Physiologically based demographic models streamline identification and collection of data in evidence‐based pest risk assessment

Abstract

The distribution and abundance of species that cause economic loss (i.e., pests) in crops, forests or livestock depends on many biotic and abiotic factors that are thought difficult to separate and quantify on geographical and temporal scales. However, the weather‐driven biology and dynamics of such species and of relevant interacting species in their food chain or web can be captured via mechanistic physiologically based demographic models (PBDMs). These models can be implemented in the context of a geographic information system (GIS) to predict the potential geographic distribution and relative abundance of pest species given observed or climate change scenarios of weather. PBDMs may include bottom‐up effects of the host on pest dynamics and, if appropriate, the top‐down action of natural enemies. When driven by weather, PBDMs predict the phenology, age structure and abundance dynamics at one or many locations enabling the distribution of the interacting species to be predicted across wide geographic areas. PBDMs are able to capture relevant ecosystem complexity within a modest number of measurable parameters because they use the same ecological models of analogous resource acquisition and allocation processes across all trophic levels. The use of these analogies makes parameter estimation easier as the underlying functions are known. This is a significant advantage in cases where the biological data available to build an evidence base for pest risk assessment is sparse.