ORIGINAL PAPER

Environmental Drivers and Distribution Patterns of CarnivoranAssemblages (Mammalia: Carnivora) in the Americas: Past to Present

Andrés Arias-Alzate1,2& José F. González-Maya3 & Joaquín Arroyo-Cabrales4 & Rodrigo A. Medellín5

&

Enrique Martínez-Meyer2

# Springer Science+Business Media, LLC, part of Springer Nature 2020

AbstractUnderstanding species distributions and the variation of assemblage structure in time and space are fundamental goals ofbiogeography and ecology. Here, we use an ecological niche modeling and macroecological approach in order to assess whetherconstraints patterns in carnivoran richness and composition structures in replicated assemblages through time and space shouldreflect environmental filtering through ecological niche constraints from the Last Inter-glacial (LIG), Last Glacial Maximum(LGM) to the present (C) time. Our results suggest a diverse distribution of carnivoran co-occurrence patterns at the continentalscale as a result of spatial climatic variation as an important driver constrained by the ecological niches of the species. Thisinfluence was an important factor restructuring assemblages (more directly on richness than composition patterns) not only at thecontinental level, but also from regional and local scales and this influence was geographically different throughout the space inthe continent. These climatic restrictions and disruption of the niche during the environmental changes at the LIG-LGM-Ctransition show a considerable shift in assemblage richness and composition across the Americas, which suggests an environ-mental filtering mainly during the LGM, explaining between 30 and 75% of these variations through space and time, with moreaccentuated changes in North than South America. LGMwas likely to be critical in species functional adaptation and distributionand therefore on assemblage structuring and rearranging from continental to local scales through time in the continent. Still,extinction processes are the result of many interacting factors, where climate is just one part of the picture.

Keywords Ecological niche . Carnivoran . Extinction . Climate change . Late Pleistocene . Paleodistributions

Introduction

Understanding how species are distributed, their determinantsand constraints, and how they are spatially structured in as-semblages through time and space is a fundamental goal ofmacroecology and ecology (Brown et al. 1995; Ferraz et al.2012; Agosta and Bernardo 2013). Species can vary in size (inmammals, which span 12 orders of magnitude), location,shape, and occupancy (Gaston 2003; Davies et al. 2009) andare the result of responses to ecological rules such as climaticconditions, species dispersal abilities, historical events, phylo-genetic inertia, and/or biotic interactions (Dynesius andJansson 2000; Blomberg and Garland 2002; Steinitz et al.2006; Davies et al. 2009; Blois et al. 2013, 2014).Understanding the main drivers of changes in such parameterscan also shed light in terms of local and global extinctions. Animportant issue is whether these processes such as climaticconditions have the same effect to structure assemblage pat-terns and species distributions at different spatial and temporal

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s10914-020-09496-8) contains supplementarymaterial, which is available to authorized users.

* Andrés Arias-Alzateaariasa@ces.edu.co

1 Facultad de Ciencias y Biotecnología, Universidad CES, Calle 10A #22-04, Medellín, Colombia

2 Instituto de Biología, Universidad Nacional Autónoma de México,Circuito exterior s/n, Ciudad Universitaria, Coyoacán,CP04510 México City, Mexico

3 Proyecto de Conservación de Aguas y Tierras, ProCAT Colombia/Internacional, Carrera 11 # 96-43, Of. 303, Bogotá, Colombia

4 Laboratorio de Arqueozoología “Ticul Álvarez Solórzano”,Subdirección de Laboratorios y Apoyo Académico, InstitutoNacional de Antropología e Historia, Moneda # 16, Col. Centro,06006 México City, Mexico

5 Instituto de Ecología, Universidad Nacional Autónoma de México,Circuito exterior s/n, Ciudad Universitaria, Coyoacán,CP04510 México City, Mexico

Journal of Mammalian Evolutionhttps://doi.org/10.1007/s10914-020-09496-8

scales (i.e., from continental to regional and/or to local scales)(Arias-Alzate et al. 2017). However, accurate information onpast and present distributional patterns for many species isoften scarce at broad scales, and the mechanisms, determi-nants, and constraints of these ranges at these scales are stillpoorly understood (Graham 2001; Prevosti et al. 2005;Martínez-Meyer et al. 2004; Davies et al. 2009; Nogués-Bravo 2009). Nevertheless, the understanding of the relation-ship between the influence of environmental drivers and as-semblages measures (i.e., richness and composition) have re-ceived more attention, especially when extreme climaticevents (e.g., glaciations) can change the outcome of the eco-logical patterns and processes (Thibault and Brown 2008;Davies et al. 2011).

These patterns are largely governed by environmental fac-tors (e.g., climatic conditions) that define part of the funda-mental niche of the species, where the biotic interactions areusually less perceptible (Martínez-Meyer et al. 2004; Soberónand Nakamura 2009; Lorenzen et al. 2011; Peterson et al.2011; Levinsky et al. 2013). However, elucidating the influ-ence of these processes on species coexistence and distribu-tion is not straightforward because non-random species asso-ciations are not necessarily caused only by climatic factors norspecies interactions. All these processes can operate indepen-dently or in synergy to determine assemblages patternsthrough time and space (Martínez-Meyer et al. 2004; Bloiset al. 2014; Giarla and Jansa 2015). Therefore, the centralissue is how to differentiate or elucidate the influence pro-duced by biotic interactions or dispersal limitations (eitherby barriers or by movement capacity) from those producedby environmental filtering (Dynesius and Jansson 2000;Svenning and Skov 2007; Blois et al. 2014; Giarla and Jansa2015). In this sense, the ecological niche played an importantrole to disentangle the combined effects of abiotic (e.g., cli-matic) and biotic factors and could help us to understand in-directly about the effects of others historical factors such asbarriers and accessible areas as strong forces on assemblage’sdynamics and coexistence at different scales (Davis and Shaw2001; Martínez-Meyer et al. 2004; Bofarull et al. 2008;Peterson et al. 2011; Levinsky et al. 2013; Soares 2013).Climate is an important determinant of species ecologicalniche and its geographic expression, thus, helping to under-stand extinction processes through range shifts and bottleneckevents (Martínez-Meyer et al. 2004; Nogués-Bravo et al.2008; Arias-Alzate 2016). However, extinction processes arethe result of many interacting factors (e.g., humans) that affectspecies differentially and could act differentially or asynchro-nously with additive effects at different spatial and temporalscales, where climate is just one part of the picture (Thomaset al. 2004; Cione et al. 2007; Araujo et al. 2017; Arias-Alzateet al. 2017).

