Current State of the Art for Statistical Modelling of Species Distributions

  • Troy M. Hegel
  • Samuel A. Cushman
  • Jeffrey Evans
  • Falk Huettmann


Over the past decade the number of statistical modelling tools available to ecologists to model species' distributions has increased at a rapid pace (e.g. Elith et al. 2006; Austin 2007), as have the number of species distribution models (SDM) published in the literature (e.g. Scott et al. 2002). Ten years ago, basic logistic regression (Hosmer and Lemeshow 2000) was the most common analytical tool (Guisan and Zimmermann 2000), whereas ecologists today have at their disposal a much more diverse range of analytical approaches. Much of this is due to the increasing availability of software to implement these methods and the greater computational ability of hardware to run them. It is also due to ecologists discovering and implementing techniques from other scientific disciplines. Ecologists embarking on an analysis may find this range of options daunting and many tools unfamiliar, particularly as many of these approaches are not typically covered in introductory university statistics courses, let alone more advanced ones. This is unfortunate as many of these newer tools may be more useful and appropriate for a particular analysis depending upon its objective, or given the quantity and quality of data available (Guisan et al. 2007; Graham et al. 2008; Wisz et al. 2008). Many of these new tools represent a paradigm shift (Breiman 2001) in how ecologists approach data analysis. In fact, for a number of these approaches, referring to them as new is a misnomer since they have long been used in other fields and only recently have ecologists become increasingly aware of their usefulness (Hochachka et al. 2007; Olden et al. 2008).


Random Forest Multivariate Adaptive Regression Spline Resource Selection Species Distribution Model Ecol Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Addicott JF, Aho JM, Antolin MF, et al (1987) Ecological neighborhoods: scaling environmental patterns. Oikos 49:340–346.Google Scholar
  2. Aebischer NJ, Robertson PA, Kenward RE (1993) Compositional analysis of habitat use from animal radio-tracking data. Ecol 74:1313–1325.Google Scholar
  3. Afshartous D, Wolf M (2007) Avoiding ‘data snooping’ in multilevel and mixed effects models. J R Stat Soc A 170:1035–1059.Google Scholar
  4. Aitchison J (1986) The statistical analysis of compositional data. Chapman & Hall, London.Google Scholar
  5. Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species' distributions: criteria for selecting optimal models. Ecol Model 162:211–232.Google Scholar
  6. Arthur SM, Manly BFJ, McDonald LL, Garner GW (1996) Assessing habitat selection when availability changes. Ecol 77:215–227.Google Scholar
  7. Austin, MP (1985) Continuum concept, ordination methods, and niche theory. Annu Rev Ecol Systemat 16:39–61.Google Scholar
  8. Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200:1–19.Google Scholar
  9. Bailey LL, Hines JE, Nichols JD, MacKenzie DI (2007) Sampling design trade-offs in occupancy studies with imperfect detection: examples and software. Ecol Appl 17:281–290.PubMedGoogle Scholar
  10. Baker WL (1995) Longterm response of disturbance landscapes to human intervention and global change. Landsc Ecol 10:143–159.Google Scholar
  11. Ball LC, Doherty PF Jr, McDonald MW (2005) An occupancy modeling approach to evaluating a Palm Springs ground squirrel habitat model. J Wildl Manag 69:894–904.Google Scholar
  12. Barry SC, Welsh AH (2002) Generalized additive modeling and zero inflated count data. Ecol Model 157:179–188.Google Scholar
  13. Beaumont LJ, Pitman AJ, Poulsen M, Hughes L (2007) Where will species go? Incorporating new advances in climate modeling into projections of species distributions. Glob Chang Biol 13:1368–1385.Google Scholar
  14. Bell JF (1996) Application of classification trees to the habitat preference of upland birds. J Appl Stat 23, 349–359.Google Scholar
  15. Betts MG, Ganio L, Huso M, Som N, Huettmann F, Bowman J, Wintle JA (2008). The ecological importance of space in species distribution models: A comment on Dormann et al. Ecography 32:1–5.Google Scholar
  16. Boyce MS, McDonald LL (1999) Relating populations to habitats using resource selection functions. Trends Ecol Evol 14:268–272.PubMedGoogle Scholar
