Expert system for modelling stopover site selection by barnacle geese

Mitra Shariati, Andrew K. Skidmore, Roshanak Darvishzadeh, Klaus-Michael Exo, Andrea Kölzsch, Larry Griffin, Julia Stahl, David Cabot and Albertus G. Toxopeus

Ecological Modelling, 2017, vol. 359, issue C, 398-405

Abstract: The study of stopover sites has received a lot of attention in avian ecology, being especially important for many long-distance migrants, some of which have to pause several times during migration. The survival of many migratory birds depends primarily on food availability at these stopovers. However, previous studies show that there is a lack of knowledge about site selection where migratory birds stop to refuel energy stores. In the present study, a Bayesian expert system has been used to incorporate environmental parameters, to determine their relationship with the presence of barnacle geese at stopover sites. Data on stopover sites was obtained from satellite-tracked barnacle geese (Branta leucopsis) for three different breeding populations in the Western Palearctic (i.e. Russian, Svalbard and Greenland). The results from the present study showed that the posterior probability of presence at the stopover sites obtained from the Bayesian model was close to one. Therefore, the Bayesian expert system detected the stopover sites of the geese correctly and can be used as a proper method for modelling the presence of barnacle geese at the stopover sites in the future. This study introduces a new method into movement ecology to identify and predict the importance of different environmental parameters for stopover site selection by migratory geese. This is particularly important from both a conservation and an agro-economic point of view with the goal of reducing possible conflicts between geese and agricultural interests.

Keywords: Foraging habitat; GPS locations; Human disturbance; Roosting areas; Site safety (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:359:y:2017:i:c:p:398-405

DOI: 10.1016/j.ecolmodel.2017.06.018

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