Déficits de conocimiento y el efecto de los incendios forestales en la conservación de la biodiversidad en Guanajuato, México
DOI:
https://doi.org/10.22201/ib.20078706e.2024.95.5323Palabras clave:
Fuego, Patrones, Prioridades, Riqueza, Modelos de distribución de especiesResumen
Los déficits en el conocimiento podrían modificar los patrones de distribución geográfica y limitar las acciones para conservar la biodiversidad, incluso en taxones bien conocidos. Además, los incendios forestales también pueden modificar esos patrones, pero los efectos potenciales de ambos no han sido probados. Nuestro objetivo fue analizar el efecto de los déficits Linneano y Wallaceano en la primera evaluación de los impactos de los incendios forestales en 22 especies de anfibios y 13 de mamíferos en Guanajuato, México. Evaluamos esos déficits utilizando los estimadores Chao2 y Qs y con mapas de riqueza de especies. Para evaluar los efectos de incendios forestales, elaboramos un mapa de recurrencia de incendios y cuantificamos el área quemada dentro de las distribuciones de las especies y en 24 áreas naturales protegidas (ANP). El déficit Linneano mostró que faltan algunas especies por registrar para ambos taxones, mientras que el déficit Wallaceano mostró una mala calidad de conocimiento. La recurrencia de incendios fue alta dentro de 5 ANP. Los patrones de riqueza afectados por los incendios cubrieron cerca de 17% de la superficie de Guanajuato. Mejorar el conocimiento de los patrones biogeográficos brindará mejores herramientas para disminuir el impacto de los incendios dentro de las ANP.
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Acevedo, P., Melo-Ferreira, J., Real, R., & Alves, P. C. (2014). Evidence for niche similarities in the allopatric sister species Lepus castroviejoi and Lepus corsicanus. Journal of Biogeography, 41, 977–986. https://doi.org/10.1111/jbi.12270
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015). spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 38, 541–545. https://doi.org/10.1111/ecog.01132
Barlow, J., Berenguer, E., Carmenta, R. & França, F. (2020). Clarifying Amazonia's burning crisis. Global Change Biology, 26, 319–321. https://doi.org/10.1111/gcb.14872op
Bivand, R., Keitt, T., Rowlingson, B., Pebesma, E., Sumner, M., Hijmans, R. et al. (2015). Package ‘rgdal’. Bindings for the Geospatial Data Abstraction Library. Retrieved on December, 2020 from: http://rgdal.r-forge.r-project.org
Chisholm, R. A., Wijedasa, L. S., & Swinfield, T. (2016). The need for long-term remedies for Indonesia’s forest fires. Conservation Biology, 30, 5–6. https://doi.org/10.1111/cobi.12662
Chuvieco, E., Giglio, L. & Justice, C. (2008). Global characterization of fire activity: toward defining fire regimes from Earth observation data. Global Change Biology, 14, 1488–1502. https://doi.org/10.1111/j.1365-2486.2008.01585.x
Clivillé, S., Montori, A., Llorente, G. A., Santos, X., & Carretero, M. A. (1997). El impacto de los incendios forestales sobre los anfibios. Quercus, 138, 10–13.
Cobos, M. E., Peterson, A. T., Barve, N., & Osorio-Olvera, L. (2019). Kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ, 7, e6281. https://doi.org/10.7717/peerj.6281
Colwell, R. K., & Coddington, J. A. (1994). Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society B, 345, 101–118. https://doi.org/10.1098/rstb.1994.0091
Conabio (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad). (2012). La biodiversidad en Guanajuato: estudio de estado (Vol. II). Guanajuato, Mexico: Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (Conabio)/ Instituto de Ecología del Estado de Guanajuato (IEE).
