Uncovering the decision rules behind collective foraging in spider monkeys

Palacios-Romo, T.M.; Castellanos, Francisco; Ramos‐Fernández, Gabriel

doi:10.1016/j.anbehav.2019.01.011

Cited by 21 publications

(31 citation statements)

References 55 publications

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“…We also found that the presence of highly connected individuals (or "hubs") in a random, scale-free network is important for collective learning. This is coincident with the results of an empirical study of spider monkeys [42] that found that more central individuals lead collective movements and are followed to available feeding trees that they know about more frequently than non-central individuals.…”

Section: Discussionsupporting

confidence: 86%

“…Similarly, in foraging groups, six or seven neighbours may be a good compromise between the benefit of gathering valuable information from others and limited sensory and memory capacities. In an empirical study aimed specifically at uncovering the rules by which spider monkeys share information about feeding trees [42], individuals that know about available feeding trees share this information with an average of 4.25 (±3.75 S.D.) individuals over several detailed observations at feeding trees.…”

Section: Discussionmentioning

confidence: 99%

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Collective learning from individual experiences and information transfer during group foraging

Falcón-Cortés

Boyer

Ramos‐Fernández

2019

J. R. Soc. Interface.

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Living in groups brings benefits to many animals, such as protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviours within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviours can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent-based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behaviour within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Collective learning from individual experiences and information transfer during group foraging

Falcón-Cortés

Boyer

Ramos‐Fernández

2019

J. R. Soc. Interface.

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“…Studies over the past decades have substantially advanced our understanding of the mechanisms by which social structure—the content, quality and patterning of social connections among individuals in a population (Hinde, 1976)—arises, and how social structure shapes social processes (Sueur, Romano, Sosa, & Puga‐Gonzalez, 2019). For example, (dis)assortativity around individual traits and states—for example, genetic relatedness, sex, age, dominance, promiscuity, behavioural repertoire—can influence social interactions (Croft et al., 2009; Farine, Montiglio, & Spiegel, 2015; McDonald, Spurgin, Fairfield, Richardson, & Pizzari, 2019; Pike, Samanta, Lindstrom, & Royle, 2008); early‐life conditions affect adult social decisions (Farine, Spencer, & Boogert, 2015); and the social environment can affect collective decision‐making (Palacios‐Romo, Castellanos, & Ramos‐Fernandez, 2019; Strandburg‐Peshkin, Farine, Crofoot, & Couzin, 2017) or dispersal and recruitment (Armansin et al., 2020; Ilany, Barocas, Koren, Kam, & Geffen, 2013; McDonald, 2007). Sometimes, who individuals are connected to, or the overall structure of populations, can also simply arise from limitations in where individuals can move and, therefore, who they can encounter (e.g.…”

Section: Introductionmentioning

confidence: 99%

The importance of individual‐to‐society feedbacks in animal ecology and evolution

Cantor

Maldonado-Chaparro

Beck

et al. 2020

Journal of Animal Ecology

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“…The circuit of individual strategies that we infer here is, at least in part, a reflection of information sharing about available patches. Following another individual when ignorant is a simple mechanism of information sharing (Palacios-Romo et al, 2019), that could be reflected in the dyadic weights we have measured. This would lead to a fully connected circuit with information about food sources promoting a flexible grouping pattern that matches heterogeneity in the environment.…”

Section: Discussionmentioning

confidence: 99%

“…Fission-fusion social dynamics, in which individuals fission and fuse into subgroups of varying size, is a collective pattern arising from individual decisions (Sueur et al, 2011 ; Ramos-Fernández et al, 2018 ). These dynamics are thought to be adaptive, as they allow individuals to forage more efficiently in heterogeneous environments, share information about the location of resources, and adjust the size of their subgroups to resource availability (Aureli et al, 2008 ; Sueur et al, 2011 ; Palacios-Romo et al, 2019 ). The individual, strategic decisions to leave or join subgroups, how these decisions influence subgroup size distributions, and whether these are a good fit or even predicted by environmental states, are open questions.…”

Section: Introductionmentioning

confidence: 99%

Collective Computation in Animal Fission-Fusion Dynamics

et al. 2020

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Recent work suggests that collective computation of social structure can minimize uncertainty about the social and physical environment, facilitating adaptation. We explore these ideas by studying how fission-fusion social structure arises in spider monkey (Ateles geoffroyi) groups, exploring whether monkeys use social knowledge to collectively compute subgroup size distributions adaptive for foraging in variable environments. We assess whether individual decisions to stay in or leave subgroups are conditioned on strategies based on the presence or absence of others. We search for this evidence in a time series of subgroup membership. We find that individuals have multiple strategies, suggesting that the social knowledge of different individuals is important. These stay-leave strategies provide microscopic inputs to a stochastic model of collective computation encoded in a family of circuits. Each circuit represents an hypothesis for how collectives combine strategies to make decisions, and how these produce various subgroup size distributions. By running these circuits forward in simulation we generate new subgroup size distributions and measure how well they match food abundance in the environment using transfer entropies. We find that spider monkeys decide to stay or go using information from multiple individuals and that they can collectively compute a distribution of subgroup size that makes efficient use of ephemeral sources of nutrition. We are able to artificially tune circuits with subgroup size distributions that are a better fit to the environment than the observed. This suggests that a combination of measurement error, constraint, and adaptive lag are diminishing the power of collective computation in this system. These results are relevant for a more general understanding of the emergence of ordered states in multi-scale social systems with adaptive properties-both natural and engineered.

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Uncovering the decision rules behind collective foraging in spider monkeys

Cited by 21 publications

References 55 publications

Collective learning from individual experiences and information transfer during group foraging

Collective learning from individual experiences and information transfer during group foraging

The importance of individual‐to‐society feedbacks in animal ecology and evolution

Collective Computation in Animal Fission-Fusion Dynamics

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