1-Lopez Arevalo Et Al 2011

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Local knowledge and species distribution models’ contribution towards mammalian conservation Hugo Fernando López-Arévalo a,b,, Sonia Gallina b,1 , Rosario Landgrave c , Enrique Martínez-Meyer d , Lyssette E. Muñoz-Villers e,2 a Instituto de Ciencias Naturales, Universidad Nacional de Colombia, A.A. 7495, Bogotá D.C., Colombia b Instituto de Ecología, INECOL, A.C. Red Biología y Conservación de Vertebrados, km 2.5 Carretera Antigua a Coatepec No. 351, Apartado Postal 91070, Xalapa, Veracruz, Mexico c Instituto de Ecología, INECOL, A.C. Red Ecología Funcional, km 2.5 Carretera Antigua a Coatepec No. 351, Apartado Postal 91070, Xalapa, Veracruz, Mexico d Universidad Nacional Autónoma de México, Instituto de Biología, Laboratorio de Análisis Espaciales, Ciudad de México 04510, Mexico e Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331-8615, USA a r t i c l e i n f o  Article history: Received 12 November 2009 Received in revised form 9 January 2011 Accepted 20 January 2011 Available online xxxx Keywords: Conservation method Distribution model Local knowledge Medium-sized mammal Mexico a b s t r a c t Landscape-scale studies facilitate species diversity analysis according to environmental heterogeneity and human activity. This study was aimed at using local knowledge as a tool for testing predictive mod- els’ validity for assessing the spatial distribution of medium-sized mammalian richness, identifying local patterns of species richness and evaluating local protected areas’ role in the conservation of mammals. Distribution maps were generated for historically recorded species using genetic algorithm for rule-set prediction (GARP). The landscape was reclassied as habitat, hospitable matrix and inhospitable matrix in the sec ond sce nar io and a thi rd scenario was gen era tedlimiti ng spe cie s dis tri but ion by usi ng thehome range. The local richness predicted by all scenarios varied from 1 to 32 species per cell while gamma diversity was 34. The 72 structured interviews led to recording 3–17 species (a total of 27). There have bee n no rep orts of nine wil d spe cie s ove r the last 2 yea rs.Curren tly pr otected areas can not sup por t via ble populations of the species so recorded so shade coffee plantations must adopt conservation strategies. Hist orica l inve ntories over estimate expec ted richn ess;however, combining GARP -gen erated models with the infor matio n obtai ned from local inhabitants and exper ts allows rapi d regio nal evalu ation of medi um- sized mammalian richness and the identication of extinct species, declining populations and abundant species. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction Landscape studies involving investigations into spatial patterns and ecological processes converge on broad spatiotemporal scales; they have been identied as a priority for both ecological research and their applica tion to envi ronm enta l pro blems ( Turner and Gardner, 1991). Species diversity may be analyzed on the land- scape scale as a function of (but not just) the physical and biolog- ical environment’s heterogeneity and also as the effect of human activities on species’ distribution and abundance ( Franklin, 1993; Halffter, 1998). Me asu rin g the num ber of sp eci es in thelandsc ap e all ows the ef- fects of forest fragmentation on species’ permanence or extinction to be detected. Such measures also allow spatial patterns to be identied (Lomolino, 2001), such as those occurring along urban– rural gradients where richness is greater in rural areas (see review by McKinney (2002)). Identifying such patterns leads to forming conservation strategies on different geographic scales ( Funk and Richardson, 2002). Simulation models have been useful in selecting biodiversity cons ervat ion -pro moti ng actio n on diff eren t scal es, there by pre- dicting species distribution, the effects of climate change and con- icts with human activity (Rodríguez et al., 2007). The copious amount of information available on biodiversity (Bisby, 2000; Edwards et al., 2000 ) and current digital data pro- cessing capacity offer several tools for modeling species distribu- tion. The compar ative analysis of diff eren t methods using the same data-set highlights how presence-only data are useful for modelin g spec ies dist ribut ions and dem onst rate diff eren ces in pre- dict ive perf ormance amo ng mod elin g methods, desp ite subs tanti al variation at regional and species levels ( Elith et al., 2006). The diff eren ce betw een differen t models’ pred ictio ns may be expl ained 0006-3207/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2011.01.014 Corresponding author. Address: Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Carrera 30 No. 45-03 Bogotá D.C., A.A. 7495, Colombia. Tel.: +57 1 316 5000x11525, fax: +57 1 316 5000x11502. E-mail addresses: h[email protected] , h[email protected] (H.F. López -Aré- valo), [email protected] (S. Gallina), [email protected] (R. Landgrave), [email protected] (E. Martí nez-M eyer), Lyssette. [email protected] (L.E. Muñoz-Villers). 1 Tel.: +52 228 842 1800x4110. 2 Tel.: +1 541 737 4952. Biological Conservation xxx (2011) xxx–xxx Contents lists available at ScienceDirect Biological Conservation journal homepage: www.elsevier.com/locate/biocon Please cite this article in press as: López-Arévalo, H.F., et al. Local knowledge and species distribution models’ contribution towards mammalian conser- vation. Biol. Conserv. (2011), doi:10.1016/j.biocon.2011.01.014

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Local knowledge and species distribution models’ contribution towardsmammalian conservation

Hugo Fernando López-Arévalo a,b,⇑, Sonia Gallina b,1, Rosario Landgrave c, Enrique Martínez-Meyer d,Lyssette E. Muñoz-Villers e,2

a Instituto de Ciencias Naturales, Universidad Nacional de Colombia, A.A. 7495, Bogotá D.C., Colombiab Instituto de Ecología, INECOL, A.C. Red Biología y Conservación de Vertebrados, km 2.5 Carretera Antigua a Coatepec No. 351, Apartado Postal 91070, Xalapa, Veracruz, Mexicoc Instituto de Ecología, INECOL, A.C. Red Ecología Funcional, km 2.5 Carretera Antigua a Coatepec No. 351, Apartado Postal 91070, Xalapa, Veracruz, Mexicod Universidad Nacional Autónoma de México, Instituto de Biología, Laboratorio de Análisis Espaciales, Ciudad de México 04510, Mexicoe Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331-8615, USA

a r t i c l e i n f o

 Article history:

Received 12 November 2009Received in revised form 9 January 2011Accepted 20 January 2011Available online xxxx

Keywords:

Conservation methodDistribution modelLocal knowledgeMedium-sized mammalMexico

a b s t r a c t

Landscape-scale studies facilitate species diversity analysis according to environmental heterogeneityand human activity. This study was aimed at using local knowledge as a tool for testing predictive mod-els’ validity for assessing the spatial distribution of medium-sized mammalian richness, identifying localpatterns of species richness and evaluating local protected areas’ role in the conservation of mammals.Distribution maps were generated for historically recorded species using genetic algorithm for rule-setprediction (GARP). The landscape was reclassified as habitat, hospitable matrix and inhospitable matrixin the second scenario and a third scenario was generated limiting species distribution by using the homerange. The local richness predicted by all scenarios varied from 1 to 32 species per cell while gammadiversity was 34. The 72 structured interviews led to recording 3–17 species (a total of 27). There havebeen no reports of nine wild species over the last 2 years. Currently protected areas cannot support viablepopulations of the species so recorded so shade coffee plantations must adopt conservation strategies.

Historical inventories overestimate expected richness; however, combining GARP-generated models withthe information obtained from local inhabitants and experts allows rapid regional evaluation of medium-sized mammalian richness and the identification of extinct species, declining populations and abundantspecies.

Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Landscape studies involving investigations into spatial patternsand ecological processes converge on broad spatiotemporal scales;they have been identified as a priority for both ecological researchand their application to environmental problems (Turner andGardner, 1991). Species diversity may be analyzed on the land-scape scale as a function of (but not just) the physical and biolog-

ical environment’s heterogeneity and also as the effect of humanactivities on species’ distribution and abundance (Franklin, 1993;Halffter, 1998).

Measuring the number of species in the landscape allows the ef-fects of forest fragmentation on species’ permanence or extinctionto be detected. Such measures also allow spatial patterns to beidentified (Lomolino, 2001), such as those occurring along urban–rural gradients where richness is greater in rural areas (see reviewby McKinney (2002)). Identifying such patterns leads to formingconservation strategies on different geographic scales (Funk andRichardson, 2002).

Simulation models have been useful in selecting biodiversityconservation-promoting action on different scales, thereby pre-dicting species distribution, the effects of climate change and con-flicts with human activity (Rodríguez et al., 2007).

The copious amount of information available on biodiversity(Bisby, 2000; Edwards et al., 2000) and current digital data pro-cessing capacity offer several tools for modeling species distribu-tion. The comparative analysis of different methods using thesame data-set highlights how presence-only data are useful formodeling species distributions and demonstrate differences in pre-dictive performance among modeling methods, despite substantialvariation at regional and species levels (Elith et al., 2006). Thedifference between different models’ predictions may be explained

0006-3207/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.biocon.2011.01.014

⇑ Corresponding author. Address: Instituto de Ciencias Naturales, UniversidadNacional de Colombia, Carrera 30 No. 45-03 Bogotá D.C., A.A. 7495, Colombia. Tel.:+57 1 316 5000x11525, fax: +57 1 316 5000x11502.

E-mail addresses: [email protected], [email protected] (H.F. López-Aré-valo), [email protected] (S. Gallina), [email protected] (R.Landgrave), [email protected] (E. Martínez-Meyer), [email protected] (L.E. Muñoz-Villers).

