Niche similarities among white-eared opossums (Mammalia, Didelphidae): Is ecological niche modelling...

Niche similarities among white-eared opossums (Mammalia,Didelphidae): Is ecological niche modelling relevant to settingspecies limits?LUANA J. TOCCHIO, RODRIGO GURGEL-GONC�ALVES, LUIS E. ESCOBAR & ANDREW TOWNSEND PETERSON

Submitted: 10 January 2014Accepted: 9 August 2014doi:10.1111/zsc.12082

Tocchio, L.J., Gurgel-Gonc�alves, R., Escobar, L.E. & Peterson, A.T. (2014). Niche similari-ties among white-eared opossums (Mammalia, Didelphidae): is ecological niche modellingrelevant to setting species limits? —Zoologica Scripta, 00, 000–000.A complex of white-eared opossums (Didelphis spp.) is distributed across three distinct areasof South America, but recent taxonomic treatments have disagreed regarding species limitsin the group. We used ecological niche modelling to test whether ecological niches havebeen conserved or have diverged among the three forms in this group. Differences in com-binations of niche and range were clear; however, when hypotheses of accessible areas foreach species were considered, coarse-grained niche dimensions (i.e. climatic dimensions)were seen not to differ across the complex. We discuss implications of these results for taxo-nomic recognition of species based on geographic and ecological characteristics and theimplications of using ENM approaches to setting species limits. We suggest that ENMshould be used to explore speciation mechanisms, rather than being applied to questions ofsetting species limits.Corresponding author: Luana J. Tocchio, Laborat�orio de Parasitologia M�edica e Biologia deVetores, Faculdade de Medicina, �Area de Patologia, Universidade de Bras�ılia, Asa Norte, CEP70.910-900, Distrito Federal, Brazil. E-mail: [email protected] J. Tocchio, and Rodrigo Gurgel-Gonc�alves, Laborat�orio de Parasitologia M�edica e Biologiade Vetores, Faculdade de Medicina, �Area de Patologia, Universidade de Bras�ılia, Asa Norte, CEP70.910-900, Distrito Federal, Brazil. E-mails: [email protected], [email protected] E. Escobar, Center for Global Health and Translational Science, State University of New YorkUpstate Medical University, Syracuse, NY, 13210 USA and Facultad de Ecolog�ıa y RecursosNaturales, Universidad Andres Bello, Av. Rep�ublica 440, Santiago, Chile. E-mail: [email protected] T. Peterson, Biodiversity Institute, University of Kansas, Lawrence, KS, 66045 USA.E-mail: [email protected]

IntroductionEcological niche modelling (ENM) is a tool that permitsexploration of geographic and ecological phenomena basedon known occurrences of species and landscape characteris-tics (Peterson et al. 2011). ENM has been applied broadly tomany challenges related to delineating distributions of spe-cies (Peterson et al. 2002; Illoldi-Rangel et al. 2004; McNy-set 2005). Niche characteristics also have been applied toimprove inferences about species limits and speciation mech-anisms (Graham et al. 2004; Raxworthy et al. 2007; Rissler &Apodaca 2007; Stockman & Bond 2007; Mart�ınez-Gordilloet al. 2010). As an example, Mart�ınez-Gordillo et al. (2010)reported ecological niche differences among Mexicanrodents, attributing their findings to allopatric speciation.Understanding and delineating distributions of species is

essential to an integrated view of ecology, biogeography andevolution (Cerqueira 1985; Guisan & Thuiller 2005). ENMoffers an effective approach to investigating patterns of nicheconservatism or divergence (Peterson et al. 1999; Raxworthyet al. 2007; Warren et al. 2008; Peterson 2011), making pos-sible empirical tests of their influence in structuring species’distributions (Pearson et al. 2007).The issue of species delimitation has long been confused

with that of species concepts (De Queiroz 2007). From anecological point of view, focus is on groups of organismsthat share the same ecological niche (Andersson 1990), butother properties must be considered in the process of set-ting species limits (De Queiroz 2007). Diverse operationalcriteria must be considered to support recognition of spe-cies taxa (Raxworthy et al. 2007; Ahmadzadeh et al. 2013).

