THE SCOPE OF TRADITIONAL AND GEOMETRIC MORPHOMETRICS FOR INFERENCES OF DIET IN CARNIVOROUS FOSSIL MAMMALS

SERGIO D. TARQUINI 1 *, M. AMELIA CHEMISQUY 1,2 , SANDRINE LADEVÈZE 3 , AND FRANCISCO J. PREVOSTI 1,2 1-Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja (CRILAR -Provincia de La Rioja, UNLaR, SEGEMAR, UNCa, CONICET).

Entre Ríos y Mendoza s/n (5301), Anillaco, Argentina. starquini92@gmail.com; amelych80@gmail.com; protocyon@hotmail.com 2-Departamento de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de la Rioja (UNLaR). Av. Luis M. de la Fuente s/n (5300), La Rioja, Argentina. quantitative traits of tooth shape (i.e., morphometric ratios or angles) with the different items consumed by the animal. However, because different inferences of diet for the same species can be found in the literature, it is crucial to study the power of different proxies. In this work, we compared the utility of classic morphometric indices (RGA, AI and angle α) and three-dimensional landmarks configuration to infer diet. Based on a previously published dataset from our working group, we calculated the classic morphometric indices and performed three classification methods: linear discriminant analysis, quadratic discriminant analysis, and weighted random forest. Our results demonstrate that when using the geometric morphometrics approach, the diet of species is better reclassified than when using morphometric indices. Furthermore, intraspecific variation appears fundamental when making paleoecological inferences. In inferring ecological characteristics of extinct animals, studying morphological variation in extant organisms is a safeguard to prevent misinterpretations when reconstructing past ecologies.

KEYWORDS. Diet classification. Carnivora. Morphometric indices. Weighted random forest.

RESUMEN. ALCANCE DE LA MORFOMETRÍA TRADICIONAL Y

GEOMÉTRICA A LA HORA DE INFERIR LA DIETA EN LOS MAMÍFEROS FÓSILES CARNÍVOROS. La morfología del molar es uno de los proxies más utilizados para las inferencias paleoecológicas en los mamíferos. Desde el siglo XIX, varios autores asociaron algunos rasgos morfológicos dentales con la dieta del animal mediante análisis cualitativos y descriptivos. Más tarde, a partir del siglo pasado, diferentes estudios sobre la función dental han asociado varios rasgos cuantitativos de la forma del diente (es decir, proporciones o ángulos morfométricos) con el porcentaje de carne de vertebrados que consume el animal. Sin embargo, debido a que en la literatura se pueden encontrar diferentes inferencias de la dieta para la misma especie, es crucial llevar a cabo estudios que comparen la precisión de diferentes proxies. En este trabajo, comparamos la utilidad de los índices morfométricos clásicos (RGA, AI y ángulo α) y la configuración de landmarks tridimensionales con respecto a las inferencias de la dieta.

Sobre la base de un conjunto de datos previamente publicado por nuestro grupo de trabajo, calculamos los índices morfométricos clásicos y realizamos tres métodos de clasificación: análisis discriminante lineal, análisis discriminante cuadrático y análisis de bosques aleatorios ponderados. Nuestros resultados demuestran que al usar la configuración de landmarks, la dieta de las especies se reclasifica mejor que cuando se usan índices morfométricos. Además, considerar la variación intraespecífica al hacer inferencias paleoecológicas parece fundamental. Teniendo el propósito de inferir características ecológicas en animales extintos, la consideración de la variación morfológica en los organismos actuales es una garantía para evitar cualquier interpretación errónea en las reconstrucciones de ecologías pasadas.

PALABRAS CLAVES. Clasificación de la dieta. Carnivora. Índices morfométricos.

Bosques aleatorios ponderados.

THE analysis of diets is an important component of ecological and paleoecological studies [START_REF] Price | Tempo of trophic evolution and its impact on mammalian diversification[END_REF][START_REF] Kissling | Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide[END_REF][START_REF] Davis | The temporal scale of diet and dietary proxies[END_REF]Croft et al., 2018a). Many proxies have been used to determine the diet of mammalian species, including direct observations of feeding behavior, stomach and fecal contents, dental microwear and mesowear, isotope analysis of nitrogen and carbon, and craniodental morphology, among others (see review in [START_REF] Davis | The temporal scale of diet and dietary proxies[END_REF].

Although often considered to represent the same parameter, these different proxies offer different evidences of the items consumed, have different scopes (i.e., extant and/or fossil species), and are constrained by different methodological aspects (i.e., cost, difficulty and time of study, availability of data) [START_REF] Evans | Shape descriptors as ecometrics in dental ecology[END_REF][START_REF] Davis | The temporal scale of diet and dietary proxies[END_REF]. In particular, the study of molar morphology combines low cost and low methodological difficulty, thus making it one of the most widely used proxies in paleoecological studies of mammals (e.g., [START_REF] Marshall | Evolution of the Borhyaenidae, extinct South American predaceous marsupials[END_REF][START_REF] Goin | Orientación de las crestas cortantes en el molar tribosfénico. Sus implicancias funcionales en didelfoideos (Marsupialia) fósiles y vivientes[END_REF][START_REF] Van Valkenburgh | Cope's Rule, Hypercarnivory, and extinction in North American canids[END_REF][START_REF] Vizcaíno | Dental occlusal surface area in relation to body mass, food habits and other biologic features in[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF]Croft et al., 2018b;[START_REF] Harper | Reigitherium (Meridiolestida, Mesungulatoidea) an enigmatic Late Cretaceous mammal from Patagonia, Argentina: morphology, affinities, and dental evolution[END_REF].

The versatility of tribosphenic molars has been shown to be a key innovation in the evolution of mammals, increasing the taxonomic diversity and ecological disparity of this group throughout their evolutionary history [START_REF] Woodburne | The evolution of tribospheny and the antiquity of mammalian clades[END_REF][START_REF] Luo | Transformation and diversification in early mammal evolution[END_REF]. Molars are also usually the sole identifiable remains of an extinct mammal, and therefore have been intensively used for taxonomic classifications in addition to dietary reconstructions (e.g., [START_REF] Marshall | Evolution of the Borhyaenidae, extinct South American predaceous marsupials[END_REF][START_REF] Ungar | Mammal teeth: origin, evolution, and diversity[END_REF][START_REF] Goin | Contribución al conocimiento de los representantes extintos de Lutreolina Thomas, 1910 (Mammalia, Marsupialia, Didelphidae)[END_REF]. Since the 19th century, several authors have associated certain dental morphological traits with dietary habits by means of qualitative and descriptive analyses (e.g., [START_REF] Lund | Cuop-d'oeil sur les espèces éteintes de Mammifères du Brésil; extrait de quelques mémoires présentés à l'Académie royal des Sciences de Copenhague[END_REF][START_REF] Ryder | On the mechanical genesis of tooth-forms[END_REF][START_REF] Cope | The origin of the specialized teeth of the Carnivora[END_REF][START_REF] Butler | The evolution of carnassial dentitions in the mammalia[END_REF][START_REF] Marshall | Evolution of the Borhyaenidae, extinct South American predaceous marsupials[END_REF][START_REF] Reig | Systematic relationships of the living and neocenozoic American 'opossumlike' marsupials (suborder Didelphimorphia), with comments on the classification of these and the Cretaceous and Paleogene New World and European Metatherians[END_REF][START_REF] Berta | Quaternary evolution and biogeography of the large South American Canidae (Mammalia: Carnivora)[END_REF].