Conafor (Comisión Nacional Forestal). (2020). Estimación de la tasa de deforestación bruta en México para el periodo 2001-2018 mediante el método muestreo. Documento técnico. Comisión Nacional Forestal. Jalisco, México. Retrieved in 2022 from: http://www.conafor.gob.mx:8080/documentos/docs/1/7768Documento%20tecnico%202020%20Deforestacion%20Bruta%20Final.pdf
DOF (Diario Oficial de la Federación). (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. Mexico City, Mexico. Retrieved on March 3rd, 2021 from: https://dof.gob.mx/nota_detalle_popup.php?codigo=5173091
DOF (Diario Oficial de la Federación). (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, publicada el 30 de diciembre de 2010. Secretaría de Gobernación (SEGOB). Mexico City, Mexico. Retrieved on March 03rd, 2021 from: https://www.dof.gob.mx/nota_detalle.php?codigo=5578808&fecha=14/11/2019#gsc.tab=0
Diniz-Filho, J. F, Jardim, L., Guedes, J. J., Meyer, L., Stropp, J., Frateles, L. E. et al. (2023). Macroecological links between the Linnean, Wallacean, and Darwinian shortfalls. Frontiers of Biogeography, 15, e59566. http://dx.doi.org/10.21425/F5FBG59566
Diniz-Filho, J. A. F., Loyola, R. D., Raia, P., Mooers, A. O., & Bini, L. M. (2013) Darwinian shortfalls in biodiversity conservation. Trends in Ecology and Evolution, 28, 689–695. https://doi.org/10.1016/j.tree.2013.09.003
Escalante, T., Noguera-Urbano, E. A., & Corona, W. (2018). Track analysis of the Nearctic region: Identifying complex areas with mammals. Journal of Zoological Systematics and Evolutionary Research, 56, 466–477. https://doi.org/10.1111/jzs.12211
Escalante, T., Varela-Anaya, A. M., Noguera-Urbano, E. A., Elguea-Manrique, L. M., Ochoa-Ochoa, L. M., Gutiérrez-Velázquez, A. L. et al. (2020). Evaluation of five taxa as surrogates for conservation prioritization in the Transmexican Volcanic Belt, México. Journal for nature conservation, 54, e125800. https://doi.org/10.1016/j.jnc.2020.125800
Farfán, M., Pérez-Salicrup, D. R., Flamenco-Sandoval, A., Nicasio-Arzeta, S., Mas, J. F., & Ramírez-Ramírez, I. (2018). Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere. Madera y Bosques, 24, 1–15. https://doi.org/10.21829/myb.2018.2431591
Farfán, M., Flamenco-Sandoval, A., Rodríguez-Padilla, C. R., Rodrigues-de Sousa Santos, L., González-Gutiérrez, I., & Gao, Y. (2020). Cartografía de la probabilidad de ocurrencia a incendios forestales para el estado de Guanajuato: Una aproximación antrópica de sus fuentes de ignición. Acta Universitaria, 30, 1–15. https://doi.org/10.15174/au.2020.2953
Farfán, M., Dominguez, C., Espinoza, A., Jaramillo, A., Alcántara, C., Maldonado, V. et al. (2021). Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato. Environmental Monitoring and Assessment, 193, 1–14. https://doi.org/10.1007/s10661-021-09494-0
Farrús, M., Anguita, J., Hernando, J., & Cerdà, R. (2007). Fusión de sistemas de reconocimiento basados en características de alto y bajo nivel. In III Congreso da Sociedade Española de Acústica Forense: actas do Congreso; 2005 oct 27-28; Santiago de Compostela, España. Santiago de Compostela: Dirección Xeral de Creación e Difusión Cultural.
Ferreira, B. M., Soares-Filho, B. S., & Quintão, F. M. (2019). The Dinamica EGO virtual machine. Science of Computer Programming, 173, 3–20. https://doi.org/10.1016/j.scico.2018.02.002
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315. https://doi.org/10.1002/joc.5086
Flores-Villela, O., & Ochoa-Ochoa, L. M. (2020). Compilación de bases de datos de la Herpetofauna mexicana. Museo de Zoología “Alfonso L. Herrera”, Facultad de Ciencias, Universidad Nacional Autónoma de México. Ciudad de México.
