Hs

CD

a

ARRA

KBCCMMMRS

I

dsaDpb2atColl

(

1d

Journal for Nature Conservation 20 (2012) 18– 29

Contents lists available at ScienceDirect

Journal for Nature Conservation

j our na l ho mepage: www.elsev ier .de / jnc

abitat suitability modelling for species at risk is sensitive to algorithm andcale: A case study of Blanding’s turtle, Emydoidea blandingii, in Ontario, Canada

atherine S. Millar ∗, Gabriel Blouin-Demers1

epartment of Biology, University of Ottawa, 30 Marie Curie, Ottawa, ON K1N 6N5, Canada

r t i c l e i n f o

rticle history:eceived 29 March 2011eceived in revised form 14 July 2011ccepted 17 July 2011

eywords:oosted regression treeshelonianonservationaximum entropy modellingulti-algorithmulti-scale

a b s t r a c t

Species distribution modelling (SDM) can help conservation by providing information on the ecologicalrequirements of species at risk. We developed habitat suitability models at multiple spatial scales fora threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored theeffect of background data selection and modelling algorithm selection on habitat suitability predictions.We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boostedregression trees; and maximum entropy modelling. The area under the receiver characteristic operat-ing curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912when using random background and from 0.727 to 0.741 with target-group background. E. blandingiihabitat suitability was best predicted by air temperature, wetland area, open water area, road density,and cropland area. Habitat suitability increased with increasing air temperature and wetland area, anddecreased with increasing cropland area. Low road density and open water increased habitat suitabil-

eptilespecies distribution modelling

ity, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps forspecies at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offerinsight on the habitat requirements of species at risk and help guide the development of managementplans. Our results suggest that E. blandingii management plans should promote the protection of terres-trial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currentlyoccupied habitat, and identify movement corridors for isolated populations.

© 2011 Elsevier GmbH. All rights reserved.

ntroduction

As human populations and resource consumption increase, sooes the pressure on endangered species. The primary causes ofpecies endangerment in North America are habitat destructionnd alteration (Dobson et al. 1997; Kerr & Cihlar 2004; Kerr &eguise 2004). In Canada, biodiversity is especially high in tem-erate southern regions, where human-dominated land uses areoth intensive and widespread (Kharouba et al. 2008; White & Kerr006). Unsurprisingly, endangered species are also disproportion-tely concentrated in these areas (Kharouba et al. 2008).In an efforto protect biodiversity, several nations have enacted legislation. Inanada, the Species at Risk Act (SARA 2003) affords legal protection

f species at risk, and their critical habitat, on federal lands. Simi-arly, Ontario’s Endangered Species Act (OESA 1971, 2007) providesegal protection on provincial lands. Under both SARA and OESA, all

∗ Corresponding author. Tel.: +1 613 859 2565.E-mail addresses: catmillar@gmail.com (C.S. Millar), gblouin@uottawa.ca

G. Blouin-Demers).1 Tel.: +1 613 562 5800x6749; fax: +1 613 562 5486.

617-1381/$ – see front matter © 2011 Elsevier GmbH. All rights reserved.oi:10.1016/j.jnc.2011.07.004

species at risk must be given a recovery strategy (OESA 1971, 2007;SARA 2003) and this recovery strategy is the mechanism used toidentify the critical habitat. A similar process is triggered in theUSA under the Endangered Species Act (ESA 1973, 1978).

Species distribution models (SDMs) can be very helpful tools inthe arsenal of conservation biologists because they may provideuseful information on the ecological requirements of an organ-ism (Mateo-Thomás & Olea 2009), facilitate fieldwork by predictingareas of potential occurrence (Araújo & Williams 2000), and enablepredictions on the effects of climate or land use changes on the dis-tribution and persistence of species (Araújo et al. 2004; Petersonet al. 2004; Thuiller et al. 2005). Effective conservation actions,however, require reliable distribution models. Thus, it is importantthat the effects of scale, modelling algorithm, and background dataselection are assessed in SDM studies involving species at risk.

Differences in selection pressures and limiting factors can some-times lead to differing patterns of selection at multiple scales(Compton et al. 2002; Luck 2002; Orians & Wittenberger 1991).

Thus, a hierarchical habitat suitability modelling approach atconsecutively smaller scales has recently been employed (Mateo-Thomás & Olea 2009; Martínez et al. 2003). Furthermore, the choiceof modelling algorithm and background data in SDM influence

C.S. Millar, G. Blouin-Demers / Journal for N

Table 1The fourteen environmental variables used for predicting E. blandingii habitat suit-ability in Ontario.

Variable (units) Range Code Source(s)

Land cover (%) OMNR (1998)Wetlands 0–100 WetlOpen water 0–100 WaterForests 0–100 ForestPlantations 0–100 PlantSettlements and

developed land0–100 Urban

Cropland 0–100 CropPastures and fields 0–100 PastMine tailings,

quarries and bedrockoutcrops

0–100 Rock

Alvar 0–100 AlvarCutovers and burns 0–100 CB

Mean monthlyprecipitations (mm)(April–October)

67.27–89.47 Prec Hijmans et al.(2005)

Mean monthlymaximal temperature(◦C) (April–October)

18.9–25.2 Tmax Hijmans et al.(2005)

Terrain ruggedness(TRI Index)

0–26.64 TRI OMNR (2005)and Riley et al.(1999)

Total road density(m/m2)

0–0.026 RD OMNR (2006)

mVT

amorsa(oobdba(p

a(rbbtog2d

fNaihb

odel results (Chefaoui & Lobo 2008; Elith et al. 2006; Jiménez-alverde et al. 2009; Lobo & Tognelli 2011; Phillips et al. 2009;homaes et al. 2008).

The data available for modelling the geographic distribution of species can vary in extent, type, and quality. In addition, severalodelling techniques are available. For species at risk, the majority

f the distributional information available consists of occurrenceecords in atlases, museums, and herbaria. Thus, absence data forpecies at risk are rarely available or reliable (Anderson et al. 2003)nd false absences can decrease the reliability of prediction modelsAnderson 2003; Loiselle et al. 2003). Moreover, occurrence data areften spatially biased towards accessible areas. To correct estimatesf species distributions, Phillips et al. (2009) proposed choosingackground data with the same observation bias as the occurrenceata. When the sampling bias is not known, it can be approximatedy combining occurrence records for a target group of species thatre all collected or observed using the same methods. Phillips et al.2009) found that target-group background improved the averageerformance of all the modelling methods examined.

In a review of modelling techniques that do not require explicitbsences, modelling algorithms using presence/pseudo-absencebackground) data consistently outperformed modelling algo-ithms using presence-only data (Elith et al. 2006). The choice ofackground data (pseudo-absence data generation), however, haseen shown to have as large an effect on predictive performance ashe choice of modelling method (Phillips et al. 2009). Few studiesf species at risk, however, use multiple modelling methodolo-ies (Gibson et al. 2007; Gray et al. 2010; Mateo-Thomás & Olea009). Even fewer studies of species at risk explore backgroundata selection.

