Geographic variation in skull shape and size of the Pampas fox Lycalopex gymnocercus (Carnivora: Canidae) in Argentina

Mammalian Biology 97 (2019) 50–58

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Original investigation

Geographic variation in skull shape and size of the Pampas fox Lycalopex gymnocercus (Carnivora: Canidae) in Argentina Mauro Ignacio Schiaffini a,∗ , Valentina Segura b , Francisco Juan Prevosti c,d a CIEMEP, Centro de Investigación Esquel de Monta˜ na y Estepa Patagónica (Universidad Nacional de la Patagonia San Juan Bosco- Consejo Nacional de Investigaciones Científicas y Técnicas), LIEB, Laboratorio de Investigaciones en Evolución y Biodiversidad, Roca 780, Esquel, Argentina b UEL, Unidad Ejecutora Lillo (Consejo Nacional de Investigaciones Científicas y Técnicas-Fundación Miguel Lillo), Miguel Lillo 251, San Miguel de Tucumán, Argentina c CRILAR, Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja, Provincia de La Rioja, UNLaR, SEGEMAR, UNCa, CONICET, Entre Ríos y Mendoza s/n, 5301, Anillaco, La Rioja, Argentina d UNLaR, Departamento de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de La Rioja, Av. Luis M. de la Fuente S/N, 5300, La Rioja, La Rioja, Argentina

a r t i c l e

i n f o

Article history: Received 13 August 2018 Accepted 1 April 2019 Available online 26 April 2019 Handled by Laura Wilson Keywords: Bergmann’s rule Environmental predictors Geometric morphometric Resource’s rule South American foxes

a b s t r a c t South American foxes are included in the monophyletic genus Lycalopex, with several recent species. Here, the influence of environment about cranial size and shape variations of Lycalopex gymnocercus was explicitly addressed. 3D landmark-based methodology was used to acquire morphometric data. Each record locality was georeferenced and assigned both environmental variables and ecoregion membership. Size and shape changes were analyzed with regression and redundancy analyses, including the study of -spatial autocorrelation. An association of smaller specimens with arid and colder environments was found. Bergmann’s rule does not hold for this species. Pampas foxes from humid and warm areas display morphological traits related to more carnivorous diets, whereas those from arid and cold envi˜ ronments should display more hypocarnivorous traits. We found that 15% of shape variation explained by environment was independent of allometry, suggesting that both are independent components of the total cranial shape in Pampas foxes. ¨ Saugetierkunde. ¨ © 2019 Deutsche Gesellschaft fur Published by Elsevier GmbH. All rights reserved.

Introduction Canids arrived in South America (SA) after the rise of the Panamanian Isthmus around 3 Ma, and had a high diversification rate that generated the living and extinct species known for this continent (Prevosti and Forasiepi, 2018). Living SA canids include 10 species within six genera: Atelocynus Cabrera, 1940, Cerdocyon Hamilton Smith, 1839, Chrysocyon Hamilton Smith, 1839, Lycalopex Burmeister, 1854, Speothos Lund, 1839 and Urocyon Baird, 1857. Except for Urocyon, the remaining genera belong to a monophyletic group, the “South American foxes”, which is supported by molecular and morphological characters (Bardeleben et al., 2005; Prevosti, 2010; Austin et al., 2013; Zrzavy´ et al., 2018). The first records of the monophyletic genus Lycalopex date from the late Pliocene, including five extant species: L. culpaeus (Molina, 1782), L. fulvipes (Martin, 1837), L. sechurae Thomas 1900, L. vetulus (Lund, 1842); and L. gymnocercus (Fischer, 1814), and two fossil species, L. cultri-

∗ Corresponding author. E-mail address: mschiaffi[email protected] (M.I. Schiaffini).

