The reliability of sockeye-salmon (Oncorhynchus nerka) count data collected by a dual-frequency, identification sonar (DIDSON) system is evaluated on the basis of comparisons with visual counts of unconstrained migrating salmon and visual counts of salmon constrained to passing through an enumeration fence. Regressions fitted to the DIDSON count data and the visual count data from the enumeration fence were statistically indistinguishable from a line with slope = 1.0 passing through the origin, which we interpret as agreement in both counts. In contrast, the regressions fitted to the DIDSON count data and the unconstrained visual count data had slopes that were significantly <1.0 (p < 0.001) and are consistent with an interpretation of systematic bias in these data. When counts of both unconstrained and constrained fish from the DIDSON system were ≥50 fish event−1, repeated counts of the DIDSON files were observed to produce the same counts 98–99% of the time, respectively, and based on the coefficient of variation, counts of individual passage events varied <3% on average. Therefore, the DIDSON count data exhibit high precision among different observers. As an enumeration fence provides a complete census of all fish passing through it, we conclude that fish-count data produced by the DIDSON imaging system are as accurate as visual counts of fish passing through an enumeration fence when counts range up to 932 fish event−1, the maximum count recorded during our study, regardless of the observer conducting the count. These conclusions should be applicable to typical riverine applications of the DIDSON system in which the bottom and surface boundaries are suitable for acoustic imaging, the migrating fish are adult salmon, and the transducer is carefully aimed so that the beams ensonify the area through which the salmon are migrating.
-Milling behaviour is problematic when using hydroacoustics to estimate the number of migrating fish in rivers. Milling behaviour was observed for adult sockeye salmon migrating upstream in the Wannock River, to their spawning grounds in the tributaries of Owikeno Lake in the central coast area of British Columbia, Canada. We classified the acoustic salmon tracks to separate the milling fish from the actively migrating fish in an attempt to obtain an estimate of sockeye salmon flux as they migrate to their spawning grounds. We used discriminant function analysis and found that three variables measured on each track were sufficient for the classification of the acoustic tracks into milling and non-milling categories with an approximate classification accuracy of 98%. The method we present can also be used to separate tracks of targets of interest from noise or debris tracks that occur in the acoustic data, if discrete track characteristics are ascertained.Key words: Split-beam / Acoustic noise / Track characteristics / Discriminant function Résumé -Classification des types de comportement du saumon en migration en utilisant les caractéristiques des données acoustiques d'un sondeur mono-faisceau. Les comportements non-migratoires posent problème quand on veut estimer le nombre de poissons en migration dans les fleuves, en utilisant l'hydroacoustique. Ce comportement non-migratoire est observé chez les saumons rouge (Oncorhynchus nerka) lorsqu'ils remontent le fleuve Wannock pour aller vers leurs aires de ponte situées dans les affluents du lac Owikeno, sur la côte de Colombie Britannique au Canada. Nous classons les suivis (échos-traces) acoustiques des saumons afin de séparer les poissons qui ne migrent pas, des poissons en migration active, dans le but d'obtenir une estimation des flux de saumons rouge lors de leur remontée vers les zones de ponte. Nous utilisons l'analyse de fonction discriminante et nous trouvons que trois variables, mesurées sur chaque déplacement, sont suffisantes pour classer les suivis acoustiques en catégories migrante et non-migrante, avec une précision approximative de 98 %. La méthode que nous présentons peut aussi être utilisée pour séparer les déplacements de cibles présentant un intérêt, des suivis de débris et du bruit présents dans les données acoustiques, si des caractéristiques discrètes sont fixées.
Reliable data on the number of salmon entering tributaries of the Fraser River to spawn (escapement) is needed for Pacific salmon management. Existing escapement techniques are costly and the number of populations requiring assessments has risen because of stock rebuilding efforts. The efficacy of a DIDSON acoustic imaging system for salmon stock assessment was investigated. Sixteen potential sites within the Fraser watershed were surveyed and based on channel morphology, bottom morphology, flow pattern, fish behavior and location relative to spawning grounds, ten sites in six rivers meet the needs of fisheries managers and the DIDSON system for escapement estimates. Fish count data from the DIDSON were compared to data from a counting fence (used as a standard) using regression techniques, resulting in relationships with slopes ranging from 0.98 to 1.02. The precison of DIDSON counts >50 (measured by CV) among three readers was 1.7%. This work supports the conclusion that the DIDSON system can deliver escapement estimates whose accuracy, precision and scientific defensibility is consistent with or better than existing escapment techniques and at a lower operating cost to assessment programs. [Work supported by the Southern Boundary Restoration and Enhancement Fund of the Pacific Salmon Commission.]
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.