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Ecological Modelling 246 (2012) 86– 90

Contents lists available at SciVerse ScienceDirect

Ecological Modelling

jo u r n al hom ep age : www.elsev ier .com/ locate /eco lmodel

he usefulness of elevation as a predictor variable in speciesistribution modelling

nouschka R. Hofa,b,∗, Roland Janssona, Christer Nilssona

Landscape Ecology Group, Department of Ecology and Environmental Science, Umeå University, SE-901 87 Umeå, SwedenDepartment of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden

r t i c l e i n f o

rticle history:eceived 4 April 2012eceived in revised form 29 June 2012ccepted 24 July 2012vailable online 14 August 2012

eywords:iodiversity

a b s t r a c t

Species distribution models (SDMs) are increasingly used to forecast impacts of climate change on speciesgeographic distributions, but the reliability of predictions is scrutinized. The main limitation of SDMslies in their assumption that species’ ranges are determined mostly by climate, which is arguable. Forinstance, biotic interactions, habitat and elevation may affect species ranges. The inclusion of habitat-related variables as predictors in SDMs is generally accepted, but there is no consensus regarding theinclusion of elevation. A review of randomly chosen literature revealed that elevation is used as a predictorvariable by just over half of the papers studied with no apparent trends as to why, except that papers

limate changelevationammals

lantspecies distribution modelling

predicting mammal species distributions for large regions included elevation more often than not, andthat papers that predicted mammal ranges for small regions tended to exclude elevation. In addition, wecompared the performance of SDMs with and without elevation as a predictor variable for the distributionof north European mammals and plants and found that the difference between their performances isstatistically significant for mammals, slightly favouring exclusion of elevation. No differences were foundfor plants.

. Introduction

Species distribution models (SDMs) are widely used to predictast, current and future geographic distributions of species. Theirpplication has considerably increased with the rising awareness ofhe impact future climate change can have on species (e.g. Pereirat al., 2010). Although there are concerns regarding the reliabilityf such models in forecasting impacts of climate change on biodi-ersity (Beale et al., 2008; Davis et al., 1998), it is still believed thathese models can provide useful information when used carefullyAraújo et al., 2009; Pearson and Dawson, 2003). In fact, projec-ions arising from these models have been used in assessments oflimate-change enhanced range shifts and extinction risks for manypecies. Indeed, a search on the appearance of ‘species distributionodelling’ in the articles in Web of Science reveals a steady increase

n published papers on the subject amounting to over 500 a year inhe past 5 years.

Nevertheless, the reliability of outputs is still debated, and theres consensus that the major limitation of the use of SDMs is thathey assume that species’ geographic ranges are determined mostly

∗ Corresponding author at: Department of Ecology and Environmental Science,meå University, SE-901 87 Umeå, Sweden. Tel.: +46 907866377;

ax: +46 907867860.E-mail address: Anouschka.Hof@emg.umu.se (A.R. Hof).

304-3800/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolmodel.2012.07.028

© 2012 Elsevier B.V. All rights reserved.

by climate (Pearson and Dawson, 2003), which is disputable. Pre-dation, competition and food availability might considerably alterthe distribution of species, which should preferably be taken intoaccount when applying SDMs (Callaghan et al., 2004; Davis et al.,1998). In addition, both habitat and elevation can restrict speciesranges (Harris and Pimm, 2008; Sekercioglu and Schneider, 2008),and have shown to be important in explaining the distribution ofspecies (Luoto and Heikkinen, 2008; Newbold et al., 2009; Virkkalaet al., 2010). Whereas the inclusion of habitat-related variables inSDMs is generally accepted, the inclusion of elevation is debated.While authors like Luoto and Heikkinen (2008) and Virkkala et al.(2010) showed that the accuracy of the predicted ranges of speciesmay increase notably when including an elevational range in thegrid-cells (NB thus not the mean elevation of a grid-cell) of variablesused in SDMs, others, like Austin (2002), advocate applying SDMswithout elevation set as one of the variables. The major argumentgenerally used for not including elevation in SDMs is that species donot respond directly to elevation, but rather to changes in abioticvariables regulated by elevation. However, it may be argued thatelevation is a surrogate for other non-climate related factors thatmay restrict species geographically, e.g. food availability (Remontiet al., 2009), or for climatic parameters when spatially explicit esti-

mates of climate are unavailable. We conducted a literature reviewof 75 papers that apply species distribution modelling for a range ofpurposes and spatial scales, and examine whether or not elevationis used as a predictor variable in specific situations, or whether it is

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andom. We further compared the prediction performance of SDMsith and without elevation as a predictor variable by assessing the

urrent distribution of native mammal and plant species of north-rn Europe and found that the difference between the performancef models that include and those that exclude elevation is slight buttatistically significant for mammals. No differences were observedor plants.

