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SDM Background and Literature

Advances in information technology and worldwide efforts to compile, digitize, and make biodiversity data available (e.g., NatureServe [www.natureserve.org], Global Biodiversity Information Facility [www.gbif.org]) have recently improved our perception of the diverse scales of anthropogenic alteration of the environment. Simultaneously, development of new tools and techniques help summarize and utilize these biodiversity datasets. Species distribution modeling (SDM) is one such tool that is increasingly used in many disciplines, including applied fields of systematic conservation planning, climate change studies, disease ecology (Sarkar et al. 2010, Peterson et al. 2008, Gonzales et al. 2010, Moffett et al. 2007), and invasive species research.  Our recent publication (Labay et al. 2011) demonstrates a probabilistic approach to fill gaps in existing collection data as a means to establish historical baseline conditions. The number of publications on SDM’s utility in the field of conservation have increased recently, and include applications towards invasive plant spread (Merow et al. 2011), mammalian conservation (Lopez-Arevalo et al. 2011), fish species conservation (Sindt et al. 2011), conservation planning protocols (Lawler et al. 2011; Carvalho et al. 2011), forest management (Falk & Mellert 2011), and species or system response to climate change (Graham et al. 2011; Falk & Mellert 2011). We are encouraged by the continued growth and utility of this tool for use in conservation, and have intentions of continuing research with these models to further our understanding about stream fishes in Texas.

Figure 1. Percentage growth from 2002 to 2010 of peer-reviewed literature in ecology in general and species distribution or ecological niche modeling specifically. Data obtained by searching papers indexed by BIOSIS Previews by topic with general ecology indicated by topic = "ecolog*" and ecological niche modeling with topic = "ecologi* niche model*" or topic = "species distribution model*". 

SDM Application and Methodology Literature

Applications of Species Distribution Models

(modified from Guisan & Thuilller 2005)

Example applications relevant to Texas

Beard, C.B.et al., 2003. Chagas Disease in a Domestic Transmission Cycle in SouthernTexas, USA. , 9(1), pp.103-105.

Drake, J.M. & Bossenbroek, J.M.,2004. The Potential Distribution of Zebra Mussels in the United States. BioScience,54(10), p.931.

Fitzpatrick, M.C. & Weltzin, J.F.,2005. Ecological niche models and the geography of biological invasions: areview and a novel application. Invasive plants: ecological and agriculturalaspects, pp.45–60.

González, C. et al., 2010. Climate Changeand Risk of Leishmaniasis in North America: Predictions from Ecological NicheModels of Vector and Reservoir Species. PLoS neglected tropical diseases. 4(1)

Herborg, L.M., Mandrak, N.E., et al.,2007. Comparative distribution and invasion risk of snakehead (Channidae) andAsian carp (Cyprinidae) species in North America. Canadian Journal ofFisheries and Aquatic Sciences, 64(12), pp.1723–1735.


Labay B, Cohen AE, Sissel B, Hendrickson DA, Martin FD, et al. (2011) Assessing Historical Fish Community Composition Using Surveys, Historical Collection Data, and Species Distribution Models. PLoS ONE 6(9): e25145. doi:10.1371/journal.pone.0025145 

Martínez-Meyer, E., Peterson, A.T. &Navarro-Sigüenza, A.G., 2004. Evolution of seasonal ecological niches in thePasserina buntings (Aves: Cardinalidae). Proceedings of the Royal Society B:Biological Sciences, 271(1544), pp.1151-1157.

Mau-Crimmins, T.M., Schussman, H.R. &Geiger, E.L., 2006. Can the invaded range of a species be predictedsufficiently using only native-range data?: Lehmann lovegrass (Eragrostislehmanniana) in the southwestern United States. Ecological Modelling,193(3-4), pp.736-746.

Pyron, R.A., Burbrink, F.T. & Guiher,T.J., 2008. Claims of Potential Expansion throughout the U.S. by InvasivePython Species Are Contradicted by Ecological Niche Models. PLoS ONE,3(8), p.e2931.

Ron, S.R., 2005. Predicting theDistribution of the Amphibian Pathogen Batrachochytrium dendrobatidis in theNew World1. Biotropica, 37(2), pp.209-221.

