TRANSACTIONS OF THE AMERICAN FISHERIES SOCIETY
Volume 126 September 1997 Number 5
Transactions American e oth f Fisheries Society 126:725-734. 1997 American Fisheries Society 1997
Factors Influencing Behavio Transferabilitd ran f yo Habitat Model Benthia r sfo c Stream Fish KEVI . LEFTWICNN H U.S. Forest Sen>ice, Southern Research Station, Center for Aquatic Technology Transfer Department of Fisheries and Wildlife Sciences Virginia Polytechnic Institute Stateand University Blacksburg, Virginia 24061-0321,USA PAUL L. ANGERMEIER U.S. Geological Survey, Virginia Cooperative FishWildlifeand Research Unit* Department of Fisheries and Wildlife Sciences Virginia Polytechnic Institute and State University C. ANDREW DOLLOFF U.S. Forest Sen1 ice. Southern Research Station. Coldwaier Fisheries Unit Department Fisheriesof Wildlifeand Sciences Virginia Polytechnic Institute and State University
Abstract.—We examine e predictivdth e powe d transferabilitan r f habitat-baseo y d modely b s comparing associations of tangerine darter Percina aurantiaca and stream habitat at local and regional scale n Norti s h Fork Holston River (NFHR d Littlan ) e River, Virginia r modelOu . s correctly predicte presence dth r absenco e f tangerino e e darter (loca % NFHn i s64 r l Rmodelfo ) (regiona% and78 l model samplee th f o ) d habitat-units (i.e., pools, runs, riffles) distributioe Th . n of tangerine darters apparently was influenced more by regional variables than local variables. Data from Littl ehistorica7 Rive3 d ran l records from Virginia were use asseso dt s transferability r modelou f o s developed from NFHR data generaln I . t transfemodele no th , d di sr wel Littlo t l e River; all models predicted that either no (regional model) or few (local model) habitat-units in Little River would contain tangerine darter even though the species was observed in 83% of the habitat-units sampled. Conversely regionae th , l model correctly predicted presenc f tangerino e e darters for 95% of the historical records. Principal components analysis showed extensive overlap in NFH d LittlRan e River habitat which suggest streamo stw thae e ecologicallth ar st y similar. The suitability of Little River for tangerine darters was shown more clearly by principal components analysis than by our models. Because different limiting factors may apply in different systems, the elimination of potentially important ecological variables may compromise model transferabiiity. A hierarchical approach to habitat modeling, with regard to variable retention, may improve transferability of habitat models.
1 The Unit is jointly sponsored by the U.S. Geological Survey, the Virginia Department of Game and Inland Fisheries Virginid an , a Polytechnic Institut Statd ean e University. 725 726 LEFTWICH ET AL.
Models are widely used to predict occurrence, for variatio t multiplna e spatiotemporal scalee sar density, and habitat associations of aquatic species likely to have greater predictive power than those (Berry 1984; Fausc t alhe . 1988; Hobb Hand san - focusin singla n go e scale. ley 1990) and include effects of human impacts. Currently well-developeo n , d framework exists Most habitat model premfisr e basee sth fo har n d-o for predicting transferabilit f habitao y t models. e thais t physical characteristics influence - fisoc h Many habitat t transferablmodelno e ar s e across currence, abundance, and production (Hubert and spatiotemporal scales (Bowlby and Roff 1986; An- Rahel 1989) d numerouan , s investigators have germeier 1987; Layher et al. 1987; Pajak and Ne- demonstrated these relationships by incorporating s 1987ve ; Huber Rahed an t l 1989) t sombu , e have various physica chemicad an l l variables into their transferred successfully (e.g. Belau . 1989)al t de . models (Fausc . 1988)al t e h . Relationship- be s Limited predictive abilit transferabilitd yan y yma tween habitat and fish populations, however, are causee b parn di failury b t includo et "truee eth " comple ofted xan n poorly understood (Huberd an t limiting habitat variable y higb hr o s spatiotem- Rahel 1989), especially for nongame species. poral variability in these variables. Thus, habitat models often lack precision for the In this stud examinee yw predictive dth e power system in which they were developed or are not d transferabilitan f habitat-baseo y d modely b s transferable among systems (Layher et al. 1987). comparing association f tangerino s e darters Per- Limitations to fish distribution and abundance cina aurantiaca and stream habitat at two spatial ma viewee yb hierarchya s da . Ultimately spea , - scales. Tangerine darters were selecte r studdfo y cie s limitei s physiologica it y b d l toleranco t e because thee relativelar y y commo d easilan n y physicochemical features such as dissolved oxy- sighted by using underwater observation tech- gen, pH, and temperature. Within the range of its niques (Greenburg 1991; Etnier and Starnes 1993; physiological tolerances and dispersal abilities, a Jenkin Burkhead san d 1994). Tangerine darter- sin species may be limited by availability of suitable habit deep