The Blanding’s turtle (Emydoidea blandingii) is a semi-aquaticreshwater turtle, it has one of the smallest global ranges of anyorth American turtle (COSEWIC 2005), and it is listed as a species

t risk in 17 of the 18 state or provincial jurisdictions acrossts range (NatureServe 2009). Thus, E. blandingii require criticalabitat delineation. Like most reptiles, the primary threat to E.landingii is habitat loss and degradation (Harding 1997; Van Dam

ature Conservation 20 (2012) 18– 29 19

1993). Furthermore, incompatible adjacent land uses may impedemigration among local populations (deMaynadier & Hunter 2000;Houlahan & Findlay 2003) and increase mortality through roadkill (Beaudry et al. 2008; Marchand & Litvaitis 2004; Steen &Gibbs 2004). The goals of this study were to determine whichfactors influence the distribution of E. blandingii and to identifyregions of significance for sustaining E. blandingii. We also wishedto examine the effects of scale and background data selection onhabitat suitability models for a species at risk in a highly alteredlandscape: southern Ontario, Canada. We used sighting records,province-wide climatic information, and high-resolution landcover data to populate two species distribution modelling algo-rithms: boosted regression trees (BRTs); and maximum entropymodelling (Maxent). To account for hierarchical differences inselection, we modeled habitat suitability at three spatial scales cor-responding to approximations of daily, intermediate, and annualmovements of E. blandingii. To examine the effect of samplingbias on model results, we used both a random and a target-groupapproach to background (pseudo-absence) data selection.

Methods

Data sources and study area

Sighting dataWe obtained E. blandingii occurrence records, with accuracy

<100 m, in Ontario from the Natural Heritage and Information Cen-ter (Oldham & Weller 2010). To reduce autocorrelation, from thisdataset of 1035 sightings points we selected the maximum numberof non-overlapping points that were at least 1 km apart using theFocus tool (Holland et al. 2004). This left 616 sightings points, 60%of which were chosen at random and used to build the models andthe other 40% were kept for independent evaluation.

Environmental predictor variablesWe used 14 environmental predictor variables (Table 1) that

were not strongly correlated (all |r| < 0.8, mean |r| = 0.125) at all spa-tial scales examined. We derived 10 classes of land cover from the28 present in the Ontario Land Cover Dataset (Table 2; OMNR 1998).These data were collected between 1986 and 1997 at a spatial res-olution of 25 m. We measured terrain ruggedness using the terrainruggedness index (TRI) developed by Riley et al. (1999) and theOntario Provincial Digital Elevation Model (DEM) v.2.0.0 at a 25 mresolution, re-sampled from 10 m resolution using bilinear inter-polation (OMNR 2006). We created road density raster files with aspatial resolution of 25 m using the Ontario Road Network (OMNR2006) (accuracy of 10 m) by dividing the length of road segmentsby surface area. We downloaded mean maximum monthly temper-ature and mean monthly precipitation rasters from the WorldClimonline database (Hijmans et al. 2005) at a spatial resolution of 30 arcs and averaged these rasters over the active season (April–October).

To take into account the vagility of E. blandingii, we extractedenvironmental data within three circular buffers around eachpresence/pseudo-absence location based on daily and annual E.blandingii movements. Thus, we read the environmental data asthe percentage of x cover within the buffer area or the meanvalue of x within the buffer area. The radii of these three circu-lar buffers corresponded to: (1) the highest published mean dailydistance moved by E. blandingii (ca. 250 m per day; Millar & Blouin-Demers 2011); (2) an intermediate value of 500 m; and (3) thehighest published mean annual E. blandingii home range length,

divided by two (ca.1 km; Hamernick 2000). These three buffer areasrepresent all the habitats available to the turtles on a daily, inter-mediate, and annual basis and were interpreted as three scales ofselection. We conducted all spatial analyses using ArcGIS Desktop

20 C.S. Millar, G. Blouin-Demers / Journal for N

Table 2Descriptions of the grouped land cover classes used for model building.

Land cover classes Descriptions

Wetl Includes all marshes, treed fens, open fens, treedbogs, open bogs, and swamps.

Water Includes all water bodies that are not categorisedas wetlands (i.e. rivers, streams, and lakes).

Forest Forested areas with greater than 30% forest canopyclosure. Includes dense coniferous forests, densedeciduous forests, mixed mainly coniferous forest,mixed mainly deciduous forests, sparse coniferousforest, and dense deciduous forests; excludesplantations.

Plant Mature conifer plantations, mostly pine, occurringin evenly spaced rows; excludes artificiallyregenerated cutovers and burns.

Urban Clearings for human settlement and economicactivity.

Crop Row crops, hay and open soil in areas ofagricultural land use.

Past Open grassland with sparse shrubs mapped inagricultural areas; includes orchards.

Rock Clearings for mining activity, aggregate quarriesand bedrock outcrops.

Alvar Homogeneous areas of dry grassland growing onthin soils over a limestone substrate.

CB Forest clear-cuts and burns. Includes new cutovers,new burns, and old cutovers and burns.

9Hawf

S

sglfbff

P

ge2(TrwE4TC

M

sltae

.3 (ESRI 1995–2010), ArcInfo Workstation (ESRI 1982–1996), andawth’s Tools (Beyer 2004). Lambert’s Conformal Conic Projection,n equal-area projection, was used and all environmental rastersere re-sampled to a pixel size of 100 m using bilinear interpolation

or habitat suitability mapping.

tudy areaWe clipped the provincial data 1 km north of the most northerly

ighting for E. blandingii and all data were restricted to the sameeographic area. Open water areas in the Great Lakes >300 m fromand were judged inaccessible to E. blandingii and were removedrom all environmental datasets and habitat suitability maps. Foroth Maxent and BRTs, the model predictions are given in logisticormat and can be interpreted as the predicted habitat suitabilityor E. blandingii, ranging from 0 (low) to 1 (high).

seudo-absencesSince both BRTs and Maxent require data akin to absences, we

enerated a random sample of 16 666 background sites from thenvironmental data (minimum of 8000 background sites; Phillips008). We used sixty percent of these points for model building10 000 points) and forty percent for model testing (6666 points).o examine more closely the effect of sampling bias in occurrenceecords on habitat suitability models, we ran additional analysesith target background sharing the same sampling biases as our

. blandingii sightings. These target background data consisted of833 NHIC sightings of other Ontario turtles (Trachemys scripta,errapene carolina, Sternotherus odoratus, Graptemys geographica,hrysemys picta, Chelydra serpentina, and Apalone spinifera).

odel building

Elith et al. (2006) compared 16 modelling methods over 226pecies from six regions of the world and found that machine-

earning methods consistently outperformed other methods. Forhis reason, we chose two machine-learning methods that weremong the top performing models in Elith et al. (2006): maximumntropy modelling (Maxent); and boosted regression trees (BRTs).

ature Conservation 20 (2012) 18– 29

Maxent estimates species’ distributions by finding the distribu-tion of maximum entropy (i.e. closest to uniform) subject to theconstraint that the expected value of each environmental variable(or its transform and/or interaction) under this estimated distribu-tion matches its empirical average (Phillips et al. 2006).

BRTs combine two algorithms; the boosting algorithm thatiteratively calls the regression-tree algorithm to construct a com-bination of trees (Elith et al. 2008). Multiple regression trees areconstructed by iteratively fitting new trees to the residual errors ofthe existing tree assemblage (De’ath 2007). Existing trees are notchanged through iterations, and the final model is a linear combi-nation of all the trees in the assemblage (Elith et al. 2008).

BRTsWe fitted BRT models in R version 2.7.2 (R Development Core

Team 2008), using the gbm package version 1.5–7 (Ridgeway 2006)and additional code (Elith et al. 2008). Based on preliminary testsand past studies, we built models using a tree complexity of 5 anda bag fraction of 0.5 (Elith et al. 2008; Millar 2010). For all models,we used 10-fold cross validation on the training data to deter-mine the optimal number of trees (nt) and the fastest learning rate(lr) reaching at least 1000 trees was selected (Elith et al. 2008).We reduced the number of environmental variables in each modelusing the area under the operating curve (AUC) as an evaluationmetric (Doetsch et al. 2009). We removed candidate variables in abackwards fashion, beginning with the lowest overall contributors,until test AUC values reached their maximum.

MaxentWe ran Maxent models in Maxent version 3.3.1 (Phillips et al.

