SciELO - Scientific Electronic Library Online

vol.13 número3Relative abundance and activity patterns of mesomammals in central AndesVariation in population density of the Andean night monkey (Aotus lemurinus) in areas with different landscape characteristics índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO



versión On-line ISSN 2007-3364

Therya vol.13 no.3 La Paz sep. 2022  Epub 21-Nov-2022 


Land use change and its implications for biodiversity and jaguar conservation

Elizabeth Jean Painter1 

Octavio Cesar Rosas-Rosas1  * 

Louis C. Bender2 

Luis Antonio Tarango-Arámbula1 

Juan Felipe Martínez-Montoya1 

Juan de Dios Guerrero-Rodríguez3 

Adrián Silva-Caballero4 

1 Posgrado en Innovación en Manejo de Recursos Naturales, Colegio de Postgraduados, Campus San Luis Potosí. Iturbide 73, CP. 78620, Salinas de Hidalgo. San Luis Potosí, México. E-mail: (EJP), (OCR-R), (LAT-A), (JFM-M).

2 Extension Animal Sciences and Natural Resources, New Mexico State University. Las Cruces, New Mexico, 88003, U.S.A. E-mail: (LCB).

3 Posgrado en Estrategias para el Desarrollo Agrícola Regional, Colegio de Postgraduados, Campus Puebla. Km. 125.5 Carretera Federal México-Puebla, CP. 72760, Puebla. Puebla, México. E-mail: (JDG-R),

4 Posgrado en Recursos Genéticos y Productividad-Ganadería, Colegio de Postgraduados. Campus Montecillo. Carretera México-Texcoco Km. 36.5, C.P. 56230, Montecillo. Texcoco, México. E-mail: (AS-C).


Protected areas are frequently established to prevent declines in biodiversity, but their effectiveness in preserving biodiversity can depend on how land outside their borders is managed. We evaluated how land use changes from 1989 to 2016 in the Reserva de la Biosfera Sierra del Abra Tanchipa (RBSAT) landscape might affect the role of the RBSAT for conservation of biodiversity, with an emphasis on conservation of jaguars, a keystone species. We estimated the rate of land use change within and surrounding the RBSAT, a 215 km2 natural reserve in San Luis Potosí, Mexico, from 1989 to 2016 using supervised classifications of satellite imagery. We also analyzed the distribution of two GPS collared male jaguars. The RBSAT and surrounding landscape became increasingly fragmented and impacted by human use over the previous ca. 30 years. The largest increases were seen in infrastructure and intensive agriculture, while the largest decreases were seen in pasture, tropical deciduous forest, and secondary vegetation. Jaguars were located more frequently than expected in secondary vegetation, the most common cover class, which decreased from 34.8 % of the landscape to 32.1 % by 2016. Only 23 % of jaguar locations fell within the boundaries of the RBSAT, due to increases in preferred habitat attributes of jaguars and prey outside the Reserve. Increasing fragmentation compromises the RBSAT’s role as a biodiversity reserve, especially for interior-dependent species. Fragmentation and edge habitats in combination with increasing agriculture enhance suitability of the landscape surrounding the RBSAT for prey of jaguar, and only 23 % of jaguar locations were within the RBSAT itself. This increases the likelihood of jaguar-related conflicts in surrounding communities. Regional landscape planning should include policies that incentivize practices that maintain remaining larger habitat patches and minimize the likelihood of human-wildlife conflicts.

Keywords: Land classification; land use change; natural reserve; México; Panthera onca


Las áreas protegidas se establecen con frecuencia para evitar la disminución de la biodiversidad, pero su eficacia para preservar la biodiversidad puede depender de cómo se gestione la tierra fuera de sus fronteras. Se evaluó cómo los cambios en el uso del suelo de 1989 a 2016 en el paisaje de la Reserva de la Biosfera Sierra del Abra Tanchipa (RBSAT) podrían afectar el papel de la RBSAT para la conservación de la biodiversidad, con énfasis en la conservación de los jaguares, una especie clave. Se estimó la tasa de cambio de uso del suelo dentro y alrededor de la RBSAT, una reserva natural de 215 km2 en San Luis Potosí, México, de 1989 a 2016 utilizando clasificaciones supervisadas de imágenes satelitales. También se analizó la distribución de dos jaguares machos con collar GPS. La RBSAT y el paisaje circundante se fragmentaron cada vez más y se vieron afectados por el uso humano durante los ca. 30 años. Los mayores aumentos se observaron en infraestructura y agricultura intensiva, mientras que las mayores disminuciones se observaron en pastos, bosque tropical caducifolio y vegetación secundaria. Los jaguares se ubicaron con mayor frecuencia de lo esperado en la vegetación secundaria, la clase de cobertura más común, que disminuyó del 34,8 % del paisaje al 32,1 % en 2016. Solo el 23 % de las ubicaciones de jaguares se ubicaron dentro de los límites de la RBSAT, debido a aumentos en la preferencia. atributos de hábitat de jaguares y presas fuera de la Reserva. La creciente fragmentación compromete el papel del RBSAT como reserva de biodiversidad, especialmente para las especies dependientes del interior. La fragmentación y los hábitats de borde en combinación con el aumento de la agricultura mejoran la idoneidad del paisaje que rodea a la RBSAT para las presas del jaguar, y solo el 23 % de las ubicaciones de los jaguares estaban dentro de la propia RBSAT. Esto aumenta la probabilidad de conflictos relacionados con jaguares en las comunidades aledañas. La planificación del paisaje regional debe incluir políticas que incentiven prácticas que mantengan los parches de hábitat más grandes restantes y minimicen la probabilidad de conflictos entre humanos y vida silvestre.


