A Systematic Analysis of Factors Affecting the Performance of Climatic Envelope Models

Abstract

Effective application of species distribution models requires some knowledge concerning the accuracy of model predictions. Yet very few studies have attempted to systematically analyze factors affecting the predictive power of distribution models. This study fills this gap for Climatic Envelope Models, which have been applied extensively for a variety of conservation and management purposes. We hypothesized that model predictions are influenced by properties of the data (both quantity and quality) and distribution properties of the modeled species. Hypotheses concerning the effects of both types of factors were tested by analyzing distribution patterns of 192 species of woody plants in Israel. Analyses were based on Monte Carlo simulations and standard statistical tests. The total number of observations had a strong positive effect on model performance; but on average, 50-75 observations were sufficient to obtain the maximal accuracy. Climatic bias (the degree of sampling bias with respect to climatic conditions) had a significant negative effect on predictive accuracy. Climatic completeness (the degree to which the climatic range occupied by the species is covered by the observations) had a negative effect on model performance-a result contradicting our original hypothesis. Among the species properties, commonness had a positive effect while niche width had a negative one. Niche position with respect to rainfall and temperature was also important in determining the accuracy of model predictions. The overall results are discussed with respect to trade-offs between commission and omission errors and the potential implications of scale dependency.

Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques

“…specialists) yield models with higher accuracy than those with larger areas of occupancy (i.e. generalists), this hypothesis have been verified in various of ecosystems, among the species as butterfly, insect, reptile, bird, and mammal (Stockwell and Peterson, 2002;Brotons et al, 2004;Segurado and Araujo, 2004;Kadmon et al, 2003;Segurado and Araujo, 2004;Hernandez et al, 2006;Tsoar et al, 2007). Our research has verified this hypothesis to be plausible to fish species in lake ecosystems.…”

“…Some others explained these as the difference of species' niche width, since former studies have found that species with narrow, well-defined niches which also have better-defined climate and habitat requirements could be better modelled than those with broader niches (Boone and Krohn, 1999;Pearce and Ferrier, 2000;Kadmon et al, 2003). But our data couldn't well support and explain the species niche hypothesis, further study should take more factors into account in order to better understand the mechanism of uncertainty in species distribution models from species attributes and characteristics.…”

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

“…Despite all the effort in recent years that has gone into identifying and understanding the major sources of uncertainty associated with the use of bioclimate envelopes to model and predict current and future species distributions and developing performance criteria for different models (e.g. Kadmon et al 2003;Araújo et al 2005b;Guisan & Thuiller 2005;Luoto et al 2005;Thuiller et al 2005;Araújo & Guisan 2006;Araújo & Rahbek 2006;Elith et al 2006;Heikkinen et al 2006;Lawler et al 2006;Araújo & Luoto 2007;Botkin et al 2007;Heikkinen et al 2007;Luoto et al 2007;Beale et al 2008;Green et al 2008;Luoto & Heikkinen 2008;Araújo et al 2009;Engler et al 2009;Titeux et al 2009;Randin et al 2009b;Franklin 2010;Hoffman et al 2010;Mouton et al 2010;Smulders et al 2010), the key assumption remains, namely that climate is assumed to limit the observed distribution in bioclimate envelope models. The greatest uncertainty is whether this assumption holds for the species modelled today (Beale et al 2008;Duncan et al 2009;Chapman 2010), and hence in predictions for the future (Dormann 2007a) and in the use of such bioclimatic-envelope models as a basis for inferring past climate from fossil remains.…”

Potential distribution and geographic characteristics of wild populations of Vanilla planifolia (Orchidaceae) Oaxaca, Mexico

“…Esto representa una reducción del 66 % de la superficie potencial total. Dicha reducción del hábitat podría deberse a la exactitud del modelo de predicción dado que el aumento del número de plantas ubicadas, define las condiciones ambientales a un nivel mucho más exacto; esto se ha observado en estudios de especies de distribución restringida (Kadmon, Farber, & Danin, 2003;Papeş, & Gaubert 2007;Mateo, Felicísimo, & Muñoz, 2010).…”