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ta tesofForest ServicePacific NorthwestResearch StationResearch PaperPNW-RP-455December 1992

G e n o t y p e ~ E n v i ro n m e n t I n te r a ctio n :~A C a s e S t u d y fo r D o u g l a s -F i rin W e s t e rn O r e g o nRober t K . Camp bel l

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A u t h o r ROBERT K. CAMPBELL is a principal plant geneticist, Forestry Sciences Laboratory,3200 S.W. Jefferson Way, Corvallis, OR 97331.

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A b s t r a c t C am pbel l , Rob er t K . 1992. Genotype * env i ronment in terac tion : a case s tudy forDouglas-f i r in western O regon. Res. Pap. PNW-R P-455. Port land, OR: U.S.Department of Agricul ture, Forest Serv ice, Paci f ic Northwest Research Stat ion.21 p.

Unrecognized genotype x envi ronment interact ions (g,e) can bias genet ic-gainpredict ions and models for predict ing growth dynamics or species perturbat ions byglobal cl imate change. This study tested six sets of famil ies in 10 plantation sites in a78-thousand-hectare breeding zone. Plantat ion di f ferences accounted for 71 percentof sums of squares (15-year heights), repl icat ions an addi t ional 4.4 percent, fami l ies1.9 percent, the first principal component of interaction effects 3.5 percent, and thesecond princ ipal component 1.2 percent. Resul ts in this s tudy and in a larger survey(87 sets in 10 breeding zones) were s imi lar: 51 percent of sets indicated s igni f icantg*e. In 46 percent of sets, the g*e interact ion-fami ly variance rat io was greater than 1in 35 percent, greater than 1.5; and in 10 percent, greater than 5.Keywords : Pseudotsuga menziesii,geneti c variation, tree height, stabi l ity, A M M Imodel , Eberhardt-Ru ssel l coeff ic ients.

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Summary I f ef fects of genotype * environment interact ion (g,e) contr ibute substant ial ly to theperformance of indiv idual trees or fami l ies, considerable research on intr insic causesmay be just i f ied. Ignored interact ions may bias genetic-gain predict ions and modelsfor predict ing growth dynamics or species perturbat ions by global c l imate change.Questions about g*e in coast Douglas- f i r led to this study, which included six sets ofabout 30 fami l ies each, tested in 10 plantat ions wi thin one smal l breeding zone inwestern Oregon. Fami l ies in sets resul ted from open-pol l inat ion of parents of var iousand loosely c lustered or igins. Except for one set, al l parents were nat ive to thebreeding zone. Data were analyzed by ANOVA, Gauch's AMMI, and Eberhardt -Russel l models. The g*e var iance rat ios ( [var iance g*e]/ [var iance fami ly effects])obta ined by AN OV A for height changed f rom age 5 to 15; rat ios became great lysmal ler (1) for four sets . The A MM I modelanalysis of 15-year data suggested a larger contr ibut ion of g*e than did the ANOVAmodel. In the AMMI model , averaged over sets, plantat ions accounted for 71 percentof sums of squares, repl icat ions an addi t ional 4.4 percent, fami l ies 1.9 percent, thef i rst pr incipal component of interact ion effects 3.5 percent, and the second pr incipalcomponent 1.2 percent. Interact ion effects therefore contr ibuted about 2.5 t imesmore var iat ion in plot means than did fami ly effects. As suggested by eigenvectorcoeff ic ients and Eberhardt-Russel l s lope coeff ic ients, interact ions involved fami l ieswith great ly di f ferent capabi l i t ies for growing in plantat ions of low or high si teproduct iv i t ies. A signi f icant fract ion of g*e effects was associated wi th parent treeor igin, thereby suggest ing an adaptat ional aspect of interact ion. Because this studyindicated amounts of g*e larger than previously reported for coast Douglas- f i r , sets inother breeding zones were surveyed to place resul ts in a larger context. Resul ts inthis study and in the survey (87 sets in 10 breeding zones) were simi lar : 51 percentof sets indicated signi f icant (P

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Table 1- -Plantat ion character is t ics

Plan ta tion To wns h ip Sec.15-year meanPlant ingRange E leva t ion da te He igh t Surv iva l

209 11 S. 32210 11 S. 09212 10 S. 29214 11 S. 04215 12 S. 34216 11 S. 2030 9 11 S. 28318 10 S. 29518 13 S. 08520 14 S. 21

Meters Mete rs Percen t01 E. 260 1974 9 .02 90 .601 E. 320 1974 10 .45 85 .701 E. 36 6 1974 10 .37 81 .001 E. 427 1974 10 ,04 94 .001 E. 442 1974 9 .56 94 .902 E. 442 1974 4 .33 83 .801 E. 224 1974 9 .65 88 .503 E. 550 1974 8.71 96.402 E. 550 1974 7 .74 72 .001 E. 610 1976 5 .25 84 .1

M a t e r i a l s a n dM e t h o d sThe Snow Peak Coopera t ive inc ludes abou t 78 000 hecta res par t i t ioned in to twobreed ing zon es, one fo r h igh e levat ion and one fo r low (S i len and Whe at 1979) . Y ie ldt r ia ls in bo th were begun in 1973 to tes t open-po l lina t ion p rogeny f rom 180 paren tt rees in the low z one and 210 in the h igh zone . Th i r t y fami lies usua l ly m ade up aset , each family tested by 120 t rees as four ind iv iduals per repl icat ion per p lantat ion.Seed l ings in fami l ies were p lan ted randomly (noncon t iguous p lo ts ) w i th in rep l ica tionsa t 3 -meter spac ing . Treehe igh ts were measured a t ages 5 , 10 , and 15 years .In the low zone , paren ts were tes ted in 6 se ts in 10 p lan ta t ions an d in . the h igh zone ,in 7 se|s in 9 p lantat ions. Each p lantat ion included three repl icat ions of a set . Ex-per imenta l des ign d id no t d if fer be tween zones. Characte r is t ics o f p lan ta t ions a regiven in table 1. In the low zone at age 15, the average height across p lantat ionswas 8.5 mete rs and the average surv iva l was 87 percen t . Some fami l ies never the lesswere no t inc luded in ana lyses because o f miss ing p lo ts o r miss ing in fo rmat ion abou tparent or ig in, which le f t a range (27-30) of family numbers in the sets.Due to num erous c onst ra in ts , paren ts and p lan ta t ions d id no t p rov ide a represen t -a t ive sampl ing o f the b reed ing zone . A l though a l l pa ren ts came genera l ly f rom acommon reg ion , the se ts o f paren ts sampled somewhat d i f fe ren t geograph ic a reas( f ig . 1) . Notably , parents of famil ies in set 6 spread across a much larger area thand id the paren ts in o ther se ts . P lan ta t ions tended to samp le ma in ly the easte rnone-quarter to one-half o f the area sampled by parents ( f ig . 1) .

