The use of singlebeam echo‐sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo‐sounder depth

Abstract

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%-10% of the world's seafloor has been mapped at high resolution, as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echosounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type.Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.

Mesophotic benthic communities associated with a submerged palaeoshoreline in Western Australia

“…Relatively high-resolution bathymetry data already existed for Area 5 as part of the Geoscience Australia 30 m data product for the northwest shelf [ 32 ]. For newly acquired multibeam data, depth values between multibeam swaths were estimated using universal block kriging interpolation [ 33 ] and the interpolated rasters were gridded at a resolution of a 10 m pixel. Universal block kriging was used rather than a spline or IDW because it models the spatial relationship in the data implicitly and applies the most appropriate interpretation via kriging with the associated accuracy and error analysis.…”

Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS

“…The automated length measurement of fish in stereo-videos using the method described in this study could be integrated with the opticacoustic approach to capitalise on the strengths of both methods. Combining acoustic surveys with stereo-BRUVS, such as the preliminary work by Landero-Figueroa et al (2016), or other sampling techniques can help overcome the limitations of each method and provide more accurate and comprehensive information on fish populations for stock assessment and ecosystem-based fisheries management. This non-invasive approach enables continuous monitoring of fish populations without harming the organisms or their habitats, offering a promising alternative for sustainable fishery management.…”

Characterizing bedforms in shallow seas as an integrative predictor of seafloor stability and the occurrence of macrozoobenthic species

“…Some studies indeed show that bedform variation may explain important variation in macrobenthic species distribution in near-shore systems as well (Holzhauer et al, 2019(Holzhauer et al, , 2022van der Wal, Ysebaert, et al, 2017b), but seascape-wide mapping studies are still lacking, especially for the more shallow parts of soft-bottom coastal systems. While the seafloor of deeper parts of coastal shelves and seas is often mapped using multibeam echosounders, this technique is more challenging to apply in more shallow estuaries and barrier island inlet lagoons, as the obtained swath width is limited by the depth, making the mapping of large areas expensive (Landero Figueroa et al, 2021). Single-beam echosounding is a cheaper method for mapping bathymetry but has a smaller coverage and is usually interpolated to larger areas at coarser resolution.…”