Assessing habitat-suitability models with a virtual species

Assessing habitat-suitability models with a virtual species This paper compares two habitat-suitability assessing methods, the Ecological Niche Factor Analysis (ENFA) and the Generalised Linear Model (GLM), to see how well they cope with three different scenarios. The main difference between these two analyses is that GLM is based on species presence/absence data while ENFA on presence data only. A virtual species was created and then dispatched in a geographic information system model of a real landscape following three historic scenarios: (1) spreading, (2) at equilibrium, and (3) overabundant species. In each situation, the virtual species was sampled and these simulated data sets were used as input for the ENFA and GLM to reconstruct the habitat suitability model. The results showed that ENFA is very robust to the quality and quantity of the data, giving good results in the three scenarios. GLM was badly affected in the case of the spreading species but produced slightly better results than ENFA when the species was overabundant; at equilibrium, both methods produced equivalent results. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of both analyses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Assessing habitat-suitability models with a virtual species

Ecological Modelling, Volume 145 (2) – Nov 15, 2001

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Publisher
Elsevier
Copyright
Copyright © 2001 Elsevier Ltd
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(01)00396-9
Publisher site
See Article on Publisher Site

Abstract

This paper compares two habitat-suitability assessing methods, the Ecological Niche Factor Analysis (ENFA) and the Generalised Linear Model (GLM), to see how well they cope with three different scenarios. The main difference between these two analyses is that GLM is based on species presence/absence data while ENFA on presence data only. A virtual species was created and then dispatched in a geographic information system model of a real landscape following three historic scenarios: (1) spreading, (2) at equilibrium, and (3) overabundant species. In each situation, the virtual species was sampled and these simulated data sets were used as input for the ENFA and GLM to reconstruct the habitat suitability model. The results showed that ENFA is very robust to the quality and quantity of the data, giving good results in the three scenarios. GLM was badly affected in the case of the spreading species but produced slightly better results than ENFA when the species was overabundant; at equilibrium, both methods produced equivalent results. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of both analyses.

Journal

Ecological ModellingElsevier

Published: Nov 15, 2001

References

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