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.

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.

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 behaviour and public health policy, continue to play a major role in the endemic load of disease and mortality. In this paper, we analyse 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 favour variants that increase the endemic load, we explore the impact of vaccination strategies and non-pharmaceutical interventions (NPIs) 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.

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.

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.

Unbalanced selection: the challenge of maintaining a social polymorphism when a supergene is selfish.

Supergenes often have multiple phenotypic effects, including unexpected detrimental ones, because recombination suppression maintains associations among co-adapted alleles but also allows the accumulation of recessive deleterious mutations and selfish genetic elements. Yet, supergenes often persist over long evolutionary periods. How are such polymorphisms maintained in the face of selection, drive and drift? We present a population genetic model that investigates the conditions necessary for a stable polymorphic equilibrium when one of the supergene haplotypes is a selfish genetic element. The model fits the characteristics of the Alpine silver ant, Formica selysi, in which a large supergene underlies colony social organization, and one haplotype distorts Mendelian transmission by killing progeny that did not inherit it. The model shows that such maternal-effect killing strongly limits the maintenance of social polymorphism. Under random mating, transmission ratio distortion prevents rare single-queen colonies from invading populations of multiple-queen colonies, regardless of the fitness of each genotype. A stable polymorphic equilibrium can, however, be reached when high rates of assortative mating are combined with large fitness differences among supergene genotypes. The model reveals that the persistence of the social polymorphism is non-trivial and expected to occur only under restrictive conditions that deserve further empirical investigation. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

Parental Population Range Expansion before Secondary Contact Promotes Heterosis.

AbstractPopulation genomic analysis of hybrid zones is instrumental to our understanding of the evolution of reproductive isolation. Many temperate hybrid zones are formed by the secondary contact between two parental populations that have undergone postglacial range expansion. Here, we show that explicitly accounting for historical parental isolation followed by range expansion prior to secondary contact is fundamental to explaining genetic and fitness patterns in these hybrid zones. Specifically, ancestral population expansion can result in allele surfing, where neutral or slightly deleterious mutations drift to high frequency at the expansion front. If these surfed deleterious alleles are recessive, they can contribute to substantial heterosis in hybrids produced at secondary contact, counteracting negative effects of Bateson-Dobzhansky-Muller incompatibilities (BDMIs) and hence weakening reproductive isolation. When BDMIs are linked to such recessive deleterious alleles, the fitness benefit of introgression at these loci can facilitate introgression at the BDMIs. The extent to which this occurs depends on the strength of selection against the linked deleterious alleles and the distribution of recombination across the chromosome. Finally, surfing of neutral loci can alter the expected pattern of population ancestry; thus, accounting for historical population expansion is necessary to develop accurate null genomic models of secondary contact hybrid zones.

The need for linked genomic surveillance of SARS-CoV-2.

Genomic surveillance during the coronavirus disease 2019 (COVID-19) pandemic has been key to the timely identification of virus variants with important public health consequences, such as variants that can transmit among and cause severe disease in both vaccinated or recovered individuals. The rapid emergence of the Omicron variant highlighted the speed with which the extent of a threat must be assessed. Rapid sequencing and public health institutions' openness to sharing sequence data internationally give an unprecedented opportunity to do this; however, assessing the epidemiological and clinical properties of any new variant remains challenging. Here we highlight a "band of four" key data sources that can help to detect viral variants that threaten COVID-19 management: 1) genetic (virus sequence) data; 2) epidemiological and geographic data; 3) clinical and demographic data; and 4) immunization data. We emphasize the benefits that can be achieved by linking data from these sources and by combining data from these sources with virus sequence data. The considerable challenges of making genomic data available and linked with virus and patient attributes must be balanced against major consequences of not doing so, especially if new variants of concern emerge and spread without timely detection and action.

An Empiricist's Guide to Using Ecological Theory.

AbstractA scientific understanding of the biological world arises when ideas about how nature works are formalized, tested, refined, and then tested again. Although the benefits of feedback between theoretical and empirical research are widely acknowledged by ecologists, this link is still not as strong as it could be in ecological research. This is in part because theory, particularly when expressed mathematically, can feel inaccessible to empiricists who may have little formal training in advanced math. To address this persistent barrier, we provide a general and accessible guide that covers the basic, step-by-step process of how to approach, understand, and use ecological theory in empirical work. We first give an overview of how and why mathematical theory is created, then outline four specific ways to use both mathematical and verbal theory to motivate empirical work, and finally present a practical tool kit for reading and understanding the mathematical aspects of ecological theory. We hope that empowering empiricists to embrace theory in their work will help move the field closer to a full integration of theoretical and empirical research.

Fixation and effective size in a haploid-diploid population with asexual reproduction.

The majority of population genetic theory assumes fully haploid or diploid organisms with obligate sexuality, despite complex life cycles with alternating generations being commonly observed. To reveal how natural selection and genetic drift shape the evolution of haploid-diploid populations, we analyze a stochastic genetic model for populations that consist of a mixture of haploid and diploid individuals, allowing for asexual reproduction and niche separation between haploid and diploid stages. Applying a diffusion approximation, we derive the fixation probability and describe its dependence on the reproductive values of haploid and diploid stages, which depend strongly on the extent of asexual reproduction in each phase and on the ecological differences between them.