ICES Journal of Marine Science: Journal du Conseil

Dunn, Muriel; McGowan-Yallop, Chelsey; Pedersen, Geir; Falk-Petersen, Stig; Daase, Malin; Last, Kim; Langbehn, Tom J; Fielding, Sophie; Brierley, Andrew S; Cottier, Finlo; Basedow, Sünnje L; Camus, Lionel; Geoffroy, Maxime

doi: 10.1093/icesjms/fsad192pmid: N/A

Classification of zooplankton to species with broadband echosounder data could increase the taxonomic resolution of acoustic surveys and reduce the dependence on net and trawl samples for ‘ground truthing’. Supervised classification with broadband echosounder data is limited by the acquisition of validated data required to train machine learning algorithms (‘classifiers’). We tested the hypothesis that acoustic scattering models could be used to train classifiers for remote classification of zooplankton. Three classifiers were trained with data from scattering models of four Arctic zooplankton groups (copepods, euphausiids, chaetognaths, and hydrozoans). We evaluated classifier predictions against observations of a mixed zooplankton community in a submerged purpose-built mesocosm (12 m3) insonified with broadband transmissions (185–255 kHz). The mesocosm was deployed from a wharf in Ny-Ålesund, Svalbard, during the Arctic polar night in January 2022. We detected 7722 tracked single targets, which were used to evaluate the classifier predictions of measured zooplankton targets. The classifiers could differentiate copepods from the other groups reasonably well, but they could not differentiate euphausiids, chaetognaths, and hydrozoans reliably due to the similarities in their modelled target spectra. We recommend that model-informed classification of zooplankton from broadband acoustic signals be used with caution until a better understanding of in situ target spectra variability is gained.

Yang, Yang; Gastauer, Sven; Proud, Roland; Mangeni-Sande, Richard; Everson, Inigo; Kayanda, Robert J; Brierley, Andrew S

doi: 10.1093/icesjms/fsad137pmid: N/A

Lake Victoria is the second-largest freshwater lake in the world, and fish from the lake are a vital food resource for millions of people living around it. The silver cyprinid (Rastrineobola argentea), a small schooling pelagic species known in Tanzania as “dagaa” contributes ca. 55% to the total annual catch (ca. 0.51 million tonnes (MT) in 2014). The acoustic target strength (TS, dB re 1 m2) of dagaa, a key factor for biomass estimation, is however not well described, and is a major source of uncertainty in biomass estimation. In this study, we developed a Kirchhoff-ray mode (KRM) model to predict the TS of dagaa at standard fisheries survey frequencies. The model was based on the morphology of the body and the dual-chambered swimbladder, as obtained from X-ray images of fish ranging in total length (TL) between 2.8 and 5.4 cm. The results suggested that the swimbladder (which comprises 2.6 to 8.2% of body volume) accounts for ca. 65 to 90% of the total backscattering at 120 kHz. The predicted TS was highly dependent on tilt angle, varying by 14.0 dB at 120 kHz across the tilt range 65–115˚ (where 0˚ is head up and 180˚ is tail up), and TS variability with tilt generally increased with increasing frequency. The tilt angle of acoustically tracked individual fish indicated a distribution of tilt angles with a mean and s.d. of 93.5 and 15.1°. Our model suggested a new tilt-averaged TS–TL relationship for dagaa [$T{S}_{120\ kHz} = 19.49\log ( {TL} ) - 70.3$], which leads to a TS 1.5 dB higher than the value in the relationship presently used to estimate stock biomass. The new relationship will lead to a substantial reduction (by ca. 30%) in estimated biomass. The discrepancies between the mean relative frequency response of the in situ measurements of backscatter from dagaa and the KRM model predictions were in the range of -2.9–3.1 dB at frequencies from 45 to 250 kHz. The KRM modelling and in situ broadband measurements of dagaa will be beneficial for acoustic identification and behavioural studies of dagaa, and will enable improved biomass assessment, thereby underpinning sustainable long-term management.

Horne, John K; Swan, Jackson A; Tracy, Tommy J; Holtgrieve, Gordon W

doi: 10.1093/icesjms/fsad196pmid: N/A

Freshwater fisheries in developing regions provide livelihoods and nutrition for millions of people worldwide. These fisheries are frequently data poor, which limits fisheries management. The seasonal Cambodian dai platform fishery on the Tonle Sap River is one of the best monitored inland fisheries in Southeast Asia, yet catch sampling is limited and there is no fishery-independent monitoring. A monitoring system is needed to characterize fish migration and mortality, be cost effective, and be deployable in areas with minimal infrastructure. We integrated a Simrad wide-band transceiver mini echosounder (200 kHz), solar power, and an Internet of Things communications module as an autonomous, automated monitoring package for the deployment on upstream and downstream commercial fishing platforms. The solar panel and controller supply direct current power to the echosounder, communications module, and battery for power during dark hours. Echosounders are programmed to sample at 1 Hz for 15 min every hour. The communications module is a built cellular endpoint that accesses a local wireless network to transmit raw data files to a data server. Data are downloaded from the server for processing and analysis. This expandable system provides a flexible management tool that can be deployed at any location with wireless communication capability.

