Cladistics Cladistics 28 (2012) 317–329 10.1111/j.1096-0031.2011.00385.x Detecting areas of endemism with a taxonomically diverse data set: plants, mammals, reptiles, amphibians, birds, and insects from Argentina Claudia Szumika,*, Lone Aagesenb, Dolores Casagrandaa, Vanesa Arzamendiac, Diego Baldod, Lucı́a E. Clapsa, Fabiana Cuezzoa, Juan M. Dı́az Gómeze, Adrián Di Giacomof, Alejandro Giraudoc, Pablo Goloboffa, Cecilia Gramajog, Cecilia Kopuchianh, Sonia Kretzschmard, Mercedes Lizarraldea, Alejandra Molinaa, Marcos Mollerachi, Fernando Navarroa, Soledad Nomdedeub, Adela Panizzab, Verónica V. Pereyraa, Marı́a Sandovali, Gustavo Scrocchid and Fernando O. Zuloagab a Instituto Superior de Entomologı´a (INSUE-CONICET), Miguel Lillo 205, 4000 Tucumán, Argentina; bInstituto de Botánica Darwinion (CONICET-ANCEFN), Labarden 200, CC22, San Isidro, B1642HYD Buenos Aires, Argentina; cInstituto Nacional de Limnologı´a (CONICET-UNL), Ciudad Universitaria, El Pozo s ⁄ n, 3016 Santa Fe, Argentina; dInstituto de Herpetologı´a (FML), Miguel Lillo 251, 4000 Tucumán, Argentina; eInstituto de Bio y Geociencias (IBIGEO), Facultad de Ciencias Naturales, Mendoza 2, 4400 Salta, Argentina; fLaboratorio de Ecologı´a y Comportamiento Animal (FCEN-UBA), Ciudad Universitaria, C1428EHA Buenos Aires, Argentina; gDivisión Entomologı´a (FML), Miguel Lillo 251, 4000 Tucumán, Argentina; hDivisión Ornitologı´a (MACN), Av. Angel Gallardo 470, C1405DJR Buenos Aires, Argentina; iPrograma de investigaciones en Biodiversidad Argentina (PIDBA-FCN), Miguel Lillo 205, 4000 Tucumán, Argentina Accepted 16 October 2011 Abstract The idea of an area of endemism implies that different groups of plants and animals should have largely coincident distributions. This paper analyses an area of 1152 000 km2, between parallels 21 and 32S and meridians 70 and 53W to examine whether a large and taxonomically diverse data set actually displays areas supported by different groups. The data set includes the distribution of 805 species of plants (45 families), mammals (25 families), reptiles (six families), amphibians (five families), birds (18 families), and insects (30 families), and is analysed with the optimality criterion (based on the notion of endemism) implemented in the program NDM ⁄ VNDM. Almost 50% of the areas obtained are supported by three or more major groups; areas supported by fewer major groups generally contain species from different genera, families, or orders.  The Willi Hennig Society 2011. The present study aims to evaluate the distributional concordance among a diverse group of taxa, by using an optimality criterion specifically designed for detecting areas of endemism. In other words, this is one of the first approximations to analyse total evidence in a biogeographical context. Studies of endemicity for taxonomically wide samples are difficult because specialists in a given group seldom have access to first-hand information on the distribution of other groups; the only way to *Corresponding author: E-mail address: szu.claudia@gmail.com  The Willi Hennig Society 2011 cover a wide array of diverse taxa is to have studies in which numerous authors, with different specialties, collaborate. In the past two decades, a considerable number of empirical studies to define and quantify areas of endemism have been published. Part of that production can be related to the development of different methods of analysis, starting with parsimony analysis of endemism (PAE; Morrone, 1994), which highlighted the need to formalize and assess the identification of areas of endemism with clear and accessible protocols. Several alternative methods followed PAE (e.g. Geraads, 1998; 318 C. Szumik et al. / Cladistics 28 (2012) 317–329 Linder, 2001; Garcı́a-Barros et al., 2002; Hausdorf and Hennig, 2003), trying to improve on the original idea. Szumik et al. (2002) and Szumik and Goloboff (2004) proposed a method that takes into account the spatial component of endemism (ignored by the other methods) and allows for non-hierarchical results (required by most other methods). It is clear that the notion of endemism includes a spatial concept by definition but Szumik et al. (2002, p. 806) are the first to point out that the spatial component has been previously ignored: ‘‘A method used to identify areas of endemism must consider the taxa occurring in a given area and their position in space. This spatial component has not been included in pre-existing clustering methods, and thus those methods (designed only to recover hierarchy) cannot be adopted for identification of areas of endemism.’’ Method of analysis aside, most of these local and global empirical studies were focused on a particular group of taxa (genus, family, order, or class), analysed with a single cell size (e.g. 1 · 1, 2 · 2). Nevertheless, the concept of an area of endemism implies distributional concordance among different groups, not within a single group: ‘‘An endemic taxon is restricted to a region and is found nowhere else. The range of distribution of a taxon is determined by both historical and current factors. Whatever the factors are, if they affect (or have affected) in a similar way different taxonomic groups, there will be congruence in the patterns of endemicity in different groups. Thus, areas that have many different groups found there and nowhere else can be defined as areas of endemism.’’ (Szumik et al., 2002, p. 806) This paper represents the first attempt to bring together a set of high-quality data provided by a large number of specialists for diverse groups of plants, mammals, reptiles, amphibians, birds, and insects. Additionally, a comparison between the results and some of the previous hypotheses, focusing in particular on Cabrera and WillinkÕs biogeographical division, is presented (Fig. 1). Materials and methods The 805 species analysed represent 53 orders, 129 families and 463 genera (Table 1; see also Appendix 1 for a list of number of species per order and family). The species were chosen because either (i) they were previously used as typical of some biogeographical area, or (ii) they have narrow distributions in the study region. Almost all the records used are connected to actual specimens in one of the major collections in Argentina (the only exception being birds, for which sighting is widely considered as acceptable for identification). Many of these vouchers are the result of years of collection and taxonomic study by the authors. The data Fig. 1. Biogeographical divisions for the study region according to Cabrera and Willink (1973). set contains almost 14 500 records. In many cases (especially birds and mammals) the records were insufficient to assess the real distribution of the taxa; in those cases, the presumed distribution was estimated by the respective specialist (see Fig. 2 for a map of species diversity). The present data set is unique among biogeographical studies not only for the number and diversity of plant and animal taxa, but also because it was compiled, edited, and corroborated by 25 practising taxonomists, whose work specializes in the study region. Thus, it differs substantially from data sets constructed by downloading data from biodiversity websites. The study region is a rectangular area between 21 and 32S and 70 and 53W in Argentina, comprising more than 1152 000 km2 of the Neotropical region, equivalent to the area of South Africa (as analysed by Linder, 2001), or twice that of Spain and Portugal (as analysed by Garcı́a-Barros et al., 2002). The high biogeographical diversity of this zone is well known (e.g. Cabrera and Willink, 1973; Fig. 1). Other studies by Vervoorst (1979), Dinerstein et al. (1995), and Morrone (2001, 2006) are also important. Some of these studies (Cabrera and Willink, 1973; Vervoorst, 1979) did not use the term ‘‘endemic’’ or ‘‘endemism’’ but simply listed plants and a few animals characterizing each of the biogeographical ‘‘divisions’’ proposed. Because of C. Szumik et al. / Cladistics 28 (2012) 317–329 the complexity of the region, there is substantial disagreement among the proposals. It is also clear that many of these biogeographical divisions continue outside the study region (e.g. extending into Bolivia); moreover, some of the taxa included here are absent outside of the study region, while others are present. It should therefore be noted that whenever we report an area of endemism defined by species that are distributed outside the study region as well, what we present may be only a patch of the area. It remains to be seen whether such areas persist in future studies as an area of endemism defined by the same species. However, a compilation of distributions such as the present