The order Carnivora represents an excellent group to assessquestions related to the biogeography and ecological niche

implications in the evolutionary patterns that took place con-tinentally, thanks to its high taxonomic diversity and well-resolved phylogeny (Goswami and Friscia 2010; Nyakaturaand Bininda-Emonds 2012). Here, we used an ecologicalniche modeling and macroecological approach in order to as-sess the influence of past climate change on carnivoran assem-blage richness and composition patterns from continental tolocal levels over the last 130 K years in the Americas and ifthis influence reflects environmental filtering through ecolog-ical niche constraints. It is important to note that relativelylittle attention has been given to understanding the underlyingcauses of non-random patterns such as geographical distribu-tion and assemblage structuring (Collins et al. 2011; Gotelliand Ulrich 2012; Blois et al. 2014); therefore, revealing thesepatterns and some of their mechanisms would improve ourknowledge on the macroecological history of carnivoran as-semblages in the Americas.

Materials and Methods

Species Geographical Distribution (GD) Patterns Data

Species GD patterns came from an ecological niche modeling(ENM) approach done by Arias-Alzate (2016) and Arias-Alzate et al. (2017), which were estimated as described below.

The species studied were based on the assumption that allextant species were also present during the late Pleistocene, andthe extinct species disappeared at the end of the Pleistocene-early Holocene approximately 12Kyr (Webb 2006; Davieset al. 2009). The species criteria for that study included taxo-nomic validity, proper chronological dating, and reported in-formation about the species’ presence during the study period.The list and the taxonomy for living species were based onWilson and Reeder (2005), Wilson and Mittermeier (2009),and IUCN Red List of Threatened Species (version 2018.2.).For extinct species, the taxonomy was based on the fossil re-cords and following Berta (1985), Barnett et al. (2005),Cisneros (2005), Cione et al. (2007), Prevosti and Rincón(2007), Soibelzon and Prevosti (2008), Arroyo-Cabrales et al.(2010), and Ferrusquía-Villafranca et al. (2010).

The study area was defined as the entire American conti-nent; this represents the area of accessibility M sensu Petersonet al. (2011), as all historical and ecological processes of colo-nization and dispersal of the species of interest occurred in thecontinent (during and after the Great American BioticInterchange; GABI). Namely, during the GABI in several in-dependent migration events (in a step-like pattern) the extantand extinct species or their ancestor (from Holarctic origin)dispersed from North America to South America through thePanamanian Isthmus, or subsequently diversified in SouthAmerica (most of these species are endemic) and colonizedCentral and North America later (e.g., Eira barbara, Procyon

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cancrivorus, Speothos venaticus; Webb 2006; Prevosti andSoibelzon 2012; Bacon et al. 2015; Prevosti and Forasiepi2018). It is important to note that the Andes mountain rangehad already emerged by this time (i.e., GABI), reaching heightsof over 3500 m since 10 Ma (Gregory-Wodzicki 2000), sothese areas did not represent an important barrier for thesespecies during migration events, as both small-carnivoran,mesocarnivoran, and large- and hypercarnivoran species wererepresented during these migration events, where species colo-nized even higher altitudes (e.g., the Andean weasel, Mustelafrenata; the grison,Galictis vittata; the tayra, Eira Barbara; themountain lion, Puma concolor; the jaguar, Panthera onca; thespectacled bear, Tremarctos ornatus, for some examples), andhigher latitudes (e.g., the dire wolf, Canis dirus†; the jaguar,Panthera onca mesembrina†; the spectacled bear, Tremarctosornatus; the ancestor of the South American short-faced bears,Arctotherium tarijense†, Arctotherium wingei†, for some ex-amples). Thus, many of the barriers that these species face orfaced were mainly climatic and / or biological in nature (i.e.,species competition). We also highlighted the use of two dif-ferentMs., one for LIG and C times (M1) and one for the LGM(M2) as during this period the continental area was wider giventhe lowering of sea level that exposed areas that are now sub-merged (Varela and Fariña 2016).

The subsequent analyzes performed by Arias-Alzate(2016) and Arias-Alzate et al. (2017) were done following ageoinformatics approach (Arroyo-Cabrales et al. 2010) wherea database containing confirmed records of extant and extinctterrestrial carnivoran species was constructed (G+, sensuPeterson et al. 2011). The records were obtained through adetailed search of the scientific literature (i.e., ISI Web ofScience and Google Scholar) through key words (i.e.,Pleistocene, Carnivora, Felidae, Canidae, and specific and sci-entific names), online databases, and museum specimens fromonline databases of biological collections and our own recordsfrom extensive fieldwork in some countries (e.g., Colombia,Costa Rica, Mexico). Then, sets of climatic data were con-structed from available past projections and current bioclimat-ic variables at the global scale, specifically climatic layersfrom three periods: 1) Last Inter-glacial (LIG, ~120–140 KyrBP; Otto-Bliesner et al. 2006), 2) Last Glacial Maximum(LGM, ~21 Kyr BP; Farrera et al. 1999; Braconnot et al.2007), and 3) Current (C, Hijmans et al. 2005). All layers werein a 0.08333° resolution (~10 arcmin). These layers wereprocessed and extracted using the Ms. (i.e., M1 and M2) de-scribed above.

