Search

Menu
Search
in Ecology

Range-wide assessment of habitat suitability for jaguars using multiscale species distribution modelling

42 Views


Abstract

Jaguars (Panthera onca) are highly sensitive to persecution, habitat loss, and fragmentation, making the identification of suitable habitat critical for conservation planning. Using GPS telemetry data from 172 individuals across seven countries – the largest jaguar dataset to date – we developed multiscale Resource Selection Functions (RSFs) incorporating 15 environmental covariates to model habitat suitability across the species’ historic range. Jaguars selected productive habitats near water and strongly avoided human-modified landscapes, including areas with high human population density and livestock presence. The resulting habitat suitability surface showed strong predictive performance (AUC = 0.88; Boyce Index = 0.91) and correlated with known density estimates and distribution models. Jaguar Conservation Units (JCUs) and Protected Areas (PAs) contained 68.7% and 53.9% of predicted suitable habitat, respectively, while occupying only a third of the range. Non-designated lands, though comprising just 4% of the range, held nearly 10% of total suitability. The Amazon and Mayan Forests were identified as core strongholds, while ecoregion-based modelling revealed additional areas of high suitability in the Pantanal, Gran Chaco, Cerrado, and coastal Mexico. While Brazil encompassed the largest extent of highly suitable habitat, countries such as Paraguay, Argentina, and the United States gained conservation relevance under the ecoregion-stratified scenario.

Similar content being viewed by others

Impending anthropogenic threats and protected area prioritization for jaguars in the Brazilian Amazon

Article
Open access
15 February 2023

Reconciling biome-wide conservation of an apex carnivore with land-use economics in the increasingly threatened Pantanal wetlands

Article
Open access
23 November 2021

Wildfires disproportionately affected jaguars in the Pantanal

Article
Open access
13 October 2022

Data availability

GPS telemetry data for 117 of the 172 jaguar individuals used in this study are publicly available via Morato et al. (2018)82. The remaining 55 individuals were provided by collaborators and remain under the stewardship of their respective research groups; these data are not publicly available due to ongoing research use and data-sharing agreements. However, access to these data may be granted upon reasonable request to the corresponding authors, pending approval from the original data providers. The resulting habitat suitability surface generated by this study will be made openly available on the Zenodo repository upon manuscript acceptance (currently accessible for peer review at: https://zenodo.org/records/15824344?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjY3YTJjNzhjLTVmMzItNDFhZi04YmY1LTk0NTQzZmFkYjgyZSIsImRhdGEiOnt9LCJyYW5kb20iOiJiYTI3YTBjNDRhOGRkNjk3NWI1ZGI1OWEyMDRkYWU3NCJ9._5XjPvxWOw4azyxXv4Ww-eaoHm1FG54BexND5TEEsmBnBTahFRBpbdScnwD_8McXtH-eNHyVaqA6hoWbieufCQ).

References

  1. Sunquist, M. E. & Sunquist, F. C. Ecological constraints on predation by large felids. Carnivore Behav. Ecol. Evol. https://doi.org/10.1007/978-1-4757-4716-4_11 (1989).

    Google Scholar 

  2. Seymour, K. L. Panthera Onca. Mammalian Species. 340, 1–9 (1989).

    Google Scholar 

  3. Quigley, H. et al. Pantera Onca. IUCN Red List. Threatened Species. 8235, 8 (2017).

    Google Scholar 

  4. Ceballos, G. et al. Beyond words: From Jaguar population trends to conservation and public policy in Mexico. PLoS ONE. 16, 1–22 (2021).

    Google Scholar 

  5. de la Torre, J. A., González-Maya, J. F., Zarza, H., Ceballos, G. & Medellín, R. A. The jaguar’s spots are darker than they appear: Assessing the global conservation status of the jaguar Panthera Onca. Oryx 52, 300–315 (2018).

    Google Scholar 

  6. Alvarenga, G. C. et al. Jaguar (Panthera onca) density and population size across protected areas and Indigenous lands in the Amazon biome, its largest stronghold. Biol. Conserv. 303, 111010 (2025).

    Google Scholar 

  7. Berzins, R. et al. Distribution and status of the jaguar in the Guiana Shield. Cat News Special Issue 16, 1–22 (2023).

  8. Jędrzejewski, W. et al. Estimating species distribution changes due to human impacts: The 2020’s status of the jaguar in South America. CATnews Special Issue 16, 44–55 (2023).

  9. Meyer, C. B. & Thuiller, W. Accuracy of resource selection functions across spatial scales. Divers. Distrib. 12, 288–297 (2006).

    Google Scholar 

  10. Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: A review. Landsc. Ecol. 27, 777–797 (2012).

    Google Scholar 

  11. Mateo Sánchez, M. C., Cushman, S. A. & Saura, S. Scale dependence in habitat selection: The case of the endangered brown bear (Ursus arctos) in the Cantabrian range (NW Spain). Int. J. Geogr. Inf. Sci. 28, 1531–1546 (2014).

    Google Scholar 

  12. McGarigal, K., Zeller, K. A. & Cushman, S. A. Multi-scale habitat selection modeling: Introduction to the special issue. Landsc. Ecol. 31, 1157–1160 (2016).

    Google Scholar 

  13. Cushman, S. A. et al. Simulating multi-scale optimization and variable selection in species distribution modeling. Ecol. Inf. 83, 102832 (2024).

