ARTÍCULOS Y NOTAS

A predictive distribution model for Andinomys edax (Rodentia: Cricetidae) in Argentina

Pablo Jayat¹, Silvia Pacheco² and E. Ortiz³

¹ Laboratorio de Investigaciones Ecológicas de las Yungas (LIEY), Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán, Yerba Buena, Tucumán, Argentina [Correspondencia: <pjayat@proyungas.org.ar>].
² Sistema de Información Geográfica Ambiental, Fundación ProYungas, Yerba Buena, Tucumán, Argentina.
³ Cátedra de Paleontología, Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán, San Miguel de Tucumán, Tucumán, Argentina.

ABSTRACT: We studied the distribution of Andinomys edax (Rodentia, Cricetidae, Sigmodontinae) in northwestern Argentina. Presence data obtained from field records, museum specimens and literature were used to model the potential distribution of this species. Andinomys edax was known in Argentina from only 41 occurrence localities, most of them restricted to Jujuy province. Here we add new localities from Catamarca, Jujuy, Salta and Tucumán provinces. We used the maximum entropy method (Maxent) and seven environmental variables to produce a potential distribution map of the species. The 7.015 cumulative threshold (corresponding to the equal test of sensitivity and specificity) was the most efficient hypothetical distribution according to the omission rate. The area under the curve (AUC) was of 0.987 for training data. The model indicates that A. edax is mainly distributed on the more humid eastern montane ranges in the region, associated to high altitude grasslands and ecotonal zones between 1500 and 4000 m in Yungas, Prepuna, Puna and High Andean environments. These results, as well as the known fossil record for the species in Argentina, indicate the ecological plasticity of A. edax, present in the region at least since Middle-Upper Pleistocene times.

RESUMEN: Un modelo predictivo de distribución para Andinomys edax (Rodentia: Cricetidae) en Argentina. Se estudió la distribución de Andinomys edax (Rodentia, Cricetidae, Sigmodontinae) en el noroeste de Argentina. Para modelar la distribución potencial de la especie se usaron datos de presencia obtenidos a partir de registros de campo, de especímenes depositados en colecciones y de la literatura. Andinomys edax se conocía en Argentina por sólo 41 localidades, la mayoría de ellas en la provincia de Jujuy. En este trabajo se adicionan nuevas localidades para las provincias de Catamarca, Jujuy, Salta y Tucumán. El mapa de distribución potencial de la especie fue modelado utilizando el método de máxima entropía (Maxent) y siete variables ambientales. De acuerdo con la tasa de omisión, el umbral acumulativo de 7.015 (correspondiente al test de igualdad de sensitividad y especificidad) determinó la distribución hipotética más eficiente. El área bajo la curva (AUC) para el test ROC fue de 0.987 para los datos de entrenamiento. De acuerdo con el modelo, A. edax se distribuye principalmente sobre las laderas orientales húmedas de los cordones montañosos de la región, asociada a pastizales de altura y sus ecotonos entre 1500 y 4000 m, en ambientes de Yungas, Prepuna, Puna y Altos Andes. Estos resultados, así como el registro fósil de la especie en Argentina, indican una gran plasticidad ecológica de A. edax, presente en la región al menos desde el Pleistoceno medio-superior.

Key words. Maxent; Northwestern Argentina; Potential distribution; Sigmodontinae.

Palabras clave. Distribución potencial; Maxent; Noroeste de Argentina; Sigmodontinae.

INTRODUCCIÓN

Among Neotropical mammals, the subfamily Sigmodontinae (Rodentia, Cricetidae) is one of the most diversified and complex groups, with approximately 380 species (Musser and Carleton, 2005). In the last decade, phylogenetic analyses based on molecular characters have identified several sigmodontine genera that cannot be placed into any of the previously recognized suprageneric groups (Smith and Patton, 1999; D'Elía, 2003; D'Elía et al., 2003). Smith and Patton (1999) treated these genera as "unique lineages" and D'Elía (2003) referred to them as genera without clear phylogenetic relationships, as Sigmodontinae incertae sedis. One of these unique lines is the monotypic genus Andinomys Thomas (D'Elía et al., 2005, 2006), previously assigned to the tribe Phyllotini based on its morphology (e.g. Hershkovitz, 1962; Olds and Anderson, 1989; Steppan, 1995). Andinomys edax Thomas 1902, the only species of the genus, is a very distinctive species among sigmodontines by its particular cranial, dental (Hershkovitz, 1962; Steppan, 1995), and molecular characters (D'Elía et al., 2005).
Andinomys edax has a broad distribution in the Central Andean region of South America, from southeastern Peru, through southwestern Bolivia and northernmost Chile to northwestern Argentina (Pine et al., 1979; Redford and Eisenberg, 1992; Anderson, 1997; Eisenberg and Redford, 1999; D'Elía et al., 2006). In Argentina A. edax is known from a few records and, as most sigmodontine species, no potential distribution map has been built for the species. The aim of this contribution is to model the potential distribution for Andinomys edax and provide new occurrence localities in Argentina.