2006; http://www.cs.princeton.edu/∼schapire/Maxent) using thedefault parameters. These default parameters have been shown tobe well suited to a wide range of presence-only datasets (Phillips& Dudík 2008), most notably datasets with 11–13 environmentalvariables and >100 occurrences. We built and tested models on 10replicate random partitions of the presence and pseudo-absencedata. As before, these sets consisted of 60% training data and 40%testing data. To facilitate comparison between the two modellingmethodologies, we used the same presence and pseudo-absencelocalities. As before, we used recursive feature elimination based onAUC values to drop unimportant variables from the Maxent models.

Model evaluation

ROC and AUCA common approach to evaluate and compare models of species

distributions is the receiver operating characteristic curve (ROC)(Hanley & McNeal 1983). The main advantage of ROC analysis isthat the area under the ROC curve (AUC) provides a single mea-sure of model performance, independent of any particular choiceof a threshold that would convert continuous outputs into binaryoutputs. The ROC curve is obtained by plotting sensitivity on they-axis and 1-specificity on the x-axis for all possible thresholds.When using presence-only data, AUC represents the probabilitythat when we randomly pick one positive and one random sample,the classifier will assign a higher score to the positive sample thanto the random (Phillips et al. 2006). Classically, models with AUCvalues above 0.75 were considered informative and models withAUC exceeding 0.9 were considered excellent (Elith 2002; Swets1988). When using presence-only data, however, AUC values must

be interpreted with caution (Jiménez-Valverde 2011; Phillips et al.2006). While a score of 0.5 still indicates discrimination that is nobetter than random, the maximum value attainable is typically lessthan 1 (Phillips et al. 2006; Wiley et al. 2003).

C.S. Millar, G. Blouin-Demers / Journal for Nature Conservation 20 (2012) 18– 29 21

Table 3Predictive performance of BRT and Maxent models built using random and target background data for each spatial scale examined. The threshold independent area under thereceiver characteristic operating curve (AUC) is reported for model building (training) and when tested on independent data (test). The point biserial correlation coefficient(COR) is also reported for all model data (all).

Model Scale BRT Maxent

AUC COR AUC COR

Test Training All Test Training All

Random 1 km 0.882 0.945 0.518 0.878 0.904 0.360500 m 0.883 0.960 0.530 0.887 0.916 0.389250 m 0.912 0.956 0.547 0.898 0.904 0.379

Target 1 km 0.739 0.816 0.270 0.741 0.793 0.236500 m 0.741 0.819 0.299 0.731 0.791 0.245

P

ti(t(vbCt2atp

V

nweaen

icistv

raTopcru

R

M

sbo

250 m 0.728 0.823

oint-biserial correlation coefficientAnother threshold independent model evaluation metric is

he point-biserial correlation coefficient (COR). COR is mathemat-cally equivalent to the Pearson (product moment) correlationMurphy & Winkler 1992) and represents the correlation betweenhe presence/pseudo-absence records and the model predictionsZheng & Agresti 2000). Unlike AUC, which is based on predictedalues only, COR is sensitive to both model discrimination and cali-ration as it is dependent on both actual and predicted values. Thus,OR provides additional information on the distribution of predic-ions (Elith et al. 2006; Murphy & Winkler 1992; Phillips & Elith010). The higher the correlation value, the more discriminativend/or better calibrated the model is. For the same reasons as AUC,he maximum achievable COR value is less than 1 when the truerobability of presence is not binary (presence-only data).

ariable contributions and response curves

With BRTs, the importance of each input variable is based on theumber of times this variable was selected for splitting in the treeeighted by the squared improvement to the model as a result of

ach of those splits. This importance measure is then averaged overll trees (Freidmand & Meulman 2003). The relative contribution ofach variable is then scaled so that the sum adds to 100, with higherumbers indicating stronger influence over the response.

With Maxent, the percent contributions are determined by thencrease in the gain of the model due to the modification of aoefficient for a single feature. Each step of the Maxent algorithmncreases the gain of the model by modifying the coefficient for aingle feature; the program then assigns the increase in the gaino the environmental variable(s) that the feature depends on, con-erting to percentages at the end of the training process.

To investigate the effect of each environmental variable on theesponse after accounting for the average effect of all other vari-bles in the model, we examined the partial dependence plots.hese plots, however, might not accurately reflect the true impactf each predictor alone on the response if strong interactions areresent or if predictors are strongly correlated. For this reason, wereated dependence plots for the top five contributing variables byunning univariate models for each variable at each spatial scale,sing both algorithms.

esults

odel performance

All models performed reasonably well according to classicaltandards (Table 3) and closely fitted the presence points of E.landingii in the study area. Similar AUC and COR values werebtained with both algorithms at all spatial scales (Table 3).

0.332 0.727 0.778 0.238

Interestingly, COR values were much higher for BRT models thanfor Maxent models. The predictive ability of models decreasedconsiderably when we used target background (Table 3).

Variable contributions and response curves

Random backgroundWhen using random background, the top five explanatory vari-

ables at all three spatial scales averaged across the two modellingmethods were: (1) maximum air temperature during the activeseason; (2) road density; (3) total wetland area; (4) total openwater area; and (5) total cropland area (Fig. 1). The responsecurves produced by univariate models of the five most importantpredictor variables were almost identical across the three spatialscales (Millar 2010), thus only results from the 1 km analysis areillustrated (Fig. 2). At all three spatial scales, E. blandingii habitatsuitability increased with increasing air temperature and wetlandarea, and decreased with increasing cropland area. Low road den-sity and open water increased habitat suitability, while high levelsof either variable decreased habitat suitability (Fig. 2). Followingthese top five predictors, terrain ruggedness, mean monthly pre-cipitations during the active season, total forest cover, and totalrock cover were of medium importance (Fig. 1). Alvar, pastures andfields, settlements and developed lands, and cutovers and burnswere of less importance (Fig. 1). Overall habitat suitability increasedslightly with forest, rock, and alvar cover types and decreasedslightly with terrain ruggedness and crop, pasture, urban, and cutsand burns cover types.

Target backgroundWhen using target background, the overall contribution of roads

to all models dropped by approximately 10–15% (Fig. 1). Fur-thermore, the dependency plots for univariate target backgroundmodels built using only road density as the explanatory vari-able showed that habitat suitability dramatically decreased withincreasing road density. Also, the overall contribution of water tohabitat suitability models dropped by approximately 10% (Fig. 1),although the univariate response plots remained the same. Finally,cropland area was of much greater importance in models builtwith target background (Fig. 1). As before, increasing croplandarea greatly decreased habitat suitability for E. blandingii whereasincreasing forest area increased habitat suitability.

Effect of scaleThe importance of road density increased as the buffer size

decreased and the overall importance of maximal temperaturedecreased as the buffer size decreased (Fig. 1).

22 C.S. Millar, G. Blouin-Demers / Journal for Nature Conservation 20 (2012) 18– 29

Fig. 1. Average contribution of predictor variables in E. blandingii habitat suitability models built using I) BRTs or II) Maxent and i) random background or ii) target backgrounddata at three different spatial scales.

D

iBgts

H

R

a

ifferences between Maxent and BRT modelsMean precipitation and forested area were of little importance

n Maxent models despite being of high to medium importance inRT models. Water area, wetland area, and cropland area were ofreater importance in Maxent models than in BRT models. Finally,he increase in the overall importance of road density at the localcale was much more apparent in Maxent models (Fig. 1).

abitat suitability predictions

andom vs. target backgroundHS predictions varied markedly between random background

nd target background models (Figs. 3 and 4). In target background

models, a much greater extent of the study area was consideredmedium to high quality habitat (Fig. 4). Furthermore, in target back-ground models, the difference in HS between the southern andnorthern parts of our study area is emphasised. The northern partof our study area seems to host much higher quality habitat thanthe southern part of our study area. This trend is most noticeablearound the Canadian Shield (Figs. 3 and 4).