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1 1 0

1 0 0

9 0

8 0

7 0

i l

R . 3 _ . . , - 8 :1, , 4 I ' : i l

: 1 !~, - I . - ~ 1" '1 7

I= s j

1 1 9. . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . ,

6 0 1 0Planta t ions Sets

# ~ ' 150 1 = 2o9 I 1 . 2 - 2 1 o L . . J

o m m ~ I8 3 = 2 1 2 , 2 ' ,4 0 L . . J 4 - 2 1 4 P , - ~ -i a ! 5 = 2 1 5 ~ _ . _ Je*3 0 - 6 = 2 1 6 " . . . . :

7 = 309 : . . . . . " :I I 8 - 3 , s _ _ I s J2 0 -

9 = 5 1 8 r ~1 1 1 0 = 5 2 0

1 0 -

0 ' 1 ' 0 2C) :30 40 5 0 60W e s t - - ~ E a s t d i s t a n c e ( k m )

F igu re 1 - -G eog raph ic d imens ions o f p lan ta t ion and pa ren t l o ca t ions G ivenfo r each se t i s a rec tang le con fo rming t o max im um la t i tud ina l and long i t u -d ina l e x t remes o f l o ca t ions o f t he pa ren t s o f fami l i e s t es ted w i t h in a se t

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Data were t rans formed to common logar ithms to he lp equa l ize var ian ces acrossp lanta t ions and then w ere ana lyzed by three models . The f i rs t (ana lys is o f var iance[ANO VA ] model ) , app l ied to 5- and 15-year data , is used as an in it ia l ana lys is inmost y ield t r ia ls :

Yerg = u + Br + Ee + ele r + Gg + (E G )eg + e2erg ,whereYerg = the y ield of fami ly g in repl icat ion r in plantat ion e,u = the grand mean,E = the p lanta t ion mean de v ia t ions ,B = the b lock mean dev ia t ions ,e l = poo led dev ia t ions o f rep li ca t ions wi th in p lanta t ions ,G = the fami l y mean dev ia t ions ,(EG) = i n terac ti on dev i a t i ons ,ande2 = error dev ia t ions .The second model (add i t i ve main e f fec ts - -mul t ip l i ca t i ve in terac t ions [AMMI ] ) wasused on ly to ana lyze the 1 5-year data . In the AM M I model , in terac t ion e f fec ts arepar t i ti oned in to princ ipa l components (Gauch 1988). A l im i ta tion o f the AN O V A m odeli s that i ts in terac t ion term co nvey s none o f the response pat terns o f ind iv idua l geno-types or envi ronments. Al l interact ion ef fects are pooled in a g-e sums of squares(SS) , o f ten wi th many degrees o f f reedom (G-1) (E-1) . The impor tance o f a la rge SSin pred ic t ing fami l y per formance may be d iscounted when the mean square (andconsequent ly the g*e component of variance) is smal l or s tat is t ical ly nonsigni f icant.Data obtained in several smal l repl icates of a y ield t r ia l over many plantat ion s i tescan be expected to be especial ly noisy. Because of th is , potent ial ly useful informat ionabout the pred ic tab le pe r formance o f genotypes across p lanta t ion s i tes may be los t.To provide y ield est imates c loser to t rue means (and therefore of more value forpred ic t ion) , pat terns must be d is t ingu ished f rom no ise. The ANOVA model does nothave this capabi l i ty . Several methods, inc luding the E-R model discussed in the nextparagraph, have been developed to part i t ion g*e in at tempts to minimize this short-coming o f the fi rst model (L in and o thers 198 6) . The AM M I model was chosen herebecause i t makes fewer assum pt ions about the s t ruc ture o f in terac t ions than do o thermodels (Gauch 1988). The model is :

Yerg = u + B r + Ee + e ler + G g + ,T_,~.nagnben + Rge + e 2erg ,whereXn = the eigenvalue of pr inc ipal component (PC) ax is n,agn = the fami l y PC vec tor coef f i c ient fo r PC ax is n ,b e n = the plantat ion PC vector coeff ic ient for PC axis n,n = the number of axes retained in the model ,R e g = the res idual interact ion ef fect, and oth er terms a re as in the f i rs t model.The degrees o f freedom ass igned to PC ax is n is (G + E - 1 - 2n) (Gauch 1988).

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The th i rd mode l ( the we l l -known Eberhard t -Russe l l [E-R ] [1966] mod i f ica t ion o f theF in lay -Wi lk inson mode l [1963 ] ) was used on the 15-year da ta express ly to p rov ideest imates o f two param eters , wh ich then cou ld be com pared w i th resu l ts from theAMMI mode l . The purpose o f the mode l was to regress each fami ly in the tes t onan env i ronmenta l index to p rov ide s lope and dev ia t ion param eters de scr ibed in thefo l lowing:

Yeg = Ug + Sgle + Deg ,w h e r eYeg = the family mean of the gth family in the e h p lantat ion,ug:,= the mea n of the gth family over a l l p lantat ions,le = an e nvironm ental index ob ta ined as a mean of a ll famil ies with in a set at the e hp lan ta t ion minus the g rand mean,Sg = the regression coef f ic ient (s lope) measuring the response of the gth family overthe vary ing env i ronmenta l indexes, andDeg = the deviat ion f rom regression of the gth family at the e th plantat ion.For breeding endeavors, rank is an important at t r ibute of a family , perhaps asimportant as i ts contr ibut ion to family and in teract ion var iance. Ranking may or maynot be af fected by in teract ion, or ranking may be af fected in only some port ion of thefamil ies. Two common quest ions exist about the re lat ion of rank to in teract ion: Dorankings change appreciably f rom plantat ion to p lantat ion in h ighest ranked famil ieseven in sets of famil ies exhib i t ing large amounts of in teract ion? I f so, does th isd if ference in var iabl i ty of p lantat ion ranks ref lect var iabi l i ty in degrees of genotype xenv i ronment in te ract ion in upper and lower ranks? These quest ions were examinedby analyzing var iat ion among the p lantat ion ranks of famil ies in 15-year averageheight growth over a range of expected ranks. Plantat ion rank is def ined here as theranking of a family with in a set tested in a p lantat ion. Expected rank is rank of thefamily as determined f rom the average performance of the family over a l l 10 p lan-tat ions. The f i rst nul l hypothesis tested was as fo l lows: var ia t ion among the 10 p lan-tat ion ranks of a family d id not d i f fer regardless of the expected rank of the family .The second q uest ion led to a second nu l l hypo thes is : fo r fami l ies w i th g iven expectedranks, p lantat ion ranks do not d i f fer among p lantat ions. The f i rst hypothesis wastested by regress ion o f the s tandard dev ia t ion o f p lan ta t ion ranks on exp ected ranks.The tes t was res tr ic ted to the top th i rd o f ex pected ranks in each se t , becaus egenet ic se lec t ions usua l ly a re res t r ic ted to th is upper g roup . The hypo thes is wou ldbe d isproved i f a re lat ion existed between expected rank and var iabi l i ty in p lantat ionranks. The second nu ll hypo thes is was tes ted by f irs t g roup ing fami l ies o f g ivenexpected ranks in to c lasses (1 -5 , 6-10 , 26-30 , fo r example ) , then averag ing p lan-tat ion ranks across expected ranks. With each set act ing as a repl icat ion, Fr iedman'snonparamet r ic tes t o f randomized b locks (Soka l and Roh l f 1969) was used to tes tvar iat ion in ranking among p lantat ions. In the absence of rank-change interact ions,p lantat ion ranks should not d i f fer .For analysis of the associat ion of g*e ef fects with parent- t ree locat ion, famil iesw i th in se ts were c lus te red geograph ica l ly ( la ti tude and depar tu re ) by the SAS(1 9 87 ) p ro ce d u re , CLU STER .

