Biology subjects

Farine, D. R.

Publications and source records attributed to Farine, D. R..

33 records · Page 2Linked to original sources

bioRxiv2021

Costs dictate strategic investment in dominance interactions

Dominance is important for access to resources. As dominance interactions are costly, individuals should be strategic in who they interact with. One hypothesis is that individuals should direct costly interactions towards those closest in rank, as they have most to gain--in terms of attaining or maintaining dominance--from winning such interactions. Here, we show that male vulturine guineafowl (Acryllium vulturinum), a gregarious species with steep dominance hierarchies, strategically express higher-cost aggressive interactions towards males occupying ranks immediately below themselves in their groups hierarchy. In contrast, lower-cost aggressive interactions are expressed towards group members further down the hierarchy. By directly evaluating differences in the strategic use of higher- and lower-cost aggressive interactions towards competitors, we show that individuals disproportionately use highest-cost interactions--such as chases--towards males found one to three ranks below themselves. Our results support the hypothesis that the costs associated with different interaction types can determine their expression in social groups with steep dominance hierarchies.

animal behavior and cognition↗

bioRxiv2020

Social network architecture and the tempo of cumulative cultural evolution

The ability to build upon previous knowledge--cumulative cultural evolution--is a hallmark of human societies. While cumulative cultural evolution depends on the interaction between social systems, cognition and the environment, there is increasing evidence that cumulative cultural evolution is facilitated by larger and more structured societies. However, such effects may be interlinked with patterns of social wiring, thus the relative importance of social network architecture as an additional factor shaping cumulative cultural evolution remains unclear. By simulating innovation and diffusion of cultural traits in populations with stereotyped social structures, we disentangle the relative contributions of network architecture from those of population size and connectivity. We demonstrate that while more structured networks, such as those found in multilevel societies, can promote the recombination of cultural traits into high-value products, they also hinder spread and make products more likely to go extinct. We find that transmission mechanisms are therefore critical in determining the outcomes of cumulative cultural evolution. Our results highlight the complex interaction between population size, structure and transmission mechanisms, with important implications for future research.

evolutionary biology↗

bioRxiv2020

Social information use shapes the coevolution of sociality and virulence

AO_SCPLOWBSTRACTC_SCPLOWSocial contacts can facilitate the spread of both survival-related information and infectious diseases, but little is known about how these processes combine to shape host and parasite evolution. Here, we use a theoretical model that captures both infection and information transmission processes to investigate how host sociality (contact effort) and parasite virulence (disease-associated mortality rate) (co)evolve. We show that selection for sociality (and in turn, virulence) depends on both the intrinsic costs and benefits of social information and infection as well as their relative prevalence in the population. Specifically, greater sociality and lower virulence evolve when the risk of infection is either low or high and social information is neither very common nor too rare. Lower sociality and higher virulence evolve when the prevalence patterns are reversed. When infection and social information are both at moderate levels in the population, the direction of selection depends on the relative costs and benefits of being infected or informed. We also show that sociality varies inversely with virulence, and that parasites may be unable to prevent runaway selection for higher contact efforts. Together, these findings provide new insights for our understanding of group living and how apparently opposing ecological processes can influence the evolution of sociality and virulence in a range of ways.

evolutionary biology↗

bioRxiv2020

Permutation tests for hypothesis testing with animal social data: problems and potential solutions

O_LIPermutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis. C_LIO_LITwo common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control "nuisance effects" like spatial, temporal, or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network. C_LIO_LIWe demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data. C_LIO_LIRegressions on simulated datasets suggest that this "double permutation" approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations, or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%. C_LIO_LIThe double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses, and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis. C_LI

animal behavior and cognition↗

bioRxiv2020

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. For example, it is well known that the social structure of animal populations can shape spreading dynamics, but it remains underexplored to what extent such dynamics are determined by the underlying habitat configuration. To address this gap, we propose a framework and model inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animals habitats, and highlight the need for a bottom-up generative model that can depict realistic variations in habitat structural connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we use our framework to demonstrate how transmission properties, such as the spread of a pathogen, can be impacted by both local connectivity and landscape-level characteristics of the habitat. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