Effect of spatial scale

Spatial scale did not have a large effect on habitat suitability pre-

dictions. The effect of roads, however, becomes more noticeable insouthern Ontario at the 250 m scale; a cross-hatch pattern becomesapparent (Fig. 3).

C.S. Millar, G. Blouin-Demers / Journal for Nature Conservation 20 (2012) 18– 29 23

Fig. 2. Response curves produced by univariate models of the five most important predictor variables in E. blandingii habitat suitability models built using a 1 km buffer, I)B respoa f each

M

mb

RTs or II) Maxent, and i) random background or ii) target background data. Similarnd represent the predicted probability of suitable conditions based on the effect o

axent vs. BRTWe observed a marked difference between habitat suitability

aps produced by BRTs and Maxent models. The relationshipetween predicted suitability for BRT and Maxent rasters was

nse curves were obtained at the 500 m and 250 m scale. Y axes are on the logit scale predictor variable independently.

exponential (Fig. 5). Areas predicted to be low quality habitat wereconsistent across the two modelling algorithms; however BRT wasmore conservative than Maxent in the assessment of higher qualityhabitat (Figs. 3–5). Pearson’s product moment correlations values

24 C.S. Millar, G. Blouin-Demers / Journal for Nature Conservation 20 (2012) 18– 29

F ulti-f ffer ana

bwr

D

fchass

B

V

haTcvK

asiSbot

ig. 3. A) Study site extent (gray) and E. blandingii sighting data (�) used to build mor E. blandingii across their range in Ontario using random background, B) 1 km bund Maxent.

etween Maxent and BRT HS maps, using random background,ere 0.66, 0.65, and 0.63 for the 1 km, 500 m, and 250 m analyses,

espectively.

iscussion

Species distribution modelling is a powerful yet affordable toolor conservation practitioners and our results highlight its appli-ability to species at risk. We discuss the main factors influencingabitat suitability for E. blandingii in Ontario, examine potentialreas of occurrence in Ontario, discuss current and future con-ervation concerns, highlight methodological considerations, anduggest future areas of study.

iological interpretations

ariable contributions and response curvesAs revealed by both Maxent and BRT modelling, temperature,

abitat type, and road density are important environmental factorsffecting the geographical distribution of E. blandingii in Ontario.his is not surprising given that other studies have shown thatlimatic conditions and land cover characteristics are highly rele-ant indicators of habitat suitability for reptiles (Fisher et al. 2004;aliontzopoulou et al. 2008; Tingley & Herman 2009).

Mean monthly maximum air temperature remained, on aver-ge, the best overall predictor of habitat suitability at all spatialcales. This can be expected since E. blandingii are ectotherms and,n Ontario, they are at the northern extreme of their global range.

imilar to a study of painted turtles by Frazer et al. (1991), E.landingii habitat suitability was highest in drier hotter areas. Webserved a positive relationship between wetland area and habi-at suitability. Once again, this is unsurprising as E. blandingii are

scale habitat suitability models. Also illustrated is the predicted habitat suitabilityd BRTs, C) 250 m buffer and BRTs, D) 1 km buffer and Maxent, and E) 250 m buffer

freshwater turtles known to occupy a variety of eutrophic wetlands(Joyal et al. 2001; Millar & Blouin-Demers 2011; Pappas & Brecke1992; Ross & Anderson 1990; Rowe & Moll 1991). Open water hada more complex effect on habitat suitability. This non-linear rela-tionship could be explained by the transient use of open water byturtles. Open water is often used as a travel corridor by E. blandingiito reach new wetlands, aestivation sites, or nesting sites, but it isnot the primary habitat for this species.

E. blandingii are vagile and often use upland corridors formovement between wetlands, aestivation, and thermoregulation.Similar to past studies (Attum et al. 2008; Gibbons 2003), our resultsstrongly suggest that the nature of these corridors will influencehabitat quality for E. blandingii. Attum et al. (2008) found thatforested area within buffer zones of up to 250 m was an importantpredictor of E. blandingii habitat use. Thus, undisturbed terrestrialhabitat, such as forested, alvar, or rocky outcrop, increases overallhabitat suitability for E. blandingii, probably by increasing connec-tivity.

Sampling bias and target background choiceWhen using random background, road density was one of the

top five predictor variables for E. blandingii habitat suitability atall three spatial scales. The positive effect of road density at lowlevels could be attributed to sampling bias in the sighting records,the documented affinity that turtles possess for roads, and habitatfragmentation. First, the sighting records from the NHIC have beencollected haphazardly and opportunistically. As such, areas that aremore accessible to humans are better sampled and tend to have

more sightings than remote areas, whether or not E. blandingii aremore abundant. Second, roadsides create artificially disturbed andopen habitats that may be attractive for thermoregulation or nest-ing (Carr 1952; Haxton 2000; Joyal et al. 2001; Wood & Herlands

C.S. Millar, G. Blouin-Demers / Journal for Nature Conservation 20 (2012) 18– 29 25

F ing tara

1s1is

ntsHbesmiba

dadiwio

reda

ig. 4. Predicted habitat suitability for E. blandingii across their range in Ontario usnd Maxent, and D) 250 m buffer and Maxent.

997). Finally, roads built between adjacent wetlands or alonghorelines fragment E. blandingii habitat (Evink 1980; Johnston994; Mitsch & Gosselink 2000). Thus, turtles are often found cross-

ng roads to reach adjacent wetlands, aestivation sites, or nestingites.

At medium to high values, however, road density becomes aegative predictor of habitat suitability. The second biggest threato reptile populations is road mortality, especially in long-livedpecies with naturally poor recruitment (Congdon et al. 1993;erman et al. 2003; Row et al. 2007). The results from the targetackground analysis, where sampling bias is accounted for (Phillipst al. 2009), suggest that the effect of roads on E. blandingii habitatuitability is less important than observed in random backgroundodels and that the relationship is negative. It is important to keep

n mind, however, that any selection for roads could be maskedy the use of target background consisting of species that are alsottracted to roads.

Similarly, the importance of open water at all spatial scalesecreased when using target background. This phenomenon couldrise because the majority of turtles in the target backgroundataset are residents of both riparian and wetland habitats or ripar-

an habitats only, whereas E. blandingii reside almost exclusively inetlands. Again, the choice of target background had a noticeable

mpact on the resulting models. Clearly more taxon specific studiesn the appropriate selection of target background are needed.

Although the use of target background supported our earlier

esults, obtained using random background data, and effectivelyliminated the sampling bias towards roads, the overall pre-ictive performance of models decreased. A study by Chefaouind Lobo (2008) demonstrated that the random selection of

get background, A) 1 km buffer and BRTs, B) 250 m buffer and BRTs, C) 1 km buffer

pseudo-absences or their selection from environmental locali-ties similar to those of the species presence data generated themost constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas.When comparing AUC and COR values between models gener-ated using random pseudo-absences and those generated usingtarget-group pseudo-absences, it seems that models built usingtarget-group data suffered from lower calibration and lower dis-criminatory power. These results are similar to those of Lütolf et al.(2006) and contrast those of Phillips et al. (2009). These differencescould be attributed to the lack of proper presence–absence recordsfor model testing. Clearly, however, the method of pseudo-absenceselection strongly conditions the resulting model.

