Biology subjects

Franks, D. W.

Publications and source records attributed to Franks, D. W..

11 recordsLinked to original sources

bioRxiv2025

A landscape of intergroup conflict shapes den site choice in banded mongooses

BackgroundFor many social animals, intergroup conflict has major impacts on fitness and should therefore influence how groups navigate their environment. Yet most studies of group movement focus on the behaviour of single groups, usually foraging groups studied in isolation of all others. In many systems rival groups present both threats and opportunities, contributing to a landscape of intergroup conflict. This landscape could have a profound impact on group movement. MethodsHere we test how the potential for intergroup conflict influences movement decisions of wild cooperatively breeding banded mongooses, using 14 years of behavioural and GPS data. In this species, encounters between groups can present both risks (e.g. injury/mortality) and opportunities (e.g., extragroup mating). We expected that, to minimise risks and maximise rewards, the motivation to engage in conflict would depend on the groups state. Such that vulnerable groups (those that were babysitting young offspring) would select den sites closer to the core of their territory than groups without young offspring. ResultsWe found support for this pattern; non-babysitting groups were unusually risk prone choosing den locations in areas of frequent use by outgroups. During times of heightened recent conflict even choosing to den closer to the core of their rivals territory than their own. ConclusionsThese results suggest that groups of banded mongooses choose den sites that reflect not only the risks but also the potential rewards of defensive or competitive claims on space. Rival groups thus form an integral part of the social landscape shaping patterns of collective movement.

animal behavior and cognition↗

bioRxiv2025

Predicting kinship dynamics during pre- and post reproductive life stages

Individuals relatedness to their groups often changes with age, potentially favouring age-linked trends in social behaviours, and these kinship dynamics have been invoked to explain the evolution of life history traits such as early reproductive cessation and post-reproductive helping. While models showed that simple demographic parameters (e.g., the rates of male and female philopatry vs dispersal, and of local vs non-local mating) suffice to predict the patterns of kinship dynamics breeders experience, they make the unrealistic assumptions that survival and fecundity are age-independent, thus fail to capture the full complexity of kinship dynamics. In particular, they cannot consistently predict how individuals relatedness to their groups should change during their pre- and post-reproductive phases. Here, we extend existing models of kinship dynamics to allow any age-linked changes in mortality and fecundity of both sexes. We describe the new model, and demonstrate its improved predictive power, by comparing the observed kinship dynamics of the killer whales to the predictions of the previously available models (assuming constant mortality and fecundity) and of our new model (considering any observed age-specific survival and fecundity). We show the new model better predicts the data, successfully capturing the observed three-stage female kinship dynamics: an individuals relatedness to her group decreases initially while juvenile, increases across reproductive lifespan, and decreases again during post-reproductive life. These predictions demonstrate the power of our model in generating new insights into the theory of social life history evolution (e.g., explaining why post-reproductive lifespan evolved yet are constrained).

evolutionary biology↗

bioRxiv2024

Analysis of sparse animal social networks

Low-density social networks can be common in animal societies, even among species generally considered to be highly social. Social network analysis is commonly used to analyse animal societal structure, but edge weight (strength of association between two individuals) estimation methods designed for dense networks can produce biased measures when applied to low-density networks. Frequentist methods suffer when data availability is low, because they contain an inherent flat prior that will accept any possible edge weight value, and contain no uncertainty in their output. Bayesian methods can accept alternative priors, so can provide more reliable edge weights that include a measure of uncertainty, but they can only reduce bias when sensible prior values are selected. Currently, neither accounts for zero-inflation, so they produce edge weight estimates biased towards stronger associations than the true social network, which can be seen through diagnostic plots of data quality against output estimate. We address this by adding zero-inflation to the model, and demonstrate the process using group-based data from a population of male African savannah elephants. We show that the Bayesian approach performs better than the frequentist to reduce the bias caused by these problems, though the Bayesian requires careful consideration of the priors. We recommend the use of a Bayesian framework, but with a conditional prior that allows the modelling of zero-inflation. This reflects the fact that edge weight derivation is a two-step process: i) probability of ever interacting, and ii) frequency of interaction for those who do. Additional conditional priors could be added where the biology requires it, for example in a society with strong community structure, such as female elephants in which kin structure would create additional levels of social clustering. Although this approach was inspired by reducing bias observed in sparse networks, it could have value for networks of all densities.

