Elsevier

Ecological Modelling

Volume 117, Issue 1, 1 April 1999, Pages 159-164
Ecological Modelling

Short communication
Sensitivity of distributional prediction algorithms to geographic data completeness

https://doi.org/10.1016/S0304-3800(99)00023-XGet rights and content

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.

Introduction

An emerging field in landscape ecology and conservation biology is that of building ecological niche models to predict geographic distributions of species (e.g. Breininger et al., 1991, Dewey et al., 1991, Pereira and Itami, 1991, Stoms et al., 1992, Herr and Queen, 1993, Mladenoff et al., 1995, Sperduto and Congalton, 1996, Bian and West, 1997, Li et al., 1997). Especially challenging has been developing such models from point data, especially data such as museum specimen locality data, which are rarely accompanied by data indicating absence or abundance at sites. Several analytical approaches have been applied to these challenges, including logistic regression, discriminant function analysis, approaches based on distance measures, and parallelipiped set-based approaches.

Although broad comparisons of efficiency have not yet been carried out, our preliminary trials (L.G. Ball et al., in preparation) indicate that an especially promising algorithm is an expert-system approach based on a genetic algorithm, developed by David Stockwell (Stockwell and Noble, 1991, Stockwell, 1993). This algorithm, called Genetic Algorithm for Rule-set Prediction (GARP) produces a model of species, niches in geographic space based on heterogeneous rule-sets (see Methods). GARP appears especially well-suited to reducing error in predicted distributions, both in the form of omission of real distributional areas and inclusion of areas not holding actual populations.

One dimension of the challenge of distributional prediction that has not seen careful testing is that of the depth or completeness of geographic information necessary for accurate predictions. The purpose of the present contribution is to analyze—for a particular geographic and taxonomic challenge—the completeness of geographic data necessary for accurate distributional prediction, and if possible, what particular thematic coverages are especially important.

Section snippets

Methods

The three species employed in this study were chosen to represent distinct distributional areas in diverse ecosystems across North America. The Pygmy Nuthatch (Sitta pygmaea) is distributed in coniferous forests throughout western North America south to central Mexico. The Barred Owl (Strix varia) is distributed in deciduous and coniferous forests almost exclusively in eastern North America, and locally south to southern Mexico. Finally, the Lark Bunting (Calamospiza melanocorys) is distributed

Results

Predictive accuracy was clearly related to the number of coverages, with low accuracy associated with few coverages, and highest accuracy generally achieved with the full or near-full complement of coverages. Fig. 1 illustrates the highly accurate prediction based on all eight coverages, compared with an example prediction based on a single coverage, for Pygmy Nuthatches. In each case, the decline in correspondence of the predicted distributional area with known distributional points in the

Methodology

This preliminary study assessed the amount of geographic information necessary for accurate distributional predictions. The conclusion was simple in the case of North America; five coverages, one of which being that of annual mean temperature, were necessary for consistently accurate prediction. Inclusion of additional coverages and themes did not greatly affect the resulting accuracy. This result, of course, depends to some degree on the particular species, and on the array of geographic

Acknowledgements

We thank Dr David Stockwell for his generous and capable assistance in all phases of this study. Thanks also to Dr Bruce Peterjohn, of the Breeding Bird Survey, for providing critical avian distributional data. Finally, this study was supported financially by the National Geographic Society, the National Science Foundation, the Kansas EPSCor, and the University of Kansas Honors Program.

References (15)

There are more references available in the full text version of this article.

Cited by (0)

View full text