Toward Harm or$zation b etweenDevelopment and Environmental

Conservation in Biolargiial Production

zB-29 February zoo8

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Habitat suitability model of Javan Hawk-Eagle (Spizuetus burtelsi)

Using Logistic and Autologistic Regression Models in West Java, lndonesia

Syartiniliar), Satoshi Tsuyuki2) and Lilik Budi Prasetyo3)

1) Graduate School ofAgricultural and Life Sciences, The University of Tokyo

1-l-1 Yayoi, Bunkyo-ku, Tokyo I l3-8657, Japan

e-mail : lia@fr.a.u-tokyo. ac j p

2) Graduate School ofAgricultural and Life Sciences, The University of Tokyo

l-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan

e-mail : tsuyuki@fr.a.u-tokyo.ac j p3) Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry Bogor Agricultural

University (IPB), Kampus IPB Darmaga, PO.Box 168, Bogor 16001,Indonesia

e-mail : lbpras@indo.net.id

Abstract

Little is known about modeling the habitat suitability of the Javan Hawk-eagle (Spizaetus

bartelsi) based on predictions formulated from habitat requirements in West Java or

throughout Java Island. This paper proposes a new approach to develop habitat suitability

models of Javan Hawk-Eagle (JHE) using the application of logistic regression (LR) and

autologistic regression (ALR) coupled with RAMAS GIS, and creating pseudo-absences data

using a normalized difference vegetation index (l.tDVI) from remote sensing data. Habitat

requirements of 11 nest sites in Gunung Gede Pangrango National Park (TNGP), West Java,

Indonesia were analyzed and quantified, and the model was validated in southern parts of

West Java. The final LR model was used as the starting point for fitting ALR models that

account for spatial autocorrelation through the addition of an autocovariate variable using

several different neighborhood sizes ranging from 450 m (15*15 moving window size) to

1500 m (50*50 moving window size) using 300 m interval. The best model was the ALR

model with a 1500 m autocovariate (ALR_50) that agreed with the distance between nests in

TNGP and the mean home range size in Java. This model showed a significant increase in

overall accuracy and successfully removed misclassified pixels.

Keywords: Autocovariate, GIS, Remote sensing, spatial autocorrelation, species occurrence,

Spizaetus bartelsi

46

Introduction

The Javan Hawk-Eagle (Spizaelus bartelsi) is an endemic raptor and evergreen-forestspecialist in the remaining original natural forests of Java Indonesia (Whitten et al., 1996).

The Javan Hawk-Eagle (JHE) is included in the IUCN Red List of Threatened Species(CITES Appendix 2) as one of the world's rarest and most endangered raptor categories.

Small population size, severe habitat loss, forest fragmentation, and illegal hunting have allcontributed to the "endangered" status of this species (Birdlife International,2000,2001).Presidential Decree (KEPPRES) No. 411993 declared the JHE, which resembles the

mythological Garuda bird displayed on the Indonesian coat of arms. to be Indonesian'snational rare animal.

Recently, the increased availability of remote-sensed data, GIS, and advances in statisticalcomputation capabilities has allowed the development of more powerful techniques in habitatmodeling (Bailey et aI.,2002). Generalized linear and additive models (GLM and GAM),implemented within a geographical information system (GIS), have become very popular forpredicting species'distributions (Guisan et a|.,2002; Zaniewski et a\.,2002; Engler et al.,2004).

Spatial autocorrelation is frequently encountered in ecological data, and many ecologicaltheories and models implicitly assume an underlying spatial pattern in the distribution oforganisms and their environment (Legendre and Fortin, 1989). Typically, species abundances

are positively autocorrelated, such that nearby points in space tend to have more similarvalues than would be expected by random chance. Furthermore, the probability of JHEoccurrence in one pixel might not be independent of the likelihood of whether JHEs occur in aneighboring pixel. This will generate spatial autocorrelation that cannot be modeledsatisfactorily by environmental covariates. Thus, incorporating spatial autocorrelation(autocovariate) into logistic regression models could solve this problem, which would resultin model improvements such as increased predictive accuracy and model versatility (Augustinet a|.,1996; Koutsias, 2003; Piorecky et a\.,2006).

