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

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.

How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end

“…These conflicting results are in accordance to the recent discussions in the literature about the relative power and predictive ability of GARP (McNyset, 2005;McNyset and Blackburn, 2006;Stockman et al, 2006;White and Kerr, 2006;Fitzpatrick et al, 2007;Tsoar et al, 2007). Also, as recently pointed out by Peterson et al (2008), the tests by Elith et al (2006) were actually performed using high-quality data and designed to evaluate ENMs in a situation of fine-scale modeling of species distribution details.…”

Distribución de las especies de Aphodiinae (Coleoptera, Scarabaeoidea, Aphodiidae) en México

“…Para la realización de este tipo de modelos suele utilizarse información biológica procedente de grupos y regiones no tan insuficientemente prospectados como en nuestro caso, pero es indudable que los resultados de estas técnicas, aunque deben siempre considerase con precaución (Jiménez-Valverde et al 2008), adquieren un interés especial cuando son capaces de realizar predicciones bajo las condiciones de carencia de información que generalmente abundan en los grupos y en las regiones mas diversificadas (Soberón & Peterson 2004, Whittaker et al 2005, Cayuela et al 2009). El procedimiento metodológico seguido en este trabajo permite corregir la frecuente sobrepredicción de este tipo de modelizaciones (Fielding & Haworth 1995, Araújo & Williams 2000, Stockwell & Peterson 2002, Brotons et al 2004, Segurado & Araújo 2004, Stockman et al 2006, especialmente cuando los valores de riqueza se obtienen a través del sumatorio de mapas estimados individuales (Hortal & Lobo 2006), y también validar las predicciones de riqueza obtenidas utilizando los resultados de diversos estimadores no-paramétricos. Pese a ello, puede decirse que la aproximación seguida en éste y en otros trabajos (Trotta-Moreu et al 2008, Verdú & Lobo 2008, Pineda & Lobo 2009, Trotta-Moreu & Lobo 2010) es atrevida y de dudosa eficiencia ya que, como nosotros mismos estimamos, proporciona un error de comisión promedio del 94% y un error de omisión medio del 28% (71% y 18%, respectivamente, en Geotrupidae; Trotta-Moreu & Lobo 2010).…”

ModEco: an integrated software package for ecological niche modeling

“…There are many environmental niche modeling packages available; for example, MaxEnt (Phillips et al 2006), and GARP (Stockwell andPeters 1999). Existing comparisons between different niche models do not show consistent conclusions (Lek et al 1996, Mastrorillo et al 1997, Stockwell and Peterson 2002, Elith et al 2006, Graham et al 2006, Stockman et al 2006 in part due to the fact that the comparisons were primarily conducted on different platforms, which could implement the training and testing differently. Therefore, there is a need to develop an integrated platform to model species distribution data.…”