Community Structure and Habitat Selection of Mammals in a Protected Area of the Sierra Madre de Chiapas

Community Structure and Habitat Selection of Mammals in a Protected Area of the Sierra Madre de Chiapas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Community Structure and Habitat Selection of Mammals in a Protected Area of the Sierra Madre de Chiapas Jenner Rodas-Trejo, Paola Ocampo González, Sergio López, César Tejeda Cruz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6248323/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study evaluated the influence of landscape elements on the community structure and habitat selection of medium- and large-sized mammals in La Frailescana Natural Resource Protection Area, Chiapas, Mexico. Specifically, we analyzed the influence of environmental variables (distance to water bodies, altitude, and vegetation types) and anthropogenic factors (distance to human settlements, roads, and agricultural areas) on the mammal community. We installed 21 camera trap stations, accumulating 1,549 camera-days of sampling effort. Diversity and relative abundance indices were calculated, and generalized linear models were applied to evaluate the relationship between landscape variables and recorded mammals. We recorded 19 species of medium- and large-sized mammals, belonging to 12 families and 7 orders. The most abundant species were Pecari tajac u and Nasua narica . Distance to water bodies had a significant negative effect on species abundance and richness, highlighting the importance of these water resources. Responses to human infrastructure revealed that P . tajacu , Urocyon cinereoargenteus , Odocoileus virginianus , and Puma concolor were more abundant away from human settlements, while rural roads generated varied responses. The results underscore the importance of considering landscape heterogeneity in conservation strategies. We recommend implementing measures that prioritize the conservation of key habitats, ensure connectivity between forest fragments, and minimize anthropogenic impacts to guarantee the persistence of biodiversity in the region. Camera trapping Connectivity Habitat selection La Frailescana Protected Natural Area Terrestrial mammals Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Human activities have modified natural landscapes worldwide since ancient times, leading to habitat loss, fragmentation, and degradation. These changes disrupt ecosystem balance and affect species distribution and diversity patterns (Mayani-Parás et al. 2020 ; Torres-Romero et al. 2020 ; Chi et al. 2020 ). Such transformations are primarily driven by land-use changes due to agricultural expansion, infrastructure development, and human settlement growth (Hoffmann et al. 2011 ; Torres et al. 2016 ; Allan et al. 2019 ). Mammals, as key components of terrestrial ecosystems, play crucial ecological roles, including trophic regulation, seed dispersal, and landscape modification (Jones and Safi 2011 ; Lacher et al. 2019 ). However, due to their spatial requirements, population densities, and susceptibility to anthropogenic pressures, they are particularly vulnerable to landscape changes and habitat connectivity loss (Ceballos et al. 2017 ; Lacher et al. 2019 ). These impacts not only directly affect populations but also trigger cascading effects that alter overall ecosystem dynamics (Jones and Safi 2011 ). The Natural Resources Protection Area La Frailescana (La Frailescana) covers 116,743 hectares in the Sierra Madre of Chiapas, one of Mexico’s most biodiverse regions. It serves as a strategic biological corridor connecting the La Sepultura and El Triunfo Biosphere Reserves, facilitating species movement and dispersal between these protected areas (Lorenzo et al. 2017 ; De la Torre et al. 2019 ). This region features an altitudinal gradient ranging from 800 to 2,280 meters above sea level, with annual precipitation between 800 and 4,000 mm and temperatures from 12 to 34.5°C. These environmental conditions have resulted in a mosaic of seven vegetation types across rugged terrain, including montane cloud forest, pine forest, and oak forest (CONANP, 2019 ). However, the expansion of productive activities such as agriculture and extensive cattle ranching poses a threat to these ecosystems and their function as a corridor (Pérez et al. 2006; CONANP 2008 ; CONANP 2019 ). Understanding the factors influencing the diversity and abundance of terrestrial mammals is essential for designing effective conservation strategies (Sathyakumar et al. 2011 ). Environmental variables such as habitat type, vegetation structure, and resource availability determine habitat suitability for different species (Imre and Derbowka 2011 ; Ferreguetti et al. 2019 ). Assessing these patterns is crucial to understanding the mechanisms driving species responses to anthropogenic habitat modifications (Pacifici et al. 2020 ), particularly in protected areas where conservation and human development objectives converge (Olmos-Martínez et al. 2022 ). In this context, this study evaluates how landscape elements influence the diversity and distribution patterns of medium- and large-sized mammals in La Frailescana. Specifically, we analyze the influence of natural variables (distance to water bodies, elevation, and vegetation types) and anthropogenic variables (distance to human settlements, roads, and agricultural areas). The study aims to: (1) characterize species richness, diversity, and relative abundance through camera trapping; (2) analyze community structure using diversity indices and occupancy patterns; (3) determine the influence of these variables on community parameters; and (4) identify species-specific responses to landscape variables. Materials and Methods Study Area The study was conducted in the La Frailescana Natural Resource Protection Area (93°37'36"W, 16°16'08"N), located in the Sierra Madre of Chiapas, Mexico (Fig. 1 ). This area covers 116,743 ha, with elevations ranging from 800 to 2,280 m asl, annual precipitation between 800 and 4,000 mm, and temperatures from 12°C to 34.5°C (DOF, 1979; CONANP, 2008 ). The vegetation comprises seven types: montane cloud forest (25.15%; 29,360 ha), pine forest (37.18%; 43,409 ha), oak forest (0.81%; 942 ha), pine-oak forest (17.68%; 20,638 ha), tropical evergreen forest (0.31%; 363 ha), and tropical dry forest (1.25%; 1,463 ha). The area also includes agricultural activities (mainly maize and coffee), extensive cattle ranching (17.61%; 20,568 ha), and 162 human settlements (Pérez et al. 2006; CONANP 2019 ; CEIEG 2021 ). Data Collection Twenty-one camera trapping stations were installed between November 2021 and March 2023, distributed across four sites with montane cloud forest within La Frailescana (Fig. 1 ). Each station consisted of a Moultrie® A-25i camera trap, placed at least 1,000 m apart to ensure spatial independence, and installed 50 cm above the ground in areas with evidence of animal activity. The cameras operated continuously, programmed to take three successive images per trigger with no delay between detections. Camera locations ranged from 922 to 1,856 m asl. Species identification was performed using Camelot software, considering only terrestrial mammals > 0.5 kg. Independent records were defined as those separated by ≥ 60 minutes for the same species at the same station (Cusack et al. 2015 ; Ferreira et al. 2022 ; Rodas-Trejo 2024 ). Landscape Metrics Eight covariates were analyzed to assess the influence of environmental and anthropogenic factors on mammal richness and abundance. Environmental factors included altitude, distance to streams, and distance to pine forest. These environmental covariates are useful for identifying key areas for species persistence and survival (Joly and Myers, 2001 ; Rhim et al. 2014 ). Specifically, the distance to pine forest was included because pine forests are a critical vegetation type in the Sierra Madre de Chiapas, providing essential resources such as shelter, food, and connectivity corridors for many mammal species. This variable helps to understand habitat preferences and the role of pine forests in maintaining biodiversity and facilitating species movement across the landscape. Anthropogenic factors included distances to infrastructure and human settlements: main roads (paved with high traffic), rural roads (unpaved with low traffic), rural villages (fewer than 250 inhabitants), major towns (more than 250 inhabitants), and agricultural-livestock systems. These variables have been shown to significantly impact species distribution and survival and are key drivers of land cover change both globally and in Mexico (Mendoza-Ponce et al. 2018 ; Allan et al. 2019 ). The minimum distances between each camera trap station and landscape elements were calculated using land-use cartographic data from CEIEG ( 2021 ), employing the st_distance() function from the sf package in R (Pebesma, 2018 ). Correlations among covariates were assessed using the Variance Inflation Factor (VIF) with the car package in R. Covariates were standardized (mean = 0, variance = 1), and those with VIF > 5 were excluded to prevent multicollinearity issues in subsequent analyses (Zuur et al. 2010; Fox and Weisberg 2019 ; Hernández et al. 2024 ). Spatial autocorrelation was evaluated using Moran’s I index with the nb2listw() function from the spdep package in R (Bivand et al. 2023). This analysis quantified spatial dependence among sampling stations, considering both geographic distances and species abundances, allowing for the establishment of neighborhood structures between sites (Carroll and Pearson 2000 ; Vieira et al. 2008 ). Data Analysis Mammal diversity was analyzed using Hill numbers (qD), which provide effective species numbers at different diversity orders: 0D (total species richness), 1D (exponential of Shannon, common species), and 2D (inverse of Simpson, dominant species). The iNEXT package was used to construct interpolation and extrapolation curves for species richness. Sampling robustness was evaluated through non-parametric estimators (Chao, Jackknife1, and Bootstrap) to compare observed and expected richness (Hill 1973 ; Jost 2007 ; Chao et al. 2014 ; Hsieh et al. 2016). The Relative Abundance Index (RAI) was calculated for each species as: RAI = independent recordscamera-days×100RAI = camera-days independent records ×100 (O'Brien, 2010 ). Naïve occupancy was obtained to determine the proportion of sites occupied by each species (Soto-Werschitz et al. 2023 ). The relationship between spatial distribution and abundance was evaluated using the correlation between RAI and naïve occupancy (Mandujano and Pérez-Solano 2019 ). Rank-abundance curves were generated to visualize the hierarchical structure of the community (Rocchini and Neteler 2012 ). The relationship between landscape variables and species abundance and richness patterns was analyzed using generalized linear models (GLMs) with the MASS package, considering species with more than 14 independent records (Smith and Warren 2019 ; Ripley 2023 ). Poisson models were initially used, but in cases of overdispersion and poor model fit, negative binomial models were applied (Hoef and Boveng 2007 ). Model selection was based on the Akaike Information Criterion (AIC) (Burnham and Anderson 2002 ). In some cases, models were simplified by reducing the number of variables to improve fit. Results Abundance and species composition The total sampling effort was 1,549 camera-days, documenting 19 species of medium- and large-sized wild mammals, belonging to 12 families and 7 orders. Carnivora and Cetartiodactyla dominated taxonomic diversity, with five and two families, respectively (Table 1 ). Species richness per station ranged from 2 to 10 species (mean = 5.95 ± 2.80 SD). Regarding conservation status, Tapirus bairdii is classified as Endangered, Leopardus wiedii and Panthera onca as Near Threatened, Mazama temama as Data Deficient, and the remaining 15 species as Least Concern (IUCN 2022 ). Table 1 Species richness of mammals, conservation status categories, independent observations, Relative Abundance Index, and naïve occupancy in the La