Dataset Information

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.

SUBMITTER: Barker BS

PROVIDER: S-EPMC7775054 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Barker Brittany S BS Coop Leonard L Wepprich Tyson T Grevstad Fritzi F Cook Gericke G

PloS one 20201231 12

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 Unit ...[more]

PMID: 33382722

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Project description:IntroductionDecision support models that predict both when and where to expect emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), are needed for the development and implementation of effective management strategies against this major invasive pest of ash (Fraxinus species) in North America and other regions such as Europe. We present a spatialized model of phenology and climatic suitability for EAB for use in the Degree-Days, Risk, and Phenological event mapping (DDRP) platform, which is an open-source decision support tool to help detect, monitor, and manage invasive threats.MethodsWe evaluated the model using presence records from three geographic regions (China, North America, and Europe) and a phenological dataset consisting primarily of observations from the northeastern and midwestern United States. To demonstrate the model, we produced phenological event maps for a recent year and tested for trends in EAB's phenology and potential distribution over a recent 20-year period.ResultsOverall, the model exhibited strong performance. Presence was correctly estimated for over 99% of presence records and predicted dates of adult phenological events corresponded closely with observed dates, with a mean absolute error of ca. 7 days and low estimates of bias. Climate stresses were insufficient to exclude EAB from areas with native Fraxinus species in North America and Europe; however, extreme weather events, climate warming, and an inability for EAB to complete its life cycle may reduce suitability for some areas. Significant trends toward earlier adult emergence over 20 years occurred in only some areas.DiscussionNear real-time model forecasts for the conterminous United States are available at two websites to provide end-users with decision-support for surveillance and management of this invasive pest. Forecasts of adult emergence and egg hatch are particularly relevant for surveillance and for managing existing populations with pesticide treatments and parasitoid introductions.

Project description:BackgroundAedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens. Many recent dengue fever outbreaks in China have been caused solely by the vector. Mapping of the potential distribution ranges of Ae. albopictus is crucial for epidemic preparedness and the monitoring of vector populations for disease control. Climate is a key factor influencing the distribution of the species. Despite field studies indicating seasonal population variations, very little modeling work has been done to analyze how environmental conditions influence the seasonality of Ae. albopictus. The aim of the present study was to develop a model based on available observations, climatic and environmental data, and machine learning methods for the prediction of the potential seasonal ranges of Ae. albopictus in China.MethodsWe collected comprehensive up-to-date surveillance data in China, particularly records from the northern distribution margin of Ae. albopictus. All records were assigned long-term (1970-2000) climatic data averages based on the WorldClim 2.0 data set. Machine learning regression tree models were developed using a 10-fold cross-validation method to predict the potential seasonal (or monthly) distribution ranges of Ae. albopictus in China at high resolution based on environmental conditions. The models were assessed based on sensitivity, specificity, and accuracy, using area under curve (AUC). WorldClim 2.0 and climatic and environmental data were used to produce environmental conduciveness (probability) prediction surfaces. Predicted probabilities were generated based on the averages of the 10 models.ResultsDuring 1998-2017, Ae. albopictus was observed at 200 out of the 242 localities surveyed. In addition, at least 15 new Ae. albopictus occurrence sites lay outside the potential ranges that have been predicted using models previously. The average accuracy was 98.4% (97.1-99.5%), and the average AUC was 99.1% (95.6-99.9%). The predicted Ae. albopictus distribution in winter (December-February) was limited to a small subtropical-tropical area of China, and Ae. albopictus was predicted to occur in northern China only during the short summer season (usually June-September). The predicted distribution areas in summer could reach northeastern China bordering Russia and the eastern part of the Qinghai-Tibet Plateau in southwestern China. Ae. albopictus could remain active in expansive areas from central to southern China in October and November.ConclusionsClimate and environmental conditions are key factors influencing the seasonal distribution of Ae. albopictus in China. The areas predicted to potentially host Ae. albopictus seasonally in the present study could reach northeastern China and the eastern slope of the Qinghai-Tibet Plateau. Our results present new evidence and suggest the expansion of systematic vector population monitoring activities and regular re-assessment of epidemic risk potential.

Dataset Information

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

Publications

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

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