dens (Gervais and Ameghino, 1880) and L. ensenadensis (Ameghino, 1888) (see Prevosti and Forasiepi, 2018). The Pampas or gray fox L. gymnocercus from Argentina is a medium-sized fox (between 2.4–8 kg in weight, Lucherini et al., 2004), with an omnivorous diet based on resource availability (Lucherini and Luengos-Vidal, 2008; Sillero-Subiri, 2009). The diet of Pampas fox consists of rodents, lagomorphs, birds, carrion, vegetables and arthropods in various proportions, with a marked trend to the consumption of small mammals and lagomorphs as main food items (Marquet et al., 1993; Johnson and Franklin, 1994; Correa and Roa, 2005; García and Kittlein, 2005; Zapata et al., 2005; ˜ Varela et al., 2008; Zuniga et al., 2008; Canel et al., 2016). The study of the correlation between diet and shape of the feeding apparatus indicated that omnivorous taxa display hypocarnivorous morphology, whereas those taxa that prey mainly on vertebrate flesh are called hypercarnivorous (see Ewer, 1973; Van Valkenburgh, 1989, 2007; Damasceno et al., 2013; Schiaffini and Prevosti, 2014; Meloro et al., 2015, 2017). Hypocarnivores are characterized by developed postcanine dentition, molars with large talonids, and narrow and long rostrum, while hypercarnivores display the opposite pattern: short rostrum, reduced dentition, reduced grinding areas and

https://doi.org/10.1016/j.mambio.2019.04.001 ¨ Saugetierkunde. ¨ Published by Elsevier GmbH. All rights reserved. 1616-5047/© 2019 Deutsche Gesellschaft fur

M.I. Schiaffini et al. / Mammalian Biology 97 (2019) 50–58

enlarged slicing areas in molars (Ewer, 1973; Van Valkenburgh, 2007). The genus Lycalopex was described as having a non-specialist cranial shape compared to other SA canids (Bubadué et al., 2016) and as being an omnivorous and opportunistic feeder (see references above). The Pampas fox was particularly classified as a small vertebrate specialist (Slater et al., 2009). However, while most past studies were related to differences between species; intraspecific variation has been particularly neglected. The Pampas fox is distributed from southeastern Brazil, eastern Bolivia, Paraguay and Uruguay to southern Chile and Argentina (see Lucherini and Luengos-Vidal, 2008; Sillero-Subiri, 2009). Although usually described as an open areas dweller (e.g., steppes, grasslands, scrublands), it also occurs in temperate and humid forests in its southernmost regions (i.e., Valdivian Temperate Forest sensu Olson et al., 2001). Its ranges from southern Brazil to Tierra del Fuego (see Sillero-Subiri, 2009) encompassing habitats with very different temperature and precipitation ranges (Garreaud et al., 2009), which could be associated with a large change in ecoregion types (sensu Olson et al., 2001). Several studies have addressed the influence of temperature on mammal size, mainly its increase associated with decreasing temperature or increasing latitude, a correlation also known as the “Bergmann’s rule” (Bergmann, 1847; see also Geist, 1987; Ashton et al., 2002; Ochocinska and Taylor, 2003; Blackburn and Hawkins, 2004; Meiri and Dayan, 2003; Meiri et al., 2004; Meiri, 2011; Clauss et al., 2013). A correlation of size, availability and characteristics of resources has also been proposed, with findings indicating that species become larger or smaller depending on the abundance of resources, which are in turn affected by rainfall, temperature and seasonality (see McNab, 2010). Size in mammals is important not only for its relationship with physiological processes (McNab, 1974), but also for its role as a mechanism to reduce niche overlap and competitive pressure (Sicuro, 2011). In particular, size has been used as a taxonomic character supposedly useful to separate L. gymnocercus (larger) from L. griseus (smaller, see Zunino et al., 1995; Lucherini and Luengos-Vidal, 2008; Prevosti et al., 2013). The relationship between morphology, climatic/environmental and ecoregional variations in SA carnivorans has received much less attention than the relationship between size and environment (but see Morales and Giannini, 2010; Bubadué et al., 2016; Zurano et al., 2017), particularly at the species level (but see Martínez et al., 2013, 2018; Schiaffini, 2016). For L. gymnocercus, skull shape variation has been related to environmental proxies (i.e., latitude and longitude, see Zunino et al., 1995; Prevosti et al., 2013); however, no detailed analysis of specific environmental variables has been performed to date. In this work, we analyzed the correlation between specific environmental variables and skull size and shape variations in L. gymnocercus from Argentina.

