1932

Abstract

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

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2009-12-01
2024-06-14
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Literature Cited

  1. Addicott JF, Aho JM, Antolin MF, Padilla DK, Richardson JS. et al. 1987. Ecological neighborhoods: scaling environmental patterns. Oikos 49:340–46 [Google Scholar]
  2. Allen TFH, Starr TB. 1982. Hierarchy: Perspectives for Ecological Complexity Chicago: Univ. Chicago Press [Google Scholar]
  3. Araújo MB, Guisan A. 2006. Five (or so) challenges for species distribution modelling. J. Biogeogr. 33:1677–88 [Google Scholar]
  4. Araújo MB, New M. 2007. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22:42–47 [Google Scholar]
  5. Araújo MB, Pearson RG, Thuiller W, Erhard M. 2005. Validation of species-climate impact models under climate change. Global Change Biol. 11:1504–13 [Google Scholar]
  6. Austin MP. 1985. Continuum concept, ordination methods and niche theory. Annu. Rev. Ecol. Syst. 16:39–61 [Google Scholar]
  7. Austin MP. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Model. 157:101–18 [Google Scholar]
  8. Austin MP. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol. Model. 200:1–19 [Google Scholar]
  9. Austin MP, Cunningham RB. 1981. Observational analysis of environmental gradients. Proc. Ecol. Soc. Aust. 11:109–19 [Google Scholar]
  10. Austin MP, Nicholls AO, Margules CR. 1990. Measurement of the realized qualitative niche: environmental niches of five eucalypt species. Ecol. Monogr. 60:161–77 [Google Scholar]
  11. Barry SC, Elith J. 2006. Error and uncertainty in habitat models. J. Appl. Ecol. 43:413–23 [Google Scholar]
  12. Beever EA, Swihart RK, Bestelmeyer BT. 2006. Linking the concept of scale to studies of biological diversity: evolving approaches and tools. Divers. Distrib. 12:229–35 [Google Scholar]
  13. Berteaux D, Humphries MM, Krebs CJ, Lima M, McAdam AG. et al. 2006. Constraints to projecting the effects of climate change on mammals. Clim. Res. 32:151–58 [Google Scholar]
  14. Booth TH, Nix HA, Hutchinson MF, Jovanovic T. 1988. Niche analysis and tree species introduction. For. Ecol. Manag. 23:47–59 [Google Scholar]
  15. Box EO. 1981. Predicting physiognomic vegetation types with climate variables. Vegetatio 45:127–39 [Google Scholar]
  16. Burgman MA, Fox JC. 2003. Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Anim. Conserv. 6:19–28 [Google Scholar]
  17. Burgman MA, Lindenmayer DB, Elith J. 2005. Managing landscapes for conservation under uncertainty. Ecology 86:2007–17 [Google Scholar]
  18. Burnham KP, Anderson DR. 2002. Model Selection and Inference: A Practical Information-Theoretic Approach. New York: Springer-Verlag488, 2nd. [Google Scholar]
  19. Cade BS, Noon BR, Flather CH. 2005. Quantile regression reveals hidden bias and uncertainty in habitat models. Ecology 86:786–800 [Google Scholar]
  20. Capen DE. 1981. The use of multivariate statistics in studies of wildlife habitat. Gen.Tech. Rep. RM-87 Rocky Mt. For. Range Exp. Stn., USDA For. Serv [Google Scholar]
  21. Caruana R, Niculescu-Mizil A. 2006. An empirical comparison of supervised learning algorithms Presented at Proc. Int. Conf. Machine Learn., 23rd, Pittsburgh, PA [Google Scholar]
  22. Cushman SA, McGarigal K. 2002. Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecol. 17:637–46 [Google Scholar]
  23. De Marco P, Diniz-Filho JAF, Bini LM. 2008. Spatial analysis improves species distribution modelling during range expansion. Biol. Lett. 4:577–80 [Google Scholar]
  24. Dormann CF. 2007. Promising the future? Global change projections of species distributions. Basic Appl. Ecol. 8:387–97 [Google Scholar]
  25. Dormann CF, McPherson JM, Araujo MB, Bivand R, Bolliger J. et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–28 [Google Scholar]
