Guiding large-scale management of invasive species using network metrics

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ABSTRACT 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. Access through your institution Buy


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NETWORKS DO NOT REPRESENT UNSEEN BIODIVERSITY Article Open access 10 June 2021 INVACOST, A PUBLIC DATABASE OF THE ECONOMIC COSTS OF BIOLOGICAL INVASIONS WORLDWIDE Article Open access 08


September 2020 MIXED EFFECTS OF A NATIONAL PROTECTED AREA NETWORK ON TERRESTRIAL AND FRESHWATER BIODIVERSITY Article Open access 13 September 2023 DATA AVAILABILITY The network data used in


this study were previously reported37 and are available at https://conservancy.umn.edu/handle/11299/216936. The minimal dataset supporting this study, including network data, lake metadata


including infestation status and geospatial data delineating county boundaries are available50. CODE AVAILABILITY Analysis used R v.4.0.2 (2020-06-22) using packages dplyr (v.1.0.7), purrr


(v.0.3.4), ggplot2 (v.3.3.3), igraph (v.1.2.5), quantreg (v.5.61) and Rglpk (v.0.6-4). Full analysis code underlying all analyses are available50. REFERENCES * Banks, N. C., Paini, D. R.,


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https://doi.org/10.6084/m9.figshare.14402447 (2021). Download references ACKNOWLEDGEMENTS We thank A. Kinsley for comments on a previous draft. Funding for this research was provided by


Resources for the Future and the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation (NSF) DBI-1639145. The Northern Research


Station, USDA Forest Service also provided support. L.E.D. acknowledges support from NSF OCE-2049360. AUTHOR INFORMATION Author notes * Jaime Ashander Present address: Eastern Ecological


Science Center, Patuxent Research Refuge (Formerly the Patuxent Wildlife Research Center), US Geological Survey, Laurel, MD, USA AUTHORS AND AFFILIATIONS * Resources for the Future,


Washington, DC, USA Jaime Ashander, Kailin Kroetz & Rebecca Epanchin-Niell * School of Sustainability, Arizona State University, Tempe, AZ, USA Kailin Kroetz * Department of Agricultural


and Resources Economics, University of Maryland, College Park, MD, USA Rebecca Epanchin-Niell * Department of Fisheries, Wildlife, and Conservation Biology, College of Food, Agricultural,


and Natural Resource Sciences, University of Minnesota, St Paul, MN, USA Nicholas B. D. Phelps * Northern Research Station, USDA Forest Service, St Paul, MN, USA Robert G. Haight *


Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA Laura E. Dee Authors * Jaime Ashander View author publications You can also search for this author


inPubMed Google Scholar * Kailin Kroetz View author publications You can also search for this author inPubMed Google Scholar * Rebecca Epanchin-Niell View author publications You can also


search for this author inPubMed Google Scholar * Nicholas B. D. Phelps View author publications You can also search for this author inPubMed Google Scholar * Robert G. Haight View author


publications You can also search for this author inPubMed Google Scholar * Laura E. Dee View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS


J.A., L.E.D. and K.K. conceived the study. J.A., L.E.D., R.E.-N. and K.K. designed the research. N.B.D.P. and R.G.H. contributed data or analytic tools. J.A. performed the research. J.A.,


L.E.D., R.E.-N. and K.K. wrote the paper and all authors edited the paper. CORRESPONDING AUTHOR Correspondence to Jaime Ashander. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare


no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Sustainability_ thanks Jonathan Bossenbroek and the other, anonymous, reviewer(s) for their contribution to the peer


review of this work. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Sections 1–8, Algorithms 1 and 2, Figs. 1–7, Tables 1–5 and References. REPORTING SUMMARY RIGHTS AND PERMISSIONS Reprints


and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Ashander, J., Kroetz, K., Epanchin-Niell, R. _et al._ Guiding large-scale management of invasive species using network metrics. _Nat


Sustain_ 5, 762–769 (2022). https://doi.org/10.1038/s41893-022-00913-9 Download citation * Received: 30 July 2021 * Accepted: 06 May 2022 * Published: 14 July 2022 * Issue Date: September


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