riffles, runs, and pools in relatively physical habitat, which typicall s describei y n di clear, moderate to large tributaries of the Tennes- term featuref so s like water depth, current velocity, e Rivese r (Howell 1971; Greenburg 1991; Etnier substrate type, and cover. Physicochemical and and Starnes 1993; Jenkin Burkhead an s d 1994). structural features form habitat templates that con- These habitats are amenable to detailed under- strain the types of life histories that can persist in water observations of habitat use and accurate es- localita y (Southwood 1977; PofWard an f d 1990; timate f abundanceso . Townsend and Hildrew 1994). Finally, biotic fea- We first tested the null hypothesis that tangerine tures suc foods ha , competitors, predators disd ,-an darters were uniformly distributed among stream ease may further restrict distribution and abun- habitats. Secondly testee w ,e nul dth l hypothesis dance l thesAl . e limitation t concurrentlac s o t y that habitat models develope streae on n mdi were generate spatial variatio fisn ni h distributiond san applicabl l streamal n ei s where tangerine darters abundance, which can be modeled at a wide range occur. We also examined patterns of model pre- of spatial scales. Accurate predictions of fish dis- dictabilit transferabilitd yan compariny yb g logis- tributio d abundancan n y requirma e e complex tic regression models, which retained only a few models to account for variation both within and e variableoth f s measured, wit ordination a h f no among spatial scales. habitats based on all measured variables. Ideally, species-habitat associations shoule b d investigated across all pertinent scales (Morris Methods 1987) becaus differentiae th f eo l effect regionalf so - Study streams.—North Fork Holston River and local-scale pattern theid an s r interactionn o s (NFHR Littld an ) e River uppe,e botth n hri Ten- species distribution patterns r exampleFo . , Smogor nessee River system of southwestern Virginia, et al. (1995) found that a single large-scale variable were selected for study because of high water clar- (distanc e oceanth o t e) predicte e distributiodth n ity, the presence of substantial populations of tan- and abundanc e Americath f eo l Anguillanee ros- gerine darters (P. L. Angermeier, unpublished trata better than did local-scale variables (i.e., data), and similar water chemistry characteristics depth, current velocity, substrate, cover), whereas (Zipper et al. 1992). . (1990Rosal t e s ) demonstrated both larged an - North Fork Holston Rive sixth-ordea s i r r trib- local-scale features (stream size and microhabitat, utar f Holstoyo n River, Tennessee (Figurs i t I . 1) e respectively) influence distributiodthe baythe -nof a clear, slow moving, moderate gradient, warm- ou darter Etheostoma rubrum. Models that account water stream characterize alternatiny b d g pool- TRANSFERABILITY OF HABITAT MODELS 727 N T Section 1
North Fork Holston section 2
Section 3
Section 4
Section 5
20km VA
TN
\ Tazewell co Little River US 19
Clinch River
m 3k Russel. Co l FIGURE 1.—North Fork Holston River (NFHR d Littlan ) e Rive southwesn i r t Virginia. Broken line n NFHo s R indicate endpoinl rive th e f studso y reaches. Fish samplin Littln gi e River starte labelee th t da d forended dan t da the Clinch River confluence. riffle morphology and a rocky substrate (Hill et al. through an agricultural valley without major in- 1975; Jenkins and Burkhead 1994). Land use is dustry (Jenkin Burkhead san d 1994). predominantly agriculture (livestocw ro d an k Habitat sampling.—We surveye contigu0 18 d - crops) majoA . r sourc f industriaeo l pollutioo nt ous river kilometers of NFHR from the Tennessee- NFHR originated from Saltville, Virginia, during Virginia border to the confluence of Lick Creek the late 1800s to the early 1970s (Jenkins and near Nebo, Virginia (stream width in the study Burkhead 1994). Although the aquatic ecosystem ranged from 14 to 74 m during base flow), between below Saltville was changed dramatically by Jund Septembean e r 1992 (Figur . Thi1) e s area chemical pollution (Young-Morgan Associates encompasse knowe sth n rang tangerinf eo e darters 1990), substantial recover occurres yha recenn di t in NFHR (Jenkin d Burkheaan s d - 1994)di e W . years (Hill et al. 1975; Feeman 1980; Jenkins and vide rivee dth r into five section f similaso r length Burkhead 1994). for samplin randomld gan y selecte sampline dth g Little River is a moderate gradient, fourth-order order of the sections (Figure 1). tributary of Clinch River, Virginia (Jenkins and We surveye e loweth d rive 6 1 r r kilometerf o s Burkhead 1994; Figure 1), characterized by an al- Little River (stream width in the study ranged from ternating pool-riffle morphology with gravel, cob durinm - 2 3 g 1 o 4t bas e flow) d betweean y nMa ble, and bedrock substrates. Only the lower 25 July 1993 (Figure 1). We restricted sampling to river kilometers support a warmwater fish fauna this section because of low water clarity in the (Jenkins and Burkhead 1994). Little River flows upper portio streame th f no . 728 LEFTWICH ET AL.