one offers an opportunity to provide first-step testable hypotheses of areas of endemism for future analyses of neighbouring regions or analyses at more inclusive scales. One could be tempted to criticize the present study by claiming that the study region is inadequate or not natural, or that the taxa present outside of this region must be ignored or eliminated from the analysis. However, this would be equivalent to using the same Table 1 Number of orders, families, genera, and species for each major taxonomic group analysed Plants Insects Reptiles Amphibians Birds Mammals Total Orders Families Genera Species 27 5 1 1 9 10 53 45 30 6 5 18 25 129 115 177 21 20 46 84 463 187 300 89 41 49 140 805 Fig. 2. Species diversity in the study region on cells of 0.25 · 0.25. 319 arguments that were misdirected against phylogenetic analyses in the past, when criticizing it for dealing with possibly incomplete monophyletic groups (Sokal, 1975, p. 258; see rebuttal by Farris, 1979, p. 486). The tree resulting from a cladistic analysis for a specific set of taxa makes a statement only about the relationships of the included taxa; those taxa not included in the analysis could land—in future analyses—on any branch of the tree. The present analysis, likewise, specifies—for each cell in the grid—the membership, or lack thereof, to a given area; nothing is stated or implied about cells that would occupy an extended grid. The data set was analysed with the heuristic algorithms of NDM-VNDM ver. 2.7 (Goloboff, 2007), which apply the methodology of Szumik and Goloboff (2004). The method, which is grid-dependent, basically evaluates spatial concordance among two or more taxa for a given set of cells (area of endemism): assigning a score of endemicity for a given taxon, according to how well the taxon distribution matches a given set of cells (area). Then, the total endemicity score for a given set of cells (area) is the summation of the individual taxon scores (for details see the methodological explanation of Szumik and Goloboff, 2004; and the empirical case of Navarro et al., 2009). Beyond the formula and ⁄ or the criteria, the programs NDM ⁄ VNDM were developed with the idea that: ‘‘An explicit method to identify areas of endemism should relate relevant evidence and conclusions… Acceptance of those conclusions (i.e., boundaries of areas) that are best supported by available evidence requires (in principle, at least) evaluation of all possible conclusions, selecting the ones judged as optimal based on the established criterion.’’ (Szumik et al., 2002, p. 806) 320 C. Szumik et al. / Cladistics 28 (2012) 317–329 With few exceptions, previous analyses provide no justification for the cell size selected. It has been proposed (Aagesen et al., 2009; Casagranda et al., 2009) that using several grid sizes provides a kind of measure of support for a particular area of endemism. More importantly, the shape and size of some areas of endemism may make them hard to identify if only a single grid size is used—using several grid sizes increases the probability of finding all areas (especially in cases such as the Andes, where steep and rugged terrain leads to very small areas, detectable only with small grid sizes). Thus, three grid sizes (0.25, 0.50 and 1, where 1 is equivalent to almost 100 km) were used. Given that some sets of cells (areas of endemism) differ little in both the composition of cells and their endemic taxa, the results were grouped with the consensus option of VNDM (see Aagesen et al., 2009; Navarro et al., 2009, for additional discussion of search protocols). The consensus option used here combines all the areas of endemism that share a (user-defined) percentage of endemic taxa with at least some other area in the consensus. Results In total, 126 consensus areas (Table 2) were obtained, 24 (19%) of which were defined by a single taxonomic group (mostly plants or insects, and rarely by mammals or amphibians). In all these areas with a unique taxonomic group, however, the endemic taxa belonged to different genera, families, and orders. Overall, 47.6% of the consensus areas were supported by three or more taxonomic groups when comparing the total of 126 consensus areas under the three different grid sizes (Table 2). Instead of discussing each of the resulting areas found by the present analysis (beyond