  17. Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FKA (2002) Evaluating resource selection function. Ecol Model 157:281–300.Google Scholar
  18. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Monterey.Google Scholar
  19. Breiman L (1996) Bagging predictors. Mach Learn (24)2:123–140Google Scholar
  20. Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16:199–215.Google Scholar
  21. Brezger A, Kneib T, Lang S (2008) BayesX: software for Bayesian inference in structured additive regression models.Google Scholar
  22. Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds) (2001) Advanced distance sampling. Oxford University Press, Oxford.Google Scholar
  23. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edition. Springer, New York.Google Scholar
  24. Buskirk SW, Millspaugh JJ (2006) Metrics for studies of resource selection. J Wildl Manag 70:358–366.Google Scholar
  25. Cade BS, Noon BR (2003) A gentle introduction to quantile regression for ecologists. Front Ecol Environ 1:412–420.Google Scholar
  26. Cade BS, Noon BR, Flather CH (2005) Quantile regression reveals hidden bias and uncertainty in habitat models. Ecol 86:786–800.Google Scholar
  27. Calenge C (2006) the package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol Model 197:516–519.Google Scholar
  28. Cameron AC, Trivedi PK (1998) Regression analysis of count data. Cambridge University Press, Cambridge.Google Scholar
  29. Casella G, Edward IG (1992) Explaining the Gibbs sampler. Am Stat 46:167–174.Google Scholar
  30. Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEboost: Improving prediction of the minority class in boosting. In: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 107–119.Google Scholar
  31. Chase JM, Leibold MA (2003) Ecological niches. University of Chicago Press, Chicago, IL.Google Scholar
  32. Chatterjee S, Hadi AS (2006) Regression analysis by example, 4th edition. Wiley, New York.Google Scholar
  33. Chen C, Liaw A, Breiman L (2004) Using random forest to learn imbalanced data.
  34. Chrisman N (1989) Error in categorical maps: Testing versus simulation. In 9th Int. Symp Comput Assis Cart, ASPRS/ACSM, pp. 521–529.Google Scholar
  35. Clark JS (2005) Why environmental scientists are becoming Bayesians. Ecol Lett 8:2–14.Google Scholar
  36. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psych Meas 20:37–46Google Scholar
  37. Cohen J (1968) Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psych Bull 70:213–20.Google Scholar
  38. Collins SL, Knapp AK, Briggs JM, et al (1998) Modulation of diversity by grazing and mowing in native tallgrass prairie. Science 280:745–747.PubMedGoogle Scholar
  39. Cooch E, White G (2007) Program MARK: a gentle introduction, 6th edition.
  40. Cooper AB, Millspaugh JJ (1999) The application of discrete choice models to wildlife resource selection studies. Ecol 80:566–575.Google Scholar
  41. Cooper WS (1913) The climax forest of Isle Royale, Lake Superior, and its development. Botanical Gaz 55:1–235.Google Scholar
  42. Craig, E., and F. Huettmann. (2008). Using “blackbox” algorithms such as TreeNet and Random Forests for data-mining and for finding meaningful patterns, relationships and outliers in complex ecological data: an overview, an example using golden eagle satellite data and an outlook for a promising future. Chapter IV in Intelligent Data Analysis: Developing New Methodologies through Pattern Discovery and Recovery (Hsiao-fan Wang, Ed.). IGI Global, Hershey, PA, USA.Google Scholar
  43. Craiu RV, Duchesne T, Fortin D (2008) Inference methods for the conditional logistic regression model with longitudinal data. Biom J 50:97–109.PubMedGoogle Scholar
  44. Cushman SA, McGarigal K (2003) Hierarchical, multiscale decomposition of species-environment relationships. Landsc Ecol 17:637–646.Google Scholar
  45. Cushman SA, McKenzie D, Peterson DL, et al (2007) Research agenda for integrated landscape modeling. Gen. Tech. Rep. RMRSGTR-194. US Department of Agriculture, Forest Service, Rocky Mountain Research Station. Fort Collins.Google Scholar
  46. De'ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for the analysis of complex ecological data. Ecol 81:3178–3192.Google Scholar
  47. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A (2009) e1071: Misc functions of the Department of Statistics (e1071), TU Wien. R package version 1.5–19.Google Scholar