    Google Scholar 

  14. Macdonald, D. W. et al. Multi-scale habitat modelling identifies Spatial conservation priorities for Mainland clouded leopards (Neofelis nebulosa). Divers. Distrib. 00, 1–16 (2019).

    Google Scholar 

  15. Sanderson, E. W. et al. Planning to save a species: The jaguar as a model. Conserv. Biol. 16, 58–72 (2002).

    Google Scholar 

  16. Alvarenga, G. C. et al. Multi-scale path-level analysis of jaguar habitat use in the Pantanal ecosystem. Biol. Conserv. 253, 108900 (2021).

  17. Balbuena-Serrano, Á. et al. Connectivity of priority areas for the conservation of large carnivores in Northern Mexico. J. Nat. Conserv. 65, 126116 (2022).

  18. Ferraz, K. M. P. M. et al. Distribuição potencial e adequabilidade ambiental dos biomas brasileiros à ocorrência da onça-pintada. in Plano Nacional para Conservação da Onça-Pintada (eds. Paula, R. C. de, Desdiez, A. & Cavalcanti., S.) 125–206 (Instituto Chico Mendes de Conservação da Biodiversidade, ICMBio, Brasília, 2013).

  19. Petracca, L. S. et al. Robust inference on large-scale species habitat use with interview data: The status of jaguars outside protected areas in Central America. J. Appl. Ecol. 55, 723–734 (2018).

    Google Scholar 

  20. Figel, J. J., Botero-Cañola, S., Lavariega, M. C. & Luna-Krauletz, M. D. Overlooked jaguar guardians: Indigenous Territories and range-wide conservation of a cultural icon. Ambio https://doi.org/10.1007/s13280-022-01754-8 (2022).

    Google Scholar 

  21. Paviolo, A. et al. A biodiversity hotspot losing its top predator: The challenge of jaguar conservation in the Atlantic forest of South America. Sci. Rep. 6, 37147 (2016).

  22. Sollmann, R., Torres, N. M. & Silveira, L. Jaguar conservation in Brazil: The role of protected areas. CAT News 4, 15–20 (2008).

  23. De Angelo, C., Paviolo, A., Wiegand, T., Kanagaraj, R. & Di Bitetti, M. S. Understanding species persistence for defining conservation actions: A management landscape for jaguars in the Atlantic Forest. Biol. Conserv. 159, 422–433 (2013).

    Google Scholar 

  24. Gese, E. M., Terletzky, P. A., Cavalcanti, S. M. C. & Neale, C. M. U. Influence of behavioral state, sex, and season on resource selection by jaguars (Panthera onca): Always on the prowl? Ecosphere 9, e02341 (2018).

    Google Scholar 

  25. Craighead, K., Yacelga, M., Wan, H. Y., Vogt, R. & Cushman, S. A. Scale-dependent seasonal habitat selection by jaguars (Panthera onca) and pumas (Puma concolor) in Panama. Landsc. Ecol. 37, 129–146 (2022).

    Google Scholar 

  26. Conde, D. A. et al. Sex matters: Modeling male and female habitat differences for jaguar conservation. Biol. Conserv. 143, 1980–1988 (2010).

    Google Scholar 

  27. Colchero, F. et al. Jaguars on the move: Modeling movement to mitigate fragmentation from road expansion in the Mayan Forest. Anim. Conserv. 14, 158–166 (2011).

    Google Scholar 

  28. Rodríguez-Soto, C., Monroy-Vilchis, O. & Zarco-González, M. M. Corridors for jaguar (Panthera onca) in Mexico: Conservation strategies. J. Nat. Conserv. 21, 438–443 (2013).

    Google Scholar 

  29. Harmsen, B. J. et al. Long term monitoring of jaguars in the Cockscomb Basin Wildlife Sanctuary, Belize; implications for camera trap studies of carnivores. PLoS ONE. 12, 1–19 (2017).

    Google Scholar 

  30. Thompson, J. J. et al. Environmental and anthropogenic factors synergistically affect space use of jaguars. Curr. Biol. 31, 3457–3466e4 (2021).

    Google Scholar 

  31. Srbek-Araujo, A. C. Do female jaguars (Panthera onca Linnaeus, 1758) deliberately avoid camera traps? Mammalian Biol. 88, 26–30 (2018).

    Google Scholar 

  32. Cerqueira, R. C., de Rivera, O. R., Jaeger, J. A. G. & Grilo, C. Direct and indirect effects of roads on space use by jaguars in Brazil. Sci. Rep. 11, 1–9 (2021).

    Google Scholar 

  33. Alegre, V. B. et al. The effect of anthropogenic features on the habitat selection of a large carnivore is conditional on sex and circadian period, suggesting a landscape of coexistence. J. Nat. Conserv. 73, 126412 (2023).

  34. Jędrzejewski, W. et al. Estimating large carnivore populations at global scale based on spatial predictions of density and distribution – Application to the Jaguar (Panthera onca). PLoS ONE. 13, 1–25 (2018).

    Google Scholar 

  35. Tôrres, N. M. et al. Can species distribution modelling provide estimates of population densities? A case study with jaguars in the neotropics. Divers. Distrib. 18, 615–627 (2012).

    Google Scholar 

  36. Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American Marten (Martes americana) in the Northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).

    Google Scholar 

  37. Wan, H. Y. et al. Meta-replication reveals nonstationarity in multi-scale habitat selection of Mexican spotted Owl. Condor: Ornithological Appl. 119, 641–658 (2017).

