MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 265: 197–212, 2003 Published December 31

INTRODUCTION

Conservation managers need to predict the abun-dance and distribution of fish species in estuarieslacking fisheries-independent monitoring data and toassess the possible effects that hydrodynamic changescaused by environmental perturbations may have onfish populations. Both needs can be met by modelswhich accurately describe the relationship betweenspecies abundance and the local environment, given

information on environmental conditions or predic-tions on conditions following hydrological perturba-tion. Attempts have been made to model fish abun-dance or distribution quantitatively (parametrically) orqualitatively (or nonparametrically) as a response toenvironmental conditions (Rubec et al. 1999, Brown etal. 2000). However, these approaches fail to eithermodel the relationship between environment andabundance realistically or in a quantifiable manner(Hastie & Tibshirani 1990).

© Inter-Research 2003 · www.int-res.com*Email: s.kupschus@cefas.co.uk

Development and evaluation of statistical habitatsuitability models: an example based on juvenile

spotted seatrout Cynoscion nebulosus

Sven Kupschus*

Fish and Wildlife Conservation Commission, Florida Marine Research Institute, 100 8th Avenue SE, St. Petersburg, Florida 33701, USA

*Present address: The Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK

ABSTRACT: Conservation and fisheries managers require models that describe the abundance anddistribution of fishes in order to protect and manage exploited fish stocks in the face of anthropogeni-cally induced habitat loss and exploitation. In this study, models describing environmental preferenceswere developed for juvenile spotted seatrout Cynoscion nebulosus within 3 Florida estuaries: theIndian River Lagoon, Tampa Bay, and Charlotte Harbor. A generalized additive model (GAM) wasdeveloped to describe the environmental preferences, spatial distribution, and temporal fluctuations ofjuvenile seatrout in each estuary based on a 4 yr time-series of fisheries-independent catches. All3 models indicated similar environmental preferences for all populations examined and were alsoqualitatively consistent with the findings from other studies. Consequently, habitat-preference modelsbased on GAMs are useful tools to predict fish abundances in estuaries lacking fisheries-independentdata, given knowledge of the local environmental conditions. These initial findings were further sup-ported by quantitative analyses of the models’ abilities to predict abundance in independent datasets,despite complications with multicollinearity of independent variables and temporal differences in therecruitment periodicity between the 3 Florida populations. Estimates of environmentally based habitatvalue on a relative scale will aid conservation managers in protecting vital nursery habitats in estuariescurrently lacking fisheries-independent information and in predicting the effects of future environ-mental change. Finally, assuming similar environmental conditions between years, the year effectfrom these models can serve as an index of relative abundance for fisheries managers to predict futurerecruitment strength and to tune catch-at-age stock-assessment models.

KEY WORDS: Cynoscion nebulosus · Fish distributions · Habitat suitability modelling · Generalizedadditive models (GAM)

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Mar Ecol Prog Ser 265: 197–212, 2003

Generalized additive models (GAMs) combine theflexibility of non-parametric models with the quantifi-able statistical evaluation of error structures and modelselection criteria. They represent a flexible semi-parametric modelling approach to determining therelationships between dependent and independentvariables and, in contrast to traditional parametrichabitat preference models, are not restricted to un-realistic monotonic multi-parameter models, or by theusual assumption of normality (McCullagh & Nelder1989, Neter et al. 1996). The model-free parameterestimation through multiple flexible splines and likeli-hood-based error estimation covering a multitude ofpossible error functions, can overcome many of therestrictions of earlier habitat preference models(Hastie & Tibshirani 1990). Spline functions are notmonotonic or tied to preconceived notions about therelationship between dependent and independentvariables and can therefore more realistically describethe multitude of physiological and behavioural effectsthat determine the distribution of fishes. The numberof possible error structures available due to the use ofthe log-likelihood approach allows for modelling thenon-normal errors typical of survey data. GAMs havealready been successfully employed to elucidatecomplex relationships between fish abundance andenvironmental conditions (Schwartzman et al. 1994,Bigelow et al. 1999, Maravelias et al. 2000). However,their ability to predict temporal and spatial variationsin fish distributions in independent datasets has yetto be rigorously examined.

The aim of this study is not to develop the best pos-sible model describing juvenile fish distributions in allestuaries, rather the aim is to test the ability of GAMmodels developed based on data in one estuary to de-scribe the spatial and temporal distribution of fishes inother estuaries given local sets of environmental pre-dictors. As a proof of concept of the use of GAMs inmodelling fish distributions independent of a particularestuary, this study developed spatially and temporallydynamic models describing the distribution of spottedseatrout (Cynoscion nebulosus) based on their habitatpreferences in 3 different estuaries. An in-depth inves-tigation of habitat preferences and spatial distributionof juvenile spotted seatrout is fitting for several reasons.First, due to its considerable recreational fisheries valueto Florida (Chester & Thayer 1990), much is knownabout its biology (Moffet 1961, Iversen & Tabb 1962,Tabb 1966, Peebles & Tolley 1988, McMichael & Peters1989, Nelson & Leffler 2001). Such published informa-tion is critical to qualitatively verify the model results interms of the species ecology and to judge the suitabilityof models for describing habitat usage and recruitment,even though nursery habitat is poorly defined and itsultimate role in determining recruitment strength is not

