The dynamic of the thermohaline structure of the upper ocean, which depends on ocean-atmosphere interactions, drives most near surface oceanic processes, including the control of gases and heat fluxes, and nutrient availability in the photic layer. The thermohaline structure of the southwestern tropical Atlantic (SWTA), a key region for diagnosing variation of the Atlantic Meridional Overturning Circulation, has prime impact on global climate. Characterising the thermohaline structure is typically based on the application of classical statistical methods on vertical profiles. Such approach has important limitations since classical methods do not explicitly contemplate the vertical nature of the profiles. Functional Data Analysis (FDA) is a new alternative to solve such drawbacks. Here, we apply an FDA approach to characterise the 3D canonical thermohaline structure of the SWTA in austral spring and fall. Our results reveal a clear spatial pattern with the presence of three areas with significantly different thermohaline structure. Area 1, mostly located along the continental slope, reflects the western boundary current system, with low static stability and high frequency of occurrence of barrier layer (BL). Conversely, Area 2, located along the Fernando de Noronha chain, presents strong static stability with a well-marked thermocline. This area, under the influence of the eastern Atlantic, is characterised by a low BL frequency, which is seasonally modulated by the latitudinal oscillation of the Intertropical Convergence Zone, controlling the regime of precipitation. In turn, Area 3 behaves as a transition zone between A1 and A2 with the presence of the water core of maximum salinity in subsurface, and therefore presence of strong-moderate BL. Beyond this study, FDA approach emerges as a powerful way to describe, characterise, classify and compare ocean patterns and Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site.processes. It can be applied to in situ data but could also be used to deeply and comprehensively explore ocean model output. Highlights► The thermohaline structure drives most near surface oceanic processes. ► A functional data analysis approach is used to characterise in 3D the thermohaline structure of the southwestern tropical Atlantic in spring and fall. ► We reveal a clear spatial pattern with the presence of three areas with significantly different thermohaline structure. ► The picture provided can serve as a reference for diagnosing future variation in the Atlantic Meridional Overturning Circulation.

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.

At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate their generalization properties. From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as ‘fine-tuning’. Starting from a convolutional network pre-trained on Guanay cormorant data reduced by two the size of the dataset needed to accurately predict dives in a tropical booby from Brazil. We believe that the networks trained in this study will provide relevant starting point for future fine-tuning works for seabird trajectory segmentation.

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Climate change is expected to affect marine mercury (Hg) biogeochemistry and biomagnification. Recent modeling work suggested that ocean warming increases methylmercury (MeHg) levels in fish. Here, we studied the influence of El Ninõ Southern Oscillations (ENSO) on Hg concentrations and stable isotopes in time series of seabird blood from the Peruvian upwelling and oxygen minimum zone. Between 2009, La Ninã (2011 and El Ninõ conditions (2015−2016) were accompanied by sea surface temperature anomalies up to 3 °C, oxycline depth change (20−100 m), and strong primary production gradients. Seabird Hg levels were stable and did not co-vary significantly with oceanographic parameters, nor with anchovy biomass, the primary dietary source to seabirds (90%). In contrast, seabird Δ 199 Hg, proxy for marine photochemical MeHg breakdown, and δ 15 N showed strong interannual variability (up to 0.8 and 3‰, respectively) and sharply decreased during El Ninõ. We suggest that lower Δ 199 Hg during El Ninõ represents reduced MeHg photodegradation due to the deepening of the oxycline. This process was balanced by equally reduced Hg methylation due to reduced productivity, carbon export, and remineralization. The non-dependence of seabird MeHg levels on strong ENSO variability suggests that marine predator MeHg levels may not be as sensitive to climate change as is currently thought.

