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

• Published in: Ecological Applications
• Main Authors: Johnson, Fred A., Alhainen, Mikko, Fox, Anthony D., Madsen, Jesper, Guillemain, Matthieu
• Other Authors: U.S. Geological Survey
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2018
• Subjects: Anser fabalis; Fennoscandia; taiga
• Online Access: http://dx.doi.org/10.1002/eap.1659; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Feap.1659; https://onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1659; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/eap.1659; https://esajournals.onlinelibrary.wiley.com/doi/am-pdf/10.1002/eap.1659; https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1659

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.