L’UNIVERSITÉ BORDEAUX 1ori-oai.u-bordeaux1.fr/pdf/2012/DO_VAN_TU_2012.pdf · 2013. 1. 15. · these prÉsentÉe À l’universitÉ bordeaux 1 ecole doctorale sciences et environnements

THESE PRÉSENTÉE À

L’UNIVERSITÉ BORDEAUX 1 ECOLE DOCTORALE SCIENCES ET ENVIRONNEMENTS

Par M. Van Tu DO POUR OBTENIR LE GRADE DE

DOCTEUR SPÉCIALITÉ : BIOGÉOCHIMIE ET ÉCOSYSTÈMES

EVOLUTION ET SANTÉ DES HERBIERS À Zostera noltii DANS

LE BASSIN D’ARCACHON À TRAVERS LA DYNAMIQUE DE

LA MACROFAUNE BENTHIQUE ASSOCIÉE

EVOLUTION AND HEALTH OF SEAGRASS Zostera noltii IN

ARCACHON BAY THROUGH THE DYNAMICS OF

ASSOCIATED BENTHIC MACROFAUNA

Soutenue le : 17 Septembre 2012

Après avis de :

M. Ángel BORJA, Chercheur, AZTI – Tecnalia, Espagne Rapporteur

Mme Sarah CULLOTY, Chercheur, Université de Cork, Irelande Rapporteur

Devant la commission d’examen formée de :

M. Ángel BORJA, Chercheur, AZTI – Tecnalia, Espagne Rapporteur

Mme Sarah CULLOTY, Chercheur, Université de Cork, Irelande Rapporteur

M. Antoine GRÉMARE, Professeur, Université Bordeaux 1 Examinateur

M. Xavier de MONTAUDOUIN, Maître de Conférences, Université Bordeaux 1 Directeur de thèse

N0 d’ordre : 4561

Remerciements

Je remercie Antoine Grémare de m’avoir accueilli dans son laboratoire EPOC (UMR 5805) et

d’avoir accepté de participer à mon jury.

Je remercie sincèrement Ángel Borja et Sarah Culloty d’avoir accepté d’être les rapporteurs

de mon travail de thèse et de participer à mon jury.

Cette thèse doit son existence tout d'abord à Xavier de Montaudouin qui m’a fait l’honneur

d’accepter la direction de mes recherches. Ses conseils et remarques pertinents m'ont toujours

été d'une grande aide. Son soutien affectif lors des moments très difficiles de ma vie familiale

est très appréciable. Je tiens à lui exprimer toute ma gratitude.

Je remercie l’ensemble des mes collègues de l’équipe ECOBIOC du laboratoire EPOC, avec

qui j’ai eu des discussions et des échanges scientifiques très fructueux. Mes remerciements

vont particulièrement à Hugues Blanchet, qui avec sa grande disponibilité m’a guidé dans la

classification de la faune benthique et surtout dans l’analyse des données, notamment sous

Primer. Ses suggestions brillantes m’ont beaucoup inspiré. La relecture et les corrections

qu’il a faites rendent mon mémoire plus lisible et plus compréhensible.

Je tiens à remercier chaleureusement Nicolas Lavesque pour ses conseils pertinents, pour son

aide dans la détermination des espèces, la vérification des bases de données et des

synonymies, dans l’analyse des données et la conception des cartes.

J’exprime ma reconnaissance à Cindy Binias qui m’a aidé lors des analyses portant sur les

maladies de la palourde et de la coque (trématodiase, perkinsose, maladie du muscle marron).

Lors de mon travail de recherche, plusieurs collègues m’ont fait découvrir différents outils de

travail. Je remercie Florence Jude-Lemeilleur, Line Bourasseau et Cécile Dang de m’avoir

initié aux méthodes d’analyse de Perkinsus et de la BMD.

La collecte des échantillons sur le terrain a été intense. Je remercie sincèrement Francis

Prince, Henri Bouillard, Pascal Lebleu, Benoît Gouillieux et Laurent Letort pour leur aide

dans ce travail.

Je remercie également Nguyen Van Tien, Le Quang Tuan, Tu Lan Huong, Cao Van Luong de

m’avoir fourni des ressources documentaires appropriées concernant les herbiers et les cartes

du Vietnam.

Je remercie à ce sujet Agnès Massonneau pour son aide à trouver certains articles de

recherche dans mon domaine.

Un grand merci à Sarah Culloty qui s’est montrée une lectrice et une correctrice infatigable

des manuscrits pour la sauvegarde de la langue anglaise ! Ce texte doit beaucoup à son sens

aigu de l’observation et à ses compétences en langue anglaise.

Merci beaucoup à Marie-Claude Duck qui a réservé les billets d’avion lors de mes voyages

de visite familiale au Vietnam et à Florence Daniel qui a rendu mon séjour (hébergement) à la

Station Marine le plus agréable possible.

Ce travail de recherche a été fait dans de très bonnes conditions grâce aux concours financiers

de divers organismes. Je remercie les responsables du Syndicat Intercommunal du Bassin

d’Arcachon, le Ministère de l'Écologie et du Développement Durable (MEDAD) d’avoir

accordé le financement pour la recherche dans le cadre des projets LITEAU2-3, QuaLiF et

REPAMEP, respectivement (Coord. : X. de Montaudouin).

Je remercie le CROUS de Bordeaux qui a assuré toutes les démarches administratives lors de

mon séjour en France.

La réalisation de ce travail de recherche a été possible grâce à une bourse octroyée par le

Ministère de l’Education et de la Formation vietnamien. Je remercie chaleureusement les

responsables de ce programme très innovateur.

Je remercie l’ensemble de mes proches et de mes amis qui m’ont soutenu durant les années

de thèse. Merci enfin à ma famille, mes parents et frères et sœurs de m’avoir accompagné

dans mes choix, assisté quand j’en avais besoin et de m’avoir toujours donné toute latitude.

Résumé

L’objectif général était d’évaluer la réponse du macrobenthos à la dynamique d’un

herbier marin à Zostera noltii (colonisation, maturation, destruction, restauration), dans le

Bassin d’Arcachon, une lagune du sud-ouest de la France.

Colonisation – Quand l’herbier commence à se développer, la structure de la

macrofaune diverge immédiatement entre habitats d’herbier et de sables nus, sans cependant

que les indice biotiques testés (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic

Opportunistic Polychaetes Amphipods Index), BENTIX) varient. De même, la population du

bivalve dominant, la coque (Cerastoderma edule), souffre du développement de l’herbier. Sa

communauté parasitaire (trématodes) est modifiée, sans que cela n’influe sur la dynamique

des coques.

Maturation – A l’échelle du Bassin, le développement de l’herbier (considéré comme un

signe de “bonne santé”) a été comparé à la santé de deux bivalves endogés dominants, la

palourde japonaise (Ruditapes philippinarum) et la coque (C. edule), évaluée en termes de

prévalence de maladie. Aucune corrélation n’existe entre le taux de recouvrement d’herbier et

la prévalence de trois maladies (trématodiase, perkinsose, maladie du muscle marron).

Destruction - Restauration – Entre 2002 et 2010, la surface d’herbier a diminué de 1/3. En

termes de structure de communautés et d’espèces dominantes, peu de différences ont été

notées au sein de chaque année (entre les 12 stations) et entre années, indépendamment du

déclin de l’herbier. Parmi les indicateurs biotiques, l’indice multivarié MISS est en

adéquation avec la relative similarité de la structure de la macrofaune benthique entre les

groupes discriminés par l’analyse MDS.

En 2005, des activités de dragage dans le Bassin d’Arcachon ont abouti à

l’enfouissement de 0,32 km2 d’herbier à Z. noltii. La structure du macrobenthos a été

immédiatement modifiée sans retour à l’état initial sur les zones couvertes de sable. En

revanche, le macrobenthos (endofaune) s’est rapidement rétabli dans les zones couvertes de

vase alors que l’herbier n’a commencé à se développer qu’au bout de 5 ans après les travaux.

Le dernier chapitre de cette thèse donne un bref aperçu des connaissances actuelles

sur les herbiers vietnamiens et des possibles travaux scientifiques à y mener.

Abstract

The overall objective was to assess macrobenthos response to marine Zostera noltii

seagrass dynamics (colonization, maturation, destruction, restoration), in Arcachon Bay, a

French South-western lagoon.

Colonization – When seagrass starts to develop, the structure of macrofauna community

immediately diverges between sand and seagrass habitats, without however modifying tested

biotic indices (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic Opportunistic

Polychaetes Amphipods Index), BENTIX). As well, population of the dominant bivalve, the

cockle (Cerastoderma edule), suffers from seagrass development. Their parasite (trematode)

community are impacted, but not sufficiently to explain cockle deficit in seagrass.

Maturation – At the scale of the Bay, seagrass development (considered as a sign of “good

health”) is compared to the fitness of the two dominant infaunal bivalves, the Manila clam

(Ruditapes philippinarum) and the cockle (Cerastoderma edule), measured in terms of

disease prevalence. There was no correlation among seagrass cover rate and the prevalence of

three diseases: trematodiosis, perkinsosis and Brown Muscle Disease.

Destruction - Restoration – Between 2002 and 2010, seagrass cover decreased by 1/3. When

looking at community structure and dominant species, there were moderate differences within

(among 12 stations) and among years, independently of seagrass decline. Among biotic

indicators, multivariate index MISS was in adequation with the relative similarity of

macrofauna structure among groups discriminated by MISS analysis.

In 2005, dredging activities in Arcachon Bay led in burying 0.32 km2 of Z. noltii.

Macrobenthos structure was immediately modified and did not recover in places buried by

sand. Conversely, macrobenthos (infauna) recovered rapidly in areas cover by mud, while

seagrass began to develop again five years after work.

The last chapter of the thesis provides a brief insight of the seagrass in Vietnam, the

actual knowledge and what could be investigated.

Tóm tắt (Résumé en Vietnamien)

Mục đích của luận án là đánh giá các phản ứng của động vật đáy cỡ lớn với những

biến đổi của thảm cỏ biển Zostera noltii (phát triển, suy thoái, phá hủy và phục hồi) ở Vịnh

Arcachon, Tây-Nam nước Pháp.

Phát triển – Khi thảm cỏ biển bắt đầu phát triển, cấu trúc của quần xã động vật đáy lập tức

khác biệt so với quần xã động vật đáy ở khu vực không có cỏ biển (vùng cát). Tuy nhiên, các

chỉ số sinh học (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic Opportunistic

Polychaetes Amphipods Index), BENTIX) không thay đổi. Chiếm ưu thế trong quần xã động

vật đáy, quần thể Sò (Cerastoderma edule) chịu tác động từ sự phát triển của thảm cỏ biển.

Quần xã kí sinh trùng sán lá (trematode) trong quần thể Sò cũng bị tác động nhưng không đủ

nhiều để giải thích sự suy giảm của quần thể Sò trong thảm cỏ biển.

Suy thoái – Trên quy mô của Vịnh, khi thảm cỏ biển phát triển (dấu hiệu của hệ sinh thái

“khỏe mạnh”), sức khỏe của hai quần thể chiếm ưu thế là Nghêu (Ruditapes philippinarum)

và Sò (Cerastoderma edule) được đánh giá dựa trên tỷ lệ nhiễm bệnh. Không có mối liên hệ

về tỷ lệ bao phủ của thảm cỏ biển với tỷ lệ nhiễm 3 loại bệnh (trematode, perkinsosis, Brown

Muscle Disease).

Phá hủy và phục hồi – Từ năm 2002 đến 2010, thảm cỏ biển bị suy giảm 1/3 diện tích bao

phủ. Tuy nhiên, biến đổi xảy ra trong quần xã động vật đáy và những loài chiếm ưu thế chỉ ở

mức trung bình và độc lập với sự suy giảm của cỏ biển. Trong số các chỉ số sinh học, chỉ có

chỉ số đa biến MISS phản ánh được sự tương đồng của cấu trúc các nhóm quần xã động vật

đáy được tách biệt bởi phân tích đa chiều MDS.

Năm 2005, các hoạt động nạo vét trong vịnh Arcachon dẫn đến việc chôn lấp 0.32

km2 cỏ biển Z. noltii. Ở khu vực bị bao phủ bởi cát, cấu trúc quần xã động vật đáy ngay lập

tức biến đổi và không thể phục hồi. Ngược lại, ở khu vực bị bao phủ bởi bùn, quần xã động

vật đáy đã phục hồi trong khi khi thảm cỏ biển bắt đầu phát triển lại sau 5 năm.

Chương cuối cùng của luận án đưa ra một cái nhìn tổng quan về thảm cỏ biển ở Việt

Nam và những vấn đề cần nghiên cứu.

SOMMAIRE/CONTENTS

Chapter 1 - General introduction ............................................................................................1

Chapter 2 - Seagrass colonization: knock-on effect on zoobenthic community,

populations and individuals’ health ......................................................................................13

1. Introduction......................................................................................................................14

2. Materials and methods .....................................................................................................16

2.1. Study area..................................................................................................................16

2.2. Sampling procedure ..................................................................................................18

2.3. Data analysis .............................................................................................................19

3. Results..............................................................................................................................20

3.1. Development of the seagrass bed and modifications of sediment characteristics ....20

3.2. Macrobenthic community .........................................................................................21

3.3. Cockle population and related trematodes................................................................28

4. Discussion ........................................................................................................................32

4.1. Kinetic of seagrass development and associated macrofauna ..................................32

4.2. Seagrass development and benthic community health .............................................35

4.3. Seagrass development and cockle population health................................................36

Chapter 3 - Environmental factors contributing to the development of Brown Muscle

Disease and Perkinsosis in Manila clams (Ruditapes philippinarum) and trematodiasis in

cockles (Cerastoderma edule) of Arcachon Bay ....................................................................41

1. Introduction......................................................................................................................42

2. Material and Methods ......................................................................................................44

2.1. Study site...................................................................................................................44


2.3. Environmental factors...............................................................................................46

2.4. Bivalve models and associated pathology ................................................................49

3. Results..............................................................................................................................51

3.1. Environmental factors...............................................................................................51

3.2. Manila clam and Perkinsosis ....................................................................................52

3.4. Cockle and trematodes..............................................................................................56

4. Discussion ........................................................................................................................60

Chapter 4 - Limited consequences of seagrass decline on benthic macrofauna and

associated biotic indicators.....................................................................................................65

1. Introduction......................................................................................................................66

2. Materials and methods .....................................................................................................68

2.1. Study area..................................................................................................................68


2.3. Data analysis .............................................................................................................71

3. Results..............................................................................................................................73

3.1. Macrobenthic community structure ..........................................................................73

3.2. Biotic Indices ............................................................................................................80

4. Discussion ........................................................................................................................80

4.1. Associated macrofauna in seagrass...........................................................................80

4.2. Benthic community in declined seagrass mudflats...................................................82

4.3. Biotic Indices ............................................................................................................84

Chapter 5 - Seagrass destruction: benthic community alteration, secondary production

loss, biotic index reaction and recovery possibility..............................................................89

1. Introduction......................................................................................................................90

2. Material and methods.......................................................................................................92

2.1. Study site...................................................................................................................92

2.2. Macrofauna sampling................................................................................................94

2.3. Sediment and seagrass leaves analysis .....................................................................94

2.4. Estimated loss of secondary production ...................................................................95

2.5. Data analysis .............................................................................................................95

3. Results..............................................................................................................................99

3.1. Seagrass and sediment disposal ................................................................................99

3.2. Main macrozoobenthic assemblages identified in the dataset ................................101

3.3. Trend in the numerical descriptor of the macrofauna assemblages........................102

3.4. Dynamic of impact and recovery of macrobenthic community..............................105

3.5. Loss of secondary production .................................................................................109

3.6. Biotic Indices ..........................................................................................................111

4. Discussion ......................................................................................................................113

4.1. Seagrass destruction and recolonization .................................................................113

4.2. Benthic community alteration and recovery possibility .........................................114

4.3. Secondary production loss ......................................................................................116

4.4. Biotic indices reaction.............................................................................................116

Chapter 6 - Perspective in Vietnam ....................................................................................119

1. Seagrass species in Vietnam ..........................................................................................119

2. Biodiversity in seagrass .................................................................................................122

3. Decline of seagrass in Vietnam......................................................................................122

4. Use of seagrasses in Vietnam ........................................................................................123

5. Threats to seagrass in Vietnam ......................................................................................123

6. Response to threats ........................................................................................................124

7. Researches on seagrass in Vietnam ...............................................................................124

8. Management seagrass beds in Vietnam .........................................................................125

8.1. In terms of science ..................................................................................................126

8.2. In terms of management..........................................................................................127

8.3. In terms of society...................................................................................................127

9. My contribution to Vietnamese seagrass .......................................................................127

Chapter 7 - General discussion - Conclusion .....................................................................129

1. Benthic macrofauna and Zostera noltii seagrass ...........................................................129

2. Seagrass and bivalve health ...........................................................................................131

3. Seagrass and biotic indices ............................................................................................131

References ..............................................................................................................................137

Chapter 1- General introduction

1

Chapter 1 - General introduction

The importance of benthic macrofauna in the functioning of marine ecosystems is

particularly evident in shallow waters (e.g. coastal systems) where its biomass represents a

large proportion of total living mass (Bouma et al., 2009a). Benthic macrofauna largely

participate to food webs and transfers of energy among the different compartments of marine

systems (Reiss and Kroncke, 2005). However, coastal ecosystems are often made of a mosaic

of different habitats. Each habitat has its own characteristics and shelters particular benthic

assemblages (Blanchet et al., 2004; Boström et al., 2006b). There is usually a drastic

difference between hard substrate communities and soft sediment communities, but such

differences also occur among habitats of each of these categories. For soft sediments of

coastal areas, it is often accepted that muddy sediments shelter higher benthic macrofauna

diversity, along with higher abundance and higher biomass than sandy sediments (Bachelet et

al., 1996). Higher organic matter content in and on muddy sediments partly explains this

tendency. Beyond grain-size characteristics of the sediment (often related to organic matter

content), spatial heterogeneity is a major key factor explaining macrofauna distribution, the

general trend being that such “ecosystem engineer” attracts a particularly high diversity of

fauna, with high biomass and abundance, due to niche diversity. In our temperate coastal

waters, many such heterogeneous ecosystems have been described, such as oyster-reefs,

maerl bottoms, Sabellaria reefs, mussel beds, etc., but seagrass beds are the most studied

around the world (Siebert and Branch, 2006; Bouma et al., 2009b; Brun et al., 2009; van

Katwijk et al., 2010). The reason for this interest is certainly related to the multi-functional

role of seagrass. Beyond its architectural complexity that attracts fauna, seagrass is a direct or

indirect (i.e. support to epibionts) source of food for many organisms, participates in

sediment biogeochemistry (roots and rhizomes), traps contaminants, serves as a nursery for

juveniles from the open ocean or for spawning grounds for adults also coming from the open

ocean. Thus, seagrass does shelter important macrofauna and consequently does play an

important role in coastal food webs and other processes (Hemminga and Duarte, 2000;

Larkum et al., 2006).

Seagrass species belong to are angiosperms. They are rhizomatous and clonal plants,

occupying space through the reiteration of shoots. Their leaves and roots are produced as


2

result of rhizome extension. This asexual process appears to be the main mechanism for

seagrass proliferation, although species also reproduce sexually. Seagrasses form highly

productive ecosystems, rivalling with the most productive biomes on earth. Their meadows

generally occupy 0-30 m depth littoral fringes off all the continents except Antarctica (Figure

1.1). Seagrass store a large fraction of their substantial production, being responsible for

about 15% of the carbon storage in the ocean. In addition, seagrass exports up to 24% of their

net production to adjacent ecosystems and seaward, acting as important trophic links with

other ecosystems. In addition to their high primary production, seagrass performs many other

functions in the ecosystem such as provision of food for coastal food webs, provision of

oxygen to waters and sediments, carbon sequestration from the atmosphere, organic carbon

export to adjacent ecosystems, sediment stabilization, prevention of sediment resuspension,

improvement of water transparency, shoreline protection, habitat for microbes, invertebrates

and vertebrates (often endangered or commercially important) and trapping and cycling of

nutrients. These functions render seagrass meadows unique, ranking amongst the most

valuable ecosystems in the biosphere, due to the important services they provides (see review

in Duarte, 2002).

On the other hand, seagrass habitats are endangered worldwide. They are vulnerable

ecosystems (Holmer and Marba, 2010), and the services they provide are threatened by the

immediate impacts of coastal development and growing human populations as well as by the

impacts of climate change and ecological degradation (Duffy, 2006; Orth et al., 2006; Airoldi

and Beck, 2007). When assessed globally, seagrass meadows rank among the most threatened

ecosystems on Earth (Duarte et al., 2008). Indeed, seagrass area cover is declining across the

globe and the rate of loss is accelerating (Duarte, 2002; Orth et al., 2006; Hughes et al., 2009;

Waycott et al., 2009; Costello and Kenworthy, 2011) (Figure 1.2). The consequences of

continuing seagrass decline extends far beyond the areas where seagrasses grow (Heck et al.,

2008). Seagrass losses also threaten the future of endangered species such as dugong,

manatee, green turtle (Waycott et al., 2009), salmon (Hughes et al., 2009). Seagrass losses

decrease primary production, carbon sequestration and nutrient cycling in the coastal zone

(Worm et al., 2006). If the current rate of seagrass loss is sustained or continues to accelerate,

the ecological losses will also increase, causing even greater ill-afforded economic losses

(Waycott et al., 2009).


3

Figure 1.1. Global seagrass distribution shown as blue points and polygons (data from 2005 UNEP-WCMC) and geographic bioregions: 1. Temperate North Atlantic, 2. Tropical Atlantic, 3. Mediterranean, 4. Temperate North Pacific, 5. Tropical Indo-Pacific, 6. Temperate Southern Oceans (source : Short et al., 2007).

Figure 1.2. Global map indicating changes in seagrass area plotted by coastline regions. Changes in seagrass areal extent at each site are defined as declining (red) or increasing (green) when areal extent changed by >10%, or no detectable change (yellow) when final area was within ±10% of the initial area. There were 131 sites in North America, 34 sites in Europe, and 40 sites in Australia (source Waycott et al., 2009).


4

If the relationship between seagrass and epifauna, and particularly motile megafauna,

is well documented, the link between seagrass and infauna is less well understood (Fredriksen

et al., 2010). Indeed, most of the approaches have consisted of comparing areas with and

without seagrass, with a major bias: factors inducing the absence or presence of seagrass

could also be the factors structuring the different benthic assemblages in areas with and

without this vegetation (notion of “confounding factor”) (Fonseca et al., 1990; Orth, 1992;

Edgar et al., 1994; Boström and Bonsdorff, 1997; Connolly, 1997; Sheridan, 1997;

Hemminga and Duarte, 2000; Beck et al., 2001; Crooks, 2002; Heck et al., 2003;

Nagelkerken and van der Velde, 2004; Nakaoka, 2005; Fredriksen et al., 2010). The major

aim of this thesis was to investigate the dynamics of seagrass associated fauna (and

particularly infauna) in the framework of seagrass dynamics: seagrass colonization,

seagrass chronic decline, seagrass brutal destruction and seagrass restoration. In other

words, does seagrass dynamics affect associated infauna and at which scale? Our

primary approach will be through detailed community analyses using modern tools,

developed for example in PRIMER software (Multidimensional scaling (MDS), Similarity

Percentage (SIMPER) …). We consider that this is the most accurate way to describe benthic

communities and their temporal evolution. Then, we will concomitantly measure the

fluctuation of other parameters related to seagrass studies, like: 1) population dynamics and

parasite infection of some dominant infaunal species; and 2) Biotic indices developed or not

in the framework of the European Water Framework Directive (WFD).

1) Population dynamics of marine invertebrates (i.e. proxy of population health) is

controlled by a variety of abiotic and biotic factors, including parasitism. For example, this is

the case for some commercial marine bivalves such as the edible cockle (Cerastoderma

edule) and the Manila clam (Ruditapes philippinarum) that host a number of common marine

parasites which can occasionally be responsible for mass mortalities and economic losses

(Paillard, 2004; Villalba et al., 2004). Among parasites, trematodes occurrence in ecosystems

have an ambiguous status in determining community health: their presence is a sign of good

health, but in return they can badly affect some dominant populations. Indeed, the diversity of

trematodes appears to be a good index of the health of benthic communities. In areas where

the human impact is weak such as in the natural reserves, the parasitic trematode fauna

generally includes a large number of species (Bartoli and Boudouresque, 1997). A diverse

and abundant community of parasites might be reflective of a diverse and abundant


5

community of hosts. Thus, we are left with the apparent quandary that a diverse and healthy

ecosystem should also be one with many parasites (Lafferty, 2003). Conversely, trematodes

may have detrimental effects on population that they parasitize, begetting mass mortality.

For example, parasites can influence the behaviour of the host (Marcogliese, 2002;

Moore, 2002), reduce the impact of herbivores (Thaler et al., 1999) or make hosts more

susceptible to predators (Hudson et al., 1992; Packer et al., 2003); these indirect effects

coupled with the direct effects on abundance of hosts can have an important role in

influencing how energy flows through communities (Hudson et al., 2006). Knowledge of

parasite diversity is thus not only valuable in itself in assessing a neglected part of

biodiversity but it might also serve as a valuable and convenient proxy for ecosystem health

(Hudson et al., 2006). There is now increasing evidence that parasites can be a good proxy

for estimating the health of an ecosystem, not only because they integrate biodiversity over a

period of time, but also because there is growing evidence that some parasites remove

environmental toxins when they are ingested by their hosts (Sures, 2004). As already

mentioned, seagrass bed attracts benthic macrofauna including species that are potential first

intermediate hosts (such as Hydrobia ulvae, Littorina littorea and Nassarius reticulatus) for

trematodes species (de Montaudouin et al., 2012). Moreover, biotic processes which are

affected by ecosystem engineer as seagrass are the major drivers to explain trematode

parasite distribution within a wide range of scales (de Montaudouin and Lanceleur, 2011).

However, the relationship between seagrass status and parasite communities has still received

little attention (Gam et al. 2009a)..

2) Finally, apart from seagrass extent, macrofauna structure and dominant population

dynamics, a way to evaluate and quantify ecological status (ES) of ecosystems consists of

calculating biotic indices (BI) and comparing them to pristine/reference situations. That was

one of the objectives of the European WFD. The WFD establishes a basis for the protection

and improvement of transitional (i.e. estuarine) and coastal waters, amongst other systems. Its

final objective is to achieve not less than ‘good ecological status’ for all waters, by 2015

(EEC, 2000). Ecological assessment is based upon the status of biological,

hydromorphological and physico-chemical quality elements. The biological elements to be

considered are phytoplankton, macroalgae, angiosperms, benthic macroinvertebrates and, in

transitional waters only, fishes. The Ecological Status (ES) of a water body is determined by


6

comparing data obtained from monitoring networks (Ferreira et al., 2007) with reference

(undisturbed) conditions, thus deriving an ecological quality ratio (EQR) (see review in Borja

et al. (2009a)). The EQR is expressed as a numerical value lying between 0 and 1; ‘High

status’ is represented by values close to 1, whilst ‘Bad status’ values lie close to 0. The range

is divided into five ES classes, ‘High’, ‘Good’, ‘Moderate’, ‘Poor’ and ‘Bad’ (Borja et al.,

2009a). Among the elements by the WFD to assess aquatic ecosystems’ ecological status are

the benthic macroinvertebrates. Their location at the sediment/water interface and their life

history traits and characteristics make them highly suitable to assess environmental

conditions. Indeed, most benthic taxa are relatively sedentary, long-lived and thus, unable to

avoid adverse environmental conditions integrating and reflecting the history of local effects

of stress over time. Besides, macrobenthic assemblages comprise species that show different

tolerances to stress and are sensitive indicators of change (Verissimo et al., 2012). Moreover,

macrofauna are dominated by species with different mobility, life-cycles and tolerance to

stress, which covers the WFD demand of integrating differently sensitive species. The

response of macrobenthic communities to several types of stress is well studied, based on

multivariate analyses that takes into account variations in species diversity and their relative

abundance between perturbed and control sites. Based on this knowledge it is possible to

determine a priori the ecological indicator behaviour and thus its appropriateness to detect

changes in the variable of interest (Patricio et al., 2009). Beyond WFD framework, a variety

of indices are available, which measure the status of ecological conditions and trends in

succession in marine benthic systems (Reiss and Kroncke, 2005). Some of these indices will

be tested in this thesis, the idea being to see how they “react” according to seagrass dynamics.

After this introduction (Chapter 1), the thesis will be structured in four major

chapters investigating the relationships between seagrass and associated macrofauna (and

evaluation of ecological quality) at different spatial and temporal scales (Table 1.1).

Comparison of benthic macrofauna and seagrass presence is often biased by the fact

that the occurrence or not of the seagrass (and therefore of the benthic macrofauna) is related

to other factors, like depth, sediment characteristics, etc. In Chapter 2, we monitored the

colonization of a sandflat by seagrass Zostera noltii and surveyed the associated fauna

modifications. Thus, we considered that the only difference between both habitats was the

presence or not of vegetation. In terms of biomass, macrobenthos was dominated by cockles.


7

The edible cockle Cerastoderma edule (L.) is one of the most common intertidal bivalves on

the sandy shores and estuaries of the north-eastern Atlantic. It ranges from the Barents Sea to

the Moroccan coasts (Gam et al., 2009b). Like other suspension feeders, cockles are

parasitized mostly through their ventilatory activity (Wegeberg et al., 1999). We tested the

influence of seagrass colonization on cockle dynamics but also on cockle infection by

trematode parasites. This study was also the occasion to compare the behaviour of different

biotic indices (AMBI (AZTI’s Marine Biotic Index (Borja et al., 2000))), BOPA (Benthic

Opportunistic Polychaetes Amphipods Index (Dauvin and Ruellet, 2007)) and BENTIX

(Simboura and Zenetos, 2002)), in relation with Z. noltii seagrass development.


8

Table 1.1. The structure of the present thesis

CHAPTER SPATIALSCALE

TEMPORALSCALE

SEAGRASSSTATUS

1. Introduction

STUDIEDVARIABLES

6. Perspectives in Vietnam

4. Limited consequences of seagrassdecline on benthic macrofauna and associated biotic indicators

Arcachon Bay(12 stations– 156 km2)

Two occasions (spring 2002& spring 2010)

5. Seagrass destruction: benthic community alteration, secondary production loss, biotic index reaction and recovery possibility

0.3 km2 8 years

2. Seagrass colonization: knock-on effect on zoobenthic community, populations and individuals’ health

1 station (2 km2) 4 years

Healthy seagrassvs

Declining seagrass

- Seagrass cover- Sediment- Asscociated macrobenthos- AMBI, BOPA, BENTIX- MISS

Seagrass

Burial

Recovery

- Seagrass cover- Sediment- Associated macrobenthos- Secondary production loss- AMBI, BOPA, BENTIX- MISS, d-MISS

- Sediment - Associated macrobenthos- Cockle dynamics- Trematode parameters- AMBI, BOPA, BENTIX

Unvegetated

Colonization

Vegetatated

7. General discussion - Conclusion

3. Correlation between bivalves health and environmental parameters, including Zosteranoltii seagrass bed cover, in Arcachon Bay

Arcachon Bay(39 stations– 156 km2)

Winter 2009

Seagrassdistribution & environment characteristics

- Trematodes- Brown Muscle Disease- Perkinsosis


9

In Chapter 3, we worked on a mature seagrass bed at the scale of Arcachon Bay. The

aim was to compare the density of seagrass leaves with the health of two dominant bivalves,

the native cockle (Cerastoderma edule) and the exotic Manila clam (Ruditapes philippinarum

Adams & Reeve, 1850). The idea was to compare two different approaches concerning the

concept of health: 1) to evaluate the seagrass leaves density which is a proxy of seagrass

health and which could itself be related to the notion of ecosystem health; 2) to assess bivalve

fitness through the prevalence of potential parasites/diseases which can also be an element to

evaluate ecosystem health. In other words, is seagrass leaves density (negatively) correlated

with disease(s) in dominant bivalves (Manila clams and edible cockles)? The Manila clam is

an endemic species from Indo-Pacific waters. This species was introduced to Europe at the

beginning of the 1970s for culture purposes, initially to France (1972) and later to England,

Spain and Italy (Flassch and Leborgne, 1992). It was introduced to Arcachon Bay (SW

France) in 1980 where it rapidly escaped from parks, colonized seagrass Z. noltii beds and

underwent intensive exploitation by fishermen. In 2010, Arcachon Bay harbored the most

important stock of Manila clams in France (5773 metric tons, t) and ranked first in terms of

national production (713 t yr–1 in 2008) (Sanchez et al., 2010). For the cockle, the health was

measured in terms of trematode load. Depending on the specific trematode species present,

molluscs can serve as first host or second intermediate host. In their first intermediate host,

these parasites reproduce asexually, generally in the gonad and the digestive gland of their

host, leading at the individual scale to growth disturbance (Curtis, 1995; Gorbushin, 1997;

Mouritsen et al., 1999; Probst and Kube, 1999; Curtis et al., 2000), reproduction failure

(Schulte-Oehlmann et al., 1997; Oliva et al., 1999; Krist, 2001; Rice et al., 2006; Lajtner et

al., 2008) and sometimes death (de Montaudouin et al., 2003; Desclaux et al., 2004;

Thieltges, 2006b). Prevalence is generally low (<5%) (Thieltges et al., 2008) and effects on

the host population are not measurable (Kube et al., 2006). However, cases of episodic high

prevalence leading to host mortalities are cited (Jonsson and Andre, 1992; de Montaudouin et

al., 2003; Fredensborg et al., 2006; Thieltges, 2006b). Conversely, trematode parasites using

molluscs as their second intermediate host can display high prevalence (de Montaudouin et

al., 2000; Thieltges and Reise, 2006; Gam et al., 2008). Their larvae remain in the tissues of

their host as metacercariae. This larval stage is often considered as energically inert and

causes little or no immediate physiological or behavioural responses in the adult host

(Lauckner, 1983). They can however impact their host when the number of metacercariae is


10

high (Lauckner, 1987b; Desclaux et al., 2004; Desclaux et al., 2006) or when the host is at the

juvenile stage (Lauckner, 1987a; Wegeberg et al., 1999). Nevertheless few studies have ever

tried to estimate the impact of trematode parasites on the second host bivalve population

dynamics (Gam et al., 2009b). For Manila clams, health status was estimated in terms of

infection by the two major diseases in the lagoon for this bivalve: perkinsosis and brown

muscle disease (BMD). Perkinsosis, caused by the protozoan Perkinsus sp. affects numerous

molluscan species all over the world and can lead to mass mortalities (Azevedo, 1989;

Burreson and Ragone Calvo, 1996; Goggin, 1996; Da Ros et al., 1998; Park and Choi, 2001;

Leite et al., 2004; Cremonte et al., 2005; Villalba et al., 2005). In Korea, this parasite has

been the cause of a severe decrease in clam populations since 1993 (Park and Choi, 2001).

The impact of perkinsosis on molluscs depends especially on infection intensity level but is

also related to environmental conditions (Park and Choi, 2001; Leite et al., 2004; Dang et al.,

2010a). Adverse environmental conditions could increase the impact of Perkinsus sp. on

clams (Dang, 2009). For instance, energy consumption in adult clams with heavy infections

would exceed the energy available for growth (Casas et al., 2002; Villalba et al., 2004). This

would result in a lower condition index and a lower growth rate. Furthermore, to explain the

influence of perkinsosis on clam health, besides the fact that Perkinsus sp. consumes energy

at the expense of the clam, a high concentration of Perkinsus in the gills may decrease

filtration efficiency. This could lead to a decrease of oxygen and food availability for clams

and have direct repercussions on clam metabolism (Dang, 2009).

Seagrass in Arcachon Bay undergoes severe stress at both a global and local scale. At the Bay

scale (Chapter 4) and since 2005, this seagrass has undergone a regression that has been

estimated at -33% of the vegetated surface (Plus et al., 2010). Our aim was to compare

macrobenthos structure before seagrass regression (2002) when Z. noltii covered all our

investigated stations, and eight years later (2010) when seagrass had disappeared in 80% of

the investigated areas. Concomitantly, we compared the results from the three different

univariate biotic indices (AMBI, BOPA, BENTIX) and a multivariate index MISS

(Macrobenthic Index in Sheltered Systems).

Apart from this chronic decline (with no clear reasons to this day), Arcachon Bay

seagrass Z. noltii suffered from a more immediate and rapid aggression: burial due to

sediment disposal (anthropogenic activity). In Chapter 5, we investigated the effects (if any)


11

on associated macrofauna and estimated secondary production loss in relation to time and

sediment grain-size. We compared the different biotic indices (BI) that were already

developed and in use but we also tested another BI that was recently developed (MISS for

Macrobenthic Index in Sheltered Systems). MISS, however, is an uneasy and time-

consuming multivariate BI to be used due to the necessity to obtain biomass. Then, we tested

a derived version (d-MISS) without biomass.

Chapter 6 was the opportunity to give an overview of marine seagrass in my native

country, Vietnam, and to summarize the information available in this region for this topic. I

have evoked, on the basis of what I learned during my PhD, what could be my propositions to

go on working on this subject in my country.

12

Chapter 2- Seagrass colonization and macrobenthos

13

Chapter 2 - Seagrass colonization: knock-on effect on zoobenthic

community, populations and individuals’ health

Published: Do, V.T., de Montaudouin, X., Lavesque, N., Blanchet, H., Guyard, H. (2011).

Estuarine, Coastal and Shelf Science 95: 458-469.

Abstract

This study provided evidence that Zostera noltii presence affects macrofauna

community structure independently from median sediment grain-size and that the notion of

ecosystem health is rather subjective: in the present case, we recorded “good health” in terms

of seagrass development, “no impact” in terms of macrobenthic biotic indices and “negative

effect” for a given key-population. The occurrence and development of a Zostera noltii

seagrass bed was surveyed at Banc d’Arguin, Arcachon Bay (France), to estimate the

modification of the macrozoobenthic community and of the dynamics of a key-population for

the local ecosystem, –the cockle Cerastoderma edule. Even though median grain-size of the

sediment decreased only at the very end of the survey, i.e. when seagrass totally invaded the

area, most of the macrofauna community characteristics (such as abundance and biomass)

increased as soon as Z. noltii patches appeared. The structure of the macrofauna community

also immediately diverged between sand and seagrass habitats, without however modifying

the tested biotic indices (BENTIX, BOPA, AMBI). The health of the cockle population

(growth, abundance, recruitment) was impacted by seagrass development. Related parasite

communities slowly diverged between habitats, with more parasites in the cockles from

seagrass areas. However, the number of parasites per cockle was always insufficient to alter

cockle fitness.

Keywords: Zostera noltii seagrass, WFD, macrozoobenthic community, Cerastoderma

edule, parasite


14

1. Introduction

The presence of seagrass beds is known to enhance species diversity (Orth et al.,

1984; Edgar, 1990; Edgar, 1994; Boström and Bonsdorff, 1997; Hemminga and Duarte,

2000; Fredriksen et al., 2010). The influence of these meadows is both structural in that it

increases the complexity of the habitat, allowing different species to occupy various

ecological niches within an area (Orth et al., 1984); and trophic because it supports epiphytes,

a resource for many grazers (Duffy et al., 2003). Most studies aiming to highlight the effect

of seagrass on diversity have compared species richness in vegetated and non-vegetated areas

(Fonseca et al., 1990; Orth, 1992; Edgar et al., 1994; Boström and Bonsdorff, 1997;

Connolly, 1997; Sheridan, 1997; Hemminga and Duarte, 2000; Beck et al., 2001; Crooks,

2002; Heck et al., 2003; Nagelkerken and van der Velde, 2004; Nakaoka, 2005; Fredriksen et

al., 2010). A difference in zoobenthic community structure between both habitats was always

associated with enhancement of abundance, biomass and species richness in seagrass.

However, these observed differences could also be due to confounding factors, the

presence/absence of seagrass being itself related to contrasted environmental features

(hydrodynamics, depth, grain-size, etc.) (Boström et al., 2006a). Furthermore, how and which

infaunal species responds to the more complex sediment environment created by the seagrass

and how this response may vary across different spatial scales remains unclear (Fredriksen et

al., 2010).

In addition to the development of the seagrass being related to modification of the

associated benthic macrofauna, the present study aimed to discuss the notion of health for a

given ecosystem. In our case study, health of the ecosystem was investigated according to

three approaches: 1) the development of the seagrass which is included in the European

Water Framework Directive (WFD) quality developments (Borja et al., 2000; Simboura and

Zenetos, 2002; Dauvin and Ruellet, 2007); as the WFD considers seagrass as a useful

indicator in terms of depth limit (Krause-Jensen et al., 2005), species composition (the

presence of disturbance-sensitive species), abundance and ecological quality (Foden and

Brazier, 2007); 2) the structure of the associated benthic community which is another quality

element in the WFD. Biotic Indices (BIs) have been developed for assessing the ecology

status (ES); BIs based on the classification of species into ecological groups according to

their level of sensitivity/tolerance to stress; 3) the health of the dominant species in terms of


15

biomass, the cockle Cerastoderma edule. This bivalve is considered as an engineer species

impacting the ecosystem functioning. Indeed, as a suspension feeder, the cockle plays an

important role in benthoplanctonic coupling: putting trophic pressure on phytoplanktonic

supply (Smaal et al., 1986; Smaal, 1997; Widdows and Navarro, 2007), sedimentation of silt

and clay (Widdows et al., 1998; Ciutat et al., 2006) and competition with other filter-feeders.

As an infaunal species, the cockle bioturbates the sediment, modifies bacterial profiles,

structures macrofaunal communities (Flach and Bruin, 1993) and participates in resuspension

processes (Flach and Bruin, 1993; Goñi-Urriza et al., 1999; Ciutat et al., 2006). We

hypothesised that the development of seagrass blades would modify hydrodynamics,

sediment characteristics and shelter availability in such a way that recruitment and predation

would be altered, with a knock-on effect on cockle density and consequently on intraspecific

competition. Cockles fitness was evaluated through three parameters, namely shell growth,

abundance (adults and recruits) and pathogen load. Indeed apart from benthic free living

macrofauna, the study was extended to the parasite fauna (trematodes) of cockles in order to

assess whether or not seagrass presence could facilitate the infection of this key-species (the

cockle) by these potential pathogens.

Cockles are the preferred intermediate hosts for many trematodes (Lauckner, 1983;

Thieltges and Reise, 2006; de Montaudouin et al., 2009). These parasites can exert a pressure

on cockle population dynamics (Blanchet et al., 2003; Gam et al., 2009b). However, these

parasites can be affected by the presence of seagrass, either directly by perturbing propagules

dispersal (Bartoli and Boudouresque, 1997; Gam et al., 2009b) or through their impact on the

dynamics of potential hosts involved in trematode cycles (Hechinger and Lafferty, 2005;

Thieltges and Reise, 2006). The general objective of this study was to monitor the evolution

of a seagrass development in a bare sandflat and to discuss ecosystem quality. Three

approaches have been developed in order to: 1) assess the effect of the colonization of

seagrass beds on the structure of macrofauna in terms of abundance, biomass, species

richness, 2) compare the assessment of ecological status by three biotic indices to evaluate

the “health” quality of the environment, 3) evaluate how the seagrass development may affect

the fitness of a given key-population (cockle).


16

2. Materials and methods

2.1. Study area

Banc d’Arguin (Figure 2.1) is a National Nature Reserve including sand dunes and

semi-sheltered sandflats. It is located at the oceanic entrance of Arcachon Bay – a macrotidal

lagoon situated on the French south-western Atlantic coast (44° 40’ N, 1° 10’ W). A strong

characteristic of this area relates to the large-scale mobility of sand banks and their change of

morphology, due to wind, waves and spring tides. When an area becomes sheltered, a

seagrass bed may develop over a large surface area but may also disappear within a few

months or a few years, buried under sand or eroded by new channels. Consequently, this site

provides a good opportunity to investigate seagrass and its associated macrofauna dynamics.

