Data Processing and Analysis: Literature Update. FISH 538 Emma DeWitt Cotter

Transcription

1 Data Processing and Analysis: Literature Update FISH 538 Emma DeWitt Cotter

2 Calibration Results Acoustic Data Hydrographic data Abundance Estimations Known Target Strength Curves Survey Track Geography of Site Biological Samples

3 Echo data Abundance estimations for a site

4 Big Changes in the Last 10 Years More data! Longer deployments enabled by ocean observatories Use of multi-frequency and broadband devices Faster computers

5 Classification of Coexisting Species Mean Volume Backscatter (MVBS) More accurate, objective information about more species using multiple frequencies More widely used for classification Kang M., Furusawa, M., and Miyashita, M. (2002). Effective and accurate use of difference in mean volume backscattering strength to identify fish and plankton. J. Mar. Sci. 59:

6 Classification of Coexisting Species 1 18 khz 2 28 khz khz Jech, J.M. and Michaels W.L. (2006). A multifrequency method to classify and evaluate fisheries acoustics data. Can. J. Fish. Aquat. Sci. 63:

7 Classification of Coexisting Species Sato, M., Horne, J., Parker-Stetter, S., & Keister, J. (2015). Synthetic echograms generated from the relative frequency response. Fisheries Research, (172),

8 Automated Target Classification Applies an established statistical method (probabilistic clustering) to acoustic data Widespread uses including insurance, biology, and meteorology Determine the intrinsic grouping in a set of unlabeled data Anderson, C.I.H., Horne, J.K., and Boyle, J. (2007). Classifying multi-frequency fisheries acoustic data using a robust probabilistic classification technique. J. Acoustical Soc. Am, 121 (6). Figure courtesy of::

9 Automated Target Classification Each sample is assigned probabilities of membership to each cluster group Determined optimum number of clusters for two data sets Gulf of Alaska: low-diversity, well known system Mid-Atlantic Ridge: high diversity, less well known system Cluster number dependent on site and survey goals Able to capture biological features of data

10 Automated Target Classification Saturation Large Fish Saturation Bottom + Intense Schools Fish High Sv Low Sv Bottom + Intense Schools Small Fish Zooplankton Background Background Zooplankton Gulf of Alaska

11 Automated Target Classification Mid-Atlantic Ridge 2 Clusters: Non noise vs intense noise features 3 Clusters saturation, bubbles, non-noise features 13 Clusters fish tracks, biota, bubbles, saturation, dropped pings

12 Spatially and Temporally Resolved Data First example Jiang et al 2007 Net-based studies do not provide high temporal resolution and are difficult to execute over long periods of time ADCP backscatter data used to observe diel and annual trends in migration ( ) Abundance data shown as a function of time of year, time of day, and depth Jiang S, Dickey TD, Steinberg DK, Madin LP (2007). Temporal variability of zooplankton biomass from ADCP backscatter time series data at the Bermuda Testbed Mooring site. Deep-Sea Res I 54:

13 Spatially and Temporally Resolved Data Jiang S, Dickey TD, Steinberg DK, Madin LP (2007). Temporal variability of zooplankton biomass from ADCP backscatter time series data at the Bermuda Testbed Mooring site. Deep-Sea Res I 54:

14 Spatially and Temporally Resolved Data Sato et al 2013 VENUS Cabled observatory in British Columbia 2 years of echosounder data Observed diel vertical migration of euphausiids (krill) Sato, M., Dower, J.F., Kunze, E., Dewey, R. (2013). Second-order seasonal variability in diel vertical migration timing of euphausiids in a coastal inlet. Mar. Ec. Progress Ser. 480:

15 Spatially and Temporally Resolved Data Sato, M., Dower, J.F., Kunze, E., Dewey, R. (2013). Second-order seasonal variability in diel vertical migration timing of euphausiids in a coastal inlet. Mar. Ec. Progress Ser. 480: