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Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot

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Abstract

Ecological niche modeling (ENM) has become an important tool in conservation biology. Despite its recent success, several basic issues related to algorithm performance are still being debated. We assess the ability of two of the most popular algorithms, GARP and Maxent, to predict distributions when sampling is geographically biased. We use an extensive data set collected in the Brazilian Cerrado, a biodiversity hotspot in South America. We found that both algorithms give richness predictions that are very similar to other traditionally used richness estimators. Also, both algorithms correctly predicted the presence of most species collected during fieldwork, and failed to predict species collected only in very few cases (usually species with very few known localities, i.e., <5). We also found that Maxent tends to be more sensitive to sampling bias than GARP. However, Maxent performs better when sampling is poor (e.g., low number of data points). Our results indicates that ENM, even when provided with limited and geographically biased localities, is a very useful technique to estimate richness and composition of unsampled areas. We conclude that data generated by ENM maximize the utility of existing biodiversity data, providing a very useful first evaluation. However, for reliable conservation decisions ENM data must be followed by well-designed field inventories, especially for the detection of restricted range, rare species.

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Acknowledgements

We thank D. Shepard and A. T. Peterson for comments on the manuscript. Fieldwork was funded by Conservation International—Brazil, and field support was provided by Pequi, a Brazilian nongovernmental organization. Work on PNCM was authorized by IBAMA permit # 13204-1. We thank, P. Valdujo, S. Balbino, R. Recoder, and R. Bosque for help during fieldwork. This study was submitted in partial fulfilment of GCC’s PhD degree at the University of Oklahoma. The species locality database was assembled as part of doctoral studies conducted by CN, supported by a FAPESP fellowship (# 02/00015-3). We thank J. Caldwell, M. Kaspari, J. Kelly, T. Rashed, and L. Vitt, for serving on GCC’s doctoral committee and providing comments. GCC is supported by a Fulbright/CAPES PhD fellowship (15053155-2018/04-7). GRC by CNPq grant (# 302343/88-1). Portions of this research were supported by a National Science Foundation grant to Laurie J. Vitt and Janalee P. Caldwell (DEB-0415430).

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  1. Centro de Biociências, Departamento de Botânica, Ecologia e Zoologia, Universidade Federal do Rio Grande do Norte, Campus Universitário, Lagoa Nova, 59072-970, Natal, RN, Brazil

    Gabriel C. Costa

  2. Departamento de Zoologia, Universidade de Brasília, CEP: 70910-900, Brasília, DF, Brazil

    Cristiano Nogueira, Ricardo B. Machado & Guarino R. Colli

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  1. Gabriel C. Costa
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  2. Cristiano Nogueira
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  4. Guarino R. Colli
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Correspondence to Gabriel C. Costa.

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Costa, G.C., Nogueira, C., Machado, R.B. et al. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. Biodivers Conserv 19, 883–899 (2010). https://doi.org/10.1007/s10531-009-9746-8

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