MATERIAL AND METHODS

For the ecoregions nomenclature we followed Burkart et al. (1999) and for the belts of the Yungas ecoregion we followed Brown et al. (2001). Distributional data of A. edax were obtained from field work through captured specimens (JPJ, PEO) and owl pellets samples (PEO-e), study of museum specimens and bibliographic revision. The new specimens studied, here informed with their field numbers, will be deposited at the Colección de Mamíferos Lillo (CML), San Miguel de Tucumán, Tucumán, Argentina. The studied collections were CML and Museo de Ciencias Naturales "Bernardino Rivadavia" (MACN), Buenos Aires, Argentina. Information on all specimens examined is provided in the Appendix. Geographic coordinates were obtained with a GPS and from maps of the Instituto Geográfico Militar (IGM, Argentina, scales 1:250 000 and 1:500 000) with five decimal degrees precision.
We used seven environmental variables to build the distribution model. These variables constitute a subset of 16 climatic, six vegetation and three topographic variables analyzed, that were selected with the object of avoid multicollinearity. All seven variables had Pearson correlation values (r) lower than 0.7 for P = 0.05. The spatial resolution of variables was 30 arc seconds, approximately equal to 0.72 km² in the study area (Hijmans et al., 2004). Climatic variables were obtained from the web page of WORLDCLIM (http://biogeo.berkeley.edu/ worldclim) and included temperature mean diurnal range, maximum temperature of the warmest month, temperature annual range, and annual precipitation. The topographic variable (altitude) was obtained from the digital elevation model (DEM) produced by NASA (Shuttle Radar Topographic Mission [SRTM]), available from http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp. Vegetation variables consist of two estimates of the Normalized Difference Vegetation Index (NDVI), available from http://edcdaac.usgs.gov/1KM/ 1kmhomepage.htlm. These variables were calculated from maximum monthly values (sensu Parra et al., 2004) and are: medium "greenness" (number of months where each pixel had a value between 109 and 150; Holben, 1986) and annual seasonality (100 x [overall maximum - overall minimum] / overall maximum; Hurlbert and Haskell, 2003). We used NDVI data from two years, April 1992 to March 1993 and February 1995 to January 1996.
We used Maxent (Phillips et al., 2006) to model the Andinomys edax distribution. This general-purpose method estimates a target probability distribution by finding the probability distribution of maximum entropy, subject to a set of constraints representing the incomplete information about the target distribution (Phillips et al., 2006; Phillips and Dudík, 2008). When applied to presence-only species distribution, the pixels of the study area make up the space on which the Maxent probability distribution is defined, pixels with known species occurrence records constituted the sample points, and the constraints are expressed in terms of functions of the environmental variables (Phillips et al., 2006; Phillips and Dudík, 2008). The best threshold, which defines the species distribution, can be determined through the performance of the omission rate and predicted area as a function of the cumulative threshold (Phillips et al., 2006). The Maxent procedure randomly selects 75% of the presence data to build the model (Appendix). We validated the model by a threshold-independent test, the receiver operating characteristic (ROC) analysis (Fielding and Bell, 1997; McPherson et al., 2004). The ROC characterizes the performance of the model at all possible thresholds by a single number, the area under the curve (AUC).

RESULTS RESULTS

Distributional records

We obtained 41 records from the literature, for Jujuy, Salta, Tucumán and Catamarca provinces (Fig. 1a and ; Thomas, 1913, 1921; Yepes, 1935; Hershkovitz, 1962; Heinonen Fortabat and Bosso, 1994; Mares et al., 1997; Díaz, 1999; Díaz and Barquez, 1999, 2007; Díaz et al., 2000; Jayat et al., 2008). In this contribution we add five new localities from Catamarca, Jujuy, Salta and Tucumán provinces (Fig. 1a and Appendix).

Fig. 1. a) Recording localities of Andinomys edax in the ecoregions of northwestern Argentina; b) Potential distribution of Andinomys edax in Argentina.