Effect of spatial scaleThe observed differences in variable contributions in habitat

suitability models at differing spatial scales support the affirma-tion that habitat selection is hierarchical (Johnson 1980). Thesedifferences may reflect the nature of the environmental variablesthemselves or differences in habitat selection. Local site selectionmay be less impacted by broad-scale climatic conditions, such asmean maximum temperature, than home range placement within alandscape. The increase in the importance of road density at smallerspatial scales may reflect increased homogeneity in road densityvalues as spatial scale increases. We did not observe an effect of

spatial scale in models built using target background data. Thisis unsurprising since the environmental variables that were themost impacted by scale in random background models were of littleimportance in target background models.

26 C.S. Millar, G. Blouin-Demers / Journal for N

Fig. 5. Predicted habitat suitability at 10 000 random points across the study area inthe 1 km BRT model as a function of predicted habitat suitability at the same pointsi

H

phauptfieo

M

GtafotltfhttHo2ibtmet

n the 1 km Maxent model.

abitat suitability predictionsFor the most part, E. blandingii sightings are in areas of high

redicted habitat suitability. There are several areas with very lowabitat suitability, however, that have E. blandingii sightings, somes recent as 2002. These areas could potentially represent pop-lation sinks due to habitat alteration and destruction over theast few decades, although further research would be necessaryo determine current population trends. Abundance data and con-rmed absences are not available for this species at the spatialxtent of analysis and thus inferences about population declinesr increases cannot be made.

axent versus BRTs

Both algorithms consistently performed better than random.iven the differences in predicted habitat suitability maps between

he two algorithms and the importance of consistent habitat suit-bility predictions in a conservation context, we recommend thatuture studies employ more than one modelling methodology. Theverall importance of some predictor variables varied between thewo modelling algorithms suggesting that these two methods canead to different models. Despite these predictive differences, theop five predictor variables and the shape of the response curvesor each variable were almost identical. Furthermore, we observedigh fidelity between the models for areas with high and low habi-at suitability. BRT and Maxent share one very important feature:hey have a high level of flexibility in fitting complex responses.owever, this expressiveness, a well-developed ability to expressr demonstrate the complex relationships in the data (Elith et al.006), needs to be controlled so that models are not overfit, lead-

ng to the use of regularisation techniques that achieve a balanceetween complexity and parsimony (Hastie et al. 2001). Based on

he difference in training and test AUC values, BRT models had

ore overfitting and retained more predictor variables than Max-nt models and this could be due to the different regularisationechniques utilised by the algorithms. Furthermore, COR values

ature Conservation 20 (2012) 18– 29

suggest that BRT models were more discriminative and better cal-ibrated than Maxent models.

Maxent predicted the largest distribution of high quality habi-tat over geographical space. Depending on the use of the maps,both algorithms have their advantages. If the objective is to delin-eate protected areas for rare or endangered species, overestimatingareas of high quality habitat may be preferable to underestimatingthe habitat needed (Zaniewski et al. 2002). Optimistic predictionsproved false could however damage public support (Zaniewski et al.2002). Furthermore, conservation dollars are not unlimited andthus models delimiting smaller patches of core habitat could bemore helpful.

Methodological considerations

The modelling techniques that we used assess the suitabilityof habitat for E. blandingii in Ontario and do not reflect actualpopulation demographics or distributions. Areas of predicted pres-ence will typically be larger than the species’ realised distribution(Phillips et al. 2006). This can be due to several factors, such asgeographic barriers to dispersal (Peterson et al. 1999), biotic inter-actions (Anderson et al. 2002), and human modification of theenvironment (Anderson & Martínez-Meyer 2004). Although wehave removed areas where E. blandingii are inferred not to inhabit,thereby increasing the reliability of the habitat suitability esti-mate, there are many ecological factors that are not modelled.These include, but are not limited to, population dynamics, meta-population dynamics, and biotic-interactions (Araújo & Williams2000).

Unfortunately, presence-only data have errors and biases asso-ciated with them, reflecting the frequent haphazard manner inwhich samples were accumulated (Hijmans et al. 2000). Moreover,the number of occurrences may be too small to estimate the param-eters of the model reliably (Stockwell & Peterson 2002) and/or theset of environmental variables may not be sufficient to describe allthe parameters of the species’ fundamental niche (Phillips et al.2006). Even after considering the potential problems associatedwith presence-only records, however, many argue that presence-only data may be more appropriate than unreliable/incompletepresence–absence data because false absences can introduce con-founding information and decrease the reliability of predictionmodels (Chefaoui & Lobo 2008; Elith & Leathwick 2009).

Although AUC is the most widely used metric of SDM perfor-mance, this measure depends solely on the relative ordering ofmodel (i.e., predicted) values at test sites (Lobo et al. 2008; Phillips &Elith 2010). Thus, AUC values do not give any information on modelcalibration, the second crucial notion in SDM model evaluation(Lobo et al. 2008; Phillips & Elith 2010). Furthermore, when usinglarge sets of pseudo-absence data the number of false absences(commission errors) is likely to be considerably higher than thenumber of false presences. It is important to note, however, thatAUC weighs omission and commission errors equally and thereforemay be misleading (Lobo et al. 2008; Jiménez-Valverde 2011). Thus,in recent studies the capacity of AUC to assess the performanceof presence-only distribution models has been questioned (Loboet al. 2008; Jiménez-Valverde 2011). It has been suggested thatAUC may only be truly informative when there are true instances ofabsence available and the objective is the estimation of the realiseddistribution (Jiménez-Valverde 2011).

The prevalence of E. blandingii in our study area was low (ca. 4%).Highly unbalanced designs (such as those having many pseudo-absences) facilitate the correct classification of the absence zone,

but increase misclassification of the presence zone (Lobo & Tognelli2011). To avoid having an excessively unbalanced design (King &Zeng 2000), we used just under 100 times more pseudo-absencesthan presences, as recommended by Lobo and Tognelli (2011). It

l for N

ipao>

ddaiw&setrttidf

C

fshcazvccotisrntwit

blgdE(istarm1(hosftG

C.S. Millar, G. Blouin-Demers / Journa

s important to note, however, that machine learning algorithmserform well even in very unbalanced designs (Prati et al. 2004)nd that the negative effect of prevalence on model accuracy wasnly found to be significant for datasets with prevalence <1% or99% (Jiménez-Valverde et al. 2009).

In this study, we used the highest resolution possible for all ouratasets. Kaliontzopoulou et al. (2008) and Heikkinen et al. (2007)emonstrated that fine-resolution models have greater predictivebility than coarser resolution ones. Given the ecological character-stics of E. blandingii (small body size, medium-sized home range),

e believe the use of a finer-resolution dataset is necessary (Guisan Hofer 2003). Furthermore, Tingley and Herman (2009) demon-trated that accounting for the effect of land cover can improve thexplanatory and predictive power of bioclimatic models for tur-les at a regional scale. High resolution land cover data may allowefinement of habitat suitability predictions by identifying areashat are climatically suitable, but that are inhospitable owing tohe effects of habitat loss and degradation (Pearson et al. 2004). Byncluding environmental features such as cropland area and roadensity, we were better able to characterise the habitat suitabilityor E. blandingii.

onservation implications

The overall negative impact of cropland on habitat suitabilityor Blanding’s turtles strongly suggests that the terrestrial habitaturrounding wetlands is an essential component of E. blandingiiabitat. This impact was evident at all spatial scales studied, indi-ating that agricultural lands within 1 km of residential wetlandsre having deleterious effects on E. blandingii habitat. Large bufferones need to be established around residential wetlands to pre-ent further alteration of terrestrial corridors, thereby maintainingonnectivity between wetlands. Furthermore, known terrestrialorridors linking wetlands to nesting sites, aestivation sites, andther neighbouring wetlands should be protected. These terres-rial areas are more than just neutral buffers, they are vital habitatsn the life cycle of this species’ and are essential to its continuedurvival (Gibbons 2003; Millar & Blouin-Demers 2011). However,eforestation and prevention of further agricultural use alone mayot be enough to assist in the recovery of E. blandingii popula-ions. Identifying landscapes that have a low density of roads oretland assemblages that are distant from roads would be an

mportant consideration for securing habitats capable of sustaininghis species (Litvaitis & Tash 2008).

