A framework to integrate innovations in invasion science for proactive management
- PMID: 35451197
- DOI: 10.1111/brv.12859
A framework to integrate innovations in invasion science for proactive management
Abstract
Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.
Keywords: IAS; big data analytics; bioinformatics; environmental DNA; genetics; infectious disease ecology; invasive species; nuisance species; remote sensing; species distribution modeling.
© 2022 Cambridge Philosophical Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
References
REFERENCES
-
- Abeysinghe, T., Simic Milas, A., Arend, K., Hohman, B., Reil, P., Gregory, A. & Vázquez-Ortega, A. (2019). Mapping invasive Phragmites australis in the Old Woman Creek Estuary using UAV remote sensing and machine learning classifiers. Remote Sensing 11, 1380.
-
- Allen, J. M. & Bradley, B. A. (2016). Out of the weeds? Reduced plant invasion risk with climate change in the continental United States. Biological Conservation 203, 306-312.
-
- Allendorf, F. W., Funk, W. C., Aitken, S. N., Byrne, M. & Luikart, G. (2022). Conservation and the Genomics of Populations, Third Edition (). Oxford University Press, Oxford, UK.
-
- Anton, V., Hartley, S. & Wittmer, H. U. (2018). Evaluation of remote cameras for monitoring multiple invasive mammals in New Zealand. New Zealand Journal of Ecology 42, 74-79.
-
- Aota, T., Ashizawa, K., Mori, H., Toda, M. & Chiba, S. (2021). Detection of Anolis carolinensis using drone images and a deep neural network: an effective tool for controlling invasive species. Biological Invasions 23, 1321-1327.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Miscellaneous