Many areas are experiencing major declines in biodiversity (Polasky et al. 2007), and the establishment of protected areas is a common method used to minimize losses. However, the effectiveness of protected areas in preserving biodiversity and supporting local economies depends in part on management of lands surrounding protected areas (Brandon et al. 2017). Large carnivores are especially difficult to maintain through protected areas due to large home ranges and the negative attitudes they evoke from some communities (Kellert et al. 1996; Inskip and Zimmermann 2009; Cortina-Villar et al. 2012).

These issues are concerns with the Reserva de la Biosfera Sierra del Abra Tanchipa (RBSAT), in the state of San Luis Potosi, northeastern México, which is in a landscape dominated by agricultural and livestock production. This small (215 km2) reserve was established in 1994 as a National Protected Area for the myriad of unique flora and fauna found in this environmental link among the peaks of the Sierra Madre Oriental (SMO). There are 25 species of birds and eight species of mammals considered endangered, threatened, or with restricted distributions in RBSAT (SEMARNAT 2010, 2019; Durán-Fernández et al. 2020), including many interior species (i. e., species that require the interior of large contiguous patches of vegetation in a similar successional state ,Villard 1998, such as the elegant eagle (Spizaetus ornatus), red brocket deer (Mazama temama), and ocelot (Leopardus pardalis). However, the keystone species of the RBSAT and SMO is the jaguar (Panthera onca), and the RBSAT functions as a critical corridor in maintaining connectivity among jaguar populations in the SMO (Dueñas-López et al. 2015; Dueñas-López and Huerta-Rodríguez 2020). Ongoing studies of jaguar ecology (Rosas-Rosas et al. 2016; Silva-Caballero et al. 2022) further confirm the importance of the RBSAT landscape for this keystone large carnivore. Because of its role as a keystone carnivore, needs for large areas of minimally impacted habitat, and vulnerability to human-related mortality (Ripple et al. 2014), the jaguar is an effective management indicator species (MIS) for conservation of the RBSAT landscape, meaning that its status and trend provide insight into the overall health of the ecological system (Committee of Scientists 1999; Rosas-Rosas et al. 2020).

The highly productive valleys that border the Reserve are heavily impacted by human land use, including cattle ranching, cultivated crops, urban and rural developments, and mining operations (Dueñas-López et al. 2015; Durán-Fernández et al. 2020). The RBSAT is situated in the middle of two counties, Ciudad Valles and Tamuín, which have growing populations and robust local economies. Ciudad Valles had a 35 % increase in population from 1990 to 2015 (INEGI 2015) and is the number one producer of sugar cane in San Luis Potosí, with a production valued at $ 49 million USD in 2016 (SIAP 2017). The population of Tamuín increased 13 % from 1990 to 2015, and Tamuín leads the state in beef production with an economic impact of $ 269 million in 2016 (INEGI 2015; SIAP 2018). Cattle numbers more than doubled in Tamuín from 107,752 to 290,300, 2008 - 2016 (SIAP 2018), primarily related to feedlot production, but decreased in Ciudad Valles from 2,634 to 2,245.

Despite the ecological role of RBSAT as a biodiversity preserve, the types and rate of land use change remain unquantified around the RBSAT. Like many other areas in México, the landscape of the RBSAT is likely threatened by isolation, and land use surrounding the reserve has a major impact on its effectiveness in the conservation efforts of the SMO (Ortega-Huerta 2007; Chapa-Vargas and Monzalvo-Santos 2012; Rosas-Rosas et al. 2020). Likewise, jaguars and other sensitive wildlife move outside the boundaries of the RBSAT (Rosas-Rosas et al. 2016; Silva-Caballero et al. 2022), which highlights the need for coordinated management of the surrounding landscape to maintain viable habitat as well as decrease the likelihood of conflicts with local livestock producers and other residents of surrounding communities. Understanding the historic and current patterns of land use around RBSAT will inform strategies for future development and conservation efforts to preserve the ecological function of the Reserve and promote coexistence with its keystone species. Therefore, our goal was to document the type and rate of land use change within the RBSAT and its surrounding landscape over the last 27 years and assess how land use changes might influence efforts to conserve jaguars, one of the keystone species of the RBSAT, and thus maintain the role of the RBSAT landscape for conservation of biodiversity.

Materials and methods

Study Area. Our study area included the RBSAT and a 10 km buffer of the surrounding landscape, representing 1,080 km2 of tropical deciduous forest, secondary vegetation, agricultural and livestock production areas, rural communities, and gravel mines (Figure 1). The RBSAT is located between 22° 04’ 38”, 22° 23’ 56” N and -98° 53’ 07”, -99° 00’ 44” and contains 214 km2 of contiguous dry tropical deciduous forest, ranging in elevation from 120 to 820 masl (Arriaga et al. 2000; CONANP 2014). The climate is classified as sub-humid with summer rains from June to September, an average annual rainfall of 965 mm, and an annual average temperature of 25.7° C (CONANP 2014). The Reserve provides habitat for 420 species of plants, 259 species of birds, 56 species of mammals, 50 species of reptiles, and 17 species of amphibians (Durán-Fernández et al. 2020).

Figure 1 The study area, including the Reserva de la Biosfera Sierra del Abra Tanchipa and ten km of its surrounding landscape, and the complex matrix of land use from a satellite image captured on March 25, 2016. 