  26. Drake JM, Randin C, Guisan A. 2006. Modelling ecological niches with support vector machines. J. Appl. Ecol. 43:424 [Google Scholar]
  27. Drielsma M, Ferrier S. 2009. Rapid evaluation of metapopulation persistence in highly variegated landscapes. Biol. Conserv. 142:529–40 [Google Scholar]
  28. Dungan JL, Perry JN, Dale MRT, Legendre P, Citron-Pousty S. et al. 2002. A balanced view of scale in spatial statistical analysis. Ecography 25:626–40 [Google Scholar]
  29. Elith J, Graham C. 2009. Do they? How do they? WHY do they differ? … on finding reasons for differing performances of species distribution models. Ecography 32:66–77 [Google Scholar]
  30. Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S. et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–51 [Google Scholar]
  31. Elith J, Leathwick JR. 2009. Conservation prioritization using species distribution models. Spatial Conservation Prioritization: Quantitative Methods and Computational Tools A Moilanen, KA Wilson, HP Possingham Oxford: Oxford Univ. Press70–93 [Google Scholar]
  32. Elith J, Leathwick JR, Hastie T. 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77:802–13 [Google Scholar]
  33. Ferrier S, Guisan A. 2006. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43:393–404 [Google Scholar]
  34. Ferrier S, Watson G. 1997. An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. Consult. Rep. NSW Natl. Parks Wildl. Serv. Dep. Environ., Sport Territ., Environ. Aust., Canberra. http://www.deh.gov.au/biodiversity/publications/technical/surrogates/ [Google Scholar]
  35. Ferrier S, Watson G, Pearce J, Drielsma M. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity: the north-east New South Wales experience. I. Species-level modelling. Biodivers. Conserv. 11:2275–307 [Google Scholar]
  36. Fitzpatrick MC, Weltzin JF, Sanders NJ, Dunn RR. 2007. The biogeography of prediction error: Why does the introduced range of the fire ant overpredict its native range?. Global Ecol. Biogeog. 16:24–33 [Google Scholar]
  37. Fleishman E, MacNally R, Fay JP, Murphy DD. 2001. Modeling and predicting species occurrence using broad-scale environmental variables: an example with butterflies of the Great Basin. Conserv. Biol. 15:1674–85 [Google Scholar]
  38. Foody GM. 2008. GIS: biodiversity applications. Prog. Phys. Geog. 32:223–35 [Google Scholar]
  39. Franklin J. 2009. Mapping Species Distributions: Spatial Inference and Prediction Cambridge, UK: Cambridge Univ. Press In press [Google Scholar]
  40. Gaston KJ, Chown SL, Evans KL. 2008. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35:483–500 [Google Scholar]
  41. Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT. 2004a. New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol. 19:497–503 [Google Scholar]
  42. Graham CH, Ron SR, Santos JC, Schneider CJ, Moritz C. 2004b. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58:1781–93 [Google Scholar]
  43. Grinnell J. 1904. The origin and distribution of the chestnut-backed chickadee. Auk 21:364–65 [Google Scholar]
  44. Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8:993–1009 [Google Scholar]
  45. Guisan A, Zimmermann NE. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135:147–86 [Google Scholar]
  46. Hamazaki T. 2002. Spatiotemporal prediction models of cetacean habitats in the mid-western North Atlantic ocean (From Cape Hatteras, North Carolina, USA to Nova Scotia, Canada). Mar. Mamm. Sci. 18:920–39 [Google Scholar]
  47. Hastie T, Tibshirani R, Friedman JH. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag744, 2nd. [Google Scholar]
  48. Heikkinen R, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geog. 30:751–77 [Google Scholar]
  49. Hoffmann A, Kellermann V. 2006. Revisiting heritable variation and limits to species distribution: recent developments. Isr. J. Ecol. Evol. 52:247–61 [Google Scholar]
