2022
DOI: 10.1111/2041-210x.13901
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Deep learning as a tool for ecology and evolution

Abstract: Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at r… Show more

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Cited by 114 publications
(86 citation statements)
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“…Machine learning approaches are becoming widely used in ecology in recent years (Borowiec et al, 2021). Here, we presented an excellent case study of machine learning assisted data collection that can obtain biologically meaningful results in existing datasets without further manual intervention.…”
Section: Discussionmentioning
confidence: 99%
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“…Machine learning approaches are becoming widely used in ecology in recent years (Borowiec et al, 2021). Here, we presented an excellent case study of machine learning assisted data collection that can obtain biologically meaningful results in existing datasets without further manual intervention.…”
Section: Discussionmentioning
confidence: 99%
“…Recent advances in deep learning (Borowiec et al, 2021) and computer vision (Weinstein, 2018a) allow quick and reliable information to be extracted from field data (Valletta et al, 2017). For example, machine learning methods have been successfully applied to solve problems with species identification (see Wäldchen and Mäder, 2018), bird song complexity measurement (see Pearse et al, 2018;Priyadarshani et al, 2018), social behaviour measurement (see Robie et al, 2017), and individual identification (Bogucki et al, 2019;Ferreira et al, 2020;Körschens et al, 2018;Schofield et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
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“…Calibration of the BIS device was conducted by using deep learning, which enabled the automated adipose tissue identification and quantification. Deep learning approaches in ecology facilitate the classification, regression, and modeling of data (Borowiec et al, 2022 ). For example, deep learning has been used to identify, classify, and estimate the density of individuals, populations, and species (Christin et al, 2019 ).…”
Section: Discussionmentioning
confidence: 99%
“…Biological models have become an essential tool to better understand our world (Jackson et al, 2000; Shoemaker et al, 2021). They are now becoming more and more numerous and sophisticated, even more so with the recent progress made in machine learning, and especially deep learning (Borowiec et al, 2022; Christin et al, 2019; Guo et al, 2020). Such fast evolution, however, introduces challenges that make the creation and comparison of new models difficult, hampering their reproducibility and research potential.…”
Section: Introductionmentioning
confidence: 99%