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Ecological niche models in phylogeographic studies: applications, advances and precautions
dc.contributor.authorAlvarado‐serrano, Diego F.en_US
dc.contributor.authorKnowles, L. Laceyen_US
dc.date.accessioned2014-03-05T18:18:49Z
dc.date.available2015-04-16T14:24:20Zen_US
dc.date.issued2014-03en_US
dc.identifier.citationAlvarado‐serrano, Diego F. ; Knowles, L. Lacey (2014). "Ecological niche models in phylogeographic studies: applications, advances and precautions." Molecular Ecology Resources 14(2): 233-248.en_US
dc.identifier.issn1755-098Xen_US
dc.identifier.issn1755-0998en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/106061
dc.description.abstractThe increased availability of spatial data and methodological developments in species distribution modelling has lead to concurrent advances in phylogeography, broadening the scope of questions studied, as well as providing unprecedented insights. Given the species‐specific nature of the information provided by ecological niche models ( ENM s), whether it is on the environmental tolerances of species or their estimated distribution, today or in the past, it is perhaps not surprising that ENM s have rapidly become a common tool in phylogeographic analysis. Such information is essential to phylogeographic tests that provide important biological insights. Here, we provide an overview of the different applications of ENM s in phylogeographic studies, detailing specific studies and highlighting general limitations and challenges with each application. Given that the full potential of integrating ENM s into phylogeographic cannot be realized unless the ENM s themselves are carefully applied, we provide a summary of best practices with using ENM s. Lastly, we describe some recent advances in how quantitative information from ENM s can be integrated into genetic analyses, illustrating their potential use (and key concerns with such implementations), as well as promising areas for future development.en_US
dc.publisherUniversity of Michigan Pressen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherPhylogeographyen_US
dc.subject.otherCoalescent Modellingen_US
dc.subject.otherEcological Niche Modelsen_US
dc.titleEcological niche models in phylogeographic studies: applications, advances and precautionsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelEcology and Evolutionary Biologyen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/106061/1/men12184-sup-0001-FigS1.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/106061/2/men12184.pdf
dc.identifier.doi10.1111/1755-0998.12184en_US
dc.identifier.sourceMolecular Ecology Resourcesen_US
dc.identifier.citedreferenceRay N, Currat M, Foll M, Excoffier L ( 2010 ) SPLATCHE2: a spatially explicit simulation framework for complex demography, genetic admixture and recombination. Bioinformatics, 26, 2993 – 2994.en_US
dc.identifier.citedreferenceThuiller W, Brotons L, Araujo MB, Lavorel S ( 2004 ) Effects of restricting environmental range of data to project current and future species distributions. Ecography, 27, 165 – 172.en_US
dc.identifier.citedreferenceThuiller W, Lafourcade B, Engler R, Araujo MB ( 2009 ) BIOMOD ‐ a platform for ensemble forecasting of species distributions. Ecography, 32, 369 – 373.en_US
dc.identifier.citedreferenceVan Niel KP, Laffan SW, Lees BG ( 2004 ) Effect of error in the DEM on environmental variables for predictive vegetation modelling. Journal of Vegetation Science, 15, 747 – 756.en_US
dc.identifier.citedreferenceWagner HH, Fortin MJ ( 2013 ) A conceptual framework for the spatial analysis of landscape genetic data. Conservation Genetics, 14, 253 – 261.en_US
dc.identifier.citedreferenceWalker PA, Cocks KD ( 1991 ) HABITAT: a procedure for modeling a disjoint environmental envelope for a plant or animal species. Global Ecology and Biogeography Letters, 1, 108 – 118.en_US
dc.identifier.citedreferenceWarren DL ( 2012 ) In defense of ‘niche modeling’. Trends in Ecology & Evolution, 27, 497 – 500.en_US
dc.identifier.citedreferenceWarren DL, Seifert SN ( 2011 ) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21, 335 – 342.en_US
dc.identifier.citedreferenceWarren DL, Glor RE, Turelli M ( 2008 ) Environmental equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62, 2868 – 2883.en_US
dc.identifier.citedreferenceWarren DL, Glor RE, Turelli M ( 2010 ) ENMTools: a toolbox for comparative studies of environmental niche models. Ecography, 33, 607 – 611.en_US
dc.identifier.citedreferenceWegmann D, Excoffier L ( 2010 ) Bayesian inference of the demographic history of chimpanzees. Molecular Biology and Evolution, 27, 1425 – 1435.en_US
dc.identifier.citedreferenceWegmann D, Currat M, Excoffier L ( 2006 ) Molecular diversity after a range expansion in heterogeneous environments. Genetics, 174, 2009 – 2020.en_US