Potential distribution

The performance of the omission rate and predicted area as a function of the cumulative threshold indicates that the equal test of sensitivity and specificity threshold (7.015) represents the best distributional hypothesis for the distributional record data and environmental variables considered in our study. Additionally, the ROC analysis, with an AUC of 0.987 for training data, indicates a good per formance of the model.
The potential distribution map indicates that A. edax is mainly distributed between 1500 and 4000 m elevation on the more humid eastern montane ranges in the region, on Cordillera Oriental, Sierras Centrales and northernmost Sierras Pampeanas. The species seems mainly associated with high altitude grasslands and related ecotonal areas of Altos Andes and Yungas ecoregions (sensu Burkart et al., 1999). Moreover, on western slopes the distributional hypothesis indicates that it occurs in grassy areas of Monte de Sierras y Bolsones through the entire region and in the most humid places of Puna in Jujuy province (Fig. 1b). A few pixels in the model also indicate the potential presence of A. edax for the Chaco Seco ecoregion in northeastern Salta and in lower belts of Yungas forest in central Tucumán (Fig. 1b).
The diverse environmental conditions where A. edax lives, from Puna (mean annual rainfall <350 mm; mean annual temperature <10°C) to Yungas (mean annual rainfall >1500 mm; mean annual temperature >16 °C), suggests that this species has a high ecological plasticity. This can also be inferred from the fossil record. Andinomys edax has been an ubiquitous sigmodontine in northwestern Argentina since, at least, the Late Pleistocene times (Ortiz and Jayat, 2007a), and all the small mammal fossil assemblages where it has been found suggest a wide range of paleoenvironmental conditions (Ortiz, 2001; Ortiz and Pardiñas, 2001; Teta and Ortiz, 2002; Ortiz and Jayat, 2007a,b).

