Biology subjects

Plotkin, J. B.

Publications and source records attributed to Plotkin, J. B..

12 recordsLinked to original sources

bioRxiv2026

Evolution of the division of labor between two interacting public tasks

Individuals in many species perform several costly tasks whose benefits are shared with neighbors: bacteria secrete distinct public metabolites, plants emit distinct defensive volatiles, and human foragers acquire distinct shared foods. Performing one of these tasks often changes the benefits and costs of the other: a forager who brings in one kind of food provides different nutritional benefits to neighbors than one who brings in several kinds, and the difference depends on what the neighbors bring. Here we ask which divisions of labor evolve when two tasks interact. We model the interaction at both the group and the individual level, and we derive the long-run outcomes of the replicator dynamics as functions of the task payoffs and the interaction parameters. Interactions between tasks produce long-run outcomes that cannot occur when the tasks are independent. In particular, specialists, who perform one task but not the other, can coexist in a stable division of labor only if the tasks interact, and in most cases only if the interaction is more favorable when different individuals perform the two tasks than when one individual performs both. We also show that a specialized division of labor can be bistable with a non-specialized one, and we prove that, when both tasks are identical anti-coordination games, individuals in the specialized population have higher fitness. This bistability produces hysteresis in both the division of labor and fitness as interaction parameters vary. Populations with identical payoffs can therefore evolve to divisions of labor with different mean fitness.

evolutionary biology↗

bioRxiv2026

The effects of group size and assortment on the evolution of division of labor

Stable variation in public-good production can generate biological division of labor. Two key drivers are the size of interacting groups and the degree of assortment (relatedness) among individuals with similar investment levels. Here we extend adaptive-dynamic models of continuous public-goods investment by allowing assortment in group formation. We show that increasing group size typically enlarges the range of benefit and cost curvatures that permit evolutionary branching, whereas assortment tends to shrink this range and prevents branching under complete relatedness. For a broad class of models, branching requires diminishing marginal public benefits and diminishing marginal private costs, with costs decreasing faster than benefits. Finally, we analyze post-branching dynamics for one and two public goods, and find that assortment can stabilize the resulting two-type division of labor. Together, these results show how group size, assortment, and payoff curvature jointly determine when heterogeneity in public-goods production can evolve.

evolutionary biology↗

bioRxiv2026

Eco-evolutionary games in noisy environments

Cooperative and competitive interactions among individuals harvesting resources can shape environmental states, such as prey abundance. In turn, environmental conditions feed back to influence strategic interactions. Eco-evolutionary game theory studies how these feedbacks shape the co-evolution of behavior and environment. Existing models typically assume deterministic, noise-free environmental dynamics. However, real environments are inherently stochastic, for example due to finite resources, and noise can qualitatively alter social outcomes. Here, we incorporate stochastic environmental dynamics into eco-evolutionary game theory. When environmental change is slow relative to strategy updates, we show that behavior reflects a mixture of the games associated with low and high environmental states, often yielding outcomes qualitatively distinct from deterministic predictions. In particular, environmental stochasticity can eliminate bistability and enforce dominance of a single behavior. When environmental dynamics are faster, populations have less opportunity to track fluctuations, and behavior converges toward strategies that are optimal on average. Stochasticity can even causes persistent oscillations in the tragedy of commons, in regimes where classical models predict stability. Our framework provides a tractable approach for analyzing social behavior linked to environmental dynamics how noise shapes long-term eco-evolutionary outcomes.

evolutionary biology↗

bioRxiv2025

Evolution of division of labor on two public goods with continuous investments

Species throughout the tree of life have evolved to produce multiple public goods, and they often exhibit division of labor, meaning that subpopulations have different allocations of effort across the goods. Despite a robust theoretical literature on mechanisms that lead to division of labor in specific domains, a general evolutionary game-theoretical analysis remains incomplete. Here we model labor as a continuous quantity and the resulting public goods as functions of labor contributed by all individuals using adaptive dynamics. We derive general conditions for the evolution of division of labor and categorize what forms of division may arise. We show that the adaptive dynamics of division of labor has three potential outcomes that differ in expected payoff and variance in payoff. Our findings indicate that the evolution of division of labor can have a diverse range of possible outcomes, with important consequences for fitness, variance in payoff, and the distribution of effort across the population.