GBIF.org. (2020a). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.bbt2tq
GBIF.org. (2020b). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.j9evdt
GBIF.org. (2020c). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.z8nbtw
GBIF.org. (2020d). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.x4qxcx
GBIF.org. (2020e). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.apt9wm
GBIF.org. (2020f). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.bugsgy
GBIF.org. (2020g). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.x33mhm
GBIF.org. (2020h). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.esbshy
GBIF.org. (2020i). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.ju93kd
GBIF.org. (2020j). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.jrv9uw
GBIF.org. (2020k). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.gmjecq
GBIF.org. (2020l). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.q7kvhy
GBIF.org. (2020m). GBIF Occurrence Download Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.jjrncx
GBIF.org. (2020n). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.7hypb5
GBIF.org. (2020o). GBIF Occurrence Download Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.vncwkt
GBIF.org. (2020p). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.sbrrgp
GBIF.org. (2020q). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.t6y7vb
GBIF.org. (2020r). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.ksdpx3
GBIF.org. (2020s). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.uy2d7k
GBIF.org. (2020t). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.py7yby
GBIF.org. (2020u). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.8c8umf
GBIF.org. (2020v). GBIF Occurrence Download. Retrieved on November 11th, 2020 from: https://doi.org/10.15468/dl.t92jw8
GBIF.org. (2020w). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.wumbut
GBIF.org. (2020x) GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.8tc8xr
GBIF.org. (2020y). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.tb25b4
GBIF.org. (2020z). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.paayuf
GBIF.org. (2020aa). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.8fzeuv
GBIF.org. (2020ab). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.vfa2qq
GBIF.org. (2020ac). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.ug5cus
GBIF.org. (2020ad). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.gpsjwg
GBIF.org. (2020ae). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.4ufzz3
GBIF.org. (2020af). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.dwvs69
GBIF.org. (2020ag). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.upjycdg
GBIF.org. (2022ah). GBIF Occurrence Download. Retrieved on December 7th, 2020 from: https://doi.org/10.15468/dl.6tt8z6
Geary, W. L., Tulloch, A. I. T., Ritchie, E. G., Doherty, T. S., Nimmo, D. G., Maxwell, M. A. et al. (2023). Identifying historical and future global change drivers that place species recovery at risk. Global Change Biology, 29, 2953–2967.
https://doi.org/10.1111/gcb.16661
González, T. M., González-Trujillo, J. D., Muñoz, A., & Armenteras, D. (2021). Differential effects of fire on the occupancy of small mammals in neotropical savanna-gallery forests. Perspectives in Ecology and Conservation, 19, 179–188. https://doi.org/10.1016/j.pecon.2021.03.005
Gotelli, N. J., & Colwell, R. K. (2011). Estimating species richness. In A. E. Magurran, & B. J. McGill (Eds.), Biological diversity: frontiers in measurement and assessment (pp. 39–54). Oxfordshire, England: Oxford University Press.
He, T., Lamont, B. B., & Pausas, J. G. (2019). Fire as a key driver of Earth's biodiversity. Biological Reviews, 94, 1983–2010. https://doi.org/10.1111/brv.12544
Hortal, J., De Bello, F., Diniz-Filho, J. A. F., Lewinsohn, T. M., Lobo, J. M., & Ladle, R. J. (2015). Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46, 523–549. https://doi.org/10.1146/annurev-ecolsys-112414-054400
Hu, F. S., Higuera, P. E., Duffy, P., Chipman, M. L., Rocha, A. V., Young, A. M., & Dietze, M. C. (2015). Arctic tundra fires: natural variability and responses to climate change. Frontiers in Ecology and the Environment, 13, 369–377. https://doi.org/10.1890/150063
IUCN (International Union for Conservation of Nature). 2001. IUCN Red List categories and criteria: Version 3.1. Prepared by IUCN Species Survival Commission. World Conservation Union, Gland, Switzerland and Cambridge, United Kingdom.
IUCN (International Union for Conservation of Nature). (2012). IUCN Red List Categories and Criteria: Version 3.1. (2nd ed.). IUCN, Gland, Switzerland.
IUCN (International Union for Conservation of Nature). (2016). Sciurus oculatus. The IUCN Red List of Threatened Species. Retrieved in 2022 from: e.T20017A22246721. https://dx.doi.org/10.2305/IUCN.UK.2016-2.RLTS.T20017A22246721.en
IUCN (International Union for Conservation of Nature). (2017). Sorex saussurei. The IUCN Red List of Threatened Species. Retrieved in 2022 from: e.T41416A22317311. https://dx.doi.org/10.2305/IUCN.UK.2017-2.RLTS.T41416A22317311.en
Jain, A., Nandakumar, K., Ross, A. (2005). Score normalization in multimodal biometric systems. Pattern Recognition, 38, 2270–2285. https://doi.org/10.1016/j.patcog.2005.01.012
Joppa, L. N., Roberts, D. L., Myers, N., & Pimm, S. L. (2011). Biodiversity hotspots house most undiscovered plant species. Proceedings of the National Academy of Sciences, 108, 13171–13176. https://doi.org/10.1073/pnas.1109389108
Kass, J. M., Vilela, B., Aiello-Lammens, M. E., Muscarella, R., Merow, C., & Anderson, R. P. (2017). Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. Methods in Ecology and Evolution, 9, 1151–1156. https://doi.org/10.1111/2041-210X.12945
Kelly, L. T., Giljohann, K. M., Duane, A., Aquilué, N., Archibald, S., Batllori, E. et al. (2020). Fire and biodiversity in the Anthropocene. Science, 370, 1–10. https://doi.org/10.1126/science.abb0355
Kindt, R., & Coe, R. (2005). Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. Nairobi, Kenya: World Agroforestry Centre (ICRAF).