yet clear. Second, comparable fisheries-independentdata sets are available for 3 Florida estuaries to facili-tate quantitative comparison of models derived fromfisheries-independent collections made in one estuarywith the observed abundances of fishes collected inother estuaries (foreign models). Third, because thehigh level of fishing pressure and anthropogenically in-duced environmental change, there is an urgent needfor tools to aid conservationists and fisheries managersin protecting these economically important resources(Helser et al. 1993, Rubec et al. 1999). Because of theirlimited migratory behaviour and their absolute depen-dence on estuarine habitats (Moffet 1961, Iversen &Tabb 1962, Tabb 1966), seatrout are threatened by lo-calized extinction through naturally or anthropogeni-cally induced habitat perturbations. This paper as-sesses the ability of GAM model to describe distri-butions based solely on environmental conditions andto examine where such models might help conservationmanagers in predicting fish distribution in estuarieslacking fisheries-independent monitoring information.In addition, an assessment was made of the year-effectfrom such pre-recruit models as a means of predictingfuture recruitment, which is important to fisheries man-agers.

MATERIALS AND METHODS

Study sites. Juvenile seatrout in 3 estuaries locatedat comparable latitudes were examined in this study, 2populations on the west and 1 population on the eastcoast of central Florida (Fig. 1). Tampa Bay (TB) andCharlotte Harbor (CH) are bays separated from theGulf of Mexico by barrier island systems. These 2 estu-aries receive significant freshwater inflows fromseveral mainland tributaries, creating a conventionalsalinity gradient, with salinities decreasing at increas-ing distances from the Gulf of Mexico. The IndianRiver Lagoon (IR), adjacent to the Atlantic Ocean, ismade up of 2 parallel basins: the more westerly IndianRiver and the less extensive Banana River. In starkcontrast to the tidally driven estuaries on the westcoast, the IR hydrography is largely controlled byprevailing wind patterns (Smith 1987). The lagoonreceives freshwater, but due to the location of itstributaries and the position of man-made connectionsto the Atlantic, the lagoon’s unconventional salinitygradient is largely perpendicular to its net flow(DaCosta et al. 1987).

Despite hydrographic and zoogeographic differ-ences, species composition in catches and availablehabitat types were similar in all 3 estuaries. Vegetatedhabitats included seagrass meadows, made up of Halo-dule wrightii, Thalassia testudinum or Syringodium

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filiforme, and mangrove fringing forest, predominantlymade up of red mangroves (Rhizophora mangle).

Sample collection. Sampling in each estuaries wasconducted from January 1996 to December 1999 usinga monthly, multigear stratified random sampling (StRS)design, stratified spatially by zones (Fig. 1b–d) andproportionally stratified by habitat type within zones.Some changes to the sampling design, were made overthe 4 yr period for logistical reasons, slightly compli-

cating the interpretation of the results. The stratifiedrandom sampling design in the IR was amended inJanuary 1998 to include the more southerly Zone Hand to reduce the monthly sampling effort in Zones A,B and E to a single fall sampling period (Fig. 1b). In TB,sampling in parts of Zone E was eliminated duringJanuary to May 1998, after which monitoring in thearea resumed at effort levels lower than before(Fig. 1c). In CH, Zone D was dropped from the

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Fig. 1. (a) Locations of the 3 study estuaries in Florida and the proportionally prestratified zones of the sampling designs in (b) the Indian River Lagoon, (c) Tampa Bay and (d) Charlotte Harbor

a)

c) d)

b)

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sampling design (Fig. 1d) and sampling effort wasdecreased in all other zones in 1998.

Three different techniques, boat, offshore, and beachsets, were used to deploy a 21.3 m by 1.8 m center-bagseine (3.2 mm #35 knotless nylon Delta mesh) in orderto effectively measure fish abundances in a widevariety of habitats (Nelson 2002). Sample sites werechosen randomly without replacement from a sam-pling universe unique to each deployment technique,consisting of 1’ latitude by 1’ longitude grids. Boat andoffshore sets were proportionally prestratified, andbeach sets were unstratified so that it was possible tocollapse the stratified design into a simple randomdesign for the purpose of this analysis.

Boat sets were used only in the tributaries of the westcoast estuaries to sample steep riverine banks, whileriverine habitats were not sampled at all in the IR. Thenet was set in a semicircle from the shore by boat andmanually retrieved to the shore. Beach sets sampledthe estuarine shoreline and were set out perpendicularto the shore and dragged for 9.1 m parallel to the shorebefore being retrieved similar to the boat sets. Off-shore sets were used to capture fish away from theimmediate shoreline by dragging the net for 9.1 m inwater <1.5 m and retrieving it around a pivot-pole toconcentrate the sample in the bag. The area swept dif-fered for the 3 techniques, covering 68, 140 and 338 m2

for boat sets, offshore sets, and beach sets, respectively. At least 40 specimens were measured to the nearest

mm standard length (SL) from each set, and in case ofgreater sample numbers the remainder were countedand raised by the length–frequency distribution. Syn-optic water-quality data were collected, and additionalvariables on the spatial, temporal and biological prop-erties of each sampling location were measured foreach sample. For a more detailed description of thesampling design, deployment methods and environ-mental variables collected, a full procedure manualcan be obtained from the Fisheries-Independent Mon-itoring Program.1

Numerical analysis. Three independent models ofthe habitat preferences of juvenile seatrout weredeveloped, each based on the data from one of 3 estu-aries, CH, IR and TB. Only catch data for seatrout lessthan 50 mm SL were analysed, because larger seatrouthave the ability to effectively avoid the sampling gear,and they tend to occupy alternative habitats as a con-sequence of ontogenetic behavioural changes such asdiet shifts (McMichael & Peters 1989). Additionally, theinitiation of schooling behaviour has also been docu-

mented for this species at >50 mm length (Tabb 1961).As larger specimens school, they are encounteredmore contagiously than would be expected in a ran-dom distribution. That is, the mere presence of fishincreases the probability of higher abundance, whilethe absence of fish reduces the probability of highabundance. Schooling fish are better modelled as anegative binomial error distribution. In any case,mixing the assumed Poisson error distribution of the<50 mm seatrout with the negative binomial error dis-tribution of larger schooling individuals would invali-date the error distribution of the model and lead tounrealistic estimates of confidence limits for parame-ters and hinder model selection through improperevaluation of the deviances.