animal behavior and cognition↗

bioRxiv2023

Group size modulates kinship dynamics and selection on social traits

An individuals relatedness to its group may change with age due to demographic processes, and such kinship dynamics can shape age-linked social behaviors. Existing models, however, have focused almost exclusively on dispersal and mating, leaving the consequences of group size local variation unexplored. Here, we extend these models to incorporate such variation within a single, genetically connected meta-population. We then contrast predicted kinship dynamics and age-linked selection for helping/harming in coexisting smaller and larger groups in three typical social mammal systems characterized by distinct patterns of dispersal and mating, followed by exploring how group size local variation impacts female and male kinship dynamics across broader demographic contexts to clarify the population-genetic mechanisms. We show that, within the adult reproductive lifespans of these social systems, an individuals age-specific relatedness to others is higher, and changes faster (especially when younger), in smaller groups; consequently, smaller groups favor more extreme helping/harming (especially when younger) -- in social system with bi-sexual philopatry with non-local mating (e.g., whales) that favors female shifts from harming to helping with age (which explains the evolution of menopause and post-reproductive helping), such shifts are earlier in smaller groups. Further explorations suggest that, in a genetically connected population with group size local variation, group size effects on local relatedness are shaped by the relative strengths of ancestry dilution versus lineage coalescence, and kinship dynamics in a group reflect not only its local demographic conditions, but also those in coexisting groups of different size. Collectively, our study generates new insights into how female and male kinship dynamics emerge and vary under within-population group-size heterogeneity, and how such locally dynamic, varying kinship environments may help to explain variation in age-linked trends in social traits, such as the timing of menopause in social mammals.

evolutionary biology↗

bioRxiv2023

Bayesian inference of toothed whale lifespans

Accurate measures of lifespan and age-specific mortality are important both for understanding life history evolution and informing conservation and population management strategies. The most accurate data to estimate lifespan are from longitudinal studies, but for many species - especially those such as toothed whales that are wide-ranging and inhabit difficult-to-access environments - these longitudinal data are not available. However, other forms of age-structured data - such as from mass-strandings - are available for many toothed species, and using these data to infer patterns of age-specific mortality and lifespan remains an important outstanding challenge. Here we develop and test a Bayesian mortality model to derive parameters of a mortality function from age-structured data while accounting for potential error introduced by mistakes in age estimation, sampling biases and population growth. We then searched the literature to assemble a database of 269 published age-structured toothed whale datasets. We applied our mortality model to derive lifespan estimates for 32 species of female and 33 species of male toothed whale. We also use our model to characterise sex differences in lifespan in toothed whales. Our mortality model allows us to curate the most complete and accurate collection of toothed whale lifespans to date.

evolutionary biology↗

bioRxiv2023

The Evolution of Democratic Peace

A major goal in evolutionary biology is to elucidate common principles that drive human and other animal societies to adopt either a warlike or peaceful nature. One proposed explanation for the variation in aggression between human societies is the democratic peace hypothesis. According to this theory, autocracies are more warlike than democracies because autocratic leaders can pursue fights for private gain. However, autocratic and democratic decision-making processes are not unique to humans and are widely observed across a diverse range of non-human animal societies. We use evolutionary game theory to evaluate whether the logic of democratic peace may apply across taxa; specifically adapting the classic Hawk-Dove model to consider conflict decisions made by groups rather than individuals. We find support for the democratic peace hypothesis without mechanisms involving complex human institutions and discuss how these findings might be relevant to non-human animal societies. We suggest that the degree to which collective decisions are shared may explain variation in the intensity of intergroup conflict in nature.

evolutionary biology↗

bioRxiv2021

BISoN: A Bayesian Framework for Inference of Social Networks

O_LISocial networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses. C_LIO_LIWe introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods used in social network analysis. C_LIO_LIWe show how the framework can be applied to common types of data and how various types of downstream statistical analyses can be performed, including non-random association tests and regressions on network properties. C_LIO_LIOur framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made example R scripts available to enable adoption of the framework. C_LI

animal behavior and cognition↗

bioRxiv2021

Common Permutation Methods in Animal Social Network Analysis Do Not Control for Non-independence

