References
Abatzoglou, J. T. et al. 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. - Sci Data 5: 170191.
Abrahms, B. et al. 2019. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. - Diversity and Distributions 25: 1182–1193.
Anderson, R. P. 2003. Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela. - J. Biogeogr. 30: 591–605.
Andrew, M. E. and Fox, E. 2020. Modelling species distributions in dynamic landscapes: The importance of the temporal dimension. - J. Biogeogr. 159: 2533.
Arguez, A. and Vose, R. S. 2011. The Key to Deriving Alternative Climate Normals. - Bull. Am. Meteorol. Soc. 92: 699–704.
Barber, R. A. et al. 2022. Target‐group backgrounds prove effective at correcting sampling bias in Maxent models. - Divers. Distrib. 28: 128–141.
Bateman, B. L. et al. 2012. Nice weather for bettongs: using weather events, not climate means, in species distribution models. - Ecography 35: 306–314.
Bauerfeind, S. S. and Fischer, K. 2014. Simulating climate change: temperature extremes but not means diminish performance in a widespread butterfly. - Popul. Ecol. 56: 239–250.
Berrar, D. 2019. Cross-Validation. - In: Ranganathan, S. et al. (eds), Encyclopedia of Bioinformatics and Computational Biology. Academic Press, pp. 542–545.
Bland, J. M. and Altman, D. G. 1986. Statistical methods for assessing agreement between two methods of clinical measurement. - Lancet 1: 307–310.
Bland, J. M. and Altman, D. G. 1995. Comparing methods of measurement: why plotting difference against standard method is misleading. - Lancet 346: 1085–1087.
Boria, R. A. et al. 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. - Ecol. Modell. 275: 73–77.
Bouckaert, R. R. 2003. Choosing between two learning algorithms based on calibrated tests. - Proceedings of the Twentieth International Conference on International Conference on Machine Learning 3: 51–58.
Brodie, S. et al. 2021. Exploring timescales of predictability in species distributions. - Ecography 44: 832–844.
Crego, R. D. et al. 2022. Implementation of species distribution models in Google Earth Engine. - Divers. Distrib. 28: 904–916.
Dobson, R. et al. 2023. dynamicSDM : An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution. - Methods Ecol. Evol. in press.
Feldmeier, S. et al. 2018. Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models. - Diversity and Distributions 24: 1047–1060.
Fick, S. E. and Hijmans, R. J. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. - Int. J. Climatol. 37: 4302–4315.
Frederiksen, M. et al. 2008. The demographic impact of extreme events: stochastic weather drives survival and population dynamics in a long-lived seabird. - J. Anim. Ecol. 77: 1020–1029.
Gardner, A. S. et al. 2021. Accounting for inter‐annual variability alters long‐term estimates of climate suitability. - J. Biogeogr. 48: 1960–1971.
GBIF.org 2022. GBIF. - GBIF Occurrence Download
Guevara, L. 2020. Altitudinal, latitudinal and longitudinal responses of cloud forest species to Quaternary glaciations in the northern Neotropics. - Biol. J. Linn. Soc. Lond. 130: 615–625.
Guevara, L. and Sánchez-Cordero, V. 2018. Patterns of morphological and ecological similarities of small-eared shrews (Soricidae, Cryptotis) in tropical montane cloud forests from Mesoamerica. - System. Biodivers. 16: 551–564.
Guevara, L. et al. 2018. Toward ecologically realistic predictions of species distributions: A cross-time example from tropical montane cloud forests. - Glob. Chang. Biol. 24: 1511–1522.
Hansen, M. C. et al. 2013. High-resolution global maps of 21st-century forest cover change. - Science 342: 850–853.
Harris, I. et al. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. - Sci Data 7: 109.
Hernández-Flores, S. D. and Rojas-Martínez, A. E. 2010. Lista actualizada y estado de conservación de los mamíferos del Parque Nacional El Chico, Hidalgo, México. - Acta Zool. Mex. 26: 563–583.
Hijmans, R. J. 2022. Spatial Data Analysis [R package terra version 1.5-17]. in press.
Hijmans, R. J. et al. 2021. Species distribution modeling [R package dismo version 1.3-5]. in press.
Hirzel, A. H. et al. 2006. Evaluating the ability of habitat suitability models to predict species presences. - Ecol. Modell. 199: 142–152.
Ingenloff, K. and Peterson, A. T. 2021. Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina. - Methods Ecol. Evol. 12: 311–321.
Karger, D. N. et al. 2017. Climatologies at high resolution for the earth’s land surface areas. - Sci Data 4: 170122.
Kass, J. M. et al. 2021. ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions. - Methods Ecol. Evol. 12: 1602–1608.
Levin, S. A. 1992. The problem of pattern and scale in ecology: The Robert H. macarthur award lecture. - Ecology 73: 1943–1967.
Livezey, R. E. et al. 2007. Estimation and Extrapolation of Climate Normals and Climatic Trends. - J. Appl. Meteorol. Climatol. 46: 1759–1776.