. Materials and methods

For the literature review ‘species distribution model*’ was useds a search term in Web of Science, refined by the query “MaxEnt orARP or Artificial Neural Networks or BIOCLIM”, popular machine

earning methods to model species distributions. The papers wereestricted to studies from 2010 and 2011, and those that onlypplied hind-casting were excluded since the use of elevation as

predictor variable is not recommended due to possible changesn elevation when studies deal with long time spans such as, as farack as the last glacial maximum. In total 75 papers were randomlyxtracted from the remainder, a number deemed sufficient for oururpose. For each paper we determined whether or not elevationincluding standard deviation of elevation and elevational range,xcluding slope) was used as a predictor variable, whether one orultiple species were studied, which taxonomic group was stud-

ed, at which resolution the study took place (small ≤1 km2 vs. large1 km2), and how large the studied area was (small <50,000 km2 vs.arge >50,000 km2).

Since MaxEnt (Phillips et al., 2006) is currently one of the mostrequently applied species distribution models (SDMs), and hashown to excel (Elith et al., 2006; Hijmans and Graham, 2006),e decided to use this algorithm for our study. We used theefault convergence threshold (106) and maximum number of iter-tions (500) values. We applied hinge features as is recommendedy Phillips and Dudik (2008). The environmental variables used

n the models consisted of the commonly used 19 bioclimaticariables derived from recent (1950–2000) monthly tempera-ure and rainfall values described and available at WorldClimhttp://www.worldclim.org/futdown.htm [developed by Hijmanst al., 2005]) and four habitat variables representing the main veg-tation zones in northern Europe (boreal needle leaved forests,rasslands, shrub areas, and shade intolerant broadleaved forests)rovided by Wolf et al. (2008). Elevation (average per grid cell) islso available at WorldClim. The climate and elevation data fromorldClim were available at the 30′′ (∼1 km2) scale. The vegeta-

ion data were available at the 25′ scale and interpolated to the0′′ scale in ArcGis (9.3.1 by ESRI) by means of the natural neigh-our method. We used all climatic variables available to us althoughhere was a certain level of auto-correlation between the variables,hich is common with climatic data. The use of auto-correlated

ariables is debated (Hijmans and Graham, 2006; Jimenez-Valverdet al., 2010); we decided to take advantage of the regularizationpplication in MaxEnt (Graham and Hijmans, 2006), which dealsith the selection of environmental variables (regulating some

o zero). This application has shown to perform well and outper-orms other methods to pre-select variables (Wollan et al., 2008). Inddition, MaxEnt minimizes auto-correlation between variables; itives more weight to variables that have a high correlation withhe occurrence data (Elith et al., 2011).

We obtained occurrence data for 54 native mammal species andor 117 species of plants occurring in northern Europe for the period1-01-2000 until 01-01-2010 from national species occurrence

atabases (Artsobservasjoner [http://www.artsobservasjoner.no],rtportalen [http://www.artportalen.se], and Hattika [http://ww.hatikka.fi]), and from the data portal of the Global Biodiver-

ity Information Facility (http://data.gbif.org). Since we wanted to

elling 246 (2012) 86– 90 87

have a minimum of 30 occurrences for each species, we obtainedextra data for the arctic fox (Alopex lagopus) from a report byAngerbjörn et al. (2005). We wanted to include this species in thestudy since it is critically endangered in Fennoscandia accordingto the International Union for the Conservation of Nature (IUCN).Since occurrence data available are often biased due to differencesin sampling effort, we randomly deleted excessive occurrence databy using a raster (grid size 10 km2) with the aim to have not morethan one randomly chosen occurrence data point per species per10 km2 grid-cell. This did not affect the resolution of our environ-mental data and predictions which was at the 30′′ scale. We wereunfortunately unable to ground-truth the occurrence records andhad to assume a degree of reliability of the datasets we used. Obvi-ous errors were omitted; we however acknowledge the fact thatsome error might have remained, which might potentially haveaffected the outcome of our study. The Fennoscandian part of thestudy area was used as a background from which pseudo-absenceswere drawn, as occurrence data from the Russian part were lack-ing. By means of randomized partition, 30% of the occurrence datawere set aside as ‘test’ data, comparing the Area Under the Curve(AUC) of a Receiver Operating Characteristic (ROC) plot (Hanley andMcNeil, 1982; Phillips et al., 2006) of these models with the AUCfrom ‘train’ models. Individual thresholds were applied to trans-form the continuous probability of presence generated by MaxEntto binary presence/absence data. Thresholds were chosen basedupon the ROC plot based approach (Cantor et al., 1999), which isexpressed by the shortest distance of the curve to the top-left cornerin the ROC plot and has shown to be one of the superior methods totransform continuous probabilities of species occurrence to binarypresence/absence occurrence (Liu et al., 2005).