    Google Scholar 

  38. Morato, R. G. et al. Resource selection in an apex predator and variation in response to local landscape characteristics. Biol. Conserv. 228, 233–240 (2018).

    Google Scholar 

  39. Villalva, P., Palomares, F. & Zanin, M. Effect of uneven tolerance to human disturbance on dominance interactions of top predators. Conserv. Biol. 39, 1–10 (2024).

    Google Scholar 

  40. Eriksson, C. E. et al. Extensive aquatic subsidies lead to territorial breakdown and high density of an apex predator. Ecology (2021). https://doi.org/10.1002/ecy.3543

    Google Scholar 

  41. Azevedo, F. C. C. & Verdade, L. M. Predator-prey interactions: Jaguar predation on Caiman in a floodplain forest. J. Zool. 286, 200–207 (2012).

    Google Scholar 

  42. Schaller, G. B. & Crawshaw, P. G. Movement patterns of jaguar. Biotropica 12, 161–168 (1980).

    Google Scholar 

  43. Khamila, C. N. et al. There is a trade-off between forest productivity and animal biodiversity in Europe. Biodivers. Conserv. 32, 1879–1899 (2023).

    Google Scholar 

  44. Peña-Mondragón, J. L., Castillo, A., Hoogesteijn, A. & Martínez-Meyer, E. Livestock predation by jaguars Panthera onca in South-Eastern Mexico: The role of local peoples’ practices. Oryx 51, 254–262 (2016).

    Google Scholar 

  45. Stryker, J. A. Thermoregulatory Behaviour Assessment and Thermal Imaging of Large Felids (The University of Guelph, 2016).

    Google Scholar 

  46. Rabinowitz, A. & Zeller, K. A. A range-wide model of landscape connectivity and conservation for the jaguar, Panthera Onca. Biol. Conserv. 143, 939–945 (2010).

    Google Scholar 

  47. Astete, S. et al. Living in extreme environments: Modeling habitat suitability for jaguars, pumas, and their prey in a semiarid habitat. J. Mammal. 98, 464–474 (2017).

    Google Scholar 

  48. Morato, R. G., Ferraz, K. M. P. M. D. B., De Paula, R. C. & Campos, C. B. De. Identification of priority conservation areas and potential corridors for jaguars in the Caatinga Biome, Brazil. PLoS ONE 9, e92950 (2014).

  49. de la Torre, J. A., Núñez, J. M. & Medellín, R. A. Habitat availability and connectivity for jaguars (Panthera onca) in the Southern Mayan forest: Conservation priorities for a fragmented landscape. Biol. Conserv. 206, 270–282 (2017).

    Google Scholar 

  50. Ramirez-Reyes, C., Bateman, B. L. & Radeloff, V. C. Effects of habitat suitability and minimum patch size thresholds on the assessment of landscape connectivity for jaguars in the Sierra Gorda, Mexico. Biol. Conserv. 204, 296–305 (2016).

    Google Scholar 

  51. Penjor, U., Kaszta, Ż., Macdonald, D. W. & Cushman, S. A. Prioritizing areas for conservation outside the existing protected area network in Bhutan: The use of multi-species, multi-scale habitat suitability models. Landsc. Ecol. 36, 1281–1309 (2021).

    Google Scholar 

  52. Chiaverini, L. et al. Multi-scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands. Anim. Conserv. 25, 660–679 (2022).

    Google Scholar 

  53. Macdonald, D. W. et al. Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation? Biodivers. Conserv. 29, 4035–4057 (2020).

    Google Scholar 

  54. Zeller, K. A., Vickers, T. W., Ernest, H. B. & Boyce, W. M. Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study. PLoS ONE. 12, e0179570 (2017).

    Google Scholar 

  55. Calderón, A. P. et al. Occupancy models reveal potential of conservation prioritization for Central American jaguars. Anim. Conserv. https://doi.org/10.1111/acv.12772 (2022).

    Google Scholar 

  56. Redo, D. J., Grau, H. R., Aide, T. M. & Clark, M. L. Asymmetric forest transition driven by the interaction of socioeconomic development and environmental heterogeneity in Central America. Proc. Natl. Acad. Sci. U S A. 109, 8839–8844 (2012).

    Google Scholar 

  57. Wultsch, C. et al. Genetic diversity and population structure of Mesoamerican jaguars (Panthera onca): Implications for conservation and management. PLoS ONE. 11, 1–25 (2016).

    Google Scholar 

  58. Meyer, N. F. V. et al. Effectiveness of Panama as an intercontinental land bridge for large mammals. Conserv. Biol. 0, 1–13 (2019).

    Google Scholar 

  59. Motlagh, J. A. Terrifying Journey Through the World’s Most Dangerous Jungle,. Outside Online (2016).

  60. Obinna, D. N. Camino de La muerte: Crossing the Darién gap and migration in the Americas. Migr. Dev. 13, 7–24 (2024).

    Google Scholar 

  61. Jędrzejewski, W. et al. Jaguars (Panthera onca) in the Llanos of Colombia and Venezuela: Estimating distribution and population size by combining different modeling approaches. in Neotropical Mammals (eds Mandujano, S., Naranjo, E. J. & Ponce, A. G. P. et al.) (Springer, Cham, 2023). https://doi.org/10.1007/978-3-031-39566-6_9

    Google Scholar 

  62. Hyde, M. et al. Tourism-supported working lands sustain a growing jaguar population in the Colombian Llanos. Sci. Rep. 13, 1–11 (2023).

