Background: Considerable progress in our understanding of long-distance migration has been achieved thanks to the use of small lightweight geolocator devices. Such global location sensors (GLS) are particularly suitable for studying non-breeding movement and behaviour due to their small size and low energy consumption allowing multiyear deployment. Errors of geolocation are however important, dicult to estimate, have a complex structure leading to poor precision and accuracy. Therefore, understanding movement ecology of short-distance migrants or resident birds during extensive time periods remains challenging. We aimed at elucidating the sex-specic marine space uses of a resident tropical seabird, the masked booby (Sula dactylatra) over the full annual life cycle, including the breeding and non-breeding periods.Methods: A total of 34 GLS were deployed on male and female masked boobies at the Fernando de Noronha archipelago (Brazil), and 31 of them were recovered and provided year-round data. Error range of geographical positions and habitat use of masked boobies were estimated from light-derived positions and temperature data. Synchronicity between movement and saltwater immersion data was investigated through a wavelet analysis.Results: Masked boobies showed a resident behaviour over their entire annual cycle. We inferred from the wavelet analysis that birds traveled way and back from the colony on consecutive trips of short length (≈ 2-4 days) and short range (≈ 100-300 km) at the east of the colony. Trip duration and range depended on the sex of the individual and on the time of the year. Females had farther ranges than males during the pre-breeding period. Trip duration increased gradually from the end of the breeding period to the post-breeding period, probably due to the release of the central-place breeding constraints.Conclusions: Despite inherent limits of light-based geolocation, synchronicity analysis of geolocation data revealed year round whereabouts of a resident tropical seabird and sex-specic movement behaviour. Such an approach based on the estimation of synchronicity between light-based coordinates data and any other external data (behavioural or environmental) could be used more broadly to investigate resident or short-migrants animal movement based on GLS data.
Seabirds are considered as suitable indicators for the study of marine ecosystems, since their foraging strategies provide a real-time response to complex ecosystem dynamics. By deploying GPS sensors on seabirds it is possible to obtain their trajectories, and deep learning have recently shown promising results for the classification of animal behaviour from trajectory data. Yet there is still lot of investigation needed in terms of network architectures, data representation but also to demonstrate the generalization properties of these approaches. From a database of about 250 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has consisted in training deep networks in a supervised manner for the prediction of dives from trajectory data. In this study, we confirm that deep learning allows better dive prediction than usual methods such as Hidden Markov Models for two distinct seabirds species. We propose a novel deep learning model for trajectory data. It combines the computational efficiency of convolutional neural networks to distance-matrix-based representations of trajectory data. Our model considerably increases the ability of deep networks to infer behaviour, as well as their stability to different data inputs. The considered trajectory data representation might enable deep networks to better capture spatial information than from longitude and latitude time-series considered in previous works.
Considerable progress in our understanding of long-distance migration has been achieved thanks to the use of small lightweight geolocator devices (GLS). Errors of geolocation are however important, difficult to estimate, have a complex structure leading to poor precision and accuracy. Thus, the study of short-distance migrants or resident birds remains challenging. Here we aimed at elucidating the sex-specific marine space uses of a resident tropical seabird, the masked booby ( Sula dactylatra ) over the full annual life cycle at the Fernando de Noronha archipelago (Brazil), using GLS and synchronicity analyses between movement and saltwater immersion data. Masked boobies (n = 31) showed a resident behaviour over their entire annual cycle. We inferred from the wavelet analysis that birds traveled way and back from the colony on consecutive trips of short length (~ 2-4 days) and short range (~ 100-300 km) from the east of the colony. Duration and range of trips depended on the sex of the individual and on the time of the year. Trip duration increased gradually from the end of the breeding period to the post-breeding period, probably due to the release of the central-place breeding constraints. During the pre-breeding period, females had farther ranges eastward and spent more time in water than males. Despite inherent limits of light-based geolocation, synchronicity analysis of geolocation data revealed year round whereabouts of the resident tropical masked booby and sex-specific movement behaviour, which could be used more broadly to investigate resident or short-migrants animal movement based on GLS data.
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