To assess the accuracy of the SDMs we published the AUC valuesof both the training and testing data, and to assess the precisionwe compared the predictions of the current ranges generated bySDMs with ranges published by the IUCN for mammals and withranges in Hultén and Fries (1986) for plants. We expressed thesimilarity between these using the average of the percentage ofthe predicted current range that lay within the published rangeand the percentage of the published range that was covered by thepredicted current range. We assessed the performance of modelsthat excluded elevation as a predictor variable and of models thatincluded elevation as a predictor variable for each species.

3. Results

The literature review revealed that 53% of the 75 reviewedstudies included elevation as a predictor variable. Of these 40studies, one used standard deviation of elevation, one used ele-vational range, and one used both standard deviation of elevationand elevational range. The remaining 37 studies that included ele-vation used elevation as is. In total 32 studies (47%) did not includeelevation as a predictor variable (Supplementary material, TableS1). Studies that included or excluded elevation were not biasedtowards single versus multiple species studies (Pearson Chi Square�2 = 1.172, df = 1, p = 0.279), nor towards large versus small studyregions (Pearson Chi Square �2 = 0.030, df = 1, p = 0.861), or withregard to the resolution used (Pearson Chi Square �2 = 3.198, df = 1,p = 0.074). In total 3 out of 10 bird papers, 4 out of 9 insect papers, 11out of 15 mammal papers, 12 out of 24 plant papers, 7 out of 9 reptileand amphibian papers, 2 out of 5 papers dealing with other species,and 1 out of 3 papers dealing with a variety of taxonomic fam-ily groups, included elevation as a predictor variable. There were

no trends visible for the different taxonomic groups with regard tosingle versus multiple species, large versus small study regions andthe resolution used, according to Fisher’s Exact Test, but mammalpapers which included elevation were more often based on large

88 A.R. Hof et al. / Ecological Modelling 246 (2012) 86– 90

Table 1The mean precision of the models for the species assessed. Coverage expresses the percentage of the published range that was covered by the predicted current range. Overlapexpresses the percentage of the predicted current range that lay within the published range. Precision expresses the average of the coverage and the overlap.

Family Scenario Parameter Mean se

Mammals (n = 54)

Elevation included

Train AUC 0.935 0.006Test AUC 0.902 0.010Coverage 45% 3%Overlap 77% 3%Precision 61% 3%

Elevation not included

Train AUC 0.932 0.006Test AUC 0.901 0.008Coverage 49% 3%Overlap 81% 3%Precision 65% 2%

Plants (n = 14)

Elevation included

Train AUC 0.952 0.006Test AUC 0.921 0.011Coverage 37% 6%Overlap 72% 8%Precision 54% 4%Train AUC 0.940 0.007Test AUC 0.917 0.012

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tudy regions (n = 10 out of 11) and those that excluded elevationere more often based on small study regions (n = 3 out of 4) than

xpected (Fisher’s Exact Test �2 = 6.516, df = 1, p = 0.033).

.1. Mammals

Elevation explained on average 13% (se = 2%, median = 4%) of theotential distribution of the mammals and it was the most impor-ant predictor variable (i.e. contributing the highest percentage tohe generated prediction as set by MaxEnt) for ten out of 54 mam-

al species in the European Subarctic when it was included inhe models. It was the second most important predictor variableor a further 11 mammal species. When elevation was excludeds a predictor variable especially the predictor variables ‘annualean temperature’ and ‘mean temperature of the warmest quarter’

ppeared more frequently as top predictor variable in the modelshan they did when elevation was included. Although elevationas the explanatory variable explaining most variation for tenammal species, excluding it improved the precision of the pre-

ictions in eight of these with on average 13% (se = 4). For thether two species including it did increase the precision of theodel (mean = 1%, se = 4%). In cases when elevation was not the

op explanatory variable when included in the models, the preci-ion increased on average 5% (se = 1) for 32 species when excludingt. However, including it still lead to higher precisions by on average% (se = 1) for 12 species.