More recently, different studies of tooth function have correlated several quantitative traits of tooth shape (i.e., morphometric ratios or angles) with the different items consumed by the animal. These quantitative tools continue to be used today, and while some have used isolated indices (e.g., [START_REF] Crusafont-Pairó | A biometric study of the evolution of fissiped carnivores[END_REF][START_REF] Goin | Orientación de las crestas cortantes en el molar tribosfénico. Sus implicancias funcionales en didelfoideos (Marsupialia) fósiles y vivientes[END_REF]Strait, 1993a, b;[START_REF] Evans | Dental ecomorphology of extant European carnivorans[END_REF][START_REF] Vizcaíno | Dental occlusal surface area in relation to body mass, food habits and other biologic features in[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF], others have used several indices to define a morphospace (e.g., [START_REF] Kay | The functional adaptions of primate molar teeth[END_REF][START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Popowics | Postcanine dental form in the Mustelidae and Viverridae (Carnivora: Mammalia)[END_REF][START_REF] Wesley-Hunt | The morphological diversification of carnivores in North America[END_REF][START_REF] Friscia | An ecomorphological analysis of extant small carnivorans[END_REF][START_REF] Asahara | Unique pattern of dietary adaptation in the dentition of Carnivora: its advantage and developmental origin[END_REF]Croft et al., 2018b). Moreover, with the development of new technologies, new dental shape descriptors have been developed to study molar morphology [START_REF] Evans | Shape descriptors as ecometrics in dental ecology[END_REF]. On the one hand, some authors have defined metrics to reflect occlusal surface topography, such as the Dirichlet normal energy, relief index, and orientation patch count [START_REF] Zuccotti | Technical note: Modeling primate occlusal topography using geographic information systems technology[END_REF][START_REF] Ungar | A solution to the worn tooth conundrum in primate functional anatomy[END_REF][START_REF] Evans | High-level similarity of dentitions in carnivorans and rodents[END_REF][START_REF] Boyer | Relief index of second mandibular molars is a correlate of diet among prosimian primates and other euarchontan mammals[END_REF][START_REF] Bunn | Comparing Dirichlet normal surface energy of tooth crowns, a new technique of molar shape quantification for dietary inference, with previous methods in isolation and in combination[END_REF][START_REF] Smits | Functional constraints on tooth morphology in carnivorous mammals[END_REF]. On the other hand, other approaches to study tooth morphology have arisen with the development of geometric morphometric techniques, where shape is captured through the use of landmark coordinates (either two-or three-dimensional) and analyzed as a whole, taking into account several shape changes instead of a few measurements [START_REF] Vizcaíno | Forma y función en paleobiología de vertebrados[END_REF]. For example, some authors have digitized the molars with the aim of assessing the relationships between molar shape and diet: elliptic Fourier analysis has been applied in rodents [START_REF] Cano | Dietary ecology of Murinae (Muridae, Rodentia): a geometric morphometric approach[END_REF]; 2D geometric morphometrics has been used in rodents [START_REF] Caumul | Phylogenetic and environmental components of morphological variation: skull, mandible, and molar shape in marmots (Marmota, Rodentia)[END_REF], primates [START_REF] White | Geometric morphometric investigation of molar shape diversity in modern lemurs and lorises[END_REF], rhinos [START_REF] Piras | Functional and phylogenetic constraints in Rhinocerotinae craniodental morphology[END_REF], and in didelphid marsupials [START_REF] Chemisquy | Evolution of molar shape in didelphid marsupials (Marsupialia: Didelphidae): Analysis of the influence of ecological factors and phylogenetic legacy[END_REF][START_REF] Magnus | Phylogeny explains better than ecology or body size the variation of the first lower molar in didelphid marsupials[END_REF]; and 3D geometric morphometrics has been used in primates [START_REF] Cooke | Paleodiet of extinct platyrrhines with emphasis on the Caribbean forms: three-dimensional geometric morphometrics of mandibular second molars[END_REF][START_REF] Singleton | Allometric and metameric shape variation in Pan mandibular molars: a digital morphometric analysis[END_REF] and in carnivorous species [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF].

The strong form-function relationship of the dentition of living mammals has allowed paleontologists to make inferences of diet in fossil species (e.g., [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Goin | Orientación de las crestas cortantes en el molar tribosfénico. Sus implicancias funcionales en didelfoideos (Marsupialia) fósiles y vivientes[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF]Zimicz, 2014a;Croft et al., 2018b). Within a historical framework, diet reconstructions are crucial for constructing trophic webs, examining evolutionary changes in lineages, and inferring ancient climates and habitats [START_REF] Pascual | Evolving climates and mammal faunas in Cenozoic South America[END_REF][START_REF] Van Valkenburgh | Cope's Rule, Hypercarnivory, and extinction in North American canids[END_REF][START_REF] Dalerum | Resolving temporal variation in vertebrate diets using naturally occurring stable isotopes[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF][START_REF] Echarri | Mandible morphology and diet of the South American extinct metatherian predators (Mammalia, Metatheria, Sparassodonta)[END_REF]. However, in a recent study, we demonstrated the need to take certain precautions before making inferences, because the morphology of the molar not only correlates with diet but also is influenced by the phylogenetic position of each taxa [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF]. In any case, the use of different techniques to analyze molar morphology generates the following question: are all methodologies utilized to quantify dental morphology equally reliable? Usually, inferences of diet in South American predatory mammals are tackled with traditional morphometric analyses, using measurements from the lower molars such as the relative grinding area index or the angle of the paracristid to the tooth row (see Materials and Methods for further details; e.g., [START_REF] Van Valkenburgh | Iterative evolution of hypercarnivory in canids (Mammalia: Carnivora): evolutionary interactions among sympatric predators[END_REF][START_REF] Goin | Orientación de las crestas cortantes en el molar tribosfénico. Sus implicancias funcionales en didelfoideos (Marsupialia) fósiles y vivientes[END_REF]Zimicz, 2014b;[START_REF] Prevosti | Evolution of South American mammalian predators during the Cenozoic: paleobiogeographic and paleoenvironmental contingencies[END_REF]. However, morphometric ratios or angles only represent some aspects of the shape, and the geometric and spatial relationships among the measurements are not considered [START_REF] Bookstein | Morphometric tools for landmark data: geometry and biology[END_REF][START_REF] Rohlf | A revolution in morphometrics[END_REF][START_REF] Adams | Geometric morphometrics: ten years of progress following the 'revolution[END_REF][START_REF] Zelditch | Geometric morphometrics for biologists[END_REF][START_REF] Slice | Geometric morphometrics[END_REF][START_REF] Mitteroecker | Advances in geometric morphometrics[END_REF]. In other words, shape can be better approximated by landmark coordinates than by linear measurements, as has been demonstrated in numerous different test cases (e.g., [START_REF] Fabre | Linear versus geometric morphometric approaches for the analysis of head shape dimorphism in lizards[END_REF][START_REF] Schmieder | Bat species comparisons based on external morphology: a test of traditional versus geometric morphometric approaches[END_REF][START_REF] Lallensack | Geometric morphometric analysis of intratrackway variability: a case study on theropod and ornithopod dinosaur trackways from Münchehagen (Lower Cretaceous, Germany)[END_REF]. Subsequently, new approaches using 2D geometric morphometrics have been used to study the mandibles of South American predators [START_REF] Meloro | The shape of the mandibular corpus in large fissiped carnivores: allometry, function and phylogeny[END_REF]Prevosti et al., 2012a;[START_REF] Echarri | Mandible morphology and diet of the South American extinct metatherian predators (Mammalia, Metatheria, Sparassodonta)[END_REF]. However, it is important to note that some problems have arisen with the use of 2D techniques for studying three-dimensional structures, such as the photographic distortion and the difficulty to capture shape variation as a whole [START_REF] Álvarez | Two-versus three-dimensional morphometric approaches in macroevolution: insight from the mandible of Caviomorph Rodents[END_REF][START_REF] Collins | Does my posterior look big in this? The effect of photographic distortion on morphometric analyses[END_REF]. This led us to hypothesize that 3D geometric morphometrics may turn out to be a more powerful predictive tool than other previously used techniques based on linear measurements, as it represents different aspects of the molar at the same time.

MATERIALS AND METHODS

Samples and landmarks

The results of this study are based on the dataset of [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF], which includes digitalizations of the first lower molar (m1) of 213 specimens corresponding to 67 extant species of Carnivora, and the fourth lower molars (m4) of 14 specimens corresponding to 4 extant species of Marsupialia, including both Didelphimorphia and Dasyuromorphia (Supplementary Online Information 1). The carnivoran m1 and carnivorous marsupial m4 are considered to be functionally analogous teeth following [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF]. The specimens belonging to the insectivore category from the parent dataset were excluded due to small sample size (only three species: Dasyurus viverrinus, Galerella sanguínea, and Otocyon megalotis). The classification of dietary categories in this study follows [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF]: Hypercarnivores -feed primarily on other vertebrates; Mesocarnivores -feed mainly on other vertebrates, but also plants and invertebrates; Omnivores -meat, plants, and invertebrates represent a similar proportion of the diet; Herbivores -feed mostly on plant material; Piscivores -feed mostly on fish.

The digitalization comprises nine three-dimensional landmarks (ldk): paraconid (ldk 1), carnassial notch in the paracristid (ldk 2), protoconid (ldk 3), metaconid (ldk 4), hypoconid (ldk 6), entoconid (ldk 7), and the mesial and distal edges of trigonid and talonid (ldk 5, 8 and 9) (Fig. 1). We also recorded ten semi-landmarks to delimit the base of the crown (Fig. 1). For information regarding landmark and semilandmark treatment, see [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF].

Morphometric indices

In the literature, several indices have been used to analyze the molar morphology of carnivorous mammals. The indices are relationships between measurements, such as the shear ratio (Strait, 1993a(Strait, , 1993b;;[START_REF] Hogue | Molar crests and body mass as dietary indicators in marsupials[END_REF] and the relative lower grinding area (RGA; [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF]. RGA is measured as the square root of the area of the m1 talonid divided by the relative blade length (BL). The area was estimated as the product of maximum width (WT) and length (LT) of the talonid of m1, and the BL was estimated as the length of the m1 trigonid.

