Developing the use of convolutional neural networking in benthic habitat classification and species distribution modelling. (28th October 2020)

• Record Type: Journal Article
• Authors: Fincham, Jennifer I; Wilson, Christian; Barry, Jon; Bolam, Stefan; French, Geoffrey
• Editors: Proud, Roland
• Is Part Of: ICES journal of marine science. Volume 77:Number 7/8(2020)
• Page Start: 3074
• Page End: 3082
• Publication Date: 2020-10-28
• Subjects: benthic habitats -- convolutional neural networks -- demersal species -- machine learning -- species distribution; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsaa208
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Management of the marine environment is increasingly being conducted in accordance with an ecosystem-based approach, which requires an integrated approach to monitoring. Simultaneous acquisition of the different data types needed is often difficult, largely due to specific gear requirements (grabs, trawls, and video and acoustic approaches) and mismatches in their spatial and temporal scales. We present an example to resolve this using a convolutional neural network (CNN), using ad hoc multibeam data collected during multi-disciplinary surveys to predict the distribution of seabed habitats across the western English Channel. We adopted a habitat classification system, based on seabed morphology and sediment dynamics, and trained a CNN to label images generated from the multibeam data. The probability of the correct classification by the CNN varied per habitat, with accuracy above 60% for 85% of habitats in a training dataset. Statistical testing revealed that the spatial distribution of 57 of the 100 demersal fish and shellfish species sampled across the region during the surveys possessed a non-random relationship with the multibeam-derived habitats using CNN. CNNs, therefore, offer the potential to aid habitat mapping and facilitate species distribution modelling at the large spatial scales required under an ecosystem-based management framework.