Variation, Use, and Misuse of Statistical Models: A Review of the Effects on the Interpretation of Research Results

Chapter

Abstract

The field of predictive habitat modeling evolved somewhat separately within the sub-disciplines of theoretical ecology, wildlife management, and landscape ecology. This chapter suggests that this is due to slightly different worldviews, cultures, and research applications within each subfield (Table 11.1). Within the theoretical ecology literature, models of all kinds (e.g., movement, foraging, competition, demographic) have been widespread for many years. The evolution from descriptive models of habitat quality (e.g., Whittaker and McCuen 1976), to mathematical formulations of niche (e.g., Austin 1985), to spatially-explicit predictive habitat models (e.g., Saarenmaa et al. 1988) was a gradual one. The driving force in this literature appears to be underlying theoretical formulations of a host of ecological processes and interactions (e.g., population dynamics, movement, predation, competition).

Keywords

Entropy Posit Autocorrelation 

Notes

Acknowledgments

F. Huettmann, M. Hooten, T. Lookingbill and two anonymous reviewers provided helpful comments on an earlier draft of this chapter. Also thanks to N. Laite for assistance with compilation of journal articles for the meta-analysis.

References

  1. Anderson DR, Burnham KP, Thompson WL (2000) Null hypothesis testing: problems, prevalence, and an alternative. J Wildl Manag 64:912–923.CrossRefGoogle Scholar
  2. Anderson DR, Link WA, Johnston DJ, Burnham KP (2001) Suggestions for presenting the results of data analysis. J Wildl Manag 65:373–378.CrossRefGoogle Scholar
  3. Anderson DR, Burnham KP (2002) Avoiding pitfalls when using information-theoretic methods. J Wildl Manag 66:912–918.CrossRefGoogle Scholar
  4. Austin MP (1985) Continuum concept, ordination methods, and niche theory. Annu Rev Ecol Syst 16:39–61.CrossRefGoogle Scholar
  5. Borra S, DiCiaccio A (2002) Improving nonparametric regression methods by bagging and boosting. Comput Stat Data Anal 38:407–420.CrossRefGoogle Scholar
  6. Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FKA (2002) Evaluating resource selection functions. Ecol Modell 157:281–300.CrossRefGoogle Scholar
  7. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group. Belmont, California.Google Scholar
  8. Breiman L (2001a) Random forests. Mach Learn 45:5–32.CrossRefGoogle Scholar
  9. Breiman L (2001b) Statistical modeling: the two cultures. Stat Sci 16: 199–231.CrossRefGoogle Scholar
  10. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition. Springer, New York.Google Scholar
  11. Busby JR (1991) BIOCLIM – a bioclimate analysis and prediction system. In: Margules CR, Austin MR (eds) Nature conservation: cost effective biological surveys and data analysis. CSIRO, Melbourne.Google Scholar
  12. Carpenter G, Gillison AN, Winter J (1993) DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodivers Conserv 2:667–680.CrossRefGoogle Scholar
  13. Clark JD, Dunn JE, Smith KG (1993) A multivariate model of female black bear habitat use for a geographic information system. J Wildl Manag 7:519–526.CrossRefGoogle Scholar
  14. Chamberlin TC (1965) The method of multiple working hypotheses. Science 148:754–759.Google Scholar
  15. De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecol 81:3178–3192.CrossRefGoogle Scholar
  16. deFrutos A, Olea PP, Vera R (2007) Analyzing and modelling spatial distribution of summering lesser kestrel: the role of spatial autocorrelation. Ecol Modell 200:33–44.CrossRefGoogle Scholar
  17. Dettmers R, Buehler DA, Bartlett JB (2002) A test and comparison of wildlife-habitat modeling techniques for predicting bird occurrence at a regional scale. Pages 607–615 In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, W. A. Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC.Google Scholar
  18. Dzeroski S, Drumm D (2003) Using regression trees to identify the habitat preference of the sea cucumber (Holothuria leucospilota) on Rarotonga, Cook Islands. Ecol Modell 170:219–226.CrossRefGoogle Scholar
  19. Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMC, Peterson AT, Philips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmerman NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151.CrossRefGoogle Scholar
  20. Elith J, Leathwick J (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Divers Distrib 13:265–275.CrossRefGoogle Scholar
