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Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

Fourcade, Yoan, Engler, Jan O., Rödder, Dennis, Secondi, Jean·2014·PLoS ONE
DOI 10.1371/journal.pone.0097122PMC PMC4018261
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Figure 1 Workflow used in analyses. Original datasets of a virtual and 2 real species were altered to create 12 bias combining 4 bias types and 3 bias intensities. Five methods of sampling bias correction were employed to assess the improvement in the modeled distribution relative to the original distribution using MAXENT. Correction performance was assessed using AUC and 3 measures of overlap between the corrected the original unbiased model.
Figure 1 Workflow used in analyses. Original datasets of a virtual and 2 real species were altered to create 12 bias combining 4 bias types and 3 bias intensities. Five methods of sampling bias correction were employed to assess the improvement in the modeled distribution relative to the original distribution using MAXENT. Correction performance was assessed using AUC and 3 measures of overlap between the corrected the original unbiased model.CC BY · PubMed Central
Figure 2 Locations of records used for modeling. (A) Virtual species; (B) Chrysemys picta; (C) Plethodon cylindraceus
Figure 2 Locations of records used for modeling. (A) Virtual species; (B) Chrysemys picta; (C) Plethodon cylindraceusCC BY · PubMed Central
Figure 3 Generation of sampling bias for the virtual species. To generate artificial sampling bias, the original dataset (here the virtual species) was altered into 4 different types of bias (rows), each with 3 intensities (columns).
Figure 3 Generation of sampling bias for the virtual species. To generate artificial sampling bias, the original dataset (here the virtual species) was altered into 4 different types of bias (rows), each with 3 intensities (columns).CC BY · PubMed Central
Figure 4 Evaluation indices of biased models across bias intensities. Reduction of AUC between unbiased and biased models (in percentage), Schoener's D overlaps between biased and unbiased models computed on SDMs (Dgeo) and in environmental space (Denv), and Gover the binary distribution overlap (mean ± SD). Dark grey bars: Chrysemys picta, light grey bars: Plethodon cylindraceus, black bars: virtual species.
Figure 4 Evaluation indices of biased models across bias intensities. Reduction of AUC between unbiased and biased models (in percentage), Schoener's D overlaps between biased and unbiased models computed on SDMs (Dgeo) and in environmental space (Denv), and Gover the binary distribution overlap (mean ± SD). Dark grey bars: Chrysemys picta, light grey bars: Plethodon cylindraceus, black bars: virtual species.CC BY · PubMed Central
Figure 5 Rank of each method to correct sampling bias. Mean ranks ± standard-error for the performance of each method to correct sampling bias for each species (Chrysemys picta: left, Plethodon cylindraceus: center, virtual species: right), following 3 measures of correction performance: ΔAUC (left), ΔDgeo (centre), and ΔGover (right). For each type of bias and bias intensity, the method which results in the most efficient correction is set to 1 whereas the least powerful method is set to 5. The plotted values are the mean rank across the 4 types of bias and 3 intensities.
Figure 5 Rank of each method to correct sampling bias. Mean ranks ± standard-error for the performance of each method to correct sampling bias for each species (Chrysemys picta: left, Plethodon cylindraceus: center, virtual species: right), following 3 measures of correction performance: ΔAUC (left), ΔDgeo (centre), and ΔGover (right). For each type of bias and bias intensity, the method which results in the most efficient correction is set to 1 whereas the least powerful method is set to 5. The plotted values are the mean rank across the 4 types of bias and 3 intensities.CC BY · PubMed Central

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Research works printed but not linked: 82

  1. A Purvis, 2000, Predicting extinction risk in declining species, Proc R Soc B Biol Sci, 267, 1947, 10.1098/rspb.2000.1234 · DOI · Paper page
  2. GM Mace, 2008, Quantification of extinction risk: IUCN's system for classifying threatened species, Conserv Biol, 22, 1424, 10.1111/j.1523-1739.2008.01044.x · DOI
  3. G Harris, 2008, Range size and extinction risk in forest birds, Conserv Biol, 22, 163, 10.1111/j.1523-1739.2007.00798.x · DOI
  4. ASL Rodrigues, 2006, The value of the IUCN Red List for conservation, Trends Ecol Evol, 21, 71, 10.1016/j.tree.2005.10.010 · DOI
  5. N Myers, 2000, Biodiversity hotspots for conservation priorities, Nature, 403, 853, 10.1038/35002501 · DOI · Paper page
  6. R Mittermeier, 1998, Biodiversity hotspots and major tropical wilderness areas: approaches to setting conservation priorities, Conserv Biol, 12, 516, 10.1046/j.1523-1739.1998.012003516.x · DOI
  7. W Reid, 1998, Biodiversity hotspots, Trends Ecol Evol, 13, 275, 10.1016/S0169-5347(98)01363-9 · DOI
  8. A Moilanen, 2005, Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems, Proc R Soc B Biol Sci, 272, 1885, 10.1098/rspb.2005.3164 · DOI · Paper page
  9. Peterson A (2006) Uses and requirements of ecological niche models and related distributional models. Biodivers Informatics: 59–72. · DOI
  10. Ferrier S (1984) The status of the Rufous Scrub-bird Atrichornis rufescens: habitat, geographical variation and abundance. PhD thesis. Armidale, Australia: University of New England.