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Zoologica Scripta

In this context, modern approaches generally assessmultiple sources of information in species delimitation.Padial et al. (2010) discussed these approaches and showedthat (i) integration by cumulation identifies species limitsvia divergence in one or more not-necessarily-overlappingtaxonomic characters (for example, mtDNA or morphol-ogy), (ii) integration by congruence identifies species limitsas the intersection of evidence from two or moreindependent taxonomic characters (for example, mtDNAplus morphology), (iii) both approaches have the limitationsand (iv) a general protocol for such integrative taxonomycombines the advantages of cumulative and congruenceapproaches. ENM is a tool that can be used to exploreniche divergence or conservatism among organisms(Arribas et al. 2012), so niche-related characters may offerother line of evidence by which to assess lineageseparation.The opossums of South America can be divided in two

main groups: those with black ears (Didelphis marsupialisLinnaeus, 1758 and D. aurita Wied-Neuwied, 1826) vs.those with white ears. The white-eared opossums aredistributed widely in open areas, mountains and forests(Cerqueira 1985) and occur in three contrasting areas of thecontinent: open areas of Argentina, Paraguay, Uruguay,Bolivia, and central and southern Brazil; the Andes; andhumid forests of Guyana, Suriname and southern Venezuela(Cerqueira 1985; Voss & Emmons 1996; Brown 2004;Cerqueira & Tribe 2007). Currently, the group is consideredto comprise three species: D. albiventris, D. pernigra andD. imperfecta (Lemos & Cerqueira 2002; Ventura et al. 2002).However, the white-eared opossums were, until relatively

recently, considered as a single species, D. albiventris Lund,1841. Cerqueira (1985) indicated that D. albiventris includedtwo subspecies: D. a. albiventris and D. a. azarae; a third sub-species in Venezuela was recognized as D. a. imperfectus(Mondolfi & P�erez-Hern�andez 1984). However, thisclassification was not accepted widely, and Gardner (1993)considered all white-eared opossums as D. albiventris. Later,Voss & Emmons (1996) recognized D. imperfecta as a fullspecies, and Anderson (1997) recognized two subspecies asoccurring in Bolivia (D. a. albiventris and D. a. pernigra).Finally, a morphological study of the group found cleardiscontinuities in several independent sets of characters andsuggested recognition of all three forms as full species(Lemos & Cerqueira 2002).Other characters would ideally be taken into account,

including phylogenetic relationships, gene flow andecological characteristics. Ecological segregation has beensuggested to play important roles in lineage divergence(Salomon 2011; Singh 2012), although this point has beendebated (Peterson et al. 1999) and is certainly highly scale-dependent (Sober�on 2007). Although maps of occurrences

of white-eared opossums have been developed (Cerqueira1985), no studies have analysed their geographicdistributions comparatively in ecological and environmentaldimensions. Cerqueira (1985) analysed factors structuringthe distribution of this group when it was considered as asingle species (D. albiventris). This situation thus begsdetailed analysis using geographic and ecological tools, tounderstand factors that shape ecological niches and geo-graphic distributions, providing information relevant tospecies status and speciation processes in the group. Hence,here, we tested whether ecological niches have divergedsignificantly among the three distributional areas of thesemarsupials in South America. Such evaluation in thisintriguing case allowed us to reflect more generally on theutility of ENM for species delimitation.

Materials and methodsOccurrence dataOccurrence data were obtained from previous studies ofthe group (Lemos & Cerqueira 2002; Carmignotto 2004;Adler et al. 2006; Cerqueira & Tribe 2007; C�aceres et al.2008; Aragona & Marinho-Filho 2009). The number andquality of these data are important for analyses ofgeographic distributions, so we used (in largest part)records associated with museum voucher specimens toassure quality, reliability and verifiability. Data from onlinedatabases were not used, to avoid confusions resulting fromoutdated taxonomic arrangements. We analysed 448localities: 398 for D. albiventris, 37 for D. pernigra and 13for D. imperfecta (Fig. 1). Geographic coordinates of namedplaces were obtained from online gazetteers (http://www.fallingrain.com/world).

