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Applications for deep learning in ecology
Open accessPublished version of a preprint
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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“ML has emerged as a promising means of connecting the dots between big data and actionable ecological insights29”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [29]
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Metadata
- Authors
- Christin, S., Hervet, É., & Lecomte, N.
- Affiliations
- Université de Moncton (CA)
- Corresponding author
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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