S-Plus (Statistical analysis software, Insightful) wasused to develop a separate GAM (Venables & Ripley1999) with an assumed Poisson error distribution foreach estuary system to describe the abundance ofseatrout as a response to day of the year, year, temper-ature, salinity, water depth at the seine bag, percentseagrass cover, and bottom type (mud, sand,mud–sand mix, oysters with mud, oysters with sand).The choice of the environmental variables was basedon either their reported performance as suitable indi-cators of seatrout abundance or their ability to charac-terize ichthyofaunal assemblages in estuaries (Kup-schus & Tremain 2001). The year effect wasparameterised as a classification variable in the modelin order to examine interannual changes in the abun-dance of seatrout in the fisheries independent surveywhich were not explained by differences in the envi-ronmental conditions encountered during sampling.

The differences in effort of each of the deploymenttechniques associated with differences in the areaswept needed to be accounted for in the analysis. Fre-quently, this is accomplished by dividing the catch bythe area to arrive at a catch per unit effort dependentvariable. This approach, however, disrupts the errorstructure, since for very small areas the problem tendstowards a binomial problem (a fish caught or not),while on a larger spatial scale the problem tendstowards a Poisson problem (how many fish arecaught). In this study, to compensate for the differ-ences in effort between the techniques, while main-taining the appropriate error structure the area cov-ered was divided by the largest area (338 m2), and theresulting ratios were used as an offset in the GAManalysis (Venables & Ripley 1999).

A number of the measured environmental variableswere highly correlated, because of the interplay of thesampling design and the individual hydrographiccharacteristics found in each estuary. This multi-collinearity can complicate or invalidate the interpreta-tion of the results of any regression analysis, particu-

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1Fisheries Independent Monitoring Program Procedure Man-ual, available from Robert McMichael, Jr, Florida MarineResearch Institute, 100 8th Ave SE, St Petersburg, Florida33701, USA

Kupschus: Statistical habitat suitability modeling

larly in linear regression models. To mitigate againstthis, one of a pair of correlated variables is usuallyexcluded from models (Geary & Leser 1968, Yeo 1984).In the present study correlated variables were re-tained, because correlation does not make them neces-sarily redundant (Hamilton 1987). Instead, to avoidincluding truly redundant variables in the analysis, yetretain all important environmental information in theanalysis, the Akaike information criterion (AIC)(McCullagh & Nelder 1989) was used in a bidirectionalstepwise procedure to select the most parsimoniousmodel. Implementing this procedure through theS-Plus step.gam function (Venables & Ripley 1999)enabled the objective selection of significant environ-mental variables and avoided multicollinearity prob-lems in the development of models. The final GAMwas of the form:

where α is the intercept of the linear predictor, β is theslope of the i th linear component, ƒ is the spline func-tion of the j th smoothed component, and ε is a Gauss-ian error term (Hastie & Tibshirani 1990).

Originally, the AIC was seen as an objective criterionnot only for the selection of independent variables, butalso to choose the degrees of freedom (df) for the splinefunction. However, the resultant models had unjustifi-ably high degrees of freedom associated with onlyminimal deviation of the overall trend from splinefunctions with a lesser degree of freedom. The phe-nomenon was explained by Hilborn & Mangel (1997),who described the tendency of the AIC to overfit themodel beyond the complexity necessary to describethe relationship between the independent variableand its partial effect when the true error distributionwas more contagious (contained higher frequencies ofsmall and large values) than expected for a Poissondistribution. To avoid overfitting, the final model wasscrutinized by reducing the spline complexity (degreesof freedom for each bicubic spline smoother; Hastie& Tibshirani 1990). If the reduction in the degrees offreedom for the individual splines subjectively changedthe overall partial relationship in the new model, theoriginal complexity of the splines was maintained;otherwise the spline complexity was reduced andtested again.

Independent validation and transferability. Theflexibility of splines enables GAMs to realistically por-tray complex relationships between fish and their envi-ronment, but this flexibility is also their weakness.When the exact error structure is unknown, the pre-dicted means become susceptible to the effects ofoutliers. The validity of each model was tested byconfronting the model with independent data from

other estuaries (foreign data) to evaluate its predictiveutility in other systems. The expected mean abun-dance for each of the 3 survey data sets was predictedfrom the environmental conditions by all 3 modelsdeveloped in this study. The summed deviances be-tween observed and predicted values were used tocalculate the explained deviance for all model andestuary combinations, to give an independent measureof the accuracy and the interestuarine transferabilityof each of the models. Systematic bias in the transfer-ability of models was examined by studying the corre-lation between the sample predictions from each of the3 models for a particular estuary.