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R e s u l t s As measured by the relat ive contr ibut ions to plot var iance of average fami ly effects(Og2) and g*e interact ion effects ((~ge2), response to environm ent wa s a highly var i -able property of Douglas- f i r in breeding zones of the Snow Peak ;ooperat ive. Est i -mates of var iance depended strongly on the combinat ion of parents and plantat ionsinvolved in tests. Ident ical ly designed tests s imi lar ly instal led (same year and crews)in high- and low-elevat ion zones, for example, exhibi ted great ly di f ferent expressionsof genetic var iat ion in 15-year heights in the two zones. In s ix of seven sets (-30fami l ies per set) in the high zone, var iances among fami l ies were higher than in anyof the six sets in the low-zone test ( f ig. 2). The probabi l i ty of this occurr ing by chanceis 0.004. Though trees in high-zone sets were shorter (by 4 percent) and test errorvar iances were larger (by 23 percent) than in the low-zone sets, interact ion contr ib-uted considerably more to total genetic effects (~g2 + O.ge ) in low-zone sets. Theexper ime ntal error wa s sma l l in ei ther case: the average coeff ic ient of var iat ion,

CV=(er ror s tandard dev iat ion x 100) /mean log height ) ,was 2.4 percent for the low zone and 2.8 percent for the high zone. Low-zone sets(Snow Peak low) also di f fered great ly from high-zone sets (Snow Peak high) in theratios of interaction variance to family var iance (O'ge2/O'g2). In lo w-z on e sets, the ratiovar ied from much less than one to much more than one. In high-zone sets, i t neverexceeded one. This var iat ion in genetic expression among low-zone sets is notunusual , however. I t represents the range of genetic effects seen in many cooper-at ives in the Paci f ic Northwest. Further analyses were restr icted to these six low-zone sets.The large (~ge2/~g2 ratios found in some low-zone sets impl ied substant ial changesin fami ly rank across plantat ions. The rank changes, however, were not necessar i lycharacter ist ic of al l fami l ies. To determine i f ranks change appreciably in fami l ies wi thhigh expected ranks, two nul l hypotheses were tested. The f i rst , that var iat ion amongplantat ion ranks did not di f fer regardless of expected rank of a fami ly, was disprovedin wi thin-set tests. Regressions of standard deviat ion of plantat ion ranks on expectedranks were not s igni f icant wi thin sets. But, when data from al l s ix sets were combinedand analyzed, var iabi l i ty in plantat ion ranks was found to increase by 0.20 uni ts ofstandard deviat ion for each uni t decrease in expected rank from 1 to 10 (P=0.009,R2=0.11). Highest ranked fami l ies therefore showed more stabi l i ty in plantat ion rankthan did fami l ies of lower expected rank. The sec ond null hypo thesis ( that for fami l ieswith given expected ranks, plantat ion ranks do not di f fer) was disproved for fami l ieswith high and low expected ranks. In the absence of rank-change interact ions, plan-tations should not differ, beyond error, from the average for al l plantations. In fact,the f ive fami l ies of highest rank (mean=3) consistent ly deviated from expected ranks(P=0.004). They ranked higher than the average in some plantat ions (table 2; forexample, p lantat ions 214 and 216) and lower than the average in others (518 and520). The f ive fami l ies of lowest expected rank (mean=28) ranked higher than theaverage in plantat ions 518 and 520 and lower than the average in 209 and 318(P=0.003). For intermediate expected ranks, the nul l hypothesis was not disproved(P=0.76) . Fami lies w i th in termediate rank ings, f rom 6-10 (mean=8 ) for example(table 2), did not show consistent ranking di f ferences among plantat ions. Rankchanges were substant ial even among fami l ies wi th the high average expected rankof three. In some plantat ions, ranks were almost twice as large as in other planta-t ions (table 2). This occurred even though three of the sets (1, 2, and 4) included inthe average plantat ion ranks exhibi ted no apparent g*e interact ion by the ANOVAmodel (fig. 2, table 3).

6

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1 8 -1 6 - J1 4 -

12-1 0 -

8 '

Snow PAak

t~ t

t t *

t ~ t

~tt t ~ t~ t tt t t

t

NSFamilyv a r i a n c e

t t~

NS

NSNSNSN SN SN S

T - - - -InteractionvarianceF igure 2- -D is t r ibut ion of gene t i c var iances w i th in the 13 sets f romSnow Peak h igh- and low -e levat ion breeding zones . Tota l var ianceis the sum of var iances am ong fami l ies , fami l y p lantat ion in ter -act ion (goe) ef fects, and e xpe rime ntal error. P robab i l i ties that theef fects c reat ing the var iances are due to chance are NS=P>0.05, =P0.01, **=P0.001, ***=P

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T a b le 2 - - A v e r a g e a c t u a l f a m i ly r a n k s w i t hi n s e t s a n d p l a n t a t io n s f o r f a m i li e s o f g i v e n e x p e c t e d r a n k( r an k b ased o n 15 -year he i g hts as av er ag ed o v er a l l p l an ta t i o n s) fo r 3 c l asses o f ex p ected r an k , h i g hest ,i n te r med i a te , an d l o west

Plantat ion aExpec tedrank Se t 210 212 214 309 215 209 318 519 520 2163.0 1 7 .4 7 .0 5 .8 7 .2 7 .4 9 .8 10.0 10.2 5 .8 6 .0 7 .72 6 .8 5 .2 11.0 10.6 9 .6 8 .2 6 .8 12.2 13.8 8 .8 9 .33 6 .6 11.6 10.0 11.2 8 .6 8 .4 10.0 12.4 11.4 7 .6 9 .84 4 .6 8 .4 4 .2 7 .0 10.4 6 .4 13.2 14.6 11.2 7 .4 8 .75 9 .8 11.6 5 .2 7 .6 10.6 12.4 12.6 16 .4 17.4 6 .2 11.06 8 .6 13.8 5 .2 9 .6 8 .4 6 .0 5 .8 13.8 17.8 10.8 10.07.3 9 .6 6 .9 8 .9 9 .2 8 .5 9 .7 13.3 12.9 7 .8 9 .48 .0