Afterwards, with this previous information, Arias-Alzate(2016) and Arias-Alzate et al. (2017) performed the ENMvia maximum entropy approach with the following parame-terization. One hundred replicate models using five hundrediterations per replicate for each species using a random 70:30split (bootstrap method) of the total number of occurrencerecords (G+) for calibration and validation, respectively. In

the case of living species, the models were generated usingthe current presence records over M1 (Current bioclimaticlayers), and hindcast over the past climatic layers and validat-ed them with the fossil records (M1 to M1 with LIG climaticlayers, and M1 to M2 with LGM climatic layers). For extinctspecies, given that fossil records are scarce and to avoid losinginformation, the proper procedure was to generate the modelsusing all records for extinct species together to calibrate andvalidate the models over the Inter-glacial climatic layer period(M1 with LIG climatic layers) and then project them over theLast Glacial Maximum time (M2 with LGM climatic layers).As the objective was not focused on model or algorithm com-parison, it was opted for one modeling method (MaxEnt3.3.3 K; Phillips et al. 2006; Phillips and Dudík 2008). Thisalgorithm has shown good performance and is robust withsmall sample sizes (Elith and Graham 2009; Peterson et al.2011; Santika 2011; Muscarella et al. 2014). ENM withMaxEnt estimates the suitability of condition across the land-scape by contrasting the environmental conditions where thespecies has been recorded, against a sample of backgroundpixels across the study area via a Bayesian procedure of modelfitting under the maximum entropy principle (Phillips andDudík 2008). The results during the niche modelling proce-dure are frequently interpreted as probability of presence(Phillips et al. 2017) or as the areas where ecological condi-tions are suitable for the establishment of the species (thepotential distribution of the species, sensu Peterson et al.2011). Detailed explanations of MaxEnt and newimplementations can be found elsewhere (Phillips et al.2006, 2017; Phillips and Dudík 2008; Elith et al. 2011;Merow et al. 2013). To test for model discriminatory abilityand performance, the AUCTest (Area under the ROC Curvebased on the validation data) was used, which is implementedin MaxEnt (Phillips et al. 2017). Despite that AUC has beencriticized as a method for evaluating model quality, especiallyto compare models from different algorithms (Lobo et al.2008; Peterson et al. 2007), the AUCTest still has been shownto be a useful measure for ordinal score and unique models inagreement with previous works (McPherson et al. 2004;Thuiller et al. 2005; Elith et al. 2011; Marino et al. 2011;Santika 2011; Muscarella et al. 2014; Phillips et al. 2017).Statistical significance of models was evaluated with a bino-mial test, which determines whether the model differs fromnull expectations.

Assemblage Hot Spots, Composition, and RichnessPatterns

In order to assess whether constraints patterns in richness(species number) and composition (species identity) structuresin the assemblages through time and spaces should reflectenvironmental filtering through ecological niche constraints,herein we generated a grid of 7760 1 × 1 degree cells over the

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continental scale for these three periods (LIG, LGM, and C).With these grids for each time and with the extant and extinctspecies GD (aforementioned in the previous section), an over-lapping count analysis was performed using the Hawth tools(Beyer 2004) in order to extract the species co-occurrence as aproxy of the species assemblage richness and compositionpatterns present in each cell at each time in the Americas.

To validate if the carnivoran assemblages’ measures(i.e., richness and composition) generated from individualspecies GD could provide accurate richness and composi-tion patterns, we then made a Cross-validation of theseestimation (niche-based GD) with the assemblage’s pat-terns estimated with the extant carnivoran species extentof occurrences (EOO) of the IUCN. These assemblage pat-terns based on EOO were estimated using the same proce-dure mentioned above (see Online Resource 2 for a moredetailed explanation). Afterwards, we validated and esti-mated the accuracy of these approaches comparing bothassemblages’ patterns using a Pearson correlation test andthe Jaccard index with a p < 0.001 statistically significanceat 99% confidence level (see Online Resource 2). All geo-graphic analyses were performed on a GeographicInformation System using ArcGIS 9.3 software (ESRI2001) and the statistical analysis were performed usingthe Infostat software (Di Rienzo et al. 2016). We then es-timated the values for four environmental determinants ineach cell for each period based on the aforementionedglobal scale climatic variables. These climatic conditionshave proven to be useful and informative as drivers ofmammalian biodiversity patterns and ecosystem function-ing at global scale in previous studies (Croft 2001; Safiet al. 2011; González-Maya 2015): i) mean precipitation,ii) precipitation seasonality, iii) mean temperature, and iv)temperature range.

To assess assemblage richness and composition shift pat-terns over the continent for the transition between periods(LIG-LGM and LGM-C), we first generated an OrdinaryLeast Squares (OLS) regression in order to select the bestexplaining models (shifts in assemblages’ richness and com-position patterns) using the variable combinations (González-Maya et al. 2016a, 2016b). For each measure (richness andcomposition) we generated all possible variable combinationswith no replacement, resulting in 15 possible models, and thenselected the best competingmodels based on the lower AkaikeInformation Criterion values (AIC) (Wagenmaker and Farrell2004; González-Maya et al. 2016a, 2016b). We used the R2 asan indicator of the proportion of variation that is explained bythe resulting models (higher R2 values were preferred).

After selecting the best models, we assessed multicollinearityto see which variables are potentially redundant in influencingmodel patterns by extracting the estimated coefficients andVariance Inflation Factor (VIF); VIF values greater than 7.5 areconsidered suspicious and redundant, so one of the two variables

should be removed (Fotheringham et al. 1998; O’Brien 2007).Once the best model was selected, we tested for spatial differ-ences from random expectation: a Moran’s I spatial autocorrela-tion test of the residuals was used (Brunsdon et al. 2010) in orderto assess if other important variables are potentially missing inthe model. We also estimated the Koenke studentized Breusch-Pagan statistic (K(BP)) and its probability, in order to assess thereliability of standard errors when heteroscedasticity is present. Incases where the K(BP) was significant, we used the robust prob-ability instead of the raw probability estimation. Significantheteroscedasticity and stationarity indicate that the effects of thedrivers (environmental drivers) over the assemblage richness andcomposition shifts would be different in magnitude and thatchanges are not homogeneous along geographic space.

