Identifying marine invasion hotspots using stacked species distribution models

Autor: Devin A. Lyons, Lanli Guo, Thomas W. Therriault, M. Angelica Peña, Claudio DiBacco, David Brickman, Zeliang Wang, Andrea Moore, J. Ben Lowen
Rok vydání: 2020
Předmět:
Zdroj: Biological Invasions. 22:3403-3423
ISSN: 1573-1464
1387-3547
Popis: Early detection and management of aquatic invasive species requires identification of those areas most at risk of invasion (i.e., hotspots). Here we identify present-day and future hotspots of invasion risk for marine invertebrates and algae in nearshore habitats of the northwest Atlantic and northeast Pacific using more than 12 years of monitoring data in conjunction with other occurrence data and stacked species distribution models. The stacked species distribution models predicted the general patterns of observed invasive species richness in both study areas (Atlantic: r2 = 0.52, Pacific: r2 = 0.42). In the northwest Atlantic, we identified hotspots through much of Massachusetts, New Hampshire and southern Maine, and in several bays in southwestern New Brunswick and Nova Scotia. In the northeast Pacific, much of the southern Salish Sea was identified as a hotspot, as were a few areas along the outer coast of Washington and Oregon. Projecting our species distribution modelling results to 2075 (climate scenario RCP 8.5), we found that existing hotspots are likely to expand slightly in the Atlantic, while in the Pacific existing hotspots are predicted to shift or expand, new hotspots are likely to appear, and areas with few invasive species attaining moderate invasive species richness. Our results suggest that climate change will have larger effects on the distributions of our focal invasive species on the Pacific coast compared to the Atlantic. Resultant hotspot maps provide an integrated perspective and guidance to managers tasked with prioritizing locations for monitoring and implementing policy related to marine invasive species, with projected hotspots making planning for future changes in invasion risk possible.
Databáze: OpenAIRE
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