Biology subjects

Madgwick, P.

Publications and source records attributed to Madgwick, P..

3 recordsLinked to original sources

bioRxiv2026

Evaluating pesticide mixtures for resistance management in asexual insect pests

Many economically important insect pests reproduce through asexual or partially asexual life cycles, yet how reproductive mode influences insecticide resistance management remains unclear. The choice of resistance management strategy has been suggested to differ for sexual and asexual pests. For instance, current IRAC guidance suggests that pesticide mixtures are less effective in non-mating pests than in sexually reproducing populations. Here, stochastic evolutionary simulations are used to compare resistance evolution under sequences and mixtures across four reproductive modes observed in pests of economic importance: sexual reproduction, obligate parthenogenesis, cyclical parthenogenesis and haplodiploidy. Contrary to current expectations, mixtures are not disadvantaged in asexual populations and, in some cases, lead to delayed resistance evolution compared to sexually reproducing populations. These differences arise as the result of reduced genetic recombination which constrained the assembly and spread of multi-resistant genotypes. Overall, these findings suggest that mixtures remain a viable resistance management strategy for pests with asexual reproduction.

evolutionary biology↗

bioRxiv2026

Constraining pesticide resistance using evolution-informed selection regimes

The rapid evolution of pesticide resistance in sexually reproducing pests threatens global food security, yet the evolutionary principles needed to design durable resistance management strategies remain poorly tested experimentally. Theory predicts that deploying multiple pesticide compounds simultaneously should suppress resistance more effectively than sequential rotations, but empirical support in sexual pest populations has remained inconclusive. Here, we directly test how selection regime shapes resistance evolution using a genetically defined, obligately mating Caenorhabditis elegans system. We evolved large dioecious populations from a near-isogenic ancestor carrying two major-effect resistance alleles under contrasting pesticide deployment regimes. We show that compound mixtures combined with a substantial refuge consistently produced the strongest constraint on resistance evolution, markedly slowing the spread of resistance even when resistance alleles were neither recessive nor rare. Species-agnostic computational simulations reproduced the overall evolutionary dynamics, suggesting broad applicability. Overall, our results provide direct experimental evidence that pesticide resistance evolution can be predictably constrained by manipulating selection regimes.

evolutionary biology↗

bioRxiv2025

The evolution of public goods altruism

Organisms often behave altruistically by investing resources into public goods production. Because the rewards of public goods are freely available to everyone in a group, they are potentially vulnerable to exploitation by cheaters. Classic kin selection models offer solutions to this public goods dilemma in terms of why public goods investment can be favoured, while game theoretical models offer insights into the conditions where exploitative cheaters can persist. However, existing theory does not provide a unified framework for understanding how much individuals should actually invest in altruism or how this investment strategy relates to why cheaters can coexist with altruists. Here we fill this gap by considering how resource allocation trade-offs shape the generosity of altruists, how this investment decision, in turn, sets the conditions for cheater coexistence, and how local competition impacts these outcomes. We find that, whenever altruists can evolve the optimal level of public goods investment, they exclude cheaters, revealing that stable altruists/cheater coexistence requires overly generous altruists--an outcome frequently driven by mechanistic constraints that limit the range of behaviours in biological systems. Importantly, we show that local competition tends to prevent such coexistence. By capturing key biologically relevant factors, our model provides a compelling framework for interpreting patterns of variation in altruistic behaviour in natural populations, and a means of generating testable predictions.

evolutionary biology↗