dc.identifier.citedreferenceWegmann D, Leuenberger C, Neuenschwander S, Excoffier L ( 2010 ) ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics, 11, 1 – 7.en_US
dc.identifier.citedreferenceWenger SJ, Olden JD ( 2012 ) Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution, 3, 260 – 267.en_US
dc.identifier.citedreferenceWerneck FP, Nogueira C, Colli GR, Sites JW, Costa GC ( 2012 ) Climatic stability in the Brazilian Cerrado: implications for biogeographical connections of South American savannas, species richness and conservation in a biodiversity hotspot. Journal of Biogeography, 39, 1695 – 1706.en_US
dc.identifier.citedreferenceWieczorek J, Guo QG, Hijmans RJ ( 2004 ) The point‐radius method for georeferencing locality descriptions and calculating associated uncertainty. International Journal of Geographical Information Science, 18, 745 – 767.en_US
dc.identifier.citedreferenceWilliams JW, Jackson ST ( 2007 ) Novel climates, no‐analog communities, and ecological surprises. Frontiers in Ecology and the Environment, 5, 475 – 482.en_US
dc.identifier.citedreferenceWisz MS, Pottier J, Kissling WD et al. ( 2013 ) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15 – 30.en_US
dc.identifier.citedreferenceWooten JA, Camp CD, Rissler LJ ( 2010 ) Genetic diversity in a narrowly endemic, recently described dusky salamander, Desmognathus folkertsi, from the southern Appalachian Mountains. Conservation Genetics, 11, 835 – 854.en_US
dc.identifier.citedreferenceZellmer AJ, Knowles LL ( 2009 ) Disentangling the effects of historic vs. contemporary landscape structure on population genetic divergence. Molecular Ecology, 18, 3593 – 3602.en_US
dc.identifier.citedreferenceAllal F, Sanou H, Millet L et al. ( 2011 ) Past climate changes explain the phylogeography of Vitellaria paradoxa over Africa. Heredity, 107, 174 – 186.en_US
dc.identifier.citedreferenceAnderson RP ( 2013 ) A framework for using niche models to estimate impacts of climate change on species distributions. Annals of the New York Academy of Sciences, 1297, 8 – 28.en_US
dc.identifier.citedreferenceAnderson RP, Gonzalez I ( 2011 ) Species‐specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecological Modelling, 222, 2796 – 2811.en_US
dc.identifier.citedreferenceAnderson RP, Raza A ( 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.en_US
dc.identifier.citedreferenceArenas M, Ray N, Currat M, Excoffier L ( 2012 ) Consequences of range contractions and range shifts on molecular diversity. Molecular Biology and Evolution, 29, 207 – 218.en_US
dc.identifier.citedreferenceAustin MP ( 2002 ) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101 – 118.en_US
dc.identifier.citedreferenceAustin MP, Van Niel KP ( 2011 ) Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography, 38, 1 – 8.en_US
dc.identifier.citedreferenceBanta JA, Ehrenreich IM, Gerard S et al. ( 2012 ) Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecology Letters, 15, 769 – 777.en_US
dc.identifier.citedreferenceBarbet‐Massin M, Jiguet F, Albert CH, Thuiller W ( 2012 ) Selecting pseudo‐absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3, 327 – 338.en_US
dc.identifier.citedreferenceBarbujani G, Oden NL, Sokal RR ( 1989 ) Detecting regions of abrupt change in maps of biological variables. Systematic Zoology, 38, 376 – 389.en_US
dc.identifier.citedreferenceBarnes BV, Wagner WH ( 2004 ) Michigan Trees: A Guide to the Trees of the Great Lakes Region. University of Michigan Press, Ann Arbor, Michigan.en_US
dc.identifier.citedreferenceBarry S, Elith J ( 2006 ) Error and uncertainty in habitat models. Journal of Applied Ecology, 43, 413 – 423.en_US
dc.identifier.citedreferenceBarve N, Barve V, Jimenez‐Valverde A et al. ( 2011 ) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222, 1810 – 1819.en_US
dc.identifier.citedreferenceBeatty GE, Provan J ( 2010 ) Refugial persistence and postglacial recolonization of North America by the cold‐tolerant herbaceous plant Orthilia secunda. Molecular Ecology, 19, 5009 – 5021.en_US
dc.identifier.citedreferenceBeaumont MA ( 2002 ) Approximate Bayesian Computation in Evolution and Ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379 – 406.en_US
dc.identifier.citedreferenceBeaumont MA, Nielsen R, Robert C et al. ( 2010 ) In defence of model‐based inference in phylogeography. Molecular Ecology, 19, 436 – 446.en_US
dc.identifier.citedreferenceBertorelle G, Benazzo A, Mona S ( 2010 ) ABC as a flexible framework to estimate demography over space and time: some cons, many pros. Molecular Ecology, 19, 2609 – 2625.en_US
dc.identifier.citedreferenceBeukema W, De Pous P, Donaire D et al. ( 2010 ) Biogeography and contemporary climatic differentiation among Moroccan Salamandra algira. Biological Journal of the Linnean Society, 101, 626 – 641.en_US
dc.identifier.citedreferenceBolstad P ( 2008 ) GIS fundamentals: A First Text on Geographic Information Systems. Eider Press, White Bear Lake, Minnesota.en_US
