Biology subjects

Tinsley, M. C.

Publications and source records attributed to Tinsley, M. C..

4 recordsLinked to original sources

bioRxiv2024

Exploiting pathogen defence trade-offs to manage risks of crop pests evolving resistance to biocontrol.

Pathogens often exert strong selection, yet host populations maintain considerable genetic variation for resistance, possibly due to environmental heterogeneity causing fitness trade-offs. These trade-offs could help explain genetic variation for pathogen defence, and also constrain resistance evolution to microbial pesticides (an underappreciated risk). However, the presence and strength of such trade-offs remain unclear. We investigate whether pathogen identity or host diet has a stronger effect on the fitness of resistance alleles. We quantify genetic variation and covariation for pathogen resistance in an insect pest across distinct pathogen and plant diet combinations. We demonstrate substantial heritability, indicating considerable risks of biopesticide resistance. Contrary to conventional thinking in host-pathogen biology, we found no strong genetic trade-offs for resisting two fungal pathogen species, but changes in plant diet dramatically altered selection for resistance, revealing diet-mediated genetic trade-offs affecting pest survival. Our data suggest that trade-offs not strictly related to immunocompetence could nevertheless maintain genetic variation for pathogen defence in natural and agricultural landscapes.

evolutionary biology↗

bioRxiv2022

resevol: an R package for spatially explicit models of pesticide resistance given evolving pest genomes

The evolution of pesticide resistance is a widespread problem with potentially severe consequences for global food security. We introduce the resevol R package, which simulates individual-based models of pests with evolving genomes that produce complex, polygenic, and covarying traits affecting pest life history and pesticide resistance. Simulations are modelled on a spatially-explicit and highly customisable landscape in which crop and pesticide application and rotation can vary, making the package a highly flexible tool for both general and tactical models of pest management and resistance evolution. We present the key features of the resevol package and demonstrate its use for a simple example simulating pests with two covarying traits. The resevol R package is open source under GNU Public License. All source code and documentation are available on GitHub.

evolutionary biology↗

bioRxiv2021

Predictors of virus prevalence and diversity across a wild bumblebee community

Viruses are key regulators of natural populations. Despite this, we have limited knowledge of the diversity and ecology of viruses that lack obvious fitness effects on their host. This is even the case in wild host populations that provide ecosystem services, where small fitness effects may have major ecological and financial impacts in aggregate. One such group of hosts are the bumblebees, which have a major role in the pollination of food crops and have suffered population declines and range contractions in recent decades. In this study, we used a multivariate generalised linear mixed model to investigate the ecological factors that determine the prevalence of four recently discovered bumblebee viruses (Mayfield virus 1, Mayfield virus 2, River Liunaeg virus and Loch Morlich virus), and two previously known viruses that infect both wild bumblebees and managed honeybees (Acute bee paralysis virus and Slow bee paralysis virus). We show that the recently discovered bumblebee viruses were more genetically diverse than the viruses shared with honeybees, potentially due to spillover dynamics of shared viruses. We found evidence for ecological drivers of prevalence in our samples, with relatively weak evidence for a positive effect of precipitation on the prevalence of River Luinaeg virus. Coinfection is potentially important in shaping prevalence: we found a strong positive association between River Liunaeg virus and Loch Morlich virus presence after controlling for host species, location and other relevant ecological variables. This study represents a first step in the description of predictors of bumblebee infection in the wild not driven by spillover from honeybees.

microbiology↗

bioRxiv2018

Host evolutionary history predicts virus prevalence across bumblebee species

Why a pathogen associates with one host but not another is one of the most important questions in disease ecology. Here we use transcriptome sequencing of wild-caught bumblebees from 13 species to describe their natural viruses, and to quantify the impact of evolutionary history on the realised associations between viruses and their pollinator hosts. We present 37 novel virus sequences representing at least 30 different viruses associated with bumblebees. We verified 17 of them by PCR and estimate their prevalence across species in the wild. Through small RNA sequencing, we demonstrate that at least 10 of these viruses form active infections in wild individuals. Using a phylogenetic mixed model approach, we show that the evolutionary history of the host shapes the current distribution of virus/bumblebee associations. Specifically, we find that related hosts share viral assemblages, viruses differ in their prevalence averaged across hosts and the prevalence of infection in individual virus-host pairings depends on precise characteristics of that pairing. Authors SummaryDespite the importance of disease in the regulation of animal populations, our understanding of the distribution of pathogen burden across wild communities remains in its infancy. In this study, we investigated the distribution of viruses across natural populations of 13 different bumblebee species in Scotland. We first searched for viruses using a metatranscriptomic approach, finding at least 30 new viruses of bumblebees, and assayed a subset of them for their presence and absence in different host species. Then, in the first application of these methods to an animal-virus system, we used co-phylogenetic mixed models to investigate the factors that lead to species being having different prevalences for a subset of these viruses. While much of the variation in the prevalence of the viruses can be explained by the idiosyncrasies of individual bumblebee-virus pairings, there is a phylogenetic signal with related bumblebee species being infected at similar frequencies by the same sets of viruses. Consistent with previous work, our study indicates that, while in general the interaction between a host and a virus may be unpredictable, closely related species are more likely to exhibit similar patterns of infection.

evolutionary biology↗