Summary1. A forager's optimal patch-departure time can be predicted by the prescient marginal value theorem (pMVT), which assumes they have perfect knowledge of the environment, or by approaches such as Bayesian updating and learning rules, which avoid this assumption by allowing foragers to use recent experiences to inform their decisions. 2. In understanding and predicting broader scale ecological patterns, individual-level mechanisms, such as patch-departure decisions, need to be fully elucidated. Unfortunately, there are few empirical studies that compare the performance of patch-departure models that assume perfect knowledge with those that do not, resulting in a limited understanding of how foragers decide when to leave a patch. 3. We tested the patch-departure rules predicted by fixed rule, pMVT, Bayesian updating and learning models against one another, using patch residency times (PRTs) recorded from 54 chacma baboons (Papio ursinus) across two groups in natural (n = 6175 patch visits) and field experimental (n = 8569) conditions. 4. We found greater support in the experiment for the model based on Bayesian updating rules, but greater support for the model based on the pMVT in natural foraging conditions. This suggests that foragers may place more importance on recent experiences in predictable environments, like our experiment, where these experiences provide more reliable information about future opportunities. 5. Furthermore, the effect of a single recent foraging experience on PRTs was uniformly weak across both conditions. This suggests that foragers' perception of their environment may incorporate many previous experiences, thus approximating the perfect knowledge assumed by the pMVT. Foragers may, therefore, optimize their patch-departure decisions in line with the pMVT through the adoption of rules similar to those predicted by Bayesian updating.
In social groups, individuals' dominance rank, social bonds, and kinship with other group members have been shown to influence their foraging behavior. However, there is growing evidence that the particular effects of these social traits may also depend on local environmental conditions. We investigated this by comparing the foraging behavior of wild chacma baboons, Papio ursinus, under natural conditions and in a field experiment where food was spatially clumped. Data were collected from 55 animals across two troops over a 5-month period, including over 900 agonistic foraging interactions and over 600 food patch visits in each condition. In both conditions, low-ranked individuals received more agonism, but this only translated into reduced foraging performances for low-ranked individuals in the high-competition experimental conditions. Our results suggest one possible reason for this pattern may be low-ranked individuals strategically investing social effort to negotiate foraging tolerance, but the rank-offsetting effect of this investment being overwhelmed in the higher-competition experimental environment. Our results also suggest that individuals may use imbalances in their social bonds to negotiate tolerance from others under a wider range of environmental conditions, but utilize the overall strength of their social bonds in more extreme environments where feeding competition is more intense. These findings highlight that behavioral tactics such as the strategic investment of social effort may allow foragers to mitigate the costs of low rank, but that the effectiveness of these tactics is likely to be limited in certain environments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.