Search

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]

Simple Summary: Knowledge of where invasive species could potentially establish (potential distribution) is critical to prioritizing and addressing biological invasion threats. In this study, we predicted the potential distribution of Calonectriapseudonaviculata (Cps), an invasive fungal pathogen that blights boxwood, an iconic landscape plant, major evergreen nursery crop, and keystone forest species. We used climate data, presence records from Europe and western Asia, and multiple modeling approaches to predict the potential distribution of Cps at regional and global scales and to explore the roles of temperature and moisture in shaping its distribution. Model predictions were validated using an independent presence record dataset. A consensus map of model predictions revealed that Cps could potentially spread and establish well beyond its currently invaded range in Europe, western Asia, New Zealand, United States and Canada. These include a number of not-yet-invaded areas in eastern and southern Europe, North America, and many regions of the world where boxwood is native. This knowledge informs policymakers and other stakeholders in these areas on the need for implementing a strict phytosanitary protocol for risk mitigation of accidental introduction, having an effective surveillance for early detection, and developing a recovery plan for the pathogen when accidental introductions occur. Boxwood blight caused by Cps is an emerging disease that has had devastating impacts on Buxus spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of Cps detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for Cps establishment. Most regions of the world where Buxus and its congeners are native are also at risk of establishment. These findings provide the first insights into Cps global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range. [ABSTRACT FROM AUTHOR]