Environmental dataThe environmental data include the physical environmentthat imposes physiological restrictions on populations thattranslate into long-term persistence (or not) in a geo-graphic area (Sober�on & Peterson 2005). Selection of idealenvironmental variables varies according to the speciesinvolved, the extent of the study area and the temporalcontext (Peterson et al. 2011). Environmental variableswere selected to match temporal and geographic scale ofknown occurrences of the three species (i.e. climate, rain-fall, elevation). This information was extracted from theWorldClim dataset (Hijmans et al. 2005). The environmen-tal database used in the analysis covers Central and SouthAmerica at a spatial resolution of 2.50 (5 9 5 km per pixel).A Spearman correlation test was used to detect and excludehighly correlated variables (r ≥ 0.9) from analysis.To identify variables that most influenced distributions

of the three populations, we used a jackknife test on theoriginal environmental variables, as described by Pearson

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Niche similarities among white-eared opossums � L. J. Tocchio et al.

et al. (2007). This test consists of removal without replace-ment of one environmental variable at a time from the fulllist, and recalculation of the model to quantify the contri-bution of each variable in model performance. Additionally,we used NicheA, a programme that supports detailed visu-alization and analysis of distributions in environmental andgeographic dimensions, to explore distributions of knownoccurrences with respect to environmental variation (Qiaoet al. 2014).Principal components analysis (PCA) in Statistica (StatSoft

Inc., Tulsa, OK, USA) was used (i) to summarize multidi-mensional environmental variation across known occur-rences of opossums and test whether opossum populationsfall into easily discernible sets of environmental conditionswithout a priori group designation; these initial analyses weredeveloped without regard to conditions available across thecontinent or across the accessible areas for each species (seebelow). (ii) We used PCA on the original climate variablesacross the entire study region to transform the often-corre-lated original environmental variables into new, uncorrelatedvariables; this procedure also allowed us to display the envi-ronmental space efficiently in NicheA, summarizing most ofthe environmental information in a three-dimensional sce-nario. Finally, we used a map of ecoregions (WWF 2013) torelate records of each opossum species to ecoregions presentin South America. Ecoregion boundaries are useful whenobserving patterns of biodiversity and were used by Cerque-

ira (1985) to investigate biogeographic limits between opos-sums in the same area, before the recognition of the threespecies. Maps were edited using ARCVIEW (version 3.3;ESRI, Redlands, CA, USA).

Ecological niche modellingEstablishing appropriate areas for model calibration andanalysis is a critical step in calibrating and comparing eco-logical niche models: they must be customized to each spe-cies involved, based on biogeographic and ecologicalfeatures (Barve et al. 2011). These calibration areas, termedM in the BAM diagram (Peterson et al. 2011), were pos-ited for this study based on hypotheses of relevant biogeo-graphic barriers (i.e. Amazon River, Andes; Fig. 1). TheBAM diagram is a representation of the three factors thatinteract to shape the species’ ecological niche (Sober�on &Peterson 2005): biotic (B), abiotic (A) and movement (M).B indicates the area suitable for the species in terms of bio-logical interactions, such as competition, which may restrictspecies’ distributions. A is the set of areas suitable for thespecies in terms of environmental variables independent ofthe species’ populations, such as climate and topography.Finally, M is the area to which the species has had accessvia dispersal over time.Ecological niche modelling uses associations between

environmental variables and occurrence sites to define suit-able environmental conditions (Peterson et al. 2011).ENMs were calibrated using a maximum entropy methodimplemented in MAXENT v3.2.1 (Phillips et al. 2006; http://www.cs.princeton.edu/schapire/maxent). Maxent fits modelsbased on the probability distributions that show maximumentropy (i.e. closest to uniform), subject to the constraintsof sites of known occurrence with respect to environmentalconditions across the study area (Phillips et al. 2006).To provide a quantitative evaluation of model perfor-

mance, distributional data were separated into two sets: arandom 50% of points for model calibration and the other50% for model evaluation. Models were calibrated acrossM hypotheses for each species (see above), so as to restrictmodel evaluation to regions potentially accessible andinhabited by the species – that is, if the species is not pres-ent, we can then hypothesize that absence should be a con-sequence of unsuitable conditions. In the light of knownproblems with the customary receiver operating character-istic (ROC) approach (Lobo et al. 2008), we used a partialROC approach that focuses model evaluation on meaning-ful predictions and allows differential weighting of omissionand commission error components (Peterson et al. 2008),using software recently made available (http://hdl.handle.net/1808/10059). Very generally, we restricted ROC spaceto predictions corresponding to omission error ≤5%, sub-sampled 50% of available evaluation data 1000 times and