1 Tel.: +52 228 842 1800x4110.2 Tel.: +1 541 737 4952.

Biological Conservation xxx (2011) xxx–xxx

Contents lists available at ScienceDirect

Biological Conservation

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / b i o c o n

Please cite this article in press as: López-Arévalo, H.F., et al. Local knowledge and species distribution models’ contribution towards mammalian conser-vation. Biol. Conserv. (2011), doi:10.1016/j.biocon.2011.01.014

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by two modeling techniques’ characteristics: data input require-ments (presence/absence vs. presence-only approaches) and theassumptions made by each algorithm when extrapolating beyondthe range of data used for building a particular model (Pearsonet al., 2006). Genetic algorithm for rule-set prediction (GARP) is acommonly used model for studying species distribution; it is a ge-netic algorithm that brings together several approaches for model-ing artificial intelligence-based distribution. GARP searches fornon-random correlations between species presence and environ-mental parameters using several types of rules (for a detailedexplanation see Anderson et al., 2003). Combining GARP resultswith land use and plant cover data leads to closely approximatingspecies’ real distribution (Sánchez-Cordero et al., 2005).

GARP was found to be useful in predicting potential Insectivora,Chiroptera, Rodentia and Artiodactyla species’ distribution in thestate of Oaxaca, Mexico; however, as expected, its accuracy de-pended on the number of available records (Illoldi-Rangel et al.,2004). It has been applied to studying the distribution and ecolog-ical relationships of other mammals based on information ob-tained from scientific collections’ records (Anderson et al., 2003;Anderson and Martinez-Meyer, 2004). GARP also provides betterresolution when detecting richness patterns compared to species’aggregation methods and the use of vegetation type as a descriptorof richness (Stockwell and Peterson, 2003).

GARP presents the highest over-prediction (Elith and Graham,2009) compared to other species distribution models, althoughcomparison results can vary according to the available information,species type, the models being compared and an investigator’sinterpretation (Stockwell and Peterson, 2003: Elith et al., 2006;Phillips, 2008). Even though discussion about the parameters forselecting the best model still continue (Segurado and Araújo,2004; Elith and Graham, 2009), field predictions must be verified.Using local inhabitants’ ecological knowledge regarding their landcan be an alternative for verifying a model’s predictions because, inmany cases, this source provides more and better information thanthat obtained by western science (Huntington, 2000). This is more

evident in fauna conservation planning and management-relatedresearch and projects (Becker and Ghimire 2003; Sheil et al.,2006; Anadón et al., 2009). Interviews with local people have beenused to complete local fauna lists and establish regional distribu-tion patterns (Hall and Dalquest, 1963), identify species used forfood, medicine and as pets, for detecting variations in their popu-lations and making management proposals (Anadón et al., 2009).

Studying Mexican mammals has led to mapping their generaldistribution patterns (Rodríguez et al., 2003; Escalante et al.,2004) and proposing strategies for their conservation, prioritybeing given to those areas having a greater concentration of endan-gered and endemic species and those having limited distribution(Ceballos et al., 1998). Records are now thus available for mostmammalian species in scientific collections in Mexico (Espinoza-

Medinilla et al., 2006) and abroad (López-Wilches, 2003).Medium-sized mammals(i.e. those weighing > 200 g) arean eco-

logically diverse group and most species can be easily identified.Their study requires a variety of methodologies and great effortsare needed for obtaining just a few data which are usually difficultto test statistically. Their natural history is known from studiesabout their geographical distribution (Ceballos and Oliva, 2005)andthese canprove useful forproposing local conservationprojects.

Veracruz is one of the states in Mexico having the highest mam-malian diversity (Gaona et al., 2003; Ceballos and Oliva, 2005). Halland Dalquest (1963) listed the mammals of Veracruz with informa-tion about their natural history; later, more general studies in Mex-ico updated the number of species in the state (Ramírez-Pulidoet al., 1996).

Research into the effect of vegetation cover change on thisgroup of mammals has indicated that arboreal species and those

depending on the forest are most affected by fragmentation(Crooks, 2002; Da Silva and Mendes Pontes, 2008; Lauranceet al., 2008) while other species’ populations might increase in het-erogeneous environments by using forest edges, crop fields and thesuburban environment (Crooks and Soulé, 1999; Crooks, 2002;McKinney, 2002; Daily et al., 2003). Some investigations havefound that the shade provided by coffee plantations maintains highmammalian diversity and that of other vertebrates in other Mexicanregions and the tropics (Gallina et al., 1996, 2008; Greenberg et al.,1997; Moguel and Toledo, 1999; Cruz-Lara et al., 2004).

Mexican coffee plantation distribution overlaps the tropicalmontane cloud forest (TMCF3), an ecological area covering 1% of the country. Although it still occupies most of its original distribu-tion (around 8000 km2), its effective area has been reduced tosmall fragments incapable of long-term support for this habitat’stypical flora and fauna (Challenger, 1998). TMCF in Veracruz hasthe highest deforestation rate for this type of tropical forest inthe world (Aldrich et al., 2000) and it has been estimated that itcontains 10–12% of Mexican plant and animal species (Ramamoor-thy et al., 1993; Rzedowski, 1996).

TMCF remnants in central Veracruz are immersed in changingmatrices which can attenuate the effect of forest fragmentation;matrices include a mixture of shaded coffee crops, disturbed forestand secondary vegetation (Gallina et al., 1996; Williams-Lineraet al., 2002) or irreversibly deteriorated vegetation resulting fromconstruction projects and urban sprawl. Accelerated cloud forestdestruction is a recent phenomenon in this area and the area westof Xalapa has lost 90% of its natural forests since 1993 (Williams-Linera et al., 2002).

This study’s main goal was to evaluate the effectof current land-scape composition and spatial configuration on medium-sizedmammals’ species richness. The following three approaches wereused. Medium-sized mammalian species distribution in the upperAntigua river basin in central Veracruz was modeled to obtain spe-cies richness models which were tested for their predictive valueby using local knowledge. Possible species richness patterns related

to altitude and human presence were identified. Local protectedareas’ current role regarding medium-sized mammals in the regionwas evaluated.

2. Materials and methods

 2.1. Study area

The study was carried out between February 2007 and July2008 in the upper Antigua river basin (UARB)4 in Veracruz, Mexico(1325 km2); it is located between 19°100-19°340 N and 96°500-97°160 W (Fig. 1), lying at over 3600 m (600–4200 masl). The upperpart of the basin (2500–4200 masl) is highly dissected, having 20–45° slopes while slopes range from 3° to 10° in the lower part. The

climate is humid temperate in almost all the basin (Muñoz-Villersand López Blanco, 2007). According to Rzedowski (1978, 1990), thebasin’s wooded areas are pine, oak-pine, cloud and deciduous for-est. Cloud forest was the dominant cover from 1990 to 2003 for26.5% of the area; 21,100 ha of cloud forest were transformed intopastures and crop fields during a 13-year period (Muñoz-Villersand López Blanco, 2007).

 2.2. Species list 

The following literature was consulted for obtaining a list of medium-sized mammals (those weighing > 200 g) for the studyarea: Hall and Dalquest (1963), Gallina et al. (1996), González-

3Tropical montane cloud forest.4 Upper Antigua river basin.

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Romero and López-González (1993), Gaona et al. (2003) andGallina et al. (2008). Mexican mammal collection databases fromthe USA and Canada (López-Wilches, 2003) were also used.Unpublished literature (theses, technical reports) containingmammalian inventories within or near the study area were alsochecked. The geographical information for each species recorded

in Mexican specimen collections was obtained from CONABIO(2007).

  2.3. Biophysical information

Cartographic information regarding Mexican climate andtopography was used for generating the distribution models. In to-tal, 19 climate data layers were used from the WorldClim database1,3 (http://www.worldclim.org; 1 k m2/resolution). A description of 

this information can be found in Hijmans et al. (2005). The topo-graphical layers were obtained from a digital elevation model of 

Fig. 1. Location of the upper Antigua river basin in the state of Veracruz, Mexico and the model test sites evaluated. 1. Yerbabuena, 2. Xico, 3. Cosautlan, 4. Ixhuacan de losReyes. Protected areas: (A) Mount Orizaba (Pico de Orizaba), (B) the Cofre de Perote Mountain, (C) San Juandel Monte, (D) Texolo Waterfalls, (E) Cerro de las culebras, (F) Fco.

 Javier Clavijero (including the Botanical Garden) (G) Barragán, (H) El Tejar Garnica, (I) Macuiltepetl, (J) Molino de San Roque, (K) Cerro de la Galaxia, (L) La Martinica, (M) ElCastillo.

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North America, obtained from the United States Geological Survey(30 s resolution) (USGS, 2007). The digital cartographic informa-tion (20 m grid) regarding the study area’s land cover types wasobtained from Muñoz-Villers and López Blanco (2007).

 2.4. Designing the model

 2.4.1. Modeling the niche with GARP 

Distribution maps for each species were obtained by modelingthe niche using a desktop genetic algorithm for rule-set prediction(GARP) (http://www.lifemapper.org/desktopgarp/ ).

One hundred distribution models were generated for each spe-cies; 60% of the records were used as training data with extrinsicomission for species having more than 30 unique localities. Themodels were generated using 100% of the data with intrinsic omis-sion for species having less than 30 localities. The ten best modelshaving the least omission errors were selected (Anderson et al.,2003) and their geographic predictions (criteria – at least 90% of the records for each species) were added to obtain a final potentialdistribution map for each species in Mexico.