Although some studies have been conducted on JHE breeding biology (T.{ijman et a\.,2000),density, age-structure and population numbers (van Balen et a1.,2001), home range size

(Gjershaug et aL.,2004), habitat use and habitat segregation Qrtrijman and van Balen, 2003;Nijman, 2004), there is little information on modeling the habitat suitability of JHE based onhabitat requirements in West Java or throughout Java island. The lack of such informationrepresents a gap in our knowledge of habitat conservation for this raptor species. Therefore,this study aimed 1) to develop habitat suitability models of JHE based on environmentalvariables of the current nest distribution from 1996-2006 using logistic regression (LR), 2) toexamine the use of autologistic regression (ALR) in order to improve the accuracy of the

logistic regression (LR) model, and 3) to validate the habitat suitability model by testing it in

southern parts of West Java.

Methods

Study area

This study was conducted in the Gunung Gede-Pangrango National Park (TNGP) and its

surrounding areas, and the model was validated in southern parts of West Java, Indonesia

(Figure 1). TNGP (latitude 56o42',30" - S6'52',10", longitude E106'49',35" - 810J"2',05",

encompassing2l,9T6 ha) was founded on March 6, 1980 as one of the first five national parks

in Indonesia which contains one of Java's few remaining larger habitat areas for JHE. TNGP

was named for two volcanoes in the park: Mount Gede (2,958 m) and Mount Pangrango

(3,019 m). TNGP supports imporlant submontane and montane tropical rainforests, including

some of the best remaining Javan montane forests. The nearby Telaga Warna Nature Reserve

surrounds a small lake and the reserve proper covers afi area of 368.25 ha.

Figure 1. Study area.

The average annual temperature varies between 22'C and 26.5"C. The annual mean rainfall

ranges from 2,000 mm up to 4,500 mm, and there is little seasonal variation with only a slight

decrease in rainfall from May-September (Metereological and Geophysical Agency ofIndonesia (BMG), 1993-2002 periods). Direct observations were conducted in

September-October 2005 and September 2006. Field surveys were conducted between 09:00

and 12:00 hours during clear days and about 300 m from the nest tree to avoid disturbance to

the JHE. Interviews were also carried out with local people who live nearby Gunung Baud

(Telaga Warna Nature Reserve) and key individuals from the NGOs, The Indonesian Institute

of Sciences (LIPI), and TNGP concerning the conservation of the JHE.

4B

Presence nests

A total of 11 nest sites and approximately 44 individuals were recorded in 1996-2006. Sixnests were located in TNGP, three nests were found in Telaga Warna Nature reserve, and twonests were noted in a protection forest. Data regarding the JHE's nests from 1996 to 2006were provided by Raptor Conservation Society (Suparman, 2005), a local NGO focusing on

monitoring JHE's nest sites in TNGP and its surrounding areas; these data are not publiclyavailable.

Absence nests/ pseudo-absence nests

In this study, no absence data were available. Thus, in order to use GLM when no absence

data are available, one approach is to generate 'pseudo-absences' (Zaniewski et ctl., 2002;Engler et al., 2004; Pearce and Boyce , 2006) and to use them in the model as absence data forthe relevant species. Many studies have sought to apply presence-absence techniques topresence-only data by generating pseudo-absence data from background areas from whichspecies dataare missing (Stockwell and Peterson,2002; Zaniewski et a\.,2002; Engler et al.,2004; Pearce and Boyce,2006).In this paper, we proposed a new approach to determine the

pseudo-absences data through random selection of the pixels within the 2 km from forestbuffer area. This buffer area was generated from forest boundaries, which was obtained bythresholding the normalized difference vegetation index O,IDVI) using RS data. It was

assumed that the area was still covered by vegetation, but was not suitable as JHE nesting site,

and JHE presence had never been reported.

Environmental variables and GIS coverage

In general, environmental variables were chosen based on perceived and documented

importance to JHE. However, van Balen et al. (2001) determined density of JHE using the

following parameters: altitude, climate, ruggedness and fragmentation. In this study weselected several environmental variables (covariates) i.e. terrain factor (slope (SLP), elevation(ELV)), vegetation cover O{DVI), proximity to a water source (DRV), proximity to humanactivity (distance to the nearest main road (DRD) and distance to the nearest settlement(DRS)), and five autocovariates to address the quantification of JHE distribution. The terainfactor is similar to the altitude and ruggedness parameters in van Balen's study. The proximityto a water source and proximity to human activity parameters were added because of thepresent nest-site condition is commonly located nearby small rivers and settlements.