Fig. 1 Occurrences and hypothesized accessible areas (M, dashedlines) for Didelphis pernigra (squares), D. imperfecta (circles) and D.albiventris (triangles). Grey shading was used for D. pernigra forbetter visualization.


L. J. Tocchio et al. � Niche similarities among white-eared opossums

presented results in terms of ratios of observed areas underthe ROC curves (AUCs) to null expectations of AUC(Peterson et al. 2008). Probabilities were determined bydirect count of null replicate frequencies of AUC ratiosfalling below 1.0.We used background similarity tests to assess similarity in

pairwise combinations of species, using two similarity mea-sures (Schoener’s D and Hellinger’s I) to compare observedvalues against null distributions (Warren et al. 2008); thisapproach is most appropriate for these questions, as it allowsaccessible areas to be taken into account for robust compari-sons (Barve et al. 2011; Peterson 2011). Observed and nullmodels were calibrated across the M of the target speciesbased on all occurrence data for each species and projectedacross the M hypotheses for both species under comparison.Model transfers were developed via Maxent’s projectionoptions, but without clamping or extrapolation permitted, tofocus on conditions actually supported by data (Owens et al.2013) and to make these tests conservative (i.e. with noclamping or extrapolation permitted, it should be easier toconclude niche differentiation). To avoid artefactual conclu-sions deriving from regional patterns of overfitting in Max-ent (Peterson et al. 2007), all models (observed and null)were thresholded using minimum training presenceapproaches (Pearson et al. 2007) prior to model comparisons.D and I were calculated in R using the phyloclim and rasterpackages (R Core Team 2012).To provide a complementary assessment of niche simi-

larity but without control of accessible areas, we used nicheidentity tests (Warren et al. 2008) in ENMTOOLS, version1.3. We again measured niche overlap using Schoener’s Dfor the three species, but using the entire study area extent(i.e. most of South America; Fig. 1) to calibrate ENMs.This test assesses whether niche models based on actualoccurrence data (i.e. partitioned by true species identity)were more different from one another than a null distribu-tion of similarity values based on a random remixture ofthe occurrence data (maintaining observed sample sizes).Hence, observed values of ENMs between each pair ofspecies were compared against similarity values derivedfrom 500 replicate resamplings. Models were transformedto binary prior to measuring similarities, using a minimumtraining occurrence threshold approach. We note that thisapproach is known to be overly sensitive for testing nicheconservatism appropriately (Peterson 2011), but include itfor purposes of comparison.

ResultsSix of the original variables (19 climatic descriptors andelevation) were highly correlated with other variables andwere excluded from analysis. All models showed closecorrespondence to known distributions of species, and all

partial ROC tests indicated significant predictive ability ofmodels (i.e. better than null expectations; P < 0.001), suchthat we are confident that the environmental and occurrencedata used in this study will suffice to characterize opossumdistributions in the region. Didelphis pernigra showed adistribution across much of the Andean region, whereasD. imperfecta was predicted to range across northern SouthAmerica, mainly in the humid forested areas of Venezuelaand Guyana. The predicted distribution of D. albiventrisextended broadly across the savannah and pampas regions ofUruguay and Argentina and the dry savannahs and scrub-lands of the cerrado and caatinga regions of Brazil (Fig. 2,Table 1). Model predictions suggested potential areas ofco-occurrence between putative species: (i) betweenD. albiventris and D. pernigra in Bolivia, and (ii) betweenD. imperfecta and D. pernigra in northern Venezuela.Didelphis albiventris and D. imperfecta showed similar pat-

terns of elevational occurrence, whereas D. pernigraoccurred at higher elevations (Fig. 3A). The ecologicalniche of D. imperfecta was characterized as correspondingto high temperature and low annual temperature range; D.pernigra occupied areas with lower temperatures; Finally,D. albiventris occurred in regions with intermediate temper-atures and higher annual temperature ranges (Fig. 3B).Jackknife tests indicated that variables most influencinggeographic distributions of D. imperfecta and D. albiventris

