Evaluation of the ecological niche model approach in spatial conservation prioritization

Autoři: Fumiko Ishihama aff001;  Akio Takenaka aff001;  Hiroyuki Yokomizo aff001;  Taku Kadoya aff001
Působiště autorů: National Institute for Environmental Studies, Onogawa, Tsukuba, Ibaraki, Japan aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226971


Ecological niche models (ENMs) are widely used in spatial prioritization for biodiversity conservation (e.g. selecting conservation areas). However, it is unclear whether ENMs are always beneficial for such purposes. We quantified the benefit of using ENMs in conservation prioritization, comparing the numbers of species covered by conservation areas selected on the basis of probabilities estimated by ENMs (ENM approach) and those selected on the basis of raw observation data (raw-data approach), while controlling survey range, survey bias, and target size of conservation area. We evaluated three ENM algorithms (GLM, GAM, and random forests). We used virtual community data generated by simulation for the evaluation. ENM approach was effective when survey bias is strong, survey range is narrow, and target size of conservation area is moderate. The percentage of cases in which the ENM approach outperformed the raw-data approach ranged from 0.0 to 33% (GLM), 31% (GAM), and 75% (random forests) depending on conditions. The number of rare species (< 20 presence records) included in the conservation area based on the ENM approach was less than, or the same as, that of the raw-data approach. The unexpectedly limited cases in which the ENM approach was effective in the present research may depend on the conservation target we used (to cover as many species as possible in conservation area). Our results highlight urgent need for evaluating ENM’s effectiveness under other conservation targets for wise use of ENM in conservation prioritization.

Klíčová slova:

Algorithms – Biodiversity – Community structure – Conservation science – Distribution curves – Ecological niches – Machine learning algorithms – Species diversity


1. Margules CR, Sarkar S. Systematic conservation planning. New York: Cambridge University Press; 2007. 270pp p.

2. Fuller RA, McDonald-Madden E, Wilson KA, Carwardine J, Grantham HS, Watson JEM, et al. Replacing underperforming protected areas achieves better conservation outcomes. Nature. 2010;466(7304):365–7. doi: 10.1038/nature09180 20592729

3. Pimm SL, Jenkins CN, Abell R, Brooks TM, Gittleman JL, Joppa LN, et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science. 2014;344(6187):1246752. doi: 10.1126/science.1246752 24876501.

4. Sarkar S, Pressey RL, Faith DP, Margules CR, Fuller T, Stoms DM, et al. Biodiversity Conservation Planning Tools: Present Status and Challenges for the Future. Ann Rev Env Resour. 2006;31(1):123–59. doi: 10.1146/annurev.energy.31.042606.085844

5. Rondinini C, Wilson KA, Boitani L, Grantham H, Possingham HP. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett. 2006;9(10):1136–45. doi: 10.1111/j.1461-0248.2006.00970.x 16972877

6. Freitag S, Jaarsveld ASV. Sensitivity of selection procedures for priority conservation areas to survey extent, survey intensity and taxonomic knowledge. Proc R Soc Lond, Ser B: Biol Sci. 1998;265(1405):1475–82. doi: 10.1098/rspb.1998.0460 PMC1689332.

7. Gladstone W, Davis J. Reduced survey intensity and its consequences for marine reserve selection. Biodivers Conserv. 2003;12(7):1525–36. doi: 10.1023/a:1023637917029 WOS:000182721700015.

8. Gaston KJ, Rodrigues ASL. Reserve Selection in Regions with Poor Biological Data. Conserv Biol. 2003;17(1):188–95. doi: 10.1046/j.1523-1739.2003.01268.x

9. De Ornellas P, Milner-Gulland EJ, Nicholson E. The impact of data realities on conservation planning. Biol Conserv. 2011;144(7):1980–8.

10. Andelman SJ, Willig MR. Alternative configurations of conservation reserves for paraguayan bats: Considerations of spatial scale. Conserv Biol. 2002;16(5):1352–63. doi: 10.1046/j.1523-1739.2002.01119.x WOS:000178183600022.

11. Elith J, Leathwick JR. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics. 2009;40(1):677–97. doi: 10.1146/annurev.ecolsys.110308.120159

12. Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, et al. Predicting species distributions for conservation decisions. Ecol Lett. 2013;16(12):1424–35. doi: 10.1111/ele.12189 WOS:000326114000011. 24134332

13. Elith J, Leathwick J. The contribution of species distribution modelling to conservation prioritization. In: Moilanen A, Wilson KA, Possingham HP, editors. Spatial conservation prioritization. New York: Oxford University Press; 2009. p. 70–93.

14. Moilanen A. Reserve selection using nonlinear species distribution models. The American Naturalist. 2005;165(6):695–706. doi: 10.1086/430011 15937749.