Leyte-Manrique, A., Mata-Silva, V., Baez-Montes, O., Fucsko, L. A., DeSantis, D. L., Garcia-Padilla, E. et al. (2022). The herpetofauna of Guanajuato, Mexico: composition, distribution, and conservation status. Amphibian & Reptile Conservation, 16, 133–180.
Lomolino, M. V. (2004). Conservation biogeography. In M. V. Lomolino, & L. R. Heaney (Eds.), Frontiers of biogeography: new directions in the geography of nature (pp. 293–296). Sunderland, Massachusetts: Sinauer.
Luedtke, J. A., Chanson, J., Neam, K., Hobin, L., Maciel, A. O., Catenazzi, A. et al. (2023). Ongoing declines for the world’s amphibians in the face of emerging threats. Nature, 622, 308–314. https://doi.org/10.1038/s41586-023-06578-4
Mandeville, P. B. (2008). Tips bioestadísticos: tema 18 ¿Por qué se deben centrar las covariables en regresión lineal? Ciencia UANL, 11, 300–305.
Martínez-Torres, H., Cantú-Férnandez, M., Ramírez, M. I., & Pérez-Salicrup, D. R. (2015). Fires and fire management in the Monarch Butterfly Biosphere Reserve. In K. S. Oberhauser, K. R. Nail, & S. Altizer (Eds.), Monarchs in a changing world: biology and conservation of an iconic butterfly (pp. 179–189). Ithaca, New York: Cornell University Press.
Montgomery, D., & Peck, E. A. (1992). Introduction to linear regression analysis. Second Edition. Hoboken, Nueva Jersey: John Wiley & Sons Ltd.
Morrone, J. J., Escalante, T., & Rodríguez-Tapia, G. (2017). Mexican biogeographic provinces: map and shapefiles. Zootaxa, 4277, 277–279. https://doi.org/10.11646/zootaxa.4277.2.8
Murguía-Romero, M., & Villaseñor, J. L. (2000). Estimating the quality of the records used in quantitative biogeography with presence‐absence matrices. Annales Botanici Fennici, 37, 289–296.
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2014). Where is positional uncertainty a problem for species distribution modelling? Ecography, 37, 191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x
Nasa Earth Data Cloud. (2020). Datos de incendios activos. NASA Official: Cerese Alber Retrieved on November 3rd, 2021 from: https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/activefiredata
Oliveira, U., Paglia, A. P., Brescovit, A. D., de Carvalho, C. J. B., Silva, D. P., Rezende, D. T. et al. (2016). The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Diversity and Distributions, 22, 1232–1244. https://doi.org/10.1111/ddi.12489
Osorio-Olvera, L., Lira-Noriega, A., Soberón, J., Peterson, A. T., Falconi, M., Contreras-Díaz, R. G. et al. (2020). NTBOX: an R package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods in Ecology and Evolution, 11, 1199-1206. https://doi.org/10.1111/2041-210X.13452
Pearson, R. G., Raxworthy, C. J., Nakamura, M., & Townsend, A. (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x
Peterson, A. T., Papeş, M., & Soberón, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Pilliod, D. S., Bury, R. B., Hyde, E. J., Pearl, C. A., & Corn, P. S. (2003). Fire and amphibians in North America. Forest Ecology and Management, 178, 163–181. https://doi.org/10.1016/S0378-1127(03)00060-4
Pyne, S. J. (2021). The Pyrocene: how we created an Age of Fire, and what happens next. Oakland, California: University of California Press.
QGIS Development Team. (2020). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Beaverton, Oregon. Retrieved on March 2nd, 2021 from: https://qgis.org
Riddle, B. R., Ladle, R. J., Lourie, S. A., & Whittaker, R. J. (2011). Basic Biogeography: estimating biodiversity and mapping nature. In R. J. Ladle, & R. J. Whittaker (Eds.), Conservation Biogeography (pp. 45–92). Hoboken, New Jersey: Wiley-Blackwell.