We use the index as defined by [START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF], modified from [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF], which has two advantages: first, it can be applied in fossils that only have the carnassial preserved; and second, the RGA value can be compared in both Carnivora and Metatheria. Other indices include morphometric angles, such as those that describe the relationship between the paracristid and the mesiodistal axis (lower angle, AI; [START_REF] Goin | Orientación de las crestas cortantes en el molar tribosfénico. Sus implicancias funcionales en didelfoideos (Marsupialia) fósiles y vivientes[END_REF] or the relationship between the height of the protoconid and the length of the talonid (angle α; Crusafont-Pairó andTruyols-Santonja, 1953, 1956;[START_REF] Wesley-Hunt | The morphological diversification of carnivores in North America[END_REF][START_REF] Meloro | Cats and dogs down the tree: the tempo and mode of evolution in the lower carnassial of fossil and living Carnivora[END_REF].

Using the configuration of landmarks mentioned above, we calculated the following morphometric indices with Rhinoceros 3D V5.0 software: angle α, AI, and RGA. The angle α was calculated using ldks 3, 8 and 9. For AI, ldk 1, 3, 8 and 9 were used. RGA was calculated using ldks 5, 8 and 9 and the two semilandmarks that define the maximum width of the talonid (Fig. 1).

We also calculated alternative RGAs taking into account a more accurate measurement of the blade length (BL', i.e., distance between the paraconid, ldk 1, and the protoconid, ldk 3) and a more accurate measurement of the area of the m1 talonid (i.e., polygon area (PA) formed by all semilandmarks of talonid) (Fig. 2). However, neither using the BL' nor using the PA led to better discriminating functions; consequently, the detailed results are not reported here.

Statistical analyses

We used the scores of between-group PCA (bg-PCA) conducted in [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF] for the new analyses (see below). The bg-PCA can be understood as a PCA in two steps: first, we made a PCA with the average shape of each dietary category, and then we plotted the entire dataset on these axes [START_REF] Mitteroecker | Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics[END_REF][START_REF] Seetah | Can morphospace shed light on cave bear spatial-temporal variation? Population dynamics of Ursus spelaeus from Romualdova pećina and Vindija, (Croatia)[END_REF]. We used the axes of bg-PCA as representatives of shape, which brings about two advantages. First, when the axes of the PCA are constructed, the variance of the whole sample is maximized, while in the bg-PCA, the variance of the dietary categories is maximized but without distorting the geometry of the multivariate space [START_REF] Klingenberg | Distances and directions in multidimensional shape spaces: implications for morphometric applications[END_REF][START_REF] Mitteroecker | Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics[END_REF]. Second, fewer axes are obtained in bg-PCA (the number of categories minus one, in this case four) than in PCA, which avoids problems with the quantity of variables when using PC axes in discriminant analyses [START_REF] Kovarovic | Discriminant function analyses in archaeology: Are classification rates too good to be true?[END_REF] but without discarding any morphological information.

In order to study the behavior of morphometric indices, we performed three analyses. First, we evaluated the covariation between indices to test independence in the case of using them as variables of the same analysis. We performed a Spearman correlation since the Shapiro-Wilk test demonstrated that the indices are not normally distributed. The Shapiro-Wilk test, the Spearman correlation and regression analyses were performed using the stats package for the software R 3.5.1 [START_REF] Core | R: a language and environment for statistical computing[END_REF].

Second, we conducted a multivariate regression to quantify how much of the change in the shape (Procrustes coordinates) could be explained by the different indices. Finally, we performed a simple linear regression between the various indices and the axes of the bg-PCA. Following the regressions, a permutation test was performed to evaluate the complete independence between the dependent and independent variables. Regression analysis and the permutation test were performed using the software MorphoJ 1.06b [START_REF] Klingenberg | MorphoJ: an integrated software package for Geometric Morphometrics[END_REF].

Although a PCA or bg-PCA would allow us to see the graphical distribution of the diet taking into account the values of the different predictor variables (angle α, AI, RGA, or landmark configuration), it is important to quantify the relative performance of each predictor variable to infer diets. That is the reason why we conducted several linear discriminant analyses (LDA): some with the scores of the bg-PCA (to analyze the scope of geometric morphometrics) and others with the morphometric indices, together and separately (to analyze the scope of traditional morphometrics). Given the known problems of performing a LDA with geometric morphometrics data [START_REF] Kemsley | Discriminant analysis of high-dimensional data: a comparison of principal components analysis and partial least squares data reduction methods[END_REF][START_REF] Sheets | Comparison of geometric morphometric outline methods in the discrimination of age-related differences in feather shape[END_REF], and to avoid a bias due to the analysis used, we also carried out two learning methods for classification: quadratic discriminant analyses (QDA) and random forest analyses (RF). The QDA is very similar to the LDA but does not assume identical covariance for each class and produces a quadratic boundary between the classes [START_REF] Venables | Modern applied statistics with S, 4th ed[END_REF][START_REF] Dixon | Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization, and support vector machines, as dependent on[END_REF]. The RF is a nonmetric method based on bootstrap resampling techniques of decision trees, which have made it a powerful tool with greater precision than neural networks, classification trees, linear and quadratic DAs, and k-nearest neighbors [START_REF] Breiman | Random Forest[END_REF][START_REF] Maroco | Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests[END_REF].

However, the regular RF is constructed to minimize the overall error rate, so it tends to be biased toward the majority class [START_REF] Chen | Using random forest to learn imbalanced data[END_REF][START_REF] Zhu | Applying a weighted random forests method to extract karst sinkholes from LiDAR data[END_REF]. Given that our dataset is unbalanced (i.e., there are six herbivores, 105 hypercarnivores, 34 mesocarnivores, 71 omnivores, and 13 piscivores), we proceeded to perform a weighted random forest (WRF), as proposed by [START_REF] Chen | Using random forest to learn imbalanced data[END_REF], which avoids the bias towards the most numerous classes. To evaluate the performance of classification methods based on different predictors, we compared the percentage of correct posterior reclassification (PCPR) and the error rate (% Error) calculated from the confusion matrix. This is obtained in an innate way by the bootstrap resampling in the WRF and from the crossvalidation procedure in both LDA and QDA [START_REF] Breiman | Random Forest[END_REF][START_REF] Venables | Modern applied statistics with S, 4th ed[END_REF][START_REF] Kovarovic | Discriminant function analyses in archaeology: Are classification rates too good to be true?[END_REF]. PCPR was calculated globally and for each category; it is understood as the number of animals correctly reclassified in a dietary category divided by the total of animals belonging to that category. % Error was calculated for each category as the number of animals wrongly reclassified in a dietary category divided by the total of animals reclassified in that category. Additionally, as mentioned above, molar morphology has a significant phylogenetic signal, so that within each family, the degree of morphological variation is variable [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF]. For that reason, we repeated the classification methods only with the species of the Canidae subgroup, which is also a group where morphometric indices are widely used. LDA, QDA and WRF were performed using the MASS and randomForest packages [START_REF] Liaw | Classification and regression by randomForest[END_REF][START_REF] Venables | Modern applied statistics with S, 4th ed[END_REF]) in R 3.5.1 [START_REF] Core | R: a language and environment for statistical computing[END_REF].

Intraspecific variation

When reviewing previous works, we noted that a few authors have considered intraspecific variation, whereas the majority have not. To compare criteria, we analyzed RGA values calculated from unpublished measurements taken by calipers (Prevosti, 2006) and compared using the average RGA value per species to using RGA values for each specimen separately.

RESULTS

The morphometric indices showed high and significant correlations: RGA vs. angle α (ρ = -0.87, p-value < 0.0001), RGA vs. AI (ρ = 0.88, p-value < 0.0001) and AI vs. angle α (ρ = -0.77, p-value < 0.0001). The regressions against the Procrustes coordinates showed that the indices have a low correlation with shape variation: RGA explained 53.30% of the change in the shape (p-value < 0.0001), AI explained 37.37% (p-value < 0.0001) and angle α explained 49.42% (p-value < 0.0001). By contrast, bg-PC1 (the axis that has the highest proportion of shape change in the bg-PCA) explained the shape variation better than any of the morphometric indices (76.59% percent of variance). Finally, when we analyzed the covariation between the bg-PCs and the morphometric indices, we observed that a large portion of variation explained by bg-PC1 could also be explained by the morphometric indices (percentage of covariance greater than 60%; Tab. 1), whereas there was a low covariation between the morphometric indices and the other axes of bg-PCA (percentage of covariance less than 20%; Tab. 1).

The three classification methods (LDA, QDA and WRF) returned a similar pattern of global PCPR (Tab. 2). The only differences observed were in the performance of the indices when comparing them to each other. However, in all cases when using the scores of the bg-PCA, the diet of a species was better reclassified than when using morphometric indices. Hereafter, we describe the results of WRF to transmit the idea more clearly, which are shown in Table 2 together with the results of the other two analyses. Using all the axes of the bg-PCA, the WRF correctly reclassified 89.96% of the specimens; using only the first axis, the PCPR was 74.67% (Tab. 2). On the other hand, none of the analyses using individual morphometric indices exceeded 66 PCPR (Tab. 2). Even using all of the indices together, the PCPR was only 75.98% (Tab. 2).

