Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).
Google Scholar
Bradshaw, C. J. A. et al. Underestimating the challenges of avoiding a ghastly future. Front. Environ. Sci. 1, 615469. https://doi.org/10.3389/fcosc.2020.615419 (2021).
Google Scholar
IUCN. The IUCN Red List of Threatened Species. IUCN, Gland, Switzerland. http://www.iucnredlist.org (2020).
Murray, K. A., Verde Arregoitia, L. D., Davidson, A., Di Marco, M. & Di Fonzo, M. M. I. Threat to the point: Improving the value of comparative extinction risk analysis for conservation action. Glob. Chang. Biol. 20, 483–494 (2014).
Google Scholar
Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).
Google Scholar
Chichorro, F., Juslén, A. & Cardoso, P. A review of the relation between species traits and extinction risk. Biol. Conserv. 237, 220–229 (2019).
Google Scholar
Ripple, W. J. et al. Bushmeat hunting and extinction risk to the world’s mammals. R. Soc. Open. Sci. 3, 160498. https://doi.org/10.1098/rsos.160498 (2016).
Google Scholar
Hoffmann, M. et al. The changing fates of the world’s mammals. Phil. Trans. R. Soc. B. 366, 2598–2610 (2011).
Google Scholar
Verde Arregoitia, L. D. Biases, gaps, and opportunities in mammalian extinction risk research. Mammal. Rev. 46, 17–29 (2016).
Google Scholar
Di Marco, M. et al. Drivers of extinction risk in African mammals: The interplay of distribution state, human pressure, conservation response and species biology. Philos. Trans. R. Soc. Lond. B. 369, 1–12 (2014).
Google Scholar
Di Marco, M., Collen, B., Rondinini, C. & Mace, G. M. Historical drivers of extinction risk: Using past evidence to direct future monitoring. Proc. R. Soc. B. 282, 20150928. https://doi.org/10.1098/rspb.2015.0928 (2015).
Google Scholar
Bogoni, J. A., Ferraz, K. M. & Peres, C. A. Continental-scale local extinctions in mammal assemblages are synergistically induced by habitat loss and hunting pressure. Biol. Conserv. 272, 109635. https://doi.org/10.1016/j.biocon.2022.109635 (2022).
Google Scholar
Yusefi, G. H., Faizolahi, K., Darvish, J., Safi, K. & Brito, J. C. The species diversity, distribution and conservation status of the terrestrial mammals of Iran. J. Mammal. 100, 55–71 (2019).
Google Scholar
Keil, P. et al. Spatial scaling of extinction rates: Theory and data reveal nonlinearity and a major upscaling and downscaling challenge. Glob. Ecol. Biogeogr. 27, 2–13 (2018).
Google Scholar
Howard, C., Flather, C. H. & Stephens, P. A. A global assessment of the drivers of threatened terrestrial species richness. Nat. Commun. 11, 993. https://doi.org/10.1038/s41467-020-14771-6 (2020).
Google Scholar
Rodríguez, J. P. The difference conservation can make: integrating knowledge to reduce extinction risk. Oryx 51, 1–2 (2017).
Google Scholar
Cardillo, M. & Meijaard, E. Are comparative studies of extinction risk useful for conservation?. Trends Ecol. Evol. 27, 167–171 (2012).
Google Scholar
Davidson, A. D. et al. Geography of current and future global mammal extinction risk. PLoS ONE 12, e0186934. https://doi.org/10.1371/journal.pone.0186934 (2017).
Google Scholar
Collen, B., Bykova, E., Ling, S., Milner-Gulland, E. J. & Purvis, A. Extinction risk: A comparative analysis of central Asian vertebrates. Biodivers. Conserv. 15, 1859–1871 (2006).
Google Scholar
Peñaranda, D. A. & Simonetti, J. A. Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline. Conserv. Biol. 29, 834–843 (2015).
Google Scholar
Fritz, S. A., Bininda-Emonds, O. R. & Purvis, A. Geographical variation in predictors of mammalian extinction risk: Big is bad, but only in the tropics. Ecol. Lett. 12, 538–549 (2009).
Google Scholar
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, 909–914 (2004).
Google Scholar
Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. B. 275, 1441–1448 (2008).
Google Scholar
Yackulic, C. B., Sanderson, E. W. & Uriat, M. Anthropogenic and environmental drivers of modern range loss in large mammals. Proc. Natl. Acad. Sci. USA 108, 4024–4029 (2011).
Google Scholar
Ripple, W. J. et al. Are we eating the world’s megafauna to extinction?. Conserv Lett 12, e12627. https://doi.org/10.1111/conl.12627 (2019).
Google Scholar
Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. PNAS https://doi.org/10.1073/pnas.1702078114 (2017).
Google Scholar
Bodmer, R. E., Eisenberg, J. E. & Redford, K. H. Hunting and the likelihood of extinction of Amazonian mammals. Conserv. Biol. 11, 460–466 (1997).