TABLE 1.—Ranges for categorical classes of continuous transect, continuously searchin tangerinr gfo e dart- habitat variables as transformed into discrete variables (by ers. Transects were locate areae th greatesf sn o di t quantiles)% 25 r o usin% . Habitat-unig50 t measurements flow in riffles and runs and about midway between were use n logistii d c regression model d chi-squaran s e goodness-of-fit tests. the middle of the stream and the stream margin on each sid poolsr efo . Habitat variable Class 1 Class 2 Class 3 Class 4 Sighting distance (distance from an observer to Elevation (m) <408 409-463 464-536 >536 a visible Secchi disk) was measured at the begin- Length (m) <100 >IOO ninf dive o f eac go y rh da samplin wheneved gan r Width (m) <21 22-31 32-44 >44 visibility changed notably. Water clarit cons ywa - Depth (cm) <25 26-50 51-75 >75 Distanco et sidered suitabl underwater efo r observations when next similar sighting distanc greates ewa visibilit; r m tha 5 n1. y uni) (m t <1(X) >1(X) during sampling ranged from NFH1. n 4.i 5o t 1 m R Pool : riffle <0.82 0.83-1.64 1.65-3.33 >3.33 and from 1.5 to 2.9 m in Little River. Habitat variables.—Variables used in our anal- yses were (1) stream order, (2) stream elevation, (3) We used multistage, stratified sampling design poo rifflo t l e ratio (P:R) ) habita(4 , t type ) dom(5 , - and visual estimation technique estimato st e total inant substrate, (6) subdominant substrate, (7) dis- surface are f selecteao d habitat types (Hankid nan nearese tancth o et t habitat-uni f similao t r type) (8 , Reeves determino 1988t d )an distributioe eth f no habitat-unit length, (9) habitat-unit width, and (10) tangerine darters in NFHR and Little River. We depth-clas habitat-unite th f so estimatee W . d mean based strata within reaches on naturally occurring elevation and stream order (Strahler 1957) of each habitat-units including pools, riffles, and runs. sampled habitat-unit from USGS 7.5; topographic A two-person crew classified and inventoried all maps (1:24,000 scale). We estimated P:R by divid- habita NFHn i t Littld Ran e River study areas dur- ing the surface area of pools by the surface area of ing periods of base flow. On the first of two trips runriffled san sm upstrea withim 0 0 50 n50 d man down each river cree on ,w member identified each downstrea f eacmo h habitat-unit sample r fishdfo . habitat-uni typy b t e (pool, run r riffleo , ; Plaitt se If an adjacent habitat-unit exceeded 500 m in al. 1983), visually classified the dominant and sub- length, a near-equal length of stream in the opposite dominant substrat y particlb e e sizy usin(b e a g direction was included in the calculation of P:R. modified Wentworth scale), and estimated wetted exampler Fo adjacene th f i , t unit upstrea sama mf o - stream width e otheTh . r crew member estimated pled unit was a 800-m-long pool, then all of the the depth-class (Table 1) of each habitat-unit by downstream sample unitth f o s e withim uni 0 t n80 measuring depth (to the nearest 0.1 m) at about 3- were included in the estimate of P:R. m intervals while traveling downstream and across Habitat models.—To tesfirse th t t null hypothesis the channel in a zigzag pattern. (tangerine darters were uniformly distributed) in the We measure e lengtth d f eaco h h habitat-unit NFHR usee w , d multiple stepwise logistic regres- wit optican ha l range finder usee W .d these mea- sion (multivariate analysis) to generate three models surement observationd san landmarkf so locato st e to estimat probabilite eth y (chi-square probabilities each habitat-unit on U.S. Geological Survey 0.05< ; SAS 1989 tangerinf o ) e darter occurrence (USGS) 7.5' topographic maps. We measured wet- (dependent variable) in NFHR habitat-units at two ted stream widths (for calibration of habitat esti- spatial scales (regiona local)d an usele W .d pres- mates) onl thosn yi e units selecte r fisdfo h sam- ence-absence data because it is more robust to sam- pling. We measured habitat and looked for tan- pling biases than measures of densities (Green gerine darter pools6 2 run8 n riffles7 si 3 4 , sd an , 1979). in NFHR (10.8 e habitat-unitsth f %o n 1i 1 d an ) firse Th t model