Frailescana Natural Resources Protection Area. Taxa Common name Obs RAI Naïve UICN CARNIVORA Canidae Urocyon cinereoargenteus Gray Fox 30 1.93 0.29 LC Mephitidae Conepatus leuconotus American Hog-nosed Skunk 4 0.25 0.10 LC Spilogale angustifrons Southern Spotted Skunk 3 0.19 0.14 LC Mustelidae Eira barbara Tayra 16 1.03 0.24 LC Felidae Leopardus pardalis Ocelot 15 0.96 0.43 LC Leopardus wiedii Margay 27 1.74 0.19 NT Panthera onca Jaguar 4 0.25 0.14 NT Puma concolor Puma 14 0.90 0.29 LC Puma yagouaroundi Jaguarundi 4 0.25 0.19 LC Procyonidae Nasua narica White-nosed Coati 140 9.03 0.48 LC Bassariscus sumichrasti Cacomixtle 2 0.12 0.05 LC CETARTIODACTYLA Tayassuidae Pecari tajacu Collared Peccary 224 14.39 0.76 LC Cervidae Mazama temama Central American Red Brocket 16 1.03 0.33 DD Odocoileus virginianus White-tailer Deer 38 2.45 0.62 LC CINGULATA Dasypodidae Dasypus novemcinctus Nine-banded Armadillo 14 0.90 0.33 LC DIDELPHIMORPHIA Didelphidae Didelphis marsupialis Common opossum 49 3.16 0.43 LC PERISSODACTYLA Tapiridae Tapirus bairdii Baird's Tapir 3 0.19 0.10 EN PILOSA Myrmecophagidae Tamandua mexicana Northern Tamandua 2 0.12 0.10 LC RODENTIA Cuniculidae Cuniculus paca Agouti 36 2.32 0.38 LC Obs = Number of independent observations recorded; RAI = Relative Abundance Index; DD = Data deficient, LC = Least concern, NT = Near Threatened, CR = Critically endangered, A = Threatened, P = Endangered, Sites = sites where the CTs were installed, Group = Groups identified in the cluster analysis (IUCN, 2022 ). The species with the highest RAI were: Pecari tajacu (RAI = 14.39), N asua narica (RAI = 9.03), Didelphis marsupialis (RAI = 3.16), Odocoileus virginianus (RAI = 2.45), Cuniculus paca (RAI = 2.32). In contrast, those with the lowest RAI were: Tamandua mexicana (RAI = 0.13), Bassariscus sumichrasti (RAI = 0.13), T. bairdii (RAI = 0.19), and Spilogale angustifrons (RAI = 0.19). Naïve occupancy showed a similar pattern with P. tajacu , O. virginianus , and N. narica occupying the highest proportion of sites (76%, 62%, and 48% respectively), while B. sumichrasti , T. mexicana , T. bairdii , S. angustifrons , and P. onca occupied less than 15% of the sites (Table 1 ). The strong correlation (78%) between RAI and naïve occupancy (Fig. 3 ), particularly evident in P. tajacu and N. narica , along with the rank-abundance curve, confirm a typical community structure with a few dominant species and several rare ones (Fig. 4 ). Hill numbers revealed a well-sampled and complete community, with an observed richness of 19 species (0D, 95% CI: 19.00–20.67), which aligns with the asymptotic estimation, suggesting that the sampling captured most of the species present. The diversity of common species (1D = 8.47, 95% CI: 8.35–9.23) indicates moderate evenness in the community, while the effective number of dominant species (2D = 5.32, 95% CI: 5.28–5.91) suggests a hierarchical structure with a few predominant species (Fig. 2 ). Non-parametric species richness estimators yielded values close to observed richness. The Chao estimator suggested a richness of 19.16 species (SE = 0.52), first-order Jackknife estimated 19.95 species (SE = 0.95), and Bootstrap estimated 19.83 (SE = 0.80). Moran’s I test produced a statistic of 0.003, with an expected value of -0.05, indicating no statistically significant evidence of spatial autocorrelation in the total mammal abundance data within the study area. This suggests that mammal abundance is uniformly distributed without significant clustering or dispersion patterns. The covariates distance to major populations and distance to main roads showed multicollinearity > 5 and were excluded from the GLM analyses. Habitat Preferences and Landscape Features Generalized linear models revealed significant patterns in mammal responses to the evaluated variables. Abundance showed a significant negative relationship with distance to water bodies (z = -3.468, P < 0.05), suggesting that individuals concentrate in greater numbers near water sources and their abundance decreases as the distance increases. Conversely, a positive relationship was observed with distance to pine forests (z = 2.145, P < 0.05) and human settlements (z = 3.637, P < 0.05), suggesting that species tend to be more abundant in areas farther from these landscape elements. In contrast, species richness showed only one significant relationship, manifesting as a negative association with distance to water bodies (z = -2.313, P < 0.05). This underscores the critical importance of water resources for maintaining species diversity in the area (Table 2 and Fig. 5 ). Table 2 Generalized Linear Models (GLM) of the relationship between environmental variables and the patterns of abundance and richness of medium and large mammals in La Frailescana, Chiapas. Covariates: distances to agricultural areas (dist_agri_farm), pine forests (dist_pine), water bodies (dist_stre), rural roads (dist_rural_road), altitude (dist_altitude), and villages (dist_town). Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘.’ 1.AIC = Akaike Information Criterion. Variable Estimate Std. Error z value P value Sig AIC Abundance --------: ----------: -------: ----------------: :--- 1030 (Intercept) 0.2152 0.1385 1.554 0.1202 dist_agri_farm -0.1739 0.2099 -0.829 0.4073 dist_pine 0.3969 0.1851 2.145 0.0320 * dist_stre -0.5554 0.1602 -3.468 0.0005 *** dista_rural_road -0.0463 0.2037 -0.227 0.8201 dist_town 0.6025 0.1657 3.637 0.0003 *** dist_altitude -0.2427 0.1687 -1.439 0.1501 Richness --------: ----------: -------: ----------------: :--- 105 (Intercept) 1.7414 0.0934 18.638 < 2e-16 *** dist_agri_farm -0.1370 0.1422 -0.963 0.3353 dist_pine 0.0982 0.1254 0.783 0.4338 dist_stre -0.2596 0.1122 -2.313 0.0207 * dista_rural_road 0.0355 0.1326 0.268 0.7891 dist_town 0.1311 0.1084 1.209 0.2267 dist_altitude 0.0113 0.1168 0.096 0.9232 Pecari tajacu --------: ----------: -------: ----------------: :--- 136 (Intercept) 4.7403 1.6145 2.936 0.0033 ** dist_agri_farm -0.0008 0.0006 -1.405 0.1599 dist_pine 0.0005 0.0003 1.716 0.0861 . dist_stre -0.0093 0.0034 -2.727 0.0064 ** dista_rural_road -0.0010 0.0007 -1.609 0.1075 dist_town 0.0008 0.0002 4.101 4.11E-05 *** dist_altitude -0.0025 0.0011 -2.327 0.0200 * Leopardus pardalis --------: ----------: -------: ----------------: :--- 50.2 (Intercept) -2.7550 1.7520 -1.573 0.1158 dist_agri_farm -0.0005 0.0005 -0.951 0.3418 dist_pine -0.0009 0.0004 -2.253 0.0243 * dist_stre -0.0078 0.0066 -1.184 0.2365 dista_rural_road 0.0011 0.0009 1.218 0.2234 dist_town 0.0001 0.0003 0.217 0.8283 Mazama temama --------: ----------: -------: ----------------: :--- 51.8 (Intercept) -3.65658 3.636409 -1.006 0.3146 dist_pine 0.00056 0.000417 1.345 0.1788 dista_rural_road -0.0019 0.000877 -2.172 0.0298 * dist_altitude 0.00358 0.002156 1.66 0.0969 . Odocoileus virginianus --------: ----------: -------: ----------------: :--- 78.7 (Intercept) 1.5446 1.1521 1.341 0.1801 dista_rural_road -0.0016 0.0007 -2.265 0.0235 * dist_town 0.0004 0.0002 2.01 0.0444 * Nasua narica --------: ----------: -------: ----------------: :--- 101 (Intercept) -0.9349 2.0040 -0.467 0.6409 dist_pine 0.0009 0.0005 1.781 0.0749 . dista_rural_road 0.0013 0.0009 1.474 0.1404 dist_stre -0.0215 0.0070 -3.056 0.0022 ** Didelphis marsupialis --------: ----------: -------: ----------------: :--- 82.4 (Intercept) -1.2347 1.8487 -0.668 0.5042 dist_pine 0.0007 0.0004 1.57 0.1164 dist_town 0.0003 0.0003 1.127 0.2599 dist_stre -0.0112 0.0059 -1.916 0.0553 . Cuniculus paca --------: ----------: -------: ----------------: :--- 45.2 (Intercept) 5.4880 2.1130 2.598 0.0094 ** dist_agri_farm 0.0011 0.0011 1.013 0.3111 dist_pine 0.0000 0.0006 -0.058 0.9541 dist_stre -0.0055 0.0042 -1.302 0.1930 dista_rural_road 0.0005 0.0013 0.343 0.7313 dist_town 0.0000 0.0004 -0.107 0.9149 dist_altitude -0.0057 0.0019 -2.92 0.0035 ** Urocyon cinereoargenteus --------: ----------: -------: ----------------: :--- 50.7 (Intercept) 3.1154 0.8848 3.521 0.0004 *** dist_agri_farm -0.0021 0.0009 -2.261 0.0237 * dist_stre -0.0263 0.0089 -2.946 0.0032 ** dista_rural_road -0.0031 0.0006 -4.867 1.13E-06 *** dist_town 0.0013 0.0003 5.087 3.64E-07 *** Leopardus wiedii --------: ----------: -------: ----------------: :--- 48.8 (Intercept) -3.3773 2.5738 -1.312 0.19 dist_agri_farm -0.0017 0.0009 -1.855 0.0637 . dist_pine -0.0017 0.0007 -2.5 0.0124 * dist_stre -0.0137 0.0054 -2.535 0.0113 * dista_rural_road 0.0065 0.0014 4.696 2.65E-06 *** dist_town -0.0011 0.0006 -1.83 0.0672 . Dasypus novemcinctus --------: ----------: -------: ----------------: :--- 54.1 (Intercept) -5.1191 2.9708 -1.723 0.0849 . dist_pine 0.0014 0.0007 1.831 0.0670 . dist_stre -0.0098 0.0062 -1.572 0.1160 dist_town 0.0005 0.0003 1.512 0.1306 Puma concolor --------: ----------: -------: ----------------: :--- 53.9 (Intercept) -1.7575 2.3800 -0.738 0.4603 dist_pine 0.0006 0.0005 1.154 0.2484 dista_rural_road -0.0013 0.0010 -1.277 0.2018 dist_stre -0.0049 0.0067 -0.737 0.4610 dist_town 0.0007 0.0004 1.826 0.0679 . Eira barbara --------: ----------: -------: ----------------: :--- 39.3 (Intercept) -1.81654 4.092988 -0.444 0.6572 dista_rural_road 0.0035 0.00158 2.217 0.0267 * dist_altitude -0.00326 0.001645 -1.979 0.0478 * dist_agri_farm -0.00185 0.001256 -1.473 0.1407 The species-level analysis revealed different habitat selection patterns. P. tajacu showed a strong positive association with distance to settlements (z = 4.101, P < 0.05), indicating a preference for areas farther from human habitation. It also exhibited significant negative relationships with distance to water bodies (z = -2.727, P < 0.05) and altitude (z = -2.327, P < 0.05), suggesting a preference for locations near rivers or streams and at lower elevations. Additionally, a marginal positive trend was observed with distance to pine forests (z = 1.716, P = 0.08). O. virginianu s selected areas far from settlements (z = 2.01, P < 0.05) but close to rural roads (z = -2.26, P < 0.05), suggesting that the species may use roads as movement corridors while avoiding areas with higher human presence. M . temama showed a strong negative association with distance to rural roads (z = -2.172, P < 0.05), indicating a preference for areas nearby. A marginal positive trend with altitude (z = 1.660, P = 0.09) was also observed, suggesting a slight preference for higher elevations (Table 2 , Fig. 5 ). C. paca exhibited a strong negative relationship with altitude (z = -2.92, P < 0.05), favoring lower areas. D. marsupialis only showed a marginal preference for areas near streams (z = -1.916, P = 0.056). Dasypus novemcinctus displayed a marginal positive relationship with distance to pine forests (z = 1.831, P = 0.067), suggesting a slight preference for areas farther from these forest formations. N. narica had a strong negative relationship with distance to water bodies (z = -3.056, P < 0.05), indicating a clear preference for areas near water sources. Additionally, a marginal positive trend was observed with distance to pine forests (z = 1.781, P = 0.07). Urocyon cinereoargenteus preferred areas far from settlements (z = 5.087, P < 0.05), while showing significant negative relationships with distance to rural roads (z = -4.867, P < 0.05), water bodies (z = -2.946, P < 0.05), and agricultural zones (z = -2.261, P < 0.05). This pattern suggests that the species favors areas away from settlements but close to rural roads, water sources, and agricultural zones. Eira barbara exhibited a significant positive relationship with distance to rural roads (z = 2.217, P < 0.05), indicating a preference for areas farther from roads. It also showed a significant negative relationship with altitude (z = -1.979, P < 0.05), demonstrating a clear selection for lower-altitude areas (Table 2 , Fig. 5 ). Felids exhibited species-specific response patterns. Leopardus pardalis showed a preference for areas near pine forests (z = -2.531, P < 0.05). In contrast, L . wiedi i presented a more complex pattern, selecting areas away from rural roads (z = 4.696, P < 0.001) but showing significant negative relationships with distance to pine forests (z = -2.500, P < 0.05) and water bodies (z = -2.535, P < 0.05), indicating a preference for