Google Scholar
Lee, T. M. & Jetz, W. Unravelling the structure of species extinction risk for predictive conservation science. Proc. R. Soc. B. 278, 1329–1338 (2011).
Google Scholar
Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. Roy. Soc Open Sci 3, 160252. https://doi.org/10.1098/rsos.160252 (2016).
Google Scholar
Firouz, E. The complete fauna of Iran. I. B. (Tauris and Co Ltd, London, 2005).
Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).
Google Scholar
Davidson, A. D., Hamilton, M. J., Boyer, A. G., Brown, J. H. & Ceballos, G. Multiple ecological pathways to extinction in mammals. Proc. Natl. Acad. Sci. USA 106, 10702–10705 (2009).
Google Scholar
Hill, J., DeVault, T. & Belant, J. Comparative influence of anthropogenic landscape pressures on cause-specific mortality of mammals. Perspect. Ecol. Conserv. 20, 38–44 (2022).
DOE-GIS. Areas under protection by the Department of Environment of Iran. Department of the Environment of Iran: GIS and Remote Sensing Section (2016).
Kolahi, M., Sakai, T., Moriya, K. & Makhdoum, M. F. Challenges to the future development of Iran’s protected areas system. Environ. Manage. 50, 750–765 (2012).
Google Scholar
Morrison, J. M., Sechrest, W., Dinerstein, E., Wilcove, D. S. & Lamoreux, J. L. Persistence of large mammal faunas as indicators of human impact. J. Mammal. 88, 1363–1380 (2007).
Google Scholar
Ghoddousi, A. et al. The decline of ungulate populations in Iranian protected areas calls for urgent action against poaching. Oryx 53, 151–158 (2017).
Google Scholar
Soofi, M. et al. Assessing the relationship between illegal hunting of ungulates, wild prey occurrence and livestock depredation rate by large carnivores. J. Appl. Ecol. 56, 365–374 (2019).
Google Scholar
Khalatbari, L., Yusefi, G. H., Martinez-Freiria, F., Jowkar, H. & Brito, J. C. Availability of prey and natural habitats are related with temporal dynamics in range and habitat suitability for Asiatic cheetah. Hystrix Ital. J. Mammal. 29, 145–151 (2018).
Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103 (2015).
Google Scholar
Hoffmann, M. et al. The difference conservation makes to extinction risk of the world’s ungulates. Conserv. Biol. 29, 1303–1313 (2015).
Google Scholar
Yusefi, G. H. Conservation biogeography of terrestrial mammals in Iran diversity distribution and vulnerability to extinction. Front Biogeogr 13(2), 49765. https://doi.org/10.21425/F5FBG49765 (2021).
Google Scholar
Faurby, S. & Svenning, J.-C. A species-level phylogeny of all extant and late Quaternary extinct mammals using a novel heuristic-hierarchical Bayesian approach. Mol. Phylogenet. Evol. 84, 14–26 (2015).
Google Scholar
R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2021).
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).
Google Scholar
Jones, K. et al. PanTHERIA: A species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648. https://doi.org/10.1890/08-1494.1 (2009).
Google Scholar
González-Suárez, M., Lucas, P. M. & Revilla, E. Biases in comparative analyses of extinction risk: Mind the gap. J. Anim. Ecol. 81, 1211–1222 (2012).
Google Scholar
Wang, Y. et al. Ecological correlates of extinction risk in Chinese birds. Ecography 41, 782–794 (2018).
Google Scholar
Wildlife Conservation Society-WCS, and Center for International Earth Science Information Network-CIESIN, Columbia University. Last of the wild project, Version 2, 2005 (LWP-2): Global human influence index (HII) Dataset. https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic (2005).
ESRI ArcGIS Desktop10.6. Redlands, CA: Environmental Systems Research Institute (2017).
Hijmans, R. J. et al. raster: geographic data analysis and modeling. https://cran.r-project.org/web/packages/raster/index.html (2018).
Pebesma, E. et al. rgdal: bindings for the geospatial data abstraction library. https://cran.r-project.org/web/packages/rgdal/index.html (2018).
Bivand, R. et al. maptools: tools for reading and handling spatial objects. https://cran.r-project.org/web/packages/maptools/ index.html (2018).
Purvis, A. Phylogenetic approaches to the study of extinction. Annu. Rev. Ecol. Evol. Syst. 39, 301–319 (2008).
Google Scholar
Venables, W. N. & Ripley, B. D. Modern applied statistics with S (Springer, 2002).
Google Scholar
Gittleman, J. L. & Kot, M. Adaptation: Statistics and a null model for estimating phylogenetic effects. Syst. Zool. 39, 227–241 (1990).
Google Scholar
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodal inference in ecology and solution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).
Google Scholar
Bartón, K. MuMIn: multi-model inference R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).
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