The studied site consisted of a 2000-m2 intertidal area within the Integral Protection Zone of

the reserve. This area was exempt from direct human activity (e.g. fishing, walking,

anchoring). Salinity remained high year-round (31–34) and surface sediment temperature in

the intertidal fluctuates between -0.2 °C in winter and 30.0 °C in summer (Dang et al.,

2010b).. The benthic macrofauna was described in 1988 (Bachelet and Dauvin, 1993) and in

2002 (Blanchet et al., 2004) when the whole area was free of seagrass. Several marine bird

species winter, nest or migrate, including species that are potential definitive hosts for

trematodes. The surrounding waters are inhabited by many fish species (e.g. bass, mullet,

goby, sole) which are also potential hosts for trematodes.

The cockle population at Banc d’Arguin is located between 0.9 and 1.9 m above chart

datum and has been characterised by highly fluctuating abundances, fast growth rates and

short lifespan (de Montaudouin, 1996; Gam et al., 2009b, 2010).


17

44° 40’

C

D

A

B

Banc d’Arguin

1° 10’

N

Figure 2.1. Studied site (A: Banc d’Arguin, Arcachon Bay) and pictures of Zostera noltii seagrass bed expansion over time (B: February 2005, early settlement; C: November 2006, 50% spreading; D: November 2009, 100% spreading).


18

2.2. Sampling procedure

2.2.1. Benthic macrofauna and associated parameters

Five sampling campaigns were carried out in the Zostera noltii patches and the

adjacent sand area over three years (February, June and November 2005, November 2006 and

November 2009) corresponding to different stages of the seagrass development in the studied

area (Figure 2.1). Sampling consisted of collecting the top 20 cm of the sediment with a

0.0225 m² corer, with six replicates per situation (seagrass vs. sand). Sediment was sieved

through a 1-mm mesh; the sieve residue was fixed in 4% buffered formalin and stained with

Rose Bengal. In seagrass beds, Zostera noltii leaves were cut and preserved in formalin. The

top 3-cm sediment layer was also sampled in each replicate for grain-size analysis. In the

laboratory, macrofauna was sorted, identified when possible to the species level, and counted.

Biomass was determined as ash-free dry weight (AFDW) after desiccation (60 °C, 48 h) and

calcination (450 °C, 4 h). Zostera noltii leaves were desiccated (60 °C) until a constant dry

weight was obtained. Sediment grain-size characteristics (median grain-size, percentage of

silt and clays) were determined after sieving pre-weigh dried sediment through a wet column

of sieves with decreasing apertures (1000 μm, 500 μm, 250 μm, 125 μm and 63 μm).

Percentage of organic matter in the sediment was assessed after ignition (450 °C, 4 h) of a

dried aliquot of sediment.

2.2.2. Cockle-Trematode systems

Cockle abundances were determined using quadrates (6 samples × 0.25 m² × 2

habitats) connected to macrofauna cores (that were sampled in the middle of each quadrat).

The top 5 cm depth was sampled with a shovel and sieved on a 1-mm mesh. Individuals were

counted and shell lengths were measured with an electronic calliper to the nearest mm.

Cockle recruitment period was considered as simultaneous in bare sand and seagrass patches.

Consequently, mean shell length of a cohort was an estimate of growth after separating the

different cohorts. Trematode communities in seagrass beds and sand were monitored and

compared when the number of cockles was sufficient (at least 10 individuals) in both

habitats. The study concentrated on the two existing cohorts (2003 and 2004) at the beginning

of the study (February 2005), cohort 2004 until its disappearance after November 2005, and


19

cohort 2008 in November 2009. Five cockles per quadrat and per cohort were dissected for

trematode diagnosis. The flesh was squeezed between two large glass slides and observed

through a binocular microscope with transmitted light. Identification of trematode species

was performed using the key and related references proposed by de Montaudouin et al.

(2009).

2.3. Data analysis

2.3.1. Biotic Indices

Three currently available univariate Biotic Indices (BIs) were tested, namely AMBI

(Borja et al., 2000), BENTIX (Simboura and Zenetos, 2002; Simboura et al., 2005) and

BOPA (Dauvin and Ruellet, 2007). AMBI (AZTI Marine Biotic Index) is based on previous

work from Grall and Glémarec (Grall and Glémarec, 1997). It considers five ecological

groups (available on web page: http://ambi.azti.es) ranging from sensitive species (EGI) to

first-order opportunistic species (EGV) (Borja et al., 2000). BENTIX considers only two

groups: sensitive (GS) and tolerant species (GT), which correspond to ecological groups I and

II, and ecological groups III to V of the AMBI, respectively. The BOPA (Benthic

Opportunistic Polychaetes Amphipods index) is based on the ratio of opportunistic

polychaetes (i.e. polychaetes of ecological groups IV and V of the AMBI) and amphipods

(except Jassa genus).

2.3.2. Statistical analysis

Analysis of variance was applied to assess differences between sand and seagrass in

terms of biomass, abundance, number of species (S), Shannon index (H’), Piélou’s evenness

index (J’) and abundance of parasites in cockles. Prior to ANOVA, homogeneity of variance

was tested by Cochran C test. If significant heterogeneity was identified, data were log10(x+1)

or arcsin√p (for percentages data) transformed, which was sufficient to achieve homogeneity

of variance. Normality of data was assumed. All statistical analyses were performed with

STATISTICA® 7.1 software (StatSoft).


20

2.3.3. Multivariate Analysis

Multivariate analysis was performed to compare macrozoobenthic communities

structure between seagrass and sand areas. Abundances were square-root transformed to

minimize the influence of the most dominant taxa. A non-metric multidimensional scaling

(MDS) based on Bray-Curtis similarity coefficient was used to obtain an ordination plot.

These analyses were performed using PRIMER® – v6 package (Clarke and Warwick, 2001;

Clarke and Gorley, 2006).

3. Results

3.1. Development of the seagrass bed and modifications of sediment characteristics

3.1.1. Seagrass development

In February 2005, Zostera noltii represented small <2 m diameter patches scattered on

a medium sand intertidal flat. In November 2006, seagrass patches occupied half of the flat

and in November 2009, bare sand areas became rare (Figure 2.1). Within the seagrass bed,

biomass of leaves varied according to seasons, but when considering a similar month

(November), an increase was observed from 2005 (55 g DW m-2) to 2006 (99 g DW m-2) and

2009 (290 g DW m-2) (Table 2.1).

3.1.2. Sediment characteristics

Together with seagrass bed extension, the surface sediment characteristics changed

(Table 2.1). In bare sand, the grain-size remained stable (327-357 µm). It also remained

similar in the seagrass (p > 0.05), except at the very end in 2009 (p < 0.05) when the seagrass

covered almost the whole of the flat, allowing finer particles to deposit. However, at that

time, sediments were still medium sands (median = 299 µm). Conversely, silt and clay

content and organic matter content rapidly increased in the seagrass to reach values that were

on average 2.3 fold higher than in bare sand (Table 2.1).


21

Table 2.1. Mean biomass of seagrass leaves (g DW m-2), sediment median particle size (µm), silt and clay and organic matter content in the sediment (%) (± 1 standard error), at each sampling date. P-value was calculated from the comparison between bare sand and seagrass. ns: p > 0.005.

Parameter Sampling date Bare sand Seagrass bed P-value Zostera noltii Feb 2005 50.5 (± 1.2) Jun 2005 181.0 (± 20.9) Nov 2005 55.4 (± 15.6) Nov 2006 99.1 (± 21.9) Nov 2009 290.2 (± 40.9)

Feb 2005 344 (± 4) 354 ( ±9) ns Jun 2005 358 (± 4) 357 ( ±14) ns Nov 2005 342 (± 3) 340 ( ±2) ns

Sediment median particle size

Nov 2006 337 (± 1) 334 ( ±1) ns Nov 2009 327 (± 4) 299 ( ±15) < 0.05 Silt & Clay Feb 2005 1.3 (± 0.1) 2.4 ( ±0.1) < 0.001 Jun 2005 0.7 (± 0.1) 1.4 ( ±0.3) < 0.05 Nov 2005 0.6 (± 0.0) 2.5 ( ±0.5) < 0.01 Nov 2006 2.6 (± 0.6) 5.0 ( ±0.8) < 0.05 Nov 2009 0.6 (± 0.0) 2.3 ( ±0.3) < 0.01 Organic matter Feb 2005 5.7 (± 0.9) 8.0 ( ±0.9) ns Jun 2005 3.2 (± 0.2) 10.3 ( ±1.6) < 0.01 Nov 2005 3.9 (± 1.7) 8.0 ( ±1.2) ns Nov 2006 0.5 (± 0.0) 1.7 ( ±0.3) < 0.01 Nov 2009 3.2 (± 0.1) 10.3 ( ±1.5) < 0.01

3.2. Macrobenthic community

A data matrix of ‘10 stations-dates × 96 species’ was analysed. This matrix was

obtained without removing any species.

3.2.1. Identification of Assemblages

The MDS stress level (< 0.1) corresponded to a good ordination without misleading

interpretation. The ANOSIM test showed significant differences in faunistic composition

between sand and seagrass for each date (R = 0.95, p = 0.002). Based on a 60% similarity

level, the MDS ordination plot (Figure 2.2) allowed the identification of three different


22

groups. There was a distinct separation between bare sand and seagrass bed assemblages.

Within seagrass beds, the benthic assemblages in 2009 were clearly separated from the

assemblage of the seagrass bed during the former sampling dates. When pooling all dates, 34

species out of 96 were found in the seagrass bed only (with Aphelochaeta marioni and

Bittium reticulatum as the main species), 13 species were found in bare sand only (with

Ampelisca brevicornis and Cyclope neritea as dominant species) and the remaining 47

species occurred in both habitats (with Notomastus latericeus and Heteromastus filiformis as

dominant species) (Table 2.2).

Figure 2.2. MDS ordination plots of benthic assemblages (square-root transformed data).

Zn: Zostera noltii; S: bare sand; (year_month).


23

Table 2.2. List of species with mean abundance > 50 ind.m-2 in at least one of three habitats. (G): gastropod; (B): bivalve; (O): oligochaete; (P): polychaete; (Op): ophiuroid; (A): amphipod; (D): decapod; (Ma): malacostraca; (N): nemert. Epifauna species are in bold.

Sand (2005-2009) Z. noltii (2005-2006) Z. noltii (2009)Species Rank Mean Rank Mean Rank Mean

Hydrobia ulvae (G) 1 3964 1 13607 9 407 Notomastus latericeus (P) 2 397 4 607 3 2400 Heteromastus filiformis (P) 3 366 2 754 1 3385 Cerastoderma edule (B) 4 206 5 307 - 0 Ampelisca brevicornis (A) 5 74 31 6 - 0 Scrobicularia plana (B) 6 58 16 89 - 0 Ruditapes philippinarum (B) 7 53 12 133 27 7 Cyclope neritea (G) 8 50 22 31 - 0 Abra segmentum (B) 9 49 8 222 25 37 Prionospio sp. (P) 10 46 13 126 20 67 Nemertina (N) 11 28 19 59 13 163 Glycera spp. (P) 12 27 20 39 18 96 Euclymene oerstedii (P) 13 27 20 39 11 281 Poecilochaetus serpens (P) 14 15 26 19 19 81 Nassarius reticulatus (G) 15 13 14 120 22 59 Aphelochaeta marioni (P) 16 12 3 687 2 2526 Tubificoides benedii (O) 17 12 15 115 12 222 Melinna palmata (P) 18 9 23 30 10 385 Bittium reticulatum (G) 19 4 7 224 5 874 Littorina littorea (G) 19 4 9 187 15 133 Euclymene collaris (P) 19 4 24 30 14 148 Pseudopolydora spp. (P) 19 4 25 24 20 67 Capitella capitata (P) 23 3 11 139 - 0 Mytilus edulis (B) 24 1 6 304 - 0 Gammarus sp. (A) 24 1 18 61 4 2363 Microdeutopus gryllotalpa (A) 24 1 26 19 7 652 Platynereis dumerilii (P) - 0 10 150 26 15 Aonides oxycephala (P) - 0 17 76 17 111 Gibbula umbilicalis (G) - 0 26 19 24 52 Melita palmata - 0 29 9 6 844 Ophiura sp. (Op) - 0 30 6 15 133 Gammarella fucicola (A) - 0 - 0 8 511 Nebalia strausi (Ma) - 0 - 0 23 52

3.2.2. Modification of macrofaunal characteristics

A rapid contrast in quantitative parameters (biomass, abundance and species richness)

was observed between the seagrass bed and bare sand. While the seagrass bed consisted of


24

small patches (February 2005), biomass and species richness in bare sand and seagrass

patches were similar (p > 0.05), while abundance was double in the seagrass bed (12363

ind.m-2 against 6400 ind.m-2) (p < 0.01) (Figure 2.3). When seagrass started to expand (June

2005) until the almost entire coverage of the area (in November 2009), macrofauna biomass

(Figure 2.3A), abundance (Figure 2.3B) and species richness (Figure 2.3C) were higher in the

seagrass patches (p < 0.01). Diversity H’ was less or similar for a short time only (until

November 2006), but also became higher in the seagrass (p < 0.01) at the end of the study, in

November 2009 (Figure 2.3D). The Evenness index J’ was always small (<0.8) in both

habitats but higher in the seagrass at the end (Figure 2.3D).

Species were gathered into five trophic groups based on the feeding types (Fauchald

and Jumars, 1979; Hily and Bouteille, 1999): (1) grazers, (2) deposit feeders, (3) scavengers,

(4) predators and (5) suspension feeders. The biomass of each trophic group was compared

between bare sand and seagrass bed (Figure 2.4). At the beginning of seagrass expansion in

February 2005, grazers and scavengers were the groups showing higher biomass (p < 0.05) in

the seagrass bed. In June 2005, grazers, predators and suspension feeders displayed higher

biomass (p < 0.05) in seagrass. In November 2005 and in November 2006, all trophic groups

(except predators) had higher levels (p < 0.05) in the seagrass bed. At the end of the survey,

when the seagrass bed occupied most of the area, all trophic groups (except suspension

feeders) showed higher biomass (p < 0.05) in the seagrass bed.

In terms of position related to substratum, epifauna always displayed higher

abundance, biomass and species richness (p <0.05) in seagrass compared to bare sand, except

abundance in November 2009 (p > 0.05) which was considered low for seagrass epifauna

(Figure 2.5). Hydrobia ulvae explained most of these differences. For infauna, abundance and

biomass remained similar until November 2005 and became higher in seagrass (p < 0.05)

thereafter (Figure 2.5A and B). Species richness was higher in seagrass from June 2005, with

increasing differences until November 2009 (× 1.5 to × 2.5) (Figure 2.5C).


25

0

20

40

60

80

100

120

140

160

180

Feb.-05 Jun.-05 Nov.-05 Nov.-06 Nov.-09

Mea

n bi

omas

s(g

AFD

W m

-2)

ns

A

0

5000

10000

15000

20000

25000

30000

35000


Mea

n ab

unda

nce

(indi

vidu

als

m-2

) B

Hu4748/9844

Hu4830/22674

Hu2867/12511

Hu3422/9400

Hu3956/407

0

5

10

15

20

25

30


Mea

n nu

mbe

r of s

peci

es

ns

C

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Feb.-05 Jun.-05 Nov.-05 Nov.-06 Nov.-09M

ean

H'

D

nsns

0.5 0.4 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.7

nsns

Figure 2.3. Mean biomass (A), abundance of macrofauna (including Hydrobia ulvae (Hu) density) (B), number of species (C) and Shannon index with Piélou’s evenness values on top of the figure (D) (+ 1 SE) in bare sand (in white) and in seagrass (in black) at different dates. ns: not significant, p > 0.05; : p < 0.05; : p < 0.01; : p < 0.001.


26

Figure 2.4. Mean biomass (g AFDW.m-2) of trophic groups (+ 1 SE) in bare sand (in white) and in the seagrass bed (in black) at different dates. A: predators; B: deposit feeders; C: grazers; D: scavengers; E: suspension feeders. ns: not significant, p > 0.05; : p < 0.05; : p < 0.01; : p < 0.001.

0

2

4

6

8

10

12


pred

ator

s (g

AFD

W m

-2)

ns

A

ns

ns

0

10

20

30

40

50

60

70

80


depo

sit f

eede

rs (g

AFD

W m

-2)

B

ns

ns

0

5

10

15

20

25

30

35

40


graz

ers

(g A

FDW

m-2

)

C

0

2

4

6

8

10

12

14


scav

enge

rs (g

AFD

W m

-2)

D

ns

0

20

40

60

80

100

120


susp

ensi

on fe

eder

s (g

AFD

W m

-2)

E

nsns


27

Figure 2.5. Mean biomass (A), abundance (B), number of species (C) (+ 1 SE) of epifauna (epi.) and infauna (inf.) in bare sand (in white) and in seagrass (in black) at different dates. ns: not significant, p > 0.05; : p < 0.05; : p < 0.01; : p < 0.001.

3.2.3. Assessment of the ecological quality of seagrass and bare sand by biotic indices

Over the years, BOPA index fluctuated without a clear solid trend but corresponded to

a good or high ecological quality. AMBI index classified the quality of both benthic

communities as good during the whole period. The BENTIX index produced a lower

assessment than AMBI and BOPA and classified the ecosystem quality from poor to

moderate (Figure 2.6). From February to June 2005, ANOVA did not indicate any significant

difference in these biotic indices values between seagrass and bare sand. However, in

November 2005, 2006 and 2009, there were significant differences between seagrass and bare

sand alternatively in AMBI, BOPA and BENTIX values (p < 0.05).

0

5000

10000

15000

20000

25000

30000

epi. inf. epi. inf. epi. inf. epi. inf. epi. inf.


Mea

n ab

unda

nce

(indi

vidu

als

m-2

)

ns ns

A

ns ns

A

ns

0

20

40

60

80

100

120

140



Mea

n bi

omas

s (g

AF

DW

m-2

)

ns

B

ns

02468

101214161820



Mea

n nu

mbe

r of

spe

cies

ns

C


28

Figure 2.6. Biotic indices, with thresholds used to classify index values and ecological quality of the ecosystem (ES) in bare sand and (in white) and in seagrass bed (in black) at different dates for A: BOPA index; B: AMBI; C: BENTIX.

3.3. Cockle population and related trematodes

3.3.1. Cockle dynamics

Mean shell length of cockles (corresponding to growth if recruitment occurred

simultaneously in the whole area) from the vegetated habitat was smaller (<0.05) than in the

sand habitat in most situations, i.e. different dates and cohorts, and was never larger (Table

2.3). As long as seagrass was present only in patches, from February 2005 to November

2006, the abundance of cockles in seagrass patches was similar to or higher (p < 0.05) than in

bare sand. Between November 2006 and November 2009, the cockle populations completely

collapsed in the seagrass that covered most of the area at that time with cockles remaining

0

0.04

0.08

0.12

0.16

0.2

0.24

0.28

0.32


BO

PA

high

good

moderate

poor

bad

A

0

1

2

3

4

5

6


AM

BI

high

good

moderate

poor

badB

0

1

2

3

4

5

6


BE

NTI

X

high

good

moderate

poorbad

C


29

only in bare sand close to the seagrass. With the sand/seagrass surface ratio decreasing, the

density of cockles tended to increase in bare sands. At the only occasion when we monitored

recruitment (June 2005), the number of recruits was 4 times higher in the bare sand but

collapsed largely over the following five months (November 2005, Table 2.3).

Table 2.3. Shell length and abundance (± 1 SE) of cockles in bare sand and seagrass. P- value was calculated from the comparison between both habitats.

Sampling date

Cockle cohort

Sand Zostera bed P- value

Feb. 2005 2003 Shell length (mm, ± SE) 28.3 (± 0.1) 26.9 (± 0.6) < 0.05 Abundance (ind.m-2) 70.0 (± 7.5) 137.3 (± 23.2) < 0.05 2004 Shell length (mm, ± SE) 23.0 (± 0.2) 21.0 (± 0.5) < 0.05 Abundance (ind.m-2) 62.7 (± 3.8) 107.3 (± 25.2) ns Jun. 2005 2003 Shell length (mm, ± SE) 31.5 (± 0.2) 29.1 (± 0.3) < 0.001 Abundance (ind.m-2) 65.0 (± 8.9) 156.0 (± 30.0) < 0.01 2004 Shell length (mm, ± SE) 27.2 (± 0.2) 23.9 (± 0.2) < 0.001 Abundance (ind.m-2) 85.0 (± 9.0) 133.3 (± 31.8) ns 2005 Abundance (recruits.m-2) 503.7 (± 79.2) 133.2 (± 50.1) <0.01 Nov. 2005 2003 Shell length (mm, ± SE) 33.8 (± 0.1) 32.5 (± 0.3) < 0.05 Abundance (ind.m-2) 6.0 (± 2.7) 20.0 (± 5.3) ns 2004 Shell length (mm, ± SE) 29.2 (± 0.4) 27.4 (± 0.3) < 0.05 Abundance (ind.m-2) 66.7 (± 22.0) 87.0 (± 18.7) ns 2005 Abundance (recruits.m-2) 1.3 (± 0.8) 12.7 (± 4.4) < 0.05 Nov. 2006 2005 Shell length (mm, ± SE) 35.4 (± 0.2) - (± -) - Abundance (ind.m-2) 18.7 (± 7.1) 0.0 (± 0.0) < 0.01 2006 Shell length (mm, ± SE) 21.3 (± 0.4) - (± -) - Abundance (ind.m-2) 15.3 (± 3.2) 0.0 (± 0.0) < 0.05 Nov. 2009 2008 Shell length (mm, ± SE) 32.5 (± 0.7) 31.2 (± 0.5) ns Abundance (ind.m-2) 4.7 (± 1.9) 3.3 (± 1.6) ns 2009 Shell length (mm, ± SE) 17.3 (± 0.4) - (± -) - Abundance (ind.m-2) 102.0 (± 55.1) 0.0 (± 0.0) < 0.01


30

3.3.2. Parasites community

Cockles were infected by five trematode species. In February 2005, when the seagrass

bed just started to grow, the dominant trematode species were Himasthla interrupta and

Meiogymnophallus minutus. In both cohorts (2003 and 2004), the average parasite abundance

per cockle of these trematode species was similar between habitats (p > 0.05) (Figure 2.5A

and B). In June 2005, the abundance of H. interrupta increased in the bare sand only and

became higher than in seagrass (p < 0.05) (Figure 2.7C). All other trematode species

displayed similar abundance levels in both habitats. In November 2005, H. interrupta

abundance increased 5-fold and became similar in seagrass bed and bare sand. M. minutus

abundance increased and became higher in bare sand (Figure 2.7D). Thereafter, until 2008

cockles disappeared from the seagrass preventing any comparison. In November 2009, the

2008 cockle cohort maintained itself in both habitats, with parasite abundance remaining

higher in the seagrass bed (p < 0.001) for the four dominant trematode species (Figure 2.7E).


31

020406080

100120140160180200

Hq Hi Ca Mm Pb

Met

acer

caria

e nu

mbe

r. co

ckle

-1February 2005: cohort 2003

(shell length: 27-28 mm)A

ns

ns

Hu4748/9844

Sp22/59

Hu4748/9844

Nr30/207

0

20

40

60

80

100

120

140

160

Hq Hi Ca Mm Pb

Met

acer

caria

e nu

mbe

r. co

ckle

-1

June 2005: cohort 2004(shell length: 24-27 mm)

ns nsns

C

Nr7/74

Hu4830/22674 Hu

4830/22674

Sp244/163

020406080

100120140160180200

Hq Hi Ca Mm Pb

Met

acer

caria

e nu

mbe

r. co

ckle

-1

February 2005: cohort 2004(shell length: 21-23 mm)

B

nsns

ns

Nr30/207

Hu4748/9844

Sp22/59

Hu4748/9844

0

100

200

300

400

500

600

Hq Hi Ca Mm Pb

Met

acer

caria

e nu

mbe

r. co

ckle

-1

November 2005: cohort 2004(shell length: 32-34 mm)

ns

nsns

D

Nr7/89

Hu2867/12511

Hu2867/12511

Sp22/119

050

100150200250300350400450500

Hq Hi Ca Mm Pb

Met

acer

caria

e nu

mbe

r. co

ckle

-1

November 2009: cohort 2008(shell length: 31-33 mm)

ns

E

ns

Nr15/59 Hu

3956/407Hu

3956/407

Sp0/0

Figure 2.7. Parasite abundance (mean ± 1 standard error) and host density (ind m-2) in bare sand (in white) and in seagrass bed (in black). When available, mean density (ind m-2) of the upper host (=first intermediate host) was determined (sand/seagrass). Nr: Nassarius reticulatus; Hu: Hydrobia ulvae; Sp: Scrobicularia plana. ns: not significant, p > 0.05; : p < 0.05; : p < 0.01; : p < 0.001. A (cockle cohort 2003) and B (cockle cohort 2004): in February 2005; C (cockle cohort 2004): in June 2005; D (cockle cohort 2004): in November 2005; E (cockle cohort 2008): in November 2009.


32

4. Discussion

4.1. Kinetic of seagrass development and associated macrofauna

The structure of benthic communities is largely influenced by sediment grain-size and

by seagrass presence. However, whether sediment characteristics or seagrass is the dominant

driver remains unclear. The originality of this study was to demonstrate that the occurrence of

a seagrass bed rapidly modified the structure of benthic communities, independently from the

median grain-size of the sediment. Three main stages were identified according to the

development of Zostera noltii bed: early settlement, 50% spreading and 100% spreading

(Figure 2.8).

Early settlement•Silt and clay (×1.9)•Organic matter (×1.4)• Median grain-size: ns

50% spreading•Silt and clay (×2.7)•Organic matter (×2.8)• Median grain-size: ns

100% spreading•Silt and clay (×4.2)•Organic matter (×3.2)•Median grain-size (×1.2)

Population (cockles)

•Length (×1.1)•Abundance (×1.8) •H. interrupta (equal)•M. minutus (equal)•H. quissetensis (equal)•C. arguinae (equal)

Community

•Abundance (×1.9)•Biomass (equal)•Species richness (equal)•H’ (1.2)•BOPA (×0.4)•AMBI (equal)•BENTIX (equal)


•Length (×1.1)•Abundance (×1.6)•H. interrupta (×1.4)•M. minutus (×1.1)•H. quissetensis (×2.5)•C. arguinae (equal)•Community

•Abundance (×3.9)•Biomass (×4.5)•Species richness (×2.0)•H’ (×1.1)•BOPA (×2.3)•AMBI (equal)•BENTIX (equal)


•Length (×1.1)•Abundance (×1.4)•H. interrupta (×4.8)•M. minutus (×4.1)•H. quissetensis (×3.4)•C. arguinae (×2.8)

Community

•Abundance (×7.8)•Biomass (×3.0)•Species richness (×2.6)•H’ (×1.8)•BOPA (×1.4)•AMBI (× 1.1)•BENTIX (× 1.4)

Figure 2.8. Summary of seagrass colonization and its main effects on sediments, cockles population, and macrofauna structure (comparison between bare sand and seagrass bed).


33

In early settlement (February 2005), seagrass was clustered as small patches on a

sheltered sandy beach. Concerning sediment characteristics, only silt and clays and organic

matter content were significantly higher in seagrass compared to bare sand, while median

grain-size remained similar. However, the seagrass macrofauna assemblage was immediately

separated from the sand assemblage. The abundance of macrofauna increased in the seagrass

bed but biomass and species richness remained similar. Most of the abundance increase was

due to the grazing gastropod Hydrobia ulvae (78% of total abundance). This species was one

of the most abundant species in both habitats (bare sand and seagrass) and is reputed to

display higher abundance in seagrass beds (Cardoso et al., 2008). Higher abundance of

macrofauna in vegetated habitats has often been reported (Fonseca et al., 1990; Orth, 1992;

Boström and Bonsdorff, 1997; Fredriksen et al., 2010) and different mechanisms can be

explained, for example 1) decreased predation efficiency due to high habitat complexity

(Orth et al., 1984); 2) habitat preference of dense seagrass by prey as an escape mechanism

from predation (Fonseca and Fisher, 1986; Webster et al., 1998; Boström et al., 2006b); 3)

stabilisation of sediments that accumulate organic material, allowing increased settlement and

growth of infauna (Neckles et al., 1993; Fredriksen et al., 2005); juveniles and adults are also

prevented from being resuspended and transported away (Fonseca et al., 1990); and 4) a high

content of organic matter, which may be common in seagrass meadows, attracts a certain type

of infauna such as deposit-feeding polychaetes (Fredriksen et al., 2010).

When seagrass beds spread over 50% of the available intertidal area (from June 2005

to November 2006), both silt and clays and organic matter contents went on increasing in the

seagrass bed, while median grain-size remained similar between both habitats and compared

to initial values. All macrofauna parameters (biomass, abundance, species richness and H’)

were enhanced in the seagrass beds. Moreover, the structures of both communities were

clearly contrasted. The development of the seagrass bed initially favoured grazers, predators

and suspension feeders in terms of abundance. Seagrass thus appeared as a structuring factor

with a higher strength than sediment. In other words, seagrass development, associated with

increased organic matter, changed the trophic structure of the assemblage in the seagrass bed

independently of the median grain-size. The role of seagrass in structuring communities was

determined in many studies (Stoner, 1980; Orth et al., 1984; Edgar et al., 1994; Webster et

al., 1998; Honkoop et al., 2008; Fredriksen et al., 2010). Stoner (1980) concluded that the

biomass of benthic vegetation, independent of sediment granulometry, exerts a strong


34

influence on the abundance, dominance, diversity, and trophic organization of macrobenthic

infauna and epifauna.

This study also confirmed that vegetation cover is a major factor increasing mobile

epibenthic fauna abundance, biomass and species richness as suggested by several former

studies (Edgar, 1990; Edgar, 1999; Cottet et al., 2007). Greater number of niches and more

food can explain this trend (Nakaoka, 2005). The role of seagrass on infauna is more

controversial. Cottet et al. (2007) suggested that the structuring effect of seagrass is mitigated

in subtidal and high tidal levels, whereas this effect is stronger at low intertidal level (our

study case). At that level, seagrass protects macrofauna against desiccation while rhizomes

and roots create spatial complexity within sediment enable oxygenation (Osenga and Coull,

1983; Hedge and Kriwoken, 2000). At Banc d’Arguin, the discrimination between seagrass

and unvegetated sediments took longer (few months) than for epifauna but was achieved at

all levels (biomass, abundance and species richness). Macrofaunal assemblages modification

primarily concerned deposit-feeders (e.g. capitellid polychaetes, Abra segmentum) hence

rejecting the hypothesis that roots and rhizomes involve loss of burrowing species (Talley

and Levin, 2001).

When the seagrass bed covered approximately the whole flat with only minor sand

patches (November 2009), all sediment characteristics were different between both habitats,

including a significant decrease in the sediment median grain-size in the seagrass bed. This

trend was consistent with previous results highlighting that the seagrass beds accumulate fine

sediments and organic matter (Yang, 1998; Agawin and Duarte, 2002; Leonard et al., 2002;

Bos et al., 2007; van Katwijk et al., 2010). These major changes in the environmental factors

lead to sharp distinctions in community structure between both habitats. Biomass, abundance

of macrofauna, species richness and H’ diversity were significantly higher in the seagrass

bed. Three deposit-feeding polychaetes, Heteromastus filiformis, Aphelochaeta marioni, and

Notomastus latericeus, became the most abundant species. However, the large bivalves

almost disappeared in the seagrass, such as Mytilus edulis, Cerastoderma edule and

Ruditapes philippinarum. The obvious separation of macrofaunal assemblage in 2009 (≈

100% coverage) from those in 2005 (patches) and 2006 (50% coverage) confirmed the

difference between patchiness and continuity in structuring macrofaunal assemblages. In

addition to the generalized idea that macrophytes enhance macrofaunal densities (Heck and


35

Orth, 1980; Borg et al., 2010), a patchy distribution and the presence of edges have been

found to support higher faunal densities than continuous or dense patches (Bouma et al.,

2009b). Our results confirmed the difference in community structure between reticulate

seagrass beds and continuous meadows, but not the decrease of faunal abundance. Indeed, if

Hydrobia ulvae which accounted for nearly 80% of the total macrobenthic abundance is

excluded from the analyses, the faunal density was much higher in the continuous meadows.

4.2. Seagrass development and benthic community health

AMBI, BOPA and BENTIX have been developed and adapted to the objectives of the

WFD. In our study, although the values of AMBI, BOPA and BENTIX alternatively

displayed a difference between seagrass and sand from autumn 2005 to autumn 2009, ES did

not change in both habitats. AMBI and BOPA fairly assessed the quality of the ecosystem in

Banc d’Arguin while BENTIX underestimated it. Comparisons between these indices

revealed that BENTIX did not provide results coherent with the two other indices nor with

what was expected from a seagrass bed in a non-polluted oceanic area. The limitations of the

use of the BENTIX index are met in the case of transitional waters (estuaries and lagoons)

where the natural conditions favour the presence of tolerant species in very high densities. In

this case, undisturbed lagoons or estuaries may appear with low quality status if the BENTIX

index is used. Interpreting different benthic indices developed for different habitats to yield a

common assessment for management purposes is further complicated when the indices are

based on different combinations of metrics (Teixeira et al., 2010). The discrepancy between

indices is mentioned in previous studies (Blanchet et al., 2008).

A comparison of AMBI or BOPA values between habitats or among dates confirmed

that seagrass cover estimation must remain a WFD parameter to be measured. Indeed, BIs

alone did not reveal the transition of habitat from sand to continuous meadow. More exactly,

they were unable to pick-up differences between communities because indices are calculated

according to the sensitivity of a species and not to the identity of the species. Therefore, when

a habitat changes but remain sensitive, as in the present case, BIs cannot detect community

changes. Such lack of reactivity concerning benthic habitat modification was already

demonstrated (Lavesque et al., 2009). However, it is also important to highlight that most BIs

(BOPA, BENTIX) used in this study were originally developed for subtidal communities. For


36

intertidal environments, the thresholds between ES classes should be revised and

‘Acceptable’ (Good and High status) and ‘Not acceptable’ (Moderate or Worse status)

redefined (Blanchet et al., 2008).

4.3. Seagrass development and cockle population health

Cockle population health was estimated through their growth (compared shell length),

their abundance (recruitment, mortality), and their parasite load (trematodes). Mean shell

length was always smaller in seagrass. The vegetation limits hydrodynamics and alters

suspension feeders filtration efficiency (Coen and Heck, 1991; Irlandi and Peterson, 1991;

Irlandi, 1996; Bouma et al., 2009b). However, the changes in the sediment dynamics within

vegetation explain that some investigations have demonstrated increased growth within

seagrass beds while others have not (Irlandi, 1996; Reusch, 1998). The consequence of the

development of seagrass on cockle abundance depended on whether cockles were already

settled or not (a posteriori recruitment). Intraspecific competition can be responsible for a

cockle growth deficit (Jensen, 1992): in the present study, cockle abundance reached values

for which such a process was already observed in this site (de Montaudouin and Bachelet,

1996). When seagrass developed in an established adult cockle population, cockles

abundance remained higher than in bare sand. Seagrass can act as a refuge against predation

(Orth et al., 1984; Boström and Bonsdorff, 1997), and particularly against birds that are the

main predators of adult cockles (Reise, 1985). When seagrass was already present and

recruitment rate was high, the number of recruits was significantly lower in seagrass bed

possibly due to predation that is higher in the seagrass for small prey such as juvenile cockles

(Edgar, 1999). Indeed, predators exhibited higher biomass in the mature seagrass, at the end

of our monitoring. That can also be due to the effect of hydrodynamics and sedimentary

impact of seagrass on recruitment processes. Some species such as mussels that need a hard

substratum to settle are favoured by the presence of seagrass (Reusch, 1998). For an infaunal

bivalve, the presence of high density of leaves, roots and rhizomes can interfere with

settlement (Neira et al., 2006). In addition, increased organic matter deposition in seagrass

can create an unsuitable habitat for buried bivalves with hypoxic conditions and enhanced

sulphide concentrations at the sediment surface (Rosenberg et al., 1991). Considering

parasites, the initial presence of the seagrass bed did not modify trematode infection in


37

cockles between seagrass and bare sand. This may be due to the complex trematode life cycle

that requires time to settle. In June 2005, infection levels differed in cockles from bare sand

but there was no relation with the potential first intermediate host abundance (Hydrobia

ulvae). Four to nine months after seagrass settlement, prevalence of infection of cockles with

two trematode species differed between both habitats. At the completion of seagrass

development, four out of five trematode species exhibited higher infection in the seagrass

bed. Therefore, with time, trematode communities diverged in relation to seagrass presence

as shown in a Moroccan lagoon (Gam et al., 2009a). The occurrence of the ‘upstream’ first

intermediate host is considered as a strong driver to explain ‘downstream’ infection

(Thieltges and Reise, 2007). The correlation between both factors (abundance of first

intermediate host and infection intensity in the second intermediate host) depends on the

scale studied (Wiens, 1989) and can disappear at the small scale (Poulin and Mouritsen,

2004). This is the case in most of our results where a higher infection in one habitat rarely

corresponded to higher first intermediate host abundance. However, we do not have any data

concerning the prevalence of infection of first intermediate hosts in seagrass and bare sands.

As previously mentioned, parasites can alter cockle fitness. In Arguin, parasites

induce mortality when mean Meiogymnophallus minutus abundance in cockles reaches 500

individuals per cockle (Gam et al., 2009b), but this threshold is lower for both Curtuteria

arguinae (∼50 individuals cockle-1) (Desclaux et al., 2006) and Himasthla quissetensis (∼32

individuals cockle-1) (Desclaux et al., 2004). Comparing these thresholds with our results, C.

arguinae (157 individuals cockle-1) would be the main trematode species that could increase

cockle mortality.

Conclusion

The development of the seagrass bed induced a large-scale change in benthic

communities, including trematode assemblages. It is however complex to determine if this

seagrass extension leads to an increase in ecosystem quality. The WFD considered that for

Good Ecological Status “The levels of… angiosperm abundance are consistent with

undisturbed conditions” (EEC, 2000). Our results highlight a rapid modification of benthic


38

community structure, but it is not possible to say whether this is a positive or negative change

remains subjective. The different tested Biotic Indices (BIs) did not reveal any change in

relation to seagrass development. That meant that these BIs are not useful in detecting habitat

modification on these flats. On the other hand, it can also be considered that these BIs

“behaved” independently of benthic habitat change in order to assess the water mass ES as

required by the WFD. Finally, seagrass bed development had a rather negative role on cockle

populations. This negative effect was certainly due to the physical presence of Zostera noltii

(and hydrodynamic consequences) than the parasite’s impact. Indeed, although trematode

parasite can alter their host-population dynamics, infection rate observed in the present study

(i.e. number of metacercariae per cockle) was smaller than what has been observed elsewhere

(Desclaux et al., 2004; Desclaux et al., 2006; Gam et al., 2009b).

39

+++

In Chapter 2, we showed that seagrass presence modify trematode community in

cockles, with an increase of parasite. Three hypotheses can be advanced to explain why

parasite infection was higher in seagrass bed compared to bare sand: 1) The presence of

seagrass may perturb parasite locomotion between both hosts. This hypothesis can be rejected

because parasites displayed higher abundance in cockles from the seagrass. 2) Larger cockles

are more infected because they filtrate more water. This hypothesis can be excluded because

cockles were smaller in the seagrass compared to bare sand. 3) The presence of seagrass

attracts the hosts that will emit the parasites toward the cockles. It is highly plausible but

these data are lacking in the present study.

Nevertheless, seagrass dynamics seems related to host-parasite dynamics. In the next

chapter, we will test correlation among seagrass cover, some environment factors as salinity,

emersion rate, temperature, sediment characteristics and the distribution of different

parasites/pathologies. Then, we will consider parasite in some dominant infaunal species

(Manila clam (Ruditapes philippinarum) and cockle (Cerastoderma edule) as an element

among others to assess seagrass ecosystem fitness. Up to now, the relationship between

seagrass presence/absence and parasite remained poorly documented (Gam et al. 2009a). The

hypothesis is that seagrass, as an engineer habitat, can affect both biotic and abiotic factors

which contribute to the parasite community.

This study was the occasion to develop a tool allowing us to rapidly estimate sea grass

(leaves) biomass from several field campaigns. Indeed, the traditional method of

measurement sea grass biomass requires a lot of time (sorting, dry weighting). Alternative

methods using video camera cannot be applied in Arcachon Bay intertidal mudflats due to

navigation security and/or high turbidity). Here, we proposed a new method based on

simplified photography analysis.

+++

40

Chapter 3- Seagrass and bivalve health

41

Chapter 3 - Correlation between bivalves health and

environmental parameters, including Zostera noltii seagrass bed

cover, in Arcachon Bay

In revision in Marine Ecology : An Evolutionary Perpective, with the title “Environmental

factors contributing to the development of Brown Muscle Disease and Perkinsosis in Manila

clams (Ruditapes philippinarum) and trematodiasis in cockles (Cerastoderma edule) of

Arcachon Bay”

Abstract

The aim of the present study was to identify environmental factors that could explain

the distribution of different pathologies of commercially exploited bivalves, in an Atlantic

lagoon, Arcachon Bay. In particular, the role of salinity gradient as a driver was explored.

The Manila clam Ruditapes philippinarum underwent two severe pathologies, i.e. perkinsosis

which is due to a protozoan parasite and brown muscle disease (BMD) the etiological agent

of which remains unknown. Perkinsus olseni infection was very low in a small low-salinity

area but, at the scale of the entire lagoon, was more influenced by organic matter content in

the sediment and by emersion rate. BMD prevalence was also 2.6 times higher in the higher

organic content area but was also negatively correlated with salinity. The sympatric cockle

(Cerastoderma edule) was affected by eight trematodes. These parasites have a complex life

cycle which generally involves three free-living species. The distribution of the different

trematode communities was rather patchy within Arcachon Bay with no clear relationship

with measured environmental factors. The dominance of trematode species could be due to

the presence of the other hosts involved in their life cycle which makes it more difficult to

detect a major environmental driver.

This survey demonstrated that salinity is not the major factor explaining disease

distribution in this temperate lagoon. This result has consequences in the research of “refuge

areas” (free of diseases) or “hot spots” (heavy infection) for high economic value species.

Keywords: bivalves, diseases, distribution, Arcachon Bay, parasites


42

1. Introduction

In transitional ecosystems, salinity is generally a strong driver of community structure

(Blanchet et al., 2005). This is particularly true in systems where the gradient is obvious like

in estuaries (Wolff, 1973; Ysebaert and Herman, 2002; Rybarczyk and Elkaim, 2003).

Typically, three types of faunal communities are encountered, in euryhaline, polyhaline,

mesohaline and oligohaline waters respectively, all of these also having both pelagic

(Herman et al., 1968; Orsi and Mecum, 1986; Baretta and Malschaert, 1988; David et al.,

2006) and benthic components (Attrill and Rundle, 2002; Ysebaert and Herman, 2002).

Parasite species may also display a strong relationship with the haline gradient when their life

cycle involves a free stage that is sensitive to water mass characteristics. This has been

described in protozoans like Perkinsus spp. that parasitize numerous mollusc species (Ahn

and Kim, 2001; La Peyre et al., 2006). When the host dies, the parasite evolves into a stage,

the hypnospore, which spends some time in the water before being inhaled by a new host.

The various different species of Perkinsus genus are known to be limited by low salinity

(Leite et al., 2004). Most trematode species also display free-swimming larval stages in their

life cycles, usually including two such stages. The parasite sexually reproduces in the

definitive host. Eggs are emitted into the water with faeces and develop into miracidium

larvae that infect the first intermediate host, always a mollusc. Asexual reproduction in this

host leads to the formation of a new type of larvae, cercariae, which are shed into the water

and swim or drift before infection of the second intermediate host. There, larvae remain in a

latent stage, the metacercariae, and wait for their host to be predated on by the final host in

order to achieve their life cycle. Many studies demonstrated that these swimming larvae were

sensitive to salinity (Mouritsen, 2002; Koprivnikar et al., 2010), suggesting that this factor

could contribute to explaining the structure of parasite communities.

