The current and future uses of machine learning in ecosystem service research

Scowen, Matthew; Athanasiadis, Ioannis N.; Bullock, James M. ORCID: https://orcid.org/0000-0003-0529-4020; Eigenbrod, Felix; Willcock, Simon. 2021 The current and future uses of machine learning in ecosystem service research. Science of the Total Environment, 799, 149263. 9, pp. 10.1016/j.scitotenv.2021.149263

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Official URL: http://dx.doi.org/10.1016/j.scitotenv.2021.149263

Abstract/Summary

Machine learning (ML) expands traditional data analysis and presents a range of opportunities in ecosystem service (ES) research, offering rapid processing of ‘big data’ and enabling significant advances in data description and predictive modelling. Descriptive ML techniques group data with little or no prior domain specific assumptions; they can generate hypotheses and automatically sort data prior to other analyses. Predictive ML techniques allow for the predictive modelling of highly non-linear systems where casual mechanisms are poorly understood, as is often the case for ES. We conducted a review to explore how ML is used in ES research and to identify and quantify trends in the different ML approaches that are used. We reviewed 308 peer-reviewed publications and identified that ES studies implemented machine learning techniques in data description (64%; n = 308) and predictive modelling (44%), with some papers containing both categories. Classification and Regression Trees were the most popular techniques (60%), but unsupervised learning techniques were also used for descriptive tasks such as clustering to group or split data without prior assumptions (19%). Whilst there are examples of ES publications that apply ML with rigour, many studies do not have robust or repeatable methods. Some studies fail to report model settings (43%) or software used (28%), and many studies do not report carrying out any form of model hyperparameter tuning (67%) or test model generalisability (59%). Whilst studies use ML to analyse very large and complex datasets, ES research is generally not taking full advantage of the capacity of ML to model big data (1138 medium number of data points; 13 median quantity of variables). There is great further opportunity to utilise ML in ES research, to make better use of big data and to develop detailed modelling of spatial-temporal dynamics that meet stakeholder demands.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.scitotenv.2021.149263
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-)
ISSN:	0048-9697
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	machine learning, ecosystem services, big data, methodology, validation, data-driven modelling
NORA Subject Terms:	Ecology and Environment
Date made live:	03 Sep 2021 15:30 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/531001

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.scitotenv.2021.149263

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)

ISSN:

0048-9697

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

machine learning, ecosystem services, big data, methodology, validation, data-driven modelling