  21. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813.CrossRefPubMedGoogle Scholar
  22. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49.CrossRefGoogle Scholar
  23. Ferrier S, Drielsma M, Manion G, Watson G (2002) Extended statistical approaches to modelling spatial pattern in biodiversity: the northeast New South Wales experience II. Community level modelling. Biodivers Conserv 11:2309–2338.CrossRefGoogle Scholar
  24. Friedman JH (1991) Multivariate adaptive regression splines (with discussion). Ann Stat 19:1–141.CrossRefGoogle Scholar
  25. Friedman JH, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–407.CrossRefGoogle Scholar
  26. Garzon MB, Blazek R, Neteler M, Sanchez de Dios R, Ollero HS, Furlanello C (2006) Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecol Modell 197:383–393.CrossRefGoogle Scholar
  27. Graham CH, Ferrier S, Huettman F, Mortiz C, Peterson AT (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503.CrossRefPubMedGoogle Scholar
  28. Graham CH, Elith J, Hijmans RJ, Guisan A, Peterson AT, Loiselle BA, NCEAS PSDWG (2008) The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol 45:239–247.CrossRefGoogle Scholar
  29. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186.CrossRefGoogle Scholar
  30. Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT (2007a) What matters for predicting the occurrences of trees: techniques, data or species’ characteristics? Ecol Monogr 77:615–630.CrossRefGoogle Scholar
  31. Guisan A, Graham CH, Elith J, Huettmann F, NCEAS SDMG (2007b) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340.CrossRefGoogle Scholar
  32. Guthrey FS, Brennan LA, Peterson MJ, Lusk JJ (2005) Information theory in wildlife science: critique and viewpoint. J Wildl Manag 69:457–465.CrossRefGoogle Scholar
  33. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36.PubMedGoogle Scholar
  34. Hirzel AH, Arlettaz R (2003) Modeling habitat suitability for complex species distributions by environmental-distance geometric mean. Environ Manag 32:614–623.CrossRefGoogle Scholar
  35. Holloway GL, Malcolm JR (2006) Sciurid habitat relationships in forests managed under selection and shelterwood silviculture in Ontario. J Wildl Manag 70:1735–1745.CrossRefGoogle Scholar
  36. Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 15:651–674.CrossRefGoogle Scholar
  37. Jelaska SD, Antoni O, Nikoli T, Hrsak V, Plazibat M, Krizan J (2003) Estimating plant species occurrence in MTB/64 quadrants as a function of DEM-based variables-a case study for Medvednica Nature Park, Croatia. Ecol Modell 170:333–343.CrossRefGoogle Scholar
  38. Johnson DH (1999) The insignificance of statistical significance testing. J Wildl Manag 63:763–772.CrossRefGoogle Scholar
  39. Johnson DH (2002) The role of hypothesis testing in wildlife science. J Wildl Manag 66:272–286.CrossRefGoogle Scholar
  40. Keating KA, Cherry S (2004) Use and interpretation of logistic regression models in habitat selection studies. J Wildl Manag 68:774–789.CrossRefGoogle Scholar
  41. Li J, Hilbert DW (2008) LIVES: a new habitat modelling technique for predicting the distribution of species’ occurrences using presence-only data based on limiting factor theory. Biodivers Conserv 17:3079–3095.CrossRefGoogle Scholar
  42. Link WA, Barker RJ (2006) Model weights and the foundations of multimodel inference. Ecology 87:2626–2635.CrossRefPubMedGoogle Scholar
  43. Lippitt CD, Rogan J, Toledana J, Sangermano F, Eastman JR, Mastro V, Sawyer A (2008) Incorporating anthropogenic variables into a species distribution model to map gypsy moth risk. Ecol Modell 210:339–350.CrossRefGoogle Scholar
  44. MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL, Hines JE (2006) Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Burlington, MA.Google Scholar
  45. Maisonneuve C, Belanger L, Bordage D, Jobin B, Grenier M, Beauliu J, Gabor S, Filion B (2006) American black duck and mallard duck breeding distribution and habitat relationships along a forest-agricultural gradient in southern Quebec. J Wildl Manag 70:450–459.CrossRefGoogle Scholar
  46. Manly BJF (1985) Measuring selectivity from multiple choice feeding-preference experiments. Biometrics 5:709–715.Google Scholar
  47. Manly BJF, McDonald LL, Thomas DL (1993) Resource selection by animals: statistical design and analysis for field studies. 1st edition. Chapman and Hall, London.Google Scholar