Marcelino, J. et al. 2020. Extreme events are more likely to affect the breeding success of lesser kestrels than average climate change. - Sci. Rep. 10: 7207.
Mayen-Zaragoza, M. et al. 2019. First record of shrews (Eulipotyphla, Soricidae) in the Sierra de Otontepec, an isolated mountain in Veracruz, Mexico. - Therya 10: 59.
Merow, C. et al. 2022. Operationalizing expert knowledge in species’ range estimates using diverse data types. - Frontiers of Biogeography 14: e53589.
Milanesi, P. et al. 2020. Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models. - Ecol. Evol. 10: 1087–1092.
Moran-Ordonez, A. et al. 2018. Modelling species responses to extreme weather provides new insights into constraints on range and likely climate change impacts for Australian mammals. - Ecography 41: 308–320.
Nadeau, C. and Bengio, Y. 2003. Inference for the Generalization Error. - Mach. Learn. 52: 239–281.
Nadeau, C. et al. 2017. Coarse climate change projections for species living in a fine-scaled world. - Glob. Chang. Biol. 23: 12–24.
Pacifici, M. et al. 2013. Generation length for mammals. - Nature Conservation 5: 89–94.
Pang, S. E. H. et al. 2022. Occurrence–habitat mismatching and niche truncation when modelling distributions affected by anthropogenic range contractions. - Divers. Distrib. 28: 1327–1343.
Paz, A. et al. 2022. A framework for near-real time monitoring of diversity patterns based on indirect remote sensing, with an application in the Brazilian Atlantic rainforest. - PeerJ 10: e13534.
Perez-Navarro, M. A. et al. 2022. Comparing climatic suitability and niche distances to explain populations responses to extreme climatic events. - Ecography 2022: e06263.
Peterson, A. T. 2001. Predicting species’ geographic distributions based on ecological niche modeling. - Condor 103: 599.
Peterson, A. T. et al. 2005. Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases. - Trans. R. Soc. Trop. Med. Hyg. 99: 647–655.
Phillips, S. J. and Dudík, M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. - Ecography 31: 161–175.
Phillips, S. J. et al. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. - Ecol. Appl. 19: 181–197.
Phillips, S. J. et al. 2017. Opening the black box: an open-source release of Maxent. - Ecography 40: 887–893.
Pinilla-Buitrago, G. E. 2023. Predicting potential range shifts using climatic time series and niche models: A Neotropical montane shrew’s case. - Ecol. Inform. 77: 102212.
Reside, A. E. et al. 2010. Weather, not climate, defines distributions of vagile bird species. - PLoS One 5: e13569.
Roubicek, A. J. et al. 2010. Does the choice of climate baseline matter in ecological niche modelling? - Ecol. Modell. 221: 2280–2286.
Sánchez-Cordero, V. and Guevara, L. 2016. Modelado de la distribución potencial de las musarañas (Mammalia, Soricidae). - Instituto de Biología. Universidad Nacional Autónoma de México. Comisión nacional para el conocimiento y uso de la biodiversidad.
Schloss, C. A. et al. 2012. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. - Proc. Natl. Acad. Sci. U. S. A. 109: 8606–8611.
Smith, A. B. et al. 2019. Alternatives to genetic affinity as a context for within-species response to climate. - Nat. Clim. Chang. 9: 787–794.
Stewart, S. B. et al. 2021. Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species. - Ecography 44: 626–639.
Syfert, M. M. et al. 2013. The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models. - PLoS One 8: e55158.
Urban, M. C. et al. 2013. Moving forward: dispersal and species interactions determine biotic responses to climate change. - Ann. N. Y. Acad. Sci. 1297: 44–60.
VanDerWal, J. et al. 2013. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. - Nat. Clim. Chang. 3: 239–243.
Warren, D. L. and Seifert, S. N. 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. - Ecol. Appl. 21: 335–342.
Welch, H. et al. 2018. Using temporally explicit habitat suitability models to assess threats to mobile species and evaluate the effectiveness of marine protected areas. - J. Nat. Conserv. 41: 106–115.
Wilcoxon, F. 1945. Individual Comparisons by Ranking Methods. - Biometrics Bulletin 1: 80–83.
Wilks, D. S. and Livezey, R. E. 2013. Performance of Alternative “Normals” for Tracking Climate Changes, Using Homogenized and Nonhomogenized Seasonal U.S. Surface Temperatures. - J. Appl. Meteorol. Climatol. 52: 1677–1687.
Williams, H. M. et al. 2017. A temporally explicit species distribution model for a long distance avian migrant, the common cuckoo. - J. Avian Biol. 48: 1624–1636.
Zimmermann, N. E. et al. 2009. Climatic extremes improve predictions of spatial patterns of tree species. - Proceedings of the National Academy of Sciences 106: 19723–19728.
Zizka, A. et al. 2019. CoordinateCleaner : Standardized cleaning of occurrence records from biological collection databases. - Methods Ecol. Evol. 10: 744–751.