Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management

Abstract

Readily accessible and easily understood forecasts of the phenology of invasive insects have the potential to support and improve strategic and tactical decisions for insect surveillance and management. However, most phenological modeling tools developed to date are site-based, meaning that they use data from a weather station to produce forecasts for that single site. Spatial forecasts of phenology, or phenological maps, are more useful for decision-making at area-wide scales, such as counties, states, or entire nations. In this review, we provide a brief history on the development of phenological mapping technologies with a focus on degree-day models and their use as decision support tools for invasive insect species. We compare three different types of phenological maps and provide examples using outputs of web-based platforms that are presently available for real-time mapping of invasive insects for the contiguous United States. Next, we summarize sources of climate data available for real-time mapping, applications of phenological maps, strategies for balancing model complexity and simplicity, data sources and methods for validating spatial phenology models, and potential sources of model error and uncertainty. Lastly, we make suggestions for future research that may improve the quality and utility of phenological maps for invasive insects.

Editorial: Forest insect invasions – risk mapping approaches and applications

“…Maps that predict the phenology of invasive insects can support efforts to detect and control populations because decision-makers often target life stages that are more observable (e.g., larvae vs. adults of wood-boring beetles) or more susceptible to control tactics such as pesticide treatments ( 24 , 25 ). Similarly, maps that predict the risk of establishment and spread of invasive insects can support surveillance programs by identifying areas that have both suitable environments for population persistence and a high likelihood of pest arrival ( 26 – 28 ).…”