Sensitivity of distributional prediction algorithms to geographic data completeness

Sensitivity of distributional prediction algorithms to geographic data completeness The sensitivity of one algorithm for prediction of geographic distributions of species from point data to depth of geographic information was tested for three species of North American birds. Test species were chosen to represent three distinct distributional patterns—western North America (Pygmy Nuthatch Sitta pygmaea ) , eastern North America (Barred Owl Strix varia), and the Great Plains in the central part of the continent (Lark Bunting Calamospiza melanocorys ) . Distributional predictions were made using the expert-system algorithm Genetic Algorithm for Role-set Prediction (GARP). Depth of geographic information was manipulated by rarifying the number of coverages on which predictions were based, from the full complement of eight down to one, using a combination of jackknifing and bootstrapping. In all three species, five of the eight coverages were necessary to arrive at the asymptotic maximum predictive efficiency and to avoid broad variance in resulting predictive efficiencies. Annual mean temperature was a critical variable, in some cases more important than inclusion of additional coverages, to producing accurate distributional predictions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Sensitivity of distributional prediction algorithms to geographic data completeness

Ecological Modelling, Volume 117 (1) – Apr 1, 1999

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Publisher
Elsevier
Copyright
Copyright © 1999 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(99)00023-X
Publisher site
See Article on Publisher Site

Abstract

The sensitivity of one algorithm for prediction of geographic distributions of species from point data to depth of geographic information was tested for three species of North American birds. Test species were chosen to represent three distinct distributional patterns—western North America (Pygmy Nuthatch Sitta pygmaea ) , eastern North America (Barred Owl Strix varia), and the Great Plains in the central part of the continent (Lark Bunting Calamospiza melanocorys ) . Distributional predictions were made using the expert-system algorithm Genetic Algorithm for Role-set Prediction (GARP). Depth of geographic information was manipulated by rarifying the number of coverages on which predictions were based, from the full complement of eight down to one, using a combination of jackknifing and bootstrapping. In all three species, five of the eight coverages were necessary to arrive at the asymptotic maximum predictive efficiency and to avoid broad variance in resulting predictive efficiencies. Annual mean temperature was a critical variable, in some cases more important than inclusion of additional coverages, to producing accurate distributional predictions.

Journal

Ecological ModellingElsevier

Published: Apr 1, 1999

References

  • A regression model for the spatial distribution of red-crown crane in Yancheng Biosphere Reserve, China
    Li, W.; Wang, Z.; Ma, Z.; Tang, H.

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