Award details

Understanding and predicting the diffusion of innovations in animal populations

Reference	BB/I007997/1
Principal Investigator / Supervisor	Professor Kevin Laland
Co-Investigators / Co-Supervisors	Dr William Hoppitt, Dr Glenna Nightingale
Institution	University of St Andrews
Department	Biology
Funding type	Research
Value (£)	399,010
Status	Completed
Type	Research Grant
Start date	03/10/2011
End date	02/10/2014
Duration	36 months

Abstract

Interest in social learning, the process whereby animals acquire skills and knowledge by observing other animals, has also been fuelled by high-profile reports of inter- and intra-population variation in the behavioural repertoires of nonhuman primates and cetaceans, and of the rapid spread of novel behavioural innovations in animal populations. However, claims that such variation represent animal traditions remain controversial in the absence of clear methods for ruling out alternatives, such as genetic differences between populations, or asocial learning in response to varying environmental resources, while field researchers have generally been unable to prove that innovations spread by social learning. What is required are new methods allowing researchers to (1) identify and quantify social learning among animals living in groups, (2) explain and predict the rate, path and pattern of diffusion of learned innovations, (3) identify the psychological mechanisms that underpin socially transmitted diffusions, and (4) identify the social learning strategies that animals deploy in natural contexts. This project deploys innovative empirical and theoretical techniques, including the development of novel analytical tools, to investigate animal social learning and the diffusion of innovations. These tools include network-based and option-bias diffusion analyses and stochastic mechanism-fitting models using MCMC and ABC. As a result of the research described in this proposal, researchers will be able to predict the spread of innovations, detect social learning, and identify its mechanisms and strategies, in natural populations and captive social groups. The methods will be developed using experimental data derived from a captive starling population and tested on chimpanzee, capuchin, meerkat and New Caledonian crow data. The experiments and theory together will lead to a deeper understanding of social learning and the spread of innovations in animals, including humans.

Summary

The aim of this research is to increase our understanding of the processes by which new knowledge and skills is transferred between animals, including humans, through copying ('social learning'). For human beings, imitation and teaching are thought to be the most important processes by which information is transferred from knowledgeable to naive individuals. However, in other animals there is little evidence that teaching occurs but nonetheless inexperienced and young individuals pick up clues from more knowledgeable others concerning, for instance, the identity of predators or how best to process foods. This social learning can generate 'traditions' for performing particular behaviour patterns, for instance, eating specific foods, or taking particular pathways. As a consequence, the behaviour of animals may vary from one population to the next, not just because they possess different genes, or are exposed to different environmental resources, but also because they have learned different habits from more experienced members of their population. A challenge for researchers studying animal behaviour is to identify these 'traditions' and to work out how novel behaviour and skills ('innovations') spread through populations. Currently it is very difficult for researchers to tell if animals are copying each other outside of the laboratory context, or to specify the psychological and social processes that underlie the spread of innovations. This research programme will generate useful data using laboratory populations of starlings and then use mathematics to develop statistical packages that specify when animals acquire their behaviour through social learning, how novel behaviours spread through social learning in animal populations, and what learning rules are deployed. The usefulness of these statistical tools will then be tested in natural or naturalistic populations of New Caledonian crows, meerkats, chimpanzees and capuchin monkeys - animals renowned for their traditional behaviour - working with expert collaborators from Oxford, Cambridge and Durham universities. These statistical tools will be made available as freeware to a variety of researchers (biologists, psychologists, anthropologists, archaeologists, economists) interested in detecting and understanding social learning and predicting the diffusion of innovations, including technological innovations in humans. As there are some parallels between the spread of information and certain transmittable diseases through populations, the project also contains a pilot study to investigate whether the developed statistical tools potentially can be applied to predict disease flow, using a guppy model system. This project involves collaborations with outstanding researchers at Princeton, Oxford, Cambridge, Durham and Exeter Universities and the MPI Leipzig.

Impact Summary

The primary users for this project are other academics, but the research has considerable potential for impact outside of the university environment: 1) Sales and marketing. The statistical tools developed as part of this project potentially have a commercial outlet, being of interest to business, sales people, economists, market researchers and management consultants seeking to predict the spread of technological innovations to target potential buyers. By identifying the contexts under which individuals copy, and from whom they learn, we allow knowledge flow to be predicted. By providing estimates of the likelihood that particular individuals, or categories of individual, will be the next to acquire a behaviour, we generate information that could be exploited by business to target sales effectively. By the end of this project we will have a robust and reliable set of statistical methods that will predict the diffusion of innovations. We will explore the market for producing a commercially viable product targeted to businesses and other outlets seeking to predict the diffusion of innovations. 2) Robotics. Increasingly, researchers studying robotics and artificial intelligence are paying attention to animal social learning as part of endeavours to develop 'imitating robots' and related technology. Part of this technological challenge relates to how to specify what should and what should not be imitated by machines, without detracting from the flexibility that such machines are designed to confer. By identifying the contexts under which animals copy others, our project potentially delivers 'when-to-copy' rules crucial for the development of intelligent imitating machines. 3) Conservation and restocking. As a direct result of our basic research, findings from studies of social learning in non-commercial fish species have been applied to develop efficient training procedures for enhancing the life-skills of hatchery-reared fishes. We have devised procedures through which a minority of knowledgeable fish can pass on their lifeskills to the majority, at low cost (Brown & Laland, 2001). These procedures are now being implemented in commercial hatcheries, to the benefit of the fishes' welfare and the hatcheries' economic returns. More generally, many endangered species are bred in captivity and released back into the wild for restocking, with mixed results. Zoos are now using social learning protocols to ensure released animals are appropriately pre-trained. 4) Managing captive populations. Large numbers of animals are held in captivity, in farms, zoos and wildlife parks, and research laboratories. Knowledge of social learning can be used to enhance their welfare. Social learning can have positive and negative effects on captive populations. Social learning protocols have been used to introduce naïve animals to novel foods, to teach foraging skills, and to control animal movements; conversely, social learning may propagate stereotypies and stress responses. A better understanding of social learning potentially allows such factors to be controlled. By providing rigorous means for studying learning mechanisms in the wild, our methods make a modest contribution to reducing the need for keeping animals in captivity, contributing to the BBSRC policy priority of 3Rs in animal research. 5) Disease. Research into the spread of learned information and infectious disease has been largely independent, yet shares the characteristic that the likelihood of transmission depends on the degree of contact between individuals. It is plausible that that ability to predict the spread of innovations could provide tools that transfer to predicting the spread of diseases, allowing individuals most at risk to be identified. We will conduct a pilot study to test this. If successful, there are exciting possibilities for better predicting the spread of disease in nonhuman animal and human populations, and identifying 'at risk' categories.

Committee	Research Committee A (Animal disease, health and welfare)
Research Topics	Animal Welfare, Neuroscience and Behaviour
Research Priority	Technology Development for the Biosciences
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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