Sarkar, Sahotra et al., 2010. ChagasDisease Risk in Texas. PLoS Negl Trop Dis, 4(10), p.e836.

Quantifying the environmental niche of species  

Austin, M.P., Nicholls, A.O. &Margules, C.R., 1990. Measurement of the realized qualitative niche:environmental niches of five Eucalyptus species. Ecological Monographs,60(2), pp.161–177.

Broennimann, O. et al., 2011. Measuringecological niche overlap from occurrence and spatial environmental.

Vetaas, O.R., 2002. Realized andpotential climate niches: a comparison of four Rhododendron tree species. Journalof Biogeography, 29(4), pp.545–554.

Warren, D.L., Glor, R.E. & Turelli,M., 2010. ENMTools: a toolbox for comparative studies of environmental nichemodels. Ecography, 33(3), pp.607–611.

Wiens, J.J. et al., 2010. Nicheconservatism as an emerging principle in ecology and conservation biology. Ecologyletters.

Testing biogeographical, ecological and evolutionary hypotheses

Anderson, R.P., Peterson, A.T. & Gómez-Laverde,M., 2002. Using niche-based GIS modeling to test geographic predictions ofcompetitive exclusion and competitive release in South American pocket mice. Oikos,pp.3–16.

Fitzpatrick, M.C. et al., 2007. Thebiogeography of prediction error: why does the introduced range of the fire antover-predict its native range? Global Ecology and Biogeography, 16(1),pp.24–33.

Graham, C.H. et al., 2004. Integratingphylogenetics and environmental niche models to explore speciation mechanismsin dendrobatid frogs. Evolution, 58(8), pp.1781–1793.

Leathwick, J.R., 1998. Are New Zealand’sNothofagus species in equilibrium with their environment? Journal ofVegetation Science, pp.719–732.

Wiens, J.J. et al., 2010. Nicheconservatism as an emerging principle in ecology and conservation biology. Ecologyletters.

Assessing species invasion and proliferation

Beerling, D.J., Huntley, B. & Bailey,J.P., 1995. Climate and the distribution of Fallopia japonica: use of anintroduced species to test the predictive capacity of response surfaces. Journalof Vegetation Science, pp.269–282.

Elith, J., Kearney, M. & Phillips,S., 2010. The art of modelling range-shifting species. Methods in ecologyand evolution.

Ficetola, G.F., Thuiller, W. & Miaud,C., 2007. Prediction and validation of the potential global distribution of aproblematic alien invasive species-the American bullfrog. Diversity andDistributions, 13(4), pp.476–485.

Peterson, A.T. & Vieglais, D.A.,2001. Predicting species invasions using ecological niche modeling: newapproaches from bioinformatics attack a pressing problem. BioScience,51(5), pp.363–371.

Peterson, A.T., Papes, M. & Kluza,D.A., 2003. Predicting the potential invasive distributions of four alien plantspecies in North America. Weed Science, 51(6), pp.863–868.

Vaclavik, T. & Meentemeyer, R.K.,2009. Invasive species distribution modeling (iSDM): Are absence data anddispersal constraints needed to predict actual distributions? Ecologicalmodelling, 220(23), pp.3248–3258.

Assessing the impact of climate and land use on species distributions

Diniz-Filho, J.A.F. et al., 2009.Partitioning and mapping uncertainties in ensembles of forecasts of speciesturnover under climate change. Ecography, 32(6), pp.897–906.

Falk, W. & Mellert, K.H., 2011.Species distribution models as a tool for forest management planning underclimate change: risk evaluation of Abies alba in Bavaria. Journal ofVegetation Science.

Hijmans, R.J. & Graham, C.H., 2006.The ability of climate envelope models to predict the effect of climate changeon species distributions. Global Change Biology, 12(12), pp.2272–2281.

Thomas, C.D., Cameron, A., et al., 2004.Extinction risk from climate change. Nature, 427(6970), pp.145–148.

Thuiller, W., 2004. Patterns anduncertainties of species’ range shifts under climate change. Global ChangeBiology, 10(12), pp.2020–2027.

Wiens, J.A. et al., 2009. Niches, models,and climate change: Assessing the assumptions and uncertainties. Proceedingsof the National Academy of Sciences, 106(Supplement 2), p.19729.