Likewise, to explore if spatial mismatching for each periodoccurred and if selected models did not perform adequately(i.e., showing at least one important variable was missing fromthe model), we performed a hot-spots analysis using the resid-uals of selected models based on the Getis-Ord Gi* test byestimating Z-values (i.e., standard deviations) and its associ-ated probability for each cell on the continent (Getis and Ord2010; Ord and Getis 2010; González-Maya et al. 2016a,2016b). This analysis identifies where clusters of high orlow richness values are more marked and are significant(p < 0.05) than one from theoretical complete random expec-tation as the null hypothesis (Green and Ebdon 1977).Mapping these high and low values allowed us to highlightif these carnivoran species clusters were different for eachperiod in the continent and thus indicating if one or moreexplanatory variables are missing in the model for that cell(i.e., partial spatial fitting of the overall model) and couldmediate and play an important role on these patterns (Getisand Ord 2010; Ord and Getis 2010; González-Maya et al.2016a, 2016b).

Afterwards, we performed a geographically weighted re-gression (GWR) to identify the spatial influence of the envi-ronmental factors within the continental grid cells over eachassemblage measure (i.e., influence from continental to locallevel), allowing us to identify a spatial heterogeneity (e.g.,heteroscedasticity and stationarity) due to heterogeneous en-vironmental influence over the continent for the transitionbetween periods (LIG-LGM and LGM-C; González-Mayaet al. 2016a, 2016b). As the influence of different variablesis likely spatially defined, we selected GWR as an appropriatemethod capable of identifying heterogeneity patterns at ourspatial scales (Fotheringham et al. 2002; Brunsdon et al.2010; González-Maya et al. 2016a, 2016b). All analyses andspatial statistical tests (using the spatial statistics tools, consid-ered significant at p < 0.05 at 95% confidence level) wereperformed on a Geographic Information System usingArcGIS 9.3 software (ESRI 2001). All data generated andanalyzed here are available on reasonable request from thefirst author.

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Results

Species Geographical Distribution (GD) Patterns Data

For all (~88 spp.) but eight carnivoran species, no potentialdistribution models for each period were considered due tolack of records (Mustela africana, Cuon alpinus, Speothospacivorus, and Enhydra macrodonta), due to ranges restrictedto islands (Urocyon litoralis and Procyon pygmea, which arerecently separated from two continental species that were iso-lated after the last glacial maximum), and due to a recentlydescribed new species and separated from Nasuella olivacea(Nasuella meridensis) and Leopardus trigrinus (Leopardusguttullus). In terms of model performance for all species,AUCTest values were all higher than 0.81, indicating a goodperformance and model discriminatory ability. With respect tothe model validation, all species models presented a statisticalsignificance (p < 0.05).

Assemblage Hot Spots, Composition, and RichnessPatterns

We found a diverse distribution of carnivoran co-occurrencepatterns at the continental scale as a result of spatial climaticvariation constrained by the ecological niches of the species(Fig. 1). The areas with more stable climatic conditions overthe Neotropics upon these periods suggest that these condi-tions enable the persistence of some assemblages to presenttimes (Fig. 2). The spatial hot spots analysis allowed us toidentify statistically significant and noticeable changes in as-semblages’ structure during the LIG-LGM-C transitions. Theresults show high and marked hot spots during the LIG at midto low latitudes in North America and in the Andes of South

America. Likewise, during the LGM a significant change wasobserved towards the west coast of North America, CentralAmerica, and South America, particularly in the Andes, and inthe north and central regions (Fig. 2).

Correlation among assemblages’ richness patterns (nichebase vs EOO) was high (Pearson = 0.90, p < 0.001) (Fig. 1,Online Resource 2). Both analyses showed relatively highrichness from mid to low latitudes in the tropical zone and inthe Andes region and low richness toward high latitudes in thecontinent (Fig. 1). On the other hand, regarding the composi-tion patterns, both approaches provided relatively accuratecharacterizations across the majority of the grid cells with highJaccard values (Fig. 2 Online Resource 2). Even though, littlespatial differences among approaches persist. Thus, this nichebase characterization gives us good reliability for interpretingthe other estimations (see Online Resource 2 for a moredetail).

Carnivoran assemblages’ richness shows a latitude struc-turing pattern and a significant shift from the LIG to C periodacross the Americas (Fig. 3). The best model (model with thelower AIC value, Table 1) that accounted for and explainedrichness shifts includes mean temperature, temperature range,and precipitation seasonality as important drivers for richnesspatterns at continental scales (LIG-LGM OLS R2 = 0.328;LGM-C OLS R2 = 0.433 (Table 1, Fig. 3). The LIG had agreater carnivoran species richness concentrated from low tomid latitudes in North and South America in the subtropicalregions. However, during the LIG-LGM transition, due toglacial conditions, low temperatures and drier environments,this pattern changed with high loss of species from north andcentral of North America (Fig. 3). During the LGM-C transi-tion the biogeographic assemblages' richness patterns tend torecover toward the temperate areas of North America (i.e.,

Fig. 1 Carnivoran assemblages’ richness patterns over the last 130 K years in America. a. LIG, b. LGM, and c. C periods. Note the high richnessreduction in North America during LGM

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grasslands, savannas, temperate coniferous forests, temperatebroadleaf, and mixed forests) but with species loss in theNeotropics (Fig. 3). On the contrary, carnivoran assemblages’composition structure shows a different pattern. The best mod-el that accounted for and explained composition shifts includ-ed mean temperature, temperature range, mean precipitation,and precipitation seasonality as main drivers (LIG-LGMOLSR2 = 0.274; LGM-C OLS R2 = 0.346, (Table 2, Fig. 3).During the LIG-LGM phase the environmental drivers causedgreater shifts in carnivoran composition at mid to high lati-tudes in North America, and at mid latitudes and in the west-ern coasts of South America, suggesting an environmentalfiltering. During the LGM-C transition, the main environmen-tal effects were in the Neartic region and the austral zone ofSouth America (Fig. 3).