dc.identifier.citedreferenceter Braak CJF ( 1986 ) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167 – 1179.en_US
dc.identifier.citedreferenceBraconnot P, Otto‐Bliesner B, Harrison S et al. ( 2007 ) Results of PMIP2 coupled simulations of the Mid‐Holocene and Last Glacial Maximum – Part 1: experiments and large‐scale features. Climate of the Past, 3, 261 – 277.en_US
dc.identifier.citedreferenceBrown JL, Knowles LL ( 2012 ) Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika. Molecular Ecology, 21, 3757 – 3775.en_US
dc.identifier.citedreferenceBuckley TR, Marske KA, Attanayake D ( 2009 ) Identifying glacial refugia in a geographic parthenogen using palaeoclimate modelling and phylogeography: the New Zealand stick insect Argosarchus horridus (White). Molecular Ecology, 18, 4650 – 4663.en_US
dc.identifier.citedreferenceBusby JR ( 1991 ) A bioclimatic analysis and prediction system. In: Nature Conservation: Cost Effective Biological Surveys and Data Analysis (eds Margules CR & Austin MP ), pp. 64 – 68. CSIRO, Sydney, New South Wales.en_US
dc.identifier.citedreferenceCarnaval AC, Hickerson MJ, Haddad CFB, Rodrigues MT, Moritz C ( 2009 ) Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science, 323, 785 – 789.en_US
dc.identifier.citedreferenceCarpenter G, Gillison AN, Winter J ( 1993 ) DOMAIN: a flexible modeling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation, 2, 667 – 680.en_US
dc.identifier.citedreferenceCarstens BC, Richards CL ( 2007 ) Integrating coalescent and ecological niche modeling in comparative phylogeography. Evolution, 61, 1439 – 1454.en_US
dc.identifier.citedreferenceChan YL, Hadly EA ( 2011 ) Genetic variation over 10 000 years in Ctenomys: comparative phylochronology provides a temporal perspective on rarity, environmental change and demography. Molecular Ecology, 20, 4592 – 4605.en_US
dc.identifier.citedreferenceChan LM, Brown JL, Yoder AD ( 2011 ) Integrating statistical genetic and geospatial methods brings new power to phylogeography. Molecular Phylogenetics and Evolution, 59, 523 – 537.en_US
dc.identifier.citedreferenceChapman DS, Purse BV ( 2011 ) Community versus single‐species distribution models for British plants. Journal of Biogeography, 38, 1524 – 1535.en_US
dc.identifier.citedreferenceChen C, Durand E, Forbes F, Francois O ( 2007 ) Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Molecular Ecology Notes, 7, 747 – 756.en_US
dc.identifier.citedreferenceCorander J, Marttinen P ( 2006 ) Bayesian identification of admixture events using multilocus molecular markers. Molecular Ecology, 15, 2833 – 2843.en_US
dc.identifier.citedreferenceCordellier M, Pfenninger M ( 2008 ) Climate‐driven range dynamics of the freshwater limpet, Ancylus fluviatilis (Pulmonata, Basommatophora). Journal of Biogeography, 35, 1580 – 1592.en_US
dc.identifier.citedreferenceCrandall ED, Treml EA, Barber PH ( 2012 ) Coalescent and biophysical models of stepping‐stone gene flow in neritid snails. Molecular Ecology, 21, 5579 – 5598.en_US
dc.identifier.citedreferenceCrego RD, Nielsen CK, Didier KA ( 2013 ) Climate change and conservation implications for wet meadows in dry Patagonia. Environmental Conservation, CJO2013. doi: 10.1017/S037689291300026X.en_US
dc.identifier.citedreferenceCsillery K, Blum MGB, Gaggiotti OE, Francois O ( 2010 ) Approximate Bayesian Computation (ABC) in practice. Trends in Ecology & Evolution, 25, 410 – 418.en_US
dc.identifier.citedreferenceD'Amen M, Zimmermann NE, Pearman PB ( 2013 ) Conservation of phylogeographic lineages under climate change. Global Ecology and Biogeography, 22, 93 – 104.en_US
dc.identifier.citedreferenceDavis AJ, Jenkinson LS, Lawton JH, Shorrocks B, Wood S ( 1998 ) Making mistakes when predicting shifts in species range in response to global warming. Nature, 391, 783 – 786.en_US
dc.identifier.citedreferenceDe'ath G, Fabricius KE ( 2000 ) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178 – 3192.en_US
dc.identifier.citedreferenceDormann CF, Schymanski SJ, Cabral J et al. ( 2012 ) Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography, 39, 2119 – 2131.en_US
dc.identifier.citedreferenceDyer RJ, Nason JD, Garrick RC ( 2010 ) Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Molecular Ecology, 19, 3746 – 3759.en_US
dc.identifier.citedreferenceEdwards DL, Keogh JS, Knowles LL ( 2012 ) Effects of vicariant barriers, habitat stability, population isolation and environmental features on species divergence in the south‐western Australian coastal reptile community. Molecular Ecology, 21, 3809 – 3822.en_US
dc.identifier.citedreferenceElith J, Graham CH ( 2009 ) Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32, 66 – 77.en_US
dc.identifier.citedreferenceElith J, Leathwick J ( 2007 ) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions, 13, 265 – 275.en_US