  48. Donovan TM, Hines J (2007) Exercises in occupancy modelling and estimation.
  49. Dormann CF (2007) Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob Ecol Biogeogr 16:129–138.Google Scholar
  50. Drake JM, Randin C, Guisan A (2006) Modelling ecological niches with support vector machines. J Appl Ecol 43:424–432.Google Scholar
  51. Elder JF (2003) The Generalization Paradox of Ensembles. J Computational Graph Stat 12:853–864.Google Scholar
  52. Elith J, Leathwick J (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Divers Dist 13:265–275.Google Scholar
  53. Elith J, Graham CH, Anderson RP, et al (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecogr 29:129–151.Google Scholar
  54. Elith J, Ferrier S, Huettmann F, Leathwick J (2005) The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. Ecol Model 186:280–289.Google Scholar
  55. Ellison, AM (2004) Bayesian inference in ecology. Ecol Lett 7:509–520.Google Scholar
  56. Evans JS, Cushman SA (2009) Gradient modeling of conifer species using random forests. Landsc Ecol 24:678–683.Google Scholar
  57. Fawcett, T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874.Google Scholar
  58. Ferrier S (2002) Mapping spatial pattern in biodiversity for regional conservation planning: where to go from here? Syst Biol 51:331–363.PubMedGoogle Scholar
  59. Ferrier S, Drielsma M, Manion G, Watson G (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodivers Cons 11:2309–2338.Google Scholar
  60. Ferrier S, Manion G, Elith J, Richardson K (2007) Using generalized dissimilarity modelling to analyze and predict patterns of beta diversity in regional biodiversity assessment. Divers Dist 13:252–264.Google Scholar
  61. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Env Cons 24:38–49.Google Scholar
  62. Fletcher D, MacKenzie DI, Villouta E (2005) Modelling skewed data with many zeros: a simple approach combining ordinary and logistic regression. Env Ecol Stat 12:45–54.Google Scholar
  63. Friedman JH (1991) Multivariate adaptive regression splines (with discussion). Ann Stat 19:1–141.Google Scholar
  64. Gelfand AE, Latimer A, Wu S, Silander JA Jr (2006) Building statistical models to analyze species distributions. In: Clark JS, Gelfand AE (eds) Hierarchical modelling for the environmental sciences: statistical methods and applications, pp. 77–97. Oxford, New York.Google Scholar
  65. Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge.Google Scholar
  66. Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis, 2nd edition. Chapman & Hall/CRC, New York.Google Scholar
  67. Getz WM, Wilmers CC (2004) A local nearest-neighbour convex-hull construction of home ranges and utilization distributions. Ecography 27:489–505.Google Scholar
  68. Getz WM, Fortmann-Roe S, Cross PC, Lyons AJ, et al (2007) LoCoH: nonparametric kernel methods for constructing home ranges and utilization distributions. PLOS Biol 2:e207. doi:10.1371/journal.pone.0000207Google Scholar
  69. Gillies CS, Hebblewhite M, Nielsen SN, et al (2006) Application of random effects to the study of resource selection by animals. J Anim Ecol 75:887–898.PubMedGoogle Scholar
  70. Glenn SM, Collins SL (1992) Effects of scale and disturbance on rates of immigration and extinction of species in prairies. Oikos 63:273–280.Google Scholar
  71. Graham CH, Elith J, Hijmans RJ, et al (2008) The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol 45:239–247.Google Scholar
  72. Graham CH, Ferrier S, Huettmann F, et al (2004) New development in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503.PubMedGoogle Scholar
