Document of bibliographic reference 288180

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

Abstract

The INFOMAR (Integrated Mapping of Irelands Marine Resource) initiative has acoustically mapped and classified a significant proportion of Irelands Exclusive Economic Zone (EEZ), and is likely to be an important tool in Irelands’ efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables.To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay were also assessed using the Infaunal Quality Index.ANNs that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for ANNs using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the sparsely distributed data within the study area.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000412252800024

Bibliographic citation

O’Carroll, J.P.J.; Kennedy, R.; Ren, L.; Nash, S.; Hartnett, M.; Brown, C. (2017). A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland. Est., Coast. and Shelf Sci. 197: 258-270. https://dx.doi.org/10.1016/j.ecss.2017.08.005

Topic

Marine

Is peer reviewed

true