The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

PLoS ONE

Volumn 8, Issue 2, 2013, Pages

  Syfert, Mindy M ^a,b   Smith, Matthew J ^b   Coomes, David A ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIODIVERSITY; COMPUTER PROGRAM; CONTROLLED STUDY; ECOLOGY; FERN; GEOGRAPHIC DISTRIBUTION; MAXENT SOFTWARE; NEW ZEALAND; NONHUMAN; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; SAMPLING ERROR; SPECIES DISTRIBUTION; TOPOGRAPHY; TREE; VASCULAR PLANT;

BIODIVERSITY; FERNS; MODELS, BIOLOGICAL; NEW ZEALAND; PROBABILITY; SOFTWARE; TREES;

EID: 84874016128     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0055158     Document Type: Article

References (60)

1. Thuiller W, Albert C, Araujo MB, Berry PM, Cabeza M, et al. (2008) Predicting global change impacts on plant species' distributions: Future challenges. Perspectives in Plant Ecology Evolution and Systematics 9: 137-152.
2. Elith J, Leathwick JR, (2009) Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology Evolution and Systematics 40: 677-697.
3. Guisan A, Thuiller W, (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993-1009.
4. Araújo MB, Luoto M, (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16: 743-753.
5. Yates CJ, McNeill A, Elith J, Midgley GF, (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.
6. Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT, (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution 19: 497-503.
7. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, et al. (2008) Effects of sample size on the performance of species distribution models. Diversity and Distributions 14: 763-773.
8. 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: 3-22.
9. Franklin J (2009) Mapping Species Distributions: Spatial Inference and Prediction. Cambridge, UK: Cambridge University Press. 338 p.
10. 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.
11. Williams JN, Seo CW, Thorne J, Nelson JK, Erwin S, et al. (2009) Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions 15: 565-576.
12. Mateo RG, Croat TB, Felicísimo ÁM, Muñoz J, (2010) Profile or group discriminative techniques? Generating reliable species distribution models using pseudo-absences and target-group absences from natural history collections. Diversity and Distributions 16: 84-94.
13. Hernandez PA, Graham CH, Master LL, Albert DL, (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29: 773-785.
14. Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, et al. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19: 181-197.
15. Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, et al. (2012) Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution: early view.
16. Reddy S, Davalos LM, (2003) Geographical sampling bias and its implications for conservation priorities in Africa. Journal of Biogeography 30: 1719-1727.
17. Kadmon R, Farber O, Danin A, (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecological Applications 14: 401-413.
18. Hortal J, Jimenez-Valverde A, Gomez JF, Lobo JM, Baselga A, (2008) Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117: 847-858.
19. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W, (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution 3: 327-338.
20. Zaniewski AE, Lehmann A, Overton JM, (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling 157: 261-280.
21. Lutolf M, Kienast F, Guisan A, (2006) The ghost of past species occurrence: improving species distribution models for presence-only data. Journal of Applied Ecology 43: 802-815.
22. 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. Journal of Applied Ecology 41: 263-274.
23. Chefaoui RM, Lobo JM, (2008) Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling 210: 478-486.
24. Phillips SJ, Dudik M, (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161-175.
25. Warren DL, Seifert SN, (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications 21: 335-342.
26. Phillips SJ, Anderson RP, Schapire RE, (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259.
27. Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, et al. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17: 43-57.
28. Fielding AH, Bell JF, (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38-49.
29. Guisan A, Zimmermann NE, (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135: 147-186.
30. Bystriakova N (2008) The Ecology and Biogeography of Tree Ferns [PhD]. Cambridge, UK: University of Cambridge. 209 p.
31. Korall P, Pryer KM, Metzgar JS, Schneider H, Conant DS, (2006) Tree ferns: Monophyletic groups and their relationships as revealed by four protein-coding plastid loci. Molecular Phylogenetics and Evolution 39: 830-845.