  73. Gu W, Swihart RK (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol Cons 116:195–203.Google Scholar
  74. Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the stage. Ecol Model 157:89–100.Google Scholar
  75. Guisan A, Graham CH, Elith J, et al (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Dist 13:332–340.Google Scholar
  76. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009.Google Scholar
  77. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186.Google Scholar
  78. Guo Q, Kelly M, Graham CH (2005) Support vector machines for predicting distribution of sudden oak death in California. Ecol Model 182:75–90.Google Scholar
  79. Haegeman B, Loreau M (2008) Limitations of entropy maximization in ecology. Oikos 117:1700–1710Google Scholar
  80. Hall LS, Krausman PR, Morrison ML (1997) The habitat concept and a plea for common terminology. Wildl Soc Bull 25:173–182.Google Scholar
  81. Hand DJ, Till RJ (2001) A simple generalization of the area under the ROC curve to multiple class classification problems. Mach Learn 45:171–186Google Scholar
  82. Harte J, Zillio T, Conlisk E, Smith AB (2008) Maximum entropy and the state-variable approach to macroecology. Ecol 89:2700–2711.Google Scholar
  83. Hastie T, Tibshirani RJ (1990) Generalized additive models. Chapman & Hall, London.Google Scholar
  84. Hastie T, Tibshirani RJ (1996) Discriminant analysis by Gaussian mixtures. J R Stat Soc Ser B 58:155–176.Google Scholar
  85. He F, Gaston KJ (2000) Estimating species abundance from occurrence. Am Nat 156:553–559.Google Scholar
  86. Hepinstall JA, Marzluff JM, Handcock MS, Hurvitz P (2004) Incorporating resource utilization distributions into the study of resource selection: dealing with spatial autocorrelation. In: Huzurbazur SV (ed) Resource selection methods and applications, pp. 12–19. Omnipress, Madison.Google Scholar
  87. Hirzel AH, Guisan A (2002) Which is the optimal sampling strategy for habitat suitability modelling. Ecol Model 157:331–341.Google Scholar
  88. Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological niche factor analysis: how to compute habitat-suitability maps without absence data? Ecol 83:2027–2036.Google Scholar
  89. Hirzel AH, Posse B, Oggier P.-A., et al (2004) Ecological requirements of reintroduced species and the implications for release policy: the case of the bearded vulture. J Appl Ecol 41:1103–1116.Google Scholar
  90. Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199:142–152.Google Scholar
  91. Hobbs NT, Hanley TA (1990) Habitat evaluation: do use/availability data reflect carrying capacity? J Wildl Manag 54:515–522.Google Scholar
  92. Hochachka WM, Caruana R, Fink D, et al (2007) Data-mining discovery of pattern and process in ecological systems. J Wildl Manag 71:2427–2437.Google Scholar
  93. Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edition. Wiley, New York.Google Scholar
  94. Hsu C-W, Chang C-C, Lin C-J (2009) A practical guide to support vector classification.
  95. Huettmann F (2007). Constraints, suggested solutions and an outlook towards a new digital culture for the oceans and beyond: experiences from five predictive GIS models that contribute to global management, conservation and study of marine wildlife and habitat, in: Vanden Berghe, E. et al. (Ed.) Proceedings of ‘Ocean Biodiversity Informatics’: an international conference on marine biodiversity data management Hamburg, Germany, 29 November — 1 December, 2004. IOC Workshop Report, 202, VLIZ Special Publication 37: pp. 49–61.