Nine sheets of digital topographic maps scale 1:25,000 generated by the NationalCoordination Agency for Surveys and Mapping, Indonesia were used. A digital elevation

model (DEM) was generated from the contour data by a triangulated irregular networkcreation. For the southern parts of West Java, shuttle radar topography mission (SRTM)digital terain elevation data was used. Raster layers of slope (SLP), aspect (ASP) and

49

elevation (ELV) were derived from DEM using TNT mips 7.0 software. Layers of slope and

aspect were furlher combined to generate a Sun Index (SI) layer according to the equation

below (Wilson et aL.,2003; Gibson et a|.,2004) as follows

SI: cos(asPect) x tan(sloPe) x 100

The SI was used on the premise that the amount of solar radiation received at a site will be

influenced by the steepness and orientation of the landscape at the site. LANDSAI ETM+

2001112122 scene 122165 (path/row) and LANDSAT ETM+ 2003llll9 scene 121165

(path/row) were used to calculate the NDVI for model development and model validation.

The normalized difference vegetation index (NDVI) is an index that provides a standardrzed

method of comparing amount of vegetation between satellite images. NDVI can be used as an

indicator of relative biomass and greenness (Chen, 1998). The formula to calculate NDVI is:

NDVI: (near IR band - red band) / (near IR band t red band)

NDVI ranges from -1.0 to 1.0, but for vegetation, the NDVI typically ranges between 0.1 and

0.7. Higher index values are associated with higher levels of healthy vegetation cover,

whereas clouds and snow will cause index values to approach zero, making it appear as if the

vegetation is less green.

Roads, rivers and settlement data were obtained from digital form generated by the National

Coordination Agency for Surveys and Mapping, Indonesia. Distance to the nearest main road

(DRD), river/stream (DRV) and settlement (DRS) were measured from a distance raster, which

was generated from rasterizing the vector data and overlaid with the nest-site locations. A11

these layers were prepared as raster data with a pixel size 30*30 m for model creation and

90*90 m for model validation.

Logistic and autologistic regression model creation

Model development began with a logistic regression using SPSS and subsequent input of the

model in RAMAS GIS Spatial data (Akcakaya and Root, 2002) to produce a habitat

suitability model. The habitat suitability value ranges from 0.0 (not suitable) up to 1.0

(suitable). We use a threshold value of 0.5 to identifu an area where a subpopulation may

survive. The formulas used to express the ordinary LR and the ALR functions are given

below.

Ordinary LR function: Pi=

'. *r[-[r, .Ir,',,)]

D:I,r-

L\ l' ))

ALR function:

50

where P; is probability, x7 is covariate, i is pixel, 00, F,, c are estimated coefficients, and fr is

the number of covariates. The hnal LR models were then used as starting points for fittingALR models. ALR models account for spatial autocorrelation through the addition of an

autocovariate variable. Autocovariate variables were calculated for several different

neighborhood sizes, i.e.,450 m,750 m, 1,050 m, 1,350 m, and 1,500 mthatwere estimated

using a moving window of 15* 15,25*25,35*35, 45*45, and 50+50 pixels, respectively. The

autocovariate variable can be estimated from predicted probabilities of the occupation

estimated by an ordinary logistic regression model (Augustin et al., 1996; Osborne et al.,

2001; Koutsias, 2003; Piorecky

Autocovariate was calculated as:Lt,,,,p and Prescott, 2006)'

ltTOCO| , = '-':I,,,ui=l

rvhere wi,being the inverse of the Euclidean distance between i and j, whilepj represents the

predicted probability estimated by LR. This autologistic term was entered into the regression

and the mode was rerun. In image-processing terms, the autologistic term acts as a smoothing

filter, removing isolated pixels and consolidating habitat patches defined as suitable (Osborne

et a|.,2001).

)Iodel selection

\{odel selection was based on the following criteria: 1) variability accounted for in the model'Nagelkerk. R'' 1P.rg et a\.,2002;Piorecky and Prescott,2006),2) predictive power (Kunkel

and Pletscher, 2000; Piorecky and Prescott,20061, Ottaviani et aL.,2004). Predictive power

was assessed with the classification tables and a receiver operating characteristic (ROC) plots.

