Variety is the spice of life: nongenetic variation in life histories influences population growth and evolvability.

Individual vital rates are key determinants of lifetime reproductive success, and variability in these rates shapes population dynamics. Previous studies have found that this vital rate hetero- geneity can influence demographic properties including population growth rates, however, the explicit effects of the amount of variation within and the covariance between vital rates that can also vary throughout the lifespan on population growth remains unknown. Here, we explore the analytical consequences of nongenetic heterogeneity on long-term population growth rates and rates of evolution by modifying traditional age-structured population projection matrices to incorporate variation among individual vital rates. The model allows vital rates to be permanent throughout life ("fixed condition") or to change over the lifespan ("dynamic condition"). We reduce the complexity associated with adding individual heterogeneity to age-structured models through a novel application of matrix collapsing ("phenotypic collapsing"), showing how to col- lapse in a manner that preserves the asymptotic and transient dynamics of the original matrix. The main conclusion is that nongenetic individual heterogeneity can strongly impact the long-term growth rate and rates of evolution. The magnitude and sign of this impact depends heavily on how the heterogeneity covaries across the lifespan of an organism. Our results emphasize that nongenetic variation cannot simply be viewed as random noise, but rather that it has consistent, predictable effects on fitness and evolvability.

F ST between haploids and diploids in species with discrete ploidy phases.

Many organisms alternate between distinct haploid and diploid phases, which generates population structure according to ploidy level. In this research, we consider a haploid-diploid population using statistical approaches developed for spatially subdivided populations, where haploids represent one "patch" and diploids another "patch". In species with alternating generations, sexual reproduction causes movement from diploids to haploids (by meiosis with recombination) and from haploids to diploids (by syngamy). Thus, an allele in one ploidy phase can be said to "migrate" to the other ploidy phase by sexual reproduction and to "remain" in the same ploidy phase by asexual reproduction. By analyzing a coalescent model of the probability of identity by descent and by state for a haploid-diploid system, we define FST-like measures of differentiation between haploids and diploids and show that these measures can be simplified as a function of the extent of sexuality in each ploidy phase. We conduct simulations with an infinite-alleles model and discuss a method for estimating the degree of effective sexuality from genetic data sets that uses the observed FST measures of haploid-diploid species.

The limit to evolutionary rescue depends on ploidy in yeast exposed to nystatin.

The number of copies of each chromosome, or ploidy, of an organism is a major genomic factor affecting adaptation. We set out to determine how ploidy can impact the outcome of evolution, as well as the likelihood of evolutionary rescue, using short-term experiments with yeast (Saccharomyces cerevisiae) in a high concentration of the fungicide nystatin. In similar experiments using haploid yeast, the genetic changes underlying evolutionary rescue were highly repeatable, with all rescued lines containing a single mutation in the ergosterol biosynthetic pathway. All of these beneficial mutations were recessive, which led to the expectation that diploids would find alternative genetic routes to adaptation. To test this, we repeated the experiment using both haploid and diploid strains and found that diploid populations did not evolve resistance. Although diploids are able to adapt at the same rate as haploids to a lower, not fully inhibitory, concentration of nystatin, the present study suggests that diploids are limited in their ability to adapt to an inhibitory concentration of nystatin, while haploids may undergo evolutionary rescue. These results demonstrate that ploidy can tip the balance between adaptation and extinction when organisms face an extreme environmental change.

Planting long-lived trees in a warming climate: Theory shows the importance of stage-dependent climatic tolerance.

