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 echo-sounders (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.