Machine learning for ecosystem services

Willcock, Simon; Martínez-López, Javier; Hooftman, Danny A.P.; Bagstad, Kenneth J.; Balbi, Stefano; Marzo, Alessia; Prato, Carlo; Sciandrello, Saverio; Signorello, Giovanni; Voigt, Brian; Villa, Ferdinando; Bullock, James M. ORCID: https://orcid.org/0000-0003-0529-4020; Athanasiadis, Ioannis N.. 2018 Machine learning for ecosystem services. Ecosystem Services, 33 (B). 165-174. 10.1016/j.ecoser.2018.04.004

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Official URL: http://dx.doi.org/10.1016/j.ecoser.2018.04.004

Abstract/Summary

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ecoser.2018.04.004
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-) UKCEH Fellows
ISSN:	2212-0416
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	ARIES, artificial intelligence, big data, data driven modelling, data science, machine learning, mapping, modelling, uncertainty, Weka
NORA Subject Terms:	Ecology and Environment
Date made live:	23 May 2018 10:33 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/520147

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.ecoser.2018.04.004

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)
UKCEH Fellows

ISSN:

2212-0416

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

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

Additional Keywords:

ARIES, artificial intelligence, big data, data driven modelling, data science, machine learning, mapping, modelling, uncertainty, Weka