areas close to these landscape features. Additionally, marginal negative trends were observed with distance to agricultural zones (z = -1.85, P = 0.063) and human settlements (z = -1.83, P = 0.067), suggesting a slight preference for areas near these landscape elements. Puma concolo r exhibited only a marginal positive trend with distance to human settlements (z = 1.826, P < 0.067), suggesting a slight preference for areas farther from direct human influence (Table 2 , Fig. 5 ). Poisson error distribution models fit well for species richness and the abundances of L. pardalis , C . paca , as well as for L . wiedii , E . barbara , and U . cinereoargenteus in models with a reduced number of variables. Negative binomial distribution provided a better fit for total abundance and for P . tajacu , as well as for O . virginianus , M . temama , N . narica , D. marsupialis , D . novemcinctus , and P . concolor in simplified models with fewer variables. Discussion The Sierra Madre of Chiapas harbors a remarkable diversity of medium- and large-sized mammals. This species richness is maintained within the complex of protected areas that form the Sierra Madre of Chiapas, including the La Frailescana, La Sepultura, and El Triunfo Biosphere Reserves, which together constitute a strategic biological corridor for mammal conservation in the region (Lorenzo et al. 2017 ; De la Torre et al. 2019 ). Abundance and Species Composition Of the total medium- and large-sized mammal species documented for La Sepultura, El Triunfo, and La Frailescana, 63.33% of the expected species were recorded, with 11 species remaining undetected (Medinilla et al. 2004 ; Medinilla et al. 2014 ; CONANP 2019 ). The absence of species such as Procyon lotor , Dasyprocta punctata , and Canis latrans , among others, could be explained by inherent limitations of camera trapping and the concentration of sampling in only one vegetation type, which may have biased the detection of species associated with other habitats present in the area (Burton et al. 2015 ; Andrade-Ponce et al. 2021 ). The results from La Frailescana reveal a structured and diverse community of medium- and large-sized mammals, with 19 species displaying complex response patterns to landscape characteristics and human influence. The hierarchical structure of the community, evidenced by Hill numbers (0D = 19, 1D = 8.47, 2D = 5.32), indicates a distribution in which approximately 45% of species are common and 28% are dominant, suggesting a relatively balanced community. The mammal community structure in La Frailescana exhibited clear hierarchical patterns, with species such as P. tajacu (RAI = 14.39) and N. narica (RAI = 9.03) dominating the assemblage. The strong correlation (78%) between RAI and naïve occupancy indicates that the most abundant species also occupy a larger proportion of the landscape, a pattern that may be related to the ability of these species to adapt to heterogeneous landscapes (Cove et al. 2014 ; Falconi-Briones et al. 2022 ). The presence of protected and disturbance-sensitive species such as T. bairdii, P. onca, P. concolor, L. wiedii , and L. pardalis at the 21 sampling stations confirms the importance of the area for regional conservation. In the case of T. bairdii and P. onca , both species were recorded in La Frailescana at low abundances, consistent with the findings of De la Torre et al. ( 2019 ) and Rivero et al. ( 2021 ). The low number of independent records obtained for both species (n < 14) limited statistical evaluation of their relationship with landscape variables. However, distribution patterns indicated that both species select areas of higher elevation and greater topographic complexity, particularly in pine-oak and cloud forests. The convergence of both species in high and topographically complex areas may represent a response to increased anthropogenic pressures in lower and more accessible areas (Gonzalez-Maya et al. 2009 ). De la Torre et al. ( 2018 , 2019 ) documented that the primary threat to P. onca in La Frailescana was livestock conflict, whereas for T. bairdii , it was poaching. This may explain why both species seek refuge in higher elevations where the habitat remains more preserved and less accessible, suggesting that the conservation of these mountainous areas is crucial for their persistence in the region. Habitat Preferences and Landscape Features Results revealed that the distance to water bodies emerged as a critical factor in the spatial structuring of the mammal community, a pattern consistent with findings in other Neotropical ecosystems (Reyna-Hurtado et al. 2010 ; Delgado-Martínez et al. 2023 ). The significant negative relationship between overall abundance and species richness with distance to water bodies suggests that this resource acts as a structuring element of the landscape (Reyna-Hurtado et al. 2010 ; Chamaillé-Jammes et al. 2016 ). This pattern was particularly evident in five species ( N. narica, P. tajacu, L. wiedii, D. marsupialis , and U. cinereoargenteus ), which showed a strong association with areas near water. The association of N. narica , P. tajacu , and D. marsupialis with these water bodies may be explained by multiple factors, including the need for thermoregulation, other physiological processes, or the greater availability of food resources in these areas due to the higher plant species richness found along riparian zones (Hafez 1964 ; Brown et al. 2008 ; Reyna-Hurtado et al. 2010 ). Additionally, riparian habitat strips function as natural corridors that facilitate species movement and dispersal between habitat fragments, which could explain the observed abundance and richness patterns near these areas (Brown et al. 2008 ). For L. wiedii and U. cinereoargenteus , the association with areas close to water could be related to hunting strategies, as water bodies may attract potential prey (Harris et al. 2015 ). The dependence of overall richness and abundance, as well as that of certain species, suggests that water bodies should be considered critical elements in the area's management and conservation strategies. The response to human infrastructure revealed that P. tajacu, U. cinereoargenteus (P < 0.001), O. virginianus (P < 0.05), and P . concolor (P < 0.1) were more abundant farther from human settlements. For P . tajacu and O. virginianus , this pattern could be related to the fact that these species are frequently hunted for subsistence in rural Neotropical communities (Nájera et al. 2018 ), while for P . concolor and U. cinereoargenteus , avoidance may be linked to reduced prey availability and as a strategy to minimize encounters with humans due to human-wildlife conflicts over livestock predation, which often results in retaliatory hunting (Rodas-Trejo et al. 2016 ; Nájera et al. 2018 ; De la Torre et al. 2019 ). The response to rural roads showed complex and contrasting patterns among species. While L. wiedii exhibited strong avoidance ( P < 0.001), consistent with findings by Goulart et al. ( 2009 ) in the Atlantic Forest of southern Brazil, where L. wiedii preferentially selected narrow trails and areas with dense forest cover while avoiding wider roads and open areas, other species showed more flexible responses. For instance, O. virginianus exhibited a dual response: while it avoided human settlements, it was more abundant near rural roads ( P < 0.05), suggesting that it uses these roads as corridors though maintaining a safe distance from human-populated areas to reduce the risk of poaching or predation (Ramos-Robles et al. 2013 ; Henderson et al. 2023 ; Ganz et al. 2024 ). Similarly, M. temama displayed a complex pattern reflecting its habitat specialization. Although it showed a positive association with rural roads ( P < 0.05), it also tended to use higher elevation areas. This apparent contradiction can be explained by the species' strategy of using rural roads as movement corridors to access different patches of suitable habitat, while favoring higher elevation areas where the most conserved zones of the reserve are located. These elevated areas likely provide anti-predator advantages and access to specific food resources, such as dense forest cover, which offers vertical protection and foraging opportunities (Contreras-Moreno et al. 2016 ; CONANP 2019 ; Vazquez and Tessaro 2016 ). This dual behavior highlights the species' ability to balance mobility and safety in a heterogeneous landscape. In the case of U. cinereoargenteus , this species avoided human settlements but showed higher abundance near rural roads ( P < 0.05). This ecological flexibility reflects its ability to exploit heterogeneous landscapes and coexist with human activities, which aligns with its generalist and opportunistic habits in terms of both habitat use and diet (Gallina et al. 2016 ; Wong-Smer et al. 2022 ). E. barbara showed a preference for avoiding roads and low-altitude areas (P < 0.05), findings consistent with the literature. Bianchi et al. ( 2021 ) found that the presence of this species is positively related to forest cover and proximity to water bodies, while its presence decreases in landscapes dominated by grasslands or near human infrastructure such as roads and buildings. Goulart et al. ( 2009 ) also reported that E. barbara avoids wide roads and prefers moving through animal paths and areas with dense vegetation cover, suggesting a greater dependence on habitat structure. The decrease in its presence with altitude could be related to changes in the availability of shelter and food resources, as well as to forest structure, which favors its scansorial behavior, and to more favorable microclimatic conditions for a species that maintains high metabolic rates (Bianchi et al. 2021 ). The results on habitat preferences of mammals in La Frailescana reveal interesting patterns that partially align with those reported by Lorenzo et al. ( 2017 ) for temperate and pine forests in Chiapas. Felids such as L. pardalis and L . wiedii showed a clear preference for areas near pine forests, consistent with studies highlighting the importance of these ecosystems for carnivorous species that require well-preserved and heterogeneous habitats (Di Bitetti et al. 2008 ; Espinosa et al. 2017 ). In contrast, more generalist species such as N. narica , P. tajacu , and D. novemcinctus tended to increase in abundance farther from pine forests, which could be explained by their adaptability to disturbed habitats and ecotones, as also suggested in studies describing their plasticity in response to land-use changes (De Matos Dias et al. 2018 ; Mendoza et al. 2019 ). Overall mammal abundance showed a positive pattern away from pine forests, which may seem contradictory to reports by Lorenzo et al. ( 2017 ) regarding high diversity in these ecosystems in Chiapas. However, our findings suggest that this pattern could be explained by the numerical dominance of generalist species in the study area, as well as by the conservation status and specific configuration of pine forests in La Frailescana. These factors emerged as key determinants in the distribution patterns of mammalian fauna in our study, highlighting how local conditions and species composition can influence ecological patterns differently than those reported in broader regional studies. Lastly, the results of this study emphasize the need to understand how landscape heterogeneity and habitat characteristics influence mammal distribution. Given the multiple threats faced by wildlife in the Sierra Madre de Chiapas, including habitat loss, poaching, and human encroachment, it is crucial to strengthen conservation strategies that mitigate these impacts (Lorenzo et al. 2017 ; De la Torre et al. 2018 , 2019 ; Rivero et al. 2021 ). In particular, improving knowledge on the distribution and ecological requirements of endemic and endangered species will provide a stronger basis for evidence-based conservation planning. Conservation Implications The findings of this study highlight the importance of preserving landscape heterogeneity in La Frailescana to ensure the persistence of medium- and large-sized mammals. The identification of water bodies as structuring