Missing in action: Species competition is a neglected predictor variable in species distribution modelling

Abstract

The central role of species competition in shaping community structure in ecosystems is well appreciated amongst ecologists. However species competition is a consistently missing variable in Species Distribution Modelling (SDM). This study presents results of our attempt to incorporate species competition in SDMs. We used a suit of predictor variables including Soil Adjusted Vegetation Index (SAVI), as well as distance from roads, settlements and water, fire frequency and distance from the nearest herbivore sighting (of selected herbivores) to model individual habitat preferences of five grazer species (buffalo, warthog, waterbuck, wildebeest and zebra) with the Ensemble SDM algorithm for Gonarezhou National Park, Zimbabwe. Our results showed that distance from the nearest animal sighting (a proxy for competition among grazers) was the best predictor of the potential distribution of buffalo, wildebeest and zebra but the second best predictor for warthog and waterbuck. Our findings provide evidence to that competition is an important predictor of grazer species’ potential distribution. These findings suggest that species distribution modelling that neglects species competition may be inadequate in explaining the potential distribution of species. Therefore our findings encourage the inclusion of competition in SDM as well as potentially igniting discussions that may lead to improving the predictive power of future SDM efforts.

Species distribution models for Peruvian plantcutter improve with consideration of biotic interactions

“…Clearly, developing models that include biotic interactions is difficult. Some studies have developed models with default parameterization and without control of the complexity of models generated by interaction (Hof et al 2012); another study attempted to document interference competition with distance from the nearest animal (Mpakairi et al 2017), but this latter approach is fixed in space, and is hard to project to novel places or sets of conditions. In our study, every model (including for each of the plant species) was customized and controlled carefully for complexity.…”

Importance of biotic predictors in estimation of potential invasive areas: the example of the tortoise beetleEurypedus nigrosignatus, in Hispaniola

“…As species’ geographic distributions are understood to coincide in large part with the intersection of their movement capacities, abiotic conditions, and biotic interactions (Hutchinson, 1957; Soberón & Peterson, 2005), understanding how to integrate these three sets of variables in ecological niche models is essential. Previous studies investigating implications of biotic interactions in niche modeling have offered evidence that the explanatory power of models at scales could be improved with addition of information on biotic interactors (Leathwick, 2002; Meier et al, 2010; Jaeschke et al, 2012; Giannini et al, 2013; Lira-Noriega, Soberón & Miller, 2013; Mpakairi et al, 2017; Atauchi, Peterson & Flanagan, 2018).…”

Human settlement drives African elephant (Loxodonta africana) movement in the Sebungwe Region, Zimbabwe

“…The variable was included since human‐dominated landscapes affect elephant habitat selection and resource use (Barnes, Barnes, Alers, & Blom, ; Lewis, ). The use of distance from settlement as a proxy has been used in several studies including in Mpakairi et al, (). All settlements in the study area were digitised from very high‐resolution images freely available on the Google Earth platform.…”