Deep inference of seabird dives from GPS-only records: Performance and generalization properties

Abstract

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.

Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets

“…Some tropical species often forage opportunistically, with prey-capture attempts occurring within directional transit [ 24 , 49 ], making it difficult for behavioural models to differentiate foraging from travelling locations. Although opportunistic foraging appears to cause a higher classification error for foraging compared to other behaviours in tropical sullids [ 16 , 26 , 37 ], the error rate in tropicbirds is particularly high, suggesting that this species may use opportunistic foraging more frequently than other tropical species.…”

“…While some differences in model performance among studies can be attributed to the type of model and/or validation method [ 16 – 20 ], performance is highly dependant on how distinct behaviour-specific movement patterns are [ 16 , 18 , 20 – 22 ]. For example, in heterogeneous systems, where resources are patchily distributed in space and time in a predictable manner, animals typically follow the concepts of ARS and OFT, using commuting trips to actively seek out rich foraging patches while quickly bypassing nutrient poor areas, resulting in a clear separation between the movement patterns of travelling and foraging [ 17 , 23 ].…”

“…Data acquired from these sensors can be incorporated into behavioural models, allowing for more accurate classification. Although several modelling techniques can be used to incorporate these data, HMMs have drawn particular attention due to their relatively high accuracy [ 22 , 34 ], their robustness at lower GPS resolution [ 16 , 20 , 22 ], and the development of the flexible user-friendly R packages that can incorporate information from additional data streams, even when collected at different time resolutions (e.g. ‘moveHMM’ and ‘momentuHMM’; [ 33 , 35 , 36 ]).…”

Exploring deep learning techniques for wild animal behaviour classification using animal‐borne accelerometers

“…Although not an acceleration-based behaviour classification, Browning et al (2018) used a multi-layer perceptron to predict diving behaviour in three seabird species using GPS data. Roy et al (2022) extended their work by using convolutional neural networks (CNNs) and U-Net to predict seabird diving. Recently, Hoffman et al (2023) applied deep learning models such as CNN and gated recurrent unit to datasets of nine species.…”

“…Roy et al. (2022) extended their work by using convolutional neural networks (CNNs) and U‐Net to predict seabird diving. Recently, Hoffman et al.…”