Interlinking backscatter, grain size and benthic community structure

The relationship between acoustic backscatter, sediment grain size and benthic community structure is examined using three different quantitative methods, covering image- and angular response-based approaches. Multibeam time-series backscatter (300 kHz) data acquired in 2008 off the coast of East Anglia (UK) are compared with grain size properties, macrofaunal abundance and biomass from 130 Hamon and 16 Clamshell grab samples. Three predictive methods are used: 1) image-based (mean backscatter intensity); 2) angular response-based (predicted mean grain size), and 3) image-based (1st principal component and classification) from Quester Tangent Corporation Multiview software. Relationships between grain size and backscatter are explored using linear regression. Differences in grain size and benthic community structure between acoustically defined groups are examined using ANOVA and PERMANOVA+. Results for the Hamon grab stations indicate significant correlations between measured mean grain size and mean backscatter intensity, angular response predicted mean grain size, and 1st principal component of QTC analysis (all p <0.001). Results for the Clamshell grab for two of the methods have stronger positive correlations; mean backscatter intensity (r2 = 0.619; p <0.001) and angular response predicted mean grain size (r2 = 0.692; p <0.001). ANOVA reveals significant differences in mean grain size (Hamon) within acoustic groups for all methods: mean backscatter (p <0.001), angular response predicted grain size (p <0.001), and QTC class (p = 0.009). Mean grain size (Clamshell) shows a significant difference between groups for mean backscatter (p = 0.001); other methods were not significant. PERMANOVA for the Hamon abundance shows benthic community structure was significantly different between acoustic groups for all methods (p <0.001). Overall these results show considerable promise in that more than 60% of the variance in the mean grain size of the Clamshell grab samples can be explained by mean backscatter or acoustically-predicted grain size. These results show that there is significant predictive capacity for sediment characteristics from multibeam backscatter and that these acoustic classifications can have ecological validity.