An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species

Amy K. Stockman, David A. Beamer and Jason E. Bond

ABSTRACT

One of the primary goals of any systematic, taxonomic or biodiversity study is the

characterization of species distributions. While museum collection data are important

for ascertaining distributional ranges, they are often biased or incomplete. The

Genetic Algorithm for Rule-set Prediction (GARP) is an ecological niche modelling

method based on a genetic algorithm that has been argued to provide an accurate

assessment of the spatial distribution of organisms that have dispersal capabilities.

The primary objective of this study is to evaluate the accuracy of a GARP model to

predict the spatial distribution of a non-invasive, non-vagile invertebrate whose

full distributional range was unknown. A GARP predictive model based on seven

environmental parameters and 42 locations known from historical museum records for

species of the trapdoor spider genus Promyrmekiaphila was produced and subsequently

used as a guide for ground truthing the model. The GARP model was neither a

significant nor an accurate predictor of spider localities and was outperformed by

more simplistic BIOCLIM and GLM models. The isolated nature of Promyrmekiaphila

populations mandates that environmental layers and their respective resolutions

are carefully chosen for model production. Our results strongly indicate that, for

modelling the spatial distribution of low vagility organisms, one should employ a

modelling method whose results are more conducive to interpretation than models

produced by a ‘black box’ algorithm such as GARP.

Keywords

Araneae, conservation, ecological niche modelling, Mygalomorphae, predicting

species occurrences, Promyrmekiaphila

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