Spatial comparisons. In contrast to models, mapsare static reproductions of a state at a given time. Assuch, they cannot account for or predict changes inthe spatial distribution over time, and hence theyunderestimate the fluctuations in the distribution overtime. This makes them unsuitable as a managementtool, or a way of statistically comparing models, how-ever, they represent a useful visual assessment ofthe ability of the models to capture interannual andintra-annual changes in juvenile seatrout abundance.To examine the spatial and temporal dynamics inyears and in months over years, surfaces of the local-ized, model-predicted abundances were interpolatedby inverse distance weighting (IDW) from all collec-tions made in one year and all collections made inMay, July, and September in all 4 years (the start,middle, and end of the main recruitment season,respectively). Observed abundance surfaces for theequivalent time periods were also interpolated, butrather than using the IDW method for predictions,observations were kriged (variable radius 12 nearestneighbours) in order to take into account the samplevariance associated with observations. The resultingsurfaces were standardized by the overall surfacemean and variance in order to more easily comparethe models and observations in light of differences inthe mean abundances and coefficients of variationsfor the different surfaces.

RESULTS

Three independent GAMs predicting seatrout abun-dance were developed, each based on the data fromone of the 3 Florida estuaries. The variables chosen bythe stepwise model selection procedure for inclusionwere the same in all models and the degrees of free-dom chosen for each spline function were the sameand their shapes similar (Figs. 2-4). The modelsexplained 36.64 (IR), 42.54 (CH), and 44.04% (TB) ofthe deviance in the data from which they were derived(native data) (Table 1).

abundance = exp ( ) ( )α β ε+ + ƒ +

==

∑∑ X Xi jj

k

i

n

11

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Environmental variables

The partial effects of temperature indicated anincreased abundance of seatrout as water tempera-tures increased to 28°C for all estuaries, after whichabundances declined in the IR (Fig. 2d),continued to increase in TB (Fig. 3d), andremained constant in CH (Fig. 4d). Thescale (range on the y-axis) of the partialeffect, and hence its importance, wassmaller in the IR than in TB or CH. Thepartial effect of salinity implied a uni-modal relationship between salinity andseatrout abundance in all estuaries with amaximum of 20 ppt (IR, 20 ‰; TB, 17 ‰;CH, 18 ‰; Figs. 2e, 3e & 4e). As for tem-perature, the scale of the salinity effectwas smaller in the IR than in the other 2estuaries (Figs. 2e, 3e & 4e). The partialeffect of depth was unimodal in TB andCH, rising to a maximum near 1m in bothestuaries, whereas in the IR, it continuedto rise beyond the range of the sampleddepths. The scale of the effect of depthwas largest in the IR, smaller in TB, andsmallest in CH (Figs. 2f, 3f & 4f). The par-tial effect of percent seagrass cover rosefrom 0 to about 60% cover in all estuaries,

after which it declined in the IR and CH and remainedconstant in TB. The percent-seagrass-cover partial wascomparable in size in all models (Figs. 2g, 3g & 4g). Ofthe frequently sampled bottom types (sand, sand–mudmix, mud), the sand–mud mix was the most productive

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15 20 25 30 35 0 10 20 30 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0 20 40 60 80 100

M MO MX S SO

Bottom Type96 97 98 99

Year

0 100 200 300

Day of the year

Water depth [m]Temperature [°C] Salinity [‰]

% Seagrass cover

a) b) c)

d) e) f )

g)Fig. 2. Cynoscion nebulosus. Habitat-preference model of Indian River Lagoonjuvenile spotted seatrout. y-axes represent the partial effect of the independentvariable on the x-axes. Classification variables are (a) bottom type (M = mud,MO = mud and oysters, MX = mud–sand mix, S = sand, SO = sand and oysters orrock) and (b) year. Continuous variables are (c) day of the year (spline[s] with7 df) (d) temperature (spline with 2 df), (e) salinity (spline with 2 df), (f) waterdepth (spline with 2 df) and (g) percent seagrass cover (spline with 3 df).Rug plots along the x-axis indicate the distribution of samples across each

independent variable

Table 1. Catch statistics, model performance and transferability for theenvironmental dataset from each of the 3 study estuaries. Numbers in bold

indicate characteristics for foreign models

Characteristic Indian Tampa CharlotteRiver Bay Harbor

DataNumber of samples 1603 2605 1862Sum of fish caught in all samples 1465 2556 1593Mean catch (fish haul–1) 0.91 0.98 0.86% occurrence 19.15 21.00 19.33Total deviation 6606.36 10 409.35 6969.47

ModelIndian River model

Mean predictions (fish haul–1) 0.91 0.62 0.76Residual deviance 4185.61 7479.90 5851.97% deviance explained 36.64 28.14 15.69

Tampa Bay modelMean predictions (fish haul–1) 1.88 0.98 1.35Residual deviance 6051.40 5824.85 5616.98% deviance explained 8.40 44.04 19.41

Charlotte Harbor modelMean predictions (fish haul–1) 1.55 0.63 0.86Residual deviance 6080.55 7932.81 4004.84% deviance explained 7.96 23.79 42.54

Kupschus: Statistical habitat suitability modeling 203

M MO MX S SO 96 97 98 99 0 100 200 300

10 15 20 25 30 35 0 10 20 30 40 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0 20 40 60 80 100

Bottom Type Year Day of the year

Water depth [m]Temperature [°C] Salinity [‰]

% Seagrass cover

a) b) c)

d) e) f )

g)

M MO M

X S SO 96 97 98 99 0 100 200 300

15 20 25 30 35 0 10 20 30 40 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0 20 40 60 80 100

Bottom Type Year Day of the year

Water depth [m]Temperature [°C] Salinity [‰]

% Seagrass cover

a) b) c)

d) e) f )

g)

