Dealing with Noisy Absences to Optimize Species Distribution Models: An Iterative Ensemble Modelling Approach

PLoS ONE

Volumn 7, Issue 11, 2012, Pages

  Lauzeral, Christine ^a,b   Grenouillet, Gaël ^a,b   Brosse, Sébastien ^a,b  

^a UNIVERSITÉ PAUL SABATIER   (France)

^b CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ANALYTIC METHOD; AREA UNDER THE CURVE; ARTICLE; CLASSICAL ENSEMBLE MODELLING; CLIMATE; CONTROLLED STUDY; ECOLOGICAL NICHE; FRANCE; INTERMETHOD COMPARISON; ITERATIVE ENSEMBLE MODELLING; MATHEMATICAL MODEL; NOISE; PERFORMANCE; PREDICTION; PROCESS OPTIMIZATION; RELIABILITY; SPECIES DISTRIBUTION;

BIODIVERSITY; CONSERVATION OF NATURAL RESOURCES; ECOLOGY; ECOSYSTEM; ENVIRONMENT; MODELS, THEORETICAL;

EID: 84869214025     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0049508     Document Type: Article

References (62)

1. Morin X, Thuiller W, (2009) Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology 90: 1301-1313.
2. Kearney M, Porter W, (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. Ecology Letters 12: 334-350.
3. Guisan A, Thuiller W, (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993-1009.
4. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151.
5. Lobo JM, Jiménez-Valverde A, Hortal J, (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33: 103-114.
6. Lobo JM, (2008) More complex distribution models or more representative data? Biodiversity Informatics 5: 14-19.
7. Rowe RJ, Finarelli JA, Rickart EA, (2010) Range dynamics of small mammals along an elevational gradient over an 80-year interval. Glob Change Biol 16: 2930-2943.
8. Moritz C, Patton JL, Conroy CJ, Parra JL, White GC, et al. (2008) Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322: 261-264.
9. Kéry M, Gardner B, Monnerat C, (2010) Predicting species distributions from checklist data using site-occupancy models. J Biogeogr 37: 1851-1862.
10. Gallien L, Douzet R, Pratte S, Zimmermann NE, Thuiller W (2012) Invasive species distribution models- how violating the equilibrium assumption can create new insights. Global Ecol Biogeogr.
11. Hirzel AH, Hausser J, Chessel D, Perrin N, (2002) Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology 83: 2027-2036.
12. Farber O, Kadmon R, (2003) Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecoll Model 160: 115-130.
13. Wisz MS, Guisan A, (2009) Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecology 9: 8.
14. Zaniewski AE, Lehmann A, Overton JM, (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Model 157: 261-280.
15. Engler R, Guisan A, Rechsteiner L, (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41: 263-274.
16. Chefaoui RM, Lobo JM, (2008) Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol Model 210: 478-486.
17. Capinha C, Leung B, Anastácio P, (2011) Predicting worldwide invasiveness for four major problematic decapods: an evaluation of using different calibration sets. Ecography 34: 448-459.
18. Stokland JN, Halvorsen R, Støa B, (2011) Species distribution modelling-Effect of design and sample size of pseudo-absence observations. Ecol Model 222: 1800-1809.
19. Kadmon R, Farber O, Danin A, (2003) A systematic analysis of factors affecting the performance of climatic envelope models. Ecol Appl 13: 853-867.
20. Olden JD, Jackson DA, (2002) A comparison of statistical approaches for modelling fish species distributions. Freshwater Biol 47: 1976-1995.
21. Segurado P, Araújo MB, (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31: 1555-1568.
22. Manel S, Dias JM, Ormerod SJ, (1999) Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecol Model 120: 337-347.
23. Elith J, Leathwick JR, (2009) Species distribution models: Ecological explanation and prediction across space and time. Annu Rev Ecol Evol S 40: 677-697.
24. Araújo MB, Whittaker RJ, Ladle RJ, Erhard M, (2005) Reducing uncertainty in projections of extinction risk from climate change. Global Ecol Biogeogr 14: 529-538.
25. Araújo MB, New M, (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22: 42-47.
26. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W, (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15: 59-69.
27. Grenouillet G, Buisson L, Casajus N, Lek S (2011) Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34 9-17.
28. Brown JH, (1984) On the relationship between abundance and distribution species. Am Nat 124: 225-279.
29. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A, (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 1965-1978.
30. Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G, (2010) Uncertainty in ensemble forecasting of species distribution. Glob Change Biol 16: 1145-1157.
31. Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC, (2005) Climate change threats to plant diversity in Europe. P Natl A Sci USA 102: 8245-8250.