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Forests; the center includes moist grassland in the Humid Pampas, and the south includes moist and cold Valdivian Temperate Forests. Data acquisition and Geometric morphometric analyses The sample consisted of 395 adult crania (based on fully erupted permanent dentition), which is part of the data of Prevosti et al. (2013). The specimens belong to the following institutions: Colección Félix de Azara (CFA, Ciudad Autónoma de Buenos Aires), Colección Mamíferos Lillo (CML, San Miguel de Tucumán), Field Museum of Natural History (FMNH, Chicago); Colección Grupo Ecología Comportamental de mamíferos (GECM, Bahía Blanca), Laboratorio de Investigaciones en Evolución y Biodiversidad (LIEB, Esquel), Museo de La Plata (MLP, La Plata), Museo Argentino de Ciencias Naturales (MACN, Ciudad Autónoma de Buenos Aires), National Museum of Natural History, Smithsonian Institution (NMNH, Washington); see Supplementary material Appendix A. Thirty-eight cranial landmarks (see Supplementary material Fig. 1 and Supplementary material Appendix B), types 1 and 2 (sensu Bookstein, 1997), were digitized in 3D with a Microscribe MX 6DOF System (GoMeasured3D, Amherst, VA, USA). The landmarks were digitized in a hemi-cranium and the configuration was reflected in the plane of symmetry defined by sagittal landmarks using Rfunction AMP.r developed by Annat Haber (http://life.bio.sunysb. edu/morph/). This procedure allowed us to improve visualization and avoid putative alignment artifacts. Landmark configurations were superimposed through generalized Procrustes analysis (GPA, Goodall, 1991; Rohlf, 1999), which minimizes the sum of squared distances between homologous landmarks by translating, rotating and scaling to best fit, in MorphoJ v 1.06d (Klingenberg, 2011). Centroid size (Cs) was used as size estimator (Zelditch et al., 2004). Climatic variables We determined geographical coordinates for each recorded locality and assigned the following values to each record: 19 bioclimatic variables plus altitude from the “Worldclim” database (see www.worldclim.org) at a spatial resolution of 30 arc-s 1 km2 (Hijmans et al., 2005a); an indicator of available energy at a spatial resolution of 2◦ (Net Primary Productivity, Npp Foley et al., 1996; Kucharik et al., 2000) and a vegetation index at a spatial resolution of 250 m (Enhanced Vegetation Index, EVI downloaded from http:// daac.ornl.gov). We also assigned each locality to an ecoregion following the biogeographic scheme of Olson et al. (2001). All these methodological steps were followed using DIVA-GIS v 7.5 (Hijmans et al., 2005b) and QGIS v 2.18.10 (QGIS Development Team, 2017). Data analyses

Material and methods Study area We analyzed specimens from northern to southern Argentina from ca. 22 ◦ S to 55 ◦ S, covering approximately 3700 km. This wide latitudinal range determines differences in solar radiation incidence, which in turn affects temperature and photoperiod, among other factors (Bianchi and Cravero, 2010). Distance from sea and altitude (e.g., Andean and pre-Andean mountains) in turn influence precipitation patterns (Cravero et al., 2017). Thus, northern and southern Argentina might be considered subtropical and temperate regions, respectively. The study area is included in the Neotropical realm, and encompasses 16 ecoregions (sensu Olson et al., 2001). The north encompasses from the cold and dry shrubby steppe in Central Andean Puna to moist forests of Alto Parana Atlantic

Several studies have reported a low sexual dimorphism in the cranium of L. gymnocercus (Zunino et al., 1995; Prevosti and Lamas, 2006; Prevosti et al., 2013). We evaluated sexual dimorphism using Principal Component Analysis in MorphoJ v 1.06d (Klingenberg, 2011), and found a high overlap between males and females. Additionally, we performed a “nonparametric MANOVA” with the function Adonis developed by Martin Henry H. Stevens and available in package vegan (Oksanen et al., 2008). We also did not find sexual dimorphism in this test (F = 1.35, p = 0.1282). For these reasons and because splitting the dataset according to sex would reduce the number of samples, we performed the subsequent analyses with males and females together (other studies including both sexes in the same analyses were conducted in Machado and HingstZaher, 2009; Martínez et al., 2013; Prevosti et al., 2013; Zurano et al., 2017).

