Movement and conformity interact to establish local behavioural traditions in animal populations.
Ontology highlight
ABSTRACT:
SUBMITTER: Somveille M
PROVIDER: S-EPMC6319775 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
Publications
Movement and conformity interact to establish local behavioural traditions in animal populations.
Somveille Marius M Firth Josh A JA Aplin Lucy M LM Farine Damien R DR Sheldon Ben C BC Thompson Robin N RN
PLoS computational biology 20181220 12
The social transmission of information is critical to the emergence of animal culture. Two processes are predicted to play key roles in how socially-transmitted information spreads in animal populations: the movement of individuals across the landscape and conformist social learning. We develop a model that, for the first time, explicitly integrates these processes to investigate their impacts on the spread of behavioural preferences. Our results reveal a strong interplay between movement and co ...[more]
Similar Datasets
Project description:Cultural traditions have been observed in a wide variety of animal species. It remains unclear, however, what is required for social learning to give rise to stable traditions: what level of precision and what learning strategies are required. We address these questions by fitting models of cultural evolution to learned bird song. We recorded 615 swamp sparrow (Melospiza georgiana) song repertoires, and compared syllable frequency distributions to the output of individual-based simulations. We find that syllables are learned with an estimated error rate of 1.85% and with a conformist bias in learning. This bias is consistent with a simple mechanism of overproduction and selective attrition. Finally, we estimate that syllable types could frequently persist for more than 500 years. Our results demonstrate conformist bias in natural animal behaviour and show that this, along with moderately precise learning, may support traditions whose stability rivals those of humans.
| S-EPMC6010409 | biostudies-literature
Project description:One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and levels of analysis. Music sampling is an ideal research model for this process because samples are known to be culturally transmitted between collaborating artists, and sampling events are reliably documented in online databases. The aim of the current study was to determine whether frequency-based bias has played a role in the cultural transmission of music sampling traditions, using a longitudinal dataset of sampling events across three decades. Firstly, we assessed whether turn-over rates of popular samples differ from those expected under neutral evolution. Next, we used agent-based simulations in an approximate Bayesian computation framework to infer what level of frequency-based bias likely generated the observed data. Despite anecdotal evidence of novelty bias, we found that sampling patterns at the population-level are most consistent with conformity bias. We conclude with a discussion of how counter-dominance signalling may reconcile individual cases of novelty bias with population-level conformity.
| S-EPMC6774939 | biostudies-literature
Project description:Social learning is important to the life history of many animals, helping individuals to acquire new adaptive behavior. However despite long-running debate, it remains an open question whether a reliance on social learning can also lead to mismatched or maladaptive behavior. In a previous study, we experimentally induced traditions for opening a bidirectional door puzzle box in replicate subpopulations of the great tit Parus major Individuals were conformist social learners, resulting in stable cultural behaviors. Here, we vary the rewards gained by these techniques to ask to what extent established behaviors are flexible to changing conditions. When subpopulations with established foraging traditions for one technique were subjected to a reduced foraging payoff, 49% of birds switched their behavior to a higher-payoff foraging technique after only 14 days, with younger individuals showing a faster rate of change. We elucidated the decision-making process for each individual, using a mechanistic learning model to demonstrate that, perhaps surprisingly, this population-level change was achieved without significant asocial exploration and without any evidence for payoff-biased copying. Rather, by combining conformist social learning with payoff-sensitive individual reinforcement (updating of experience), individuals and populations could both acquire adaptive behavior and track environmental change.
| S-EPMC5544276 | biostudies-other
Project description:BackgroundSocial conformity is a cornerstone of human culture because it accelerates and maintains the spread of behaviour within a group. Few empirical studies have investigated the role of social conformity in the maintenance of traditions despite an increasing body of literature on the formation of behavioural patterns in non-human animals. The current report presents a field experiment with free-ranging marmosets (Callithrix jacchus) which investigated whether social conformity is necessary for the maintenance of behavioural patterns within groups or whether individual effects such as habit formation would suffice.MethodsUsing a two-action apparatus, we established alternative behavioural patterns in six family groups composed of 36 individuals. These groups experienced only one technique during a training phase and were thereafter tested with two techniques available. The monkeys reliably maintained the trained method over a period of three weeks, despite discovering the alternative technique. Three additional groups were given the same number of sessions, but those 21 individuals could freely choose the method to obtain a reward. In these control groups, an overall bias towards one of the two methods was observed, but animals with a different preference did not adjust towards the group norm. Thirteen of the fifteen animals that discovered both techniques remained with the action with which they were initially successful, independent of the group preference and the type of action (Binomial test: exp. proportion: 0.5, p<0.01).ConclusionsThe results indicate that the maintenance of behavioural patterns within groups 1) could be explained by the first rewarded manipulation and subsequent habit formation and 2) do not require social conformity as a mechanism. After an initial spread of a behaviour throughout a group, this mechanism may lead to a superficial appearance of conformity without the involvement of such a socially and cognitively complex mechanism. This is the first time that such an experiment has been conducted with free-ranging primates.
| S-EPMC2636861 | biostudies-literature
Project description:State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone.
