Biology subjects

Osmond, M.

Publications and source records attributed to Osmond, M..

7 recordsLinked to original sources

bioRxiv2026

The probability of evolutionary rescue with competition: scaling-up from a single species to a community

When ecological communities experience environmental change, continued coexistence may require rapid adaptation to avoid extinction, i.e., evolutionary rescue. This rescue can be inhibited by competition. Combining Lotka-Volterra and population genetic models, we investigate how both within- and between-species competition impact the probability of rescue. With a single species we derive closed-form approximations that quantify the negative impact of competition. We then use a two-species model to examine how the sweep of a rescue mutant in one species inhibits establishment of rescue mutants in another, a process we term survival interference. This interference is strongest when the stronger competitor begins recovering first, creating eco-evolutionary priority effects despite conditions permitting deterministic coexistence. Finally, considering larger competitive communities, we document how survival interference reduces the number of rescued species but weakens with species richness and unevenness. These results move us closer to predicting evolutionary rescue in ecological communities.

ecology↗

bioRxiv2025

Host constraints on viral recombination and emergence

Recombination (including reassortment) is a salient force in viral evolution and has been implicated in the emergence of several zoonotic pathogens in humans. Viral recombination occurs during simultaneous infection of an individual host with multiple genotypes (co-infection). Thus, processes which affect the incidence of a disease in the host population affect how often viral genotypes recombine. We investigate whether and how host traits affect the realized rate of viral recombination using a mathematical model that makes feedbacks between viral evolution and host ecology (in particular, lifespan) explicit. Our main result is that viruses of host species that are short-lived tend to recombine more frequently than those of relatively long-lived hosts. This is because of differences in population density and, thus, the prevalence of (co-)infection at equilibrium. Using highly pathogenic avian influenza sequence data, we test the prediction that recombination is elevated in short-lived hosts. In agreement with this prediction, the magnitude of statistical associations between mutations on different segments of the flu genome increases with host body size, a proxy for lifespan. Similarly, estimates of the reassortment rate from phylogenetic network analyses decrease with body size. We discuss the implications of these findings for disease emergence. Subject categoryEvolution Subject areasevolution, health and disease and epidemiology, ecology

evolutionary biology↗

bioRxiv2025

Quasi epigenetic equilibrium: the implications of plasticity and variability on evolution and extinction

The phenotypic effects of epigenetic modifications, and thus their evolutionary consequences, depend on how the modifications interact with the underlying genetics and the surrounding environment. These interactions lead to a complex model that has so far prevented general analytical progress, and thus limited our understanding. Here, we show that the timescale difference between epigenetic and genetic changes create a quasi-epigenetic equilibrium (QEE). The QEE allows us to tackle the complexity of population epigenetic models by reducing them to their underlying population genetic models with effective parameters. Using this technique, we show how epigenetics modifies key evolutionary parameters, such as the strength of selection and dominance, which can have drastic evolutionary consequences on mutation-selection balance. Further, we show how the QEE allows us to analytically investigate the effect of epigenetics on the probability of a population escaping extinction in a harsh environment via adaptation - evolutionary rescue - by altering the number of potential rescue lineages and their probability of establishing. These calculations show that whether epigenetics helps or hurts population persistence depends non-trivially on the frequency and stability of epigenetic modifications.

evolutionary biology↗

bioRxiv2024

When does stronger selection increase the probability of evolutionary rescue?

Populations may be rescued from extinction via sufficiently rapid adaptive evolution. Evolution is faster with stronger selection but this may come with a demographic cost, creating opposing effects on evolutionary rescue. The outcome of this trade-off determines the optimal strategy for avoiding herbicide/drug resistance evolution. Here we examine the effect of stronger selection, and the associated demographic change, across four models of evolutionary rescue. We find that stronger selection cannot facilitate rescue in two quite different population genetic models unless it increases the absolute fitness of the rescue homozygote. Similarly, in a quantitative genetic model of rescue, where stronger selection leaves maximum fitness unchanged, stronger selection cannot facilitate rescue despite elevating the rate of evolution. We also explore a quantitative genetic model of rescue in a gradually changing environment, generalizing the finding that an intermediate selection strength maximizes survival at steady-state to a wider class of fitness functions.

evolutionary biology↗

bioRxiv2023

Unicellular propagules, cancer, and competition: Are unicellular propagules part of the optimal life-history strategy in the face of both cancer and competition?

