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The spatial ecology of climate influences species distributions: the case of North American amphibians


The spatial ecology of climate influences species distributions: the case of North American amphibians

Rimok, Gabrielle L. (2021) The spatial ecology of climate influences species distributions: the case of North American amphibians. Masters thesis, Concordia University.

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Species distributions are largely determined by three main drivers: abiotic environmental conditions, dispersal, and biotic interactions. Because abiotic environmental conditions determine habitat suitability, they also have direct implications on the capacity of species to disperse and how species interact with one another in space. However, it is specifically the variability in abiotic environmental conditions (i.e., environmental heterogeneity) and how they are spatially structured (i.e., environmental spatial autocorrelation - ESA) that determines whether or not a habitat, or even a landscape, is environmentally suitable for species establishment. Environmental heterogeneity itself is spatially structured; where environmental conditions/features that are closer together in space tend to be more similar than those farther apart. As such, the spatial structure of environmental features (i.e., ESA) mimics dispersal networks because spatial patterns in environmental heterogeneity affect the strategies and energetic costs (and their associated fitness consequences) involved in movement and dispersal among patches. At broad spatial scales, species distributions are shaped by environmental conditions, namely, those of climate. Climatic conditions thus also impose important physiological and life history constraints on species and in accordance with environmental features, are also often heterogeneous and spatially structured. Yet, how they affect and contribute to species distributions remains unknown. Here, we use species distribution models (SDMs) in a novel framework in which we demonstrate for the first time, the influence of climate heterogeneity (within and between patches) and climate spatial structure on species distributions. We evaluated six different SDMs testing both the individual and combined effects of climate variables (i.e., between and within-patch climate heterogeneity and climate spatial structure) on species distributions, using 301 North American amphibian species as a case study. Our results demonstrate that a model using climate spatial structure as a predictor alone explained species distributions better than any other model in the majority of species. Although a model including both climate heterogeneity (within and between-patch) and climate spatial structure as predictors was only the best model for a handful of species, we provide critical evidence that there is added value in considering climate spatial structure when fitting different SDMs for the same species. Most importantly, we demonstrate that climate spatial structure and heterogeneity are important mechanisms driving species distributions in North America.

Divisions:Concordia University > Faculty of Arts and Science > Biology
Item Type:Thesis (Masters)
Authors:Rimok, Gabrielle L.
Institution:Concordia University
Degree Name:M. Sc.
Date:15 November 2021
Thesis Supervisor(s):Peres-Neto, Pedro R.
Keywords:Environmental Heterogeneity, Environmental Spatial Autocorrelation, Climate Heterogeneity, Climate Spatial Autocorrelation, Climate Spatial Structure, Species Distribution Models, SDMs, Amphibians, North America, Species Distributions, Spatial Ecology, Spatial Autocorrelation, Local Spatial Autocorrelation, Local Moran's I, Ecology, Species, Climate
ID Code:990061
Deposited By: Gabrielle Rimok
Deposited On:16 Jun 2022 15:08
Last Modified:16 Jun 2022 15:08


Ai, D., D. Gravel, C. Chu, and G. Wang. 2013. Spatial structures of environment and of dispersal impact species distribution in competitive metacommunities. PLoS one 8:e68927.
Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6):716–723.
Alford, R. A., and S. J. Richards. 1999. Global amphibian declines: a problem in applied ecology. Annual Review of Ecology and Systematics 30:133-165.
Anderson, R.P. and A. Raza. 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography 37: 1378-1393.
Anselin, L. 1995. Local Indicators of Spatial Association—LISA. Geographical Analysis 27(2):93–115.
Araújo, K. C., A. Guzzi, and R. Avila. 2018. Influence of habitat heterogeneity on anuran diversity in Restinga landscapes of the Parnaíba River delta, northeastern Brazil. ZooKeys 757:69–83.