Classification tables display the number of correct and incorrect predictions made for the

current data sets, using a cut-off value of 0.5. The probability values greater than the cut-offvalue identifi, sites where JHE distribution predicted. ROC plots assess model success across

a full range of dichotomies and not just at a single cut-off point. The area under the curve

(AUC) of the ROC is a single measure of overall fit, ranging from 0.5 for chance performance

to 1 .0 for a perfect fit (Osborne et al., 2001 ; Piorecky and Prescott,2006).

Model Validation

In the validation, two types of errors can be detected: an omission error when the model

predicts an unsuitable habitat, even though the species has indeed been found in that location,

and a commission error when the model predicts a suitable habitat but the species has not

been reported there. Commission effors are unavoidable as not all suitable habitats are likelyto be occupied concurrently (Boone and Krohn, 1999; Ottaviani et a1.,2004).Clearly,

apparent commission errors may be caused by incomplete field surveys or low detectability of

51

species (Ottaviani et al., 2004). The validation phase is crucial in assessing the accuracy of

any predictions. This is achieved by testing the predicted probability distribution of a species

represented by the habitat suitability model against evidence recorded in the field. To veriff

that predictions are robust, general, and unbiased, model predictions must be compared with

independent data sets (Guisan and Zimmermann, 2000; Ottaviani et aI.,2004).In this study,

we tested the model in southern parts of West Java, and the result was compared with the

localities records of JHE derived from the Indigenous Nature Conservation Society (YPAL)

(Setiadi et a1.,2000) and field survey data of 2006. YPAL recorded JHE presence in 21

locations. These localities records are used as primary data for error calculation of model

validation in this study. The entire process throughout this study is shown in Figure 2.

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Figure 2. Flowchart of this study.

Results

Both the ordinary LR and the ALR models contained three environmental variables and a

constant, while the ALR model also contained a spatial autocovariate, SLP, ELV and NDVI

were the three significant environmental variables (P < 0.01). The preference for steep slopes

is probably related to protection against either man or predators. Indeed, Wells (1985)

considered JHE as a slope specialist, and it may be a genuine slope species with topographic

relief as special demand. Moreover, JHE is largely restricted to rugged, hilly terrain, and

generally encountered in undulating, hilly, or mountainous terrain (van Balen et a1.,2000).

The elevation above sea level was identified as another significant variable, and these results

are similar to those of other raptor species such as Gypaetus barbatus (Doninar et al., 1993),

Falco eleonorae (Urios and Martinez-Abrain,2005). NDVI used as an indicator of relative

biomass and greenness where in this model similar to vegetation cover conditions. Other

i\Z

studies used NDVI to distinguish wildlife habitats (Rogers et al., 1997) and to monitor habitat

ranges (Osborne et aI.,2001).

The best overall model was decided by comparing the -2 Log Likelihood (-2LL), maximum

data variability Qrlagelkerke R2;, overall accuracy, and AUC (Table 1). All ALR models

obtained lower -2LL, higher overall accuracy and accounted for more data variability than the

LR model. The overall accuracy of the LR model, which has been estimated based on the

correct classified observations of reference data was 91 .30%. Addition of an autocovariate

variable using all rectangular moving window sizes significantly increased overall accuracy

from 91 .30Yo to 95.65%. Based on visual comparison, all the predicted probability models by

LR and ALR (Figure 3) showed that certain cases of misclassif,red pixels still exist, especially

located on the top of Mount Gede-Pangrango (+ 3. 000 m asl). Only ALR_50 successfully

removed the misclassified pixels. This finding shows that the ALR model significantly

improved the LR model using neighborhood sizes starting from the 1500 m autocovariate.

Thus ALR_50 was selected as the best ALR model, and was able to classify JHE distribution

more correctly. Equations for the final LR model and ALR model are follows:

LR:

Pi : 1/(exp(-0.3804 [SLPI-0.0148 IELVI+0.1229 [NDVI] +19.6s18)+1)

ALR-50:

Pi:1/(exp(-0.2371[SLPI+0.0070[ELV]-0.0189[NDVII-19.8063[AUTOCOV]+6.8s92)+1)

Model validation

Final LR and ALR predicted probability models were tested in southern parts of West Java

(Fig. 4), and subsequent omission and commission errors were calculated based upon JHE

survey data derived from YPAL (Setiadi et aL.,2000) and ground checks in2006. Omission

errors for LR and ALR_50 were found in the same locations, i.e., Leuweung Sancang (locality

number: 18), Mt. Tampo Mas (locality number: 13), and Mt. Jagat (locality number: 20).

