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Predicting species distributions in poorly-studied landscapes

Publication ,  Journal Article
Hernandez, PA; Franke, I; Herzog, SK; Pacheco, V; Paniagua, L; Quintana, HL; Soto, A; Swenson, JJ; Tovar, C; Valqui, TH; Vargas, J; Young, BE
Published in: Biodiversity and conservation
June 2008

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.

Duke Scholars

Published In

Biodiversity and conservation

DOI

EISSN

1572-9710

ISSN

0960-3115

Publication Date

June 2008

Volume

17

Issue

6

Start / End Page

1353 / 1366

Related Subject Headings

  • Ecology
  • 4104 Environmental management
  • 4102 Ecological applications
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management
  • 0501 Ecological Applications
 

Citation

APA
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ICMJE
MLA
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Hernandez, P. A., Franke, I., Herzog, S. K., Pacheco, V., Paniagua, L., Quintana, H. L., … Young, B. E. (2008). Predicting species distributions in poorly-studied landscapes. Biodiversity and Conservation, 17(6), 1353–1366. https://doi.org/10.1007/s10531-007-9314-z
Hernandez, P. A., I. Franke, S. K. Herzog, V. Pacheco, L. Paniagua, H. L. Quintana, A. Soto, et al. “Predicting species distributions in poorly-studied landscapes.” Biodiversity and Conservation 17, no. 6 (June 2008): 1353–66. https://doi.org/10.1007/s10531-007-9314-z.
Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL, et al. Predicting species distributions in poorly-studied landscapes. Biodiversity and conservation. 2008 Jun;17(6):1353–66.
Hernandez, P. A., et al. “Predicting species distributions in poorly-studied landscapes.” Biodiversity and Conservation, vol. 17, no. 6, June 2008, pp. 1353–66. Epmc, doi:10.1007/s10531-007-9314-z.
Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL, Soto A, Swenson JJ, Tovar C, Valqui TH, Vargas J, Young BE. Predicting species distributions in poorly-studied landscapes. Biodiversity and conservation. 2008 Jun;17(6):1353–1366.
Journal cover image

Published In

Biodiversity and conservation

DOI

EISSN

1572-9710

ISSN

0960-3115

Publication Date

June 2008

Volume

17

Issue

6

Start / End Page

1353 / 1366

Related Subject Headings

  • Ecology
  • 4104 Environmental management
  • 4102 Ecological applications
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management
  • 0501 Ecological Applications