Able-Bodied Wild Chimpanzees Imitate a Motor Procedure Used by a Disabled Individual to Overcome Handicap

Chimpanzee culture has generated intense recent interest, fueled by the technical complexity of chimpanzee tool-using traditions; yet it is seriously doubted whether chimpanzees are able to learn motor procedures by imitation under natural conditions. Here we take advantage of an unusual chimpanzee population as a ‘natural experiment’ to identify evidence for imitative learning of this kind in wild chimpanzees. The Sonso chimpanzee community has suffered from high levels of snare injury and now has several manually disabled members. Adult male Tinka, with near-total paralysis of both hands, compensates inability to scratch his back manually by employing a distinctive technique of holding a growing liana taut while making side-to-side body movements against it. We found that seven able-bodied young chimpanzees also used this ‘liana-scratch’ technique, although they had no need to. The distribution of the liana-scratch technique was statistically associated with individuals' range overlap with Tinka and the extent of time they spent in parties with him, confirming that the technique is acquired by social learning. The motivation for able-bodied chimpanzees copying his variant is unknown, but the fact that they do is evidence that the imitative learning of motor procedures from others is a natural trait of wild chimpanzees

Harnessing learning biases is essential for applying social learning in conservation

Social learning can influence how animals respond to anthropogenic changes in the environment, determining whether animals survive novel threats and exploit novel resources or produce maladaptive behaviour and contribute to human-wildlife conflict. Predicting where social learning will occur and manipulating its use are, therefore, important in conservation, but doing so is not straightforward. Learning is an inherently biased process that has been shaped by natural selection to prioritize important information and facilitate its efficient uptake. In this regard, social learning is no different from other learning processes because it too is shaped by perceptual filters, attentional biases and learning constraints that can differ between habitats, species, individuals and contexts. The biases that constrain social learning are not understood well enough to accurately predict whether or not social learning will occur in many situations, which limits the effective use of social learning in conservation practice. Nevertheless, we argue that by tapping into the biases that guide the social transmission of information, the conservation applications of social learning could be improved. We explore the conservation areas where social learning is highly relevant and link them to biases in the cues and contexts that shape social information use. The resulting synthesis highlights many promising areas for collaboration between the fields and stresses the importance of systematic reviews of the evidence surrounding social learning practices.BBSRC David Phillips Fellowship (BB/H021817/1

Social preferences and network structure in a population of reef manta rays

Understanding how individual behavior shapes the structure and ecology ofpopulations is key to species conservation and management. Like manyelasmobranchs, manta rays are highly mobile and wide ranging species threatened byanthropogenic impacts. In shallow-water environments these pelagic rays often formgroups, and perform several apparently socially-mediated behaviors. Group structuresmay result from active choices of individual rays to interact, or passive processes.Social behavior is known to affect spatial ecology in other elasmobranchs, but this isthe first study providing quantitative evidence for structured social relationships inmanta rays. To construct social networks, we collected data from more than 500groups of reef manta rays over five years, in the Raja Ampat Regency of West Papua.We used generalized affiliation indices to isolate social preferences from non-socialassociations, the first study on elasmobranchs to use this method. Longer lastingsocial preferences were detected mostly between female rays. We detectedassortment of social relations by phenotype and variation in social strategies, with theoverall social network divided into two main communities. Overall network structurewas characteristic of a dynamic fission-fusion society, with differentiated relationshipslinked to strong fidelity to cleaning station sites. Our results suggest that fine-scaleconservation measures will be useful in protecting social groups of M. alfredi in theirnatural habitats, and that a more complete understanding of the social nature of mantarays will help predict population response

Inferring learning strategies from cultural frequency data

Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population. We review the previous methodology that has attempted to infer the underlying social learning processes from such data, showing that they may apply statistical methods with insufficient power to draw reliable inferences. We then introduce a generative inference framework that allows robust inferences on the social learning processes that underlie cultural frequency data. Using developments in population genetics—in the form of generative simulation modelling and approximate Bayesian computation—as our model, we demonstrate the strength of this method with an example based on simulated data