Iterative Models for Early Detection of Invasive Species across Spread Pathways

Abstract

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

“…For example, as remote sensing technology improves spatial resolution and coverage, these new environmental data can be incorporated into the environmental variable library to provide additional predictive power that has been shown to effectively map invasive spices populations in localized examples [9]. As shown with gypsy moth, operational iterative modeling can improve models over time and lead to better informed management actions [15]. Our models represent the first iteration of these predictions.…”

“…Making the most credible SDM takes considerable effort and often requires collecting information that is not readily available (e.g., on species absence and detection probability), while a fully automated process using a standard set of existing information may produce many models quickly but has less credibility. Iterative modeling, the incremental improvement of SDMs based on added information and modified modeling processes over time, provides a way to minimize the tradeoffs between quality and quantity of models created and to objectively demonstrate improvement of models with the use of additional information, including new data, expert review, and field validation [13][14][15].…”

Impacts of Nonnative Species on the Health of Natural and Planted Forests

“…To overcome issues related to uncertainty and stationarity, Cook et al [1] used iterative distribution models to direct early detection of invasive European gypsy moth (Lymantria dispar dispar L.) in the United States. They concluded that incorporating human-assisted pathway predictors can greatly improve early detection of invasive species.…”

Right place. Right time. Right tool: guidance for using target analysis to increase the likelihood of invasive species detection

“…The computations that constitute target analysis are largely built on existing information and use statistical sampling theory (Russell et al 2017;Berec et al 2015;Hoffman et al 2016; US Forest Service n.d.; http:// www.landscapetoolbox.org, accessed 24 September 2018). Modeling helps extend estimates about the invasive species in space and/or time (Cook et al 2019;Wang et al 2014). Mapping is used to consider important geospatial information, jurisdictional boundaries, and logistical aspects of surveillance strategy.…”

“…Department of Agriculture On an annual basis, USDA's Animal and Plant Health Inspection Service (APHIS) has been using aspects of target analyses to forecast the likelihood of detecting European gypsy moth (Lymantria dispar dispar) outside of the existing federal quarantine area. Because pathways for spread differ across space (from natural spread over relatively short distances to human-assisted spread over long distances), the gypsy moth model is regionalized to capture these different pathways (Cook et al 2019). The resulting model output is used to guide sampling, directing more effort to high-probability locations (USDA 2014).…”