DISCUSSION AND CONCLUSION

With a few outstanding exceptions (e.g., Akodon lutescens, A. simulator, Phyllotis osilae, and P. xanthopygus), most of northwestern sigmodontine species are known by a few occurrence localities. Until recently, the distributional information for Andinomys edax was scarce and concentrated to the northern extreme of this region, with almost 60% of the distributional records in Jujuy province (Díaz, 1999; Díaz and Barquez, 2007). Recently, we added several records for the species in high altitudinal grassland environments, most of them for the south of northwestern Argentina (Jayat et al., 2008). Here we offer all the records at hand for the species in the region, filling a noteworthy gap of information.
Predictive modelling of species occurrences has become an important tool in biogeographical and ecological studies, with many contributions in the last 20 years (Scott et al., 2002; Guisan and Thuiller, 2005; Guisan et al., 2006). The proliferation of new technologies, such as Geographic Information Systems, new statistical packages and specific programs dedicated to prediction of distributions, as well as the growing accessibility to global databases, have successfully promoted the development of this topic. Among several available modelling techniques, Maxent has proved to be a good tool for determining distributional areas without absence data, few recording localities, limited environmental data and for a number of taxonomical groups and geographic areas (Phillips et al., 2006; Benito de Pandos and Peñas de Giles, 2007; Guisan et al., 2007a,b; Giovanelli et al., 2008; Ortega-Huerta and Townsend Peterson, 2008). Many additional advantages for Maxent procedure have been delineated in Phillips et al. (2006).
We use a "correlative approach" to ecological niche modeling (Soberón and Peterson, 2005). In this approach, the algorithm (in this case Maxent) searches the map for areas that are "similar" ecologically (often climatic) to those where the species is known to occur, and so, the resulting model constitute a geographic representation of the fundamental ecological niche (FN) and not the distributional area of the species (Soberón and Peterson, 2005). This last area is determined by a mixture of biotic and abiotic factors in a complex spatial context. Notwithstanding, we tried to approach to the distributional area of A. edax using our own knowledge about areas in northwestern Argentina where the species is absent and taking advantage of the spatial scale of our study. In this way, although the distribution of A. edax extends northward to southern Peru, we choose to model its distribution only in Argentina. We have much more information for their distribution in this country (presence and absence data), we examined most of the cited specimens, and we know in detail the geographic features and environmental characteristics of northwestern Argentina, which allowed us to interpret and evaluate the performance of the model. The regional scale of this analysis indicates, at least theoretically, a low influence of biotic factors on the resulting model (Soberón and Peterson, 2005).
The model obtained for A. edax in the study area is a good approximation according to our field experience and has relatively sound validation values. Notwithstanding, new occurrence records could improve the distributional hypothesis obtained here. In our model building we used seven environmental variables related to climatic, topographic and vegetation characteristics of the area; however, better results can be obtained by using more related species life requirements variables (e.g. food resources availability). Unfortunately, as for many sigmodontine species of northwestern Argentina, there is not enough information about habitat requirements for A. edax.
Albeit all the A. edax records in Argentina come mainly from highland grasslands of Jujuy, Salta, Tucumán and Catamarca provinces, the potential distribution indicates its presence in several mountainous areas in La Rioja and Córdoba provinces, such as Sierras de Velazco, Famatina, Sierras Grandes and Sierras Chicas (Fig. 1b). Until now, there were no known records for the species in these areas, despite environmental similarities with nearby areas situated northward in Catamarca province. This absence can be explained by the lack of sampling in Sierra de Velazco and Famatina ranges, both large and unexplored areas. However, the absence of A. edax in Sierras Grandes could be related to historical causes, being the species unable to colonize these areas across regions occupied by extensive dry and warm Chaco environments. Alternatively, a possible colonization of Sierras Grandes during Quaternary times followed by local extinction can not be ruled out. It is necessary to highlight that many surveys were accomplished in this hilly area but A. edax has never been registered (Polop, 1989, 1991; Priotto et al., 1996; Altrichter et al., 2001, 2004; Kufner et al., 2004).
The model predicts the presence of the species for extensive areas of Monte de Sierras y Bolsones environments in all the study area. We think that the more xeric areas in this ecoregion are not a good environment for A. edax. The presence of the species in areas such as Campo del Arenal (Catamarca) and low elevation places of the Valles Calchaquíes (Tucumán and Salta) constitutes probably an overestimation of the real distribution. Most of locations in Monte de Sierras y Bolsones where A. edax was registered are associated to humid microenvironments, with nearby watercourses flanked by dense bunch grasses. Probably, the prediction of the species presence in xeric areas of the Monte is consequence of the use of owl pellets records (localities 10, 14 and 29). We think that using owl pellets samples for predicting species distribution in areas with high environmental heterogeneity, and sharply contrasting conditions in short distances, must be taken with caution.
A few low elevation areas (below 1000 m) are considered as an adequate environment for A. edax by the Maxent model. One of these areas is located in Selva Montana of Yungas, around the Sierra de San Javier, Tucumán (Fig. 1). From this area there is one record for Horco Molle, a noticeably different locality from the rest of sites where A. edax has been trapped, with more humid and warmer climatic conditions and a very different vegetation structure. This apparently incongruent record, as well as the distributional prediction, is probably the result of inadequate sampling in this intermediate altitudinal belt. The sigmodontines of the Yungas ecoregion in northwestern Argentina have been relatively well surveyed at low (Selva Pedemontana) and high (Bosque Montano and Pastizales de Neblina) elevations (Jayat et al., 2008, in press), but there are very few collecting localities in Selva Montana, between 700 and 1400 m elevation. The second low elevation area includes very small patches of Chaco environments in northeastern Salta (Fig. 1b). Our field experience and all the available distributional evidence indicate that this is clearly an overestimation of the real distribution, and we think the species is not present in the Chacoan region. In spite of more than 100 localities until now surveyed, Andinomys edax was not registered in this xeric ecoregion.
In Salta province the present record of the species is very poor, restricted to only five localities in central and northernmost areas. In spite of these few records, the potential distribution map predicts the presence of A. edax for large areas in the eastern slopes of Sierra de Santa Victoria and Sierra de Zenta, in the northern part of the province, where we think the species is indeed present (Fig. 1b).
The model also indicates the A. edax presence in highland grasslands of isolated eastern mountain systems of all the region, such as Sierra de Santa Bárbara, Sierra del Centinela (Jujuy), Sierra de Metán, Sierra de la Candelaria (Salta), Sierra de Medina, Sierra del Campo (Tucumán), and Sierra de Ancasti (Catamarca) (Fig. 1b). In these systems we recorded other two sigmodontine species (Necromys lactens and Phyllotis osilae) restricted to highland grassland environments through most of Northwestern Argentina and usually registered simpatrically with A. edax (Jayat and Pacheco, 2006; Jayat et al., 2006, 2008). This similar distributional pattern together with the proximity of these systems respect to areas of proved presence, suggest that A. edax is also present in these areas.
Although A. edax is mainly a dweller of high altitude grasslands and related ecotonal highland systems, it inhabits humid places of very dissimilar ecoregions, being part of very different micromammal assemblages from the Middle-Upper Pleistocene to present time. Notwithstanding the noteworthy Quaternary environmental changes that have impacted on small mammal communities in northwestern Argentina (Ortiz et al., 2000a; Ortiz and Pardiñas, 2001; Pardiñas et al., 2002; Teta and Ortiz, 2002; Ortiz and Jayat, 2007a, b), A. edax is present in most of the studied fossil assemblages in this region.
Clearly, the distributional hypothesis obtained here can be improved with additional field data coming from northwestern Argentina, including all its distributional area in South America, and with a better knowledge on the ecological requirements of the species. However, this hypothesis represents the best available approximation and constitutes an objective tool for conservation planning in Argentina. This distributional model, and those obtained for other species in this region (e.g. Porcasi et al., 2005; Jayat and Pacheco, 2006), can constitute the base for further studies on ecology and biogeography of the sigmodontines in northwestern Argentina and the foundations for the development of appropriate conservation policies based on areographic characteristics such as size of total area, shape and connectivity, hot spot areas, and endemicity.

