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Modelling the environmental niche of a decliningfarmland bird speciesM. Girardello a & F. Morelli ba Centre for Ecology and Hydrology, MacLean Building, Wallingford, Oxfordshire, UKb DiSTeVA, University of Urbino, Scientific Campus, 61029, Urbino, Italy

Version of record first published: 01 May 2012

To cite this article: M. Girardello & F. Morelli (2012): Modelling the environmental niche of a declining farmland bird species,Italian Journal of Zoology, 79:3, 434-440

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Italian Journal of Zoology, September 2012; 79(3): 434–440

Modelling the environmental niche of a declining farmland bird species

M. GIRARDELLO1 & F. MORELLI2*

1Centre for Ecology and Hydrology, MacLean Building, Wallingford. Oxfordshire, UK and 2DiSTeVA, University of Urbino,Scientific Campus, 61029 Urbino, Italy

(Received 24 May 2011; accepted 8 February 2012)

AbstractSpecies distribution models are used increasingly in both applied and theoretical research to understand attributes of species’environmental requirements and to predict how species are distributed. However, recent advances in species distributionmodelling have shown that the use of a single modelling method can lead to biased conclusions about the determinants ofspecies’ distributions. Here we illustrate the application of an alternative modelling framework whereby multiple methodsare employed for both making inference and predicting species distributions. We used six modelling techniques to quantifythe environmental niche of the Red-backed shrike Lanius collurio, a threatened farmland bird species. Our results show that,while the importance and the direction of the effect of the enviromental variables are broadly concordant with what is knownabout habitat selection in the Red-backed shrike, there was variation in the predictive performance and the variables judgedas important by the models. We conclude that the use of an analytical framework based on multiple modelling methodscould be useful, not only to highlight the uncertainties derived from the use of a single best model, but also to make morerobust inferences on species distributions and make more accurate predictions.

Keywords: Red-backed shrike, species distribution modelling, BIOMOD, GIS

Introduction

Species distribution models are currently recognizedas helpful tools for providing valuable and quanti-tative information by revealing the most importantresources required by a species (Guisan & Thuiller2005). Models can efficiently guide decision mak-ers and wildlife managers in processes of protection,management or conservation planning (e.g. Ortega-Huerta & Peterson 2004; Pawar et al. 2007) or canbe used to develop environmental impact assessmentprograms (e.g. Seiler 2005; Pearce-Higgins et. al.2009). Furthermore, if coupled with geographicinformation systems (GIS) technology, species dis-tribution models can be used for producing mapsthat display the spatial configuration of the suitablehabitats, which enables protection, management andrestoration strategies to be implemented within aspatial context (e.g. Martinez et al. 2006; Santoset al. 2006; Rayner et al. 2007). Besides revealinghabitat selection patterns, the application of speciesdistribution models to areas where environmental

*Correspondence: F. Morelli, DiSTeVA, University of Urbino, Scientific Campus, 61029 Urbino, Italy. Email: federico.morelli@uniurb.it

conditions are known but where species distribu-tions are unknown yields habitat suitability maps(Buermann et al. 2008).

While the benefits of using species distributionmodels are numerous, species distribution modellingis complicated by numerous uncertainties (Thuiller2004; Guisan & Thuiller 2005), including uncer-tainty derived from the use of different algorithms.A recently emerging alternative modelling frame-work, for reducing uncertainty among models, is onethat makes use of multiple models in order to mini-mize the risk of modelling failure that can arise fromindividual models (Araújo & New 2007). Here, weillustrate an application of the use of multiple meth-ods, using a declining farmland bird species as a casestudy. Our study species, the Red-backed Shrike,Lanius collurio Linnaeus, 1758, is a farmland bird(Lefranc & Worfolk 1997) that has experienced a dra-matic decline throughout much of its European rangeduring the past century (Yosef 1994; Olsson 1995;Tryjanowski et al. 2000; Golawski & Meissner 2008;

ISSN 1125-0003 print/ISSN 1748-5851 online © 2012 Unione Zoologica Italianahttp://dx.doi.org/10.1080/11250003.2012.666572

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Modelling the niche of declining farmland bird 435

PECBMS 2009; Brambilla et al. 2010). The specificaims of our research were to: i) quantify the impor-tance of a series of environmental factors affectingthe distribution of the Red-backed Shrike and ii) testthe predictive performance of our models.

Materials and methods

Species and environmental data

Data on the distribution of the Red-backed Shrikewere collected during 2009 in central Italy. Thestudy area is located between the Marche and EmiliaRomagna regions (Figure 1). From early on Mayto mid of June the study area was monitored withtransect counts with recording of observations andvocalizations every five days to search for breed-ing Red-backed Shrikes territories. After locating thebreeding pairs by direct observation during arrival,courtship and nest-building, the occupied territorywas surveyed at least once every three days duringthe breeding period to locate the nests (Blondel &Frochot 1987; Bibby et al. 2000). Inspections werecarried out on shrubs that evidenced major activi-ties of male and female birds (nest building activ-ities, material transport and territorial behaviour).Because this species is markedly territorially, the useof such records is powerful to define an occupiedterritory (Karlsson 2004; Tryjanowski & Golawski2004; Brambilla et al. 2009). Because Red-backedShrikes are particularly sensitive to disturbance dur-ing nest building, egg-laying and early incubation(Tryjanowski & Kuzniak 1999) we minimized thenumber of visits to each nesting site. For every sitewe recorded geographic coordinates using a GPS.

Eleven environmental variables were used forthe analyses (Table I). Altitude, slope and aspect

Table I. Environmental variables used for the study.

Description Units

Slope ◦Aspect sin{aspect x π/180}Altitude mUrban areas m2

Non-irrigated arable land m2

Arable land (with trees) m2

Vineyards m2

Woodland m2

Pastures-Permanent Grassland m2

Uncultivated fields with shrubs m2

Uncultivated fields with trees m2

variables were derived from a digital elevation model(DEM) (http://srtm.csi.cgiar.org/), available in araster format. Aspect values were transformed to alinear scale such that north-facing sites had highscores, south facing sites had low scores and botheast and west-facing sites scored zero (sin{aspect ×π/18}). Eight land-cover types were derived froma regional land-cover database (AA.VV. 2001). Thebaseline resolution of all the environmental layers was90 m (the lowest possible resolution for the DEM,which was the layer with the coarser spatial resolu-tion). For each 90 × 90 m grid cell of the landscape,we therefore calculated the area (m2) of each of theeight cover types. The environmental data were thenmatched to the species data by overlaying the mapscontaining the species occurrence records and thosecontaining the environmental data.

Data analysis

We modelled the Red-backed Shrike distribu-tion using the BIOMOD package for R (Thuiller2003; Thuiller et al. 2009). BIOMOD allows the

Figure 1. Location of the study area within Italy and distribution of the sample sites, the altitude is represented in gray-scale from black(lowland) to white (highland).

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436 M. Girardello and F. Morelli

development of nine species distribution models withdifferent methods. For the purpose of the presentanalysis we used six methods: 1) Generalized LinearModels (GLM; McCullagh & Nelder 1989) withlinear terms. A stepwise procedure using the AICcriterion was used to select the best model; 2)Classification Tree Analysis (CART; Breiman 1984)using a 10-fold cross-validation to select the besttrade-off between the number of leaves and theexplained deviance; 3) Multiple Adaptive RegressionSplines (MARS; Friedman 1991). MARS combinesclassical linear regression, mathematical construc-tions of splines and binary recursive partitioning toproduce a local model in which relationships betweenresponse and predictors are either linear or non-linear. MARS automatically selects the amount ofsmoothing required for each predictor as well asthe interaction order of the predictors. It is con-sidered a projection method where variable selec-tion is not a concern but the maximum level ofinteraction needs to be determined. Here we tooka conservative approach and specified only two-level interactions. 4) Mixture Discriminant Analysis(MDA); (Hastie et al. 1994). MDA is a method forclassification (supervised) based on mixture mod-els. It is an extension of the well-known LinearDiscriminant Analysis (LDA). MDA assumes thatthe distribution of the class of each environmen-tal variable follows a Gaussian distribution. MDAenhances the LDA, allowing the classifier to han-dle different prototype classes such as a mixture ofGaussians. The environmental parameters form pri-mal classes, which are divided into sub-classes. Theclassification results from these subclasses, a mix-ture density, describe the distribution density of theprimal classes of environmental variables. Differentregression methods can be used in the optimal scal-ing process. We use MARS to increase the pre-dictive power of the models. 5) Random Forests(RF; Breiman 2001). Random Forests belongs tomachine learning methods and generates hundredsrandom classification trees. A training set of eachtree is chosen, as many times as there are obser-vations, among the whole set of observations. Foreach node of trees, the decision is taken accord-ing to randomly selected environmental parameters.Trees thus constructed are not pruned and are aslarge as possible. After the trees have been built,data are entered into them and each grid squarewill be classified by all trees. At the end of the run,the classification given by each tree is consideredas a “vote”, and the classification of a grid squarecorresponds to the majority vote among all trees.The output of Random Forests depends primarily onthe number of predictors selected randomly for the