Fig. 3. Cynoscion nebulosus. Habitat-preference model of Tampa Bay juvenile spotted seatrout. See Fig. 2 for explanation

Fig. 4. Cynoscion nebulosus. Habitat-preference model of Charlotte Harbor juvenile spotted seatrout. See Fig. 2 for explanation

Mar Ecol Prog Ser 265: 197–212, 2003

bottom type according to both the IR and TB models,whereas sand was the most productive bottom type inCH (Figs. 2a, 3a & 4a). The effect of bottom types con-taining rock or oysters, irrespective of the presence ofmud or sand, fluctuated widely between models.These differences may indicate behavioural differ-ences between populations, but it is likely that theoverall rarity of the latter 2 substrates and the differ-ences in their prevalence in each estuary account forthe disparity. The relative importance of bottom typewas significant, but small in all models.

Temporal variables

In all models, the partial effect of day (January 1 =1 through December 31 = 365) implied a general sea-sonal increase in abundance of seatrout from Maythrough August (Figs. 2c, 3c & 4c). The timing andexact form of the effect however was different betweenthe models. The IR model indicated a plateau of great-est abundance lasting from Day 125 to Day 250 of theyear, with abundances declining sharply outside thisperiod (Fig. 2c). CH and TB models both indicatedlonger periods of maximum abundance, lasting fromDay 100 to Day 280 (Fig. 3c & 4c). The effect of day wasbimodal in both estuaries, with local maxima aroundDay 180 and Day 230, but in CH, the second peak waslarger than the first, whereas in TB the peaks weresymmetrical. The day partial represented the mostimportant variable describing the abundance of sea-trout in each model, with the greatest importanceimplied for the IR model.

The interannual patterns of abundance were specificto each estuary. IR abundances declined from 1996to 1997, but have risen since (Fig. 2b), whereas TBseatrout abundances continued to decline until 1998,recovering in 1999 to levels seen in 1996 (Fig. 3b). CHabundances increased from 1996 to 1997 but havedeclined since (Fig. 4b). The partial effect of year wassmall, yet significant, in all estuaries.

Spatial comparison between catches and predictions

Distinct interannual differences in the spatial dis-tribution of seatrout in each estuary were predicted bythe models derived from their respective data (nativemodels), as indicated by the interpolated surfacesproduced from observed and predicted point data(Figs. 5–7). Predictions matched not only the spatialpattern in the relative abundance but also the actualmagnitude of mean catches (e.g., TB 1996 to 1999:Fig. 5). Intra-annual variations in the spatial distribu-tion of seatrout within CH (Fig. 6) and TB were also

obvious. In CH and TB, these spatial dynamics couldlargely be explained by native and foreign models ascaused by monthly changes in the environmental con-ditions, although the CH model consistently overpre-dicted the abundance of seatrout in the lower part ofestuary (Fig. 6), due to an insufficient sampling densityassociated with the changes in sampling design toZone D. Despite good interannual correlation betweenthe observed and predicted abundances in the IR,monthly spatial comparisons were less consistent. TheIR model demonstrated poor predictive ability, particu-larly in the Banana River region (Fig. 7a,b), indicatingthat there was some spatially systematic bias notexplained by the environmental conditions included inthe model. This part of the estuary is a wildlife refuge,and the salinity regime is strongly influenced by thepresence of the Canaveral locks in this area resultingin unusual salinity fluctuations, so that the discrepan-cies are likely a reflection of the unusual conditionsexperienced in the area.

Validation and transferability

Seatrout abundances predicted for each of the estu-aries by their respective native model were signifi-cantly correlated (α = 0.05) with abundances predictedfor the same samples by foreign models (Fig. 8,Table 2). Although the IR model faired worst inexplaining deviance in the native data, it transferredbest between estuaries, explaining 28.14 and 15.69%of the devia nce in the TB and CH data, respectively(Table 1). The CH model only accounted for 7.96% ofthe deviance in the IR data and 23.79% of the deviancein the TB data. The TB model produced the worst fitfor foreign data (IR data 8.40%; CH data 19.41%), butexplained the largest amount of deviance of any modelfor its native data (44.04%).

Comparisons of the standardized kriged model pre-dictions by year and month showed spatially consistentdistribution patterns between models (Fig. 7). Themodel prediction surfaces, although not dissimilar tosurfaces derived from observed data from each estu-

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Table 2. Pearson’s correlation coefficients for all combinationsof model to model comparisons of abundance predictions foreach sample in each of the 3 datasets from the 3 Florida estu-aries. IR: Indian River; TB: Tampa Bay; CH: Charlotte Harbor.

All correlations were significant at the α = 0.05 level

IR-data TB-data CH-data

IR-model × CH-model 0.515 0.589 0.564IR-model × TB-model 0.674 0.767 0.751TB-model × CH-model 0.609 0.700 0.608

Kupschus: Statistical habitat suitability modeling 205

Fig. 5. Cynoscion nebulosus. TampaBay standardized relative abundancesurfaces interpolated from predicted(inverse distance weighting) and ob-served (kriged) annual point sampleabundances and subsequently stan-dardized by mean and variance, indi-cating interannual differences in thedistribution accounted for by the TBmodel: (a) observed 1996, (b) pre-dicted 1996, (c) observed 1997, (d) pre-dicted 1997, (e) observed 1998, (f) pre-dicted 1998, (g) observed 1999 and