  50. Hooten MB, Wikle CK, Dorazio RM, Royle JA. 2007. Hierarchical spatiotemporal matrix models for characterizing invasions. Biometrics 63:558–67 [Google Scholar]
  51. Hortal J, Jiménez-Valverde A, Gómez JF, Lobo JM, Baselga A. 2008. Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117:847–58 [Google Scholar]
  52. Huston MA. 2002. Critical issues for improving predictions. Predicting Species Occurrences: Issues of Accuracy and Scale JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall et al.7–24 Covelo, CA: Island Press [Google Scholar]
  53. Iverson LR, Prasad AM, Bossenbroek J, Sydnor D, Schwartz MW. 2009. Modeling potential movements of an ash threat: the emerald ash borer. Advances in Threat Assessment and Their Application to Forest and Rangeland Management. http://www.threats.forestencyclopedia.net J Pye, M Raucher [Google Scholar]
  54. Jiménez-Valverde A, Lobo JM, Hortal J. 2008. Not as good as they seem: the importance of concepts in species distribution modelling. Divers. Distrib. 14:885–90 [Google Scholar]
  55. Jones J. 2001. Habitat selection studies in avian ecology: a critical review. Auk 118:557–62 [Google Scholar]
  56. Kearney M, Phillips BL, Tracy CR, Christian KA, Betts G. et al. 2008. Modelling species distributions without using species distributions: the cane toad in Australia under current and future climates. Ecography 31:423–34 [Google Scholar]
  57. Kearney M, Porter WP. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12:334–50 [Google Scholar]
  58. Keith DA, Akçakaya HR, Thuiller W, Midgley GF, Pearson RG. et al. 2008. Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biol. Lett. 4:560–63 [Google Scholar]
  59. Kitchener AC, Dugmore AJ. 2000. Biogeographical change in the tiger, Panthera tigris. Anim. Conserv. 3:113–24 [Google Scholar]
  60. Kozak KH, Graham CH, Wiens JJ. 2008. Integrating GIS-based environmental data into evolutionary biology. Trends Ecol. Evol. 23:141–48 [Google Scholar]
  61. Latimer AM, Wu SS, Gelfand AE, Silander JA. 2006. Building statistical models to analyze species distributions. Ecol. Appl. 16:33–50 [Google Scholar]
  62. Leathwick JR, Austin MP. 2001. Competitive interactions between tree species in New Zealand's old-growth indigenous forests. Ecology 82:2560–73 [Google Scholar]
  63. Leathwick JR, Elith J, Chadderton L, Rowe D, Hastie T. 2008. Dispersal, disturbance, and the contrasting biogeographies of New Zealand's diadromous and nondiadromous fish species. J. Biogeogr. 35:1481–97 [Google Scholar]
  64. Leathwick JR, Whitehead D. 2001. Soil and atmospheric water deficits and the distribution of New Zealand's indigenous tree species. Funct. Ecol. 15:233–42 [Google Scholar]
  65. Legendre P. 1993. Spatial autocorrelation: trouble or new paradigm?. Ecology 74:1659–73 [Google Scholar]
  66. Levin SA. 1992. The problem of pattern and scale in ecology. Ecology 73:1943–67 [Google Scholar]
  67. Mac Nally R. 2000. Regression and model-building in conservation biology, biogeography and ecology: the distinction between—and reconciliation of—‘predictive’ and ‘explanatory’ models. Biodivers. Conserv. 9:655–71 [Google Scholar]
  68. MacArthur RH. 1958. Population ecology of some warblers of northeastern coniferous forests. Ecology 39:599–619 [Google Scholar]
  69. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA. et al. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–55 [Google Scholar]
  70. Mackey BG, Lindenmayer DB. 2001. Towards a hierachical framework for modelling the spatial distribution of animals. J. Biogeogr. 28:1147–66 [Google Scholar]
  71. Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP. 2002. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Dordrecht: Kluwer221, 2nd. [Google Scholar]
