Abstract:Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the OPEN ACCESS Remote Sens. 2014, 6 2155 structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments.

With the implementation of marine spatial planning in many coastal regions of the world, there is a need to understand how marine species and communities respond to environmental heterogeneity. Predictive modelling approaches are one efficient method for associating marine communities with variations across the seascape. These approaches, along with increasing access to spatially explicit environmental data, provide improved opportunities for modelling fish assemblages. Baited remote underwater video stations (BRUVS) are a popular means of gathering fish assemblage data in the coastal zone, but have biases in bait attraction, trophic groups sampled, and behavioral conditions. To account for these biases, spatial and temporal scales of analyses must be considered. In this study, we combined time-series BRUVS observations with seafloor and oceanographic variables in generalized additive models to model patterns of relative species richness and abundance in temperate coastal fish assemblages across multiple habitat types, functional trophic groups, and spatial scales from 5-500 m. We show that the spatial and temporal scale of analyses and behavioral characteristics of target species (such as mobility) are important considerations when predicting the spatial distribution of a particular assemblage or functional subset. The resulting models performed well, with prediction accuracies up to 79% while explaining between 24 and 83% of variance. These models were then used to extrapolate assemblage characteristics over broader areas of the seafloor to expand our understanding of fish distributions, providing valuable insights for marine spatial planning, including marine protected area assessment.