evolutionary biology↗

bioRxiv2021

Drift as a driver of language change: An artificial language experiment

Over half a century ago, George Zipf observed that more frequent words tend to be older. Corpus studies since then have confirmed this pattern, with more frequent words being replaced and regularized less often than less frequent words. Two main hypotheses have been proposed to explain this: that frequent words change less because selection against innovation is stronger at higher frequencies, or that they change less because stochastic drift is stronger at lower frequencies. Here, we report the first experimental test of these hypotheses. Participants were tasked with learning a miniature language consisting of two nouns and two plural markers. Nouns occurred at different frequencies and were subjected to treatments that varied drift and selection. Using a model that accounts for participant heterogeneity, we measured the rate of noun regularization, the strength of selection, and the strength of drift in participant responses. Results suggest that drift alone is sufficient to generate the elevated rate of regularization we observed in low-frequency nouns, adding to a growing body of evidence that drift may be a major driver of language change.

evolutionary biology↗

bioRxiv2020

What we talk about when we talk about color

Names for colors vary widely across languages, but color categories are remarkably consistent (Berlin & Kay, 1969). Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies (Regier et al., 2007). But the mappings from colors to words are not identical across languages, which may reflect communicative needs - how often speakers must refer to objects of different color (Gibson et al., 2017). Here we quantify the communicative needs of colors in 130 different languages by means of a novel inference algorithm. We find that communicative needs are not uniform: some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Our analysis also reveals a hidden diversity in the communicative needs of colors across different languages, which is partly explained by differences in geographic location and the local biogeography of linguistic communities. Accounting for language-specific, non-uniform communicative needs improves predictions for how a language maps colors to words, and how these mappings vary across languages. Our account closes an important gap in the compression theory of color naming, while opening new directions to study cross-cultural variation in the need to communicate different colors and its impact on the cultural evolution of color categories.

animal behavior and cognition↗

bioRxiv2020

A machine-vision approach for automated pain measurement at millisecond timescales

Objective and automatic measurement of pain in mice remains a barrier for discovery in both basic and translational neuroscience. Here we capture rapid paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches. Our statistical software platform, PAWS (Pain Assessment at Withdrawal Speeds), uses a univariate projection of paw position over time to automatically quantify fast paw dynamics at the onset of paw withdrawal and also lingering pain-related behaviors such as paw guarding and shaking. Applied to innocuous and noxious stimuli across six inbred mouse strains, a linear discriminant analysis reveals a two-dimensional subspace that separates painful from non-painful stimuli on one axis, and further distinguishes the severity of pain on the second axis. Automated paw tracking combined with PAWS reveals behaviorally-divergent mouse strains that display hypo- and hyper-sensitivity to mechanical stimuli. To demonstrate the efficacy of PAWS for detecting hypersensitivity to noxious stimuli, we chemogenetically activated pain-aversion neurons in the amygdala, which further separated the behavioral representation of pain-related behaviors along a low-dimensional path. Taken together, this automated pain quantification approach should increase the ease and objectivity of collecting rigorous behavioral data, and it is compatible with other neural circuit dissection tools for determining the mouse pain state.

neuroscience↗

bioRxiv2018

Evolution of empathetic moral evaluation

Social norms can promote cooperation in human societies by assigning reputations to individuals based on their past actions. A good reputation indicates that an individual is worthy of help and is likely to reciprocate. A large body of research has established the norms of moral assessment that promote cooperation and maximize social welfare, assuming reputations are objective. But if there is no centralized institution to provide objective moral evaluation, then opinions about an individuals reputation may differ across a population. Here we use evolutionary game theory to study the effects of empathy - the capacity to make moral evaluations from the perspective of another person. We find that empathetic moral evaluation tends to foster cooperation by reducing the rate of unjustified defection. The norms of moral evaluation previously considered most socially beneficial depend on high levels of empathy, whereas different norms are required to maximize social welfare in populations unwilling or incapable of empathy. We demonstrate that empathy itself can evolve through social contagion and attain evolutionary stability under most social norms. We conclude that a capacity for empathetic moral evaluation represents a key component to sustaining cooperation in human societies: cooperation requires getting into the mindset of others whose views differ from our own.

