Back

Modeling control of invasive fire ants by gene drive

Liu, Y.; Champer, S. E.; Champer, J.

2025-03-10 ecology
10.1101/2025.03.04.641467 bioRxiv
Show abstract

Solenopsis invicta, commonly known as the fire ant, is characterized by aggressive behavior and exceptional invasive capabilities, rendering conventional control methods largely ineffective. Here, we explore the implementation of homing suppression gene drive in fire ants by developing a spatially explicit model that incorporates both monogyne and polygyne colony structures, enabling comprehensive evaluation of genetic control strategies. Ants may present unique challenges for gene drive due to their colony structure and haplodiploidy. Our results reveal that after an extended period of time, gene drive effectively eliminates polygyne colonies, but monogyne populations often persist at low level. Though standard suppression drives in haplodiploids have reduced power, new dominant-sterile resistance or two-target strategies, as well as drives that affect the colony structure, can restore high suppressive capability. Interspecific competition can also exert a positive effect on gene drive-mediated population suppression dynamics. In particular, a gene drive release during the invasion phase significantly enhances population suppression, enabling native ants to successfully recolonize their original habitats. We also identify several conserved female fertility genes in fire ants, together with gRNA targets that may support efficient, low-resistance suppression drive designs. Overall, we conclude that while gene drive in fire ants may take place over extended time scales, its long-term results, even with imperfect efficiency, are quite promising.

Published in Advanced Science (predicted rank #22) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.