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Keywords:

  • Alouatta pigra ;
  • dietary preference;
  • food competition;
  • foraging;
  • movement monitoring;
  • residence time;
  • resource selection function

Summary

  1. Most animals need to actively search for food to meet energetic requirements and live in heterogeneous environments where food resources have complex spatio-temporal patterns of availability. Consequently, foraging animals need to find a balance between effort and resource allocation while accounting for intrinsic and extrinsic factors, which are often overlooked when modelling foraging behaviour.
  2. We identified the decision rules for foraging in black howler monkeys (Alouatta pigra), according to food preferences, locations of high-quality patches and previously eaten trees, phenology of food resources and hunger state. We depicted foraging in two stages: (i) the choice of the immediate next tree and (ii) the time spent on this tree. We used a recently developed model for inference of movement processes, incorporating resource selection functions into a Markov chain framework.
  3. We found that monkeys tend to move to preferred tree species at each step. However, we did not find conclusively that, at each step, monkeys direct their movements to reach high-quality patches. In fact, they were using these patches intensively, thus limiting the possibility to move towards other high-quality patches. Time spent on a tree was positively and strongly affected by the presence of preferred food items, but not by its species. We also showed that time spent on trees increased as a function of satiation state. We suggest that the strategy adopted by black howlers tends to be efficient because choosing preferred trees at each step and spending spend more time where preferred resources are available should favour energy intake and restrain movement costs.
  4. This study showcases a modelling framework that can be widely used in ecology to describe movements as a combination of multiple attraction and repulsion sources, such as mates and competitors.