  96. Huettmann F. and A.W. Diamond (2006). Large-Scale Effects on the Spatial Distribution of Seabirds in the Northwest Atlantic. Landscape Ecology 21:1089–1108.Google Scholar
  97. Hutchinson GE (1957) Concluding remarks. Cold Spring Harb Symp Quant Biol 22:415–427.Google Scholar
  98. Huzurbazar SV (ed) (2003) Resource selection methods and applications. Omnipress, Madison, WI.Google Scholar
  99. Jachowski DS (2007) Resource selection by black-footed ferrets in relation to the spatial distribution of prairie dogs. Thesis, University of Missouri-Columbia, Columbia.Google Scholar
  100. Johnson CJ, Seip DR (2008) Relationship between resource selection, distribution, and abundance: a test with implications to theory and conservation. Pop Ecol 50:145–157.Google Scholar
  101. Johnson CJ, Nielsen SE, Merrill EH, et al (2006) Resource selection functions based on use-availability data: theoretical motivation and evaluation methods. J Wildl Manag 70:347–357.Google Scholar
  102. Johnson CJ, Seip DR, Boyce MS (2004) A quantitative approach to conservation planning: using resource selection functions to map the distribution of mountain caribou at multiple scales. J Appl Ecol 41:238–251.Google Scholar
  103. Johnson DH (1980) The comparison of usage and availability measurements for evaluating resource preference. Ecol 61:65–71.Google Scholar
  104. Kearney M (2006) Habitat, environment and niche: what are we modelling? Oikos 115:186–191.Google Scholar
  105. Keating KA, Cherry S (2004) Use and interpretation of logistic regression in habitat-selection studies. J Wildl Manag 68:774–789.Google Scholar
  106. Kecman V (2005) Support vector machines: an introduction. In: Wang L (ed) Support vector machines: theory and applications, pp. 1–47. Springer, New York.Google Scholar
  107. Kernohan BJ, Gitzen RA, Millspaugh JJ (2001) Analysis of animal space use and movements. In Millspaugh JJ, Marzluff JM (eds) Radio tracking and animal populations, pp 125–166. Academic, San Diego, CA.Google Scholar
  108. Kothari R, Dong M (2001) Decision Trees for Classification: A Review and Some New Results. In: Pal SR, Pal NR (eds) Lecture Notes in Pattern Recognition. World Scientific, Singapore.Google Scholar
  109. Koenker R (2005) Quantile regression. Cambridge University Press, Cambridge.Google Scholar
  110. Kohonen T (1995) Self-Organizing Maps. Series in Information Sciences, Vol. 30. 2nd edition. Springer, Heidelberg.Google Scholar
  111. Kynn, M (2005) Eliciting expert knowledge for Bayesian logistic regression in species habitat modelling. Dissertation. Queensland University of Technology, Brisbane.Google Scholar
  112. La Morgia V, Bona F, Badino G (2008) Bayesian modelling procedures for the evaluation of changes in habitat suitability: a case study of roe deer in the Italian Alps. J Appl Ecol. doi: 10.1111/j.1365–2664.2008.01452.x.Google Scholar
  113. Latimer AM, Wu S, Gelfand AE, Silander JA Jr (2006) Building statistical models to analyze species distributions. Ecol Appl 16:33–50.PubMedGoogle Scholar
  114. Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199:188–196.Google Scholar
  115. Leathwick JR, Rowe D, Richardson J, et al (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshw Biol 50:2034–2052.Google Scholar
  116. Lele SR, Keim JL (2006) Weighted distributions and estimation of resource selection probability functions. Ecol 87:3021–3028.Google Scholar
  117. Leopold A (1933) Game management. Charles Scribner, New York.Google Scholar
  118. Levin SA (1992) The problem of pattern and scale in ecology. Ecol 73:1943–1967.Google Scholar
  119. Lilburne L, Gatelli D, Tarantola S (2006) Sensitivity analysis on spatial models: a new approach. In: Caetano M and Painho M (eds) Seventh International Symposium on Spatial Accur Assess Nat Res Env Sci, pp 329–338.Google Scholar
  120. Lobo JM, Jimenez-Valverde A, Real R (2007) AUC: a misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17:145–151.Google Scholar
  121. Long JS (1997) Regression models for categorical and limited dependent variables. Sage, Thousand Oaks.Google Scholar
  122. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS-a Bayesian modelling framework: concepts, structure and extensibility. Stat Comp 10:325–337.Google Scholar