Land Use Change Data. We selected satellite images used to estimate land use change based on strict criteria, including temporal availability (during the dry season) and the percentage of cloud coverage (< 10 %). These criteria emphasize the contrast between dense forest, secondary vegetation, pastures, and agriculture (most notably sugar cane, which is a crop that stays green throughout the year), facilitating the differentiation among land uses (Fichera et al. 2012; Mei et al. 2016).

We analyzed two 30-m spatial resolution satellite images, downloaded from the online platform EarthExplorer (USGS 2018). The most appropriate available images from the end of the dry season with no cloud coverage were from the Landsat 4 Thematic Mapper (TM; 7 March 1989) and Landsat 8 Operational Land Imager (OLI; 25 March 2016), which spanned a period of 27 years. We were interested in maximizing the time span as much as possible, and these images fit all criteria. We followed the methodological processing (i. e., preprocessing of the satellite images, supervised classification, and accuracy assessment; see below) of data common to analyses of land use change (e. g., Reyes-Hernández et al. 2006; Sahagún-Sánchez et al. 2011; Horvat 2013; Fernández-Landa et al. 2016; Mei et al. 2016). We used the software programs QGIS (QGIS Development Team 2018) and SAGA GIS (Conrad et al. 2015) for all analyses.

Preprocessing. Geographic, radiance, and atmospheric interference are the three components of preprocessing corrections for satellite images commonly applied before classification (Horvat 2013). Geographic corrections were not necessary for our images, as the Landsat images we used had been geo-referenced by the USGS (2015) to the Universal Transverse Mercator (UTM) projection. We corrected for radiance using the Semi-Automatic Classification Plugin for QGIS (SCP; Congedo 2018). Last, we corrected for atmospheric interference using a Dark Object Subtraction (DOS1) correction within the SCP plugin (Hadjimitsis et al. 2010). We then clipped the corrected images to the study area.

Supervised Classification. We included six classes in our supervised classification: agriculture, infrastructure, pasture, secondary vegetation, tropical deciduous forest, and water. We selected these classes based on two cartographic maps from the National Institute of Statistics Geography and Information Technology (INEGI 2018), Series I from 1985 and Series VI from 2016 (Table 1), which included 13 land use types (LUTs) within our study area.

We developed a training file based on the above classes using three data inputs (Foody 2002; Reyes-Hernández et al. 2006; Grinand et al. 2013). The first level of data was INEGI Series I and VI land use and vegetation maps. The second level were the satellite images projected in natural color (Landsat 8, bands 7 - 5 - 3; Landsat 4, bands 3 - 2 - 1) and false color composite images that highlight vegetation (Landsat 8, bands 6 - 5 - 4 and 4 - 3 - 2; Landsat 4, bands 5 - 4 - 3 and 4 - 3 - 2). These provided greater detail than the land use and vegetation maps. The third input was personal knowledge of the region provided by the authors. We used the classification algorithm K-nearest Neighbors Classification in SAGA GIS (Qian et al. 2014; Conrad et al. 2015) for our supervised classification, with the training file serving as the basis for the classification. We ran 15 iterations of the classification; after every iteration, we reviewed the classification results, and strengthened the training files in misclassified areas.

Accuracy Assessment. We used the final classifications from 1989 and 2016 and two verification files for accuracy assessment. The verification files included high-resolution (1-m; Planet Team 2017) satellite imagery from March 2016 and field surveys used to classify areas with a high level of certainty to serve as controls. We then ran the Confusion Matrix (Grid/Polygon) module in SAGA GIS (Conrad et al. 2015) for each image, which compared the classification and the verification files to assess their level of agreement. This generated the overall accuracy and kappa index values.

Land Use Trends. We ran the Confusion Matrix (Grid/Grid) module in SAGA GIS (Conrad et al. 2015) with the two classified images to calculate the land use change from 1989 to 2016. We calculated rate of change following Miranda-Aragón et al. (2013) and Sahagún-Sánchez et al. (2011) using RC = ,(A2 / A1) ^ (1 / n) - 1,, where RC = rate of change, A1 = area in the first year (ha), A2 = area in the final year (ha), and n = number of years in the analysis period. Positive percentages represent increases in area and negative percentages represent losses in area, and the larger the number, the greater the magnitude of change.

Jaguar and Land Use Change. We used jaguar movement data from an ongoing project in the SMO (Rosas-Rosas et al. 2016; Silva-Caballero et al. 2022) to analyze the relationship between land use change and occurrence of jaguars. Data was collected from two male jaguars in 2016 and 2017 fitted with GPS collars (VECTRONIC Aerospace GmbH; Rosas-Rosas et al. 2016). We used 100 randomly selected GPS records within the study area from each jaguar and assigned a land use class to each record using the plugin Add Grid Values to Points in SAGA GIS (Conrad et al. 2015). We then calculated the percentage of records within each class.

To address the potential effects of land use change on jaguars, we compared the location points with a transition change map that described how each pixel shifted in its land use from 1989 to 2016. We used the Chi-square goodness of fit test and Bonferroni simultaneous confidence intervals to test whether jaguar records were random or if they were associated with certain land use classes (Neu et al. 1974; Byers et al. 1984). We excluded water as a potential land use type in the analysis due to its small percentage of surface area.

Table 1 Original categories of land use types (LUTs) from INEGI Series I and VI, and the six classes established for use in this study. 