Climate change poses a particular threat to long-lived trees, which may not adapt or migrate fast enough to keep up with rising temperatures. Assisted gene flow could facilitate adaptation of populations to future climates by using managed translocation of seeds from a warmer location (provenance) within the current range of a species. Finding the provenance that will perform best in terms of survival or growth is complicated by a trade-off. Because trees face a rapidly changing climate during their long lives, the alleles that confer optimal performance may vary across their lifespan. For instance, trees from warmer provenances could be well adapted as adults but suffer from colder temperatures while juvenile. Here we use a stage-structured model, using both analytical predictions and numerical simulations, to determine which provenance would maximize the survival of a cohort of long-lived trees in a changing climate. We parameterize our simulations using empirically estimated demographic transition matrices for 20 long-lived tree species. Unable to find reliable quantitative estimates of how climatic tolerance changes across stages in these same species, we varied this parameter to study its effect. Both our mathematical model and simulations predict that the best provenance depends strongly on how fast the climate changes and also how climatic tolerance varies across the lifespan of a tree. We thus call for increased empirical efforts to measure how climate tolerance changes over life in long-lived species, as our model suggests that it should strongly influence the best provenance for assisted gene flow.

Modelling daisy quorum drive: A short-term bridge across engineered fitness valleys.

Engineered gene-drive techniques for population modification and/or suppression have the potential for tackling complex challenges, including reducing the spread of diseases and invasive species. Gene-drive systems with low threshold frequencies for invasion, such as homing-based gene drive, require initially few transgenic individuals to spread and are therefore easy to introduce. The self-propelled behavior of such drives presents a double-edged sword, however, as the low threshold can allow transgenic elements to expand beyond a target population. By contrast, systems where a high threshold frequency must be reached before alleles can spread-above a fitness valley-are less susceptible to spillover but require introduction at a high frequency. We model a proposed drive system, called "daisy quorum drive," that transitions over time from a low-threshold daisy-chain system (involving homing-based gene drive such as CRISPR-Cas9) to a high-threshold fitness-valley system (requiring a high frequency-a "quorum"-to spread). The daisy-chain construct temporarily lowers the high thresholds required for spread of the fitness-valley construct, facilitating use in a wide variety of species that are challenging to breed and release in large numbers. Because elements in the daisy chain only drive subsequent elements in the chain and not themselves and also carry deleterious alleles ("drive load"), the daisy chain is expected to exhaust itself, removing all CRISPR elements and leaving only the high-threshold fitness-valley construct, whose spread is more spatially restricted. Developing and analyzing both discrete patch and continuous space models, we explore how various attributes of daisy quorum drive affect the chance of modifying local population characteristics and the risk that transgenic elements expand beyond a target area. We also briefly explore daisy quorum drive when population suppression is the goal. We find that daisy quorum drive can provide a promising bridge between gene-drive and fitness-valley constructs, allowing spread from a low frequency in the short term and better containment in the long term, without requiring repeated introductions or persistence of CRISPR elements.

Endemic does not mean constant as SARS-CoV-2 continues to evolve.

COVID-19 has become endemic, with dynamics that reflect the waning of immunity and re-exposure, by contrast to the epidemic phase driven by exposure in immunologically naïve populations. Endemic does not, however, mean constant. Further evolution of SARS-CoV-2, as well as changes in behavior and public health policy, continue to play a major role in the endemic load of disease and mortality. In this article, we analyze evolutionary models to explore the impact that a newly arising variant can have on the short-term and longer-term endemic load, characterizing how these impacts depend on the transmission and immunological properties of the variants. We describe how evolutionary changes in the virus will increase the endemic load most for a persistently immune-escape variant, by an intermediate amount for a more transmissible variant, and least for a transiently immune-escape variant. Balancing the tendency for evolution to favor variants that increase the endemic load, we explore the impact of vaccination strategies and non-pharmaceutical interventions that can counter these increases in the impact of disease. We end with some open questions about the future of COVID-19 as an endemic disease.

The prokaryotic and eukaryotic microbiome of Pacific oyster spat is shaped by ocean warming but not acidification.