https://doi.org/10.1002/9781444390001.ch4
Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez-Valverde, A., Ricotta, C. et al. (2011). Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Progress in Physical Geography: Earth and Environment, 35, 211–226. https://doi.org/10.1177/0309133311399491
Rodrigues, J. F. M., Villalobos, F., Iverson, J. B., & Diniz-Filho, J. A. F. (2019). Climatic niche evolution in turtles is characterized by phylogenetic conservatism for both aquatic and terrestrial species. Journal of Evolutionary Biology, 32, 66–75. https://doi.org/10.1111/jeb.13395
Roth, D., Moreno-Sánchez, R., Torres-Rojo, J. M., & Moreno-Sánchez, F. (2016). Estimation of human induced disturbance of the environment associated with 2002, 2008 and 2013 land use/cover patterns in Mexico. Applied Geography, 66, 22–34. https://doi.org/10.1016/j.apgeog.2015.11.009
RStudio Team. (2020). RStudio: integrated development for R. RStudio, PBC, Boston, Massachusetts. Retrieved in 2020 from: http://www.rstudio.com/
Salazar, D. N., Farfán-Gutiérrez, M., & Arellano-Reyes, M. A. (2019). Cartografía de la severidad de los incendios forestales (2017, 2018, 2019) en el estado de Guanajuato empleando imágenes Sentinel-2. Jóvenes en la Ciencia, 5, 1-6.
Sánchez O., Charre-Medellín, J. F., Téllez-Girón, G., Báez-Montes, Ó., & Magaña-Cota, G. (2016). Mamíferos silvestres de Guanajuato: actualización taxonómica y diagnóstico de conservación. In M. Briones-Salas, Y. Hortelano-Moncada, G. Magaña-Cota, G. Sánchez-Rojas, & J. E. Sosa-Escalante, (Eds.), Riqueza y conservación de los mamíferos en México a nivel estatal (pp. 243–280). Mexico City: Instituto de Biología, UNAM/ Asociación Mexicana de Mastozoología/ Universidad de Guanajuato.
Santos-Barrera, G., & Flores-Villela, O. (2004). Lithobates megapoda. IUCN Red List of Threatened Species. Versión 2013.2. Cambridge, United Kingdom. Retrieved on April 1st, 2021 from: www.iucnredlist.org
SMAOT (Secretaría de Medio Ambiente y Ordenamiento Territorial). (2022). Áreas Naturales Protegidas. Gobierno del Estado de Guanajuato, Guanajuato. Retrieved on October 26th, 2022 from: https://smaot.guanajuato.gob.mx/sitio/areas-naturales-protegidas
Soberón, J., & Peterson, A. T. (2005). Interpretation of Models of Fundamental Ecological Niches and Species’ Distributional Areas. Biodiversity Informatics, 2, 1–10. https://doi.org/10.17161/bi.v2i0.4
Shlisky, A., Waugh, J., González, P., González, M., Manta, M., Santoso, H. et al. (2007). Fire, ecosystems and people: threats and strategies for global biodiversity conservation. Global Fire Initiative Technical Report 2007-2; Arlington, VA: The Nature Conservancy.
Tessarolo, G., Ladle, R. J., Lobo, J. M., Rangel, T. F. & Hortal, J. (2021), Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models. Ecography, 44, 1743–1755. https://doi.org/10.1111/ecog.05793
Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R., & Legendre, F. (2017). Taxonomic bias in biodiversity data and societal preferences. Scientific Reports, 7, 1–14. https://doi.org/10.1038/s41598-017-09084-6
Tyukavina, A., Potapov, P., Hansen, M. C., Pickens, A. H., Stehman, S. V., Turubanova, S. et al. (2022). Global trends of forest loss due to fire from 2001 to 2019. Frontiers in Remote Sensing, 3, 1–20. https://doi.org/10.3389/frsen.2022.825190
United States Geological Survey (USGS). (2021). Archivo EROS de USGS-Elevación digital- Elevación global de 30 segundos de arco (GTOPO30). Retrieved on June 18th, 2021 from: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second-elevation-gtopo30
Vergara-Asenjo, G., Alfaro, F. M., & Pizarro-Araya, J. (2023) Linnean and Wallacean shortfalls in the knowledge of arthropod species in Chile: Challenges and implications for regional conservation. Biological Conservation, 281, 110027. https://doi.org/10.1016/j.biocon.2023.110027
Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21, 335–342. https://doi.org/10.1890/10-1171.1
Zamudio, S. (2012). Diversidad de ecosistemas del estado de Guanajuato. In Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (Conabio)/ Instituto de Ecología del estado de Guanajuato (IEE) (Eds.), La biodiversidad de Guanajuato: estudio de estado. II (pp. 19–55). Mexico City: Conabio/ IEE.
