Epidemiological landscape models reproduce cyclic insect outbreaks

Abstract

Forest insect outbreaks can have large impacts on ecosystems and understanding the underlying ecological processes is critical for their management. Current process-based modeling approaches of insect outbreaks are often based on population processes operating at small spatial scales (i.e. within individual forest stands). As such, they are difficult to parameterize and offer limited applicability when modeling and predicting outbreaks at the landscape level where management actions take place. In this paper, we propose a new process-based landscape model of forest insect outbreaks that is based on stand defoliation, the Forest-Infected-Recovering-Forest (FIRF) model. We explore both spatially-implicit (mean field equations with global dispersal) and spatially-explicit (cellular automata with limited dispersal between neighboring stands) versions of this model to assess the role of dispersal in the landscape dynamics of outbreaks. We show that density-dependent dispersal is necessary to generate cyclic outbreaks in the spatially-implicit version of the model. The spatially-explicit FIRF model with local and stochastic dispersal displays cyclic outbreaks at the landscape scale and patchy outbreaks in space, even without density-dependence. Our simple, process-based FIRF model reproduces large scale outbreaks and can provide an innovative approach to model and manage forest pests at the landscape scale.