characterized regional conditions pools run0 riffles2 2 Littl,3 n sd i an ,e River (35.2% and included four independent variables: (1) stream habitat-units)e th f o . width, (2) elevation, (3) stream order, and (4) P:R. We made underwater observations during the The second model characterized local conditions second trip down each section of river to determine and included six independent variables: (1) habitat e distributioth f tangerino n e darters within each type dominan) (2 , t substrate ) subdominan(3 , t sub- selected habitat-unit o observers,Tw . equipped strate ) habitat-uni(4 , t length) ) depth(6 (5 ,d an , with face-masks and snorkels, entered the water distance to the next similar unit. The third model e downstreaath t f eaco d h men selecte d habitat- included all ten independent variables. Continuous uniproceeded an t d slowly upstream alon glineaa r habitat variables were transformed into discrete TRANSFERABILIT HABITAF YO T MODELS 729
variables by dividing the data into either two or TABLE 2.—Summary of stepwise logistic regression four classes by using 50% or 25% quantiles, re- model o estimatt s e probabilit f presenco y f tangerino e e spectively (Table 1). We verified the three logistic darter Nortn i s h Fork Holston River, Virginia. Parameter regression models using univariate analyses (chi- estimates are maximum-likelihood estimates, and signifi- canc f eaceo h variabl bases i e Waln do d chi-square. Sig- square goodness-of-fit tests; P < 0.05) to examine nificanc eacof e h mode baseis a chi-squarl don the eof associations between tangerine darter presence and -2 log-likelihood statistic (SAS 1989); DU represenLs dis- individual variables used in logistic regression tance to next similar unit, and S represents subdominant models. substrate. Principal components analysis (PCA).—We used Parameter Chi- (multivariatA PC e analysis 1989S SA ;ordinato )t e Variable estimate square df Probability fish sampling units from both streams, l baseal n do 2 10 habitat variables examino t , effece e th f scalo t e Regional model (x = 43.81, df = 2, P = 0.0001) variabld an e reductio moden no l transferability- Be . Intercept 0.92 9.60 0.0020 Elevation > 463 m -1.94 15.74 0.0001 caus ordinae eth r datl t violatedatou ase n ai e dth 4th order -4.14 15.17 0.0001 assumptio f multivariatno e normality requirer dfo Local model (\2 = 18.90, df = 3, P = C.0003) statistical inference from PCA, we used this method Intercept 0.20 0.46 0.0498 only to illustrate similarity of sites across multiple Depth < 25 cm -1.36 6.61 0.0102 ecological variables (Tabachnick and Fidell 1989; Depth 51-7m 5c 1.36 4.46 0.0347 Johnso Wicherd nan n 1992). D U< 10 0 m -0.93 4.61 0.0317 2 W dato e performetw a n sets o NFH) (1 :A RdPC Combined model (\ = 56.88, df = 4, P =: 0.0001) ) NFH(2 Littld sited Ran an se River sites com- Intercept l.ll 7.86 0.0050 bined comparee W . ordinatioe dth NFHf no R sites Width > 43 m 1.15 3.91 0.0479 Elevatio3 46 > n m -2.19 16.26 0.0001 containing tangerine darters with the results of our 4th order -4.18 14.62 0.0001 logistic regression model (containin habn te -l gal Pebble (5) -1.92 8.84 0.0030 itat variables) and univariate analyses to assess the usefulness of PCA for identifying distribution pat- terns. If the ordination patterns of tangerine darters corresponded wit e patternhth f distributioo s n ni darter NFHRn i s e regionaTh . l mode r NFHfo l R r previouou s analyses , woultheA nPC d providea indicated tangerine darters selected the higher or- reliable depictio similarite th f no f habitat-unityo s der, lower elevation section streae th f mso (Table when both streams are ordinated together. 2). The logistic equation correctly predicted pres- Model transferability.—To test our second null encabsencd an e f tangerino e f eo darter0 9 r fo s hypothesis (habitat models developed in one 116 (78%) sampled habitat-units (critical proba- stream were applicable in all streams where tan- bilit S 1989= 0.5y SA ,e regiona; TablTh . 