In lagoons, however, salinity gradients are not always so pronounced and other

structuring factors may interfere in the process such as emersion rate, seagrass occurrence,

and sediment grain-size (Bachelet et al., 1996; Marzano et al., 2003; Blanchet et al., 2004). It

results in a mosaic of communities where the major structuring factors are not always easy to

detect. In relation to parasites, there is little knowledge on the factors that drive their

distribution in these sheltered areas. For fishermen, such knowledge could contribute to


43

identifying ‘refuge areas’ that are more or less exempt from infectious diseases (Hoffmann et

al., 2009).

Arcachon Bay is a good system to illustrate and study these questions. It is a typical

tidal lagoon with a cape sheltering most of the system and with fresh water inputs. Previous

studies performed on free-living intertidal fauna demonstrated that the salinity gradient was

not the only driver of the distribution of pelagic (Vincent, 2002) and benthic communities

(Bachelet and Dauvin, 1993; Blanchet et al., 2004). Arcachon Bay also ranks in first position

in terms of Manila clam Ruditapes philippinarum production in France and periodically

sustains a small cockle Cerastoderma edule fishery also. Both bivalves undergo pathologies.

Perkinsosis (Perkinsus olseni) is very prevalent in Manila clams in the whole bay with no

detected gradient (Dang et al., 2010a). Recently, in 2005, a new disease of the bivalve was

described for the first time, Brown Muscle Disease (BMD) (Dang et al., 2008). The infectious

agent is still unknown, although a virus is suspected (Dang et al., 2009). Finally, trematodes

are abundant, mostly in cockles. A community of 13 species was identified in previous

studies (Desclaux et al., 2002; de Montaudouin et al., 2009) but the global distribution of

these parasites at the lagoon scale remains unknown. Trematodes induce a less severe impact

on their cockle hosts than that observed in Manila clam infected with Perkinsosis and BMD,

except when the intensity (number of parasite individuals per infected host (Bush et al.,

1997)) becomes high in cockles infected as second intermediate hosts (Desclaux et al., 2004)

or when the cockle is the first intermediate host (Jonsson and Andre, 1992; Thieltges, 2006a).

Our specific aims were: 1) to describe a series of biotic and abiotic factors in

Arcachon Bay in order to identify homogeneous entities, i.e. groups of stations that were

defined by environmental characteristics with similar values (of salinity, temperature,

emersion rate, grain-size median, seagrass biomass…); 2) to assess levels of infection in

cockles and clams; 3) to identify environmental factors correlated with the different diseases.

The general aim was to explore whether it is possible to identify ‘hot spots’ (accumulation of

infections) or ‘refuge areas’ (sites free of pathogens) in relation to some biotic and/or abiotic

factors.


44

2. Materials and Methods

2.1. Study site

Arcachon Bay (44°40’N, 1°10W) is a macrotidal (tidal range = 0.9-4.9) coastal

lagoon situated on the South Western coast of France. This 156-km² ecosystem is connected

to the Atlantic Ocean by a 2-3-km wide and 12-km long channel. Channels represent 41 km²

of the lagoon surface and penetrate between large intertidal areas (115 km²). A significant

proportion of these tidal flats (46 km²) are covered by Zostera noltii seagrass beds (Plus et al.,

2010). Arcachon Bay receives freshwater inputs from its North-Eastern and Southern parts

but mainly by a river (Leyre) located in the South-Eastern end of the lagoon. The balance

between marine and continental water inputs and the slow renewal of water by tides induce

salinity and temperature gradients (Robert et al., 1987).


In October and November 2009, a total of 39 stations were sampled along two axes

(i.e. two subareas) drawn between the most seaward part of the lagoon and the most

landward, within the Manila clam and cockle habitat (Figure 3.1). Stations were sampled at

low tide. Six 0.25-m² quadrats were sampled by hand to collect clams and cockles. When the

number of collected individuals was insufficient, they were collected haphazardly in the

immediately surrounding area. When all stations were sampled, we selected 28-36-mm shell

length individuals that corresponded to the length range that was common to all stations.

Sediment was sampled for organic matter analysis and grain-size determination (median).

Pictures were taken for seagrass biomass evaluation (Fujifilm FinePix S9500 camera, 1600 ×

1200 pixels) (see Figure 3.1).


45

Figure 3.1. Principal Component Analysis (PCA) based on 21 environmental factors (A) from 33 stations (B). Four groups can be separated. Organic Matter content in the sediment contributed to Axis 1, while salinity and temperature contributed to Axis 2. Group 1: blue; Group 2: green; Group 3: violet; and Group 4: red. White : No values. Stars represent stations where bivalves were collected while yellow disks represent stations that were surveyed within ARCHYD network in order to obtain ground-truth values of salinity and temperature for model validation. Black disk situates Eyrac tide gauge to calibrate sea surface height with the model and to deduce emersion time.


46

2.3. Environmental factors

2.3.1. Grain size and organic matter

At each station, the 3 first cm of sediment was collected and subsequently analysed

for grain-size distribution (wet sieving) and organic matter (loss of weight of dry sediment at

450°C for 4 h).

2.3.2. Seagrass leaf biomass

A rapid but reliable method of assessing Zostera noltii’s leaf biomass was developed

for this study, during a preliminary sampling trip.

Fifteen 15 cm × 15 cm quadrats were delicately laid over the sediment surface, at low

tide. These quadrats were visually selected to represent a large range of vegetation cover,

from 0% (bare sediment) to 100%. For each quadrat, a numeric photograph was taken

perpendicularly, one meter above the surface.

Then, leaves were cut at their base with scissors. Back at the lab, each sample was

washed, weighed (fresh weight) and dried at 60°C for 48 hours to obtain a dry weight.

On each photograph, polygons corresponding to bare sediment have been drawn using

image analysis software. The surface of these polygons was automatically calculated and the

seagrass cover was deduced from the quadrate surface. The leaves’ dry weight and the

surface cover were correlated after logarithmic transformation of both variables (and after

removing the picture without grass). The equation being as follows:

Loge(DW) = 1.450 × Loge(S) – 1.733, with R = 0.98 (n=14 pictures)

Where Loge(S) = 0.690 × Loge(DW) + 1.195

DW is Zostera noltii leaves dry weight in g.m-2 and S is the percentage of sediment

covered by Z. noltii. The biomass in fresh weight (FW) could also be obtained

FW = 13.2 × DW, R=0.97 (n=15)

Therefore from a numerically identified picture, it was possible to obtain a biomass

with reliable precision. Of course, the more the vegetation cover approaches 100% cover, the


47

less precise the method becomes, because 100% cover may correspond to many biomass

values, depending on seagrass bed thickness. Then, the drawing of polygons allowed us

obtaining the relationship to calculate leave biomass from seagrass cover but was highly time

consuming for routine survey and not really worth comparing to direct biomass assessment.

Consequently, we used a line-drawing method in order to rapidly estimate foliage biomass.

This method consisted of drawing three equidistant lines across each numerical image

and counting the intersections between lines and leaves. Consequently, the 15 cm × 15 cm

frame must be disposed with leaves perpendicular to one of the square sides and lines have to

be drawn at right angle to leaves. The best correlations were found after logarithmic

transformation of both variables (intersection and biomass).

Loge(DW) = 1.514 × Loge (mean number of intercepts per line) – 1.911, with R =

0.98 (n=14 pictures)

This method was utilized to determine aerial biomass in the 39 investigated stations

(10 frames per station). Less than ten minutes per photograph, including line drawing, were

necessary to estimate aerial biomass.

2.3.3. Temperature, salinity, emersion rate

The high number of sampling sites and the necessity to determine the general

environmental patterns defining the “living conditions” at each sampling site were

incompatible with the setting up of an experimental protocol. Thus, temperature, salinity and

emersion rates for each sampling site, were obtained by means of a mathematical model

(MARS, (Lazure and Dumas, 2008)), which was previously applied and validated on

Arcachon Bay (Plus et al., 2009). This three-dimensional hydrodynamic model calculates free

surface height variations, current speed and direction, water temperature and salinity, at a 235

m resolution on the horizontal plan (10 meshes on the vertical) and at a time step ranging

from 10 to 60 seconds. The model was launched for a three-year period (November 2006 to

November 2009), and the following parameters were recorded for each site:

• Temperature and salinity minima, maxima and means.

• Percent of time spent in emersion.


48

• Frequency histograms for temperature (percent of time spent at temperatures

below the following thresholds: 8, 12, 16 and 20°C). These threshold values cover

the range of values that is found in this area and allow detecting particularly low

and high temperatures from “rare events”.

• Frequency histograms for salinity (percent of time spent at salinities below the

following thresholds: 13, 28 and 34). These threshold values cover the range of

values that is found in this area and allow detecting particularly low and high

salinities from “rare events”.

Boundary conditions were provided by the global tidal solution FES99 (Lefèvre et al.,

2002) and the atmospheric forcing – air temperature, atmospheric pressure, nebulosity,

relative humidity and surface wind stress – was provided by the ARPEGE model (Météo-

France).

Comparisons between available ground-truth values and model simulations were

performed in order to validate the mathematical model on the study period (from November

2006 to November 2009). Temperature and salinity observed data were taken from the

ARCHYD database (Ifremer), selecting four stations located along the clam sampling axes

(Figure 3.1). Sea surface height (SSH) observations at the Eyrac tide gauge (Figure 3.1) were

provided by the REFMAR website (refmar.shom.fr) and remain the property of the SHOM

(Naval Hydrographic and Oceanographic Service) and the Gironde DDTM (Sea and Territory

Departmental Directorate). Model evaluation was performed following Piñeiro et al. (2008),

regressing observed vs. predicted values and testing the significance of slope=1 and

intercept=0. This analysis was complemented by RMSD (root mean squared deviation) and

EFF (model efficiency) calculations:

n

YYRMSD

n

iiobsi∑

=−

= 1mod )²(

( )

( )∑

∑

=

=

−

−−= n

ii

n

iii

YY

YYEff

1

2obsobs

1

2obsmod

1

where Yimod and Yiobs are respectively the predicted and observed values and n is the

total number of values.

Table 3.1 summarizes the results of the model validation. Observed vs. predicted

values regressions showed that the model behave satisfactorily. Best model performances


49

were obtained for sea surface elevation and temperature, with a very high percentage of

variance in observed values explained by the model (respectively 97% and 98%). Worst

model performance was obtained for salinity but the coefficient of determination still remain

high (85%). All tests for slope=1 and intercept=0 were passed and model efficiency was close

to 1. Theil's partial coefficients show that most of the errors in model predictions were due to

unexplained variance and not to bias or to misleading. Temperature, salinity and SSH root

mean squared deviations are 0.78°C, 0.79 and 19 cm, respectively.

Table 3.1. Regressions parameters (slope a and bias b, for the Yobs = aYmod + b equation), coefficient of determination (r2), Theil's partial inequality coefficients (Ubias, Uslope and Uerror,, are the proportions of observed variance not explained by the predicted values but due to respectively, mean differences between observed and predicted values, slope error and unexplained variance), root mean squared deviation (RMSD, expressed in the same units as the variables) and model efficiency (Eff, the closer Eff is to 1, the better is the model), for observed vs. predicted variables (temperature, TEMP, salinity, SAL and sea surface height, SSH).

SSH Temperature Salinity a 1.008 0.958 0.910 Significance of test a=1 0.20 0.09 0.18 b 0.016 0.376 3.086 Significance of test b=0 0.33 0.35 0.16 Degree of freedom 915 34 34 r2 0.97 0.98 0.85 Ubias (%) 0.034 0.125 0.039 Uslope (%) 0.002 0.071 0.049 Uerror (%) 0.964 0.804 0.912 RMSD 0.19 0.78 0.79 EFF 0.96 0.98 0.84


50

2.4. Bivalve models and associated pathology

2.4.1. Manila clam and Perkinsus

All collected Manila clams (Ruditapes philippinarum) belonging to shell length class

28-36 mm were opened (34 stations out of 39 investigated stations harboured Manila clams

with adequate shell length) and gills samples were excised. Five gills from clams with

identical lengths were pooled and weighed for analyses of Perkinsus infection levels which

were determined by the FTM (fluid thioglycollate medium) assay (Ray, 1966). Depending on

clam availability, between 2 and 6 pools (except 1 pool in a single station (n°21)) were

obtained. For induction of prezoosporangia (hypnospores), gills samples were placed in

separate 15 mL tubes containing 9.5 mL FTM supplemented with 66 µg mL-1 streptomycin,

32 µg mL-1 penicillin G and, 40 µg mL-1 nystatin (final concentrations), to prevent bacterial

and fungal activity. The tubes were incubated at room temperature for 7 days, in the dark.

After incubation, the samples were stored at 4°C until hypnospore numeration. To lyse

tissues, samples were centrifuged at 2500 rd/min (664 g) for 10 min. Pellets were added with

5 mL NaOH 2N, and incubated at 60°C for at least 1 hour. This step was repeated before

pellets were rinsed twice with 10 mL 0.1 M phosphate-buffered saline (PBS). Final pellets

were resuspended in 1 mL PBS and hypnospores were counted twice using a Malassez

counting chamber.

The concentration of Perkinsus was correlated to the different variables of the

environment (Pearson correlation, after verifying normality of residuals), and was compared

among the four spatial groups (Kruskal Wallis test due to heteroscedasticity) and between the

two axes, i.e. between the two subareas of the lagoon (Student t-test) (Statistica 7 software).

2.4.2. Manila clam and BMD

All collected clams belonging to the 28-36-mm shell length class were opened and an

index of the pathology (Muscle Print Index, MPI) estimated. On the posterior muscle (the

only affected one), the MPI was used to designate the surface colonized by the brown muscle

print on a scale of 0 to 4 as follows: (0 (healthy), 1 (0-25%), 2 (25-50%), 3 (50-75%) and 4

(75-100%)). When both valves displayed different pathology indices, the highest category


51

was selected to characterize the stage of BMD. Prevalence was defined as the percentage of

infected hosts (Bush et al., 1997).

2.4.3. Cockle and trematodes

When possible, five cockles between 13 and 29-mm shell length per station were

opened. The flesh was separated and squeezed between two large glass slides. Trematodes

were identified and counted under a stereomicroscope (de Montaudouin et al., 2009).

Trematode abundance was defined as the mean number of metacercariae per individual host,

and prevalence as the percentage of infected hosts (Bush et al., 1997). A correspondence

analysis was performed on data which consisted of a ‘35 stations × 8 trematode species’

matrix when each species occurred. Data (averaged metacercariae abundance) were

log10(x+1) transformed. In the case of Bucephalus minimus, it was not possible to separate

and count sporocysts and a value of 1 was arbitrarily imposed in the matrix. Particular

attention was devoted to the identification of the ‘contributive’ taxa. A taxa was determined

‘contributive’ when its contribution to the dimension’s inertia was at least twice the mean

theoretical contribution of a taxon. Considering that the 8 taxa of the matrix contributed to

100% inertia, a “contributive” taxon inertia should arbitrarily be over (100/8) × 2 = 25%.

3. Results

3.1. Environmental factors

The Principal Component Analysis separated 4 spatial groups (Figure 3.1A). Group 1

isolated a small number of stations at the mouth of the two freshwater inputs (Canal des

Etangs and Leyre) (Figure 3.1B). This group was characterized by low mean salinity (22.6),

high frequency of T<16°C (50%), high organic matter and silt and clay contents in the

sediment (7.8 and 38.5%, respectively), null emersion time, and low seagrass coverage (7%)

(Table 3.2). Group 2 gathered stations in oceanic position with high median grain size (191

µm), high mean salinity (32.8), mean temperature similar to elsewhere in the lagoon (17°C)

but with low occurrence of cold events, i.e. low frequency of T<8°C (4.2%), low organic


52

matter and silt and clay content in the sediment (3.2 and 16.3%, respectively) (Figure 3.1,

Table 3.2). Groups 3 and 4 displayed medium mean salinity (30.1), medium frequency of

T<8°C (7.7%) (Table 3.2). Axis 2 displayed a higher percentage of silt and clay, organic

matter in the sediment and emersion time and more extreme water temperatures than Axis 1

(Table 3.3).

3.2. Manila clam and Perkinsosis

The mean concentration of Perkinsus in the bay was 62,000 cells.g-1 (gill fresh weight

FW) and could reach 209,000 cells.g-1 (gill FW) (station 33, Axis 2, Figure 3.2). It was

positively correlated to organic matter concentration in the sediment, percentage of emersion,

distance to Leyre river and negatively correlated to frequency of T<16°C and distance to

Canal des Etangs (Table 3.4). There was a significant difference of Perkinsus concentration

among spatial groups (Kruskal Wallis, df=3, H=22.58, p<0.001), Group 1 (7,400 cells.g-1

(gill FW)) being different from Groups 2, 3 and 4 that displayed similar infection (70,193

cells.g-1 (gill FW)). Perkinsus abundance was three times higher in Axis 2 (mean = 95,701

cells.g-1 gill fresh weight) than in Axis 1 (mean = 28,917 cells.g-1 gill fresh weight) (Student

t-test, df=33, t=-4.62, p<0.001).


53

Table 3.2. Different characteristics of the environment averaged for each of the four groups that were defined by the Principal Component Analysis (Figure 3.2). ANOVA (F value) or Kruskal Wallis tests (H) were performed to compare values among groups. Superscript letters gather groups that are similar for a given parameter, values that are different from values of the three other groups (p<0.05) are in bold.

Group 1 Group 2 Group 3 Group 4 F H p

Median (µm) 103 b

±52.8 191.2 a

±85.3 96.5 b

±37.8 108.3 b

±22.5 13.23 0.004

Silt (%) 38.5 a

±11.3 16.3 b

±16.3 38.8 a

±3.2 29.5 a

±8.3 8.32 <0.001

Organic matter (%) 7.8 bc

±2.6 3.2 a

±2.5 9.1 c

±1.3 6.2 b

±2.5 7.91 0.000 Sediment

Seagrass cover (%) 6.2 a

±10.8 72.2 b

±22.7 40.1 ab

±23.4 44.9 ab

±26.7 6.24 0.002

Mean 22.5 c

±3.7 32.8 b

±0.3 30.3 a

±0.3 29.9 a

1.4 23.16 <0.001

F <13‰ 16.4 a

±8.7 0.0 b

±0 0.1 ab

±0.3 0.3 ab

±0.9 14.64 0.002

F <28‰ 66.6 a

±19.0 1.8 b

±0.7 20.1 b

±3.9 22.7 b

±13.1 22.87 <0.001 Salinity

F <34‰ 99.9 a

±0.0 75.1 b

±7.8 99.8 a

±0.1 98.8 a

±1.5 21.92 <0.001

Mean 16.7 a

±0.2 16.7 a

±0.1 17.0 b

±0.1 17.0 b

±0.1 31.80 <0.001

Minimal 1.2 bc

±0.4 3.1 a

±0.9 0.8 c

±0.5 1.9 b

±0.9 9.55 <0.001

Maximal 34.4 ±0.5

33.1 ±2.2

34.1 ±2.1

32.2 ±2.6 1.36 0.274

F < 8°C 9.5 a

±0.3 4.2 b

±1.1 7.9 a

±0.2 7.5 a

±0.7 23.74 <0.001

F <12°C 31.6 a

±0.7 27.1 b

±0.2 28.0 a

±0.2 28.1 a

±0.5 23.11 <0.001

F <16°C 50.4 a

±0.7 48.4 ab

±0.4 48.0 b

±0.2 48.3 ab

±0.3 12.13 0.006

Temperature

F < 20°C 62.7 ac

±1.3 63.1 a

±1.4 59.5 b

±0.3 59.9 cb

±0.7 23.07 <0.001

Current F<0.25m/s 93.3 ±6.9

95.6 ±4.5

91.3 ±10.4

82.2 ±15.7 4.97 0.174

% Emersion time 0.0 a

±0 30.9 b

±16.9 32.9 b

±11.4 13.0 a

±15.1 6.26 0.002

Clam density (ind/m²) 16.2 ab

±28.1 6.3 a

±7.9 51.4 b

±41.7 23.9 ab

±18.6 10.84 0.012

Leyre 5.9 ab

±8.1 13.8 a

±1.1 12.4 ab

±1.3 8.2 b

±4.3 10.66 0.013

Canal des étangs 9.8 ab

±8.1 8.2 ab

±0.9 3.3 a

±1.3 8.2 b

±4.1 9.07 0.028 Distance (km)

Atlantic ocean 12.7 a

±1.9 3.0 b

±1.0 9.6 a

±0.4 9.5 a

±1.9 22.06 <0.001


54

Table 3.3. Different characteristics of the environment in both axes (Figure 3.1). Student t-test (t value) was performed to compare values between axes. Values that are different (p<0.05) are in bold.

Axis 1 Axis 2 t p

Median (µm) 131.9 127.4 0.20 0.838 Silt (%) 23.5 32.1 -2.12 0.041

Organic matter (%) 4.3 7.3 -3.11 0.004 Sediment

Seagrass cover (%) 44.6 51 -0.60 0.550 Mean 29.5 30.9 -1.38 0.178

F <13‰ 2.6 0.8 1.02 0.315 F <28‰ 25.2 15.2 1.49 0.144

Salinity

F <34‰ 91.2 92.3 -0.27 0.790 Mean 16.9 16.9 0.30 0.765

Minimal 2.5 1.7 2.28 0.029 Maximal 32.1 33.7 -2.05 0.048 F < 8°C 6.6 6.9 -0.39 0.700 F <12°C 28.2 27.9 0.69 0.489 F <16°C 48.6 48.3 1.33 0.191

Temperature

F< 20°C 61.3 60.9 0.57 0.573 Current F<0.25m/s 85.3 91.9 -1.53 0.135 % Emersion time 11.4 28.3 -3.16 0.003

Clam density (ind/m²) 17.6 27.7 -1.11 0.273 Leyre 7.2 13.3 -5.10 <0.001

Canal des étangs 10.9 4.5 7.83 <0.001 Distance (km)

Atlantic ocean 8.4 7.4 0.83 0.414


55

0

50 000

100 000

150 000

200 000

250 000

1 2 3 5 6 7 8 9 10 11 12 13 14 16 17 38 39

Perk

insu

s ce

ll/g

(gill

fresh

wei

ght)

Stations

Axis 1

West East

0

50 000

100 000

150 000

200 000

250 000

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Perk

insu

s ce

ll/g

(gill

fresh

wei

ght)

Stations

Axis 2

South North

Figure 3.2. Abundance of Perkinsus olseni (cells/g (fresh weight) in gills) per station in Arcachon Bay.


56

3.3. Manila clam and Brown Muscle Disease

The mean Brown Muscle Disease prevalence per station in Manila clams throughout

the bay was 11% (Figure 3.3). BMD prevalence and Muscle Print Index were (MPI)

positively correlated with silt and organic matter contents in the sediment and with frequency

S<34. They were negatively correlated with minimal temperatures. Besides, MPI was

positively correlated with mean water temperature and with Manila clam density (Table 3.4).

However, the influence of freshwater input was not similar between Canal des Etangs and

Leyre. There was no difference of prevalence between spatial groups (one-way ANOVA,

F3,28=2.03, p=0.13). However, Muscle Print Index increased from the most oceanic spatial

group (G2) (MPImean= 0.8) to the more continental ones (G3 and G4) (MPImean= 2.4) groups

(one-way ANOVA, F3,28=11.74, p<0.001). Prevalence was 2.6 times higher along Axis 2

(16.1%) (See Figure 3.1 for localisation of both axes) than along Axis 1 (4.5%) (U-Mann &

Whitney, Z=-2.70, p=0.007) (Figure 3.3). MPI was similar in both axes (1.84) (U-Mann &

Whitney, Z=-1.55, p=0.12).

3.4. Cockle and trematodes

Cockles were present in 35 stations out of the 39 investigated stations. A total of eight

trematode taxa were found: seven species utilize the cockle as second intermediate host. One

of them belongs to Himasthla genus and could be a complex of two species, H. quissetensis

and H. continua. The 8th species, Bucephalus minimus, utilizes the cockle as first intermediate

host with a global prevalence of 13% (N=144 cockles). Dimension 1 of the Correspondence

Analysis (44.6% of inertia) discriminated H. interrupta whereas dimension 2 (23.6% inertia)

discriminated Curtuteria arguinae and H. spp. (Figure 3.4A). Three trematode communities

can be identified in the bay (Figure 3.4B) : 1) H. interrupta was present in the northern part

of the bay only and characterized (mean abundance per station was comprised between 3 and

22 metacercariae.cockle-1) and was accompanied by a high B. minimus prevalence (24% with

N=25 cockles) ; 2) Curtuteria arguinae was the dominant trematode in the south-west end of

the bay, i.e. the most oceanic part (mean abundance per station comprised between 9 and 43

metacercariae.cockle-1, N=34 cockles), often accompanied by Diphterostomum brusinae; 3)

the south-eastern end of the bay was characterized by a higher abundance in H. spp. (mean


57

abundance per station comprised between 9 and 43 metacercariae.cockle-1, N=34 cockles)

that represented the only trematode taxa present here.

Figure 3.3. Brown Muscle Disease (BMD) prevalence per station in Arcachon Bay. Mean BMD prevalence per axis (i.e. subarea) is mentioned.


58

Table 3.4. Correlation between Perkinsus concentration, Brown Muscle Disease (BMD) prevalence, BMD’s Muscle Print Index (MPI) and different parameters of the environment (N=34). Significant correlations (p<0.05) are in bold.

R= Pearson correlation coefficient

BMD

Perkinsus concentration (Cells/g of gills) Prevalence (%) MPI

R p R p R p

Median (µm) -0.04 0.829 -0.27 0.141 -0.28 0.119

Silt (%) 0.25 0.149 0.45 0.010 0.39 0.025

Organic matter (%) 0.35 0.040 0.52 0.003 0.42 0.015 Sediment

Seagrass cover (%) 0.09 0.594 0.07 0.713 -0.35 0.051

Mean 0.29 0.101 -0.10 0.580 -0.26 0.145

F <13‰ -0.31 0.073 0.08 0.672 -0.03 0.865

F <28‰ -0.27 0.127 0.07 0.681 0.29 0.098 Salinity

F <34‰ 0.03 0.875 0.37 0.039 0.72 <0.001

Minimal -0.29 0.093 -0.37 0.034 -0.46 0.008

Maximal 0.29 0.090 0.12 0.514 -0.02 0.900

Mean 0.09 0.614 0.12 0.6644 0.66 <0.001

F < 8°C 0.00 0.990 0.32 0.071 0.65 <0.001

F <12°C -0.26 0.131 0.18 0.330 0.21 0.245

F <16°C -0.35 0.043 -0.00 0.987 -0.24 0.176

Temperature

F < 20°C -0.17 0.344 -0.23 0.213 -0.73 <0.001

Current F<0.25m/s 0.09 0.626 -0.16 0.390 -0.20 0.270

% Emersion time 0.34 0.048 0.13 0.491 0.03 0.875

Clam density (ind/m²) 0.10 0.569 -0.12 0.502 0.37 0.037

Leyre 0.43 0.011 0.28 0.114 -0.28 0.113

Canal des étangs -0.51 0.002 -0.60 <0.001 -0.20 0.260 Distance (km)

Atlantic ocean -0.14 0.422 0.20 0.264 0.62 <0.001


59

Figure 3.4. Correspondence Analysis discriminating the 34 stations harbouring cockles in relation to trematode species in cockles (A). Different communities of trematodes in cockles from Correspondence Analysis (B). Dominant species are indicated and discriminated by different colours. Stations with no characteristic trematode community are in green.

A

B


60

4. Discussion

The aim of the study was to correlate the distribution of three types of bivalve

diseases within a lagoon (Arcachon Bay) in order to identify the main drivers. Salinity is

often cited as a strong contributing factor (Ahn and Kim, 2001; Elandalloussi et al., 2008) but

many confounding factors may intervene along the gradient. The unusual feature of Arcachon

Bay was the presence of two major freshwater inputs that induce a bi-directional salinity-

temperature gradient, East-West and North-South. We demonstrated that the effect of salinity

was restricted to a very narrow area around the mouth of the river, for perkinsosis only.

Indeed, Perkinsus concentration was significantly lower than elsewhere in Group 1 only,

corresponding to 3 stations near river mouths. The distribution of all diseases was explained

less within “oceanic vs. continental” axis than between both northwest and southwest

subareas of the lagoon. This suggests that factors other than salinity were contributing.

The Principal Component Analysis discriminated groups of stations in accordance

with what could be expected in such an ecosystem: an oceanic-influenced group (Group 2), a

continent-influenced group (Group 1) and two intermediates groups (Groups 3 and 4)

(Bouchet, 1993).

Concerning perkinsosis, the mean abundance of infection was high compared to

previous studies (Lassalle et al., 2007) but was lower than what was assessed in Arcachon

Bay in 2006 (96,000 cells.g-1 (gill FW) (Dang et al., 2010a). This difference can be explained

by the distribution of sampling stations, which in the present study took into account a

broader area including stations with low infection. Salinity is an important factor structuring

the abundance of Perkinsus spp. (Leite et al., 2004). For P. olseni, the optimal salinity range

is 25-35 (Auzoux-Bordenave et al., 1995) and high infection generally corresponds to high

salinity (Burreson and Ragone Calvo, 1996; Cigarría et al., 1997; Park and Choi, 2001). In

Arcachon Bay a similar gradient was formerly obtained (Dang et al., 2010a) but mainly due

to stations sampled near freshwater inputs. In the present study, the refuge function of low-

salinity areas was evident (Group 1), but concerned less than 5% of the axes length (on the

other hand one should verify whether these low salinity areas are effective for commercially

relevant growth rates in the shellfish?). Consequently, the salinity gradient out of these areas

was not high enough to induce a perkinsosis infection gradient. Unexpectedly, the highest


61

difference in perkinsosis infection was observed between Axes 1 and 2. The environmental

characteristics that were significantly different between axes mainly concerned the sediment

and emersion. The highest content in silt and clay, and organic matter in the sediment

coincided with the highest emersion rate, the highest extreme temperatures and eventually the

highest perkinsosis infection (Axis 2). Higher infection in muddy sediment than in sands was

already reported (Choi et al., 2002). Our hypothesis is also that the highest emersion rate

(higher average hypsometric level) is due to (and/or is the consequence) a smaller input of

oceanic water during flow-tides, a water lower turnover and a higher sedimentation of fine

particles. This lower turn-over is consistent with an unpublished report mentioning that 1/3 of

the water mass transits through Axis 2 against 2/3 through Axis 1 (SOGREAH, 2001). That

would increase the retention of Perkinsus hypnospores and facilitate infection between clams.

A similar observation was made with BMD. Interpretation is more difficult due to the

lack of knowledge concerning the infectious agent which might be a virus (Dang et al., 2009).

Here again, Axis 2 displayed higher prevalence in relation to sediment characteristics (but

similar MPI). This axis was also characterized by higher clam densities (+60%, although

p>0.05) which could facilitate disease transmission.

The situation in relation to trematodes was different because many species were

involved and the distribution corresponded to a community analysis pattern. At the lagoon

scale, three communities were discriminated by one or two dominant trematode species,

certainly in relation to the presence of their other hosts (Sapp and Esch, 1994; Hechinger and

Lafferty, 2005; Byers et al., 2008), i.e. first intermediate and definitive hosts which are also

sensitive to environment characteristics. Concerning the influence of freshwater input,

trematode communities were also different between both areas (Leyre and Canal des Etangs).

This monitoring only dealt with correlations. Relationships between causes and

consequences were not demonstrated but the identification of two different sub-ecosystems

arose, independently of salinity gradient. For Manila clams, the notion of ‘refuge’ concerns a

very small area and is not relevant for BMD. Hence, this is not interesting in terms of fishery

management. However, the difference in infection between both axes is important and gives

arguments to develop fisheries models by subareas (Bald et al., 2009) and not at the whole

the ecosystem. For cockles, trematode communities have a patchy distribution but the level of


62

infection remained low compared to known pathological thresholds (Desclaux et al., 2004;

Gam et al., 2009b) and should, therefore, have a low impact in this system.

63

+++

In Chapter 2, we studied dynamics of macrofauna structure in a growing seagrass

bed. The result displayed that the presence of seagrass beds is correlated with high species

diversity, high biomass, and high abundance. The question is what happens with macrofauna

when seagrass disappears. In the next chapter, we will study the relation between chronic

seagrass decline and associated macrofauna. The main hypothesis is that influence of

seagrass is both structural and trophic. Therefore, if seagrass cover declines, it is expected

that it will be accompanied by a loss of diversity, biomass and abundance, a loss of ecological

services and major modifications of coastal systems functioning. This will be considered by

comparing different situations in terms of benthic macrofauna structures.

Biotic Indices (BI) are usually tested to assess Ecological Status (ES). However, BIs

were performed in this study less to assess ES than to observe their “behaviour” in a changing

habitat. Three of the most utilized univariate BIs, i.e. AMBI (AZTI’s Marine Biotic Index

(Borja et al., (2000)), BOPA (Benthic Opportunistic Polychaetes Amphipods Index (Dauvin

and Ruellet (2007)) and BENTIX (Simboura and Zenetos (2002)), were tested. Besides, the

multivariate index MISS (Macrobenthic Index of Sheltered System (Lavesque et al., 2009))

was also tested. This index was preliminary investigated and successfully applied in

Arcachon Bay (Lavesque et al. 2009). Hence, we wanted to test MISS again with a new set of

data. We also selected these different biotic indices for their potential ability to capture an

ecological situation. Each index may give different information which may be (or may be

not) relevant in our study case. Some of them (AMBI in M-AMBI, BENTIX) are indeed used

within the WFD but others are not (BOPA, MISS).

+++

64

Chapter 4 – Seagrass decline and macrobenthos

65

Chapter 4 - Limited consequences of seagrass decline on benthic

macrofauna and associated biotic indicators

Under Review: V. Tu Do, Hugues Blanchet, Xavier de Montaudouin, Nicolas Lavesque.

Estuaries and Coasts.

Abstract

Marine phanerogams are ecosystem engineers, as their presence induces major

environmental changes that impact on the benthic fauna. Consequently, modifications to the

structure of benthic communities would be expected to be associated with seagrass decline.

Since 2005, Zostera noltii seagrass beds in Arcachon Bay, the largest in Europe, have

undergone a severe decline. Twelve stations distributed throughout the lagoon were sampled

in 2002, and all were found to be densely planted at that time. Subsequently the same stations

were revisited in 2010 and seagrass cover had drastically decreased by that time. Based on

benthic macrofauna, Multidimensional scaling (MDS) analysis identified four groups. Years

were separated. In 2002, two groups were distinct in relation to the water body, since in 2010

separation between the two other groups was related to seagrass occurrence. When looking at

community structure and dominant species there were moderate differences within and

between years, independent of seagrass decline. Seagrass loss did not drastically modify the

species composition as they were preserved in the remaining seagrass patches. However,

there was a drop in macrofauna abundance in unvegetated muddy compared with abundance

in the remaining seagrass areas. Epifauna was particularly affected by seagrass decline.

Among Biotic Indicators (BI) based on macrofauna, multivariate BI MISS (Macrobenthic

Index of Sheltered Systems) was in agreement with the similarity of macrofauna structure

among groups, while other tested BI (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic

Opportunistic Polychaetes Amphipods), BENTIX) performed badly in relation to seagrass

occurrence. However, no index detected seagrass loss, highlighting the necessity to maintain

a separate survey on seagrass cover.

Keywords: Seagrass, Benthic macrofauna, WFD, Arcachon Bay


66

1. Introduction

Seagrass beds are considered as an important component of coastal ecosystems

(contributing to nutrient cycles, biodiversity, food resource (direct or indirect), sediment

stabilisation, etc.). More particularly they are considered to contribute to structuring

macrofaunal communities and stimulating (alpha)-diversity (Hemminga and Duarte, 2000).

Unfortunately, seagrass meadows are also extremely sensitive to environmental

perturbations, resulting in a global decline at an accelerating rate (Short and Wyllie-

Echeverria, 1996; Hemminga and Duarte, 2000; Orth et al., 2006; Waycott et al., 2009;

Costello and Kenworthy, 2011).

Although natural factors such as ‘wasting disease’ played a role in seagrass

regressions, anthropogenic activities are considered to be primarily responsible (Hemminga

and Duarte, 2000; Waycott et al., 2009). Coastal eutrophication, over-exploitation of

predators such as fish, coastal development such as dredging or harbour construction and

many other anthropogenic activities can lead to the irreversible elimination of seagrasses

from coastal ecosystems (Short and Burdick, 1996; Hemminga and Duarte, 2000; Baden et

al., 2003; Pillay et al., 2010). Waycott et al. (2009) reported that threats to coastal ecosystems

as a result of seagrass losses include loss of habitat and marine biodiversity, sediment

erosion, degradation of water quality, decrease of primary production, carbon sequestration

and nutrient cycling. The decimation of seagrass meadows has also been associated with the

collapse of scallop fisheries, major declines in abundance of waterfowl, the extinction of

some associated invertebrate species (Carlton et al., 1991; Orth et al., 2006), reduction of

abundance of fish and shrimp (Bell et al., 2002), influence of predator–prey dynamics

(Irlandi, 1994; Hovel and Lipcius, 2001; Hovel, 2003) and reduction of faunal growth rates

(Irlandi, 1996; Irlandi et al., 1999), species diversity and abundance (Turner et al., 1999), and

alterations in epifauna community structure (Reed and Hovel, 2006).

In general, despite growing concern regarding the decline of these ecosystems, few

studies directly report on the consequences of such losses, with a shortage of historical data

being a particular hindrance. Most investigations were based on comparisons between areas

with and without the habitat of interest, or on experimental manipulation, which usually

cannot replicate the large spatiotemporal scales characteristic of habitat loss (see review in

Pillay et al., 2010). In Arcachon Bay, where the largest European intertidal seagrass bed of


67

dwarf grass (Zostera noltii) occurs (Auby and Labourg, 1996), a 1/3rd decrease of the

occupied surface has been observed particularly in recent years (since 2005) (Plus et al.,

2010). The exact reason for the loss of seagrass in Arcachon Bay is still unclear, but Plus et

al. (2010) suggested exploring such factors as eutrophication, increased geese grazing,

wasting disease, herbicide contamination or dredging activities that take place permanently in

the Bay for harbour and channel maintenance as well as for supplying beaches with sand and

fighting against coastal erosion. These activities often increase the seawater suspension

matter, thus lowering the available light for the seagrass canopy (Erftemeijer and Robin,

2006). Abundance of macrofauna can be used as a powerful tool to detect even slight

environmental changes (Blanchet et al., 2005). The composition and structure of benthic

macrofauna is one of the indicated biological quality elements to be used in transitional

(estuaries and lagoons) and coastal waters for quality status assessment within the European

Water Framework Directive (WFD 2000/60/EC). Benthic communities may constitute a sort

of memory for the system illustrating stressors that have occurred locally and over a period of

time (Patricio et al., 2009). Within the WFD, benthic invertebrates are one of the biological

elements to be used for the assessment of ecological status (ES) of surface and transitional

water bodies (EEC, 2000). Several biological indices based on the benthic macrofauna

assemblages have been recently developed to assess ES of marine waters within the WFD

(Borja et al., 2000; Simboura and Zenetos, 2002; Rosenberg et al., 2004; Borja et al., 2007;

Muxika et al., 2007; Borja et al., 2009a).

The benthic macrofauna of the seagrass bed in Arcachon Bay was sampled in 2002 at

twelve stations, when the seagrass bed fully extended over the tidal flats (Blanchet et al.,

2004). In order to assess the consequences of seagrass decreases on associated macrofauna,

these twelve stations were re-sampled in 2010. The ratio of selected unvegetated stations

(80% of all stations) corresponded to the ratio of unvegetated surface in the surveyed area of

the lagoon. Our particular objectives were 1) to identify possible changes to the macrofauna

in terms of community structure, biomass, abundance, species richness and trophic groups

and evaluate the concomitant effects of annual variability and seagrass decline; 2) to test

performance of Biotic Indices (BIs) in detecting the changes to ecological status (ES).


68


2.1. Study area

Arcachon Bay is a triangular-shaped macro-tidal lagoon (180 km2), located on the

French Atlantic coast (44° 40’ N, 1° 10’ W) (Figure 4.1). It communicates with the Atlantic

Ocean through a narrow (2-km wide) entrance. The tide is semi-diurnal and the tidal

amplitude varies from 0.8 to 4.6 m. The average water temperatures are 22.5 °C and 6 °C in

summer and winter respectively, and fluctuations in freshwater contributions from rivers and

rainwater influence water salinity between 22 and 35. Many small streams run into the

lagoon, but the two main rivers, the Leyre and Canal des Etangs, contribute 73% and 24%,

respectively, of the total annual freshwater inflow (813 million m3). The total lagoon surface

(180 km2) can be divided into two parts: the subtidal channels (63 km2), and the intertidal

area (117 km2). The main channels have a maximum depth of 25 m and are extended by a

secondary network of shallower channels. The intertidal area comprises sandy to sandy-mud

flats (Plus et al., 2010). Most of these flats (61 km2 in 2005 (Plus et al., 2010)) were still

covered by the largest Zostera noltii seagrass bed in Europe (Auby and Labourg, 1996). The

lower part of the intertidal is generally devoted to Japanese oyster (Crassostrea gigas)

culture, which constitutes a major activity at this site. Adjacent to the mudflats, and lining the

channels, eelgrass (Z. marina) occupies the subtidal sector (Blanchet et al., 2008).

The background chemical pollution in Arcachon Bay is low (Benoit, 2005; de Wit et

al., 2005; Lavesque et al., 2009). Its catchment area is dominated by pine forestry (79%) and

intensive agriculture occupies only 9% of the surface (de Wit et al., 2005). As a consequence,

nutrient inputs to the lagoon are moderate and their concentrations in water remain low

(Castel et al., 1996; Bachelet et al., 2000). Some developments of green macroalgae (mainly

Monostroma obscurum and Enteromorpha spp.) occurred in the early 1990s, but these signs

of moderate eutrophication have not been observed since. The catchment area is poorly

industrialised, and heavy metal contamination is low (Benoit, 2005). Consequently, the

overall water quality of the lagoon can be considered as satisfactory (Lavesque et al., 2009).


69

Group B

Group A

Group D

Group C

2002 2010

Figure 4.1. Studied site (Arcachon Bay) with position of the twelve sampled stations. Station groups issued from data analysis are indicated and discriminated both years (A and B in 2002, and C and D in 2010).


2.2.1. Macrofauna

Two sampling campaigns were carried out in twelve stations over two years (in spring

2002 and spring 2010, except one station that was sampled in August (see discussion))

corresponding to fully extended Z. noltii seagrass bed in 2002 and after the decline of Z. noltii

bed in 2010 (Blanchet et al., 2004; Plus et al., 2010) (Figure 4.1). These twelve stations were

selected along the widest salinity gradient within the seagrass bed, with 33 (annual average)

in the outer position (station 175) to 22 in the inner position (station 126). Sampling consisted


70

of collecting the top 20 cm of the sediment with a 0.15×0.15 m2 corer, with four replicates

per station (replicates being separated by ca. 10 m). Sediment was sieved through a 1-mm

mesh; the remaining fraction was fixed in 4% buffered formalin and stained with Rose

Bengal. In the laboratory, macrofauna was sorted, identified under the stereomicroscope

when possible to the species level, and counted. Biomass of whole organisms was determined

per species as ash-free dry weight (AFDW) after desiccation (60°C, 48 h) and calcination

(450°C, 4 h). Trophic groups were based on literature description (Fauchald and Jumars,

1979; Bachelet, 1981; Sauriau et al., 1989; Hily and Bouteille, 1999).

2.2.2. Sediment and seagrass leaves analysis

The top 3-cm sediment layer was also sampled in each replicate for granulometric

analysis (median grain-size). Median grain-size was determined after sieving weighed dried

sediment through a wet column of sieves with decreasing apertures (1000 μm, 500 μm, 250

μm, 125 μm and 63 μm).

In 2002, Zostera noltii leaves were cut in each macrofauna sample and desiccated

(60°C) until a constant dry weight was obtained. In 2010, a new method of assessing Zostera

noltii leaf biomass was developed. Fourteen [15 cm × 15 cm]-quadrats were delicately laid

over the sediment surface, at low tide. For each quadrat, a numeric picture was taken

perpendicularly, one meter above the surface. The method consisted of drawing three

equidistant lines across each numerical image and counting the intersections between lines

and leaves. Then biomass and percentage of coverage could be obtained, using the following

relationships:

Loge(DW) = 1.514 × Loge(mean number of intercepts per line) – 1.911, with R = 0.98

(n = 14 pictures)

Loge(S) = 0.690 × Loge(DW) + 1.195 with R = 0.98 (n = 10 pictures)

Where DW is Zostera noltii leaves dry weight in g.m-2 and S is the percentage of

sediment surface covered by Z. noltii. Ten pictures per station were analysed.


