Community density patterns estimated by species distribution modeling: the case study of an insect virus interaction

Abstract

AbstractTime delays complicates the analysis of trophic dependence, which requires large time series data to study local associations.Here we propose using species distribution modeling. This approach removes confounding time lag effects and allows using data obtained separately in the different species.Since the approach is correlative, it cannot be interpreted in terms of causality.We apply the method to the interaction between the invasive potato moth Tecia solanivora and its granulovirus PhoGV in the Northern Andes. Host density was analyzed based on 1206 pheromone trap data from 106 sampled sites in Ecuador, Colombia and Venezuela. Virus prevalence was evaluated in 15 localities from 3 regions in Ecuador and Colombia. glm models were optimized for both variables on bioclimatic variables. Predicted virus prevalence was not significantly correlated to host density in the sampled virus sites. Across the climatic range covered by the study, correlation was R=−0.053. Of the total population of insect in this range, 26% were expected to be infected.Infection status was also analyzed for spatial structure at different scales: storage bag, storage room, field, locality, country. Locality and storage bag explained respectively 8% and 26% of the total deviance in infection status in glm analysis. Field and storage structure differed within locality but not always in the same direction.This basic method may help studying statistical relationships between species density across a number of trophic models making use of existing non sympatric data, with none or limited additional sampling effort.