Ens, Nicholas J; Dower, John F; Gauthier, Stéphane

doi: 10.1093/icesjms/fsad177pmid: N/A

Diel vertical migration (DVM) is a behaviour observed across zooplankton taxa in marine and limnetic systems worldwide. DVM influences biogeochemical cycling and carbon drawdown in oceanic systems and alters prey availability for zooplanktivorous species. DVM has been well studied among zooplankton, and many exogenous and endogenous triggers as well as adaptive significances have been hypothesized. However, second-order variability in DVM timing, the deviation of DVM times to respective dawn and dusk times throughout the year, is a less-studied phenomenon that can help identify the factors influencing migration timing as well as demonstrate the changes of DVM behaviours within and across systems. Here, we quantified seasonal trends in second-order variability of DVM timing of euphausiids at Brooks Peninsula, Clayoquot Canyon, and Saanich Inlet near Vancouver Island, British Columbia, Canada, over multiple years using upward-facing moored echosounders. We used generalized additive mixed models to characterize this seasonality. DVM timing relative to civil twilight times showed strong seasonality at all locations, with euphausiids remaining near the surface longer than expected in spring and summer, and shorter than expected in winter. Euphausiids spent less time near the surface at Brooks Peninsula and Clayoquot Canyon than at Saanich Inlet throughout the year. Increased primary productivity in Saanich Inlet, which reduced light penetration and hid euphausiids from visual predators, likely drove this difference. Our findings confirm that proper understanding of DVM behaviours must account for seasonal variability due to context-specific oceanographic and ecological parameters. This is particularly pertinent when attempting to model the biogeochemical or predator–prey interactions influenced by DVM behaviours.

Urmy, Samuel S; De Robertis, Alex; Bassett, Christopher

doi: 10.1093/icesjms/fsad102pmid: N/A

Identifying sound-scattering organisms is a perennial challenge in fisheries acoustics. Most practitioners classify backscatter based on direct sampling, frequency-difference thresholds, and expert judgement, then echo-integrate at a single frequency. However, this approach struggles with species mixtures, and discards multi-frequency information when integrating. Inversion methods do not have these limitations, but are not widely used because species identifications are often ambiguous and the algorithms are complicated to implement. We address these shortcomings using a probabilistic, Bayesian inversion method. Like other inversion methods, it handles species mixtures, uses all available frequencies, and extends naturally to broadband signals. Unlike previous approaches, it leverages Bayesian priors to rigorously incorporate information from direct sampling and biological knowledge, constraining the inversion and reducing ambiguity in species identification. Because it is probabilistic, a well-specified model should not produce solutions that are both wrong and confident. The model is based on physical scattering processes, so its output is fully interpretable, unlike some machine learning methods. Finally, the approach can be implemented using existing Bayesian libraries and is easily parallelized for large datasets. We present examples using simulations and field data from the Gulf of Alaska, and discuss possible applications and extensions of the method.

Sibley, E C P; Madgett, A S; Lawrence, J M; Elsdon, T S; Marnane, M J; Fernandes, P G

doi: 10.1093/icesjms/fsad156pmid: N/A

Imaging sonars (ISs) are high-frequency acoustic devices that are increasingly being used to study fish in marine and freshwater habitats. Acoustic devices are limited in quantifying species richness, and previous attempts to identify fish species using IS have mostly focused on assemblages of low species richness or high morphological diversity. This study aimed to determine the ability of IS for identifying fish species at a subtropical artificial reef off Perth, Western Australia. Several fish traits that could be defined using IS were identified and described for all fish species observed with simultaneous optical footage. These traits were used to create a clustering algorithm to infer the species identity of IS detections of the five most abundant species at the reef. The identities of all fish from two species (Chromis westaustralis and Neatypus obliquus) were inferred with 100% success, though no individuals from the remaining three species (Seriola dumerili, Coris auricularis, and Pempheris klunzingeri) were correctly identified. An alternative clustering-based approach to categorising fish detected by IS independent of taxonomic inference was also implemented. Overall, this study demonstrates that IS can identify reef fish with variable success, and proposes an alternative method for describing fish assemblages irrespective of species identity.