  123. MacKenzie DI (2005) What are the issues with presence-absence data for managers? J Wildl Manag 69:849–860.Google Scholar
  124. MacKenzie DI (2006) Modeling the probability of resource use: the effect of, and dealing with, detecting a species imperfectly. J Wildl Manag 70:367–374.Google Scholar
  125. MacKenzie DI, Nichols JD, Lachman GB, et al (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecol 83:2248–2255.Google Scholar
  126. MacKenzie DI, Nichols JD, Royle JA, et al (2005) Occupancy estimation and modelling: inferring patterns and dynamics of species occurrence. Elsevier, San Diego, CA.Google Scholar
  127. MacKenzie, DI, Royle JA (2005) Designing occupancy studies: general advice and allocating survey effort. J Appl Ecol 42:1105–1114.Google Scholar
  128. Maclure M, Willett WC (1987) Misinterpretation and misuse of the kappa statistic. Am J Epidemiol 126:161–169.PubMedGoogle Scholar
  129. Magness, D.R., F. Huettmann, and J.M. Morton. (2008). Using Random Forests to provide predicted species distribution maps as a metric for ecological inventory & monitoring programs. Pages 209–229 in T.G. Smolinski, M.G. Milanova & A-E. Hassanien (eds.). Applications of Computational Intelligence in Biology: Current Trends and Open Problems. Studies in Computational Intelligence, Vol. 122, Springer, Berlin. 428 pp.Google Scholar
  130. Manel S, William HC, Ormerod SJ (2001) Evaluating presence-absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931Google Scholar
  131. Manly BFJ, McDonald LL, Thomas D, et al (2002) Resource selection by animals: statistical design and analysis for field studies, 2nd edition. Kluwer, Boston, MA.Google Scholar
  132. Martin TG, Wintle BA, Rhodes JR, et al (2005) Zero tolerance ecology: improving ecological inference by modelling the source of zeros. Ecol Lett 8:1235–1246.Google Scholar
  133. Marzluff JM, Millspaugh JJ, Hurvitz P, Handcock MA (2004) Relating resources to a probabilistic measure of space use: forest fragments and Steller's jays. Ecol 85:1411–1427.Google Scholar
  134. McCarthy MA (2007) Bayesian methods for ecology. Cambridge University Press, Cambrige.Google Scholar
  135. McCracken ML, Manly BFJ, Vander Heyden M (1998) The use of discrete-choice models for evaluating resource selection. J Agric Biol Environ Stat 3:268–279.Google Scholar
  136. McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edition. Chapman & Hall, London.Google Scholar
  137. McDonald TL, Manly BFJ, Nielson RM, Diller LV (2006) Discrete-choice modelling in wildlife studies exemplified by northern spotted owl nighttime habitat selection. J Wildl Manag 70:375–383.Google Scholar
  138. McLoughlin PD, Boyce MS, Coulson T, Clutton-Brock T (2006) Lifetime reproductive success and density-dependent, multi-variable resource selection. Proc R Soc B 273:1449–1454.PubMedGoogle Scholar
  139. McLoughlin PD, Dunford JS, Boutin S (2005) Relating predation mortality to broad-scale habitat selection. J Anim Ecol 74:701–707.Google Scholar
  140. Millspaugh JJ, Nielson RM, McDonald L, et al (2006) Analysis of resource selection using utilization distributions. J Wildl Manag 70:384–395.Google Scholar
  141. Mitchell SC (2005) How useful is the concept of habitat? — A critique. Oikos 110:634–638.Google Scholar
  142. Mobæk R, Mysterud A, Loe LE, Holand Ø, Austrheim G (2009) Density dependent and temporal variability in habitat selection by a large herbivore; an experimental approach. Oikos 118:209–218.Google Scholar
  143. Monserud R A, Leemans R (1992) Comparing global vegetation maps with the kappa statistic. Ecol Model 62:275–293.Google Scholar
  144. Mooney HA, Godron M (eds) (1983) Disturbance and ecosystems. Springer, New York.Google Scholar
  145. Murphy MA, Evans JS, Storfer AS (Accepted) Quantifying ecological process at multiple spatial scales using landscape genetics: Bufo boreas connectivity in Yellowstone National Park. Ecol.Google Scholar
  146. Nielsen SE, Johnson CJ, Heard DC, Boyce MS (2005) Can models of presence-absence be used to scale abundances? Two case studies considering extremes in life history. Ecography 28:197–208.Google Scholar
