Detection method for diel vertical migration pattern using 2D cross‐correlation with ADCP backscatter time‐series data

Diel vertical migration (DVM), which refers to the global daily migration of zooplankton and micronekton (hereafter ‘zooplankton’), serves an important function in oceanic ecosystems. DVM can be recognized as a variation in the sound scattering layer (SSL), but no standard exists for identifying SSL...

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Bibliographic Details
Published in:Methods in ecology and evolution 2022-07, Vol.13 (7), p.1475-1487
Main Authors: Chun, Sehwa, La, Hyoung Sul, Son, Wuju, Kim, Young Cheol, Cho, Kyoung‐Ho, Yang, Eun Jin
Format: Article
Language:English
Subjects:
acoustic backscatter
Acoustics
ADCP
Automation
Backscattering
Correlation coefficient
Correlation coefficients
cross‐correlation
diel vertical migration
Inspection
Marine ecosystems
Marine environment
Parameter identification
Quantitative analysis
Sound scatter
sound scattering layers
Vertical migrations
Zooplankton
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Summary:Diel vertical migration (DVM), which refers to the global daily migration of zooplankton and micronekton (hereafter ‘zooplankton’), serves an important function in oceanic ecosystems. DVM can be recognized as a variation in the sound scattering layer (SSL), but no standard exists for identifying SSLs, and no definition has been proposed to describe the basic features of DVM. Hence, a standardized DVM detection method is needed to report consistent results and efficiently compare the parameters over broad ocean areas and long time‐scales. We developed an automated, quantitative method for detecting DVM and identifying the characteristic parameters using 2D cross‐correlation. We established the DVM trajectory model with linear and sinusoidal parts featuring a specific height and width. The 2D cross‐correlation method was applied to acoustic echogram images of the volume backscattering strength (Sv, dB re 1 m−1) with a synthetic image made from the DVM trajectory model. From the cross‐correlation coefficients, we found the candidate lines exhibiting the strongest possibility of being DVM trajectories. We tested the DVM detection method on the acoustic echograms for 273 days, of which Sv was gathered by a bottom‐moored, upward‐looking acoustic Doppler current profiler (ADCP). For each identified DVM trajectory, four quantitative parameters describing their spatial and temporal structures (the maximum and minimum depths and ascent and descent times) were determined, and the existence of multi‐layer was recognized. We confirmed that this method showed better performance than the prior method based on the weighted mean depth (WMD), and as a result of visual scrutiny inspection, our method showed 88% DVM detection performance. Our goal was to suggest a robust, quantitative, automated DVM detection method using 2D cross‐correlation. The method was successfully applied to detect DVM trajectories and define the characteristic parameters regardless of fluctuation or the multi‐layer structure due to its robustness and simple model. Analysing DVM behaviours across environments with this method could help reveal the significance of these behaviours in ocean environments.
ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.13871