Araújo, M. B., R. P. Anderson, A. M. Barbosa, C. M. Beale, C. F. Dormann, R. Early, R. A. Garcia, A. Guisan, L. Maiorano, B. Naimi, R. B. O’Hara, N. E. Zimmermann, and C. Rahbek. 2019. Standards for distribution models in biodiversity assessments. Science Advances 5:eaat4858.
Araújo, M. B., and R. G. Pearson. 2005. Equilibrium of species’ distributions with climate. Ecography 28:693–695.
Araújo, M.B., R. G. Pearson, W. Thuiller, and M. Erhard. 2005. Validation of species-climate impact models under climate change. Global Change Biology 11:1504–1513.
Barve, N., V. Barve, A. Jiménez-Valverde, A. Lira-Noriega, S. P. Maher, A. T. Peterson, J. Soberón, and F. Villalobos. 2011. The crucial role of accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222:1810-1819.
Basile, M., F. Valerio, R. Balestrieri, M. Posillico, R. Bucci, T. Altea, B. De Cinti, and G. Matteucci. 2016. Patchiness of forest landscape can predict species distribution better than abundance: the case of a forest-dwelling passerine, the short-toed treecreeper, in central Italy. PeerJ 4:e2398.
Beisner, B. E., P. R. Peres-Neto, E. S. Lindström, A. Barnett, and M. L. Longhi. 2006. The role of environmental and spatial processes in structuring lake communities from bacteria to fish. Ecology 87:2985–2991.
Ben-Hur, E., and R. Kadmon. 2020. An experimental test of the area-heterogeneity tradeoff. Proceedings of the National Academy of Sciences 117:4815–4822.
Bishop, P. J., A. Angulo, J. P. Lewis. R. D. Moore, G. B. Rabb, and J. Garcia. 2012. The amphibian extinction crisis – what will it take to put the action into the Amphibian Conservation Action Plan? Surveys and Perspectives Integrating Environment and Society [accessed November 1st, 2021]; 5 (IUCN Commissions).
Biswas, S. R., R. L. MacDonald, and H. Y. H. Chen. 2017. Disturbance increases negative spatial autocorrelation in species diversity. Landscape Ecology 32(823-834).
Bonebrake, T. C., and C. A. Deutsch. 2012. Climate heterogeneity modulates impact of warming on tropical insects. Ecology 93:449–455.
Boone, M. D., P. S. Corn, M. A. Donnelly, E. E. Little, and P. H. Niewiarowski. 2003. Physical stressors. In: Linder G. L., Krest, S.K., Sparling D. W, editors. Amphibian decline: an integrated analysis of multiple stressor effects. Pensacola, Florida: SETAC. p.129-151.
Brodman, R. 2009. A 14-year study of amphibian populations and metacommunities. Herpetological Conservation and Biology 4:106-119.
Büchi, L., P-A. Christin, and A. H. Hirzel. 2009. The influence of environmental spatial structure on the life-history traits and diversity of species in a metacommunity. Ecological Modelling 220:2857-2864.
Büchi, L., and S. Vuilleumier. 2012. Dispersal strategies, few dominating or many coexisting: the effect of environmental spatial structure and multiple sources of mortality. PLoS one 7:e34733.
Büchi, L. and Vuilleumier. 2014. Coexistence of specialist and generalist species is shaped by dispersal and environmental factors. The American Naturalist 183:612-624.
Button K. S., J. P. A. Ioannidis, C. Mokrysz, B. A. Nosek, J. Flint, E. S. J. Robinson, and M. R. Munafò. 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews 14:365-376.
Catenazzi, A. 2015. State of the world’s amphibians. Annual Review of Environment and Resources 40:91-119.
Catford, J. A., J. R. U. Wilson, P. Pyšek, P. E. Hulme, and R. R. Duncan. 2021. Addressing context dependency in ecology. Trends in Ecology and Evolution 2914:1-13.
Chesson, P. 2000. General theory of competitive coexistence in spatially-varying environments. Theoretical Population Biology 58:211–237.
