Discriminating between possible foraging decisions using pattern-oriented modelling:the case of pink-footed geese in Mid-Norway during their spring migration

• Published in: Ecological Modelling
• Main Authors: Chudzińska, Magda, Ayllón, Daniel, Madsen, Jesper, Nabe-Nielsen, Jacob
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2016
• Subjects: Agent-based simulation model; Anser brachyrhynchus; Heterogeneous landscape; Learning; Optimal foraging; Arctic; Norway
• Online Access: https://risweb.st-andrews.ac.uk/portal/en/researchoutput/discriminating-between-possible-foraging-decisions-using-patternoriented-modelling(c79b426e-cce0-4f62-821e-e88993e87445).html; https://doi.org/10.1016/j.ecolmodel.2015.10.005; https://research-repository.st-andrews.ac.uk/bitstream/10023/12519/1/Chudzinska_2016_EM_Pattern_orientedModelling_CC.pdf; http://www.sciencedirect.com/science/article/pii/S0304380015004639?via%3Dihub#sec0180

Foraging decisions and their energetic consequences are critical to capital Arctic-breeders migrating in steps, because there is only a narrow time window with optimal foraging conditions at each step. Optimal foraging theory predicts that such animals should spend more time in patches that enable them to maximise the net rate of energy and nutrient gain. The type of search strategy employed by animals is, however, expected to depend on the amount of information that is involved in the search process. In highly dynamic landscapes, animals are unlikely to have complete knowledge about the distribution of the resources, which makes them unable to forage on the patches that enable them to maximise their net energy intake. Random search may, however, be a good strategy in landscapes where patches with profitable resources are abundant. We present simulation experiments using an individual-based model (IBM) to test which foraging decision rule (FDR) best reproduces the population patterns observed in pink-footed geese during spring staging in an agricultural landscape in Mid-Norway. Our results suggested that while geese employed a random search strategy, they were also able to individually learn where the most profitable patches were located and return to the patches that resulted in highest energy intake. Such asocial learning is rarely reported for flock animals. The modelled geese did not benefit from group foraging, which contradicts the results reported by most studies on flocking birds. Geese also did not possess complete knowledge about the profitability of the available habitat. Most likely, there is no one single optimal foraging strategy for capital breeders but such strategy is site and species-specific. We discussed the potential use of the model as a valuable tool for making future risk assessments of human disturbance and changes in agricultural practices.