Accounting for spatially biased sampling effort in presence-only species distribution modelling

Diversity and Distributions

Volumn 21, Issue 5, 2015, Pages 595-608

  Stolar, Jessica ^a   Nielsen, Scott E ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Biased sampling effort; Maxent bias file; Presence only data; Prior sample weight; Species distribution modelling; Virtual species

Indexed keywords

ACCURACY ASSESSMENT; BRYOPHYTE; BUTTERFLY; DATA SET; ECOLOGICAL MODELING; NUMERICAL MODEL; POPULATION DISTRIBUTION; PREDICTION; RARE SPECIES; SAMPLING; SPATIAL ANALYSIS; TAXONOMY; VASCULAR PLANT; WEIGHT;

ALBERTA; CANADA;

BRYOPHYTES; PAPILIONOIDEA; TRACHEOPHYTA;

EID: 84926286790     PISSN: 13669516     EISSN: 14724642     Source Type: Journal    
DOI: 10.1111/ddi.12279     Document Type: Article

References (78)

1. Alberta Biodiversity Monitoring Institute (2012) ABMI Wall-to-wall land cover map. Available at: http://www.abmi.ca/abmi/rawdata/geospatial/gisdownload.jsp?categoryId=2&subcategoryId=4 (accessed 12 August 2014).
2. Allouche, O., Tsoar, A. & Kadmon, R. (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223-1232.
3. Anderson, R.P. (2003) Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela. Journal of Biogeography, 30, 591-605.
4. Araújo, M.B. & Guisan, A. (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677-1688.
5. Argáez, J.A., Christen, J.A., Nakamura, M. & Sobeŕn, J. (2005) Prediction of potential areas of species distributions based on presence-only data. Environmental and Ecological Statistics, 12, 27-44.
6. Austin, M.P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101-118.
7. Barbet-Massin, M., Jiguet, F., Albert, C.H. & Thuiller, W. (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3, 327-338.
8. Beale, C.M. & Lennon, J.J. (2011) Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 247-258.
9. Beyer, H.L. (2012) Geospatial modelling environment. Spatial Ecology LLC. Available at: http://www.spatialecology.com/gme/ (accessed 14 December 2012).
10. Boakes, E.H., McGowan, P.J.K., Fuller, R.A., Chang-qing, D., Clark, N.E., O'Connor, K. & Mace, G.M. (2010) Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biology, 8, e1000385.
11. Botts, E.A., Erasmus, B.F.N. & Alexander, G.J. (2011) Geographic sampling bias in the South African Frog Atlas Project: implications for conservation planning. Biodiversity and Conservation, 20, 119-139.
12. Boyce, M.S., Vernier, P.R., Nielsen, S.E. & Schmiegelow, F.K.A. (2002) Evaluating resource selection functions. Ecological Modelling, 157, 281-300.
13. Boyd, D.R. (2003) Unnatural law: rethinking Canadian environmental law and policy. UBC Press, Vancouver, BC.
14. Center for International Earth Science Information Network (CIESIN), International Food Policy Research Institute (IPFRI), the World Bank & Centro Internacional de Agricultura Tropical (CIAT) (2004) Global Rural-Urban Mapping Project (GRUMP): urban/rural population grids. CIESIN, Columbia University, Palisades, NY. Available at: http://sedac.ciesin.columbia.edu/data/collection/grump-v1 (accessed 12 August 2014).
15. Chefaoui, R.M., Lobo, J.M. & Hortal, J. (2011) Effects of species' traits and data characteristics on distribution models of threatened invertebrates. Animal Biodiversity and Conservation, 34, 229-247.
16. Dennis, R. & Thomas, C. (2000) Bias in butterfly distribution maps: the influence of hot spots and recorder's home range. Journal of Insect Conservation, 4, 73-77.
17. Di Lorenzo, B., Farcomeni, A. & Golini, N. (2011) A Bayesian model for presence-only semicontinuous data, with application to prediction of abundance of Taxus Baccata in two Italian regions. Journal of Agricultural, Biological, and Environmental Statistics, 16, 339-356.
18. Dudík, M., Phillips, S.J. & Schapire, R.E. (2004) Performance guarantees for regularized maximum entropy density estimation. Proceedings of the 17th Annual Conference on Computational Learning Theory, pp. 472-486. ACM Press, New York.
19. Dudík, M., Phillips, S.J. & Schapire, R.E. (2005) Correcting sample selection bias in maximum entropy density estimation. Advances in neural information processing systems 18 (ed. by Y. Weiss, B. Scholkopf and J.C. Platt), pp. 323-330. MIT Press, Cambridge, MA.