The study area was delimited using Arcview 3.2 (ESRI, 1996) onthe country-scale maps so generated. The maps were then com-bined to obtain expected species richness and their compositionvalues for different areas of the basin; the results will hereinafterbe referred to as the GARP scenario (GARPS).

 2.4.2. Modeling using vegetation cover and land use type

Vegetation cover and land use maps were reclassified, consider-ing published information about each species’ natural history(Table 1), vegetation type where it had been reported (Ceballosand Oliva, 2005) and local experts’ advice for determining whetherthere were any relationship between theoretical species richnessdistribution and current land cover in the study area. Such reclas-sification of cover type was based on Tischendorf et al.’s proposal

(2003) in which habitat is defined as being the cover type wherespecies could establish viable populations, hospitable matrix isthe cover type which could facilitate movement between areashaving desirable habitat and inhospitable matrix is where species’presence is low or null. A map was obtained for each species byusing this new classification (Table 1).

The habitat distribution maps for each species and the distribu-tion map obtainedfor each species with GARP were spatially addedwith Arcview 3.2 (ESRI, 1996) to adjust the final fundamentalniche distribution model (generated by GARP) to the species’ realniche (Sánchez-Cordero et al., 2005; Soberón and Peterson, 2005;Peterson et al., 2006).

The niche modeled with GARP and the area overlapping be-tween the niche and the existence of habitat was taken for finally

defining species’ presence (i.e. species absence was assumed inareas where species’ presence was not predicted by niche variablesbut there was suitable habitat). A new species richness map calledHABITATS was obtained for the basin by adding each of thesemaps.

 2.4.3. Modeling based on the home range

A third scenario was generated by selecting contiguous habitatareas which were equal to or larger than twice the smallest homerange recorded for each species. A value of 1 was assumed at thesesites for the probability of species presence (HRANGES). Speciesdiversity and composition were calculated inside the studied areabased on the resulting distribution model. The size of the distribu-

tion areas obtained with the GARPS and HRANGES scenarios werecompared for each species.

 2.5. Testing the model

Four circular model test sites were selected (Yerbabuena, Xico,Ixhuacán and Cosautlán) for evaluating the scenarios generatedwith the models; they covered around 50 km2 corresponding to15% of the entire area (Fig. 1). The selected sites did not overlap,were accessible most of the time and contained most of the landuse types common to the region. Field surveys supported by a pho-tographic guide of the mammals were used with local adult inhab-itants, with more than 5 years living in the area. We focus on thepresence or absence of species, their newest record and sites wereobserved. A 1:50,000 map and a GPS (Garmin Etrex) were used forlocating the sites indicated by interviewees (<30 m error).Researchers who worked in model test sites were also interviewed.

Field interviews were complemented by looking for pelts, otherhunting trophies and pets. Tracks, scats and observations in thewild were recorded during trekking while direct observations of run-over animals were recorded on the highways. Ten cameratraps were set up at each test site in different parts of the forestand their adjacent vegetation cover during three periods of fiveconsecutive nights.

Species accumulation curves (using the Chao 1 non-parametricestimator) were generated with EstimateS ver. 8.0 software(Colwell, 2006) based on the information obtained during theinterviews.

Similarity between model test sites and field observations wascompared for each scenario using Jaccard’s index (Magurran,1988). The difference between the expected richness for each sce-nario and that observed in the field via interviews was evaluatedusing Dunnett’s a posteriori comparison of means (Zar, 1996)where a zero value indicates equality. Different landscape charac-teristics were calculated for each site using the Patch Analystextension for Arcview 3.2 (MacGarigal and Marks, 1994). The im-pact of road density was calculated on a radius of 50 km per cell(Table 2).

 2.6. Identifying species richness patterns

100 samples were randomly re-sampled with 10,000 repeti-tions from all cells in each scenario to determine the existence of an altitude-associated species richness pattern. A correlation test(Zar, 1996) was also made for species richness obtained in the field,human population density and road density to identify a possibleurban–rural gradient.

 2.7. The role of the protected areas

The federal, state-owned and municipal-protected areas in thebasin were identified; these were superimposed on the HRANGESscenario and the richness verification points obtained in the field.The potential for conserving local mammalian biodiversity andpossible management action according to the present species’ bio-logical characteristics, location and size was discussed.

3. Results

 3.1. Species richness

Thirty-four medium-sized mammal species had previouslybeen recorded for the basin: three marsupials, two edentates,one primate, one lagomorph, seven rodents, three artiodactylsand 17 carnivores (Table 1), giving a total of 159 records obtainedfrom unpublished and published information for the UARB for 16

different localities (Ramírez-Pulido et al., 1996; González-Romeroand López-González, 1993; Gallina et al., 1996; García, 2007;

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 Table 1

Medium-sized mammal that are probably present in the studied area and the number of records used in the predictive distribution model. Degree of dependence on the forest:

High, H, Moderate, M, Low, L. Locomotion: Arboreal, Ar, Scansorial, S, Terrestrial, T, Aquatic, Aq, Fossorial, F. Rain-fed agriculture, 1, Coffee plantations, 2, Agroforestry systems, 3,

Sugar cane, 4, Coniferous forest, 5, Pine-oak forest, 6, Tropical montane cloud forest, 7, Water bodies, 8, Alpine grassland, 9, Cultivated grassland, 10, Tropical deciduous forest, 11,

Bare soil, 12, Urban areas, 13.

Species Common name Records Dependence Locomotion Habitat Hospitablematrix

Inhospitable matrix

Didelphis marsupialis Opossum 77 L S 3,11 1,2,4,7,8,10,13 5,6,9,12

TlacuacheDidelphis virginiana Opossum 170 L S 3,5,6,7,11 1,2,8,13 4,9,10,12

Tlacuache

Philander opossum Four-eyed opossum 84 M Ar 3,7,11 8 1,2,4,5,6,9,10,12,13Tlacuache cuatro ojos, chipe

Dasypus

novemcinctus

Armadillo 105 M T 3,5,6,7,11 1,2,4,10 8,9,12,13

Armadillo, Tochi

Tamandua mexicana Mexican collared anteater 54 H Ar 3,7,11 1,2,4,5,6,8,9,10,12,13Oso hormiguero, Chupa miel, brazo fuerte

 Ateles geoffroyi Spider monkey 68 H Ar 7,11 1,2,3,4,5,6,8,9,10,12,13Mono araña

Canis latrans Coyote 153 M T 3,5,6,7,10,11,12 1,2,9 4,8,13Coyote

Urocyoncinereoargenteus Grey fox 210 M T 3,5,6,7,9,10,11, 1,2,4,12,13 8

Zorra gris

Puma yagouaroundi Jaguarundi 45 M T 7,11 2,3, 1,4,5,6,8,9,10,12,13Leoncillo, jaguarundi, Onza real

Leopardus pardalis Ocelot 55 H T 7,11 2,3 1,4,5,6,8,9,10,12,13Ocelote

Leopardus wiedii Margay 36 M Ar 6,7,11 3 1,2,4,5,8,9,10,12,13Tigrillo, gato montes

aLynx rufus Mountain lion 127 L T 5,6,9 2,3,7 1,4,8,10,11,12Gato montes

Puma concolor  Puma 65 M T 5,6,7,11 9 1,2,3,4,8,10,12,13Puma

Lontra longicaudis River otter, water dog 187 H Aq 7,8,11 3 1,2,4,5,6,9,10,12,13Nutria de río, perro de agua

Eira barbara Tayra 25 H S 7,11 1,2,3,4,5,6,8,9,10,12,13Viejo del bosque

Mustela frenata Long-Tailed Weasel 100 L T 3,5,6,7,11 1,2,4,8,9,13 12Comadreja

Galictis vittata Greater grison 19 H T - Ar 7,8,11 1,2,3,4, 5,6,9,10,12,13,Grisón

Conepatus

leuconatus

Hog-nosed skunk 166 M T 5,6,7,11 1,2,3,9,10 4,8,12

Zorrillo

Mephitis macroura Hooded skunk 135 H T 2,5,6,7,11 1,2,3,9,10 4,8,12,13Zorrillo listado

Potos flavus Kinkajou, honey bear 88 H Ar 7,11 3 1,2,4,5,6,8,9,10,12,13Martucha, mico de noche

Bassariscus astutus Ringtail, miner’s cat 138 L S 3,5,6,7,11 1,2,9,10,13 4,8,12

Cacomixtle, SietilloBassariscus

sumichrasti

Cacomistle 16 H Ar 3,5,6,7,11 1,2,4,8,9,10,12,13

Cacomistle, Cacomixtle, Sietillo

Nasua narica White-nosed coatimundi 144 H S 3,5,6,7,11 1,2,10 4,8,9,12,13Tejón, Coatí

Procyon lotor  Racoon 170 L S 5,6,7,8,11 1,2,3, 4,9,10,12,13Mapache

Odocoileus

virginianus

White-tailed deer 248 M T 5,6,9,10 1,2,3,7,11 4,8,12,13

Venado cola blanca

Mazama americana Red brocket 47 H T 7,11 1 2,3,4,5,6,8,9,10,12,13Temazate

Pecari tajacu Peccary 89 H T 6,7,11 1,2,3,4,5 8,9,10,12,13Pecari de collar

(continued on next page)

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Tlapaya, 2008; Gallina et al., 2008). Sylvilagus brasiliensis and Lynx

rufus were also included in the surveys and analysis due to theirpresence in areas very near to the river basin (Table 1). The zoologicalnomenclature used followed Wilson and Reeder (2005).