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In order to analyze size and shape variation due to only geographic variables, the data (i.e., Procrustes coordinates, centroid size and climatic variables) from each locality were averaged. Thus, of 395 specimens taken from Prevosti et al. (2013) dataset, we worked with size and shape data from 122 localities. The presence of spatial autocorrelation (i.e., lack of independence in values of nearby localities, Dormann et al., 2007) was tested with Moran’s I coefficients in SAM v 4.0 (Rangel et al., 2010) or with DurbinWatson test with package “lmtest” (Hothorn et al., 2018) in R v 3.4.0 (R Core Development Team, 2017). Spatial Eigenvector Mapping (SEVM) was used to include spatial predictors (i.e., Spatial Filters, SFs) in subsequent models (Diniz-Filho and Bini, 2005; Kühn and Dormann, 2012), combining these SFs with environmental predictors in fully spatial explicit models. The first two SFs, which possess the largest eigenvalues (i.e., broad-scale space patterns; Diniz-Filho and Bini, 2005) were used as covariables. The Variance Inflation Factor was used to measure the multicollinearity in the data (VIF < 10, see Chatterjee and Hadi, 2006; Dormann et al., 2013). A recent example of this methodology is provided in Schiaffini (2016). Before conducting the subsequent analyses, we calculated correlation coefficients between all environmental variables and eliminated those with r>0.7. Therefore, we worked with mean annual temperature (Bio1), mean diurnal range (Bio2), Isothermality (Bio3), mean temperature of driest quarter (Bio9), annual precipitation (Bio12), precipitation seasonality (Bio15), Net primary productivity (Npp) and Enhanced vegetation index (EVI). Size analysis. Box plots of Cs according to ecoregions were created in R v 3.4.0 (R Development Core Team, 2017). To analyze size explained by ecoregions, we performed partial redundancy analysis (pRDA) in R v 3.4.0 (R Core Development Team, 2017). Size was used as response variable, a matrix of membership of each locality to an ecoregion (i.e., as a binary code, see Morales and Giannini, 2010; Schiaffini, 2016) was used as explanatory variables, and the first two spatial filters (related to broad-scale patterns, Diniz-Filho and Bini, 2005) were used as constraints. The influence of environmental variables on size was explored with ordinary least-squared regression analysis (OLS). Multiple regression analyses were also performed to investigate the contribution of each variable to size variation in the presence of other predictors. Spatial autocorrelation was analyzed in residuals of the created models. Multi-model selection based on Akaike criterion (Akaike, 1973) was used to identify the best model supported by the data (Johnson and Omland, 2004; Diniz-Filho et al., 2008). Model Averaging was applied when several models displayed equivalent support (i.e., AIC<2; Burnham and Anderson, 2002; Diniz-Filho et al., 2008) (see Johnson and Omland, 2004; Schiaffini, 2016). These tests were performed in SAM v 4.0 (Rangel et al., 2010). Shape analysis. Allometry was tested with multivariate regression between Procrustes coordinates and Log Cs with 10,000 permutations in MorphoJ v 1.06d (Klingenberg, 2011). To analyze shape variation only due to geographic variables and not to allometry, a multivariate regression between Procrustes coordinates and Log Cs was performed in MorphoJ v 1.06d (Klingenberg, 2011; Segura and Prevosti, 2012), and subsequent analyses were performed with residuals of this regression (hereafter referred to as “Procrustes coordinates”). As in size analyses, RDA was performed to study influence of ecoregions, but using a matrix of Procrustes coordinates (i.e., shape) instead of Cs, as explained above. Redundancy analysis (RDA) is an asymmetric canonical analysis, in which response and explanatory matrices do not play the same role, but the variation in one matrix is explained by another matrix containing response or explanatory variables (Legendre et al., 2011; see also Rao, 1964). RDA and variance partition analyses were performed between Procrustes coordinates and environmental predictors, separately and together, using the package “vegan” (Oksanen et al., 2008) in R