It is interesting to note that the model with the scores of the bg-PCA had an excellent reclassification (higher PCPR and lower % Error) in the hypercarnivorous and piscivorous categories, and a good reclassification (higher PCRP and average % Error) in the mesocarnivorous and omnivorous categories, while it had a bad reclassification (low PCPR and average % Error) in the herbivorous category (Supplementary Online Information 2). Otherwise, the models with the morphometric indices tended to reclassify the different dietary categories with higher % Error (regardless of the value of PCPR) than the model with bg-PCs (Supplementary Online Information 2).

Additionally, the indices tend to have a bias to hypercarnivores since it is usually the category with the highest PCPR and the lowest % Error (Supplementary Online Information 2).

When we subdivided the dataset and scrutinized only the Canidae, the results did not change substantially. The scores of the bg-PCA still better reclassified the diet of species than the morphometric indices (Tab. 3 and Supplementary Online Information 3). The discriminant function obtained with the scores of the bg-PCA had a 92.31 PCPR, while that obtained with the indices the PCRP did not exceed 68% (Tab. 3).

Finally, in the intraspecific variation test, we observed that when using the average value of RGA, the separation between dietary categories was clearer than when using all the values of each specimen (Supplementary Online Information 4). Using the average values, under this example, the diets could be classified with a series of logical rules: a taxon can be considered hypercarnivorous when the RGA index is lower than 0.45; mesocarnivorous when the RGA index range from 0.46 to at least 0.47; omnivorous when the RGA index ranges from 0.60 (perhaps lower) to 0.68; and insectivorous when the RGA index is larger than 1. However, when the index is between 0.48 and 0.59, the classification is ambiguous, and the taxon may be omnivorous or mesocarnivorous. On the other hand, if intraspecific variation is considered, the superposition zone is much larger. If the value of RGA is lower than 0.40, the taxon is hypercarnivorous; between 0.40 and 0.42, it may be hypercarnivorous or mesocarnivorous; between 0.43 and 0.48, it may be hypercarnivorous, mesocarnivorous or omnivorous; between 0.48 and 0.69, it may be mesocarnivorous or omnivorous; and larger than 1, it is insectivorous (Supplementary Online Information 4).

DISCUSSION

Because of the hardness of dental tissues, teeth are the mammalian body parts most likely to be preserved in the fossil record and, consequently, the elements most frequently found and studied [START_REF] Carlson | Skeletal biomineralization: patterns, processes and evolutionary trends[END_REF][START_REF] Benton | Introduction to paleobiology and the fossil record[END_REF][START_REF] Ungar | Mammal teeth: origin, evolution, and diversity[END_REF].

Combined with the importance of the teeth in the acquisition and processing of food, this makes tooth morphology an important source of information for understanding and inferring the ecological habits of fossil species (e.g., [START_REF] Van Valen | Evolution of dental growth and adaptation in mammalian carnivores[END_REF][START_REF] Kay | The functional adaptions of primate molar teeth[END_REF][START_REF] Marshall | Evolution of the Borhyaenidae, extinct South American predaceous marsupials[END_REF][START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF][START_REF] Solé | Evolution of the hypercarnivorous dentition in mammals (Metatheria, Eutheria) and its bearing on the development of tribosphenic molars[END_REF][START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF]. Bearing that in mind, there have been different inferences of diet for the same species in the literature, making it necessary to carry out studies that include diverse proxies (e.g., [START_REF] Soibelzon | South American giant short-faced bear (Arctotherium angustidens) diet: evidence from pathology, morphology, stable isotopes, and biomechanics[END_REF][START_REF] Pineda-Munoz | Inferring diet from dental morphology in terrestrial mammals[END_REF] or to compare the power of different proxies. Our results indicate that 3D geometric morphometrics is a very powerful tool to study molar morphology and is better than traditional morphometrics when it comes to predicting feeding habits. The discriminant function obtained (regardless of the chosen classification method) displays a greater predictive power than that obtained from the indices (Tabs. 2 and 3). Since the PCPR tends to increase when adding explanatory variables, we performed the classification methods with the first bg-PC to compare against isolated indices, and with the first three bg-PCs against the combined indices. All comparisons showed that geometric morphometric data are better for classifying diets than classical morphometric indices (Tabs. 2 and 3). Although the bg-PC1 explained more shape variation than any of the indices, the variation within this first axis could be explained partially using the indices (e.g., specimens at the positive end of bg-PC1 have a molar with the paracristid parallel to the mesiodistal axis and the talonid is reduced or absent; [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF].

It is noteworthy that the bg-PCA, based on geometric data, provided a diffused low degree of separation between diet categories, with different degrees of overlap among them. Nevertheless, as we discussed in a previous work [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF], this might be in part related to the intrinsic definition of diets and the disadvantage of the discretization of a continuous variable such as diet into classes, whose chosen limits are questionable ( [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Van Valkenburgh | Cranial and dental adaptations to predation in canids[END_REF]Prevosti et al., 2012b;[START_REF] Echarri | Mandible morphology and diet of the South American extinct metatherian predators (Mammalia, Metatheria, Sparassodonta)[END_REF]. This is why this problem is not intrinsically related to geometric morphometric, and in studies that include a broad taxonomic sampling (several species across all Carnivora), it appears difficult to choose a unique value of RGA (or equivalent index) to separate each dietary categories ( [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Friscia | An ecomorphological analysis of extant small carnivorans[END_REF][START_REF] Zimicz | Ecomorfología de los marsupiales paleógenos de América del Sur[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF]. Some authors have chosen to use several indices to define morphospaces that better separate the categories ( [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Friscia | An ecomorphological analysis of extant small carnivorans[END_REF], although these ratios have high correlation values between them (as also calculated here). This is due to the fact that the indices are calculated with anatomical points or landmarks in common. This issue is easily circumvented with geometric morphometrics because it avoids any overrepresentation of data by working directly with the landmarks coordinates.

As we mentioned in the introduction, to improve the accuracy of dietary inferences from dental morphology it is necessary to take into account the phylogenetic framework [START_REF] Tarquini | Evolution of the carnassial in living mammalian carnivores (Carnivora, Didelphimorphia, Dasyuromorphia): diet, phylogeny, and allometry[END_REF]. This is the reason why we restricted our dataset to a less inclusive clade, the Canidae (Tab. 3 and Supplementary Online Information 3).

Although there are differences in the performance of the indices when comparing them to each other (e.g., RF and LDA indicated that the AI is the best diet estimator, while QDA indicated that the RGA is the best one), they all agreed that the scores of the bg-PCA better reclassified the diet of species. When examining where the differences in the reclassification were, the confusion matrix showed that, whatever the classification method, the morphometric indices principally struggled to discern correctly hypercarnivores form mesocarnivores. [START_REF] Van Valkenburgh | Iterative evolution of hypercarnivory in canids (Mammalia: Carnivora): evolutionary interactions among sympatric predators[END_REF] pointed out that although the molars of hypercarnivorous canids (Canis lupus, Cuon alpinus, Lycaon pictus, and Speothos venaticus) are specialized, they do not reach the cat-like extreme condition seen in felids and, to a lesser degree, in hyaenids. For this reason, when we calculated the indices, hypercarnivores and mesocarnivores overlapped and were reclassified erroneously (Tab. 4). This problem was not mentioned by previous authors, since they worked with the average of the indices (see below).

When reviewing previous works, we also noted that another criterion that differs in the methodologies is the use of the average value of the index per species versus the use of the observed variation (i.e., including the value of each specimen separately). In this work, we considered all specimens individually in order to properly account for intraspecific morphological variation. [START_REF] Goin | Orientación de las crestas cortantes en el molar tribosfénico. Sus implicancias funcionales en didelfoideos (Marsupialia) fósiles y vivientes[END_REF] calculated a series of basic statistics (such as mean, variance, standard deviation, etc.) to evaluate intraspecific morphological variation in the molars of didelphids, whereas in the majority of the other works ( [START_REF] Valkenburgh | Carnivore dental adaptations and diet: a study of trophic diversity within guilds[END_REF][START_REF] Prevosti | The evolution of the Cenozoic terrestrial mammalian predator guild in South America: competition or replacement?[END_REF], the authors only used the average value, without specifying exactly how many specimens were measured for each species.

To compare criteria, we analyzed the indices calculated from the landmarks and others from unpublished measurements taken by calipers (Prevosti, 2006) and discovered that if the maximum and minimum values of each species are considered, the ranges of overlap between the dietary categories used increases (Supplementary Online Information 4). In fact, it is possible that the use of a central tendency statistic minimizes the real overlap that exists between categories. However, this problem persists when there are few specimens per species (which is commonly the case in fossil taxa), since the average value of the specimens examined is not likely to be representative of the actual average value for the species, which in turn is likely to lead to misinterpretations in the ecology of the fossil species analyzed.

CONCLUSION

This work quantitatively evaluated the power of different proxies to describe the molar morphology of extant carnivore mammals and how it relates to diet. Our results demonstrated that 3D geometric morphometrics is better than traditional morphometrics to predict feeding habits, as using landmarks provides more information than working with isolated measurements or angles of teeth. Moreover, we discussed the importance of considering the intraspecific variation. As the ultimate purpose of these analyses is to infer ecological characteristics in fossils, misinterpretations in the ecology of the fossil specimens can be avoided when the variation of extant organisms is considered.