dc.identifier.citedreferenceElith J, Leathwick JR ( 2009 ) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology Evolution and Systematics, 40, 677 – 697.en_US
dc.identifier.citedreferenceElith J, Graham CH, Anderson RP et al. ( 2006 ) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129 – 151.en_US
dc.identifier.citedreferenceElith J, Leathwick JR, Hastie T ( 2008 ) A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802 – 813.en_US
dc.identifier.citedreferenceElith J, Kearney M, Phillips S ( 2010 ) The art of modelling range‐shifting species. Methods in Ecology and Evolution, 1, 330 – 342.en_US
dc.identifier.citedreferenceElith J, Phillips SJ, Hastie T et al. ( 2011 ) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43 – 57.en_US
dc.identifier.citedreferenceEstoup A, Lombaert E, Marin JM et al. ( 2012 ) Estimation of demo‐genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics. Molecular Ecology Resources, 12, 846 – 855.en_US
dc.identifier.citedreferenceExcoffier L, Foll M, Petit RJ ( 2009 ) Genetic consequences of range expansions. Annual Review of Ecology Evolution and Systematics, 40, 481 – 501.en_US
dc.identifier.citedreferenceFerrier S, Guisan A ( 2006 ) Spatial modelling of biodiversity at the community level. Journal of Applied Ecology, 43, 393 – 404.en_US
dc.identifier.citedreferenceFielding AH, Bell JF ( 1997 ) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38 – 49.en_US
dc.identifier.citedreferenceFitze PS, Gonzalez‐Jimena V, San‐Jose LM et al. ( 2011 ) Integrative analyses of speciation and divergence in Psammodromus hispanicus (Squamata: Lacertidae). Bmc Evolutionary Biology, 11, 1 – 21.en_US
dc.identifier.citedreferenceFlorio AM, Ingram CM, Rakotondravony HA, Louis EE, Raxworthy CJ ( 2012 ) Detecting cryptic speciation in the widespread and morphologically conservative carpet chameleon (Furcifer lateralis) of Madagascar. Journal of Evolutionary Biology, 25, 1399 – 1414.en_US
dc.identifier.citedreferenceFortin R, Dumont P, Guenette S ( 1996 ) Determinants of growth and body condition of lake sturgeon (Acipenser fulvescens). Canadian Journal of Fisheries and Aquatic Sciences, 53, 1150 – 1156.en_US
dc.identifier.citedreferenceFournier‐Level A, Korte A, Cooper MD et al. ( 2011 ) A Map of Local Adaptation in Arabidopsis thaliana. Science, 334, 86 – 89.en_US
dc.identifier.citedreferenceFranklin J, Miller JA ( 2009 ) Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press, Cambridge and New York.en_US
dc.identifier.citedreferenceFriedl MA, McIver DK, Hodges JCF et al. ( 2002 ) Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83, 287 – 302.en_US
dc.identifier.citedreferenceFuchs J, Parra JL, Goodman SM et al. ( 2013 ) Extending ecological niche models to the past 120000 years corroborates the lack of strong phylogeographic structure in the Crested Drongo (Dicrurus forficatus forficatus) on Madagascar. Biological Journal of the Linnean Society, 108, 658 – 676.en_US
dc.identifier.citedreferenceGalbreath KE, Hafner DJ, Zamudio KR ( 2009 ) When cold is better: climate‐driven elevation shifts yield complex patterns of diversification and demography in an alpine specialist (American pika, Ochotona princeps ). Evolution, 63, 2848 – 2863.en_US
dc.identifier.citedreferenceGonzalez SG, Soto‐Centeno JA, Reed DL ( 2011 ) Population distribution models: species distributions are better modeled using biologically relevant data partitions. BMC Ecology, 11, 1 – 10.en_US
dc.identifier.citedreferenceGoovaerts P ( 1998 ) Geostatistical tools for characterizing the spatial variability of microbiological and physico‐chemical soil properties. Biology and Fertility of Soils, 27, 315 – 334.en_US
dc.identifier.citedreferenceGraham CH, Ron SR, Santos JC, Schneider CJ, Moritz C ( 2004 ) Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution, 58, 1781 – 1793.en_US
dc.identifier.citedreferenceGraham CH, VanDerWal J, Phillips SJ, Moritz C, Williams SE ( 2010 ) Dynamic refugia and species persistence: tracking spatial shifts in habitat through time. Ecography, 33, 1062 – 1069.en_US
dc.identifier.citedreferenceGray LK, Hamann A ( 2013 ) Tracking suitable habitat for tree populations under climate change in western North America. Climatic Change, 117, 289 – 303.en_US
dc.identifier.citedreferenceGreenstein BJ, Pandolfi JM ( 2008 ) Escaping the heat: range shifts of reef coral taxa in coastal Western Australia. Global Change Biology, 14, 513 – 528.en_US
dc.identifier.citedreferenceGuillemaud T, Beaumont MA, Ciosi M, Cornuet JM, Estoup A ( 2010 ) Inferring introduction routes of invasive species using approximate Bayesian computation on microsatellite data. Heredity, 104, 88 – 99.en_US
dc.identifier.citedreferenceGuisan A, Thuiller W ( 2005 ) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993 – 1009.en_US
dc.identifier.citedreferenceGuisan A, Zimmermann NE ( 2000 ) Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147 – 186.en_US
