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Simple Summary: Phenological maps can depict the development and seasonal activities (phenology) of invasive insects at area-wide scales, such as counties, states, or entire nations. When regularly updated using real-time and forecast climate data, these maps may improve the timeliness of early detection and control tactics that target specific life stages. Rapid responses to invasive insects may increase the likelihood that populations are eradicated or controlled before they can spread or increase in size. In this review, we provide a brief history of phenological mapping, compare three types of maps that are commonly used for real-time decision support, and summarize climate datasets that may be used for mapping. We also present applications of phenological maps for assessing establishment risk, investigating pest–host interactions, and measuring climate-driven changes in pest phenology. Next, we discuss model complexity, potential sources of model error and uncertainty, methods for evaluating map predictions, and recommendations for future research. The development of additional real-time climate datasets and pest models will allow expanded use of phenological maps to help control invasive insects under current and future climates. 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. [ABSTRACT FROM AUTHOR]

A wide variety of organisms use the regular seasonal changes in photoperiod as a cue to align their life cycles with favorable conditions. Yet the phenological consequences of photoperiodism for organisms exposed to new climates are often overlooked. We present a conceptual approach and phenology model that maps voltinism (generations per year) and the degree of phenological mismatch that can arise when organisms with a short‐day diapause response are introduced to new regions or are otherwise exposed to new climates. Our degree‐day‐based model combines continent‐wide spatialized daily climate data, calculated date‐specific and latitude‐specific day lengths, and experimentally determined developmental responses to both photoperiod and temperature. Using the case of the knotweed psyllid Aphalara itadori, a new biological control agent being introduced from Japan to North America and Europe to control an invasive weed, we show how incorporating a short‐day diapause response will result in geographic patterns of attempted voltinism that are strikingly different from the potential number of generations based on degree‐days alone. The difference between the attempted and potential generations represents a quantitative measure of phenological mismatch between diapause timing and the end of the growing season. We conclude that insects moved from lower to higher latitudes (or to cooler climates) will tend to diapause too late, potentially resulting in high mortality from inclement weather, and those moved from higher to lower latitude (to warmer climates) may be prone to diapausing too early, therefore not fully exploiting the growing season and/or suffering from insufficient reserves for the longer duration in diapause. Mapped output reveals a central region with good phenology match that shifts north or south depending on the geographic source of the insect and its corresponding critical photoperiod for diapause. These results have direct relevance for efforts to establish populations of classical biocontrol agents. More generally, our approach and model could be applied to a wide variety of photoperiod‐ and temperature‐sensitive organisms that are exposed to changes in climate, including resident and invasive agricultural pests and species of conservation concern. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]