(h) predicted 1999

>3

2.5 to 2

2 to 2.5

1.5 to 2

1 to 1.5

0.5 to 1

0 to 0.5

–0.5 to 0

–1to –0.5

–1.5 to –1

–2 to –1.5

–2.5 to 2

–3 to –2.5

<–3

Interpolated relativeabundance in stan-dard deviation units

Mar Ecol Prog Ser 265: 197–212, 2003206

>3

2.5 to 2

2 to 2.5

1.5 to 2

1 to 1.5

0.5 to 1

0 to 0.5

–0.5 to 0

–1to –0.5

–1.5 to –1

–2 to –1.5

–2.5 to 2

–3 to –2.5

<–3

Fig. 6. Cynoscion nebulosus. Charlotte Harborstandardized relative abundance surfaces inter-polated from predicted (inverse distanceweighting) and observed (kriged) monthly pointsample abundances and subsequently stan-dardized by mean and variance, indicatingseasonal differences in the distribution in partaccounted for by the CH (Charlotte Harbor)model: (a) observed May 1996–1999, (b) pre-dicted May 1996–1999, (c) observed July1996–1999, (d) predicted July 1996–1999,(e) observed September 1996–1999 and (f) pre-dicted September 1996–1999. The outer part ofthe estuary was sampled only in 1996 and 1997,so the number of samples is too small for appro-

priate interpolation in this area

Interpolated relative abun-dance in standard deviationunits

Kupschus: Statistical habitat suitability modeling 207

Fig. 7. Cynoscion nebulosus. Indian River Lagoon stan-dardized relative abundance surfaces interpolated fromthe IR (Indian River), TB (Tampa Bay) and CH (CharlotteHarbor) seatrout abundance model predictions (inversedistance weighting) and observed (kriged) 1996 pointsample abundances and subsequently standardized bymean and variance. (a) IR observed abundances, (b) IRmodel predictions, (c) TB model predictions and (d) CH

model predictions

>3

2.5 to 2

2 to 2.5

1.5 to 2

1 to 1.5

0.5 to 1

0 to 0.5

–0.5 to 0

–1to –0.5

–1.5 to –1

–2 to –1.5

–2.5 to 2

–3 to –2.5

<–3

Interpolated relative abundance instandard deviation units

Mar Ecol Prog Ser 265: 197–212, 2003

ary, usually resembled each other more than theyresembled the observed data. As with the nativemodel, the foreign models were inconsistent predictorsof the spatial distribution of seatrout in the BananaRiver region of the IR (Fig. 7c,d), again confirming theunusual status of this area.

DISCUSSION

Models of seatrout habitat preferences indepen-dently developed for 3 Florida populations showed thatjuvenile spotted seatrout responded similarly to sal-inity, temperature, depth, and percent seagrass cover.Habitat preferences were alike for all 3 estuarypopulations and were similar to documented habitatpreferences of this species. The GAMs described therelationships between species abundance and envi-ronment and supplied a robust measure of the inter-annual variation in abundance. However, based onpublished information of the biology and detailedexamination of the sampling designs used in this study,it was clear that the models were susceptible to mis-interpretation when independent variables were cor-related. The predicted spatial distribution of juvenilesbased on the spatial configuration of habitat character-istics in the estuaries closely matched the observeddistribution of catches. What follows is a detailedreview of biological inferences from the models andhow the results compare with previously reportedfindings for this species. This review is critical in orderto aid in model interpretation, assess transferability,and judge model utility in conserving and managingfish stocks.

Day was a prominent descriptor of seatrout abun-dance in all models, despite the protracted spawningperiod reported by Moody (1950), Tucker & Faulkner(1987), Peebles & Tolley (1988), and McMichael &Peters (1989). The CH and TB models showed abimodal relationship between day and abundance,which was similar to reports of temporal fluctuationsin juvenile abundance in Florida by Rutherford etal. (1989) and McMichael & Peters (1989). Seatroutspawning intensity was reported to be similarlybimodal elsewhere along the Gulf Coast (Hein &Shepard 1979). Presumably, the bimodality in juvenileabundance within a year reflected temporal variationsin reproductive activity (Nelson & Leffler 2001). Incontrast, the IR model indicated a unimodal seasonalpattern of juvenile abundance, concordant with uni-modal reproductive patterns reported from the IndianRiver Lagoon (Crabtree & Adams 1998). The impor-tance of the intra-annual variation in larval supply,whether caused by variation in reproductive effort ordifferential survival of larvae, greatly limits the abun-

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Fig. 8. Cynoscion nebulosus. Correlation between the abun-dances predicted by foreign models with those predictedby the native model for the datasets from (a) Indian River (IR),(b) Tampa Bay (TB) and (c) Charlotte Harbor (CH). Predic-tions of the native model are always plotted on the x-axis,with the y-axis representing estuary foreign models. The lineindicates the expected 1:1 relationship if transferability on

the absolute scale were applicable

Kupschus: Statistical habitat suitability modeling

dance of juveniles, which was reflected in the primaryimportance of day in all models.