  72. Maravelias CD, Reid DG. 1997. Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalised additive models. Mar. Ecol. Prog. Ser. 147:1–9 [Google Scholar]
  73. McCune B. 2006. Non-parametric models with automatic interactions. J. Veg. Sci. 17:819–30 [Google Scholar]
  74. Midgley GF, Hughes GO, Thuiller W, Rebelo AG. 2006. Migration rate limitations on climate change-induced range shifts in Cape Proteaceae. Divers. Distrib. 12:555 [Google Scholar]
  75. Miller J, Franklin J, Aspinall R. 2007. Incorporating spatial dependence in predictive vegetation models. Ecol. Model. 202:225–42 [Google Scholar]
  76. Miller JR, Turner MG, Smithwick EAH, Dent CL, Stanley EH. 2004. Spatial extrapolation: the science of predicting ecological patterns and processes. BioScience 54:310–20 [Google Scholar]
  77. Moisen GG, Frescino TS. 2002. Comparing five modeling techniques for predicting forest characteristics. Ecol. Model. 157:209–25 [Google Scholar]
  78. Murray A. 1866. The Geographical Distribution of Mammals London: Day & Son [Google Scholar]
  79. Nix H. 1986. A biogeographic analysis of Australian elapid snakes. Atlas of Elapid Snakes of Australia R Longmore 4–15 Canberra: Aust. Gov. Publ. Serv. [Google Scholar]
  80. Olden JD, Lawler JJ, Poff NL. 2008. Machine learning methods without tears: a primer for ecologists. Q. Rev. Biol. 83:171–93 [Google Scholar]
  81. Pearce J, Ferrier S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133:225–45 [Google Scholar]
  82. Pearce JL, Boyce MS. 2006. Modelling distribution and abundance with presence-only data. J. Appl. Ecol. 43:405–12 [Google Scholar]
  83. Pearson RG. 2007. Species’ Distribution Modeling for Conservation Educators and Practitioners. Synthesis. New York: Am. Mus. Natl. Hist. http://ncep.amnh.org [Google Scholar]
  84. Pearson RG, Dawson TP. 2003. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Global Ecol. Biogeog. 12:361–71 [Google Scholar]
  85. Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L. et al. 2006. Model-based uncertainty in species range prediction. J. Biogeogr. 33:1704–11 [Google Scholar]
  86. Peterson AT. 2006. Uses and requirements of ecological niche models and related distributional models. Bioinformatics 3:59–72 [Google Scholar]
  87. Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190:231–59 [Google Scholar]
  88. Phillips SJ, Dudik M, Elith J, Graham C, Lehmann A. et al. 2009. Sample selection bias and presence-only models of species distributions. Ecol. Appl. 19:181–97 [Google Scholar]
  89. Planque B, Buffaz L. 2008. Quantile regression models for fish recruitment environment relationships: four case studies. Mar. Ecol. Prog. Ser. 357:213–23 [Google Scholar]
  90. Poff NL. 1997. Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. J. North Am. Benthol. Soc. 16:391–409 [Google Scholar]
  91. Prasad AM, Iverson LR, Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–99 [Google Scholar]
  92. Rangel TFLVB, Diniz-Filho JAF, Bini LM. 2006. Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecol. Biogeog. 15:321–27 [Google Scholar]
  93. Reineking B, Schröder B. 2006. Constrain to perform: regularization of habitat models. Ecol. Model. 193:675–90 [Google Scholar]
  94. Richards CL, Carstens BC, Lacey Knowles L. 2007. Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses. J. Biogeogr. 34:1833–45 [Google Scholar]
  95. Rouget M, Richardson DM. 2003. Inferring process from pattern in plant invasions: a semimechanistic model incorporating propagule pressure and environmental factors. Am. Nat. 162:713–24 [Google Scholar]
  96. Royle JA, Dawson DK, Bates S. 2004. Modeling abundance effects in distance sampling. Ecology 85:1591–97 [Google Scholar]
  97. Ruegg KC, Hijmans RJ, Moritz C. 2006. Climate change and the origin of migratory pathways in the Swainson's thrush, Catharus ustulatus. J. Biogeogr. 33:1172–82 [Google Scholar]