Evolutionary transitions in individuality introduce new levels of selection and thus enable discordant selection, threatening the stability of the transition. Cheating is such a problem for multicellularity. So why have so many transitions to multicellularity persisted? One possibility is that the unicellular propagule maintains cooperation among cells by purging cheaters. The evolution of propagule size has been modeled previously, but in the absence of competition between individuals, which may often select for larger propagules. How does the nature of competition between individuals affect the optimal propagule size in the presence of cheating? Here we take a model of early multicellularity, add phenotypic switching between cheating and cooperative phenotypes, and simulate size-dependent competition on a lattice, which allows us to tune the strength of interspecific vs. intraspecific competition via dispersal. As expected, cheating favors strategies with unicellular propagules while size-dependent competition favors strategies with few large propagules (binary fragmentation). How these opposing forces resolve depends on dispersal. Local dispersal, which intensifies intraspecific competition, favors binary fragmentation, which reduces intraspecific competition for space, with one unicellular propagule. Global dispersal instead favours multiple fission when cheating is common. We also find that selfishness promotes smaller body size, despite direct opposing selection from competition. Our results shed light on the evolution of multicellular life cycles and the prevalence of a unicellular stage in the multicellular life cycle across the tree of life. Author summaryA multicellular organism is a group of cooperating cells. But wherever there is cooperation there is the temptation to cheat. Having offspring that start as a single cell (a unicellular bottleneck) has been hypothesized as an adaptation to purge lineages of cheating cells. We model the evolution of offspring size but add competition between individuals, which may select against small unicellular offspring. We find that having some unicellular offspring is still a successful strategy, but how many depends on the nature of competition.

evolutionary biology↗

bioRxiv2021

Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies

Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these patterns of movement shape the underlying genealogies along the genome leading to geographic patterns of isolation by distance in contemporary population genetic data. However, extracting the enormous amount of information contained in genealogies along recombining sequences has, until recently, not been computationally feasible. Here we capitalize on important recent advances in genome-wide gene-genealogy reconstruction and develop methods to use thousands of trees to estimate per-generation dispersal rates and to locate the genetic ancestors of a sample back through time. We take a likelihood approach in continuous space using a simple approximate model (branching Brownian motion) as our prior distribution of spatial genealogies. After testing our method with simulations we apply it to Arabidopsis thaliana. We estimate a dispersal rate of roughly 60km2 per generation, slightly higher across latitude than across longitude, potentially reflecting a northward post-glacial expansion. Locating ancestors allows us to visualize major geographic movements, alternative geographic histories, and admixture. Our method highlights the huge amount of information about past dispersal events and population movements contained in genome-wide genealogies.

evolutionary biology↗

bioRxiv2017

Quantification of cellular distribution as Poisson process in 3D matrix using a multiview light-sheet microscope

The absence of quantitative in vitro cell-extracellular matrix models represents an important bottleneck for basic research and human health. Randomness of cellular distributions provides an opportunity for the development of a quantitative in vitro model. However, quantification of the randomness of random cell distributions is still lacking. In this paper, we have imaged cellular distributions in an alginate matrix using a multiview light-sheet microscope and developed quantification metrics of randomness by modeling it as a Poisson process, a process that has constant probability of occurring in space or time. Our light-sheet microscope can image more than 5 mm thick optically clear samples with 2.9 {+/-}0.4 m depth-resolution. We applied our method to image fluorescently labeled human mesenchymal stem cells (hMSCs) embedded in an alginate matrix. Simulated randomness agrees well with the experiments. Quantification of distributions and validation by simulations will enable quantitative study of cell-matrix interactions in tissue models.

biophysics↗