evolutionary biology↗

bioRxiv2018

Codon usage influences fitness through RNA toxicity

Many organisms are subject to selective pressure that gives rise to unequal usage of synonymous codons, known as codon bias. To experimentally dissect the mechanisms of selection on synonymous sites, we expressed several hundred synonymous variants of the GFP gene in Escherichia coli, and used quantitative growth and viability assays to estimate bacterial fitness. Unexpectedly, we found many synonymous variants whose expression was toxic to E. coli. Unlike previously studied effects of synonymous mutations, the effect that we discovered is independent of translation, but it depends on the production of toxic mRNA molecules. We identified RNA sequence determinants of toxicity, and evolved suppressor strains that can tolerate the expression of toxic GFP variants. Genome sequencing of these suppressor strains revealed a cluster of promoter mutations that prevented toxicity by reducing mRNA levels. We conclude that translation-independent RNA toxicity is a previously unrecognized obstacle in bacterial gene expression.\n\nSignificance statementSynonymous mutations in genes do not change protein sequence, but they may affect gene expression and cellular function. Here we describe an unexpected toxic effect of synonymous mutations in Escherichia coli, with potentially large implications for bacterial physiology and evolution. Unlike previously studied effects of synonymous mutations, the effect that we discovered is independent of translation, but it depends on the production of toxic mRNA molecules. We hypothesize that the mechanism we identified influences the evolution of endogenous genes in bacteria, by imposing selective constraints on synonymous mutations that arise in the genome. Of interest for biotechnology and synthetic biology, we identify bacterial strains and growth conditions that alleviate RNA toxicity, thus allowing efficient overexpression of heterologous proteins.

synthetic biology↗

bioRxiv2018

Selection for protein stability enriches for epistatic interactions

A now classical argument for the marginal thermodynamic stability of proteins explains the distribution of observed protein stabilities as a consequence of an entropic pull in protein sequence space. In particular, most sequences that are sufficiently stable to fold will have stabilities near the folding threshold. Here we extend this argument to consider its predictions for epistatic interactions for the effects of mutations on the free energy of folding. Although there is abundant evidence to indicate that the effects of mutations on the free energy of folding are nearly additive and conserved over evolutionary time, we show that these observations are compatible with the hypothesis that a non-additive contribution to the folding free energy is essential for observed proteins to maintain their native structure. In particular through both simulations and analytical results, we show that even very small departures from additivity are sufficient to drive this effect.

evolutionary biology↗

bioRxiv2017

riboviz: analysis and visualization of ribosome profiling datasets

Using high-throughput sequencing to monitor translation in vivo, ribosome profiling can provide critical insights into the dynamics and regulation of protein synthesis in a cell. Since its introduction in 2009, this technique has played a key role in driving biological discovery, and yet it requires a rigorous computational toolkit for widespread adoption. We developed a processing pipeline and browser-based visualization, riboviz, that allows convenient exploration and analysis of riboseq datasets. In implementation, riboviz consists of a comprehensive and flexible backend analysis pipeline that allows the user to analyze their private unpublished dataset, along with a web application for comparison with previously published public datasets.\n\nAvailability and implementationJavaScript and R source code and extra documentation are freely available from https://github.com/shahpr/RiboViz, while the web-application is live at www.riboviz.org.

bioinformatics↗

bioRxiv2016

Phenotypic plasticity can facilitate evolutionary rescue

Environmental variation is commonplace, but unpredictable. Populations that encounter a deleterious environment can sometimes avoid extinction by rapid evolutionary adaptation. Phenotypic variability, whereby a single genotype can express multiple different phenotypes, might play an important role in rescuing such populations from extinction. This type of evolutionary bet-hedging need not confer a direct benefit to a single individual, but it may increase the chance of long-term survival of a lineage. Here we develop a population-genetic model to explore how partly heritable phenotypic variability influences the probability of evolutionary rescue and the mean duration of population persistence in changing environments. We find that the probability of population persistence depends non-monotonically on the degree of phenotypic heritability between generations: some heritability can help avert extinction, but too much heritability removes any benefit of phenotypic variability. We discuss the implications of these results in the context of therapies designed to eradicate populations of pathogens or aberrant cellular lineages.

evolutionary biology↗