Choi, W., R. Tareghian, J. Choi, and C. Hwang. 2014. Geographically heterogeneous temporal trends of extreme precipitation in Wisconsin, USA during 1950-2006. International Journal of Climatology 34:2841–2852.
Cohen, J. M., D. J. Civitello, A. J. Brace, E. M. Feichtinger, C. N. Ortega, J. C. Richardson, E. L. Sauer, X. Liu, and J. R. Rohr. 2016. Spatial scale modulates the strength of ecological processes driving disease distributions. Proceedings of the National Academy of Sciences 113: E359-E364.
Colquhoun, D. 2014. An investigation of the false discovery rate and the misinterpretation of p-values. The Royal Society of Open Science 1:140216.
Corn, P. S. 2005. Climate change and amphibians. Animal Biodiversity and Conservation 28:59-67.
Cornell, H. V., and J. H. Lawton. 1992. Species interactions, local and regional processes, and limits to the richness of ecological communities: a theoretical perspective. Journal of Animal Ecology 61:1-12
Costa, G. C., C. Nogueira, R. B. Machado, and G. R. Colli. 2007. Squamate richness in the Brazilian Cerrado and its environmental-climatic associations. Diversity and Distributions 13: 714-724.
Cottenie K. 2005. Integrating environmental and spatial processes in ecological community dynamics: meta-analysis of metacommunities. Ecology Letters 8:1175–1182.
Couto, A. P., E. Ferreira, R. T. Torres, and C. Fonseca. 2017 Local and landscape drivers of pond-breeding amphibian diversity at the northern edge of the Mediterranean. Herpetologica 73:10-17.
D’Amen, M., C. Rahbek, N. E. Zimmermann, and A. Guisan. 2017. Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews 92:169-187.
Di Marco, M., J. E. M. Watson, H. P. Possingham, and O. Venter. 2017. Limitations and trade-offs in the use of species distribution maps for protected area planning. Journal of Applied Ecology 54:402–411.
Diniz-Filho, J. A. F., and L. M. Bini. 2005. Modelling geographical patterns in species richness using eigenvector-based spatial filters: spatial filtering of richness data. Global Ecology and Biogeography 14:177–185.
Diniz-Filho, J. A. F., L. M. Bini, C. M. Vieira, D. Blamires, L. C. Terribile, R. P. Bastos, G. de Oliveira, and B. de S. Barreto. 2008. Spatial patterns of terrestrial vertebrate species richness in the Brazilian Cerrado. Zoological Studies 47:146-157.
Donelle, L. 2018. The effects of the spatial structure of the environment on species coexistence and related consequences to local and regional community structure. Montreal, Quebec, Canada: Concordia University.
Dormann, C. F., J. M. McPherson, M. B. Araújo, R. Bivand, J. Bolliger, G. Carl, R. Davies, A. Hirzel, W. Jetz, D. Kissling, I. Kühn, R. Ohlemüller, P. R. Peres-Neto, R. Björn, B. Schröder, F. M. Schurr, and R. Wilson. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609-628.
Dray, S. 2011. A new perspective about Moran’s coefficient: spatial autocorrelation as a linear regression problem. Geographical Analysis 43:127-141.
Dufour, A., F. Gadallah, H. H. Wagner, A. Guisan, and A. Buttler. 2006. Plant species richness and environmental heterogeneity in a mountain landscape: effects of variability and spatial configuration. Ecography 29:573–584.
Elith, J., and J. R. Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40:677-697.
Esparza-Orozco, A., A. Lira-Noriega, J. F. Martínez-Montoya, L. F. Pineda-Martínez, and S de J. Méndez-Gallegos. 2020. Influences of environmental heterogeneity on amphibian composition at breeding sites in a semiarid region of Mexico. Journal of Arid Environments 182:104259.
Ficetola, G. F., C. Rondinini, A. Bonardi, V. Katariya, E. Padoa-Schioppa, and A. Angulo. 2014. An evaluation of the robustness of global amphibian range maps. Journal of Biogeography 41:211–221.
Fick, S. E., and R. J. Hijmans. 2017. WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37: 4302-4315.