28.0

1 17.0 13.2 9 .0 6 .0 14.6 13.8 7 .6 9 .0 21.2 12.4 12.42 11.2 7 .6 13.4 15.0 12.0 10.6 13.2 19.2 14.6 7 .6 12.43 10.2 9 .0 11.2 12.4 14.0 14.4 10.2 10.4 11.6 10.0 11.34 16 .8 11.0 12.6 13.2 11.6 13.8 8 .4 13.0 5 .6 16 .4 12.25 10.0 14.4 15.4 13.4 7 .8 10.4 9 .0 15.4 15.2 18 .2 12.96 15.6 15.6 13.0 17.2 9 .2 13.8 17.8 16 .0 15.0 10.6 14.413.5 11.8 12.4 12.9 11.5 12.8 11.0 13.8 13.9 12.5 12.61 25.6 23.8 22.8 22.8 16 .4 23.6 24.0 18 .8 16 .2 18 .8 21.32 23.2 21.6 26 .8 19.6 26 .6 28 .0 20.6 17.8 20.0 23.4 22.83 21.8 24.2 22.6 24.0 21.4 26 .2 25.8 24.6 16 .8 20.6 22.9_4 25.2 28 .0 22.4 22.2 25.2 25.6 27.0 11.6 17.0 16 .8 22.15 21.2 15.6 20.0 24.2 23.0 21.6 24.6 21.0 16 .4 20.0 20.86 21.8 19.8 24.6 22.8 22.8 23.2 25.4 "19.6 20.6 18 .2 21.923.1 22.2 23.2 22.6 22.6 24.7 24.6 18 .9 17.8 19.6 21.9

a Plantations are ordered by productivity as measured by average 15-year height, from highest ( left) to lowest (r ight).The above repor ts the express ion o f genet ic var ia t ion in terms of mean squares andvar iance compon ents . V ar iance componen ts , espec ia l ly , a re par t o f the vocabulary o fthe c lass i f ica t ion model for descr ib ing var ia t ion in exper iments . T ree bree ders re ly ones t imates o f compon ents for he lp in eva luat ing a l ternat ive breed ing des igns . Resu l tsf rom regress ion m odels prov ide, however , a m ore usefu l bas is for judg ing potent ia lef fects of interact ion in respect to growth model ing. In regression models, sumsqu.ares can be part i t ioned so thei r relat ive importance in predict ing performanceat plantat ion s i tes can be judged. In this paragraph, the importance of the variousdes ign var iab les i s repor ted in a regress ion contex t . In or thogonal des igns , thef rac t ion o f the sum s of squares accounted for by a s ta t i s t ica l ly s ign i f icant var iab leprovides an est imate of the relat ive importance of the variable in describing variat ion

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T a b l e 3 - - M e a n s q u a r e s a n d d e g r e e s o f f re e d o m ( in p a r e n t h e s e s ) a s s o c i a t e d w i th s e t s o f f a m i li e s , m a i ne f f e c ts ( p la n ta t ions , re p l ic a t ions , a nd f a mi l ie s ) , a nd w i th In te ra c t ions , Inc lu d ing p r inc ipa l c ompone nts( P C1 . P C3 ) o f th e f a mi ly p la n ta t ion in te ra c t ion

SetsSou rcevariation 1 2 3 4 5 6. * * * * * * * * * * * * * * * * *Plan tat ions (E ) ( 9 )2 .180 .6 . . ( 9 )1 .518 .3 . . ( 9 )1 .2742. ( 9 ) 1 .4 72 ,2 , . ( 9 ) 1 .5 51 .6 . , ( 9)2.460 .6 * *Reps in E (20).0197,** (20).0420*** (20).0246*** (20).0359.** (20 ).07 25 (20).0512.**Families (G) (28) .0145 (28 ) .0147 (28 ) .0117.. (28) .0149 (29).00 74ns (26) .0132. .G x E: (252).0053,n.s, (252).0070 .n.s. (252 ).00 56,,. (252).0067 ,n,s. (26 1).0 06 0.. (234).0 06 9.,.PC 1-- (36) .0144, (36) .0186 (36) .0220 (36) .0237 (37).0133ns (34) .0191.. .Clus ters (CL) a (12) .02 38 .. (16).0140n,s, (12) .0203.n.s. (13) .0247.n.s. (10 ).00 75 ... (15),0 397Within CL (24).0098 (20) .0223. (25) .0219 (23).0231 (27) .0 155,, (19) .0029.n.s.PC2- - ( 34 ) .0064ns (34) .0127.. (34) .0077 " (34) .0 097 :,. (35).0115as (32) .0104CL (12) .0056ns (16) .0192 (12).0107 ns (13).0231 (10).0005.** (15).00 60 nsWithin CL (22) .0063 ns (18) .0063ns (23) .0054ns (21).0013 ns (25).0147 (17) .0128 ..PC 3 -- (32).0091 ns (32) .003 3 ns (32) .01 60 ns (33) .0071 ns (30) .00 87 *Residual 1 b (216).00 38 ns (216).0051 ns (216) .002 9ns (216) .0039 ns (224).00 48 '~ (200).00 48nsResidual 2 c (182).00 33 ns (182).0037ns (182).0020ns (182).0028ns (189).0036ns (168).0039nsResidual 3 (138).00 27nsError (560) .0050 (560) .0067 (560) .0 0 41 (560) .0058 (580) .0051 (520) .0040

n s = P > 0 . 0 5 .* = P < 0 . 0 5 > 0 . 0 1 .* * = 0 . 0 1 > P > 0 . 0 0 1 .* * * = P < 0 . 0 0 1 .a V a r i a t i o n a m o n g p a r e n t - t r e e c l u s t e r s t e s t e d b y v a r i a n c e w i t h i n c l u s te r s . V a r i a t i o n w i t h i n c l u s t e r s t e s t e d b y e r r o r .b R e s i d u a l m e a n s q u a r e s o f i n te r a c t io n e f f e c ts a f t e r re m o v i n g P C 1 .c R e s i d u a l m e a n s q u a r e s o f i n t e r a c t io n e f f e c t s a f t e r r e m o v i n g P C 2 .

among plot means. In this context, R 2 ( the coeff icient of determination = sumsquares fo r effects/total sum squares) measures the proport ion of the i total sums ofsquares "explained" by design var iables and their interactions. Averaged over al lsets, plantations accounted for 71 percen t (R2=0.71) of sums of s quares ( fig. 4).Replications within plantations contributed an additional 4.4 percent, families 1.9percent, PC1 3.5 percent, and PC2 1.2 percent. Set 1 was not included in thecalculation of the average fraction added by PC2. Genetic effects ( including g,e)explained about 9 percent as much var iat ion as did plantations and about 1.5 t imesmore than did repl ications. Note that g*e effects accou nted for almost 2.5 t imes m orevariation than did averag e family effects. Even in sets 1 and 2, in which interactionswere stat ist ical ly nonsigni f icant in ANOVA analyses, the contr ibutions of PC1 andPC2 were as large as that of families.

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Age15

Set6 * * *

" " " " = " " / S SSS S /S /

" ~ . . . . . I / " /" - . 7 -% % % , % % ~ ~ , , ,,,,,,~ , , , = , = ,m

2 * * *

3 * *

5 ~ t1 *

Familyv a r i a n c e

~ ' ~ . J 5 *% '~"~ "~, ~ %-_ 4 NS~ . . ~ " " 4 N S

" ~ 1 N S3 N S2 NSIIn teract ionv a r i a n c e

Figure 3--Distr ibu tion of genetic variance, at two ages, wi thin the sixsets for the Snow Peak low-elevation zone. Total variance is the sumof variance s among families, fam ily plantation interaction (g.e) effects,and exp erimen tal error. P robabil i ties that the variation in effects creatingthe variances is due to chance are NS=P>O.05, *=P 0.01, *=P 0.001, ***=P

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. . . J* * * J / I/

t t t ~S e t /1 /

* * * S J

jS J "p0 0 0 0 0

/ . . .

PCl-INT PC2-1NTD e g re e s o f f r e e d o m

PC3-1NTFigure 4--F or each set, the percentage of total variability (total sum s ofsquares) in p lot means explained by plantation (PL T), replications withinplantations (RE PS), families (FAMS), and the first (PCI-INT), second(PC2-1NT), and third (PC3-1NT) principal components of fam ily plantation(g.e) interaction. Dimension on the abscissa is measured in degrees offreedom associated with each d esign category; the steeper the line, thelarger the me an square for the e ffect. The probabilities that effects aredue to chance are NS=P> 0.05, *=P0.01, **=P0.001, **=P< 0.00 1. E ffects of plantations and replications are all significantat P

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Table 4 - -E igenvec t or coe f f i c ient s o f t h e 1s t (P C1) and 2d (P C2 ) p r inc ipa l com ponent s o f t h e w i t h in -se t "gen ot y pe , , env i ronm ent In t e rac t ion e f fec t s asso c ia t ed w i th p lant a t ionsP lan ta t ion

2 0 9 2 1 0 2 1 2 2 1 4 2 1 5 2 1 6 3 0 9 3 1 8 5 1 8 5 2 0S e t P C 1 P C 2 PC 1 P C 2 P C 1 P C 2 P C 1 P C 2 PC 1 P C 2 PC 1 P C 2 P C 1 P C 2 P C 1 P C 2 P C 1 P C 2 P C 1 P C 21 0 . 1 0 - - 0 . 21 - - 0 . 2 4 - - 0 . 0 9 - - 0 . 0 2 - - - 0 . 4 5 - - 0 .2 1 - - 0 . 2 6 - - 0 . 0 5 - - - 0 .7 5 - -2 - . 1 3 - 0 . 0 2 - . 0 7 0 . 0 7 - . 11 0 . 7 6 - . 0 8 - 0 . 1 2 - . 0 8 0 . 0 5 - 0 . 2 6 - 0 . 5 9 - . 0 4 0 . 0 1 - . 1 2 - 0 . 15 - . 0 4 0 . 0 8 . 93 - 0 . 103 . 1 9 - . 1 3 . 1 0 - . 0 5 . 1 0 - . 2 1 . 1 8 . 0 2 . 1 2 - . 0 1 - . 1 7 . 8 8 . 1 7 - . 1 8 . 1 7 . 1 4 . 0 4 - . 2 2 - . 9 0 - . 2 44 . 2 6 . 2 9 . 1 8 . 2 2 . 1 9 . 11 . 1 3 . 0 6 . 1 7 . 0 6 . 0 5 - . 1 2 . 0 6 - . 1 4 . 0 5 - . 0 2 - . 8 7 . 3 6 - . 2 2 - . 8 35 . 1 4 - . 2 9 .0 1 - . 1 9 - . 3 1 - . 1 1 . 0 4 - . 0 9 . 0 7 - . 0 3 . 0 6 - . 3 2 . 0 9 . 0 2 - . 1 6 - . 1 7 . 6 7 . 8 3 - . 6 2 . 6 26 - . 2 5 .1 1 - . 1 6 - . 0 1 - . 4 3 . 0 3 - . 0 4 - . 0 2 - . 0 5 - . 0 5 . 29 . 6 5 - . 1 8 . 0 9 - . 0 6 - . 0 1 . 12 - . 7 3 . 7 6 - . 0 6- - = n o t c a l c u l a t e d b e c a u s e m e a n s q u a r e s f o r t h i s f a c to r w a s n o t s t a t is t i ca l ly s i g n i f ic a n t ( P > 0 . 0 5 ) .

Famil ies also contr ibuted var iously to interact ion effects; eigenvector coeff ic ients(PC1 and PC2) of the four most react ive fami l ies in each set are given in table 5.A disadvantage of the AMMI model is that i t does not provide readi ly interpretablefami ly means. Fortunately, in several sets, the fami ly eigenvector coeff ic ients appar-ent ly measured about the same features of interact ion as are descr ibed by the E-Rslope coeff ic ients ( table 5). An E-R slope above 1 denotes a fami ly that performsrelat ively better in the more product ive plantat ions than in the less product ive ones,when compared to the performance of other fami l ies in the set. In sets 3 and 6,larger eigenvector coeff ic ients we re associated w i th larger s lope coeff ic ients. In sets1 and 2, on the other hand, negative eigenvector coeff ic ients were associated wi ththe larger E-R slope coeff ic ients. The latter relat ion demonstrates the fact that thesign of an eigenvector coeff ic ient is not inherent ly associated wi th the size of theslope coeff ic ient. The E-R slopes therefore cannot be used as subst i tutes foreigenvector coeff ic ients in performance evaluat ion.Eigenvector coeff ic ients in tables 4 and 5 document the presence of plantat ions andfami l ies that strongly part ic ipated in interact ions. These very react ive ent i t ies seemedto account for most of the interact ion in sets 1, 2, and 3, but interact ion appeared tobe more deeply imbedded in other sets. When components of var iance were est i -mated f rom the or ig inal data set by ANO VA, the g*e componen t wa s larger than thefami ly component in sets 3, 4, 5, and 6 (compare f igs. 5 and 6). After removing themost react ive plantat ion (for example, 520 in set 1, table 4), or the two most react ivefami l ies (for example, 225 and 201 in set 1, table 5), the g*e component was nolonger larger than the fami ly component in set 3. When the two most react ive plan-tat ions or the four most reactive fami lies (two each from PC 1 and PC2) w ereremoved, g*e var iance was larger than fami ly var iance in only two sets. Even afterremoving four fami l ies, which resul ted in negative ANOVA est imates of g*e com-ponents of var iance for sets 1, 2, and 3, the AMMI analysis suggested a remainingcomponent of interact ion wi thin the sets. Mean squares for PC1 were st i l l s igni f icantand the proport ions of the sum squares explained by PC1 were st i l l almost as largeas those explained by fami ly effects ( f ig. 7).

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T a b l e 5 - - - E i g e n v e c t o r c o e f f i c i e n t s o f 1 s t a n d 2 d p r i n c i p a l c o m p o n e n t s ( P C 1 , P C 2 ) a n d E b e r h a r d t a n dR u s s e l l ( 1 9 6 6 ) s l o p e a n d d e v i a t i o n c o e f f i c ie n t s f o r r e a c ti v e f a m i l ie s I n g e n o t y p e - e n v i r o n m e n t I n te r a c ti o n ePC1 PC2

Vector VectorSet Fam ily coefficent Slope Deviation coefficient Slope Deviation

3

225 0.419 0.65(28) 0.0006 (24)211 .277 1.1 2( 27 ) .0033(2)206 -.219 1.16(5) .0001 (17)201 -.42 6 1.43(1 ) .0017(7)240 .257 1.17(4) .0046(3)239 .256 1.0 5( 10 ) .0029(4)257 -.26 6 1.29(2) .0012(16)248 -.68 9 1.25(3) .0122(1)261233252296 .882267 .227276 - .108269 - .19328 329126 4295

1.87(1) .0133(1)1.14(4) .0026(2).99(12) .0012(8).72(29) .0069(17)

30 8 .237 .72(28) .0021 (9)331 .212 .94(16) .0027(6)313 -.43 4 1.15(7) .0086(2)322 -.46 9 1.22(3) .0086(1 )30029 9409 .448346 .338401 -.297352 - .3353443514O6334517 .567504 .285416 - .317511 -.45641350 350 9411

