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Individual-Based Models for Incorporating Landscape Processes in the Conservation and Management of Aquatic Systems

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Abstract

Purpose of Review

Ecological models can provide critical guidance to conservation programs both as problem-solving tools and by projecting future outcomes, specifically when time and resources limit directly testing alternative management approaches and scenarios. Due to the complexity of aquatic systems, environmental and climatic factors co-vary, multiple risk factors interact, and driving ecological and evolutionary processes are characterized by non-linear, higher-order interactions. Recent modeling advancements allow for better accounting of variation across time and space in ecological and genetic processes, but more progress is needed to inform conservation and address biodiversity decline. Modeling approaches that can explicitly incorporate the ongoing, rapid transformation of climate and landscapes and demogenetic and eco-evo consequences are useful for supporting and informing conservation planning strategies. In this narrative perspective, we present the history and role of individual-based models (IBMs) in aquatic systems to guide management.

Recent Findings

We present exemplary cases that cover (1) the conservation and management of native species in systems impacted by invasive species, (2) life history evolution impacts on the management of fisheries, (3) predictions of the interaction between changing environments and management decisions, and (4) testing factors that drive system dynamics in order to prioritize management decisions. We summarize potential platforms and software available to researchers and managers and discuss future opportunities and challenges.

Summary

While this review focuses on the use of IBMs in aquatic systems, we assert that this foundational knowledge is applicable across systems and encourages researchers and managers to consider incorporating individual-based modeling perspectives to inform conservation as appropriate.

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Acknowledgements

We would like to thank the invitation to submit this review for consideration. In addition, Steve Railsback provided very helpful feedback prior to the peer review process. We would also like to thank anonymous reviewers.

Funding

This publication was made possible by the NSF Idaho and National EPSCoR Programs and by the National Science Foundation under award numbers OIA-1757324 and OIA-1826801.

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Authors and Affiliations

  1. School of Natural Resource Sciences, North Dakota State University, 58108-6050, Fargo, ND, US

    Travis Seaborn

  2. Computational Ecology Lab, School of Public and Community Health Sciences, University of Montana, Missoula, MT, USA

    Casey C. Day & Erin L. Landguth

  3. Department of Biological Sciences, Boise State University, Boise, ID, USA

    Stephanie J. Galla

  4. Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA

    Tomas O. Höök

  5. Illinois-Indiana Sea Grant College Program, West Lafayette, IN, USA

    Tomas O. Höök

  6. US Forest Service, Salmon, ID, USA

    Elizabeth Jossie

  7. Department of Mathematics and Statistics, University of Wyoming, Laramie, WY, USA

    Rongsong Liu

  8. Seattle City Light, Seattle, WA, USA

    Ryan K. Simmons

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  1. Travis Seaborn
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  6. Erin L. Landguth
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Contributions

All authors helped lead the design, strucure, and writing of the main text and edited the manuscript. S.G. prepared Fig. 1. C.C.D prepared Fig. 2.

Corresponding author

Correspondence to Travis Seaborn.

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Conflict of Interest

Travis Seaborn and Stephanie Galla received financial support from the National Science Foundation grants listed under funding. All other authors declare no conflict of interest.

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Seaborn, T., Day, C.C., Galla, S.J. et al. Individual-Based Models for Incorporating Landscape Processes in the Conservation and Management of Aquatic Systems. Curr Landscape Ecol Rep 8, 119–135 (2023). https://doi.org/10.1007/s40823-023-00089-8

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