71

2.3. Data analysis


Four currently available Biotic Indices (BIs) were tested, namely AMBI (AZTI’s

Marine Biotic Index) (Borja et al., 2000), BENTIX (Simboura and Zenetos, 2002; Simboura

et al., 2005), BOPA (Benthic Opportunistic Polychaetes Amphipods) (Dauvin and Ruellet,

2007) and MISS (Macrobenthic Index in Sheltered Systems) (Lavesque et al., 2009). The

three first indices are based on the classification of species into ecological groups according

to their level of sensitivity/tolerance to pollution. MISS is a multimetric approach using 16

metrics describing the biological integrity of the macrofauna. Ecological status (ES) and

thresholds used to classify index values are reported in Table 4.1.

2.3.2. Multivariate Analysis

Abundances were log10(x+1) transformed to minimize the influence of the most

dominant taxa. A non-metric multidimensional scaling (MDS) based on Bray-Curtis

similarity coefficient was carried out to visually assess differences in macrofaunal

assemblages among stations of the two sampling campaigns (2002 and 2010). Four groups

were identified with the decision that a group should include at least two stations. SIMPER

tests were performed to determine which species contributed to within-group similarity.

These analysis were performed using PRIMER® – v6 (Clarke and Warwick, 2001; Clarke and

Gorley, 2006).

2.3.3. Statistical analysis

Statistical tests were applied to assess the differences between the four groups

identified by MDS in terms of biomass of seagrass leaves, median grain-size and macrofauna

(biomass, abundance, number of species per station (S), trophic groups biomass). ANOVA

test was used when homogeneity of variance (Cochran C test) was achieved. In the case of

variance heterogeneity, data were log10(x+1) transformed. When homogeneity of variances

was not achieved, nonparametric tests (Kruskal-Wallis) were applied. All statistical analyses

were performed with STATISTICA® 7.1 software (StatSoft).


72

Table 4.1. Indices used in this study to assess Ecological Status (ES) and thresholds used to classify index values

ES status Biotic Indices

Number of ecological groups

Computation of the indices Acceptable Not acceptable

References

AMBI 5 0 EGI + 1.5 EGII + 3 EGIII + 4.5 EGIV + 6EGV based on percentage of ecological groups

0 to 3.3 3.3 to 7 Borja et al. (2000)

BENTIX 2 6 EGI&II+ 2 EGIII-V based on percentage of ecological groups

3 to 6 (for mud) 0 to 3 (for mud) Simboura and Zenetos (2002)

BOPA 2 log10 [(fp/fa + 1) + 1] based on ratio of ecological groups

0 to 0.13966 0.13966 to 0.30103 Dauvin and Ruellet (2007)

MISS Sixteen parameters were classified in three categories describing macrofauna assemblages

0.4 to 1 0 to 0.4 Lavesque et al. (2009)

EG for AMBI and BENTIX: ecological groups as determined by Borja et al. (2000): EGI: very sensitive to organic enrichment; EGII: indifferent to enrichment; EGIII: tolerant to excess organic matter enrichment; EGIV: second-order opportunistic species; EGV: first-order opportunistic species; For BOPA: fp: opportunistic polychaetes frequency; fa: amphipods frequency


73

3. Results

3.1. Macrobenthic community structure

When all dates and stations were combined, 110 taxa were found. At 50% similarity

level, a MDS plot showed a separation among four groups of stations, two in 2002 (A and B)

and two in 2010 (C and D), corresponding to different benthic assemblages (Figure 4.2).

Group A

Group B

Group C

(g DW m-2)

Similarity level 50%Group D

Zostera leaf biomass

Figure 4.2. Non metric multidimensional scaling (MDS) of stations based on the Bray-Curtis similarity matrix on Log10(x+1)-transformed abundance data and gathered at 50% similarity level. Zostera leaves biomass is indicated a bubble plot. For example 02-97 means station 97 (Figure 4.1) sampled in 2002.

Group A clustered 4 stations sampled in 2002 and located closer to the ocean in more

saline water than the other groups (Figure 4.1). Average Z. noltii leaf biomass was 70 g DW

m-2 corresponding to 60% cover of the sediment surface (Table 4.2). Sediment consisted of

mud (grain size median = 45 µm) with 62% of the weight composed of silt and clay. Mean

macrofauna abundance was 28125 ind. m-2 with a mean of 29 species per station (Table 4.2,

Figure 4.3). Group A was characterized by both the numerical dominance and major

contribution (SIMPER test) of the oligochaete Tubificoides benedii (60% of total abundance),


74

the polychaetes Heteromastus filiformis and Melinna palmata and the bivalve Abra

segmentum (Table 4.3, Figure 4.3). The epifauna represented 12.5% of total abundance, half

of it due to Hydrobia ulvae (Figure 4.3). Total biomass was 21.6 g AFDW m-2 distributed

among deposit feeders (27%), grazers (16%), predators (27%) and suspension-feeders (27%)

(Table 4.2). One fourth (5.3 g AFDW m-2) of the total biomass consisted in epifauna. This

epifauna was mostly composed of motile species, Hydrobia ulvae being dominant (21%)

(Figure 4.3) followed by Demospongiae (15%), Idotea chelipes (12%), Nassarius reticulatus

(12%), etc. Infauna biomass (16.4 AFDW m-2) was dominated by Manila clams Ruditapes

philippinarum (45%) (Figure 4.3), followed by T. benedeni (10%), Loripes lacteus (9%) and

Marphysa sansuinea (8%).

Group B clustered 8 stations also sampled in 2002 but located in an inner position in

the lagoon (Figure 4.1), and therefore having a lower average salinity. Mean Z. noltii leaf

biomass (83 g DW m-2), sediment median grain size (36 µm) and silt and clay content (69%)

were similar to that found in Group A (p>0.05) (Table 4.2). Mean macrofauna abundance

(24286 ind. m-2) was also similar to what was observed in Group A but number of species per

station was lower (20) (p<0.001) and species composition was different (Table 4.2). Group B

was particularly characterized by a high abundance of Hydrobia ulvae (63% of total

abundance) (Figure 4.3) explaining that epifauna represented 52% of abundance and that total

biomass (24.2 g AFDW m-2) was dominated by grazers (43%) that were mainly H. ulvae

(88%) (Table 4.2). Suspension-feeders represented the other important fraction of total

biomass (42%) mainly due to Manila clams R. philippinarum that represented 67% of infauna

biomass.

Group C clustered 8 stations sampled in 2010 in oceanic as well as in the inner

position (Figure 4.1). The major difference compared to all other groups was the scarcity of

seagrass leaves (3 g DW m-2 corresponding to a cover of 6% of the surface sediment).

Sediment were slightly more sandy (median = 87 µm with 42% silt and clay content) than in

Group B (Table 4.2). Compared to 2002 (Groups A and B), macrofauna abundance was

divided by 2.7 (Figure 4.3, Table 4.2), but the dominant and contributive species were the

same (Hydrobia ulvae for epifauna, Heteromastus filiformis and Tubificoides benedii for

infauna). The only new dominant species was the polychaete Pygospio elegans with

abundance (899 ind. m-2) 3 to 450 times higher than in other groups (Table 4.3). Biomass was

similar to Groups A and B (Figure 4.2, Table 4.2). Half of it was due to suspension feeders


75

and in particular to the bivalves Ruditapes philippinarum (83% of suspension-feeders

biomass), Musculista senhousia (10%), Loripes lacteus (6%) and Cerastoderma edule (3%).

Infauna biomass was dominated by R. philippinarum (63%) and the large polychaete Melinna

palmate (15%) (Figure 4.3). Epifauna biomass was dominated by the grazer H. ulvae (75%),

followed by the exotic bivalve Musculista senhousia (16%). Species richness per station was

the lowest together with Group B (Figure 4.2, Table 4.2) mainly due to epifauna collapse (4

species only compared with 7-9 in other groups).

Group D clustered only 2 stations corresponding to the only sampled stations where Z.

noltii seagrass continued to flourish in 2010 (biomass leaves = 140 g DW m-2, corresponding

86% cover of the sediment surface) (Table 4.2). Sediments consisted of mud (median = 50

µm, with 54% silt and clay). Mean macrofauna abundance was similar to Group C (13556

ind. m-2) (p>0.05) (Table 4.2, Figure 4.3). Dominant and contributive species were partly

different with the occurrence of Bittium reticulatum (epifauna), and Melinna palmata and

Aphelochaeta marioni (infauna). Biomass was particularly high (128.8 g AFDW m-2) mainly

due to suspension feeders of infauna (in particular Manila clam Ruditapes philippinarum)

(80% of infauna biomass) and epifauna (Mussel Mytilus edulis with 37% of epifauna

biomass; N. reticulatus with 19%) (Table 4.3, Figure 4.3). Mean species richness per station

was relatively high (26) like in Group A.


76

Table 4.2. Average values (min - max) of environmental factors: salinity, tidal level (m), distance to the ocean (km), Zostera noltii leaves biomass (g DW m-2) and cover (% of sediment surface), median grain-size (µm), silt and clay content (%); quantitative parameters of macrofauna assemblages: mean abundance (ind. m-2), mean biomass (g AFDW m-2), mean number of species per station, biomass of each trophic group (g AFDW m-2), abundance (ind. m-2) and biomass (g AFDW m-2) of epifauna and infauna in four groups identified by MDS and statistic test results for the difference between four groups. Mean comparison: values are ranked and lines gather groups that are not significantly different (p>0.05).

MDS groups Test, p value Mean comparison Parameters A B C D

Habitat Mean annual salinity (range) 32 (30-33) 29 (22-31) 31 (29-33) 30 (19-35) Tidal level (m, range) 2.1 (1.5-2.4) 2.3 (1.7-2.8) 2.4 (2.2-2.6) 1.6 (1.5-1.7) Distance to the ocean (km, range) 12 (9-14) 16 (15-17) 14 (9-17) 15 (14-16) Seagrass biomass (g DW m-2, ±SD) 70 (±18) 83 (±14) 3 (±1) 140 (±52) Anova, p<0.001 C<A<B<D

Seagrass cover (% of sediment surface, range) 60 (37-62) 67 (37-100) 6 (0-12) 86 (72-100) Kruskal-Wallis, p<0.005 C<A<B<D

Median Grain-size (µm, ±SD) 45 (±8) 36 (±7) 87 (±22) 50 (±10) Anova, p<0.05 B<A<D<C

Silt & Clay content (%, ±SD) 62 (±5) 69 (±6) 42 (±7) 54 (±4) Anova, p<0.05 C<D<A<B

Macrofauna Mean abundance (ind. m-2 ±SD) 28125 (±3131) 24286 (±4145) 9726 (±1282) 13556 (±1347) Anova, p<0.001 C<D<B<A

Abundance epifauna (ind. m-2, ±SD) 3525 (±1865) 16046 (±1908) 5585 (±1308) 4322 (±1749) Anova, p<0.001 A<D<C<B

Abundance infauna (ind. m-2, ±SD) 24586 (±3771) 8232 (±2804) 4140 (±582) 9222 (±2956) Kruskal-Wallis, p<0.01 C<B<D<A

Mean biomass (g AFDW m-2, ±SD) 21.6 (±2.5) 24.2 (±2.8) 20.5 (±3.6) 128.8 (±46.1) Anova, p<0.001 C<A<B<D

Biomass epifauna (g AFDW m-2, ±SD) 5.3 (±1.6) 10.8 (±1.2) 6.2 (±1.8) 30.7 (±10.0) Anova, p<0.001 A<C<B<D

Biomass infauna (g AFDW m-2, ±SD) 16.4 (±2.6) 13.4 (±2.4) 14.3 (±2.9) 98.0 (±36.4) Anova, p<0.001 B<A<C<D

Mean number of species per station (±SD) 29 (±1) 20 (±1) 20 (±2) 26 (±1) Anova, p<0.001 C=B<D<A

Biomass deposit feeders (g AFDW m-2, ±SD) 4.9 (±0.3) 2.3 (±0.5) 4.4 (±0.7) 7.7 (±1.8) Kruskal-Wallis, p<0.01 B<C<A<D

Biomass grazers (g AFDW m-2, ±SD) 3.0 (±0.9) 10.4 (±1.1) 4.9 (±1.1) 8.8 (±4.1) Anova, p<0.01 A<C<D<B

Biomass predators (g AFDW m-2, ±SD) 2.9 (±1.0) 1.3 (±0.4) 0.8 (±0.2) 3.3 (±1.7) Kruskal-Wallis, p>0.05 Biomass scavengers (g AFDW m-2, ±SD) 0.7 (±0.5) 0.0 0.0 6.3 (±1.2) Kruskal-Wallis, p<0.001 C=B<A<D

Biomass suspension feeders (g AFDW m-2, ±SD) 10.2 (±2.7) 10.2 (±2.4) 10.5 (±3.2) 102.7 (±44.3) Anova, p<0.01 A=B<C<D


77

Tb

Tb

Tb

Hf

Hf

HfPe

As

As

Hu

Hu

Mp

Am

BrHu

3 525 ind.m-2

24 586 ind.m-2

16 046 ind.m-2

8 232 ind.m-2

5 585 ind.m-2

4 140 ind.m-2

4,322 ind.m-2

9 222 ind.m-2

GR

OU

P A

(200

2 –

OU

TER

LA

GO

ON

-SE

AG

RA

SS)

5.3 g m-2

16.4 g m-2

10.8 g m-2

13.4 g m-2

EpifaunaInfauna

EpifaunaInfauna

6.2 g m-2

14.3 g m-2

30.7 g m-2

98.0 g m-2

9 species

20 species

7 species

13 species

GR

OU

P C

(201

0 –

WH

OLE

LA

GO

ON

–B

AR

E M

UD

)

4 species

16 species17 species

9 species

GR

OU

P D

(201

0 –

WH

OLE

LA

GO

ON

-SEA

GR

ASS

)

Hu

GR

OU

P B

(200

2 –

INN

ER L

AG

OO

N -

SEA

GR

ASS

)

2002

2010

DC

A B

Hu

Rp

Tb

Rp

Hu

Rp

Hu

Mp

Mp

Rp

Me

Mp

DeIcNr

Ms

Ll

Nr

Figure 4.3. Average abundance (ind. m-2), biomass (g AFDW m-2) and species richness per station of epifauna and infauna among four groups (A, B, C and D) discriminated by MDS at 50% similarity (see Figure 4.2). Am: Aphelochaeta marioni; As: Abra segmentum; Br: Bittium reticulatum; De: Demospongiae; Hu: Hydrobia ulvae; Hf: Heteromastus filiformis; Ic: Idotea chelipes; Ll: Loripes lacteus; Me: Mytilus edulis; Mp: Melinna palmata; Ms: Musculista senhousia; Nr: Nassarius reticulatus; Pe: Pygospio elegans; Tb: Tubificoides benedii. Scale for biomass is different in group D.


78

Table 4.3. List of the main species contributing to the within-station groups similarity (SIMPER procedure). Contribution to similarity is given as percentage and rank. Top four dominant species per Group are in bold. Species are only ranked when mean abundance > 50 ind.m-2 in at least one group. Blank cell = absent species. Ecological groups (I to V) are ascribed according to AZTI data base (http://ambi.azti.es), see Table 1.

A B C D Abundance Contribution Abundance Contribution Abundance Contribution Abundance Contribution

Zoological group Position Trophic group Taxon Rank Mean Rank % Rank Mean Rank % Rank Mean Rank % Rank Mean Rank % Cnidaria epifauna predator Anthozoa (II) - 42 11 1.47 - 42 11 1.27 - 15 13 1.32 - 22 Nemertea infauna predator Nemertea (III) 9 350 7 4.3 14 83 7 3.23 - 33 - 44 19 2.14 Mollusca Polyplacophora epifauna grazer Polyplacophora (II) - 3 3 - 7 20 56 Mollusca Gastropoda epifauna grazer Bittium reticulatum (I) 17 147 8 2.99 7 211 - 36 4 1600 14 2.14 Hydrobia ulvae (III) 3 1844 6 4.44 1 15264 1 49.84 1 5461 1 27.5 3 1756 1 13.7 Littorina littorea (II) - 3 10 131 9 2.32 - 3 7 344 8 3.71 Rissoa membranacea (I) 8 161 6 3.64 - 6 scavenger Nassarius reticulatus (II) - 22 15 111 6 4.28 Mollusca Bivalvia epifauna suspension feeder Modiolus modiolus (I) 10 286 7 Mytilus edulis (III) 13 156 18 2.14 infauna deposit feeder Abra segmentum (III) 4 1236 3 9.73 3 603 4 5.8 7 107 7 4.8 9 233 5 6.77 suspension feeder Loripes lacteus (I) 13 178 17 0.79 - 29 12 1.18 - 6 - 44

Ruditapes philippinarum (III) 24 50 16 0.8 9 144 8 3.13 8 74 8 3 5 633 7 4.28

Annelida Polychaeta infauna deposit feeder Ampharete acutifrons (I) 7 489 Aonides oxycephala (III) - 44 14 0.9 - 7 - 11 Aphelochaeta marioni (IV) 15 158 10 2.46 12 104 13 1.09 6 424 5 7.03 2 3000 3 10 Clymenura clypeata (III) - 36 - 18 16 78 Euclymene oerstedi (I) - 11 - 4 11 189

Heteromastus filiformis (IV) 2 2414 2 12.89 4 514 3 5.95 4 706 3 10.9 6 622 2 10.7

Manayunkia aestuarina (II) - 14 5 458 Melinna palmata (III) 5 1119 4 8.24 - 1 3 860 6 5.91 1 3122 4 9.33 Notomastus latericeus (III) - 17 - 7 - 44 Paraonidae (NA) 12 183 13 1.13 - 1 - 14 11 1.69

Pseudopolydora antennata (IV) - 3 - 24 - 29 12 1.47

Pseudopolydora spp. (IV) - 1 8 278 Pygospio elegans (III) - 6 6 313 2 899 2 12 - 44

(continued on the next page)


79

A B C D Abundance Contribution Abundance Contribution Abundance Contribution Abundance Contribution

Zoological group Position Trophic group Taxon Rank Mean Rank % Rank Mean Rank % Rank Mean Rank % Rank Mean Rank % Streblospio shrubsolii (III) - 25 15 64 10 1.35 - 22 predator Diopatra sp. (I) - 3 - 44 12 3.03 Glycera spp. (II) 20 67 12 1.36 - 10 10 58 9 2.88 16 78 17 2.14 Nephtys hombergii (II) - 3 - 18 14 1.23 - 33 13 3.03 Phyllodocidae (II) - 17 18 0.76 - 1 - 1 Syllidae (NA) 23 53 Annelida Oligochaeta infauna deposit feeder Tubificoides benedii (V) 1 16844 1 28.73 2 5431 2 7.7 5 582 4 8.1 14 133 Phoronida infauna suspension feeder Phoronis psammophila (II) 18 142 - 3 9 69 12 156 Crustacea Amphipoda epifauna deposit feeder Ampithoe sp. (I) - 6 - 4 19 67 10 3.03 Ericthonius difformis (I) 22 64 8 Siphonoecetes sp. (I) - 4 16 78 9 3.71 infauna deposit feeder Melita palmata (I) - 35 - 1 10 222

Perioculodes longimanus (I) 21 64 - 24 - 11

grazer Microdeutopus gryllotalpa (III) 11 222 - 11

suspension feeder Corophium insidiosum (III) - 42 15 0.81 Crustacea Cumacea epifauna deposit feeder Iphinoe trispinosa i (I) 19 81 8 Crustacea Decapoda epifauna predator Carcinus maenas (III) - 25 - 17 - 13 10 1.73 - 22 15 2.14 Hippolyte sp. (I) 16 153 6 Crustacea Isopoda epifauna grazer Idotea chelipes (II) 6 706 5 5.89 11 125 5 3.78 - 18 - 33 infauna deposit feeder Cyathura carinata (III) 14 161 - 3 - 24 16 2.14 Insecta Diptera infauna grazer Chironomidae (III) 8 369 9 2.48 13 92 - 4 - 44 11 3.03 Dolichopodidae (IV) - 33 - 25 - 29 15 1.18 - 11

Table 4.3. (continued)


80

3.2. Biotic Indices

The four investigated indices were compared among groups and provided different

results in terms of ecological quality status but also in terms of temporal trend with year.

AMBI considered that the 2010 groups (C and D) had a better status than in 2002 (Groups A

and B) without any relationship to seagrass presence (Table 4.4). BOPA was rather optimistic

but with an opposite trend (Group B with a better ES than group D) and, in 2010, with a

better ES when seagrass was absent (Group C). BENTIX provided poor ES status in all

groups except in group D where it was good. MISS maintained its ES as high in the four

groups.

Table 4.4. Biotic indices and ecological status (ES) in the four groups A, B, C and D discriminated by MDS (Figure 4.2)

AMBI BENTIX BOPA MISS Groups ES Value ES Value ES Value ES Value A poor 4.57 poor 2.41 good 0.061401 high 0.84 B moderate 3.33 poor 2.26 high 0.025416 high 0.88 C good 3.32 poor 2.12 good 0.134524 high 0.80 D good 2.73 good 3.05 moderate 0.165953 high 0.80

4. Discussion

The present study showed that the biomass of seagrass dramatically declined in 2010

in ten sampled stations out of twelve, as described by Plus et al. (2010) on larger scale. The

concomitant effects on associated benthic macrofauna were rather moderate, with a

significant decrease of abundance and diversity (measured as species richness per station),

while biomass remained stable.

4.1. Associated macrofauna in seagrass

Groups A, B and D allowed comparison of macrofauna communities in seagrass with

different conditions. Benthic communities of Groups A and B were sampled simultaneously

(in spring 2002), in similar sediments (mud), both with dense Z. noltii seagrass but they


81

differed in terms of the surrounding water bodies. Both groups of stations were situated in

two different water bodies, the external neritic waters (A) and the intermediate neritic water

(B) (Bouchet, 1993). Species composition was also similar between Groups A and B and

differences were due to contrasted dominance in terms of abundance and biomass of

Hydrobia ulvae and Tubificoides benedii, and occurrence of some polychaete species. Indeed,

Hydrobia ulvae was present and abundant in both groups, taking advantage of the presence of

Z. noltii (and probably of associated periphyton (Cardoso et al., 2005)). However, Hydrobia

ulvae abundance was 8 times lower in oceanic Group A. A possible explanation is stronger

hydrodynamic conditions in the outer lagoon washing out these mudsnails (Zühlke and Reise,

1994). Abundance of Tubificoides benedii in Group A is usual (Bachelet et al., 2000) while it

is surprisingly low in Group B, without any explanation. Finally, species that were restricted

to a single group are characterized by the absence or reduction of the larval pelagic phase

(Ampharete acutifrons, Clymenura clypeata, Manayunkia aestuarina). In contrast, species

with widespread spatial distribution display a longer pelagic phase (e.g. Heteromastus

filiformis, Glycera spp.) (Cazaux, 1973; Wolff, 1973; Marcano and Cazaux, 1994; Blanchet

et al., 2004).

Group D encompassed the last two stations of our survey that still harboured dense

seagrass in 2010. One of these stations was not sampled in spring like the other ones but in

summer. This may have biased results but previous 2-year monitoring in the same area

determined that biomass and abundance of macrofauna sieved with a 1-mm mesh was

relatively constant among seasons (Bachelet et al., 2000). Compared to Groups A and B

(seagrass in 2002), macrofauna abundance in Group D was half. This could be due to

interannual fluctuation, but this abundance (13556 ind. m-2) is also low compared with those

observed in other studies in Arcachon Bay (50000-200000 in Castel et al. 1989; Bachelet et

al. 2000). An alternative hypothesis is that Group D characterized a relictual fragmented

seagrass within a large unvegetated mudflat (see Group C). Group D was also characterized

by the largest biomass of all groups mainly due to the settlement of a population of Manila

clams Ruditapes philippinarum. This exotic bivalve was introduced in Arcachon Bay in 1980

and rapidly naturalized in the intertidal Z. noltii seagrass beds (Goulletquer et al., 1987).


82

4.2. The benthic community in reduced seagrass mudflats

Group C corresponded in 2010 to stations where seagrass leaf biomass was

dramatically low, i.e. between 0 and 7 g DW m-2 depending on the station. Most stations in

Group C were located at few meters from stations sampled in 2002 (Groups A and B).

Structurally complex habitats strongly shape the physical environment, e.g. by modifying

light conditions, hydrodynamics, sedimentation, providing shelter and refuges, and buffering

the effects of disturbances. When these habitats are lost, many of these functions are also lost

(Airoldi et al., 2008). Blanchet et al. (2005) hypothesized that Zostera noltii meadows had a

structuring effect on benthic communities when leaf biomass exceeds 28 g DW m-2. In the

present study, seagrass leaves declined from 79.5 g DW m-2 in 2002 to 2.2 g DW m-2 in 2010.

Consequently, the benthic communities should have been altered by these changes. However,

the difficulty is in distinguishing natural year-to-year variations (Boström et al., 2002) from

effects of seagrass decline on macrofauna. Therefore, our approach consisted of a comparison

between changes in macrofauna between 2002 and 2010 in stations where the seagrass had

almost disappeared (Groups A and B vs. Group C) with stations where the seagrass remained

(Groups A and B vs. Group D).

Lower abundance of macrofauna in unvegetated habitats compared to vegetated

habitats has often been reported (Fonseca et al., 1990; Orth, 1992; Boström and Bonsdorff,

1997; Cottet et al., 2007; Fredriksen et al., 2010; Do et al., 2011) and different mechanisms

for this were proposed, such as: 1) decreased predation efficiency due to high habitat

complexity (Orth et al., 1984); 2) habitat preference of dense seagrass by prey as an escape

mechanism from predation (Fonseca and Fisher, 1986; Webster et al., 1998; Boström et al.,

2006b); 3) stabilisation of sediments that accumulate organic material, allowing increased

settlement and growth of infauna (Neckles et al., 1993; Fredriksen et al., 2005); juveniles and

adults are also prevented from being resuspended and transported away (Fonseca et al.,

1990); and 4) a high content of organic matter, which may be common in seagrass meadows,

attracts a certain type of infauna such as deposit-feeding polychaetes (Fredriksen et al., 2010;

Do et al., 2011). Consequently, our results in Group C are consistent with the literature, as we

observed a 2.4 magnitude drop in abundance. However, the same decline in magnitude was

observed in Group D (same year but in seagrass) (i.e. a loss of 11000-15000 ind. m-2), but the


83

species involved were different and the final macrofauna community structure discriminated

two groups (C and D) in relation to Z. noltii presence or absence.

Thus, the scenario that we propose and seems the most plausible is: 1) In 2002, the

vast Z. noltii seagrass bed sheltered a relatively homogeneous associated macrofauna

dominated in terms of abundance by grazers (and especially Hydrobia ulvae) and several

opportunistic deposit feeders. Biomass was dominated by the Manila clam Ruditapes

philippinarum (infauna) and the mudsnail Hydrobia ulvae (epifauna). A few species, by their

abundance, allowed discrimination of two subcommunities, one related to oceanic water

(Group A) and the other to more sheltered water (Group B). 2). In 2010, seagrass beds had

almost disappeared in 83% of the investigated area. Thus the ecosystem engineer role of the

seagrass (Jones et al., 1994) has also disappeared. Then, as expected by numerous studies

comparing vegetated and unvegetated sediments (Fonseca et al., 1990; Boström and

Bonsdorff, 1997; Fredriksen et al., 2010; Do et al., 2011), abundance and species richness

(here calculated by station in order to minimize the unbalanced design of the groups with

different number of stations) decreased. This decrease is sufficient to discriminate Group C,

but however the species were quite similar to those observed in 2002 (Groups A and B). The

deposit-feeder Tubificoides benedii has particularly declined. There is no explanation, since

this oligochaete is abundant in all marine stressed habitats, often characterized by hypoxic

condition (Giere et al., 1999) and is not dependent on seagrass presence. Another logical

consequence of seagrass disappearance is the scarcity of grazing epifauna. 3) Group C

represented a dominant habitat in 2010 and the loss of macrofauna abundance had

consequences on the remaining small areas where seagrass remained. Group D clustered

stations considered as belonging to “fragmented seagrass”, i.e. matching the general deficit of

macrofauna abundance but containing some characteristic epibenthic species that could graze

on Z. noltii blades (Bittium reticulatum, Littorina littorea, Rissoa membranacea, etc.). Group

D was also characterized by a very high suspension-feeders biomass, dominated by R.

philippinarum and Mytilus edulis. However, at a the lagoon scale, there is no attended

modification of ecosystem functioning because Group D represents a small percentage of the

total investigated area (<20%).


84

4.3. Biotic Indices

In terms of seagrass cover, the Water Framework Directive (WFD) considers that

Groups A, B and D situations corresponded to the conditions of a ‘Good’ Ecological Status

(ES) and C should be considered as ‘moderate’ or ‘poor’ (Foden and Brazier, 2007).

Consequently and when extrapolating to benthic fauna indicators, MISS was rather more

efficient than other indices (AMBI, BOPA, BENTIX) to assess ES of the investigated area

although it did not detect seagrass loss. Indeed, AMBI showed an improvement in ES while

seagrass were in decline in 2010. This was due to the decrease in abundance of highly

dominant species belonging to ecological groups III to V (rather opportunistic) such as

Hydrobia ulvae (group III), Tubificoides benedii (group IV), Heteromastus filiformis (group

V) (Table 4.1 for ecological groups definition). BENTIX assessed incorrectly and failed to

detect the change of ES in almost all stations. The limitation of the BENTIX index is in

estuaries or lagoons where the natural conditions favour the presence of tolerant species in

very high densities like in the present study. BOPA remained optimistic in all situations (high

ES) except in group D that was, however, characterized by a flourishing seagrass. Generally,

most of these Biotic Indices (BENTIX and BOPA) perform badly in semi-enclosed

ecosystems where the natural benthic habitat consists of muddy, organic matter enriched

sediments (Blanchet et al., 2008; Lavesque et al., 2009).

MISS with 16 metrics describing community, trophic composition and pollution

indicators is supposed to be more efficient than other indices in detecting perturbations in this

kind of ecosystem (Lavesque et al., 2009). However, it failed to detect any seagrass changes.

On the other hand, it has been previously mentioned that macrofauna changes were moderate

among groups. In this context, MISS was consistent with benthic community similarities

among the four groups.

Conclusions

We expected more significant changes to have occurred due to seagrass decline

because Z. noltii is an engineer species. In fact the changes were not so important either

because the seagrass did not entirely disappear at the lagoon scale (25% loss between 2005

and 2007) and/or because the benthic system is resistant to this change. Of course, this study

did not take into account motile epifauna that are the species most affected by seagrass


85

regression (Boström et al., 2006a). Our results emphasized the necessity to integrate

numerous parameters in order to correctly describe ecosystem trajectories. Evolution with

time, seagrass development/regression, or benthic fauna structure do not always evolve in the

same way and so cannot always be described by the biotic indicators present.

86

87

+++

The main objective of this thesis was to correlate seagrass evolution with associated

macrofauna community changes. After analysing the changes in benthic macrofauna

structures in relation with seagrass colonization (Chapter 2) or seagrass destruction

(Chapter 4), the next chapter will focus on the response of macrofauna to severe seagrass

damage and to seagrass recover.

In chapters 2 and 4, we showed that changes of benthic community structures are

often subtle and cannot be detected by most biotic indices. In such situation (i.e. seagrass

changes), different Biotic Indices (BIs) are not accurate enough to detect changes of benthic

communities in this situation and should not be utilized in this sense. In the next chapter, we

will monitor community structure changes and will also test BIs in the case of seagrass buried

by sediment disposal at a large spatial scale (0.32 km²).

This survey was the occasion to test again a multivariate BI that was experimented in

Arcachon Bay, few years ago, MISS (Lavesque et al. 2009). However, we also tried to

simplify MISS.

Eventually, due to the difficulty that we had in the previous chapter to discriminate

differences in macrofauna structures or BIs between vegetated and unvegetated substrates, we

also had a new approach in comparing secondary production among these different situations.

+++

88

Chapter 5- Seagrass burial, macrobenthos and Biotic Indices

89

Chapter 5 - Seagrass burial by dredged sediments: benthic

community alteration, secondary production loss, biotic index

reaction and recovery possibility

Published: V. Tu Do, Xavier de Montaudouin, Hugues Blanchet, Nicolas Lavesque (2012).

Marine Pollution Bulletin 64: 2340-2350.

Abstract

In 2005, dredging activities in Arcachon Bay led to the burying of 320,000 m2 of Zostera

noltii intertidal seagrass. Recovery by macrobenthos and seagrass was monitored. Six months

after the work, seagrass was absent and macrobenthos drastically different from surrounding

vegetated stations was observed. Due to sediment dispersal, the disposal area was rapidly

divided into a sandflat with a specific benthic community maintained until the end of the

survey in 2010, and a mudflat where associated fauna became similar to those in adjacent

seagrass beds. The macrobenthic community needed 3 years to recover while seagrass needed

5 years to recover in the station impacted by mud. The secondary production loss due to this

dredging activity was low. In this naturally carbon enriched system, univariate biotic indices

did not perform well in detecting seagrass destruction and recovery. MISS (Macrobenthic

Index of Sheltered Systems) gave more relevant conclusions and a simplified version was

tested with success, at this local scale.

Keywords: Seagrass, macrobenthos, sediment disposal, secondary production, WFD


90

1. Introduction

The disposal of dredged material constitutes one of the most important problems in

coastal zone management (Van Dolah et al., 1984; Bolam and Rees, 2003; Bolam and

Whomersley, 2005). Coastal works (e.g. harbors, docks, breakwaters), beach stabilization,

dredging and excess siltation from changes in land catchments, are examples of

anthropogenic activities that result in changes of the sedimentary dynamics and consequent

seagrass loss. Frequently, such human-induced activities result in complete, perhaps

irreversible, disappearance of seagrass meadows from coastal areas (Cabaco et al., 2008).

Many studies concerning the effects of dredged material deposition on benthic

macroinvertebrates and physical environment have been carried out. The effects of dredge

material relocation are smothering (Stronkhorst et al., 2003), chemical contamination (Bolam

et al., 2006), changes in sedimentology (Harvey et al., 1998; Essink, 1999), increased levels

of organic carbon, reduction in abundance, number of species and diversity (Van Dolah et al.,

1984; Wildish and Thomas, 1985; Cruz-Motta and Collins, 2004), and increased dominance

of tolerant and opportunistic species (Rees et al., 1992). Impacts of burial are the most

obvious effects of dredged material placement on benthic organisms in the short term, both at

intertidal and subtidal placement sites (Roberts et al., 1998; Powilleit et al., 2006; Bolam,

2011). Since different types of effects were identified in these studies, it is impossible to draw

a general conclusion about the impact of dredged material deposition on the benthic

community structure (Harvey et al., 1998). In addition, benthic community recovery after

dredged material deposition has not been well studied, particularly with reference to seagrass

habitats (Sheridan, 2004).

A long term and large-scale spatial study on effects of seagrass bed burial was

initiated in Arcachon Bay, following sediment disposal. Arcachon Bay harbours the largest

Zostera noltii seagrass bed in Europe (Auby and Labourg, 1996), which occupies most

intertidal areas. Arcachon Bay is also an important site for oyster farming which implies

regular cleaning of oyster parks that are rapidly invaded by non-exploited, “wild” oysters

(Crassostrea gigas). Empty shells and live animals are traditionally buried in large holes

(“souilles”) dug in remote areas, within the lagoon. In 2002, these “souilles” were full and

stakeholders decided to dig another one, near the previous one, in a seagrass area. It consisted


91

of a 5000-m2 pit able to receive 100,000 m3 of shells. Works were implemented during the

winter of 2004 and extracted sediments were disposed over a 20,000 m2 seagrass bed, rapidly

dispersing within a total impacted area of 300,000 m2 (×10 cm thickness). Between 2002 and

2010, a benthic survey was performed in the primary disposal area, in the secondary sediment

spreading area and in controlled stations. Our aim was: (1) to monitor macrobenthic

communities in and out of the impacted area in terms of biomass, abundance, diversity,

structure and trophic groups. (2) To assess the loss of secondary production related to

seagrass destruction, Z. noltii biomass being a major component influencing the overall

macrobenthic production (Dolbeth et al., 2011). Besides, secondary production is one of the

most comprehensive measurements of ecosystem health (Dolbeth et al., 2005). It may reveal

greater insights into ecosystem change than static parameters such as diversity, density or

biomass. Combining production with long-term datasets could increase our level of

understating system functioning (see for instance, Dolbeth et al., 2007; Pranovi et al., 2008).

To compare different biological indicators implemented or not in the Water Framework

Directive (WFD) and based on macrofauna communities structure and to observe how they

responded to this physical stress and potential seagrass recovery. Indeed, benthic macrofauna

are a powerful tool to detect even slight environment changes (Blanchet et al., 2005). It may

locally detect the level of stress and integrate the recent history of stress, constituting a sort of

memory for the system (Patricio et al., 2009). The composition and structure of benthic

macrofauna is one of the indicated biological quality elements to be used in transitional

(estuaries and lagoons) and coastal waters for ecological status assessment. Several biological

indices (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic Opportunistic Polychaetes

Amphipods Index), BENTIX and MISS (Macrobenthic Index of Sheltered System)) based on

the benthic macrofauna assemblages have been recently developed to assess ecological status

(ES) of marine waters (Borja et al., 2000; Simboura and Zenetos, 2002; Dauvin and Ruellet,

2007; Lavesque et al., 2009).

Particular attention will be devoted to a newly developed multi-metric index, MISS

(Lavesque et al., 2009). The implementation of this index in routine monitoring exposes two

major problems. Firstly, two out of 16 metrics are based on biomass (total biomass and the W

statistic). Assessing biomass is time consuming and requires sample destruction. Secondly,

five metrics are related to the identification of trophic groups which is often a hazardous task,


92

except maybe for suspension feeders. As a result, we tested MISS in different derived

versions to check whether we can simplify its calculation without degrading the information.


2.1. Study site

The study site was an intertidal mudflat called "Dispute” in the middle of Arcachon

Bay (Figure 5.1). Arcachon Bay is a triangular-shaped macro-tidal lagoon (180 km2), located

on the southwest French Atlantic coast (44°40’N, 1°10’W). It communicates with the

Atlantic Ocean through a narrow (2-km wide) entrance. The tide is semi-diurnal and the tidal

amplitude varies from 0.8 to 4.6 m. The average temperatures vary seasonally between 6 °C

and 22.5 °C. Fluctuations in freshwater contributions from rivers and rainwater influence

water salinity, ranging between 22 and 35. Many small streams run into the lagoon, but the

two main rivers, the Leyre and Canal des Etangs, contribute 73% and 24%, respectively, of

the total annual freshwater inflow (813 million m3 yr-1). The total lagoon surface (180 km2)

can be divided in two parts: the subtidal channels (63 km2), and the intertidal areas (117 km2)

(Plus et al., 2010). The main channels have a maximum depth of 25 m and are extended by a

secondary network of shallower channels. The intertidal area comprises sandy to sandy-mud

flat (Plus et al., 2010). Most of these flats (61 km2 in 2005 are covered a Z. noltii seagrass bed

(Plus et al., 2010). The lower part of the intertidal is generally devoted to Japanese oyster (C.

gigas) culture, which constitutes a major activity at this site. Adjacent to the mudflats, and

lining the channels, eelgrass (Zostera marina) occupies the subtidal sector. Sediment

temperatures vary annually between -1 and 35.4 °C (average = 15.8 °C) and salinity varies

between 18.5 and 34.5 (average = 30).

Around Dispute, four stations were monitored. Two “impacted” stations (IS: impacted

by sand and IM: impacted by mud) located in the impacted area and two “un-impacted” Z

noltii stations, with one situated near the impacted stations (PS: proximate seagrass) and one

situated far from the impacted stations (RS: remote seagrass), were monitored after the


93

operations in August 2005, 2006, 2008 and 2010 (Figure 5.1). Before the works, in August

2002, two stations corresponding to RS and PS were sampled.

A B

C

Figure 5.1. Location of Arcachon Bay on the southwest French coast (A), the study site in the lagoon (B), different stations: RS: remote seagrass; PS: proximate seagrass; IM: impacted by mud; IS: impacted by sand (C).

Atlantic Ocean

Arcachon

Dispute site

1°10 W

44°40 N

Leyre river


94

2.2. Macrofauna sampling

At low tide, the top 20 cm of the sediment was collected with a 0.0225-m2 corer, with

four replicates per station. Sediment was sieved through a 1-mm mesh; the remaining fraction

was fixed in 4% buffered formalin and stained with Rose Bengal. In the laboratory,

macrofauna was sorted, identified when possible to the species level, and counted. Biomass

was determined as ash-free dry weight (AFDW) after desiccation (60 °C, 48 h) and

calcination (450 °C, 4 h).

2.3. Sediment and seagrass leaf analysis

The top 3-cm sediment layer was also sampled for granulometric analysis. Sediment

grain-size characteristics (median grain-size, percentage of silt and clays) were determined

after sieving weighed dried sediment through a wet column of sieves with decreasing

apertures (1000, 500, 250, 125 and 63 μm). Percentage of organic matter in the sediment was

assessed after ignition (450 °C, 4 h) of a dried aliquot of sediment.

In 2002 and 2006, Z. noltii leaves were cut in each macrofauna sample and desiccated

(60 °C) until a constant dry weight was obtained. In 2010, a new method of assessing Z. noltii

leaf biomass was developed. Fourteen [15 cm × 15 cm]-quadrats were delicately laid over the

sediment surface, at low tide. For each quadrat, a numeric photograph was taken

perpendicularly, one meter above the surface. The method consisted of drawing three

equidistant lines across each numerical image and counting the intersections between lines

and leaves. Then biomass and percentage of coverage could be obtained, using the following

relationships:

Loge(DW) = 1.514 × Loge (mean number of intercepts per line) – 1.911, with R = 0.98

(n = 14 pictures)

Loge(S) = 0.690 × Loge(DW) + 1.195 with R = 0.98 (n = 10 pictures)

where DW is Z. noltii leaf dry weight in g m-2 and S is the percentage of sediment

surface covered by Z. noltii. Ten pictures per station were analysed.


95

2.4. Estimated loss of secondary production

Macrofauna species were gathered in five trophic groups based on the feeding types

(Fauchald and Jumars, 1979; Bachelet, 1981; Sauriau et al., 1989; Hily and Bouteille, 1999):

(1) deposit feeders, (2) grazers, (3) predators, (4) scavengers and (5) suspension feeders.

Then, biomass of each group was calculated. A P/B ratio was assigned for each trophic group

using values calculated by Blanchet (2004). Then at each date we subtracted the secondary

production in the impacted area from the secondary production in the seagrass to obtain a

gross estimation of secondary production loss. This value was multiplied by the surface of

destroyed seagrass and the time elapsed from the previous sampling date.

2.5. Data analysis

2.5.1. Multivariate analysis

Multivariate analysis was performed to compare macrozoobenthic communities

structure between areas. Abundances were square root-transformed to minimize the influence

of the most dominant taxa. A non-metric multidimensional scaling (n-MDS) based on Bray-

Curtis similarity coefficient was carried out to obtain an ordination plot. A Cluster Analysis

was used to determine groups of stations × dates that were homogeneous in terms of benthic

community. SIMPER analysis was performed to determine which species contributed to

between-group dissimilarity. These analyses were performed using PRIMER® – v6 package

(Clarke and Warwick, 2001; Clarke and Gorley, 2006).

In order to investigate the pattern of change of numerical descriptor of the benthic

assemblage such as total biomass, total number of species, total abundance, number of

species, abundance and biomass of epi- and infauna, biomass of the different trophic groups,

a Principal Coordinate Analysis (PCO) was performed on the matrix of Euclidean distances

among stations based on fourth-root transformed and normalized data. This analysis was

performed using PRIMER PERMANOVA package (Anderson et al., 2008).


