Differentiation of two swim bladdered fish species using next generation wideband hydroacoustics

Obtaining accurate population estimates with reduced workload and lower fish mortality in multi-mesh gillnet sampling of a large pre-alpine lake

The EU Water Framework Directive requires monitoring of the ecological status of lakes, with fish as a relevant class of biotic quality indicator, but monitoring fish populations in large lakes is demanding. This study evaluated use in Lake Constance of a novel multi-mesh gillnet modified to reduce catch numbers. In direct comparison with conventional European Committee for Standardization (CEN) nets we achieved 48% reduction in fish mortality with 38% less labour for tasks directly influenced by fish catch numbers, while maintaining comparable species composition and catch per unit effort. Comparison of mesh sizes indicated no significant reduction in species detection in area-reduced panels of the small mesh sizes, while total observed species richness was greater when using the modified nets. Differences in benthic species communities among depth strata were common, while those of pelagic zones were more homogeneous and did not differ significantly with depth. Catches of different net types from the same depth stratum did not exhibit significant differences. The dominance structure of the most common species, relevant to lake assessment, was similar in catches of both net types, suggesting overall superiority of the modified nets in Lake Constance. Sampling conducted according to standard European CEN protocol, while deploying 60% fewer nets, yielded sufficiently precise abundance estimates for monitoring shallow areas of the benthic zone. A 50% difference in the abundance of dominant species was detected among sampling events with a certainty of 95%. The sample did not provide comparable accuracy in deep benthic strata or the pelagic zone, but was adequate to record complete inventories of species present. Based on this trial data, a new stratified sampling design is proposed for monitoring large lake fish communities for ecological assessment. Depth-dependent fish communities were used to calculate the required number of nets, which resulted in a 69% reduction for the entire lake compared to the CEN calculation method. Using the modified nets increases the feasibility of performing WFD surveys, by reducing effort and cost, while the simultaneous halving of fish mortality minimises the negative impact of fish surveys.

Taxonomical classification of reef fish with broadband backscattering models and machine learning approaches

Commercially available broadband echosounders have the potential to classify acoustic targets based on their scattering responses, which are a function of their species-specific morphological and physiological properties. This is particularly important in complex environments with biologically diverse fish assemblages. Using theoretical acoustic scattering models among 130 fishes across six species, we examine the potential to classify reef fish based on the fine-scale gas-bearing swim bladder morphology quantified from three-dimensional computed-tomography models. Modeled echoes of the swim bladder for an incident broadband sound source (30-200 kHz) and across a range of orientation angles (±44°) are acoustically simulated using the boundary element method. Backscatter models present characteristics that are consistent within species and distinguishable among them. Broadband and multifrequency echoes are classified and compared with Bayesian, support vector machine, k-nearest neighbor, and convolutional neural network estimators. Classifiers have higher accuracies (>70%) when noise is not present and perform better when applied to broadband spectra than multifrequency data (42, 70, 100, 132, 160, 184 kHz). The modeling and classification approaches presented indicate that a taxonomic distinction based on morphologically dependent scattering responses is possible and may provide the capacity to acoustically discriminate among fish species.