    Google Scholar 

  63. Boron, V. et al. Jaguar densities across human-dominated landscapes in Colombia: The contribution of unprotected areas to long term conservation. PLoS ONE. 11, e0153973 (2016).

    Google Scholar 

  64. Seidl, A. F., De Silva, J. D. S. V. & Moraes, A. S. Cattle ranching and deforestation in the Brazilian Pantanal. Ecol. Econ. 36, 413–425 (2001).

    Google Scholar 

  65. Soisalo, M. K. & Cavalcanti, S. M. C. Estimating the density of a jaguar population in the Brazilian Pantanal using camera-traps and capture-recapture sampling in combination with GPS radio-telemetry. Biol. Conserv. 129, 487–496 (2006).

    Google Scholar 

  66. Devlin, A. L. et al. Drivers of large carnivore density in non-hunted, multi-use landscapes. Conserv. Sci. Pract. 5, 1–13 (2023).

    Google Scholar 

  67. de Barros, A. E. et al. Wildfires disproportionately affected jaguars in the Pantanal. Commun. Biol. 5, 1–12 (2022).

    Google Scholar 

  68. Santos, F. C., Chaves, F. M., Negri, R. G. & Massi, K. G. Fires in Pantanal: The link to agriculture conversions in Cerrado, and hydrological changes. Wetlands 44, 1–11 (2024).

    Google Scholar 

  69. Romero-Muñoz, A. et al. Habitat loss and overhunting synergistically drive the extirpation of jaguars from the Gran Chaco. Divers. Distrib. 25, 176–190 (2019).

    Google Scholar 

  70. Thompson, J. J. et al. Jaguar (Panthera onca) population density and landscape connectivity in a deforestation hotspot: The Paraguayan Dry Chaco as a case study. Perspect. Ecol. Conserv. https://doi.org/10.1016/j.pecon.2022.09.001 (2022).

    Google Scholar 

  71. Quiroga, V. A., Boaglio, G. I., Noss, A. J. & Di Bitetti, M. S. Critical population status of the jaguar Panthera onca in the Argentine Chaco: Camera-trap surveys suggest recent collapse and imminent regional extinction. Oryx 48, 141–148 (2014).

    Google Scholar 

  72. Haag, T. et al. The effect of habitat fragmentation on the genetic structure of a top predator: Loss of diversity and high differentiation among remnant populations of Atlantic Forest jaguars (Panthera onca). Mol. Ecol. 19, 4906–4921 (2010).

    Google Scholar 

  73. Roques, S. et al. Effects of habitat deterioration on the population genetics and conservation of the jaguar. Conserv. Genet. 17, 125–139 (2016).

    Google Scholar 

  74. de Paula, R. C., Campos, C. B. & Oliveira, T. G. Red List assessment for the jaguar in the Caatinga biome. Cat News Special Issue 7, 19–24 (2012).

  75. Portugal, M. P., Morato, R. G., Ferraz, K. M. P. M. D. B., Rodrigues, F. H. G. & Jacobi, C. M. Priority areas for jaguar Panthera onca conservation in the Cerrado. Oryx 54, 854–865 (2019).

    Google Scholar 

  76. Moraes, E. A. Jr. The status of the jaguar in the Cerrado. CATnews Special Issue 7, 25–28 (2012).

  77. Soterroni, A. C. et al. Expanding the soy moratorium to Brazil’s Cerrado. Sci. Adv. 5, eaav7336 (2019).

    Google Scholar 

  78. Segura-Garcia, C. et al. The fire regimes of the Cerrado and their changes through time. Philos. Trans. R. Soc. B: Biol. Sci. 380, 20230460 (2025).

  79. Pompeu, J., Assis, T. O. & Ometto, J. P. Landscape changes in the cerrado: Challenges of land clearing, fragmentation and land tenure for biological conservation. Sci. Total Environ. 906, 167581 (2024).

  80. Pessôa, A. C. M. et al. Protected areas are effective on curbing fires in the Amazon. Ecol. Econ. 214, 107983 (2023).

    Google Scholar 

  81. Qin, Y. et al. Forest conservation in Indigenous Territories and protected areas in the Brazilian Amazon. Nat. Sustain. 6, 295–305 (2023).

    Google Scholar 

  82. Moutinho, P. & Azevedo-Ramos, C. Untitled public forestlands threat Amazon conservation. Nat. Commun. 14, 1152 (2023).

    Google Scholar 

  83. Morán-López, R., Cortés Gañán, E., Uceda Tolosa, O. & Sánchez Guzmán, J. M. The umbrella effect of natura 2000 Annex species spreads over multiple taxonomic groups, conservation attributes and organizational levels. Anim. Conserv. 23, 407–419 (2020).

    Google Scholar 

  84. Zanin, M. et al. The differential genetic signatures related to climatic landscapes for jaguars and pumas on a continental scale. Integr. Zool. 16, 2–18 (2021).

    Google Scholar 

  85. Morato, R. G. et al. Jaguar movement database: A GPS-based movement dataset of an apex predator in the Neotropics. Ecology 99, 1691 (2018).

    Google Scholar 

  86. Rondinini, C. & Boitani, L. Mind the map: Trips and pitfalls in making and reading maps of carnivore distribution. in Carnivore Ecology and Conservation: A Handbook of Techniques (eds Boitani, L. & Powell, R. A.) 506 (Oxford University Press, New York, USA, (2012).

    Google Scholar 

  87. Crego, R. D., Stabach, J. A. & Connette, G. Implementation of species distribution models in Google Earth Engine. Divers. Distrib. 28, 904–916 (2022).