A better understanding of the methodology used to infer diets in fossils would allow a better comprehension of the ecology of these animals. Having more precise classifications about different ecological traits of the South American carnivorous mammals would allow us to test extinction hypothesis of some carnivore guilds with greater assurance.

3 -

 3 Centre de Recherche en Paléontologie -Paris (CR2P -CNRS, MNHN, Sorbonne Université). 8 rue Buffon CP38 (75005), Paris, France. sandrine.ladeveze@mnhn.fr 31 pages (text+references); 2 figures; 4 tables; 4 supplementary online information Proposed header: TARQUINI ET AL.: MORPHOMETRIC METHODS TO INFER DIET * Corresponding author: SERGIO D. TARQUINI ABSTRACT. Molar morphology is one of the most used proxies for paleoecological inferences in mammals. Since the 19th century, several authors associated dental morphological traits with the diet of an animal by means of qualitative and descriptive analyses. More recently, different studies of tooth function have associated various

FIGURE CAPTIONS Figure 1 .( 1 )

 CAPTIONS11 FIGURE CAPTIONS Figure 1. Diagram of landmarks and measurements taken on specimens, in labial view

Figure 2 .

 2 Figure 2. Alternative measurements taken on specimens to calculate alternative RGAs.

TABLE 1 -

 1 Results of the regression analyses between morphometric indices and bgPCs

		RGA		AI		Angle α
		%	P	%	P	%	P
	bgPC1	94.34 < 0.0001	64.97	< 0.0001 84.34	< 0.0001
	bgPC2	06.92 < 0.0001	02.85	0.0104	18.29	< 0.0001
	bgPC3	12.01 < 0.0001	04.31	0.0017	11.70	< 0.0001
	bgPC4	09.21 < 0.0001	00.26	0.4565	10.84	< 0.0001
	%, percentage of variance explained; P, probability for each analysis

TABLE 2 -

 2 Summary of Confusion matrices of the different Discriminant Analyses displaying the global PCPR (Percentage of Correct Posterior

	Reclassification)
		Lineal Discriminant Analysis	Quadratic Discriminant Analysis	Weighted Random Forests
	All (4) bg-PCs	78.17%	79.91%	89.96%
	3 bg-PCs	69.43%	72.49%	85.59%
	All (3) indices	50.22%	60.26%	75.98%
	bg-PC1	56.33%	57.64%	74.67%
	RGA	50.66%	50.66%	62.01%
	AI	46.72%	53.28%	65.07%
	Angle α	51.09%	47.16%	50.66%

TABLE 3 -

 3 Summary of Confusion matrices of the different Discriminant Analyses within Canidae displaying the global PCPR (Percentage of Correct

	Posterior Reclassification)
		Lineal Discriminant Analysis	Quadratic Discriminant Analysis	Weighted Random Forests
	All (2) bg-PCs	90.38%	90.38%	92.31%
	All (3) indices	71.15%	65.38%	69.23%
	bg-PC1	86.54%	84.62%	75.00%
	RGA	50.00%	65.38%	61.54%
	AI	57.69%	57.69%	67.31%
	Angle α	36.54%	32.69%	42.31%

TABLE 4 -

 4 Summary of dietary classification obtained from Weighted Random Forests within hypercarnivorous and mesocarnivorous Canidae

	Collection number	Species	Diet	All (3) indices	All (2) bg-PCs
	MLP-MA 1035	Canis aureus	Mesocarnivore	Omnivore	Mesocarnivore
	MACN-MA 25123	Canis latrans	Mesocarnivore Hypercarnivore Mesocarnivore
	MLP-PV S/N	Canis latrans	Mesocarnivore	Omnivore	Mesocarnivore
	MLP-MA 1027	Canis latrans	Mesocarnivore Mesocarnivore Mesocarnivore
	MACN-MA 23.15	Canis lupus	Hypercarnivore Mesocarnivore Hypercarnivore
	MACN-MA 4256	Canis lupus	Hypercarnivore Mesocarnivore Mesocarnivore
	MLP-MA 30	Canis lupus	Hypercarnivore Mesocarnivore Hypercarnivore
	AMNH(M) S/N	Cuon alpinus	Hypercarnivore Hypercarnivore Hypercarnivore
	MACN-MA 15109	Lycalopex culpeus	Mesocarnivore Mesocarnivore Mesocarnivore
	MACN-MA 20813	Lycalopex culpeus	Mesocarnivore Mesocarnivore Mesocarnivore
	MACN-MA 23719	Lycalopex culpeus	Mesocarnivore Mesocarnivore Mesocarnivore
	MACN-MA 23915	Lycalopex culpeus	Mesocarnivore Mesocarnivore Mesocarnivore
	MACN-MA 31.59	Lycalopex culpeus	Mesocarnivore Mesocarnivore Mesocarnivore
	MACN-MA 38.39	Lycalopex culpeus	Mesocarnivore Hypercarnivore Mesocarnivore
	AMNH(M) S/N	Lycaon pictus	Hypercarnivore Mesocarnivore Hypercarnivore
	MACN-MA 38249	Lycaon pictus	Hypercarnivore Mesocarnivore Hypercarnivore
	AMNH(M) S/N	Speothos venaticus Hypercarnivore Hypercarnivore Hypercarnivore
	MACN-MA 16510	Speothos venaticus Hypercarnivore Mesocarnivore Hypercarnivore
	MACN-MA 50.67	Speothos venaticus Hypercarnivore Mesocarnivore Hypercarnivore
	MACN-MA 35203	Vulpes lagopus	Mesocarnivore Mesocarnivore Hypercarnivore
	MACN-MA 4.1	Vulpes lagopus	Mesocarnivore Hypercarnivore Mesocarnivore
	MACN-MA 25149	Vulpes vulpes	Mesocarnivore Mesocarnivore	Omnivore
	MLP-MA 744	Vulpes vulpes	Mesocarnivore	Omnivore	Mesocarnivore
	MLP-PV S/N	Vulpes vulpes	Mesocarnivore Hypercarnivore Mesocarnivore

The scope of traditional and geometric morphometrics for inferences of diet in carnivorous fossil mammals Sergio D. Tarquini 1 *, M. Amelia Chemisquy 1,2 , Sandrine Ladevèze 3 , and Francisco J. Prevosti 1,2 1

  -Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja (CRILAR -Provincia de La Rioja, UNLaR, SEGEMAR, UNCa, CONICET). Entre Ríos y Mendoza s/n (5301), Anillaco, Argentina. starquini92@gmail.com; amelych80@gmail.com; protocyon@hotmail.com 2-Departamento de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de la Rioja (UNLaR). Av. Luis M. de la Fuente s/n (5300), La Rioja, Argentina. Studied species and specimens and their dietary classification. Confusion matrices of the different Discriminant Analyses using the scores of the classic indices and the bg-PCs. PCPR = Percentage of Correct Posterior Reclassification (global percentage shown in bold). Confusion matrices of the Discriminant Analyses using the scores of the classic indices and the bg-PCs within Canidae. PCPR = Percentage of Correct Posterior Reclassification (global percentage shown in bold).