v 3.4.0 (R Development Core Team, 2017). Significance was analyzed with 9999 unrestricted permutations. Shape changes were analyzed using deformation grids. Model selection was used with the Akaike information criterion (Akaike, 1973). To include the possible existence of two different lineages, our dataset was divided in two, removing the specimens from northeastern Argentina (that might belong to a different clade) and repeating all the analyses (see Chemisquy et al., 2014; Tchaika et al., 2016) The results (data not shown) were highly similar to those obtained with the complete dataset. Since only five specimens of our dataset came from northeastern Argentina, the same analyses for the second mitochondrial lineage could not be performed. Results Of 122 localities (resulting from the average of the 395 analyzed specimens), 19% occurred in Patagonian Steppe, 18% in Dry Chaco, 17% in Humid Pampas, 13% in Espinal, 8% in High Monte and Southern Andean Yungas, 5% in Low Monte, 4% in Magellanic Subpolar Forests, 3% in Humid Chaco, and 2% in Central Andean Puna and Parana Flooded Savanna (sensu Olson et al., 2001; Fig. 1). Size analysis. The largest Pampas fox specimens came from Humid Pampas and Parana Flooded Savanna, followed by Central Andean Puna, whereas the smallest specimens came from High Monte and Patagonian Steppe (Fig. 1). Ecoregion membership explained 20.85% of size variation, with Humid Pampas, Patagonian Steppe, High Monte and Espinal significantly explaining parts of the size variation (p < 0.05). The main results of OLS are presented in Table 1. Significant associations (p < 0.05) were observed between Cs and three independent variables: annual precipitation explained 8.3% of size variation, annual mean temperature explained 4.1% and vegetation explained 3.4%; all the variables had positive slopes of regression. Multiple partial regression between Cs and the three significant independent variables indicated in Table 1 plus the first two SFs shows that 27.6% of the size variation was significantly (p < 0.01) explained. Independent variables explained 9.1% alone, whereas 13.8% was explained by SFs and 7.1% was shared explained variation. VIF values were low (i.e., <10) and spatial autocorrelation was effectively taken into account. Two models were equally probable to account for size variation with AIC<2 from the model with lowest AIC (−340.751), which includes annual mean temperature, mean temperature of driest quarter, annual precipitation and the two SFs explaining 35.1% of the size variation. The second model (AIC= −338.817) included the same variables plus isothermality, explaining 35.3% of size variation. Model Averaging showed that 30% of size variation was explained by all environmental variables, with temperature of driest quarter, annual precipitation and mean annual temperature as the most important variables (Table 2). The expected values from a regression using only those three most important variables plus two SFs (explaining 32.9% of size variation, Table 3) showed that the largest Pampas fox specimens would occur in the center and east of Argentina, followed by those from the northwest, whereas smaller individuals would occur in the arid diagonal (Monte sensu lato and Patagonia, Fig. 2). Shape analysis Size significantly explained 5% of cranial shape (p < 0.01). Larger specimens showed longer cranium, narrower rostrum, smaller orbits and tympanic bullae, with smaller specimens showing the opposite pattern. All analyses were re-done using Procrustes coordinates (instead of residuals of regression between Procrustes coordinates and Cs); the results were the same (data not shown).

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Fig. 1. Boxplot of centroid size according to ecoregions. Width of each box drawn proportional to number of observations.

Table 1 Results from ordinary least square regression between centroid size and independent variables. The first two spatial filters (SFs) were used as covariables. In bold, significant p-values <0.05. Slope: Slope of each regression (rate of change in response variable as independent variables changes); P-value: probability value of each model; R2 adj: coefficient of multiple correlation (R2 ) of each analysis; R2 independent variable: R2 of independent variable after removing the effects of covariables; R2 shared independent variable-SFs: common R2 between independent variable and covariables; R2 SFs: R2 of covariable without contribution of independent variable. Independent variable

Slope

P-value

R2 adj

R2 independent variable

R2 shared independent variable-SFs

R2 SFs

Annual mean temperature Meand diurnal range Isothermality Mean temperature of driest quarter Annual precipitation Precipitation seasonality Net primary productivity Enhaced vegetation index

2.541 0.657 0.177 −0.828 3.722 0.228 1.358 2.304

0.012 0.512 0.86 0.409 <.001 0.82 0.177 0.023

0.237 0.198 0.195 0.2 0.28 0.196 0.208 0.23

0.041 0.003 <.001 0.005 0.083 <.001 0.012 0.034

0.01 0.016 0.016 0.035 0.075 0.013 0.004 0.013

0.199 0.193 0.193 0.173 0.133 0.196 0.205 0.195

Table 2 Parameter estimates across 255 ordinary least square models, using Akaike weights. Importance: sum of Akaike weights over all models in which the independent variable appears; VIF: Variance Inflation Factor; R2 : multiple correlation coefficient = 0.291.