| S-EPMC4745009 | biostudies-literature
Project description:Obesity, a major risk factor for type II diabetes, is becoming more prevalent in Western populations consuming high calorie diets while expending less energy both at the workplace and at home. Most human obesity, and probably most type II diabetes as well, reflects polygenic rather than monogenic inheritance. We have genetically dissected a polygenic mouse model of obesity-driven type II diabetes by outcrossing the obese, diabetes-prone, NZO (New Zealand Obese)/HlLt strain to the relatively lean NON (Nonobese Nondiabetic)/Lt strain, and then reciprocally backcrossing obese F1 mice to the lean NON/Lt parental strain. A continuous distribution of body weights was observed in a population of 203 first backcross males. The 22% of first backcross males developing overt diabetes showed highest peripubertal weight gains and earliest development of hyperinsulinemia. We report a complex diabetes-predisposing ("diabesity") QTL (Quantitative Trait Loci) on chromosome 1 contributing significant main effects to increases in body weight, plasma insulin, and plasma glucose. NZO contributed QTL with significant main effects on adiposity parameters on chromosomes 12 and 5. A NON QTL on chromosome 14 interacted epistatically with the NZO obesity QTL on chromosome 12 to increase adiposity. Although the main effect of the diabetogenic QTL on chromosome 1 was on rapid growth rather than adiposity, it interacted epistatically with the obesity QTL on chromosome 12 to increase plasma glucose levels. Additional complex epistatic interactions eliciting significant increases in body weight and/or plasma glucose were found between the NZO-contributed QTL on chromosome 1 and other NZO-contributed QTL on chromosomes 15 and 17, as well as with an NON-contributed QTL on chromosome 2. We further show that certain of these intergenic interactions are predicated on, or enhanced by, the maternal postparturitional environment. We show by cross-fostering experiments that the maternal environmental influence in part is because of the presence of early obesity-inducing factors in the milk of obese F1 dams. We also discuss a strategy for using recombinant congenic strains to separate and reassemble interacting QTL for future study.
| S-EPMC310941 | biostudies-literature
Project description:Conformity to the behavioural preferences of others can have powerful effects on intragroup behavioural homogeneity in humans, but evidence in animals remains minimal. In this study, we took advantage of circumstances in which individuals or pairs of captive chimpanzees, Pan troglodytes, were 'migrated' between groups, to investigate whether immigrants would conform to a new dietary population preference experienced in the group they entered, an effect suggested by recent fieldwork. Such 'migratory-minority' chimpanzees were trained to avoid one of two differently coloured foods made unpalatable, before 'migrating' to, and then observing, a 'local-majority' group consume a different food colour. Both migratory-minority and local-majority chimpanzees displayed social learning, spending significantly more time consuming the previously unpalatable, but instead now edible, food, than did control chimpanzees who did not see immigrants eat this food, nor emigrate themselves. However, following the migration of migratory-minority chimpanzees, these control individuals and the local-majority chimpanzees tended to rely primarily upon personal information, consuming first the food they had earlier learned was palatable before sampling the alternative. Thus, chimpanzees did not engage in conformity in the context we tested; instead seeing others eat a previously unpalatable food led to socially learned and adaptive re-exploration of this now-safe option in both minority and majority participants.
| S-EPMC5705092 | biostudies-literature
Project description:Neuronal intracellular chloride concentration [Cl(-)](i) is an important determinant of ?-aminobutyric acid type A (GABA(A)) receptor (GABA(A)R)-mediated inhibition and cytoplasmic volume regulation. Equilibrative cation-chloride cotransporters (CCCs) move Cl(-) across the membrane, but accumulating evidence suggests factors other than the bulk concentrations of transported ions determine [Cl(-)](i). Measurement of [Cl(-)](i) in murine brain slice preparations expressing the transgenic fluorophore Clomeleon demonstrated that cytoplasmic impermeant anions ([A](i)) and polyanionic extracellular matrix glycoproteins ([A](o)) constrain the local [Cl(-)]. CCC inhibition had modest effects on [Cl(-)](i) and neuronal volume, but substantial changes were produced by alterations of the balance between [A](i) and [A](o). Therefore, CCCs are important elements of Cl(-) homeostasis, but local impermeant anions determine the homeostatic set point for [Cl(-)], and hence, neuronal volume and the polarity of local GABA(A)R signaling.
| S-EPMC4220679 | biostudies-literature
Project description:Background:Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. Methods:We address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. Results:The overall recognition accuracy of >?94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. Conclusions:Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.
| S-EPMC6712732 | biostudies-literature
Project description:Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that is a combination of two steps: first predicting movement behavioral states and second predicting the animal's velocity. We specify this framework at the individual level as well as for collective movement. We use Random Forests, Neural and Recurrent Neural Networks to compare performance predicting one step ahead as well as long range simulations. We compare results against a custom constructed Stochastic Differential Equation (SDE) model. We apply this approach to high resolution ant movement data. We found that the individual level Machine Learning and Deep Learning methods outperformed the SDE model for one step ahead prediction. The SDE model did comparatively better at simulating long range movement behaviour. Of the Machine Learning and Deep Learning models the Long Short Term Memory (LSTM) individual level model did best at long range simulations. We also applied the Random Forest and LSTM individual level models to model gull migratory movement to demonstrate the generalizability of this framework. Machine Learning and deep learning models are easier to specify compared to traditional parametric movement models which can have restrictive assumptions. However, machine learning and deep learning models are less interpretable than parametric movement models. The type of model used should be determined by the goal of the study, if the goal is prediction, our study provides evidence that machine learning and deep learning models could be useful tools.
| S-EPMC7384613 | biostudies-literature