dc.identifier.citedreferenceGuisan A, Zimmermann NE, Elith J et al. ( 2007 ) What matters for predicting the occurrences of trees: techniques, data, or species' characteristics? Ecological Monographs, 77, 615 – 630.en_US
dc.identifier.citedreferenceGuo QH, Kelly M, Graham CH ( 2005 ) Support vector machines for predicting distribution of sudden oak death in California. Ecological Modelling, 182, 75 – 90.en_US
dc.identifier.citedreferenceHaegeman B, Etienne RS ( 2010 ) Entropy maximization and the spatial distribution of species. The American Naturalist, 175, E74 – E90.en_US
dc.identifier.citedreferenceHe Q, Edwards DL, Knowles LL ( 2013 ) Integrative test of how environments from the past to the present shape the genetic structure across landscapes. Evolution, doi: 10.1111/evo.12159.en_US
dc.identifier.citedreferenceHernandez PA, Graham CH, Master LL, Albert DL ( 2006 ) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29, 773 – 785.en_US
dc.identifier.citedreferenceHewitt G ( 2000 ) The genetic legacy of the Quaternary ice ages. Nature, 405, 907 – 913.en_US
dc.identifier.citedreferenceHickerson MJ, Carstens BC, Cavender‐Bares J et al. ( 2010 ) Phylogeography's past, present, and future: 10 years after Avise, 2000. Molecular Phylogenetics and Evolution, 54, 291 – 301.en_US
dc.identifier.citedreferenceHickerson MJ, Stone G, Lohse K et al. ( 2013 ) Recommendations for using MsBayes to incorporate uncertainty in selecting an ABC model prior: a response to Oaks et al.. Evolution, doi: 10.1111/evo.12241.en_US
dc.identifier.citedreferenceHijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A ( 2005 ) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965 – 1978.en_US
dc.identifier.citedreferenceHilbert DW, Ostendorf B ( 2001 ) The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecological Modelling, 146, 311 – 327.en_US
dc.identifier.citedreferenceHirzel AH, Hausser J, Chessel D, Perrin N ( 2002 ) Ecological‐niche factor analysis: how to compute habitat‐suitability maps without absence data? Ecology, 83, 2027 – 2036.en_US
dc.identifier.citedreferenceHirzel AH, Le Lay G, Helfer V, Randin C, Guisan A ( 2006 ) Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199, 142 – 152.en_US
dc.identifier.citedreferenceHornsby AD, Matocq MD ( 2012 ) Differential regional response of the bushy‐tailed woodrat (Neotoma cinerea) to late Quaternary climate change. Journal of Biogeography, 39, 289 – 305.en_US
dc.identifier.citedreferenceHortal J, Jimenez‐Valverde A, Gomez JF, Lobo JM, Baselga A ( 2008 ) Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos, 117, 847 – 858.en_US
dc.identifier.citedreferenceIverson LR, Schwartz MW, Prasad AM ( 2004 ) How fast and far might tree species migrate in the eastern United States due to climate change? Global Ecology and Biogeography, 13, 209 – 219.en_US
dc.identifier.citedreferenceJackson ST, Betancourt JL, Booth RK, Gray ST ( 2009 ) Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions. Proceedings of the National Academy of Sciences of the United States of America, 106, 19685 – 19692.en_US
dc.identifier.citedreferenceJacquez GM ( 1995 ) The map comparison problem: tests for the overlap of geographic boundaries. Statistics in Medicine, 14, 2343 – 2361.en_US
dc.identifier.citedreferenceJezkova T, Jaeger JR, Marshall ZL, Riddle BR ( 2009 ) Pleistocene impacts on the phylogeography of the desert pocket mouse ( Chaetodipus penicillatus ). Journal of Mammalogy, 90, 306 – 320.en_US
dc.identifier.citedreferenceJimenez‐Valverde A ( 2012 ) Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography, 21, 498 – 507.en_US
dc.identifier.citedreferenceKalkvik HM, Stout IJ, Doonan TJ, Parkinson CL ( 2012 ) Investigating niche and lineage diversification in widely distributed taxa: phylogeography and ecological niche modeling of the Peromyscus maniculatus species group. Ecography, 35, 54 – 64.en_US
dc.identifier.citedreferenceKearney M ( 2006 ) Habitat, environment and niche: what are we modelling? Oikos, 115, 186 – 191.en_US
dc.identifier.citedreferenceKearney M, Porter W ( 2009 ) Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology Letters, 12, 334 – 350.en_US
dc.identifier.citedreferenceKearney M, Porter WP, Williams C, Ritchie S, Hoffmann AA ( 2009 ) Integrating biophysical models and evolutionary theory to predict climatic impacts on species’ ranges: the dengue mosquito Aedes aegypti in Australia. Functional Ecology, 23, 528 – 538.en_US
dc.identifier.citedreferenceKitchener AC, Rees EE ( 2009 ) Modelling the dynamic biogeography of the wildcat: implications for taxonomy and conservation. Journal of Zoology, 279, 144 – 155.en_US
dc.identifier.citedreferenceKnowles LL ( 2000 ) Tests of Pleistocene speciation in montane grasshoppers (genus Melanoplus) from the sky islands of western North America. Evolution, 54, 1337 – 1348.en_US
