Biology subjects

Week, B.

Publications and source records attributed to Week, B..

7 recordsLinked to original sources

bioRxiv2026

Microbially Assisted Rescue of Host Populations

Rapid environmental change can drive populations into decline but host genomes may adapt in time to rescue the population from extinction. Host-associated microbes may alter this race. We here develop a population-genetics model to characterize the dynamics and consequences of microbe-assisted host population rescue. In our model, we replace one locus of a classical two-locus system with microbial carriage. Under weak selection the microbial variables settle to a quasi-microbial equilibrium (QME) conditional on the host allele, reducing the coupled dynamics to host allele frequency and abundance. The reduction identifies two microbial contributions that shape rescue. Additive benefits of microbial carriage on host fitness raise host growth immediately without affecting evolution and lift the floor of the demographic rescue-U. By contrast, positive interactions between the rescue allele and the microbe accelerate evolution and narrow the rescue-U instead of buffering initial decline. A microbe can therefore buy time, speed adaptation, or be required for recovery when neither contribution suffices alone. Imperfect microbial inheritance, however, can impede microbe-assisted rescue. When selection acts through fecundity and carriers transmit the microbe to only some offspring, adaptation generates non-carrying descendants faster than transmission replaces them. When that loss is sufficiently strong, recovery can fail even though the allele, the microbe, and their interaction are each individually beneficial. We call this selection-amplified dilution. Together, our model integrates evolutionary rescue theory with multilocus population genetics and evolutionary epidemiology to define a framework of microbe-assisted host rescue, thereby identifying key factors driving or compromising the rescue process.

evolutionary biology↗

bioRxiv2025

STOCHASTIC ECO-EVOLUTIONARY DYNAMICS OF MULTIVARIATE TRAITS: A Framework for Modeling Population Processes Illustrated by the Study of Drifting G-Matrices

I derive a novel stochastic equation for the evolution of the additive genetic variance-covariance matrix G in response to mutation, selection, drift, and fluctuating population size. Common wisdom holds that the effect of drift on G is simply to reduce each of its entries by a common proportional amount while preserving its orientation. In contrast, I find that drift causes significant and directional shifts in the orientation of G by driving genetic correlations to their extremes. Biologically, this is a consequence of linkage build-up introduced by drift. I compare these theoretical results to empirical observations based on experiments conducted by Phillips et al., (2001). Additionally, to derive the model of G-matrix evolution, I developed a novel synthetic framework for modelling ecological and evolutionary dynamics of populations carrying multivariate traits. This framework is optimized for deriving new models across a wide range of topics in population biology. Foundations of the framework are formalized by the theory of measure-valued processes, but application of the framework only requires multivariate calculus, and heuristics are presented in the main text for making additional calculations involving stochastic processes. Collectively, this work establishes a powerful framework enabling efficient formal analysis of integrated population processes across evolution and ecology, and its potential for making new discoveries is illustrated by novel findings on fundamental aspects of G-matrix evolution.

evolutionary biology↗

bioRxiv2024

Quantitative Genetics of Microbiome Mediated Traits

Multicellular organisms host a rich assemblage of associated microorganisms, collectively known as their "microbiomes". Microbiomes have the capacity to influence their hosts fitnesses, but the conditions under which such influences contribute to evolution are not clear. This is due in part to a lack of a comprehensive theoretical framework for describing the combined effects of host and associated microbes on phenotypic variation. Here we address this gap by extending the foundations of quantitative genetic theory to include host-associated microbes, as well as alleles of hosts, as factors that explain quantitative host trait variation. We introduce a way to partition host-associated microbiomes into components relevant for predicting a microbiome-mediated response to selection. We then apply our general framework to a simulation model of microbiome inheritance to illustrate principles for predicting host trait dynamics, and to generalize classical narrow and broad sense heritabilities to account for microbial effects. We demonstrate that microbiome-mediated responses to host-level selection can arise from various transmission modes, not solely vertical, but that the contribution of non-vertical modes can depend strongly on host life history. Our work lays a foundation for integrating microbiome-mediated host variation and adaptation into our understanding of natural variation.

