Download presentation

Presentation is loading. Please wait.

Published byColin Sheridan Modified over 4 years ago

1
WP3.1 TS equations for anchovy and sardine Lead participant: IEO Magdalena Iglesias Harmonization of the acoustic methodology in terms of applied TS equations for anchovy and sardine Harmonization of the acoustic methodology in terms of applied TS equations for anchovy and sardine

2
Methodology followed based on the proposal…… A common protocol for the analysis will be decided A common protocol for the analysis will be decided Literature review of existing TS equations (in the Mediterranean and elsewhere) will be done concerning anchovy and sardine. Literature review of existing TS equations (in the Mediterranean and elsewhere) will be done concerning anchovy and sardine. The different TS equations will be applied in past acoustic data from each area and comparisons of the resulting abundance estimates will be made. Each participant will be responsible for the analysis of the data from each respective area based on an agreed, common protocol. The different TS equations will be applied in past acoustic data from each area and comparisons of the resulting abundance estimates will be made. Each participant will be responsible for the analysis of the data from each respective area based on an agreed, common protocol.

3
Currently applied TS equations in Aegean Sea Aegean Sea Anchovy-71.2 Sardine-72.6 No TS equations available based on in situ estimations with the split beam method

4
Data requirements for in situ TS estimation Appropriate TS detection parameters Appropriate TS detection parameters Fish communities scattered enough to limit multiple targets detection Fish communities scattered enough to limit multiple targets detection Roughly monospecific fish assemblages Roughly monospecific fish assemblages Distinct length modes Distinct length modes Relationship between modes in TS distribution to catches distribution Relationship between modes in TS distribution to catches distribution

5
HCMR data for in situ TS equations Night trawling 2004-now Night trawling 2004-now Echoes were collected during trawling Echoes were collected during trawling 172 trawls in total 172 trawls in total 66 cases >90% anchovy by weight 66 cases >90% anchovy by weight 12 cases >90% sardine by weight 12 cases >90% sardine by weight 92 cases where 1 dominant species occurs either anchovy or sardine >60% by weight 92 cases where 1 dominant species occurs either anchovy or sardine >60% by weight The majority refers to 38kHz with 23 referring to 120 kHz The majority refers to 38kHz with 23 referring to 120 kHz

6
Issues for discussion Harmonize the analysis up to the maximum degree possible Harmonize the analysis up to the maximum degree possible Propose possibly the same software for all studies Propose possibly the same software for all studies Myriax Echoview Myriax Echoview Movies + (Ifremer Data) Movies + (Ifremer Data) Decide on a common protocol for analysis that will facilitate comparisons and increase the credibility of our work Decide on a common protocol for analysis that will facilitate comparisons and increase the credibility of our work

7
Ideas-Suggestions on hauls selection Night hauls preferably Night hauls preferably Monospecific hauls >90% or >85% by weight Monospecific hauls >90% or >85% by weight Having an idea on the mean TS per species if possible from monospecific hauls then Having an idea on the mean TS per species if possible from monospecific hauls then In the case of discrete TS and Length modes also hauls with 2 species and less percentage can potentially be used In the case of discrete TS and Length modes also hauls with 2 species and less percentage can potentially be used

8
Single target selection parameters Pulse length Pulse length Min TS threshold Min TS threshold Max gain compensation Max gain compensation Max phase deviation Max phase deviation Min echo length Min echo length Max echo length Max echo length Suggested by WGACEGG 08

9
Fish Track detection algorithm Echoviews default alpha and beta values, which define the algorithms sensitivity to unpredicted changes in the targets position and velocity, can be used because there was no a priori information on variability in anchovy or sardine swimming behaviour - Track Detection Major axis Minor axis Range Alpha0.70.70.7 Beta0.50.50.5 - Target gates Major axis Minor axis Range Exclusion distance (m) 440.4 Missed ping expansion (%) 000 Weight303040

10
Moreover…. Are there any available acoustic data prior or after trawling ? Are there cases where two hauls are applied to the same echogram? Decide on the approach to follow Acoustic Target Selection for analysis: Entire water column or based on the hauling depth? Acoustic Target Selection for analysis: Entire water column or based on the hauling depth? Filtering based on Tortuousity : Filtering based on Tortuousity : is used to measure the linearity of a targets swimming path. A perfectly linear track is indicated by a tortuosity value of one. To ensure that tracks were derived from a single individual and not multiple fish in close proximity, any track with a tortuousity value greater than 1.2 was excluded from analysis. Density filtering: Density filtering: Can we set a certain echo density above which we assume high occurrence of multiple echoes and overestimation of TS, therefore we do not consider these areas/ hauls? Barange & Soule 1996: 1 target per sampled volumeBarange & Soule 1996: 1 target per sampled volume Ona & Barange 1999: N of 0.03/m3 corresponds to only a one percent probability of multiple targets detected within the sample volume.Ona & Barange 1999: N of 0.03/m3 corresponds to only a one percent probability of multiple targets detected within the sample volume.

11
Regression analysis Previous studies have primarily used two methods to regress target strength and length: Previous studies have primarily used two methods to regress target strength and length: the average target strength (Foote, 1980b; Miyanohana et al., 1990).the average target strength (Foote, 1980b; Miyanohana et al., 1990). Suggested Total Length bin range for analysis : 0.5 cm, L-W relationship to be estimated Suggested Total Length bin range for analysis : 0.5 cm, L-W relationship to be estimated Use mean TL per haul (weighted with total abundance per hour sampling hour if more than 2 hauls are assigned to 1 echogram) Use mean TL per haul (weighted with total abundance per hour sampling hour if more than 2 hauls are assigned to 1 echogram) Two regressions can be conducted for each data set: Two regressions can be conducted for each data set: 1) best-fit, and1) best-fit, and 2) a slope forced to 20 (Foote, 1979).2) a slope forced to 20 (Foote, 1979). Can we apply both and use a t-test (α=0.05) to test whether slopes of the best-fit regressions are significantly different from 20. Can we apply both and use a t-test (α=0.05) to test whether slopes of the best-fit regressions are significantly different from 20. Regression fit will be evaluated based on least squares Regression fit will be evaluated based on least squares

12
Agree on common TS analysis export parameters Decide on a minimum of parameters of TS export in order to be able to post-process output and apply some filtering if necessary Decide on a minimum of parameters of TS export in order to be able to post-process output and apply some filtering if necessary Agree on a common table to present results Agree on a common table to present results

13
Planning of work Kick off meeting (March 2010) Kick off meeting (March 2010) First results of biomass estimates with different TS equations First results of biomass estimates with different TS equations First results of TS estimates per area First results of TS estimates per area Joint TS estimation (November 2010) Joint TS estimation (November 2010) Interim Report (beginning March 2011) Interim Report (beginning March 2011) Biomass estimates with agreed TS equations Biomass estimates with agreed TS equations Suggestions, Advice Suggestions, Advice Final Report (beginning March 2012) Final Report (beginning March 2012)

Similar presentations

Presentation is loading. Please wait....

OK

Objectives 10.1 Simple linear regression

Objectives 10.1 Simple linear regression

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Ppt on new company act 2013 Ppt on care of public property record Ppt on digital door lock system Ppt on wildlife conservation Ppt on sports day outfits Ppt on the road not taken theme Ppt on church leadership Ppt on hindu religion holidays Ppt on economic development and growth Ppt on db2 mainframes learning