For both parameters (richness and composition patterns)the K(BP) the statistic indicated heteroscedasticity and station-arity of the models. Showing, in this way, a heterogeneousinfluence of the environmental drivers on the assemblages’richness and composition patterns. This relationship changeswhen the magnitude of drivers changes causing the patternsnot to be constant across geographic space and time in theAmericas (Tables 1, 2). These patterns suggest a greater asso-ciation and a heterogeneous environmental influence oncarnivoran richness patterns at local-regional levels withinthe continent for the transition between periods (LIG-LGMGWR R2 = 0.67; LGM-C GWR R2 = 0.75; Table 1, Fig. 4).However, composition patterns showmore moderate environ-mental influences at local-regional levels, but with an impor-tant pattern towards north central North America (LIG-LGMGWR R2 = 0.57; LGM-C GWR R2 = 0.62; Table 2).

Discussion

Understanding carnivoran species distributions and how theyare spatially structured has received greater attention in recentyears (Goswami and Friscia 2010; Levinsky et al. 2013).Mostcarnivoran lineages in the Americas were holarctic specieswith a lower diversity during the Miocene-Pliocene comparedwith the Pleistocene (Marshall et al. 1982; Prevosti andReguero 2000; Webb 2006; Prevosti and Soibelzon 2012;Prevosti and Forasiepi 2018). This group was one of the mostsuccessful among mammalian species to participate in theGreat American Biotic Interchange (GABI as a complexprocess that persists today; Dundas 1999; Graham 2001;Webb 2006; Johnson et al. 2006; Rincón et al. 2011;Prevosti et al. 2013; Bacon et al. 2015). However, some ofthese species and other terrestrial fauna faced the “MegafaunaExtinction,”which apparently affected mostly the mammalianfaunas of the Nearctic, Neotropics, and Australasia regions(Bofarull et al. 2008; Prevosti and Soibelzon 2012).However, some species persist until today.

Our results show that carnivoran co-occurrence patterns atcontinental scales in the Americas at the end of the Pleistoceneare, in part, the result of important environmental driversconstrained by the ecological niche stability of the species,which represents the ranges of the species on the geography.This suggests that these significant climatic variations werecritical in range contractions, mainly from high to mid lati-tudes in North America and in the Andes and mid latitudes inSouth America during the LIG-LGM and LGM-C transitions,causing substantial changes on carnivoran assemblages’ rich-ness and composition at the continental scale. These spatial

Fig. 2 Hot spots of carnivoran assemblages across Americas over the last 130 K years. a. LIG, b. LGM, and c. C periods. Z-scores accounts for standarddeviations of the Getis-Ord Gi test. Red dots indicate significant (p < 0.05) and more marked associations

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analyses allowed us to identify noticeable changes that appar-ently match with the biomes displaced southward in NorthAmerica as the continental ice sheets (i.e., Laurentide IceSheet) grew and partially waned at decamillennial intervals(Dyke 2005). Equally, these changes match with the shiftingbiomes of South America during the LGM; the Andes wascovered in some parts by ice and deserts, and the north andcentral regions were a mosaic of open forest and open savanna(Cione et al. 2009). These richness and composition changesfrom LIG-LGM and LGM-C transitions highlight the role ofclimatic alterations and these environmental drivers (meanprecipitation, precipitation seasonality, mean temperature,and temperature range) as some of the primary factors withimportant effects over carnivoran assemblages that made themmore susceptible to local extinctions.

These climatic alterations have also been proposed as im-portant factors altering the distribution and diversity patternsof other vertebrate and plants communities in other regions(Graham and Mead 1987; Root 1988; Lundberg et al. 2000;Lyons 2003; Eronen and Rook 2004; Svenning and Skov

2004; Dyke 2005; Araújo et al. 2006; Blois and Hadly 2009;Croitor and Brugal 2010; Diniz-Filho et al. 2009; Li et al.2014; Villavicencio et al. 2016). However, it is important tonote that some other important factors that mainly act at localto regional scales could be missing from our approach (i.e.,species interaction, human influence, resources) and couldhelp to explain the remaining proportion of variation mainlyseen in the composition shifts during these transitions (Fig. 4).For example, competitive exclusion of similar and closelyrelated species is likely to result from competition for spaceand resources because species that occupy separate rangesthen occurred in association during the LGM, or human im-pacts and reduced herbivore populations acting at differentintensities were also critical, increasing even more the extinc-tion risk (Dyke 2005; Cione et al. 2009; Diniz-Filho et al.2009; Li et al. 2014; Villavicencio et al. 2016). Nevertheless,as the climatic variations also act differentially and it is ataxon-specific process, given that the species respond accord-ing to their physiological and ecological characteristics, phy-logenetic inertia, dispersal and differential colonization

Fig. 3 Influences of environmental drivers on carnivoran assemblages’richness shift patterns: a. LIG-LGM, b. LGM-C, and composition shiftpatterns: c. LIG-LGM, d. LGM-C. (the a. and b. legend represents

the number of species loss and gain, and c. and d. represents thepercentage of change during the transition between periods)

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capacity (which is different in relation to carnivoran species)(Blomberg and Garland 2002; Canto et al. 2010; Croitor andBrugal 2010; Lorenzen et al. 2011; Agosta and Bernardo2013; Prevosti and Forasiepi 2018), we cannot highlight ac-cording to our results that these patterns shown herein are ageneral rule (via ecological niches stability) for the othergroups (e.g., others mammals (large herbivores), birds andreptiles) with similar co-occurrence patterns at continentalscale, but it is a plausible possibility.