Land Use Class/Included LUTs I VI
Semi-permanent irrigated agriculture X
Seasonal agriculture X
Annual and semi-permanent seasonal agriculture X
Semi-permanent seasonal agriculture X
Seasonal agricultura X  
Urban zone   X
Bodies of water X X
Permanent cultivated pasture X X
Managed pasture (native species) X X
Tropical Deciduous Forest
Tropical deciduous forest X X
Secondary Vegetation
Marsh X
Tropical deciduous forest - secondary arboreal vegetation X X
Tropical deciduous forest - secondary shrub vegetation X X
Lacking vegetation   X


Accuracies of the classifications were 91 % for both 1989 and 2016. Kappa values were 83 % for 1989 and 84 % for 2016; kappa values of 81 % - 100 % indicate an almost perfect level of agreement (Landis and Koch 1977). From 1989 to 2016 the RBSAT landscape shifted from a more contiguous landscape towards greater heterogeneity and fragmentation (Figure 2). The largest increases were seen in infrastructure (+6.0 %) and intensive agriculture (+5.5 %), while the largest decreases were seen in pasture (-5.7 %), tropical deciduous forest (-3.0 %), and secondary vegetation (-2.7 %); the latter was the most common cover class, comprising 34.8 % of the landscape in 1989 and 32.1 % in 2016 (Table 2). The greatest rates of annual change were seen in infrastructure and agriculture (+4.5 % and +2.3 %, respectively), while pasture (-0.9 %) showed the greatest rate of annual loss.

Figure 2 Land use classes of the RBSAT and ten km of the surrounding landscape from 1989 and 2016 analyzed using satellite imagery. 

Table 2 Totals and change in area in hectares (ha) and percent (%) measured by the classification of satellite imagery from 1989 and 2016. 

1989 2016 Change 1989 to 2016
Ha % ha % Ha % % change/year
Water 1,180 1.1 1,215 1.1 +35 0.0 0.1
Infrastructure 2,858 2.6 9,330 8.6 +6,472 6.0 4.5
TDF 31,931 29.6 28.640 26.5 -3,291 -3.0 -0.4
Secondary veg 37,599 34.8 34,631 32.1 -2,968 -2.7 -0.3
Agriculture 6,843 6.3 12,778 11.8 +5,935 5.5 2.3
Pasture 27,610 25.6 21,428 19.8 -6,182 -5.7 -0.9

Notable results in the change matrix included 20,463 ha (19.1 %) of tropical deciduous forest that remained unchanged, the majority of which was located in the RBSAT, in which 21,464 ha was protected (Table 3). Similarly, 17,364 ha of secondary vegetation were conserved (16.2 %). Since 1989, pasture conversions included 5,661 ha to intensive agriculture, 5,692 ha to secondary vegetation, and 4,208 ha to infrastructure. Infrastructure, tropical deciduous forest, secondary vegetation, and pasture all transitioned to water, but only accounted for 0.2 % of the total area.

Table 3 Confusion matrix of land use changes in area (%) from 1989 to 2016. Unchanged land use are in *. Retrogressive land use change is italicized

    Land use classes from 2016 imagery        
Land use Water Infrastructure Tropical forest Secondary veg Agriculture Pasture Total (1989)
Water 0.90 % * 0.04 % 0.02 % 0.07 % 0.03 % 0.05 % 1.10 %
Infrastructure 0.01 % 0.8 3% * 0.09 % 0.32 % 0.48 % 0.94 % 2.67 %
TDF 0.05 % 0.43 % 19.14 % * 8.31 % 0.84 % 0.78 % 29.54 %
Secondary Veg 0.09 % 2.81 % 5.21 % 16.24 % * 3.55 % 6.84 % 34.73 %
Agriculture 0.02 % 0.65 % 0.94 % 1.70 % 1.69 % * 1.38 % 6.38 %
Pasture 0.07 % 3.94 % 1.13 % 5.32 % 5.29 % 9.82 % * 25.57 %
Total (2016) 1.14 % 8.69 % 26.52 % 31.96 % 11.88 % 19.82 %  

Expected distribution of jaguars based on the spatial availability of each land use class differed significantly from actual locations of jaguars (χ2 4 = 121.7, P < 0.001). Bonferroni CIs indicated that jaguars were found proportionately more than expected in secondary vegetation (66 % ,57 to 81 %, of locations v. 32 % expected) and were located in tropical deciduous forest (26 % ,13 to 35 %, of locations v. 27 % expected) according to its availability. Few locations were in infrastructure (3 % ,1 to 7 %, of locations v. 9 % expected), agriculture (4 % ,2 to 5 %, of locations v. 12 % expected), and pasture (2 % ,1 to 7 %, of locations v. 20 % expected). While the majority of tropical deciduous forest and other land use types were conserved from 1989 to 2016 within the RBSAT, only 23 % of jaguar locations fell within the boundaries of the RBSAT. In contrast, jaguars were comparably located in areas of stable (i. e., unchanged) secondary vegetation (22 to 38 % of locations) as well as in secondary vegetation that had previously been tropical deciduous forest in 1989 (25 %) and in secondary vegetation that had been pasture in 1989 (38 %).

Figure 3 The percent of records associated with transitions in secondary vegetation from 1989 to 2016 from two jaguars (grey and white bars) within the RBSAT landscape. The symbol > indicates a transition between land use classes from 1989 to 2016. 


The RBSAT and surrounding landscape changed considerably over the previous ca. 30 years, becoming increasingly fragmented and impacted by human use, particularly intensive agriculture and urbanization (Tables 2 and 3; Figure 2). Based on the accuracies of our supervised land use classification, these changes were real rather than artifacts of selected land use data inputs. Classifications showed high accuracy (91 % for 1989 and 2016), and kappa values (83 % for 1989 and 84 % for 2016) indicated an almost perfect level of agreement (i. e., > 81 %; Landis and Koch 1977). Our values were also similar or superior to previous classifications performed in México at the state level. For example, Chapa-Vargas and Monzalvo-Santos (2012) obtained a 72 % overall accuracy and 69 % Kappa in San Luis Potosí; Porter-Bolland et al. (2007) obtained an 87 % overall accuracy in Campeche; and Currit (2005) obtained an 86 % overall accuracy and 85 % Kappa in Chihuahua. At the national level, Velázquez et al. (2010) obtained a 71 % overall accuracy of a classification of México. Thus, our mapping accurately classified the changing land use of the RBSAT landscape.

