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Guiding large-scale management of invasive species using network metrics

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.

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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 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

  1. Banks, N. C., Paini, D. R., Bayliss, K. L. & Hodda, M. The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol. Lett. 18, 188–199 (2015).

    Article  Google Scholar 

  2. Epanchin-Niell, R. et al. Controlling invasive species in complex social landscapes. Front. Ecol. Environ. 8, 210–216 (2009).

    Article  Google Scholar 

  3. Charles, H. & Dukes, J. S. in Biological Invasions (ed. Nentwig, W.) 217–237 (Springer, 2007). https://doi.org/10.1007/978-3-540-36920-2_13

  4. Gallardo, B., Clavero, M., Sánchez, M. & Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Change Biol. 22, 151–163 (2016).

    Article  Google Scholar 

  5. Diagne, C. et al. High and rising economic costs of biological invasions worldwide. Nature 592, 571–576 (2021).

    Article  CAS  Google Scholar 

  6. Sardain, A., Sardain, E. & Leung, B. Global forecasts of shipping traffic and biological invasions to 2050. Nat. Sustain. 2, 274–282 (2019).

    Article  Google Scholar 

  7. Epanchin-Niell, R. S. & Hastings, A. Controlling established invaders: integrating economics and spread dynamics to determine optimal management. Ecol. Lett. 13, 528–541 (2010).

    Article  Google Scholar 

  8. Chades, I. et al. General rules for managing and surveying networks of pests, diseases, and endangered species. Proc. Natl. Acad. Sci. USA 108, 8323–8328 (2011).

    Article  CAS  Google Scholar 

  9. Epanchin-Niell, R. S. & Wilen, J. E. Optimal spatial control of biological invasions. J. Environ. Econ. Manag. 63, 260–270 (2012).

    Article  Google Scholar 

  10. Epanchin-Niell, R. S. & Wilen, J. E. Individual and cooperative management of invasive species in human-mediated landscapes. Am. J. Agric. Econ. 97, 180–198 (2015).

    Article  Google Scholar 

  11. Aadland, D., Sims, C. & Finnoff, D. Spatial dynamics of optimal management in bioeconomic systems. Comput. Econ. 45, 545–577 (2015).

    Article  Google Scholar 

  12. Baker, C. M. Target the source: optimal spatiotemporal resource allocation for invasive species control. Conserv. Lett. 10, 41–48 (2017).

    Article  Google Scholar 

  13. Bushaj, S., Büyüktahtakın, İ. E., Yemshanov, D. & Haight, R. G. Optimizing surveillance and management of emerald ash borer in urban environments. Nat. Res. Model. 34, e12267 (2021).

    Article  Google Scholar 

  14. Fischer, S. M., Beck, M., Herborg, L.-M. & Lewis, M. A. Managing aquatic invasions: optimal locations and operating times for watercraft inspection stations. J. Environ. Manag. 283, 111923 (2021).

    Article  Google Scholar 

  15. Büyüktahtakın, İ. E. & Haight, R. G. A review of operations research models in invasive species management: state of the art, challenges, and future directions. Ann. Oper. Res. 271, 357–403 (2018).

    Article  Google Scholar 

  16. Epanchin-Niell, R. S. Economics of invasive species policy and management. Biol. Invasions 19, 3333–3354 (2017).

    Article  Google Scholar 

  17. Bodin, Ö. et al. Improving network approaches to the study of complex social–ecological interdependencies. Nat. Sustain. 2, 551–559 (2019).

    Article  CAS  Google Scholar 

  18. Nowzari, C., Precaido, V. M. & Pappas, G. J. Analysis and control of epidemics: a survey of spreading processes on complex networks. IEEE Control Syst. 36, 26–46 (2016).

    Google Scholar 

  19. Newman, M. E. J. Spread of epidemic disease on networks. Phys. Rev. E 66, 016128 (2002).

    Article  CAS  Google Scholar 

  20. Kempe, D., Kleinberg, J. & Tardos, E. Maximizing the spread of influence through a social network. In Proc. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 137–146 (ACM Press, 2003).

  21. Pastor-Satorras, R. & Vespignani, A. Immunization of complex networks. Phys. Rev. E 65, 036104 (2002).

    Article  CAS  Google Scholar 

  22. Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015).

    Article  Google Scholar 

  23. Holme, P., Kim, B. J., Yoon, C. N. & Han, S. K. Attack vulnerability of complex networks. Phys. Rev. E 65, 056109 (2002).

    Article  CAS  Google Scholar 

  24. Muirhead, J. R. & Macisaac, H. J. Development of inland lakes as hubs in an invasion network. J. Appl. Ecol. 42, 80–90 (2005).

    Article  Google Scholar 

  25. de la Fuente, B., Saura, S. & Beck, P. S. Predicting the spread of an invasive tree pest: the pine wood nematode in southern europe. J. Appl. Ecol. 55, 2374–2385 (2018).

    Article  Google Scholar 

  26. Minor, E. S. & Urban, D. L. A graph-theory framework for evaluating landscape connectivity and conservation planning. Conserv. Biol. 22, 297–307 (2008).