96


Three currently available univariate Biotic Indices (BIs), namely AMBI (Borja et al.,

2000), BENTIX (Simboura and Zenetos, 2002), BOPA (Dauvin and Ruellet, 2007) and one

multimetric approach called MISS (Lavesque et al., 2009) were tested. Ecological quality

status and thresholds used to classify index values were reported in Table 5.1.

AMBI is based on previous work from Grall and Glémarec (1997). It considers five

ecological groups (available on web page: http://ambi.azti.es) ranging from sensitive species

(EGI) to first-order opportunistic species (EGV) (Borja et al., 2000) (Table 5.1).

BENTIX considers only two groups: sensitive (GS) and tolerant species (GT), which

correspond to ecological groups I and II, and ecological groups III to V of the AMBI,

respectively (Table 5.1).

BOPA is based on the ratio of opportunistic polychaetes (i.e. polychaetes of

ecological groups IV and V of the AMBI) and amphipods (except Jassa genus) (Dauvin and

Ruellet, 2007) (Table 5.1).


97

Table 5.1. Indices used in this study to assess Ecological Status (ES) and thresholds used to classify index values.

Ecological Status (ES) Biotic Indices

Number of ecological groups

Computation of the indices Acceptable Not acceptable

References

AMBI 5 0 EGI + 1.5 EGII + 3 EGIII + 4.5 EGIV + 6EGV based on percentage of ecological groups

0 to 3.3 3.3 to 7 Borja et al. (2000)

BENTIX 2 6 EGI&II+ 2 EGIII-V

based on percentage of ecological groups 3.5 to 6 (for sand) 3.0 to 6 (for mud)

0 to 3.5 (for sand) 0 to 3.0 (for mud)

Simboura and Zenetos (2002)

BOPA 2 log10 [(fp/fa + 1) + 1]

based on ratio of ecological groups 0 to 0.13966 0.13966 to

0.30103 Dauvin and Ruellet (2007)

MISS Sixteen parameters were classified in three

categories describing the macrofauna assemblages

0.6 to 1 0 to 0.6 Lavesque et al. (2009)

EG: ecological groups as determined by Borja et al. (2000); fp: opportunistic polychaetes frequency; fa: amphipods frequency (except Jassa sp.).


98

MISS basically consisted of including a selection of existing BIs with additional

metrics describing the community (five parameters): abundance per m2, biomass g AFDW

per m2, number of species (per 0.045 m2), Shannon Index H', and Piélou's evenness J'; trophic

composition (five parameters): grazers per m2, selective deposit feeders per m2, non-selective

deposit feeders per m2, suspension feeders per m2, carrion feeders per m2 (e.g. carnivorous,

omnivorous and scavengers); and pollution indicators (six parameters): values of two Biotic

Indices were used (AMBI and BOPA), abundance per m2 of sensitive species (e.g. EGI and

EGII), abundance per m2 of tolerant species (e.g. EGIII), abundance per m2 of opportunistic

species (e.g. EGIV and EGV). Finally, the W statistic, referring to Abundance-Biomass

Comparison (ABC curves) was computed with PRIMER® - v6 package. W measured the

extent to which biomass curves lays above the abundance curves (positive values were

expected for the undisturbed conditions, negative values for impacted samples) (Warwick,

1986). The BOPA and the AMBI indices were included as indicators of, respectively,

pollution by hydrocarbons and organic matter inputs (Lavesque et al., 2009). MISS was

inspired by the development of Indices of Biological Integrity conducted in North America

(Weisberg et al., 1997; Engle and Summers, 1999; Van Dolah et al., 1999; Llanso et al.,

2002a; Llanso et al., 2002b). Monitoring results were compared with reference conditions, in

order to derive an Ecological Status (ES) (Table 1).

The reference condition for a water body type is a description of the biological

elements, which corresponds totally, or almost totally, to undisturbed (pristine) conditions,

i.e. with no, or only a very minimal, impact from human activities (Borja and Muxika, 2005;

Muxika et al., 2007). In this study, reference conditions were derived by the software from

the data collected in the 38 stations located on normally vegetated Z. noltii beds in 2002

(Blanchet, 2004; Lavesque et al., 2009).

In addition, derived calculations of MISS were investigated. Firstly, we deleted

biomass related parameter (biomass and W values) from calculation because biomass

assessment is time consuming and it can be useful to keep samples in laboratory. Secondly,

we did not consider trophic groups because definition is often controversial (except for

suspension feeders), and many species have mixed trophic habits. Thirdly, different

combinations of the two first attempts (without ‘biomass + W + trophic groups except

suspension feeders’) were performed.


99

3. Results

3.1. Seagrass and sediment disposal

Before sediment disposal (in 2002), seagrass covered 62-68% of the sediments

surface with biomass ranging from 70 g DW m-2 to 80 g DW m-2 (Table 5.2). Six months

after this work, in August 2005, seagrass was completely destroyed in both impacted stations

(IS and IM) and remained absent up to and including 2008, included. In 2010, seagrass only

recovered in IM stations (41 g DW m-2 or 43% coverage total surface) while it was still

absent in IS station (Table 5.2). In un-impacted stations PS and RS, seagrass were always

present though its cover varied according to year (Table 5.2).

Sediments consisted of sandy muds to muddy sands in un-impacted stations with

median grain-size varying from 20 to 100 according to date and small-scale heterogeneity

(Table 5.2). In both stations submitted to sediments deposit (IM and IS), sediments in 2005

(i.e. just after the works) were not still sorted and consisted of muddy sands (median: 100-

120 µm; silt and clay: 26-28%; organic matter: 5%). With time, finer sediments were washed

out. Near the discharge place (IS) only larger material remained while fine sediments

accumulated around IM station. Hence, sediment evolved toward sands with low silt and clay

content at IS station (median: 150-190 µm; silt and clay fraction ≤23%; organic matter ≤2%)

whereas sediments consisted of muddy sand to sandy mud at station IM (median: 40-70 µm;

silt and clay: ≤60%; organic matter: 6-9%) (Table 5.2).


100

Table 5.2. Seagrass leaves biomass (g DW m-2), seagrass cover (% of sediment surface), sediment median particle size (µm), silt and clay and

organic matter content in the sediment (%) before dredging (August 2002) and after (August 2005-2006-2008-2010), “-”: missing data.

Status Date Station Seagrass leaves biomass (g DW m-2)

Seagrass coverage (%)

Median grain-size (µm) Silt and clay content (%)

Organic matter content (%)

Remote seagrass Aug. 2002 RS 80 68 20 (sandy mud) 82 5 Aug. 2005 - - 90 (muddy sand) 37 8 Aug. 2006 29 33 100 (muddy sand) 26 10 Aug. 2008 82 70 30 (sandy mud) 73 9 Aug. 2010 87 72 60 (sandy mud) 50 7 Proximate seagrass Aug. 2002 PS 70 62 20 (sandy mud) 73 7 Aug. 2005 103 52 100 (muddy sand) 31 9 Aug. 2006 9 15 90 (muddy sand) 36 8 Aug. 2008 - - 20 (sandy mud) 83 10 Aug. 2010 192 100 40 (sandy mud) 57 8 Impacted by mud Aug. 2005 IM 0 0 100 (muddy sand) 26 5 Aug. 2006 0 0 70 (muddy sand) 47 9 Aug. 2008 0 0 40 (sandy mud) 59 6 Aug. 2010 41 43 40 (sandy mud) 60 7 Impacted by sand Aug. 2005 IS 0 0 120 (muddy sand) 28 5 Aug. 2006 0 0 190 (sand) 5 2 Aug. 2008 0 0 150 (muddy sand) 23 2 Aug. 2010 0 0 180 (sand) 6 1


101

3.2. Main macrozoobenthic assemblages identified in the dataset

Multivariate analysis performed on the ‘18 stations-dates × 110 species’ data matrix

showed, at a 35% similarity level, that three different groups of stations-dates could be

identified (Cluster Analysis) (Figure 5.2). The first main group (Group 1) included all un-

impacted stations (PS and RS) as well as two IM stations from the last two dates of the

monitoring, 2008 and 2010. The second group of stations (Group 2) gathered all stations

which were impacted by sand (IS) as well as the IM station just after the sediment deposits

(in 2005) (Figure 5.2). Finally, the latter station (IM station in 2006) displayed a benthic

community differing from that of all other situations the first year after sediment disposal

(Group 3, Figure 5.2).

Similarity35%

Figure 5.2. Non metric multidimensional scaling (n-MDS) of stations based on Bray-Curtis

similarity matrix after square root-transformed abundance data. RS: remote seagrass; PS:

proximate seagrass; IM: impacted by mud; IS: impacted by sand. Groups of stations

identified by the Cluster Analysis at a 35% similarity level are identified (Group 1: ×; Group

2: ; Group 3: ).


102

According to the SIMPER analysis, the dissimilarity between groups 1 and 2 was

mainly due to: (1) a much lower abundance or disappearance of several taxa of grazing

epifauna such as Hydrobia ulvae (4193 ±951 ind. m-2 vs. 444 ±208 ind. m-2), Bittium

reticulatum, Idotea chelipes or Littorina littorea in Group 2 (Table 5.3); (2) a decrease of the

abundance level of many infaunal polychaete taxa such as Melinna palmata, Aphelochaeta

marioni, Heteromastus filiformis or Clymenura clypeata as well as other infaunal taxa such as

the bivalves Abra segmentum and Loripes lacteus (which disappeared from stations of Group

2); (3) nevertheless, few species were found at higher abundance levels in Group 2 such as

the infaunal polychaetes Nephtys hombergii and Streblospio shrubsolii, the bivalve

Cerastoderma edule and the amphipod Ampelisca sp. (Table 5.3).

3.3. Trend in the numerical descriptor of the macrofauna assemblages

The first axis of the principal coordinates analysis (PCO) extracted more than 58% of

total variation. Together with axis two, 72.5% of total variation was represented (Figure 5.3).

The ordination of samples along the first axis of the PCO clearly separated samples retrieved

within un-impacted seagrass beds (positive values) from samples collected in both impacted

areas (negative values). Nevertheless there was a noteworthy exception with samples from

2010 collected at the IM station which gathered within the data cloud corresponding to un-

impacted seagrass (Figure 5.3).

The ordination of samples obtained through the first PCO axis was positively

correlated with all numerical descriptors showing that samples collected in impacted sites

(except at station IM in 2010) displayed lower values for almost all of these descriptors

(Table 5.4). However the best correlations (Spearman coefficient of correlation >0.8) were

obtained with total, epifauna and infauna biomass, total number of species and abundance of

epifauna (Table 5.4). This analysis shows that, on a purely numeric point of view, only

station IM in 2010 displayed values comparable to un-impacted seagrass beds.


103

Table 5.3. List of the main species contributing to contribution to dissimilarity (SIMPER analysis) between groups identified by Cluster Analysis (Group 1: RS and PS stations from 2002 to 2010 and IM stations from 2008 to 2010; Group 2: IS stations from 2005 to 2010 and IM station in 2005; Group 3: IM station in 2006, see Figure 5.2). Top five dominant species in each group and top five contributed species for dissimilarity between groups are in bold.

Mean abundance ind. m-2

± standard deviation % contribution to dissimilarity between groups

Zoological group Position Trophic group Taxon Group 1 Group 2 Group 3 1 vs 2 1 vs 3 2 vs 3

Nemertea infauna predators Nemertinea 69±20 4±3 11±11 1.6 1.2 0.9 Mollusca Gastropoda epifauna grazers Hydrobia ulvae 4193±951 444±208 0 13.5 14.1 7.1 Bittium reticulatum 405±130 0 11±11 3.7 2.6 1.4 Littorina littorea 53±16 2±2 0 1.4 1.3 0.3 scavengers Nassarius reticulatus 37±8 2±2 0 1.2 1.1 0.3 Mollusca Bivalvia infauna deposit feeders Abra segmentum 219±62 22±20 56±21 3.6 1.6 2.8 Abra tenuis 3±2 11±9 22±13 0.6 1.2 1.5 suspension feeders Cerastoderma edule 45±20 53±18 0 1.5 1.2 2.6 Loripes lacteus 97±26 0 0 1.9 1.7 0 Ruditapes philippinarum 104±27 7±4 0 2.0 1.9 0.8 suspension feeders Mytilus edulis 67±42 0 0 1.3 1.1 0 Annelida Polychaeta infauna deposit feeders Aonides oxycephala 76±29 0 0 1.2 1.1 0 Aphelochaeta marioni 682±173 147±93 0 4.7 4.8 3.2 Clymenura clypeata 136±41 9±5 0 1.8 1.6 0.9 Euclymene oerstedi 47±13 2±2 0 1.2 1.0 0.2 Heteromastus filiformis 609±78 191±91 700±204 4.7 1.9 7.3 Melinna palmata 1099±220 9±9 78±21 6.7 5.2 3.2 Polydora spp. 0 2±2 22±13 0.2 1.2 1.7 Pseudopolydora spp. 67±21 38±22 33±11 1.6 1.4 1.7 Pygospio elegans 46±31 89±45 100±71 2.1 2.0 1.8 Streblospio shrubsolii 27±18 73±37 0 1.6 0.6 1.8 predators Glycera spp. 33±8 27±7 0 1.0 1.0 2.1 Nereis diversicolor 0 4±3 733±456 0.4 7.1 10.8 Nephtys hombergii 38±9 51±12 0 1.0 1.3 2.6 Annelida Oligochaeta infauna deposit feeders Tubificoides benedii 412±110 31±16 44±44 4.4 3.2 2.1 Phoronida infauna suspension feeders Phoronis psammophila 35±12 0 0 1.2 1.1 0 Crustacea Amphipoda infauna deposit feeders Ampelisca sp. 7±4 42±23 0 1.4 0.4 2.3 Corophium urdaibaiense 1±1 0 22±13 0.1 1.1 2.0 Melita palmata 40±17 33±15 22±22 1.4 1.1 1.4 grazers Microdeutopus gryllotalpa 57±35 11±8 167±38 1.2 3.0 4.5 Crustacea Isopoda infauna deposit feeders Cyathura carinata 40±14 7±4 744±286 1.2 6.2 10.5 epifauna deposit feeders Lekanesphaera spp. 2±1 24±10 0 1.2 0.1 1.6 grazers Idotea chelipes 105±33 0 0 2.1 1.9 0 Insecta Diptera infauna grazers Dolichopodidae 15±6 2±2 189±90 0.7 3.1 5.5


104

Figure 5.3. Ordination of samples obtained by the principal coordinates analysis (PCO) performed on the matrix of numerical descriptors of macrofauna assemblages. Sb: samples retrieved in un-impacted seagrass beds (RS and PS from all dates from 2002 to 2010); IM: samples retrieved in the stations with mud deposits; IS: samples retrieved in the station with sand deposits. The number indicates the year of collection from 2005 (05) to 2010 (10).

Table 5.4. Correlations (Spearman Rank Correlation Coefficient) of each numeric descriptor values with the first two axes of the principal coordinates analysis. In bold: R >0.8.

PCO axis 1 PCO axis 2 Variables (58.2% of total variation) (14.3% of total variation) Total biomass 0.91 -0.11 Total number of species 0.90 -0.24 Biomass of epifauna 0.88 0.27 Abundance of epifauna 0.85 0.22 Biomass of infauna 0.82 -0.32 Total abundance 0.80 0.27 Deposit feeders biomass 0.79 -0.29 Abundance of infauna 0.78 -0.33 Biomass of infauna 0.78 -0.45 Grazers biomass 0.77 0.50 Suspension feeders biomass 0.70 -0.14 Scavengers biomass 0.64 -0.27 Abundance of epifauna 0.63 0.68 Carnivores biomass 0.50 -0.42


105

3.4. Dynamic of impact and recovery of macrobenthic community

Six months after sediments disposal (in August 2005), both impacted areas were

covered by a mixture of sand and mud. Seagrasses were destroyed and the macrofauna was

dramatically changed in the same way at both impacted stations (IM and IS) (Figures 5.2 &

5.4). Compared to un-impacted seagrass beds, benthic macrofauna assemblages were

characterized by very low biomass (1.3 ±0.4 g AFDW m-2 vs 15.4 ±2.9 g AFDW m-2) of both

epifauna and infauna in impacted stations (Table 5.5). The number of species was also

drastically reduced with 11 ±2 taxa per station in impacted sites and 17 ±2 taxa per station in

un-impacted areas (Table 5.5).

With time, the perturbed area divided into two distinct habitats. A 2.104 m2-sandflat

(IS) near the place where sediments were initially disposed in 2005 and a larger bare mudflat

(IM, 28.104 m2), due to fine sediment migration. In August 2006, benthic communities at both

impacted sites were still very different from that of seagrass beds. In the mudflat (IM),

biomass, abundance and species richness were half those of un-impacted seagrass bed (Table

5.5). In the sandflat, the benthic community was drastically different with a particularly low

abundance (400 ind. m-2 i.e. 13% of mudflat abundance and 5% of seagrass abundance), low

biomass (0.2 g AFDW m-2 i.e. 2% of biomass in bare mudflat and 1% of biomass in seagrass

bed), and a very low number of species (5 species).

More than 3 years after the perturbation, in August 2008, the macrozoobenthic

assemblage from the mudflat (IM) was similar to that of seagrass beds according to a Cluster

Analysis (Figure 5.2) even though no seagrass recovery was observed at that time. Though

benthic assemblage was similar in terms of species composition and dominance pattern, the

benthic assemblage from station IM still displayed reduced biomass and rather low

abundance and diversity compared to seagrass beds assemblage (Figure 5.4). In the

meantime, the macrobenthic assemblage within the sandflat remained very different from that

of all other studied stations (Figure 5.2). In this station, the benthic assemblage was still

characterized by reduced diversity, abundance and biomass (Figure 5.4).

In 2010, the full recovery of the benthic assemblage in terms of species composition,

dominance pattern, abundance, biomass and diversity patterns was observed at the IM station

(Figures 5.2 & 5.4). In the meantime, Z. noltii shoots had returned at this site, 4-5 years after


106

its destruction. However, the IS site remained devoid of any seagrass cover and still displayed

a clearly different benthic community (Figure 5.2-5.4).


107

Table 5.5. Mean values (± standard deviation) of abundance, biomass, number species richness of macrofauna, biomass of epifauna and infauna and biomass of each trophic groups in un-impacted (RS, PS), IM and IS stations from 2005 to 2010.

August 2005 August 2006 August 2008 August 2010

Parameters RS, PS IM, IS RS, PS IM IS RS, PS IM IS RS, PS IM IS

Abundance (ind. m-2, ±SD) 4111±744 2700±817 7594±974 3011±648 400±151 9350±806 6011±3450 567±150 6777±580 17844±1844 1022±243

Abundance epifauna (ind. m-2, ±SD) 1611±408 933±494 1239±502 56±33 67±43 6183±1490 4589±3430 133±91 2161±664 13289±2382 489±252

Abundance infauna (ind. m-2, ±SD) 2483±460 1750±360 6339±795 2956±630 333±143 3156±1082 1422±489 411±102 4611±993 4556±1016 533±189

Biomass (g AFDW m-2, ±SD) 15.4±2.9 1.3±0.4 21.7±5.5 10.3±5.4 0.2±0.1 41.8±24.0 3.3±1.8 4.2±1.7 64.4±16.5 13.9±5.8 4.4±3.9

Biomass epifauna (g AFDW m-2, ±SD) 5.8±2.2 0.6±0.3 7.3±2.2 0.4±0.4 0.05±0.03 30.6±23.0 2.4±1.8 2.4±2.0 15.4±3.8 5.6±2.3 0.2±0.1

Biomass infauna (g AFDW m-2, ±SD) 9.0±2.3 0.7±0.1 14.2±3.8 9.9±5.4 0.1±0.1 11.2±3.3 0.9±0.3 1.6±1.1 49.0±13.0 8.3±4.5 4.2±3.9

Number species (±SD) 17±2 11±2 23±2 12±2 5±1 13±2 9±2 7±1 18±1 16±2 7±2

Deposit feeders (g AFDW m-2, ±SD) 2.4±0.6 0.4±0.1 4.9±0.7 0.8±0.2 0.05±0.0 4.2±1.7 0.8±0.2 0.4±0.2 3.9±0.8 7.3±4.1 0.9±0.8

Grazers (g AFDW m-2, ±SD) 2.9±1.9 0.6±0.3 5.1±2.3 0.3±0.1 0.03±0.02 4.6±0.9 2.2±1.7 0.1±0.1 4.4±1.4 4.4±1.4 0.1±0.1

Predators (g AFDW m-2, ±SD) 1.7±1.0 0.3±0.1 1.9±0.7 9.2±5.5 0.1±0.0 0.4±0.2 0.3±0.1 0.4±0.4 1.6±0.6 1.8±0.8 0.05±0.02

Scavengers (g AFDW m-2, ±SD) 2.5±1.2 0 1.6±0.4 0 0 1.9±1.0 0 2.0±2.0 3.2±1.0 0 0

Suspension feeders (g AFDW m-2, ±SD) 5.5±1.8 0.05±0.05 8.1±3.1 0 0.03±0.02 30.7±23.1 0.01±0.01 1.3±1.0 51.3±15.7 0.4±0.2 3.3±3.2


108

0

20

40

60

80

100

120

140

2002

2005

2006

2008

2010

2002

2005

2006

2008

2010

2005

2006

2008

2010

2005

2006

2008

2010

RS PS IM IS

Mea

n bi

omas

s (g

AFD

W m

-2) A

0

5000

10000

15000

20000

25000

30000

2002

2005

2006

2008

2010

2002

2005

2006

2008

2010

2005

2006

2008

2010

2005

2006

2008

2010

RS PS IM IS

Mea

n ab

unda

nce

(ind.

m-2

)

B

0

5

10

15

20

25

30

35

40

2002

2005

2006

2008

2010

2002

2005

2006

2008

2010

2005

2006

2008

2010

2005

2006

2008

2010

RS PS IM IS

Mea

n nu

mbe

r of s

peci

es

C

Figure 5.4. A) Mean biomass (g AFDW m-2), (B) abundance (ind. m-2), and (C) species richness of the benthic macrofauna. RS: remote seagrass; PS: proximate seagrass; IM: impacted by mud; IS: impacted by sand (Figure 5.1).


109

3.5. Loss of secondary production

The loss of secondary production increased between 2005 and 2010 in the impacted

sites, from 20 to 51 g AFDW m-2 y-1 and from 20 to 75 g AFDW m-2 y-1 in mud and in sand

respectively (Table 5.6). This increase was mostly due to suspension feeders (mussels, clams)

that progressively settled in un-impacted stations and not in impacted stations (see Table 5.3).

Indeed, the part of secondary production loss due to suspension feeders was of the order of

30% in 2005, against more than 70% in 2010. The global loss of production was 10-16 times

the higher in the area impacted by mud than area impacted by sand, mainly due to the

difference in the surface (× 14 times). Over 5.5 years and a total surface of 30 × 104 m2,

secondary production loss reached 74 t AFDW.


110

Table 5.6. Loss of secondary production by trophic group (g AFDW m-2 y-1). The total secondary production loss (total area and total duration

from the previous date. For 2005, elapsed time from the works was 0.5 year is given on last line. RS, PS, IM and IS: see Figure 5.1.

2005 2006 2008 2010 Trophic group P/B y-1

IM, IS g AFDW m-2 y-1

IM g AFDW m-2 y-1

IS g AFDW m-2 y-1

IM g AFDW m-2 y-1

IS g AFDW m-2 y-1

IM g AFDW m-2 y-1

IS g AFDW m-2 y-1

Deposit feeders 2.4 4.8 9.8 11.8 8.2 9.1 -8.2 7.2

Grazers 2.4 5.5 11.5 12.2 10.8 5.8 0.0 10.3

Predators 0.9 1.3 -6.6 1.6 0.0 -0.1 -0.2 1.4

Scavengers 0.9 2.3 1.4 1.4 1.7 -0.1 2.9 2.9

Suspension feeders 1.1 6.0 8.9 8.9 33.8 32.3 56.0 52.8

Total production 19.9 25.0 35.9 54.5 47.0 50.5 74.6

Total production loss g AFDW 2.99×106 7.04×106 0.72×106 30.49×106 1.90×106 28.30×106 2.99×106


111

3.6. Biotic Indices

3.6.1. Assessment perturbation by univariate Biotic Indices

AMBI classified all stations as “good” or “moderate” without any recognition

sediment disposal (Table 5.7). As well, BENTIX classified almost all stations as “moderate”

and “poor” without any relation to sediment disposal (Table 5.6). BOPA achieved more

contrasted results, from “poor” to “high”. This index increased the quality status of IM with

time. Conversely, BOPA gave fluctuating ecological status (ES) to control stations (RS, PS)

and observed strong amelioration of station IS that was however considered as the most

impacted during the whole monitoring (Table 5.7). In general, there was a disagreement

between the classification of univariate Biotic Indices (AMBI, BENTIX, BOPA) and they did

not detect the perturbation related to sediment disposal.

3.6.2. Assessment perturbation by multimetric approach (MISS) and derived-MISS

In seagrass, ES from MISS varied between “moderate” and “high” according to year

and position (RS, PS). In the site impacted by mud (IM), ES remained at a “moderate” level

until 2010 when it achieved “good”. In the site impacted by sand (IS), ES was “poor” until

2006, and reached moderate between 2008 and 2010 (Table 5.7). When removing biomass-

related parameters (i.e. total biomass and W), ES from MISS changed in one situation out of

18 (IS in 2005). Conversely, when MISS was calculated without trophic groups parameters, it

modified 1/3 of ES estimations, mostly by degrading them (Table 5.7).


112

Table 5.7. Biotic indices (AMBI, BENTIX, BOPA, MISS and d-MISS) and ecological status (ES) according to Table 5.1. For d-MISS: values different from MISS are in bold. RS, PS, IM and IS: see Figure 5.1.

AMBI BENTIX BOPA MISS (with 16 indices)

d-MISS (without biomass+W)

d-MISS (without trophic groups except suspension feeders)

d-MISS (without W+biomass+trophic groups except suspension-feeders)

Station Year Score ES Score ES Score ES Score ES Score ES Score ES Score ES RS 2002 3.1 good 2.53 moderate 0.00662 high 0.9 high 0.90 high 1.00 high 1.00 high 2005 3.7 moderate 2.78 moderate 0.09607 good 0.54 moderate 0.50 moderate 0.57 moderate 0.53 moderate 2006 3.7 moderate 2.46 poor 0.24761 poor 0.76 good 0.66 good 0.51 moderate 0.51 moderate 2008 3.0 good 2.46 poor 0.00619 high 0.60 moderate 0.56 moderate 0.43 moderate 0.39 poor 2010 3.5 moderate 2.66 moderate 0.25719 poor 0.69 good 0.69 good 0.52 moderate 0.52 moderate PS 2002 3.2 good 2.48 poor 0.08911 good 0.66 good 0.62 good 0.49 moderate 0.45 moderate 2005 2.4 good 2.51 moderate 0.18368 moderate 0.49 moderate 0.47 moderate 0.36 poor 0.44 poor 2006 2.5 good 2.54 moderate 0.18309 moderate 0.56 moderate 0.57 moderate 0.56 moderate 0.57 moderate 2008 3.1 good 2.54 moderate 0.20392 poor 0.73 good 0.74 good 0.53 moderate 0.54 moderate 2010 1.9 good 2.90 moderate 0.07912 good 0.52 moderate 0.60 moderate 0.42 moderate 0.50 moderate IM 2005 3.8 moderate 2.98 moderate 0.21241 poor 0.47 moderate 0.50 moderate 0.42 moderate 0.45 moderate 2006 3.5 moderate 3.09 good 0.17760 moderate 0.52 moderate 0.55 moderate 0.52 moderate 0.55 moderate 2008 3.1 good 3.01 good 0.08949 good 0.54 moderate 0.52 moderate 0.67 good 0.66 good 2010 3.0 good 2.87 moderate 0.08095 good 0.72 good 0.77 good 0.62 good 0.67 good IS 2005 3.0 good 2.69 moderate 0.15609 moderate 0.38 poor 0.42 moderate 0.36 poor 0.40 moderate 2006 3.0 good 2.48 poor 0.19562 poor 0.34 poor 0.35 poor 0.37 poor 0.38 poor 2008 1.9 good 2.63 moderate 0.10574 good 0.42 moderate 0.43 moderate 0.42 moderate 0.43 moderate 2010 2.6 good 2.72 moderate 0.09314 good 0.44 moderate 0.42 moderate 0.38 poor 0.35 poor


113

4. Discussion

4.1. Seagrass destruction and recolonization

Six months after the sediments disposal, seagrass totally disappeared in the sites that

were covered by sediment (sand and mud). The responses of seagrass to sediment burial have

been assessed in many studies (Marba and Duarte, 1994; Duarte et al., 1997; Cabaco and

Santos, 2007; Cabaco et al., 2008; Han et al., 2012) and is species specific and strongly size-

dependent (Cabaco et al., 2008). According to Cabaco and Santos (2007), Z. noltii is highly

sensitive to burial and erosion disturbances due to the small size of this species and the lack

of vertical rhizomes. Hence, most Z. noltii plants under complete experimental burial died

between the 1st and the 2nd week (Cabaco and Santos, 2007). Whereas the mortality caused

by burial increased with decreasing seagrass size, the potential to recover from disturbances

by growth is enhanced with decreasing seagrass size (Duarte et al., 1997; Peralta et al., 2005).

A trade-off related to seagrass size exists, in terms of recovery time versus resistance to

stressors, such as sediment disturbance (Han et al., 2012).

Duarte et al. (1997) found that small seagrass species, such as Halophila ovalis and

Halodule uninervis were able to recover within 4 months of burial disturbance, while Cabaco

and Santos (2007) did not observe any recovery of Z. noltii within 2 months of experimental

burial. In fact, Zostera noltii is well adapted to cope with sediment disturbances of limited

amplitude (i.e. ±6 cm) and with continuous events by rapidly relocating their rhizomes to the

preferential depth (Han et al., 2012). However, in our study, seagrass partly recovered in the

IM station only 5 years (from 2005 to 2010) after burial. This long delay could be related to

the thick layer of sediment (≥10 cm) discharged on a single occasion (Han et al., 2012).

Characteristics of sediment were also an important factor, since we observed that areas

covered by sand remained free of seagrass after 5 years. The reason is certainly not directly

linked to the sediment grain-size since Do et al. (2011) showed that Z. noltii could colonize a

sand flat within 4 years.


114

4.2. Benthic community alteration and recovery possibility

Seagrass destruction and the changes due to sediment disposal altered benthic

community. The impact depends on the amount of discharged sediment, disposal time, water

depth, currents, particle size, and other abiotic parameters (see review in Witt et al., 2004).

One of the main effects of dumping of dredged sediments relates to burial of benthos at dump

sites (Essink, 1999). Local benthos has to cope with deposition of sediments which are in

many cases strongly anaerobic. Sensitivity of benthos to being covered by dredged sediments

is strongly dependent on the thickness of sediments and their ability to restore contact with

the overlying water (Essink, 1999). The mortalities generally increase with increased

sediment depth, exotic sediment and burial time (Maurer et al., 1981a, b, 1982; Harvey et al.,

1998). Decreases in macrofaunal abundances, biomasses and species richness as a

consequence of the disposal have been reported in several studies (Harvey et al., 1998; Cruz-

Motta and Collins, 2004; Witt et al., 2004; Ware et al., 2010). Our study confirmed that

benthic macrofauna community structure at the disposal sites had changed substantially

following deposition. Indeed, 6 months after work (in August 2005), macrofauna

assemblages showed a decrease of biomass, abundance and species number in impacted (IM,

IS) stations. After 18 months (in August 2006), the macrofauna assemblages displayed a clear

difference between impacted (IM, IS) and un-impacted stations (PS, RS). Biomass,

abundance and diversity were lower in the station affected by sand disposal. Faunal

differences between the disposal sites and the reference areas were indeed correlated with

changes in the sediment composition depending on impact types. The disposal sites had a

higher proportion of mud or sand, which influenced species composition (Witt et al., 2004).

Sediment disposal affected differently benthic macrofaunal species according to their specific

feeding behavior, mobility or morphology (Pearson and Rosenberg, 1978; Van Dolah et al.,

1984; Witt et al., 2004).

Following burial, macrobenthic invertebrate recovery can occur by a combination of

three main mechanisms: planktonic recruitment of larvae, lateral migration of juveniles/adults

from adjacent un-impacted areas and/or vertical migration through the deposited material

(Bolam and Whomersley, 2005; Bolam et al., 2011). The relative importance of these

mechanisms will depend on a number of factors such as spatial scale timing, rate and depth of

placement (Bolam et al., 2006). Which mechanism ultimately predominates has important


115

implications for the rate and successional sequences of invertebrate recovery (Bolam et al.,

2010). For example, in cases where material is deposited thinly over a large area, a relatively

rapid recovery through vertical migration may occur. If the sediments are deposited at a depth

which exceeds the organisms’ burrowing and migration ability, total elimination of the

ambient community will occur (although some species may successfully be transported

within the dredged material) in the short term due to smothering; a slower recovery will then

ensue through lateral migration (days/weeks) and/or planktonic settlement

(weeks/months/years) (Bolam, 2011). Because of the thick disposal sediment layer, in our

case, few or no species were able to recolonize the disposal sites by vertical migration. On the

other hand, migration of adults from undisturbed areas and reproduction and larval

recruitment from undisturbed areas could explain the gradual re-establishment of macrofauna

(Harvey et al., 1998).

Invertebrate recovery following dredged material disposal in relatively unstressed

marine environments generally takes between 1 and 4 years, while in more naturally stressed

areas, recovery is generally achieved within 9 months, although deeper polyhaline habitats

can take up to 2 years to recover (Bolam and Rees, 2003). Differences in recovery times are

attributed to the number of successional stages required to regain the original community

composition that depends on their life-history traits (Bolam and Rees, 2003). In Upper

Laguna Madre, mollusc and polychaete species composition and densities in seagrasses that

had colonized dredging deposits required at least 10 years to become similar to communities

in adjacent natural seagrass beds (see review in Sheridan, 2004). In our study, benthic

community showed a recovery three years after sediment disposal. At that time, sediment

consisted of mud, like in 2002, but seagrass had not yet recovered. This observation

highlights the importance of sediment type for benthic organisms, especially concerning

infauna which is more independent to seagrass presence (Cottet et al., 2007).

On other hand, the recovery of seagrass after 6 years in IM station also explained the

increase of species richness, abundance and biomass. The benefits of seagrass habitats for

ecosystems’ diversity, health and functioning were broadly documented (Orth et al., 1984;

Edgar, 1990; Blanchet et al., 2004; Do et al., 2011). Sheridan (2004) reported that, once

seagrass start to cover dredged sediments, increases in densities of the associated mobile

macrofauna would be expected. The presence of intertidal seagrass potentially increases food


116

availability for both infauna and epibenthic organisms by acting as a sink for organic matter

(Asmus and Asmus, 2000). Seagrass meadows reduce water movement and increase

sedimentation rates of fine particles and detritus. The above-ground vegetation provides

habitats and substrates for free-living animals and epiphytic animals and algae also, the

below-ground rhizome network offers sediment stability, creating favorable living conditions,

including shelter from predation, for a wide range of infaunal organisms (Fredriksen et al.,

2010).

4.3. Secondary production loss

Increasing percentages of plant burial significantly increase mortality and

consequently decrease secondary production (Mills and Fonseca, 2003). Since secondary

production responds quickly following the disposal of dredged material, the response of

benthic production to disposal is more predictable than community (Wilber et al., 2008;

Bolam, 2011). In fact, our results showed that both approaches differ. While benthic

macrofauna tended to recover in terms of structure since 2008 (at least in mud), secondary

production loss reached the highest values in 2008 and kept similar in 2010.

A gross calculation however, tends to show that this production loss has small

consequence on higher trophic level. Indeed, we calculated a total loss of 74 t AFDW, over

the whole area and in 5.5 years. With a production/consumption rate of 15%, this would

consist in a loss of predator production of 11 t AFDW over 5.5 years, i.e. 1.5 t AFDW per

year (i.e. 15.2 t Fresh Weight per year) which is insignificant at the scale of the lagoon.

However this calculation is only based on trophic pathways and does not take in

consideration the effect of seagrass destruction as an habitat loss for potential predators

(Summerson and Peterson, 1984; Irlandi, 1994; Boström et al., 2006a).

4.4. Biotic indices reaction

Previous studies in Arcachon Bay already stated that some Biotic Indices (AMBI,

BENTIX, BOPA) may perform badly in semi-enclosed ecosystems that are naturally enriched

in organic carbon (Blanchet et al., 2008; Lavesque et al., 2009). The present study confirmed

that these BIs did not detect both the seagrass burial and its recovery.


117

Some previous studies have attributed a poor performance of AMBI to highlight

anthropogenic pressures (Albayrak et al., 2006; Labrune et al., 2006; Quintino et al., 2006;

Zettler et al., 2007; Simonini et al., 2009), especially when the disturbance agent is not

related to organic enrichment (Sampaio et al., 2011). AMBI has ability to detect different

anthropogenic impacts worldwide, including anoxia and hypoxia, eutrophication, nutrient

loads, sediment toxicity (metals, PAH), and aquaculture (see review in Borja and Tunberg

(2011)). However, when using AMBI, reference conditions must be assessed independently

for each habitat (Muxika et al., 2007; Borja et al., 2012). In addition, our results showed that

the AMBI classified “Good” ES whereas the BENTIX assigned ‘Moderate’ ES for most

times/sites. The disagreements between both indices were already reported in some previous

studies (Simonini et al., 2009; Do et al., 2011). It was suggested that the discrepancy in the

AMBI and BENTIX results could be ascribed to differences in: (i) the weighting of sensitive

and tolerant groups of species in the formulae; (ii) the scaling of boundary limits among

classes; (iii) the arrangement of the ‘tolerant’ species, which are weighted separately in the

AMBI, whereas the BENTIX method required all tolerant species to be weighted equally; and

(iv) the attribution of the species to the ecological groups (see reviewed in Simonini et al.,

2009).

BIs constitute an extreme in terms of data reduction from the species × abundance

tables to a single numerical value. As a consequence, they are unable to assess the drastic

changes that occur following sediment disposal. Our result showed that MISS (Macrobenthic

Index for Sheltered Systems), that includes some of the existing BIs, namely BOPA and

AMBI, together with an additional set of metrics showed better results in assessing the

seagrass burial and its recovery. However, MISS requires biomass which is destructive and

time-consuming. We showed that it was possible to calculate a derived MISS (d-MISS)

without biomass that provided very similar conclusions. This BI should be now tested in

other conditions. Conversely, we recommend to keep considering trophic group separation,

even though it is often uneasy to classify species according to a clear trophic regime.

118

119

Chapter 6 - Perspective in Vietnam

1. Seagrass species in Vietnam

Seagrasses are distributed across the globe but unlike other taxonomic groups with

worldwide distribution, they exhibit low taxonomic diversity, with approximately 60 species

worldwide (Orth et al., 2006). In Vietnam, there are 14 species of seagrass reported (Nguyen

et al., 2010) (Table 6.1). Compared to other countries in the East Asian region (China,

Vietnam, Cambodia, Thailand, Malaysia, Indonesia and Philippines), seagrass species

diversity in Vietnam ranked third after the Philippines (16 species) and Malaysia (15 species)

(UNEP, 2004).

Table 6.1. The list of seagrass species in Vietnam

N0 Taxon In the North In the South Family Hydrocharitaceae 1 Halophila beccarii Asch., 1871 + + 2 Halophila decipiens Ostenf., 1901 + + 3 Halophila ovalis (R.Br.) Hook.f., 1858 + + 4 Halophila minor (Zoll.) den Hartog, 1957 + + 5 Thalassia hemprichii (Ehrenb. ex Solms) Asch., 1871 + + 6 Enhalus acoroides (L.f.) Royle, 1839 + Family Ruppiaceae 7 Ruppia maritima L., 1753 + + Family Cymodoceaceae 8 Halodule pinifolia (Isobe et al.) den Hartog, 1964 + + 9 Halodule uninervis (Forsk.) Boiss., 1882 + 10 Syringodium isoetifolium (Asch.) Dandy, 1939 + 11 Cymodocea rotundata Ehr.. & Hempr. ex Aschers., 1870 + 12 Cymodocea serrulata (R. Br.) Aschers. & Magnus, 1870 + 13 Thalassodendron ciliatum (Forsskål) den Hartog, 1970 + Family Zosteraceae 14 Zostera japonica Ascherson & Graebner, 1907 + + Total 8 14

120

Vietnam is at the overlap of temperate and tropical seagrass species with Zostera

japonica growing intertidally in the north and mixing with Halophila ovalis, while in the

south the species composition is similar to the Philippines and Malaysia (Edmund and

Frederick, 2003). Ruppia maritima is common in coastal zones in northern Vietnam,

Halophila ovalis is widespread in coastal zones and around islands in north and central

Vietnam, while Halodule pinifolia is particularly prevalent in southern areas of Vietnam.

Seagrass beds are distributed along the entire coastline with meadows occurring from

Vietnam’s northern border with China, through to the south-western border with Cambodia

(Figure 6.1), but mostly from the middle of the southern sections. Species diversity tends to

increase gradually from the north to the south. In particular, in the north, seagrass beds such

as in Quang Ninh and Hai Phong provinces have the lowest number of species (5 species) and

the smallest coverage area (100 ha). Moreover, the meadows in this area are usually single

species. In the central part, seagrass beds in Tam Giang-Cau Hai and Lap An (Thua Thien-

Hue province) have 6 species with 1100 ha coverage area. In the South, seagrass beds in Phu

Quoc Island (Kien Giang province) are the most diverse with 9 species and coverage area of

over 10000 ha. It is usual to find 3 to 4 seagrass species per square meter in Phu Quoc Island

(Tu, 2009).

121

Dam HaTran island

Co To island

Lien Vi

Gia LuanCat Hai

Bach Long Vi islandKim Trung

Thanh Long

Xuan Hoi

Vung Ang

Cua GianhNhat Le

Cua TungCua Viet

Tam Giang – Cau Hai lagoonLang Co lagoon

Thu Bon

Ly Son island

Thi NaiCu MongO Loan

Van Phong bay

Nha PhuThuy Trieu

My Hoa

Vinh Hao

Phu Quy island

Con Dao island

Phu Quoc island

Ba Mun island

Dugong recorded Dugong sighting Record of seagrass

Figure 6.1. Map of seagrass distribution in Vietnam (source Nguyen (2004))

122

2. Biodiversity in seagrass

Very few studies have been undertaken to identify seagrass associated biota in

Vietnam as well as in East Asia (UNEP, 2004). However, the demonstration site proposals

from the seven countries of East Asia (China, Vietnam, Cambodia, Thailand, Malaysia,

Indonesia, Philippines) participating in the Project “Reversing Environmental Degradation

Trends in the South China Sea and Gulf of Thailand”, indicate that, along with 17 seagrass

species, there are at least 25 species of epiphytic algae, 21 macrobenthic algae, 10 penaeid

shrimps, 100 gastropods, 5 siganid fishes, 7 sea urchins, and 7 seahorses (UNEP, 2004). Most

of the major commercial fisheries of the region occur immediately adjacent to seagrass beds

(UNEP, 2004).

In Vietnam, according to Nguyen (2010) density of macrobenthos inside seagrass is

1.5-5.2 times higher than in sediment without seagrass. This study also found 323

macrobenthos, 219 seaweeds, 214 fishes, 178 juvenile species (including fishes, crabs and

shrimps), 60 gastropods, 10 sea cucumbers, 5 seahorses, 8 prawns (Penaeidae), 4 urchin

species in the seagrass beds (Nguyen et al., 2010).

Seagrass in Vietnam have another importance as it feeds endangered species such as

dugongs (Dugong dugon) and green turtles (Chelonia mydas). Until recently, it was widely

considered that the only remaining population of dugongs in Vietnam inhabited areas of Con

Dao National Park, an archipelago of 14 islands in the southern province of Ba Ria-Vung

Tau. The major threats to dugongs in Vietnam are hunting (not widespread), gill nets and

starvation through habitat destruction.