Suggesting unsurveyed sites of high potential of occurrence for rare species

Elith, J. & Burgman, M.A., 2002. Predictions and their validation: rare plants in the Central Highlands, Victoria, Australia. Predicting species occurrences: issues of accuracy and scale, 303, p.313.

Engler, R., Guisan, A. & Rechsteiner,L., 2004. An improved approach for predicting the distribution of rare andendangered species from occurrence and pseudo-absence data. Journal ofApplied Ecology, 41(2), pp.263–274.

Raxworthy, C.J. et al., 2003. Predictingdistributions of known and unknown reptile species in Madagascar. Nature,426(6968), pp.837–841.

Supporting management plans for habitat restoration, species recovery, and suitable repatriation sites

Anderson, J.T., Saldana Rojas, J. &Flecker, A.S., 2009. High-quality seed dispersal by fruit-eating fishes inAmazonian floodplain habitats. Oecologia, 161(2), pp.279–290.

Johnson, C.J. & Gillingham, M.P.,2005. An evaluation of mapped species distribution models used for conservationplanning. Environmental Conservation, 32(2), pp.117–128.

Lopez-Arevalo, H.F. et al., 2011. Localknowledge and species distribution models’ contribution towards mammalianconservation. Biological Conservation.

Pearce, J. & Lindenmayer, D., 1998.Bioclimatic analysis to enhance reintroduction biology of the endangeredhelmeted honeyeater (Lichenostomus melanops cassidix) in southeasternAustralia. Restoration Ecology, 6(3), pp.238–243.

Rodríguez, J.P. et al., 2007. Theapplication of predictive modelling of species distribution to biodiversityconservation. Diversity and Distributions, 13(3), pp.243–251.

Vanreusel, W., Maes, D. & Van Dyck,H., 2007. Transferability of species distribution models: a functional habitatapproach for two regionally threatened butterflies. Conservation biology,21(1), pp.201–212.

Wilson, C.D., Roberts, D. & Reid, N.,2010. Applying species distribution modelling to identify areas of highconservation value for endangered species: A case study using Margaritiferamargaritifera (L.). Biological Conservation.

Supporting conservation planning and reserve selection

Araújo, M.B. et al., 2004. Would climatechange drive species out of reserves? An assessment of existingreserve-selection methods. Global Change Biology, 10(9), pp.1618–1626.

Esselman, P.C. & Allan, J.D., 2010.Application of species distribution models and conservation planning softwareto the design of a reserve network for the riverine fishes of northeasternMesoamerica. Freshwater Biology, 56(1), pp.71-88.

Ferrier, S. et al., 2002. Extendedstatistical approaches to modelling spatial pattern in biodiversity innortheast New South Wales. I. Species-level modelling. Biodiversity andConservation, 11(12), pp.2275–2307.

Klein, C. et al., 2009. Incorporatingecological and evolutionary processes into continental-scale conservationplanning. Ecological Applications, 19(1), pp.206–217.

Kremen, C. et al., 2008. Aligningconservation priorities across taxa in Madagascar with high-resolution planningtools. Science, 320(5873), p.222.

Sarkar, S. & Margules, C., 2002.Operationalizing biodiversity for conservation planning. Journal ofbiosciences, 27(4), pp.299–308.

Sarkar, S., Pressey, R.L., et al., 2006.Biodiversity conservation planning tools: present status and challenges for thefuture. Annual Review of Environment and Resources, 31(1), p.123.

Sarkar, Sahotra et al., 2009. Systematicconservation assessment for the Mesoamerica, Chocó, and Tropical Andesbiodiversity hotspots: a preliminary analysis. Biodiversity and Conservation,18(7), pp.1793-1828.

Modelling species assemblages from individual species predictions

Ferrier, S. et al., 2002. Extendedstatistical approaches to modelling spatial pattern in biodiversity innortheast New South Wales. I. Species-level modelling. Biodiversity andConservation, 11(12), pp.2275–2307.

Guisan, A. et al., 2000. Equilibriummodeling of alpine plant distribution: how far can we go? In Vegetation andclimate. A selection of contributions presented at the 42nd Symposium of theInternational Association of Vegetation Science, Bilbao, Spain, 26-30 July1999. pp. 353–384.

