Assessing the risks and opportunities of presence-only data for conservation planning

Journal of Biogeography

Volumn 42, Issue 2, 2015, Pages 218-228

  Hermoso, Virgilio ^a   Kennard, Mark J ^a   Linke, Simon ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

Australia; Commission error; Conservation biogeography; Efficiency; Freshwater biodiversity; Omission errors; Predictive model; Sensitivity; Systematic planning; Trade offs

Indexed keywords

EID: 84920942529     PISSN: 03050270     EISSN: 13652699     Source Type: Journal    
DOI: 10.1111/jbi.12393     Document Type: Article

References (48)

1. Ball, I.R., Possingham, H.P. & Watts, M. (2009) Marxan and relatives: software for spatial conservation prioritisation. Spatial conservation prioritisation: quantitative methods and computational tools (ed. by A. Moilanen, K.A. Wilson and H.P. Possingham ), pp. 185-195. Oxford University Press, Oxford.
2. Balmford, A. & Gaston, K.J. (1999) Why biodiversity surveys are good value. Nature, 398, 204-205.
3. Benito, B.M., Cayuela, L. & Albuquerque, F.S. (2013) The impact of modelling choices in the predictive performance of richness maps derived from species-distribution models: guidelines to build better diversity models. Methods in Ecology and Evolution, 4, 327-335.
4. Boitani, L., Maiorano, L., Baisero, D., Falcucci, A., Visconti, P. & Rondinini, R. (2011) What spatial data do we need to develop global mammal conservation strategies? Philosophical Transactions of the Royal Society B: Biological Sciences, 366, 2623-2632.
5. Bombi, P., Luiselli, L. & D'Amen, M. (2011) When the method for mapping species matters: defining priority areas for conservation of African freshwater turtles. Diversity and Distributions, 17, 581-592.
6. Brotons, L., Thuiller, W., Araujo, M.B. & Hirzel, A.H. (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437-448.
7. Burbidge, A.A. (1991) Cost constraints on surveys for nature conservation. Nature conservation: cost effective biological and data surveys (ed. by C.R. Margules and M.P. Austin ), pp. 3-6. CSIRO, Melbourne.
8. Danielsen, F., Burgess, N.D., Balmford, A. et al. (2009) Local participation in natural resource monitoring: a characterization of approaches. Conservation Biology, 23, 31-42.
9. De Ornellas, P., Milner-Gulland, E.J. & Nicholson, E. (2011) The impact of data realities on conservation planning. Biological Conservation, 144, 1980-1988.
10. Elith, J. & Leathwick, J. (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions, 13, 265-275.
11. 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.
12. Fielding, A.H. & Bell, J.F. (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38-49.
13. Freeman, E. (2007) PresenceAbsence: an R package for presence-absence model evaluation. USDA Forest Service, Rocky Mountain Research Station, Ogden, UT.
14. Freitag, S. & Van Jaarsveld, A.S. (1998) Sensitivity of selection procedures for priority conservation areas to survey extent, survey intensity and taxonomic knowledge. Proceedings of the Royal Society B: Biological Sciences, 265, 1475-1482.
15. Gaston, K.J. & Rodrigues, A.S.L. (2003) Reserve selection in regions with poor biological data. Conservation Biology, 17, 188-195.
16. Geoscience Australia (2011) Environmental attributes database. Available at: http://www.ga.gov.au/ (last visited 29 November 2012).
17. Grand, J., Cummings, M.P., Rebelo, T.G., Ricketts, T.H. & Neel, M.C. (2007) Biased data reduce efficiency and effectiveness of conservation reserve networks. Ecology Letters, 10, 364-374.
18. Grantham, H.S., Moilanen, A., Wilson, K.A., Pressey, R.L., Rebelo, T.G. & Possingham, H.P. (2008) Diminishing return on investment for biodiversity data in conservation planning. Conservation Letters, 1, 190-198.
19. Grantham, H.S., Wilson, K.A., Moilanen, A., Rebelo, T. & Possingham, H.P. (2009) Delaying conservation actions for improved knowledge: how long should we wait? Ecology Letters, 12, 293-301.
20. Halpern, B.S., Regan, H.M., Possingham, H.P. & McCarthy, M.A. (2006) Accounting for uncertainty in marine reserve design. Ecology Letters, 9, 2-11.
21. Hastie, T., Tibshirani, R.J. & Friedman, J.H. (2001) The elements of statistical learning: data mining, inference and prediction. Springer-Verlag, New York.
22. Hermoso, V. & Kennard, M.J. (2012) Uncertainty in coarse conservation assessments hinders the efficient achievement of conservation goals. Biological Conservation, 147, 52-59.