3. Decline of seagrass in Vietnam

Vietnam has at least 440 km2 of seagrass beds as determined from remote sensing and

ground-truth surveys. Vietnam coastal zone has been heavily impacted by sedimentation and

domestic and agricultural pollution (Edmund and Frederick, 2003). Seagrass meadows in

Vietnam have suffered serious degradation, with approximately 40 to 50% of their areas lost

over the past 2 decades (UNEP, 2004). Especially in the North Vietnam, some seagrass areas

decreased by 85% as in Ha Coi (Quang Ninh province) and some completely disappeared as

in Tien Yen (Quang Ninh province) by construction activities and coastal development. In

123

general, very little information on seagrass loss is available from Vietnam (Nguyen et al.,

2010).

4. Use of seagrasses in Vietnam

Seagrass in Vietnam are important either for direct use or as habitat. The majority of

exploited seagrasses (particularly Zostera and Ruppia genus) are used as food for livestock

and fertilizer. The main importance of seagrass, however, is for the use of associated biota,

such as algae (e.g. Gelidiella acerosa, Gracilaria spp., Hypnea spp., Sargassum spp., and

Turbinaria spp. are of commercial value), harvesting of the swimming crabs (Portunus

pelagicus and P. sanguinolentus), sea cucumbers (commercially important species

Holothuria scabra and Halodeima atra), finfish (at least 34 commercially important species),

and seahorses (in particular Hippocampus kuda) (Tu, 2009).

5. Threats to seagrass in Vietnam

Threats to seagrasses in Vietnam include both natural and human impact. However,

the main threat is anthropogenic activities such as destructive fishing practices (e.g. explosive

fishing, trawling, electric fishing, poisons), intensive aquaculture (rapid expansion of fish

farming, shellfish culture, etc.), land reclamation (using tidal flats for agricultural purposes),

and coastal development (e.g. construction of roads, bridges, houses and ports, dredging

activities), which have resulted in sedimentation and land-based pollution. Pressure on

seagrass beds stems from the lack of public awareness of their importance (UNEP, 2004).

Natural threats include typhoons (Tonkin Gulf in northern Vietnam experiences an

annual average of 35 typhoons), turbidity and sedimentation (river runoff from agriculture,

forestry, and urban development) and freshwater runoff (particularly during the rainy season),

climate change, sea-level rise. Tropical hurricanes are associated with heavy rainfall and

runoff, which may have negative impact on seagrass distribution as a result of deteriorated

light conditions due to suspended material and increased abundance of algae blooms induced

by enhanced nutrient discharges (Carlson et al., 2010). As an example of the hurricanes effect

on seagrass, Thalassodendron ciliatum species completely disappeared in Con Dao island

after the Linda storm in 1997 (Tu, 2009).

124

6. Response to threats

To date very little work has been done in response to the specific threats to seagrass in

Vietnam as well as in the South China Sea Region. UNEP/GEF South China Sea Project

(Seagrass monitoring in Vietnam was conducted as a part of the UNEP/GEF project

“Reversing environmental degradation trends in the South China Sea and Gulf of Thailand”

(2002-2008)), two Europe-funded projects (Prediction of the Recovery and Resilience of

Disturbed Coastal Communities in the tropics (South East Asian) (code: IC18CT980292)

(1998-2003) and Biodiversity assessment of the Ha Long Bay Heritage Area and proposition

of management plans (2002-2003)) and one project funded by United States of America

(National Oceanic and Atmospheric Administration) (Monitoring of seagrass in Bai Tu Long,

Quang Ninh Province (2003-2004) have recently been completed. The other projects

attempted to predict the resilience and recovery of disturbed seagrass at two sites as Bai Bon

(Maintaining seagrass beds for biodiversity, particularly endangered species) and Thuy Trieu

(Community Based Management) (UNEP, 2004). In addition, indirectly a number of projects

and programs that deal with coastal protection in Vietnam likewise promote the protection

and conservation of seagrass habitats such as the establishment of fifteen marine protected

areas (MPA) in Vietnam.

7. Research on seagrass in Vietnam

There have been only 62 ISI-cited publications on the seagrasses of Southeast Asia in

the last three decades and most work has been done on few sites such as Northwest Luzon in

the Philippines and South Sulawesi in Indonesia (Ooi et al., 2011). There is very little

quantitative data, especially long time series, from the tropical Indo-Pacific, in particular

Southeast Asia and eastern Africa. Our understanding of the processes driving spatial and

temporal distributions of seagrass species in these regions is rudimentary and has focused

primarily on estuarine and back reef/lagoonal seagrass meadows, with little work on fore reef

systems (see review in Ooi et al. (2011)). In Vietnam, there is also little research that has

been undertaken on seagrass. Therefore, the seagrass is poorly understood in all terms of

biodiversity, ecology and the natural and human impacts upon it. From 1999 up to now, only

about 60 works have been published on seagrass ecosystems in Vietnam (see Annex 1). What

is known is most often contained in reports and workshop and conference documents that are

125

not available to the wider scientific community. Most results of research are published in the

Vietnamese language and are rarely referenced by other academics. The total number of ISI

Web of Science publications on Vietnam seagrass until now are only three (Gacia et al.,

2003; Huong et al., 2003; Marba et al., 2010). There are also a few works on Southeast Asia

mentioning Vietnam seagrass (Morton and Blackmore, 2001; Kennedy et al., 2004; Ooi et al.,

2011). In addition, the researches mainly refer to diversity studies while further studies on

ecological processes in seagrass have been poorly studied. In general, seagrass ecosystems

receive the least attention compared to other marine ecosystems such as coral reefs,

mangrove forests. Knowledge of seagrass ecosystems is incomplete and the seagrass is

gradually lost in Vietnam.

8. Management seagrass beds in Vietnam

Managing seagrass meadows requires an integrated approach (Borum et al., 2004),

including efforts to avoid excessive nutrient and organic inputs from agricultural,

aquaculture, and urban sources and to prevent sediment loading, which causes a deterioration

in the submarine light climate so critical for seagrass growth (Waycott et al., 2009). With

reference from Duarte (2002) we propose some actions for management of seagrass beds in

Vietnam (Figure 6.2).

Figure 6.2. Cooperative elements required to prevent present trend towards seagrass decline and efficiently conserve seagrass ecosystems (source: Duarte (2002))

Managerial

- Monitoring programs

Scientific

- Increased knowledge

Society

- Improved education

126

8.1. In terms of science

More research effort and monitoring programs must be conducted. The research could

be done towards:

- Research on ecological drivers of seagrasses such as disturbance events, sediment

characteristics, herbivory and light aims to understand the processes driving spatial and

temporal distribution of seagrass species.

- Research on habitat suitability models, habitat fragmentation and habitat loss in

seagrass areas; particularly study the changes in the number, shape, size, quality, and species

composition of seagrass.

- Assess the future of seagrasses under the exponentially increasing pressure of

population growth and development in coastal zones.

- Research on genetic diversity and ecological seagrass including physical and

biological characteristics such as the growth pattern, transfer of material, environmental

regulation. A focused research effort on seed bank, seedling survival and various factors

affecting the restoration process required.

- Research on transplantation to restore seagrass beds in the areas where they totally

disappeared.

- Development of a set of criteria and indicators with an associated numerical scoring

system, encompassing biological, environmental and socio-economic characteristics for

assessment ecoquality of seagrass beds.

The results of researches will be used for maintaining existing biodiversity or

restoring degraded biodiversity to former levels, removing or reducing the causes, hence

reducing the existing rates of degradation, or preventive actions that prevent the adoption of

unsustainable patterns of use, before they commence.

127

8.2. In terms of management

In Vietnam, marine and coastal resources law and policy have been ineffective in

achieving goals for sustainable development. Marine policy has mostly focused on national

security and sovereignty issues, with little emphasis on marine environmental protection

(Nguyen, 2008). Vietnam needs to issue effective policies for conservation of seagrass in

coastal management. Besides, it is urgent to establish seagrass protected areas in order to (1)

to prevent remaining seagrass beds from negative impacts and threatened species living in

this ecosystem from extinction; (2) to restore declining meadows; (3) to rationally use

resources in seagrass beds.

8.3. In terms of society

The lack of public awareness of seagrass losses contributes to continued decline of

this ecosystem. Raising awareness of people living in the coastal communities in the field of

protection, restoration and sustainable development of seagrass ecosystems is very important.

More effective communication of scientific knowledge about seagrass ecosystem is required.

Scientific understanding of the causes and consequences of ecosystem loss will be most

effective in reversing the negative trajectories of coastal ecosystems if science is converted to

public awareness, which is essential to ensure ecosystem conservation (Inglehart, 1995).

Hence effective use of formal (e.g., school programs, media) and informal (e.g., web)

education avenues and an effective partnership between scientists and media communicators

are essential to raise public awareness of issues, concerns, and solutions for seagrass

ecosystem. Only increased public understanding can ultimately inform and result in effective

management of this ecologically important ecosystem (Duarte et al., 2008).

9. My contribution to Vietnamese seagrass

My knowledge acquired during the implementation of my thesis would be helpful for

understanding seagrass in Vietnam. The new method of determining seagrass biomass by

photo will be applied in Vietnam. Besides, the study of parasites in marine organisms with

new approaches such as population dynamics, health of benthic communities will be

proposed and implemented.

128

In Vietnam, for the moment, there is not any study on the application of biotic indices

in the assessment of ecological quality of coastal environment. Although some multimetric

indices such as M-AMBI, BAT (Benthic assessment tool) and at a much lesser extent MISS

have been developed in many countries, testing these indices applicability now in Vietnam is

necessary. Of course, though some modifications must be done (reference conditions,

determination of tolerant and sensitive species, etc.), the successful application of these

indices will bring great significance in assessing the quality of coastal ecosystems in

Vietnam.

Chapter 7- General discussion - Conclusion

129

Chapter 7 - General discussion - Conclusion

1. Benthic macrofauna and Zostera noltii seagrass

Seagrasses are habitually used as biological indicators of ecosystem health

(Montefalcone, 2009). Our study confirmed a higher abundance (Fonseca et al., 1990; Orth,

1992; Boström and Bonsdorff, 1997; Cottet et al., 2007; Fredriksen et al., 2010), biomass

(Stoner, 1980) and diversity of macrofauna (Fonseca et al., 1990; Edgar et al., 1994; Boström

and Bonsdorff, 1997; Patricio et al., 2009; Fredriksen et al., 2010) in the seagrass beds

compared to unvegetated areas. Seagrass occupation induces rapid modification of benthic

community structure, but it would be subjective to conclude whether this is a positive or a

negative change. The present study highlighted the complexity in determining if seagrass

presence leads to an increase in ecosystem quality. In chapter 2, for example, we concluded

that the presence of seagrass considered as the sign of “good health” of the ecosystem had a

“moderate impact” in terms of macrobenthic biotic indices and a “rather negative effect” on

cockle population fitness.

Seagrasses are vulnerable ecosystems (Holmer and Marba, 2010). Even though, water

quality of Arcachon Bay can be considered as satisfactory (Lavesque et al., 2009), seagrass in

the lagoon have declined since 2005 (Plus et al., 2010). The observed variations in ammonia

(N-NH4) in the inner part of the lagoon are a symptom of the seagrasses disappearance and

thus, a first sign indicating a change in the Arcachon Bay ecosystem towards more instability

and vulnerability (Plus et al., 2010). Our study also detected the decrease of seagrass biomass

in ten out of twelve sampled stations in 2010 compared to in 2002. Seagrass absence is

usually related to low values of macrobenthos abundance, biomass and species richness

(Hemminga and Duarte, 2000; Deegan et al., 2002; Hughes et al., 2002; Airoldi et al., 2008).

Our results showed that seagrass decline was correlated to a moderate change of macrofauna,

with lower abundance and species richness. Seagrass cover was reduced by 25% over the

whole lagoon (Plus et al., 2010), resulting in seagrass fragmentation but still at levels below

the thresholds at which benthic communities decline dramatically. The effects of habitat loss


130

are mainly strong and negative, whereas habitat fragmentation results in weaker effects on

diversity that can be both positive and negative (Fahrig, 2003; Arponen and Boström, 2012).

The benthic macroinvertebrate fauna in Arcachon Bay in 2002 differed from that in

2010 in certain ways that would be expected in a system that had been subjected to increased

environmental stress. Seagrass loss was associated with other features that would indicate

that environmental stress had increased, such as a greater variability in composition among

samples and a reduction in diversity at all sites (Wildsmith et al., 2011). It is proposed that

the changes undergone by the benthic environment in Arcachon Bay are also due to a

combination of anthropogenic effects rather than to a single factor (seagrass loss). However,

it is difficult to unambiguously distinguish the anthropogenic disturbance effects from the

natural temporal variability (Elliott and Quintino, 2007; Patricio et al., 2009; Prato et al.,

2009; Borja and Tunberg, 2011), especially in estuarine and lagoon systems where

eurytolerant organisms are adapted to dynamic conditions and naturally high organic loading

(Rakocinski, 2012). Therefore, methods for discerning effects of anthropogenic from natural

stressors using macrobenthic process indicators will need to be developed (Rakocinski,

2012). However, it appears that only long-term study can determine whether the putative

anthropogenic impact is ‘‘real” or merely part of a long-term natural cycle. Many short-term

pollution monitoring surveys are of limited value since they fail to address natural temporal

variability. It is always critical to isolate the natural from the artificial (see review in Dauvin

(2010)). In this study, explaining the changes of macrobenthic communities was limited

because the temporal scale is only eight years (from 2002 to 2010).

Z. noltii is very sensitive to sediment disposal (Han et al., 2012) and it will take years

or decade for seagrass to recover to its former state after such an event (Bryars and

Neverauskas, 2004; Cardoso et al., 2005). In our study, seagrass had totally disappeared six

months (or less) after this destruction. After that, seagrass needed five years to recover in the

site covered by mud while it was still absent in the site covered by sand. As a result of

seagrass destruction, abundance, biomass and diversity of the benthic community in the

disposal sites have decreased after deposition. However, the macrofauna needed only three

years to show substantial recovery. According to Verissimo et al. (2012), although benthic

communities tend to respond rapidly to environmental change, recovery processes are

generally slower, and may require from two to more than twenty five years. In general, it is


131

impossible to draw a general conclusion about the impact of dredged material deposition on

the benthic community structure (Harvey et al., 1998).

While there appears little generality in the response of benthic community structure to

dredged material disposal, perhaps functional attributes such as productivity show a more

predictable response; however only by an increased number of comparable studies which

include production estimates can we begin to attempt to address this (Bolam et al., 2011).

Crisp (1984) also pointed out that the most integrative parameters of population health are

certainly the secondary production. In fact, our results showed both the structure and function

need to be analyzed in order to further understand the responses of macrofauna to sediment

disposal. However, the function features of macrofauna perhaps respond more slowly than

structure features with recovery events.

2. Seagrass and bivalve health

Parasites have a major impact on ecosystem health through their impact on driving

biodiversity and ecosystem organization (Hudson et al., 2006). Our study confirmed that

level of trematode infection in cockle population in Arcachon Bay remained low compared to

known thresholds and should have a low impact. Indeed, cockles in the lagoon were

considered healthy and lightly stressed (de Montaudouin et al., 2010). Another exploited

bivalve species in the lagoon, Manila clams seemed moderately stressed (de Montaudouin et

al., 2010). Arcachon Bay does not provide the best conditions for Manila clams, like

prolonged emersion time (Dang et al., 2010b), but the local population seemed to develop

resistance patterns, at least against metal aggression (de Montaudouin et al., 2010). Our result

also confirmed that environmental factors such as organic matter, temperature, and salinity

contribute to spatially heterogeneous distribution of the parasite and that salinity is not the

major factor explaining disease (perkinsosis, Brown Muscle Disease, trematodiosis)

distribution in this lagoon.

3. Seagrass and biotic indices

Our study demonstrated that the single indices as AMBI (AZTI's Marine Biotic

Index), BOPA (Benthic Opportunistic Polychaetes Amphipods Index) and BENTIX could

not exhaustively assess ecology quality status (ES).


132

One disadvantage of AMBI in such ecosystems (lagoons, estuaries) is that mistakes

can occur during the grouping of the species into different groups according to their response

to pollution situations. Once it draws on the response of organisms to organic inputs in the

ecosystem, it does not detect the effects caused by other types of pollution (Marin-Guirao et

al., 2005; Pinto et al., 2009).

The use of a single sensitivity/tolerance list in different geographical areas (such as in

AMBI Ecological Groups (EG)) is not appropriate (Gremare et al., 2009). The species

sensitivity/tolerance levels change with geographical location (Smith et al., 2001; Prato et al.,

2009). Therefore, AMBI classifications and local expert classifications should be the focal

point for additional investigation, both locally and internationally (Teixeira et al., 2012).

Further autoecological studies are nevertheless clearly needed to make clearer the actual

sensitivity/tolerance levels of the species. Moreover, it is important to establish the degree of

sensitivity of each species to different sources of pollution (de-la-Ossa-Carretero et al., 2012).

In addition, AMBI cannot clearly distinguish locals with high trophic diversity but

composed of a relatively high percentage of detritivore species, typical of places where an

accumulation of fine sediments and organic matter occur. This is the case in seagrass beds,

which are usually places presenting high species richness and high trophic diversity (Gamito,

2008; Gamito and Furtado, 2009; Gamito et al., 2012b). Faunal composition of healthy

benthic communities from naturally organic enriched sediments, and especially at stabilized

seagrass beds such as Zostera spp., do not reflect the theoretical model for the expected

distribution of ecological groups at unpolluted situations as described by Borja et al. (2000).

At these habitats, the relative proportion of abundance of ecological groups is more evenly

distributed, with no clear dominance of sensitive species (EG I) over the remaining groups,

and also with a typical presence of opportunistic species (EG IV and V) (Gamito et al.,

2012a).

The weaknesses of AMBI could be improved with adjustments to the index

calibration by (1) improving species classification (Teixeira et al., 2012); (2) applying a data

transformation (Warwick et al., 2010; Muxika et al., 2012; Teixeira et al., 2012); (3)

adjusting thresholds for condition categories (Teixeira et al., 2012); (4) using different types

of input data, specifically numerical abundances (NAMBI), biomass (BAMBI) and

production (PAMBI) (Warwick et al., 2010); (5) using a multivariate AMBI approach (M-

AMBI) (Teixeira et al., 2012).


133

Warwick et al. (2010) suggested that pre-treating data prior to calculating the indices,

using a spectrum of power transformations (square root, fourth root, logarithm,

presence/absence) such as are routinely used in nonparametric multivariate analyses, might

usefully down-weigh the influence of dominant species and give a better overview of the

status of assemblages (see review in Muxika et al., 2012). Ecological indices based on

relative abundances of species are often over-sensitive to the superabundance of one or a few

dominants. Moreover, it is usual to find high variation in numerical abundance, even between

replicate counts of the same species, which can affect the robustness of the index (Warwick et

al., 2010). As a result, some authors support the transformation of the data, especially prior to

multivariate analyses, in order to down-weigh the dominant species (Clarke and Warwick,

2001; Warwick et al., 2010). However, Muxika et al. (2012) stated that it is not yet feasible to

determine if it is better to calculate AMBI from abundance data, from biomass data or from

production data, or if the subsequent index will be more sensitive to pressures or impacts if

the input data are pre-treated or not. In fact, all the indices react to changes in pressures in a

similar way and follow similar improvement or degradation paths after those changes.

As similar with AMBI, BENTIX was based on the pollution resulting from organic

enrichment, their application in other pollution cases may not be successful (Marin-Guirao et

al., 2005; Dauvin, 2007; Elliott and Quintino, 2007; Dauvin et al., 2010; Muxika et al., 2012).

In addition, this index also showed some limitations when applied to estuaries and lagoons

where the natural conditions favour the presence of tolerant species in very high densities

(Simboura and Zenetos, 2002; Borja et al., 2004; Blanchet et al., 2008; Prato et al., 2009).

Indeed, the present study showed that BENTIX always give a low quality for the undisturbed

sites such as Banc d’Arguin.

BOPA is actually widely criticized because it takes into account only three categories

of organisms – opportunistic polychaetes, amphipods (except Jassa) and other species – but

only the first two have a direct effect on the index calculation (Pinto et al., 2009) and notably,

the sensitivity to pollution for the same taxonomic group differs from one species to another

(Afli et al., 2008). Another point is that it does not consider the oligochaete influence, which

may also include opportunistic species (Pinto et al., 2009).

Our results also proved that AMBI, BOPA and BENTIX rarely agree in assessing

ecological status (ES). Differences in ES assessment with AMBI and BENTIX were proven

by some previous studies (Prato et al., 2009; Simonini et al., 2009). Prato et al. (2009) gave


134

some explanations: (1) Discordances on assignment of ecological groups to many species.

Despite the recent efforts of the indices authors to revise the libraries of the species list, some

taxa resulted as ‘sensitive’ according to AMBI, while are ascribed as ‘tolerant’ according to

BENTIX. The assignment of ecological group to species is often arguable since it is based on

field remarks rather than correct knowledge of their autoecology and this may vary between

scientist and geographical area. Moreover, species react differently depending on inter-

species interaction and environmental conditions. (2) The incompleteness of check-lists. This

difficulty could lead to an exclusion of large number of individuals in applying biotic indices.

(3) The misclassification of species to EG leads to differences between AMBI and BENTIX

owing to the differential weight each index puts in the different ecological groups. BENTIX

tends to reveal extreme values in the ES because species are ascribed only to 2 EG rather than

5 EG of AMBI. In the case where communities are dominated by tolerant species, the

BENTIX index assesses a lower ES rather than AMBI. The difference was found in a

transitional system where communities were dominated by ‘EG III’ by AMBI and ‘2’ by

BENTIX and therefore the ES final assessments were always lower for BENTIX index. (4)

Features of indices pertaining to the boundary limits among quality classes. The indices

AMBI and BENTIX are widely utilized in assessing the ES in marine environments, but their

correct application in transitional systems would depend on a resettlement. As a matter of

fact, thresholds settled in the biotic index scale values need to be modified according to the

natural variability of transitional waters referring to abiotic conditions and the abundance of

opportunistic species (Prato et al., 2009).

Although many benthic indices were successfully validated during the last decade,

most indices and assessment scales were developed for local geographic regions, and often

only for specific habitats within the region. Using benthic indices for assessment over large

geographic areas can be problematic because species composition and reference conditions

change naturally with ecoregion and habitat (Borja et al., 2009b). BIs (BENTIX, BOPA) used

in this study were originally developed for subtidal communities. For intertidal environments,

the thresholds between ES classes should be revised, and ‘Acceptable’ and ‘Not acceptable’

redefined (Blanchet et al., 2008). The establishment of reference conditions is a key process

and should be habitat-specific in order to properly reflect natural benthic gradients (Dauvin et

al., 2007; Blanchet et al., 2008; de Paz et al., 2008; Puente et al., 2008; Teixeira et al., 2008a;

Gamito et al., 2012a). Thresholds of benthic indices used to define ecological status should


135

be calibrated for hydrographically and/or biogeographically different estuarine or transitional

ecosystems (Chainho et al., 2007). However, it is also difficult to determine a reference status

for estuarine (transition) and coastal waters (Dauvin, 2007; Elliott and Quintino, 2007;

Teixeira et al., 2008b). In the future validation of the reference conditions, by using data from

similar systems, both degraded and healthy conditions should be undertaken (Borja and

Tunberg, 2011). Our result also confirmed that setting the reference condition is an important

step in assessing ES. Moreover, these indices must be tested for different ecosystems,

geographies, hydrological regimes and diverse pressure types.

Several studies across the world demonstrate that none of the available indices should

be considered ideal to measure biological effects of pollution, because every index was

originally developed for one or a few stressors (Quintino et al., 2006; Salas et al., 2006;

Chainho et al., 2007). Therefore, biotic indices must be validated for other stressors such as

physical disturbance and chemical pollution (Labrune et al., 2006; Patricio et al., 2009).

Moreover, the use of single univariate indicators to assess ecological quality is a too reduced

perspective of environmental complexity (Van Hoey et al., 2010). Complex ecosystems, such

as estuaries and lagoons, can also show more complex responses in some indicators. Our

study demonstrated that MISS or d-MISS including several indices would give a better,

although far from perfect, result than univariate indices in ES assessment. Combining several

metrics, each of them providing information on a biological attribute, in such a way that,

when integrated, determines the systems’ overall status and condition. This is the main

strength of biotic indices, since they allow the integration of the ecosystem’s information and

parameters, providing a broader understanding of the system’s processes (Pinto et al., 2009).

In addition, the use of multimetric tools allows overcoming the sensitivity of single metrics

by combining several indices (Buckland et al., 2005; Teixeira et al., 2008b). Consequently,

the application of multi-metric methods (compared to single metrics) increases the

probability of a correct evaluation of the ecological conditions of the system (Quintino et al.,

2006; Borja et al., 2007; Afli et al., 2008; Lavesque et al., 2009; Van Hoey et al., 2010;

Gamito et al., 2012b).

Although benthic invertebrate fauna presents a lot of advantages for assessing

ecological quality (Dauvin, 2007; Borja and Dauer, 2008; Dauvin et al., 2012), we agree that

there are several disadvantages of the existing benthic indices based on benthic organisms:

(1) they represent a static expression of an ecological condition, (2) they are not explicitly


136

linked to changes in ecological function, (3) they may not be specific with respect to different

kinds of stressors, (4) they are subject to underlying taxonomic changes across estuarine

gradients, (5) their use can be labour intensive, and they are not applied consistently across

bio-geographic provinces (Dauvin et al., 2012). Many previous studies confirmed that benthic

macrofauna are good indicators of ecosystem health (Dauer et al., 1993; Warwick, 1993;

Reiss and Kroncke, 2005; Dauvin et al., 2010; Verissimo et al., 2012). Our study showed that

although seagrass has been decreased by 1/3rd of the occupied surface since 2005 (Plus et al.,

2010), macrofauna only changed a little in this period. Seagrass development/regression and

benthic fauna structure do not always evolve in the same way.

4. Conclusion

In fact, only one group the macrofauna is not enough to explain all of the changes in

environmental status. Some investigations have demonstrated the fundamental advantage of a

multi-species approach, with the inclusion of many taxonomic and functional groups that

have a broad range of sensitivities to any given environmental regime (Attrill and Depledge,

1997; Patricio et al., 2012). For example, macrofauna and meiobenthic nematodes may

provide different but complementary types of information, depending on the indices used and

the different “response-to-stress” times of each benthic group. Optimally, both groups should

be used in marine pollution monitoring programs (Patricio et al., 2012). Coastal lagoons are

complex systems, with considerable habitat heterogeneity and often subject to high temporal

dynamics, which constitutes a great challenge for ecological assessment programs (Gamito et

al., 2012a). The present study suggests that the necessity to integrate numerous parameters

(macrofauna, motile megafauna, meiofauna, fishes, etc.) to assess this ecosystem.

Finally, we agree that the next studies undertaken should continue to establish

ecological thresholds in order to forecast the ecosystem trajectory. The critical ecological

thresholds exist in the structural patterning of biogenic ecosystems that, when exceeded,

cause abrupt shifts in the distribution and abundance of organisms (Boström et al., 2011). A

quantitative analysis of seagrass trajectories will be critically important to forecast the likely

cumulative effects of the known and emerging stressors of seagrasses (Orth et al., 2006).

137

References

Afli, A., Ayari, R., Zaabi, S., 2008. Ecological quality of some Tunisian coast and lagoon

locations, by using benthic community parameters and biotic indices. Estuarine, Coastal

and Shelf Science 80, 269-280.

Agawin, N.S.R., Duarte, G.M., 2002. Evidence of direct particle trapping by a tropical

seagrass meadow. Estuaries 25, 1205-1209.

Ahn, K.J., Kim, K.H., 2001. Effect of temperature and salinity on in vitro zoosporulation of

Perkinsus sp. in Manila clams Ruditapes philippinarum. Diseases of Aquatic Organisms

48, 43-46.

Airoldi, L., Balata, D., Beck, M.W., 2008. The Gray Zone: Relationships between habitat loss

and marine diversity and their applications in conservation. Journal of Experimental

Marine Biology and Ecology 366, 8-15.

Airoldi, L., Beck, M.W., 2007. Loss, status and trends for coastal marine habitats of Europe.

CRC Press, Boca Raton, FL, ETATS-UNIS.

Albayrak, S., Balkis, H., Zenetos, A., Kurun, A., Kubanc, C., 2006. Ecological quality status

of coastal benthic ecosystems in the Sea of Marmara. Marine Pollution Bulletin 52,

790-799.

Anderson, M.J., Gorley, R.N., Clarke, K.R., 2008. PERMANOVA+ for PRIMER: Guide to

Software and Statistical methods. PRIMER-E Ltd., Plymouth, UK.

Arponen, H., Bostrom, C., 2012. Responses of mobile epifauna to small-scale seagrass

patchiness: is fragmentation important? Hydrobiologia 680, 1-10.

Asmus, H., Asmus, R., 2000. Material exchange and food web of seagrass beds in the Sylt-

Romo Bight: how significant are community changes at the ecosystem level? Helgoland

Marine Research 54, 137-150.

Attrill, M.J., Depledge, M.H., 1997. Community and population indicators of ecosystem

health: Targeting links between levels of biological organisation. Aquatic Toxicology

38, 183-197.

138

Attrill, M.J., Rundle, S.D., 2002. Ecotone or ecocline: ecological boundaries in estuaries.

Estuarine Coastal and Shelf Science 55, 929-936.

Auby, I., Labourg, P.J., 1996. Seasonal dynamics of Zostera noltii Hornem. in the Bay of

Arcachon (France). Journal of Sea Research 35, 269-277.

Auzoux-Bordenave, S., Vigario, A.M., Ruano, F., Domart-Coulon, I., Doumenc, D., 1995. In

vitro sporulation of the clam pathogen Perkinsus atlanticus (Apicomplexa, Perkinsea)

under various environmental conditions. Journal of Shellfish Research 14, 469-475.

Azevedo, C., 1989. Fine structure of Perkinsus atlanticus n. sp. (Apicomplexa, Perkinsea)

parasite of the clam Ruditapes decussatus from Portugal. Journal of Parasitology 75,

627-635.

Bachelet, G., 1981. Données préliminaires sur l'organisation trophique d'un peuplement

benthique marin. Vie et Milieu 31, 205-213.

Bachelet, G., Dauvin, J.-C., 1993. Distribution quantitative de la macrofaune benthique des

sables intertidaux du bassin d'Arcachon. Oceanologica Acta 16, 83-97.

Bachelet, G., de Montaudouin, X., Auby, I., Labourg, P.J., 2000. Seasonal changes in

macrophyte and macrozoobenthos assemblages in three coastal lagoons under varying

degrees of eutrophication. ICES Journal of Marine Science 57, 1495-1506.

Bachelet, G., de Montaudouin, X., Dauvin, J.-C., 1996. The quantitative distribution of

subtidal macrozoobenthic assemblages in Arcachon Bay in relation to environmental

factors: a multivariate analysis. Estuarine, Coastal and Shelf Science 42, 371-391.

Baden, S., Gullström, M., Lundén, B., Pihl, L., Rosenberg, R., 2003. Vanishing seagrass

(Zostera marina, L.) in Swedish coastal waters. AMBIO: A Journal of the Human

Environment 32, 374-377.

Bald, J., Sinquin, A., Borja, A., Caill-Milly, N., Duclercq, B., Dang, C., de Montaudouin, X.,

2009. A system dynamics model for the management of the Manila clam, Ruditapes

philippinarum (Adams and Reeve, 1850) in the Bay of Arcachon (France). Ecological

Modelling 220, 2828-2837.

Baretta, J.W., Malschaert, J.F.P., 1988. Distribution and abundance of the zooplankton of the

Ems estuary (NorthSea). Netherlands Journal of Sea Research 22, 69-81.

139

Bartoli, P., Boudouresque, C.F., 1997. Transmission failure of parasites (Digenea) in sites

colonized by the recently introduced invasive alga Caulerpa taxifolia. Marine Ecology

Progress Series 154, 253-260.

Beck, M.W., Heck, K.L., Able, K., Childers, D., Eggleston, D., Gilanders, B., Halpern, B.S.,

Hays, C., Hoshino, K., Minello, T., Orth, R.J., Sheridan, P., Weinstein, M., 2001.

Towards better identification, conservation, and management of estuarine and marine

nurseries for fish and invertebrates. Bioscience 51, 633–641.

Bell, S.S., Hall, M.O., Soffian, S., Madley, K., 2002. Assessing the impact of boat propeller

scars on fish and shrimp utilizing seagrass beds. Ecological Applications 12, 206-217.

Benoit, C., 2005. Biogéochimie et enregistrement des composés organostanniques dans les

sédiments du Bassin d'Arcachon. PhD thesis, University de Bordeaux 1, Bordeaux, p.

208.

Blanchet, H., 2004. Structure et fonctionnement des peuplements benthiques du bassin

d'Arcachon. PhD dissertation, University of Bordeaux 1, Bordeaux, p. 220.

Blanchet, H., de Montaudouin, X., Chardy, P., Bachelet, G., 2005. Structuring factors and

recent changes in subtidal macrozoobenthic communities of a coastal lagoon, Arcachon

Bay (France). Estuarine, Coastal and Shelf Science 64, 561-576.

Blanchet, H., de Montaudouin, X., Lucas, A., Chardy, P., 2004. Heterogeneity of

macrozoobenthic assemblages within a Zostera noltii seagrass bed: diversity,

abundance, biomass and structuring factors. Estuarine, Coastal and Shelf Science 61,

111-123.

Blanchet, H., Lavesque, N., Ruellet, T., Dauvin, J.-C., Sauriau, P.G., Desroy, N., Desclaux,

C., Leconte, M., Bachelet, G., Janson, A.L., Bessineton, C., Duhamel, S., Jourde, J.,

Mayot, S., Simon, S., de Montaudouin, X., 2008. Use of biotic indices in semi-enclosed

coastal ecosystems and transitional waters habitats - Implications for the

implementation of the European Water Framework Directive. Ecological Indicators 8,

360-372.

Blanchet, H., Raymond, N., de Montaudouin, X., Capdepuy, M., Bachelet, G., 2003. Effects

of digenean trematodes and heterotrophic bacteria on mortality and burying capability

140

of the common cockle Cerastoderma edule (L.). Journal of Experimental Marine

Biology and Ecology 293, 89-105.

Bolam, S.G., 2011. Burial survival of benthic macrofauna following deposition of simulated

dredged material. Environmental Monitoring and Assessment 181, 13-27.

Bolam, S.G., Barry, J., Bolam, T., Mason, C., Rumney, H.S., Thain, J.E., Law, R.J., 2011.

Impacts of maintenance dredged material disposal on macrobenthic structure and

secondary productivity. Marine Pollution Bulletin 62, 2230-2245.

Bolam, S.G., Barry, J., Schratzberger, M., Whomersley, P., Dearnaley, M., 2010.

Macrofaunal recolonisation following the intertidal placement of fine-grained dredged

material. Environmental Monitoring and Assessment 168, 499-510.

Bolam, S.G., Rees, H.L., 2003. Minimizing impacts of maintenance dredged material

disposal in the coastal environment: A habitat approach. Environmental Management

32, 171-188.

Bolam, S.G., Rees, H.L., Somerfield, P., Smith, R., Clarke, K.R., Warwick, R.M., Atkins,

M., Garnacho, E., 2006. Ecological consequences of dredged material disposal in the

marine environment: A holistic assessment of activities around the England and Wales

coastline. Marine Pollution Bulletin 52, 415-426.

Bolam, S.G., Whomersley, P., 2005. Development of macrofaunal communities on dredged

material used for mudflat enhancement: a comparison of three beneficial use schemes

after one year. Marine Pollution Bulletin 50, 40-47.

Borg, J.A., Rowden, A.A., Attrill, M.J., Schembri, P.J., Jones, M.B., 2010. Spatial variation

in the composition of motile macroinvertebrate assemblages associated with two bed

types of the seagrass Posidonia oceanica. Marine Ecology Progress Series 406, 91-104.

Borja, A., Dauer, D.M., 2008. Assessing the environmental quality status in estuarine and

coastal systems: Comparing methodologies and indices. Ecological Indicators 8, 331-

337.

Borja, A., Dauer, D.M., Gremare, A., 2012. The importance of setting targets and reference

conditions in assessing marine ecosystem quality. Ecological Indicators 12, 1-7.

141

Borja, A., Franco, J., Muxika, I., 2004. The biotic indices and the Water Framework

Directive: the required consensus in the new benthic monitoring tools. Marine Pollution

Bulletin 48, 405-408.

Borja, A., Franco, J., Perez, V., 2000. A marine Biotic Index to establish the ecological

quality of soft-bottom benthos within European estuarine and coastal environments.

Marine Pollution Bulletin 40, 1100-1114.

Borja, A., Josefson, A.B., Miles, A., Muxika, I., Olsgard, F., Phillips, G., Rodriguez, J.G.,

Rygg, B., 2007. An approach to the intercalibration of benthic ecological status

assessment in the North Atlantic ecoregion, according to the European Water

Framework Directive. Marine Pollution Bulletin 55, 42-52.

Borja, A., Miles, A., Occhipinti-Ambrogi, A., Berg, T., 2009a. Current status of

macroinvertebrate methods used for assessing the quality of European marine waters:

implementing the Water Framework Directive. Hydrobiologia 633, 181-196.

Borja, A., Muxika, H., 2005. Guidelines for the use of AMBI (AZTI's Marine Biotic Index)

in the assessment of the benthic ecological quality. Marine Pollution Bulletin 50, 787-

789.

Borja, A., Ranasinghe, A., Weisberg, S.B., 2009b. Assessing ecological integrity in marine

waters, using multiple indices and ecosystem components: Challenges for the future.


Borja, A., Tunberg, B.G., 2011. Assessing benthic health in stressed subtropical estuaries,

eastern Florida, USA using AMBI and M-AMBI. Ecological Indicators 11, 295-303.

Borum, J., Duarte, C.M., Krause-Jensen, D., Greve, T.M., 2004. European seagrasses: an

introduction to monitoring and management (The EU project Monitoring and Managing

of European Seagrasses, Copenhagen), p. 95.

Bos, A.R., Bouma, T.J., de Kort, G.L.J., van Katwijk, M.M., 2007. Ecosystem engineering by

annual intertidal seagrass beds: Sediment accretion and modification. Estuarine, Coastal


Boström, C., Bonsdorff, E., 1997. Community structure and spatial variation of benthic

invertebrates associated with Zostera marina (L.) beds in the northern Baltic Sea.

Journal of Sea Research 37, 153-166.

142

Boström, C., Bonsdorff, E., Kangas, P., Norkko, A., 2002. Long-term changes of a brackish-

water eelgrass (Zostera marina L.) Community indicate effects of coastal

eutrophication. Estuarine, Coastal and Shelf Science 55, 795-804.

Boström, C., Jackson, E.L., Simenstad, C.A., 2006a. Seagrass landscapes and their effects on

associated fauna: A review. Estuarine, Coastal and Shelf Science 68, 383-403.

Boström, C., O'Brien, K., Roos, C., Ekebom, J., 2006b. Environmental variables explaining

structural and functional diversity of seagrass macrofauna in an archipelago landscape.

Journal of Experimental Marine Biology and Ecology 335, 52-73.

Boström, C., Pittman, S.J., Simenstad, C., Kneib, R.T., 2011. Seascape ecology of coastal

biogenic habitats: advances, gaps, and challenges. Marine Ecology Progress Series 427,

191-217.

Bouchet, J.-M., 1993. Stratifications, fronts halins dans une lagune mésotidale (Bassin

d'Arcachon - France), in: Sorbe, J.-C., Jouanneau, J.-M. (Eds.), 3e Colloque

International d'Oceanographie du Golfe de Gascogne. Station Marine d'Arcachon,

Arcachon, pp. 33-39.

Bouma, T.J., Olenin, S., Reise, K., Ysebaert, T., 2009a. Ecosystem engineering and

biodiversity in coastal sediments: posing hypotheses. Helgoland Marine Research 63,

95-106.

Bouma, T.J., Ortells, V., Ysebaert, T., 2009b. Comparing biodiversity effects among

ecosystem engineers of contrasting strength: macrofauna diversity in Zostera noltii and

Spartina anglica vegetations. Helgoland Marine Research 63, 3-18.

Brun, F.G., van Zetten, E., Cacabelos, E., Bouma, T.J., 2009. Role of two contrasting

ecosystem engineers (Zostera noltii and Cymodocea nodosa) on the food intake rate of

Cerastoderma edule. Helgoland Marine Research 63, 19-25.

Bryars, S., Neverauskas, V., 2004. Natural recolonisation of seagrasses at a disused sewage

sludge outfall. Aquatic Botany 80, 283-289.

Buckland, S.T., Magurran, A.E., Green, R.E., Fewster, R.M., 2005. Monitoring change in

biodiversity through composite indices. Philosophical Transactions of the Royal Society

B-Biological Sciences 360, 243-254.

143

Burreson, E.M., Ragone Calvo, L.M., 1996. Epizootiology of Perkinsus marinus disease of

oysters in Chesapeake Bay, with emphasis on data since 1985. Journal of Shellfish

Research 15, 17-34.

Bush, A.O., Lafferty, K.D., Lotz, J.M., Shostak, A.W., 1997. Parasitology meets ecology on

its own terms: Margolis et al. revisited. Journal of Parasitology 83, 575-583.

Byers, J.E., Blakeslee, A.M.H., Linder, E., Cooper, A.B., Maguire, T.J., 2008. Controls of

spatial variation in the prevalence of trematode parasites infecting a marine snail.

Ecology 89, 439-451.

Cabaco, S., Santos, R., 2007. Effects of burial and erosion on the seagrass Zostera noltii.

Journal of Experimental Marine Biology and Ecology 340, 204-212.

Cabaco, S., Santos, R., Duarte, C.M., 2008. The impact of sediment burial and erosion on

seagrasses: A review. Estuarine, Coastal and Shelf Science 79, 354-366.

Cardoso, P.G., Brandao, A., Pardal, M.A., Raffaelli, D., Marques, J.C., 2005. Resilience of

Hydrobia ulvae populations to anthropogenic and natural disturbances. Marine Ecology


Cardoso, P.G., Raffaelli, D., Pardal, M.A., 2008. The impact of extreme weather events on

the seagrass Zostera noltii and related Hydrobia ulvae population. Marine Pollution

Bulletin 56, 483-492.

Carlson, P.R., Yarbro, L.A., Kaufman, K.A., Mattson, R.A., 2010. Vulnerability and

resilience of seagrasses to hurricane and runoff impacts along Florida's west coast.

Hydrobiologia 649, 39-53.

Carlton, J.T., Vermeij, G.J., Lindberg, D.R., Carlton, D.A., Dudley, E.C., 1991. The first

historical extinction of a marine invertebrate in an ocean basin: the demise of the

eelgrass limpet Lottia alveus. Biological Bulletin 180, 72-80.

Casas, S.M., Villalba, A., Reece, K.S., 2002. Study of perkinsosis in the carpet shell clam

Tapes decussatus in Galicia (NW Spain). I. Identification of the aetiological agent and

in vitro modulation of zoosporulation by temperature and salinity. Diseases of Aquatic

Organisms 50, 51-65.

144

Castel, J., Caumette, P., Herbert, R., 1996. Eutrophication gradients in coastal lagoons as

exemplified by the bassin d'Arcachon and the étang du Prévost. Hydrobiologia 329, 9-

28.

Castel, J., Labourg, P.-J., Escaravage, V., Auby, I., Garcia, M.E., 1989. Influence of seagrass

beds and oyster parks on the abundance and biomass patterns of meio- and

macrobenthos in tidal flats. Estuarine, Coastal and Shelf Science 28, 71-85.

Cazaux, C., 1973. Cycle et distribution des larves de polychètes; caractères du méroplancton

des différents types de masses d'eaux du Bassin d'Arcachon. Bulletin d'Ecologie 4, 257-

275.

Chainho, P., Costa, J.L., Chaves, M.L., Dauer, D.M., Costa, M.J., 2007. Influence of seasonal

variability in benthic invertebrate community structure on the use of biotic indices to

assess the ecological status of a Portuguese estuary. Marine Pollution Bulletin 54, 1586-

1597.

