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Local management in a regional context: Simulations with process-based species distribution models

Author

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  • Szewczyk, Tim M.
  • Lee, Tom
  • Ducey, Mark J.
  • Aiello-Lammens, Matthew E.
  • Bibaud, Hayley
  • Allen, Jenica M.

Abstract

Ecological models often strive to inform conservation and management decisions. Occurrence-based distribution models may aid regional management strategies, though many management decisions require information beyond the likely presence of a species provided by such models. Process-based distribution models predict geographic distributions using environmental relationships with biological processes, providing more detailed predictions and a key opportunity for data-driven management. Here, we develop and characterize a novel demography-based regional distribution model and illustrate its use by comparing four management strategies for glossy buckthorn (Frangula alnus), a bird-dispersed shrub invasive throughout the northeastern United States. On a gridded landscape in southern New Hampshire and Maine, this population-level simulation includes fruiting, seed dispersal, seed bank dynamics, germination and establishment, and annual survival, with land cover as the dominant environmental driver. We parameterize the model with field and lab studies, supplementing with published data, expert knowledge, and pattern-oriented parameterization with historical records. In a comprehensive sensitivity analysis, we found that the age at which individuals are capable of reproduction and the frequency of long distance dispersal had the strongest influence on the distribution. In our management simulations, we found that immigration prevents total eradication within any property regardless of management frequency or coordination, though management impacts are detectable in nearby un-managed cells via reduced seed deposition. The flexible model structure combines multiple disparate data sources similar to those available for many species into a synthetic framework of local and regional biological processes, allows the incorporation of specific management actions targeting particular processes and life stages into the regional context of a process-based species distribution model, and provides a robust method for evaluating potential management strategies.

Suggested Citation

  • Szewczyk, Tim M. & Lee, Tom & Ducey, Mark J. & Aiello-Lammens, Matthew E. & Bibaud, Hayley & Allen, Jenica M., 2019. "Local management in a regional context: Simulations with process-based species distribution models," Ecological Modelling, Elsevier, vol. 413(C).
    • Handle: RePEc:eee:ecomod:v:413:y:2019:i:c:s0304380019303357
    DOI: 10.1016/j.ecolmodel.2019.108827
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    References listed on IDEAS

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    1. Mestre, Frederico & Risk, Benjamin B. & Mira, António & Beja, Pedro & Pita, Ricardo, 2017. "A metapopulation approach to predict species range shifts under different climate change and landscape connectivity scenarios," Ecological Modelling, Elsevier, vol. 359(C), pages 406-414.
    2. Sims, Charles & Aadland, David & Finnoff, David, 2010. "A dynamic bioeconomic analysis of mountain pine beetle epidemics," Journal of Economic Dynamics and Control, Elsevier, vol. 34(12), pages 2407-2419, December.
    3. Menezes, Jorge F.S. & Kotler, Burt P., 2019. "The generalized ideal free distribution model: Merging current ideal free distribution models into a central framework," Ecological Modelling, Elsevier, vol. 397(C), pages 47-54.
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    5. Fern, Rachel R. & Morrison, Michael L. & Wang, Hsiao-Hsuan & Grant, William E. & Campbell, Tyler A., 2019. "Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds," Ecological Modelling, Elsevier, vol. 408(C), pages 1-1.
    6. Loehle, Craig & Weatherford, Philip, 2017. "Detecting population trends with historical data: Contributions of volatility, low detectability, and metapopulation turnover to potential sampling bias," Ecological Modelling, Elsevier, vol. 362(C), pages 13-18.
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    Cited by:

    1. Szewczyk, Tim M. & Moore, Pippa J. & Smale, Dan A. & Adams, Thomas & Burrows, Michael T., 2024. "Mechanistic simulations of kelp populations in a dynamic landscape of light, temperature, and winter storms," Ecological Modelling, Elsevier, vol. 488(C).
    2. Leins, Johannes A. & Banitz, Thomas & Grimm, Volker & Drechsler, Martin, 2021. "High-resolution PVA along large environmental gradients to model the combined effects of climate change and land use timing: lessons from the large marsh grasshopper," Ecological Modelling, Elsevier, vol. 440(C).

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