DDRP: Real-time phenology and climatic suitability modeling of invasive insects

Abstract

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Combining photoperiod and thermal responses to predict phenological mismatch for introduced insects

“…It uses a new computing platform written entirely in R, greatly simplifying model operations and parameterization for other species. The model also builds on our recently developed open source geo‐climatic phenology modeling platform, Degree‐Days, Risk, and Phenological Event Mapping (DDRP) (Barker et al, 2020), by incorporating the short‐day diapause response. Aside from the addition of photoperiod‐cued diapause, the basic operation is similar to DDRP models that have recently been developed for other species (Barker et al, 2020).…”

Surveillance for Early Detection of High-Consequence Pests and Pathogens

“…Thus, it provides a "snapshot in time" of phenology model predictions for a specific date and pest. ping (DDRP) platform (Barker et al, 2020;DDRP, 2021), the USA National Phenology Network (Crimmins et al 2017; USA-NPN, 2021), and the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS, 2020), among others.…”

“…For example, the USDA, APHIS, CAPS program supports and oversees national and state surveys targeted at species of foreign and invasive plant pests. Maps that predict the timing of phenological events such as first spring flight are important for detection and monitoring program because they facilitate timely surveys and trap placement (Barker et al, 2020, Crimmins et al, 2020. These predictions are also relevant to eradication or management of established pests, because optimally timed operations (e.g., pesticide applications, sterile insect releases, and pheromone mating disruption) are more effective and cost-efficient.…”

“…In contrast to a degree-day lookup table map, a phenological event map (or Pest Event Map; hereafter, PEM), depicts the dates on which accumulating degree-days reach a value (target degree-day total) that corresponds with a phenological event for a poikilothermic organism, an animal whose internal temperature varies considerably (Barker et al, 2020;Grevstad & Coop, 2015). As an example, Figure 7 shows PEMs produced by DDRP which depict the average oviposition date by the overwintering generation of light brown apple moth (Epiphyas postvittana) for 2020.…”

“…This situation, however, is changing. The DDRP platform, developed by researchers at Oregon State University, integrates mapping of phenology and climatic suitability to predict both where and when to expect insect pests (Barker et al, 2020).…”