Fig. 2 Potential distribution maps for Didelphis pernigra (purple),D. imperfecta (green) and D. albiventris (black) based on Maxentmodels: lines are country borders, white areas represent modelledunsuitability; coloured shading represents modelled suitability,from low (light) to high (dark).



Table 1 Distributional patterns of white-eared opossums (Didelphis spp.) based on occurrence records in 21 ecoregions and 6 biomes inSouth America. The colour of the cells indicates occurrence of species in ecoregions: white (absent), grey (low frequency, <25% of records)and black (high frequency, >25% of records)

*For this species almost 80% of all records were from the Cerrado and ecoregions with less than 1% of the records were omitted in the table.

A B

Fig. 3 Results from initial iterations of this study, which assessed niche differences without attention to environments available to thespecies across its accessible region. Geographic space: —A. Elevational and latitudinal occurrence distributions of Didelphis pernigra (purplesquares), D. imperfecta (green circles) and D. albiventris (black triangles). Climatic space: —B. values of the two first principal components ofPCA (PC1 and PC2), considering 14 climatic variables related to the occurrence areas of white-eared opossums.



were related to temperature, whereas for D. pernigra, eleva-tion was most important. Hence, at first view, the threespecies appear to inhabit distinct ecological niches.The niche identity tests, in which no consideration was

given to accessible areas, indicated D similarity values

uniformly lower than the null distribution (P < 0.002), thusrejecting the null hypothesis of niche similarity betweeneach pair of species; we emphasize that these tests willreflect dramatic contrasts in availability of conditions acrossthe accessible areas of the three species because theaccessible area is not considered.Background similarity tests, however, which take into

account the availability of conditions across accessible areas,indicated general similarity among the three putative spe-cies (Fig. 4). Occurrence data suggested broad overlap ofniches (Fig. 5); raw niche similarity values were D = 0.19and I = 0.26 (D. albiventris – D. imperfecta), D = 0.51 andI = 0.63 (D. albiventris – D. pernigra), and D = 0.32 andI = 0.42 (D. pernigra – D. imperfecta). Comparisons of theseobserved values against null distributions showed that noneof the pairwise comparisons of niches among the threeputative species was surprisingly divergent (P ≫ 0.05). Theonly comparisons indicating difference was that of D. im-perfecta against the background of D. albiventris (Fig. 4),but the reciprocal comparison was unable to reject the nullhypothesis of similarity.

DiscussionGeneralities regarding opossum nichesWe found clear indications of ecological niche conserva-tism across the evolutionary history of the three lineagesof white-eared opossums, when considered across theirentire geographic distributions at coarse resolutions, andin the context of climatic characteristics and areas that

Fig. 4 Results of background similarity test comparisons, in whichavailable environments and accessible areas were considered. Rowsrepresent occurrences, whereas columns indicate species for whichbackground areas were used in background similarity tests; theupper portion of each cell shows similarity test results based onSchoener’s D, whereas the lower portion shows results based onHellinger’s I. Grey cells indicate situations in which similarity wasnot rejected (P ≥ 0.05); white cells indicate situations in whichsimilarity is rejected (P < 0.05).