Otherwise, the results display an important latitudinal gra-dient (i.e., current richness pattern) consistent with other

approaches at the continental scale (Gittleman and Gompper2005; Schipper et al. 2008; Polly 2013; Fergnani andRuggiero 2015). The smallest carnivoran ranges are mostlypresent in the tropical region, and the largest ranges are foundfrom low to high latitudes. These patterns seem to follow inpart Rapoport’s rule, which reflects the seasonal variability ofhigh latitude environments and other climatic oscillations(e.g., Milankovitch oscillations; Stevens 1989; Dynesius andJansson 2000; Davies et al. 2011). This could explain thegreatest assemblage richness pattern towards the tropics dur-ing the LIG and C times (although, also high richness can be

Table 1 Results for best competing and selected models testing forenvironmental drivers influence on carnivoran assemblages’ richnessshift patterns using a 7760 1 × 1 degree cells in the Americas. Co:Coefficient; SE: Standard Error; P: p value; VIF: Variance Inflation

Factor, ORL: ordinary least squares; GWR: Geographic weightedregression; AIC: Akaike Information Criterion; K(BP): Koenker’sstudentized Breusch-Pagan Statistic. * indicates the selected models

LIG-LGM Models

Model Variable Co SE p Robust t Robust p VIF OLSR2 GWR R2 AIC K(BP) K(BP)-P

1* Intercept −0.599 0.132 <0.05 −6.256 0.000 0.328 0.67 53,117.48 495.590 <0.05Mean Temperature 0.443 0.010 <0.05 42.345 0.000 1.282

Temperature range 0.063 0.005 <0.05 14.336 0.000 1.284

Precipitation seasonality 0.115 0.003 <0.05 32.499 0.000 1.011

2 Intercept −0.617 0.133 <0.05 −6.421 0.000 0.328 53,118.28 513.678 <0.05Mean Temperature 0.437 0.011 <0.05 36.850 0.000

Temperature range 0.062 0.005 <0.05 14.047 0.000

Mean precipitation 0.000 0.000 0.274 1.116 0.264

Precipitation seasonality 0.113 0.003 <0.05 28.301 0.000

Intercept 0.250 0.114 <0.05 2.420 0.016 0.315 53,265.06 152.355 <0.053 Mean Temperature 0.373 0.010 <0.05 37.468 0.000

Mean precipitation 0.001 0.000 <0.05 2.960 0.003

Precipitation seasonality 0.114 0.004 <0.05 29.189 0.000

4 Intercept 0.336 0.110 <0.05 3.194 0.001 0.314 53,272.2 104.903 <0.05Mean Temperature 0.386 0.009 <0.05 43.973 0.000

Precipitation seasonality 0.119 0.003 <0.05 34.603 0.000

LGM-C Models

1* Intercept −1.679 0.096 <0.05 −17.387 0.000 0.433 0.75 49,996.54 207.303 <0.05Mean Temperature 0.465 0.007 <0.05 66.180 0.000 1.024

Temperature range 0.127 0.008 <0.05 13.063 0.000 2.626

Precipitation seasonality 0.059 0.006 <0.05 7.727 0.000 2.612

2 Intercept −1.667 0.096 <0.05 −17.511 0.000 0.433 49,996.6 158.532 <0.05Mean Temperature 0.460 0.008 <0.05 57.452 0.000

Temperature range 0.126 0.008 <0.05 13.121 0.000

Mean precipitation 0.000 0.000 0.16 1.235 0.217

Precipitation seasonality 0.057 0.006 <0.05 7.154 0.000

Intercept −1.887 0.094 <0.05 −21.374 0.000 0.426 50,088.73 535.589 <0.053 Mean Temperature 0.460 0.008 <0.05 58.442 0.000

Temperature range 0.183 0.005 <0.05 42.762 0.000

Mean precipitation 0.001 0.000 <0.05 3.484 0.001

4 Intercept −1.948 0.093 <0.05 −21.881 0.000 0.425 50,102.07 690.205 <0.05Mean Temperature 0.475 0.007 <0.05 67.932 0.000

Temperature range 0.191 0.005 <0.05 51.072 0.000

J Mammal Evol

seen in temperate latitudes in North America), as greatestranges (e.g., dire wolf, (Canis dirus†), saber cats (Smilodonfatalis† and S. populator†), jaguar (Panther onca), cougar(Puma concolor), and bush dog (Speothos venaticus)) areconfluent with the narrower ranges at more equatorial lati-tudes (e.g., Bassariscus spp., Bassaricyon spp., and Potosflavus ranges). This was also evident at the LGM for the tro-pics where geographical ranges tended to be more stable dueto less drastic variation in environmental conditions and that

many of the holarctic species had to migrate south escapingfrom unsuitable areas (Davis and Shaw 2001; Dyke 2005;Davies et al. 2011). This high richness pattern in the latePleistocene is consistent with previously suggested estima-tions, being one of the highest in richness worldwide in pro-portion to the continental area, and significantly higher thanthe richness recorded during middle Holocene and the currenttime as shown herein (Cione et al. 2003; Prevosti andVizcaíno 2006).