Eira barbara, Galictis vittata, Bassariscus sumichrasti and Dasy-

  procta mexicana presented less than 30 unique records for thecountry based on CONABIO information (2007). Eighteen specieshad been recorded more than 100 times and the remaining 14 spe-cies had intermediate record values (Table 1). The predictabilityvalues for the ten best country-scale models for the 32 species hav-ing trained and validated data varied from 0.85 to 1, having 20.3 to455.8 Chi square values ( p < 0.001).

Canis latrans and Puma concolor  distribution was not predictedon the UARB scale whilst the presence of  S. brasiliensis and L. rufus

was predicted. The basin had three main species distribution pat-

terns: species widely distributed throughout the basin (for exam-

ple, Didelphis marsupialis, Mustela frenata), species havingcontinuous distribution and a well-defined limit, possibly relatedto altitude (e.g. Sphiggurus mexicanus, E. barbara) and specieswhose distribution was fragmented (e.g. Odocoileus virginianus, B.

sumichrasti).Local species richness values using GARP (GARPS) ranged from

6 to 32 species (34 gamma diversity). Expected local richness val-ues for HABITATS and HRANGES fell between 1 and 32 species hav-ing the same value (34 spp.) for gamma diversity (Fig. 2).

The highest number of species recorded by an interviewee was20 from the 72 interviews conducted between 2007 and 2008whereas the lowest was three (median 13 species). The totalnumber of species for the basin recorded via interviews was 27(Table 3) while the median obtained with the Chao 1 non-paramet-ric estimator was 28 species (Fig. 3). Twenty-two species were

recorded using the other methods (Table 4).

 Table 1 (continued)

Species Common name Records Dependence Locomotion Habitat Hospitablematrix

Inhospitable matrix

Sciurus aureogaster  Grey squirrel 306 L Ar 3,5,6,7,11 2,13 1,4,8,9,10,12Ardilla gris

Sciurus deppei Deppe’s squirrel 113 L Ar 3,5,6,7,11 1,2,4,8,9,10,12,13Ardilla

Spermophilus

variegatus

Rock squirrel 213 L T 4,5,6,7,9,11 1,2,10 3,8,12

Ardillón

Orthogeomys

hispidus

Hispid pocket gopher 99 L F 2,3,4,7.9.11 1,10 5,6,8,9,12,13

Tuza

Sphiggurus

mexicanus

Mexican hairy dwarf porcupine, Mexican treeporcupine

58 M Ar 7,11 3 1,2,4,5,6,8,9,10,12,13

Puerco espín, Viztlacuache

Cuniculus paca Agouti, paca 45 M T 7,8,11 2,3 1,4,5,6,9,10,12,13Tepezcuintle

Dasyprocta mexicana Mexican agouti 18 H T 7,11 1,2,3,4, 5,6,8,9,1012,13Guaqueque negro

aSylvilagus

brasiliensis

Brazilian forest rabbit 32 M T 7,11 3,10 1,2,4,5,6,8,9,12,13

Conejo

Sylvilagus floridanus Eastern cottontail 382 L T 3,4,5,6,7,9,10,11 1,2, 8,12,13Conejo

a Indicates species with no record in the area but present in nearby areas and that were included in the interview.

 Table 2

Landscape variables for each of the four model test sites evaluated.

Variable Description YERBABUENA XICO IXHUACAN COSAUTLAN

Total area (ha) Total area of woody canopy 851.44 598.2 2297.56 590Number of forest

fragments patchesTotal number of forest fragments 1311 674 663 1031

Mean forest fragmentssize

The size of each forest fragments in themodel test sites

0.65 ± 3.14 (Coef var482.92)

0.89 ± 7.73 (Coef var870.75)

3.47± 45.71 (Coef var1318.91)

0.89 ± 4.19 (Coef var731.59)

Shape index Area: perimeter ratio 3.69 5.96 7.99 3.38Distance Y km (Crooks,

2002)Distance to the closest habitat that isequal to or larger in size (measured fromthe edge of each patch)

39.6 54.86 34.04 51.94

Road density The sum of the linear length of roads inthe sector multiplied by a weighting factordepending on the type of road.

0.39 a 1.38; mean1.07 + À0.199

0.34 a 1.14; mean0.66 + À0.203

0.368 a 0.745; mean0.5161+ À0.066

0.71 a 1.34; mean1.03 + À0.190

Area of shaded coffeecrops (ha)

Total area occupied by shaded coffee cropsin the model test sites

1573.64 673.84 186.44 2091.56

Area of pastures (ha) 824.28 3326.6 1551.76 500.92Agricultural area (ha) 521.84 65.88 1117.36 437.16Residential area (ha) Total area occupied in the model test sites 319 131.84 3 33.28

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The interviewees had not seen nine species in the wild for atleast the last 2 years: Ateles geoffroyi, Mazama americana, P. concol-

or, Lontra longicaudis, E. barbara, Cuniculus paca and D. mexicana.

Captive species like O. virginianus or Tayassu pecari which had beenaccidentally liberated during the past year were also mentioned bythe interviewees. The most frequently recorded species includedSciurus aureogaster, Urocyon cinereoargenteus, Orthogeomys hispi-

dus, M. frenata, D. virginiana, D. marsupialis, D. novemcinctus, S. flor-

idanus and S. brasiliensis (Fig. 4).Although it had not been recorded in previous studies, S. brasil-

iensis presence was mentioned at all the test sites by 63% of theinterviewees. Examination of a pelt confirmed its existence in thebasin. L. rufus was mentioned in 14% of the interviews and scatswhich might have belonged to this species were observed in the

upper part of the basin. C. latrans distribution was not predictedby GARP, but its presence in the basin was mentioned by 25% of 

those interviewed. The reintroduction of a pair of coyotes 7 yearsago was documented for the Xico model test site; a female wascaptured 5 years ago in Yerbabuena and a hunted animal was re-ported in January 2009.

The size of the potential distribution area calculated in GARPSfor 13 species was reduced by more than 40%. D. novemcinctus,S. floridanus, M. frenata and M. macroura were the only speciespresenting a reduction of less than 10% of their potential distribu-tion area (Fig. 5).

 3.2. Comparing scenarios by model test site

Although most current vegetation cover in the basin occurs atthe selected test sites, these represent a gradient extending from

urban to suburban areas in Yerbabuena, agricultural areas inCosautlan (mainly coffee plantations and sugarcane fields),

Fig. 2. Spatial patterns for the potential distribution of medium-sized mammal species diversity in the upper Antigua river basin. On the left, the results of the modelgenerated using the environmental variables used in GARPS and, on the right, the results of the model generated by combining available habitat and the minimum homerange size (HRANGES).

 Table 3

  Jaccard similarity index values between the different model test sites and between predicted richness by scenarios and field information.

GARP scenario GARPS Model test sites Expected richness

YERB XICO IXHU COSA

YERB 1 30XICO 0.97 1 31IXHU 0.87 0.84 1 28COSA 0.94 0.97 0.82 1 32

Total 34

Home range scenario HRANGES YERB XICO IXHU COSA Expected richness

YERB 1 28XICO 0.96 1 29IXHU 0.90 0.87 1 27

COSA 0.90 0.93 0.76 1 31Total 34

Field information YERB XICO IXHU COSA Observed richness

YERB 1 21XICO 0.86 1 20IXHU 0.69 0.79 1 23COSA 0.79 0.83 0.73 1 22

Total 27

Scenarios and field data YERB XICO IXHU COSA

GARPS 0.63 0.58 0.66 0.61HABITATS 0.70 0.63 0.85 0.69HRANGES 0.75 0.71 0.88 0.71

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pastured areas for livestock in Xico and forested areas in Ixhuacán(Table 2).

The species richness predicted by the different scenarios wasdifferent from the richness found in the field (F = 30.643; p < 0.001). The GARP scenario was different from the others, havingmore similarity with HABITATS and HRANGES. When the ninespecies which had not been recorded as living in the wild wereremoved from each scenario, the differences between the modelsand the information from the interviews decreased, but the differ-ence was still significant (F = 7.6044; p < 0.001).

The species accumulation curves and Chao 1 non-parametric

estimator indicated that asymptote was reached for all model testsites. There were small differences between observed and esti-mated richness and loss in the number of species having a singleindividual (Fig. 6).

Expected richness values for each model test site in the HABI-TATS and HRANGES scenarios were the same as those predictedby GARPS, so they were omitted from Table 3. Similarity valueswere high in all modeled scenarios (0.76 being the lowest value).According to information obtained in the field, the lowest similar-ity value was 0.69 and the highest 0.86 (Table 3). Comparing spe-cies richness values between sites revealed a spatial changebetween theoretical GARPS species richness and that obtainedfrom field information. The Ixhuacán sector (IXHU) had the lowestrichness value (28 species) in the GARPS model compared to theother GARPS model test sites. This site had the highest richness va-lue obtained with field information (23 species). Jaccard values forpredicted richness by scenario and data collected in the field werehigher than 0.50 at all test sites. HRANGES and field data had thehighest similarity values (0.71–0.88) (Table 3).