ecology↗

bioRxiv2019

Deep learning-based methods for individual recognition in small birds

O_LIIndividual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well established but often make data collection and analyses time consuming and consequently are not suited for collecting very large datasets. C_LIO_LIRecent technological and analytical advances, such as deep learning, can help overcome these limitations by automatizing data collection and analysis. Currently one of the bottlenecks preventing the application of deep learning for individual identification is the need of hundreds to thousands of labelled pictures required for training convolutional neural networks (CNNs). C_LIO_LIHere, we describe procedures that improve data collection and allow individual identification in captive and wild birds and we apply it to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata. C_LIO_LIFirst, we present an automated method that allows the collection of large samples of individually labelled images. Second, we describe how to train a CNN to identify individuals. Third, we illustrate the general applicability of CNN for individual identification in animal studies by showing that the trained CNN can predict the identity of birds from images collected in contexts that differ from the ones originally used to train the CNNs. Fourth, we present a potential solution to solve the issues of new incoming individuals. C_LIO_LIOverall our work demonstrates the feasibility of applying state-of-the-art deep learning tools for individual identification of birds, both in the lab and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual identification of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods. C_LI

ecology↗

bioRxiv2019

How to characterise shared space use networks

Studying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). Such an approach assumes that individuals that have been asynchronously detected in the same trapping location will also be more likely to have interacted. However, there is very little guidance on how much data are required for making robust co-trapping networks. In this study, we propose that co-trapping networks broadly assume that co-trapping captures shared space use (and, subsequently, likelihood of interacting), and that it may be more parsimonious to directly model shared space use. We first use empirical data to highlight that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to co-occur. We then show that a method that explicitly models individuals home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are correlated with the true shared space use network (relative to co-trapping networks constructed from space sharing events). Further, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects present in the true shared space use network. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution. Significance statementCharacterising animal social networks requires repeated (co-)observations of individuals. Collecting sufficient data to characterise the connections among individuals represents a major challenge when studying cryptic organisms--such as small rodents. This study draws from existing spatial mark-recapture data to inspire an approach that constructs networks by estimating space use overlap (representing the potential for interactions) from observations of individuals in the same location (e.g. a trap). We then use simulations to demonstrate that the method provides consistently higher correlations between inferred (or observed) networks and the true underlying network compared to current approaches, and requires fewer observations to reach higher correlations. We further demonstrate that these improvements translate to greater network accuracy and to more power for statistical hypothesis testing.

ecology↗

bioRxiv2019

Information use in foraging flocks of songbirds - no evidence for social transmission of patch quality

Animals use behavioural cues from others to make decisions in a variety of contexts. There is growing evidence, from a range of taxa, that information about the locations of food patches can spread through a population via social connections. However, it is not known whether information about the quality of potential food sources transmits similarly. We studied foraging behaviour in a population of wild songbirds with known social associations, and tested whether flock members use social information about the profitability of patches to inform their foraging decisions. We provided artificial patches (ephemeral bird feeders) that appeared identical but were either profitable (contained food) or unprofitable (contained no food). If information about patch profitability spreads via social associations, we predicted that empty feeders would only be sampled by individuals that are less connected to each other than expected by chance. In contrast, we found that individuals recorded at empty feeders were more closely associated with each other than predicted by a null model simulating random arrival of individuals, mirroring pattern of increased connectedness among individuals recorded at full feeders. We then simulated arrival under network-based diffusion of information, and demonstrate that the observed pattern at both full and empty feeders matches predictions derived from this post-hoc model. Our results suggest that foraging songbirds only use social cues about the location of potential food sources, but not their profitability. These findings agree with the hypothesis that individuals balance the relative economic costs of using different information, where the costs of personally sampling a patch upon arrival is low relative to the cost of searching for patches. This study extends previous work on information spread through avian social networks, by suggesting important links between how animals use information at different stages of the acquisition process and the emerging population-level patterns of patch use.

animal behavior and cognition↗

bioRxiv2019

Wild songbirds exhibit consistent individual differences in inter-specific social behaviour

