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.

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.

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.