 3.3. Identifying species richness patterns

All scenarios were negatively correlated with altitude( p < 0.001). This was seen most clearly in GARPS (0.974–0.95195% correlation interval) and decreased in HABITATS (À0.7294–0.472) and HRANGES (À0.721–0.468). Species richness tended todecrease with increased population density (correlation coeffi-cient = À0.402) and road density (correlation coefficient = À0.22).

 3.4. The role of protected areas

There are six protected areas within the UARB, having differentconservation categories and objectives. Five of these lie completelywithin the basin, covering 635 ha, extending from 1164 to 1200masl (Table 5). Part of the Cofre de Perote Mountain National Park(about 4500 ha) lies in the basin, its lower limit being 3000 masl(Fig. 1), representing the largest protected area in the basin. Thesmallest protected area is the 1-ha Barragán Ecological Park.

According to the HRANGES scenario, the RAMSAR site andClavijero Park include the areas having the greatest potential

number of species (27 and 28, respectively). When expected rich-ness was compared to that observed in the field, all the areashad fewer species than expected (Table 5). Cerro de las Culebras(40 ha), the San Roque Mill (18 ha) and Barragán Park (1 ha) were

Upper Basin of the La Antigua River 

0

20

40

60

80

100

0 15 30 45 60 75

Number of interviews

   N  u  m   b  e  r  o   f  s  p  e  c   i  e  s

Sobs (Mao Tau)

Chao 1 Mean

Chao 1 95% CI Lower BoundChao 1 95% CI Upper Bound

Singletons MeanDoubletons Mean

Fig. 3. Species accumulation curves obtained using the Sobs function (Mao Tau)

and the Chao 1 non-parametric estimator, based on information gathered from 72interviews.

 Table 4

Species recorded by other methods in the Upper Basin of the La Antigua river, obtained during the same period by García (2007).

Species Camera Capture Pet Observationa Furb Scat and tracks

Didelphis marsupialis X X XDidelphis virginiana X X XPhilander opossum XDasypus novemcinctus XÃ X X XTamandua mexicana XUrocyon cinereoargenteus X X X X XPuma yagouaroundi X

Leopardus pardalis XLeopardus wiedii XMustela frenata XConepatus leuconatus XMephitis macroura XPotos flavus XBassariscus astutus X XBassariscus sumichrasti XÃ XNasua narica XÃ XProcyon lotor  X X XSciurus aureogaster  X X XOrthogeomys hispidus X XSphiggurus mexicanus X XSylvilagus brasiliensis XSylvilagus floridanus XÃ X

Species total (22) 7 6 3 11 13 4

a

Includes observations in the wild and animals that had been run over by vehicles.b Observed when the field interviews were being done.

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immersed in an urban matrix, inferring that mammal populationsin these places are isolated. The Francisco Javier Clavijero Parkand the Texolo Waterfalls could support populations if some of the areas surrounding them were preserved for allowing animalsto move between them, as suggested by Halffter (2007) in his

Archipelago Reserves proposal as a complement to protected areas.The Cofre de Perote Mountain National Park, covering more

than 11,000 ha, can support local medium-sized mammal popula-tions; however, its geographical location in the upper part of thebasin means that sites having lower expected richness are pro-tected. There are no protected areas at sites lying below 1000 maslwhere the highest number of species were predicted for the basin(Figs. 1 and 2).

4. Discussion

4.1. Species richness and composition

Medium-sized mammal richness in the area arises from a com-bination of Nearctic and Neotropical species, a characteristic that

has been acknowledged as being one of the factors promotingterrestrial Mexican mammals’ high gamma and beta diversity(Rodríguez et al., 2003). This combination exemplifies the complexbiogeographical history of the cloud forest in this area (Rzedowski,1991, 1996).

GARP allowed us to model the distribution of most speciespreviously recorded (GARPS) in the UARB. This led to identifyingdifferences throughout the basin regarding medium-sized mam-mals’ theoretical species richness.

GARP’s ability to predict mammals’ macro-distribution hasbeen demonstrated in several studies using the type of environ-mental variables presented in this study (for example Andersonand Martinez-Meyer, 2004). The results obtained by combiningGARP with the actual vegetation maps have demonstrated theirusefulness in evaluating the effects of habitat transformation ona detailed scale given by fragmentation for endemic Mexicanmammals (Sánchez-Cordero et al., 2005). Two species (P. concolor 

and C. latrans) were not found in the basin according to the modelsgenerated with GARP, even though being characterized by broad

geographic distribution, wide home ranges and potential distribu-tion covering the entire country. Althoughpuma density is lower in

0

10

20

30

40

50

60

70

  A  t  C  p   D  m   E   b   L  w L   l   M  a  O  v   P  c  G  v   P

  t   P   h   L  p   H  y  C   l  S  v L  r   T  m P  f   B  s   M

  m C   l   B  a   N  n  C  m  S  d   P  r

  S   b  S  f   D  v   D  n   D  m   M  f

  O   h   U  c  S  a

Species

   N  u  m   b  e  r  o   f

   i  n   t  e  r  v   i  e  w  s

Fig. 4. Distribution of species records in the interviews (n = 72) in the upper Antigua river basin. The capital letter represents the genus and the lower case letter the species,see Table 1.

Lw

Sd Lp

Ll

DmCp Sa DvAg Hy PoNnUcBs

TmBaDm

SbPl

Lr Cm

SbOhCnMa Pt GvMmMf 

Ov Sf Eb ClPc

Dn

Pf 

-20

0

20

40

60

80

100

Lw  Lp  P f   C p  D v  H y  N n  B s  B a  S b  Lr  S b  C n  P t  M m  O v  E b  P c 

Species

   P  e  r  c  e  n   t  a  g  e  c   h  a  n  g  e

Fig. 5. Percentagechange in potentialdistributionarea between the minimum area for the home range scenario (HRANGES) andthe GARP scenario (GARPS). Thecapital letterrepresents the genus and the lower case letter the species, see Table 1.

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the southern part of the country and the coyote is naturally absentfrom the tropical rainforest and the cloud forest in southern Vera-cruz as well as the tropical evergreen forest (Leopold, 2000), thepresence of both species has been noted in the region (Gallinaet al., 1996; Gómez, unpublished result). The exclusion of both

species from the area generated by GARP can be explained by thefact that collection record distribution was mainly from northernMexico (CONABIO, 2007). This could have resulted from a lack of digitizing scientific collections, other kind of records and probablyfrom inadequate sampling which can create artificial absencesin species distribution models (Ponder et al., 2001). Godown andPeterson (2000), Loiselle et al. (2003) and Elith et al. (2006) giveexamples of GARP use and limitations in the conservation andstudy of other biological groups.

All the scenarios overestimated the richness found in the testsites due to historic records allowing the modeling of availablehabitat for nine species. However, those species were not recordedduring the field work, suggesting the existence of sites where thespecies have been locally extirpated; however, such places could

possibly be used for reintroducing them, following a posteriorianalysis (Anderson et al., 2003). Six of these species are character-

ized by having broad distribution and low density, two of themhave limited distribution and high density and one is widely dis-tributed with high density (Arita et al., 1990). The historical re-cords and the existence of potential distribution areas for thesespecies indicated that other causes such as hunting or population

isolation could explain their absence from the model test sites.The effect of hunting on the disappearance of species from areasthat still offer suitable habitat has been described for differenttropical areas (IUCN, 2002).

Overestimated species richness based on historical data hasbeen analyzed in different scenarios, including the richness of spe-cies in protected areas in Canada where it was found that the his-torical maps produced an overestimation in the area of species’occupancy, this being more evident on a fine scale than large spa-tial scales (Habib et al., 2003). Using different types of distributiondata and identifying novel tools for application to existing distribu-tion data-sets can minimize uncertainty about target attainment(Underwood et al., 2010).

Compared to other species distribution models, GARP presented

the highest over-prediction (Elith and Graham, 2009). Neverthe-less, both the quality and availability of environmental data and

 Yerbabuena

0

1020

30

40

50

60

70

80

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Number of interviews

   N  u  m   b

  e  r  o   f  s  p  e  c   i  e  s

Sobs (Mao Tau)

Chao 1 Mean

Chao 1 95% CI Lower 

BoundChao 1 95% CI Upper BoundSingletons Mean

Cosautlán

0

20

40

60

80

2 6 7 8

Number of interviews

   N  u  m   b  e  r  o   f  s  p  e  c   i  e  s

Sobs (Mao Tau)

Chao 1 Mean

Chao 1 95% CI Lower BoundChao 1 95% CI Upper BoundSingletons Mean

Xico

1

21

41

61

Number of interviews

   N  u  m   b  e

  r  o   f  s  p  e  c   i  e  s

Sobs (Mao Tau)

Chao 1 Mean

Chao 1 95% CI Lower BoundChao 1 95% CI Upper BoundSingletons Mean

Ixhuacán

0

20

40

60

80

0 1 5

3 4 9 10 11 12 1314 15 16 17 18

3 4

0 1 2 7 85 6

0 2 4 6 8 10 12 14 16 18 20

Number of interview

   N  u  m

   b  e  r  o   f  s  p  e  c   i  e  s

Sobs (Mao Tau)

Chao 1 Mean

Chao 1 95% CI Lower 

Bound

Chao 1 95% CI Upper BoundSingletons Mean

Fig. 6. Species accumulation curves obtained with the Sobs funciton (Mao Tau) and the Chao 1 non-parametric estimator, based on the interviews held at each of the fourmodel test sites.

 Table 5

Currently protected areas in the Upper Basin of the La Antigua river, Veracruz, Mexico.