dc.identifier.citedreferenceKnowles LL ( 2009 ) Statistical phylogeography. Annual Review of Ecology Evolution and Systematics, 40, 593 – 612.en_US
dc.identifier.citedreferenceKnowles LL, Alvarado‐Serrano DF ( 2010 ) Exploring the population genetic consequences of the colonization process with spatio‐temporally explicit models: insights from coupled ecological, demographic and genetic models in montane grasshoppers. Molecular Ecology, 19, 3727 – 3745.en_US
dc.identifier.citedreferenceKnowles LL, Carstens BC, Keat ML ( 2007 ) Coupling genetic and ecological‐niche models to examine how past population distributions contribute to divergence. Current Biology, 17, 940 – 946.en_US
dc.identifier.citedreferenceKozak KH, Wiens JJ ( 2010 ) Niche conservatism drives elevational diversity patterns in Appalachian salamanders. The American Naturalist, 176, 40 – 54.en_US
dc.identifier.citedreferenceKriticos DJ, Leriche A ( 2010 ) The effects of climate data precision on fitting and projecting species niche models. Ecography, 33, 115 – 127.en_US
dc.identifier.citedreferenceKriticos DJ, Webber BL, Leriche A et al. ( 2012 ) CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution, 3, 53 – 64.en_US
dc.identifier.citedreferenceLatimer AM ( 2007 ) Geography and resource limitation complicate metabolism‐based predictions of species richness. Ecology, 88, 1895 – 1898.en_US
dc.identifier.citedreferenceLawler JJ, Ruesch AS, Olden JD, McRae BH ( 2013 ) Projected climate‐driven faunal movement routes. Ecology Letters, 16, 1014 – 1022.en_US
dc.identifier.citedreferenceLawson LP ( 2010 ) The discordance of diversification: evolution in the tropical‐montane frogs of the Eastern Arc Mountains of Tanzania. Molecular Ecology, 19, 4046 – 4060.en_US
dc.identifier.citedreferenceLegendre P, Fortin MJ ( 2010 ) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Molecular Ecology Resources, 10, 831 – 844.en_US
dc.identifier.citedreferenceLehmann A, Overton JM, Leathwick JR ( 2002 ) GRASP: generalized regression analysis and spatial prediction. Ecological Modelling, 157, 189 – 207.en_US
dc.identifier.citedreferenceLehrian S, Balint M, Haase P, Pauls SU ( 2010 ) Genetic population structure of an autumn‐emerging caddisfly with inherently low dispersal capacity and insights into its phylogeography. Journal of the North American Benthological Society, 29, 1100 – 1118.en_US
dc.identifier.citedreferenceLeuenberger C, Wegmann D ( 2010 ) Bayesian Computation and Model Selection Without Likelihoods. Genetics, 184, 243 – 252.en_US
dc.identifier.citedreferenceLiu CR, Berry PM, Dawson TP, Pearson RG ( 2005 ) Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28, 385 – 393.en_US
dc.identifier.citedreferenceLiu C, White M, Newell G ( 2009 ) Measuring the accuracy of species distribution models: a review. In: 18th World IMACS/MODSIM Congress, Cairns, Australia.en_US
dc.identifier.citedreferenceLobo JM, Jimenez‐Valverde A, Real R ( 2008 ) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 145 – 151.en_US
dc.identifier.citedreferenceLombaert E, Guillemaud T, Thomas CE et al. ( 2011 ) Inferring the origin of populations introduced from a genetically structured native range by approximate Bayesian computation: case study of the invasive ladybird Harmonia axyridis. Molecular Ecology, 20, 4654 – 4670.en_US
dc.identifier.citedreferenceLozier JD, Aniello P, Hickerson MJ ( 2009 ) Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. Journal of Biogeography, 36, 1623 – 1627.en_US
dc.identifier.citedreferenceMaggini R, Lehmann A, Zimmermann NE, Guisan A ( 2006 ) Improving generalized regression analysis for the spatial prediction of forest communities. Journal of Biogeography, 33, 1729 – 1749.en_US
dc.identifier.citedreferenceMantel N ( 1967 ) The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209 – 220.en_US
dc.identifier.citedreferenceMarske KA, Leschen RAB, Buckley TR ( 2012 ) Concerted evolution versus independent evolution and the search for multiple refugia: comparative phylogeography of four forest beetles. Evolution, 66, 1862 – 1877.en_US
dc.identifier.citedreferenceMcCormack JE, Zellmer AJ, Knowles LL ( 2010 ) Does niche divergence accompany allopatric divergence in Aphelcoma jays as predicted under ecological speciation? Insights from tests with niche models. Evolution, 64, 1231 – 1244.en_US
dc.identifier.citedreferenceMcRae BH ( 2006 ) Isolation by resistance. Evolution, 60, 1551 – 1561.en_US
dc.identifier.citedreferenceMeier ES, Kienast F, Pearman PB et al. ( 2010 ) Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecography, 33, 1038 – 1048.en_US
dc.identifier.citedreferenceMonmonier MS ( 1973 ) Maximum‐difference barriers: an alternative numerical regionalization method*. Geographical Analysis, 5, 245 – 261.en_US
dc.identifier.citedreferenceMorgan K, O'Loughlin SM, Chen B et al. ( 2011 ) Comparative phylogeography reveals a shared impact of pleistocene environmental change in shaping genetic diversity within nine Anopheles mosquito species across the Indo‐Burma biodiversity hotspot. Molecular Ecology, 20, 4533 – 4549.en_US