AO_SCPCAPBSTRACTC_SCPCAPO_LINatural populations and communities consist of individuals that differ in their phenotypes. There is increasing evidence in community ecology that consistent intraspecific variation in behaviour changes the outcome of ecological interactions.\nC_LIO_LIDifferences in intra- and inter-specific interactions are expected to play a major role in determining patterns of species coexistence and community structure. However, the question of whether individuals vary in their propensity to associate with heterospecifics has been neglected.\nC_LIO_LIWe used social network analysis to characterise pattern of heterospecific associations in wild mixed-species flocks of songbirds, and assessed whether individuals adopt consistent social strategies in their broader, heterospecific, social environment. We quantified heterospecific foraging associations using data from a large automatically monitored PIT-tagged population of birds, involving more than 300 000 observations of flock membership, collected over three winters, for two tit species (Paridae), blue tits, Cyanistes caeruleus, and great tits, Parus major.\nC_LIO_LIWe assessed individual consistency in interspecific social preferences over both short-term (week-to-week) and longer-term (year-to-year) timescales for a total of 4610 individuals, and found that blue tits and great tits exhibited marked and consistent intraspecific differences in heterospecific social phenotypes in terms of both absolute and relative number of associates. Further, we found that these consistent differences were significantly greater than expected from spatial and temporal differences in population densities.\nC_LIO_LIHeterospecific associations represent a major component of the social environment for many species, and our results show that individuals vary consistently in their social decisions with respect to heterospecifics. These findings provide support for the notion that intraspecific trait variation contributes to patterns at community and ecosystem levels.\nC_LI

animal behavior and cognition↗

bioRxiv2019

How to make methodological decisions when inferring social networks

Constructing and analysing social networks data can be challenging. When designing new studies, researchers are confronted with having to make decisions about how data are collected and networks are constructed, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. We suggest an approach for making decisions when developing a network without jeopardising the validity of future hypothesis tests. We argue that choosing the best edge definition for a network can be made using a priori knowledge of the species, and testing hypotheses that are known and independent from those that the network will ultimately be used to evaluate. We illustrate this approach by conducting a pilot study with the aim of identifying how to construct a social network for colonies of cooperatively breeding sociable weavers. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then identified which combination of data collection and association definition maximised (i) the assortment of individuals into breeding groups (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). Our approach highlights how existing knowledge about a system can be used to help navigate the myriad of methodological decisions about data collection and network inference.\n\nSIGNIFICANCE STATEMENTGeneral guidance on how to analyse social networks has been provided in recent papers. However less attention has been given to system-specific methodological decisions when designing new studies, specifically on how data are collected, and how edge weights are defined from the collected data. This lack of guidance can lead researchers into being less critical about their study design and making arbitrary decisions or trying several different methods driven by a given preferred hypothesis of interest without realising the consequences of such approaches. Here we show that pilot studies combined with a priori knowledge of the study species social behaviour can greatly facilitate making methodological decisions. Furthermore, we empirically show that different decisions, even if data are collected under the same context (e.g. foraging), can affect the quality of a network.

animal behavior and cognition↗

bioRxiv2019

Development of new food-sharing relationships among nonkin vampire bats

In an individualized animal society, social bonds can foster cooperation and enhance survival and reproduction. Cooperative bonds often exist among kin, but nonkin can also develop high-investment cooperative bonds that share similarities with human friendship. How do such bonds form? One theory suggests that strangers should test the waters of a new relationship by making small initial cooperative investments and gradually escalating them with good partners. This raising-the-stakes strategy is demonstrated by human strangers in short-term economic games, but it remains unclear whether it applies to helping in a natural long-term social bond. Here we show evidence that unfamiliar vampire bats (Desmodus rotundus) selectively escalate low-cost investments in allogrooming before developing higher-cost food-sharing relationships. We introduced females from geographically distant sites in pairs or groups and observed that bats established new reciprocal grooming relationships, and that increasing grooming rates predicted the occurrence of first food donations, at which point grooming rates no longer increased. New food-sharing relationships emerged reciprocally in 14% of female pairs, typically over 10-15 months, and developed faster when strangers lacked alternative familiar partners. A gradual grooming-to-sharing transition among past strangers suggests that raising the stakes might be more evident when tracking multiple cooperative behaviours as new relationships form, rather than measuring a single behavior in an established relationship. Raising the stakes could play a similar underappreciated role across a broader spectrum of social decisions with long-term consequences, such as joining a new social group or forming a long-term pair-bond. Significance statementVampire bats form long-term cooperative social bonds that involve reciprocal food sharing. How do two unrelated bats go from being strangers to having a high-investment food-sharing relationship? We introduced unfamiliar bats and found evidence that low-cost grooming paves the way for higher-cost food donations. Food sharing emerged in a reciprocal fashion and it emerged faster when two strangers did not have access to their original groupmates. The bats that formed new food-sharing relationships had a history of escalating reciprocal grooming up until the food sharing began. Our finding that unfamiliar nonkin vampire bats appear to gradually and selectively transition from low-cost to high-cost cooperative behaviors is the first evidence that nonhuman individuals raise the stakes when forming new cooperative relationships.