Omission error rates for LR and ALR_50 were 37.5% and 20o/o, respectively, due todifference in total number of habitat patches recognized by each model. Leuweung Sancang

and Mt. Jagat are nature reserves, while Mt. Tampo Mas is a recreational park open to the

public. During ground truth checks in 2006 in Mt. Tampo Mas and Mt. Jagat, no JHE were

recorded, but we observed another raptor, Spilornis cheela, in both locations. On the other

hand, the commission error rate for LR and ALR-50 was 37.5o/o and 60Yo, respectively.

CJ

Tabel 1. Comparison of final LR and the five ALR models

DescriptionLR Best LR model

ALR_15 Includes 20 ha neighboring areas

ALR_25 Includes 56 ha neighboring areas

ALR-35 Includes 110 ha neighboring areas

ALR 45 lncludes 182 ha neighboring areas

.2LL kerke R3 Overall accurac AUC

7.4906.470

6.9537.367

6.807

0.871

0.891

0.882

0.8740.885

0.898

91.30%9s.65%95.6s%9s.65%95.65%95.65%

0.977

0.985

0.98s

0.971

0.985

0.985ALR-50 Includes 225 ha neighboring areas 6.137

Notes: - Bold face shows selected models, LR: Logistic regression,ALR: 'Autologistic regression, AUC: Area Under Cur

I

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Figure 3. JHE, habitat suitability model selection in TNGP and its surrounding areas based on

LR andALR models'

a .-i.q.:l i$ {1

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lEl! {{. ; ': *

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Figure 4. JHE habitat suitability model validation in southern parts of West Java based on LR

and ALR models.

Discussions

This study identified environmental variables affecting the distribution of JHE in West Java

and developed models to predict their suitable habitat accurately. This approach will enable

forest and wildlife managers to determine current areas of concentrated quality habitat as well

as model the effects of various conservation strategies of future habitat quality and

distribution assessments. Data for wildlife habitats in biological studies are rarely distributed

normally and ecological relationships are not often linear (Piorecky and Prescott, 2006).

However there is one fundamental problem; logistic regression is a non-spatial analysis that

requires independence of observations. When the response variable is autocorrelated, as is

typically the case with species occuffences, the assumption of independence is often invalid,

and the effects of covariates (e.g. environmental variables) that are themselves autocorrelated

tend to be exaggerated (Grumpetz et aL.,2002). Nearly all biophysical and response variables

used in logistic regression models have shown signihcant positive spatial autocorrelation,

which declined as the scale of analysis increased (Piorecky and Prescott, 2006). Through the

addition of an autocovariate term, ALR allows us to weigh the effects of different

neighborhood variables on species occuffence and to statistically control the effect of spatial

autocorrelation (Hubbell et al., 2001).

This study found that ALR models had improved overall accuracy, higher AUC values and

predictive power indicating superiority in estimating JHE, distribution. Similar results have

been reported by Agustin et a|.,1996 Osborne et aI.,2001; and Piorecky and Prescott, 2006.

These studies showed the ALR model to produces better model fitting that reflects the roles of

55

predictor variables more accurately in estimating the spatial distribution and occurrence ofwildlife. Based on visual comparison, the 1,500 m neighborhood size (moving window of

50*50 pixels) was determined as the neighborhood size that produced the most parsimonious

autologistic model because it successfully removed misclassified pixels on the top of Mount

Gede-Pangrango. This is a very interesting result because the 1,500 m autocovanate,

equivalent to 225 ha neighborhood areas, can be considered as the minimum home range size

in this study site. This result agreed with the current approximate distance between nests in

TNGP of 1.5-2 km (Suparman,2005) and the mean home range size estimated for JHE in

Java of 400 ha (Gjershaug et al., 2004). Home range size of JHE differs between various

researchers and the appropriate home range size is depending on habitat quality of each

location. The home range of JHE is overlapped but usually nest sites of breeding pairs are

spaced regularly. These results suggest that considering the species of interest's home range

size in selection of the appropriate neighborhood size may improve the ALR probability

result.

