Section: Ecology
Topic:
Biophysics and computational biology,
Ecology
Predicting species distributions in the open ocean with convolutional neural networks
Corresponding author(s): Morand, Gaétan (gaetan.morand@umontpellier.fr)
10.24072/pcjournal.471 - Peer Community Journal, Volume 4 (2024), article no. e93.
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As biodiversity plummets due to anthropogenic disturbances, the conservation of oceanic species is made harder by limited knowledge of their distributions and migrations. Indeed, tracking species distributions in the open ocean is particularly challenging due to the scarcity of observations and the complex and variable nature of the ocean system. In this study, we propose a new method that leverages deep learning, specifically convolutional neural networks (CNNs), to capture spatial features of environmental variables. This novelty eliminates the need to predefine these features before modelling and creates opportunities to discover unexpected correlations. Our aim is to present the results of the first trial of this method in the open ocean, discuss limitations and provide feedback for future improvements or adjustments. In this case study, we considered 38 taxa comprising pelagic fishes, elasmobranchs, marine mammals, marine turtles and birds. We trained a model to predict probabilities from the environmental conditions at any specific point in space and time, using species occurrence data from the Global Biodiversity Information Facility (GBIF) and environmental data from various sources. These variables included sea surface temperature, chlorophyll concentration, salinity and fifteen others. During the testing phase, the model was applied to environmental data at locations where species occurrences were recorded. The classifier accurately predicted the observed taxon as the most likely taxon in 69% of cases and included the observed taxon among the top three most likely predictions in 89% of cases. These findings show the adequacy of deep learning for species distribution modelling in the open ocean. Additionally, this purely correlative model was then analysed with explicability tools to understand which variables had an influence on the model’s predictions. While variable importance was species-dependent, we identified finite-size Lyapunov exponents (FSLEs), sea surface temperature, pH and salinity as the most influential variables, in that order. These insights can prove valuable for future species-specific ecology studies.
Type: Research article
Morand, Gaétan 1; Joly, Alexis 2; Rouyer, Tristan 1; Lorieul, Titouan 2; Barde, Julien 1
CC-BY 4.0
@article{10_24072_pcjournal_471,
author = {Morand, Ga\'etan and Joly, Alexis and Rouyer, Tristan and Lorieul, Titouan and Barde, Julien},
title = {Predicting species distributions in the open ocean with convolutional neural networks},
journal = {Peer Community Journal},
eid = {e93},
publisher = {Peer Community In},
volume = {4},
year = {2024},
doi = {10.24072/pcjournal.471},
language = {en},
url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.471/}
}
TY - JOUR AU - Morand, Gaétan AU - Joly, Alexis AU - Rouyer, Tristan AU - Lorieul, Titouan AU - Barde, Julien TI - Predicting species distributions in the open ocean with convolutional neural networks JO - Peer Community Journal PY - 2024 VL - 4 PB - Peer Community In UR - https://peercommunityjournal.org/articles/10.24072/pcjournal.471/ DO - 10.24072/pcjournal.471 LA - en ID - 10_24072_pcjournal_471 ER -
%0 Journal Article %A Morand, Gaétan %A Joly, Alexis %A Rouyer, Tristan %A Lorieul, Titouan %A Barde, Julien %T Predicting species distributions in the open ocean with convolutional neural networks %J Peer Community Journal %D 2024 %V 4 %I Peer Community In %U https://peercommunityjournal.org/articles/10.24072/pcjournal.471/ %R 10.24072/pcjournal.471 %G en %F 10_24072_pcjournal_471
Morand, Gaétan; Joly, Alexis; Rouyer, Tristan; Lorieul, Titouan; Barde, Julien. Predicting species distributions in the open ocean with convolutional neural networks. Peer Community Journal, Volume 4 (2024), article no. e93. doi : 10.24072/pcjournal.471. https://peercommunityjournal.org/articles/10.24072/pcjournal.471/
PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 10.24072/pci.ecology.100584
Conflict of interest of the recommender and peer reviewers:
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.
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