Table 3 Results of ordinary least square using most important variables according to Model Averaging. Slope: Slope of each regression (rate of change in response variable as independent variables changes); VIF: Variance Inflation Factor; P-value: probability value of each model; R2 : multiple correlation coefficient = 0.329.

Independent variable

Importance

VIF

Independent variable

Slope

VIF

P-value

Annual temperature Meand diurnal range Isothermality Mean temperature of driest quarter Annual precipitation Annual seasonality Net primary productivity Enhaced vegetation index SF1 SF2

0.782 0.3 0.28 0.935 0.893 0.255 0.277 0.289 1 1

4.581 3.983 2.916 2.196 2.876 4.83 1.417 2.576 3.225 1.88

Annual mean temperature Mean temperature of driest quarter Annual precipitation SF1 SF2

0.118 −0.12 0.071 0.194 −0.104

2.542 1.727 1.422 1.303 1.505

0.016 0.002 0.004 0.004 0.146

Ecoregion membership explained 14.2% of shape variation, with Patagonian Steppe, High Monte, Low Monte and Humid Pampas significantly explaining part of the variation (p < 0.05). Specimens with wide and robust skulls, particularly at zygomatic arches and basicranium, and shorter palates were located at the positive end

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M.I. Schiaffini et al. / Mammalian Biology 97 (2019) 50–58

Fig. 2. Estimated Log Centroid size values from multiple regression using most important 9variables according to Model Average. Larger specimens are indicated with black points, medium-sized specimens with grey triangles and smaller with white squares. Numbers represent ecoregions sensu Olson et al. (2001): 1) Central Andean Puna, 2) Southern Andean Yungas, 3) Dry Chaco, 4) Humid Chaco, 5) High Monte, 6) Low Monte, 7) Espinal, 8) Humid Pampas, 9) Parana Flooded Savanna, 10), Patagonian Steppe, 11) Magellanic Subpolar forests. Scale (black and white bar): 500 Km.

of RDA1, with the opposite pattern being observed at the negative end. Specimens with wider and more robust rostrum and lower cranium were located at the positive end of RDA2 (see Supplementary material Fig. 2). Shape changes according to ecoregions seemed to be subtle, with overlap of individuals in biplot of RDA1 vs. RDA2. However, certain segregation was observed, with foxes from High Monte being located on the positive side

of RDA1 and on the negative side of RDA2 (wide basicranium, narrow rostrum); those of Patagonian Steppe, on negative side of RDA1 and RDA2 (narrow skulls and rostrum); those of Humid Pampas on the positive side of RDA2 (wide and robust rostrum, lower skulls), and those of Low Monte on the negative side of RDA1 and spread across both sides of RDA2 (narrow skulls, longer palates).

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Table 4 Results from RDA between shape and independent variables. The first two spatial filters (SFs) were used as covariables. In bold, significant p-values <0.05. See Table 1 legend for details. Variable

p-value

R2 adj

R2 independent variable

R2 shared independent variable-SFs

R2 SFs

Annual mean temperature Mean diurnal range Isothermality Mean temperature of driest quarter Annual precipitation Precipitation seasonality Net primary productivity Enhaced vegetation index