Turtle populations may become extirpated because of a com-ination of extrinsic (e.g., habitat destruction) and intrinsic (e.g.,

imited dispersal ability) factors that can result in little or noene flow among isolated populations. Habitat suitability mapsemonstrate a clear divide between the northern and southern. blandingii populations in Ontario. Given global climate changeTravis 2003; Thomas et al. 2004) and high land conversion ratesn southern Ontario (OMNR 1992), E. blandingii populations in theouth seem to be at a higher risk of extinction with little poten-ial for gene flow between populations. Coupled with low dispersalbility (ca. 6 km per 14 years; estimate is based on the longestecorded nesting excursion by a female E. blandingii in Ontario andinimum time to sexual maturity; Edge et al. 2010; Congdon et al.,

993) and high fidelity to wetland complexes during their lifetimesEdge et al. 2010; McMaster & Herman 2000; Paterson pers. comm.),abitat fragmentation may be pushing the southern populationsf Blanding’s turtles irrevocably towards extinction. To disperse,

uitable microhabitats (such as wetlands, lakes, rivers and uplandorest) that can be used for shelter, hydration and food acquisi-ion, and safe passages over/under roads are needed (Bodie 2001;ibbons 2003).

ature Conservation 20 (2012) 18– 29 27

Species distribution modelling has been employed to delineateessential habitat for species at risk; however, most SDM studies areeither multi-scale (Gray et al. 2010) or multi-algorithm (Thomaeset al. 2008). Few studies examine both of these effects. Furthermore,the choice of background data is largely ignored in the conservationsetting, yet we have shown that background data greatly influencemodel output. We suggest that a multi-scale and multi-algorithmapproach should be used if SDM techniques are to be applied tospecies at risk. In addition, the choice of target background must beexplored in greater detail (Chefaoui & Lobo 2008; Lobo & Tognelli2011; Phillips et al. 2009), with an emphasis on regional and tax-onomic effects. By using high-resolution land cover data, sightingrecords, and two SDM algorithms we were able to build robust habi-tat suitability maps at multiple spatial scales for a species at risk, E.blandingii. Our results suggest that SDMs can be used to gain insighton the habitat requirements for species at risk and help guide thedevelopment of management plans.

Acknowledgements

Funding for this study was provided by the Natural Sciencesand Engineering Research Council of Canada and the University ofOttawa. We would like to thank Michael Oldham and the NHIC forproviding the turtle sighting data and Nancy Lemay for acquisitionof spatial datasets. Dr. Jane Elith, Dr. Steven Phillips, and Dr. MikeSawada helped troubleshoot initial spatial analyses and modellingscripts. Finally, we greatly appreciated the advice provided by Dr.Scott Findlay and Dr. Lenore Fahrig.

References

Anderson, R. P. (2003). Real vs. artefactual absences in species distributions: Tests forOryzomys albigularis (Rodentia: Muridea) in Venezuela. Journal of Biogeography,30, 591–605.

Anderson, R. P. & Martínez-Meyer, E. (2004). Modeling species’ geographic distri-butions for preliminary conservation assessments: An implementation withthe spiny pocket mice (Heteromys) of Ecuador. Biological Conservation, 116,167–179.

Anderson, R. P., Peterson, A. T. & Gómez-Laverde, M. (2002). Using niche-based GISmodeling to test geographic predictions of competitive exclusion and competi-tive release in South American pocket mice. Oikos, 98, 3–16.

Anderson, R. P., Lew, D. & Peterson, A. T. (2003). Evaluating predictive models ofspecies’ distributions: Criteria for selecting optimal models. Ecological Modelling,162, 211–232.

Araújo, M. B. & Williams, P. H. (2000). Selecting areas for species persistence usingoccurrence data. Biological Conservation, 96, 331–345.

Araújo, M. B., Cabeza, M., Thuiller, W., Hannah, L. & Williams, P. H. (2004). Wouldclimate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology, 10, 1618–1626.

Attum, O., Lee, Y. M., Roe, J. H. & Kingsbury, B. A. (2008). Wetland complexes andupland-wetland linkages: Landscape effects on the distribution of rare and com-mon wetland reptiles. Journal of Zoology, 275, 245–251.

Beaudry, F., deMaynadier, P. G. & Hunter, M. L., Jr. (2008). Identifying road mortalitythreat at multiple spatial scales for semi-aquatic turtles. Biological Conservation,141, 2550–2563.

Beyer, H. L., (2004). Hawth’s Analysis Tools for ARCGIS. In: www.spatialecology.com/htools.

Bodie, J. R. (2001). Stream and riparian management for freshwater turtles. Journalof Environmental Management, 62, 443–455.

Carr, A. (1952). Handbook of turtles: The turtles of the United States, Canada and BajaCalifornia. Ithaca: Cornell University Press.

Chefaoui, R. M. & Lobo, J. M. (2008). Assessing the effects of pseudo-absences onpredictive distribution model performance. Ecological Modelling, 210, 478–486.

Compton, B. W., Rhymer, J. M. & McCollough, M. (2002). Habitat selection by woodturtles (Clemmys insculpta): An application of paired logistic regression. Ecology,83, 833–843.

Congdon, J. D., Dunham, A. E. & van LobenSels, R. C. (1993). Delayed sexual maturityand demographics of Blanding’s turtles (Emydoidea blandingii): Implications forconservation and management of long-lived organisms. Conservation Biology, 7,826–833.

COSEWIC. (2005). COSEWIC assessment and update status report on the Blanding’sTurtle, Emydoidea blandingii, in Canada. Ottawa: Committee on the Status ofEndangered Wildlife in Canada. www.sararegistry.gc.ca/status/status e.cfm

De’ath, G. (2007). Boosted trees for ecological modeling and prediction. Ecology, 88,243–251.

2 l for N

d

D

D

E

E

E

E

E

EEE

E

F

F

F

G

G

G

G

H

H

H

H

H

H

H

H

H

H

H

J

J

J

J

8 C.S. Millar, G. Blouin-Demers / Journa

eMaynadier, P. G. & Hunter, M. L., Jr. (2000). Road effects on amphibian movementsin a forested landscape. Natural Areas Journal, 20, 56–65.

obson, A., Rodriguez, J. P., Roberts, W. M. & Wilcove, D. S. (1997). Geographicdistribution of endangered species in the United States. Science, 275, 550–553.

oetsch, P., Buck, C., Golik, P., Hoppe, N., Kramp, M., Laudenberg, J., et al. (2009).Logistic model trees with AUC split criterion for the KDD Cup 2009 Small Chal-lenge. JMLR: Workshop and Conference Proceedings, 7, 77–88.

dge, C. B., Steinberg, B. D., Brooks, R. J. & Litzgus, J. D. (2010). Habitat selectionby Blanding’s turtles (Emydoidea blandingii) in a relatively pristine landscape.Ecoscience, 17, 90–99.

lith, J. (2002). Quantitative methods for modeling species habitat: Comparativeperformance and an application to Australian plants. In S. Ferson, & M. Burgman(Eds.), Quantitative methods for conservation biology (pp. 39–58). New York:Springer-Verlag.

lith, J. & Leathwick, J. R. (2009). Species distribution models: Ecological explanationand prediction across space and time. Annual Review of Ecology, Evolution, andSystematics, 40, 677–697.

lith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., et al. (2006).Novel methods improve prediction of species’ distributions from occurrencedata. Ecography, 29, 129–151.

lith, J., Leathwick, J. R. & Hastie, T. (2008). A working guide to boosted regressiontrees. Journal of Animal Ecology, 77, 802–813.

ndangered Species Act, (1973). 16 U.S.C. $1531.SRI. (1982–1996). ArcInfo 8.1. Redlands: Environmental Systems Research Institute.SRI. (1995–2010). ArcGIS Desktop Evaluation Edition 9.3. Redlands: Environmental

Systems Research Institute.vink, G. L. (1980). Studies of causeways in the Indian River, Florida. Report FL-ER-7-80.

Tallahassee: Florida Department of Transportation.isher, J., Lindenmayer, D. B. & Cowling, A. (2004). The challenge of managing multi-

ple species at multiple scales: Reptiles in an Australian grazing landscape. Journalof Applied Ecology, 41, 32–44.

razer, N. B., Gibbons, J. W. & Green, J. L. (1991). Growth, survivorship and longevityof painted turtles Chrysemys picta in a southwestern Michigan marsh. AmericanMidland Naturalist, 125, 245–258.

reidmand, J. H. & Meulman, J. J. (2003). Multiple additive regression tress withapplication in epidemiology. Statistics in Medicine, 22, 1365–1381.

ibbons, J. W. (2003). Terrestrial habitat: A vital component for herpetofauna ofisolated wetlands. Wetlands, 23, 630–635.

ibson, L., Barrett, B. & Burbidge, A. (2007). Dealing with uncertain absences inhabitat modelling: A case study of a rare ground-dwelling parrot. Diversity andDistributions, 13, 704–713.

ray, T. N. E, Phan, C. & Long, B. (2010). Modelling species distribution at multiplespatial scales: Gibbon habitat preferences in a fragmented landscape. AnimalConservation, 13, 324–332.

uisan, A. & Hofer, U. (2003). Predicting reptile distributions at themesoscale: Relation to climate and topography. Journal of Biogeography, 30,1233–1243.

amernick, M. G. (2000). Home ranges and habitat selection of Blanding’s turtles(Emydoidea blandingii) at the Weaver Dunes, Minnesota. Final report submittedto the Nongame Wildlife Program, Minnesota Department of Natural Resources.

anley, J. A. & McNeil, B. J. (1983). A method of comparing the areas under receiveroperating characteristic curves derived from the same cases. Radiology, 148,839–843.

arding, J. H. (1997). Amphibians and Reptiles of the Great Lakes Region. Ann Arbor,M. I: U. of Mich. Press., 378 pp.

astie, T., Tibshirani, R. & Friedman, J. H. (2001). The elements of statistical learning:Data mining, inference and prediction. New York: Springer-Verlag.

axton, T. (2000). Road mortality of snapping turtles Chelydra serpentina, in centralOntario during their nesting period. Canadian Field-Naturalist, 114, 106–110.

eikkinen, R. K., Luoto, M., Virkkala, R., Pearson, R. G. & Körber, J. H. (2007). Bioticinteractions improve prediction of boreal bird distributions at macro-scales.Global Ecology and Biogeography, 16, 754–763.

erman, T. B., Boates, J. S., Drysdale, C., Eaton, S., McNeil, J., Mockford, S., et al. (2003).National recovery plan for the Blanding’s turtle (Emydoidea blandingii) Nova Scotiapopulation. Ottawa: Recovery of Nationally Endangered Wildlife Committee.

ijmans, R. J., Garrett, K. A., Huamán, Z., Zhang, D. P., Schreuder, M. & Bonierbale, M.(2000). Assessing the geographic representativeness of genebank collections:The case of Bolivian wild potatoes. Conservation Biology, 14, 1755–1765.

ijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high res-olution interpolated climate surfaces for global land areas. International Journalof Climatology, 25, 1965–1978.

olland, J. D., Bert, D. G. & Fahrig, L. (2004). Determining the spatial scale of species’response to habitat. Bioscience, 54, 227–233.

oulahan, J. E. & Findlay, C. S. (2003). The effects of adjacent land use on wetlandamphibian species richness and community composition. Canadian Journal ofFisheries and Aquatic Sciences, 60, 1078–1094.

iménez-Valverde A., (2011). Insights into the area under the receiver operatingcharacteristic curve (AUC) as a discrimination measure in species distributionmodelling. Global Ecology and Biogeography, in press.

iménez-Valverde, A., Lobo, J. M. & Hortal, J. (2009). The effect of prevalence and itsinteraction with sample size on the reliability of species distribution models.

Community Ecology, 10, 196–205.

ohnson, D. H. (1980). The comparison of usage and availability measurements forevaluating resource preference. Ecology, 61, 65–71.

ohnston, C. A. (1994). Cumulative impacts to wetlands. Wetlands, 14, 49–55.

ature Conservation 20 (2012) 18– 29

Joyal, L. A., McCollough, M. & Hunter, M. L., Jr. (2001). Landscape ecology approachesto wetland species conservation: A case study of two turtle species in southernMaine. Conservation Biology, 15, 1755–1762.

Kaliontzopoulou, A., Brito, J. C., Carretero, M. A., Larbes, S. & Harris, D. J. (2008).Modelling the partially unknown distribution of wall lizards (Podarcis) in NorthAfrica: Ecological affinities, potential areas of occurrence, and methodologicalconstraints. Canadian Journal of Zoology, 86, 992–1001.

Kerr, J. T. & Cihlar, J. (2004). Patterns and causes of species endangerment in Canada.Ecological Applications, 14, 743–753.

Kerr, J. T. & Deguise, I. (2004). Habitat loss and the limits to endangered speciesrecovery. Ecology Letters, 7, 1163–1169.

Kharouba, H. M., Nadeau, J. L., Young, E. & Kerr, J. (2008). Using species distributionmodels to effectively conserve biodiversity into the future. Biodiversity, 9, 39–47.

King, G. & Zeng, L. (2000). Logistic regression in rare events data. Political Analysis,9, 2.

Litvaitis, J. A. & Tash, J. P. (2008). An approach toward understanding wildlife-vehiclecollisions. Environmental Management, 42, 688–697.

Lobo, J. M. & Tognelli, M. F. (2011). Exploring the effects of quantity and locationof pseudo-absences and sampling biases on the performance of distributionmodels with limited point occurrence data. Journal for Nature Conservation, 19,1–7.

Lobo, J. M., Jiménez-Valverde, A. & Real, R. (2008). AUC: A misleading measure of theperformance of predictive distribution models. Global Ecology and Biogeography,17, 145–151.

Loiselle, B. A., Howell, C. A., Graham, C. H., Goerck, J. M., Brooks, T., Smith, K. G., et al.(2003). Avoiding pitfalls of using species distribution models in conservationplanning. Conservation Biology, 17, 1591–1600.

Luck, G. W. (2002). The habitat requirements of the rufous treecreeper (Climacterisrufa). Preferential habitat use demonstrated at multiple spatial scales. BiologicalConservation, 105, 383–394.

Lütolf, M., Kienast, F. & Guissan, A. (2006). The ghost of past species occurrence:Improving species distribution models for presence-only data. Journal of AppliedEcology, 43, 802–815.

Marchand, M. N. & Litvaitis, J. A. (2004). Effects of habitat features and landscapecomposition on the population structure of a common aquatic turtle in a regionundergoing rapid development. Conservation Biology, 18, 758–767.

Martínez, J. A., Serrano, D. & Zuberogoitia, I. (2003). Predictive models of habitat pref-erences for the Eurasian eagle owl Bubo bubo: A multiscale approach. Ecography,26, 21–28.

Mateo-Thomás, P. & Olea, P. P. (2009). Combining scales in habitat models to improveconservation planning in an endangered vulture. Acta Oecologica, 35, 489–498.