  11. M Austin, 2006, Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory, Ecol Modell, 199, 197, 10.1016/j.ecolmodel.2006.05.023 · DOI
  12. J Elith, 2009, Species Distribution Models: Ecological Explanation and Prediction Across Space and Time, Annu Rev Ecol Evol Syst, 40, 677, 10.1146/annurev.ecolsys.110308.120159 · DOI · Paper page
  13. J Soberón, 2005, Interpretation of models of fundamental ecological niches and species' distributional areas, Biodivers Informatics, 2, 1, 10.17161/bi.v2i0.4 · DOI
  14. J Elith, 2011, A statistical explanation of MaxEnt for ecologists, Divers Distrib, 17, 43, 10.1111/j.1472-4642.2010.00725.x · DOI · Paper page
  15. Elith J, Burgman M (2002) Predictions and their validation: Rare plants in the Central Highlands, Victoria, Australia. Predicting Species Occurrences: Issues of Accuracy and Scale. Washington, USA: Island Press. pp. 303–313.
  16. A Jimenez-Valverde, 2011, Use of niche models in invasive species risk assessments, Biol Invasions, 13, 2785, 10.1007/s10530-011-9963-4 · DOI
  17. J Jeschke, 2008, Usefulness of bioclimatic models for studying climate change and invasive species, Ann N Y Acad Sci, 1134, 1, 10.1196/annals.1439.002 · DOI
  18. S Sinclair, 2010, How useful are species distribution models for managing biodiversity under future climates, Ecol Soc, 15, 8, 10.5751/ES-03089-150108 · DOI
  19. JS Thorn, 2009, Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus), Divers Distrib, 15, 289, 10.1111/j.1472-4642.2008.00535.x · DOI
  20. Franklin J (2009) Mapping Species Distributions: Spatial Inference and Prediction. Cambridge, UK: Cambridge University Press. · DOI
  21. M Boyce, 1999, Relating populations to habitats using resource selection functions, Trends Ecol Evol, 14, 268, 10.1016/S0169-5347(99)01593-1 · DOI
  22. Manly BFJ, McDonald LL, Thomas D (1993) Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Chapman & Hall. · DOI
  23. McCullagh P, Nelder JA (1989) Generalized Linear Models. London, UK: Chapman & Hall. · DOI · Paper page
  24. Ripley BD (1996) Pattern Recognition and Neural Networks. Cambridge, UK: Cambridge University Press. · DOI
  25. Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. Proceedings of the twenty-first international conference on Machine learning. New York, New York, USA: ACM. p. 83. · DOI
  26. J Elith, 2006, Novel methods improve prediction of species' distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x · DOI · Paper page
  27. MS Wisz, 2008, Effects of sample size on the performance of species distribution models, Divers Distrib, 14, 763, 10.1111/j.1472-4642.2008.00482.x · DOI · Paper page
  28. SJ Phillips, 2006, Maximum entropy modeling of species geographic distributions, Ecol Modell, 190, 231, 10.1016/j.ecolmodel.2005.03.026 · DOI · Paper page
  29. Elith J, Leathwick JR (2006) Conservation prioritisation using species distribution modelling. In: Moilanen A, Wilson KA, Possingham HP, editors. Spatial conservation prioritization: quantitative methods and computational tools. Oxford, UK: Oxford University Press.