The increases in infrastructure and intensive agriculture and the decrease in pasture reflected a regional trend of shifting from less intensive cattle ranching towards intensive agriculture, specifically production of sugar cane and citrus (SIAP 2017). The increase in infrastructure reflected the development of commercial and residential infrastructure as a consequence of significant population growth in both Ciudad Valles (increase of 35 %) and Tamuín (increase of 14 %) from 1990 to 2015 (INEGI 2015). These trends follow a national and state-level trend of increasing fragmentation driven by anthropogenic modifications (Mas et al. 2009; Velázquez et al. 2010). For example, Miranda-Aragón et al. (2013) identified high rates of change in San Luis Potosí towards seasonal agriculture (+1.1 %) and irrigated agriculture (+4.8 %) between 1993 and 2007, similar to the annual rate of change (+2.3 %) to all forms of intensive agriculture combined that we observed from 1989 to 2016. Concurrently, less intensive agricultural uses declined, as reflected in the decrease in cover of pastures during our evaluation period. Despite the decrease in pasture, however, cattle production and associated activities are still common and economically significant in the RBSAT landscape, especially in Tamuín (Silva-Caballero et al. 2022).

These and other (e. g., the loss of tropical deciduous forest and secondary vegetation) land use trends in the RBSAT landscape could compromise the ecological role of the reserve. Increasing fragmentation of the immediate surrounding landscape decreases habitat quantity and quality for interior-dependent species such as the elegant eagle and ocelot, thus decreasing the effectiveness of the overall RBSAT landscape as a biodiversity reserve. Loss, or decreases in the size of, large patches that support interior species can affect biodiversity by negatively impacting populations of interior species (Villard 1998), as well as increasing vulnerability to humans and other human-associated threats (e. g., domestic cats and dogs; Doherty et al. 2016). Conversely, edge-dependent species, which include many of the key prey species of jaguars (Escamilla et al. 2000; Ávila-Nájera et al. 2011; Silva-Caballero et al. 2022), are benefitted by these changes in land use and increasing fragmentation of the RBSAT landscape, likely resulting in increased abundance, particularly outside of the RBSAT boundary. Consequently, most kill sites of livestock (and natural prey) by jaguars were outside the RBSAT boundary (Silva-Caballero 2019; Silva-Caballero et al. 2022).

These changes increase the likelihood of conflicts between jaguars and humans (Rosas-Rosas et al. 2015; Guerrero-Rodríguez et al. 2020), as only 23 % of jaguar locations were within the boundary of the RBSAT and jaguars were primarily associated with secondary vegetation, which is decreasing within the RBSAT itself. Better habitat quality for jaguars and their prey outside of the RBSAT boundary facilitates potential conflicts with humans. For example, livestock comprised 66 % of the biomass in jaguar diets in the RBSAT landscape, with all depredation occurring outside the RBSAT boundary (Silva-Caballero 2019; Silva-Caballero et al. 2022).

Secondary vegetation can result from abandonment of pastures or agricultural fields, and thinning or other alterations of tropical deciduous forest (Table 3; Wandelli and Fearnside 2015). Significantly, jaguars were frequently located in secondary vegetation that had transitioned from pasture and tropical deciduous forest in the RBSAT landscape (primarily outside the RBSAT border; Figure 3) in addition to their use of secondary vegetation that remained unchanged from 1989 to 2016. The apparent preference and use of secondary vegetation outside of the RBSAT boundary by jaguars in association with the preponderance of livestock in jaguar diets (Silva-Caballero et al. 2022) emphasizes the need to include local stakeholders in the greater RBSAT landscape in conservation efforts, including secondary vegetation.

Moreover, the expansion of intensive agriculture, increases in habitat edge due to fragmentation, and continued importance of livestock production and associated improvements (especially water) may attract prey species to, or increase their populations in, these land uses (Rosas-Rosas et al. 2020; Silva-Caballero et al. 2022). For example, Escamilla et al. (2000) and Ávila-Nájera et al. (2011) found that collared peccary (Pecari tajacu), white-tailed deer (Odocoileus virginianus), and white-nosed coati (Nasua narica), important prey species for jaguars in the RBSAT (Hernández-SaintMartín et al. 2015; Silva-Caballero 2019; Silva-Caballero et al. 2022, preferentially used mosaics of tropical deciduous forest, agriculture, and livestock pasture. Consequently, so did jaguars and pumas (Puma concolor; Escamilla et al. 2000; Hernández-SaintMartín et al. 2015). Further, the RBSAT lacks permanent water sources (Hernández-SaintMartín et al. 2013; Silva-Caballero et al. 2022). Consequently, wildlife, including jaguars and their prey, seek water outside of the boundaries of the Reserve during the dry season (Hernández-SaintMartín et al. 2013), which increases the vulnerability of livestock and natural prey to predation (Silva-Caballero et al. 2022). Water developments are most common in livestock enterprises, intensive agriculture (irrigation), and human developed (e. g., residential, etc.) areas, where presence of jaguars frequently results in serious conflicts (Guerrero-Rodríguez et al. 2020; Silva-Caballero et al. 2022).

