Biology subjects

Silva, L. R.

Publications and source records attributed to Silva, L. R..

8 recordsLinked to original sources

bioRxiv2026

Sex-specific Flexibility in Breeding and Helping Strategies in the Sociable Weaver (Philetairus socius)

Helping-at-the-nest is often viewed as a precursor to reproduction, but switching between breeder and helper roles has been documented in some species. Such flexibility should depend on the opportunities and benefits of helping, which could differ between sexes due to dispersal strategies and social structure. However, whether breeding-helping flexibility is widespread and sex-specific remains unknown. Here, we investigated sex-specific strategies in breeding-helping flexibility using a 10-year dataset (1955 individuals) on sociable weavers (Philetairus socius), a colonial cooperative breeder in which males are typically philopatric whereas females disperse. Both sexes helped for several years, with males helping more frequently than females and for twice as long (0-13 vs 0-10 years). Around 40% of non-dispersing birds never became breeders and 50% of individuals bred without helping first. Both sexes switched roles between- and within-seasons but males were more likely to alternate (respectively four and six times more than females and up 15 switches across seven years). Our study reveals important flexibility and sex differences in breeder-helper roles, consistent with sex-biased dispersal, spatial breeding proximity and possible indirect fitness benefits. These factors could play a role promoting the evolution of helping across life-stages, not only pre-reproduction, but also reproductive and post-reproductive individuals.

evolutionary biology↗

bioRxiv2025

Infanticide in a colonial cooperative bird is not associated with direct reproductive benefits

Infanticide, the killing of dependent young by adult conspecifics, is an extreme form of conflict that remains poorly understood. Previous studies suggest that it is associated with reproductive benefits, either by making breeders newly available for reproduction or by increasing offspring care and reducing competition for resources. In addition, infanticide may result from a reproductive trade-off, where parents kill their offspring if environmental conditions compromise their reproductive value. Here, we combine video, genetic and individual-level reproductive data collected over 12 years to describe infanticide in a highly social, cooperative bird, the sociable weaver Philetairus socius and to establish whether direct reproductive benefits or life-history trade-offs are possible motivators for this behaviour. We documented 50 cases of infanticide and identified 27 aggressors with known histories. These were mostly born outside the colonies where they committed infanticide (63%) and there was a bias towards younger individuals. Aggressors were usually not part of the victims breeding group (74%), but in two cases infanticide was committed by the breeding female, and in five by helpers. In most cases, aggressors were not reproducing at the time of the infanticide, were not recorded mating with any of the breeders or helpers from the attacked nest, nor occupied the nest from where the chicks were evicted. Only 13 of the known aggressors were confirmed as breeders after the attacks, and these mostly reproduced at the colony where the attacks were committed. Overall, we found no evidence for direct reproductive benefits of infanticide in sociable weavers. However, the younger age of aggressors, lack of genetic relatedness to the victims and future breeding at the colony suggest that competition between breeding groups, and hence indirect reproductive benefits, could provide motivation for infanticide in this population. Additional work over longer periods is needed to identify these possible indirect fitness benefits of infanticide.

animal behavior and cognition↗

bioRxiv2025

Long-term evaluation of the association between dominance, bib size, sex and age

The association between dominance and phenotypic traits ( badges of status) has been recently questioned, since it has been found to show more variation across populations and time than expected. Using an eleven-year dataset, encompassing more than 1,800 individuals, we studied the association between the size of a melanin-based plumage trait (a black patch under the beak or bib), individual attributes (age and sex) and dominance. Our study model, the sociable weaver (Philetairus socius), is a small passerine, which lives year-round in colonies of varying size. Bib sizes were obtained from pictures and automatically pre-processed with an object segmentation deep learning network. We recorded 36,346 dominance-related interactions to estimate dominance hierarchies. We then investigated the effect of sex and age on bib size, and the overall and between-year association of bib size and age with dominance for each sex. We found that bib size increased with age within individuals of both sexes. Age was a strong and consistent predictor of dominance in males, but not in females. Nevertheless, dominance was not correlated with bib size in males and in females. Finally, these relationships did not strongly vary between years. Our results suggest that males and females may use different factors to regulate dominance interactions. Furthermore, our longitudinal dataset allowed us to show that bib size is not a badge of status in our population even though its growth encodes information on age. These results highlight the importance of considering multiple traits over time when studying morphological signalling of dominance hierarchy.

animal behavior and cognition↗

bioRxiv2024

Foraging associations are related with helping interactions in a cooperatively breeding bird

Kin selection has been the main hypothesis explaining helping behaviour in cooperative breeders, with evidence being largely based on the observation that helpers tend to provide to related offspring. However, kin-biased help could conceal additional, mechanisms contributing to the maintenance of cooperation. Under pay-to-stay, group augmentation and partner choice hypotheses, a range of direct benefits can arise through helping. Here, we explored this potential mechanism by testing whether the social associations of breeding individuals were related with the help that they received from non-breeding individuals. We collected social associations from PIT-tagged sociable weavers, Philetairus socius, at RFID feeding stations, which allowed us to compare associations between breeders and either their helpers (mostly kin) or their other kin that did not help--before, during and after reproduction. Using correlative tests and data-driven simulations, we show that helpers have stronger foraging bonds with breeders than non-helping kin, and that these stronger bonds are present both prior and post breeding. Furthermore, helper-breeding female social affiliations were positively correlated with the amount of help provided. Our results suggest that direct benefits of social associations complement kin selection to determine helping decision, and that these in turn influence future social associations.

animal behavior and cognition↗

bioRxiv2024

From raw footage to behaviour: an automated pipeline for activity monitoring and behaviour identification in the wild

