A simple method for estimating species abundance from occurrence maps

A simple method for estimating species abundance from occurrence maps The issue of how to estimate species abundance from presence/absence maps has attracted much attention. Several methods have been developed to address this problem. However, those methods either overlook the structure of spatial autocorrelation of species distribution, thus leading to underestimation, or they demand extra information besides presence/absence maps. This study first developed a new method that takes account of spatial autocorrelation and only requires occurrence maps, without any extra information. This method was further improved by incorporating a correction factor to it. We used an index defined by joint counts of occupied and unoccupied cells to measure spatial autocorrelation and to correct the underestimation of the random placement model. The performance of our method was compared against four other major methods (random placement model, negative binomial model, Conlisk et al.'s method and Solow & Smith's method) using both simulated and empirical data. The results showed that the performance of our method is comparable with other methods but requires less and readily obtained input data, a property important for real applications. We suggest this simple, data‐parsimonious method be a useful alternative to the currently available methods for estimating abundance from occurrence. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Methods in Ecology and Evolution Wiley

A simple method for estimating species abundance from occurrence maps

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Publisher
Wiley
Copyright
Copyright © 2014 British Ecological Society
ISSN
2041-210X
eISSN
2041-210X
DOI
10.1111/2041-210X.12159
Publisher site
See Article on Publisher Site

Abstract

The issue of how to estimate species abundance from presence/absence maps has attracted much attention. Several methods have been developed to address this problem. However, those methods either overlook the structure of spatial autocorrelation of species distribution, thus leading to underestimation, or they demand extra information besides presence/absence maps. This study first developed a new method that takes account of spatial autocorrelation and only requires occurrence maps, without any extra information. This method was further improved by incorporating a correction factor to it. We used an index defined by joint counts of occupied and unoccupied cells to measure spatial autocorrelation and to correct the underestimation of the random placement model. The performance of our method was compared against four other major methods (random placement model, negative binomial model, Conlisk et al.'s method and Solow & Smith's method) using both simulated and empirical data. The results showed that the performance of our method is comparable with other methods but requires less and readily obtained input data, a property important for real applications. We suggest this simple, data‐parsimonious method be a useful alternative to the currently available methods for estimating abundance from occurrence.

Journal

Methods in Ecology and EvolutionWiley

Published: Jan 1, 2014

Keywords: ; ; ;

References

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