1.01(14) . 0049(1)1.13(4) .0038(3)1.14(3) .0036(4)1.06(8) .0030(7)

1.42(1 ) .0078 (1 )1.37(2) .0005(26).72(29) .0038(2).48(30) .0024(8)

-0.216

.816.197-.311

.264.250- .347-.590

.347.319-.280- .363

.382.246- .316- .353

.417.416-.208- .363

1.17(4)

.640(29).887(24)1.449(1 )

.828(27).779(28)1.185(3)1.326(2)

1.15(7)1.22(3)1.06(11 )1.39(1 )

1.04(10).98(17)1.01(15)1.15(2)

.89(24).92(29).98(15)1.24(5)

0.0046(3)

. 0 1 1 4 ( 2 ). 0 0 1 1 ( 1 7 ). 0 0 1 1 ( 1 9 )

.ooo5(25).0003(27). O O l 1 ( 9 ).0022(3)

.0086(2).0086(1).0019(11 ).0044(3)

.0045(2).0023(9).0032(5).0030(6)

.0033(4).0032(2).0011 (19).0019(9)a Rankings within sets are in parentheses.

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' ./ s~, . - ' " H

o - - ' i " - " - T - ' - - - - - - - - r --4 -2 All -1 -2 /F A M S F A M S P L T S P I T S J

S e t6

A,__ _ i N - - ' . L ' -/ / v \ ,,.l.-/ \;~Q'/ \

III

2 o

- 4 - 2 A l lF A M S F A M S -1 - 2P L T S P L T S

Figure 5--Est imated var i-ances a mon g famil ies with insets as they a re af fected byremoving the most reactive(in interactions) two to fourfamil ies (FAMS) or one totWO plan tations (PLT S) fromthe data set be fore analysis."ALU' designates est imatedvar iances in the completedata set . Total var ianceincludes family var iance,plan tation family interaction(g 'e) , and ex per imen tal error .

F igure 6 ~E st ima ted var iances o f fami ly p lan-tation interaction effects (g'e) within sets asthey are a f fected by remov ing the most reactive( in interactions) two to four famil ies (FAMS) orone to two plantat ions (PLTS) f rom the da taset before analysis. "ALL" d esignates est imatedvar iances in the comp lete data set . Total var i-anc e includes fam ily variance, p lanta tion familyinteraction (g-e), and ex perim enta l error.

1 4

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o o o set /t t t t t ~ / " J . . J , " / _ . . . . . . ~, / j ' ~ - . / . . . . . '/

t . . : . "3

6 /t

5 '

PC 1 I N T P C 2 - 1 N T PC 3 -1 NTD e g r e e s o f f r e e d o m

F igure 7 - -Fo r each set , the analyzed data d id not inc lude p lot means forthe four mos t reactive families ( in interact ions) in each set. Given a re thepercentag es of total var iabil ity in plot means e xplain ed by plantat ion (PLT),replications w ithin plantatio ns (REPS ), families (F AM S), and the first (PCI-INT.),second (PC2-1NT), and third (PC3 -1NT) pr inc ipal com ponen ts of family plan-tation (g.e) interaction. Dimension on the abscissa is measured in degrees offreedom as sociated with each design category. Thus, the steepe r the l ine, thelarger the mean square for the effect. Th e probabi li ties that effects a re due tochanc e are NS=P>0.05, *=P0.01, **=P0.001, ***=P

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D i s c u s s i o n

T a b l e 6 - - P e r c e n t a g e o f f a m i l y m e a n h e i g h t a n d In t e r a c ti o n v a r i a n c e s ( s l o p e ,d e v i a t io n s , a n d P C s ) a c c o u n t e d f o r b y c l u s t e r s o f p a r e n t t r e e s w i th i n s e t s aEberhardt-Russel l Principal compo nentsNumbers of MeanSet c lusters family height Slope Deviat ions PC1 PC2

1 5 0(0.68) 18.8(0.09) 0.2(0.42) (0.03) (0.57)2 9 9.2(.30) 0(.48) 0(.91) (.82) (.01)3 5 15.6(.12) 27.2( .03) 12.2(.17) (.54) (.08)4 6 4.6(.34 ) 26 .9(.05) 0( .81 ) (.43) (.00)5 3 0(.67 ) 15.9(.09) .1 (.38) (.89) (.99)6 8 17.8(.11) 34.7 (.04) 62 .1(.00 ) (.00) (.95)"C lus ter mean squa res were tested against within-clus ter mean squares. Probabil i ties of obtaining clustermean squares by chance are given in parentheses.A signif icant fraction of the g*e interaction was associated with parent tree origin.Parent t rees had been chosen haphazardly by locat ion in loosely ordered clusters.Clustering procedu res group ed famil ies within sets into three to nine clusters base don the geograp hic proximity ( lat i tude and de parture) of the pa rents ( table 6). In sets 1and 6 (PC1) and in sets 2 and 4 (PC 2), interact ion ef fects were significant ly largeramong clusters than within c lusters ( table 3). When cluster mean squares weresignif icant, within-clus ter mean squ ares usually were not and vice versa. E berhard t-Russell partit ioning of interaction effects also suggested an association of interactionwith clustering. Abo ut 20 percent of the variance among famil ies in E-R slopes wascontr ibuted by clustering ( table 6). Although there see med to be an a ssociat ion ofslope and eigenvector coeff ic ients in table 5, s lopes a nd PC interact ion ef fects werenot interchange ably asso ciated with parent c lusters. In sets 3 and 5, for example,PC effects apparen t ly did not c luster by parent or igin ( table 3), whe reas E-R slopesapparen t ly did ( table 6 ). In set 6, clustering acco unted for 6 2 percent o f the variancein deviations from the regression lines (table 6). In other sets, deviations were nomore closely associated with c lusters than w ere m ean family h eights ( table 6), inwhich clustering ef fects approache d stat istical s ignificance only in sets 3 and 6.Parent or igin app arent ly inf luenced interact ions (as expresse d in PC s, s lopes, anddeviat ions) more than i t did the family average heights.This study reports the occurrence of appreciable am ounts of interact ion within onebreeding zone, more than has been indicated in previous reports for coast Douglas-f i r (Adams and Joyce 1990, Kitzmil ler 1990, Stonecypher 1990). This raises thequest ion whether Snow Peak sets fair ly represented condit ions in the Pacif ic North-west. Fortunately, data are avai lable that can be used to place results within a largercontext . Analyses based on the ANOVA model exist (15-year data) for 10 breedingzone s ( including Snow Peak, high and low) in western O regon. 1 Eight of the zone scover most of the lands from lat. 43o20 N. to 45020 N. along the eastern slopesI Unpublished data. Pacific Northwest Tree ImprovementCooperatives. On fi le with Forestry Sciences Laboratory,3200 S.W. Jefferson Way, Corval l is, Oregon 97 331.