  147. Nielsen SN, Boyce MS, Stenhouse GB, Munro RHM (2002) Modeling grizzly bear habitats in the Yellowhead ecosystem of Alberta: taking autocorrelation seriously. Ursus 13:45–56.Google Scholar
  148. O'Connor R, Jones MT (1997) Hierarchical models to index the ecological health of the nation. In: Transactions of the 62nd North American Wildlife and Natural Resources Conference, pp. 501–608.Google Scholar
  149. Özesmi SL, Özesmi U (1999) An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecol Model 116:15–31.Google Scholar
  150. Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecolo-gists. Q Rev Biol 83:171–193.PubMedGoogle Scholar
  151. Popp JD, Neubauer D, Paciulli L, Huettmann F (2007). Using TreeNet for Identifying Management Thresholds of Mantled Howling Monkeys' Habitat Preferences on Ometepe Island, Nicaragua, on a Tree and Home Range Scale J Medical Biolog Sciences 1(2): 1–14. Google Scholar
  152. Pearce J, Boyce MS (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43:405–412.Google Scholar
  153. Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133:225–245.Google Scholar
  154. Pearce J, Ferrier S (2001) The practical value of modelling relative abundance of species for regional conservation planning: a case study. Biol Cons 98:33–43.Google Scholar
  155. Peterson AT, Papes M, Soberon J (2008) Rethinking receiver operating characteristic analysis applications in ecological modelling. Ecol Model 213:63–72Google Scholar
  156. Peterson AT (2003) Predicting the geography of species' invasions via ecological niche modelling. Q Rev Biol 78:419–433.PubMedGoogle Scholar
  157. Peterson AT (2006) Uses and requirements of ecological niche models and related distributional models. Biodiver Inform 3:59–72.Google Scholar
  158. Peterson AT, Papeş M, Eaton M (2007) Transferability and model evaluation in ecological niche modelling: a comparison of GARP and Maxent. Ecography 30:550–560.Google Scholar
  159. Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modelling. Proc 21st Conf Mach Learn 472–486.Google Scholar
  160. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190:231–259.Google Scholar
  161. Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175.Google Scholar
  162. Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-Plus. Springer, New York.Google Scholar
  163. Potts JM, Elith J (2006) Comparing species abundance models. Ecol Model 199:153–163.Google Scholar
  164. Prasad AM, Iverson LR, Liaw A (2006) Random forests for modeling the distribution of tree abundances. Ecosyst 9:181–199.Google Scholar
  165. Pulliam HR (2000) On the relationship between niche and distribution. Ecol Lett 3:349–361.Google Scholar
  166. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Fransisco, CA.Google Scholar
  167. Rehfeldt GE, Crookston NL, Warwell MV, Evans JS (2006) Empirical analyses of plant-climate relationships for the western United States. Int J Plant Sci 167:1123–1150.Google Scholar
  168. Reiners WA, Lang GE (1979) Vegetational patterns and processes in the balsam fir zone, White Mountains, New Hampshire. Ecol 60:403–417.Google Scholar
  169. Rittenhouse CD, Millspaugh JJ, Cooper AB, et al (2008) Modeling resource selection using polytomous logistic regression and kernel density estimates. Environ Ecol Stat 15:39–47.Google Scholar
  170. Salford Systems (2001) MARS 2.0 user's guide. Salford, San Diego, CA.Google Scholar
  171. Sattler T, Bontadina F, Hirzle AH, Arlettaz R (2007) Ecological niche modelling of two cryptic bat species calls for a reassessment of their conservation status. J Appl Ecol 44:1188–1199.Google Scholar
  172. Sawyer H, Nielson RM, Lindzey F, McDonald LL (2006) Winter habitat selection of mule deer before and during development of a natural gas field. J Wildl Manag 70:396–403.Google Scholar
  173. Schneider DC (1994) Quantitative ecology: spatial and temporal scaling. Academic, San Diego, CA.Google Scholar
  174. Scott JM, Heglund PJ, Samson F, et al (eds) (2002) Predicting species occurrences: issues of accuracy and scale. Island, Covelo.Google Scholar
  175. Seaman DE, Powell, RA (1996) An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology 77:2075–2085.Google Scholar
  176. Shan Y, Paull D, McKay RI (2006) Machine learning of poorly predictable ecological data. Ecol Model 195:129–138.Google Scholar