animal behavior and cognition↗

bioRxiv2018

Movement and conformity interact to establish local behavioural traditions in animal populations

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 conformity for determining whether local traditions establish across a landscape or whether a single preference dominates the whole population. The model is able to replicate a real-world cultural diffusion experiment in great tits Parus major, but also allows for a range of predictions for the emergence of animal culture under various initial conditions, habitat structure and strength of conformist bias to be made. Integrating social behaviour with ecological variation will be important for understanding the stability and diversity of culture in animals.

animal behavior and cognition↗

bioRxiv2017

Do Sigmoidal Acquisition Curves Indicate Conformity?

The potential for behaviours to spread via cultural transmission has profound implications for our understanding of social dynamics and evolution. Several studies have provided empirical evidence that local traditions can be maintained in animal populations via conformist learning (i.e. copying the majority). A conformist bias can be characterized by a sigmoidal relationship between a behaviors prevalence in the population and an individuals propensity to adopt that behavior. For this reason, the presence of conformist learning in a population is often inferred from a sigmoidal acquisition curve in which the overall rate of adoption for the behavior is taken as the dependent variable. However, the validity of sigmoidal acquisition curves as evidence for conformist learning has recently been challenged by models suggesting that such curves can arise via alternative learning rules that do not involve conformity. We review these models, and find that the proposed alternative learning mechanisms either rely on faulty or unrealistic assumptions, or apply only in very specific cases. We therefore recommend that sigmoidal acquisition curves continue to be taken as evidence for conformist learning. Our paper also highlights the importance of understanding the generative processes of a model, rather than only focusing solely on the patterns produced. By studying these processes, our analysis suggests that current practices by empiricists have provided robust evidence for conformist transmission in both humans and non-human animals.

animal behavior and cognition↗

bioRxiv2017

A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty

Many animal social structures are organized hierarchically, with dominant individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. As a result, there are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. In this study, we simulated interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and hierarchy steepness affect the performance of three methods, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty in the inferred hierarchy. First, we found that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the hierarchy steepness and method used. We then show that Davids score and our novel randomized Elo-rating are the two best methods, whereas the original Elo-rating and the recently described ADAGIO perform less well. Finally, we propose two simple methods to estimate uncertainty at the individual and group level. These uncertainty measures further allow to differentiate non-existent, very flat and highly uncertain hierarchies from intermediate, steep and certain hierarchies. Overall, we find that the methods for inferring dominance hierarchies are relatively robust, even when the ratio of observed interactions to individuals is as low as 10 to 20. However, we suggest that implementing simple procedures for estimating uncertainty will benefit researchers, and quantifying the shape of the dominance hierarchies will provide new insights into the study organisms.\n\nHighlightsO_LIDavids score and the randomized Elo-rating perform best.\nC_LIO_LIMethod performance depends on hierarchy steepness and sampling effort.\nC_LIO_LIGenerally, inferring dominance hierarchies requires relatively few observations.\nC_LIO_LIThe R package \"aniDom\" allows easy estimation of hierarchy uncertainty.\nC_LIO_LIHierarchy uncertainty provides insights into the shape of the dominance hierarchy.\nC_LI

animal behavior and cognition↗

bioRxiv2016

Social ecology of a woodland songbird community: from individual movements to the emergence of population social structure

Social structure can have profound evolutionary and ecological implications for animal populations. Structure can arise and be maintained via social preferences or be indirectly shaped by habitat structure. Understanding how social structure emerges is important for understanding the potential links between social structure and evolutionary and ecological processes. Here, we study a large community of wild birds fitted with uniquely-coded passive integrated transponder (PIT) tags and recorded on a grid of automated feeders fitted with radio frequency identification (RFID) antennae. We show that both large-scale and fine-scale network communities are consistent across years in this population, despite high generational turn-over. Studying the process that generates community structure, here the movement of individual birds across the woodland, suggests an important role of habitat geometry in shaping population-level social community structure. Our study highlights how relatively simple factors can produce apparent emergent social structure at the population scale, which has widespread implications for understanding eco-evolutionary dynamics.

animal behavior and cognition↗