The rapid increases in infrastructure, intensive agriculture, and habitat fragmentation have and will continue to dramatically change the RBSAT landscape, and thus affect the wildlife that utilize the landscape. Managers and stakeholders need to seek approaches that account for the response of resident wildlife including interior-dependent species and jaguars, mitigate conflicts, and protect the economic viability of adjacent communities (Olvera-Hernández et al. 2020; Silva-Caballero et al. 2022). Minimization of loss of tropical deciduous forest and other large habitat patches in the RBSAT itself is likely the only practical means of minimizing loss of biodiversity in the RBSAT landscape, especially for interior-dependent species given the already significant fragmentation in the surrounding landscape. Characteristics associated with human-jaguar conflict areas (especially with livestock) include proximity to a protected area and seasonal availability of water (Rosas-Rosas et al. 2010; Peña-Mondragón and Castillo 2013; Alfaro et al. 2016; Silva-Caballero et al. 2022). Both of these are present in the RBSAT landscape, as are associated livestock depredation conflicts (Silva-Caballero et al. 2022), and are likely to become more prevalent given the changes in land use away from natural vegetation towards intensive agricultural and infrastructure development. The combination of a decrease in preferred jaguar habitat within the RBSAT and likely increases in preferred prey outside of the RBSAT as a consequence of increasing fragmentation, intensive agriculture, and permanent water infrastructure, will likely lead to in increased conflicts between humans and jaguars (Rosas-Rosas et al. 2010; Guerrero-Rodríguez et al. 2020; Peña-Mondragón and Castillo 2013; Alfaro et al. 2016; Silva-Caballero et al. 2022).

Conservation of the RBSAT landscape is thus dependent upon a mutually beneficial relationship between the objectives of the Reserve and the economic viability of adjacent communities, necessitating management strategies that reflect the interests of both entities. The trends in land use in the RBSAT landscape towards increasingly fragmented intensive agricultural and infrastructure development are likely to decrease the ecological values of the Reserve (i. e., biodiversity, habitat linkage, jaguar conservation because of increased conflicts and illegal killing) and increase conflicts between adjacent landowners and wildlife, including large carnivores (because even perceived conflicts with large carnivores can facilitate intolerance; Rosas-Rosas et al. 2015). This outcome, unless changed, results in a loss for both the RBSAT and surrounding communities. Stakeholder driven adaptable land use planning and management based upon the necessity and expectation of shared use that is advantageous for both communities and the Reserve is necessary to minimize both the future loss of ecological values and potential human-wildlife conflicts (Rosas-Rosas et al. 2015, 2020). However, adaptive management requires structured monitoring and a willingness to change methods and alter tactics depending on the feedback and results (Williams 2011). Consequently, working with members of the community on site-specific conservation strategies is critical to maintain or enhance the ecological role of the RBSAT and its surrounding landscape.


We thank the Consejo Nacional de Ciencia y Tecnología, Mexico, for granting a scholarship for graduate studies to E. J. Painter and A. Silva-Caballero, the Colegio de Postgraduados, Campus San Luis Potosí, Campus Montecillo, Campus Puebla, staff of Africam Zoo and of the Reserva de la Biosfera Sierra del Abra Tanchipa. We would like to thank the Comisión Nacional de Áreas Naturales Protegidas for providing funding for the use of GPS collars through the Programa de Conservación de Especies en Riesgo (2015 and 2016). To carry out the captures, the necessary permit was granted by the Secretaría del Medio Ambiente y Recursos Naturales, Subsecretaría de Gestión para la Protección Ambiental y la Dirección General de Vida Silvestre (SGPA/DGVS/07159/15 and SGPA/DGVS/06011/16). We also acknowledge all project personnel, volunteers, ranchers and the Ranching Association of Tamuín for their help with field research activities.

Literature cited

Alfaro, L., et al. 2016. Factors affecting jaguar and puma predation on livestock in Costa Rica. Wildlife Biology in Practice 12:32−42. [ Links ]

Arriaga, L., et al. 2000. Regiones terrestres prioritarias de México. Comisión Nacional para el Conocimiento y uso de la Biodiversidad. México City, México. [ Links ]

Ávila-Nájera, D. et al. 2011. Conocimiento, uso y valor cultural de seis presas del jaguar (Panthera onca) y su relación con éste, en San Nicolás de los Montes, San Luis Potosí, México. Revista Mexicana de Biodiversidad 82:1020−1028. [ Links ]

Brandon, K., et al. 2005. Reconciling biodiversity conservation, people, protected areas, and agricultural suitability in Mexico. World Development 9 SPEC 33:1403−1418. [ Links ]

Byers, C. R., R. K. Steinhorst, and P. R. Krausman. 1984. Clarification of a technique for analysis of utilization-availiability data. Journal of Wildlife Management 48:1050−1053. [ Links ]

Chapa-Vargas, L., and K. Monzalvo-Santos. 2012 Natural protected areas of San Luis Potosí, Mexico: Ecological representativeness, risks, and conservation implications across scales. International Journal of Geographical Information Science 26:1625−1641. [ Links ]

Committee of Scientists. 1999. Sustaining the people’s lands: recommendations for stewardship of the National Forests and Grasslands into the next century. Committee of Scientists 15 March 1999. U.S. Department of Agriculture. Washington, U.S.A. [ Links ]

Comisión Nacional de Áreas Naturales Protegidas (CONANP). 2014. Programa de Manejo de la Reserva de la Biosfera Sierra del Abra Tanchipa. Comisión Nacional de Áreas Naturales Protegidas-Secretaría de Medio Ambiente y Recursos Naturales. México City, México. [ Links ]