the scope of the present paper), it is our aim to discuss those areas (a) (b) well supported by all or most of the different taxonomic groups used, illustrating cases where endemism can indeed be supported by widely different groups of taxa. Two such areas (supported by the six taxonomic groups) are the Atlantic Forest (Selva Paranaense—Neotropical, Fig. 3) and the north Yungas sector (tropical BermejoToldo-Calilegua, Fig. 4). Both of these areas are recovered in all grid sizes and in every case were supported by the six major taxonomic groups included in the data set (see Appendices 2 and 3). Topographically, the study region consists of lowland plains that rise from approximately 70 m in the east to approximately 300 m in the west, and the Andes in the west with deep valleys and peaks reaching above 6000 m. The complexity of the western part of the region is directly reflected in the higher number of consensus areas found west of 64W, compared with the number found east of the same longitude (see Table 3). Consensus areas that extend both east and west of 64W appear more clearly when the cell size is increased, which helps detect wide-ranging distribution patterns such as the Chaco scrubland (Fig. 5a). Given that in a region such as this it is quite impossible to have records uniformly sampled, a small grid size applied to all the records would render almost Table 2 Relationship between number of major taxonomic groups (6 to 1) supporting any of 126 consensus areas obtained for the three grid sizes No. of taxonomic groups 6 5 4 3 2 1 Total (c) Grid size 0.25 0.5 1.0 No. (%) of consensus areas 2 1 2 2 13 9 29 4 1 5 8 17 8 43 3 6 10 16 12 7 54 9 (7.1) 8 (6.4) 17 (13.5) 26 (20.6) 42 (33.3) 24 (19.0) 126 (d) Fig. 3. Atlantic Forest. (a) Concordance between the consensus areas of the three grid sizes; (b) consensus area under 1 grid size; (c) consensus area under 0.50 grid size; (d) consensus area under 0.25 grid size. C. Szumik et al. / Cladistics 28 (2012) 317–329 (a) (b) (c) 321 (d) Fig. 4. Northern Yungas. (a) Concordance between the consensus areas of the three grid sizes; (b) consensus area under 1 grid size; (c) consensus area under 0.50 grid size; (d) consensus area under 0.25 grid size. Table 3 Number of consensus areas in Northwestern Argentina (NWA), Northeastern Argentina (NEA), or in both regions (NA), for the three grid sizes Grid size Region 0.25 0.5 1.0 NWA > 64 NEA < 64 NA 20 8 1 25 10 8 24 10 20 any distribution entirely discontinuous and make large areas of endemism unrecognizable. One objection against using 1 cell size is that it could lead to overlapping different distribution patterns in the (a) same area. As an example, Fig. 5b (grid size 1) depicts a consensus of areas that lumps distribution patterns running north–south of organisms found at different altitudes. The grass species of Deyeuxia are found in Puna and High Andean environments above 3000 m, as is the case of the Llama and Vicuña. However, species from lower altitudes such as the bush Bulnesia schickendantzii (Zygophyllaceae) and the grass Panicum chloroleurum also appear as endemic to this area under grid size 1, confusing the preconceived limits of the biogeographical strata found in the Andes (Fig. 6), as the altitudinal range of the species in the area shows. Besides finding areas similar to those proposed previously, the present analysis also yielded two strongly supported distribution patterns which were found in all (b) Fig. 5. (a) Consensus area of the Chaco Scrubland under 1 grid size; (b) consensus area of Puna–High Andean under 1 grid size. 322 C. Szumik et al. / Cladistics 28 (2012) 317–329 Fig. 6. Altitude range of the species which give score to the consensus area of Puna–High Andean sector under 1 grid size (see Fig. 5b). (a) (b) (c) (d) Fig. 7. Cordillera Real. (a) Concordance between the consensus areas of the three grid sizes; (b) consensus area under 1 grid size; (c) consensus area under 0.50 grid size; (d) consensus area under 0.25 grid size. grid sizes (Figs 7 and 8). Both areas are found in topographically variable parts of the Andes and both include strong gradients in altitude, temperature, and rainfall. These areas appear as major centres of endemism in northern Argentina, resistant to change in analytical parameters (here, changes in grid sizes), and with high taxonomic diversity (with a wide array of endemic species and families). The northernmost area (Fig. 7) lies in the southern part of the Cordillera Real, occupying ca. 23 000 km2, from 2250¢ to 2450¢S and from 6450¢ to 66W. This area had also been identified in earlier studies (Aagesen et al., 2009) of the distribution of grasses within a portion of the current study region. Using a grid size of 0.5 (Fig. 7c), the area is supported by 33 species, including 21 plant species from 11 families. Ongoing studies of plant distribution have identified 47 plant species from 18 different families as strictly endemic to this area. Here, the area is also supported by 11 species and seven families of animals (see Appendix 4). The southernmost area (Fig. 8) occupies valleys, slopes, and peaks of the Sierras Calchaquies, between 2550¢ and 28S and 6450¢ and 6610¢W, with an area of 31 000 km2. Under a grid size of 0.25 (Fig. 8b) the area C. Szumik et al. / Cladistics 28 (2012) 317–329 (a) (b) (c) 323 (d) Fig. 8. Valles Calchaquies. (a) Concordance between the consensus areas of the three grid sizes; (b) consensus area under 1 grid size; (c) consensus area under 0.50 grid size; (d) consensus area under 0.25 grid size. (a) (b) Fig. 9. (a) The deciduous tropical forest (Yungas and Atlantic Forest) under 0.5 grid size; (b) the tropical tails entering Argentina in two disjoint patches under 1.0 grid size. is supported by two grass species (see Appendix 5), but 64 plant species from 21 families are known to be endemic to this area (Zuloaga et al., 2008). In addition to plants, the area is supported by 28 species of animals from eight families. Discussion The main aims of the present study were to explore to what extent different taxonomic groups can co-occur and support similar areas of endemism. The general idea of such areas is not associated with a specific causal factor; if a single factor affects the distribution of diverse groups of organisms, they will be expected to show similar spatial patterns. Regardless of whether the causal factor is historical or ecological, our results indicate that when all the evidence is analysed for a given region it is possible to obtain areas supported by diverse taxonomic groups (Navarro et al., 2009). Besides, a causal factor need not have affected the entirety of the biota, so that different groups (with different ecological requirements, for example) may have different, or even overlapping, distributional patterns. Yet, and regardless of overlaps, all the repetitive patterns are (possible sampling artefacts aside) equally real, in the sense that each of them is the result of some common factor (Szumik and Goloboff, 2004). It is also important to note that the present method allows partial 324 C. Szumik et al. / Cladistics 28 (2012) 317–329 overlapping between areas of endemism, but does not require it; cases of overlap in the results are a consequence of the data, not of the method. Almost all the main biogeographical units proposed in previous studies (Cabrera and Willink, 1973; Cabrera, 1976; Stange et al., 1976; Cracraft, 1985; Willink, 1991; Morrone, 2001, 2006) were recovered in the analysis: the Atlantic Forest (Fig. 3), the Campos (Grasslands) District, the Chaco shrubland (Fig. 5a), the deciduous tropical Yungas forest (Fig. 9a), the Puna highland, and the tropical tails entering Argentina in two disjoint patches (Fig. 9b). Each of these tropical tails represents part of a broader area that extends towards the north of the South American continent. Besides the general spatial concordance with previously suggested biogeographical units, the species that support the various areas also agree in general with previous biogeographical studies based on individual groups (plants: Aagesen et al., 2009; reptiles: Giraudo et al., 2008; Arzamendia and Giraudo, 2009; mammals: Barquez and Diaz, 2001; insects: Navarro et al., 2009; birds: Straube and Di Giacomo, 2007). It is beyond the scope of the present paper either to discuss the biogeographical units in detail or to provide extensive species lists of the supporting species for each area; these aspects will be treated in a separate publication. However, it should be noted that several of the species appearing as endemic to certain areas are currently on red-lists of threatened species at national or global level (Collar et al., 1992; Diaz