Literature DB >> 29205644

Making do with less: must sparse data preclude informed harvest strategies for European waterbirds?

Fred A Johnson1, Mikko Alhainen2, Anthony D Fox3, Jesper Madsen3, Matthieu Guillemain4.   

Abstract

The demography of many European waterbirds is not well understood because most countries have conducted little monitoring and assessment, and coordination among countries on waterbird management has little precedent. Yet intergovernmental treaties now mandate the use of sustainable, adaptive harvest strategies, whose development is challenged by a paucity of demographic information. In this study, we explore how a combination of allometric relationships, fragmentary monitoring and research information, and expert judgment can be used to estimate the parameters of a theta-logistic population model, which in turn can be used in a Markov decision process to derive optimal harvesting strategies. We show how to account for considerable parametric uncertainty, as well as for different management objectives. We illustrate our methodology with a poorly understood population of Taiga Bean Geese (Anser fabalis fabalis), which is a popular game bird in Fennoscandia. Our results for Taiga Bean Geese suggest that they may have demographic rates similar to other, well-studied species of geese, and our model-based predictions of population size are consistent with the limited monitoring information available. Importantly, we found that by using a Markov decision process, a simple scalar population model may be sufficient to guide harvest management of this species, even if its demography is age structured. Finally, we demonstrated how two different management objectives can lead to very different optimal harvesting strategies, and how conflicting objectives may be traded off with each other. This approach will have broad application for European waterbirds by providing preliminary estimates of key demographic parameters, by providing insights into the monitoring and research activities needed to corroborate those estimates, and by producing harvest management strategies that are optimal with respect to the managers' objectives, options, and available demographic information.
© 2017 by the Ecological Society of America.

Entities:  

Keywords:  Markov decision process; Taiga Bean Geese; adaptive management; demography; geese; harvest; hunting; stochastic dynamic programming; waterbirds

Mesh:

Year:  2018        PMID: 29205644     DOI: 10.1002/eap.1659

Source DB:  PubMed          Journal:  Ecol Appl        ISSN: 1051-0761            Impact factor:   4.657


  3 in total

1.  Make flying-fox hunting sustainable again: Comparing expected demographic effectiveness and hunters' acceptance of more restrictive regulations.

Authors:  Malik Oedin; Fabrice Brescia; Eric Vidal; Alexandre Millon
Journal:  Ambio       Date:  2021-10-09       Impact factor: 5.129

2.  Aggregating predictions from experts: a review of statistical methods, experiments, and applications.

Authors:  Thomas McAndrew; Nutcha Wattanachit; Graham C Gibson; Nicholas G Reich
Journal:  Wiley Interdiscip Rev Comput Stat       Date:  2020-06-16

3.  Attenuating the nonresponse bias in hunting bag surveys: The multiphase sampling strategy.

Authors:  Philippe Aubry; Matthieu Guillemain
Journal:  PLoS One       Date:  2019-03-15       Impact factor: 3.240

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.