The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of non-independence, parametric regression models can also produce accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we suggest that standard parametric models could be used in the place of permutation-based methods. Moving away from permutation-based methods could have several benefits, including reducing over-reliance on p-values, generating more reliable effect size estimates, and facilitating the adoption of alternative types of statistical analysis.

animal behavior and cognition↗

bioRxiv2021

Accuracy and Power Analysis of Social Interaction Networks

O_LIPower analysis is used to estimate the probability of correctly rejecting a null hypothesis for a given statistical model and dataset. Conventional power analyses assume complete information, but the stochastic nature of behavioural sampling can mean that true and estimated networks are poorly correlated. Power analyses do not currently take the effect of sampling into account. This could lead to inaccurate estimates of statistical power, potentially yielding misleading results. C_LIO_LIHere we develop a method for computing network correlation: the correlation between an estimated social network and its true network, using a Gamma-Poisson model of social event rates for networks constructed from count data. We use simulations to assess how the level of network correlation affects the power of nodal regression analyses. We also develop a generic method of power analysis applicable to any statistical test, based on the concept of diminishing returns. C_LIO_LIWe demonstrate that our network correlation estimator is both accurate and moderately robust to its assumptions being broken. We show that social differentiation, mean social event rate, and the harmonic mean of sampling times positively impacts the strength of network correlation. We also show that the required level of network correlation to achieve a given power level depends on many factors, but that 0.80 network correlation usually corresponds to around 0.80 power for nodal regression in ideal circumstances. C_LIO_LIWe provide guidelines for using our network correlation estimator to verify the accuracy of networks built from count data, and to conduct power analysis. This can be used prior to data collection, in post hoc analyses, or even for subsetting networks in dynamic network analysis. The network correlation estimator and custom power analysis methods have been made available as an R package. C_LI

animal behavior and cognition↗

bioRxiv2020

Calculating effect sizes in animal social network analysis

O_LIBecause of the nature of social interaction or association data, when testing hypotheses using social network data it is common for network studies to rely on permutations to control for confounding variables, and to not also control for them in the fitted statistical model. This can be a problem because it does not adjust for any bias in effect sizes generated by these confounding effects, and thus the effect sizes are not informative in the presence of counfouding variables. C_LIO_LIWe implemented two network simulation examples and analysed an empirical data set to demonstrate how relying solely on permutations to control for confounding variables can result in highly biased effect size estimates of animal social preferences that are uninformative when quantifying differences in behaviour. C_LIO_LIUsing these simulations, we show that this can sometimes even lead to effect sizes that have the wrong sign and are thus the effect size is not biologically interpretable. We demonstrate how this problem can be addressed by controlling for confounding variables in the statistical dyadic or nodal model. C_LIO_LIWe recommend this approach should be adopted as standard practice in the statistical analysis of animal social network data. C_LI

animal behavior and cognition↗

bioRxiv2020

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

O_LISocial network methods have become a key tool for describing, modelling, and testing hypotheses about the social structures of animals. However, due to the non-independence of network data and the presence of confounds, specialized statistical techniques are often needed to test hypotheses in these networks. Datastream permutations, originally developed to test the null hypothesis of random social structure, have become a popular tool for testing a wide array of null hypotheses. In particular, they have been used to test whether exogenous factors are related to network structure by interfacing these permutations with regression models. C_LIO_LIHere, we show that these datastream permutations typically do not represent the null hypothesis of interest to researchers interfacing animal social network analysis with regression modelling, and use simulations to demonstrate the potential pitfalls of using this methodology. C_LIO_LIOur simulations show that utilizing common datastream permutations to test the coefficients of regression models can lead to extremely high type I (false-positive) error rates (> 30%) in the presence of non-random social structure. The magnitude of this problem is primarily dependent on the degree of non-randomness within the social structure and the intensity of sampling C_LIO_LIWe strongly recommend against utilizing datastream permutations to test regression models in animal social networks. We suggest that a potential solution may be found in regarding the problems of non-independence of network data and unreliability of observations as separate problems with distinct solutions. C_LI

animal behavior and cognition↗