A B

C D

Fig. 5 Environmental space in NicheA:—A. visualization in three dimensions interms of principal components 1, 2 and 3;ecological niche models were displayed asminimum-volume ellipsoids used toillustrate limits of environmentaldistributions of Didelphis pernigra (purpleellipsoids), D. imperfecta (green ellipsoids)and D. albiventris (yellow ellipsoids). Thebackground cloud is derived from arandom sample of 3000 random pointsfrom across the study area (grey points).Rotations of (A) for visualization areshown in B–D.



were actually accessible to each species. These species arenot distributed randomly (obviously) with respect eitherto geography or environment; rather, interactionsbetween the two suites of factors determine the limits oftheir distributions, as already suggested by Cerqueira(1985, 1995).Cerrado, caatinga, humid pampas, chaco and Atlantic

forest are all habitats across which D. albiventris is knownto occur. According to Cerqueira (1985), human-impactedlandscapes of small islands of primary vegetation in a broadsecond-growth matrix are invaded by this species, whichhas thus expanded its area of occurrence with deforestation.Adler et al. (2006) reported specimens of this species fromParacou, in French Guyana, distant from other knownoccurrence areas of the species; D. albiventris is probably arecent colonizer in the area, taking advantage of a newhighway into the region (Adler et al. 2006).Populations of D. pernigra are restricted to the Andean

region. According to Ventura et al. (2002), the speciesoccupies elevations of 2300-3300 m in the Venezuelan An-des. Didelphis imperfecta, on the other hand, occupies for-ested areas at lower elevations, occurring at 100-2200 m inthe Venezuelan states of Bolivar and Amazonas (Venturaet al. 2002). These contrasting elevational distributions mayhelp to maintain populations of D. pernigra and D. imper-fecta separate and without interaction.At coarse resolutions, however, model predictions sug-

gested some potential areas of co-occurrence of these spe-cies. One area in Bolivia represented a potential zone ofinteraction between D. albiventris and D. pernigra, whichhad already been mentioned by Lemos & Cerqueira(2002). The potential distribution of D. imperfecta appearsto overlap with that of D. pernigra in northern Venezuela,although no sympatry has been observed so far (Lemos &Cerqueira 2002). This modelled, coarse-resolution, poten-tial co-occurrence may be related to niche conservatism inthe genus (Peterson et al. 1999; Raxworthy et al. 2003).However, other biogeographic and ecological factors

may keep pairs of species separate, even if both may beable to maintain populations in the same general areas (see,e.g. Anderson et al. 2002; Anderson 2003; Raxworthy et al.2007). In the present case, the Eastern Andes and M�eridaAndes may act as geographic barriers to dispersal, limitingcontact between populations of the two species. Curiously,even though differences were apparent in both geographicand environmental spaces, which appear to indicate nichedifferences (Figs 1 and 3A,B), these dissimilarities disap-peared when accessibility of environmental conditions wasconsidered (Figs 4 and 5). In fact, initial iterations of thisstudy centred on niche differences, but when accessible areaswere controlled carefully, differences disappeared, and theoverall picture was one of niche similarity across the group.

PCA-based visualizations of initial iterations indicated clearecological niche differentiation between the three opossumsspecies (Fig. 3A,B), but these differences were reflectingdifferences in sets of environments available to each speciesover its M.

Niche differentiation and species limitsIn recent years, a series of papers has appealed to ecologi-cal niche characteristics to inform decisions about specieslimits (Graham et al. 2004; Raxworthy et al. 2007; Rissler& Apodaca 2007; Stockman & Bond 2007; Mart�ınez-Gordillo et al. 2010). This study speaks to the same issues:whether quantitative descriptions and tests of similarityamong ecological niches of species can add to the discus-sion of species limits and mechanisms of speciation in thewhite-eared opossum complex of South America. Theanswer, however, turns out to be more complex than mighthave been anticipated: our concerns about this paradigm ofinforming decisions about species limits with data on nichedifference and geographic disjunction from ecological nichemodels fall into two major categories.First, ENM suffers from the curse of simultaneous popu-

larity and ease of execution, as has been the case in otherdifficult fields (e.g. multivariate statistics, phylogenetics).With development of convenient, user-friendly, powerfulniche-modelling platforms (e.g. Phillips et al. 2006), ENMhas become easily accessible to all, yet without detailedconceptual frameworks and analytical guides until recently(Peterson et al. 2011), leading to many analytical misstepsin many studies. Most crucial to the point at hand has beenthat of considering carefully which areas are accessible tothe species in question (Barve et al. 2011): when no explicitassumptions are stated about the calibration area, implicitassumptions are made that all sites have been accessible tothe species in question, and that unoccupied sites are likelyunoccupied because of unsuitability. These assumptionsfrequently lead to incorrect conclusions of niche difference(Peterson 2011).A conceptual framework can aid in thinking about these