Table 2 Results for best competing and selected models testing forenvironmental drivers influence on composition shift patternscarnivoran assemblages using a 7760 1 × 1 degree cells in theAmericas. Co: Coefficient; SE: Standard Error; P: p value; VIF:

Variance Inflation Factor, ORL: ordinary least squares; GWR:Geographic weighted regression; AIC: Akaike Information Criterion;K(BP): Koenker’s studentized Breusch-Pagan Statistic. * indicates theselected models

LIG-LGM Models

Model Variable Co SE p Robust t Robust p VIF R2 GWR R2 AIC K(BP K(BP)-P

1* Intercept 0.353 0.006 <0.05 73.82 0.00 0.274 0.57 4446.84 513.678 <0.05Mean Temperature −0.008 0.000 <0.05 −15.39 0.00 1.636

Temperature range 0.005 0.000 <0.05 23.57 0.00 1.317

Mean precipitation 0.000 0.000 <0.05 9.71 0.00 1.504

Precipitation seasonality 0.005 0.000 <0.05 27.03 0.00 1.195

2 Intercept 0.360 0.006 <0.05 74.52 0.00 0.265 4538.55 495.590 <0.05Mean Temperature −0.006 0.000 <0.05 −11.85 0.00

Temperature range 0.005 0.000 <0.05 25.74 0.00

Precipitation seasonality 0.005 0.000 <0.05 32.58 0.00

Intercept 0.339 0.006 <0.05 66.23 0.00 0.188 5312.81 391.469 <0.053 Mean Temperature −0.009 0.000 <0.05 −16.24 0.00

Temperature range 0.005 0.000 <0.05 22.73 0.00

Mean precipitation 0.000 0.000 <0.05 20.37 0.00

4 Intercept 0.423 0.005 <0.05 82.54 0.00 0.226 4941.16 152.355 <0.05Mean Temperature −0.013 0.000 <0.05 −28.84 0.00

Mean precipitation 0.000 0.000 <0.05 12.54 0.00

Precipitation seasonality 0.005 0.000 <0.05 27.29 0.00

LGM-C Models

1* Intercept 0.367 0.004 <0.05 77.63 0.00 0.346 0.62 2231.26 158.532 <0.05Mean Temperature 0.015 0.000 <0.05 35.91 0.00 1.314

Temperature range 0.001 0.000 <0.05 2.07 0.04 2.634

Mean precipitation 0.000 0.000 <0.05 −14.26 0.00 1.537

Precipitation seasonality −0.007 0.000 <0.05 −22.01 0.00 2.786

2 Intercept 0.374 0.004 <0.05 75.20 0.00 0.323 2497.7 207.303 <0.05Mean Temperature 0.012 0.000 <0.05 29.50 0.00

Temperature range 0.001 0.000 0.15 1.17 0.24

Precipitation seasonality −0.009 0.000 <0.05 −24.55 0.00

Intercept 0.395 0.004 <0.05 74.92 0.00 0.284 2935.16 535.589 <0.053 Mean Temperature 0.015 0.000 <0.05 30.57 0.00

Temperature range −0.006 0.000 <0.05 −26.40 0.00

Mean precipitation 0.000 0.000 <0.05 −18.05 0.00

4 Intercept 0.368 0.004 <0.05 79.24 0.00 0.345 2235.08 133.698 <0.05Mean Temperature 0.015 0.000 <0.05 36.46 0.00

Mean precipitation 0.000 0.000 <0.05 −14.17 0.00

Precipitation seasonality −0.007 0.000 <0.05 −34.60 0.00

J Mammal Evol

Even though it appears that the composition shifts at thesescales where slightly less influenced by these climatic driversdue to the low variation explained by the composition modelscompared with the richness pattern (see Table 2), it is clear thatin some regions both richness and composition pattern shifteddramatically. For example, these shifts were more pronouncedin the Nearctic region because much of this area was coveredby the Laurentian Glacier during the LGM, including Canadaand a large portion of the United States (Dyke 2005). Thus,carnivoran ranges responded accordingly with most species’characteristics shifting larger distances to more suitable areas,

southward to the southern United States and northern Mexicoand westward to the western coast of the United States andCanada. Yet, species with both temperate and tropical distri-butions where less affected, so these shift patterns were appar-ently less severe in South America from low to mid latitudes.These results are consistent with the impact of Quaternaryclimate oscillations on other mammals in other regions(Davies et al. 2011; Lorenzen et al. 2011; Levinsky et al.2013; Blois et al. 2014). The loss of jaguar populations dis-tributed at higher latitudes (P. onca augusta from NorthAmerica and P. onca mesembrina from South America) and

Fig. 4 Geographically weighted regression showing the differential andlocal influence by environmental drivers on carnivoran assemblagesduring the transition between periods. Richness shift patterns: a. LIG-

LGM, b. LGM-C. Composition shift patterns: c. LIG-LGM, d. LGM-C. (legend represents Local R2 values)

J Mammal Evol

range constriction from high latitudes towards the tropics witha more stable range is an example (Arias-Alzate et al. 2017).

It is important to note that this minor effect on compositionpatterns perhaps could also be explained because after this lastperiod of time (LGM-C transition) not considerablecarnivoran species turnover occurred as previously happened(Prevosti and Soibelzon 2012; Silvestro et al. 2015). For ex-ample, Prevosti and Soibelzon (2012) suggested a first turn-over during the Pre-Ilinonian (North America) and Rio LLico(South America) Ages/stages (~478–424 ka), and a secondturnover during the Ilinonian (North America) and SantaMaria (South America) Ages/stages (~200–130 ka).Apparently, these ages/stages correspond with two glacial pe-riods proposed by Porter (1981) and Cohen and Gibbard(2010), where species replacement emerged apparently bycompetition with the entrance of new carnivoran species withsimilar ecological trait-space niches (e.g., Cyonasua meranivs Nasua nasua; Galictis henningi vs Galicitis cuja;Lyncodon bosei vs Lyncodon patagonicus; Arctotheriumangustidens vs Arctotherium tarijense; Brachyprotomaoptusata vs Mephitis mephitis; Martes diluviana vs Martespennanti; Smilodon gracilis vs Smilodon fatalis; Arctoduspristinus vs Arctodus simus).

