Document of bibliographic reference 331391

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

A new machine learning approach to seabed biotope classification

Abstract

Effective management in the marine environment requires a thorough understanding of the distribution of natural resources, including that of the benthos, the animals living in and on the seabed. Hitherto, it has been difficult to identify broadscale patterns in the benthos as the faunal clusters identified from individual surveys are not directly comparable. As a result, much reliance has been placed on one-off broadscale spatial surveys or matching samples to a common set of biotopes. In this study, new benthic macrofaunal data from discrete surveys are matched to existing broadscale cluster groups identified using unsupervised machine learning (k-means). This objective approach allows for continual improvements in our understanding of macrofaunal distribution patterns, thereby supporting ongoing conservation and marine spatial planning efforts. Other benefits are discussed. Finally, an R shiny web application is presented, allowing users to biotope match their own data.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000594827600005

Bibliographic citation

Cooper, K.M.; Barry, J. (2020). A new machine learning approach to seabed biotope classification. Ocean Coast. Manag. 198: 105361. https://dx.doi.org/10.1016/j.ocecoaman.2020.105361

Is peer reviewed

true

Access rights

open access

Is accessible for free

true