evolutionary biology↗

bioRxiv2024

The Evolution of Microbiome-Mediated Traits

The role of host-associated microbiomes in shaping host fitness is becoming increasingly recognized, and may have important evolutionary consequences. The degree to which microbiomes contribute to host evolution depends on how they are inherited. To understand the conditions under which host evolution is mediated by microbiome inheritance, we introduce a simple mathematical model. Our model tracks the evolution of a quantitative host trait that is mediated by host genotype and host microbiome. Unlike host genotype, the host microbiome may have several routes of transmission, including social contact and environmental acquisition. Our model takes a breeding-value approach to formally generalize classical quantitative genetic theory to account for these various modes of microbiome inheritance. A key prediction of our model is that, as long as microbiomes are transmitted between host generations, their mode of transmission has no effect on host trait dynamics. To understand the conditions required for this result, we analyze microbiome-explicit simulations that break important model assumptions. We then discuss avenues to generalize our model and connections with related concepts. Taken together, this work provides novel insights into the dynamics of non-Mendelian populations, and makes first steps towards establishing a theory of microbiome-mediated trait evolution.

evolutionary biology↗

bioRxiv2022

A model of coevolution and local adaptation between hosts and parasites in continuous space

Previous host-parasite coevolutionary theory has focused on understanding the determinants of local adaptation using spatially discrete models. However, these studies fall short of describing patterns of host-parasite local adaptation across spatial scales. In contrast, empirical work demonstrates patterns of adaptation depend on the scale at which they are measured. Here, we propose a model of host-parasite coevolution in continuous space that naturally leads to a scale-dependent definition of local adaptation and a formal definition for the spatial scale of coevolution. In agreement with empirical findings, our model implies patterns of adaptation vary across spatial scales. When measured on spatial scales shorter than the scale of coevolution, we find the farther dispersing species is locally adapted. However, when measured at longer spatial scales, the opposite pattern is observed. We discuss our results in relation to those found using spatially discrete models and to conclusions drawn from empirical studies, and provide an example of our how our results can be used to inform the design of empirical studies.

evolutionary biology↗

bioRxiv2020

A White Noise Approach to Evolutionary Ecology

Although the evolutionary response to random genetic drift is classically modelled as a sampling process for populations with fixed abundance, the abundances of populations in the wild fluctuate over time. Furthermore, since wild populations exhibit demographic stochasticity, it is reasonable to consider the evolutionary response to demographic stochasticity and its relation to random genetic drift. Here we close this gap in the context of quantitative genetics by deriving the dynamics of the distribution of a quantitative character and the abundance of a biological population from a stochastic partial differential equation driven by space-time white noise. In the process we develop a useful set of heuristics to operationalize the powerful, but abstract theory of white noise and measure-valued stochastic processes. This approach allows us to compute the full implications of demographic stochasticity on phenotypic distributions and abundances of populations. We demonstrate the utility of our approach by deriving a quantitative genetic model of diffuse coevolution mediated by exploitative competition for a continuum of resources. In addition to trait and abundance distributions, this model predicts interaction networks defined by rates of interactions, competition coefficients, or selection gradients. Analyzing the relationship between selection gradients and competition coefficients reveals independence between linear selection gradients and competition coefficients. In contrast, absolute values of linear selection gradients and quadratic selection gradients tend to be positively correlated with competition coefficients. That is, competing species that strongly affect each others abundance tend to also impose selection on one another, but the directionality is not predicted. This approach contributes to the development of a synthetic theory of evolutionary ecology by formalizing first principle derivations of stochastic models that underlie rigorous investigations of the relationship between feedbacks of biological processes and the patterns of diversity they produce.

ecology↗

bioRxiv2020

A unified model of species abundance, genetic diversity, and functional diversity revealsthe mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Reconciling the relative importance of these processes is hindered by current theory, which tends to focus on a single spatial, temporal or taxonomic scale. We introduce a mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. We demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. We combine our massive eco-evolutionary synthesis simulations (MESS) with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of spatial scales.

evolutionary biology↗