elements of the mammal community suggests the need to establish specific protection measures for these areas, including maintaining riparian zones and regulating human activities in their vicinity. Furthermore, the avoidance of human settlements by sensitive species indicates that fragmentation and anthropogenic pressure can negatively affect wildlife distribution, emphasizing the urgency of management strategies that minimize the impact of agricultural expansion and infrastructure development. The presence of threatened species such as T. bairdii and P. onca in high-elevation areas underscores the need to strengthen the protection of these mountainous regions. Implementing biological corridors and restoring degraded habitats in key areas particularly those with high species diversity and low human presence can enhance landscape connectivity and reduce the effects of population isolation. This study provides valuable information to guide conservation policies in the region, promoting the design of evidence-based strategies that integrate the protection of critical habitats with sustainable development compatible with biodiversity conservation. Declarations Competing Interests The authors declare that they have no competing interests. Author Contributions Statement Conceptualization: J.R.T., S.L. Data curation: J.R.T. Formal analysis: J.R.T., C.T.C, S.L. Investigation: J.R.T., C.T.C, S.L., P.O.G. Methodology: J.R.T., C.T.C, S.L., Resources: J.R.T., P.O.G. Writing –original draft: J.R.T. Writing –review & editing: J.R.T., C.T.C, S.L., P.O.G. All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Funding J.R.T. received a scholarship (CVU: 206503) from the Consejo Nacional de Humanidades, Ciencias y Tecnologías of Mexico (CONAHCYT). This article contains part of the results from the thesis project for the Doctorado en Ciencias en Biodiversidad y Conservación de Ecosistemas Tropicales at the Universidad de Ciencias y Artes de Chiapas (UNICACH). IDEA WILD for the equipment donated for field sampling. Acknowledgments J.R.T. thanks the National Council of Humanities, Sciences, and Technologies of Mexico (CONAHCYT) for the scholarship granted (CVU: 206503), which made this work possible. This article presents part of the results obtained in the thesis project for the Doctorate in Sciences in Biodiversity and Conservation of Tropical Ecosystems at the University of Sciences and Arts of Chiapas (UNICACH). 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Methods Ecol Evol 1:3–14. https://doi.org/10.1111/j.2041-210x.2009.00001.x Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 31 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 19 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6248323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434683980,"identity":"fc40ae66-77f2-4f59-968f-f39e6195c805","order_by":0,"name":"Jenner Rodas-Trejo","email":"","orcid":"","institution":"Universidad Autonoma de Chiapas","correspondingAuthor":false,"prefix":"","firstName":"Jenner","middleName":"","lastName":"Rodas-Trejo","suffix":""},{"id":434683981,"identity":"9319149d-121e-45d7-a56b-e0445961dca3","order_by":1,"name":"Paola Ocampo González","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACxgNw5gcQIUGEHrgWxhkka2HmIUaLOfvhAwd+MNjI8087e/Cxbdsdef7ZDaybefBosexJSzjYw5BmOON2XrJxbtszwxl3DrDdnIFHi8GBHIMDPAyHExhu55hJ57YdZtwgkcB24wM+LeffGBz8A9QiD9Ji2XbYHqwlAZ+WGzkGh0G2GIC0MLYdTiRoi+WMZwmHZQzSDDcC/WLYc+5w8owbiW14/WLOn3zw4ZsKG3m527kHH/woO2zbPyP52G18IWaAIOHKGBvwaIBpYUDRMgpGwSgYBaMAFQAAHzJTXRzYYY4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5186-3581","institution":"Universidad Autonoma de Chiapas","correspondingAuthor":true,"prefix":"","firstName":"Paola","middleName":"Ocampo","lastName":"González","suffix":""},{"id":434683982,"identity":"e6a399a8-c3be-4dd7-857e-4ed051c8d7ae","order_by":2,"name":"Sergio López","email":"","orcid":"","institution":"Universidad de Ciencias y Artes de Chiapas","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"López","suffix":""},{"id":434683983,"identity":"5da5ff2b-4b7e-47ac-920f-9cc446ae9f71","order_by":3,"name":"César Tejeda Cruz","email":"","orcid":"","institution":"Universidad de Ciencias y Artes de Chiapas","correspondingAuthor":false,"prefix":"","firstName":"César","middleName":"Tejeda","lastName":"Cruz","suffix":""}],"badges":[],"createdAt":"2025-03-18 01:26:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6248323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6248323/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80553267,"identity":"b3b0e6f8-89f9-4521-86a4-5672dd5ebc58","added_by":"auto","created_at":"2025-04-14 15:15:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":399211,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and location of the 21 sampling sites in the La Frailescana Natural Resources Protection Area.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248323/v1/a77eb8226a0bcef2049d88fd.jpeg"},{"id":80554348,"identity":"e035c367-c2e3-4893-9a43-14faa0cd2b89","added_by":"auto","created_at":"2025-04-14 15:23:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49313,"visible":true,"origin":"","legend":"\u003cp\u003eRarefaction curves of diversity based on the sample and extrapolated curves with 95% confidence intervals. Expected diversity \u003csup\u003eq\u003c/sup\u003eD is shown as a function of the number of individuals with \u003cem\u003eq\u003c/em\u003e = 0, 1, 2.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6248323/v1/b2bd9047ad49419c5a03c397.png"},{"id":80553269,"identity":"efc860cc-bfcd-4ee3-a93e-5133684b3c25","added_by":"auto","created_at":"2025-04-14 15:15:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129776,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the Relative Abundance Index and naïve occupancy. Bas_sum= \u003cem\u003eBassariscus sumichrasti\u003c/em\u003e, Con_leu= \u003cem\u003eConepatus leucotonus\u003c/em\u003e, Cun_pac = \u003cem\u003eCuniculus paca\u003c/em\u003e, Das_nov= \u003cem\u003eDasypus novemcinctus\u003c/em\u003e, Did_mar= \u003cem\u003eDidelphis marsupialis\u003c/em\u003e, Eir_bar= \u003cem\u003eEira barbara\u003c/em\u003e, Leo_par= \u003cem\u003eLeopardus pardalis\u003c/em\u003e, Leo_wie= \u003cem\u003eLeopardus wiedii\u003c/em\u003e, Maz_tem= \u003cem\u003eMazama temama\u003c/em\u003e, Nas_nar= \u003cem\u003eNasua narica\u003c/em\u003e, Odo_vir= \u003cem\u003eOdocoileus virginianus\u003c/em\u003e, Pan_onc= \u003cem\u003ePanthera onca\u003c/em\u003e, Pec_taj= \u003cem\u003ePecari tajacu\u003c/em\u003e, Pum_con= \u003cem\u003ePuma concolor\u003c/em\u003e, Pum_yag= \u003cem\u003ePuma yagouaroundi\u003c/em\u003e, Spi_ang= \u003cem\u003eSpilogale angustifrons\u003c/em\u003e, Tam_mex= \u003cem\u003eTamandua mexicana\u003c/em\u003e, Tap_bai= \u003cem\u003eTapirus bairdii\u003c/em\u003e, Uro_cin= \u003cem\u003eUrocyon cinereoargenteus\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248323/v1/6695c6480a1fac61f0b956dc.jpeg"},{"id":80553271,"identity":"4297d7d0-fd64-499b-b53b-fb05703061bc","added_by":"auto","created_at":"2025-04-14 15:15:30","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53577,"visible":true,"origin":"","legend":"\u003cp\u003eRank-abundance curve. Species are identified by their codes, as shown in Figure 3.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248323/v1/6ad764e32d1c8763439ad0b6.jpeg"},{"id":80553270,"identity":"def003cf-3697-4290-855c-f8e14f7b86b2","added_by":"auto","created_at":"2025-04-14 15:15:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44433,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the relative abundance of mammals and landscape variables in La Frailescana, Chiapas. The heat map shows the z statistics of the GLMs, where blue (z \u0026gt; 0) indicates positive associations and red negative associations (z \u0026lt; 0), with the color intensity reflecting the magnitude of the effect. Variables: distances to agricultural areas (dist_agri_farm), pine forests (dist_pine), water bodies (dist_stre), rural roads (dist_rural_road), villages (dist_town), and altitude (dis_altitude). Species are identified by their codes, as shown in Figure 3. Significant (P\u0026lt;0.05) and marginally significant (P\u0026lt;0.1) relationships are shown. Blank cells indicate the absence of a significant effect.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248323/v1/5b4eeed86bfa58ed64e2e772.jpeg"},{"id":80555091,"identity":"33b786b5-611d-4e93-b85e-392814e271fa","added_by":"auto","created_at":"2025-04-14 15:31:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1922509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6248323/v1/7749f43b-3277-4e7f-86a1-9dc381aee608.pdf"}],"financialInterests":"","formattedTitle":"Community Structure and Habitat Selection of Mammals in a Protected Area of the Sierra Madre de Chiapas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman activities have modified natural landscapes worldwide since ancient times, leading to habitat loss, fragmentation, and degradation. These changes disrupt ecosystem balance and affect species distribution and diversity patterns (Mayani-Par\u0026aacute;s et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Torres-Romero et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such transformations are primarily driven by land-use changes due to agricultural expansion, infrastructure development, and human settlement growth (Hoffmann et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Torres et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Allan et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMammals, as key components of terrestrial ecosystems, play crucial ecological roles, including trophic regulation, seed dispersal, and landscape modification (Jones and Safi \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lacher et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, due to their spatial requirements, population densities, and susceptibility to anthropogenic pressures, they are particularly vulnerable to landscape changes and habitat connectivity loss (Ceballos et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lacher et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These impacts not only directly affect populations but also trigger cascading effects that alter overall ecosystem dynamics (Jones and Safi \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Natural Resources Protection Area La Frailescana (La Frailescana) covers 116,743 hectares in the Sierra Madre of Chiapas, one of Mexico\u0026rsquo;s most biodiverse regions. It serves as a strategic biological corridor connecting the La Sepultura and El Triunfo Biosphere Reserves, facilitating species movement and dispersal between these protected areas (Lorenzo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; De la Torre et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This region features an altitudinal gradient ranging from 800 to 2,280 meters above sea level, with annual precipitation between 800 and 4,000 mm and temperatures from 12 to 34.5\u0026deg;C. These environmental conditions have resulted in a mosaic of seven vegetation types across rugged terrain, including montane cloud forest, pine forest, and oak forest (CONANP, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the expansion of productive activities such as agriculture and extensive cattle ranching poses a threat to these ecosystems and their function as a corridor (P\u0026eacute;rez et al. 2006; CONANP \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; CONANP \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the factors influencing the diversity and abundance of terrestrial mammals is essential for designing effective conservation strategies (Sathyakumar et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Environmental variables such as habitat type, vegetation structure, and resource availability determine habitat suitability for different species (Imre and Derbowka \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ferreguetti et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Assessing these patterns is crucial to understanding the mechanisms driving species responses to anthropogenic habitat modifications (Pacifici et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), particularly in protected areas where conservation and human development objectives converge (Olmos-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, this study evaluates how landscape elements influence the diversity and distribution patterns of medium- and large-sized mammals in La Frailescana. Specifically, we analyze the influence of natural variables (distance to water bodies, elevation, and vegetation types) and anthropogenic variables (distance to human settlements, roads, and agricultural areas). The study aims to: (1) characterize species richness, diversity, and relative abundance through camera trapping; (2) analyze community structure using diversity indices and occupancy patterns; (3) determine the influence of these variables on community parameters; and (4) identify species-specific responses to landscape variables.