Pacific oysters (Magallana gigas, a.k.a. Crassostrea gigas), the most widely farmed oysters, are under threat from climate change and emerging pathogens. In part, their resilience may be affected by their microbiome, which, in turn, may be influenced by ocean warming and acidification. To understand these impacts, we exposed early-development Pacific oyster spat to different temperatures (18°C and 24°C) and pCO2 levels (800, 1,600, and 2,800 µatm) in a fully crossed design for 3 weeks. Under all conditions, the microbiome changed over time, with a large decrease in the relative abundance of potentially pathogenic ciliates (Uronema marinum) in all treatments with time. The microbiome composition differed significantly with temperature, but not acidification, indicating that Pacific oyster spat microbiomes can be altered by ocean warming but is resilient to ocean acidification in our experiments. Microbial taxa differed in relative abundance with temperature, implying different adaptive strategies and ecological specializations among microorganisms. Additionally, a small proportion (~0.2% of the total taxa) of the relatively abundant microbial taxa were core constituents (>50% occurrence among samples) across different temperatures, pCO2 levels, or time. Some taxa, including A4b bacteria and members of the family Saprospiraceae in the phyla Chloroflexi (syn. Chloroflexota) and Bacteroidetes (syn. Bacteroidota), respectively, as well as protists in the genera Labyrinthula and Aplanochytrium in the class Labyrinthulomycetes, and Pseudoperkinsus tapetis in the class Ichthyosporea were core constituents across temperatures, pCO2 levels, and time, suggesting that they play an important, albeit unknown, role in maintaining the structural and functional stability of the Pacific oyster spat microbiome in response to ocean warming and acidification. These findings highlight the flexibility of the spat microbiome to environmental changes.IMPORTANCEPacific oysters are the most economically important and widely farmed species of oyster, and their production depends on healthy oyster spat. In turn, spat health and productivity are affected by the associated microbiota; yet, studies have not scrutinized the effects of temperature and pCO2 on the prokaryotic and eukaryotic microbiomes of spat. Here, we show that both the prokaryotic and, for the first time, eukaryotic microbiome of Pacific oyster spat are surprisingly resilient to changes in acidification, but sensitive to ocean warming. The findings have potential implications for oyster survival amid climate change and underscore the need to understand temperature and pCO2 effects on the microbiome and the cascading effects on oyster health and productivity.

Density-Dependent Selection during Range Expansion Affects Expansion Load in Life History Traits.

AbstractModels of range expansion have independently explored fitness consequences of life history trait evolution and increased rates of genetic drift-or "allele surfing"-during spatial spread, but no previous model has examined the interactions between these two processes. Here, using spatially explicit simulations, we explore an ecologically complex range expansion scenario that combines density-dependent selection with allele surfing to asses the genetic and fitness consequences of density-dependent selection on the evolution of life history traits. We demonstrate that density-dependent selection on the range edge acts differently depending on the life history trait and can either diminish or enhance allele surfing. Specifically, we show that selection at the range edge is always weaker at sites affecting competitive ability (K-selected traits) than at sites affecting birth rate (r-selected traits). We then link differences in the frequency of deleterious mutations to differences in the efficacy of selection and rate of mutation accumulation across distinct life history traits. Finally, we demonstrate that the observed fitness consequences of allele surfing depend on the population density in which expansion load is measured. Our work highlights the complex relationship between ecology and expressed genetic load, which will be important to consider when interpreting both experimental and field studies of range expansion.

When and how can we predict adaptive responses to climate change?

Predicting if, when, and how populations can adapt to climate change constitutes one of the greatest challenges in science today. Here, we build from contributions to the special issue on evolutionary adaptation to climate change, a survey of its authors, and recent literature to explore the limits and opportunities for predicting adaptive responses to climate change. We outline what might be predictable now, in the future, and perhaps never even with our best efforts. More accurate predictions are expected for traits characterized by a well-understood mapping between genotypes and phenotypes and traits experiencing strong, direct selection due to climate change. A meta-analysis revealed an overall moderate trait heritability and evolvability in studies performed under future climate conditions but indicated no significant change between current and future climate conditions, suggesting neither more nor less genetic variation for adapting to future climates. Predicting population persistence and evolutionary rescue remains uncertain, especially for the many species without sufficient ecological data. Still, when polled, authors contributing to this special issue were relatively optimistic about our ability to predict future evolutionary responses to climate change. Predictions will improve as we expand efforts to understand diverse organisms, their ecology, and their adaptive potential. Advancements in functional genomic resources, especially their extension to non-model species and the union of evolutionary experiments and "omics," should also enhance predictions. Although predicting evolutionary responses to climate change remains challenging, even small advances will reduce the substantial uncertainties surrounding future evolutionary responses to climate change.