and Ojeda, 2000; Lavilla et al., 2000; Barquez et al., 2006; Lopez-Lanus et al., 2008; BirdLife International, 2011). Acknowledgements Helpful comments and constructive criticism from editors James Carpenter and Dennis Stevenson and three anonymous reviewers are greatly appreciated. Special thanks to our colleagues Norberto Giannini and Osvaldo Morrone for their generous contribution to this analysis; we are also grateful to Adriana Chalup and Arturo Roig Alsina, who helped us with their remarks, criticism, and general info on Lepidoptera and Hymenoptera. The team would also like to thank the following for funding our project: FONCyT (PICT 1314), CONICET (PIP 0355, 0805, 2422, PIPI 0019), CIUNT (G430), CAID (PJ47-383 and PI47-234). Preliminary versions of this study were presented at the VII Reunión Argentina de Cladı́stica y Biogeografı́a XXVII (San Isidro, 2007) and the XXVII Meeting of the Willi Hennig Society—VIII Reunión Argentina de Cladı́stica y Biogeografı́a (San Javier, 2008). We thank the organizers and colleagues for discussion and comments. Luisa Montivero helped with the English text. References Aagesen, L., Szumik, C., Zuloaga, F.O., Morrone, O., 2009. Biogeography of the South America highlands—recognizing the Altoandina, Puna, and Prepuna through the study of Poaceae. Cladistics 25, 295–310. Arzamendia, V., Giraudo, A.R., 2009. Influence of great South American Rivers of the Plata basin in distributional patterns of tropical snakes: a panbiogeographic analysis. J. Biogeogr. 36, 1739–1749. 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Zuloaga, F.O., Morrone, O., Belgrano, M.J., 2008. Catálogo de las Plantas Vasculares del Cono Sur. Monogr. Syst. Bot. Mo. Bot. Gard. 107, 609–967. 326 C. Szumik et al. / Cladistics 28 (2012) 317–329 Appendix 1 Names of orders and families used in the study (with number of species per family) Order Plants Aquifoliales Asparagales Asterales Brassicales Caryophyllales Cucurbitales Cyatheales Dipsacales Fabales Gentianales Incertae sedis Lamiales Liliales Malpighiales Malvales Myrtales Oxalidales Pandanales Piperales Poales Polypodiales Ranunculales Sapindales Solanales Reptiles Squamata Mammals Artiodactyla Family n Aquifoliaceae Amaryllidaceae Anthericaceae Asteraceae Campanulaceae Brassicaceae Bromeliaceae Amaranthaceae Cactaceae Caryophyllaceae Portulacaceae Podocarpaceae Begoniaceae Cyatheaceae Valerianaceae Fabaceae Betulaceae Apocynaceae Gentianaceae Rubiaceae Boraginaceae Acanthaceae Calceolariaceae Gesneriaceae Lamiaceae Lauraceae Orobanchaceae Plantaginaceae Scrophulariaceae Verbenaceae Alstroemeriaceae Euphorbiaceae Malvaceae Myrtaceae Onagraceae Oxalidaceae Velloziaceae Aristolochiaceae Piperaceae Cyperaceae Poaceae Aspleniaceae Papaveraceae Anacardiaceae Convolvulaceae Solanaceae Zygophyllaceae 1 3 1 32 1 2 12 1 4 1 1 1 2 1 1 7 1 6 2 3 1 1 2 1 3 1 1 1 1 6 3 5 5 1 1 2 1 2 3 2 38 2 1 1 4 11 1 Boidae Dipsadidae Elapidae Leptotyphlopidae Liolaemidae Viperidae 2 21 4 7 47 8 Camelidae Cervidae Tayassuidae 2 6 3 Order Cingulata Didelphimorphia Lagomorpha Perissodactyla Pilosa Primates Rodentia Birds Charadriiformes Columbiformes Galliformes Gruiformes Passeriformes Piciformes Psittaciformes Tinamiformes Trochiliformes Amphibians Anura Insects Lepidoptera Diptera Hemiptera Hymenoptera Family n Dasypodidae Caluromyidae Didelphidae Leporidae Tapiridae Bradypodidae Myrmecophagidae Atelidae Cebidae Chinchillidae Erethizontidae Hydrochoeridae Myocastoridae 10 1 22 1 1 1 2 1 2 3 2 1 1 Charadriidae Recurvirostridae Columbidae Cracidae Rallidae Cinclidae Formicariidae Fringillidae Furnariidae Mimidae Rhinocryptidae Thamnophilidae Tyrannidae Picidae Ramphastidae Psittacidae Tinamidae Trochilidae 1 1 1 2 2 1 2 9 8 1 1 2 5 4 1 3 1 3 Bufonidae Hylidae Leptodactylidae Microhylidae Strabomantidae 8 16 15 1 1 Geometridae Noctuidae Asilidae Asteiidae Bibionidae Chloropidae Clusiidae Ephydridae Micropezidae Mycetophilidae Pipunculidae Platypezidae Sciomycidae Stratiomydae Syrphidae Tabanidae Tachinidae Dactylopidae Diaspididae Apidae 21 64 2 1 3 5 1 24 5 2 7 3 1 3 5 4 5 5 23 14 C. Szumik et al. / Cladistics 28 (2012) 317–329 327 Appendix 1 (Continued) Order Carnivora Chiroptera Family n Order Family n Canidae Felidae Mephitidae Mustelidae Procyonidae Molossidae Noctilionidae Phyllostomidae Vespertilionidae 5 9 1 5 2 18 2 17 24 Crabronidae