15. Freitag S, Nicholls AO, Jaarsveld AS. Nature reserve selection in the Transvaal, South Africa: what data should we be using? Biodivers Conserv. 1996;5(6):685–98.

16. Wilson KA, Westphal MI, Possingham HP, Elith J. Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biol Conserv. 2005;122(1):99–112.

17. Carvalho SB, Brito JC, Pressey RL, Crespo E, Possingham HP. Simulating the effects of using different types of species distribution data in reserve selection. Biol Conserv. 2010;143(2):426–38.

18. Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, et al. Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning. Conserv Biol. 2003;17(6):1591–600. doi: 10.1111/j.1523-1739.2003.00233.x

19. Esselman PC, Allan JD. Application of species distribution models and conservation planning software to the design of a reserve network for the riverine fishes of northeastern Mesoamerica. Freshwat Biol. 2011;56(1):71–88.

20. Peralvo M, Sierra R, Young K, Ulloa C. Identification of Biodiversity Conservation Priorities using Predictive Modeling: An Application for the Equatorial Pacific Region of South America. Biodivers Conserv. 2007;16(9):2649–75.

21. Pawar S, Koo MS, Kelley C, Ahmed MF, Chaudhuri S, Sarkay S. Conservation assessment and prioritization of areas in Northeast India: Priorities for amphibians and reptiles. Biol Conserv. 2007;136(3):346–61. doi: 10.1016/j.biocon.2006.12.012 WOS:000246947300002.

22. Leathwick J, Moilanen A, Francis M, Elith J, Taylor P, Julian K, et al. Novel methods for the design and evaluation of marine protected areas in offshore waters. Conserv Lett. 2008;1(2):91–102. doi: 10.1111/j.1755-263X.2008.00012.x WOS:000207586900006.

23. Linke S, Pressey RL, Bailey RC, Norris RH. Management options for river conservation planning: condition and conservation re-visited. Freshwat Biol. 2007;52(5):918–38. doi: 10.1111/j.1365-2427.2006.01690.x WOS:000245987200012.

24. Ortega Huerta MA. Fragmentation patterns and implications for biodiversity conservation in three biosphere reserves and surrounding regional environments, northeastern Mexico. Biol Conserv. 2007;134(1):83–95. doi: 10.1016/j.biocon.2006.08.007 WOS:000243630200009.

25. Cabeza M, Moilanen A. Design of reserve networks and the persistence of biodiversity. Trends Ecol Evol. 2001;16(5):242–8. doi: 10.1016/s0169-5347(01)02125-5 11301153

26. Reports on 5th National Basic Survey on Natural Environment [Internet]. Biodiversity Center of Japan, Ministry of the Environment. [cited August, 2012]. Available from: http://www.biodic.go.jp/ne_research_e.html#id03.

27. McGill BJ, Etienne RS, Gray JS, Alonso D, Anderson MJ, Benecha HK, et al. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecol Lett. 2007;10(10):995–1015. doi: 10.1111/j.1461-0248.2007.01094.x 17845298

28. Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H, Lentini PE, et al. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecol Biogeogr. 2015;24(3):276–92. doi: 10.1111/geb.12268 WOS:000349391600002.

29. Breiman L. Random forests. Machine Learning. 2001;45(1):5–32. doi: 10.1023/a:1010933404324 WOS:000170489900001.

30. Wilson KA, Cebeza M, Klein CJ. Fundamental concepts of spatial conservation prioritization. In: Moilanen A, Wilson KA, Possingham HP, editors. Spatial conservation prioritization. 1 st ed. New York: Oxford University Press; 2009. p. 16–27.

31. Kirkpatrick JB. An iterative method for establishing priorities for the selection of nature reserves: An example from Tasmania. Biol Conserv. 1983;25(2):127–34. http://dx.doi.org/10.1016/0006-3207(83)90056-3.

32. Mehri A, Salmanmahiny A, Mirkarimi SH, Rezaei HR. Use of optimization algorithms to prioritize protected areas in Mazandaran Province of Iran. J Nat Conserv. 2014;22(5):462–70. https://doi.org/10.1016/j.jnc.2014.05.002.

33. Polasky S, Camm JD, Solow AR, Csuti B, White D, Ding R. Choosing reserve networks with incomplete species information. Biol Conserv. 2000;94(1):1–10.

34. Sarkar S, Pappas C, Garson J, Aggarwal A, Cameron S. Place prioritization for biodiversity conservation using probabilistic surrogate distribution data. Divers Distrib. 2004;10(2):125–33.

35. Tole L. Choosing reserve sites probabilistically: A Colombian Amazon case study. Ecol Model. 2006;194(4):344–56.

36. Arponen A, Moilanen A, Ferrier S. A successful community-level strategy for conservation prioritization. J Appl Ecol. 2008;45(5):1436–45.

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