Table 1 shows the mean precision of all the models. For theajority of the mammal species (n = 44), the percentage with which

he IUCN range was covered by and overlapped with the predictedange decreased when elevation was taken into account while mod-lling their current distributions (Paired t-test; coverage: t = 2.675,f = 53, p = 0.010, overlap: t = 3.067, df = 53, p = 0.003). Therefore,or most of the species the predicted distribution was more ingreement with the published range when elevation was not useds a predictor variable (Paired t-test; t = 3.907, df = 53, p < 0.001).espite these significant differences, the precision of two-third of

he models for mammals (n = 36) differed with five or less percenthen in- or excluding elevation (mean = 5%, n = 54, se = 1%, min = 0%,ax = 28%).

.2. Plants

Elevation explained on average only 3% (se = 1%, 1st quar-ile < 1%, median = 1%, 3rd quartile = 2%) of the potential distribution

Coverage 35% 6%Overlap 72% 8%Precision 54% 4%

of the 117 plant species. It further appeared as the most importantpredictor variable for only three out of the 117 species and for fouras the second most important predictor variable. Due to the rela-tive weakness of elevation as a predictor variable for the potentialdistribution of plants, we felt that a comparison of the precision ofpredictions generated by models including and excluding elevationas a predictor variable for all 117 plant species was futile, especiallysince for mammals, the difference in precision between the modelsthat included and those that excluded elevation as a predictor vari-able was significantly and positively correlated with the percentageof the potential distribution that was explained by elevation when itwas included (Pearson correlation; r = 0.427, n = 54, p = 0.001). Fur-ther analyses for the plant species were therefore based upon 14plant species; the seven species for which elevation appeared asone of the two most important predictor variables in the modelsincluding elevation, and seven randomly chosen species for whichelevation did not appear as such.

For half of the 14 plant species (Table 1), the precision of the pre-dictions decreased when elevation was taken into account whilemodelling their current distributions. For four of these specieselevation was one of the two variables explaining most of thedistribution when it was taken into account. Differences in theprecision between models including and excluding elevation werehowever small (mean = 3%, n = 14, se = 1%, min = 0%, max = 10%) andnot significant (Wilcoxon signed ranks test; coverage: Z = −0.345,df = 13, p = 0.730, overlap: Z = −0.973, df = 13, p = 0.331). This wastrue both when the species for which elevation was one of thetwo variables explaining most of the distribution were taken intoaccount and when they were excluded (Wilcoxon signed ranks test,elevation important; coverage: Z = −0.338, df = 6, p = 0.735, over-lap: Z = −0.169, df = 6, p = 0.866. Elevation not important; coverage:Z = −0.169, df = 6, p = 0.866, overlap: Z = −1.014, df = 6, p = 0.310).

3.3. Model accuracy

Training AUC values were higher for models that included ele-vation (mammals mean = 0.935, se = 0.006, plants mean = 0.952,se = 0.006) than for those that excluded elevation (mammalsmean = 0.932, se = 0.006, plants mean = 0.940, se = 0.007 [mammals:

Paired t-test; t = −4.762, df = 53, p < 0.001, plants: Wilcoxon signedranks test; Z = −3.297, df = 6, p = 0.001]). Differences between test-ing AUC values were however not significant (mammals: Pairedt-test; t = −0.207, df = 53, p = 0.837, plants: Wilcoxon signed ranks

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A.R. Hof et al. / Ecologica

est; Z = −1.965, df = 6, p = 0.287). Both the training and the test-ng AUC values were significantly, but negatively, correlated withhe precision of the predictions for mammals (Pearson correla-ion; training values: r = −0.521, n = 54, p < 0.001, testing values:

= −0.377, n = 54, p = 0.005), but not for plants (Pearson correla-ion; training values: r = −0.153, n = 14, p = 0.603, testing values:

= −0.117, n = 14, p = 0.690).

. Discussion

From our literature review, we observed no obvious trendsegarding taxa, spatial scale, resolution and number of speciestudied, with regard to including or excluding elevation as a pre-ictor variable, but for one factor. Papers that aimed to predicthe distributions of mammals in large regions included elevationignificantly more often than not, whilst papers aiming to predictistributions of mammals in small regions tended to exclude eleva-ion. The absence of more trends is likely due to lack of consensusbout the usefulness of elevation as a predictor variable in SDMs.ur study revealed that for mammals, including elevation as a pre-ictor variable generally led to more conservative predictions ofurrent species distributions in comparison to the presumed rangesublished by the IUCN, than excluding elevation (according to ourlassification, our study region would be classified as large). Wessume that forecasts of species geographic distributions will beffected in a similar matter, which in turn affects their reliabilitynd power. Since underpredictions are regarded as an error in theontext of climate change (Thuiller et al., 2004), we do not advocatehe use of elevation as a predictor variable for mammals. However,lthough differences were statistically significant for mammals,hey were small. Besides, there were no significant differencesbserved for the plant species we investigated. Moreover, not allredictions responded in a similar matter; for a number of mam-als elevation appeared as the top explaining variable when it was