Flater, D. 2011. Understanding geodesic buffering. URL: https://www.esri.com/news/arcuser/0111/geodesic.html [accessed on 17 May 2021].
Fortin, M-J., M. R. T. Dale, and J. M. ver Hoef. 2006. Spatial Analysis in Ecology. Encyclopedia of Environmetrics 4:2051-2058.
Fortin, M-J., M. R. T. Dale, J. M. ver Hoef. 2013. Spatial Analysis in Ecology. In: El-Shaarawi, A. H., and Piegorsch W. W., editors. Encyclopedia of Environmetrics. Second Edition: John Wiley & Sons, Ltd.
Fournier, B., H. Vázquez‐Rivera, S. Clappe, L. Donelle, P. H. P. Braga, and P. R. Peres‐Neto. 2020. The spatial frequency of climatic conditions affects niche composition and functional diversity of species assemblages: the case of Angiosperms. Ecology Letters 23:254–264.
Fu, W. J., P. K. Jiang, G. M. Zhou, and K. L. Zhao. 2014. Using Moran’s I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences 11:2401–2409.
Fujiwara, M. and T. Takada. 2017. Environmental stochasticity. In: eLS. Chichester, United Kingdom: John Wiley & Sons, Ltd.
Garcia, R. A., M. Cabeza, C. Rahbek, and M. B. Araújo. 2014. Multiple dimensions of climate change and their implications for biodiversity. Science 344:1247579.
Gelman, A., and J. Hill. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge, United Kingdom: Cambridge University Press.
Gelman, A., and Y-S. Su. 2016. arm: data analysis using regression and multilevel/hierarchical models. R package version 1.9-1. Retrieved from: https://CRAN.R-project.org/package=arm
Griffith, D. A., and P. R. Peres-Neto. 2006. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87:2603–2613.
Guan, Y., H. Lu, L. He, H. Adhikari, P. Pellikka, E. Maeda, and J. Heiskanen. 2020. Intensification of the dispersion of the global climatic landscape and its potential as a new climate change indicator. Environmental Research Letters 15:114032.
Guan, Y., H. Lu, Y. Jiang, P. Tian, L. Qiu, P. Pellikka, and J. Heiskanen. 2021. Changes in global climate heterogeneity under the 21st century global warming. Ecological Indicators 130:108075
Guerra, C., and E. Aráoz. 2015. Amphibian diversity increases in an heterogeneous agricultural landscape. Acta Oecologica 69:78–86.
Guisan, A., and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8:993–1009.
Guisan, A., W. Thuiller, and N. E. Zimmermann. 2017. Habitat suitability and distribution models with applications in R. Cambridge, United Kingdom: Cambridge University Press.
Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135:147-186.
Hand, D. J., and C. Anagnostopoulos. 2013. When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance? Pattern Recognition Letters 34:492-495.
Hefley, T. J., and M. B. Hooten. 2015. On the existence of maximum likelihood estimates for presence-only data. Methods in Ecology and Evolution 6:648-655.
Herkt, K. M. B., A. K. Skidmore, and J. Fahr. 2017. Macroecological conclusions based on IUCN expert maps: a call for caution. Global Ecology and Biogeography 26:930–941.
Hof, C. 2010. Species distributions and climate change: current patterns and future scenarios for biodiversity. Copenhagen, Denmark: University of Copenhagen.
Howard. S. D., and D. P. Bickford. 2014. Amphibians over the edge: silent extinction risk of data deficient species. Diversity and Distributions 20: 837-846.
Hsiung, H-Y., B-H. Huang, J-T. Chang, Y-M. Huang, C-W. Huang, and P-C. Liao. 2017. Local climate heterogeneity shapes populations genetic structure of two undifferentiated insular Scutellaria species. Frontiers in Plant Science 8:1–17.
Huang, Y., Q. Dai, Y. Chen, H. Wan, J. Li, and Y. Wang. 2011. Lizard species richness patterns in China and its environmental associations. Biodiversity and Conservation 20:1399–1414.