0.0315 0.3977 0.2233 0.0196 0.0004 0.1401 0.002 0.0622

0.0459 0.0415 0.0427 0.04727 0.05209 0.04359 0.05185 0.0454

0.00471 0.0003 0.00149 0.00599 0.01082 0.00231 0.01057 0.00412

0.01274 0.00488 0.00451 0.00194 0.0036 0.01841 0.00209 0.00433

0.02854 0.0364 0.0367 0.03933 0.03768 0.02286 0.03919 0.03695

The main results of RDAs are presented in Table 4. Significant associations (p < 0.05) were observed between shape and four independent variables, with low percentages of explained shape variation: annual precipitation (1.08%), net primary productivity (1.05%), mean temperature of driest quarter (0.59%) and mean annual temperature (0.47%). RDA using only these variables significantly (p < 0.01) explained 7.3% of shape variation. The Durbin-Watson test indicated that no spatial autocorrelation remained on the residuals of this model (DW = 1.972, p = 0.3919) and VIF values were low (<10). Model selection indicated that the best model (AIC=−731.08) included mean annual temperature, mean temperature of driest quarter, annual precipitation and precipitation seasonality, explaining 8.4% of shape variation. Other five models with AIC<2 explained similar percentages of shape variation (data not shown). Discussion Body size of Pampas fox was found to vary geographically between 2.4 and 8 kg (Lucherini and Luengos-Vidal, 2008) and traditionally used as a supposedly useful taxonomic trait to separate species (see Zunino et al., 1995; Lucherini and Luengos-Vidal, 2008; Prevosti et al., 2013). Some studies have related this change in body size to competition processes, and found that in the southern parts of the species distribution, where L. gymnocercus (=Dusicyon griseus) is sympatric with L. culpaeus (=D. culpaeus), size divergence evolved, allowing both species to partition food resources (Fuentes and Jaksic, 1979). On the other hand, changes in body size were related to an ecogeographic pattern, with increasing size toward higher latitudes, as expected according to Bergmann’s rule (Jiménez et al., 1999). As L. griseus might be conspecific to L. gymnocercus (Prevosti et al., 2013), at least in most of Argentina, body size variation needs to be studied in this geographic range of distribution. A relationship between one environmental proxy (i.e., longitude but not latitude) and body size variation has been found by Prevosti et al. (2013) and linked to precipitation changes. Here, we found evidence that body size variation in Pampas fox can be related to temperature and precipitation patterns. Annual mean temperature showed a positive correlation with body size (i.e., depicted as cranial centroid size), contradicting Bergmann’s rule. This rule has been traditionally explained by heat conservation, arguing that larger animals have better heat retention due to lower surface to volume ratio than smaller animals (Meiri, 2011). However, this argument has been criticized because larger animals lose more heat (in absolute terms) that smaller ones (McNab, 1971, 2010). According to our results, the size of the Pampas fox seems to increase with an increase in temperature. Annual precipitation and vegetation also showed positive slopes of regression with size, indicating that Pampas fox specimens occurring in humid and warm areas might be of larger size. The ecoregion analysis showed similar results. In arid regions, although precipitation is the limiting factor (Reynolds et al., 2004), temperature also has an important influence, given that low temperatures might reduce or inhibit plant growth (Yom-

Tov and Geffen, 2006). Body size in Pampas fox seems to be related to a combination of precipitation and temperature variables, with smaller specimens in arid and colder ecoregions (i.e., Patagonian Steppe, Monte sensu lato) and larger specimens in more humid and warmer environments (i.e., Humid Pampas sensu Olson et al., 2001). Our results would be in agreement with several theoretical and empirical studies. Rosenzweig (1968) found Annual Evapotranspiration (i.e., a productivity proxy) to be a good predictor of carnivore size, particularly in cold and/or arid environments. Yom-Tov and Geffen (2006) showed that body size in some mammalian groups (including carnivorans) was related to covariation between temperature and moisture. According to McNab (2010), the size of the species varies with abundance of resources, and in turn is affected by rainfall, temperature and seasonality. Gortázar et al. (2000) showed that size of the red fox Vulpes vulpes was related to environmental productivity, with larger foxes occurring in more productive environments. Szuma (2008) showed that condylobasal length and dentition size of the artic fox Alopex lagopus was related to availability of food resources, with larger specimens being present in more productive and stable environments. Schiaffini (2016) found that size of hog-nosed skunk Conepatus chinga was also related to environmental productivity, with larger specimens occurring in more productive areas. Recently, Martínez et al. (2018) found that a portion of skull size variation in culpeo fox Lycalopex culpaeus was related to precipitation and biomass variables. Two recent studies addressed size and shape changes of all extant SA canids, with different results: one found that size changes are not related to climate, but to trophic specializations (Zurano et al., 2017), whereas the other formulates that canid assemblages are controlled by climate, with smaller species occurring farther south and with non-specialist phenotypes being present in highly seasonal environments (Bubadué et al., 2016). Although the study scale of these studies was quite different from the one applied in the present work, we found a closer relationship with results reported by Bubadué et al. (2016), given that climatic variation was responsible for about 30% of size variation. Also in agreement with that study, we found some morphological traits, such as elongated rostrum and narrow cranium, related to arid and cold environments (i.e., Patagonian Steppe, Low Monte, see Results), a phenomenon that might be related to a more hypocarnivorous diet (Ewer, 1973; Van Valkenburgh, 2007) that includes several food items (e.g., fruits, insects). Moreover, specimens from warm and more humid areas (i.e., Humid Pampas) display shorter, thicker rostrum and wider zygomatic breadth, and might be more related to more flesh-vertebrate consumers. A recent study showed an increase in flexibility, evolvability and allometry of morphological traits related to snout length in Canidae (Machado et al., 2018). As these facial traits display a great variance, they represent an increased capacity to respond to natural selection (Machado et al., 2018), allowing foxes to change the facial region according to available resources. A shorter rostrum allows an increase in bite force by reducing the distance of the jaw out-lever, whereas a longer rostrum provides a fast jaw closing, but reduces bite force (Slater