    Article  Google Scholar 

  27. Morel-Journel, T., Assa, C. R., Mailleret, L. & Vercken, E. Its all about connections: hubs and invasion in habitat networks. Ecol. Lett. 22, 313–321 (2019).

    Google Scholar 

  28. Perry, G. L. W., Moloney, K. A. & Etherington, T. R. Using network connectivity to prioritise sites for the control of invasive species. J. Appl. Ecol. 54, 1238–1250 (2017).

    Article  Google Scholar 

  29. Kvistad, J. T., Chadderton, W. L. & Bossenbroek, J. M. Network centrality as a potential method for prioritizing ports for aquatic invasive species surveillance and response in the Laurentian Great Lakes. Manag. Biol. Invasions 10, 403 (2019).

    Article  Google Scholar 

  30. Haight, R. G., Kinsley, A. C., Kao, S.-Y., Yemshanov, D. & Phelps, N. B. Optimizing the location of watercraft inspection stations to slow the spread of aquatic invasive species. Biol. Invasions 23, 3907–3919 (2021).

    Article  Google Scholar 

  31. McEachran, M. C. et al. Stable isotopes indicate that zebra mussels (Dreissena polymorpha) increase dependence of lake food webs on littoral energy sources. Freshw, Biol. 64, 183–196 (2019).

    Article  CAS  Google Scholar 

  32. Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. in Invasive Aquatic Species of Europe. Distribution, Impacts and Management (eds Leppäkoski, E. et al.) 433–446 (Springer, 2002).

  33. Prescott, T. H., Claudi, R. & Prescott, K. L. Impact of Dreissenid mussels on the infrastructure of dams and hydroelectric power plants. In Quagga and Zebra Mussels (eds Nalepa, T. F. & Schloesser, D. W.) 243–258 (CRC Press, 2013).

  34. Invasive Species of Aquatic Plants and Wild Animals in Minnesota: Annual Report for 2020 (Minnesota Department of Natural Resources, 2020).

  35. Kanankege, K. S., Alkhamis, M. A., Phelps, N. B. & Perez, A. M. A probability co-kriging model to account for reporting bias and recognize areas at high risk for zebra mussels and eurasian watermilfoil invasions in Minnesota. Front. Vet. Sci. 4, 231 (2018).

    Article  Google Scholar 

  36. Mallez, S. & McCartney, M. Dispersal mechanisms for zebra mussels: population genetics supports clustered invasions over spread from hub lakes in Minnesota. Biol. Invasions 20, 2461–2484 (2018).

    Article  Google Scholar 

  37. Kao, S.-Y. Z. et al. Network connectivity of Minnesota waterbodies and implications for aquatic invasive species prevention. Biol. Invasions 23, 3231–3242 (2021).

    Article  Google Scholar 

  38. Kleinberg, J. M. Authoritative sources in a hyperlinked environment. In Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms 668–677 (1998).

  39. McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).

    Article  CAS  Google Scholar 

  40. Bossenbroek, J. M., Kraft, C. E. & Nekola, J. C. Prediction of long-distance dispersal using gravity models: zebra mussel invasion of inland lakes. Ecol. Appl. 11, 1778–1788 (2001).

    Article  Google Scholar 

  41. Leung, B., Bossenbroek, J. M. & Lodge, D. M. Boats, pathways, and aquatic biological invasions: estimating dispersal potential with gravity models. Biol. Invasions 8, 241–254 (2006).

    Article  Google Scholar 

  42. Beger, M. et al. Integrating regional conservation priorities for multiple objectives into national policy. Nat. Commun. 6, 8208 (2015).

  43. Runting, R. K. et al. Larger gains from improved management over sparing–sharing for tropical forests. Nat. Sustain. 2, 53–61 (2019).

    Article  Google Scholar 

  44. Kinsley, A. C. et al. AIS Explorer: prioritization for watercraft inspections. A decision-support tool for aquatic invasive species management. J. Environ. Manage. 314, 115037 (2022).

    Article  Google Scholar 

  45. Vander Zanden, M. J. & Olden, J. D. A management framework for preventing the secondary spread of aquatic invasive species. Can. J. Fish. Aquat. Sci. 65, 1512–1522 (2008).

    Article  Google Scholar 

  46. Kanankege, K. S. et al. Lessons learned from the stakeholder engagement in research: application of spatial analytical tools in one health problems. Front. Vet. Sci. 7, 254 (2020).

    Article  Google Scholar 

  47. Kroetz, K. & Sanchirico, J. The bioeconomics of spatial-dynamic systems in natural resource management. Annu. Rev. Resour. Econ. 7, 189–207 (2015).

    Article  Google Scholar 

  48. Cade, B. S. & Noon, B. R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420 (2003).

    Article  Google Scholar 

  49. Koenker, R. in Asymptotic Statistics (eds Mandl, P. & Hušková, M.) 349–359 (Springer, 1994).

  50. Ashander, J. Analysis code and data for ‘Guiding large-scale management of invasive species using network metrics’. figshare https://doi.org/10.6084/m9.figshare.14402447 (2021).

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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.

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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). https://doi.org/10.1038/s41893-022-00913-9

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