McMaster, N. L. & Herman, T. B. (2000). Occurrence, habitat selection, and move-ment patterns of juvenile Blanding’s Turtles (Emydoidea blandingii) in KejimkujikNational Park, Nova Scotia. Chelonian Conservation and Biology, 3, 602–610.

Millar, C. S. (2010). The spatial ecology of Blanding’s turtles (Emydoidea blandingii):From local movement patterns, home ranges and microhabitat selection toOntario-wide habitat suitability modelling. M.Sc. Thesis, University of Ottawa.

Millar, C. S.,& Blouin-Demers, G., (2011). Spatial ecology and seasonal activity ofBlanding’s turtles (Emydoidea blandingii) in Ontario, Canada. Journal of Herpetol-ogy, 45, in press.

Mitsch, W. J. & Gosselink, J. G. (2000). Wetlands (3rd ed.). New York: John Wiley andSons.

Murphy, A. H. & Winkler, R. L. (1992). Diagnostic verification of probability forecasts.International Journal of Forecasting, 7, 435–455.

NatureServe. (2009). NatureServe Explorer: An online encyclopedia of life [web applica-tion]. Version 7.1. Arlington: NatureServe. http://www.natureserve.org/explorerAccessed 20.04.10

Oldham, M. J. & Weller, W. F. (2010). Ontario Herpetofaunal Atlas. Natu-ral Heritage Information Centre: Ontario Ministry of Natural Resources.http://nhic.mnr.gov.on.ca/MNR/nhic/herps/ohs.html

OMNR. (1992). Manual of implementation guidelines for the wetlands policy statement.Toronto: Ontario Ministry of Natural Resources.

OMNR. (1998). Ontario 28-Class Land Cover Database. Peterborough: Ontario Ministryof Natural Resources.

OMNR. (2005). Provincial Digital Elevation Model Version 2.0.0. Peterborough: OntarioMinistry of Natural Resources.

OMNR. (2006). Ontario Road Network. Peterborough: Ontario Ministry of NaturalResources.

Ontario Endangered Species Act. S.O., (2007) c. 27.Orians, G. H. & Wittenberger, J. F. (1991). Spatial and temporal scales in habitat

selection. The American Naturalist, 137, 29–49.Pappas, M. J. & Brecke, B. J. (1992). Habitat selection of juvenile Blanding’s turtles,

Emydoidea blandingii. Journal of Herpetology, 26, 233–234.Pearson, R. G., Dawson, T. P. & Lin, C. (2004). Modelling species distributions in

Britain: A hierarchical integration of climate and land-cover data. Ecography,27, 285–298.

Peterson, A. T, Soberon, J. & Sanchez-Cordero, V. (1999). Conservatism of ecologicalniches in evolutionary time. Science, 285, 1265–1267.

Peterson, A. T., Martínez-Meyer, E. & Gonzalez-Salazar, C. (2004). Reconstructingthe pleistocene geography of the Aphelocoma jays (Corvidae). Diversity andDistributions, 10, 237–246.

Phillips, S. J. (2008). Transferability, sample selection bias and background data inpresence-only modeling: A response to Peterson et al. Ecography, 31, 272–278.

Phillips, S. J. & Dudík, M. (2008). Modeling of species distributions with Maxent:New extensions and a comprehensive evaluation. Ecography, 31, 161–175.

l for N

P

P

P

P

R

R

R

R

R

R

SS

S

Zaniewski, A. E, Lehmann, A. & McCOverton, J. (2002). Predicting species spatial dis-

C.S. Millar, G. Blouin-Demers / Journa

hillips, S. J. & Elith, J. (2010). POC plots: calibrating species distribution models withpresence-only data. Ecology, 91, 2476–2484.

hillips, S. J., Anderson, R. P. & Schapire, R. E. (2006). Maximum entropy modelingof species geographic distributions. Ecological Modelling, 190, 231–259.

hillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., et al. (2009).Sample selection bias and presence-only distribution models: Implications forbackground and pseudo-absence data. Ecological Applications, 19, 181–197.

rati, R. C., Batista, G. E. A. P. A. & Monard, M. C. (2004). Class imbalances versus classoverlapping: An analysis of a learning system behaviour. III Mexican Interna-tional Conference on Artificial Intelligence. Lecture Notes in Computer Science (pp.312–321). Mexico City, Mexico: Springer-Verlag.

Development Core Team. (2008). R: A language and environment for statisticalcomputing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org

idgeway, G. (2006). Generalized boosted regression models. Documentation onthe R Package ‘gbm’, version 1.5-7. http://www.i-pensieri.com/gregr/gbm.shtmlAccessed March 2008

iley, S. J., DeGloria, S. D. & Elliot, R. (1999). A terrain ruggedness index that quantifiestopographic heterogeneity. Intermountain Journal of Science, 5, 23–27.

oss, D. A. & Anderson, R. K. (1990). Habitat use, movements, and nesting of Emy-doidea blandingii in Central Wisconsin. Journal of Herpetology, 24, 6–12.

ow, J. R, Blouin-Demers, G. & Weatherhead, P. J. (2007). Demographic effects ofroad mortality in black ratsnakes (Elaphe obsoleta). Biological Conservation, 137,117–124.

owe, J. W. & Moll, E. O. (1991). A radiotelemetric study of activity and movementsof the Blanding’s turtle (Emydoidea blandingii) in Northeastern Illinois. Journalof Herpetology, 25, 178–185.

pecies at Risk Act S.C. (2003). c. 29.teen, D. A. & Gibbs, J. P. (2004). Effects of roads on the structure of freshwater turtle

populations. Conservation Biology, 18, 1143–1148.tockwell, D. R. B. & Peterson, A. T. (2002). Effect of sample size on accuracy of species

distribution models. Ecological Modelling, 148, 1–13.

ature Conservation 20 (2012) 18– 29 29

Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240,1285–1293.

Thomaes, A., Kervyn, T. & Maes, D. (2008). Applying species distribution modellingfor the conservation of the threatened saproxylic Stag Beetle (Lucanus cervus).Biological Conservation, 141, 1400–1410.

Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. (2005). Climatechange threats to plant diversity in Europe. Proceeding of the National Academyof Science of the United States of America, 102, 8245–8250.

Tingley, R. & Herman, T. B. (2009). Land-cover data improve bioclimatic models foranurans and turtles at a regional scale. Journal of Biogeography, 36, 1656–1672.

Travis, J. M. J. (2003). Climate change and habitat destruction: A deadly anthro-pogenic cocktail. Proceedings of the Royal Society B: Biological Sciences, 270,467–473.

Van Dam, B. (1993). Element stewardship abstract for Emydoidea blandingii (Holbrock)Blanding’s turtle. Lansing, M. I: Michigan Natural Features Inventory., 25 pp.

White, P. J. & Kerr, J. T. (2006). Contrasting spatial and temporal global changeimpacts on butterfly species richness during the 20th century. Ecography, 29,908–918.

Wiley, E. O., McNyset, K. M., Peterson, A. T., Robins, C. R. & Stewart, A. M. (2003).Niche modeling and geographic range predictions in the marine environmentusing a machine-learning algorithm. Oceanography, 16, 120–127.

Wood, R. C. & Herlands, R. (1997). Turtles and tires: The impact of roadkills on thenorthern diamondback terrapin, Malaclemys terrapin, populations on the CapeMay peninsula, southern New Jersey, USA. In Proceedings: Conservation, restora-tion, and management of tortoises and turtles – An international conference. NewYork: New York Turtle and Tortoise Society. (pp. 46–53).

tributions using presence-only data: A case study of native New Zealand ferns.Ecological Modelling, 157, 261–280.

Zheng, B. & Agresti, A. (2000). Summarizing the predictive power of a generalizedlinear model. Statistics in Medicine, 19, 1771–1781.