Incorporating ecology into gene drive modelling.

Gene drive technology, in which fast-spreading engineered drive alleles are introduced into wild populations, represents a promising new tool in the fight against vector-borne diseases, agricultural pests and invasive species. Due to the risks involved, gene drives have so far only been tested in laboratory settings while their population-level behaviour is mainly studied using mathematical and computational models. The spread of a gene drive is a rapid evolutionary process that occurs over timescales similar to many ecological processes. This can potentially generate strong eco-evolutionary feedback that could profoundly affect the dynamics and outcome of a gene drive release. We, therefore, argue for the importance of incorporating ecological features into gene drive models. We describe the key ecological features that could affect gene drive behaviour, such as population structure, life-history, environmental variation and mode of selection. We review previous gene drive modelling efforts and identify areas where further research is needed. As gene drive technology approaches the level of field experimentation, it is crucial to evaluate gene drive dynamics, potential outcomes, and risks realistically by including ecological processes.

Inferring the ecological and evolutionary determinants of community genetic diversity.

Understanding the relative contributions of ecological and evolutionary processes to the structuring of ecological communities is needed to improve our ability to predict how communities may respond to future changes in an increasingly human-modified world. Metabarcoding methods make it possible to gather population genetic data for all species within a community, unlocking a new axis of data to potentially unveil the origins and maintenance of biodiversity at local scales. Here, we present a new eco-evolutionary simulation model for investigating community assembly dynamics using metabarcoding data. The model makes joint predictions of species abundance, genetic variation, trait distributions and phylogenetic relationships under a wide range of parameter settings (e.g. high speciation/low dispersal or vice versa) and across a range of community states, from pristine and unmodified to heavily disturbed. We first demonstrate that parameters governing metacommunity and local community processes leave detectable signatures in simulated biodiversity data axes. Next, using a simulation-based machine learning approach we show that neutral and non-neutral models are distinguishable and that reasonable estimates of several model parameters within the local community can be obtained using only community-scale genetic data, while phylogenetic information is required to estimate those describing metacommunity dynamics. Finally, we apply the model to soil microarthropod metabarcoding data from the Troodos mountains of Cyprus, where we find that communities in widespread forest habitats are structured by neutral processes, while high-elevation and isolated habitats act as an abiotic filter generating non-neutral community structure. We implement our model within the ibiogen R package, a package dedicated to the investigation of island, and more generally community-scale, biodiversity using community-scale genetic data.

Predation shifts coevolution toward higher host contact rate and parasite virulence.

Hosts can avoid parasites (and pathogens) by reducing social contact, but such isolation may carry costs, e.g. increased vulnerability to predators. Thus, many predator-host-parasite systems confront hosts with a trade-off between predation and parasitism. Parasites, meanwhile, evolve higher virulence in response to increased host sociality and consequently, increased multiple infections. How does predation shift coevolution of host behaviour and parasite virulence? What if predators are selective, i.e. predators disproportionately capture the sickest hosts? We answer these questions with an eco-coevolutionary model parametrized for a Trinidadian guppy-Gyrodactylus spp. system. Here, increased predation drives host coevolution of higher grouping, which selects for higher virulence. Additionally, higher predator selectivity drives the contact rate higher and virulence lower. Finally, we show how predation and selectivity can have very different impacts on host density and prevalence depending on whether hosts or parasites evolve, or both. For example, higher predator selectivity led to lower prevalence with no evolution or only parasite evolution but higher prevalence with host evolution or coevolution. These findings inform our understanding of diverse systems in which host behavioural responses to predation may lead to increased prevalence and virulence of parasites.

Ecomorphological convergence in planktivorous surgeonfishes.