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of the Coast R ange. The other two zone s extend from lat . 44030 N. to 45030 N.along the west s ide of the Cascade Range. The zones range in s ize from about 30to 80 thousand hectares. Progeny tests in the zones inc lude 87 sets, usual ly of 28 to30 fami l ies, each tested in f rom 5 to 10 plantat ions. Fi f ty-one percent of these setsexhibited signif icant (P

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The relat ively large amounts of g*e wi thin breeding zones may simply ref lect var i -able environments and var iat ion among Douglas- f i r populat ions wi thin the zones. Inwestern Oregon, the major determinants of s i te product iv i ty for Douglas- f i r are soi ldepth and drainage, and cold- or drought-condi t ioned growing-season length. M anylocal topographical factors, such as slope, cold-air drainage, or aspect, may achieveimportance because they inf luence temperature and avai lable moisture at cr i t icalt imes. Rain shadow is one such local factor that f igures prominently in the distr ibut ionof precipi tat ion in the region (Froehl ich and others 1982). Cl imate in western Oregonis general ly Medi terranean and therefore moisture l imi t ing; vegetat ion and si teproduct iv i ty c losely ref lect var iat ion in annual precipi tat ion (Frankl in and Dyrness1973). Snow and rain arr ive in storms from the Paci f ic Ocean, and amounts areaffected by prominent r idges and mountains. Highest annual precipi tat ions are foundon highest mountains and lowest precipi tat ions in adjacent inter ior val leys (Froehl ichand others 1982). I t therefore is not surpr is ing that plantat ions di f fered considerablyin untransformed heights at age 15 even wi thin breeding zones; wi thin the surveyedzones, shortest plantat ions averaged only 57 percent of heights in the tal lest plan-tat ions. Furthermore, previous studies have shown strong gradients of geographicgenetic var iat ion in Douglas- f i r that is associated wi th rain shadows (Campbel l 1986;Gr i f f in 1978; Sorensen 1979, 1983). Interact ions may be a ref lect ion of var iablepopulat ion responses' in the part icular ly var iable environments associated wi th rainshadows. This would account for the greater prevalence of interact ion in breedingzones in the lee of the C oast Range.Genotype * environment interact ion in amounts indicated by this study could representa ser ious source of error in models developed to predict growth in plantat ions or innat ive stands in a changing environment. In Snow Peak low, stat ist ical ly s igni f icantg*e effects accounted for 19 percent of that proport ion of var iat ion among plot meansnot explained by plantat ion di f ferences. These effects represent potent ial error in anyglobal-c l imate model that assumes a consistent response in Douglas- f i r to environ-mental change. The er ror could be larger or smal ler dep endin g on the degree ofenvironmental response and the genotypes involved. The interact ion seen here alsoshould preclude using seed-orchard seed in breeding zones outside the ones inwhich parents have been tested. Interact ions of considerable magni tude occur wi thinexist ing breeding zones and intui t ively we might expect fami ly performance to beeven less predictable in larger zones; evidence exists to suggest that genotype isa very large component of s i te product iv i ty wi thin a regional sett ing (Monserud andRehfeldt 1990). The main reason for advis ing caution stems, however, from quest ionsconcerning an understanding of the measurement, dynamics, and causes of ge inDouglas-fir.The measurement and interpretat ion of g*e effects have received sustained attent ionover the years (Al lard and Bradshaw 1964, Eberhardt and Russel l 1966, Finlay andWilk inson 1963, Freeman 1973). Unti l very recently effor ts have been concentratedon the wi thin- tr ial accuracy of a model 's f i t to i ts own data: the measure of "post-dict ive" success as contrasted to "predict ive" success (Gauch 1988). The lattermeasures the f i t between a model developed from part of the data and a separateval idat ion set of data. I t is not c lear whether models recommended by stat ist ical testsfor Snow Peak sets would be predict ively accurate for other tr ials of the same setseven in the same breeding zones. Gauch (1988) employed a data-spl i t t ing val idat ionprocedure to evaluate a soybean yield tr ial and found that only the f i rst PC axisimproves predict ion. In his case, the f i rst PC accounted for 16 percent of the plot

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var iat ion exclusive of p lantat ion ef fects. Including PC1 in the model reduced thesums o f squared d i f fe rences be tween mode l bu i ld ing and va l ida t ion da ta by 12percent . Because p lot error precludes perfect predict ion of va l idat ion data, PC1undoubted ly exp la ined m ore than 12 percen t o f the sum s o f squared "p red ic tab le"d if ferences. The model that included the f i rst PC predicted val idat ion data bet ter thandid a l l the data used to construct the model. Noise in the data set obviously obscuredtrue y ie ld pat terns exist ing with in the exper iment. One test is not , o f course, suf f ic ientbasis for recommending inclusion of only one, or of even one, s ignif icant PC in apredict ion model. The test does point out , however, that best postd ict ive modelsare not necessar i ly the best predict ive models. The opt imum structure of predict ivemodels for Douglas-f i r obviously bears invest igat ion.Another source o f uncer ta in ty ove r our ab i li ty to p red ic t g rowth per fo rmance inDouglas-f i r ar ises f rom quest ions about the consistency of in teract ion ef fects. InSnow Peak low, the sets d i f fered great ly in est imated amounts of g,e, as d id setsin the larger survey sample. And removing react ive famil ies or p lantat ions f rom theSnow Peak data sets af fected the est imated re lat ive amounts of g*e var iously in thedif ferent sets ( f igs. 5-7) . Furthermore, sets showed two ent ire ly d i f ferent dynamics ofg*e expression with t ree age; in two sets, th e ~ge2/~g 2 rat io dec reas ed dramat ica l lywith increasing age, but in the other four sets, the rat io increased just as dramat ica l ly .The var iabi l i ty of g*e est imates in space and t ime reported here and previously(Adam s and Joyce 1990, K i t zmi lle r 1990, Stonecy pher 1990) fo r coast Doug las- f ir ,lead to a host of quest ions about i ts cause. Of f i rst importance are the physio logicalmechanisms involved (Wheeler and others 1990). Is in teract ion a funct ion main ly ofgenet ic var ia t ion in growth potent ia l or of growth t iming? I f growth-t iming ef fects exist ,a re they mat te rs o f t iming w i th in years o r be tween years? Some o ther quest ionsre late to the st ructure of sets. Would the nonreact ive "stable" famil ies with in apresent set reta in their stabi l i t ies i f tested with in a d i f ferent family matr ix? Does thewith in-set d ist r ibut ion i tse lf (o f family heights, for example) af fect est imates of in ter-act ion; is i t possib le to preclude in teract ion with in a set by inadvertent or purposefu lchoice of a d ist r ibut ion of famil ies, half with very poor growth rates and half with veryfast growth rates, for example? Would in teract ions be essent ia l ly e l iminated i f famil ieswith in sets a l l or ig inated f rom one local stand; is in teract ion character ist ica l ly afea tu re o f compar isons o f geno types f rom somewhat d i f fe ren t popu la t ions?Genotype * env i ronment in te ract ion seems to be ub iqu i tous and o f t roub lesomemagn i tude in Doug las-f i r in weste rn O regon, even in b reed ing z ones tha t se ldomexceed 80 thousand hecta res. There a re many quest ions abou t i t , o f wh ich theabove quest ions a re a sma l l sample . Cons iderab le work on the measurement ,dynamics, and cause of in teract ion therefore seems just i f ied. In teract ion must beunders tood and measurab le in suppor t o f increas ing p rec is ion o f mode ls fo rpredict ing stand development, y ie ld, and growth responses in a changing cl imate.The work a lso is needed to improve se lec t ion p rocedures fo r t ree b reed ing . Forthe p resen t , however , gene t ic improvem ent cou ld p robab ly be a ided by e f fo rts torecon f igure b reed ing z ones. Ava i lab le da ta w i th in the reg ion shou ld be exam inedto determine whether a signif icant f ract ion of g*e is indeed associated with s i teproduct iv i ty , as i t seems to be in Snow Peak. I f i t is , part i t ion ing the area in to lowand h igh s i te f rac t ions may reduce in te ract ion and increase var iance among averagefamily ef fects. By so doing, i t a lso is possib le that zone sizes could be increasedsignif icant ly without sacr i f ic ing genet ic gains or adaptat ion (Stonecypher 1990).