APPENDIX

List of the presence localities and data sources for Andinomys edax in Argentina. Asterisks indicate the localities representing new records for the species. See appendix here.

ACKNOWLEDGMENTS

We thank J. Babot, J. Ballesteros, J. Carilla, D. Juri, E. Martin, D. Miotti, G. Namen, J. Powell, F. Scacchi and J. Torres Dowdall for helping us during field work. S. McLaren provided us with photographic material of the Carnegie Museum of Natural History, USA. We are indebted to the following curators: R. Bárquez (Colección de Mamíferos Lillo, Tucumán, Argentina), J. Powell (Colección Paleovertebrados Lillo, Tucumán, Argentina) and O. Vaccaro (Museo Argentino de Ciencias Naturales "Bernardino Rivadavia", Buenos Aires, Argentina). Two anonymous reviewers improved significantly the original manuscript. Financial support was provided by Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT), Consejo de Investigaciones de la Universidad Nacional de Tucumán (CIUNT), Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET) and Fundación ProYungas. This work was possible thanks to the institutional support of the Laboratorio de Investigaciones Ecológicas de las Yungas and Cátedra de Paleontología, Universidad Nacional de Tucumán.

LITERATURE CITED

1. ALTRICHTER M, M KUFNER, L GIRAUDO, G GAVIER, D TAMBURINI, A CALABRESE, M SIRONI, and L ARGUELLO. 2001. Comunidades de micromamíferos de bosque serrano y pastizal de altura de la Reserva La Quebrada, Córdoba, Argentina. Vida Silvestre Neotropical 10:58-61         [ Links ]

2. ALTRICHTER M, M KUFNER, L GIRAUDO, G GAVIER, D TAMBURINI, M SIRONI and L ARGUELLO. 2004. Comunidades de micromamíferos de bosque serrano y pastizal de altura en la Sierra Chica, Córdoba, Argentina. Ecología Aplicada 3:122-127.         [ Links ]

3. ANDERSON S. 1997. Mammals of Bolivia, taxonomy and distribution. Bulletin of the American Museum of Natural History 231:1-652.         [ Links ]

4. BENITO DE PANDO B and J PEÑAS DE GILES. 2007. Aplicación de modelos de distribución de especies a la conservación de la biodiversidad en el sureste de la Península Ibérica. GeoFocus (Artículos) 7:1578-5157.         [ Links ]

5. BROWN AD, HR GRAU, LR MALIZIA, and A GRAU. 2001. Argentina. Pp. 623-659, in: Bosques Nublados del Neotrópico (M Kappelle y AD Brown, eds.). Instituto Nacional de Biodiversidad (INBio). Costa Rica.         [ Links ]

6. BURKART R, N BARBARO, RO SANCHEZ, and DA GÓMEZ. 1999. Eco-regiones de la Argentina. Administración de Parques Nacionales, PRODIA.         [ Links ]

7. CABRERA AL. 1976. Regiones fitogeográficas de la República Argentina. Enciclopedia Argentina de Agricultura y Jardinería 2:1-85.         [ Links ]

8. CAPLLONCH P, A AUTINO, MM DÍAZ, RM BÁRQUEZ, and M GOYTIA. 1997. Los mamíferos del Parque Biológico Sierra de San Javier, Tucumán, Argentina: observaciones sobre su sistemática y distribución. Mastozoología Neotropical 4:49-71.         [ Links ]

9. D´ELÍA G. 2003. Phylogenetics of Sigmodontinae (Rodentia, Muroidea, Cricetidae), with special reference to the akodont group, and with additional comments on historical biogeography. Cladistics 19:307-323.         [ Links ]