  48. Manly BJF, McDonald LL, Thomas DL, McDonald TL, Erickson WP (2003) Resource selection by animals: statistical design and analysis for field studies. 2nd edition. Kluwer Academic Publishers, Dordrecht, NL.Google Scholar
  49. Miller J, Franklin J (2002) Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol Modell 157:227–247.CrossRefGoogle Scholar
  50. Moisen GG, Freeman EA, Blackard JA, Frescino TS, Zimmermann NE, Edwards TC (2006) Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecol Modell 199:176–187.CrossRefGoogle Scholar
  51. Naugle DE, Higgins KF, Nusser SM, Johnson WC (1999) Scale-dependent habitat -use in three species of prairies wetland birds. Landsc Ecol 14:267–276.CrossRefGoogle Scholar
  52. Olivier F, Wotherspoon SJ (2005) GIS-based application of resource selection functions to the prediction of snow petrel distribution and abundance in East Antarctica: comparing models at multiple scales. Ecol Modell 189:105–129.CrossRefGoogle Scholar
  53. Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Modell 133:225–245.CrossRefGoogle Scholar
  54. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259.CrossRefGoogle Scholar
  55. Pittman SJ, Chistensen JD, Caldow C, Menza C, Monaco ME (2007) Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean. Ecol Modell 204:9–21.CrossRefGoogle Scholar
  56. Ribe R, Morganti R, Hulse D, Shull R (1998) A management driven investigation of landscape patterns of northern spotted owl nesting territories in the high Cascades of Oregon. Landsc Ecol 13:1–13.CrossRefGoogle Scholar
  57. Robinson DH, Wainer H (2002) On the past and future of null hypothesis significance testing. J Wildl Manag 66:263–271.CrossRefGoogle Scholar
  58. Rotenberry JT, Knick ST, Dunn JE (2002) A minimalist approach to mapping species’ habitat: Pearson’s planes of closest fit. Pages 281–289 In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, USA.Google Scholar
  59. Saarenmaa H, Stone ND, Folse LJ, Packard JM, Grant WE, Makela ME, Coulson RN (1988) An artificial intelligence modelling approach to simulating animal/habitat interactions. Ecol Modell 44:125–141.CrossRefGoogle Scholar
  60. Sleep DJH, Drever MC, Nudds TD (2007) Statistical versus biological hypothesis testing: response to Steidl. J Wildl Manag 71:2120–2121.CrossRefGoogle Scholar
  61. Soberón JM, Llorente JB, Onate L (2000) The use of specimen-label databases for conservation purposes: an example using Mexican Papilionid and Pierid butterflies. Biodivers Conserv 9:1441–1466.CrossRefGoogle Scholar
  62. Steidl RJ (2006) Model selection, hypothesis testing, and risks of condemning analytical tools. J Wildl Manag 70:1497–1498.CrossRefGoogle Scholar
  63. Steidl RJ (2007) Limits of data analysis in scientific inference: reply to Sleep et al. J Wildl Manag 71:2122–2124.CrossRefGoogle Scholar
  64. Stockwell DRB, Peters AT (1999) The GARP modelling system: problems and solutions to automated spatial prediction. Int J Geogr Inf Sci 13: 143–158.CrossRefGoogle Scholar
  65. Tsoar A, Allouch O, Steinitz O, Rotem D, Kadmon R (2007) A comparative evaluation of preesence-only methods for modelling species distribution. Divers Distrib 13:397–405.CrossRefGoogle Scholar
  66. Vayssiéres MP, Plant RE, Allen-Diaz BH (2000) Classification trees: an alternative non-parametric approach for predicting species distributions. J Veg Sci 11:679–694.CrossRefGoogle Scholar
  67. Whittaker GA, McCuen RH (1976) A proposed methodology for assessing the quality of wildlife habitat. Ecol Modell 2:251–272.CrossRefGoogle Scholar
  68. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, NCEAS PSDWG (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:736–773.Google Scholar
  69. Yang X, Skidmore AK, Melick DR, Zhou Z, Xu J (2006) Mapping non-wood forest product (matsutake mushrooms) using logistic regression and a GIS expert system. Ecol Modell 198:208–218.CrossRefGoogle Scholar
  70. Yen PPW, Huettmann F, Cooke F (2004) A large-scale model for the at-sea distribution and abundance of Marbeled Murrelets (Brachyramphus marmoratus) during the breeding season in coastal British Columbia, Canada. Ecol Modell 171:395–413.CrossRefGoogle Scholar
  71. Zaniewski AE, Lehmann A, Overton JMC (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Modell 157:261–280.CrossRefGoogle Scholar

Copyright information

© Springer Science+BUsiness Media, LLC 2011

Authors and Affiliations

  1. 1.Department of BiologyMemorial UniversitySt. John’sCanada

Personalised recommendations