  177. Skrondal A, Rabe-Hesketh S (2004) Generalized latent variable modeling: multilevel, longitudinal, and structural equation models. Chapman & Hall, Boca Raton, FL.Google Scholar
  178. Soberón J (2007) Grinnelian and Eltonian niches and geographic distribution of species. Ecol Lett 10:1115–1123.PubMedGoogle Scholar
  179. Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species' distributional areas. Biodiver Inform 2:1–10.Google Scholar
  180. Sousa WP (1984) The role of disturbance in natural communities. Ann Rev Ecol Systematics 15:353–391.Google Scholar
  181. Stockwell D, Peters D (1999) The GARP modelling system: problems and solutions to automated spatial prediction. Int J Geog Inf Sci 13:143–158.Google Scholar
  182. Strickland MD, McDonald LL (2006) Introduction to the special section on resource selection. J Wildl Manag 70:321–323.Google Scholar
  183. Sutton T, de Giovanni R, de Siqueira MF (2007) Introducing openModeller: a fundamental niche modelling framework. OSGeo J 1:1–6.Google Scholar
  184. Ter Braak CJF (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67:1167–1179.Google Scholar
  185. Thomas DL, Taylor EJ (1990) Study designs and tests for comparing resource use and availability. J Wildl Manag 54:322–330.Google Scholar
  186. Thomas DL, Taylor EJ (2006) Study designs and tests for comparing resource use and availability II. J Wildl Manag 70:324–336.Google Scholar
  187. Thomas DL, Johnson D, Griffith B (2006) A Bayesian random effects discrete-choice model for resource selection: population-level selection inference. J Wildl Manag 70:404–412.Google Scholar
  188. Turner MG, Gardner RH, O'Neill RV (2003) Landscape ecology in theory and practice. Springer, New York.Google Scholar
  189. Tyre AJ, Tenhumberg H, Field SA, et al (2003) Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecol Appl 13:1790–1801.Google Scholar
  190. Van Horne B (1983) Density as a misleading indicator of habitat quality. J Wildl Manag 47:893–901.Google Scholar
  191. Vaz S, Martin CS, Eastwood PD, et al (2008) Modelling species distributions using regression quantiles. J Appl Ecol 45:204–217.Google Scholar
  192. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edition. Springer, New York.Google Scholar
  193. Watt AS (1947) Pattern and process in the plant community. J Ecol 35:1–22.Google Scholar
  194. Welsh AH, Cunningham RB, Donnelly CF, Lindenmayer DB (1996) Modelling the abundance of rare species: statistical models for counts with extra zeros. Ecol Model 88:297–308.Google Scholar
  195. White PS (1979) Pattern, process and natural disturbance in vegetation. Botanical Rev 45:229–299.Google Scholar
  196. Whittaker RH (1975) Communities and Ecosystems. Macmillan, New York.Google Scholar
  197. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397.Google Scholar
  198. Wiens TS, Dale BC, Boyce MS, Kershaw PG (2008) Three way k-fold cross-validation of resource selection functions. Ecol Model 212:244–255.Google Scholar
  199. Wintle BA, Bardos DC (2006) Modeling species-habitat relationships with spatially autocorrelated observational data. Ecol Appl 16:1945–1958.PubMedGoogle Scholar
  200. Wisz MS, Hijmans RJ, Li J, et al (2008) Effects of sample size on the performance of species distribution models. Divers Dist 14:763–773.Google Scholar
  201. Wood SN (2006) Generalized additive models: an introduction with R. CRC/Chapman & Hall, London.Google Scholar
  202. Wood SN, Augustin NH (2002) GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol Model 157:157–177.Google Scholar
  203. Worton BJ (1989) Kernel methods for estimating the utilization distribution in home-range studies. Ecol 70:164–168.Google Scholar
  204. Yen P, Huettmann F, Cooke F (2004). Modelling abundance and distribution of Marbled Murrelets (Brachyramphus marmoratus) using GIS, marine data and advanced multivariate statistics. Ecol Mod 171:395–413.Google Scholar

Copyright information

© Springer 2010

Authors and Affiliations

  • Troy M. Hegel
    • 1
  • Samuel A. Cushman
    • Jeffrey Evans
    • Falk Huettmann
      1. 1.Yukon Government, Environment YukonWhitehorseCanada

      Personalised recommendations