	Species Felinae Feliformia Eutheria Carnivora Felidae Acinonyx jubatus Felis catus Herpailurus yagouaroundi Leopardus colocolo Leopardus geoffroyi Species Leopardus geoffroyi Leopardus guigna Leopardus pardalis Leopardus tigrinus Leopardus wiedii Lynx rufus Species Puma concolor Pantherinae Panthera leo Panthera onca Panthera pardus Panthera tigris Species Panthera tigris Panthera uncia Herpestidae Herpestinae Herpestes ichneumon Hyaenidae Hyaeninae Crocuta crocuta Hyaena hyaena Viverridae Genettinae Genetta genetta Species Genetta genetta Caniformia Canidae Canis aureus Canis latrans Canis lupus Cerdocyon thous Chrysocyon brachyurus Species Cuon alpinus Lycalopex culpaeus Lycalopex griseus Lycalopex gymnocercus Lycalopex vetulus Lycaon pictus Species Galictis cuja Galictis vittata Ictionyx libyca Species Guloninae Eira barbara Species Neovison vison Taxidiinae Taxidea taxus Procyonidae Species Metatheria Dasyuromorphia Dasyuridae Dasyurinae Dasyurus geoffroii SUPPLEMENTARY ONLINE INFORMATION 2. Linear Discriminant Analysis Collection number Digitized molar MACN-MA 49.36 m1 MACN-MA 23688 m1 MACN-MA 23700 m1 MACN-MA 23701 m1 MACN-MA 23702 m1 MACN-MA 17254 m1 MACN-MA 23692 m1 MACN-MA 25772 m1 MACN-MA 33.4 m1 MACN-MA 50104 m1 MACN-MA 50555 m1 MACN-MA 16489 m1 MACN-MA 22928 m1 MACN-MA 22934 m1 MACN-MA 23176 m1 MACN-MA 30103 m1 MACN-MA 15425 m1 MACN-MA 22022 m1 Collection number Digitized molar MACN-MA 24214 m1 MACN-MA 23329 m1 MACN-MA 34337 m1 MACN-MA 39209 m1 MLP-MA 1297 m1 MACN-MA 13464 m1 MACN-MA 13465 m1 MACN-MA 50.98 m1 MACN-MA 50101 m1 MACN-MA 50540 m1 MACN-MA 51132 m1 MACN-MA 23709 m1 MACN-MA 51121 m1 MACN-MA 52.57 m1 MACN-MA 23694 m1 MACN-MA 24899 m1 MACN-MA 50.94 m1 MACN-MA 50.95 m1 MACN-MA 50.97 m1 MACN-MA 51137 m1 MACN-MA 25.118 m1 MLP-PV S/N m1 Collection number Digitized molar MACN-MA 13339 m1 MACN-MA 20629 m1 MACN-MA 30250 m1 MACN-MA 32.80 m1 MACN-MA 36614 m1 MACN-MA 49295 m1 MACN-MA 23.1 m1 MACN-MA 25158 m1 MACN-MA 29904 m1 MACN-MA 4254 m1 MACN-MA 4330 m1 MACN-MA 4337 m1 MACN-MA 21622 m1 MACN-MA 25.66 m1 MACN-MA 25769 m1 MACN-MA 4301 m1 MACN-MA 7.8 m1 MACN-MA 8.43 m1 MLP-MA 1040 m1 MACN-MA 25.54 m1 MACN-MA 26.83 m1 MLP-MA 1048 m1 MLP-MA 1051 m1 Collection number Digitized molar MLP-MA 112 m1 MLP-MA 11-IV-48-1 m1 MACN-MA 8.23 m1 MLP-MA 1029 m1 MLP-MA 1323 m1 MACN-MA 24525 m1 MACN-MA 33277 m1 MACN-MA 15.28 m1 MACN-MA 25103 m1 MACN-MA 4.6 m1 MLP-MA 1039 m1 MACN-MA 21104 m1 MACN-MA 21105 m1 Collection number Digitized molar MACN-MA 21106 m1 MACN-MA 21107 m1 MACN-MA 23.43 m1 MLP-MA 1035 m1 MACN-MA 25123 m1 MLP-MA 1027 m1 MLP-PV S/N m1 MACN-MA 23.15 m1 MACN-MA 4256 m1 MLP-MA 30 m1 MACN-MA 14322 m1 MACN-MA 14681 m1 MACN-MA 16189 m1 MACN-MA 20456 m1 MACN-MA 48.10 m1 MACN-MA 50.61 m1 MACN-MA 19146 m1 MACN-MA 23984 m1 MACN-MA 24043 m1 MACN-MA 24750 m1 MACN-MA 3.73 m1 MACN-MA 53.49 m1 Collection number Digitized molar AMNH(M) S/N m1 MACN-MA 15109 m1 MACN-MA 20813 m1 MACN-MA 23719 m1 MACN-MA 23915 m1 MACN-MA 31.59 m1 MACN-MA 38.39 m1 MACN-MA 15186 m1 MACN-MA 20205 m1 MACN-MA 20207 m1 MACN-MA 20278 m1 MACN-MA 20814 m1 MACN-MA 36480 m1 MACN-MA 14319 m1 MACN-MA 16041 m1 MACN-MA 16049 m1 MACN-MA 29.35 m1 MACN-MA 49134 m1 MACN-MA 49148 m1 MLP-PV S/N m1 AMNH(M) S/N m1 MACN-MA 38249 m1 Collection number Digitized molar MACN-MA 23291 m1 MACN-MA 23793 m1 MACN-MA 50.91 m1 MACN-MA 24.12 m1 MACN-MA 31214 m1 Collection number Digitized molar MACN-MA 20457 m1 MACN-MA 31.61 m1 MACN-MA 47202 m1 MACN-MA 50.90 m1 Collection number Digitized molar MACN-MA 19187 m1 MACN-MA 19188 m1 MLP-PV S/N m1 Collection number Digitized molar FMNH 35329 m4 Linear Discriminant Analysis All (4) bg-PCs Herbivore Hypercarnivore Mesocarnivore Omnivore Piscivore PCPR Sex Provenance -Africa --------♀ Bolivia ♀ Misiones, Argentina ♂ Salta, Argentina ♂ Mendoza, Argentina ♂ -♂ Misiones, Argentina -Neuquén, Argentina ♀ Salta, Argentina -Santa Cruz, Argentina -La Pampa, Argentina ♂ Neuquén, Argentina ♂ Córdoba, Argentina -Bs. As., Argentina Sex Provenance ♂ La Rioja, Argentina ♀ Neuquén, Argentina ♀ La Pampa, Argentina ♀ La Pampa, Argentina -Chubut, Argentina ♀ Bolivia ♀ Misiones, Argentina ♂ Misiones, Argentina ♀ Tucumán, Argentina ♂ Santa Fé, Argentina ♂ Bolivia ♂ Misiones, Argentina -Misiones, Argentina ♂ Misiones, Argentina -Misiones, Argentina ♀ Misiones, Argentina ♂ Bolivia -Misiones, Argentina ♂ Bolivia ♂ Bolivia ♂ Mexico ♂ Millbridge, Maine, USA Sex Provenance -San Luis, Argentina -La Pampa, Argentina ♂ Neuquén, Argentina ♀ San Luis, Argentina ♂ Santa Cruz, Argentina -Salta, Argentina -Africa -Africa ♀ Africa -Africa -Africa -Africa -Misiones, Argentina -Misiones, Argentina ♀ Tucumán, Argentina -South America -Bolivia -Bolivia -Africa -Sumatra -India ----Sex Provenance -India ---Asia -Africa -Africa -Africa -Africa -Africa -Africa -Africa ---Spain -Spain Sex Provenance -Spain -Spain -Africa ---North America -Africa -South Dakota, USA -Russia -Europe --♂ Santa Fe, Argentina ♂ Salta, Argentina ♂ Misiones, Argentina ♀ Salta, Argentina ♀ Salta, Argentina ♀ Bolivia ---Corrientes, Argentina -Corrientes, Argentina -Paraguay ----Sex Provenance -♂ Neuquén, Argentina ♂ Neuquén, Argentina ♂ Jujuy, Argentina ♀ Ecuador ----♂ Neuquén, Argentina ♂ Río Negro, Argentina ♂ Río Negro, Argentina ♀ Río Negro, Argentina ♀ Río Negro, Argentina ♀ Salta, Argentina ♂ La Pampa, Argentina ♂ Córdoba, Argentina ♂ La Pampa, Argentina ♀ La Pampa, Argentina ♀ Salta, Argentina ♀ La Pampa, Argentina -Brazil -Africa -Africa Sex Provenance ♂ Bs. As., Argentina -Bs. As., Argentina ♀ Bolivia -Africa Patquía, La Rioja, Sex Provenance ♂ Iguazú, Misiones, Argentina ♀ Bolivia ♀ Bolivia ♀ Misiones, Argentina Sex Provenance ♀ Bs. As., Argentina ♀ Chubut, Argentina --Sex Provenance ♀ Australia Herbivore 5 0 0 1 0 83.33 Hypercarnivore 0 93 6 5 1 88.57 Mesocarnivore 0 0 28 3 3 82.35 Omnivore 17 0 8 40 6 56.34 Quadratic Discriminant Analysis All (3) indices Herbivore Hypercarnivore Mesocarnivore Omnivore Piscivore PCPR Herbivore 3 0 0 3 0 50.00 Hypercarnivore 1 77 25 0 2 73.33 Mesocarnivore 0 3 28 2 1 82.36 Omnivore 10 0 34 20 7 28.17 Quadratic Discriminant Analysis All (4) bg-PCs Herbivore Hypercarnivore Mesocarnivore Omnivore Piscivore PCPR Herbivore 2 0 0 4 33.33 Hypercarnivore 0 93 8 4 88.57 Mesocarnivore 0 0 28 6 82.35 Omnivore 9 1 14 47 66.20 Weighted Random Forests All (3) indices Herbivore Hypercarnivore Mesocarnivore Omnivore Piscivore PCPR Herbivore 1 1 1 3 16.67 Hypercarnivore 0 93 9 3 88.57 Mesocarnivore 0 12 14 8 41.18 Omnivore 0 2 3 62 87.32 Weighted Random Forests All (4) bg-PCs Herbivore Hypercarnivore Mesocarnivore Omnivore Piscivore PCPR Herbivore 3 0 0 3 50.00 Hypercarnivore 0 97 2 6 92.38 Mesocarnivore 0 1 29 4 85.29 Omnivore 1 1 5 64 90.14 Quadratic Discriminant Analysis All (2) bg-PCs Hypercarnivore Mesocarnivore Omnivore PCPR Hypercarnivore 8 1 0 88.89 Mesocarnivore 1 14 0 93.33 Omnivore 0 3 25 89.29 Weighted Random Forests All (2) bg-PCs Hypercarnivore Mesocarnivore Omnivore PCPR Hypercarnivore 8 1 0 88.89 Mesocarnivore 0 14 1 93.33 Omnivore 0 2 26 92.86 SUPPLEMENTARY ONLINE INFORMATION 3. Linear Discriminant Analysis %Error 11.11 22.22 0.00 90.38 %Error 0.00 17.65 3.70 92.31 ♂ Argentina Lyncodon patagonicus MLP-MA 29-XII-00-17 m1 -Río Negro, Argentina Lutrinae Lontra felina MACN-MA 34602 m1 -Isla de los Estados, Argentina MACN-MA 13073 m1 ♂ Misiones, Argentina MACN-MA 24.13 m1 ♀ Paraguay MACN-MA 24662 m1 --MACN-MA 24665 m1 -America MACN-MA 24666 m1 ♀ Delta del Paraná, Argentina MACN-MA 25357 m1 ♀ Bs. As., Argentina MACN-MA 30234 m1 -Chaco, Argentina MACN-MA 30246 m1 -Chaco, Argentina Lontra longicaudi MACN-MA 5.11 m1 -Santa Fé, Argentina Lontra provocax MLP-MA 1282 m1 -Tierra del Fuego, Argentina MACN-MA 25783 m1 --Pteronura braziliensis MACN-MA 33155 m1 -Bolivia MACN-MA 50.92 m1 ♂ Salta, Argentina MACN-MA 52.38 m1 ♂ Ecuador Martes martes MACN-MA 4.50 m1 --Melinae MACN-MA 41227 m1 -Europe MACN-MA 4250 m1 -Southern Europe Meles meles MACN-MA 5.36 m1 -Europe Mustelinae Mustela eversmanni MACN-MA 35206 m1 -Russia Mustela nivalis MLP-MA 1018 m1 -Europe MACN-MA 20645 m1 -Spain MACN-MA 25148 m1 -Europe Mustela putorius MLP-MA 6-111-36-35 m1 -Spain Mustela sibirica MACN-MA 35207 m1 ♂ Russia MACN-MA 16327 m1 ♂ Chubut, Argentina MACN-MA 17825 m1 ♂ Bs. As., Argentina MACN-MA 17826 m1 ♂ Chubut, Argentina Neovison vison MACN-MA 19186 m1 ♀ Bs. As., Argentina MACN-MA 31.68 m1 -Ecuador MACN-MA 50.81 m1 ♂ Bolivia Bassaricyon alleni