23. Hermoso, V., Linke, S., Prenda, J. & Possingham, H.P. (2011) Addressing longitudinal connectivity in the systematic conservation planning of fresh waters. Freshwater Biology, 56, 57-70.
24. Hermoso, V., Kennard, M.J. & Linke, S. (2013) Data acquisition for conservation assessments: is the effort worth it? PLoS ONE, 8, e59662.
25. Hermoso, V., Kennard, M.J. & Linke, S. (2014) Evaluating the costs and benefits of systematic data acquisition for conservation assessments. Ecography, doi:10.1111/ecog.00792.
26. Hirzel, A. & Guisan, A. (2002) Which is the optimal sampling strategy for habitat suitability modelling. Ecological Modelling, 157, 331-341.
27. Hirzel, A.H., Helfer, V. & Metral, F. (2001) Assessing habitat suitability models with a virtual species. Ecological Modelling, 145, 111-121.
28. 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.
29. Kirkpatrick, J.B. (1983) An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biological Conservation, 25, 127-134.
30. Knight, A.T., Smith, R.J., Cowling, R.M., Desmet, P.G., Faith, D.P., Ferrier, S., Gelderblom, C.M., Grantham, H., Lombard, A.T., Maze, K., Nel, J.L., Parrish, J.D., Pence, G.Q.K., Possingham, H.P., Reyers, B., Rouget, M., Roux, D. & Wilson, K.A. (2007) Improving the key biodiversity areas approach for effective conservation planning. BioScience, 57, 256-261.
31. Langford, W.T., Gordon, A. & Bastin, L. (2009) When do conservation planning methods deliver? Quantifying the consequences of uncertainty. Ecological Informatics, 4, 123-135.
32. Leathwick, J.R., Rowe, D., Richardson, J., Elith, J. & Hastie, T. (2005) Using multivariate adaptive regression splines to predict the distribution of New Zealand's freshwater diadromous fish. Freshwater Biology, 50, 2034-2052.
33. 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.
34. Loiselle, B.A., Howell, C.A., Gaham, C.H., Goerck, J.M., Brooks, T., Smith, K.G. & Williams, P. (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17, 1591-1600.
35. MacLeod, C.D., Mandleberg, L., Schweder, C., Bannon, S.M. & Pierce, G.J. (2008) A comparison of approaches for modelling the occurrence of marine animals. Hydrobiologia, 612, 21-32.
36. Maidment, D.R. (2002) Arc Hydro: GIS for water resources. ESRI Press, Redlands, CA.
37. Phillips, S.J., Anderson, R.B. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.
38. Possingham, H.P., Ball, I.R. & Andelman, S. (2000) Mathematical methods for identifying representative reserve networks. Quantitative methods for conservation biology (ed. by S. Ferson and M. Burgman ), pp. 291-305. Springer-Verlag, New York.
39. Possingham, H.P., Grantham, H. & Rondinini, C. (2007) How can you conserve species that haven't been found? Journal of Biogeography, 34, 758-759.
40. Pusey, B., Kennard, M., Burrows, D., Perna, C., Kyne, P., Cook, B. & Hughes, J. (2011) Freshwater fish. Aquatic biodiversity in northern Australia: patterns, threats and future (ed. by B.J. Pusey ), pp 71-92. Charles Darwin University Press, Darwin.
41. Rodrigues, A.S.L. (2011) Improving coarse species distribution data for conservation planning in biodiversity-rich, data-poor, regions: no easy shortcuts. Animal Conservation, 14, 108-110.
42. Rodrigues, A.S.L., Gaston, K.J. & Gregory, R.D. (2000) Using presence-absence data to establish reserve selection procedures that are robust to temporal species turnover. Proceedings of the Royal Society B: Biological Sciences, 267, 897-902.
43. Rondinini, C., Wilson, K., Boitani, L., Grantham, H. & Possingham, H.P. (2006) Tradeoffs of different types of species occurrence data for use on systematic conservation planning. Ecology Letters, 9, 1136-1145.
44. Tsoar, A., Allouche, O., Steinitz, O., Rotem, D. & Kadmon, R. (2007) A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions, 13, 397-405.
45. Tulloch, A.I.T., Mustin, K., Possingham, H.P., Szabo, J.K. & Wilson, K.A. (2012) To boldly go where no volunteer has gone before: predicting volunteer activity to prioritize surveys at the landscape scale. Diversity and Distributions, 19, 465-480.
46. Vaughan, P. & Ormerod, S.J. (2003) Modelling the distribution of organisms for conservation: optimising the collection of field data for model development. Conservation Biology, 17, 1601-1611.
47. Wessels, K.J., Van Jaarsveld, A.S., Grimbeek, J.D. & Van der Linde, M.J. (1998) An evaluation of the gradsect biological survey method. Biodiversity and Conservation, 7, 1093-1121.
48. Wilson, K.A., Westphal, M.I., Possingham, H.P. & Elith, J. (2005) Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation, 122, 99-112.