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the La Frailescana Natural Resource Protection Area (93\u0026deg;37'36\"W, 16\u0026deg;16'08\"N), located in the Sierra Madre of Chiapas, Mexico (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This area covers 116,743 ha, with elevations ranging from 800 to 2,280 m asl, annual precipitation between 800 and 4,000 mm, and temperatures from 12\u0026deg;C to 34.5\u0026deg;C (DOF, 1979; CONANP, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The vegetation comprises seven types: montane cloud forest (25.15%; 29,360 ha), pine forest (37.18%; 43,409 ha), oak forest (0.81%; 942 ha), pine-oak forest (17.68%; 20,638 ha), tropical evergreen forest (0.31%; 363 ha), and tropical dry forest (1.25%; 1,463 ha). The area also includes agricultural activities (mainly maize and coffee), extensive cattle ranching (17.61%; 20,568 ha), and 162 human settlements (P\u0026eacute;rez et al. 2006; CONANP \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; CEIEG \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eTwenty-one camera trapping stations were installed between November 2021 and March 2023, distributed across four sites with montane cloud forest within La Frailescana (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each station consisted of a Moultrie\u0026reg; A-25i camera trap, placed at least 1,000 m apart to ensure spatial independence, and installed 50 cm above the ground in areas with evidence of animal activity. The cameras operated continuously, programmed to take three successive images per trigger with no delay between detections. Camera locations ranged from 922 to 1,856 m asl. Species identification was performed using Camelot software, considering only terrestrial mammals\u0026thinsp;\u0026gt;\u0026thinsp;0.5 kg. Independent records were defined as those separated by \u0026ge;\u0026thinsp;60 minutes for the same species at the same station (Cusack et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ferreira et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rodas-Trejo \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLandscape Metrics\u003c/h3\u003e\n\u003cp\u003eEight covariates were analyzed to assess the influence of environmental and anthropogenic factors on mammal richness and abundance. Environmental factors included altitude, distance to streams, and distance to pine forest. These environmental covariates are useful for identifying key areas for species persistence and survival (Joly and Myers, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rhim et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Specifically, the distance to pine forest was included because pine forests are a critical vegetation type in the Sierra Madre de Chiapas, providing essential resources such as shelter, food, and connectivity corridors for many mammal species. This variable helps to understand habitat preferences and the role of pine forests in maintaining biodiversity and facilitating species movement across the landscape. Anthropogenic factors included distances to infrastructure and human settlements: main roads (paved with high traffic), rural roads (unpaved with low traffic), rural villages (fewer than 250 inhabitants), major towns (more than 250 inhabitants), and agricultural-livestock systems. These variables have been shown to significantly impact species distribution and survival and are key drivers of land cover change both globally and in Mexico (Mendoza-Ponce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Allan et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe minimum distances between each camera trap station and landscape elements were calculated using land-use cartographic data from CEIEG (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), employing the st_distance() function from the sf package in R (Pebesma, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Correlations among covariates were assessed using the Variance Inflation Factor (VIF) with the car package in R. Covariates were standardized (mean\u0026thinsp;=\u0026thinsp;0, variance\u0026thinsp;=\u0026thinsp;1), and those with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 were excluded to prevent multicollinearity issues in subsequent analyses (Zuur et al. 2010; Fox and Weisberg \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatial autocorrelation was evaluated using Moran\u0026rsquo;s I index with the nb2listw() function from the spdep package in R (Bivand et al. 2023). This analysis quantified spatial dependence among sampling stations, considering both geographic distances and species abundances, allowing for the establishment of neighborhood structures between sites (Carroll and Pearson \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vieira et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eMammal diversity was analyzed using Hill numbers (qD), which provide effective species numbers at different diversity orders: 0D (total species richness), 1D (exponential of Shannon, common species), and 2D (inverse of Simpson, dominant species). The iNEXT package was used to construct interpolation and extrapolation curves for species richness. Sampling robustness was evaluated through non-parametric estimators (Chao, Jackknife1, and Bootstrap) to compare observed and expected richness (Hill \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Jost \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Chao et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hsieh et al. 2016).\u003c/p\u003e \u003cp\u003eThe Relative Abundance Index (RAI) was calculated for each species as: RAI\u0026thinsp;=\u0026thinsp;independent recordscamera-days\u0026times;100RAI\u0026thinsp;=\u0026thinsp;camera-days independent records \u0026times;100 (O'Brien, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Na\u0026iuml;ve occupancy was obtained to determine the proportion of sites occupied by each species (Soto-Werschitz et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The relationship between spatial distribution and abundance was evaluated using the correlation between RAI and na\u0026iuml;ve occupancy (Mandujano and P\u0026eacute;rez-Solano \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rank-abundance curves were generated to visualize the hierarchical structure of the community (Rocchini and Neteler \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between landscape variables and species abundance and richness patterns was analyzed using generalized linear models (GLMs) with the MASS package, considering species with more than 14 independent records (Smith and Warren \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ripley \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Poisson models were initially used, but in cases of overdispersion and poor model fit, negative binomial models were applied (Hoef and Boveng \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Model selection was based on the Akaike Information Criterion (AIC) (Burnham and Anderson \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In some cases, models were simplified by reducing the number of variables to improve fit.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAbundance and species composition\u003c/h2\u003e \u003cp\u003eThe total sampling effort was 1,549 camera-days, documenting 19 species of medium- and large-sized wild mammals, belonging to 12 families and 7 orders. Carnivora and Cetartiodactyla dominated taxonomic diversity, with five and two families, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Species richness per station ranged from 2 to 10 species (mean\u0026thinsp;=\u0026thinsp;5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80 SD). Regarding conservation status, \u003cem\u003eTapirus bairdii\u003c/em\u003e is classified as Endangered, \u003cem\u003eLeopardus wiedii\u003c/em\u003e and \u003cem\u003ePanthera onca\u003c/em\u003e as Near Threatened, \u003cem\u003eMazama temama\u003c/em\u003e as Data Deficient, and the remaining 15 species as Least Concern (IUCN \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecies richness of mammals, conservation status categories, independent observations, Relative Abundance Index, and na\u0026iuml;ve occupancy in the La Frailescana Natural Resources Protection Area.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNa\u0026iuml;ve\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUICN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCARNIVORA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrocyon cinereoargenteus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGray Fox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMephitidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConepatus leuconotus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican Hog-nosed Skunk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpilogale angustifrons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Spotted Skunk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMustelidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEira barbara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTayra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFelidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcelot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus wiedii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMargay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanthera onca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaguar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePuma concolor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePuma yagouaroundi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaguarundi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcyonidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNasua narica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite-nosed Coati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBassariscus sumichrasti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCacomixtle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCETARTIODACTYLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTayassuidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePecari tajacu\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollared Peccary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMazama temama\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral American Red Brocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOdocoileus virginianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite-tailer Deer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCINGULATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDasypodidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDasypus novemcinctus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNine-banded Armadillo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIDELPHIMORPHIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDidelphidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDidelphis marsupialis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon opossum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePERISSODACTYLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTapiridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTapirus bairdii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaird's Tapir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePILOSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyrmecophagidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTamandua mexicana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern Tamandua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRODENTIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuniculidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCuniculus paca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgouti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eObs\u0026thinsp;=\u0026thinsp;Number of independent observations recorded; RAI\u0026thinsp;=\u0026thinsp;Relative Abundance Index; DD\u0026thinsp;=\u0026thinsp;Data deficient, LC\u0026thinsp;=\u0026thinsp;Least concern, NT\u0026thinsp;=\u0026thinsp;Near Threatened, CR\u0026thinsp;=\u0026thinsp;Critically endangered, A\u0026thinsp;=\u0026thinsp;Threatened, P\u0026thinsp;=\u0026thinsp;Endangered, Sites\u0026thinsp;=\u0026thinsp;sites where the CTs were installed, Group\u0026thinsp;=\u0026thinsp;Groups identified in the cluster analysis (IUCN, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe species with the highest RAI were: \u003cem\u003ePecari tajacu\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;14.39), \u003cem\u003eN\u003c/em\u003easua \u003cem\u003enarica\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;9.03), \u003cem\u003eDidelphis marsupialis\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;3.16), \u003cem\u003eOdocoileus virginianus\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;2.45), \u003cem\u003eCuniculus paca\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;2.32). In contrast, those with the lowest RAI were: \u003cem\u003eTamandua mexicana\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;0.13), \u003cem\u003eBassariscus sumichrasti\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;0.13), \u003cem\u003eT. bairdii\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;0.19), and \u003cem\u003eSpilogale angustifrons\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;0.19). Na\u0026iuml;ve occupancy showed a similar pattern with \u003cem\u003eP. tajacu\u003c/em\u003e, \u003cem\u003eO. virginianus\u003c/em\u003e, and \u003cem\u003eN. narica\u003c/em\u003e occupying the highest proportion of sites (76%, 62%, and 48% respectively), while \u003cem\u003eB. sumichrasti\u003c/em\u003e, \u003cem\u003eT. mexicana\u003c/em\u003e, \u003cem\u003eT. bairdii\u003c/em\u003e, \u003cem\u003eS. angustifrons\u003c/em\u003e, and \u003cem\u003eP. onca\u003c/em\u003e occupied less than 15% of the sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The strong correlation (78%) between RAI and na\u0026iuml;ve occupancy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), particularly evident in \u003cem\u003eP. tajacu\u003c/em\u003e and \u003cem\u003eN. narica\u003c/em\u003e, along with the rank-abundance curve, confirm a typical community structure with a few dominant species and several rare ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHill numbers revealed a well-sampled and complete community, with an observed richness of 19 species (0D, 95% CI: 19.00\u0026ndash;20.67), which aligns with the asymptotic estimation, suggesting that the sampling captured most of the species present. The diversity of common species (1D\u0026thinsp;=\u0026thinsp;8.47, 95% CI: 8.35\u0026ndash;9.23) indicates moderate evenness in the community, while the effective number of dominant species (2D\u0026thinsp;=\u0026thinsp;5.32, 95% CI: 5.28\u0026ndash;5.91) suggests a hierarchical structure with a few predominant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Non-parametric species richness estimators yielded values close to observed richness. The Chao estimator suggested a richness of 19.16 species (SE\u0026thinsp;=\u0026thinsp;0.52), first-order Jackknife estimated 19.95 species (SE\u0026thinsp;=\u0026thinsp;0.95), and Bootstrap estimated 19.83 (SE\u0026thinsp;=\u0026thinsp;0.80).\u003c/p\u003e \u003cp\u003eMoran\u0026rsquo;s I test produced a statistic of 0.003, with an expected value of -0.05, indicating no statistically significant evidence of spatial autocorrelation in the total mammal abundance data within the study area. This suggests that mammal abundance is uniformly distributed without significant clustering or dispersion patterns. The covariates distance to major populations and distance to main roads showed multicollinearity\u0026thinsp;\u0026gt;\u0026thinsp;5 and were excluded from the GLM analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHabitat Preferences and Landscape Features\u003c/h3\u003e\n\u003cp\u003eGeneralized linear models revealed significant patterns in mammal responses to the evaluated variables. Abundance showed a significant negative relationship with distance to water bodies (z = -3.468, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that individuals concentrate in greater numbers near water sources and their abundance decreases as the distance increases. Conversely, a positive relationship was observed with distance to pine forests (z\u0026thinsp;=\u0026thinsp;2.145, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and human settlements (z\u0026thinsp;=\u0026thinsp;3.637, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that species tend to be more abundant in areas farther from these landscape elements. In contrast, species richness showed only one significant relationship, manifesting as a negative association with distance to water bodies (z = -2.313, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This underscores the critical importance of water resources for maintaining species diversity in the area (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Models (GLM) of the relationship between environmental variables and the patterns of abundance and richness of medium and large mammals in La Frailescana, Chiapas. Covariates: distances to agricultural areas (dist_agri_farm), pine forests (dist_pine), water bodies (dist_stre), rural roads (dist_rural_road), altitude (dist_altitude), and villages (dist_town). Signif. codes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05 \u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo;.\u0026rsquo; 1.AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePecari tajacu\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.11E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.7550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMazama temama\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.65658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.636409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOdocoileus virginianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNasua narica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDidelphis marsupialis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.2347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCuniculus paca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrocyon cinereoargenteus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus wiedii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.3773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.65E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDasypus novemcinctus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.1191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePuma concolor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.7575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_stre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEira barbara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------------:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e:---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.81654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.092988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edista_rural_road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edist_agri_farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe species-level analysis revealed different habitat selection patterns. \u003cem\u003eP. tajacu\u003c/em\u003e showed a strong positive association with distance to settlements (z\u0026thinsp;=\u0026thinsp;4.101, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a preference for areas farther from human habitation. It also exhibited significant negative relationships with distance to water bodies (z = -2.727, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and altitude (z = -2.327, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a preference for locations near rivers or streams and at lower elevations. Additionally, a marginal positive trend was observed with distance to pine forests (z\u0026thinsp;=\u0026thinsp;1.716, P\u0026thinsp;=\u0026thinsp;0.08). \u003cem\u003eO. virginianu\u003c/em\u003es selected areas far from settlements (z\u0026thinsp;=\u0026thinsp;2.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but close to rural roads (z = -2.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the species may use roads as movement corridors while avoiding areas with higher human presence. \u003cem\u003eM\u003c/em\u003e. \u003cem\u003etemama\u003c/em\u003e showed a strong negative association with distance to rural roads (z = -2.172, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a preference for areas nearby. A marginal positive trend with altitude (z\u0026thinsp;=\u0026thinsp;1.660, P\u0026thinsp;=\u0026thinsp;0.09) was also observed, suggesting a slight preference for higher elevations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eC. paca\u003c/em\u003e exhibited a strong negative relationship with altitude (z = -2.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), favoring lower areas. \u003cem\u003eD. marsupialis\u003c/em\u003e only showed a marginal preference for areas near streams (z = -1.916, P\u0026thinsp;=\u0026thinsp;0.056). \u003cem\u003eDasypus novemcinctus\u003c/em\u003e displayed a marginal positive relationship with distance to pine forests (z\u0026thinsp;=\u0026thinsp;1.831, P\u0026thinsp;=\u0026thinsp;0.067), suggesting a slight preference for areas farther from these forest formations. \u003cem\u003eN. narica\u003c/em\u003e had a strong negative relationship with distance to water bodies (z = -3.056, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a clear preference for areas near water sources. Additionally, a marginal positive trend was observed with distance to pine forests (z\u0026thinsp;=\u0026thinsp;1.781, P\u0026thinsp;=\u0026thinsp;0.07). \u003cem\u003eUrocyon cinereoargenteus\u003c/em\u003e preferred areas far from settlements (z\u0026thinsp;=\u0026thinsp;5.087, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while showing significant negative relationships with distance to rural roads (z = -4.867, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), water bodies (z = -2.946, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and agricultural zones (z = -2.261, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This pattern suggests that the species favors areas away from settlements but close to rural roads, water sources, and agricultural zones. \u003cem\u003eEira barbara\u003c/em\u003e exhibited a significant positive relationship with distance to rural roads (z\u0026thinsp;=\u0026thinsp;2.217, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a preference for areas farther from roads. It also showed a significant negative relationship with altitude (z = -1.979, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), demonstrating a clear selection for lower-altitude areas (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFelids exhibited species-specific response patterns. \u003cem\u003eLeopardus pardalis\u003c/em\u003e showed a preference for areas near pine forests (z = -2.531, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, \u003cem\u003eL\u003c/em\u003e. \u003cem\u003ewiedi\u003c/em\u003ei presented