32. Korall P, Conant DS, Metzgar JS, Schneider H, Pryer KM, (2007) A molecular phylogeny of scaly tree ferns (Cyatheaceae). American Journal of Botany 94: 873-886.
33. Wiser SK, Bellingham PJ, Burrows LE, (2001) Managing biodiversity information: development of New Zealand's National Vegetation Survey databank. New Zealand Journal of Ecology 25: 1-17.
34. Coomes DA, Allen RB, Scott NA, Goulding C, Beets P, (2002) Designing systems to monitor carbon stocks in forests and shrublands. Forest Ecology and Management 164: 89-108.
35. Richardson SJ, Peltzer DA, Hurst JM, Allen RB, Bellingham PJ, et al. (2009) Deadwood in New Zealand's indigenous forests. Forest Ecology and Management 258: 2456-2466.
36. Wiser SK, Hurst JM, Wright EF, Allen RB, (2011) New Zealand's forest and shrubland communities: a quantitative classification based on a nationally representative plot network. Applied Vegetation Science 14: 506-523.
37. Payton IJ, Newell CL, Beets PN (2004) New Zealand Carbon Monitoring System: indigenous forest and shrubland data collection manual. Manaaki Whenua - Landcare Research and Forest Research, NZ.
38. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A, (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.
39. Trabucco A, Zomer RJ (2009) Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Dataset. CGIAR-CSI GeoPortal, Available: http://www.csi.cgiar.org. Accessed: 2012 Dec 19.
40. Trabucco A, Zomer RJ (2010) Global High-Resolution Soil-Water Balance Geospatial Database. CGIAR-CSI GeoPortal Available: http://www.cgiar.csi.org. Accessed December 19 2012.
41. Stephenson NL, (1998) Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. Journal of Biogeography 25: 855-870.
42. Stephenson NL (2000) Climate, Vegetation, and Considerations for Restoration. U.S. Geological Survey Open-File Report 00-62.
43. Zuur AF, Ieno EN, Elphick CS, (2010) A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution 1: 3-14.
44. Lehmann A, Leathwick JR, Overton JM, (2002) Assessing New Zealand fern diversity from spatial predictions of species assemblages. Biodiversity and Conservation 11: 2217-2238.
45. Woodward FI, Williams BG, (1987) Climate and plant distribution at global and local scales. Plant Ecology 69: 189-197.
46. Frank DA, Inouye RS, (1994) Temporal Variation in Actual Evapotranspiration of Terrestrial Ecosystems- Patterns and Ecological Implications. Journal of Biogeography 21: 401-411.
47. Elith J, Graham CH, (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32: 66-77.
48. R Development Core Team (2010) R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available: http://www.r-project.org/. Accessed September 18 2011.
49. Sing T, Sander O, Beerenwinkel N, Lengauer T, (2005) ROCR: visualizing classifier performance in R. Bioinformatics. 21: 3940-3941.
50. Pearce J, Ferrier S, (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling 133: 225-245.
51. Phillips SJ, Elith J, (2010) POC plots: calibrating species distribution models with presence-only data. Ecology 91: 2476-2484.
52. Stokland JN, Halvorsen R, Stoa B, (2011) Species distribution modelling-effect of design and sample size of pseudo-absence observations. Ecological Modelling 222: 1800-1809.
53. Wisz M, 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.
54. VanDerWal J, Shoo LP, Graham C, William SE, (2009) Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? Ecological Modelling 220: 589-594.
55. Elith J, Kearney M, Phillips S, (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution 1: 330-342.
56. Newbold T, Reader T, Zalat S, El-Gabbas A, Gilbert F, (2009) Effect of characteristics of butterfly species on the accuracy of distribution models in an arid environment. Biodiversity and Conservation 18: 3629-3641.
57. Thuiller W, Brotons L, Araujo MB, Lavorel S, (2004) Effects of restricting environmental range of data to project current and future species distributions. Ecography 27: 165-172.
58. Hanspach J, Kühn I, Schweiger O, Pompe S, Klotz S, (2011) Geographical patterns in prediction errors of species distribution models. Global Ecology and Biogeography 20: 779-788.
59. Rivers MC, Taylor L, Brummitt NA, Meagher TR, Roberts DL, et al. (2011) How many herbarium specimens are needed to detect threatened species? Biological Conservation 144: 2541-2547.
60. Brummitt NA, Bachman SP, Moat J, (2008) Applications of the IUCN Red List: towards a global barometer for plant diversity. Endangered Species Research 6: 127-135.