Biology subjects

Allaine, D.

Publications and source records attributed to Allaine, D..

2 recordsLinked to original sources

bioRxiv2025

Same-sex sexual behaviours in a wasp despite sex recognition cues: the broad mating filter hypothesis

Same-sex sexual behaviour (SSB) in animals, once considered an evolutionary dead end, is surprisingly widespread, particularly among male insects. These behaviours may arise from either adaptive or non-adaptive processes. Over shorter timescales, SSB can occur when courtship costs are low or when missing a mating opportunity is costly, leading to a reduced behavioural threshold for courtship--a "broad mating filter," as predicted by threshold acceptance theory. We investigated SSB in males of the parasitoid wasp Venturia canescens (Hymenoptera: Ichneumonidae), a species where encounters are infrequent and male courtship is energetically not costly. We hypothesised that males exhibit a broad mating filter, accepting conspecifics for courtship regardless of sex. First, behavioural assays assessed whether SSB correlated with higher reproductive success. Second, we compared cuticular hydrocarbon (CHC) profiles between sexes, as these compounds may serve as cues for sex recognition. Our chemical analyses revealed clear quantitative CHC differences between males and females, indicating potential for sex recognition. Nonetheless, 18% of males displayed SSB, and these behaviours had no detectable effect on either courting or mating success. Moreover, males receiving SSB did not experience any reproductive advantage. Our findings support the hypothesis of a broad mating filter in V. canescens males. In this species, where conspecific encounters are rare, SSB appears to arise from reduced discrimination rather than adaptive benefits, consistent with a low-cost courtship strategy and high opportunity costs of missed mating events.

animal behavior and cognition↗

bioRxiv2020

Revisiting giraffe photo-identification using deep learning and network analysis

An increasing number of ecological monitoring programs rely on photographic capture-recapture of individuals to study distribution, demography and abundance of species. Photo-identification of individuals can sometimes be done using idiosyncratic coat or skin patterns, instead of using tags or loggers. However, when performed manually, the task of going through photographs is tedious and rapidly becomes too time consuming as the number of pictures grows. Computer vision techniques are an appealing and unavoidable help to tackle this apparently simple task in the big-data era. In this context, we propose to revisit animal re-identification using image similarity networks and metric learning with convolutional neural networks (CNNs), taking the giraffe as a working example. We first developed an end-to-end pipeline to retrieve a comprehensive set of re-identified giraffes from about 4, 000 raw photographs. To do so, we combined CNN-based object detection, SIFT pattern matching, and image similarity networks. We then quantified the performance of deep metric learning to retrieve the identity of known individuals and detect unknown individuals never seen in the previous years of monitoring. After a data augmentation procedure, the re-identification performance of the CNN reached a Top-1 accuracy of about 90%, despite the very small number of images per individual in the training data set. While the complete pipeline succeeded in re-identifying known individuals, it slightly under-performed with unknown individuals. Fully based on open-source software packages, our work paves the way for further attempts to build automatic pipelines for re-identification of individual animals, not only in giraffes but also in other species.

ecology↗