  30. RJ Hijmans, 2005, Very high resolution interpolated climate surfaces for global land areas, Int J Climatol, 25, 1965, 10.1002/joc.1276 · DOI
  31. KH Kozak, 2008, Integrating GIS-based environmental data into evolutionary biology, Trends Ecol Evol, 23, 141, 10.1016/j.tree.2008.02.001 · DOI
  32. R Dennis, 2000, Bias in butterfly distribution maps: the influence of hot spots and recorder's home range, J Insect Conserv, 4, 73, 10.1023/A:1009690919835 · DOI
  33. R Kadmon, 2004, Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models, Ecol Appl, 14, 401, 10.1890/02-5364 · DOI
  34. PJ Leitão, 2011, Effects of geographical data sampling bias on habitat models of species distributions: a case study with steppe birds in southern Portugal, Int J Geogr Inf Sci, 25, 439, 10.1080/13658816.2010.531020 · DOI
  35. N Bystriakova, 2012, Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models, Syst Biodivers, 10, 1, 10.1080/14772000.2012.705357 · DOI
  36. TC Edwards, 2006, Effects of sample survey design on the accuracy of classification tree models in species distribution models, Ecol Modell, 199, 132, 10.1016/j.ecolmodel.2006.05.016 · DOI
  37. B Loiselle, 2008, Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?, J Biogeogr, 35, 105, 10.1111/j.1365-2699.2007.01779.x · DOI
  38. S Reddy, 2003, Geographical sampling bias and its implications for conservation priorities in Africa, J Biogeogr, 30, 1719, 10.1046/j.1365-2699.2003.00946.x · DOI
  39. MM Syfert, 2013, The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models, PLoS One, 8, e55158, 10.1371/journal.pone.0055158 · DOI
  40. GC Costa, 2009, Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot, Biodivers Conserv, 19, 883, 10.1007/s10531-009-9746-8 · DOI
  41. J Beck, 2013, Spatial bias in the GBIF database and its effect on modelling species' geographic distributions, Ecol Inform, 19, 10, 10.1016/j.ecoinf.2013.11.002 · DOI
  42. Y Fourcade, 2013, Confronting expert-based and modelled distributions for species with uncertain conservation status: a case study from the Corncrake (Crex crex), Biol Conserv, 167, 161, 10.1016/j.biocon.2013.08.009 · DOI
  43. MB Araújo, 2006, Five (or so) challenges for species distribution modelling, J Biogeogr, 33, 1677, 10.1111/j.1365-2699.2006.01584.x · DOI
  44. SJ Phillips, 2008, Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson, et al. (2007), Ecography, 31, 272, 10.1111/j.0906-7590.2008.5378.x · DOI
  45. SJ Phillips, 2009, Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data, Ecol Appl, 19, 181, 10.1890/07-2153.1 · DOI · Paper page
  46. Dudík M, Schapire RE, Phillips SJ (2005) Correcting sample selection bias in maximum entropy density estimation. Appearing in Advances in Neural Information Processing Systems. <volume>Vol</volume>: . 18.
  47. D Rödder, 2009, Niche shift versus niche conservatism? Climatic characteristics of the native and invasive ranges of the Mediterranean house gecko (Hemidactylus turcicus), Glob Ecol Biogeogr, 18, 674, 10.1111/j.1466-8238.2009.00477.x · DOI
  48. PE Osborne, 2002, Should data be partitioned spatially before building large-scale distribution models?, Ecol Modell, 157, 249, 10.1016/S0304-3800(02)00198-9 · DOI
  49. S Kramer-Schadt, 2013, The importance of correcting for sampling bias in MaxEnt species distribution models, Divers Distrib, 19, 1366, 10.1111/ddi.12096 · DOI · Paper page
  50. S Varela, 2014, Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models, Ecography
  51. RA Boria, 2014, Spatial filtering to reduce sampling bias can improve the performance of ecological niche models, Ecol Modell, 275, 73, 10.1016/j.ecolmodel.2013.12.012 · DOI
  52. SD Veloz, 2009, Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models, J Biogeogr, 36, 2290, 10.1111/j.1365-2699.2009.02174.x · DOI
  53. O Arino, 2008, GLOBCOVER The most detailed portrait of Earth, ESA Bull - Eur Sp Agency, 136, 24
  54. P Maisongrande, 2004, VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products, Int J Remote Sens, 25, 9, 10.1080/0143116031000115265 · DOI
  55. RK Heikkinen, 2006, Methods and uncertainties in bioclimatic envelope modelling under climate change, Prog Phys Geogr, 6, 751, 10.1177/0309133306071957 · DOI
  56. RD Sagarin, 2002, The “abundant centre” distribution: to what extent is it a biogeographical rule?, Ecol Lett, 5, 137, 10.1046/j.1461-0248.2002.00297.x · DOI
  57. K McCarthy, 2012, Predicting Species Distributions from Samples Collected along Roadsides, Conserv Biol, 26, 68, 10.1111/j.1523-1739.2011.01754.x · DOI
  58. F Ihlow, 2012, On the brink of extinction? How climate change may affect global chelonian species richness and distribution, Glob Chang Biol, 18, 1520, 10.1111/j.1365-2486.2011.02623.x · DOI