Labay, B.J. et al., (In Press). Assessing historical fish community composition using surveys, historical collection data, and species distribution models. PLoS ONE.

Leathwick, J.R., Whitehead, D. &McLeod, M., 1996. Predicting changes in the composition of New Zealand’sindigenous forests in response to global warming: a modelling approach. EnvironmentalSoftware, 11(1-3), pp.81–90.

Building bio- or ecogeographic regions

No Published example found

Improving the calculation of ecological distance between patches in landscape meta-population dynamic and gene flow models

 No Published example found      

Methodology Literature for Species Distribution Models

Reviews of SDM literature

Elith, J. et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), pp.129–151.
Elith, J. & Leathwick, J.R., 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, pp.677–697.

Franklin J. 2010. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge, UK: Cambridge Univ. Press. 320 pages

Guisan A, Zimmermann NE. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135:147–86

Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993–1009.

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

Schröder B. 2008. Challenges of species distribution modeling belowground. J. Plant Nutr. Soil Sci. 171:325–37

Stauffer DE. 2002. Linking populations and habitats: Where have we been? Where are we going? In Predicting Species Occurrences: Issues of Accuracy and Scale, ed. JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall, et al., pp. 53–61.

General literature on best practices and technique

Elith, J. & Leathwick, J.R., 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, pp.677–697.

Jiménez-Valverde, A., Lobo, J.M. & Hortal, J., 2008. Not as good as they seem: the importance of concepts in species distribution modelling. Diversity and Distributions, 14(6), pp.885–890.

Peterson, A.T. (2007) Uses and requirements of ecological niche models and related distributional models. Biodiversity Informatics, 3, 59–72.

    Model evaluation

Elith, J. & Graham, C.H., 2009. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32(1), pp.66–77.

Lobo, J.M., Jiménez-Valverde, A. & Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17(2), pp.145–151.

Wisz, M.S. et al., 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), pp.763–773.

Jiménez-Valverde, A., Lobo, J.M. & Hortal, J., 2008. Not as good as they seem: the importance of concepts in species distribution modelling. Diversity and Distributions, 14(6), pp.885–890.

    Sampling Bias and Species Occurrence Data

Anderson, R.P. & Gonzalez Jr, I., 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecological Modelling. 222(15), pp. 2796-2811

Costa, G.C. et al., 2009. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. Biodiversity and Conservation, 19(3), pp.883-899.

Graham, C.H. et al., 2008. The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 45(1), pp.239–247.

Johnson, C.J. & Gillingham, M.P., 2008. Sensitivity of species-distribution models to error, bias, and model design: an application to resource selection functions for woodland caribou. Ecological Modelling, 213(2), pp.143–155.

Leitão, P.J., Moreira, F. & Osborne, P.E., 2011. Effects of geographical data sampling bias on habitat models of species distributions: a case study with steppe birds in southern Portugal. International Journal of Geographical Information Science, 25(3), pp.439-454.

Lobo, J.M., 2008. More complex distribution models or more representative data? Biodiversity Informatics, 5(0).

Newbold, T., 2010. Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Progress in Physical Geography, 34(1), pp.3 -22.

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

Phillips, S.J. & Dudík, M., 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), pp.161–175.

Phillips, S.J. & Elith, J., 2010. POC plots: calibrating species distribution models with presence-only data. Ecology, 91(8), pp.2476–2484.

Rota, C.T. et al., 2011. Does accounting for imperfect detection improve species distribution models? Ecography. 34(4) pp. 659-670.

Veloz, S.D., 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography (J. Biogeogr.), 36, pp.2290–2299.

Wisz, M.S. et al., 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), pp.763–773.

Published predictive SDM packages

Published SDM methods


    Algorithm & Explanation

Elith, J. et al., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions.

Phillips, S.J. & Dudík, M., 2008.Modeling of species distributions with Maxent: new extensions and acomprehensive evaluation. Ecography, 31(2), pp.161–175.

Phillips, S.J., Anderson, R.P. & Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), pp.231–259.

    Application (examples of published studies post 2006)

Carnaval, A.C. & Moritz, C. (2008) Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest. Journal of Biogeography, 35, 1187–1201.