The dominance of day in the model was diminishedin CH and TB models that were associated with con-current increased importance of temperature. In addi-tion to the quantitative differences in the importance oftemperature, the relationship between temperatureand abundance, at first glance, appeared to differbetween populations. The optimum temperature forthe species is around 28°C (Wohlschlag & Wakeman1978), consistent with the predicted abundance in-creases up to this temperature in all models. Highertemperatures were reported to adversely affect thephysiology of this species, leading to stress and even-tually death (Vetter 1977), so that declining abun-dances would be expected at such temperatures. Theanticipated decline in abundance was only observed inthe IR model, with unrealistic increases in abundancepredicted by the CH and TB models. The differences inthe importance and shape of the temperature partialbetween the west coast estuaries and the IR can beexplained by differences in the degree of correlationbetween temperature and day within each estuary.The large latitudinal expanse and shallow depth of theIR (Smith 1987) leads to a greater temporal variabilityin temperatures, resulting in less of a correlationbetween temperature and the day of the year in the IRthan in the other bay systems. Therefore, the IR modelcan correctly attribute the deviance to day, while theCH and TB models associate some of the deviance dueto day with the temperature effect. The predicted highabundances at high temperatures are scaled back byan excessive dip in the day effect during the summerperiod when temperatures are above 28°C. Multi-collinearity between day and temperature, rather thandifferences in the biology of the populations, was themost likely cause for the difference in the day andtemperature effect, particularly given the evidence offatalities at high temperatures.

Percent seagrass cover was positively correlated toseatrout abundance in all models, second only to dayin influence on seatrout distribution after accountingfor the collinearity between temperature and day. Theprominence attributed to seagrass cover in these mod-els was consistent with reports by McMichael & Peters(1989), Chester & Thayer (1990), and Gilmore (1977).Despite the predicted high-ranking importance of sea-grass, juvenile seatrout have also been frequentlyencountered in areas devoid of seagrass, usually inprotected backwaters and tributaries over muddy bot-tom (Peebles & Tolley 1988, Peebles et al. 1991, Llansóet al. 1998). Even estuaries apparently lacking sea-grass altogether have sustained viable populations ofseatrout (Darnell 1958). This presents evidence thatseagrass itself is not a facultative requirement of

seatrout but rather that percent seagrass cover servedas an indirect measure of the ability to find food (Orthet al. 1984) and avoid predators (Hindell et al. 2000)because of habitat complexity. This function may befacilitated in other estuaries by shallow backwaters(Ruiz et al. 1993, Halpin 2000, Paterson & Whitfield2000). Quantitative measures of food availability andprotection from predators offered by different habitatswould be necessary in order to accurately predict thedistribution of seatrout in estuaries lacking seagrass.

All 3 models largely concur on the shape and theimportance of the salinity partial, indicating maximalseatrout abundances at 20 ppt. This value is also closeto the physiological optimum of this species (Wohl-schlag & Wakeman 1978). The divergence of the IRmodel from the other estuaries at low salinities resultsfrom the lack of sampling in the suboptimal riverinehabitats (Peebles et al. 1991) in the IR that are fre-quently sampled in TB and CH. The few IR samplestaken in low-salinity environments are more represen-tative of short-term conditions incidental to samplingand not true long-term oligohaline conditions. Conse-quently, the IR model overpredicts the abundance ofseatrout in mesohaline environments when comparedto the other models. In contrast, the CH and TB modelscorrectly predicted few seatrout at low salinities, butunfortunately, the low-salinity river sites in these estu-aries also yielded samples from the deepest waterdepths because of the boat-set technique employedsolely in this habitat. Inevitably, this led to the lowabundance associated with low salinities also beingassociated with increasing depth in the west coastmodels. In contrast, the depth partial in the IR, whererivers were not sampled, continued to increase for theentire length of the depth gradient as reported else-where (Rutherford et al. 1989). It is likely that theobserved differences between models in the salinityand depth effects were again a result of samplingdesign induced multicollinearity and not behaviouraldivergence of the populations. The high degree of cor-relation of salinity and depth in the CH and TB data,the ecological evidence, and the transferability per-formance of the IR model all attested to the greaterplausibility of the latter model and demonstrated thatmulticollinearity is hampering the effectiveness of theCH and TB models when predicting abundances in theIR data.

Transferability

Transferability, in this case, is the ability of a modelderived from data in one estuary to predict the abun-dance of seatrout in a second estuary based solely onmeasured environmental conditions in the latter estu-

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ary. The developed models were able to achievereasonable transferability when confronted with non-native environmental data, explaining significant por-tions of the total deviance in the data from otherestuaries. Observed abundances were significantlycorrelated to predicted abundances for all foreignmodels, although the relationship was temporallybiased and the overall relationship between modelpredictions always differed from the expected one-to-one relationship.

The 3 models implied 3 distinct estuary-specificyear effects, indicating 3 independent populations,each with its own specific population dynamics. Thistype of information gleaned from these models hasimportant implications for fisheries managers, whomust ultimately consider such effects in producingcoast wide stock assessments in order not to allowlocalized populations to collapse. It might be arguedthat the sampling-design changes implemented atthe end of 1997 invalidate the use of the year effectas a measure representative of estuarine recruitmentdynamics. The year effect was smallest in TB, theestuary that has had the most modest sampling designchanges, and was largest in the IR, the estuary withthe most significant design changes. In CH only smallchanges in abundance were observed across the sam-pling design transition—1997 and 1998 data weremore similar to each other than to the 1996 and 1999data. Consequently, differences could not be attrib-uted solely to changes in the sampling design, andabundances must at least in part be controlled by dif-ferences in the population dynamics between estuar-ies. The unique interannual trends meant that modelswere not useful in predicting the number of seatroutfound in an area of a foreign estuary, but could be aninvaluable tool in grading the suitability of habitats ina foreign estuary.