  59. RM Chefaoui, 2008, Assessing the effects of pseudo-absences on predictive distribution model performance, Ecol Modell, 210, 478, 10.1016/j.ecolmodel.2007.08.010 · DOI
  60. N Barve, 2011, The crucial role of the accessible area in ecological niche modeling and species distribution modeling, Ecol Modell, 222, 1810, 10.1016/j.ecolmodel.2011.02.011 · DOI
  61. P Acevedo, 2012, Delimiting the geographical background in species distribution modelling, J Biogeogr, 39, 1383, 10.1111/j.1365-2699.2012.02713.x · DOI
  62. CF Dormann, 2007, Methods to account for spatial autocorrelation in the analysis of species distributional data: a review, Ecography, 30, 609, 10.1111/j.2007.0906-7590.05171.x · DOI · Paper page
  63. D Rödder, 2009, Global Amphibian Extinction Risk Assessment for the Panzootic Chytrid Fungus, Diversity, 1, 52, 10.3390/d1010052 · DOI
  64. Stiels D, Schidelko K, Engler JO, Elzen R, Rödder D (2011) Predicting the potential distribution of the invasive Common Waxbill Estrilda astrild (Passeriformes: Estrildidae). J Ornithol: 769–780. · DOI
  65. S Dray, 2007, The ade4 package: implementing the duality diagram for ecologists, J Stat Softw, 22, 1, 10.18637/jss.v022.i04 · DOI
  66. S Gonzalez, 2011, Population distribution models: species distributions are better modeled using biologically relevant data partitions, BMC Ecol, 11, 20, 10.1186/1472-6785-11-20 · DOI
  67. JM Lobo, 2008, AUC: a misleading measure of the performance of predictive distribution models, Glob Ecol Biogeogr, 17, 145, 10.1111/j.1466-8238.2007.00358.x · DOI · Paper page
  68. A Jimenez-Valverde, 2012, Discrimination capacity in species distribution models depends on the representativeness of the environmental domain, Glob Ecol Biogeogr, 22, 508, 10.1111/geb.12007 · DOI
  69. EA Freeman, 2008, {PresenceAbsence}: An R Package for Presence Absence Analysis, J Stat Softw, 23, 1, 10.18637/jss.v023.i11 · DOI · Paper page
  70. T Schoener, 1968, The Anolis lizards of Bimini: resource partitioning in a complex fauna, Ecology, 49, 704, 10.2307/1935534 · DOI
  71. DL Warren, 2008, Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution, Evolution, 62, 2868, 10.1111/j.1558-5646.2008.00482.x · DOI
  72. J Bray, 1957, An ordination of the upland forest communities of southern Wisconsin, Ecol Monogr, 7, 325, 10.2307/1942268 · DOI
  73. D Rödder, 2011, Quantitative metrics of overlaps in Grinnellian niches: advances and possible drawbacks, Glob Ecol Biogeogr, 20, 915, 10.1111/j.1466-8238.2011.00659.x · DOI
  74. O Broennimann, 2012, Measuring ecological niche overlap from occurrence and spatial environmental data, Glob Ecol Biogeogr, 21, 481, 10.1111/j.1466-8238.2011.00698.x · DOI
  75. C Liu, 2005, Selecting thresholds of occurrence in the prediction of species distributions, Ecography, 28, 385, 10.1111/j.0906-7590.2005.03957.x · DOI · Paper page
  76. MB Araújo, 2005, Validation of species–climate impact models under climate change, Glob Chang Biol, 11, 1504, 10.1111/j.1365-2486.2005.01000.x · DOI
  77. JA Swets, 1988, Measuring the accuracy of diagnostic systems, Science, 240, 1285, 10.1126/science.3287615 · DOI · Paper page
  78. A Jimenez-Valverde, 2014, Threshold-dependence as a desirable attribute for discrimination assessment: implications for the evaluation of species distribution models, Biodivers Conserv, 23, 369, 10.1007/s10531-013-0606-1 · DOI
  79. W Thuiller, 2004, Effects of restricting environmental range of data to project current and future species distributions, Ecography, 27, 165, 10.1111/j.0906-7590.2004.03673.x · DOI
  80. JJ VanDerWal, 2009, Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?, Ecol Modell, 220, 589, 10.1016/j.ecolmodel.2008.11.010 · DOI · Paper page
  81. WM Adams, 2004, Biodiversity conservation and the eradication of poverty, Science, 306, 1146, 10.1126/science.1097920 · DOI
  82. M Hoffmann, 2010, The impact of conservation on the status of the world's vertebrates, Science, 330, 1503, 10.1126/science.1194442 · DOI · Paper page

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Metadata

Authors
Fourcade, Y., Engler, J. O., Rödder, D., & Secondi, J.
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-05-12
Volume
9
Issue
5
Pages
e97122
ISSN
1932-6203
DOI
10.1371/journal.pone.0097122
PMC
PMC4018261
Cited by
1,098
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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CoChat. (2026). Citation check: Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias. https://app.cochat.ai/papers/10.1371/journal.pone.0097122
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Fourcade, Y., Engler, J. O., Rödder, D., & Secondi, J. (2014). Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias. PLoS ONE. https://doi.org/10.1371/journal.pone.0097122