Eumenidae Formicidae Ichneumonidae Pompilidae Vespidae Embioptera 26 7 11 1 4 38 Anisembiidae Archembiidae Teratembiidae 3 9 7 Appendix 2 Endemic species of the consensus area ‘‘Atlantic Forest’’ (Fig. 3) Grid size Species BOT Aristolochia burkartii BOT Asplenium claussenii BOT Mikania summinima BOT Vernonia spicata BOT V. teyucuarensis BOT Viguiera misionensis BOT Borreria loretiana BOT Mecardonia grandiflora BOT Eugenia lilloana BOT Hyptis australis BOT Jacquemontia laxiflora BOT Melica hunzikeri BOT Mesosetum comatum BOT Peperomia misionense BOT P. subpubistachya BOT Siphocampylus yerbalensis BOT Dyckia niederleinii REP Bothrops cotiara REP B. jararaca REP B. jararacussu REP B. moojeni REP Micrurus corallinus MAM Vampyressa pusilla MAM Histiotus velatus MAM Cynomops abrasus MAM Molossops neglectus MAM Micoureus demerarae MAM Monodelphis iheringii MAM M. scalops MAM M. sorex MAM Caluromys lanatus MAM Chironectes minimus MAM Didelphis aurita MAM Gracilinanus microtarsus MAM Metachirus nudicaudatus MAM Pteronura brasiliensis AVE Aramides saracura Grid size 0.25 0.50 0.99 0.98 0.56 0.81 0.96 1.00 0.99 1.00 0.93 0.75 1.00 0.96 1.00 0.99 0.99 0.99 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.50 1.00 1.00 0.50 1.00 1.00 1.00 1.00 0.50 1.00 1.00 1.00 1.00 0.98 1.00 Species 0.25 0.50 1.00 0.63 0.88 0.63 0.79 0.79 0.52 0.80 0.90 0.96 0.79 0.92 0.63 0.79 0.70 0.88 0.80 0.83 0.90 AVE Pionopsitta pileata AVE Stephanoxis lalandi AVE Thalurania glaucopis AVE Ramphastos dicolorus AVE Philydor lichtensteini AVE Sclerurus scansor AVE Hypoedaleus guttatus AVE Mackenziaena leachii AVE Pyriglena leucoptera AVE Mionectes rufiventris AVE Tangara seledon ANF Aplastodiscus perviridis ANF Hypsiboas curupi ANF H. faber ANF Scinax perereca INS Euclysia columbipennis INS Oxydia gilva INS Cliobata guttipennis INS Paralimna molosus INS Polistes melanosoma INS Parachartergus fraternus INS Agelaia angulata INS A. pallipes pallipes INS Synoeca surinama INS Protonectarina sylveirae INS Protopolybia sedula INS Myschocyttarus rotundicollis INS Montezumia ferruginea INS M. aurata INS M. brethesi INS Monobia apicalipennis INS Acromyrmex laticeps INS Pseudomyrmex schuppi INS Archembia dilate INS Diradius plaumanni INS D. unicolor INS Oligembia mini 1.00 0.95 0.93 0.50 0.96 1.00 0.96 1.00 0.91 0.92 0.97 0.99 0.99 0.92 0.98 0.95 0.31 0.90 1.00 0.98 1.00 0.50 0.70 0.75 1.00 0.63 1.00 0.63 0.75 0.95 0.95 0.88 0.88 0.63 0.80 0.80 0.84 0.84 0.88 0.70 0.80 0.70 0.52 0.70 0.95 0.90 0.50 0.80 0.57 0.90 0.75 1.00 0.89 1.00 1.00 1.00 1.00 1.00 1.00 0.96 1.00 0.78 0.81 0.93 1.00 0.75 1.00 1.00 1.00 1.00 1.00 0.54 1.00 0.80 0.75 1.00 1.00 0.84 0.72 0.95 1.00 1.00 0.88 0.70 0.88 0.96 0.92 0.95 0.95 1.00 0.92 0.70 0.70 0.90 0.70 The numbers are the maximum endemicity scores for the species (among all areas included in the consensus) for the three grid sizes (37 species in 0.25, 48 species in 0.50, and 67 species in 1.00). Blanks indicate that the species is not endemic for the area. 328 C. Szumik et al. / Cladistics 28 (2012) 317–329 Appendix 3 Endemic species of the consensus area ‘‘Northern Yungas’’ (Fig. 4) Grid size Species BOT *Anatherostipa brevis BOT *Elymus tilcarensis BOT Nassella punensis BOT *Dicliptera cabrerae BOT *Eupatorium saltense BOT *Nassella yaviensis BOT *Rebutia marsoneri BOT *Nototriche sleumeri BOT *Barbaceniopsis humahuaquensis BOT *Vernonia lipeoensis BOT *Silene haumanii BOT *Adesmia friesii BOT *Nototriche friesii BOT Salvia calolophos BOT *Arachis monticola BOT *Psychotria argentinensis BOT *Solanum caesium BOT Solanum toldense BOT Alsophila odonelliana BOT* Aristida pubescens BOT* Muhlenbergia atacamensis BOT Eragrostis andicola BOT Nassella novari BOT* Senecio punae BOT Mutisia hamata BOT Chuquiraga atacamensis BOT* Metastelma microgynostegia BOT Conyza coronopifolia BOT* Senecio jujuyensis BOT* Senecio tilcarensis BOT* Stevia jujuyensis BOT* Stevia yalae BOT* Solanum calileguae BOT* Ipomoea volcanensis BOT Bomarea boliviensis BOT Begonia boliviensis BOT Muhlenbergia phalaroides BOT Calceolaria elatior BOT *Gamochaeta longipedicellata BOT*Laennecia altoandina BOT* Macropharynx meyeri BOT* Valeriana altoandina BOT* Bartsia jujuyensis BOT Begonia micranthera BOT* Macropharynx meyeri BOT* Mikania jujuyensis 0.25 0.92 0.95 0.92 0.95 Grid size 0.50 1.00 Species 0.80 0.83 0.92 0.78 0.78 0.51 0.59 BOT* Solanum