a more complex pattern, selecting areas away from rural roads (z\u0026thinsp;=\u0026thinsp;4.696, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but showing significant negative relationships with distance to pine forests (z = -2.500, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and water bodies (z = -2.535, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a preference for areas close to these landscape features. Additionally, marginal negative trends were observed with distance to agricultural zones (z = -1.85, P\u0026thinsp;=\u0026thinsp;0.063) and human settlements (z = -1.83, P\u0026thinsp;=\u0026thinsp;0.067), suggesting a slight preference for areas near these landscape elements. \u003cem\u003ePuma concolo\u003c/em\u003er exhibited only a marginal positive trend with distance to human settlements (z\u0026thinsp;=\u0026thinsp;1.826, P\u0026thinsp;\u0026lt;\u0026thinsp;0.067), suggesting a slight preference for areas farther from direct human influence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePoisson error distribution models fit well for species richness and the abundances of \u003cem\u003eL. pardalis\u003c/em\u003e, \u003cem\u003eC\u003c/em\u003e. \u003cem\u003epaca\u003c/em\u003e, as well as for \u003cem\u003eL\u003c/em\u003e. \u003cem\u003ewiedii\u003c/em\u003e, \u003cem\u003eE\u003c/em\u003e. \u003cem\u003ebarbara\u003c/em\u003e, and \u003cem\u003eU\u003c/em\u003e. \u003cem\u003ecinereoargenteus\u003c/em\u003e in models with a reduced number of variables. Negative binomial distribution provided a better fit for total abundance and for \u003cem\u003eP\u003c/em\u003e. \u003cem\u003etajacu\u003c/em\u003e, as well as for \u003cem\u003eO\u003c/em\u003e. \u003cem\u003evirginianus\u003c/em\u003e, \u003cem\u003eM\u003c/em\u003e. \u003cem\u003etemama\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e. \u003cem\u003enarica\u003c/em\u003e, \u003cem\u003eD. marsupialis\u003c/em\u003e, \u003cem\u003eD\u003c/em\u003e. \u003cem\u003enovemcinctus\u003c/em\u003e, and \u003cem\u003eP\u003c/em\u003e. \u003cem\u003econcolor\u003c/em\u003e in simplified models with fewer variables.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe Sierra Madre of Chiapas harbors a remarkable diversity of medium- and large-sized mammals. This species richness is maintained within the complex of protected areas that form the Sierra Madre of Chiapas, including the La Frailescana, La Sepultura, and El Triunfo Biosphere Reserves, which together constitute a strategic biological corridor for mammal conservation in the region (Lorenzo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; De la Torre et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAbundance and Species Composition\u003c/h2\u003e \u003cp\u003eOf the total medium- and large-sized mammal species documented for La Sepultura, El Triunfo, and La Frailescana, 63.33% of the expected species were recorded, with 11 species remaining undetected (Medinilla et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Medinilla et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; CONANP \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The absence of species such as \u003cem\u003eProcyon lotor\u003c/em\u003e, \u003cem\u003eDasyprocta punctata\u003c/em\u003e, and \u003cem\u003eCanis latrans\u003c/em\u003e, among others, could be explained by inherent limitations of camera trapping and the concentration of sampling in only one vegetation type, which may have biased the detection of species associated with other habitats present in the area (Burton et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Andrade-Ponce et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results from La Frailescana reveal a structured and diverse community of medium- and large-sized mammals, with 19 species displaying complex response patterns to landscape characteristics and human influence. The hierarchical structure of the community, evidenced by Hill numbers (0D\u0026thinsp;=\u0026thinsp;19, 1D\u0026thinsp;=\u0026thinsp;8.47, 2D\u0026thinsp;=\u0026thinsp;5.32), indicates a distribution in which approximately 45% of species are common and 28% are dominant, suggesting a relatively balanced community. The mammal community structure in La Frailescana exhibited clear hierarchical patterns, with species such as \u003cem\u003eP. tajacu\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;14.39) and \u003cem\u003eN. narica\u003c/em\u003e (RAI\u0026thinsp;=\u0026thinsp;9.03) dominating the assemblage. The strong correlation (78%) between RAI and na\u0026iuml;ve occupancy indicates that the most abundant species also occupy a larger proportion of the landscape, a pattern that may be related to the ability of these species to adapt to heterogeneous landscapes (Cove et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Falconi-Briones et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of protected and disturbance-sensitive species such as \u003cem\u003eT. bairdii, P. onca, P. concolor, L. wiedii\u003c/em\u003e, and \u003cem\u003eL. pardalis\u003c/em\u003e at the 21 sampling stations confirms the importance of the area for regional conservation. In the case of \u003cem\u003eT. bairdii\u003c/em\u003e and \u003cem\u003eP. onca\u003c/em\u003e, both species were recorded in La Frailescana at low abundances, consistent with the findings of De la Torre et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Rivero et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The low number of independent records obtained for both species (n\u0026thinsp;\u0026lt;\u0026thinsp;14) limited statistical evaluation of their relationship with landscape variables. However, distribution patterns indicated that both species select areas of higher elevation and greater topographic complexity, particularly in pine-oak and cloud forests. The convergence of both species in high and topographically complex areas may represent a response to increased anthropogenic pressures in lower and more accessible areas (Gonzalez-Maya et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). De la Torre et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) documented that the primary threat to \u003cem\u003eP. onca\u003c/em\u003e in La Frailescana was livestock conflict, whereas for \u003cem\u003eT. bairdii\u003c/em\u003e, it was poaching. This may explain why both species seek refuge in higher elevations where the habitat remains more preserved and less accessible, suggesting that the conservation of these mountainous areas is crucial for their persistence in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHabitat Preferences and Landscape Features\u003c/h2\u003e \u003cp\u003eResults revealed that the distance to water bodies emerged as a critical factor in the spatial structuring of the mammal community, a pattern consistent with findings in other Neotropical ecosystems (Reyna-Hurtado et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Delgado-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The significant negative relationship between overall abundance and species richness with distance to water bodies suggests that this resource acts as a structuring element of the landscape (Reyna-Hurtado et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chamaill\u0026eacute;-Jammes et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This pattern was particularly evident in five species (\u003cem\u003eN. narica, P. tajacu, L. wiedii, D. marsupialis\u003c/em\u003e, and \u003cem\u003eU. cinereoargenteus\u003c/em\u003e), which showed a strong association with areas near water. The association of \u003cem\u003eN. narica\u003c/em\u003e, \u003cem\u003eP. tajacu\u003c/em\u003e, and \u003cem\u003eD. marsupialis\u003c/em\u003e with these water bodies may be explained by multiple factors, including the need for thermoregulation, other physiological processes, or the greater availability of food resources in these areas due to the higher plant species richness found along riparian zones (Hafez \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Brown et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Reyna-Hurtado et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, riparian habitat strips function as natural corridors that facilitate species movement and dispersal between habitat fragments, which could explain the observed abundance and richness patterns near these areas (Brown et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For \u003cem\u003eL. wiedii\u003c/em\u003e and \u003cem\u003eU. cinereoargenteus\u003c/em\u003e, the association with areas close to water could be related to hunting strategies, as water bodies may attract potential prey (Harris et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The dependence of overall richness and abundance, as well as that of certain species, suggests that water bodies should be considered critical elements in the area's management and conservation strategies.\u003c/p\u003e \u003cp\u003eThe response to human infrastructure revealed that \u003cem\u003eP. tajacu, U. cinereoargenteus\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eO. virginianus\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and \u003cem\u003eP\u003c/em\u003e. \u003cem\u003econcolor\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1) were more abundant farther from human settlements. For \u003cem\u003eP\u003c/em\u003e. \u003cem\u003etajacu\u003c/em\u003e and \u003cem\u003eO. virginianus\u003c/em\u003e, this pattern could be related to the fact that these species are frequently hunted for subsistence in rural Neotropical communities (N\u0026aacute;jera et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while for \u003cem\u003eP\u003c/em\u003e. \u003cem\u003econcolor\u003c/em\u003e and \u003cem\u003eU. cinereoargenteus\u003c/em\u003e, avoidance may be linked to reduced prey availability and as a strategy to minimize encounters with humans due to human-wildlife conflicts over livestock predation, which often results in retaliatory hunting (Rodas-Trejo et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; N\u0026aacute;jera et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; De la Torre et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe response to rural roads showed complex and contrasting patterns among species. While \u003cem\u003eL. wiedii\u003c/em\u003e exhibited strong avoidance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with findings by Goulart et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) in the Atlantic Forest of southern Brazil, where \u003cem\u003eL. wiedii\u003c/em\u003e preferentially selected narrow trails and areas with dense forest cover while avoiding wider roads and open areas, other species showed more flexible responses. For instance, \u003cem\u003eO. virginianus\u003c/em\u003e exhibited a dual response: while it avoided human settlements, it was more abundant near rural roads (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that it uses these roads as corridors though maintaining a safe distance from human-populated areas to reduce the risk of poaching or predation (Ramos-Robles et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Henderson et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ganz et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, \u003cem\u003eM. temama\u003c/em\u003e displayed a complex pattern reflecting its habitat specialization. Although it showed a positive association with rural roads (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), it also tended to use higher elevation areas. This apparent contradiction can be explained by the species' strategy of using rural roads as movement corridors to access different patches of suitable habitat, while favoring higher elevation areas where the most conserved zones of the reserve are located. These elevated areas likely provide anti-predator advantages and access to specific food resources, such as dense forest cover, which offers vertical protection and foraging opportunities (Contreras-Moreno et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; CONANP \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vazquez