| Summary: | Our understanding of how energy shapes animal behavioural decisions has been limited by the difficulty of measuring directly the energy gain and expenditure in free-living animals. Mechanistic models that simulate energy gain and expenditure from estimable parameters can overcome these limitations and hence could help scientists to gain a predictive understanding of animal behaviour. Such models could be used to test mechanistic explanations of observed patterns of resource use within a landscape, such as behavioural decisions to switch among food resources. Here, we developed mechanistic models of the instantaneous and daily rates of net energy gain for two species of migratory swans, the Bewick’s swan (Cygnus columbianus bewickii) and whooper swan (Cygnus cygnus), that feed on root and cereal crops within an agricultural landscape in eastern England. Field data show that both species shift from using predominantly root crops (e.g. sugar beet and potatoes) in early winter to using mostly cereals (e.g. wheat) in late winter. Our models correspondingly predicted that swans could achieve the greatest rates of net energy gain on root crops in early winter and on cereal crops in late winter. The change from root crops to cereal crops providing the greatest net rates of energy gain was predicted to occur at the same time as the birds’ switch from feeding predominantly on root crops to predominantly cereal crops (between December and January). We used Monte Carlo simulations to account for variance in model parameters on predictions of energy gain and profitability. A sensitivity analysis indicated that predictions of net energy gain were most sensitive to variance in the intake rate and food quantity parameters. The agreement between our model estimates of energy gain and the observed shifts in resource use observed among the overwintering swans suggests that maximising net rates of energy gain is an important resource selection strategy among overwintering birds. A mechanistic understanding of where and when birds ...
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