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Predicting species distributions in poorly-studied landscapes

Biodiversity and Conservation volume 17, pages 1353–1366 (2008)Cite this article

Abstract

Conservationists are increasingly relying on distribution models to predict where species are likely to occur, especially in poorly-surveyed but biodiverse areas. Modeling is challenging in these cases because locality data necessary for model formation are often scarce and spatially imprecise. To identify methods best suited to modeling in these conditions, we compared the success of three algorithms (Maxent, Mahalanobis Typicalities and Random Forests) at predicting distributions of eight bird and eight mammal species endemic to the eastern slopes of the central Andes. We selected study species to have a range of locality sample sizes representative of the data available for endemic species of this region and also that vary in their distribution characteristics. We found that for species that are known from moderate numbers (N = 38–94) of localities, the three methods performed similarly for species with restricted distributions but Maxent and Random Forests yielded better results for species with wider distributions. For species with small numbers of sample localities (N = 5–21), Maxent produced the most consistently successful results, followed by Random Forests and then Mahalanobis Typicalities. Because evaluation statistics for models derived from few localities can be suspect due to the poor spatial representation of the evaluation data, we corroborated these results with review by scientists familiar with the species in the field. Overall, Maxent appears to be the most capable method for modeling distributions of Andean bird and mammal species because of the consistency of results in varying conditions, although the other methods have strengths in certain situations.

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Acknowledgments

We are indebted to the Gordon and Betty Moore Foundation for their generous financial support and to J. Cavelier for inspiring this study. We thank the curators at the following natural history museums for providing locality records for bird and mammal species: AMNH, ANSP, CBF, CBG, CM, DMNH, FMNH, KU, LSUMZ, MUSM, MNK, MSB, MVZ, UMMZ. J. Fjeldså, D. Lane, and J. O’Neill kindly made unpublished locality data available to us. We are also grateful to D. Lane and J. O’Neill, for their careful review of the locality data.

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Authors and Affiliations

  1. 2 Parr Street, Toronto, ON, Canada, M6J 2E3

    P. A. Hernandez

  2. NatureServe, Arlington, VA, USA

    P. A. Hernandez, L. Paniagua, J. J. Swenson & B. E. Young

  3. Museo de Historia Natural, Universidad Nacional Mayor de San Marcos, Lima, Peru

    I. Franke, V. Pacheco & H. L. Quintana

  4. Asociación Armonía – BirdLife International, Santa Cruz de la Sierra, Bolivia

    S. K. Herzog

  5. Centro de Datos para la Conservación, Universidad Nacional Agraria La Molina, Lima, Peru

    A. Soto & C. Tovar

  6. Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA

    T. H. Valqui

  7. Colección Boliviana de Fauna, Museo Nacional de Historia Natural, La Paz, Bolivia

    J. Vargas

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  1. P. A. Hernandez
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  2. I. Franke
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Correspondence to P. A. Hernandez.

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Hernandez, P.A., Franke, I., Herzog, S.K. et al. Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17, 1353–1366 (2008). https://doi.org/10.1007/s10531-007-9314-z

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  • DOI: https://doi.org/10.1007/s10531-007-9314-z

Keywords

  • Maxent
  • Mahalanobis Typicalities
  • Model evaluation
  • Species distribution models
  • Random Forests