20. Dudík, M., Phillips, S.J. & Schapire, R.E. (2007) Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. Journal of Machine Learning Research, 8, 1217-1260.
21. Eddy, S.R. (2004) What is Bayesian statistics? Nature Biotechnology, 22, 1177-1178.
22. Elith, J. & Graham, C.H. (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32, 66-77.
23. Elith, J. & Leathwick, J.R. (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 677-697.
24. Elith, J., Graham, C.H., Anderson, R.P. et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129-151.
25. 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.
26. ESRI (2011) ArcGIS desktop: release 10. Environmental Systems Research Institute, Redlands, CA.
27. Evans, J. (2004) Topographic ruggedness index. Available at: http://arcscripts.esri.com/details.asp?dbid=12435 (accessed 12 August 2014).
28. Fagan, W.F. & Kareiva, P.M. (1997) Using compile species lists to make biodiversity comparisons among regions: a test case using Oregon butterflies. Biological Conservation, 80, 249-259.
29. 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, 9, e97122.
30. Frair, J.L., Nielsen, S.E., Merrill, E.H., Lele, S.R., Boyce, M.S., Munro, R.H.M., Stenhouse, G.B. & Beyer, H.L. (2004) Removing GPS collar bias in habitat selection studies. Journal of Applied Ecology, 41, 202-212.
31. GBIF (2012) Global Biodiversity Information Facility. Available at: http://data.gbif.org/welcome.htm (accessed 14 December 2012).
32. Gelfand, A.E., Silander, J.A., Jr, Wu, S., Latimer, A., Lewis, P.O., Rebelo, A.G. & Holder, M. (2003) Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis, 1, 41-92.
33. Golini, N. (2012) Bayesian modeling of presence-only data. PhD Thesis. Università di Roma, Sapienza.
34. Government of Alberta (2012) Alberta Conservation Information Management System. Available at: http://www.albertaparks.ca/albertaparksca/management-land-use/alberta-conservation-information-management-system-(acims).aspx (accessed 31 August 2011).
35. Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing (2008) Atlas of Canada 1, 000, 000 national frameworks data, protected areas. Available at: http://www.geogratis.gc.ca/download/frameworkdata/protected_areas/ (accessed 12 August 2014).
36. Graham, C.H., Ferrier, S., Huettman, F., Moritz, C. & Peterson, A.T. (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19, 497-503.
37. Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993-1009.
38. Guisan, A. & Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147-186.
39. 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.
40. Hastie, T. & Tibshirani, R. (1986) Generalized additive models. Statistical Science, 1, 297-310.
41. Hirzel, A.H., Helfer, V. & Metral, F. (2001) Assessing habitat-suitability models with a virtual species. Ecological Modelling, 145, 111-121.
42. Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology, 83, 2027-2036.
43. Hortal, J., Jiménez-Valverde, A., Gómez, J.F., Lobo, J.M. & Baselga, A. (2008) Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos, 117, 847-858.
44. Hui, C., Foxcroft, L.C., Richardson, D.M. & MacFadyen, S. (2011) Defining optimal sampling effort for large-scale monitoring of invasive alien plants: a Bayesian method for estimating abundance and distribution. Journal of Applied Ecology, 48, 768-776.
45. Jiménez-Valverde, A. (2012) Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography, 21, 498-507.
46. Jiménez-Valverde, A., Lobo, J.M. & Hortal, J. (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Diversity and Distributions, 14, 885-890.
47. Keating, K.A. & Cherry, S. (2004) Use and interpretation of logistic regression in habitat-selection studies. Journal of Wildlife Management, 68, 774-789.
48. Latimer, A.M., Wu, S., Gelfand, A.E. & Silander, J.A., Jr (2006) Building statistical models to analyze species distributions. Ecological Applications, 16, 33-50.
49. Li, W. & Guo, Q. (2013) How to assess the prediction accuracy of species presence-absence models without absence data? Ecography, 36, 788-799.
50. Lobo, J.M., Jiménez-Valverde, A. & Real, R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 145-151.
51. Lobo, J.M., Jiménez-Valverde, A. & Hortal, J. (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography, 33, 103-114.