    Google Scholar 

  88. Zeller, K. A. et al. Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: Pumas as a case study. Landsc. Ecol. 29, 541–557 (2014).

    Google Scholar 

  89. Sillero, N. & Barbosa, A. M. Common mistakes in ecological niche models. Int. J. Geogr. Inf. Sci. 35, 213–226 (2021).

    Google Scholar 

  90. Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83, 2049–2055 (2002).

    Google Scholar 

  91. Johnson, D. H. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71 (1980).

    Google Scholar 

  92. Rodríguez-Soto, C. et al. Predicting potential distribution of the jaguar (Panthera onca) in Mexico: Identification of priority areas for conservation. Divers. Distrib. 17, 350–361 (2011).

    Google Scholar 

  93. Searle, C. E. et al. Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data. J. Appl. Ecol. 59, 2346–2359 (2022).

    Google Scholar 

  94. Ávila–Nájera, D. M., Chávez, C., Pérez–elizalde, S., Palacios–pérez, J. & Tigar, B. Coexistence of jaguars (Panthera onca) and pumas (Puma concolor) in a tropical forest in South–Eastern Mexico. Anim. Biodivers. Conserv. 43, 55–66 (2020).

    Google Scholar 

  95. Schoen, J. M., DeFries, R. & Cushman, S. Open-source, environmentally dynamic machine learning models demonstrate behavior-dependent utilization of mixed-use landscapes by jaguars (Panthera onca). Biol. Conserv. 302, 110978 (2025).

    Google Scholar 

  96. Bogoni, J. A., Peres, C. A. & Ferraz, K. M. P. M. B. Extent, intensity and drivers of mammal defaunation: A continental-scale analysis across the neotropics. Sci. Rep. 10, 1–16 (2020).

    Google Scholar 

  97. Gilbert, M. et al. Global cattle distribution in 2010 (5 minutes of arc). Harv. Dataverse https://doi.org/10.7910/DVN/GIVQ75 (2018).

    Google Scholar 

  98. Gilbert, M. et al. Global goats distribution in 2010 (5 minutes of arc). Harv. Dataverse https://doi.org/10.7910/DVN/OCPH42 (2018).

    Google Scholar 

  99. Gilbert, M. et al. Global sheep distribution in 2010 (5 minutes of arc). Harv. Dataverse https://doi.org/10.7910/DVN/BLWPZN (2018).

  100. Montalvo, V. H., Sáenz-Bolaños, C., Carrillo, E. & Fuller, T. K. A review of environmental and anthropogenic variables used to model jaguar occurrence. Neotropical Biology Conserv. 18, 31–51 (2023).

    Google Scholar 

  101. Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).

    Google Scholar 

  102. Jenness, J. Topographic Position Index (tpi_jen.avx) Extension for ArcView 3.x, v.1.-3a. Preprint at (2006). http://www.jennessent.com/arcview/tpi.htm

  103. Amatulli, G., McInerney, D., Sethi, T., Strobl, P. & Domisch, S. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Sci. Data. 7, 1–18 (2020).

    Google Scholar 

  104. Weill, R. R. & Brady, N. C. The Nature and Properties of Soils (Pearson, 2016).

    Google Scholar 

  105. Cushman, S. A., Elliot, N. B., Macdonald, D. W. & Loveridge, A. J. A multi-scale assessment of population connectivity in African lions (Panthera leo) in response to landscape change. Landsc. Ecol. 31, 1337–1353 (2016).

    Google Scholar 

  106. Macdonald, D. W. et al. Multi-scale habitat selection modeling identifies threats and conservation opportunities for the Sunda clouded Leopard (Neofelis diardi). Biol. Conserv. 227, 92–103 (2018).

    Google Scholar 

  107. R Core Team. R: A language and environment for statistical computing. Preprint at. (2018).

  108. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://Preprint at (2016). http://www.qgis.org/en/site/

  109. Gorelick, N. et al. Google Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Google Scholar 

  110. Morato, R. G. et al. Space use and movement of a Neotropical top predator: The endangered jaguar. PLoS ONE. 11, 1–17 (2016).

    Google Scholar 

  111. Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6

    Google Scholar 

  112. De Jay, N. et al. mRMRe: An R package for parallelized mRMR ensemble feature selection. Bioinformatics 29, 2365–2368 (2013).

    Google Scholar 

  113. Fletcher, R. & Fortin, M. J. Spatial ecology and conservation modeling. Springer Nat. Switz. https://doi.org/10.1007/978-3-030-01989-1 (2018).

    Google Scholar 

  114. Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  115. Hijmans, R. terra: Spatial Data Analysis. Preprint at (2024).

  116. Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Modell. 157, 281–300 (2002).

    Google Scholar 

  117. Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Modell. 199, 142–152 (2006).

    Google Scholar 

  118. Nagy-Reis, M. et al. NEOTROPICAL CARNIVORES: A data set on carnivore distribution in the Neotropics. Ecology 101, 1–5 (2020).

    Google Scholar 

  119. Robin, X. et al. pROC: An open-source package for R and S + to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).

    Google Scholar 

  120. MMAMC, M. do M. A. e M. C. Cadastro Nacional de Florestas Públicas (CNFP). (2022).

  121. Nijhawan, S. Conservation units, priority areas and dispersal corridors for jaguars in Brazil. Cat News Special Issue 7, 43–47 (2012).