MACN-MA 50.82 m1 ♂ Bolivia MACN-MA 49402 m1 ♀ Bolivia MACN-MA 49415 m1 ♂ Misiones, Argentina MACN-MA 49457 m1 ♂ Misiones, Argentina MACN-MA 50.77 m1 ♀ Misiones, Argentina Nasua nasua MACN-MA 50535 m1 ♂ Misiones, Argentina MACN-MA 13816 m1 -Jujuy, Argentina MACN-MA 16190 m1 -Tucumán, Argentina MACN-MA 17116 m1 -Santa Fe, Argentina MACN-MA 32254 m1 ♀ Corrientes, Argentina MACN-MA 33.7 m1 ♀ Jujuy, Argentina Procyon cancrivorus MACN-MA 41109 m1 ♀ San Javier, Santa Fe, Argentina MLP-MA 1005 m1 --Procyon lotor MLP-PV S/N m1 ♀ Massachusetts, USA Thylacinidae Thylacinus cynocephalus FMNH 81522 m4 -Tasmania Didelphimorphia Didelphidae Didelphinae MACN-MA 13139 m4 ♀ Concordia, Entre Ríos, Argentina MACN-MA 24149 m4 ♂ Bs. As., Argentina MACN-MA 24150 m4 -Corrientes, Argentina MACN-MA 24169 m4 ♂ Concordia, Entre Ríos, Argentina MACN-MA 49.49 m4 -Corrientes, Argentina Didelphis albiventris MACN-MA 49.51 m4 ♂ Corrientes, Argentina MACN-MA 13265 m4 ♂ Bs. As., Argentina MACN-MA 24110 m4 ♂ Formosa, Argentina MACN-MA 24111 m4 -Corrientes, Argentina MACN-MA 24112 m4 ♂ Corrientes, Argentina MACN-MA 24780 m4 -Corrientes, Argentina Lutreolina crassicaudata MACN-MA 50477 m4 ♂ Bs. As., Argentina All (3) indices Herbivore Hypercarnivore Mesocarnivore Omnivore Piscivore PCPR Herbivore 3 0 0 2 1 Piscivore 0 0 0 0 13 100.00 %Error 77.27 0.00 33.33 18.37 43.48 78.17 Piscivore 1 0 0 2 10 76.92 %Error 80.00 3.75 67.82 25.93 50.00 60.26 Piscivore 0 0 0 0 13 100.00 %Error 81.81 1.06 44.00 22.95 0.00 79.91 Piscivore 0 0 0 9 30.77 %Error 0.00 13.89 48.15 27.06 50.00 75.98 Piscivore 0 0 0 0 13 100.00 %Error 25.00 2.02 19.44 27.66 0.00 89.96 All (3) indices All (3) indices All (2) bg-PCs Hypercarnivore 4 5 0 44.44 Hypercarnivore 2 7 0 22.22 Hypercarnivore Mesocarnivore Omnivore PCPR 50.00 Hypercarnivore 0 70 33 1 1 66.67 Mesocarnivore 0 1 23 6 4 67.65 Omnivore 17 0 26 13 15 3 bg-PCs Herbivore 5 0 0 1 0 83.33 RGA Herbivore 3 0 0 1 2 50.00 3 bg-PCs Herbivore 4 0 0 2 66.67 RGA Herbivore 2 0 0 3 33.33 3 bg-PCs Herbivore 1 0 0 5 16.67 Hypercarnivore 8 1 0 Mesocarnivore 4 9 2 60.00 Mesocarnivore 4 8 3 53.33 88.89 Mesocarnivore 0 15 0 Omnivore 1 6 21 75.00 Omnivore 0 2 26 92.86 100.00 Omnivore 0 4 24 %Error 55.56 55.00 8.70 65.38 %Error 66.67 52.94 10.34 69.23 85.71 18.31 Piscivore 1 0 1 5 6 Hypercarnivore 0 91 7 5 2 86.67 Hypercarnivore 0 73 30 0 2 69.52 Hypercarnivore 0 88 13 4 83.81 Hypercarnivore 0 85 11 8 80.95 Hypercarnivore 0 99 3 3 94.29 %Error 0.00 25.00 0.00 Bg-PC1 Bg-PC1 90.38 46.15 %Error 85.71 1.41 72.29 51.85 77.78 50.22 RGA Herbivore 3 0 0 1 2 Mesocarnivore 0 4 23 6 1 67.65 Omnivore 20 0 17 27 7 38.03 Piscivore 0 0 0 0 13 100.00 %Error 80.00 4.21 51.06 30.77 43.48 69.43 Mesocarnivore 0 2 26 0 6 76.47 Omnivore 19 0 33 3 16 4.23 Piscivore 0 0 1 1 11 84.62 %Error 86.36 2.67 71.11 40.00 70.27 50.66 Mesocarnivore 0 4 25 5 73.53 Omnivore 13 0 22 36 50.70 Piscivore 0 0 0 0 13 100.00 %Error 76.47 4.35 58.33 23.40 0.00 72.49 Mesocarnivore 1 12 8 12 23.53 Omnivore 1 9 9 44 61.97 Piscivore 1 2 1 6 23.08 %Error 60.00 21.30 62.07 39.73 78.57 62.01 Mesocarnivore 0 4 21 9 61.76 Omnivore 1 2 6 62 87.32 Piscivore 0 0 0 0 13 100.00 %Error 50.00 5.71 30.00 21.52 0.00 85.59 Hypercarnivore 8 1 0 88.89 Hypercarnivore 8 1 0 88.89 All (3) índices Hypercarnivore 5 4 0 Mesocarnivore 1 13 1 86.67 Mesocarnivore 1 9 5 60.00 55.56 Mesocarnivore 6 9 0 Omnivore 0 5 23 82.14 Omnivore 0 6 22 78.57 55.56 Omnivore 3 2 23 %Error 11.11 33.33 4.17 84.62 %Error 11.11 43.75 18.52 75.00 82.14 50.00 Hypercarnivore 0 72 31 2 0 68.57 Mesocarnivore 0 1 26 6 1 76.47 Omnivore 21 0 31 12 7 16.90 Piscivore 1 0 1 8 3 23.08 %Error 88.00 1.37 70.79 58.62 76.92 50.66 AI Herbivore 3 2 0 1 0 50.00 Hypercarnivore 1 90 12 2 0 85.71 Mesocarnivore 7 14 8 0 5 23.53 Omnivore 22 9 28 3 9 4.23 Piscivore 4 0 4 2 3 23.08 %Error 91.89 21.74 84.62 62.50 82.35 46.72 Angle α Herbivore 4 0 0 0 2 Hypercarnivore 4 70 20 9 2 Mesocarnivore 1 3 24 5 1 Omnivore 21 0 30 15 5 Piscivore 4 0 0 5 4 %Error 88.24 4.11 67.57 55.88 71.43 51.09 %Error 100.00 77.78 45.00 36.54 30.77 Omnivore 3 14 11 39.29 21.13 Mesocarnivore 3 8 4 53.33 70.59 Hypercarnivore 0 4 5 0.00 66.67 Angle α 66.67 bg-PC1 Herbivore 3 0 0 3 0 50.00 Hypercarnivore 0 80 21 4 0 76.19 Mesocarnivore 0 2 27 0 5 79.41 Omnivore 24 0 13 8 26 11.27 Piscivore 0 0 0 2 11 84.62 %Error 88.89 2.44 55.74 52.94 73.81 56.33 AI Herbivore 3 2 0 0 1 50.00 Hypercarnivore 0 92 10 0 3 87.62 Mesocarnivore 0 16 6 1 11 17.65 Omnivore 9 10 23 10 19 14.08 Piscivore 0 0 2 0 11 84.62 %Error 75.00 23.33 85.37 9.09 75.56 53.28 Angle α Herbivore 3 0 0 1 2 50.00 Hypercarnivore 4 70 25 1 5 66.67 Mesocarnivore 1 3 26 2 2 76.47 Omnivore 21 0 32 4 14 5.63 Piscivore 5 0 1 2 5 38.46 %Error 91.18 4.11 69.05 60.00 82.14 47.16 bg-PC1 Herbivore 3 0 0 3 50.00 Hypercarnivore 0 88 13 4 83.81 Mesocarnivore 0 3 26 0 76.47 Omnivore 25 0 16 5 25 7.04 Piscivore 0 0 1 2 10 76.92 %Error 89.29 3.30 53.57 64.29 75.00 57.64 AI Herbivore 0 2 1 3 0.00 Hypercarnivore 2 87 10 4 82.86 Mesocarnivore 0 12 10 11 29.41 Omnivore 2 3 11 49 69.01 Piscivore 0 1 1 8 23.08 %Error 100.00 17.14 69.70 34.67 75.00 65.07 Angle α Herbivore 0 1 0 2 %Error 64.29 40.00 0.00 RGA RGA 71.15 bg-PC1 Herbivore 5 0 0 1 83.33 Hypercarnivore 0 92 10 2 87.62 Mesocarnivore 0 9 17 5 50.00 Omnivore 2 3 7 52 Hypercarnivore 7 2 0 77.78 Hypercarnivore 1 7 1 11.11 Bg-PC1 Hypercarnivore 8 1 0 Mesocarnivore 10 3 2 20.00 Mesocarnivore 6 6 3 40.00 88.89 Mesocarnivore 0 14 1 Omnivore 2 2 24 85.71 Omnivore 0 3 25 89.29 93.33 Omnivore 0 5 23 %Error 63.16 57.14 7.69 65.38 %Error 85.71 62.50 13.79 61.54 82.14 73.24 Piscivore 0 1 2 5 %Error 0.00 30.00 4.17 AI AI 86.54 38.46 %Error 28.57 12.38 52.78 20.00 68.75 Hypercarnivore 5 4 0 55.56 Hypercarnivore 6 2 1 66.67 RGA 74.67 Hypercarnivore 0 9 0 Mesocarnivore 6 5 4 33.33 Mesocarnivore 4 6 5 40.00 0.00 Mesocarnivore 11 2 2 Omnivore 1 7 20 71.43 Omnivore 1 4 23 82.14 13.33 Omnivore 4 0 24 %Error 58.33 68.75 83.33 57.69 %Error 45.45 50.00 20.69 67.31 85.71 0.00 Hypercarnivore 2 75 14 13 %Error 100.00 81.82 7.69 Angle α Angle α 50.00 71.43 Mesocarnivore 0 12 9 12 26.47 Omnivore 3 16 11 32 45.07 Piscivore 2 3 1 7 0.00 %Error 100.00 29.91 74.29 51.52 100.00 %Error 58.33 68.75 16.67 57.69 50.66 Hypercarnivore 2 3 4 22.22 Hypercarnivore 1 2 6 11.11 AI Hypercarnivore 5 4 0 Mesocarnivore 2 7 6 46.67 Mesocarnivore 2 4 9 26.67 55.56 Mesocarnivore 6 5 4 Omnivore 3 17 8 28.57 Omnivore 6 5 17 60.71 33.33 Omnivore 1 7 20 71.43 %Error 71.43 74.07 55.56 32.69 %Error 88.89 63.64 46.88 42.31	Diet (reference) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Diet (reference) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Diet (reference) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Diet (reference) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Sunquist & Sunquist, 2009) Hypercarnivore (Rosalino et al., 2009) Hypercarnivore (Holekamp & Kolowski, 2009) Mesocarnivore (Holekamp & Kolowski, 2009) Mesocarnivore (Torre et al., 2003) Diet (reference) Mesocarnivore (Torre et al., 2003) Mesocarnivore (Sillero-Zubiri, 2009) Mesocarnivore (Sillero-Zubiri, 2009) Hypercarnivore (Sillero-Zubiri, 2009) Omnivore (Sillero-Zubiri, 2009) Omnivore (Sillero-Zubiri, 2009) Diet (reference) Hypercarnivore (Sillero-Zubiri, 2009) Mesocarnivore (Sillero-Zubiri, 2009) Omnivore (Sillero-Zubiri, 2009) Omnivore (Sillero-Zubiri, 2009) Omnivore (Sillero-Zubiri, 2009) Hypercarnivore (Sillero-Zubiri, 2009) Diet (reference) Hypercarnivore (Larivière & Jennings, 2009) Hypercarnivore (Larivière & Jennings, 2009) Mesocarnivore (Larivière & Jennings, 2009) Diet (reference) Omnivore (Larivière & Jennings, 2009) Diet (reference) Hypercarnivore (Larivière & Jennings, 2009) Mesocarnivore (Larivière & Jennings, 2009) Diet (reference) Mesocarnivore (Baker, 2015) Hypercarnivore (Larivière & Jennings, 2009) Piscivore (Kruuk, 2006) Piscivore (Kruuk, 2006) Piscivore (Kruuk, 2006) Piscivore (Kruuk, 2006) Mesocarnivore (Larivière & Jennings, 2009) Omnivore (Larivière & Jennings, 2009) Hypercarnivore (Larivière & Jennings, 2009) Hypercarnivore (Larivière & Jennings, 2009) Hypercarnivore (Larivière & Jennings, 2009) Hypercarnivore (Larivière & Jennings, 2009) Hypercarnivore (Larivière & Jennings, 2009) Herbivore (Kays, 2009) Omnivore (Kays, 2009) Omnivore (Kays, 2009) Omnivore (Kays, 2009) Hypercarnivore (Helgen & Veatch, 2015) Omnivore (Vieira & Astúa de Moraes, 2003) Mesocarnivore (Vieira & Astúa de Moraes, 2003)