Miniature electronic devices have recently enabled ecologists to document relatively large amounts of animal trajectories. Modelling such trajectories may contribute to explaining the mechanisms underlying observed behaviours and to clarifying ecological processes at the scale of the population by simulating multiple trajectories. Existing approaches to animal movement modelling have mainly addressed the first objective, and are often limited when used for simulation purposes. Individual‐based models generally rely on ad hoc formulation and their empirical parametrization lacks generability, while random walks based on mathematically sound statistical inference typically consist of first‐order Markovian models calibrated at the local scale which may lead to overly simplistic description and simulation of animal trajectories. We investigate a recent deep learning tool—generative adversarial networks (GAN)—to simulate animal trajectories. GANs consist of a pair of deep neural networks that aim to capture the data distribution of some experimental dataset. They enable the generation of new instances of data that share statistical properties. This study aims at identifying relevant deep network architectures to simulate central‐place foraging trajectories, as well as at evaluating GANs drawbacks and benefits over classical methods, such as state‐switching hidden Markov models (HMM). We demonstrate the outstanding ability of deep convolutional GANs to simulate and to capture medium‐ to large‐scale properties of seabird foraging trajectories. GAN‐derived synthetic trajectories reproduced the Fourier spectral density of observed trajectories better than those simulated using HMMs. However, unlike HMMs, GANs do not adequately capture local‐scale descriptive statistics, such as step speed distributions. GANs provide a new likelihood‐free approach to calibrate complex stochastic processes and thus open new research avenues for animal movement modelling. We discuss the potential uses of GANs in movement ecology and future developments to better capture local‐scale features. In this context, embedding HMM‐based priors in GAN schemes appears as a promising research direction.

Seabirds play important roles as marine ecosystem sentinels. Studying their at-sea ecology is essential for understanding how environmental variability affects their populations. However, the at-sea ecology of small-sized temperate seabirds remains poorly studied. We explored the at-sea ecology of the Critically Endangered MacGillivray’s prion Pachyptila macgillivrayi breeding on the subtropical Saint Paul Island. Using global location sensor loggers and stable isotope analysis, we investigated movements, migratory strategies, at-sea activity and moulting period, and characterized the isotopic niche of tracked individuals. During incubation, MacGillivray’s prions remained in temperate waters north of the Subtropical Front, possibly feeding on prey caught in cold eddies. During the inter-breeding period, individuals wintered almost equally to the north and south of the Subtropical Front in 2 distinct sectors (Tasman Sea and Southwest Indian Ridge). Daily activity varied seasonally, and individuals overwintering in the Tasman Sea spent more time flying at night when moonlight intensity was high. Moulting occurred after the breeding period and lasted longer compared to other prion species. Isotopic data suggest a higher dietary proportion of low trophic-level prey for MacGillivray’s prions than for Antarctic and slender-billed prions, highlighting trophic segregation in relation to bill width. Our study provides new evidence to understand the suite of adaptations allowing the abundant prion species to coexist by feeding on prey of different sizes. Contrary to the majority of seabird species, MacGillivray’s prions from Saint Paul Island exhibited 2 migratory tactics with associated differences in at-sea activity, leading to questions about the origin of these differences.

1. Miniature electronic device such as GPS have enabled ecologists to document relatively large amount of animal trajectories. Modeling such trajectories may attempt (1) to explain mechanisms underlying observed behaviors and (2) to elucidate ecological processes at the population scale by simulating multiple trajectories. Existing approaches to animal movement modeling mainly addressed the first objective and they are yet soon limited when used for simulation. Individual-based models based on ad-hoc formulation and empirical parametrization lack of generability, while state-space models and stochastic differential equations models, based on rigorous statistical inference, consist in 1st order Markovian models calibrated at the local scale which can lead to overly simplistic description of trajectories. 2. We introduce a 'state-of-the-art' tool from artificial intelligence - Generative Adversarial Networks (GAN) - for the simulation of animal trajectories. GAN consist in a pair of deep neural networks that aim at capturing the data distribution of some experimental dataset, and that enable the generation of new instances of data that share statistical similarity. In this study, we aim on one hand to identify relevant deep networks architecture for simulating central-place foraging trajectories and on the second hand to evaluate GAN benefits over classical methods such as state-switching Hidden Markov Models (HMM). 3. We demonstrate the outstanding ability of GAN to simulate 'realistic' seabirds foraging trajectories. In particular, we show that deep convolutional networks are more efficient than LSTM networks and that GAN-derived synthetic trajectories reproduce better the Fourier spectral density of observed trajectories than those simulated using HMM. Therefore, unlike HMM, GAN capture the variability of large-scale descriptive statistics such as foraging trips distance, duration and tortuosity. 4. GAN offer a relevant alternative to existing approaches to modeling animal movement since it is calibrated to reproduce multiple scales at the same time, thus freeing ecologists from the assumption of first-order markovianity. GAN also provide an ultra-flexible and robust framework that could further take environmental conditions, social interactions or even bio-energetics model into account and tackle a wide range of key challenges in movement ecology.