气候变化领域集成服务门户(CCPortal): Guiding large-scale management of invasive species using network metrics

CCPortal

DOI	10.1038/s41893-022-00913-9
	Guiding large-scale management of invasive species using network metrics
	Ashander, Jaime; Kroetzt, Kailin; Epanchin-Niell, Rebecca; Phelps, Nicholas B. D.; Haight, Robert G.; Dee, Laura E.
发表日期	2022
ISSN	2398-9629
起始页码	762
结束页码	769
卷号	5 期号:9 页码:8
英文摘要	Biological invasions involve complex interactions between social and environmental factors, challenging effective management. This study represents the invasion of Minnesota lakes by zebra mussels as a network of interactions and finds that using network metrics can guide effective management. Complex socio-environmental interdependencies drive biological invasions, causing damages across large spatial scales. For widespread invasions, targeting of management activities based on optimization approaches may fail due to computational or data constraints. Here, we evaluate an alternative approach that embraces complexity by representing the invasion as a network and using network structure to inform management locations. We compare optimal versus network-guided invasive species management at a landscape-scale, considering siting of boat decontamination stations targeting 1.6 million boater movements among 9,182 lakes in Minnesota, United States. Studying performance for 58 counties, we find that when full information is known on invasion status and boater movements, the best-performing network-guided metric achieves a median and lower-quartile performance of 100% of optimal. We also find that performance remains relatively high using different network metrics or with less information (median >80% and lower quartile >60% of optimal for most metrics) but is more variable, particularly at the lower quartile. Additionally, performance is generally stable across counties with varying lake counts, suggesting viability for large-scale invasion management. Our results suggest that network approaches hold promise to support sustainable resource management in contexts where modelling capacity and/or data availability are limited.
学科领域	Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS记录号	WOS:000825199100004
来源期刊	NATURE SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/272671
作者单位	Resources for the Future; Arizona State University; Arizona State University-Tempe; University System of Maryland; University of Maryland College Park; University of Minnesota System; University of Minnesota Twin Cities; United States Department of Agriculture (USDA); United States Forest Service; University of Colorado System; University of Colorado Boulder; United States Department of the Interior; United States Geological Survey
推荐引用方式 GB/T 7714	Ashander, Jaime,Kroetzt, Kailin,Epanchin-Niell, Rebecca,et al. Guiding large-scale management of invasive species using network metrics[J],2022,5(9):8.
APA	Ashander, Jaime,Kroetzt, Kailin,Epanchin-Niell, Rebecca,Phelps, Nicholas B. D.,Haight, Robert G.,&Dee, Laura E..(2022).Guiding large-scale management of invasive species using network metrics.NATURE SUSTAINABILITY,5(9),8.
MLA	Ashander, Jaime,et al."Guiding large-scale management of invasive species using network metrics".NATURE SUSTAINABILITY 5.9(2022):8.