3-Centre de Recherche en Paléontologie -Paris (CR2P -CNRS, MNHN, Sorbonne Université). 8 rue Buffon CP38 (75005), Paris, France. sandrine.ladeveze@mnhn.fr SUPPLEMENTARY ONLINE INFORMATION 1.

Sergio D. Tarquini 1 *, M. Amelia Chemisquy 1,2 , Sandrine Ladevèze 3 , and

ACKNOWLEDGEMENTS

We thank D. Flores and P. Teta (MACN-ma) and I. Olivares (MLP-ma) for access to specimens under their care; J. Rajmil, C. Bustamante, W. Bustamante, and M. Mignana for replicas of molars deposited in foreign collections; M. Macchioli Grande for helping with the English version of the manuscript; D. Croft, R. Engelman, A. Friscia, and A.

Otero for providing useful comments that helped improve the manuscript. This is a contribution to projects PICT-2015-0966 and PUE 2016-0125.

Species

The RGA values used in this study correspond to measurements taken by calliper (Prevosti, 2006). We compared the use of the average value of the RGA per species, versus the use of the observed variation (i.e., including the value of each specimen separately). In the first case, we averaged the RGA per species, obtaining a single value per species. Under this example, the diets could be classified with a series of logical rules: e.g., a taxon can be considered hypercarnivorous when the RGA index is lower than 0.45; insectivore when the RGA index is larger than 1. Although the limit between omnivores and mesocarnivores is ambiguous and it is usually determined arbitrarily. In the second case, we used the intraspecific variation including the maximum and minimum values for each species. Considering these values, the overlap between the diet categories is much greater. The limits between hypercarnivores and mesocarnivores, hypercarnivores and omnivores, and omnivores and mesocarnivores, are ambiguous.

RGA AVERAGED

Species

MAXIMUM AND MINIMUM RGA FOR EACH SPECIES


Investigating the role of a fungal oxidase-peroxidase tandem in plant pathogenicity

Bastien Bissaro 1 , Sayo Kodama 2 , Mireille Haon 1 , David Ribeaucourt 1,3 , Michael Lafond 3 , Harry Brumer 4 , Richard O'Connell 5 , Yasuyuki Kubo 2 , Jean-Guy Berrin