3) el gerine darters occur) e use three w , th d e logistic model correctly predicted presence- (80ob f %o regression models develope NFHr dfo predicRo t t servations) slightly more accurately than absence the presence and absence of tangerine darters in (76% of observations) of tangerine darters. habitat-units of Little River. We verified model The local model indicated that tangerine darters predictions with underwater observations. selected habitat-units with a distance to the next We also compared the regional model predic- similar habitat-uni f greateo t witm r thah0 10 n tion f tangerinso e darters presence with data from depth avoidet s bu betwee m c d 5 habitat7 n d 51an - historical collection sites from the upper Tennessee units with depths less than 25 cm (Table 2). The drainage in Virginia (Burkhead and Jenkins 1991; logistic equation predicted presence of tangerine Jenkin Burkhead san d 1994)Powe th site n -ei On . darter observatione th f so correctl% 64 sr (Tayfo - ell River (sixth-order tributary of Clinch River), ble 3). The local model correctly predicted darters five sites in Copper Creek (fifth-order tributary of presence (66 f %observationso ) slightly mor- eac Clinch River)site1 3 Clincn i sd an , h River (sev- curately than absence (64 f %observations)o . enth-order tributary of Tennessee River) were used The regression model developed from all 10 teso regionae t th t l model e degreTh . f modeo e l habitat variables retaine same dth e variablee th s sa transferabilit s assesse proportioe wa yth y db f no regional mode t alsbu lo indicate a positivd - as e correct predictions. sociation with streams greater than 43 m wide and Results negativa e association with pebbl subdomia s ea - Logistic Regression nant substrate (Table 2). No significant variables All three of our logistic regression models in the local model emerged in this model, which showed a nonuniform distribution of tangerine correctly predicted absence (80 %observationsf o ) 730 LEFTWIC. AL T HE
TABLE 3.—Classification table r thsfo e multiple logistic substrate, observe e regressioth n i d n mode- in l regression model Tabln i o sestimatt 2 e e probabilitf o y cludin l habitagal t variables, wer t significanteno . presenc f tangerineo e darter Nortn si h Fork Holston Riven Little River d historicaan , l records. Classificatione ar s Principal Components Analysis based on critical values of 0.5 (SAS 1989). Visual representation of PCA closely corre- Numbe f habitat-unito r s sponded with the results of the logistic regressions Datt ase Predicted with fish and univariate analyse regionae th t a s l scald ean and Observec observation with fish Present Absent Correct with the logistic regressions at the local scale. Tan- gerine darters were less commo lowen ni r order, North Fork Holston River higher elevation portion f NFHo s R (Figure 3A; Regional Present 50 40 10 Tabl . Habitat-unite4) s wit highese hth t densities Absent 66 16 50 of tangerine darters (greater than 200/ha; K. N. Total 116 56 60 78% Leftwich, unpublished data) were most common Local Present 50 33 17 in moderate depths near the middle of NFHR, as Absent 66 24 42 indicated by stream order and elevation (Figure Total 116 67 69 64% 3A). Combined Principal components analysis was partially suc- Present 50 39 11 Absent 66 13 53 cessfu t distinguishina l g NFHR sites from Little Total 116 52 64 79% River sites but showed overlap (similarity) of nu- Little River merous sites in both streams. The majority of sites Regional occupied by tangerine darters in NFHR and Little Present 52 0 52 River were similar (Figure 3B; Table 4). Absent 11 0 11 Total 63 0 63 17% Model Transferability Local Present 53 7 46 The logistic regression models developed from Absent 10 2 8 data collected in NFHR performed poorly when Total 63 9 54 24% Combined used to predict occurrence of tangerine darters in Present 0 0 52 Little River (Table 3). The regional and local-re- Absent 63 0 11 gional (combined) models correctly predicted only Total 63 0 63 17% the absenc tangerinf eo e darter eved san n then only Historical records in 17% of the 63 sampled units. These models Regional predicted that no habitat-units would contain tan- Present 37 35 2 Absent 0 0 0 gerine darters even thoug- e specieob th h s wa s Total 37 35 2 95% served in 83 percent of the units sampled. The local model performed slightly better than the