  122. UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). (UNEP-WCMC and IUCN www.protectedplanet.net., 2022).

  123. Griffith, D. M., Nivelo-Villavicencio, C., Rodas, F., Puglla, B. & Cisneros, R. New altitudinal records of Panthera onca (Carnivora: Felidae) in the Andean region of Ecuador. Mammalia 86, 190–195 (2021).

    Google Scholar 

  124. Zeller, K. Jaguars in the New Millennium Data Set Update: The State of the Jaguar in 2006 1–77 (Wildlife Conservation Society, Bronx, 2007).

  125. Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baurch-Murdo, S. & Kiesecker, J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob Chang. Biol. 00, 1–16 (2019).

    Google Scholar 

  126. Sorichetta, A. et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Sci. Data https://doi.org/10.1038/sdata.2015.45 (2015).

    Google Scholar 

  127. Li, X., Zhou, Y., Zhao, M. & Zhao, X. A harmonized global nighttime light dataset 1992–2018. Sci. Data. 7, 1–9 (2020).

    Google Scholar 

  128. Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).

  129. Giglio, L., Justice, C., Boschetti, L. & Roy, D. MCD64A1 MODIS/Terra + Aqua burned area monthly L3 global 500m SIN grid V006. NASA EOSDIS Land. Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.006 (2015).

    Google Scholar 

  130. Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-Day L3 global 250m SIN grid V006. NASA EOSDIS Land. Processes Distrib. Act. Archive Cent. https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).

    Google Scholar 

  131. Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra gross primary productivity 8-Day L4 global 500m SIN grid V006. NASA EOSDIS Land. Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).

    Google Scholar 

  132. DiMiceli, C. et al. MOD44B MODIS/Terra vegetation continuous fields yearly L3 global 250m SIN grid V006. (2015). https://doi.org/10.5067/MODIS/MOD44B.006

  133. Potapov, P. et al. Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2020.112165 (2020).

    Google Scholar 

  134. Wan, Z., Hook, S. & Hulley, G. MODIS/Terra land surface Temperature/Emissivity 8-Day L3 global 1km SIN grid V061. NASA EOSDIS Land. Processes DAAC. https://doi.org/10.5067/MODIS/MOD11A2.061 (2021).

    Google Scholar 

  135. Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L. & Karger, D. CHELSA-BIOCLIM + A novel set of global climate-related predictors at kilometre-resolution. EnviDat https://doi.org/10.16904/envidat.332 (2022).

    Google Scholar 

  136. Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4. CGIAR-CSI SRTM 90m (2008). https://srtm.csi.cgiar.org

  137. Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

  138. Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).

    Google Scholar 

Download references

Acknowledgements

GCA doctorate is supported by a grant to David Macdonald at WildCRU from the Robertson Foundation. We are extremely grateful to the Alianza WWF-Fundación, Telmex/Telcel, the Universidad Nacional Autónoma de México (project DGAPA, PAPIT IN208017), and Amigos de Calakmul A.C. for their financial support. We extend special thanks to the ejidos Caobas and Laguna Om, as well as the Calakmul Biosphere Reserve, for granting permission to conduct our research on their lands. The Instituto Homem Pantaneiro is grateful to ICMBio/CENAP, Panthera Brasil, and Onçafari for their valuable partnerships. The Mamirauá Institute thanks CNPq for the scholarships, and the communities of Mamirauá Sustainable Development Reserve for their essential field support – especially Lázaro Pinto dos Santos (Lazinho) and Railgler Gomes dos Santos (Raí), in memoriam. ACSA thanks the Espírito Santo Research and Innovation Support Foundation (FAPES) for funding the project (FAPES 510/2016), as well as for the Capixaba Researcher Fellowship (FAPES 404/2022). SAC thanks NASA for the project grant 80NSSC25K7244. All figures were created by GCA using QGIS v3.36.0 (https://qgis.org). Editorial assistance was provided by ChatGPT (OpenAI) to improve clarity and language use.

Funding

GCA doctorate is supported by a grant to David Macdonald at WildCRU from the Robertson Foundation. ACSA was supported by the Espírito Santo Research and Innovation Support Foundation (FAPES) for funding the project (FAPES 510/2016), as well as for the Capixaba Researcher Fellowship (FAPES 404/2022). SAC was supported by a NASA project grant number 80NSSC25K7244.

Author information

Authors and Affiliations

  1. Wildlife Conservation Research Unit (WildCRU), Department of Biology, University of Oxford, Life and Mind Building, South Parks Road, Oxford, OX1 3EL, UK

    Guilherme Costa Alvarenga, Caroline C. Sartor, Samuel A. Cushman, Alexandra Zimmermann & David W. Macdonald

  2. Grupo de Pesquisa em Ecologia e Conservação de Felinos na Amazônia, Mamirauá Institute for Sustainable Development (MISD), Estrada do Bexiga, nº 2584, Tefé, AM, Brazil

    Guilherme Costa Alvarenga, Diogo Maia Gräbin, Emiliano E. Ramalho & Marcos Roberto Monteiro de Brito

  3. Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

    Żaneta Kaszta

  4. Programa de Pós-Graduação em Ciência Animal e Programa de Pós-Graduação em Ecologia de Ecossistemas, Universidade Vila Velha, Av. Comissário José Dantas de Melo, 21, Boa Vista, Vila Velha, Espírito Santo, 29102-920, Brazil

