Biology subjects

Yeakel, J. D.

Publications and source records attributed to Yeakel, J. D..

9 recordsLinked to original sources

bioRxiv2026

Collective learning and manifold behaviors in predator groups

Collective foraging in animal groups often relies on behavioral diversity, with individuals adopting different, sometimes complementary roles during shared tasks. However, most theoretical models predict that individuals responding to similar information cues in a shared environment should converge on a single optimal behavioral strategy. Using a spatially explicit multi-agent deep reinforcement learning model embedded in a three-species food chain, we show that stable behavioral diversity can emerge spontaneously among initially naive agents. Rather than converging on a single optimum, agents differentiate along a low-dimensional manifold of sensorimotor control, reflecting tradeoffs in speed regulation, spatial exploration, and deterministic turning rules. While multiple strategies yield comparable individual energetic returns, they are not interchangeable; group performance depends on how specific strategies combine to produce spatial resource partitioning and distributed directional influence. Replacing co-learned individuals with similarly competent agents trained in other groups disrupts these interaction structures and strongly reduces total energy acquisition. These results demonstrate that coordinated collective behavior and diverse, compatible strategies can arise endogenously from shared learning histories, but that this form of collective performance is path dependent and may be fragile to changes in group composition.

ecology↗

bioRxiv2025

Island Biogeography Theory of coevolution in pollination networks

Island ecosystems have been pivotal in understanding community assembly and biodiversity, from the competing roles of colonization and extinction, to the influence of spatial structure, species interactions, and evolutionary processes. Network theory has helped our understanding of how ecological interactions shape island biogeography dynamics, using empirical and theoretical studies to explore species-rich island community structures. Mutualistic networks on islands differ from those on the mainland by supporting fewer species and more super-generalists, resulting in a more nested community structure. Coevolution, the reciprocal adaptation between interacting species, can shape traits and interactions within these networks, influencing their assembly. Here we explore how colonization, extinction, and coevolution can intersect to shape species traits and the structure of mutualistic networks. Using a stochastic dynamic model, we integrate Island Biogeography Theory with coevolutionary dynamics, examining pollination networks to understand trait-matching in evolving communities. Our results show that highly nested and connected mainland communities contribute to greater instability on islands, particularly under intermediate extinction rates. While super-generalist species persist longer, their persistence does not greatly exceed those species with fewer interactions beyond a threshold, which itself varies with the extinction rate. Additionally, coevolution leads to greater traitsimilarity within communities on islands. Our findings highlight the critical role of coevolution in shaping island communities, especially on small islands where the extinction rate is expected to be elevated.

evolutionary biology↗

bioRxiv2025

Generalized dynamics of cooperating bacteria

Bacterial cooperation involves the exchange of metabolites, which can range from costless byproducts of metabolism to intentionally produced and costly molecules. These interactions occur across spatial scales, from direct cell contact to the diffusion of metabolites in the environment. Due in part to this variety of interaction modes, the impacts of mutualism on bacterial community dynamics remain unclear. Using generalized models, we derive conditions for the onset of different dynamical behaviors in communities of bacterial cooperators across these scenarios. These include exchanges of low-cost metabolites, costly cross-feeding, and cases where bacteria produce either most or only a small fraction of available metabolites. Stability depends strongly on metabolic production costs and the balance between metabolite uptake and production. We further show that perturbations to bacteria have larger impacts than to metabolites. Finally, we demonstrate that spatial metabolite diffusion drives pattern formation, emphasizing the links between local stability and spatial structures in bacterial cooperation.

ecology↗

bioRxiv2022

Rising above the noise: The influence of populationdynamics on the evolution of acoustic signaling

Acoustic signaling is employed by many sexually reproducing species to select for mates and enhance fitness. However, signaling in dense populations can create an auditory background, or chorus, which can interfere with a signal receivers phonotactic selectivity, or the ability to distinguish signals. Feedback between the strength of an individuals signal, phonotactic selectivity, and population size, may interactin complex ways to impact the evolution of the signaling trait within a population, potentially leading to the emergence of silence. Here we formulate a general model that captures the dynamic feedback between individual acoustic signalers, phonotactic selectivity, and the populationlevel chorus to explore the eco-evolutionary dynamics of an acoustic trait. We find that population dynamics has a significant influence on the evolutionary dynamics of the signaling trait, and that very sharp transitions separate conspicuous from silent populations. Our framework also reveals that increased phonotactic selectivity promotes the stability of signaling populations. We suggest that understanding the relationship between factors influencing population size such as environmental productivity, as well as factors influencing phonotactic selectivity such as anthropogenic noise, are central to understanding the complex mosaic of acoustically signaling and silent populations.