re- gional model but predicted the correct condition slightly more accurately than presence (78% o funite th fof s o rsample onl% y24 d e (TablTh . e3) observations). regional model, however, correctly predicted pres- ence of tangerine darters for 95% of the 37 his- Chi-Square Analysis torical records; it incorrectly predicted the absence Tangerine darters used the entire range of habitat of the darters for only two collections from the available but preferred habitat-units in fifth- and upper Clinch River (Table 3). sixth-order reaches at lower stream elevations that were longer than 100 m and wider than 32 m (Fig- Discussion urThes. e2) e relationships corresponded with both Our results provided evidence to reject a uni- the regional regressio nregressioe modeth d an l n form distribution of tangerine darter in NFHR. Our model includin l habitagal t variable provided san d models demonstrated habitat selectivit y tanb y - further evidence that tangerine darters avoidee dth gerine darter in NFHR at all spatial scales. Further, smaller, upstream sectio NFHe th f nRo study area. all analyses showed a relationship between tan- Chi-square analyse t supporno regresd e sdi th t - gerine darter distributio stread nan m siz NFHRn ei . sion models that contained local-scale variables. Several analyses provided evidence thadise th t- The relationships between tangerine darters and tribution of the species is influenced more by re- depth, observe locae th ln d i regressio n modeld an , gional than local patterns in NFHR. We found no between tangerine darter pebble-subdominand san t significant relationships between presenc dartf eo - >ecie s a abse i :ceptio n o f tw c la l variable s c JR E 2 . — Observ e hic h th e regio n th e chi-squar B a. 3' o vi 3
M rt 3C S" ^ *"• *3 s3 «r< H-1 t^y *"* !1
Low (Order) High O High (Elevation) Low Q_
B s
Low (Order) High Narrow (Stream Width) Wide High (Elevation) Low PCI FIGURE 3.—Plot f habitat-unitso principan i s l components (PC) space habita0 base1 n do t variables ) Plof (A .o t habitat-unit Nortn i s h Fork Holston River (NFHR). Open circles represent sites lacking tangerine darters, solid circles represent sites containing tangerine darters, and solid squares, encompassed by a polygon, represent sites with tangerine darter densities greater than 200/ha ) Plo f habitat-unit(B . o t NFHn si Littld Ran e River. Circled san triangles represent habitat-units in NFHR and Little River, respectively, where tangerine darters were present (closed) and absent (open). Polygons encompass sites occupie tangeriny db e darter NFHn si Littld Ran e River.
factors may be ranked in different orders among or nearly absent from Little River. The PCA plot systems. Thus, reduced or simple models may not of habitat-units from both rivers (Figure 3), how- transfer well becaus y variableeke s that explain ever, showed that many habitat-unit Littln si e Riv- species distribution somn si e system t othno -t sbu similae ar r e thoso rt e occupie tangeriny db e dart- ers have been removed. n NFHRi e s ordinatioer Th . f habitat-unitso n , Because habitat models are typically construct- l variablesbaseal n do , more clearly showee dth eliminatiny b d e g some variables, some resolution suitability of Little River habitat for tangerine dart- in habitat association e lostb y . Logistima s - cre ers than did the logistic regression models, which gression models develope r tangerinfo d e darters were base fewen do r variables. incorrectly predicted the species to be either absent A hierarchical approac modelino ht g habita- as t TRANSFERABILIT HABITAF YO T MODELS 733