On the fast track: hybrids adapt more rapidly than parental populations in a novel environment.

Rates of hybridization are predicted to increase due to climate change and human activity that cause redistribution of species and bring previously isolated populations into contact. At the same time climate change leads to rapid changes in the environment, requiring populations to adapt rapidly in order to survive. A few empirical cases suggest hybridization can facilitate adaptation despite its potential for incompatibilities and deleterious fitness consequences. Here we use simulations and Fisher's Geometric model to evaluate the conditions and time frame of adaptation via hybridization in both diploids and haplodiploids. We find that hybrids adapt faster to new environments compared to parental populations in nearly all simulated scenarios, generating a fitness advantage that can offset intrinsic incompatibilities and last for tens of generations, regardless of whether the population was diploid or haplodiploid. Our results highlight the creative role of hybridization and suggest that hybridization may help contemporary populations adapt to the changing climate. However, adaptation by hybrids may well happen at the cost of reduced biodiversity, if previously isolated lineages collapse into one.

Schrödinger's yeast: the challenge of using transformation to compare fitness among Saccharomyces cerevisiae that differ in ploidy or zygosity.

How the number of genome copies modifies the effect of random mutations remains poorly known. In yeast, researchers have investigated these effects for knock-out or other large-effect mutations, but have not accounted for differences at the mating-type locus. We set out to compare fitness differences among strains that differ in ploidy and/or zygosity using a panel of spontaneously arising mutations acquired in haploid yeast from a previous study. To ensure no genetic differences, even at the mating-type locus, we embarked on a series of transformations, which first sterilized and then temporarily introduced plasmid-borne mating types. Despite these attempts to equalize the haplotypes, fitness variation introduced during transformation swamped the differences among the original mutation-accumulation lines. While colony size looked normal, we observed a bi-modality in the maximum growth rate of our transformed yeast and determined that many of the slow growing lines were respiratory deficient ("petite"). Not previously reported, we found that yeast that were TID1/RDH54 knockouts were less likely to become petite. Even for lines with the same petite status, however, we found no correlation in fitness between the two replicate transformations performed. These results pose a challenge for any study using transformation to measure the fitness effect of genetic differences among strains. By attempting to hold haplotypes constant, we introduced more mutations that overwhelmed our ability to measure fitness differences between the genetic states. In this study, we transformed over one hundred different lines of yeast, using two independent transformations, and found that this common laboratory procedure can cause large changes to the microbe studied. Our study provides a cautionary tale of the need to use multiple transformants in fitness assays.

Conceptual and empirical bridges between micro- and macroevolution.

Explaining broad molecular, phenotypic and species biodiversity patterns necessitates a unifying framework spanning multiple evolutionary scales. Here we argue that although substantial effort has been made to reconcile microevolution and macroevolution, much work remains to identify the links between biological processes at play. We highlight four major questions of evolutionary biology whose solutions require conceptual bridges between micro and macroevolution. We review potential avenues for future research to establish how mechanisms at one scale (drift, mutation, migration, selection) translate to processes at the other scale (speciation, extinction, biogeographic dispersal) and vice versa. We propose ways in which current comparative methods to infer molecular evolution, phenotypic evolution and species diversification could be improved to specifically address these questions. We conclude that researchers are in a better position than ever before to build a synthesis to understand how microevolutionary dynamics unfold over millions of years.

Braiding Indigenous rights and endangered species law.

Recovery targets fall short of culturally meaningful abundance.

Opposing effects of plant traits on diversification.

Species diversity can vary dramatically across lineages due to differences in speciation and extinction rates. Here, we explore the effects of several plant traits on diversification, finding that most traits have opposing effects on diversification. For example, outcrossing may increase the efficacy of selection and adaptation but also decrease mate availability, two processes with contrasting effects on lineage persistence. Such opposing trait effects can manifest as differences in diversification rates that depend on ecological context, spatiotemporal scale, and associations with other traits. The complexity of pathways linking traits to diversification suggests that the mechanistic underpinnings behind their correlations may be difficult to interpret with any certainty, and context dependence means that the effects of specific traits on diversification are likely to differ across multiple lineages and timescales. This calls for taxonomically and context-controlled approaches to studies that correlate traits and diversification.