  98. Sagarin RD, Gaines SD, Gaylord B. 2006. Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol. Evol. 21:524–30 [Google Scholar]
  99. Saunders SC, Chen J, Drummer TD, Gustafson EJ, Brosofske KD. 2005. Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses. Ecol. Complex. 2:87–105 [Google Scholar]
  100. Schimper AFW. 1903. Plant-Geography upon a Physiological Basis Transl. WR Fisher Oxford: Clarendon Press (From German) [Google Scholar]
  101. Schröder B. 2008. Challenges of species distribution modeling belowground. J. Plant Nutr. Soil Sci. 171:325–37 [Google Scholar]
  102. Schröder B, Seppelt R. 2006. Analysis of pattern–process interactions based on landscape models—overview, general concepts, and methodological issues. Ecol. Model. 199:505–16 [Google Scholar]
  103. Schurr FM, Midgley GF, Rebelo AG, Reeves G, Poschlod P. et al. 2007. Colonization and persistence ability explain the extent to which plant species fill their potential range. Global Ecol. Biogeog. 16:449–59 [Google Scholar]
  104. Soberon J. 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10:1115–23 [Google Scholar]
  105. St-Louis V, Pidgeon AM, Clayton MK, Locke BA, Bash D. et al. 2009. Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography 32:468–80 [Google Scholar]
  106. Stauffer DE. 2002. Linking populations and habitats: Where have we been? Where are we going?. Predicting Species Occurrences: Issues of Accuracy and Scale JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall et al.53–61 Covelo, CA: Island Press [Google Scholar]
  107. Steyerberg EW, Eijkemans MJC, Habbema JDF. 1999. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J. Clin. Epidemiol. 52:935–42 [Google Scholar]
  108. Stockwell D, Peters D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13:143–58 [Google Scholar]
  109. Swenson NG. 2008. The past and future influence of geographic information systems on hybrid zone, phylogeographic and speciation research. J. Evol. Biol. 21:421–34 [Google Scholar]
  110. Thuiller W, Richardson DM, Pysek P, Midgley GF, Hughes GO. et al. 2005. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biol. 11:2234–50 [Google Scholar]
  111. Tobalske C. 2002. Effects of spatial scale on the predictive ability of habitat models for the Green Woodpecker in Switzerland. Predicting Species Occurrences: Issues of Accuracy and Scale JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall et al.197–204 Covelo, CA: Island Press [Google Scholar]
  112. Venables WN, Dichmont CM. 2004. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fish. Res. 70:319–37 [Google Scholar]
  113. Vierling KT, Vierling LA, Gould WA, Martinuzzi S, Clawges RM. 2008. Lidar: shedding new light on habitat characterization and modeling. Front. Ecol. Environ. 6:90–98 [Google Scholar]
  114. Wharton TN, Kriticos DJ. 2004. The fundamental and realized niche of the Monterey Pine aphid, Essigella californica (Essig) (Hemiptera : Aphididae): implications for managing softwood plantations in Australia. Divers. Distrib. 10:253–62 [Google Scholar]
  115. Whittaker RJ. 1956. Vegetation of the Great Smoky Mountains. Ecol. Monogr. 26:1–80 [Google Scholar]
  116. Whittingham MJ, Stephens PA, Bradbury RB, Freckleton RP. 2006. Why do we still use stepwise modelling in ecology and behavior?. J. Anim. Ecol. 75:1182–89 [Google Scholar]
  117. Williams JW, Jackson ST, Kutzbac JE. 2007. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl. Acad. Sci. USA 104:5738–42 [Google Scholar]
  118. Wright JW, Davies KF, Lau JA, McCall AC, McKay JK. 2006. Experimental verification of ecological niche modeling in a heterogeneous environment. Ecology 87:2433–39 [Google Scholar]
  119. Zimmermann NE, Kienast F. 1999. Predictive mapping of Alpine grasslands in Switzerland: species versus community approach. J. Veg. Sci. 10:469–82 [Google Scholar]
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