dc.identifier.citedreferenceMoussalli A, Moritz C, Williams SE, Carnaval AC ( 2009 ) Variable responses of skinks to a common history of rainforest fluctuation: concordance between phylogeography and palaeo‐distribution models. Molecular Ecology, 18, 483 – 499.en_US
dc.identifier.citedreferenceNeuenschwander S, Largiader CR, Ray N et al. ( 2008 ) Colonization history of the Swiss Rhine basin by the bullhead ( Cottus gobio ): inference under a Bayesian spatially explicit framework. Molecular Ecology, 17, 757 – 772.en_US
dc.identifier.citedreferenceNielsen R, Beaumont MA ( 2009 ) Statistical inferences in phylogeography. Molecular Ecology, 18, 1034 – 1047.en_US
dc.identifier.citedreferenceOrtego J, Riordan EC, Gugger PF, Sork VL ( 2012 ) Influence of environmental heterogeneity on genetic diversity and structure in an endemic southern Californian oak. Molecular Ecology, 21, 3210 – 3223.en_US
dc.identifier.citedreferencePatterson BD, Ceballos G, Sechrest W et al. ( 2007 ) Digital Distribution Maps of the Mammals of the Western Hemisphere, version 3.0. NatureServe, Arlington, Virginia.en_US
dc.identifier.citedreferencePearson R ( 2010 ) Species’ distribution modeling for conservation educators and practitioners. Lessons in Conservation, 3, 56 – 89.en_US
dc.identifier.citedreferencePearson RG, Thuiller W, Araujo MB et al. ( 2006 ) Model‐based uncertainty in species range prediction. Journal of Biogeography, 33, 1704 – 1711.en_US
dc.identifier.citedreferencePearson RG, Raxworthy CJ, Nakamura M, Peterson AT ( 2007 ) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102 – 117.en_US
dc.identifier.citedreferencePeterson AT ( 2011 ) Ecological niche conservatism: a time‐structured review of evidence. Journal of Biogeography, 38, 817 – 827.en_US
dc.identifier.citedreferencePeterson AT, Soberon J, Sanchez‐Cordero V ( 1999 ) Conservatism of ecological niches in evolutionary time. Science, 285, 1265 – 1267.en_US
dc.identifier.citedreferencePeterson AT, Papes M, Eaton M ( 2007 ) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography, 30, 550 – 560.en_US
dc.identifier.citedreferencePeterson AT, Papes M, Soberon J ( 2008 ) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63 – 72.en_US
dc.identifier.citedreferencePeterson AT, Soberón J, Pearson RG et al. ( 2011 ) Ecological Niches and Geographic Distributions. Princeton University Press, Princeton, New Jersey.en_US
dc.identifier.citedreferencePhillips SJ, Anderson RP, Schapire RE ( 2006 ) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231 – 259.en_US
dc.identifier.citedreferencePhillips SJ, Dudik M, Elith J et al. ( 2009 ) Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecological Applications, 19, 181 – 197.en_US
dc.identifier.citedreferencePolly PD, Eronen JT ( 2011 ) Mammal associations in the Pleistocene of Britain: implications of ecological niche modelling and a method for reconstructing palaeoclimate. In: The Ancient Human Occupation of Britain (eds Ashton N, Lewis S & Stringer C ), pp. 257 – 282. Elsevier, Amsterdam.en_US
dc.identifier.citedreferenceRalston J, Kirchman JJ ( 2012 ) Continental‐scale genetic structure in a broeal forest migrant, the blackpoll warbler ( Setophaga striata ). The Auk, 129, 467 – 478.en_US
dc.identifier.citedreferenceRandin CF, Dirnbock T, Dullinger S et al. ( 2006 ) Are niche‐based species distribution models transferable in space? Journal of Biogeography, 33, 1689 – 1703.en_US
dc.identifier.citedreferenceRay N, Currat M, Berthier P, Excoffier L ( 2005 ) Recovering the geographic origin of early modern humans by realistic and spatially explicit simulations. Genome Research, 15, 1161 – 1167.en_US
dc.identifier.citedreferenceRee RH, Sanmartin I ( 2009 ) Prospects and challenges for parametric models in historical biogeographical inference. Journal of Biogeography, 36, 1211 – 1220.en_US
dc.identifier.citedreferenceRichards CL, Carstens BC, Knowles LL ( 2007 ) Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses. Journal of Biogeography, 34, 1833 – 1845.en_US
dc.identifier.citedreferenceRissler LJ, Apodaca JJ ( 2007 ) Adding more ecology into species delimitation: ecological niche models and phylogeography help define cryptic species in the black salamander (Aneides flavipunctatus). Systematic Biology, 56, 924 – 942.en_US
dc.identifier.citedreferenceRobert CP, Cornuet JM, Marin JM, Pillai NS ( 2011 ) Lack of confidence in approximate Bayesian computation model choice. Proceedings of the National Academy of Sciences of the United States of America, 108, 15112 – 15117.en_US
dc.identifier.citedreferenceRoberts DR, Hamann A ( 2012a ) Method selection for species distribution modelling: are temporally or spatially independent evaluations necessary? Ecography, 35, 792 – 802.en_US
