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Reducing matrix population models with application to social animal species

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  • Marescot, Lucile
  • Gimenez, Olivier
  • Duchamp, Christophe
  • Marboutin, Eric
  • Chapron, Guillaume

Abstract

Stage-structured matrix models are commonly used to inform management decisions for species with complex life cycles. These models require information on the number or proportion of individuals in each stage. However, complex life cycles, such as those in species exhibiting a complex social organization, can make these data difficult to obtain. The discrete time structure of matrix models makes them reducible, meaning that full models can be simplified by removing some stages. We illustrate the method by reducing the life cycle of wolf (Canis lupus) on which culling and conservation plans often lead to controversial debates. Starting from a 4-stage matrix incorporating social stages, we obtained several reduced models of increasing simplicity all showing similar demographic outcomes to the full model. We found that asymptotic growth rates of reduced models were in close agreement with empirical data. Our approach can offset the lack of information on individual stage abundance and therefore be valuable when using matrix models for wildlife management when data on certain stages are sparse.

Suggested Citation

  • Marescot, Lucile & Gimenez, Olivier & Duchamp, Christophe & Marboutin, Eric & Chapron, Guillaume, 2012. "Reducing matrix population models with application to social animal species," Ecological Modelling, Elsevier, vol. 232(C), pages 91-96.
    • Handle: RePEc:eee:ecomod:v:232:y:2012:i:c:p:91-96
    DOI: 10.1016/j.ecolmodel.2012.02.017
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    References listed on IDEAS

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    1. Gimenez, Olivier & Lebreton, Jean-Dominique & Gaillard, Jean-Michel & Choquet, Rémi & Pradel, Roger, 2012. "Estimating demographic parameters using hidden process dynamic models," Theoretical Population Biology, Elsevier, vol. 82(4), pages 307-316.
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    Cited by:

    1. Logofet, Dmitrii O., 2017. "Aggregation may or may not eliminate reproductive uncertainty," Ecological Modelling, Elsevier, vol. 363(C), pages 187-191.
    2. Hatlauf, J. & Kunz, F. & Griesberger, P. & Sachser, F. & Hackländer, K., 2024. "A stage-based life cycle implementation for individual-based population viability analyses of grey wolves (Canis lupus) in Europe," Ecological Modelling, Elsevier, vol. 491(C).
    3. Bauduin, Sarah & Grente, Oksana & Santostasi, Nina Luisa & Ciucci, Paolo & Duchamp, Christophe & Gimenez, Olivier, 2020. "An individual-based model to explore the impacts of lesser-known social dynamics on wolf populations," Ecological Modelling, Elsevier, vol. 433(C).
    4. Santostasi, Nina Luisa & Ciucci, Paolo & Bearzi, Giovanni & Bonizzoni, Silvia & Gimenez, Olivier, 2020. "Assessing the dynamics of hybridization through a matrix modelling approach," Ecological Modelling, Elsevier, vol. 431(C).
    5. Thibaut Couturier & Sarah Bauduin & Guillelme Astruc & Aurélie Blanck & Coline Canonne & Thierry Chambert & Jules Chiffard & Alix Cosquer & Sarah Cubaynes & Laurence Curtet & Emmanuelle Dortel & Nolwe, 2023. "Building spaces of interactions between researchers and managers," Post-Print hal-04125409, HAL.
    6. Chapron, Guillaume & Wikenros, Camilla & Liberg, Olof & Wabakken, Petter & Flagstad, Øystein & Milleret, Cyril & Månsson, Johan & Svensson, Linn & Zimmermann, Barbara & Åkesson, Mikael & Sand, Håkan, 2016. "Estimating wolf (Canis lupus) population size from number of packs and an individual based model," Ecological Modelling, Elsevier, vol. 339(C), pages 33-44.

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