Brandl, H. B. | bioRxiv Science

bioRxiv2023

The physiological costs of spatial positioning and leadership in collective movements

Individuals can gain substantial benefits from collective actions1-7. However, collective behaviours introduce new challenges, like coordinating actions, maintaining cohesion, and meeting the needs of different individuals. When making collective movements, leaders are typically thought to gain disproportionate benefits through the choice of more beneficial resources3 and/or earlier access to resources8. However, reaping these benefits can also have costs. Being at the front of a group can increase physical exertion4,9,10 and predation risk11,12. Moreover, ending up in a leadership position (i.e., at the front), is a process of negotiation in many animal groups. Within-group differences in directional preferences are typically resolved by some individuals initiating directional movements, after which they are either followed (if they are successful in leading) or return to the group (if they fail)13-30. By combining data on movement initiations (using whole-group GPS tracking31 and individual heart rate from implanted ECG loggers) in wild vulturine guineafowl, we found significant increases in heart rate (and decreases in heart rate variability) during collective movements. We found that attempting--and failing--to initiate directional movement was particularly costly, with the highest costs when consensus among group members was low and when individuals acted against the majority. Increases in heart rate and decreases in its variability can indicate physiological stress, entailing increased energy expenditure and long-term physiological damage. These results suggest that behaviours often thought beneficial to individuals (by influencing group behaviours) are also physiologically costly, representing a constraint on group-living and explaining why sometimes individuals opt out of contributing to leadership.

ecology↗

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

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