A multiscale approach to mapping seabed sediments.

• Published in: PLOS ONE
• Main Authors: Benjamin Misiuk, Vincent Lecours, Trevor Bell
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2018
• Subjects: Medicine; R; Science; Q; Nunavut; Canada; Qikiqtarjuaq
• Online Access: https://doi.org/10.1371/journal.pone.0193647; https://doaj.org/article/ba3908aaf80b4599b29a81575c3c6c89

Benthic habitat maps, including maps of seabed sediments, have become critical spatial-decision support tools for marine ecological management and conservation. Despite the increasing recognition that environmental variables should be considered at multiple spatial scales, variables used in habitat mapping are often implemented at a single scale. The objective of this study was to evaluate the potential for using environmental variables at multiple scales for modelling and mapping seabed sediments. Sixteen environmental variables were derived from multibeam echosounder data collected near Qikiqtarjuaq, Nunavut, Canada at eight spatial scales ranging from 5 to 275 m, and were tested as predictor variables for modelling seabed sediment distributions. Using grain size data obtained from grab samples, we tested which scales of each predictor variable contributed most to sediment models. Results showed that the default scale was often not the best. Out of 129 potential scale-dependent variables, 11 were selected to model the additive log-ratio of mud and sand at five different scales, and 15 were selected to model the additive log-ratio of gravel and sand, also at five different scales. Boosted Regression Tree models that explained between 46.4 and 56.3% of statistical deviance produced multiscale predictions of mud, sand, and gravel that were correlated with cross-validated test data (Spearman's ρmud = 0.77, ρsand = 0.71, ρgravel = 0.58). Predictions of individual size fractions were classified to produce a map of seabed sediments that is useful for marine spatial planning. Based on the scale-dependence of variables in this study, we concluded that spatial scale consideration is at least as important as variable selection in seabed mapping.