Choi, H.-S., Park, K.-I., Lee, K.-W., Matsuoka, K., 2002. Infection intensity, prevalence, and

histopathology of Perkinsus sp. in the Manila clam, Ruditapes philippinarum, in

Isahaya bay, Japan. Journal of Shellfish Research 21, 119-125.

Cigarría, J., Rodríguez, C., Fernández, J.M., 1997. Impact of Perkinsus sp. on Manila clam

Ruditapes philippinarum beds. Diseases of Aquatic Organisms 29, 117-120.

Ciutat, A., Widdows, J., Readman, J.W., 2006. Influence of cockle Cerastoderma edule

bioturbation and tidal-current cycles on resuspension of sediment and polycyclic

aromatic hydrocarbons. Marine Ecology Progress Series 328, 51-64.

Clarke, K.R., Gorley, R.N., 2006. PRIMER v6: User Manual/Tutorial. PRIMER-E Ltd,

Plymouth.

Clarke, K.R., Warwick, R.M., 2001. Change in marine communities: an approach to

statistical analysis and interpretation. PRIMER-E Ltd, Plymouth.

Coen, L.D., Heck, K.L., 1991. The interacting effects of siphon nipping and habitat on

bivalve (Mercenaria mercenaria (L.)) growth in a subtropical seagrass (Halodule

wrightii Aschers) meadow. Journal of Experimental Marine Biology and Ecology 145,

1-13.

145

Connolly, R.M., 1997. Differences in composition of small, motile invertebrate assemblages

from seagrass and unvegetated habitats in a southern Australian estuary. Hydrobiologia

346, 137-148.

Costello, C.T., Kenworthy, W.J., 2011. Twelve-year mapping and change analysis of eelgrass

(Zostera marina) areal abundance in Massachusetts (USA) identifies statewide declines.

Estuaries and Coasts 34, 232-242.

Cottet, M., de Montaudouin, X., Blanchet, H., Lebleu, P., 2007. Spartina anglica eradication

experiment and in situ monitoring assess structuring strength of habitat complexity on

marine macrofauna at high tidal level. Estuarine, Coastal and Shelf Science 71, 629-

640.

Cremonte, F., Figueras, A., Burreson, E.M., 2005. A histopathological survey of some

commercially exploited bivalve molluscs in northern Patagonia, Argentina. Aquaculture

249, 23-33.

Crisp, D.J., 1984. Energy flow measurements, in: Holme N. A.. McIntyre, A.D. (Ed.),

Methods for the study of marine benthos. Blackwell Scientific Publications, Oxford, pp.

284-372.

Crooks, J.A., 2002. Characterizing ecosystem-level consequences of biological invasions: the

role of ecosystem engineers. Oikos 97, 153-166.

Cruz-Motta, J.J., Collins, J., 2004. Impacts of dredged material disposal on a tropical soft-

bottom benthic assemblage. Marine Pollution Bulletin 48, 270-280.

Curtis, L.A., 1995. Growth, trematode parasitism, and longevity of a longlived marine

gastropod (Ilyanassa obsoleta). Journal of the Marine Biological Association of the

United Kingdom 75, 913-925.

Curtis, L.A., Kinley, J.L., Tanner, N.L., 2000. Longevity of oversized individuals: growth,

parasitism, and history in an estuarine snail population. Journal of the Marine

Biological Association of the United Kingdom 80, 811-820.

Da Ros, L., Marin, M.G., Nesto, N., Ford, S.E., 1998. Preliminary results of a field study on

some stress-related parameters in Tapes philippinarum naturally infected by the

protozoan Perkinsus sp. Marine Environmental Research 46, 249-252.

146

Dang, C., 2009. Dynamique des populations de palourdes japonaises (Ruditapes

philippinarum) dans le bassin d'Arcachon, conséquences sur la gestion des populations

exploitées. PhD dissertation, Université de Bordeaux 1, Bordeaux, p. 355.

Dang, C., de Montaudouin, X., Caill-Milly, N., Trumbic, Z., 2010a. Spatio-temporal patterns

of perkinsosis in the Manila clam Ruditapes philippinarum from Arcachon Bay (SW

France). Diseases of Aquatic Organisms 91, 151-159.

Dang, C., de Montaudouin, X., Gam, M., Paroissin, C., Bru, N., Caill-Milly, N., 2010b. The

Manila clam population in Arcachon Bay (SW France): Can it be kept sustainable?


Dang, C., de Montaudouin, X., Gonzalez, P., Mesmer-Dudons, N., Caill-Milly, N., 2008.

Brown Muscle Disease (BMD), an emergent pathology affecting Manila clam

Ruditapes philippinarum in Arcachon bay (SW France). Diseases of Aquatic Organisms

80, 219-228.

Dang, C., Gonzalez, P., Mesmer-Dudons, N., Bonami, J.R., Caill-Milly, N., de Montaudouin,

X., 2009. Virus-like particles associated with Brown Muscle Disease in Manila clam

(Ruditapes philippinarum) in Arcachon Bay (France). Journal of Fish Diseases 32, 577-

584.

Dauer, D.M., Luckenbach, M.W., Rodi, A.J., 1993. Abundance biomass comparison (ABC

method): effects of an estuarine gradient, anoxic/hypoxic events and contaminated

sediments. Marine Biology 116, 507-518.

Dauvin, J.-C., 2007. Paradox of estuarine quality: Benthic indicators and indices, consensus

or debate for the future. Marine Pollution Bulletin 55, 271-281.

Dauvin, J.-C., 2010. Benthic time-series observations in North-eastern European Marine

Station: Is a European label needed? Marine Pollution Bulletin 60, 483-488.

Dauvin, J.-C., Alizier, S., Rolet, C., Bakalem, A., Bellan, G., Gomez Gesteira, J.L., Grimes,

S., de-la-Ossa-Carretero, J.A., Del-Pilar-Ruso, Y., 2012. Response of different benthic

indices to diverse human pressures. Ecological Indicators 12, 143-153.

Dauvin, J.-C., Bellan, G., Bellan-Santini, D., 2010. Benthic indicators: From subjectivity to

objectivity - Where is the line? Marine Pollution Bulletin 60, 947-953.

147

Dauvin, J.-C., Ruellet, T., 2007. Polychaete/amphipod ratio revisited. Marine Pollution

Bulletin 55, 215-224.

Dauvin, J.-C., Ruellet, T., Desroy, N., Janson, A.L., 2007. The ecological quality status of the

Bay of Seine and the Seine estuary: Use of biotic indices. Marine Pollution Bulletin 55,

241-257.

David, V., Chardy, P., Sautour, B., 2006. Fitting a predator-prey model to zooplankton time-

series data in the Gironde estuary (France): Ecological significance of the parameters.

Estuarine, Coastal and Shelf Science 67, 605-617.

de-la-Ossa-Carretero, J.A., Del-Pilar-Ruso, Y., Gimenez-Casalduero, F., Sanchez-Lizaso,

J.L., Dauvin, J.-C., 2012. Sensitivity of amphipods to sewage pollution. Estuarine,

Coastal and Shelf Science 96, 129-138.

de Montaudouin, X., 1996. Factors involved in growth plasticity of cockles Cerastoderma

edule (L.), identified by field survey and transplant experiments. Journal of Sea

Research 36, 251-265.

de Montaudouin, X., Bachelet, G., 1996. Experimental evidence of complex interactions

between biotic and abiotic factors in the dynamics of an intertidal population of the

bivalve Cerastoderma edule. Oceanologica Acta 19, 449-463.

de Montaudouin, X., Binias, C., Lassalle, G., 2012. Assessing parasite community structure

in cockles Cerastoderma edule at various spatio-temporal scales. Estuarine, Coastal and

Shelf Science 110, 54-60.

de Montaudouin, X., Blanchet, H., Kisielewski, I., Desclaux, C., Bachelet, G., 2003.

Digenean trematodes moderately alter Hydrobia ulvae population size structure. Journal

of the Marine Biological Association of the United Kingdom 83, 297-305.

de Montaudouin, X., Kisielewski, I., Bachelet, G., Desclaux, C., 2000. A census of

macroparasites in an intertidal bivalve community, Arcachon Bay, France.

Oceanologica Acta 23, 453-468.

de Montaudouin, X., Lanceleur, L., 2011. Distribution of parasites in their second

intermediate host, the cockle Cerastoderma edule: community heterogeneity and spatial

scale. Marine Ecology Progress Series 428, 187-199.

148

de Montaudouin, X., Paul-Pont, I., Lambert, C., Gonzalez, P., Raymond, N., Jude, F.,

Legeay, A., Baudrimont, M., Dang, C., Le Grand, F., Le Goic, N., Bourasseau, L.,

Paillard, C., 2010. Bivalve population health: Multistress to identify hot spots. Marine

Pollution Bulletin 60, 1307-1318.

de Montaudouin, X., Thieltges, D.W., Gam, M., Krakau, M., Pina, S., Bazairi, H.,

Dabouineau, L., Russell-Pinto, F., Jensen, K.T., 2009. Review - Digenean trematode

species in the cockle Cerastoderma edule: identification key and distribution along the

north-eastern Atlantic shoreline. Journal of the Marine Biological Association of the


de Paz, L., Patricio, J., Marques, J.C., Borja, A., Laborda, A.J., 2008. Ecological status

assessment in the lower Eo estuary (Spain). The challenge of habitat heterogeneity

integration: A benthic perspective. Marine Pollution Bulletin 56, 1275-1283.

de Wit, R., Leibreich, J., Vernier, F., Delmas, F., Beuffe, H., Maison, P., Chossat, J.-C.,

Laplace-Treyture, C., Laplana, R., Clavé, V., Torre, M., Auby, I., Trut, G., Maurer, D.,

Capdeville, P., 2005. Relationship between land-use in the agro-forestry system of les

Landes, nitrogen loading to and risk of macro-algal blooming in the Bassin d'Arcachon

coastal lagoon (SW France). Estuarine, Coastal and Shelf Science 62, 453-465.

Deegan, L.A., Wright, A., Ayvazian, S.G., Finn, J.T., Golden, H., Merson, R.R., Harrison, J.,

2002. Nitrogen loading alters seagrass ecosystem structure and support of higher

trophic levels. Aquatic Conservation: Marine and Freshwater Ecosystems 12, 193-212.

Desclaux, C., de Montaudouin, X., Bachelet, G., 2002. Cockle emergence at the sediment

surface: 'favourization' mechanism by digenean parasites? Diseases of Aquatic

Organisms 52, 137-149.

Desclaux, C., de Montaudouin, X., Bachelet, G., 2004. Cockle Cerastoderma edule

population mortality: role of the digenean parasite Himasthla quissetensis. Marine

Ecology Progress Series 279, 141-150.

Desclaux, C., Russell-Pinto, F., de Montaudouin, X., Bachelet, G., 2006. First record and

description of Metacercariae of Curtuteria arguinae n. sp. (Digenea :

Echinostomatidae), parasite of cockles Cerastoderma edule (Mollusca : Bivalvia) in

Arcachon Bay, France. Journal of Parasitology 92, 578-587.

149

Do, V.T., de Montaudouin, X., Lavesque, N., Blanchet, H., Guyard, H., 2011. Seagrass

colonization: Knock-on effects on zoobenthic community, populations and individual

health. Estuarine, Coastal and Shelf Science 95, 458-469.

Dolbeth, M., Cardoso, P.G., Ferreira, S.M., Verdelhos, T., Raffaelli, D., Pardal, M.A., 2007.

Anthropogenic and natural disturbance effects on a macrobenthic estuarine community

over a 10-year period. Marine Pollution Bulletin 54, 576-585.

Dolbeth, M., Cardoso, P.G., Grilo, T.F., Bordalo, M.D., Raffaelli, D., Pardal, M.A., 2011.

Long-term changes in the production by estuarine macrobenthos affected by multiple

stressors. Estuarine, Coastal and Shelf Science 92, 10-18.

Dolbeth, M., Lillebo, A.I., Cardoso, P.G., Ferreira, S.M., Pardal, M.A., 2005. Annual

production of estuarine fauna in different environmental conditions: An evaluation of

the estimation methods. Journal of Experimental Marine Biology and Ecology 326,

115-127.

Duarte, C.M., 2002. The future of seagrass meadows. Environmental Conservation 29, 192-

206.

Duarte, C.M., Dennison, W.C., Orth, R.J.W., Carruthers, T.J.B., 2008. The charisma of

coastal ecosystems: Addressing the imbalance. Estuaries and Coasts 31, 233-238.

Duarte, C.M., Terrados, J., Agawin, N.S.R., Fortes, M.D., Bach, S., Kenworthy, W.J., 1997.

Response of a mixed Philippine seagrass meadow to experimental burial. Marine


Duffy, J.E., 2006. Biodiversity and the functioning of seagrass ecosystems. Marine Ecology


Duffy, J.E., Richardson, J.P., Canuel, E.A., 2003. Grazer diversity effects on ecosystem

functioning in seagrass beds. Ecology Letters 6, 637-645.

Edgar, G.J., 1990. The influence of plant structure on the species richness, biomass and

secondary production of macrofaunal assemblages associated with Western Australian

seagrass beds. Journal of Experimental Marine Biology and Ecology 137, 215-240.

Edgar, G.J., 1994. Observations on the size-structure of macrofaunal assemblages. Journal of

Experimental Marine Biology and Ecology 176, 227-243.

150

Edgar, G.J., 1999. Experimental analysis of structural versus trophic importance of seagrass

beds. I. Effects on macrofaunal and meiofaunal invertebrates. Vie et Milieu-Life and

Environment 49, 239-248.

Edgar, G.J., Shaw, C., Watsona, G.F., Hammond, L.S., 1994. Comparisons of species

richness, size-structure and production of benthos in vegetated and unvegetated habitats

in Western Port, Victoria. Journal of Experimental Marine Biology and Ecology 176,

201-226.

Edmund, P.G., Frederick, T.S., 2003. World Atlas of Seagrasses. University of California

Press, London, England.

EEC, 2000. Directive 2000/60/EC of the European parliament and of the Council of 23

October 2000 establishing a framework for Community action in the field of water

policy. Official Journal of the European Communities 43, 1-72.

Elandalloussi, L.M., Carrasco, N., Roque, A., Fernández-Tejedor, M., Furones, D., 2008.

Occurence of Perkinsus sp. in two clam species (Ruditapes philippinarum and R.

decussatus) from the Ebro delta, Spain. Bulletin of the European Association of Fish

Pathologists 28, 1-9.

Elliott, M., Quintino, V., 2007. The estuarine quality paradox, environmental homeostasis

and the difficulty of detecting anthropogenic stress in naturally stressed areas. Marine


Engle, V., Summers, J., 1999. Refinement, validation, and application of a benthic condition

index for Northern Gulf of Mexico estuaries. Estuaries and Coasts 22, 624-635.

Erftemeijer, P.L.A., Robin, L.R.R., 2006. Environmental impacts of dredging on seagrasses:

A review. Marine Pollution Bulletin 52, 1553-1572.

Essink, K., 1999. Ecological effects of dumping of dredged sediments; options for

management. Journal of Coastal Conservation 5, 69-80.

Fahrig, L., 2003. Effects of habitat fragmentation on biodiversity. Annual Review of Ecology

Evolution and Systematics 34, 487-515.

Fauchald, K., Jumars, P.A., 1979. The diet of worms: a study of polychaete feeding guilds.

Oceanography and Marine Biology: an Annual Review 17, 193-284.

151

Ferreira, J.G., Vale, C., Soares, C.V., Salas, F., Stacey, P.E., Bricker, S.B., Silva, M.C.,

Marques, J.C., 2007. Monitoring of coastal and transitional waters under the EU water

framework directive. Environmental Monitoring and Assessment 135, 195-216.

Flach, E.C., Bruin, W.d., 1993. Effects of Arenicola marina and Cerastoderma edule on

distribution, abundance and population structure of Corophium volutator in

Gullmarsfjorden Western Sweden. Sarsia 78, 105-118.

Flassch, J.P., Leborgne, Y., 1992. Introduction in Europe, from 1972 to 1980, of the Japanese

Manila clam (Tapes philippinarum) and the effects on aquaculture production and

natural settlement. ICES Marine Science Symposia 194, 92-96.

Foden, J., Brazier, D.P., 2007. Angiosperms (seagrass) within the EU water framework

directive: A UK perspective. Marine Pollution Bulletin 55, 181-195.

Fonseca, M.S., Fisher, J.S., 1986. A comparison of canopy friction and sediment movement

between four species of seagrass with reference to their ecology and restoration. Marine


Fonseca, M.S., Kenworthy, W.J., Colby, D.R., Rittmaster, K.A., Thayer, G.W., 1990.

Comparisons of fauna among natural and transplanted eelgrass Zostera marina

meadows: criteria for mitigation. Marine Ecology Progress Series 65, 251-264.

Fredensborg, B.L., Mouritsen, K.N., Poulin, R., 2006. Relating bird host distribution and

spatial heterogeneity in trematode infections in an intertidal snail-from small to large

scale. Marine Biology 149, 275-283.

Fredriksen, S., Christie, H., Saethre, B.A., 2005. Species richness in macroalgae and

macrofauna assemblages on Fucus serratus L. (Phaeophyceae) and Zostera marina L.

(Angiospermae) in Skagerrak, Norway. Marine Biology Research 1, 2-19.

Fredriksen, S., De Backer, A., Bostrom, C., Christie, H., 2010. Infauna from Zostera marina

L. meadows in Norway. Differences in vegetated and unvegetated areas. Marine

Biology Research 6, 189-200.

Gacia, E., Duarte, C.M., Marba, N., Terrados, J., Kennedy, H., Fortes, M.D., Tri, N.H., 2003.

Sediment deposition and production in SE-Asia seagrass meadows. Estuarine, Coastal


152

Gam, M., Bazairi, H., de Montaudouin, X., 2009a. Impact de la présence d’herbiers à Zostera

noltii sur l’infestation parasitaire des coques Cerastoderma edule dans la lagune de

Merja Zerga (Maroc). Bulletin de l’Institut Scientifique, Rabat, section Sciences de la

Vie 31, 13-20.

Gam, M., Bazairi, H., Jensen, K.T., De Montaudouin, X., 2008. Metazoan parasites in an

intermediate host population near its southern border: the common cockle

(Cerastoderma edule) and its trematodes in a Moroccan coastal lagoon (Merja Zerga).

Journal of the Marine Biological Association of the United Kingdom 88, 357-364.

Gam, M., de Montaudouin, X., Bazairi, H., 2009b. Do trematode parasites affect cockle

(Cerastoderma edule) secondary production and elimination? Journal of the Marine

Biological Association of the United Kingdom 89, 1395-1402.

Gam, M., de Montaudouin, X., Bazairi, H., 2010. Population dynamics and secondary

production of the cockle Cerastoderma edule: A comparison between Merja Zerga

(Moroccan Atlantic Coast) and Arcachon Bay (French Atlantic Coast). Journal of Sea

Research 63, 191-201.

Gamito, S., 2008. Three main stressors acting on the Ria Formosa lagoonal system (Southern

Portugal): Physical stress, organic matter pollution and the land-ocean gradient.

Estuarine, Coastal and Shelf Science 77, 710-720.

Gamito, S., Furtado, R., 2009. Feeding diversity in macroinvertebrate communities: A

contribution to estimate the ecological status in shallow waters. Ecological Indicators 9,

1009-1019.

Gamito, S., Patricio, J., Neto, J.M., Marques, J.C., Teixeira, H., 2012a. The importance of

habitat-type for defining the reference conditions and the ecological quality status based

on benthic invertebrates: The Ria Formosa coastal lagoon (Southern Portugal) case

study. Ecological Indicators 19, 61-72.

Gamito, S., Patricio, J., Neto, J.M., Teixeira, H., Marques, J.C., 2012b. Feeding diversity

index as complementary information in the assessment of ecological quality status.

Ecological Indicators 19, 73-78.

153

Giere, O., Preusse, J.H., Dubilier, N., 1999. Tubificoides benedii (Tubificidae, Oligochaeta) -

a pioneer in hypoxic and sulfidic environments. An overview of adaptive pathways.


Goggin, C.L., 1996. Effect of Perkinsus olseni (Protozoa, Apicomplexa) on the weight of

Tridacna crocea (Mollusca, Bivalvia) from Lizard Island, Great Barrier Reef.

Aquaculture 141, 25-30.

Goñi-Urriza, M., de Montaudouin, X., Guyoneaud, R., Bachelet, G., de Wit, R., 1999. Effect

of macrofaunal bioturbation on bacterial distribution in marine sandy sediments, with

special reference to sulphur-oxidising bacteria. Journal of Sea Research 41, 269-279.

Gorbushin, A.M., 1997. Field evidence of trematode-induced gigantism in Hydrobia spp.

(Gastropoda: Prosobranchia). Journal of the Marine Biological Association of the


Goulletquer, P., Lombas, I., Prou, J., 1987. Influence du temps d'immersion sur l'activité

reproductrice et sur la croissance de la palourde japonaise Ruditapes philippinarum et

l'huître japonaise Crassostrea gigas. Haliotis 16, 453-462.

Grall, J., Glémarec, M., 1997. Using biotic indices to estimate macrobenthic community

perturbations in the Bay of Brest. Estuarine, Coastal and Shelf Science 44, 43-53.

Gremare, A., Labrune, C., Vanden Berghe, E., Amouroux, J.M., Bachelet, G., Zettler, M.L.,

Vanaverbeke, J., Fleischer, D., Bigot, L., Maire, O., Deflandre, B., Craeymeersch, J.,

Degraer, S., Dounas, C., Duineveld, G., Heip, C., Herrmann, M., Hummel, H.,

Karakassis, I., Kedra, M., Kendall, M., Kingston, P., Laudien, J., Occhipinti-Ambrogi,

A., Rachor, E., Sarda, R., Speybroeck, J., Van Hoey, G., Vincx, M., Whomersley, P.,

Willems, W., Wlodarska-Kowaiczuk, M., Zenetos, A., 2009. Comparison of the

performances of two biotic indices based on the MacroBen database. Marine Ecology


Han, Q., Bouma, T.J., Brun, F.G., Suykerbuyk, W., van Katwijk, M.M., 2012. Resilience of

Zostera noltii to burial or erosion disturbances. Marine Ecology Progress Series 449,

133-143.

Harvey, M., Gauthier, D., Munro, J., 1998. Temporal changes in the composition and

abundance of the macro-benthic invertebrate communities at dredged material disposal

154

sites in the Anse à Beaufils, baie des Chaleurs, Eastern Canada. Marine Pollution

Bulletin 36, 41-55.

Hechinger, R.F., Lafferty, K.D., 2005. Host diversity begets parasite diversity: bird final

hosts and trematodes in snail intermediate hosts. Proceedings of the Royal Society B-

Biological Sciences 272, 1059-1066.

Heck, K.L.J., Carruthers, T.J.B., Duarte, C.M., Hughes, A.R., Kendrick, G., Orth, R.J.,

Williams, S.W., 2008. Trophic transfers from seagrass meadows subsidize diverse

marine and terrestrial consumers. Ecosystems 11, 1198-1210.

Heck, K.L.J., Hays, G., Orth, R.J., 2003. Critical evaluation of the nursery role hypothesis for

seagrass meadows. Marine Ecology Progress Series 253, 123-136.

Heck, K.L.J., Orth, R.J., 1980. Seagrass habitats: the roles of habitat complexity, competition

and predation in structuring associated fish and motile macroinvertebrate assemblages.

In: Kennedy VS (ed) Estuarine perspectives, 449-464.

Hedge, P., Kriwoken, L.K., 2000. Evidence for effects of Spartina anglica invasion on

benthic macrofauna in Little Swanport estuary, Tasmania. Austral Ecology 25, 150-159.

Hemminga, M.A., Duarte, C.M., 2000. Seagrass Ecology. Cambridge University Press,

Cambridge.

Herman, S.S., Mihursky, J.A., McErlean, A.J., 1968. Zooplankton and environmental

characteristics of the Patuxent River Estuary 1963–1965. Chesapeake Science 9, 67-82.

Hily, C., Bouteille, M., 1999. Modifications of the specific diversity and feeding guilds in an

intertidal sediment colonized by an eelgrass meadow (Zostera marina) (Brittany,

France). Comptes Rendus De l'Academie des Sciences Serie III-Sciences de la Vie-Life

Sciences 322, 1121-1131.

Hoffmann, E., Bushek, D., Ford, S., Guo, X., Haidvogel, D., Hedgecock, D., Klinck, J.M.,

Milbury, C.A., Narvaez, D., Powell, E.N., Wang, Y., Wang, Z., Wilkin, J., Zhang, L.,

2009. Understanding how disease and environment combine to structure resistance in

estuarine bivalve populations. Oceanography 22, 212-231.

Holmer, M., Marba, N., 2010. Preface: dynamics and functions of seagrass ecosystems.


155

Honkoop, P.J.C., Berghuis, E.M., Holthuijsen, S., Lavaleye, M.S.S., Piersma, T., 2008.

Molluscan assemblages of seagrass-covered and bare intertidal flats on the Banc

d'Arguin, Mauritania, in relation to characteristics of sediment and organic matter.


Hovel, K.A., 2003. Habitat fragmentation in marine landscapes: relative effects of habitat

cover and configuration on juvenile crab survival in California and North Carolina

seagrass beds. Biological Conservation 110, 401-412.

Hovel, K.A., Lipcius, R.N., 2001. Habitat fragmentation in a seagrass landscape: patch size

and complexity control blue crab survival. Ecology 82, 1814-1829.

Hudson, P.J., Dobson, A.P., Lafferty, K.D., 2006. Is a healthy ecosystem one that is rich in

parasites? Trends in Ecology & Evolution 21, 381-385.

Hudson, P.J., Dobson, A.P., Newborn, D., 1992. Do parasites make prey vulnerable to

predation? Red grouse and parasites. Journal of Animal Ecology 61, 681-692.

Hughes, A.R., Williams, S.L., Duarte, C.M., Heck, K.L., Waycott, M., 2009. Associations of

concern: declining seagrasses and threatened dependent species. Frontiers in Ecology

and the Environment 7, 242-246.

Hughes, J.E., Deegan, L.A., Wyda, J.C., Weaver, M.J., Wright, A., 2002. The effects of

eelgrass habitat loss on estuarine fish communities of southern new England. Estuaries

25, 235-249.

Huong, T.T.L., Vermaat, J.E., Terrados, J., Van Tien, N., Duarte, C.M., Borum, J., Tri, N.H.,

2003. Seasonality and depth zonation of intertidal Halophila ovalis and Zostera

japonica in Ha Long Bay (northern Vietnam). Aquatic Botany 75, 147-157.

Inglehart, R., 1995. Public support for environmental protection: Objective problems and

subjective values in 43 societies. Ps-Political Science & Politics 28, 57-72.

Irlandi, E.A., 1994. Large- and small-scale effects of habitat structure on rates of predation:

how percent coverage of seagrass affects rates of predation and siphon nipping on an

infaunal bivalve. Oecologia 98, 176-183.

Irlandi, E.A., 1996. The effects of seagrass patch size and energy regime on growth of a

suspension-feeding bivalve. Journal of Marine Research 54, 161-185.

156

Irlandi, E.A., Orlando, B.A., Ambrose, W.G., 1999. Influence of seagrass habitat patch size

on growth and survival of juvenile bay scallops, Argopecten irradians concentricus

(Say). Journal of Experimental Marine Biology and Ecology 235, 21-43.

Irlandi, E.A., Peterson, C.H., 1991. Modification of animal habitat by large plants:

mechanisms by which seagrasses influence clam growth. Oecologia 87, 307-318.

Isobe, T., Takada, H., Kanai, M., Tsutsumi, S., Isobe, K.O., Boonyatumanond, R., Zakaria,

M.P., 2007. Distribution of polycyclic aromatic hydrocarbons (PAHs) and phenolic

endocrine disrupting chemicals in south and southeast Asian mussels. Environmental

Monitoring and Assessment 135, 423-440.

Jensen, K.T., 1992. Dynamics and growth of the cockle, Cerastoderma edule, on an intertidal

mud-flat in the Danish Wadden sea: Effects of submersion time and density.

Netherlands Journal of Sea Research 28, 335-345.

Jones, C.G., Lawton, J.H., Shachak, M., 1994. Organisms as ecosystem engineers. Oikos 69,

373-386.

Jonsson, P.R., Andre, C., 1992. Mass mortality of the bivalve Cerastoderma edule on the

Swedish west coast caused by infestation with the digenean trematode Cercaria

cerastodermae I. Ophelia 36, 151-157.

Kennedy, H., Gacia, E., Kennedy, D.P., Papadimitriou, S., Duarte, C.M., 2004. Organic

carbon sources to SE Asian coastal sediments. Estuarine, Coastal and Shelf Science 60,

59-68.

Koprivnikar, J., Lim, D., Fu, C., Brack, S.H.M., 2010. Effects of temperature, salinity, and

pH on the survival and activity of marine cercariae. Parasitology Research 106, 1167-

1177.

Krause-Jensen, D., Greve, T.M., Nielsen, K., 2005. Eelgrass as a bioindicator under the

European Water Framework Directive. Water Resources Management 19, 63-75.

Krist, A.C., 2001. Variation in fecundity among populations of snails is predicted by

prevalence of castrating parasites. Evolutionary Ecology Research 3, 191-197.

Kube, S., Kube, J., Bick, A., 2006. A loss of fecundity in a population of mudsnails Hydrobia

ventrosa caused by larval trematodes does not measurably affect host population

equilibrium level. Parasitology 132, 725-732.

157

La Peyre, M., Casas, S., La Peyre, J., 2006. Salinity effects on viability, metabolic activity

and proliferation of three Perkinsus species. Diseases of Aquatic Organisms 71, 59-74.

Labrune, C., Amouroux, J.M., Sarda, R., Dutrieux, E., Thorin, S., Rosenberg, R., Gremare,

A., 2006. Characterization of the ecological quality of the coastal Gulf of Lions (NW

Mediterranean). A comparative approach based on three biotic indices. Marine


Lafferty, K.D., 2003. Is disease increasing or decreasing, and does it impact or maintain

biodiversity? Journal of Parasitology 89, S101–S105.

Lajtner, J., Luciæ, A., Marušiæ, M., Erben, R., 2008. The effects of trematode Bucephalus

polymorphus on the reproductive cycle of the zebra mussel Dreissena polymorpha in

the Drava River. Acta Parasitologica 53, 85-92.

Larkum, A.W.D., Orth, R.J., Duarte, C.M., 2006. Seagrasses: biology, ecology, and

conservation. Springer.

Lassalle, G., de Montaudouin, X., Soudant, P., Paillard, C., 2007. Parasite co-infection of two

sympatric bivalves, the Manila clam (Ruditapes philippinarum) and the cockle

(Cerastoderma edule) along a latitudinal gradient. Aquatic Living Resource 20, 33-42.

Lauckner, G., 1983. Diseases of Mollusca: Bivalvia, in: Kinne, O. (Ed.), Diseases of Marine

Animals. Biologische Anstalt Helgoland, Hamburg, pp. 477-961.

Lauckner, G., 1987a. Ecological effects of larval trematode infestation on littoral marine

invertebrate populations. International Journal for Parasitology 17, 391-398.

Lauckner, G., 1987b. Effects of parasites on juvenile Wadden Sea invertebrates, in:

Tougaard, S., Asbirk, S. (Eds.), 5th International Wadden Sea Symposium. The national

Forest and Nature Agency and the Museum of Fisheries and Shipping, Esbjerg, pp. 103-

121.

Lavesque, N., Blanchet, H., de Montaudouin, X., 2009. Development of a multimetric

approach to assess perturbation of benthic macrofauna in Zostera noltii beds. Journal of

Experimental Marine Biology and Ecology 368, 101-112.

Lazure, P., Dumas, F., 2008. An external-internal mode coupling for a 3D hydrodynamical

model for applications at regional scale (MARS). Advances in Water Resources 31,

233-250.

158

Lefèvre, F., Lyard, F.H., Le Provost, C., Schrama, E.J.O., 2002. FES99 : a global tide finite

element solution assimilating tide gauge and altimetric information. Journal of

Atmospheric and Oceanic Technology 19, 1345-1356.

Leite, R.B., Afonso, R., Cancela, M.L., 2004. Perkinsus sp. infestation in carpet-shell clams,

Ruditapes decussatus (L), along the Portuguese coast. Results from a 2-year survey.

Aquaculture 240, 39-53.

Leonard, L.A., Wren, P.A., Beavers, R.L., 2002. Flow dynamics and sedimentation in

Spartina alterniflora and Phragmites australis marshes of the Chesapeake Bay.

Wetlands 22, 415-424.

Llanso, R.J., Scott, L.C., Dauer, D.M., Hyland, J.L., Russell, D.E., 2002a. An estuarine

benthic index of biotic integrity for the Mid-Atlantic region of the United States. I.

Classification of assemblages and habitat definition. Estuaries 25, 1219-1230.

Llanso, R.J., Scott, L.C., Hyland, J.L., Dauer, D.M., Russell, D.E., Kutz, F.W., 2002b. An

estuarine benthic index of biotic integrity for the Mid-Atlantic region of the United

States. II. Index Development. Estuaries 25, 1231-1242.

Marba, N., Duarte, C.M., 1994. Growth response of the seagrass Cymodocea nodosa to

experimental burial and erosion. Marine Ecology Progress Series 107, 307-311.

Marba, N., Duarte, C.M., Terrados, J., Halun, Z., Gacia, E., Fortes, M.D., 2010. Effects of

seagrass rhizospheres on sediment redox conditions in SE Asian coastal ecosystems.


Marcano, G., Cazaux, C., 1994. Influence of tidal advection on the larval distribution of

polychaete annelids in the channels from Arcachon Bay. Bulletin de la Societe

Zoologique de France-Evolution et Zoologie 119, 263-273.

Marcogliese, D.J., 2002. Food webs and the transmission of parasites to marine fish.

Parasitology 124, S83-S99.

Marin-Guirao, L., Cesar, A., Marin, A., Lloret, J., Vita, R., 2005. Establishing the ecological

quality status of soft-bottom mining-impacted coastal water bodies in the scope of the

Water Framework Directive. Marine Pollution Bulletin 50, 374-387.

159

Marzano, C.N., Liaci, S.L., Fianchini, A., Gravina, F., Mercurio, M., Corriero, G., 2003.

Distribution, persistence and change in the macrobenthos of the lagoon of Lesina

(Apulia, southern Adriatic Sea). Oceanologica Acta 26, 57-66.

Maurer, D., Keck, R.T., Tinsman, J.C., Leathem, W.A., 1981a. Vertical migration and

mortality of benthos in dredged material: Part I - Mollusca. Marine Environmental

Research 4, 299-319.

Maurer, D., Keck, R.T., Tinsman, J.C., Leathem, W.A., 1981b. Vertical migration and

mortality of benthos in dredged material: Part II - Crustacea. Marine Environmental

Research 5, 301-317.

Maurer, D., Keck, R.T., Tinsman, J.C., Leathem, W.A., 1982. Vertical migration and

mortality of benthos in dredged material: Part III - Polychaeta. Marine Environmental

Research 6, 49-68.

Mills, K.E., Fonseca, M.S., 2003. Mortality and productivity of eelgrass Zostera marina

under conditions of experimental burial with two sediment types. Marine Ecology


Montefalcone, M., 2009. Ecosystem health assessment using the Mediterranean seagrass

Posidonia oceanica: A review. Ecological Indicators 9, 595-604.

Moore, J., 2002. Parasites and the Behavior of Animals. Oxford University Press.

Morton, B., Blackmore, G., 2001. South China sea. Marine Pollution Bulletin 42, 1236-1263.

Mouritsen, K.N., 2002. The Hydrobia ulvae-Maritrema subdolum association: influence of

temperature, salinity, light, water-pressure and secondary host exudates on cercarial

emergence and longevity. Journal of Helminthology 76, 341-347.

Mouritsen, K.N., Gorbushin, A., Jensen, K.T., 1999. Influence of trematode infections on in

situ growth rates of Littorina littorea. Journal of the Marine Biological Association of

the United Kingdom 79, 425-430.

Muxika, I., Borja, A., Bald, J., 2007. Using historical data, expert judgement and multivariate

analysis in assessing reference conditions and benthic ecological status, according to the

European Water Framework Directive. Marine Pollution Bulletin 55, 16-29.

160

Muxika, I., Somerfield, P.J., Borja, A., Warwick, R.M., 2012. Assessing proposed

modifications to the AZTI marine biotic index (AMBI), using biomass and production.


Nagelkerken, I., van der Velde, G., 2004. A comparison of fish communities of subtidal

seagrass beds and sandy seabeds in 13 marine embayments of a Caribbean island, based

on species, families, size distribution and functional groups. Journal of Sea Research

52, 127-147.

Nakaoka, M., 2005. Plant-animal interactions in seagrass beds: ongoing and future challenges

for understanding population and community dynamics. Population Ecology 47, 167-

177.

Neckles, H.A., Wetzel, R.R., Orth, R.J., 1993. Relative effects of nutrient enrichment and

grazing on epiphyte macrophyte (Zostera marina) dynamics. Oecologia 93, 285-295.

Neira, C., Grosholz, E.D., Levin, L.A., Blake, R., 2006. Mechanisms generating modification

of benthos following tidal flat invasion by a Spartina hybrid. Ecological Applications

16, 1391-1404.

Nguyen, T.T., Nguyen, V.T., Chu, T.C., Cao, V.L., 2010. The regression of seagrass beds at

the coastal waters of Viet Nam, Scientific conference celebrating the 35th anniversary

of Viet Nam Academy of Science and Technology - Subcommittee: Marine Science and

Technology. Publishing House for Science and Technology.

Nguyen, V.T., 2004. Towards seagrass ecosystem management in Vietnam. Publishing

House for Science and Technology.

Nguyen, V.T., 2008. National Report on Seagrass of the South China Sea in Vietnam.

UNEP/GEF South China Sea Project.

Oliva, M.E., Olivares, A.N., Diaz, C.D., Pasten, M.V., 1999. Parasitic castration in

Concholepas concholepas (Gastropoda : Muricidae) due to a larval digenean in northern

Chile. Diseases of Aquatic Organisms 36, 61-65.

Ooi, J.L.S., Kendrick, G.A., Van Niel, K.P., Affendi, Y.A., 2011. Knowledge gaps in tropical

Southeast Asian seagrass systems. Estuarine, Coastal and Shelf Science 92, 118-131.

Orsi, J.J., Mecum, W.L., 1986. Zooplankton distribution and abundance in the Sacremento-

San Joaquin delta in relation to certain environmental factors. Estuaries 9, 326-339.

161

Orth, R.J., 1992. A perspective on plant–animal interactions in seagrasses: physical and

biological determinants influencing plant and animal abundance, in: John, D.M.,

Hawkins, S. J. and Price, J. H. (Ed.), Plant–animal interactions in the marine benthos.

Clarendon Press, Oxford, pp. 147–164.

Orth, R.J., Carruthers, T.J.B., Dennison, W.C., Duarte, C.M., Fourqurean, J.W., Heck, K.L.,

Hughes, A.R., Kendrick, G.A., Kenworthy, W.J., Olyarnik, S., Short, F.T., Waycott,

M., Williams, S.L., 2006. A global crisis for seagrass ecosystems. Bioscience 56, 987-

996.

Orth, R.J., Heck, K.L.J., van Montfrans, J., 1984. Faunal communities in seagrass beds: a

review of the influence of plant structure and prey characteristics on predator-prey

relationships. Estuaries 7, 339-350.

Osenga, G.A., Coull, B.C., 1983. Spartina alterniflora Loisel root structure and meiofaunal

abundance. Journal of Experimental Marine Biology and Ecology 67, 221-225.

Packer, C., Holt, R.D., Hudson, P.J., Lafferty, K.D., Dobson, A.P., 2003. Keeping the herds

healthy and alert: implications of predator control for infectious disease. Ecology

Letters 6, 797-802.

Paillard, C., 2004. A short review of brown ring disease, a vibriosis affecting clams,

Ruditapes philippinarum and Ruditapes decussatus. Aquatic Living Resources 17, 467-

475.

Park, K.I., Choi, K.S., 2001. Spatial distribution of the protozoan parasite Perkinsus sp. found

in the Manila clams, Ruditapes philippinarum, in Korea. Aquaculture 203, 9-22.

Patricio, J., Adao, H., Neto, J.M., Alves, A.S., Traunspurger, W., Marques, J.C., 2012. Do

nematode and macrofauna assemblages provide similar ecological assessment

information? Ecological Indicators 14, 124-137.

Patricio, J., Neto, J.M., Teixeira, H., Salas, F., Marques, J.C., 2009. The robustness of

ecological indicators to detect long-term changes in the macrobenthos of estuarine

systems. Marine Environmental Research 68, 25-36.

Pearson, T.H., Rosenberg, R., 1978. Macrobenthic succession in relation to organic

enrichment and pollution of the environment. Oceanogrraphy and Marine Biology

Annual Review 16, 229–311.

162

Peralta, G., Brun, F.G., Hernandez, I., Vergara, J.J., Perez-Llorens, J.L., 2005. Morphometric

variations as acclimation mechanisms in Zostera noltii beds. Estuarine, Coastal and


Pillay, D., Branch, G.M., Griffiths, C.L., Williams, C., Prinsloo, A., 2010. Ecosystem change

in a South African marine reserve (1960-2009): role of seagrass loss and anthropogenic

disturbance. Marine Ecology Progress Series 415, 35-48.

Piñeiro, G., Perelman, S., Guerschman, J.P., Paruelo, J.M., 2008. How to evaluate models:

Observed vs. predicted or predicted vs. observed? Ecological Modelling 216, 316-322.

Pinto, R., Patricio, J., Baeta, A., Fath, B.D., Neto, J.M., Marques, J.C., 2009. Review and

evaluation of estuarine biotic indices to assess benthic condition. Ecological Indicators

9, 1-25.

Plus, M., Dumas, F., Stanisière, J.Y., Maurer, D., 2009. Hydrodynamic characterization of

the Arcachon Bay, using model-derived descriptors. Continental Shelf Research 29,

1008-1013.

Plus, M., Sebastien, D., Gilles, T., Isabelle, A., de Montaudouin, X., Emery, E., Claire, N.,

Christophe, V., 2010. Long-term evolution (1988-2008) of Zostera spp. meadows in

Arcachon Bay (Bay of Biscay). Estuarine, Coastal and Shelf Science 87, 357-366.

Poulin, R., Mouritsen, K.N., 2004. Small-scale spatial variation in rates of metacercarial

accumulation by a bivalve second intermediate host. Journal of the Marine Biological

Association of the United Kingdom 84, 1209-1212.

Powilleit, M., Kleine, J., Leuchs, H., 2006. Impacts of experimental dredged material

disposal on a shallow, sublittoral macrofauna community in Mecklenburg Bay (western

Baltic Sea). Marine Pollution Bulletin 52, 386-396.

Pranovi, F., Da Ponte, F., Torricelli, P., 2008. Historical changes in the structure and

functioning of the benthic community in the lagoon of Venice. Estuarine, Coastal and


Prato, S., Morgana, J.G., La Valle, P., Finoia, M.G., Lattanzi, L., Nicoletti, L., Ardizzone,

G.D., Izzo, G., 2009. Application of biotic and taxonomic distinctness indices in

assessing the Ecological Quality Status of two coastal lakes: Caprolace and Fogliano

lakes (Central Italy). Ecological Indicators 9, 568-583.

163

Probst, S., Kube, J., 1999. Histopathological effects of larval trematode infections in

mudsnails and their impact on host growth: what causes gigantism in Hydrobia

ventrosa (Gastropoda : Prosobranchia)? Journal of Experimental Marine Biology and

Ecology 238, 49-68.

Puente, A., Juanes, J.A., Garcia, A., Alvarez, C., Revilla, J.A., Carranza, I., 2008. Ecological

assessment of soft bottom benthic communities in northern Spanish estuaries.


Quintino, V., Elliott, M., Rodrigues, A.M., 2006. The derivation, performance and role of

univariate and multivariate indicators of benthic change: Case studies at differing

spatial scales. Journal of Experimental Marine Biology and Ecology 330, 368-382.

Rakocinski, C.F., 2012. Evaluating macrobenthic process indicators in relation to organic

enrichment and hypoxia. Ecological Indicators 13, 1-12.