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et al., 2009; see also Radinsky, 1981; Van Valkenburgh, 1991, 2007; Binder and Van Valkenburgh, 2000; Damasceno et al., 2013). Thus, morphology might determine the performance of individuals and therefore, their ability to thrive in their environments (Binder and Van Valkenburgh, 2000; see also Wainright and Reilly, 1994). Regarding foxes displaying a more hypercanivorous morphology, it should be noticed that vertebrate meat is easier to digest and provides more energy than plant or arthropod food (Van Valkenburgh, 2007); therefore, foxes from more productive areas might exhibit higher basal metabolic rates and faster growth rates. This has been argued for mammals (McNab, 1986) and carnivorans in particu˜ lar (Munoz-Garcia and Williams, 2005), showing that meat-eating vertebrates display higher basal metabolic rates. Although direct relationships in cranial shape and trophic habits in such a generalist feeder might be hard to demonstrate (see Martínez et al., 2018), some dietary studies of Pampas foxes from humid areas found that mammals and/or birds were the main prey items (Jaksic et al., 1980; Johnson and Franklin, 1994; Correa and ˜ et al., 2008; Canel et al., 2016), whereas studies Roa, 2005; Zuniga conducted in drier environments found that the most consumed items were fruits and/or arthropods (Marquet et al., 1993; García and Kittlein, 2005; Varela et al., 2008). According to our results, Pampas foxes from humid and warm areas should display morphological traits related to more carnivorous diets, whereas those from arid and cold environments should display more hypocarnivorous traits (see above). ˜ We found that the 15% of shape variation explained by environment was independent of allometry. According to size-driven changes in shape, larger specimens should display a narrower rostrum (see Results). However, environmental analyses showed that larger specimens from more productive regions display broader palate and shorter rostrum. This contradiction between allometry and environmental changes in shape suggests that both allometric and environmental changes are independent components of the total cranial shape in Pampas foxes.

Conclusions The analyses of size and shape covariation with environmental variables are very important to determine properly know intraspecific limits of variation. Here we found a positive relationship between size and warmer and humid areas, suggesting that more productive ecoregions might host larger Pampas fox specimens, whereas the smaller ones would occur in more arid environments (see McNab, 2010). The evidence found did not support Bergmann’s rule, but contradicted it. Cranial shape changes were related to allometry as well as to environmental predictors, independently of each other. Specimens from warmer and more humid areas displayed a broader and shorter rostrum, indicating a morphotype adapted to a more hypercarnivorous diet. Future lines of research might be addressed to extend these analyses to other South American carnivorans, searching for similar patterns of shape and morphological variation between and within species.

Acknowledgements We acknowledge for partial financial support from Agencia Nacional de Promoción Científica y Tecnológica (PICT 2014-1930, 2014-3182, 2015-2389, 2015-966, 2016-0238, 2016-3151; PUE 0125) and CONICET. The work was greatly improved from comments of the editor and two anonymous reviewers.

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