The multicollinearity unique to the sampling designswithin each estuary represented a hindrance to trans-ferability, as indicated by the better ability of the IRmodel—the model with the least correlation amongindependent variables—to explain the most deviancein foreign data sets. Presence of multicollinearity initself does not seriously impede model predictability,provided that it is constant over the period and area ofinterest (Neter et al. 1996). In the absence of sampling-design changes, we can assume this is the case withinan estuary but certainly not between estuaries, asshown here by the poorer transfer performance of theCH and TB models. It is important to consider thesampling design in light of the species’ ecology duringthe development phase of these complex models andto remember that changes to the design will potentiallyinvalidate the use of the indices of abundance acrosssuch changes.

Implications for conservation management

In order to protect and conserve species, conserva-tion managers need information on the resourcerequirements of all life stages of a species, especiallythe vulnerable juvenile stages. The models describedhere allow quantitative evaluation of the importanthabitats of juvenile seatrout for their native estuariesbased on actual environmental data. All models clearlyindicated areas of high seatrout abundance (high suit-ability) as well as areas of low seatrout abundance (lowsuitability) in non-native estuaries. The suitability ofspecific estuarine regions was consistent between allmodels and all estuaries, despite considerable dynam-ics in space and time. Because the location of optimumconditions (e.g., nursery habitat) varies dynamically inspace and time, protection of a specific locale willunlikely suffice to ensure the survival of the species.GAMs can clearly aid conservation managers in re-viewing proposed changes to the environment anddeciding on options with the least deleterious effectsto populations.

For conservation managers it is important to identifythe areas of most suitable habitat within an estuarylacking fisheries-independent sampling data. Despitethe high residual deviance in these models, it is clearfrom the maps presented in this paper that, when envi-ronmental conditions are sampled in suitable densityin space and time, all models were capable of identify-ing the areas of most suitable habitat in native and for-eign estuaries. This justifies the use of low-explanatory-power models by conservation managers. Preferenceshould always be given to those models with least cor-relation of the independent variables in order to avoidmulticollinearity issues during transfer of models, evenif it is thought to be at the expense of explanatorypower in native estuaries.

Although transferable on a relative scale, the modelsdiscussed here were not transferable on the scale ofabsolute abundances caused by the significant contri-bution of the population-specific year effect. There-fore, the current models would not be capable ofassessing the absolute number of fish affected by envi-ronmental alterations or disasters in foreign estuaries.But this might be different for species with a wellmixed population or one that recruits from a single-ocean-spawned pool of larval recruits.

Implications for fisheries management

Fisheries managers are interested in obtaining infor-mation on the abundance of juveniles before theyenter the fishery in order to predict future stock sizesand implement management changes early. In the

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past, the most important factor in determining thesize of recruitment was thought to be the size of thespawning stock as reflected by the spawning stock bio-mass (SSB) benchmark currently used for Florida’sspotted seatrout stocks. It has, however, become in-creasingly clear that recruitment can only be poorlylinked to SSB, particularly for species restricted to veryspecific juvenile habitats, because of the variance-dampening effect enforced by its limited availability(Iles & Beverton 2000, van der Veer et al. 2000). A poorspawner–recruit relationship has been observed forFlorida spotted seatrout. Therefore, it is expected thatthe variance-generating function of the reproductivepart of the life cycle was overshadowed by the vari-ance dampening function of the other life stages.Assessment of habitat quality and availability shouldproduce better estimates of recruitment than informa-tion solely on the spawning stock size. The year effectdescribed by the models represents a robust measureof native juvenile seatrout abundance or future recruit-ment, assuming a consistent sampling design, stableenvironmental conditions, and constant niche breadth.Interannual fluctuations were estuary specific, indi-cating the different seatrout populations are less wellmixed than assumed by current stock assessments, andquantitative prediction of recruitment for foreign estu-aries is not possible from these models. In contrast toconservation managers, fisheries managers using themodels purely in the native estuaries would placeheavier emphasis on the explanatory power of themodels, rather than their transferability.

Conclusions

The prohibitive costs of intense fisheries-indepen-dent surveys has led conservation managers to look formodels that can be used to predict fish abundances insystems lacking suitable surveys (Rubec et al. 1999,Brown et al. 2000). This analysis suggests considerablesavings can be made by the development of environ-mentally driven models of species distribution basedon modern regression techniques. Generalized addi-tive models are also sure to be invaluable tools for fish-eries managers in determining useful recruitmentindices for implementation in stock assessments pro-vided that the time series are sufficiently long anduninterrupted by large-scale changes in the samplingdesign.

The ability of generalized additive models to statisti-cally deal with non-normal data and weighting of dif-ferent environmental variables as well as the flexibilityof splines used to more sensibly model the relationshipbetween abundance and environment means that indata-rich situations these models provide a temporally

and biologically more accurate picture of seatrout dis-tributions than habitat-suitability models (Rubec et al.1999). However, the susceptibility of the models tomulticollinearity issues means special attention needsto be paid to the sampling design under considerationof the biological and ecological characteristics of a spe-cies, in order to produce robust distribution models.

Acknowledgements. I thank the field crews in the IndianRiver Lagoon, Charlotte Harbor and Tampa Bay for collectionand processing of samples, and the FWC-FMRI FisheriesIndependent Monitoring Program as a whole for making thiswork possible. The project was supported in part by the fund-ing from the Department of the Interior, US Fish and WildlifeService, Federal Aid for Sportfish Restoration Project NumberF-96 and Florida Saltwater Fishing License Monies.

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Editorial responsibility: Otto Kinne (Editor), Oldendorf/Luhe, Germany

Submitted: December 10, 2002; Accepted: September 23, 2003Proofs received from author(s): December 19, 2003