Ray, S.M., 1966. A review of the culture method of detecting Dermocystidium marinum with

suggested modifications and precautions. Proceedings of the National Shellfish

Association 54, 55-69.

Reed, B.J., Hovel, K.A., 2006. Seagrass habitat disturbance: how loss and fragmentation of

eelgrass Zostera marina influences epifaunal abundance and diversity. Marine Ecology


Rees, H.L., Rowlatt, S.M., Limpenny, D.S., Rees, E.E.S., Rolfe, M.S., 1992. Benthic studies

at dredged material disposal sites in Liverpool Bay, Aquatic Environment Monitoring

Report. M.A.F.F. Directorate of Fisheries Research, Lowestoft 2, pp. 1–21.

Reise, K., 1985. Tidal flat ecology. An experimental approach to species interactions.

Springer-Verlag, Berlin.

Reiss, H., Kroncke, I., 2005. Seasonal variability of benthic indices: An approach to test the

applicability of different indices for ecosystem quality assessment. Marine Pollution

Bulletin 50, 1490-1499.

Reusch, T.B.H., 1998. Differing effects of eelgrass Zostera marina on recruitment and

growth of associated blue mussels Mytilus edulis. Marine Ecology Progress Series 167,

149-153. 167, 149-153.

164

Rice, T., McGraw, E., O'Brien, E.K., Reverter, A., Jackson, D.J., Degnan, B.M., 2006.

Parasitic castration by the digenian trematode Allopodocotyle sp. alters gene expression

in the brain of the host mollusc Haliotis asinina. Febs Letters 580, 3769-3774.

Robert, R., Guillocheau, N., Collos, Y., 1987. Hydrobiological parameters during an annual

cycle in the Arcachon basin. Marine Biology 95, 631-640.

Roberts, R.D., Gregory, M.R., Foster, B.A., 1998. Developing an efficient macrofauna

monitoring index from an impact study - A dredge spoil example. Marine Pollution

Bulletin 36, 231-235.

Rosenberg, R., Blomqvist, M., Nilsson, H.C., Cederwall, H., Dimming, A., 2004. Marine

quality assessment by use of benthic species-abundance distributions: a proposed new

protocol within the European Union Water Framework Directive. Marine Pollution

Bulletin 49, 728-739.

Rosenberg, R., Hellman, B., Johansson, B., 1991. Hypoxic tolerance of marine benthic fauna.

Marine Ecology Progress Series 79, 127-131.

Rybarczyk, H., Elkaim, B., 2003. An analysis of the trophic network of a macrotidal estuary:

the Seine Estuary (Eastern Channel, Normandy, France). Estuarine, Coastal and Shelf

Science 58, 775-791.

Salas, F., Marcos, C., Neto, J.M., Patricio, J., Perez-Ruzafa, A., Marques, J.C., 2006. User-

friendly guide for using benthic ecological indicators in coastal and marine quality

assessment. Ocean & Coastal Management 49, 308-331.

Sampaio, L., Rodrigues, A.M., Quintino, V., 2011. Can biotic indices detect mild organic

enrichment of the seafloor? Ecological Indicators 11, 1235-1244.

Sanchez, F., Caill-Milly, N., Lissardy, M., de Casamajor, M.N., Morandeau, G., 2010.

Campagne d'évaluation du stock de palourdes du bassin d'Arcachon. Année 2010.

IFREMER, pp. 1-47.

Sapp, K.K., Esch, G.W., 1994. The effects of spatial and temporal heterogeneity as

structuring forces for parasite communities in Heliosoma anceps and Physa gyrina.

American Midland Naturalist 132, 91-103.

Sauriau, P.G., Mouret, V., Rince, J.P., 1989. Trophic system of wild soft bottom molluscs in

the Marennes-Oléron France oyster farming bay. Oceanologica Acta 12, 193-204.

165

Schulte-Oehlmann, U., Oehlmann, J., Fioroni, P., Bauer, B., 1997. Imposex and reproductive

failure in Hydrobia ulvae (Gastropoda: Prosobranchia). Marine Biology 128, 257-266.

Sheridan, P., 1997. Benthos of adjacent mangrove, seagrass and non-vegetated habitats in

Rookery Bay, Florida, U.S.A. Estuarine, Coastal and Shelf Science 44, 455-469.

Sheridan, P., 2004. Recovery of floral and faunal communities after placement of dredged

material on seagrasses in Laguna Madre, Texas. Estuarine, Coastal and Shelf Science

59, 441-458.

Short, F.T., Burdick, D.M., 1996. Quantifying eelgrass habitat loss in relation to housing

development and nitrogen loading in Waquoit Bay, Massachusetts. Estuaries 19, 730-

739.

Short, F.T., Wyllie-Echeverria, S., 1996. Natural and human-induced disturbance of

seagrasses. Environmental Conservation 23, 17-27.

Siebert, T., Branch, G.M., 2006. Ecosystem engineers: Interactions between eelgrass Zostera

capensis and the sandprawn Callianassa kraussi and their indirect effects on the

mudprawn Upogebia africana. Journal of Experimental Marine Biology and Ecology

338, 253-270.

Simboura, N., Parlayotidis, P., Papathanassiou, E., 2005. A synthesis of the biological quality

elements for the implementation of the European Water Framework Directive in the

Mediterranean ecoregion: The case of Saronikos Gulf. Ecological Indicators 5, 253-266.

Simboura, N., Zenetos, A., 2002. Benthic indicators to use in Ecological Quality

classification of Mediterranean soft bottom marine ecosystems, including a new Biotic

Index. Mediterranean Marine Science 3/2, 77-111.

Simonini, R., Grandi, V., Massamba-N'Siala, G., Iotti, M., Montanari, G., Prevedelli, D.,

2009. Assessing the ecological status of the North-western Adriatic Sea within the

European Water Framework Directive: a comparison of Bentix, AMBI and M-AMBI

methods. Marine Ecology-an Evolutionary Perspective 30, 241-254.

Smaal, A.C., 1997. Food supply and demand of bivalve suspension feeders in a tidal system,

Faculty of Mathematics and Natural Sciences. PhD dissertation, University of

Groningen, Groningen, Netherlands, p. 237.

166

Smaal, A.C., Verhagen, J.H.G., Coosen, J., Haas, H.A., 1986. Interaction between seston

quantity and quality and benthic suspension feeders in the Oosterschelde, The

Netherlands. Ophelia 26, 385-399.

Smith, R.W., Bergen, M., Weisberg, S.B., Cadien, D., Dalkey, A., Montagne, D., Stull, J.K.,

Velarde, R.G., 2001. Benthic response index for assessing infaunal communities on the

southern California mainland shelf. Ecological Applications 11, 1073-1087.

SOGREAH, 2001. Bassin d'Arcachon - Amélioration de l'hydraulique réflexion sur la

programmation des travaux. SOGREAH - Syndicat Intercommunal du Bassin

d'Arcachon, pp. 1-17.

Stoner, A.W., 1980. The role of seagrass biomass in the organization of benthic macrofaunal

assemblages. Bulletin of Marine Science 30, 537-551.

Stronkhorst, J., Ariese, F., van Hattum, B., Postma, J.F., de Kluijver, M., Den Besten, P.J.,

Bergman, M.J.N., Daan, R., Murk, A.J., Vethaak, A.D., 2003. Environmental impact

and recovery at two dumping sites for dredged material in the North Sea.

Environmental Pollution 124, 17-31.

Summerson, H.C., Peterson, C.H., 1984. Role of predation in organising benthic communities

of a temperate-zone seagrass bed. Marine Ecology Progress Series 15, 63-77.

Sures, B., 2004. Environmental parasitology: relevancy of parasites in monitoring

environmental pollution. Trends in Parasitology 20, 170-177.

Talley, T.S., Levin, L.A., 2001. Modification of sediments and macrofauna by an ivasive

marsh plant. Biological Invasions 3, 51-68.

Teixeira, H., Borja, A., Weisberg, S.B., Ranasinghe, J.A., Cadien, D.B., Dauer, D.M.,

Dauvin, J.-C., Degraer, S., Diaz, R.J., Gremare, A., Karakassis, I., Llanso, R.J., Lovell,

L.L., Marques, J.C., Montagne, D.E., Occhipinti-Ambrogi, A., Rosenberg, R., Sarda,

R., Schaffner, L.C., Velarde, R.G., 2010. Assessing coastal benthic macrofauna

community condition using best professional judgement - Developing consensus across

North America and Europe. Marine Pollution Bulletin 60, 589-600.

Teixeira, H., Salas, F., Borja, A., Neto, J.M., Marques, J.C., 2008a. A benthic perspective in

assessing the ecological status of estuaries: The case of the Mondego estuary (Portugal).


167

Teixeira, H., Salas, F., Neto, J.M., Patriicio, J., Pinto, R., Verissimo, H., Garcia-Charton,

J.A., Marcos, C., Perez-Ruzafa, A., Marques, J.C., 2008b. Ecological indices tracking

distinct impacts along disturbance-recovery gradients in a temperate NE Atlantic

Estuary - Guidance on reference values. Estuarine, Coastal and Shelf Science 80, 130-

140.

Teixeira, H., Weisberg, S.B., Borja, A., Ranasinghe, J.A., Cadien, D.B., Velarde, R.G.,

Lovell, L.L., Pasko, D., Phillips, C.A., Montagne, D.E., Ritter, K.J., Salas, F., Marques,

J.C., 2012. Calibration and validation of the AZTI's Marine Biotic Index (AMBI) for

Southern California marine bays. Ecological Indicators 12, 84-95.

Thaler, J.S., Fidantsef, A.L., Duffey, S.S., Bostock, R.M., 1999. Trade-offs in plant defense

against pathogens and herbivores: A field demonstration of chemical elicitors of

induced resistance. Journal of Chemical Ecology 25, 1597-1609.

Thieltges, D.W., 2006a. Effect of infection by the metacercarial trematode Renicola roscovita

on growth in intertidal blue mussel Mytilus edulis. Marine Ecology Progress Series 319,

129-134.

Thieltges, D.W., 2006b. Parasite induced summer mortality in the cockle Cerastoderma edule

by the trematode Gymnophallus choledochus. Hydrobiologia 559, 455-461.

Thieltges, D.W., de Montaudouin, X., Fredensborg, B., Jensen, K.T., Koprivnikar, J., Poulin,

R., 2008. Production of marine trematode cercariae: a potentially overlooked path of

energy flow in benthic systems. Marine Ecology Progress Series 372, 147-155.

Thieltges, D.W., Reise, K., 2006. Metazoan parasites in intertidal cockles Cerastoderma

edule from the northern Wadden Sea. Journal of Sea Research 56, 284-293.

Thieltges, D.W., Reise, K., 2007. Spatial heterogeneity in parasite infections at different

spatial scales in an intertidal bivalve. Oecologia 150, 569-581.

Tu, T.L.H., 2009. Research on some biological and ecological characteristics of seagrass on

Quang Ninh, Hai Phong, Kien Giang coastal and propose solutions for sustainable use.

PhD dissertation, Research Institute for Marine Fisheries, Hai Phong, Viet Nam, p. 170.

Turner, S., Hewitt, J., Wilkinson, M., Morrisey, D., Thrush, S., Cummings, V., Funnell, G.,

1999. Seagrass patches and landscapes: The influence of wind-wave dynamics and

168

hierarchical arrangements of spatial structure on macrofaunal seagrass communities.


UNEP, 2004. Seagrass in the South China Sea, Techical Publication No. 3. UNEP/GEF/SCS.

Van Dolah, R.F., Calder, D.R., Knott, D.M., 1984. Effects of dredging and open-water

disposal on benthic macroinvertebrates in a South Carolina estuary. Estuaries 7, 28-37.

Van Dolah, R.F., Hyland, J.L., Holland, A.F., Rosen, J.S., Snoots, T.R., 1999. A benthic

index of biological integrity for assessing habitat quality in estuaries of the southeastern

USA. Marine Environmental Research 48, 269-283.

Van Hoey, G., Borja, A., Birchenough, S., Buhl-Mortensen, L., Degraer, S., Fleischer, D.,

Kerckhof, F., Magni, P., Muxika, I., Reiss, H., Schroder, A., Zettler, M.L., 2010. The

use of benthic indicators in Europe: From the Water Framework Directive to the Marine

Strategy Framework Directive. Marine Pollution Bulletin 60, 2187-2196.

van Katwijk, M.M., Bos, A.R., Hermus, D.C.R., Suykerbuyk, W., 2010. Sediment

modification by seagrass beds: Muddification and sandification induced by plant cover

and environmental conditions. Estuarine, Coastal and Shelf Science 89, 175-181.

Verissimo, H., Neto, J.M., Teixeira, H., Franco, J.N., Fath, B.D., Marques, J.C., Patricio, J.,

2012. Ability of benthic indicators to assess ecological quality in estuaries following

management. Ecological Indicators 19, 130-143.

Villalba, A., Casas, S.M., Lopez, C., Carballal, M.J., 2005. Study of perkinsosis in the carpet

shell clam Tapes decussatus in Galicia (NW Spain). II. Temporal pattern of disease

dynamics and association with clam mortality. Diseases of Aquatic Organisms 65, 257-

267.

Villalba, A., Reece, K.S., Camino Ordas, M., Casas, S.M., Figueras, A., 2004. Perkinsosis in

molluscs: A review. Aquatic Living Resources 17, 411-432.

Vincent, D., 2002. Dynamique et nutrition du zooplancton (Bassin d'Arcachon) : flux de

carbone et d'azote associés. Conséquences sur le pool nutritif et sur les organismes.

Université de la Méditerranée.

Ware, S., Bolam, S.G., Rees, H.L., 2010. Impact and recovery associated with the deposition

of capital dredging at UK disposal sites: Lessons for future licensing and monitoring.


169

Warwick, R.M., 1986. A new method for detecting pollution effects on marine macrobenthic

communities. Marine Biology 92, 557-562.

Warwick, R.M., 1993. Environmental impact studies on marine communities: pragmatical

considerations. Australian Journal of Ecology 18, 63-80.

Warwick, R.M., Clarke, K.R., Somerfield, P.J., 2010. Exploring the marine biotic index

(AMBI): variations on a theme by Angel Borja. Marine Pollution Bulletin 60, 554-559.

Waycott, M., Duarte, C.M., Carruthers, T.J.B., Orth, R.J., Dennison, W.C., Olyarnik, S.,

Calladine, A., Fourqurean, J.W., Heck, K.L., Hughes, A.R., Kendrick, G.A.,

Kenworthy, W.J., Short, F.T., Williams, S.L., 2009. Accelerating loss of seagrasses

across the globe threatens coastal ecosystems. Proceedings of the National Academy of

Sciences of the United States of America 106, 12377-12381.

Webster, P.J., Rowden, A.A., Attrill, M.J., 1998. Effect of shoot density on the infaunal

macro-invertebrate community within Zostera marina seagrass bed. Estuarine, Coastal


Wegeberg, A.M., de Montaudouin, X., Jensen, K.T., 1999. Effect of intermediate host size

(Cerastoderma edule) on infectivity of cercariae of three Himasthla species

(Echinostomatidae, Trematoda). Journal of Experimental Marine Biology and Ecology

238, 259-269.

Weisberg, S., Ranasinghe, J., Dauer, D., Schaffner, L., Diaz, R., Frithsen, J., 1997. An

estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries and

Coasts 20, 149-158.

Widdows, J., Brinsley, M.D., Bowley, N., Barrett, C., 1998. A benthic annular flume for in

situ measurement of suspension feeding/biodeposition rates and erosion potential of

intertidal cohesive sediments. Estuarine, Coastal and Shelf Science 46, 27-38.

Widdows, J., Navarro, J.M., 2007. Influence of current speed on clearance rate, algal cell

depletion in the water column and resuspension of biodeposits of cockles

(Cerastoderma edule). Journal of Experimental Marine Biology and Ecology 343, 44-

51.

Wiens, J.A., 1989. Spatial scaling in ecology. Functional Ecology 3, 385-397.

170

Wilber, D.H., Ray, G.L., Clarke, D.G., Diaz, R.J., 2008. Responses of benthic infauna to

large-scale sediment disturbance in Corpus Christi Bay, Texas. Journal of Experimental

Marine Biology and Ecology 365, 13-22.

Wildish, D.J., Thomas, M.L.H., 1985. Effects of dredging and dumping on benthos of Saint

John Harbor. Marine Environmental Research 15, 45-57.

Wildsmith, M.D., Rose, T.H., Potter, I.C., Warwick, R.M., Clarke, K.R., 2011. Benthic

macroinvertebrates as indicators of environmental deterioration in a large microtidal

estuary. Marine Pollution Bulletin 62, 525-538.

Witt, J., Schroeder, A., Knust, R., Arntz, W.E., 2004. The impact of harbour sludge disposal

on benthic macrofauna communities in the Weser estuary. Helgoland Marine Research

58, 117-128.

Wolff, W.J., 1973. The estuary as a habitat. An analysis of data on the soft-bottom

macrofauna of the estuarine area of the Rivers Rhine, Meuse and Scheldt. Zoologische

Verhandelingen 126, 1-242.

Worm, B., Barbier, E.B., Beaumont, N., Duffy, J.E., Folke, C., Halpern, B.S., Jackson,

J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., Sala, E., Selkoe, K.A., Stachowicz, J.J.,

Watson, R., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science

314, 787-790.

Yang, S.L., 1998. The role of Scirpus marsh in attenuation of hydrodynamics and retention of

fine sediment in the Yangtze Estuary. Estuarine, Coastal and Shelf Science 47, 227-233.

Ysebaert, T., Herman, P.M.J., 2002. Spatial and temporal variation in benthic macrofauna

and relationships with environmental variables in an estuarine, intertidal soft-sediment

environment. Marine Ecology Progress Series 244, 105-124.

Zettler, M.L., Schiedek, D., Bobertz, B., 2007. Benthic biodiversity indices versus salinity

gradient in the southern Baltic Sea. Marine Pollution Bulletin 55, 258-270.

Zühlke, R., Reise, K., 1994. Response of macrofauna to drifting tidal sediments. Helgolander

Meeresuntersuchungen 48, 277-289.

171

Annex 1. The literatures on Seagrass in Vietnam

Aryuthaka C., M. Fortes, Nguyen Van Tien, S. Satumanatpan, H. Malikuswovo, K. Sour,

X.P. Huang , K. Passfield, 2004: Seagrass in the South China Sea. UNEP /GEF/SCS

Technical publication No 3, 12 pages.

Chu The Cuong, Lang Van Keng, Nguyen Mai Luu, Tran Manh Ha, 2008. Macrobenthos

resource in seagrass beds in Phu Quoc district, Kien Giang province, UNEP/

GEF/SCS report (in Vietnamese: Nguồn lợi động vật đáy trong thảm cỏ biển Phú

Quốc, tỉnh Kiên Giang).

Do Cong Thung, 1998. Macrobenthos in seagrass beds in Quang Ninh. Annual collection of

works on Marine Environment and Resources. Publishing House for Science and

Technology, Hanoi, V, p. 103- 198 (in Vietnamese: Động vật đáy trong thảm cỏ biển

khu vực Quảng Ninh. Tuyển tập Tài nguyên và Môi trường Biển).

Do Cong Thung, 1999. Macrobenthos in seagrass beds in Lap An lagoon (Thua Thien Hue). .

Annual collection of works on Marine Environment and Resources. Publishing House

for Science and Technology, Hanoi, VI, p. 293-250 (in Vietnamese: Động vật đáy

trong thảm cỏ biển đầm Lập An (Thừa Thiên - Huế). Tuyển tập Tài nguyên và Môi

trường Biển).

Fortes M., Nguyen Van Tien, S. Satumanatpan, H. Malikuswovo, K. Sour, X.P. Huang, The

UNEP/GEF Seagrass demonstration sites in South China Sea: Milestones in Seagrass

research and coastal resource management in Southeast Asia. Selected papers of the

NaGISA World Congress. Kyoto Univ. Japan, May, 2007: p.125-137.

Gacia, E., Duarte, C.M., Marba, N., Terrados, J., Kennedy, H., Fortes, M.D., Tri, N.H., 2003.

Sediment deposition and production in SE-Asia seagrass meadows. Estuarine, Coastal


172

Huong, T.T.L., Vermaat, J.E., Terrados, J., Van Tien, N., Duarte, C.M., Borum, J., Tri, N.H.,

2003. Seasonality and depth zonation of intertidal Halophila ovalis and Zostera

japonica in Ha Long Bay (northern Vietnam). Aquatic Botany 75, 147-157.

Mai Ky Vinh, Tran Chinh Khuong and Symington, K., 2005. Application of GIS and remote

sensing for mapping seagrass distribution in Kien Giang province. National

Workshop on Protection Environment and Aquatic Resources, Hai Phong, 14-

15/1/2005 (in Vietnamese: Ứng dụng công nghệ GIS và ảnh viễn thám để xây dựng

bản đồ phân bố cỏ biển ở vùng ven biển tỉnh Kiên Giang. Hội thảo toàn quốc Bảo vệ

Môi trường và nguồn lợi thủy sản).

Marba, N., Duarte, C.M., Terrados, J., Halun, Z., Gacia, E., Fortes, M.D., 2010. Effects of

seagrass rhizospheres on sediment redox conditions in SE Asian coastal ecosystems.


Nguyen Huu Dai, 2005. Restoration and protection of seagrass - model of management and

sustainable development in coastal areas. National Workshop on Protection

Environment and Aquatic Resources, Hai Phong, 14-15/1/2005 (in Vietnamese: Phục

hồi và bảo vệ các thảm cỏ biển – Mô hình quản lý và phát triển bền vững vùng biển

ven bờ. Hội nghị toàn quốc về Môi trường và Bảo vệ nguồn lợi Thủy sản).

Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, Nguyen Xuan Vy, 2002. The decline of

seagrass beds in Khanh Hoa and their resilience. The collection papers of National

Workshop on South China Sea, Nha Trang 16-19/9/2002, p. 359-368 (in Vietnamese:

Sự suy giảm các thảm cỏ biển ở Khánh Hòa và khả năng phục hồi chúng. Tuyển tập

Báo cáo Khoa học hội nghị Khoa học toàn quốc về Biển Đông).

Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, Nguyen Xuan Vy, 2006. Chapter VIII

Solutions to protect and restore seagrass ecosystems. Summary of the Project:

"Research solutions to protect and restore coral reef ecosystems, seagrass and

pollution environment" in the Collection of the main results of the sea survey program

and technology application. Vol II. Code KC.09, p. 149-168 (in Vietnamese: Các giải

pháp bảo vệ và phục hồi hệ sinh thái cỏ biển. Tóm tắt Báo cáo Đề tài: “Nghiên cứu

giải pháp bảo vệ, phục hồi hệ sinh thái rạn san hô, cỏ biển và khắc phục ô nhiễm môi

173

trường tự sinh” trong tuyển tập các kết quả chủ yếu của chương trình Điều tra cơ bản

và nghiên cứu ứng dụng công nghệ Biển. Quyển II. Mã số KC.09).

Nguyen Thi Thu và Nguyen Huu Phung, 2002. Young-of-the-year fish species composition

in Tam Giang – Cau Hai lagoon. Annual collection of works on Marine Environment

and Resources. Publishing House for Science and Technology, Hanoi, IX, p. 283-294

(in Vietnamese: Thành phần nguồn giống cá trong đầm phá Tam Giang-Cầu Hai.

Tuyển tập Tài nguyên và Môi trường Biển).

Nguyen Thi Thu và Nguyen Manh Ha, 2008. Young-of-the-year fish, crab and shrimp in

seagrass beds in Phu Quoc Island. Report of UNEP/GEF/SCS project (in Vietnamese:

Nguồn giống tôm, cua, cá trong thảm cỏ biển Phú Quốc. Báo cáo chuyên đề - Điểm

trình diễn San hô, Cỏ biển đảo Phú Quốc thuộc Dự án UNEP/GEF/SCS).

Nguyen Thi Thu, 2001. Young-of-the-year fish, crab and shrimp in seagrass beds in Lang Co.


for Science and Technology, Hanoi, VIII, p. 211-219 (in Vietnamese: Nguồn giống

tôm, cua, cá trong thảm cỏ biển Lăng Cô. Tuyển tập Tài nguyên và Môi trường Biển).

Nguyen Van Quan, 2006. Preliminary studies on fishery resources in the seagrass beds in Phu

Quoc Island, Kien Giang province. Report Workshop on Science, Technology and

Maritime Economy for the industrialization and modernization of the country, Do

Son, Hai Phong, 25-26/10/2006, p. 126-135 (in Vietnamese: Bước đầu nghiên cứu

nguồn lợi cá trong thảm cỏ biển đảo Phú Quốc, tỉnh Kiên Giang. Báo cáo Hội thảo

Khoa học, công nghệ và kinh tế biển phục vụ sự nghiệp công nghiệp hóa và hiện đại

hóa đất nước).

Nguyen Van Tien, Le Thanh Binh, Nguyen Huu Dai, Tran Hong Ha, Tu Thi Lan Huong, Do

Nam, Dam Duc Tien, 2004. Towards managing seagrass ecosystems Vietnam.

Publishing House for Science and Technology, Hanoi, 132 p. (in Vietnamese: Tiến tới

quản lí hệ sinh thái cỏ biển Việt Nam).

Nguyen Van Tien, 1996. Data on species composition and distribution of seagrass in Thua

Thien-Hue-Da Nang coastal. Annual collection of works on Marine Environment and

Resources. Publishing House for Science and Technology, Hanoi, III, p. 263-270 (in

174

Vietnamese: Dẫn liệu về thành phần loài và phân bố của cỏ biển vùng ven biển Thừa

Thiên Huế-Đà Nẵng. Tuyển tập Tài nguyên và Môi trường biển).

Nguyen Van Tien, 1998. Preliminary studies in seagrass ecosystems Journal of Vietnamese

Environment, Association for the Protection of Nature and Environment of Vietnam.

Publishing House for Science and Technology, Hanoi, II, p. 41-50 (in Vietnamese:

Bước đầu nghiên cứu hệ sinh thái cỏ biển ở Việt Nam Tạp chí Môi trường, Hội Bảo

vệ thiên nhiên và Môi trường Việt Nam).

Nguyen Van Tien, 1998. Species composition and distribution of seagrass in Viet Nam.

Abstracts 3-rd International seagrass biology Workshop (ISBW). Quezon city,

Philippines, 19-26 Apr. 1998: p.88.

Nguyen Van Tien, 1998. Species composition and distribution of seagrasses in Quang Ninh.


for Science and Technology, Hanoi, V, p. 183-190 (in Vietnamese: Thành phần loài

và phân bố của cỏ biển ở Quảng Ninh. Tuyển tập Tài nguyên và Môi trường biển).

Nguyen Van Tien, 1998. Seagrass ecosystem management approach in Vietnam. Annual

collection of works on Marine Environment and Resources. Publishing House for

Science and Technology, Hanoi, V, p.: 220-229 (in Vietnamese: Tiếp cận quản lý hệ

sinh thái cỏ biển ở Việt Nam. Tuyển tập Tài nguyên và Môi trường biển).

Nguyen Van Tien, 1999. Data on species composition and distribution of seagrass in

Vietnam. Annual collection of works on Marine Environment and Resources.

Publishing House for Science and Technology, Hanoi, VI, p. 192-207 (in Vietnamese:

Dẫn liệu về thành phần loài và phân bố của cỏ biển Việt Nam. Tuyển tập Tài nguyên

và Môi trường biển).

Nguyen Van Tien, 1999. Seaweed-seagrass research situation in Vietnam. Publishing House

for Science and Technology, Hanoi, VI, p. 169-181 (in Vietnamese: Tình hình nghiên

cứu rong-cỏ biển ở Việt Nam. Tuyển tập Tuyển tập Tài nguyên và Môi trường biển).

175

Nguyen Van Tien, 2000. Seagrass of Vietnam. Journal of Biology Today's the biology

Vietnam, VI, 3(21)/2000, p. 44-45 (in Vietnamese: Cỏ biển Việt Nam. Tạp chí Sinh

học Ngày nay của Hội các ngành sinh học Việt Nam)

Nguyen Van Tien, 2001. Seagrass of Hai Phong. Hai Phong Journal of Science and

Economy, 5, p. 4-5 (in Vietnamese: Cỏ biển Hải Phòng. Tạp chí Khoa học & Kinh tế

Hải Phòng).

Nguyen Van Tien, 2003. Seagrass survey methods (Chapter XI). In the book "Handbook of

investigation and monitoring of biodiversity." WWF Indochina Programme. Transport

Publishing House Company Limited, p. 333-352 (in Vietnamese: Phương pháp điều

tra Cỏ Biển (chương XI). Trong sách Sổ tay hướng dẫn điều tra và giám sát đa dạng

sinh học. Chương trình WWF Đông dương. Nhà in Giao thông Vận tải).

Nguyen Van Tien, 2003. Researches in distribution of Vietnamese seagrasses. Publishing

House for Science and Technology, Hanoi, X, p. 66-88.

Nguyen Van Tien, 2004. Data on seagrasses in Quang Ninh and Thua Thien Hue province

waters. Proceedings of the Vietnam-Italy scientific conference: Biodiversity

conservation in the coastal zone of Vietnam. Publishing House of Vietnam National

University, Hanoi, p.107-112.

Nguyen Van Tien, 2005. Propose measures to protect seagrass resources in Tam Giang-Cau

Hai lagoon. Proceedings National Conference of Thua Thien-Hue, 24-26/12/2005 (in

Vietnamese: Đề xuất các giải pháp bảo vệ nguồn lợi cỏ biển đầm phá Tam Giang-Cầu

Hai. Kỉ yếu Hội thảo quốc gia về đầm phá Thừa Thiên-Huế).

Nguyen Van Tien, 2005. Seagrass ecosystems management in Phu Quy Island, Binh Thuan

province. Proceedings National Conference on Research in Life Sciences. Hanoi,

3/11/2005, Publishing House for Science and Technology, p. 1098-1101 (in

Vietnamese: Quản lí hệ sinh thái cỏ biển đảo Phú Quí (Bình Thuận). Kỉ yếu Hội nghị

toàn quốc về nghiên cứu Cơ bản trong Khoa học Sự sống).

Nguyen Van Tien, 2005. Reasonable using of seagrass in Vietnam. Proceedings National

Conference on Environmental Protection and Aquatic Resources, Hai Phong 14-

176

15/01/2005. Agriculture Publishing House, Hanoi: p. 426-433 (in Vietnamese: Sử

dụng hợp lí hệ sinh thái cỏ biển Việt Nam. Kỉ yếu Hội nghị toàn quốc về Bảo vệ Môi

trường và Nguồn lợi Thuỷ sản).

Nguyen Van Tien, 2005. Degradation of seagrass ecosystems in Vietnam and propose

mitigation measures. Proceedings National Conference on Environment. Hanoi, 21-

22/4/2005: p. 840-844 (in Vietnamese: Suy thoái hệ sinh thái cỏ biển ở Việt Nam và

đề xuất các giải pháp giảm thiểu. Kỉ yếu Hội nghị Môi trường toàn quốc. Hà Nội).

Nguyen Van Tien, 2005. Seagrass ecosystem degradation and propose a number of

management tasks. Journal of Environmental Protection, Issue 1+2/2005, p. 41-43 (in

Vietnamese: Suy thoái hệ sinh thái cỏ biển và đề xuất một số nhiệm vụ quản lí. Tạp

chí Bảo vệ Môi trường).

Nguyen Van Tien, 2006. Vietnam seagrass ecosystems. Journal of Fisheries, 11, p. 33-36 (in

Vietnamese: Hệ sinh thái cỏ biển Việt Nam. Tạp chí thuỷ sản).

Nguyen Van Tien, 2009. Some issues in marine ecosystem management in Vietnam.

Proceedings National Conference on Marine and sustainable development. Do Son,

Hai Phong on 28 - 29/11/2009. Publishing House for Science and Technology, Hanoi,

p. 152 – 157 (in Vietnamese: Một số vấn đề về quản lí hệ sinh thái Cỏ biển Việt Nam.

Kỉ yếu Hội nghị toàn quốc về Sinh vật biển và phát triển bền vững).

Nguyen Van Tien, Dam Duc Tien, 1996. The seagrass beds from some remote islands in

Vietnam. Abstracts 3th international congress on the Marine Biology of the South

China Sea (PACON). 8 Oct.-1 Nov. 1996, the University of Hong Kong.

Nguyen Van Tien, Dam Duc Tien, 1997. Species composition and distribution of seagrass in

Con Dao. Publishing House for Science and Technology, Hanoi, IV, p. 263-269 (in

Vietnamese: Thành phần loài và phân bố của cỏ biển Côn Đảo. Tuyển tập Tài nguyên

và Môi trường biển).

Nguyen Van Tien, Dam Duc Tien, 2000. Preliminary studies on seagrass in the Spratly

Islands. National Conference Proceedings biology: basic research problems in

biology. Hanoi, 7-8/10/2000, p. 293-296 (in Vietnamese: Bước đầu nghiên cứu cỏ

177

biển ở quần đảo Trường Sa. Kỉ yếu Hội nghị Sinh học Quốc gia: Những vấn đề

nghiên cứu cơ bản trong Sinh học).

Nguyen Van Tien Dang Ngoc Thanh, 2003. Ecological characteristics of seagrass beds

(Chapter 12). In the book "East Sea", Volume IV, Marine Biology and Ecology,

Vietnam National University Publishing House, Hanoi, p. 254-267 (in Vietnamese:

Đặc trưng Sinh thái các bãi cỏ biển (chương 12). Trong sách “Biển Đông”, tập IV,

Sinh vật và Sinh thái Biển).

Nguyen Van Tien Dang Ngoc Thanh, Nguyen Huu Dai, 2002. Vietnam Seagrass. Species

composition, distribution, ecology, biology. Publishing House for Science and

Technology, Hanoi, 165 p (in Vietnamese: Cỏ Biển Việt Nam. Thành phần loài, phân

bố, sinh thái, sinh học).

Nguyen Van Tien, Le Thi Thanh, 2001. Data on red algae (Hypnea) in west coast of Tonkin

Bay. Annual collection of works on Marine Environment and Resources. Publishing

House for Science and Technology, Hanoi, VIII, p. 197-210 (in Vietnamese: Dẫn liệu

về rong đông (Hypnea) bờ Tây vịnh Bắc Bộ. Tuyển tập Tài nguyên và Môi trường

biển).

Nguyen Van Tien, Le Thi Thanh, 2003. Some research results of seagrass recovery by

growing in Ha Long Bay, Quang Ninh province Annual collection of works on

Marine Environment and Resources. Publishing House for Science and Technology,

Hanoi, X, p. 262-268 (in Vietnamese: Một số kết quả nghiên cứu trồng phục hồi cỏ

biển ở vịnh Hạ Long (Quảng Ninh). Tuyển tập Tài nguyên và Môi trường biển).

Nguyen Van Tien, Le Thi Thanh, 2007. Seagrass ecosystems management in Phu Quoc

Island, Kien Giang province. Proceedings National Conference on Research in Life

Sciences, Qui Nhon in 10/8/2007. Publishing House for Science and Technology,

Hanoi, p. 603 – 606 (in Vietnamese: Quản lí hệ sinh thái Cỏ biển ở đảo Phú Quốc,

tỉnh Kiên Giang. Kỉ yếu Hội nghị toàn quốc Nghiên cứu cơ bản trong Khoa học sự

sống).

Nguyen Van Tien, Le Thi Thanh, 2008. Firstly propose on protect areas of seagrass in

Vietnam. Journal of Agriculture and Rural Development, March/2008, p. 12-18 (in

178

Vietnamese: Bước đầu đề xuất các khu bảo tồn cỏ biển ở Việt Nam. Tạp chí Nông

nghiệp và Phát triển Nông thôn).

Nguyen Van Tien, Le Thi Thanh, Tu Thi Lan Huong, 2007. Seagrass ecosystems

management in Quang Nam province. Proceedings National Conference on Ecology

and Biological Resources, Hanoi in 26/10/2007, Agriculture Publishing House, p.

141-147 (in Vietnamese: Quản lí hệ sinh thái Cỏ biển ở tỉnh Quảng Nam. Kỉ yếu Hội

nghi quốc gia về Sinh thái và Tài nguyên Sinh vật).

Nguyen Van Tien, Le Thi Thanh, Tu Thi Lan Huong, 2002. Seaweed in Phu Quoc Island,

Kien Giang. Annual collection of works on Marine Environment and Resources.

Publishing House for Science and Technology, Hanoi, IX, p. 189-194 (in Vietnamese:

Cỏ biển đảo Phú Quốc, tỉnh Kiên Giang. Tuyển tập Tài nguyên và Môi trường biển).

Nguyen Van Tien, Nguyen Huu Dai, 2002. Studies on seaweeds in Viet Nam. Annual

collection of works on Marine Environment and Resources. Publishing House for

Science and Technology, Hanoi, p. 67-75.

Nguyen Van Tien, Nguyen Huy Yet, Le Thi Thanh, 2003. First data on resource of

pharmaceuticals from marine life in Vietnam. Conference Proceedings First National

Medicine. Hanoi 11-12/3/2003, p. 95-99 (in Vietnamese: Dẫn liệu bước đầu về nguồn

dược liệu từ sinh vật biển Việt Nam. Kỉ yếu Hội nghị Dược liệu toàn quốc lần thứ

nhất).

Nguyen Van Tien, Nguyen Xuan Hoa, 2008. Seagrass resources in Thi Nai lagoon, Binh

Dinh province. Annual collection of works on Marine Environment and Resources.

Publishing House for Science and Technology, Hanoi, XIII, p. 194-203 (in

Vietnamese: Nguồn lợi thảm cỏ biển đầm Thị Nại, tỉnh Bình Định. Tuyển tập Tài

nguyên và Môi trường biển).

Nguyen Van Tien, Tu Thi Lan Huong, 2005. Ecology and management measures to restore

seagrass in Lap An lagoon, Thua Thien-Hue. Proceedings National Conference on

Environmental Protection and Aquatic Resources. Hai Phong 14-15/01/2005.

Agriculture Publishing House, p. 547-554 (in Vietnamese: Giải pháp sinh thái và quản

179

lí nhằm phục hồi thảm cỏ biển đầm Lập An, Thừa Thiên Huế. Kỉ yếu Hội nghị toàn

quốc về Bảo vệ Môi trường và Nguồn lợi thuỷ sản).

Nguyen Van Tien, Tu Thi Lan Huong, 2008. Seagrass research methods. Publishing House

for Science and Technology, Hanoi, 102 pages (in Vietnamese: Phương pháp nghiên

cứu cỏ biển).

Nguyen Van Tien, Vo Si Tuan, Nguyen Huy Yet, 1997. The results of seaweeds and seagrass

study in Spratly Archipelago during RP-VN. JOMSRE-SCS96. Proceeding of

Scientific Conference on RP-VN JOMSRE-SCS 96. Ha Noi, Vietnam 22-

23/April/1997: p.102-113.

Nguyen Xuan Hoa and Tran Cong Binh, 2002. Monitoring seagrass and dungongs in the Con

Dao 1998-2002. The collection of works in South China Sea Science Conference

Report-2000, Nha Trang, 16-19/9/2002. Agriculture Publishing House, p. 626-637 (in

Vietnamese: Giám sát thảm cỏ và dungongs ở Côn Đảo giai đoạn 1998-2002. Tuyển

tập Báo cáo Hội nghị Khoa học Biển Đông-2000).

Nguyen Xuan Hoa, Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, 1999. The seagrass

beds in South of Vietnam. Anthology of Science Report of 4th Science and

Technology Conference on National Sea, p. 967-974 (in Vietnamese: Các thảm cỏ

biển phía nam Việt Nam. Tuyển tập Báo cáo Khoa học, Hội nghị Khoa học Công

nghệ Biển toàn quốc lần thứ IV. Trang: 967-974.)

Nguyen Xuan Hoa, Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, 2000. Study the

variation of seagrass Enhalus acoroides (L.f.) Royle, Thalassia hemprichii (Ehrenb.)

Asch., Cymodocea serrulata (R.Brown) Asch. and Magn. in Khanh Hoa province.

Anthology of Science Report of Science and Technology Conference on National Sea,

2000, p. 179-180 (in Vietnamese: Nghiên cứu sự biến động các thảm cỏ biển Enhalus

acoroides (L.f.) Royle, Thalassia hemprichii (Ehrenb.) Asch., Cymodocea serrulata

(R.Brown) Asch. and Magn. ở vùng biển ven bờ tỉnh Khánh Hòa. Tuyển tập Báo cáo

Khoa học Hội nghị Khoa học Biển Đông).

Tran Duc Thanh Dinh Van Huy, Nguyen Van Tien, Nguyen Huy Yet, the 1998. First results

using satellite imagines in study distribution of seagrass, coral and seaweed in central

180

Vietnam. Annual collection of works on Marine Environment and Resources.

Publishing House for Science and Technology, Hanoi, V, p. 94-102 (in Vietnamese:

Kết quả bước đầu sử dụng tài liệu ảnh vệ tinh nghiên cứu phân bố cỏ biển, rong biển

và san hô ở miền Trung Việt Nam. Tuyển tập Tài nguyên và Môi trường Biển).

Tu Thi Lan Huong and Nguyen Van Tien, 2005. Some ecological characteristics of the

seagrass beds of Kien Giang province. 1st National Workshop on Biology and

Ecology in Hanoi, p. 758-763 (in Vietnamese: Một số đặc điểm sinh thái của các

thảm cỏ biển tỉnh Kiên Giang. Hội nghị Sinh học- Sinh thái Quốc gia lần I tại Hà

Nội).

Tu Thi Lan Huong and Nguyen Van Tien, 2008. Monitoring of seagrass resources in Bai Bon

Beach, Phu Quoc Island. Report of the project: "Prevent the trend of environmental

degradation in South China Sea and Gulf of Thailand" (in Vietnamese: Giám sát

nguồn lợi cỏ biển ở điểm trình diễn Bãi Bổn, đảo Phú Quốc. Báo cáo thuộc dự án:

“Ngăn chặn xu thế suy thoái môi trường Biển Đông và vịnh Thái Lan).

Tu Thi Lan Huong, 2005. SeagrassNet: some results of seagrass monitoring in The Vang

Island, Quang Ninh province. Annual collection of works on Marine Environment and

Resources. Publishing House for Science and Technology, Hanoi, IX, p. 189-195 (in

Vietnamese: SeagrassNet: một số kết quả giám sát cỏ biển đảo Thẻ Vàng, tỉnh Quảng

Ninh. Tuyển tập Tài nguyên và Môi trường Biển).

Tu Thi Lan Huong, 2006. An overview for seagrass managers. Journal of Environmental

Protection Agency, 10, p. 19-20 (in Vietnamese: Một cái nhìn tổng quan về cỏ biển

cho các nhà quản lý. Tạp chí của Cục Bảo vệ Môi trường).

Tu Thi Lan Huong, 2007. The role and value of seagrass ecosystems. Vietnam Sea Magazine,

3, p. 31-32 (in Vietnamese: Vai trò và giá trị hệ sinh thái cỏ biển. Tạp chí Biển Việt

Nam).

Tu Thi Lan Huong, 2008. Comparing the seasonal fluctuations of the populations of seagrass

Halophila ovalis in Vietnam coastal. 2nd National Scientific Conference on ecological

and biological resources, Hanoi 26/10/2007, p. 437-441 (in Vietnamese: So sánh sự

biến động theo mùa của các quần thể cỏ biển Halophila ovalis ở vùng triều ven biển

181

Việt Nam. Hội nghị Khoa học Toàn quốc lần thứ 2 về sinh thái và tài nguyên sinh

vật).

Tu Thi Lan Huong, Nguyen Van Tien, 2010. Seagrass in Vietnam with the challenges of

climate change. (www.kienviet.net:11/12/2010) (in Vietnamese: Thảm cỏ biển Việt

Nam với những thách thức trong điều kiện biến đổi khí hậu).

Documents

L’UNIVERSITÉ BORDEAUX 1ori-oai.u-bordeaux1.fr/pdf/2012/DO_VAN_TU_2012.pdf · 2013. 1. 15. · these prÉsentÉe À l’universitÉ bordeaux 1 ecole doctorale sciences et environnements