52. Loiselle, B.A., Jørgensen, P.M., Consiglio, T., Jiménez, I., Blake, J.G., Lohmann, L.G. & Montiel, O.M. (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? Journal of Biogeography, 35, 105-116.
53. McInerny, G.J. & Purves, D.W. (2011) Fine-scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice. Methods in Ecology and Evolution, 2, 248-257.
54. Miller, J.A. (2014) Virtual species distribution models: using simulated data to evaluate aspects of model performance. Progress in Physical Geography, 38, 117-128.
55. NatureServe (2013) NatureServe conservation status. Available at: http://www.natureserve.org/explorer/ranking.htm (accessed 8 November 2013).
56. Otegui, J., Ariño, A.H., Encinas, M.A. & Pando, F. (2013) Assessing the primary data hosted by the Spanish Node of the Global Biodiversity Information Facility (GBIF). PLoS One, 8, e55144.
57. Pearce, J. & Ferrier, S. (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133, 225-245.
58. Pearson, R.G. (2007) Species' distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History. Available at: http://ncep.amnh.org/resources.php?globalnav=resources§ionnav=modules§ionsubnav=module_files&module_id=361 (accessed 17 September 2014).
59. Pearson, R.G., Raxworthy, C.J., Nakamura, M. & Peterson, A.T. (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102-117.
60. PERG (2009) Peatlands and oil sands. Available at: http://www.gret-perg.ulaval.ca/no_cache/research/research-scope/peatlands-and-oil-sands/?tx_centrerecherche_pi1%5BshowUid%5D=770 (accessed 19 November 2014).
61. Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., Nakamura, M. & Araújo, M.B. (2011) Ecological niches and geographic distributions. Princeton University Press, Princeton, NJ.
62. Phillips, S.J., Dudík, M. & Schapire, R.E. (2004) A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning (ed. by R. Greiner and D. Schuurmans), pp. 655-662. ACM Press, New York.
63. Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.
64. Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, G.A., Leathwick, J. & Ferrier, S. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19, 181-197.
65. Ponder, W.F., Carter, G.A., Flemons, P. & Chapman, R.R. (2001) Evaluation of museum collection data for use in biodiversity assessment. Conservation Biology, 15, 648-657.
66. R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org/ (accessed 14 December 2012).
67. Reddy, S. & Dávalos, L.M. (2003) Geographical sampling bias and its implications for conservation priorities in Africa. Journal of Biogeography, 30, 1719-1727.
68. Rocchini, D., Hortal, J., Lengyel, S., Lobo, J.M., Jiménez-Valverde, A., Ricotta, C., Bacaro, G. & Chiarucci, A. (2011) Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Progress in Physical Geography, 35, 211-226.
69. Royle, J.A., Kéry, M., Gautier, R. & Schmid, H. (2007) Hierarchical spatial models of abundance and occurrence from imperfect survey data. Ecological Monographs, 77, 465-481.
70. Schulman, L., Toivonen, T. & Ruokolainen, K. (2007) Analysing botanical collecting effort in Amazonia and correcting for it in species range estimation. Journal of Biogeography, 34, 1388-1399.
71. StataCorp (2009) Stata statistical software: release 11. StataCorp LP, College Station, TX.
72. StataCorp (2012) Stata 12 help for pweight. Available at: http://www.stata.com/help.cgi?pweight (accessed 14 December 2012).
73. Swets, J.A. (1988) Measuring the accuracy of diagnostic systems. Science, 240, 1285-1293.
74. Syfert, M.M., Smith, M.J. & Coomes, D.A. (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS One, 8, e55158.
75. Tyre, A.J., Tenhumberg, B., Field, S.A., Niejalke, D., Parris, K. & Possingham, H.P. (2003) Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecological Applications, 13, 1790-1801.
76. UCLA Statistical Consulting Group (2012) R learning module: probabilities and distributions. Available at: http://www.ats.ucla.edu/stat/r/modules/prob_dist.htm (accessed 29 October 2012).
77. Ward, G., Hastie, T., Barry, S., Elith, J. & Leathwick, J.R. (2009) Presence-only data and the EM algorithm. Biometrics, 65, 554-563.
78. Zaniewski, A.E., Lehman, A. & Overton, J.McC (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling, 157, 261-280.