construction of each tree and total number of trees.We kept these to the default values (500 trees and√

(p) where p number of predictors), which are gen-erally known to perform quite well (Girardello et al.2010). 6) Boosted Regression Trees (BRT; Friedman2001). General boosting method (GBM) is a sequen-tial method based on classification trees (Ridgeway1999). The GBM is considered as a machine learn-ing method processing by sequential improvementsof the estimate residuals. To classify a vector amongseveral classes, it is possible to use a tree classifica-tion as in classification trees. However, a prior singletree classification can be improved, as long as thereis an estimate residual. This residual can be usedas an input into another classification tree, which isthen used to improve the prior classification. Thesequence is repeated as long as necessary, decreas-ing step by step the estimate residual. Each trainingsample has an attributed weight corresponding to itsdifficulty to be classified. The boosted classifier’s pre-diction is based on an accuracy weighted vote acrossthe estimated classifiers. Developing a BRT modelinvolves specifying the number of trees, the learningrate and tree complexity. The learning rate is usedto shrink the contribution of each tree as it is addedto the model and tree complexity is the number ofnodes in each individual tree. For the purpose ofthe present analysis we built our BRT model using8000 trees with a slow learning rate of 0.001 and treecomplexity of two.

Because the database contained presence-onlydata, we derived a series of absence points from theenvironmental background of the study area usingthe following procedure: i) we run a Surface RangeEnvelope SRE model with the presence-only data(i.e. the presence localities where the Red-backedShrike had been observed). We used SRE becausethis method only requires presence data. SRE iden-tifies minimum and maximum values for each envi-ronmental variable from the localities where thespecies is present, and the predicted distribution thenincludes any site with all variables falling betweenthese minimum and maximum limits (Busby 1991)ii) we randomly selected 129 pseudo-absences fromthe background where the SRE model predicted alow probability of occurrence for the species andthereby excluding sites where the environment isconsidered to be possibly favourable for the species.We then run all models using both the presence andthe pseudoabsence data.

We measured the importance of each variableusing a permutation procedure. This procedure usesthree steps to calculated the importance of a givenvariable: i) use a fitted model to predict onto the orig-inal observations ii) repeat step (i) using permuted

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Modelling the niche of declining farmland bird 437

observations (randomly reshuffled) for the variable ofinterest, while all other variables are left unchangediii) calculate a correlation coefficient between theoriginal prediction and the one of the permuted vari-able. The importance of the variable is calculatedby subtracting from 1 the mean correlation. Thismethod allows for the comparison of the importanceof a particular variable across different modellingalgorithms. We also computed the response curve ofthe Red-backed shrike occurrence against the envi-ronmental variables for each method. To do this, weused the evaluation strip (Elith et al. 2005). In theevaluation strip mehod, N-1 variables are held con-stant at their mean value whilst the variable of interestcontains 100 points varying across the maximum andthe minimum of the variable’s range. Variation inpredictions, made to these 100 cells, only reflectsthe effects of variation of the one selected variable.Thus, a plot of these predictions allows visualisationof the modelled response to the variable of inter-est, contingent on the other variables being heldconstant.

