Cited by 724Advanced Neural Network ApplicationsWildlife Ecology and ConservationSmart Agriculture and AI

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Applications for deep learning in ecology

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Christin, Sylvain, Hervet, Éric, Lecomte, Nicolas·2019·Methods in Ecology and Evolution

DOI 10.1111/2041-210x.13256

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Abstract

A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

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Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
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Show all 102 references

Research works printed but not linked: 2

Christin S., 2019, Applications for deep learning in ecology, figshare
Goodfellow I., 2014, Advances in Neural Information Processing Systems 27, 2672

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(2023) · Climate of the past · doi:10.5194/cp-2023-69-rc1
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Comment on cp-2023-69
(2023) · Climate of the past · doi:10.5194/cp-2023-69-rc2
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Reply on RC1
Holden, P. B. (2024) · Climate of the past · doi:10.5194/cp-2023-69-ac1
Reply on RC2
Holden, P. B. (2024) · Climate of the past · doi:10.5194/cp-2023-69-ac2

Authors: Christin, S., Hervet, É., & Lecomte, N.
Affiliations: Université de Moncton (CA)
Corresponding author: Lecomte, Nicolas
ORCID: Christin, Sylvain
Hervet, Éric
Lecomte, Nicolas
Venue: Methods in Ecology and Evolution
Publisher: Wiley
Published: 2019-07-31
Volume: 10
Issue: 10
Pages: 1632-1644
ISSN: 2041-210X (ISSN-L)
DOI: 10.1111/2041-210x.13256
OpenAlex: W2954932437
Cited by: 724
References: 102
Citations by year: 2026: 121 · 2025: 119 · 2024: 114 · 2023: 108 · 2022: 123 · 2021: 84 · 2020: 49 · 2019: 5
FWCI: 29.20
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc-nd
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Methods in Ecology and Evolution (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
Funders: Polar Knowledge Canada
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords: deep learning, ecological applications, species identification, animal behavior classification, biodiversity estimation, camera trap images, audio recordings, ecological monitoring, conservation

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