Identifying biodiversity hotspots by predictive models: A case study using Taiwan's endemic bird species

Zoological Studies

Volumn 48, Issue 3, 2009, Pages 418-431

  Ko, Chia Ying ^a   Lin, Ruey Shing ^b   Ding, Tzung Su ^a   Hsieh, Chih Hao ^c   Lee, Pei Fen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b ENDEMIC SPECIES RESEARCH INSTITUTE   (Taiwan)

^c NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Biodiversity; Biogeography; Conservation; Endemism; Model

Indexed keywords

AVES;

EID: 72549113774     PISSN: 10215506     EISSN: 1810522X     Source Type: Journal    
DOI: None     Document Type: Article

References (47)

1. Aitkenhead MJ, MJ Mustard, AJS McDonald. 2004. Using neural networks to predict spatial structure in ecological systems. Ecol. Model. 179: 393-403.
2. Araújo MB, M New. 2007. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22: 42-47.
3. Austin MP, JA Meyers. 1996. Current approaches to modeling the environmental niche of eucalypts: implication for management of forest biodiversity. For. Ecol. Manage. 85: 95-106.
4. Bonn A, ASL Rodriguez, KJ Gaston. 2002. Threatened and endemic species: Are they good indicators of patterns of biodiversity on a national scale? Ecol. Lett. 5: 733-741.
5. Collett D. 1991. Modeling binary data. London: Chapman and Hall.
6. Corsi F, E Dupre, L Boitan. 1999. A large-scale model of wolf distribution in Italy for conservation planning. Conserv. Biol. 13: 150-159.
7. DeLeo JM. 1993. Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. Proceeding of the Second International Symposium on Uncertainty Modeling and Analysis. College Park, MD: IEEE Computer Society Press, pp. 318-325.
8. Dickinson EC. 2003. The Howard and Moore complete checklist of the birds of the world, 3rd ed. Princeton, NJ: Princeton Univ. Press.
9. Elith J, CH Graham, RP Anderson, M Dudík, S Ferrier, A Guisan, RJ Hijmans, F Huettmann, JR Leathwick, A Lehmann, J Li, LG Lohmann, BA Loiselle, G Manion et al. 2006. Novel methods improve prediction of species'distribution from occurrence data. Ecography 29: 129-151.
10. Feria ATP, AT Peterson. 2002. Prediction of bird community composition based on point-occurrence data and inferential algorithms: a valuable tool in biodiversity assessments. Divers. Distrib. 8: 49-56.
11. Field SA, AJ Tyre, KH Thorn, PJ O'Connor, HP Possingham. 2005. Improving the efficiency of wildlife monitoring by estimating detectability: a case study of foxes (Vulpes vulpes) on the Eyre Peninsula, South Australia. Wildlife Res. 32: 253-258.
12. Fielding AH, JF Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24: 39-49.
13. Fuller T, V Sánchez-Cordero, P Illoldi-Rangel, M Linaje, S Sarkar. 2007. The cost of postponing biodiversity conservation in Mexico. Biol. Conserv. 134: 593-600.
14. Gray D, MP Scarsbrook, JS Harding. 2006. Spatial biodiversity patterns in a large New Zealand braided river. NZ J. Mar. Freshw. Res. 40: 631-642.
15. Guisan A, NE Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135: 147-186.
16. Hirzel A, A Guisan. 2002. Which is the optimal sampling strategy for habitat suitability modeling. Ecol. Model. 157: 331-341.
17. Hsu FH, CT Yao, RS Lin, CC Yang, SJ Lai. 2004. Avian species composition and distribution along elevation gradient in the southern Taiwan. Endem. Species Res. 6: 41-66. (in Chinese)
18. Jose MP, T Fernando. 1997. Prediction of functional characteristics of ecosystem: a comparison of artificial neural networks and regression models. Ecol. Model. 98: 173-186.
19. Kati V, P Devillers, M Dufrêne, A Legakis, D Vokou, P Lebrun. 2004. Hotspots, complementarity or representativeness? Designing optimal small-scale reserves for biodiversity conservation. Biol. Conserv. 120: 471-480.
20. Koh CN, PF Lee, RS Lin. 2006. Bird species richness patterns of northern Taiwan: primary productivity, human population density, and habitat heterogeneity. Divers. Distrib. 12: 546-554.