Hutchinson, G. E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 22:415-427.
IUCN 2018. The IUCN Red List of Threatened Species. Version 2020-3. https://www.iucnredlist.org. Downloaded on [16 July 2018].
Jarvie, S., and J-C. Svenning. 2018. Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change. Philosophical Transactions of the Royal Society B: Biological Sciences 373:20170446.
Jimenez, I., and R. E. Ricklefs. 2014. Diversity anomalies and spatial climate heterogeneity. Global Ecology and Biogeography 23:988–999.
Joly, P. 2019. Behavior in a changing landscape: using movement ecology to inform the conservation of pond-breeding amphibians. Frontiers in Ecology and Evolution 7:155.
Katayama, N., T. Amano, S. Naoe, T. Yamakita, I. Komatsu, S. Takagawa, M. Ueta, and T. Miyashita. 2014. Landscape heterogeneity–biodiversity relationship: effect of range size. PLoS one 9:e93359.
Kassen, R. 2002. The experimental evolution of specialists generalists, and the maintenance of diversity. Journal of Evolutionary Biology 15: 173-190.
Keller, A., M-O. Rodel, K. E. Linsenmair, and T. U. Grafe. 2009. The importance of environmental heterogeneity for species diversity and assemblage structure in Bornean stream frogs. Journal of Animal Ecology 78:305–314.
Kumar, M., H. Padalia, S. Nandy, H. Singh, P. Khaiter, and N. Kalra. 2019. Does spatial heterogeneity of landscape explain the process of plant invasion? A case study of Hyptis suaveolens from Indian Western Himalaya. Environmental Monitoring and Assessment 191:794.
Landeiro, V. L., F. Waldez, and M. Menin. 2014. Spatial and environmental patterns of Amazonian anurans: differences between assemblages with aquatic and terrestrial reproduction, and implications for conservation management. Natureza & Conservação 12:42–46.
Lannoo, M. 2005. Amphibian declines. London, England: University of California Press.
Legendre, P. 1993. Spatial Autocorrelation: trouble or new paradigm? Ecology 74:1659–1673.
Legendre, P., and L. Legendre. 2012. Chapter 14: Multiscale analysis: spatial eigenfunctions. In: P. Legendre, L. Legendre, editors. Numerical Ecology. Oxford, United Kingdom: Elsevier. p.859-906.
Legendre, P., D. Borcard, and P. R. Peres-Neto. 2005. Analyzing beta diversity: partitioning the spatial variation of community composition data. Ecological Monographs 75:435-450.
Leibold, M. A., and J. M. Chase. 2018. Metacommunity ecology. Princeton, New Jersey and Oxford, United Kingdom: Princeton University Press.
Leibold, M. A., M. Holyoak, N. Mouquet, P. Amarasekare, J. M. Chase, M. F. Hoopes, R. D. Holt, J. B. Shurin, R. Law, D. Tilman, M. Loreau, and A. Gonzalez. 2004. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters 7:601–613.
Leibold, M. A., and M. A. McPeek. 2006. Coexistence of the niche and neutral perspectives in community ecology. Ecology 87:1399–1410.
Leibold, M. A., F. J. Rudolph, F. G. Blanchet, L. De Meester, D. Gravel, F. Hartig, P. R. Peres-Neto, L. Shoemaker, and J. M. Chase. 2021. The internal structure of metacommunities. Oikos 00:1-13.
Logue, J. B., N. Mouquet, H. Peter, and H. Hillebrand. 2011. Empirical approaches to metacommunities: a review and comparison with theory. Trends in Ecology & Evolution 26:482–491.
Menge, B. A., and A. M. Olson. 1990. Role of scale and environmental factors in regulation of community structure. Trends in Ecology and Evolution 5:52-57.
Mielke, K. P., T. Claassen, M. Busana, T. Heskes, M. A. J. Huijbregts, K. Koffijberg, and A. M. Schipper. 2020. Disentangling drivers of spatial autocorrelation in species distribution models. Ecography 43:1741-1751.