10. D´ELÍA G, EM GONZÁLEZ, and UFJ PARDIÑAS. 2003. Phylogenetic analysis of sigmodontinae rodents (Muroidea), with special reference to the akodont genus Deltamys. Mammalian Biology 68:351-364.         [ Links ]

11. D'ELÍA G, UFJ PARDIÑAS, and P MYERS. 2005. An introduction to the genus Bibimys (Rodentia: Sigmodontinae): Phylogenetic position and alpha taxonomy. Pp. 211-246, in: Mammalian diversification: from Chromosomes to Phylogeography (A Celebration of the Career of James L. Patton) (E Lacey and P Myers, eds.). University of California Publication in Zoology.         [ Links ]

12. D'ELÍA G, P TETA, and UFJ PARDIÑAS. 2006. Incertae Sedis. Pp. 197-202, in: Mamíferos de Argentina: sistemática y distribución (RM Bárquez, MM Díaz and RA Ojeda, eds.). Sociedad Argentina para el Estudio de los Mamíferos.         [ Links ]

13. DALBY PL and MA MARES. 1974. Notes on the distribution of the coney rat, Reithrodon auritus, in Northwestern Argentina. American Midland Naturalist 92:205-206.         [ Links ]

14. DÍAZ MM. 1999. Mamíferos de la Provincia de Jujuy: sistemática, distribución y ecología. Unpublished Ph.D. dissertation, Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán, Tucumán, Argentina.         [ Links ]

15. DÍAZ MM and RM BARQUEZ. 1999. Contributions to the knowledge of the mammals of Jujuy Province, Argentina. The Southwestern Naturalist 44:324-333.         [ Links ]

16. DÍAZ MM and RM BARQUEZ. 2007. The Wild Mammals of Jujuy Province, Argentina: Systematics and Distribution. Pp. 417-578, in: The Quintessential Naturalist: Honoring the Life and Legacy of Oliver P. Pearson (DA Kelt, EP Lessa, J Salazar-Bravo, and JL Patton, eds.). University of California Publications in Zoology 134.         [ Links ]

17. DÍAZ MM, JK BRAUN, MA MARES, and RM BARQUEZ. 2000. An update of the taxonomy, systematic, and distribution of the mammals of Salta Province, Argentina. Occasional Papers of the Oklahoma Museum of Natural History 10:1-52.         [ Links ]

18. EISENBERG JF and KH REDFORD. 1999. Mammals of the Neotropics, Volume 3. The Central Neotropics. Ecuador, Peru, Bolivia, Brazil. The University of Chicago Press, Chicago and London.         [ Links ]

19. FIELDING AH y J F BELL. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49.         [ Links ]

20. GIOVANELLI JGR, CFB HADDAD, and J ALEXANDRINO. 2008. Predicting the potential distribution of the alien invasive American bullfrog (Lithobates catesbeianus) in Brazil. Biological Invasions 10:585-590.         [ Links ]

21. GUISAN A, CH GRAHAM, J ELITH, and F HUETTMANN. 2007a. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions 13:332-340.         [ Links ]

22. GUISAN A, A LEHMANN, S FERRIER, R ASPINALL, R OVERTON, M AUSTIN, and T HASTIE. 2006. Making better biogeographic predictions of species distribution. Journal of Applied Ecology 43:386-392.         [ Links ]

23. GUISAN A and W THUILLER. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8:993-1009.         [ Links ]

24. GUISAN A, NE ZIMMERMANN, J ELITH, CH GRAHAM, S PHILLIPS, and AT PETERSON. 2007b. What matters for predicting the occurrences of trees: techniques, data, or species' characteristics? Ecological Monographs 77:615-630.         [ Links ]

25. HEINONEN FORTABAT S and A BOSSO. 1994. Nuevos aportes para el conocimiento de la mastofauna del Parque Nacional Calilegua (Provincia de Jujuy, Argentina). Mastozoología Neotropical 1:51-60.         [ Links ]

26. HERSHKOVITZ P. 1962. Evolution of Neotropical cricetine rodents (Muridae) with special reference to the phyllotine group. Fieldiana Zoology 46:1-524.         [ Links ]

27. HIJMANS R, S CAMERON, and J PARRA. 2004. WorldClim, version 1.2. A square kilometer resolution database of global terrestrial surface climate. http://www.biogeo.berkeley.edu         [ Links ]