Our models were evaluated by randomly split-ting the original dataset into model-training (70%)and model evaluation data sets (30%). The train-ing dataset was used for the construction of themodel whereas the evaluation dataset was used totest the predictive abilities of our models. We com-puted the following measures of model performance:Cohen’s kappa (Manel & Williams 2001) and AUC(Fielding & Bell 1997). The Kappa coefficient mea-sures the correct classification rate (proportion ofcorrectly classified presences and absences) after theprobability of chance agreement has been removed(Congalton 1991). Landis and Koch (1977) pro-posed a scale to describe the degree of concor-dance based on Kappa: 0.81–1.00, almost per-fect; 0.61–0.80 substantial; 0.41–0.60 moderate;00.21–0.40 fair; 0.00–0.20 fail. Kappa is depen-dent on a single threshold and distinguish betweenpredicted presence and predicted absence and thusfalls into the class of threshold-dependent measures.We selected the probability threshold based on thecut-off level that maximizes Kappa. This can bedetermined by evaluating Kappa values at successiveprobability increments across the entire range from0.00 to 1.00 (Huntley et al. 2004). The AUC relatesrelative proportions of correctly classified (true posi-tive proportion) and incorrectly classified (false pos-itive proportion) cells over a wide and continuousrange of threshold levels (Fielding & Bell 1997). Thismakes it a threshold–independent measure (Pearce2000). The AUC ranges generally from 0.5 formodels with no discrimination ability to 1.0 formodels with perfect discrimination. An approximate

guide for classifying the accuracy of AUC is thatproposed by Swets (1988): 0.90–1.00 excellent;0.80–0.90 good; 0.70–0.80 fair; 0.60–0.70 poor;0.50–0.60 fail.

Results

We recorded a total of 129 Red-backed Shrikebreeding sites. An exploration of the major envi-ronmental features within the surroundings of thenesting sites revealed that, within a radius of 100 m,the predominant land-use range were: pastures-permanent grassland (29.9%), non-irrigated arableland (21.1%) to woodland (15.2%). The modelsshowed a variable performance when tested againstthe validation data. According to the AUC, the bestperforming method was RF, follow GLM, the GBM,FDA, MARS and CART. The results for the kappastatistic were broadly concordant with the AUC withthe exception of those for GLM and RF, where GLMperformed better than RF (Table II). Table III showsthe important predictors obtained through the per-mutation procedure. Although the ranking of theenvironmental variables (Table III) differed acrossthe modelling methods, the most influential predic-tors were generally related to slope, followed by non-irrigated arable land, altitude, woodland and pasture-grassland areas. Figure 2 shows the response curvesthe Random Forest model. The plots reveal thepresence of potential thresholds in the relationshipsbetween species occurrence and the environmentalvariables. Only five variables seem to show an effectprobability of occurrence of the species. These werealtitude, aspect, slope, non-irrigated arable land,woodland, and to a lesser extent grassland/pastureareas. Altitude and grassland had positive effect onthe probability of occurrence of the species whereasaspect, slope and woodland had a marginally negativeeffect.

Discussion

In this study, we sought to quantify the environ-mental niche of a threatened farmland bird species.Our results are broadly concordant with what has

Table II. Results of model validation. (CTA Classification Tree,GBM Boosted Regression Trees, GLM Generalised Linear Model,MARS Multivariate Adaptive Regression Spline, FDA MixtureDiscriminant Analysis, RF Random Forest).

CTA GBM GLM MARS FDA RF

AUC 0.67 0.78 0.79 0.73 0.77 0.80Kappa 0.33 0.49 0.52 0.43 0.47 0.51

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Table III. Relative importance of the variables. The names of the variables are listed in decreasing order of importance(for acronyms see Table I). The numbers (e.g., 0.41) indicate the mean decrease in the prediction accuracy of the modelwhen one particular variable is permuted while all the other variables are held constant. Higher values of mean decreasein accuracy indicate variables that are more important in determining a species’ distribution. (CTA Classification Tree,GBM Boosted Regression Trees, GLM Generalised Linear Model, MARS Multivariate Adaptive Regression Spline, FDAMixture Discriminant Analysis, RF Random Forest).

