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Open AccessArticle

Iterative Models for Early Detection of Invasive Species across Spread Pathways

1
USDA Animal and Plant Health Inspection Service, Fort Collins, CO 80526, USA
2
US Geological Survey, Fort Collins Science Center, Fort Collins, CO 80526, USA
3
USDA Animal and Plant Health Inspection Service, Buzzards Bay, MA 02542, USA
4
USDA Forest Service, Arapaho and Roosevelt National Forests and Pawnee National Grassland, Fort Collins, CO 80526, USA
5
Front Range Data Analytics LLC, Fort Collins, CO 80521, USA
6
Conservation Science Partners, Fort Collins, CO 80524, USA
*
Author to whom correspondence should be addressed.
Forests 2019, 10(2), 108; https://doi.org/10.3390/f10020108
Received: 30 November 2018 / Revised: 20 December 2018 / Accepted: 21 December 2018 / Published: 29 January 2019
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. View Full-Text
Keywords: Correlative spatial models; species distribution models; iterative modeling; forest health; stationarity; early detection; invasive species; gypsy moth; Lymantria dispar dispar; invasion pathways Correlative spatial models; species distribution models; iterative modeling; forest health; stationarity; early detection; invasive species; gypsy moth; Lymantria dispar dispar; invasion pathways
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MDPI and ACS Style

Cook, G.; Jarnevich, C.; Warden, M.; Downing, M.; Withrow, J.; Leinwand, I. Iterative Models for Early Detection of Invasive Species across Spread Pathways. Forests 2019, 10, 108.

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