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Guiding large-scale management of invasive species using network metrics


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.

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Fig. 1: Our analytical approach.
Fig. 2: Relative performance of network-guided management—measured as the number of infective boats inspected using metric-guided management as a proportion of those inspected in the integer-programming solution—for relative budget 0.1.

Data availability

The network data used in this study were previously reported37 and are available at 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.


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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.

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Authors and Affiliations



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.

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Correspondence to Jaime Ashander.

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Nature Sustainability thanks Jonathan Bossenbroek and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Sections 1–8, Algorithms 1 and 2, Figs. 1–7, Tables 1–5 and References.

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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).

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