Ecology of gene drives: Organism fitness under larval competition determines gene drive performance
- 1University of Oxford
Abstract
Gene drives are a promising technology to control disease vectors, such as mosquitoes. However, as these interventions aim to modify and/or control the population of disease vectors, a full understanding of how gene drives interact with the ecology of the target species is essential. In this study, we explore how larval competition determines gene drive performance. We first extend an agent-based model of the larval behaviour of Aedes aegypti mosquitoes to obtain detailed predictions for gene drive designs under realistic larval competition. Using Bayesian optimisation techniques, we explore the performance of different gene drive designs and find the Pareto frontier of optimal underdominant drives with different release thresholds. To understand what determines the structure of the Pareto frontier, we conduct both local and global sensitivity analyses to assess the role different parameters play in determining gene drive performance. We find that ecological parameters, which determine the behaviour of the organism, dominate over genetic parameters for much of the parameter space, although genetic parameters play an important role at the frontier. Finally, we develop stochastic and deterministic surrogate models to enable efficient comparison and simulation without imposing the computational costs of agent-based approaches.
Related
- Space versus Context: Competition for limited neural resources determines engram cell allocation in the hippocampus
- Genetic accessibility, not superior fitness, determines which anti-HBs antibody escape variants are selected in patients
- Fitness is asymptotically irrelevant under uniform competition in phenotype-structured populations
- Fire-Disturbed Soil and Intraspecific Competition Modulate Juvenile Performance of Invasive Hakea decurrens subsp. physocarpa
- Fish-derived Loigolactobacillus coryniformis LJR-37 enhances growth performance and disease resistance in tench in association with altered gut microbial ecology and predicted metabolic potential
BibTeX
@misc{hughes2026ecology,
title = {{Ecology of gene drives: Organism fitness under larval competition determines gene drive performance}},
author = {E. Hughes and M. B. Bonsall},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.03.763917},
url = {https://www.biorxiv.org/content/10.64898/2026.11.03.763917v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Ecology of gene drives: Organism fitness under larval competition determines gene drive performance
AU - E. Hughes
AU - M. B. Bonsall
PY - 2026
DA - 2026/11/03
PB - bioRxiv
DO - 10.64898/2026.11.03.763917
UR - https://www.biorxiv.org/content/10.64898/2026.11.03.763917v1
AB - Gene drives are a promising technology to control disease vectors, such as mosquitoes. However, as these interventions aim to modify and/or control the population of disease vectors, a full understanding of how gene drives interact with the ecology of the target species is essential. In this study, we explore how larval competition determines gene drive performance. We first extend an agent-based model of the larval behaviour of Aedes aegypti mosquitoes to obtain detailed predictions for gene drive designs under realistic larval competition. Using Bayesian optimisation techniques, we explore the performance of different gene drive designs and find the Pareto frontier of optimal underdominant drives with different release thresholds. To understand what determines the structure of the Pareto frontier, we conduct both local and global sensitivity analyses to assess the role different parameters play in determining gene drive performance. We find that ecological parameters, which determine the behaviour of the organism, dominate over genetic parameters for much of the parameter space, although genetic parameters play an important role at the frontier. Finally, we develop stochastic and deterministic surrogate models to enable efficient comparison and simulation without imposing the computational costs of agent-based approaches.
ER -