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. Access through your institution Buy

or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through your institution Access Nature and 54 other Nature Portfolio journals Get

Nature+, our best-value online-access subscription $29.99 / 30 days cancel any time Learn more Subscribe to this journal Receive 12 digital issues and online access to articles $119.00 per

year only $9.92 per issue Learn more Buy this article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during

checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS PROTECTED AREA

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.,

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  * Epanchin-Niell, R. et al. Controlling invasive species in complex social landscapes. _Front. Ecol. Environ._ 8, 210–216 (2009). Article  Google Scholar  * 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 * 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  * Diagne, C. et al. High and rising economic costs of

biological invasions worldwide. _Nature_ 592, 571–576 (2021). Article  CAS  Google Scholar  * 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  * 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  * 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  * Epanchin-Niell, R. S. & Wilen, J. E. Optimal spatial control of biological

invasions. _J. Environ. Econ. Manag._ 63, 260–270 (2012). Article  Google Scholar  * 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  * Aadland, D., Sims, C. & Finnoff, D. Spatial dynamics of optimal management in bioeconomic

systems. _Comput. Econ._ 45, 545–577 (2015). Article  Google Scholar  * Baker, C. M. Target the source: optimal spatiotemporal resource allocation for invasive species control. _Conserv.

Lett._ 10, 41–48 (2017). Article  Google Scholar  * 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  * 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  * 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  * Epanchin-Niell, R. S.

Economics of invasive species policy and management. _Biol. Invasions_ 19, 3333–3354 (2017). Article  Google Scholar  * 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  * 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  * Newman, M. E. J. Spread of epidemic disease on networks. _Phys. Rev. E_ 66, 016128

(2002). Article  CAS  Google Scholar  * 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). * Pastor-Satorras, R. & Vespignani, A. Immunization of complex networks. _Phys. Rev. E_ 65, 036104 (2002).

Article  CAS  Google Scholar  * 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  * 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  *

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  * 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  * 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  * 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  * 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  *

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  * 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  * 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  * 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). * 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). * _Invasive Species of Aquatic Plants and Wild Animals in Minnesota: Annual Report for 2020_ (Minnesota Department of

Natural Resources, 2020). * 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  * 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  * 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  * Kleinberg, J. M.

Authoritative sources in a hyperlinked environment. In _Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms_ 668–677 (1998). * McDonald-Madden, E. et al. Using food-web theory to

conserve ecosystems. _Nat. Commun._ 7, 10245 (2016). Article  CAS  Google Scholar  * 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  * 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  * Beger, M. et al. Integrating regional conservation

priorities for multiple objectives into national policy. _Nat. Commun_. 6, 8208 (2015). * 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  * 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  * 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  * 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  * 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  * Cade, B. S. & Noon, B. R. A gentle introduction to

quantile regression for ecologists. _Front. Ecol. Environ._ 1, 412–420 (2003). Article  Google Scholar  * Koenker, R. in _Asymptotic Statistics_ (eds Mandl, P. & Hušková, M.) 349–359

(Springer, 1994). * 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). 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

2022 * DOI: https://doi.org/10.1038/s41893-022-00913-9 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable

link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative