Biology subjects

McElreath, R.

Publications and source records attributed to McElreath, R..

13 recordsLinked to original sources

bioRxiv2026

Evolution of the motor cortex microstructure and its lateralization: a comparative study of chimpanzees and humans

Human hand coordination exceeds that of other species, including great apes, and is marked by pronounced right-hand dominance. This specialization parallels an expansion of its cortical representation, forming the hand-knob in the motor cortex. In humans, this region shows high myelination on quantitative MRI (qMRI), but whether this feature is shared with great apes remains unclear. It is also unknown whether increased right-hand dominance in humans is mirrored by greater hemispheric asymmetry in cortical microstructure. Using high-resolution qMRI, we compared motor cortex subdivisions controlling the leg, hand, and face in humans and chimpanzees. We found consistently higher myelin and iron content in the hand-knob in both species, suggesting an evolutionarily conserved role. However, only humans showed enhanced rightward lateralization. These results highlight both conserved and species-specific features of the motor cortex, offering insights into the evolution of manual dexterity and handedness.

neuroscience↗

bioRxiv2026

Single-cell quantification of the iron-neuromelanin balance in dopaminergic neurons across the lifespan

Dopaminergic neurons in the substantia nigra depend on iron for dopamine synthesis but are vulnerable to iron-induced oxidative stress. Many of these neurons synthesize neuromelanin, an iron-chelating pigment that accumulates across the lifespan and makes them vulnerable in Parkinsons disease. It remains unclear whether their selective vulnerability arises from neuromelanin overload or from the release of toxic labile iron from the oversaturated pigment. We quantified iron and neuromelanin at the single-cell level across the lifespan of chimpanzees, a species closely resembling humans in pigment and iron accumulation. Combining quantitative MRI, X-ray fluorescence imaging, and microscopic colorimetry, we found that the iron-to-neuromelanin ratio remains stable with age across large neuronal populations. Chemical equilibrium modeling of the iron binding in neuromelanin indicated that cytosolic labile iron concentrations remain low throughout adulthood. We have found no evidence for neuromelanin saturation or increased iron-mediated toxicity with age. This finding challenges the hypothesis that neuromelanin saturation drives age-related dopaminergic vulnerability. The presented method provides a quantitative framework for studying iron homeostasis in these neurons.

neuroscience↗

bioRxiv2025

Inferring the causes of animal social network structure from time-series data

Behavioural ecologists aim to understand the causes of animal social structure. Connecting theoretical models of social structure with empirical observations remains, however, a for-midable challenge. While most of the current statistical methods for animal social network analysis rely on data that are aggregated over time and summarised as one behavioural dimension (e.g., an adjacency-matrix), common behavioural sampling techniques (e.g., focal-animal sampling) produce data in continuous time, and involve different behaviours. Furthermore, empiricists in the field are generally interested in causal inference, but lack a framework to rigorously analyse focal-animal sampling data in light of transparent causal assumptions. As a consequence, common methods are often inappropriate, and can lead to wrong biological conclusions. Here, we introduce a causal Bayesian modelling framework to empirically study the causes of social network structure from focal-animal sampling data. We start by outlining a generative model that encodes how biological and measurement processes jointly produce social network data in continuous time; namely, as a temporal sequence of dyadic behavioural states (e.g., no body contact, social resting, grooming). Building upon the generative model, we develop a statistical model: a multilevel, multiplex Bayesian model that takes raw focal observations as input, and produces a posterior probability distribution for the generative parameters as output. After validating the statistical models performance with sparse data-- common in real-world settings--we illustrate its application with an empirical data set collected in wild Assamese macaques. We notably showcase how researchers can compute probabilistic estimates for well-defined causal hypotheses about the drivers of social structure. With this work, we not only contribute novel theoretical and statistical tools to the field, but also illustrate a workflow that allows researchers to iteratively translate their domain expertise into a formal analytical strategy--bridging theoretical and empirical research in behavioural ecology.

animal behavior and cognition↗

bioRxiv2025

Dynamic strategic social learning in nest-building zebra finches and its generalisability

Animals often balance asocial and social information strategically, adjusting when and from whom they copy based on context. Yet the cognition driving this dynamic--and its broader implications--remains poorly understood. We tested whether zebra finches use a copy-if-dissatisfied strategy by manipulating the quality of their initial nest-building or reproductive experience, showing them a conspecific nest-builder, and tracking subsequent material choices. Builder-males were more likely to choose the demon-strated social material--particularly at first choice--if they had previously used low-quality material. Using cognitive modelling, we estimated how latent learning mechanisms shaped decisions, identifying two asocial and two social parameters. These estimates provide the first formal evidence for the cognitive basis of nest building. Forward simulations informed--but not predetermined--by these parameters approximated observed behaviour, supporting their causal role. We then used these parameters in exploratory simulations to test how choices might shift under novel payoff contexts. We found that payoff structure--not (dis)satisfaction--was the primary driver of social material use, though higher rewards did not proportionally increase copying. These exploratory simulation results offer preliminary insight into mechanisms underlying material-use variation. Our study illustrates how computational modelling can robustly link behaviour to underlying learning mechanisms and probe the generalisability of animal cognition--a rarity in this field.

animal behavior and cognition↗

bioRxiv2024

A causal framework for the drivers of animal social network structure

AO_SCPLOWBSTRACTC_SCPLOWA major goal of behavioural ecology is to explain how phenotypic and ecological factors shape the networks of social relationships that animals form with one another. This inferential task is notoriously challenging. The social networks of interest are generally not observed, but must be approximated from behavioural samples. Moreover, these data are highly dependent: the observed network edges correlate with one another, due to biological and sampling processes. Failing to account for the resulting uncertainty and biases can lead to dysfunctional statistical procedures, and thus to incorrect results. Here, we argue that these problems should be understood--and addressed--as problems of causal inference. For this purpose, we introduce a Bayesian causal modelling framework that explicitly defines the links between the target interaction network, its causes, and the data. We illustrate the mechanics of our framework with simulation studies and an empirical example. First, we encode causal effects of individual-, dyad-, and group-level features on social interactions using Directed Acyclic Graphs and Structural Causal Models. These quantities are the objects of inquiry, our estimands. Second, we develop estimators for these effects--namely, Bayesian multilevel extensions of the Social Relations Model. Third, we recover the structural parameters of interest, map statistical estimates to the underlying causal structures, and compute causal estimates from the joint posterior distribution. Throughout the manuscript, we develop models layer by layer, thereby illustrating an iterative workflow for causal inference in social networks. We conclude by summarising this workflow as a set of seven steps, and provide practical recommendations.

animal behavior and cognition↗

bioRxiv2024

The refinement paradox and cumulative cultural evolution: collective improvement in knowledge favors conformity, blind copying and hyper-credulity

Social learning is common in nature, yet cumulative culture (where knowledge and technology increase in complexity and diversity over time) appears restricted to humans. To understand why, we organized a computer tournament in which programmed entries specified when to learn new knowledge and when to refine (i.e. improve) existing knowledge. The tournament revealed a refinement paradox: refined behavior afforded higher payoffs as individuals converged on a small number of successful behavioral variants, but refining did not generally pay. Paradoxically, entries that refined only in certain conditions did best during behavioral improvement, while simple copying entries thrived when refinement levels were high. Cumulative cultural evolution may be rare in part because sophisticated strategies for improving knowledge and technology are initially advantageous, yet complex culture, once achieved, favors conformity, blind imitation and hyper-credulity.

evolutionary biology↗

bioRxiv2022

Modelling human and non-human animal network data in R using STRAND

O_LIThere have been recent calls for wider application of generative modelling approaches in applied social network analysis. These calls have been motivated by the limitations of contemporary empirical frameworks, which have generally relied on post hoc permutation methods that do not actively account for interdependence in network data. At present, however, it remains difficult for typical end-users--e.g., field researchers--to apply generative network models, as there is a dearth of openly available software packages that make application of such methods as simple as other, permutation-based methods. C_LIO_LIHere, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of human and non-human animal social network data that can be implemented using simple, base R syntax. C_LIO_LITo facilitate ease-of-use, we provide a tutorial demonstrating how STRAND can be used to model binary, count, or proportion data using stochastic blockmodels, social relations models, or a combination of the two modelling frameworks. C_LI

animal behavior and cognition↗

bioRxiv2019

The Life History of Human Foraging: Cross-Cultural and Individual Variation

Human adaptation depends upon the integration of slow life history, complex production skills, and extensive sociality. Refining and testing models of the evolution of human life history and cultural learning will benefit from increasingly accurate measurement of knowledge, skills, and rates of production with age. We pursue this goal by inferring individual hunters of hunting skill gain and loss from approximately 23,000 hunting records generated by more than 1,800 individuals at 40 locations. The model provides an improved picture of ages of peak productivity as well as variation within and among ages. The data reveal an average age of peak productivity between 30 and 35 years of age, though high skill is maintained throughout much of adulthood. In addition, there is substantial variation both among individuals and sites. Within study sites, variation among individuals depends more upon heterogeneity in rates of decline than in rates of increase. This analysis sharpens questions about the co-evolution of human life history and cultural adaptation. It also demonstrates new statistical algorithms and models that expand the potential inferences drawn from detailed quantitative data collected in the field.

animal behavior and cognition↗

bioRxiv2018

Rapid environmental change in games: complications and curiosities

Human-induced rapid environmental change (HIREC) has recently led to alterations in the fitness and behavior of many organisms. Game theory is an important tool of behavioral ecology for analyzing evolutionary situations involving multiple individuals. However, game theory bypasses the details by which behavioral phenotypes are determined, taking the functional perspective straight from expected payoffs to predicted frequencies of behaviors. In contrast with optimization approaches, we identify that to use existing game theoretic models to predict HIREC effects, additional mechanistic details (or assumptions) will often be required. We illustrate this in relation to the hawk-dove game by showing that three different mechanisms, each of which support the same ESS prior to HIREC (fixed polymorphism, probabilistic choice, or cue dependency), can have a substantial effect on behavior (and success) following HIREC. Surprisingly, an increase in the value of resources can lead to a reduction in payoffs (and vice versa), both in the immediate- and long-term following HIREC. An increase in expected costs also increases expected payoffs. Along with these counter-intuitive findings, this work shows that simply understanding the behavioral payoffs of existing games is insufficient to make predictions about the effects of HIREC.\n\nIts the little details that are vital. Little things make big things happen.\n\nJohn Wooden

animal behavior and cognition↗

bioRxiv2017

Complex Dynamics From Simple Cognition: The Primary Ratchet Effect In Animal Culture

AO_SCPLOWBSTRACTC_SCPLOWIt is often observed that human culture, unlike most other animal culture, is cumulative: human technology and behavior is more complex than any individual could invent in their own lifetime. Cumulative culture is often explained by appeal to a combination of high-fidelity social learning and innovation, the \"ratchet effect.\" What is often overlooked is that both human and other animal culture is supported by a more primary ratchet effect that retains and increases the prevalence of adaptive behavior. This primary ratchet can arise without appeal to specialized cognitive adaptations and is plausibly more widespread in animal societies. We use a simple model to highlight how simple forms of contingent social learning can create the primary ratchet effect, dramatically increasing the prevalence of adaptive, hard to invent behavior. We investigate some ways that demography may interact with the primary ratchet to generate patterns of cultural variation. As the primary ratchet may be common to many animal societies, its cognitive components and population dynamics provide a common foundation for the study of animal culture and a necessary foundation for understanding the origins of human cumulative culture.

animal behavior and cognition↗

bioRxiv2017

The Evolution Of Covert Signaling

Human sociality depends upon the benefits of mutual aid and extensive communication. However, diverse norms and preferences complicate mutual aid, and ambiguity in meaning hinders communication. Here we demonstrate that these two problems can work together to enhance cooperation through the strategic use of deliberately ambiguous signals: covert signaling. Covert signaling is the transmission of information that is accurately received by its intended audience but obscured when perceived by others. Such signals may allow coordination and enhanced cooperation while also avoiding the alienation or hostile reactions of individuals with different preferences. Although the empirical literature has identified potential mechanisms of covert signaling, such as encryption in humor, there is to date no formal theory of its dynamics. We introduce a novel mathematical model to assess when a covert signaling strategy will evolve, as well as how receiver attitudes coevolve with covert signals. Covert signaling plausibly serves an important function in facilitating within-group cooperative assortment by allowing individuals to pair up with similar group members when possible and to get along with dissimilar ones when necessary. This mechanism has broad implications for theories of signaling and cooperation, humor, social identity, political psychology, and the evolution of human cultural complexity.

evolutionary biology↗

bioRxiv2017

Payoff-biased social learning underlies the diffusion of novel extractive foraging traditions in a wild primate

AO_SCPLOWBSTRACTC_SCPLOWThe type and variety of learning strategies used by individuals to acquire behaviours in the wild are poorly understood, despite the presence of behavioural traditions in diverse taxa. Social learning strategies such as conformity can be broadly adaptive, but may also retard the spread of adaptive innovations. Strategies like payoff-biased learning, in contrast, are effective at diffusing new behaviour but may perform poorly when adaptive behaviour is common. We present a field experiment in a wild primate, Cebus capucinus, that introduced a novel food item and documented the innovation and diffusion of successful extraction techniques. We develop a multilevel, Bayesian statistical analysis that allows us to quantify individual-level evidence for different social and individual learning strategies. We find that payoff-biased social learning and age-biased social learning are primarily responsible for the diffusion of new techniques. We find no evidence of conformity; instead rare techniques receive slightly increased attention. We also find substantial and important variation in individual learning strategies that is patterned by age, with younger individuals being more influenced by both social information and their own individual experience. The aggregate cultural dynamics in turn depend upon the variation in learning strategies and the age structure of the wild population.

animal behavior and cognition↗

bioRxiv2016

The coevolution of social learning and sensitivity to changing environments

AO_SCPLOWBSTRACTC_SCPLOWThere is widespread agreement that social and individual learning are adaptations to varying environments. However, existing theory assumes that organisms cannot detect changes in the environment and instead adapt to averages. This paper develops the first analytical model that allows for the simultaneous coevolution of socially learned traditions, reliance on social learning, and signal detection for environmental change. There are numerous conditions under which detection can be stable once common but cannot invade the population when rare. When signal detection is maintained by selection, it always leads to pure separating equilibria at which organisms always learn individually when they believe the environment has recently changed and otherwise always learn socially. Detection can increase mean fitness at equilibrium, but it may also reduce it.

evolutionary biology↗