Protected area Area (ha) Altitudinal range m a.s.l. Expected species HRANGES Observed species

Cofre de Perote Mountain National Park 11,700 3000–4282 2–9 7a

Texolo Waterfalls RAMSAR site 500 1093–1164 11–27 22b

Francisco Javier Clavijero Ecological Park 76 7–28 5a

Snake Hill (Cerro de las culebras) Ecological Park 40 1200–1325 7–15 6a

San Roque Mill Ecological Park 18 1350 7–15 3a

Barragán Ecological Park 1 1200 7 2a

References:a Subsecretaria del Medio Ambiente, 2000.b Gordillo and Cruz, Unpublished results.

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modeling techniques used can result in uncertainty and can over-or under-estimate a species’ distribution (e.g. Loiselle et al., 2003;Rodríguez et al., 2007). Testing models developed from presence–absence data has been a recurrent focus in ecological discussion(e.g. Vaughan and Ormerod, 2005) including distribution and rela-tive abundance models’ application (Royle et al., 2007; Wilsonet al., 2010).

The inverse relationship found between mammalian richnessand elevation in UARB has been empirically recognized by severalbiological research groups (Graham, 1990; Stevens, 1992; Hunterand Yonzon, 1993). The relationship for small nonvolant mammalsis curvilinear and richness is greatest at intermediate elevationsbetween 2000 and 2500 masl (Sánchez-Cordero, 2001; McCain,2004). The tendency towards a higher number of species in thelowlands than in the mountains has been identified for mammalsin Asia (Steinmetz et al., 2007).

The information gathered in the field allowed us to detect varia-tions along an urban–rural gradient, greater species richness occur-ring in rural areas.This type of relationship has been documentedinseveral studies (for a review, see McKinney, 2002) and coincideswith the results of this study which found that the site least alteredby fragmentation andurbanization (IXHU) had more species anditsspeciesrichnesswas more similarto expected richness(Tables2and3). Changes in species composition for terrestrial mammals regard-ingdifferent land use (such as that reported forthe UARB)have alsobeen documented in Costa Rica where 60 species have been histor-icallyrecorded, 37 recordedin thefieldand at least sixspeciesbeinglocally extinct. Species richness and composition were related tohabitattype, withpastureprovidingthe least diverse siteswhilefor-est remnants and coffee plantations had similar richness to that of extensive forests (Daily et al., 2003).

Mammals such as U. cinereoargenteus, D. marsupialis and D. vir-

 giniana and M. frenata were most frequently recorded and had thelargest distribution throughout the area. An increased abundanceof these species in fragmented forests has been reported in Califor-nia where fragment area and the degree of isolation are the main

factors explaining variation (Crooks, 2002). On the other hand, ahigh rate of land transformation and human activity in more than60% of the area (Muñoz-Villers and López-Blanco, 2007) could beaffecting populations on a regional scale, thereby promoting thedisappearance of big carnivores and increasing the presence of generalist mesopredators, such as the grey fox U. cinereoargenteus

or domestic mammals such as dogs and cats (Crooks and Soulé,1999).

The most frequent species (i.e. those that mentioned in at leasthalf of the interviews, Fig. 4) had wide distribution and high den-sity in tropical forest (Arita et al., 1990). However, the most abun-dant ones, U. cinereoargenteus, S. aureogaster , O. hispidus and M.

 frenata, are not limited to the forest and they do well in heteroge-neous environments, such as those in the study area including cof-

fee plantations (Gallina et al., 1996; Cruz-Lara et al., 2004),fragment edges and suburban environments (Crooks, 2002; Dailyet al., 2003). These species fall into the categories of being exploit-ers and adaptable, as defined by McKinney (2002), sometimesbecoming crop pests (González-Romero, 1980). The nine speciesthat were not recorded have broad distribution but low density(Arita et al., 1990), making them more susceptible to anthropo-genic effects in the sites evaluated here. Less common species, suchas felids and other carnivores depending on the forest, fall into thecategories that avoid urban and semi-urban environments, therebybeing more susceptible to habitat changes (McKinney, 2002).

Local inhabitants’ ability to quickly recognize medium-sizedmammal species provided valuable information about the currentrichness at each model test site. The sampling effort put into the

interviews was sufficient, given that the asymptote was reachedin the species accumulation curve and in non-parametric estima-

tors’ curves. The differences between estimated richness in the dif-ferent scenarios and observed richness indicated local inhabitants’ability to identify rare and extinct species as well as abundant andpest species. The inclusion of so-called traditional ecologicalknowledge often provides more abundant, reliable informationthan formal research (Huntington, 2000).

Interviews are a traditional method in the social sciences andhave also been used in ecological research. This is most evidentin projects focused on conservation management and planning(Becker and Ghimire, 2003; Anadón et al., 2009). Interviews havebeen used for studying endangered species, evaluating their tradi-tional uses and monitoring fauna by local communities (Lizcanoet al., 2002; Anadón et al., 2009).

Camera traps did not provide sufficient data to allow statisticalanalysis even if there have been good experiences of sampling elu-sive or rare species with this technique (Silver et al., 2004). Thishappened because there were many domestic animals and peoplein the area who activated the cameras, thereby giving uselesspictures.

The collection of medium and big mammals has little justifica-tion at the moment which is why systematizing direct or indirectobservation would allow us to model the present distribution of species. These models would offer proposals regarding specificareas and concrete local action, complementing existing nationalproposals (Ceballos et al., 1998; Ceballos, 2007; Vázquez et al.,2009).

According to some authors (Williams-Linera et al., 2002;Muñoz-Villers and López-Blanco, 2007), almost all the basin isbeing used for production activities negatively affecting the cloudforests. Nevertheless, the surrounding natural remnants matrixhas been able to maintain its current medium-size mammal rich-ness. The presence of different vegetation types is recognized asbeing important for maintaining landscape-scale assemblage of mammals (Velázquez et al., 2001).

Given the low viability of the protected areas in the basin, thespecies recorded survive due to the coffee plantations (especially

those using shade) and the inaccessibility of certain forested areas.A conservation alternative is to protect different sized areas per-mitting species establishment or dispersal to more suitable habi-tats, as if they were islands for a regional conservation scheme.This proposal is considered to be a good strategy in environmentswhich have been highly modified by humans, especially in areashaving high species turnover (Halffter, 2007; Williams-Lineraet al., 2007). Even though species turnover is low in the basin, thiswould be an appropriate strategy given the species’ vagility,increasing its viability if it sought to increase the area’s structuralconnectivity by designing corridors. Municipal and private initia-tives thus acquire more relevance since they have been referencedin different scenarios (Meisel and Woodward, 2005; Ochoa-Ochoaet al., 2009).

5. Conclusions

The historical inventories used for analyzing species distribu-tion with GARP overestimate the expected richness. CombiningGARP with the information obtained from local inhabitants and ex-perts allows rapid evaluation of medium-sized mammal richnesson a regional scale, permits extirpated species to be easily recog-nized and those populations which have become considerably de-creased (as well as abundant species) to be identified.

Combining predictive distribution models of species (GARP),delimiting species distribution according to habitat type and areaand local knowledge allow quick evaluation of medium-sized

mammalian species richness. These tools can be combined for con-servation goals and identifying research priorities.

H.F. López-Arévalo et al./ Biological Conservation xxx (2011) xxx–xxx 11

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Reduced habitat in the area being evaluated seemed to be themain cause for the local disappearance of medium-sized mammalshaving a broad home range and which depended on forested areas,although hunting, isolation of remnant populations and the intro-duction of non-native fauna may be causing less perceptible dam-age to all UARB mammalian species.

It is unlikely that wild medium-sized mammal populations willpersist in currently protected areas, except for generalists orspecies having intermediate home range, traits increasing theirprobability of surviving in the existing landscape mosaic. Theirlong-term existence will depend on the permanence of a set of habitats arranged as archipelago reserves. Species depending onarboreal structures or forests, having small home ranges, will alsopresent a higher probability of survival. Nevertheless, the currentlyprotected areas are too small to support viable populations in thelong-term.

 Acknowledgements

The first author would like to thank the Universidad Nacionalde Colombia for permission to pursue PhD studies and is grateful

to the Russell E. Train Education for Nature Program, run by theWorld Wildlife Fund (Grant RL 27) and the Consejo Nacional deCiencia y Tecnología, México (CONACYT) for partial scholarshipsawarded for carrying out his PhD studies at the Instituto deEcología, A. C. in Xalapa, Veracruz, Mexico. Grateful acknowledge-ment is extended to Dr. Gonzalo Halffter and Dr. Octavio PérezMaqueo for their valuable contributions. We thank three anonymousreviewers for providing helpful comments on previous drafts of this manuscript. Also to Bianca Delfosse and Bibiana López Canowho translated the text from the original in Spanish and JasonGarry who extensively revised it. Some of the data was obtainedfrom CONABIO projects (Comisión Nacional de Biodiversidad):Q068, T9, J123, P130, J121 and A26.

References

Aldrich, M.P., Bubb, P., Hostettler, S., Van De Wiel, H., 2000. Bosques nubladostropicales montanos. Tiempo para la acción. WWF International/UICN, TheWorld Conservation Union. Cambridge, England.

Anadón, J.D., Gimenez, A., Ballestar, R., Pérez, I., 2009. Evaluation of local ecologicalknowledge as a method for collecting extensive data on animal abundance.Conserv. Biol. 3 (3), 617–625.