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L i t e r a t u r e C i t a t i o n s Adams, W.T.; Joyce, D.G. 1990. Comparison of select ion methods for improvingvolume g rowth in young coastal D ouglas-fir. Silvae Genetica. 39: 219-226 .Allard, R.W.; Bradshaw, A.D. 1964. Implications of genotype-environmentinteractions in applied plant breeding. Crop Science. 4: 503-507.C am pbe ll, R obe rt K. 1 98 6. M app ed genetic var iat ion of Douglas-fir to guide seedtransfer in southwest O regon . Silvae Genetica. 35: 85-96 .Eberhardt, S.A.; Ru ssell, W .A. 1 96 6. Stabili ty parameters for comparing var iet ies.Crop Science. 6: 36-40.Fln lay, K .W.; Wilk inso n, G .N. 1963 . Th e analysis of adaptation in a plant-breedingprogramme. Australian Journal of Agricultural Research. 14: 742-754.Fran k l in, Jer ry F. ; Dy rness, D.T. 1973. Natural vegetat ion of O regon andWashington. G en. Tech. R ep. PNW-8 Port land, OR : U.S . Department ofAgriculture, F orest Service, P acif ic Northwest F orest and Ra nge Ex perimentStation. 417 p.Freeman, G.H . 1973. Stat istical methods for the analysis of genotype-environmentinteractions. H eredity. 31: 339-354 .Froehl ich, Henry A. ; McNabb, Dav id H. ; Gaweda, Frank. 1982. Average annualprecipitation, 196 0-1980, in southwest O regon. E xt. Se rv. Misc. Publ. 8220 ,Corvall is, O R: O regon State University. 7 p.Gauch, Hugh G., Jr. 198 8. Mod el selection and validation for yield tr ials withinteraction. Biometr ics. 44:705-715.G rif f in, A.R. 19 78. G eographic var iat ion in Dou glas-f ir from the coastal ranges inCa lifornia. I1: Predictive value of a reg ression m od el for seed ling gro wth variation.

Silvae G enetica. 27: 96-101 .K itzm il ler, Ja y H. 1990. Ge netic var iat ion and adaptabili ty of D ouglas-fir in North-western California. In: Proceedings of the joint meetings of the Western ForestG enetic Associat ion and I UFR O working parties; $2.0 2-0 5,1 2, and 14; 1990August 20-24; Olym pia, WA. Re pt. 2. 18 1. Olym pia, WA: [publisher unknow n].1 3 p .LIn, G .S.; Bin ns, M.R.; Le fkov i tch , L.P. 198 6. Stabil ity analys is : where do w estand? C rop Science. 26: 894-900.Monserud, R.A.; Rehfeldt, G.E. 1990. Genetic and environmental components ofvariation in site index in inland Douglas-fir. Forest Science. 36: 1-9.SAS I ns t i tu te Inc . 1987. SAS/ST AF ~ guide for personal computers, version 6edit ion. C ary, NC: SAS Inst itute Inc. 102 8 p.

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Silen, R.R.; Wheat, J.G. 1979. Progressive tree improvement program in coastalDouglas-fir. Journal of Forestry. 77: 78-83.Sokal, Robert R.; Rohlf , F. James. 1969. Biometry. San Francisco: W.H . Freemanand Company. 776 p.Sorensen, F.C . 1979. Provenance var iat ion in Pseudotsuga m enziesii seedlings fromth e me nziesii-var, g/auca transition zone in Oregon. Silvae Genetica. 28: 96 -103.Sorensen, F.C. 1983. Geographic variation in seedling Douglas-fir (Pseudotsugamenziesii) from the western Siskiyou M ountains in O regon . Eco logy. 64: 696 -702.Stonecypher, R.W . 199 0. Assessing effects of seed transfer for selected parents ofDouglas-fir: experimental methods and early results. In: Proceedings of the jointmeetings of the Western F orest Genetic Association and IUF RO working part ies,$2.02-05, 12, and 14; 1990 August 20-24; Olympia, WA. R ept. 2.290, Olympia,WA: [publisher unknown]. 23 p.Wheeler, N.C.; Ston ecy ph er, R .W.; Jech , K .S. 1990. Physiological character izat ionof select families to aid in source movement decisions: supplementing long-termperformance tdals. In: Proceedings of the joint meetings of the Western ForestGenetic Association and IU FR O working part ies, $2.02-05, 12, and 14; 1990August 20-24; O lympia, WA. Re pt. 2.362. O lympia, WA: [publisher unknown].1 6 p .

*U.S. GOVERNMENT PRINTING OFFICE: 1993-790- 301/80006

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Campbell, Robert K. 1992. Ge notype environmen t interaction: a case study forDouglas-fir in western Oregon. Res. Pap. PNW-RP-455. Portland, OR: U.S.Dep artme nt of Agriculture, F orest Service, Pacific Northwest Research Station.21 p.

Unrecogn ized genotyp e x e nvironm ent nteract ions (g.e) can bias genet ic-gainpredictions a nd mo dels for predicting growth dynam ics or specie s perturbations byglob al clima te change. This study tested six sets of families in 10 p lantatio n sites in a78-thousand-hectare breeding zone. Plantation differences acc ounte d for 71 perce ntof sums o f squares (15-year heights), replications a n additio nal 4.4 percent, families1.9 percent, the first principal com pon ent of interaction effects 3.5 percent, a nd thesecond pr incipal compone nt 1.2 percent. Results in th is study and in a larger survey(87 sets in 10 breeding zones) we re simi lar : 51 percent of sets indicated signif icantg.e. In 46 pe rcent of sets, the g-e interaction-family varian ce ratio wa s grea ter than 1in 35 percent, greater than 1.5; and in 10 percent, g reater than 5.Keywords: Pseudotsugamenziesii, gen etic variation, tree height, stability, AM MImo del, Eberhardt-Russell coefficients.

The Fo rest Se rvice of the U.S. Department ofAgriculture is dedicated to the principle of multipleuse m anagem ent of the N ation's forest resources forsustained yields of wood, w ater, forage, wildlife, andrecreation. Through forestry research, cooperationwith the States and private forest owners, andmanage ment of the National Fore sts and NationalG rasslands, it str ives--as directed by Con gress --toprovide increasingly greater service to a growingNation.The U.S. Department of Agriculture is an EqualOp portunity Em ployer. Applicants for all Departmentprograms w ill be given equal consideration withoutregard to age, race , color, se x, religion, or nationalorigin.Pacific Northwest Research S tation333 S.Wo First AvenueP.O. Box 3890Port land, Oregon 9720 8-3890

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