O_LIStudies of animal behaviour usually rely on direct observations or manual annotations of video recordings. However, such methods can be very time-consuming and error-prone, leading to sub-optimal sample sizes. Recent advances in deep-learning show great potential to overcome such limitations, nevertheless, most currently available behavioural recognition solutions remain focused on captivity settings. C_LIO_LIHere, we present a deployment-focused framework to guide researchers in building behavioural recognition systems from video data, using Long Short-Term Memory (LSTM) networks to classify behavioural sequences across consecutive frames. C_LIO_LILSTMs allowed to: 1) monitor nest activity by detecting the birds presence and simultaneously classifying the type of trajectory: i.e., nest-chamber entrance or exit; and 2) identify the behaviour performed: building, aggression or sanitation. Using our framework, we outperformed human annotators when jointly considering error and speed. Model performance improved with challenging training instances, and remained robust even with modest sample sizes. LSTM also outperformed YOLO ("You Only Look Once"), highlighting the critical role of temporal sequence information in behavioural analysis. C_LIO_LIWe demonstrate that our approach is replicable across three bird species and applicable to deployment videos, highlighting its value as a generalizable and transferable tool for long-term studies in the wild. C_LI DATA AVAILABILITYScripts, models, and data required to reproduce this work are available on Zenodo (DOIs: 10.5281/zenodo.18681623 and 10.5281/zenodo.18695178).

animal behavior and cognition↗

bioRxiv2024

A deep learning approach to detect and visualise sexual dimorphism in monomorphic species

Sex recognition is facilitated by dimorphism in some traits. However, humans often fail to find the traits that allow to distinguish between sexes in other species. Deep learning has the potential to surpass humans in identifying cryptic differences between sexes, but, so far, has rarely been used to assess sexual dimorphism. In this study, we evaluated (i) the ability of a fine-tuned classification neural network, EfficientNet, to find differences between sexes in a species that appears monomorphic to humans, the sociable weaver (Philetairus socius). We then assessed (ii) the benefits of Grad-CAM visualisation techniques to understand which parts of the individuals are used by the network to differentiate the sexes. We trained 10-folds cross-validation models on more than 4,500 pictures of the head from more than 1,300 individuals. Our results show that the network can predict sex of sociable weavers with an accuracy of 76%, which is considerably higher than humans performance, and that the model was similarly good at predicting females and males. When interpreting the probability of being classified to one sex, our results further reveal an effect of the interaction of sex with age on the confidence score of the models which shows that younger males are less masculine than older ones, and older females more masculine than younger ones. Finally, using Grad-CAM we found that the model mostly uses the beak region to predict the sex of individuals. Overall, this work shows that artificial intelligence has the potential to be a non-invasive sexing tool, surpassing human capabilities and aiding in pinpointing potential cryptic dimorphic body parts that have yet to be identified. In birds, half of the worlds species appear sexually monomorphic to humans, and re-evaluation of species dimorphism with this type of methods could deepen our understanding of the effect of selection on animal traits.

zoology↗

bioRxiv2019

Deep learning-based methods for individual recognition in small birds

O_LIIndividual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well established but often make data collection and analyses time consuming and consequently are not suited for collecting very large datasets. C_LIO_LIRecent technological and analytical advances, such as deep learning, can help overcome these limitations by automatizing data collection and analysis. Currently one of the bottlenecks preventing the application of deep learning for individual identification is the need of hundreds to thousands of labelled pictures required for training convolutional neural networks (CNNs). C_LIO_LIHere, we describe procedures that improve data collection and allow individual identification in captive and wild birds and we apply it to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata. C_LIO_LIFirst, we present an automated method that allows the collection of large samples of individually labelled images. Second, we describe how to train a CNN to identify individuals. Third, we illustrate the general applicability of CNN for individual identification in animal studies by showing that the trained CNN can predict the identity of birds from images collected in contexts that differ from the ones originally used to train the CNNs. Fourth, we present a potential solution to solve the issues of new incoming individuals. C_LIO_LIOverall our work demonstrates the feasibility of applying state-of-the-art deep learning tools for individual identification of birds, both in the lab and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual identification of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods. C_LI

ecology↗

bioRxiv2019

How to make methodological decisions when inferring social networks

Constructing and analysing social networks data can be challenging. When designing new studies, researchers are confronted with having to make decisions about how data are collected and networks are constructed, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. We suggest an approach for making decisions when developing a network without jeopardising the validity of future hypothesis tests. We argue that choosing the best edge definition for a network can be made using a priori knowledge of the species, and testing hypotheses that are known and independent from those that the network will ultimately be used to evaluate. We illustrate this approach by conducting a pilot study with the aim of identifying how to construct a social network for colonies of cooperatively breeding sociable weavers. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then identified which combination of data collection and association definition maximised (i) the assortment of individuals into breeding groups (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). Our approach highlights how existing knowledge about a system can be used to help navigate the myriad of methodological decisions about data collection and network inference.\n\nSIGNIFICANCE STATEMENTGeneral guidance on how to analyse social networks has been provided in recent papers. However less attention has been given to system-specific methodological decisions when designing new studies, specifically on how data are collected, and how edge weights are defined from the collected data. This lack of guidance can lead researchers into being less critical about their study design and making arbitrary decisions or trying several different methods driven by a given preferred hypothesis of interest without realising the consequences of such approaches. Here we show that pilot studies combined with a priori knowledge of the study species social behaviour can greatly facilitate making methodological decisions. Furthermore, we empirically show that different decisions, even if data are collected under the same context (e.g. foraging), can affect the quality of a network.

animal behavior and cognition↗