Anderson, R.P., Martinez-Meyer, E., 2004. Modeling species’ geographicdistributions for preliminary conservation assessments: an implementationwith the spiny pocket mice (Heteromys) of Ecuador. Biol. Conserv. 116, 167–179.

Anderson, R.P., Lew, D., Peterson, A.T., 2003. Evaluating predictivemodels of species’distributions: criteria for selecting optimal models. Ecol. Model. 162, 211–232.

Arita, H.T., Robinson, J.G., Redford, K.H., 1990. Rarity in Neotropical forest mammalsand its ecological correlates. Conserv. Biol. 4, 181–192.

Becker, C.D., Ghimire, K., 2003. Synergy between traditional ecological knowledgeand conservation science supports forest preservation in Ecuador. Conserv. Ecol.

8, 1.Bisby, F.A., 2000. The quiet revolution: biodiversity informatics and the Internet.Science 298, 2309–2312.

Ceballos, G., 2007. Conservation priorities for mammals in megadiverse Mexico: theefficiency of reserve networks. Ecol. Appl. 17, 569–578.

Ceballos, G., Oliva, G. (Eds.), 2005. Los mamíferos silvestres de México. CONABIOand Fondo de Cultura Económica, México.

Ceballos, G., Rodríguez, P., Medellín, R.A., 1998. Assessing conservation priorities inmegadiverse Mexico: mammalian diversity, endemicity, and endangerment.Ecol. Appl. 8, 8–17.

Challenger, A., 1998. Utilización y conservación de los ecosistemas terrestres deMéxico. Pasado presente y futuro. CONABIO, Instituto de Ecología y AgrupaciónSierra Madre, México.

Colwell, R.K., 2006. Estimate S: Statistical Estimation of Species Richness and SharedSpecies from Samples. <http://viceroy.eeb.uconn.edu/estimates>.

CONABIO, Comisión Nacional de Biodiversidad, 2007. Base de datos de mamíferosde México, consulted on November 1, 2007.

Crooks, K.R., 2002. Relative sensitivities of mammalian carnivores to habitatfragmentation. Conserv. Biol. 16, 488–502.

Crooks, K.R., Soulé, M.E., 1999. Mesopredator release and avifaunal extinctions in afragmented system. Nature 400, 563–565.

Cruz-Lara, L.E., Lorenzo, C., Soto, L., Naranjo, E., Ramírez-Marcial, N., 2004.Diversidad de mamíferos en cafetales y selva mediana de las cañadas de laselva Lacandona, Chiapas, México. Acta Zool. Mex. 20, 63–81.

Da Silva, A.P., Mendes Pontes, A.R., 2008. The effect of a mega-fragmentationprocess on large mammal assemblages in the highly-threatened PernambucoEndemism Centre, north-eastern Brazil. Biodiversity Conserv. 17, 1455–1464.

Daily, G.C., Ceballos, G., Pacheco, J., Suzan, G., Sánchez-Azofeifa, A., 2003.Countryside biogeography of neotropical mammals: conservationopportunities in agricultural landscape of Costa Rica. Conserv. Biol. 17, 1814–1826.

Edwards, J.L., Lane, M.A., Nielsen, E.S., 2000. Interoperability of biodiversitydatabase: biodiversity information on every desktop. Science 298, 2312–2314.

Elith, J., Graham, C.H., 2009. Do they? Howdo they? WHY do they differ?On findingreasons for differing performances of species distribution models. Ecography32, 66–77.

Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J.,Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A.,Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T.,Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberón, J.,Williams, S., Wisz, M.S., Zimmermann, N.E., 2006. Novel methods improvepredictionof species’ distributions fromoccurrence data.Ecography29, 129–151.

Escalante, T., Rodríguez, G., Morrone, J., 2004. The diversification of Nearcticmammals in the Mexican Transition Zone. Biol. J. Linn. Soc. 83, 327–339.

Espinoza-Medinilla, E., Lorenzo, C., Briones-Salas, M., 2006. Integración delconocimiento de las colecciones mastozoológicas de México. In: Lorenzo, E.,Espinoza-Medinilla, M., Briones-Salas, F.A., Cervantes (Eds.), Coleccionesmastozoológicas de México. Asociación Mexicana de Mastozoología, A.C.México, D.F. pp. 537–548.

ESRI (Environmental Systems Research Institute), 1996. Arcview GIS 3.2.Franklin, J.F., 1993. Preserving biodiversity: species, ecosystem or landscapes? Ecol.

Appl. 3, 202–205.Funk, V., Richardson, K., 2002. Systematic data in biodiversity studies: use it or lose

it. Syst. Biol. 51, 303–316.Gallina, S., Mandujano, S., González-Romero, A., 1996. Conservation of mammalian

biodiversity in coffee plantations of central Veracruz, Mexico. Agrof. Syst. 33,13–27.

Gallina, S., González-Romero, A., Manson, R.H., 2008. Mamíferos pequeños ymedianos. In: Manson, R., Hernández-Ortíz, V., Gallina, S., Melhtreter, K. (Eds.),Agroecosistemas cafetaleros de Veracruz: biodiversidad, manejo yconservación. INECOL, INE-SEMARNAT, Mexico. pp. 161-180.

Gaona, S., González-Christen, A.L., López-Wilchis, R., 2003. Síntesis delconocimiento de los mamíferos silvestres del Estado de Veracruz, México.Rev. Soc. Mex. Hist. Nat. 3 época 1, 91–123.

García, B. J., 2007. Comparación de la riqueza de mamíferos medianos en ungradiente de manejo de cafetales delcentrode Veracruz. Master thesis.Institutode Ecología A.C. Xalapa, Mexico (Unpublished results).

Godown, M.E., Peterson, A.T., 2000. Preliminary distributional analysis of US

endangered bird species. Biodivers. Conserv. 9, 1313–1322.González-Romero, A., 1980. Roedores plaga de las zonas agrícolas del DistritoFederal. Instituto de Ecología, Museo de Historia Natural de la Ciudad deMéxico, Mexico.

González-Romero, A., López-González, C., 1993. Reconocimiento preliminar de lamastofauna asociada a las zonas suburbanas de Xalapa y Coatepec. In: López-Moreno, I. (Ed.), Ecología urbana aplicada a la ciudad de Xalapa Instituto deEcología A.C., Xalapa, Mexico. pp. 221–238.

Graham, G.L., 1990. Bats vs. birds: comparisons among Peruvian vertebrate faunasalong an elevational gradient. J. Biogeogr. 17, 657–668.

Greenberg, R., Bichier, P., Sterling, J., 1997. Bird populations in rustic and plantedshade coffe plantations of Eastern Chiapas, México. Biotropica 29 (4), 501–514.

Habib, L.D., Wiersma, Y.F., Nudds, T.D., 2003. Effects of errors in range maps onestimates of historical species richness of mammals in Canadian national parks.

  J. Biogeogr. 30, 375–380.Halffter, G., 1998. Una estrategia para medir la biodiversidad a nivel del paisaje. In:

Halffter, G. (Ed.), La diversidad Biológicade Iberoamérica, vol. II, Acta Zool. Mex.,Vol. Esp., Mexico, pp. 3–18.

Halffter, G., 2007. Reservas archipiélago: un nuevo tipo de área protegida. In:

Halffter, G., Guevara, S., Melo, A. (Eds.), Hacia una cultura de conservación de ladiversidad biológica. Monografías Tercer Milenio, Zaragoza, España. pp. 281–286.

Hall, E.R., Dalquest, W.W., 1963. The mammals of Veracruz, vol. 14. University of Kansas Publications, Museum of Natural History. pp. 165–362.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very highresolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25,1965–1978.

Hunter Jr., M.L., Yonzon, P., 1993. Altitudinal distributions of birds, mammals,people, forests, and parks in Nepal. Conserv. Biol. 7, 420–423.

Huntington, H.P., 2000. Using traditional ecological knowledge in science: methodsand applications. Ecol. Appl. 10, 1270–1274.

Illoldi-Rangel, P., Sánchez-Cordero, V., Peterson, A.T., 2004. Predicting distributionsof Mexican mammals using ecological niche modeling. J. Mammal. 85, 658–662.

IUCN Species Survival Commission, 2002. Links between Biodiversity Conservation,Livelihoods and Food Security: The Sustainable Use of Wild Species for Meat.International Union for Conservation of Nature and Natural Resources, Gland,Switzerland and Cambridge, UK.

Laurance, W., Laurance, S.G., Hilbert, D.H., 2008. Long-term dynamics of afragmented rainforest mammal assemblage. Conserv. Biol. 22, 1154–1164.

12 H.F. López-Arévalo et al./ Biological Conservation xxx (2011) xxx–xxx

Please cite this article in press as: López-Arévalo, H.F., et al. Local knowledge and species distribution models’ contribution towards mammalian conser-vation. Biol. Conserv. (2011), doi:10.1016/j.biocon.2011.01.014

Page 13: 1-Lopez Arevalo Et Al 2011

8/3/2019 1-Lopez Arevalo Et Al 2011

http://slidepdf.com/reader/full/1-lopez-arevalo-et-al-2011 13/13

Leopold, A.S., 2000. Fauna Silvestre de México, second ed., Editorial Pax Mex.Lizcano, D.J., Pizarro, V., Cavelier, J., Carmona, J., 2002. Geographic distribution and

population size of the mountain tapir (Tapirus pinchaque) in Colombia. J.Biogeogr. 29, 7–15.