points. The fundamental ecological niche, NF, can be con-sidered as reflecting physiological tolerances and responses,and as such is likely to be unimodal with respect to singlevariables and convex in multivariate space. However, givenany limited accessible area M, the environments associatedwith that area [termed g (M)] are unlikely to cover theentire spectrum of relevant environmental values. As such,a reduced ‘niche’ is what is observable, which Petersonet al. (2011) termed the ‘existing fundamental ecologicalniche,’ or NF*. Note that this niche can be defined asN �

F ¼ NF \ gðMÞ, and that this observable niche is none-theless subject to influences of two factors: the fundamentalniche and representation of environments across the region



M. As a consequence, analyses of niche conservatism ordifferentiation that do not condition analyses explicitly on aspecies-specific accessible area will frequently come toincorrect conclusions (Peterson 2011); to our reading, noneof the studies cited above as exploring these ideas has spec-ified species-specific M hypotheses, such that many or allof their conclusions regarding niche difference may meritre-examination.The second major point is the strength of inference pro-

vided by these approaches, even when models are cali-brated and interpreted properly. That is, the idea is toderive information on (i) whether distributions of the formsin question show geographic continuity, and (ii) whethertheir ecological niches are similar or not – this pair of fac-tors can be visualized as a 2 9 2 table of interactions (seeFig. 6). When geographic distributions are disjunct, the sit-uation is the same as any in which disjunctions prevent a‘test of sympatry’: no extra information is provided by thedifference or lack of difference in ecological niches, andthe decision becomes a function of the species conceptbeing followed by the researcher. When geographic distri-butions are contiguous and ecological niches are distinctlydifferent, interruption of gene flow can be inferred, as withany phenotypic or genotypic trait, and most species con-cepts would conclude that species-level differentiation hasbeen achieved (Mayr 1963; De Queiroz 2007). Finally,when distributions are contiguous but niches are similar,conclusions are again limited: either no differentiation hastaken place or differentiation exists that is not manifestedin niche characteristics, and no useful information aboutspecies limits is available once again.Hence, it is not clear that ENM approaches to setting

species limits will be particularly powerful or informative.Raxworthy et al. (2007) correctly emphasized the key roleof geographic contiguity in these issues, much more crucialthan niche difference or similarity. Rissler & Apodaca

(2007) put greater weight on niche difference in these deci-sions, but clearly adhere to a viewpoint in which ecologicaldifference is a crucial step in the speciation process. Wenote that, in vicariant speciation, differentiation is fre-quently simply a function of evolution in isolation, and noecological differentiation is expected, yet vicariant specia-tion is recognized as a dominant force in biological diversi-fication, at least among vertebrates and many other animals(Barraclough & Vogler 2000; Salomon 2011).As such, we suggest that ENM approaches are best used

to explore speciation mechanisms, rather than beingapplied (weakly) to questions of setting species limits. Wenote that questions of species limits have always been com-plicated: no magic wand exists that gives us an easy answer.Morphometric study and the data on size and shape thatresult are certainly relevant, but do not give any finalanswer either. Hence, while ecological niche modelling isnot the basis for anything revolutionary in this regard, itdoes provide important and useful new data, visualizationand insight, if interpreted in an appropriate context.

AcknowledgementsATP was supported by a grant from the Institute of Interna-tional Education’s Fulbright Scholar Program. E. M. Vieiraand M. N. Ferreira provided comments on earlier drafts ofthis manuscript. Abdallah M. Samy and Lindsay P. Campbellprovided invaluable assistance with analyses. LEE thanksUniversidad Andres Bello for the grant Iniciaci�on a laInvestigaci�on #DI-412-13/I. LEE’s postdoctoral research issupported by the Global Emerging Infectious Disease Sur-veillance and Response System (GEIS) grant. Special thanksto Huijie Qiao for his assistance in NicheA analysis.

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Niche similarities among white-eared opossums (Mammalia, Didelphidae): Is ecological niche modelling...

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