Research Topic

Applications of Machine Learning to Evolutionary Ecology Data

About this Research Topic

Editors' Note: "Deadlines were extended as a result of the COVID19. Should you have any queries about your confirmed or intended contribution, please contact Dr. Juliano Morimoto directly at juliano.morimoto@abdn.ac.uk
Stay safe and sane during these difficult times"


Machine learning is the present and future of data analysis. Harnessing its analytical power has been proven useful to better understand patterns across many fields, from medical sciences to chemistry and physics. Furthermore, it is not only in science that machine learning is a hot topic, but also in technology and industry where studies that have implemented machine learning to analyze and increase industrial yield are emerging at a faster-than-ever rate. As a result, machine learning has become a powerful tool for analyzing complex datasets across science and industry. Despite this, the use of machine learning in evolutionary ecology has been limited. One reason for this could be that machine learning models are often complex and difficult to implement, combined with the seemingly small datasets often observed in evolutionary ecology studies (although not always, for example in the case of long-term studies in birds). But can implementing machine learning teach us anything about ecological data?

This Research Topic aims to place machine learning at the forefront of evolutionary ecology, highlighting the use of machine learning algorithms to biological data across disciplines and compiling the first Open Access, peer-reviewed journal issue focusing on the applications of machine learning to behavioral and evolutionary ecology data. In a sense, this collection will become a reference in this rapidly advancing field, creating a long-lasting, centralized source of research papers that apply various machine learning algorithms to tackle key gaps in our knowledge of fundamental and applied ecology.

We welcome the submission of articles that use machine learning as an underlying modeling strategy and/or primary data analysis tool. For instance, manuscripts that analyze biological data with machine learning and/or manuscripts that model biological phenomena with machine learning algorithms will be considered. We encourage submissions from across fields, including (but not limited to) behavioral ecology, evolutionary biology and applied ecology. We will also consider balanced reviews and opinions of machine learning application to a field within evolutionary ecology, as well as guidelines for the use and implementation of machine learning in ecological research.


Keywords: animal behavior, algorithm, insects, mammals, conservation, survey, modeling


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Editors' Note: "Deadlines were extended as a result of the COVID19. Should you have any queries about your confirmed or intended contribution, please contact Dr. Juliano Morimoto directly at juliano.morimoto@abdn.ac.uk
Stay safe and sane during these difficult times"


Machine learning is the present and future of data analysis. Harnessing its analytical power has been proven useful to better understand patterns across many fields, from medical sciences to chemistry and physics. Furthermore, it is not only in science that machine learning is a hot topic, but also in technology and industry where studies that have implemented machine learning to analyze and increase industrial yield are emerging at a faster-than-ever rate. As a result, machine learning has become a powerful tool for analyzing complex datasets across science and industry. Despite this, the use of machine learning in evolutionary ecology has been limited. One reason for this could be that machine learning models are often complex and difficult to implement, combined with the seemingly small datasets often observed in evolutionary ecology studies (although not always, for example in the case of long-term studies in birds). But can implementing machine learning teach us anything about ecological data?

This Research Topic aims to place machine learning at the forefront of evolutionary ecology, highlighting the use of machine learning algorithms to biological data across disciplines and compiling the first Open Access, peer-reviewed journal issue focusing on the applications of machine learning to behavioral and evolutionary ecology data. In a sense, this collection will become a reference in this rapidly advancing field, creating a long-lasting, centralized source of research papers that apply various machine learning algorithms to tackle key gaps in our knowledge of fundamental and applied ecology.

We welcome the submission of articles that use machine learning as an underlying modeling strategy and/or primary data analysis tool. For instance, manuscripts that analyze biological data with machine learning and/or manuscripts that model biological phenomena with machine learning algorithms will be considered. We encourage submissions from across fields, including (but not limited to) behavioral ecology, evolutionary biology and applied ecology. We will also consider balanced reviews and opinions of machine learning application to a field within evolutionary ecology, as well as guidelines for the use and implementation of machine learning in ecological research.


Keywords: animal behavior, algorithm, insects, mammals, conservation, survey, modeling


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Topic Editors

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Submission Deadlines

29 May 2020 Abstract
02 November 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

29 May 2020 Abstract
02 November 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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