Cordellier, M. & Pfenninger, M. (2009) Inferring the past to predict the future: climate modelling predictions and phylogeography for the freshwater gastropod Radix balthica (Pulmonata, Basommatophora). Molecular Ecology, 18, 534–544.

Elith, J., Graham, C.H., Anderson, R.P. et al. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.

Graham, C.H. & Hijmans, R.J. (2006) A comparison of methods for mapping species ranges and species richness. Global Ecology & Biogeography, 15, 578.

Kharouba, H.M., Algar, A.C. & Kerr, J.T. (2009) Historically calibrated predictions of butterfly species’ range shift using global change as a pseudo-experiment. Ecology, 90, 2213–2222.

Monterroso, P., Brito, J.C., Ferreras, P. & Alves, P.C. (2009) Spatial ecology of the European wildcat in a Mediterranean ecosystem: dealing with small radio-tracking datasets in species conservation. Journal of Zoology, 279, 27–35.

Murray-Smith, C., Brummitt, N.A., Oliveira-Filho, A.T., Bachman, S., Moat, J., Lughadha, E.M.N. & Lucas, E.J. (2009) Plant diversity hotspots in the Atlantic coastal forests of Brazil. Conservation Biology, 23, 151–163.

Lamb, J.M., Ralph, T.M.C., Goodman, S.M., Bogdanowicz, W., Fahr, J., Gajewska, M., Bates, P.J.J., Eger, J., Benda, P. & Taylor, P.J. (2008) Phylogeography and predicted distribution of African-Arabian and Malagasy populations of giant mastiff bats, Otomops spp. (Chiroptera : Molossidae). Acta Chiropterologica, 10, 21–40.

Peterson, A.T., Papeş, M. & Eaton, M., 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography, 30(4), pp.550–560.

Tinoco, B.A., Astudillo, P.X., Latta, S.C. & Graham, C.H. (2009) Distribution, ecology and conservation of an endangered Andean hummingbird: the Violet-throated Metaltail (Metallura baroni). Bird Conservation International, 19, 63–76.

Tittensor, D.P., Baco, A.R., Brewin, P.E., Clark, M.R., Consalvey, M., Hall-Spencer, J., Rowden, A.A., Schlacher, T., Stocks, K.I. & Rogers, A.D. (2009) Predicting global habitat suitability for stony corals on seamounts. Journal of Biogeography, 36, 1111–1128.

Tognelli, M.F., Roig-Junent, S.A., Marvaldi, A.E., Flores, G.E. & Lobo, J.M. (2009) An evaluation of methods for modelling distribution of Patagonian insects. Revista Chilena De Historia Natural, 82, 347–360.

Verbruggen, H., Tyberghein, L., Pauly, K., Vlaeminck, C., Van Nieuwenhuyze, K., Kooistra, W., Leliaert, F. & De Clerck, O. (2009) Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed Halimeda. Global Ecology and Biogeography, 18, 393–405.

Wang, Y., Xie, B., Wan, F., Xiao, Q. & Dai, L. (2007) The potential geographic distribution of Radopholus similis in China. Agricultural Sciences in China, 6, 1444–1449.

Warren, D. & Seifert, S., 2010. Environmental niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications.

Ward, D. (2007a) Modelling the potential geographic distribution of invasive ant species in New Zealand. Biological Invasions, 9, 723–735.

Williams, J.N., Seo, C.W., Thorne, J., Nelson, J.K., Erwin, S., O’Brien, J.M. & Schwartz, M.W. (2009) Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15, 565–576.

Wollan, A.K., Bakkestuen, V., Kauserud, H., Gulden, G. & Halvorsen, R. (2008) Modelling and predicting fungal distribution patterns using herbarium data. Journal of Biogeography, 35, 2298–2310.

Yates, C., McNeill, A., Elith, J. & Midgley, G. (2010) Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region. Diversity and Distributions, 16, 187–201.

Yesson, C. & Culham, A. (2006) A phyloclimatic study of Cyclamen. BMC Evolutionary Biology, 6, 72–95.

Young, B.F., Franke, I., Hernandez, P.A., Herzog, S.K., Paniagua, L., Tovar, C. & Valqui, T. (2009) Using spatial models to predict areas of endemism and gaps in the protection of Andean slope birds. Auk, 126, 554–565.


