A deep learning model to unlock secrets of animal movement and behaviour
MoveFormer: a Transformer-based model for step-selection animal movement modelling
Recommendation: posted 29 September 2023, validated 29 September 2023
Sueur, C. (2023) A deep learning model to unlock secrets of animal movement and behaviour. Peer Community in Ecology, 100531. 10.24072/pci.ecology.100531
The study of animal movement is essential for understanding their behaviour and how ecological or global changes impact their routines . Recent technological advancements have improved the collection of movement data , but limited statistical tools have hindered the analysis of such data [3–5]. Animal movement is influenced not only by environmental factors but also by internal knowledge and memory, which are challenging to observe directly [6,7]. Routine movement behaviours and the incorporation of memory into models remain understudied.
Researchers have developed ‘MoveFormer’ , a deep learning-based model that predicts future movements based on past context, addressing these challenges and offering insights into the importance of different context lengths and information types. The model has been applied to a dataset of over 1,550 trajectories from various species, and the authors have made the MoveFormer source code available for further research.
Inspired by the step-selection framework and efforts to quantify uncertainty in movement predictions, MoveFormer leverages deep learning, specifically the Transformer architecture, to encode trajectories and understand how past movements influence current and future ones – a critical question in movement ecology. The results indicate that integrating information from a few days to two or three weeks before the movement enhances predictions. The model also accounts for environmental predictors and offers insights into the factors influencing animal movements.
Its potential impact extends to conservation, comparative analyses, and the generalisation of uncertainty-handling methods beyond ecology, with open-source code fostering collaboration and innovation in various scientific domains. Indeed, this method could be applied to analyse other kinds of movements, such as arm movements during tool use , pen movements, or eye movements during drawing , to better understand anticipation in actions and their intentionality.
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8. Cífka, O.; Chamaillé-Jammes, S.; Liutkus, A. MoveFormer: A Transformer-Based Model for Step-Selection Animal Movement Modelling. bioRxiv 2023, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.
9. Ardoin, T.; Sueur, C. Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence. 2023, https://doi.org/10.13140/RG.2.2.30465.02402
10. Martinet, L.; Pelé, M. Drawing in Nonhuman Primates: What We Know and What Remains to Be Investigated. J. Comp. Psychol. Wash. DC 1983 2021, 135, 176-184, doi:10.1037/com0000251.
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article. The authors declared that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
This work was supported by the LabEx NUMEV (ANR-10-LABX-0020) and the REPOS project, both funded by the I-Site MUSE (ANR-16-IDEX-0006). Computations were performed using HPC/AI resources from GENCI-IDRIS (Grant AD011012019R1).
Evaluation round #1
DOI or URL of the preprint: https://doi.org/10.1101/2023.03.05.531080
Version of the preprint: 2
Author's Reply, 27 Sep 2023
Decision by Cédric Sueur, posted 18 May 2023, validated 21 May 2023
We received the comments of two reviewers. Please prepare a response. We will be pleased to review another version of your manuscript.
All the best,