This page is for informational purposes only. The main Github repositories for this work are linked below.
The Active Acoustics SI (AA-SI) seeks to support transformational advances in NMFS active acoustic survey activities. The APES-ELF project will improve echoclassification by further developing several ongoing lines of inquiry led by NMFS-Alaska Fisheries Science Center. The first is a plan to collect low-frequency backscatter data (‘ELF’) that has the potential to inform echoclassification and provide size discrimination of managed species without less frequent trawl sampling (Holliday 1972, Stanton et al. 2010, 2012, Bassett et al. 2016, Mursaline et al. in review ICES JMS). The second is application of a probabilistic echo classification approach ‘APES’, Urmy et al. 2023 that can leverage low-frequency from ELF, newly available wideband acoustic backscatter data (Bassett et al. 2018, Andersen et al. 2024, Levine et al. 2025), as well as standard multifrequency narrowband data collected during acoustic-trawl surveys for stock assessment. Surveys of walleye pollock (Gadus chalcogrammus), one of the largest fisheries in the world, will be used as the primary test case, though the method is generally applicable to any acoustic-trawl survey. Computation and storage for this work may be done in on prem or cloud environments using software such as pyEcholab and Echoview. The results will be compared to traditional survey outputs that are used in stock assessment.
APES
APES in the cloud demo
pyEcholab
References
Andersen, L.N., Dezhang Chu, Harald Heimvoll, Rolf Korneliussen, Gavin J. Macaulay, Egil Ona. 2024. Quantitative processing of broadband data as implemented in a scientific splitbeam echosounder. Methods in Ecology and Evolution 15(2): 317-328, https://doi.org/10.1111/2041-210X.14261
Bassett, Christopher, Alex De Robertis, and Christopher D. Wilson. 2018. Broadband echosounder measurements of the frequency response of fishes and euphausiids in the Gulf of Alaska, ICES Journal of Marine Science, Volume 75, Issue 3, May-June 2018, Pages 1131–1142, https://doi.org/10.1093/icesjms/fsx204
Bassett, Christopher, Thomas C. Weber, Chris Wilson, Alex De Robertis. 2016. Potential for broadband acoustics to improve stock assessment surveys of midwater fishes. J Acoust Soc Am 1 October 2016; 140 (4_Supplement): 3242–3243. https://doi.org/10.1121/1.4970256 (meeting abstract)
Holliday, D. V. 1972. Resonance structure in echoes from schooled pelagic fish. Journal of the Acoustic Society of America, 51: 1322–1332. https://doi.org/10.1121/1.1912978
Levine, Robert, C. Bassett, A. De Robertis. 2025. Broadband and narrowband echosounder signals produce comparable estimates of volume scattering. ICES Journal of Marine Science. https://academic.oup.com/icesjms/article-abstract/82/9/fsaf160/8262704
Mursaline, M.A., C. Bassett, A. De Robertis. In review. On the feasibility of resonance classification for size class inferences of walleye pollock (Gadus chalcogrammus). ICES JMS.
Stanton, T. K., Chu, D., Jech, J. M., and Irish, J. D. 2010. New broadband methods for resonance classification and high-resolution imagery of fish with swimbladders using a modified commercial broadband echosounder. – ICES Journal of Marine Science, 67: 365–378. https://doi.org/10.1093/icesjms/fsp262
Stanton, T. K., Sellers, C. J., and Jech, J. M. 2012. Resonance classification of mixed assemblages of fish with swimbladders using a modified commercial broadband acoustic echosounder at 1-6 kHz. Canadian Journal of Fisheries and Aquatic Sciences, 69: 854–868, https://doi.org/10.1139/f2012-013
Urmy, Samuel S, Alex De Robertis, and Christopher Bassett, 2023. A Bayesian inverse approach to identify and quantify organisms from fisheries acoustic data, ICES Journal of Marine Science, fsad102, https://doi.org/10.1093/icesjms/fsad102
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