During field observations, JHE were generally encountered in the hills and mountains due to

their high dependence on the presence of remaining natural forests. Natural forests on Java

have been generally cleared and remnants are now confined to mountain areas (above 1200 m

asl), with only atiny percentage of the natural lowland forest (below 1200 m asl) remaining

(Whitten et al., 1996). In the study area, the floral transition between sub-montane forest

(1,200-1,800 m asl), which is dominated by the JHE preferred tree species (Altingia excelsa),

and montane forest (1,800-3,000 m asl) occurred at 2300 m asl (Whiuen et al., 1996).

Therefore, there was no indication of suitable habitat for JHE on the top of Mount

Gede-Pangrango. In addition, two decades field observations studying forest birds in Java also

found that JHE are more frequently encountered in forest habitats (secondary forest and

evergreen forest) and between sea level and about 2,500 m asl (van Balen et a|.,2001; Nijman,

2004).

Zaniewski et al. (2002) argued that pure presence-only methods such as Ecological Niche

Factor Analysis (ENFA) are more likely to predict potential distributions that more closely

resemble the fundamental niche of the species; in contrast presence-absence modeling is more

likely to reflect the present natural distribution derived from a realized niche. However both

methods aim to predict distributions by sampling real distributions, and therefore they provide

different estimations of the realized niche of the species. Since presence-only methods do not

take into account the areas from which the species might be absent, they are less conservative

in estimating the species'realized niche (Brotons et aI.,2004). Moreover, presence-only data

is one of sources ofbias in biodiversity data and generating pseudo-absences is approach for

controlling this source of bias (Stockwell and Peterson,2002). Predicting species distribution

56

from statistical models incorporating presence-only data sets and generated pseudo-absences

could be a convenient and useful alternative when systematically gathered presence/absence

data are unavailable or impossible to obtain.

Equal omission errors have been found in both models, i.e., Leuweung Sancang (localitynumber: 18), Mt. Tampo Mas (locality number: 13) and Mt. Jagat (locality number: 20). Allthese locations have experienced similar threats, including forest conversion (illegal logging

and agricultural expansion), illegal hunting, and isolation from other patches. Habitat isolation

can have significant effects on the occupancy of suitable habitat, population persistence, and

rulnerability of an individual species to extinction (Lindenmayer and Fischer, 2006).

Moreover, illegal hunting and trade appear to be a big problem now. Since cage birds play an

important role in Indonesian culture the wild bird trade is a serious problem, particularly on

Java where JHE is severely threatened largely because of this pressure. Improved roads,

including the opening up of forests by logging concessions and plantations, have greatly

increased access for hunters and trappers. Surveys consistently show that 30-40 JHEs are

openly offered for sale in the bird markets of Java each year (IUCN, 1996) and presumably

many more undetected. Even though Indonesian law (UU No. 511990) about conservation ofnatural resources and ecosystems has been established, where rare and endangered species

receive extra protection under article 21(2) from illegal hunting, trade and ownership,

unfortunately law enforcement is still weak. Therefore, efforts to control illegal hunting and

trade must be continued by increasing patrolling and removal of snares, particularly inprotected areas, accompanied by strengthened law enforcement and awareness campaigns.

Conclusions

This study found that ALR models had improved overall accuracy, higher AUC values and

predictive power indicating superiority in estimating habitat distribution of JHE. The spatial

autocorrelation should be taken into account in predictive models to explain the habitat

suitability of JHE. The 1500 m neighborhood size (moving window of 50*50 pixels) was

determined as the neighborhood size that produced the most parsimonious autologistic model.

Predicting species distribution from statistical models incorporating presence-only data sets

and generated "pseudo-absences" could be a convenient and useful alternative when

systematically gathered presence/absence data are unavailable or impossible to obtain. The

ALR-50 was successfully validated its usefulness in southern parts of West Java with 20o/o

omission error rate. Commission errors found in this study suggest that future survey should

focus on potential locations because the commission errors may be caused by incomplete fieldsurveys or low detectability of the species.

Acknowledgements

The authors wish to express sincere thanks to the directors and staffs of Gunung

57

Gede-Pangrango National Park (TNGP) for granting permission to carry out surveys in TNGP.

We are grateful to the Raptor Conservation Society (RCS) and Indigenous Nature

Conservation Society (YPAL), especially to Mr. Usep Suparman and Ml Zaim Rakhman for

sharing information on breeding data and their guidance during field surveys. This study was

(partially) supported by The Core University Program on Applied Biosciences between Bogor

Agricultural University (IPB)/The University of Tokyo of DGHE/ JSPS.

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