ncluded in the modelling process, but the agreement of the cur-ent prediction with the published range increased when elevationas excluded, which seems counterintuitive. Further, we did not

bserve any obvious patterns amongst the species for which includ-ng elevation improved their prediction. To add to the confusion,

hen focusing upon the AUC values we observed that training AUCalues were significantly higher for models that included elevation,ut differences between testing AUC values were not significanthich indicates that including or excluding elevation as a predictor

ariable was irrelevant to the prediction (but see the discussion onUC below). These findings complicate the application of SDMs to

multitude of species, e.g. to assess future hotspots of biodiversity.It might even be argued that (future) underpredictions might

e better for those species that are limited in their range by non-limatic variables. Indeed, when the aim is to conserve a species,ver-predictions might be worse than under-predictions becausever-predictions might lead to the conservation of the ‘wrong’reas, with obvious consequences involved for, amongst others,he targeted species. Looking at for example the Fennoscandianopulation of the arctic fox, which is largely constrained by non-limatic factors such as the presence of Norway lemming (Lemmusemmus) and red fox (Vulpes vulpes) (e.g. Angerbjörn et al., 2005),he range as published by the IUCN is too generous if we are toelieve Angerbjörn et al. (2005). Even though a precision test byomparing predictions with a published range might give weight toxcluding elevation as a predictor variable for this species, includ-ng it gave a prediction closer to the actual situation. Undoubtedly

he inclusion of elevation in this case acts a surrogate for, amongstthers, Norway lemming and red fox presence, and the inclusion ofpecies interactions, when possible, is to be favoured over applying

surrogate (e.g. Hof et al., 2012) when such interactions are well

elling 246 (2012) 86– 90 89

understood. Nevertheless, when species interactions are not wellunderstood or data are not available, including elevation as a sur-rogate might be an alternative (e.g. Cianfrani et al., 2011), ratherthan creating over-complex models. It is however to be questionedwhether elevation can be a good surrogate for non-climatic vari-ables in general, since excluding elevation improved the precisionof the predictions for eight out of the ten mammal species for whichelevation was the highest explaining variable originally.

It is unfortunate that studies that aim to predict future speciesdistributions are contingent on the accuracy of published currentgeographic ranges. In our study, in contrast to the too generousrange of the arctic fox, some published ranges appear to be toorestricted; for example in the case of the lesser noctule (Nyctalusleisleri) and the pond myotis (Myotis dasycneme). The occurrencedata obtained from the various data sources suggested that thesespecies are more widely occurring in northern Europe than is cur-rently assumed by the IUCN. Nevertheless, a baseline is requiredwhen assessing the reliability of SDMs. This is increasingly impor-tant since the usefulness of the AUC of an ROC plot as an assessmentof the accuracy of predictions is strongly questioned (e.g. Lobo et al.,2008). We also doubt its usefulness; although both the training andthe testing AUC values were significantly, correlated with the preci-sion of the predictions for mammals, they were negatively so. Thismeans that AUC values were higher when a generated current pre-diction for mammals was incompatible with its published range,which is surprising. A more thorough study on the usefulness ofAUC values may be required and an effort should be made to betterassess the performance of SDMs.

As SDMs are increasingly applied to forecast effects of a chang-ing climate on species ranges meaning to aid species conservation,it is imperative that modellers do the best possible job in creatingthose predictions, even though it nevertheless remains a prediction.Seeing that there is not always consensus on best practice withinthe modelling community, the only remedy seems to be trial anderror. Our study implies that whereas the inclusion of elevation asa predictor variable did in general negatively affect the predictivepower of SDMs for mammals, this effect was small and inconsis-tent and not observed for plants. Since an increasing number ofstudies report on many species, it is often logistically impossible tocreate the best possible prediction for each species, which mightgive weight to using a broad surrogate variable such as elevation.When the objective of a study is to detect trends for a host of speciesinstead of targeting one species this difference is likely to be irrel-evant. We therefore conclude that researchers have to weigh thepros and cons of decisions like including or excluding elevation asa predictor variable before each study.

Acknowledgements

This project was funded by the Nordic Council of Ministers. Wethank Larisa Harding for the extraction of papers from Web of Sci-ence for the literature review. We thank two anonymous reviewersfor their useful comments.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.ecolmodel.2012.07.028.

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