Miller, D. A.W., E. H. C. Grant, E. Muths, S. M. Amburgey, M. J. Adams, M. B. Joseph, J. H. Waddle, P. T. J. Johnson, M. E. Ryan, B. R. Schmidt et al. 2018. Quantifying climate sensitivity and climate-driven change in North American amphibian communities. Nature Communications 9: 3926.
Mittelbach, G. G., and D. W. Schemske. 2015. Ecological and evolutionary perspectives on community assembly. Trends in Ecology and Evolution 30:241–247.
Monteiro, V. F., P. C. Paiva, and P. R. Peres-Neto. 2017. A quantitative framework to estimate the relative importance of environment, spatial variation and patch connectivity in driving community composition. Journal of Animal Ecology 86:316–326.
Mouquet, N,. and M. Loreau. 2002. Coexistence in metacommunities: the regional similarity hypothesis. American Naturalist 159:420–426.
Nathans, L.L., F. L. Oswald, and K. Nimon. 2012. Interpreting multiple linear regression: a guidebook of variable importance. Practical Assessment, Research & Evaluation 17:1-19
Nenzén, H. K., and M. B. Araújo. 2011. Choice of threshold alters projections of species range shifts under climate change. Ecological Modelling 222:3346-3354.
Olden, J. D., D. A. Jackson, and P. R. Peres-Neto. 2002. Predictive models of fish species distributions: a note on proper validation and chance predictions. Transactions of the American Fisheries Society 131:329-336.
Orrock, J. L. 2020. Deterministic insights from stochastic interactions. Proceedings of the Natural Academy of Sciences 117:6965–6967.
Ortiz-Yusty, C. E., V. Páez, and F. A. Zapta. 2013. Temperature and precipitation as predictors of species richness in northern Andean amphibians from Columbia. Caldasia 35:65-80.
Osborne, P. E., G. M. Foody, and S. Suárez-Seoane. 2007. Non-stationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions 13:313–323.
Palmer, M. W., and P. M. Dixon. 1990. Small-scale environmental heterogeneity and the analysis of species distributions along gradients. Journal of Vegetation Science 1:57-65.
Pearson, R. G. and T. P. Dawson. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography 12:361-371.
Pebesma, E., 2018. Simple features for R: standardized support for spatial vector data. The R Journal 10: 439-446.
Pebesma, E., and R. Bivand. 2005. Classes and methods for spatial data in R. R News 5:9-13.
Peres-Neto, P. R., and P. Legendre. 2010. Estimating and controlling for spatial structure in the study of ecological communities. Global Ecology and Biogeography 19:174–184.
Peres-Neto, P. R., M. A. Leibold, and S. Dray. 2012. Assessing the effects of spatial contingency and environmental filtering on metacommunity phylogenetics. Ecology 93:S14–S30.
Perry, J. N., A. M. Leibhold, M. S. Rosenberg, J. Dungan, M. Miriti, A. Jakomulska, and S. Citron-Pousty. 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography 25:578–600.
Peterson, A. T., J. Soberón, R. G. Pearson, R. P. Anderson, E. Martínez-Meyer, and M. Nakamura. 2011. Ecological niches and geographic distributions (MPB-49). Princeton, New Jersey and Oxford, United Kingdom: Princeton University Press.
Pincebourde, S., C. C. Murdock, M. Vickers, and M. W. Sears. 2016. Fine-scale microclimatic variation can shape the responses of organisms to global change in both natural and urban Environments. Integrative and Comparative Biology 56:45–61.
Presley, S. J., C. L. Higgins, and M. R. Willig. 2010. A comprehensive framework for the evaluation of metacommunity structure. Oikos 119:908–917.
Provete, D. B., T. Gonçalves-Souza, M. V. Garey, I. A. Martins, and D de C. Rossa-Feres. 2014. Broad-scale spatial patterns of canopy cover and pond morphology affect the structure of a Neotropical amphibian metacommunity. Hydrobiologia 734:69–79.
