From pest data to abundance‐based risk maps combining eco‐physiological knowledge, weather, and habitat variability. Issue 2 (24th February 2017)

• Record Type: Journal Article
• Authors: Lacasella, Federica; Marta, Silvio; Singh, Aditya; Stack Whitney, Kaitlin; Hamilton, Krista; Townsend, Phil; Kucharik, Christopher J.; Meehan, Timothy D.; Gratton, Claudio
• Is Part Of: Ecological applications. Volume 27:Issue 2(2017)
• Page Start: 575
• Page End: 588
• Publication Date: 2017-02-24
• Subjects: abundance model -- host‐specific insects -- invasive alien species -- pest management -- risk map -- soybean aphid -- species distribution modelling -- Zonation algorithm; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.1467
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance‐based risk maps using multi‐year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R 2 = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance‐based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance‐based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.