Making use of harvest information to examine alternative management scenarios: a body weight-structured model for wild boar

Authors

  • Marlène Gamelon,

    Corresponding author
    1. Université de Lyon, F-69000, Lyon, France; Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France
    2. Office National de la Chasse et de la Faune Sauvage, CNERA Cervidés Sangliers, 2 Bis Rue des Religieuses, BP 19, 52120 Châteauvillain, France
    3. Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175, campus CNRS, 1919 route de Mende, 34293 Montpellier Cedex 5, France
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  • Jean-Michel Gaillard,

    1. Université de Lyon, F-69000, Lyon, France; Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France
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  • Sabrina Servanty,

    1. USGS Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, MD 20708-4039, USA
    2. Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, 1484 Campus Delivery, Fort Collins, CO 80523-1484, USA
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  • Olivier Gimenez,

    1. Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175, campus CNRS, 1919 route de Mende, 34293 Montpellier Cedex 5, France
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  • Carole Toïgo,

    1. Office National de la Chasse et de la Faune Sauvage, CNERA Faune de Montagne, 5 allée de Bethléem, Z.I. Mayencin, 38610 Gières, France
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  • Eric Baubet,

    1. Office National de la Chasse et de la Faune Sauvage, CNERA Cervidés Sangliers, Montfort 01330 Birieux, France
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  • François Klein,

    1. Office National de la Chasse et de la Faune Sauvage, CNERA Cervidés Sangliers, 1 Place Exelmans, 55000 Bar-le-Duc, France
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  • Jean-Dominique Lebreton

    1. Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175, campus CNRS, 1919 route de Mende, 34293 Montpellier Cedex 5, France
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Correspondence author. E-mail: marlene.gamelon@univ-lyon1.fr

Summary

1. Harvest models are often built to explore the sustainability of the dynamics of exploited populations and to help evaluate hunting management scenarios. Age-structured models are commonly used for ungulate population dynamics. However, the age of hunted individuals is usually not recorded, and hunting data often only include body weight and sex limiting the usefulness of traditional models.

2. We propose a new modelling approach that fits data collected by hunters to develop management rules when age is not available. Using wild boar Sus scrofa scrofa as a case study, we built a matrix model structured according to sex and body weight whose output can be directly compared with the observed distribution of hunted individuals among sex and body weight classes.

3. In the face of the current wide scale increase in populations of wild boar, the best feasible option to stop or slow down population growth involves targeting the hunting effort to specific sex and body weight classes. The optimal harvest proportion in the target body weight classes is estimated using sensitivity analyses.

4. The number of individuals shot in each sex and body weight class predicted by our model was closely associated with those recorded in the hunting bag. Increasing the hunting pressure on medium-sized females by 14·6% was the best option to limit growth rate to a target of 0·90.

5.Synthesis and applications. We demonstrate that targeting hunting effort to specific body weight classes could reliably control population growth. Our modelling approach can be applied to any game species where group composition, phenotypic traits or coat colour allows hunters to easily identify sex and body weight classes. This offers a promising tool for applying selective hunting to the management of game species.

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