Abstract
AbstractThe increased incidence of invasive species introductions is a hallmark of global change, but their associated environmental and economic impacts are vastly underestimated. Assessing and managing the impact of invasive species requires understanding their weather driven dynamics as a basis for predicting their potential geographic distribution and relative abundance. Current de-facto standards for invasive species assessment are correlative approaches lacking mechanistic underpinnings, and hence fail to capture the weather driven biology limiting their explanatory and predictive capacity to forewarn policy makers of species invasiveness (i.e., its potential geographic distribution and relative abundance under extant and/or climate change weather). The idiosyncratic time-place nature of biological invasions and the inability of correlative approaches to incorporate biological information call for development of a unifying prospective approach across species. Physiologically based demographic models (PBDMs) provide a holistic basis for assessment of invasive species addressing many limitations of correlative approaches while accommodating higher level of biological complexity using a similar number of parameters. We use the South American tomato pinworm Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) as a case study in the Palearctic and compare the predictions of our PBDM model to those of three analyses based on the correlative CLIMEX model. The PBDM outperformed CLIMEX with comparable CLIMEX predictions only after the pest had reached its potential geographic distribution (i.e., post hoc), using 6–10 vs. 13 parameters, respectively. We suggest creating dedicated laboratories to gather appropriate biological data and developing generalized software to build mechanistic models for assessing invasive species of any taxa.
Funder
Horizon 2020 Framework Programme
Ente per le Nuove Tecnologie, l'Energia e l'Ambiente
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Economics and Econometrics,Geography, Planning and Development
Reference114 articles.
1. Abkin, M. H., & Wolf, C. (1976). Computer library for agricultural systems simulation. Distributed delay routines: DEL, DELS, DELF, DELLF, DELVF, DELLVF. Department of Agricultural Economics, Michigan State University. https://pdf.usaid.gov/pdf_docs/pnaae013.pdf
2. Bahn, V., & McGill, B. J. (2007). Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16(6), 733–742.
3. Barker, B. S., Coop, L., Wepprich, T., Grevstad, F., & Cook, G. (2020). DDRP: Real-time phenology and climatic suitability modeling of invasive insects. PLOS ONE, 15(12), e0244005. https://doi.org/10.1371/journal.pone.0244005
4. Barlow, N. D. (1999). Models in biological control: A field guide. In B. A. Hawkins & H. V. Cornell (Eds.), Theoretical approaches to biological control (pp. 43–70). Cambridge University Press.
5. Barrientos, Z. R., Apablaza, H. J., Norero, S. A., & Estay, P. P. (1998). Temperatura base y constante termica de desarrollo de la polilla del tomate, Tuta absoluta (Lepidoptera: Gelechiidae). Ciencia e Investigación Agraria, 25(3), 133–137.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献