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The way animals select their breeding habitat may have great impacts on individual fitness. This complex process depends on the integration of information on various environmental factors, over a wide range of spatiotemporal scales. For seabirds, breeding habitat selection integrates both land and sea features over several spatial scales. Seabirds explore these features prior to breeding, assessing habitats' quality. However, the information-gathering and decision-making process by seabirds when choosing a breeding habitat remains poorly understood. We compiled 49 historical records of larids colonies in Cuba from 1980 to 2020. Then, we predicted potentially suitable breeding sites for larids and assessed their breeding macrohabitat selection, using deep and machine learning algorithms respectively. Using a convolutional neural network and Landsat satellite images we predicted the suitability for nesting of non-monitored sites of this archipelago. Furthermore, we assessed the relative contribution of 18 land- and marine-based environmental covariates describing macrohabitats at three spatial scales (i.e. 10, 50 and 100 km) using random forests. Convolutional neural network exhibited good performance at training, validation and test (F1-scores >85%). Sites with higher habitat suitability (p > .75) covered 20.3% of the predicting area. Larids breeding macrohabitats were sites relatively close to main islands, featuring sparse vegetation cover and high chlorophyll-a concentration at sea in 50 and 100 km around colonies. Lower sea surface temperature at larger spatial scales was determinant to distinguish the breeding from non-breeding sites. A more comprehensive understanding of the seabird breeding macrohabitats selection can be reached from the complementary use of convolutional neural networks and random forest models. Our analysis provides crucial knowledge in tropical regions that lack complete and regular monitoring of seabirds' breeding sites.

1. 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 1st order Markovian models calibrated at the local scale which may lead to overly simplistic description and simulation of animal trajectories. 2. We investigate a recent deep learning tool - Generative Adversarial Networks (GAN) - to simulate animal trajectories. GAN 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 GAN drawbacks and benefits over classical methods, such as state-switching Hidden Markov Models (HMM). 3. We demonstrate the outstanding ability of deep convolutional GAN to simulate and to capture medium to large-scale properties of seabirds foraging trajectories. GAN-derived synthetic trajectories reproduced the Fourier spectral density of observed trajectories better than those simulated using HMM. However, unlike HMM, GAN do not adequately capture local-scale descriptive statistics, such as step speed distributions. 4. GAN provide a new likelihood-free approach to calibrate complex stochastic processes and thus open new research avenues for animal movement modelling. We discuss the

Human-induced rapid environmental changes can disrupt habitat quality in the short term. A decrease in quality of habitats associated with preference for these over other available higher quality is referred as ecological trap. In 2015, the Fundão dam containing iron mining tailings, eastern Brazil, collapsed and released about 50 million cubic meters of metal-rich mud composed by Fe, As, Cd, Hg, Pb in three rivers and the adjacent continental shelf. The area is a foraging site for dozens of seabird and shorebird species. In this study, we used a dataset from before and after Fundão dam collapse containing information on at-sea distribution during foraging activities (biologging), dietary aspects (stable isotopes), and trace elements concentration in feathers and blood from three seabird species known to use the area as foraging site: Phaethon aethereus, Sula leucogaster, and Pterodroma arminjoniana. In general, a substantial change in foraging strategies was not detected, as seabirds remain using areas and food resources similar to those used before the dam collapse. However, concentration of non-essential elements increased (e.g., Cd and As) while essential elements decreased (e.g., Mn and Zn), suggesting that the prey are contaminated by trace elements from tailings. This scenario represents evidence of an ecological trap as seabirds did not change habitat use, even though it had its quality reduced by contamination. The sinking-resuspension dynamics of tailings deposited on the continental shelf can temporally increase seabird exposure to contaminants, which can promote deleterious effects on populations using the region as foraging sites in medium and long terms.

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 fo

Marine spatial planning (MSP) is positioning itself as a rational decision-making process regulating uses of marine spaces and resources in order to reduce tensions between exploitation and conservation as well as between ocean stakeholders. As global political agendas identified marine protected areas as a key answer to biodiversity erosion, systematic reserve site selection became a critical component of MSP. Establishing an ocean zoning involves the analysis of large quantities of heterogeneous, multi-sources and spatially explicit data. This often leads to problems too complex to be solved by human intuition only, thus calling for optimisation tools to support the decisions. In that context, our work aims at informing practitioners about stakes, possibilities and limitations of MSP approach through reserve site selection tools. We first clarify the reserve site selection framework, especially the underlying mathematics - the problem formulation and the solving method. Then, we highlight potential pitfalls due to input data feeding the reserve-based planning approach. Finally, and more practically, we show to what extent parameters used in reserve selection tools shape the reserve outcome. These elements are explored and illustrated on a real case study, namely the Fernando de Noronha archipelago in the Brazilian tropical Atlantic. This work provides a brief overview of informational challenges brought by decision support tools in marine spatial planning negotiations.