TABLE 4.—Results of principal component (PC) ordi- gerine darters were uniformly distributed in nation of sampled habitat-units from North Fork Holston NFHR modelr Ou . s demonstrated habitat selectiv- River (NFHR; N= 116Littld ) an )base e63 Rived= N ( r ity by tangerine darters in NFHR at all spatial on 10 habitat variables. Eigenvectors associated with each variable for the first three PCs from the NFHR are shown scales, and habitat selectivity at the regional scale with eigenvalue f respectivo s e correlation matriced an s was verified by chi-square analyses. proportions of variance explained. Eigenvectors with ab- We also reject the hypothesis that habitat models solute values less than 0.3 are represented by dashes. developed for tangerine darters in the NFHR were applicable to all streams in southwest Virginia. Variable or statistic PC I PC II PC III Our logistic models incorrectly predicte dise dth - North Fork Holston River tribution of tangerine darters in Little River and Order 0.492 - - upper Clinch River. Although tangerine darter dis- Elevation -0.529 - - Riffle-pool - - 0.640 tribution in NFHR and most of the historic sam- Mean stream width 0.467 - - pling sites might have been explained by regional Habitat-unit type - 0.550 - variables, tangerine darter occurrenc pret no -s ewa Distanc liko et e unit - 0.432 - Habitat-unit length - -0.320 - dicte Littln di e e uppeRiveth r o rr Clinch River Depth 0.347 -0.428 - sites. Dominant substrate - - -0.562 Finally morA PC ,e clearly illustrate e suitdth - Subdominant substrate - - 0.374 ability of Little River as habitat for tangerine dart- Eigenvalue 3.039 2.088 1.225 Proportio f variancno e 0.304 0.209 0.12 3r models ou s tha d e er los di nf resolution Th .so , caused by the elimination of potentially important NFHR-Little River combined ecological variables, may compromise the trans- Order 0.500 _ _ Elevation -0.528 - - ferabilit f habitao y t models hierarchicaA . - ap l Riffle-pool - - 0.401 proac habitao ht t modeling, with regar variabldo t e Mean stream width 0.476 - - retention, may improve transferability of habitat Habitat-unit type - 0.574 - Distance to like unit - 0.346 - models across spatial scales and across stream sys- Habitat-unit length - -0.350 - tems. Depth - -0.519 - Dominant substrate - - -0.675 Acknowledgments Subdominant substrate - - 0.514 Eigenvalue 3.247 1.972 1.201 We thank Chris Arthur, Kelly Harpster, Jess Proportio f variancno e 0.325 0.197 0.120 Jones, Nancy Mason, Martin Underwood, and Car- oline Weiking for their assistance with field data collection alse W o. thank Mark Hiss Vad a yan nJi sociations may improve model transferability. A for providing statistical advice and William Ensign full-model approac usefue b y determininn i lh ma g and Mel Warren for reviewing the manuscript. the overall similarity of the systems being studied. Fundin r thigfo s projec provides wa tVir-e th y db Once ecological similarity has been established, ginia Department of Game and Inland Fisheries reduced e usemodelb identifo dt y ma s y specific anU.Se dth . FisWildlifd han e Service. habitat need limitationr so speciea f so t variousa s spatial scales r exampleFo . glochidie ,th e th f o a References Cumberland monkeyface Quadrula intermedia,a Alien, W. E. 1926. The problem of significant variables freshwater mussel, only survive on the gills of two in natural environments. Ecology 10:223-227. host-fish species in the genus Erimystax. Further, Angermeier . L 1987 . P , . Spatiotemporal variation i n this species has been found only in moderate to habitat selection by fishes in small Illinois streams. large streams, in swift-water habitats, and over Pages 52-60 in W. J. Matthews and D. C. Heins, editors. Community and evolutionary ecology of silt-free rocky substrates (Neves 1991). Quadrula North American stream fishes. Universit f Oklayo - intermedia thus i highlsa y specialized species with homa Press, Norman. very specific habitat requirements. A transferable Belaud. A., P. Chaveroche, P. Lim, and C. Sabaton. model for Q. intermedia might consist of three lev- 1989. Probability-of-use curves applie browo t d n els: (1) presence of host species, (2) stream size, trout (Salmo trutta fario L.) in rivers of southern and (3) local current velocity and substrate. A hi- France. Regulated Rivers: Researc Managed han - erarchical approach, however y onlma y, apply ment 3:321-336. Berry, K. H. 1984. Development, testing and applica- wher modeler'e eth s objectives include developing tio wildlife-habitaf no t models. Pages . Ver1-J 3n i - models with broad applicability. ner, M. L. Morrison, and C. J. Ralph, editors. Wild- In summary, we reject the hypothesis that tan- life 2000: modeling habitat relationships of terres- 734 LEFTWICH ET AL.