Loiselle, B.A., Howell, C.A., Graham, C., Goerck, J.M., Brooks, T., Smith, K.G., Williams,P.H., 2003. Avoiding pitfalls of using species distribution models in conservationplanning. Conserv. Biol. 17, 1591–1600.

Lomolino, M.V., 2001. Elevation gradients of species diversity: historical andprospective views. Global Ecol. Biogeogr. 10, 3–13.

López-Wilches, R., 2003. Base de datos de los mamíferos de México depositados en

colecciones de Estados Unidos y Canadá. Universidad Autónoma Metropolitana-Iztapalapa. <http://investigacion.izt.uam.mx/mamiferos/>.

MacGarigal, K., Marks, B.J., 1994. Fragstats: Spatial Pattern Analysis Program forQuantifying Landscape Structure. Reference Manual. Forest ScienceDepartment, Oregon State University. Corvallis, Oregon. 62 p + Append.

Magurran, A.E., 1988. Ecological Diversity and its Measurement. PrincetonUniversity Press, New Jersey.

McCain, C.M., 2004. The mid-domain effect applied to elevational gradients: speciesrichness of small mammals in Costa Rica. J. Biogeogr. 31, 19–31.

McKinney, M.L., 2002. Urbanization, biodiversity, and conservation. BioScience 52,883–890.

Meisel, J.E., Woodward, C.L., 2005. Andean orchid conservation and the role of private lands: a case study from Ecuador. Selbyana 26, 49–57.

Moguel, P., Toledo, V.M., 1999. Biodiversity conservation in trafitional coffe systemsof México. Conserv. Biol. 13, 1–21.

Muñoz-Villers, L.E., López-Blanco, J., 2007. Land use/cover changes using LandsatTM/ETM images in a tropical and biodiverse mountainous area of central-eastern Mexico. Int. J. Remote Sens. 29, 71–93.

Ochoa-Ochoa, L., Urbina-Cardona, J.N., Vázquez, L.B., Flores-Villela, O., Bezaury-Creel, J., 2009. The effects of governmental protected areas and social initiativesfor land protection on the conservation of Mexican amphibians. PLoS One 4 (9),e6878. doi:10.1371/journal.pone.0006878.

Pearson, R.G., Thuiller, W., Araujo, M.B., Martinez-Meyer, E., Brotons, L., McClean, C.,Miles, L., Segurado, P., Dawson, T.P., Lees, D.C., 2006. Model-based uncertaintyin species range prediction. J. Biogeogr. 33, 1704–1711.

Peterson, A.T., Sánchez-Cordero, V., Martínez-Meyer, E., Navarro-Siguenza, A.G.,2006. Tracking population extirpations via melding ecological niche modelingwith land-cover information. Ecol. Modell. 195, 229–236.

Phillips, S.J., 2008. Transferability, sample selection bias and background data inpresence-only modelling: a response to Peterson et al. (2007). Ecography 31,272–278.

Ponder, W.F., Carter, G.A., Flemons, P., Chapman, R.R., 2001. Evaluation of museumcollection data for use in biodiversity assessment. Conserv. Biol. 15, 648–657.

Ramamoorthy, T.P., Bye, R., Lot, A., Fa, J. (Eds.), 1993. Biological Diversity of Mexico:Origins and Distribution. Oxford University Press, New York.

Ramírez-Pulido,J.A., Castro-Campillo, Arroyo-Cabrales, J., Cervantes, F.A., 1996. Listataxonómica de los mamíferos terrestres de México. Occas. papers, Mus. Texas

Univ. 158, 1–62.Rodríguez, P., Soberón, J., Arita, H.T., 2003. El componente beta de la diversidad demamíferos de México. Acta Zool. Mex. 89, 241–259.

Rodríguez, J.P., Brotons, L., Bustamante, J., Seoane, J., 2007. The application of predictive modelling of species distribution to biodiversity. Divers. Distrib. 13,243–251.

Royle, J.A., Kéry, M., Gautier, R., Schmid, H., 2007. Hierarchical spatial models of abundance and occurrence from imperfect survey data. Ecol. Monogr. 77, 465–481.

Rzedowski, J., 1978. Vegetación de México. Ed. Limusa, México.Rzedowski, J., 1990. Vegetación Potencial, IV.8.2. Atlas Nacional de México. Vol II.

Escala 1:4 000 000. Instituto de Geografía, UNAM. México.Rzedowski, J., 1991. Diversidad y orígenes de la flora fanerogámica de México. Acta

Bot. Mex. 14, 3–21.

Rzedowski, J., 1996. Análisis preliminar de la flora vascular de los bosques mesófilosde montaña de México. Acta Bot. Mex. 35, 25–44.

Sánchez-Cordero, V., 2001. Elevational gradients of diversity for rodents and bats inOaxaca, México. Global Ecol. Biogeogr. 9, 63–76.

Sánchez-Cordero, V., Illoldi-Rangel, P., Linaje, M., Sarkar, S., Peterson, A.T., 2005.Deforestation and extant distributions of endemic Mexican mammals. Biol.Conserv. 126, 464–473.

Segurado, P., Araújo, M.B., 2004. An evaluation of methods for modeling speciesdistributions. J. Biogeogr. 31, 1555–1568.

Sheil, D., Puri, R., Wan, M., Basuki, I., van Heist, M., Liswanti, N., Rukmiyati,

Rachmatika, I., Samsoedin, I., 2006. Local people’s priorities for biodiversity:examples from the forests of Indonesian Borneo. Ambio 15, 17–24.

Silver, S.C., Ostro, L.E.T., Marsh, L.K., Maffei, L., Noss, A.J., Kelly, M.J., Wallace, R.,Gómez, H., Ayala, G., 2004. The use of camera traps for estimating jaguarPanthera onca abundance and density using capture/recapture analysis. Oryx38, 148–154.

Soberón, J., Peterson, A.T., 2005. Interpretation of models of fundamental ecologicalniches and species’ distributional areas. Biodivers. Inform. 2, 1–10.

Steinmetz, R., Chutipong, W., Seuaturien, N., 2007. Community structure of largemammals in tropical montane and lowland forest in the Tenasserim–DawnaMountains, Thailand. Biotropica 40, 344–353.

Stevens, G.C., 1992. The elevational gradient in altitudinal range: an extension of Rapoport’s latitudinal rule to altitude. Am. Nat. 140, 893–911.

Stockwell, D., Peterson, A.T., 2003. Comparison of resolution of methods used inmapping biodiversity patternsfrom point-occurrence data. Ecol. Indic. 3, 213–221.

Subsecretaria del Medio Ambiente, 2000. Áreas protegidas de Veracruz. Gobiernodel Estado de Veracruz. p. 171.

Tischendorf, L., Bender, D.J., Fahrig, L., 2003. Evaluation of patch isolation metrics inmosaic landscapes for specialist vs. generalist dispersers. Landscape Ecol. 18,41–50.

Tlapaya, R. L., 2008. Efecto de la cacería sobre la diversidad de mamíferos medianosen cafetales del centro de Veracruz. undergraduate thesis. Escuela de Biología.Benemérita Universidad Autónoma de Puebla, Mexico (Unpublished results).

Turner, M.G., Gardner, R.H., 1991. Quantitative Methods in Landscape Ecology.Springer-Verlag, New York.

Underwood, J.D., D’agrosa, C., Gerber, L.R., 2010. Identifying conservation areas onthe basis of alternative distribution data sets. Conserv. Biol. 24, 162–170.

USGS, United States Geological Service, 2007. <http://edc.usgs.gov/products/elevation/gtopo30/hydro/namerica.html>.

Vaughan, I.P., Ormerod, S.J., 2005. The continuing challenges of testing speciesdistribution models. J. Appl. Ecol. 42, 720–730.

Vázquez, L.B., Bustamante-Rodríguez, C.G., Bahena Arce, D.G., 2009. Area selectionfor conservation of Mexican mammals. Anim. Biodiv. Conserv. 32, 29–39.

Velázquez, A.F., Romero, J., Rangel-Cordero, H., Heil, G.W., 2001. Effects of landscapechanges on mammalian assemblages at Izta-Popo Volcanoes, Mexico. Biodiver.Conserv. 10, 1059–1075.

Williams-Linera, G., Manson, R.H., Isunza, E., 2002. La fragmentación del bosque

mesófilo de montaña y patrones de uso del suelo en la región oeste de Xalapa,Veracruz, México. Madera y Bosques 8, 73–89.Williams-Linera, G., Guillén Servent, G.A., Gómez García, O., Lorea Hernández, F.,

2007. Conservación en el centro de Veracruz, México. El bosque de niebla:Reserva archipiélago o corredor biológico?. In: Halffter, G., Guevara, S., Melo, A.(Eds.), Hacia una cultura de conservación de la diversidad biológica,Monografías Tercer Milenio, Zaragoza, pp. 303–310.

Wilson, G.J., Reeder, D.M., 2005. Mammal Species of the World: A Taxonomic andGeographic Reference, third ed. The John Hopkins University Press, Baltimore.

Wilson, T.L., Odei, J.B., Hooten, M.B., Edwards Jr., T.C., 2010. Hierarchical spatialmodelsfor predicting pygmy rabbit distribution andrelative abundance. J. Appl.Ecol. 47, 401–409.

Zar, J.H., 1996. Biostatistical Analysis, second ed.. Prentice-Hall Inc., EnglewoodCliffs, New Jersey.

H.F. López-Arévalo et al./ Biological Conservation xxx (2011) xxx–xxx 13