dc.identifier.citedreferenceRoberts DR, Hamann A ( 2012b ) Predicting potential climate change impacts with bioclimate envelope models: a palaeoecological perspective. Global Ecology and Biogeography, 21, 121 – 133.en_US
dc.identifier.citedreferenceRodder D, Lotters S ( 2010 ) Explanative power of variables used in species distribution modelling: an issue of general model transferability or niche shift in the invasive Greenhouse frog (Eleutherodactylus planirostris). Naturwissenschaften, 97, 781 – 796.en_US
dc.identifier.citedreferenceRow JR, Blouin‐Demers G, Lougheed SC ( 2010 ) Habitat distribution influences dispersal and fine‐scale genetic population structure of eastern foxsnakes ( Mintonius gloydi ) across a fragmented landscape. Molecular Ecology, 19, 5157 – 5171.en_US
dc.identifier.citedreferenceRoyle JA, Chandler RB, Yackulic C, Nichols JD ( 2012 ) Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions. Methods in Ecology and Evolution, 3, 545 – 554.en_US
dc.identifier.citedreferenceScoble J, Lowe AJ ( 2010 ) A case for incorporating phylogeography and landscape genetics into species distribution modelling approaches to improve climate adaptation and conservation planning. Diversity and Distributions, 16, 343 – 353.en_US
dc.identifier.citedreferenceSegurado P, Araujo MB ( 2004 ) An evaluation of methods for modelling species distributions. Journal of Biogeography, 31, 1555 – 1568.en_US
dc.identifier.citedreferenceShafer ABA, Cullingham CI, Cote SD, Coltman DW ( 2010 ) Of glaciers and refugia: a decade of study sheds new light on the phylogeography of northwestern North America. Molecular Ecology, 19, 4589 – 4621.en_US
dc.identifier.citedreferenceSillero N ( 2011 ) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling, 222, 1343 – 1346.en_US
dc.identifier.citedreferenceSmith BT, Escalante P, Banos BEH et al. ( 2011 ) The role of historical and contemporary processes on phylogeographic structure and genetic diversity in the Northern Cardinal, Cardinalis cardinalis. BMC Evolutionary Biology, 11, 1 – 12.en_US
dc.identifier.citedreferenceSoberón J ( 2007 ) Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10, 1115 – 1123.en_US
dc.identifier.citedreferenceSoberón J, Nakamura M ( 2009 ) Niches and distributional areas: concepts, methods, and assumptions. Proceedings of the National Academy of Sciences of the United States of America, 106, 19644 – 19650.en_US
dc.identifier.citedreferenceSoberón J, Peterson AT ( 2005 ) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics, 2, 1 – 10.en_US
dc.identifier.citedreferenceSokal RR, Rohlf FJ ( 1981 ) Biometry: The Principles and Practice of Statistics in Biological Research. W.H. Freeman, San Francisco, California.en_US
dc.identifier.citedreferenceStewart JR ( 2009 ) The evolutionary consequence of the individualistic response to climate change. Journal of Evolutionary Biology, 22, 2363 – 2375.en_US
dc.identifier.citedreferenceStockman AK, Bond JE ( 2007 ) Delimiting cohesion species: extreme population structuring and the role of ecological interchangeability. Molecular Ecology, 16, 3374 – 3392.en_US
dc.identifier.citedreferenceStockwell D, Peters D ( 1999 ) The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13, 143 – 158.en_US
dc.identifier.citedreferenceStockwell DRB, Peterson AT ( 2002 ) Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148, 1 – 13.en_US
dc.identifier.citedreferenceSvenning JC, Flojgaard C, Marske KA, Nogues‐Bravo D, Normand S ( 2011 ) Applications of species distribution modeling to paleobiology. Quaternary Science Reviews, 30, 2930 – 2947.en_US
dc.identifier.citedreferenceSwenson NG ( 2006 ) GIS‐based niche models reveal unifying climatic mechanisms that maintain the location of avian hybrid zones in a North American suture zone. Journal of Evolutionary Biology, 19, 717 – 725.en_US
dc.identifier.citedreferenceSynes NW, Osborne PE ( 2011 ) Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change. Global Ecology and Biogeography, 20, 904 – 914.en_US
dc.identifier.citedreferenceTaubmann J, Theissinger K, Feldheim KA et al. ( 2011 ) Modelling range shifts and assessing genetic diversity distribution of the montane aquatic mayfly Ameletus inopinatus in Europe under climate change scenarios. Conservation Genetics, 12, 503 – 515.en_US
dc.identifier.citedreferenceTelenius A ( 2011 ) Biodiversity information goes public: GBIF at your service. Nordic Journal of Botany, 29, 378 – 381.en_US
dc.identifier.citedreferenceThornton K, Andolfatto P ( 2006 ) Approximate Bayesian inference reveals evidence for a recent, severe bottleneck in a Netherlands population of Drosophila melanogaster. Genetics, 172, 1607 – 1619.en_US
dc.identifier.citedreferenceThuiller W, Araujo MB, Lavorel S ( 2003 ) Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. Journal of Vegetation Science, 14, 669 – 680.en_US
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