32. Marini MA, Barbet-Massin M, Lopes LE, Jiguet F, (2009) Predicted climate-driven bird distribution changes and forecasted conservation conflicts in a Neotropical savanna. Conserv Biol23: 1558-1567.
33. Lobo JM, Tognelli MF, (2011) Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data. J Nat Conserv 19: 1-7.
34. Jiménez-Valverde A, Lobo JM, (2007) Threshold criteria for conversion of probability of species presence to either-or presence-absence Acta Oecol. 31: 361-369.
35. Soberón J, Nakamura M, (2009) Niches and distributional areas: Concepts, methods, and assumptions. P Natl A Sci USA 106: 19644-19650.
36. Freeman EA, Moisen GG, (2008) A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol Model 217: 48-58.
37. Mouton AM, De Baets B, Van Broekhoven E, Goethals PLM, (2009) Prevalence-adjusted optimisation of fuzzy models for species distribution. Ecol Model 220: 1776-1786.
38. R Development Core Team (2011) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
39. Thuiller W, (2004) Patterns and uncertainties of species' range shifts under climate change. Glob Change Biol 10: 2020-2027.
40. 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.
41. Santika T, (2011) Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data. Global Ecol Biogeogr 20: 181-192.
42. Allouche O, Tsoar A, Kadmo R, (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43: 1223-1232.
43. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, et al. (2008) Effects of sample size on the performance of species distribution models. Divers and Distrib 14: 763-773.
44. Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT, (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34: 102-117.
45. Cubaynes S, Pradel R, Choquet R, Duchamp C, Gaillard J-M, et al. (2010) Importance of accounting for detection heterogeneity when estimating abundance: the case of french wolves. Conserv Biol 24: 621-626.
46. Cianfrani C, Le Lay G, Hirzel AH, Loy A, (2010) Do habitat suitability models reliably predict the recovery areas of threatened species? J Appl Ecol 47: 421-430.
47. Gallien L, Munkemuller T, Albert CH, Boulangeat I, Thuiller W, (2010) Predicting potential distributions of invasive species: where to go from here? Divers Distrib 16: 331-342.
48. Olden JD, Kennard MJ, Leprieur F, Tedesco PAW, K.O., Garcia-Berthou E, (2010) Conservation biogeography of freshwater fishes: recent progress and future challenges. Divers Distrib 16: 496-513.
49. Leprieur F, Beauchard O, Blanchet S, Oberdorff T, Brosse S, (2008) Fish invasions in the world's river systems: when natural processes are blurred by human activities PloS Biology. 6: e28 http://dx.doi.org/10.1371/journal.pbio.0060028.
50. Blanchet S, Leprieur F, Beauchard O, Staes J, Oberdorff T, et al. (2009) Broad-scale determinants of non-native fish species richness are context-dependent. P Roy Soc B- Biol Sci 276: 2385-2394.
51. Václavík T, Meentemeyer RK, (2009) Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions? Ecol Model 220: 3248-3258.
52. Brotons L, Thuiller W, Araújo MB, Hirzel AH, (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27: 437-448.
53. Jiménez-Valverde A, Peterson AT, J. Soberón J, Overton JM, Aragón P, et al. (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13: 2785-2797.
54. Beaumont LJ, Gallagher RV, Thuiller W, Downey PO, Leishman MR, et al. (2009) Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Divers Distrib 15: 409-420.
55. Lauzeral C, Leprieur F, Beauchard O, Duron Q, Oberdorff T, et al. (2011) Identifying climatic niche shifts using coarse-grained occurrence data: a test with non-native freshwater fish. Global Ecol Biogeogr 20: 407-414.
56. Rödder D, Lötters S, (2009) Niche shift versus niche conservatism? Climatic characteristics of the native and invasive ranges of the Mediterranean house gecko (Hemidactylus turcicus). Global Ecol Biogeogr 18: 674-687.
57. Medley KA, (2010) Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models. Global Ecol Biogeogr 19: 122-133.
58. Mack RN, Simberloff D, Lonsdale WM, Evans H, Clout M, et al. (2000) Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol Appl 10: 689-710.
59. Randin CF, Dirnbo T, Dullinger S, Zimmermann NE, Zappa M, et al. (2006) Are niche-based species distribution models transferable in space? J Biogeogr 33: 1689-1703.
60. Menke SB, Holway DA, Fisher RN, Jetz W, (2009) Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder. Global Ecol Biogeogr 18: 50-63.
61. Williams JN, Seo C, Thorne J, Nelson JK, Erwin S, et al. (2009) Using species distribution models to predict new occurrences for rare plants. Divers Distrib 15: 565-576.
62. Liu CR, Berry PM, Dawson TP, Pearson RG, (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385-393.