    Ana Carolina Srbek-Araujo

  5. Laboratório de Ecologia e Zoologia de Vertebrados (LABEV/ICB), Universidade Federal do Pará, Belém, PA, Brazil

    Ana Cristina Mendes-Oliveira & Leonardo Sena

  6. Panthera, 104 West 40th Street, 5th Floor, New York, NY, 10018, USA

    Bart Harmsen, Rebecca J. Foster & Ronaldo G. Morato

  7. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente (IBCIA), Universidad Nacional de Río Cuarto and National Scientific and Technical Research Council (CONICET), Ruta Nacional 36 Km 601, Río Cuarto, Argentina

    Carlos De Angelo

  8. Institute for the Conservation of Neotropical Carnivores, Avenida Horácio Neto, 1030, Parque Edmundo Zanoni, Atibaia, SP, Brazil

    Carolina Franco Esteves, Claudia B. de Campos, Daiana Jeronimo Polli, Emiliano E. Ramalho & Fernando C. C. Azevedo

  9. Programa de Pós-Graduação em Ecologia e Conservação, Instituto de Biociências – Inbio, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, 79070-900, Brazil

    Diego F. Passos Viana

  10. Fundación Rewilding Argentina, Scalabrini Ortiz 3355, 1425, Buenos Aires, Argentina

    Emiliano Donadio

  11. WCS Big Cat Program, New York, USA

    Esteban Payán

  12. Departamento de Ciências Naturais, Universidade Federal de São João del Rei, São João del Rei, MG, 36301-160, Brazil

    Fernando C. C. Azevedo

  13. Department of Conservation Biology, Doñana Biological Station, CSIC, Avda. Américo Vespucio 26, 41092, Isla de la Cartuja, Seville, Spain

    Francisco Palomares

  14. Wildlife Protection Solutions, 2501 Welton Street, Denver, CO, 80205, USA

    George V. N. Powell

  15. Laboratorio de Ecología y Conservación de Fauna Silvestre, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, Ciudad de México, Mexico

    Gerardo Ceballos

  16. Instituto Homem Pantaneiro, Corumbá, Mato Grosso do Sul, Brazil

    Grasiela Porfirio & Wener Hugo Arruda Moreno

  17. Departamento de Ciencias Ambientales, Universidad Autónoma Metropolitana, Unidad Lerma, CBS, Lerma de Villada, Mexico

    Heliot Zarza

  18. Primero Conservation, Box 158885935, Pinetop, AZ, USA

    Ivonne Cassaigne

  19. Centro de Pesquisa de Limnologia, Biodiversidade e Etnobiologia do Pantanal; Laboratório de Mastozoologia; Programa de Pós-graduação em Ciências Ambientais, Universidade do Estado de Mato Grosso-UNEMAT, Cáceres, MT, 78217-900, Brazil

    Juliano A. Bogoni

  20. Mamirauá Institute for Sustainable Development (MISD), Estrada do Bexiga, nº 2584, Tefé, AM, Brazil

    Louise Maranhão

  21. Associação Onçafari, São Paulo, SP, Brazil

    Marcos Roberto Monteiro de Brito

  22. San Diego Zoo Wildlife Alliance, Conservation Science and Wildlife Health, 15600 San Pasqual Valley Road, Escondido, CA, 92027, USA

    Mathias W. Tobler

  23. Natural History Museum, University of Oslo, POB 1172 Blindern, 0318, Oslo, Norway

    Øystein Wiig

  24. Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros (CENAP-ICMBio), Estrada Municipal Hisaichi Takebayashi, 8600, Atibaia, SP, 12952-011, Brazil