28. HOLBEN BN. 1986. Characteristics of maximum-value composite images for temporal AVHRR data. International Journal of Remote Sensing 7:1435-1445.         [ Links ]

29. HURLBERT A and J HASKELL. 2003. The effect of energy and seasonality on avian species richness and community composition. American Naturalist 161:83-97.         [ Links ]

30. JAYAT JP and S PACHECO. 2006. Distribución de Necromys lactens y Phyllotis osilae (Rodentia: Cricetidae: Sigmodontinae) en el noroeste argentino: modelos predictivos basados en el concepto de nicho ecológico. Mastozoología Neotropical 13:69-88.         [ Links ]

31. JAYAT JP, PE ORTIZ, and MD MIOTTI. 2008. Distribución de sigmodontinos (Rodentia: Cricetidae) en pastizales de neblina del noroeste argentino. Acta Zoológica Mexicana 24:137-177.         [ Links ]

32. JAYAT JP, PE ORTIZ, and MD MIOTTI. In press. Mamíferos de la selva pedemontana del noroeste argentino. In: Ecología, historia natural y conservación de la selva pedemontana de las yungas australes (AD Brown, PG Blendinger, T Lomáscolo, and P García Bes, eds.). Ediciones del Subtrópico.         [ Links ]

33. JAYAT JP, PE ORTIZ, P TETA, UFJ PARDIÑAS, and G D'ELÍA. 2006. Nuevas localidades argentinas para algunos roedores sigmodontinos (Rodentia: Cricetidae). Mastozoología Neotropical 13:51-67.         [ Links ]

34. KUFNER MB, G GAVIER, and D TAMBURINI. 2004. Comunidades de roedores de pampas de altura en las Sierras Grandes en Córdoba, Argentina. Ecología Aplicada 3:118-121.         [ Links ]

35. MC PHERSON JM, W JETZ, and DJ ROGERS. 2004. The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? Journal of Applied Ecology 41:811-823.         [ Links ]

36. MARES MA, RA OJEDA, JK BRAUN, and RM BÁRQUEZ. 1997. Systematics, distribution, and ecology of the mammals of Catamarca Province, Argentina. Pp. 89-141, in: Life among the Muses: Papers in Honor of James S. Findley (TL Yates, WL Gannon, and E Wilson, eds.). The Museum of Southwestern Biology, The University of New Mexico, Albuquerque.         [ Links ]

37. MUSSER GG and MD CARLETON. 2005. Superfamily Muroidea. Pp. 894-1531, in: Mammal Species of the World. A taxonomic and geographic reference (DE Wilson and DM Reeder, eds.). The Johns Hopkins University Press, Baltimore, 3rd edition.         [ Links ]

38. OLDS N and S ANDERSON. 1989. A diagnosis of the tribe Phyllotini (Rodentia, Muridae). Pp. 55-74, in: Advances in Neotropical Mammalogy (KH Redford and JF Eisenberg, eds.). Sandhill Crane Press, Gainesville, Florida.         [ Links ]

39. ORTEGA-HUERTA MA and A TOWNSEND PETERSON. 2008. Modeling ecological niches and predicting geographic distributions: a test of six presence-only methods. Revista Mexicana de Biodiversidad 79:205-216.         [ Links ]

40. ORTIZ PE. 2001. Roedores del Pleistoceno superior del valle de Tafí (Provincia de Tucumán), implicancias paleoambientales y paleobiogeográficas. Unpublished Ph.D. dissertation, Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán, Argentina.         [ Links ]

41. ORTIZ PE and JP JAYAT. 2007a. Fossil record of the Andean Rat, Andinomys edax (Rodentia: Cricetidae), in Argentina. Mastozoología Neotropical 14:77-83.         [ Links ]

42. ORTIZ PE and JP JAYAT. 2007b. Roedores sigmodontinos (Mammalia: Rodentia: Cricetidae) del límite Pleistoceno-Holoceno en el valle de Tafí, provincia de Tucumán (Argentina): tafonomía y significación paleoambiental. Ameghiniana 44:641-660.         [ Links ]

43. ORTIZ PE and UFJ PARDIÑAS. 2001. Sigmodontinos (Mammalia, Rodentia) del Pleistoceno tardío del Valle de Tafí (Tucumán, Argentina): taxonomía, tafonomía y reconstrucción paleoambiental. Ameghiniana 38:3-26.         [ Links ]

44. ORTIZ PE, UFJ PARDIÑAS, and S STEPPAN. 2000a. A new fossil phyllotine (Rodentia: Muridae) from northwestern Argentina and the relationships of Reithrodon group. Journal of Mammalogy 81:37-51.         [ Links ]