evolutionary biology↗

bioRxiv2022

Parasitoid pressures and silence evolution

Acoustic signals used by organisms to attract mates are known to attract parasitoid flies. The parasitoid flies lay their eggs inside the host signaler, eventually killing the host. We build a host-parasitoid acoustic model to investigate the effect of parasitoid flies on the signalling hosts eco-evolutionary dynamics. We used field crickets as a system to build the framework of the model. We explore how the sex ratio and the female parasitoid fecundity impact the evolution of the acoustic signal and population density of the signalling hosts. We also explore the stability of the host populations with an increase in parasitoid load. We find that up to a threshold value, an increase in parasitoid load leads to a thriving yet silent host population. Consistent with field observations, we show how this emergence of silence as an evolutionary strategy is immediate. Our results show that a drastic increase in the parasitoid load can rapidly push the signalling host population towards instability and extinction.

evolutionary biology↗

bioRxiv2018

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These issues may affect the accuracy of empirically constructed ecological networks. Yet statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying ecological networks structure. To explore properties of large-scale modular networks, we developed EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be employed in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties accurately depends both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, the modules with nested structure were the easiest to detect, regardless of sampling design. Sampling according to species degree (number of interactions) was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. We recommend that these findings be incorporated into field sampling design of projects aiming to characterize large species interactions networks to reduce sampling biases.\n\nAuthor SummaryEcological interactions are commonly modeled as interaction networks. Analyses of such networks may be sensitive to sampling biases and detection issues in both the interactors and interactions (nodes and links). Yet, statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying networks structure. For insight into ecological networks, we developed software EcoNetGen (available in R and Python). These allow the generation and sampling of several types of large-scale modular networks with predetermined topologies, representing a wide variety of communities and types of ecological interactions. Networks can be sampled according to designs employed in field observations. We demonstrate, through first uses of this software, that underlying network topology interacts strongly with empirical sampling design, and that constructing empirical networks by starting with highly connected species may be the give the best representation of the underlying network.

ecology↗

bioRxiv2017

The dynamics of starvation and recovery

The eco-evolutionary dynamics of species are fundamentally linked to the energetic constraints of its constituent individuals. Of particular importance is the interplay between reproduction and the dynamics of starvation and recovery. To elucidate this interplay, we introduce a nutritional state-structured model that incorporates two classes of consumer: nutritionally replete, reproducing consumers, and undernourished, non-reproducing consumers. We obtain strong constraints on starvation and recovery rates by deriving allometric scaling relationships and find that population dynamics are typically driven to a steady state. Moreover, these rates fall within a refuge in parameter space, where the probability of population extinction is minimized. We also show that our model provides a natural framework to predict maximum mammalian body size by determining the relative stability of an otherwise homogeneous population to a competing population with altered percent body fat. This framework provides a principled mechanism for a selective driver of Copes rule.

ecology↗

bioRxiv2017

Eco-evolutionary dynamics and collective dispersal: implications for salmon metapopulation robustness

The spatial dispersal of individuals is known to play an important role in the dynamics of populations, and is central to metapopulation theory. At the same time, local adaptation to environmental conditions creates a geographic mosaic of evolutionary forces, where the combined drivers of selection and gene flow interact. Although the dispersal of individuals from donor to recipient populations provides connections within the metapopulation, promoting demographic and evolutionary rescue, it may also introduce maladapted individuals into habitats host to different environmental conditions, potentially lowering the fitness of the recipient population. Thus, dispersal plays a dual role in both promoting and inhibiting local adaptation. Here we explore a model of the eco-evolutionary dynamics between two populations connected by dispersal, where the productivity of each is defined by a trait complex that is subject to local selection. Although general in nature, our model is inspired by salmon metapopulations, where dispersal between populations is defined in terms of the straying rate, which has been shown to be density-dependent, and recently proposed to be shaped by social interactions consistent with collective movement. The results of our model reveal that increased straying between evolving populations leads to alternative stable states, which has large and nonlinear effects on two measures of metapopulation robustness: the portfolio effect and the time to recovery following an induced disturbance. We show that intermediate levels of straying result in large gains in robustness, and that increased habitat heterogeneity promotes robustness when straying rates are low, and erodes robustness when straying rates are high. Finally, we show that density-dependent straying promotes robustness, particularly when the aggregate biomass is low and straying is correspondingly high, which has important ramifications for the conservation of salmon metapopulations facing both natural and anthropogenic disturbances.\n\nMedia SummaryMany migratory species, such as salmon, are remarkable in finding their way home. This homing has allowed fine-scale adaptations to the environments in which they evolve. But some individuals do not find their way home and instead stray to other locations, especially when there are fewer individuals to help with collective decision-making. With an eco-evolutionary model, we discovered that an intermediate and density-dependent straying rate allows linked populations to be robust to disturbance but maintain local adaptations.

ecology↗

bioRxiv2017

Analyzing ecological networks of species interactions

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

ecology↗