    Ricardo Sampaio

  25. Alianza Jaguar AC, Lab. Vida Silvestre, Fac. Biol. Universidad Michoacana, Morelia, Michoacán, Mexico

    Rodrigo Nuñez

  26. World Wide Fund for Nature (WWF) UK, The Living Planet Centre, Brewery Road, Woking, GU214LL, UK

    Valeria Boron

  27. Environmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, UK

    Yadvinder Malhi

  28. Leverhulme Centre for Nature Recovery, University of Oxford, South Parks Road, Oxford, UK

    Yadvinder Malhi

Authors

  1. Guilherme Costa Alvarenga
    View author publications

    Search author on:PubMed Google Scholar

  2. Caroline C. Sartor
    View author publications

    Search author on:PubMed Google Scholar

  3. Samuel A. Cushman
    View author publications

    Search author on:PubMed Google Scholar

  4. Alexandra Zimmermann
    View author publications

    Search author on:PubMed Google Scholar

  5. Ana Carolina Srbek-Araujo
    View author publications

    Search author on:PubMed Google Scholar

  6. Ana Cristina Mendes-Oliveira
    View author publications

    Search author on:PubMed Google Scholar

  7. Bart Harmsen
    View author publications

    Search author on:PubMed Google Scholar

  8. Carlos De Angelo
    View author publications

    Search author on:PubMed Google Scholar

  9. Carolina Franco Esteves
    View author publications

    Search author on:PubMed Google Scholar

  10. Claudia B. de Campos
    View author publications

    Search author on:PubMed Google Scholar

  11. Daiana Jeronimo Polli
    View author publications

    Search author on:PubMed Google Scholar

  12. Diego F. Passos Viana
    View author publications

    Search author on:PubMed Google Scholar

  13. Diogo Maia Gräbin
    View author publications

    Search author on:PubMed Google Scholar

  14. Emiliano Donadio
    View author publications

    Search author on:PubMed Google Scholar

  15. Emiliano E. Ramalho
    View author publications

    Search author on:PubMed Google Scholar

  16. Esteban Payán
    View author publications

    Search author on:PubMed Google Scholar

  17. Fernando C. C. Azevedo
    View author publications

    Search author on:PubMed Google Scholar

  18. Francisco Palomares
    View author publications

    Search author on:PubMed Google Scholar

  19. George V. N. Powell
    View author publications

    Search author on:PubMed Google Scholar

  20. Gerardo Ceballos
    View author publications

    Search author on:PubMed Google Scholar

  21. Grasiela Porfirio
    View author publications

    Search author on:PubMed Google Scholar

  22. Heliot Zarza
    View author publications

    Search author on:PubMed Google Scholar

  23. Ivonne Cassaigne
    View author publications

    Search author on:PubMed Google Scholar

  24. Juliano A. Bogoni
    View author publications

    Search author on:PubMed Google Scholar

  25. Leonardo Sena
    View author publications

    Search author on:PubMed Google Scholar

  26. Louise Maranhão
    View author publications

    Search author on:PubMed Google Scholar

  27. Marcos Roberto Monteiro de Brito
    View author publications

    Search author on:PubMed Google Scholar

  28. Mathias W. Tobler
    View author publications

    Search author on:PubMed Google Scholar

  29. Øystein Wiig
    View author publications

    Search author on:PubMed Google Scholar

  30. Rebecca J. Foster
    View author publications

    Search author on:PubMed Google Scholar

  31. Ricardo Sampaio
    View author publications

    Search author on:PubMed Google Scholar

  32. Rodrigo Nuñez
    View author publications

    Search author on:PubMed Google Scholar

  33. Ronaldo G. Morato
    View author publications

    Search author on:PubMed Google Scholar

  34. Valeria Boron
    View author publications

    Search author on:PubMed Google Scholar

  35. Wener Hugo Arruda Moreno
    View author publications

    Search author on:PubMed Google Scholar

  36. Yadvinder Malhi
    View author publications

    Search author on:PubMed Google Scholar

  37. David W. Macdonald
    View author publications

    Search author on:PubMed Google Scholar

  38. Żaneta Kaszta
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Guilherme Costa Alvarenga: Conceptualization, Investigation, Data curation, Methodology, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing. Caroline C. Sartor: Methodology, Formal analysis, Writing – review & editing. Samuel (A) Cushman: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision, Project administration. Alexandra Zimmermann: Writing – review & editing. Ana Carolina Srbek-Araujo: Investigation, Resources, Writing – review & editing. Ana Cristina Mendes-Oliveira: Investigation, Resources, Writing – review & editing. Bart Harmsen: Investigation, Resources, Writing – review & editing. Carlos De Angelo: Investigation, Resources, Writing – review & editing. Carolina Franco Esteves: Investigation, Resources, Writing – review & editing. Claudia (B) de Campos: Investigation, Resources, Writing – review & editing. Daiana Jeronimo Polli: Investigation, Resources, Writing – review & editing. Diego F. Passos Viana: Investigation, Resources, Writing – review & editing. Diogo Maia Gräbin: Investigation, Resources, Writing – review & editing. Emiliano Donadio: Investigation, Resources, Writing – review & editing. Emiliano E. Ramalho: Investigation, Resources, Writing – review & editing. Esteban Payán: Investigation, Resources, Writing – review & editing. Fernando (C) C. Azevedo: Investigation, Resources, Writing – review & editing. Francisco Palomares: Investigation, Resources, Writing – review & editing. George V. N. Powell: Investigation, Resources, Writing – review & editing. Gerardo Ceballos: Investigation, Resources, Writing – review & editing. Grasiela Porfirio: Investigation, Resources, Writing – review & editing. Heliot Zarza: Investigation, Resources, Writing – review & editing. Ivonne Cassaigne: Investigation, Resources, Writing – review & editing. Juliano A. Bogoni: Investigation, Resources, Writing – review & editing. Leonardo Sena: Investigation, Resources, Writing – review & editing. Louise Maranhão: Investigation, Resources, Writing – review & editing. Marcos Roberto Monteiro de Brito: Investigation, Resources, Writing – review & editing. Mathias W. Tobler: Investigation, Resources, Writing – review & editing. Øystein Wiig: Investigation, Resources, Writing – review & editing. Rebecca J. Foster: Investigation, Resources, Writing – review & editing. Ricardo Sampaio: Investigation, Resources, Writing – review & editing. Rodrigo Nuñez: Investigation, Resources, Writing – review & editing. Ronaldo G. Morato: Investigation, Resources, Writing – review & editing. Valeria Boron: Investigation, Resources, Writing – review & editing. Wener Hugo Arruda Moreno: Investigation, Resources, Writing – review & editing. Yadvinder Malhi: Writing – review & editing. David W. Macdonald: Resources, Writing – review & editing, Funding acquisition. Zaneta Kaszta: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision, Project administration.

Corresponding author

Correspondence to
Guilherme Costa Alvarenga.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Alvarenga, G.C., Sartor, C.C., Cushman, S.A. et al. Range-wide assessment of habitat suitability for jaguars using multiscale species distribution modelling.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-30512-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-025-30512-5

Keywords

  • Habitat suitability
  • Indigenous lands
  • Jaguar conservation units
  • Multiscale modelling
  • Panthera onca
  • Protected areas

Source: Ecology - nature.com

Structure and community assembly of rare bacterial community in sediments of Sancha Lake

National human footprint maps for Peru and Ecuador

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close