Morphological convergence plays a central role in the study of evolution. Often induced by shared ecological specialization, homoplasy hints at underlying selective pressures and adaptive constraints that deterministically shape the diversification of life. Although midwater zooplanktivory has arisen in adult surgeonfishes (family Acanthuridae) at least four independent times, it represents a clearly specialized state, requiring the capacity to swiftly swim in midwater locating and sucking small prey items. Whereas this diet has commonly been associated with specific functional adaptations in fishes, acanthurids present an interesting case study as all nonplanktivorous species feed by grazing on benthic algae and detritus, requiring a vastly different functional morphology that emphasizes biting behaviours. We examined the feeding morphology in 30 acanthurid species and, combined with a pre-existing phylogenetic tree, compared the fit of evolutionary models across two diet regimes: zooplanktivores and nonzooplanktivorous grazers. Accounting for phylogenetic relationships, the best-fitting model indicates that zooplanktivorous species are converging on a separate adaptive peak from their grazing relatives. Driving this bimodal landscape, zooplanktivorous acanthurids tend to develop a slender body, reduced facial features, smaller teeth and weakened jaw adductor muscles. However, despite these phenotypic changes, model fitting suggests that lineages have not yet reached the adaptive peak associated with plankton feeding even though some transitions appear to be over 10 million years old. These findings demonstrate that the selective demands of pelagic feeding promote repeated - albeit very gradual - ecomorphological convergence within surgeonfishes, while allowing local divergences between closely related species, contributing to the overall diversity of the clade.

Improved inference of population histories by integrating genomic and epigenomic data.

With the availability of high-quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, for example bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites, or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to (1) improve the accuracy of demographic inference in recent times, (2) uncover past demographic events hidden to SNP-based inference methods, and (3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demographic inference in Arabidopsis thaliana using DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregating single methylated polymorphisms (SMPs) satisfy the modeling assumptions of the SMC framework, while differentially methylated regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post-glacial population expansion of the European A. thaliana population. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next-generation inference of evolutionary history by combining information from several genetic and epigenetic markers.

Automatic inference of demographic parameters using generative adversarial networks.

Population genetics relies heavily on simulated data for validation, inference and intuition. In particular, since the evolutionary 'ground truth' for real data is always limited, simulated data are crucial for training supervised machine learning methods. Simulation software can accurately model evolutionary processes but requires many hand-selected input parameters. As a result, simulated data often fail to mirror the properties of real genetic data, which limits the scope of methods that rely on it. Here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg-gan, is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project and show that we can accurately recapitulate the features of real data.

Shorter life and reduced fecundity can increase colony fitness in virtual Caenorhabditis elegans.

In the nematode Caenorhabditis elegans, loss of function of many genes leads to increases in lifespan, sometimes of a very large magnitude. Could this reflect the occurrence of programmed death that, like apoptosis of cells, promotes fitness? The notion that programmed death evolves as a mechanism to remove worn out, old individuals in order to increase food availability for kin is not supported by classic evolutionary theory for most species. However, it may apply in organisms with colonies of closely related individuals such as C. elegans in which largely clonal populations subsist on spatially limited food patches. Here, we ask whether food competition between nonreproductive adults and their clonal progeny could favor programmed death by using an in silico model of C. elegans. Colony fitness was estimated as yield of dauer larva propagules from a limited food patch. Simulations showed that not only shorter lifespan but also shorter reproductive span and reduced adult feeding rate can increase colony fitness, potentially by reducing futile food consumption. Early adult death was particularly beneficial when adult food consumption rate was high. These results imply that programmed, adaptive death could promote colony fitness in C. elegans through a consumer sacrifice mechanism. Thus, C. elegans lifespan may be limited not by aging in the usual sense but rather by apoptosis-like programmed death.

On the effective depth of viral sequence data.

Genome sequence data are of great value in describing evolutionary processes in viral populations. However, in such studies, the extent to which data accurately describes the viral population is a matter of importance. Multiple factors may influence the accuracy of a dataset, including the quantity and nature of the sample collected, and the subsequent steps in viral processing. To investigate this phenomenon, we sequenced replica datasets spanning a range of viruses, and in which the point at which samples were split was different in each case, from a dataset in which independent samples were collected from a single patient to another in which all processing steps up to sequencing were applied to a single sample before splitting the sample and sequencing each replicate. We conclude that neither a high read depth nor a high template number in a sample guarantee the precision of a dataset. Measures of consistency calculated from within a single biological sample may also be insufficient; distortion of the composition of a population by the experimental procedure or genuine within-host diversity between samples may each affect the results. Where it is possible, data from replicate samples should be collected to validate the consistency of short-read sequence data.

Evolutionary dynamics of the cryptocurrency market.

The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1469 cryptocurrencies introduced between April 2013 and May 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.