    Algorithm & Explanation

Stockwell, D. R. B. 1999. Genetic algorithms II. Pages 123-144 in A. H. Fielding, editor. Machine learning methods for ecological applications. Kluwer Academic Publishers, Boston.

Stockwell, D. R. B., and D. P. Peters. 1999. The GARP modelling system: Problems and solutions to automated spatial prediction. International Journal of Geographic Information Systems 13:143-158.

Stockwell, D. R. B., and I. R. Noble. 1992. Induction of sets of rules from animal distribution data: A robust and informative method of analysis. Mathematics and Computers in Simulation 33:385-390.

    Application (examples of published studies post 1999)

Anderson, R. P., M. Laverde, and A. T. Peterson. 2002a. Geographical distributions of spiny pocket mice in South America: Insights from predictive models. Global Ecology and Biogeography 11:131-141.

Anderson, R. P., M. Laverde, and A. T. Peterson. 2002b. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 93:3-16.

Anderson, R. P., D. Lew, and A. T. Peterson. 2003. Evaluating predictive models of species' distributions: Criteria for selecting optimal models. Ecological Modelling, v. 162, p. 211 232

Chen, G., and A. T. Peterson. 2000. A new technique for predicting distributions of terrestrial vertebrates using inferential modeling. Zoological Research 21:231-237.

Chen, G., and A. T. Peterson. 2002. Prioritization of areas in China for biodiversity conservation based on the distribution of endangered bird species. Bird Conservation International. 12:197-209

Feria, T. P., and A. T. Peterson. 2002. Using point occurrence data and inferential algorithms to predict local communities of birds. Diversity and Distributions 8:49-56.

Godown, M. E., and A. T. Peterson. 2000. Preliminary distributional analysis of U.S. endangered bird species. Biodiversity and Conservation 9:1313-1322.

Peterson, A. T. 2001. Predicting species' geographic distributions based on ecological niche modeling. Condor 103:599-605.

Peterson, A. T., L. G. Ball, and K. C. Cohoon. 2002a. Predicting distributions of tropical birds. Ibis 144:e27-e32.

Peterson, A. T., M. A. Ortega-Huerta, J. Bartley, V. Sanchez-Cordero, J. Soberon, R. H. Buddemeier, and D. R. B. Stockwell. 2002b. Future projections for Mexican faunas under global climate change scenarios. NATURE 416:626-629.

Peterson, A. T., V. Sanchez-Cordero, C. B. Beard, and J. M. Ramsey. 2002c. Ecologic niche modeling and potential reservoirs for Chagas disease, Mexico. Emerging Infectious Diseases 8:662-667.

Peterson, A. T., V. Sanchez-Cordero, J. Soberon, J. Bartley, R. H. Buddemeier, and A. G. Navarro-Siguenza. 2001. Effects of global climate change on geographic distributions of Mexican Cracidae. Ecological Modelling 144:21-30.

Peterson, A. T., D. R. B. Stockwell, and D. A. Kluza. 2002d. Distributional prediction based on ecological niche modeling of primary occurrence data. Pages 617-623 in J. M. Scott, P. J. Heglund, and M. L. Morrison, editors. Predicting Species Occurrences: Issues of Scale and Accuracy. Island Press, Washington, D.C.

Peterson, A. T., and D. A. Vieglais. 2001. Predicting species invasions using ecological niche modeling. BioScience 51:363-371.

Stockwell, D. R. B., and A. T. Peterson. 2002a. Controlling bias in biodiversity data. Pages 537-546 in J. M. Scott, P. J. Heglund, and M. L. Morrison, editors. Predicting Species Occurrences: Issues of Scale and Accuracy. Island Press, Washington, D.C.

Stockwell, D. R. B., and A. T. Peterson. 2002b. Effects of sample size on accuracy of species distribution models. Ecological Modelling 148:1-13.


    Algorithm & Explanation

Busby, J.R. (1991) BIOCLIM – a bioclimate analysis and prediction system. Nature conservation: cost effective biological surveys and data analysis (ed. by C.R.Margules and M.P.Austin), pp. 64–68. CSIRO, Victoria, Australia.