Effective pandemic policy design through feedback does not need accurate predictions.

The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions.

Trait-dependent diversification in angiosperms: Patterns, models and data.

Variation in species richness across the tree of life, accompanied by the incredible variety of ecological and morphological characteristics found in nature, has inspired many studies to link traits with species diversification. Angiosperms are a highly diverse group that has fundamentally shaped life on earth since the Cretaceous, and illustrate how species diversification affects ecosystem functioning. Numerous traits and processes have been linked to differences in species richness within this group, but we know little about their relative importance and how they interact. Here, we synthesised data from 152 studies that used state-dependent speciation and extinction (SSE) models on angiosperm clades. Intrinsic traits related to reproduction and morphology were often linked to diversification but a set of universal drivers did not emerge as traits did not have consistent effects across clades. Importantly, SSE model results were correlated to data set properties - trees that were larger, older or less well-sampled tended to yield trait-dependent outcomes. We compared these properties to recommendations for SSE model use and provide a set of best practices to follow when designing studies and reporting results. Finally, we argue that SSE model inferences should be considered in a larger context incorporating species' ecology, demography and genetics.

Student reports of attendance at programs to reduce campus sexual assault and harassment.

OBJECTIVE: To identify factors independently associated with program participation and knowledge of campus processes to address sexual assault and harassment complaints. PARTICIPANTS: 1,182 undergraduates who completed the University of Michigan's 2015 campus climate survey on topics of sexual assault and harassment (67% response rate). METHODS: We analyze survey responses to estimate multivariable models that identify subgroups of the student population least likely to have participated in programs or to know campus processes. RESULTS: Students living off campus, not involved in major organizations, and males are less likely to report attending programming. Students not involved in major organizations and females are less likely to report knowing campus processes. CONCLUSIONS: Specific student subpopulations are more difficult to engage in programs designed to reduce sexual assault and harassment. Targeting additional effort to these groups may improve campus sexual climate. Careful analyses of campus climate survey data can help construct campus-specific priorities for these interventions.

Rapid weed adaptation and range expansion in response to agriculture over the past two centuries.

North America has experienced a massive increase in cropland use since 1800, accompanied more recently by the intensification of agricultural practices. Through genome analysis of present-day and historical samples spanning environments over the past two centuries, we studied the effect of these changes in farming on the extent and tempo of evolution across the native range of the common waterhemp (Amaranthus tuberculatus), a now pervasive agricultural weed. Modern agriculture has imposed strengths of selection rarely observed in the wild, with notable shifts in allele frequency trajectories since agricultural intensification in the 1960s. An evolutionary response to this extreme selection was facilitated by a concurrent human-mediated range shift. By reshaping genome-wide diversity across the landscape, agriculture has driven the success of this weed in the 21st century.

The phoenix hypothesis of speciation.

Genetic divergence among allopatric populations builds reproductive isolation over time. This process is accelerated when populations face a changing environment that allows large-effect mutational differences to accumulate, but abrupt change also places populations at risk of extinction. Here we use simulations of Fisher's geometric model with explicit population dynamics to explore the genetic changes that occur in the face of environmental changes. Because evolutionary rescue leads to the fixation of mutations whose phenotypic effects are larger on average compared with populations not at risk of extinction, these mutations are thus more likely to lead to reproductive isolation. We refer to the formation of new species from the ashes of populations in decline as the phoenix hypothesis of speciation. The phoenix hypothesis predicts more substantial hybrid fitness breakdown among populations surviving a higher extinction risk. The hypothesis was supported when many loci underlie adaptation. With only a small number of potential rescue mutations, however, mutations that fixed in different populations were more likely to be identical, with such parallel changes reducing isolation. Consequently, reproductive isolation builds fastest in populations subject to an intermediate extinction risk, given a limited number of mutations available for adaptation.