Qian, H. 2010. Environment-richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales. Ecological Research 25:629-637.
Qian, H. X. Wang, S. Wang, and Y. Li. 2007. Environmental determinants of amphibian and reptile species richness in China. Ecography 30: 471-482.
R Core Team. 2021. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL:https://www.R-project.org/.
Record, S., M. C. Fitzpatrick, A. O. Finley, S. Veloz, and A. M. Ellison. 2013. Should species distribution models account for spatial autocorrelation? A test of model projections across eight millennia of climate change. Global Ecology and Biogeography 22:760–771.
Ricklefs, R. E. 2004. A comprehensive framework for global patterns in biodiversity. Ecology Letters 7:1–15.
Rodríguez, M.Á., J.A., Belmontes, and B. A., Hawkins. 2005. Energy, water and large-scale patterns of reptile and amphibian species richness in Europe. Acta Oecologica 28:65-70.
Rojas-Ahumada, D. P., V. L. Landeiro, and M. Menin. 2012. Role of environmental and spatial processes in structuring anuran communities across a tropical rain forest. Austral Ecology 37:865–873.
Sarkar, D. 2008. Lattice multivariate data visualization with R. New York, USA: Springer.
Shen, G., F. He, R. Waagepetersen, I-F. Sun, Z. Hao, Z-S. Chen, and M. Yu. 2013. Quantifying effects of habitat heterogeneity and other clustering processes on spatial distributions of tree species. Ecological Society of America 94: 2436-2443.
Silva, R. A., I. A. Martins, and D de C. Rossa-Feres. 2011. Environmental heterogeneity: anuran diversity in homogeneous environments. Zoologia 28:610–618.
Singh, V., and M. K. Goyal. 2016. Changes in climate extremes by the use of CMIP5 coupled climate models over eastern Himalayas. Environmental Earth Sciences 75:839.
Smith, A., and D. M. Green. 2005. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography 28:110-128.
Smulders, M., T. A. Nelson, D. E. Jelinski, and S. E. Nielsen, and G. B. Stenhouse. 2010. A spatially explicit method for evaluating accuracy of species distribution models. Diversity and Distributions 16:996–1008.
Stein, A., K. Gerstner, and H. Kreft. 2014. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecology Letters 17:866–880.
Titley, M. A., J. L. Snaddon, and E. C. Turner. 2017. Scientific research on animal biodiversity is systematically biased towards vertebrates and temperate regions. PLoS one 12: e0189577.
Tordoni, E., V. Casolo, G. Barcaro, F. Martini, A. Rossi, and F. Boscutti. 2020. Climate and landscape heterogeneity drive spatial pattern of endemic plant diversity within local hotspots in South-Eastern Alps. Perspectives in Plant Ecology, Evolution and Systematics 43:125512.
Vellend, M. 2010. Conceptual synthesis in community ecology. The Quarterly Review of Biology 85:183–206.
Warren, D. L., A. Dornburg, K. Zapfe, and T. L. Iglesias. 2021. The effects of climate change on Australia’s only endemic Pokémon: Measuring bias in species distribution models. Methods in Ecology and Evolution 12:985–995.
Wickham, H. 2016. ggplot2: elegant graphics for data analysis. New York, USA: Springer-Verlag.
Wind, E. 1999. Effects of habitat fragmentation on amphibians: what do we know and where do we go from here? Proceedings on the Biology and Management of Species and Habitats at Risk 2:885-894.
Yuan, Y., M. Cave, and C. Zhang. 2018. Using Local Moran’s I to identify contamination hotspots of rare earth elements in urban soils of London. Applied Geochemistry 88:167–178.
Zhang, C., L. Luo, W. Xu, and V. Ledwith. 2008. Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of The Total Environment 398:212–221.
Zhang, L., J. H. Gove, and L. S. Heath. 2005. Spatial residual analysis of six modeling techniques. Ecological Modelling 186:154–177.
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