    Algorithm & Explanation

Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002) Ecological niche factor analysis: how to compute habitat suitability maps without absence data? Ecology, 83, 2027–2036.

Braunisch V., Bollmann K., Graf R. F., Hirzel A.H. (2008). Living on the edge -- Modelling habitat suitability for species at the edge of their fundamental niche. Ecological Modelling 214, 153-167

Hirzel, A. H. & Le Lay G. (2008). Habitat suitability modelling and niche theory. Journal of Applied Ecology, 45, 1372-1381.

Hirzel, A. H. (2008). Using relative capacity to measure habitat suitability. Israel Journal of Ecology and Evolution, 54, 421-434
Calenge, C., Darmon, G., Basille, M., Loison, A. &Jullien, J.M. (2008) The factorial decomposition of the Mahalanobisdistances in habitat selection studies. Ecology, 89(2), 555-66

     Application (examples of published studies obtained from http://www2.unil.ch/biomapper/bibliography.html)

Jiménez-Valverde, A., Gómez, J.F., Lobo, J.M., Baselga, A. & Hortal, J. (2008) Challenging species distribution models: the case of Maculinea nausithous in the Iberian Peninsula. Annales Zoologici Fennici, 45, 200-10.

Rivera, J.H.V., Huerta, M.A.O. & Guerrer, R. (2008) Analysis of the distribution of Orange-breasted Bunting (Passerina leclanchetii): An endemic species of Mexico's Pacific slope. Ornitologia Neotropical, 19(2), 265-74.

Mertzanis G, Kallimanis AS, Kanellopoulos N, et al. (2008) Brown bear (Ursus arctos L.) habitat use patterns in two regions of northern Pindos, Greece - management implications. Journal of Natural History 42, 301-315.

Strubbe, D. & Matthysen, E. (2008) Predicting the potential distribution of invasive ring-necked parakeets Psittacula krameri in northern Belgium using an ecological niche modelling approach. Biol Invasions, on-line.

Allouche, O., Steinitz, O., Rotem, D., Rosenfeld, A. & Kadmon, R. (2008) Incorporating distance constraints into species distribution models. Journal of Applied Ecology, 45(2), 599-609.

Long, P.R., Zefania, S., Ffrench-Constant, R.H. & Szekely, T. (2008) Estimating the population size of an endangered shorebird, the Madagascar plover, using a habitat suitability model. Animal Conservation, 11(2), 118-27.

Praca, E. & Gannier, A. (2008) Ecological niches of three teuthophageous odontocetes in the northwestern Mediterranean Sea. Ocean Science, 4(1), 49-59.

Skov, H., Humphreys, E., Garthe, S., Geitner, K., Gremillet, D., Hamer, K.C., Hennicke, J., Parner, H. & Wanless, S. (2008) Application of habitat suitability modelling to tracking data of marine animals as a means of analyzing their feeding habitats. Ecological Modelling, 212(3-4), 504-12.

Chefaoui, R.M., Lobo, J.M. 2007. Assessing the conservation status of an iberian moth using pseudo-absences. Journal of Wildlife Management 71:2507-2516.

Estrada-Peña, A., Venzal, J.M. 2007. Climate niches of tick species in the mediterranean region: Modeling of occurrence data, distributional constraints, and impact of climate change. Journal of Medical Entomology 44:1130-1138.

Sattler, T., Bontadina, F., Hirzel, A.H. & Arlettaz, R. (2007) Ecological niche modelling of two cryptic bat species calls for a reassessment of their conservation status. Journal of Applied Ecology, 44(6), 1188-99.

Jimenez-Valverde A, Ortuno VM, Lobo JM (2007) Exploring the distribution of Sterocorax Ortuno, 1990 (Coleoptera, Carabidae) species in the Iberian peninsula. Journal of Biogeography 34, 1426-1438.

Braunisch, V. and Suchant, R. 2007. A model for evaluating the 'habitat potential' of a landscape for capercaillie Tetrao urogallus: a tool for conservation planning. - Wildlife Biology 13: 21-33.


    Algorithm & Explanation

Carpenter, G., Gillison, A.N. & Winter, J. (1993) DOMAIN: a flexible modeling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation, 2, 667–680.