Congedo, L. 2018. Semi-Automatic Classification Plugin. Release . Accesed on May 18, 2018. [ Links ]

Conrad, O., et al. 2015. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosciences Model Development 8:1991−2007. [ Links ]

Cortina-Villar, S., et al. 2012. Resolving the conflict between ecosystem protection and land use in protected areas of the Sierra Madre de Chiapas, Mexico. Environmental Management 49:649−662. [ Links ]

Currit, N. 2005. Development of a remotely sensed, historical land-cover change database for rural Chihuahua, Mexico. International Journal of Applied Earth Observation and Geoinformation 7:232−247. [ Links ]

Doherty, T. S., et al. 2016. Invasive predators and global biodiversity loss. Proceedings of the National Academy of Sciences 113:11261−11265. [ Links ]

Dueñas-López, G., et al. 2015. Connectivity among jaguar populations in the Sierra Madre Oriental, Mexico. Therya 6:449−468. [ Links ]

Dueñas-López, G., and J. O. Huerta-Rodríguez. 2020. Importancia de los corredores ecológicos en la conservación del jaguar en la Sierra Madre Oriental. Pp. 115−124, in Manejo y conservación del jaguar en la Reserva de la Biosfera Sierra del Abra Tanchipa (Rosas-Rosas, O. C., A. Silva-Caballero, and A. Durán-Fernández, eds.). Colegio de Postgraduados. Texcoco, México. [ Links ]

Durán-Fernández, A., et al. 2020. Historia y conservación del jaguar en la Sierra del Abra Tanchipa. Pp. 61−85, in Manejo y conservación del jaguar en la Reserva de la Biosfera Sierra del Abra Tanchipa (Rosas-Rosas, O. C. , A. Silva-Caballero, and A. Durán-Fernández, eds.). Colegio de Postgraduados. Texcoco, México. [ Links ]

Escamilla, A., et al. 2000. Habitat mosaic, wildlife availability, and hunting in the tropical forest of Calakmul, Mexico. Conservation Biology 14:1592−1601. [ Links ]

Fernández-Landa, A., et al. 2016. An operational framework for land cover classification in the context of REDD+ mechanisms: A Case Study from Costa Rica. Remote Sensing 8:593. [ Links ]

Fichera, C. R., G. Modica, and M. Pollino. 2012. Land cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing 45:1−18. [ Links ]

Foody, G. M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment 80:185−201. [ Links ]

Grinand, C., et al. 2013. Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier. Remote Sensing of Environment 139:68−80. [ Links ]

Guerrero-Rodríguez, J. D. D., et al. 2020. Factores que determinan la depredación de ganado por grandes felinos en vida libre. Pp. 193−206, in Manejo y conservación del jaguar en la Reserva de la Biosfera Sierra del Abra Tanchipa (Rosas-Rosas, O. C. , A. Silva-Caballero, and A. Durán-Fernández, eds.). Colegio de Postgraduados. Texcoco, México. [ Links ]

Hadjimitsis, D. G., et al. 2010. Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices. Natural Hazards and Earth System Science 10:89−95. [ Links ]

Hernández-SaintMartín, A. D., et al. 2013. Activity patterns of jaguars, puma and their potential prey in San Luis Potosi, Mexico. Acta Zoológica Mexicana 29:520−533. [ Links ]

Hernández-SaintMartín, A. D., et al. 2015. Food habits of jaguar and puma in a protected area and adjacent fragmented landscape of northeastern Mexico. Natural Areas Journal 35:308-317 . [ Links ]

Horvat, Z. 2013. Using landsat satellite imagery to determine land use/land cover changes in Međimurje County, Croatia. Croatian Geographical Bulletin 28:5−28. [ Links ]

Insituto Nacional de Estadística y Geografía (INEGI). 2015. Encuesta Intercensal 2015. . Accesed on May 16, 2018. [ Links ]

Insituto Nacional de Estadística y Geografía (INEGI). 2018. Uso de suelo y vegetación. . Accesed on May 03, 2017. [ Links ]

Inskip, C., and A. Zimmermann. 2009. Human-felid conflict: A review of patterns and priorities worldwide. Oryx 43:18−34. [ Links ]

Kellert, S. R., et al. 1996. Human culture and large carnivore conservation in North America. Conservation Biology 10:977−990. [ Links ]

Landis, J. R., and G. G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33:159−174. [ Links ]

Mas, J. F., A. Velázquez, and S. Couturier. 2009. La evaluación de los cambios de cobertura/uso del suelo en la República Mexicana. Investigación Ambiental Ciencia y Política Pública 1: 23−39. [ Links ]

Mei, A., et al. 2016. Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data. Journal of African Earth Sciences 122:15−24. [ Links ]

Miranda-Aragón, L., et al. 2013. Tasa de deforestación en San Luis Potosí, México (1993-2007). Revista Chapingo, Serie Ciencias Forestales y del Ambiente 19:201−215. [ Links ]

Neu, C. W., C. R. Byers, and J. M. Peek. 1974. A technique for analysis of utilization-availability data. Journal of Wildlife Management 38:541−545. [ Links ]

Olvera-Hernández, J. I., et al. 2020. Participación social: una propuesta para la conservación. Pp. 177−192, in Manejo y conservación del jaguar en la Reserva de la Biosfera Sierra del Abra Tanchipa (Rosas-Rosas, O. C. , A. Silva-Caballero, and A. Durán-Fernández, eds.). Colegio de Postgraduados. Texcoco, México. [ Links ]

Ortega-Huerta, M. A. 2007. Fragmentation patterns and implications for biodiversity conservation in three biosphere reserves and surrounding regional environments, northeastern Mexico. Biological Conservation 134:83−95. [ Links ]