trial vertebrates. Universit f Wisconsio y n Press, Spotted bass suitability related to fish occurrence Madison. and biomas measurementd san f physicochemicaso l Bowlby. J. N., and J. C. Roff. 1986. Trout biomass and variables. North American Journal of Fisheries habitat relationships in southern Ontario streams. Management 7:238-251. Transactions of the American Fisheries Society 115: Morris . 1987W . D , . Ecological scal habitad ean t use. 503-514. Ecology 68:362-369. Burkhead, N.M., and R.E.Jenkins. 1991. Fishes. Pages Neves, R. J. 1991. Mollusks. Pages 251-320 in K. Ter- 321-408 in K. Terwilliger, coordinator. Virginia's williger, coordinator. Virginia's endangered species: endangered species: proceedings of a symposium. proceedings of a symposium. McDonald and Wood- McDonald and Woodward Publishing, Blacksburg, ward Publishing, Blacksburg, Virginia. Virginia. Pajak, P., and R. J. Neves. 1987. Habitat suitability and Etnier. D. A., and W. C. Starnes. 1993. Fishes of Ten- fish production: a model evaluation for rock bass nessee. Universit f Tennesseyo e Press, Knoxville. Virginio intw a streams. Transaction Amere th f so - Fausch, K. D.. C. L. Hawkes, and M. G. Parsons. 1988. ican Fisheries Society 116:839-850. Models that predict standing crop of stream fish Plaits Minshall. Megahan. F W . D.. . W ,W . G d an . 1983. from habitat variables: 1950-85. U.S. Forest Ser- Method r evaluatinfo s g stream, riparian biotid an , c vice General Technical Report PNW-213. conditions. U.S. Forest Service General Technical Feeman . 1980C . J , quantitativA . e surve f d fisyo an h Report INT-138. macroinvertebrate Holstoe th f so n River basin- Au : Ward. V . J Poff . d . L.1990N an , . Physical habitat tem- gust-September 1973. Tennessee Valley Authority. plat f lotio e c systems: recover e contexth n i yf o t Report WR(70)-40-4-80.1. Morris. Tennessee. historical patterns of spatiotemporial heterogeneity. Green, R. H. 1979. Sampling design and statistical Environmental Management (New York) 14:629- methods for environmental biologists. Wiley. New 645. York. Ross, S. T, J. G. Knight, and S. D. Wilkins. 1990. Lon- Greenburg, L. A. 1991. Habitat use and feeding be- gitudinal occurrenc bayoe th f euo darters (Percidae: havio f thirteeo r n specie f benthiso c stream fishes. Etheosioma rubrum) in Bayou Pierre: a response to Environmental Biology of Fishes 31:389-401. stream order or habitat availability? Polskie Archi- . OrthJ . D . Groshensd 1994an , P. .. T .Transferabilit y wum Hydrobiologii 37:221-233. f habitao t suitability criteri r smallmoutafo h bass. SAS. 1989. SAS/STAT user's guide, version 2. SAS Micropterus dolomieu. Rivers 4:194-212. Institute. Cary North Carolina. Hankin. D.G.. and G. H. Reeves. 1988. Estimating total Smogor . Angermeier L . A. . . R Gaylord,P ,K . C d ..an fish abundanc d totaan e l habitat are n smali a l 1995. Distribution and abundance of American eels streams base visuan do l estimation. Canadian Jour- in Virginia streams: tes f nulo t l models across spa- naf Fisherieo l Aquatid san c Sciences 45:834-844. tial scales. Transactions of the American Fisheries Hill. McDonough . A M. D . ,. SaylorT . F . C d . 1975an . . Society 124:789-803. Status of faunal recovery in the North Fork Holston Southwood . 1977E . R . .T , Habitat templee th , ecor fo t - River, Tennesse Virginiad an e . Proceedinge th f o s logical strategies? Journa f Animao l l Ecolog: y46 Annual Conference Southeastern Associatiof no 337-365. Gam Fisd ean h Commissioners 28(1974):398-413. Strahler . 1957N . A , . Quantitative analysi watershef so d . HanleyA . T d . an 1990Hobbs , T use/availo . D .N , - geomorphology. American Geophysical Union ability data reflect carrying capacity? Journal of Transactions 38:913-920. Wildlife Management 54:515-522. Tabachnick, B. G., and L. S. Fidell. 1989. Using mul- Howell, J. F. 1971. The life history and ecology of tivariate statistics. Harpe Rowd Yorkw ran Ne , . Percina aurantiaca (Cope). Doctoral dissertation. Townsend, C. R., and A. G. Hildrew. 1994. Species Universit f Tennesseeyo . Knoxville. trait relation i s habitaa o nt t temple r rivefo t r sys- Hubert, W. A., and F. J. Rahel. 1989. Relations of phys- tems. Freshwater Biology 31:265-275. ical habita abundanco t t f foueo r nongame fishen i s Young-Morgan Associates. 1990. Assessment of mus- high-plains streams: a test of habitat suitability in- l communitiese e Nortth sn hi Fork Holston River. dex models. North American Journal of Fisheries Young-Morgan Associates, Abingdon. Virginia. Management 9:332-340. Zipper, C. E, G. I. Holtzman, S. Rheem, G. K. Evanylo. . BurkheadM . N d an .Jenkin , 1994E. . R s . Freshwater 1992. Surface water quality trends in southwestern fishes of Virginia. American Fisheries Society. Be- Virginia, 1970-1989 . SeasonaI : l Kendall analysis. thesda, Maryland. Virginia Water Resources Research Center, Virginia Johnson, R. A., and D. W. Wichern. 1992. Applied mul- Polytechnic Institute and State University. Blacks- burg. tivariate statistical analysis. Prentice-Hall, Engle- wood Cliffs Jerseyw Ne , . Received September 11, 1996 Layher . . MaughanWardeE D G.. . . W O ,. W .d 1987an , . Accepted March 19, 1997