This glacial history has long been considered as an impor-tant factor in shaping diversity patterns worldwide (Davieset al. 2009, 2011), where the last glacial period is not theexception. These environmental factors shown herein andtheir disturbances events (e.g., glaciations) are importantdrivers that influence these ecological and evolutionarycarnivoran distribution responses, which are likely to be moredetectable at broader spatial scales (Martínez-Meyer et al.2004; Soberón and Nakamura 2009; Morris et al. 2010;Davies et al. 2011; Lorenzen et al. 2011; Levinsky et al.2013; Blois et al. 2014). Here, we provide insights that theenvironmental drivers dynamically and differentially affectedrichness and composition patterns in the Americas (moremarked on richness than composition), not only at the conti-nental level, but also from regional and local scales as recentlysuggested (Blois and Hadly 2009; Wisz et al. 2013). Theseresults are consistent with evidence on composition ofEuropean mammalian communities of the past 20 millionyears, which suggests that this composition structure remainedconstant despite the significant richness shifts of the dominantherbivore assemblages, as has also been proposed for themammalian communities in Australia (Jernvall and Fortelius2004; Prideaux et al. 2007; Blois and Hadly 2009).

These carnivoran patterns can be viewed as a description ofthe environmental and ecological niche constraints that havecontributed to shape the lineages’ geographical distributions(Cardillo et al. 2006; Davies et al. 2009; Palombo et al. 2009;Agosta and Bernardo 2013), suggesting an environmental fil-tering during the LIG-LGM-C transition. This environmentalfiltering apparently affected more the hyper-large and large

carnivoran species due to their energetic constraint, biologicalcharacteristics, area and population requirements (e.g., such aslow population size, low reproductive rates, and large homeranges) and open habitats adaptation (O’Regan et al. 2002;Cione et al. 2007; Canto et al. 2010, Lorenzen et al. 2011;Agosta and Bernardo 2013; Levinsky 2010; Levinsky et al.2013; Arias-Alzate et al. 2017). Therefore, such species wereinherently more vulnerable to energetic constraint and rangesize reductions, due to the reduction of climatically suitableareas in the LIG-LGM transition, the reason why most largerspecies needed to hold large distribution ranges in order tomaintain viable populations to persist and to avoid bottlenecksevents and extinction risk (Canto et al. 2010; Lorenzen et al.2011; Agosta and Bernardo 2013; Arias-Alzate et al. 2017).

Interestingly, more ecologically tolerant, eurytopic, andflexible species with a more stable range (i.e., more continu-ous and less fragmented ranges) could persist in the continent(Cione et al. 2007; Arias-Alzate et al. 2017). These patternsare consistent with the evolutionary dynamics and the ecolog-ical structure of carnivoran species in Europe, where the smallbody-size species with high reproductive rates and ecologicalplasticity (e.g., such as some felids and mustelids) ensuredtheir success during the late Pleistocene dramatic ecologicalchanges (Croitor and Brugal 2010). It is to say that species likelynxes (Lynx rufus and L. canadensis) that suffered drasticrange reductions, and the jaguar (Panthera onca) and puma(Puma concolor) that likely lost their higher latitudes popula-tions persist up to the present. The latter two species main-tained a more continued ranges in the tropical region andrecolonized North America after the LGM via founder effect(Culver et al. 2000; Arias-Alzate et al. 2017). Besides, in thesesuitable areas, mesocarnivores and perhaps other non-specialized carnivorans kept a variety of prey that allowedthem to endure, unlike the more hypercarnivorous speciesadapted to feed on megaherbivores that also experienced thereduction of their suitable areas summed to other anthropo-genic effects (Cione et al. 2007; Villavicencio et al. 2016).

In this sense, it is possible that many of these carnivoranspecies that persisted through the last climate change “extinc-tion filter” represent the current set of species best suited toface natural environmental changes. However, as the modelsassessed herein where not explained entirely by environmen-tal drivers, past climate change and filter effects played just apart of the puzzle about the carnivoran assemblages’ structurepatterns and extinction event in Americas. It is important tonote that other important forces, associated with biotic inter-actions and competitive effects among multiple clades, andeven with human interactions (although human migrationand its effects was not uniform throughout the Americas,Barnosky and Lindsey 2010) could mediate and play an im-portant role on these patterns from local and regional levels ashas been proposed in other studies (De Vivo and Carmignotto2004; Araújo and Luoto 2007; Cione et al. 2009; Croitor and

J Mammal Evol

Brugal 2010; Davies et al. 2011; Wisz et al. 2013; Silvestroet al. 2015; Villavicencio et al. 2016; Araujo et al. 2017).However, recent works suggested that extinction may havestarted in the north before human arrival and occurred laterin higher latitudes after humans were present and where cli-mate changes were more severe (Hubbe et al. 2007; Barnoskyand Lindsey 2010; Villavicencio et al. 2016).

Although fossil records are always limited by local condi-tions and taphonomy, and therefore spatial and temporal dis-tribution could be biased (Nogués-Bravo et al. 2008), here,instead of presenting fine temporal or spatial resolution analy-ses, due to these limitations, we aimed to show a general trendof responses of the group. Here we provide plausible hypoth-eses and new insights regarding history and spatial patterns ofamerican carnivoran species during the last 130 Kyrs. Climaticvariation and the ecological niche constraints are in part animportant role of carnivoran assemblages’ restructuring fromcontinental to local scales in the Americas. Environmental fil-tering is likely a key factor in the extinction context, increasingthe susceptibility to extinction of remaining populations, espe-cially if other stressors were operating synergistically as cur-rent times perfectly evidence.

Acknowledgements We would like to thank H. Zarza, Joe J. Figel, andA. Townsend Peterson for support, pre-review, and insightful commentson the manuscript. A. Arias-Alzate acknowledges the Posgrado enCiencias Biológicas, the Instituto de Biología-UNAM, and the scholar-ship and financial support for the Doctoral Degree Program in BiologicalSciences provided by the Consejo Nacional de Ciencia y Tecnología ofMéxico (CONACyT) (scholarship 280993). We also thank the reviewersfor their comments, which helped to improve the manuscript.

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