zuloagae BOT Parapiptadenia excelsa BOT Bocconia integrifolia BOT Cinnamomum porphyrium REP Liolaemus albiceps REP Liolaemus chaltin REP Liolaemus irregularis REP Liolaemus multicolor REP Liolaemus orientalis REP Liolaemus ornatus REP Liolaemus pulcherrimus REP Liolaemus yanalcu REP Leptotyphlops striatulus MAM Anoura caudifer MAM Cynomops planirostris MAM Cryptonanus ignitus MAM Thylamys venustus MAM Cebus apella MAM Chaetophractus nationi MAM Chinchilla brevicaudata MAM Coendou bicolor MAM Dasypus yepesi MAM Leopardus wiedii MAM Tapirus terrestris MAM Tayassu pecari AVE Atlapetes fulviceps AVE Grallaria albigula AVE Penelope dabbenei ANF Gastrotheca christiani ANF Gastrotheca chrysosticta ANF Melanophryniscus rubiventris ANF Phyllomedusa boliviana ANF Pleurodema marmoratum ANF Telmatobius atacamensis ANF Telmatobius platycephalus INS Bassania jocosa INS Oxydia optima INS Herminodes carbonelli INS Scatella balioptera INS Scatella hirticrus INS Scatella semipolita INS Scatella glabra INS Pachodynerus jujuyensis INS Montezumia fritzi INS Chelicerca tigre INS Oligembia arbol 0.83 0.83 0.83 0.83 0.76 0.61 0.66 0.79 0.60 0.76 0.84 0.52 0.72 0.92 0.63 0.72 0.69 0.83 1.00 0.83 1.00 0.75 0.70 0.83 0.75 0.75 0.66 0.91 0.64 0.71 0.72 0.72 0.94 0.87 0.75 0.86 0.77 0.74 0.94 0.93 0.78 0.92 0.84 0.80 0.77 0.77 0.82 0.95 0.71 0.76 0.86 0.85 0.72 0.85 0.75 0.77 0.74 0.70 0.73 0.83 0.64 0.70 0.77 0.88 0.82 0.85 0.79 0.82 0.25 0.90 0.50 1.00 0.67 0.82 0.6 0.78 0.53 0.64 0.83 1.00 0.89 0.62 0.94 0.83 0.89 0.83 0.60 0.56 0.68 0.51 0.86 0.96 0.86 0.55 0.91 0.64 0.64 0.78 0.92 0.94 0.91 0.91 0.57 0.69 0.68 0.69 0.75 0.83 0.89 0.81 0.69 0.93 0.59 0.58 0.75 0.7 0.85 0.79 0.83 0.92 0.66 0.53 0.72 0.78 0.51 0.81 0.66 0.97 0.75 0.89 0.8 0.76 0.62 0.63 0.9 0.92 0.54 1.00 0.73 0.90 0.77 0.89 0.90 0.66 0.70 0.80 0.88 0.79 0.77 0.57 0.54 0.83 The numbers are the maximum endemicity scores for the species (among all areas included in the consensus) for the three grid sizes (14 species in 0.25, 59 species in 0.50, and 86 species in 1.00). Blanks indicate that the species is not endemic for the area. *Only present in the study region. C. Szumik et al. / Cladistics 28 (2012) 317–329 329 Appendix 4 Endemic species of the consensus area ‘‘Cordillera Real’’ under 0.5 grid size (Fig. 7c) Species 0.50 Species 0.50 BOT *Elymus tilcarensis BOT *Rebutia marsoneri BOT *Barbaceniopsis humahuaquensis BOT *Silene haumanii BOT *Adesmia friesii BOT Salvia calolophos BOT *Arachis monticola BOT *Solanum caesium BOT Eragrostis andicola BOT* Metastelma microgynostegia BOT Conyza coronopifolia BOT* Senecio tilcarensis BOT* Stevia yalae BOT* Solanum calileguae BOT* Ipomoea volcanensis BOT Bomarea boliviensis 0.83 0.83 0.83 0.55 0.40 0.76 0.60 0.41 0.75 0.63 0.72 0.83 0.83 1.00 0.75 0.57 BOT Muhlenbergia phalaroides BOT *Gamochaeta longipedicellata BOT *Laennecia altoandina BOT* Mikania jujuyensis BOT* Solanum zuloagae REP Liolaemus irregularis REP Liolaemus pulcherrimus REP Liolaemus yanalcu REP Leptotyphlops striatulus MAM Cryptonanus ignitus ANF Gastrotheca christiani ANF Melanophryniscus rubiventris ANF Telmatobius platycephalus LEP Bassania jocosa LEP Oxydia optima LEP Herminodes carbonelli 0.71 0.43 0.71 0.71 0.67 0.47 0.83 0.47 0.52 0.68 0.78 0.49 0.71 0.52 0.89 0.81 *Only present in the study region. Appendix 5 Endemic species of the consensus area ‘‘Sierras Calchaquı́es’’ under 0.25 grid size (Fig. 8b) Species 0.25 Species 0.25 BOT *Nassella leptothera BOT *Nassella fabrisii REP Liolaemus calchaqui REP Liolaemus heliodermis REP Liolaemus pagaburoi REP Liolaemus griseus ANF Gastrotheca gracilis ANF Telmatobius laticeps INS Pero olivacea INS Epimecis curvilinear INS Bassania schreiteri INS Psaliodes prionograma INS Lissochlora sanguinipunctata INS Synchlora suppomposa INS Motya haematopis 0.90 0.93 0.85 0.91 0.85 0.90 0.87 0.88 0.96 0.96 0.90 0.93 0.96 0.90 0.98 INS INS INS INS INS INS INS INS INS INS INS INS INS INS INS 0.92 0.89 0.94 0.97 0.87 0.90 0.83 0.92 0.92 0.93 0.94 0.97 0.96 0.90 0.91 *Only present in the study region. Coxina turibia Alypia australis Aucula hilzingeri albirubra Seirocastnia praefecta Galgula castra Agrotis aspersula Platysenta glaucoptera Jurinella tucumana Mimapsilopa mathisi Nostima flavida Dactylopius zimmermanni Anochetus altisquamis Pachycondyla striata Solenopsis angulata Prionopelta punctulata