21. Lee PF, TS Ding, FH Hsu, S Geng. 2004. Breeding bird species richness in Taiwan: distribution on gradients of elevation, primary productivity and urbanization. J. Biogeogr. 31: 307-314.
22. Lek S, M Delacoste, P Baran, I Dimopoulos, J Lauga, S Aulagnier. 1996. Application of neural networks to modeling nonlinear relationships in ecology. Ecol. Model. 90: 39-52.
23. Li SH, JW Li, LX Han, CT Yao, H Shi, FM Lei, C Yen. 2006. Species delimitation in the Hwamei Garrulax canorus. Ibis 148: 698-706.
24. Li Z, Q Zhu, C Gold. 2005. Digital terrain modeling - principles and methodology. Boca Raton, FL: CRC Press.
25. Lira-Noriega A, J Soberón, AG Navarro-Sigüenza, Y Nakazawa, AT Peterson. 2007. Scale dependency of diversity components estimated from primary biodiversity data and distribution maps. Divers. Distrib. 13: 185-195.
26. Liu C, PM Berry, TP Dawson, RG Pearson. 2005. Selecting thresholds of occurrence in the prediction of species distribution. Ecography 28: 385-393.
27. MacKenzie DI. 2005a. Was it there? Dealing with imperfect detection for species presence/absence data. Aust. NZ J. Statistics 47: 65-74.
28. MacKenzie DI. 2005b. What are the issues with presenceabsence data for wildlife managers? J. Wildlife Manage. 69: 849.
29. Manel S, JM Dias, ST Buckton, SJ Ormerod. 1999a. Alternative methods for predicting species distribution: an illustration with Himalayan river birds. J. Appl. Ecol. 36: 734-747.
30. Manel S, JM Dias, SJ Ormerod. 1999b. Comparing discriminant analysis, neural networks and logistic regression for predicting species distribution: a case study with a Himalayan river bird. Ecol. Model. 120: 337-347.
31. Margules C, RL Pressey. 2000. Systematic conservation planning. Nature 405: 243-253.
32. McCullagh P, JA Nelder. 1989. Generalized linear models. London: Chapman and Hall.
33. Miller JR, MG Turner, EAH Smithwick, CL Dent, EH Stanley. 2004. Spatial extrapolation: the science of predicting ecological patterns and processes. BioScience 54: 310-320.
34. Moisen GG, TS Frescino. 2002. Comparing five modeling techniques for predicting forest characteristics. Ecol. Model. 157: 209-225.
35. Myers N, RA Mittermeier, CG Mittermeier, AB da Fonseca, J Kent. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853-858.
36. Nieh CH. 1999. Species richness pattern of breeding birds in Taiwan. MS thesis. National Taiwan Univ., Taipei, Taiwan. (in Chinese)
37. Phillips SJ, RP Anderson, RE Schapire. 2006. Maximum entropy modeling of species geographic distribution. Ecol. Model. 190: 231-259.
38. Reid WV. 1998. Biodiversity hotspots. Trends Ecol. Evol. 13: 275-280.
39. Robertson MP, CI Peter, MH Villet, BS Ripley. 2003. Comparing models for predicting species' potential distribution: a case study using correlative and mechanistic predictive modeling techniques. Ecol. Model. 164: 153-167.
40. Rodríguez JP, L Brotons, J Bustamante, J Seoane. 2007. The application of predictive modeling of species distribution to biodiversity conservation. Divers. Distrib. 13: 243-251.
41. Scachetti-Pereira R. 2001. Desktop GARP. Available at htp:// www.lifemapper.org/deskgarp
42. Scott JM, F Davis, B Csuti, R Noss, B Butterfield, C Groves et al. 1993. Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monogr. 123: 1-41.
43. Shiu HJ, PF Lee. 2003. Assessing avian point-count duration and sample size using species accumulation functions. Zool. Stud. 42: 357-367.
44. Stockman AK, DA Beamer, JE Bond. 2006. An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Divers. Distrib. 12: 81-89.
45. Stockwell D. 2007. Niche modeling. Boca Raton, FL: Chapman and Hall/CRC Press.
46. Su HJ. 1984. Studies on the climate and vegetation types of the natural forests in Taiwan (I): analysis of the variations on climate factors. Q. J. Chinese For. 17: 1-14.
47. Zaniewski AE, A Lehmann, JM Overton. 2002. Predicting species spatial distribution using presence-only data: a case study of native New Zealand ferns. Ecol. Model. 157: 261-280.