Back

Ongoing coevolution between reintroduced Phengaris teleius butterflies and their Myrmica host ants

Sanchez-Garcia, D.; Wynhoff, I.; d'Ettorre, P.; Leroy, C.; Kajzer-Bonk, J.; Maak, I. E.; Barbero, F.; Casacci, L. P.; Witek, M.

2025-12-04 ecology
10.64898/2025.12.02.691951 bioRxiv
Show abstract

Coevolutionary interactions between parasites and hosts are key drivers of biological adaptation. In this study, we explore the evolutionary response of the social parasitic butterfly Phengaris teleius to its host ant, Myrmica scabrinodis, taking advantage of a unique opportunity: the reintroduction of the butterfly in the Netherlands thirty years ago. We compared the degree of host mimicry and behavioural performance of caterpillars between the reintroduced and the Polish source population. After about thirty generations, chemical and vibroacoustical signal profiles have diverged. Chemical mimicry remained limited during the pre-adoption phase for both groups; however, in the post-adoption phase, the source population showed significantly higher chemical similarity to their local hosts. In contrast, reintroduced pre-adoption caterpillars evolved vibroacoustic signals closely resembling local hosts, also resulting in a stronger response from their local host ants. This suggests that adoption is driven by acoustics and subsequently serves as a selective filter promoting post-entry chemical refinement. Behavioural data evince that despite the differences between the different communication channels, the combination of signals remains sufficient to ensure recognition and integration in both host-parasite systems. These results illustrate how social parasites involved in multisensory mimicry can rapidly recalibrate strategies to remain functional in new ecological contexts.

Matching journals

The top 4 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.