and Tessaro \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This dual behavior highlights the species' ability to balance mobility and safety in a heterogeneous landscape. In the case of \u003cem\u003eU. cinereoargenteus\u003c/em\u003e, this species avoided human settlements but showed higher abundance near rural roads (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This ecological flexibility reflects its ability to exploit heterogeneous landscapes and coexist with human activities, which aligns with its generalist and opportunistic habits in terms of both habitat use and diet (Gallina et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wong-Smer et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eE. barbara\u003c/em\u003e showed a preference for avoiding roads and low-altitude areas (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), findings consistent with the literature. Bianchi et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that the presence of this species is positively related to forest cover and proximity to water bodies, while its presence decreases in landscapes dominated by grasslands or near human infrastructure such as roads and buildings. Goulart et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) also reported that \u003cem\u003eE. barbara\u003c/em\u003e avoids wide roads and prefers moving through animal paths and areas with dense vegetation cover, suggesting a greater dependence on habitat structure. The decrease in its presence with altitude could be related to changes in the availability of shelter and food resources, as well as to forest structure, which favors its scansorial behavior, and to more favorable microclimatic conditions for a species that maintains high metabolic rates (Bianchi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results on habitat preferences of mammals in La Frailescana reveal interesting patterns that partially align with those reported by Lorenzo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for temperate and pine forests in Chiapas. Felids such as L. pardalis and \u003cem\u003eL\u003c/em\u003e. \u003cem\u003ewiedii\u003c/em\u003e showed a clear preference for areas near pine forests, consistent with studies highlighting the importance of these ecosystems for carnivorous species that require well-preserved and heterogeneous habitats (Di Bitetti et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Espinosa et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, more generalist species such as \u003cem\u003eN. narica\u003c/em\u003e, \u003cem\u003eP. tajacu\u003c/em\u003e, and \u003cem\u003eD. novemcinctus\u003c/em\u003e tended to increase in abundance farther from pine forests, which could be explained by their adaptability to disturbed habitats and ecotones, as also suggested in studies describing their plasticity in response to land-use changes (De Matos Dias et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mendoza et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overall mammal abundance showed a positive pattern away from pine forests, which may seem contradictory to reports by Lorenzo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) regarding high diversity in these ecosystems in Chiapas. However, our findings suggest that this pattern could be explained by the numerical dominance of generalist species in the study area, as well as by the conservation status and specific configuration of pine forests in La Frailescana. These factors emerged as key determinants in the distribution patterns of mammalian fauna in our study, highlighting how local conditions and species composition can influence ecological patterns differently than those reported in broader regional studies.\u003c/p\u003e \u003cp\u003eLastly, the results of this study emphasize the need to understand how landscape heterogeneity and habitat characteristics influence mammal distribution. Given the multiple threats faced by wildlife in the Sierra Madre de Chiapas, including habitat loss, poaching, and human encroachment, it is crucial to strengthen conservation strategies that mitigate these impacts (Lorenzo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; De la Torre et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rivero et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, improving knowledge on the distribution and ecological requirements of endemic and endangered species will provide a stronger basis for evidence-based conservation planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConservation Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study highlight the importance of preserving landscape heterogeneity in La Frailescana to ensure the persistence of medium- and large-sized mammals. The identification of water bodies as structuring elements of the mammal community suggests the need to establish specific protection measures for these areas, including maintaining riparian zones and regulating human activities in their vicinity. Furthermore, the avoidance of human settlements by sensitive species indicates that fragmentation and anthropogenic pressure can negatively affect wildlife distribution, emphasizing the urgency of management strategies that minimize the impact of agricultural expansion and infrastructure development. The presence of threatened species such as \u003cem\u003eT. bairdii\u003c/em\u003e and \u003cem\u003eP. onca\u003c/em\u003e in high-elevation areas underscores the need to strengthen the protection of these mountainous regions. Implementing biological corridors and restoring degraded habitats in key areas particularly those with high species diversity and low human presence can enhance landscape connectivity and reduce the effects of population isolation. This study provides valuable information to guide conservation policies in the region, promoting the design of evidence-based strategies that integrate the protection of critical habitats with sustainable development compatible with biodiversity conservation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e \u003cp\u003eConceptualization: J.R.T., S.L. Data curation: J.R.T. Formal analysis: J.R.T., C.T.C, S.L. Investigation: J.R.T., C.T.C, S.L., P.O.G. Methodology: J.R.T., C.T.C, S.L., Resources: J.R.T., P.O.G. Writing \u0026ndash;original draft: J.R.T. Writing \u0026ndash;review \u0026amp; editing: J.R.T., C.T.C, S.L., P.O.G.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \"Ethical responsibilities of Authors\" as found in the Instructions for Authors.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eJ.R.T. received a scholarship (CVU: 206503) from the Consejo Nacional de Humanidades, Ciencias y Tecnolog\u0026iacute;as of Mexico (CONAHCYT). This article contains part of the results from the thesis project for the Doctorado en Ciencias en Biodiversidad y Conservaci\u0026oacute;n de Ecosistemas Tropicales at the Universidad de Ciencias y Artes de Chiapas (UNICACH). IDEA WILD for the equipment donated for field sampling.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eJ.R.T. thanks the National Council of Humanities, Sciences, and Technologies of Mexico (CONAHCYT) for the scholarship granted (CVU: 206503), which made this work possible. This article presents part of the results obtained in the thesis project for the Doctorate in Sciences in Biodiversity and Conservation of Tropical Ecosystems at the University of Sciences and Arts of Chiapas (UNICACH). We thank IDEA WILD for the equipment donated for field sampling. We thank the Commission of Protected Natural Areas of Mexico (CONANP), particularly the Natural Resources Protection Area office, for their invaluable support during the fieldwork.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllan JR, Watson JEM, Di Marco M, O'Bryan CJ, Possingham HP, Atkinson SC, Venter O (2019) Hotspots of human impact on threatened terrestrial vertebrates. PLoS Biol 17:e3000158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pbio.3000158\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.3000158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade-Ponce G, Cepeda-Duque JC, Mandujano S, Vel\u0026aacute;squez-C KL, Lizcano DJ, G\u0026oacute;mez-Valencia B (2021) Modelos de ocupaci\u0026oacute;n para datos de c\u0026aacute;maras trampa. 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Methods Ecol Evol 1:3\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2041-210x.2009.00001.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210x.2009.00001.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"mammalian-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mamb","sideBox":"Learn more about [Mammalian Biology](https://link.springer.com/journal/42991)","snPcode":"42991","submissionUrl":"https://www.editorialmanager.com/mamb/default2.aspx","title":"Mammalian Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Camera trapping, Connectivity, Habitat selection, La Frailescana, Protected Natural Area, Terrestrial mammals","lastPublishedDoi":"10.21203/rs.3.rs-6248323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6248323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the influence of landscape elements on the community structure and habitat selection of medium- and large-sized mammals in La Frailescana Natural Resource Protection Area, Chiapas, Mexico. Specifically, we analyzed the influence of environmental variables (distance to water bodies, altitude, and vegetation types) and anthropogenic factors (distance to human settlements, roads, and agricultural areas) on the mammal community. We installed 21 camera trap stations, accumulating 1,549 camera-days of sampling effort. Diversity and relative abundance indices were calculated, and generalized linear models were applied to evaluate the relationship between landscape variables and recorded mammals. We recorded 19 species of medium- and large-sized mammals, belonging to 12 families and 7 orders. The most abundant species were \u003cem\u003ePecari tajac\u003c/em\u003eu and \u003cem\u003eNasua narica\u003c/em\u003e. Distance to water bodies had a significant negative effect on species abundance and richness, highlighting the importance of these water resources. Responses to human infrastructure revealed that \u003cem\u003eP\u003c/em\u003e. \u003cem\u003etajacu\u003c/em\u003e, \u003cem\u003eUrocyon cinereoargenteus\u003c/em\u003e, \u003cem\u003eOdocoileus virginianus\u003c/em\u003e, and \u003cem\u003ePuma concolor\u003c/em\u003e were more abundant away from human settlements, while rural roads generated varied responses. The results underscore the importance of considering landscape heterogeneity in conservation strategies. We recommend implementing measures that prioritize the conservation of key habitats, ensure connectivity between forest fragments, and minimize anthropogenic impacts to guarantee the persistence of biodiversity in the region.\u003c/p\u003e","manuscriptTitle":"Community Structure and Habitat Selection of Mammals in a Protected Area of the Sierra Madre de Chiapas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 15:15:25","doi":"10.21203/rs.3.rs-6248323/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-31T16:00:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T08:06:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T08:51:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mammalian Biology","date":"2025-03-17T21:25:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mammalian-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mamb","sideBox":"Learn more about [Mammalian Biology](https://link.springer.com/journal/42991)","snPcode":"42991","submissionUrl":"https://www.editorialmanager.com/mamb/default2.aspx","title":"Mammalian Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0a2d905d-7a9d-4e3a-b94c-23290c77297f","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T13:14:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 15:15:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6248323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6248323","identity":"rs-6248323","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}