45. ORTIZ PE, S CIRIGNOLI, DH PODESTA, and UFJ PARDIÑAS. 2000b. New records of sigmodontine rodents (Mammalia: Muridae) from high-Andean localities of northwestern Argentina. Biogeographica 76:133-140.         [ Links ]

46. PARDIÑAS UFJ, G D´ELÍA, and PE ORTIZ. 2002. Sigmodontinos fósiles (Rodentia, Muroidea, Sigmodontinae) de América del Sur: estado actual de su conocimiento y prospectiva. Mastozoología Neotropical 9:209-252.         [ Links ]

47. PARRA J, C GRAHAM, and J FREILE. 2004. Evaluating alternative data sets for ecological niche models of birds in the Andes. Ecography 27:350-360.         [ Links ]

48. PHILLIPS SJ, RP ANDERSON, and RE SCHAPIRE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modeling 190:231-259.         [ Links ]

49. PHILLIPS SJ and M DUDÍK. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161-175.         [ Links ]

50. PINE RH, SD MILLER, and ML SCHAMBERGER. 1979. Contributions to the mammalogy of Chile. Mammalia 43:339-376.         [ Links ]

51. POLOP JJ. 1989. Distribution and ecological observation of wild rodents in Pampa de Achala, Córdoba, Argentina. Studies on Neotropical Fauna and Environment 24:53-59.         [ Links ]

52. POLOP JJ. 1991. Distribución de cricétidos en las sierras de Achala (Provincia de Córdoba, República Argentina). Revista de la Universidad Nacional de Río Cuarto 11:115-121.         [ Links ]

53. PORCASI X, GE CALDERÓN, M LAMFRI, M SCAVUZZO, MS SABATTINI, and JJ POLOP. 2005. Predictive distribution maps of rodent reservoir species of zoonosis in southern America. Mastozoología Neotropical 12:199-216.         [ Links ]

54. PRIOTTO JW, M MORANDO, and L AVILA. 1996. Nuevas citas de roedores de los pastizales de altura de la sierra de Comechingones, Córdoba, Argentina. Facena 12:135-137.         [ Links ]

55. REDFORD K and J EISENBERG. 1992. Mammals of the Neotropics, Volume 2. The Southern Cone. Chile, Argentina, Uruguay, Paraguay. University of Chicago Press, Chicago.         [ Links ]

56. SCOTT J, P HEGLUND, M MORRISON, J HAUFLER, M RAPHAEL, W WALL, and F SAMSON. 2002. Predicting species occurrence: issues of accuracy and scale. Island Press, Washington DC.         [ Links ]

57. SMITH MF and JL PATTON. 1999. Phylogenetic relationships and the radiation of sigmodontine rodents in South Americ: evidence from cytochrome b. Journal of Mammalian Evolution 6:89-128.         [ Links ]

58. SOBERÓN J and AT PETERSON. 2005. Interpretation of models of fundamental ecological niches and species' distributional areas. Biodiversity Informatics 2:1-10.         [ Links ]

59. STEPPAN SJ. 1995. Revision of the tribe Phyllotini (Rodentia: Sigmodontinae), with a phylogenetic hypothesis for the Sigmodontinae. Fieldiana Zoology, new series 80:1-112.         [ Links ]

60. TETA P and PE ORTIZ. 2002. Micromamíferos andinos holocénicos del sitio arqueológico Inca Cueva 5, Jujuy, Argentina: tafonomía, zoogeografía y reconstrucción paleoambiental. Estudios Geológicos 58:117-135.         [ Links ]

61. THOMAS O. 1913. On small mammals collected in Jujuy by Senor E. Budin. Annals and Magazine of Natural History 8:136-143.         [ Links ]

62. THOMAS O. 1919. List of mammals from the highlands of Jujuy, north Argentina, collected by Sr. E. Budin. Annals and Magazine of Natural History 9:128-135.         [ Links ]

63. THOMAS O. 1921. On a further collection of mammals from Jujuy obtained by Sr. E. Budin. Annals and Magazine of Natural History 9:608-617.         [ Links ]

64. YEPES J. 1935. Consideraciones sobre el género "Andinomys" (Cricetinae) y descripción de una forma nueva. Anales del Museo Argentino de Ciencias Naturales "Bernardino Rivadavia" 38:333-348.         [ Links ]

Recibido 29 diciembre 2008.
Aceptado 13 abril 2009.

Editor asociado: J Morrone