Peña-Mondragón, J. L., and A. Castillo. 2013. Depredación de ganado por jaguar y otros carnívoros en el noreste de México. Therya 4:431−446. [ Links ]

Planet Team. 2017. Planet application program interface: In space for life on Earth. . Accesed on May 16, 2018. [ Links ]

Polasky, S., et al. 2005 Conserving species in a working landscape: Land use with biological and economic objectives. Biodiversity Economics 15:501−530. [ Links ]

Porter-Bolland, L., E. A. Ellis, and H. L. Gholz. 2007. Land use dynamics and landscape history in La Montaña, Campeche, Mexico. Landscape and Urban Planning 82:198−207. [ Links ]

QGIS Development Team. 2018. QGIS Geographic Information System v.3.0.3-1. Open Source Geospatial Foundation. . Accesed on May 21, 2018. [ Links ]

Qian, Y., et al. 2014. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sensing 7:153−168. [ Links ]

Reyes-Hernández, H., et al. 2006. Cambios en la cubierta vegetal y uso del suelo en el área del proyecto Pujal-Coy, San Luis Potosí, México, 1973-2000. Investigaciones Geográficas 59:26−42. [ Links ]

Ripple, W. J., et al. 2014. Status and ecological effects on the world´s largest carnivores. Science 343:1241484. [ Links ]

Rosas-Rosas, O. C. , L. C. Bender, and R. Valdez. 2010. Habitat correlates of jaguar kill-sites of cattle in northeastern Sonora, Mexico. Human-Wildlife Interactions 4:103−111. [ Links ]

Rosas-Rosas, O. C. , J. de D. Guerrero-Rodríguez, and A. D. Hernández-SaintMartín. 2015. Manual de prácticas ganaderas para regiones con grandes carnívoros en la Sierra Madre Oriental. Colegio de Postgraduados-Comisión de Áreas Naturales Protegidas. Puebla, México. [ Links ]

Rosas-Rosas, O. C. , et al. 2016. Fortalecimiento de acciones para la conservación del jaguar y sus presas en el Corredor Ecológico Sierra Madre Oriental. Informe Final. Colegio de Postgraduados-Comisión Nacional de Áreas Naturales Protegidas. Available at: [ Links ]

Rosas-Rosas, O. C. , A. Silva-Caballero, and A. Duran-Fernández (eds). 2020. Manejo y conservación del jaguar en la Reserva de la Biosfera Sierra del Abra Tanchipa. Colegio de Postgraduados. Texcoco, México. [ Links ]

Sahagún-Sánchez, F. J., et al. 2011. Modelización de escenarios de cambio potencial en la vegetación y el uso de suelo en la Sierra Madre Oriental de San Luis Potosí, México. Journal of Latin American Geography 10:65−86. [ Links ]

Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT). 2010. Norma Oficial Mexicana NOM-059-SEMARNAT-2010, protección ambiental-especies nativas de México de flora y fauna silvestres-categorías de riesgo y especificaciones para su inclusión, exclusión o cambio-Lista de especies en riesgo. Diario Oficial de la Federación, December 30, 2010. [ Links ]

Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT). 2019. Modificación del Anexo Normativo III, Lista de especies en riesgo de la Norma Oficial Mexicana NOM-059-SEMARNAT-2010, protección ambiental-especies nativas de México de flora y fauna silvestres-categorías de riesgo y especificaciones para su inclusión, exclusión o cambio-Lista de especies en riesgo. Diario Oficial de la Federación, November 14, 2019. [ Links ]

Servicio de Información Agroalimentaria y Pesquera (SIAP). 2017. Producción agrícola. . Accesed on May 21, 2018. [ Links ]

Servicio de Información Agroalimentaria y Pesquera (SIAP). 2018. Producción ganadera. . Accesed on May 21, 2018. [ Links ]

Silva-Caballero, L. A. 2019. Preferencias alimentarias y su relación con la bioenergética del jaguar (Panthera onca) en la Reserva de la Biosfera Sierra del Abra Tanchipa, San Luis Potosí, México. Ph.D. dissertation. Colegio de Postgraduados, Texcoco, México. [ Links ]

Silva-Caballero, A., L. C. Bender, and O. C. Rosas-Rosas. 2022. Livestock depredation by jaguars associated with dry season core-use areas in a north-eastern Mexico agrolandscape. Western North American Naturalist 82:177-182. [ Links ]

United States Geological Survey (USGS). 2015. Global land survey (GLS) - the long term archive. . Accesed on January 20, 2017. [ Links ]

United States Geological Survey (USGS). 2018. EarthExplorer. . Accesed on January 20, 2017. [ Links ]

Velázquez, A., et al. 2010. Mapping land cover changes in Mexico, 1976-2000 and applications for guiding environmental management policy. Singapore Journal of Tropical Geography 31:152−162. [ Links ]

Villard, M. A. 1998. On forest-interior species, edge avoidance, area sensitivity and dogmas in avian conservation. Auk 115:801−805. [ Links ]

Wandelli, E. V., and P. M. Fearnside. 2015. Secondary vegetation in central Amazonia: Land-use history effects on aboveground biomass. Forest Ecology and Management 347:140−148. [ Links ]

Williams, B. K. 2011. Adaptive management of natural resources-framework and issues. Journal of Environmental Management 92:1346−1353. [ Links ]

Associated editor: Rafael Reyna

Received: February 02, 2022; Revised: March 18, 2022; Accepted: May 19, 2022; Published: August 18, 2022

*Corresponding author:

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License