SAFARIS: a spatial analytic framework for pest forecast systems

Abstract

Non-native pests and diseases pose a risk of economic and environmental damage to managed and natural U.S. forests and agriculture. The U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Plant Protection and Quarantine (PPQ) protects the health of U.S. agriculture and natural resources against invasive pests and diseases through efforts to prevent the entry, establishment, and spread of non-native pests and diseases. Because each pest or disease has its own idiosyncratic characteristics, analyzing risk is highly complex. To help PPQ better respond to pest and disease threats, we developed the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS), an integrated system designed to provide a seamless environment for producing predictive models. SAFARIS integrates pest biology information, climate and non-climate data drivers, and predictive models to provide users with readily accessible and easily customizable tools to analyze pest and disease risks. The phenology prediction models, spread forecasting models, and other climate-based analytical tools in SAFARIS help users understand which areas are suitable for establishment, when surveys would be most fruitful, and aid in other analyses that inform decision-making, operational efforts, and rapid response. Here we introduce the components of SAFARIS and provide two use cases demonstrating how pest-specific models developed with SAFARIS tools support PPQ in its mission. Although SAFARIS is designed to address the needs of PPQ, the flexible, web-based framework is publicly available, allowing any user to leverage the available data and tools to model pest and disease risks.

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

“…The simplicity of the approach and its applicability to multiple organisms has sustained its use for several years. For example, the Degree-Day, establishment Risk, and Phenological event maps (DDRP) platform [16] 1) [15]. Specific features that make the degree-day lookup table approach so common include:…”

“…Phenological maps produced using real-time and forecast climate data have the potential to support the early detection of invasive pests. For example, regularly updated phenological maps on the SAFARIS platform provide decision support for the Cooperative Agricultural Pest Survey (CAPS) program [15,16], which conducts national and statewide surveys for exotic plant pests in the United States deemed to be of regulatory…”

“…Degree-day models are widely used in decision support systems that predict the phenology of agricultural insect pests because of their simplicity and ability to accommodate multiple species with varying life histories [8][9][10][14][15][16][17][18][19][20]. The development and phenology of an organism in a degree-day model is driven by heat accumulation above a lower temperature threshold (and oftentimes below an upper temperature threshold) over a daily or weekly time step (Figure 1) [4,5,7,[21][22][23][24].…”

“…Climate data at fine-scale spatial resolution increased the prediction accuracy because local effects (e.g., of mountains, valleys, or large bodies of water) that might influence phenological events could be modeled [43][44][45]. Additionally, advancements in satellite, cellular, and internet communications allowed meteorological observations to be released within hours of being collected, enabling researchers to produce phenology maps in near real-time to gain insight into the current development and activities of a species [15,16,46,47]. For example, the advent of free, near real-time (henceforth real-time for brevity) daily climate data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) database [http://www.prism.oregonstate.edu (accessed on 19 December 2023)] in 2011 was a major breakthrough for phenological mapping for the contiguous United States (CONUS), as PRISM data prior to this time were only available as monthly grids that were released months after data collection [43,44].…”

“…The USA National Phenology Network's Pheno Forecast maps take advantage of 7-day National Digital Forecast Database (NDFD) forecasts to provide a 1-week "look ahead" prediction for CONUS [47]. SAFARIS PestCAST maps include a 1-month forecast using a 7-day NDFD forecast followed by three weeks of recent 20-year average PRISM data [15]. 4.…”