CTA GBM GLM MARS FDA RF

Slope 0.43 0.26 0.34 0.30 0.31 0.23Aspect 0.14 0.03 0 0 0 0.07Altitude 0.33 0.18 0.25 0.23 0.11 0.15Urban areas 0 0 0 0 0 0.01Non-irrigated arable land 0.27 0.23 0 0.28 0.39 0.19Arable land (with trees) 0 0 0.05 0 0 0Vineyards 0 0 0 0 0 0Woodland 0.24 0.09 0.36 0.22 0.14 0.10Pastures-Permanent Grassland 0.17 0.11 0 0.15 0.18 0.10Uncultivated fields with shrubs 0 0 0 0 0 0Uncultivated fields with trees 0 0 0.03 0 0 0

Figure 2. Response curves for the Random Forest Model (RF). Every plot depicts the probability of occurrence (y axis) of the species inrelation to a particular environmental variable (x axis).

been reported for this species in other Italian andEuropean regions (Vanhinsbergh & Evans 2002).In accordance with what is known about the ecol-ogy of the Red-backed shrike, our models showedthat the probability of occurrence of the speciesincreases with the presence of pastures and perma-nent grasslands areas, arable land and altitude whilstwoodland areas were less suitable (Vanhinsbergh &Evans 2002; Brambilla et al. 2010, Morelli 2012).Topography was also important in our study area.It is likely that topography reflects the availabil-ity of more favourable habitats in our study area.Lowland areas in the Marche region are dominated

by intensive agriculture whereas in higher altitudearea grasslands and low intensity farming are morecommon.

Most importantly, our results showed the pres-ence of variability in the ranking of the environ-mental variables and the predictive performance ofour models. This has important consequences forthe applied use of species distribution models. Whilethe current paradigm involves the search for a sin-gle best model, often based on statistical significance,many studies have shown how model performanceand prediction can vary across different modellingalgorithms (Thuiller et al. 2004; Araújo et al. 2005;

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Modelling the niche of declining farmland bird 439

Elith & Graham 2009). Models can be thought asdifferent representations of reality and there is nocertainty about which model is closer to the truth(Burnham & Anderson 2002). Examining the resultsfrom multiple models, i.e. multiple descriptions ofreality, can thus help one to make more robustinferences on species distributions (Araújo & New2007). The use of multiple modelling approacheswill surely not remove all the uncertainties, but thelikelihood of making conservation decisions basedon the results of models that are far from the truthis reduced. However, it is imperative to realize thatgood modelling performance with observed distribu-tions does not guarantee that the model produces theecologically most plausible results, especially for non-sampled parts of a given area (Pearson & Dawson2003; Pearson et al. 2006; Randin et al. 2006).Models built on a specific set of environmental vari-ables from small regions may underestimate the set ofenvironmental conditions where a species occur andproduce erroneous predictions towards the upperand lower environmental boundaries of a species(Thuiller et al. 2004).

In recent years, conservation biology has takenadvantage of the tremendous advances in earthobservation technologies and in statistics applied tomodelling ecological systems, allowing researchersto forecast population dynamics and practitioners toundertake adaptive measures based on such forecasts(Elith et al. 2006; Foody & Cutler 2006). Usingmany of the methods presented here could be use-ful to screen across a large number of variables tomodel detailed modelling exercises. Analyzing largeamounts of species distribution data has become anissue of keen interest and elucidating species distri-butions patterns has become a vital tool for conser-vation programmes, aimed at identifying importantareas for biodiversity (e.g. Girardello et al. 2009).The approach adopted here shows how multiplemodelling methods can be used to make robust infer-ence on species’ distribution patterns. It should,however, be borne in mind that the choice of theappropriate modelling method will primarily dependon the goals of the study, and one should be fullyaware of the limitations of the techniques being used(e.g. Brambilla et al. 2009). Species distributionmodels can only depict a picture of this and thisshould be understood by the modeller and the enduser.

Acknowledgements

We thank Simone Asprea, Niki Morganti, FabioPruscini and Paolo Magalotti for helping in the field.

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