1. TDW-12: 23-27th April 2018, Noumea, New Caledonia
E-Monitoring data
Introduction:
There’s a lot of people who have not met me, as this is to be my first TDW, My name is Eparama Loganimoce, from Fiji. A lot of people call me Epa in sort and you are most welcome to call me by that, if you want to. It has been 10 months now since I joined the Data Management team here in SPC and it is a pleasure to be supporting you in my role and also to be a part of this workshop. My role is the Emonitoring technical person for the region and basically I am heavily involved in receiving your Emonitoring data and importing them into Tubs. And I work very closely with Malo who is the Regional coordinator for Emonitoring and also of course with Manu, Peter Williams and our development team. So that’s basically what I do.
TDW-12: th April 2018, Noumea, New Caledonia

2. Overview E-Monitoring data summary (MH, PW, FM, FJ)
Current challenges and improvements
Aggregated catch summary (Tropical Longline Fishery)
EM reports in Dorado
EM trials - Preliminary findings (from data)
A quick overview of what this short presentation is about. I will be showing you a few slides on summary of Emonitoring data that we have so far in Tubs and those are the four countries involved so far in the ongoing preliminary period and they are Marshall Islands, Palau, FSM and Fiji
I will briefly talk on a few challenges we faced and ongoing improvements that the project team are taking.
An example of an Aggregated catch summary.
EM reports in Dorado.
And I will close with some of the findings we have learnt so far in the EM project.

3. EM data in TUBS Current challenges: Long analysis time
Submitted
Imported
Issues
Marshall 56
Palau 11
FSM 5
Fiji
150
130 20
Total
222
202
Current challenges:
Long analysis time
New initiatives to improve:
-Automatics hook counting algorithm(ongoing)
Data issues/errors
-Real-time error detections on SVM during analysis
This is a summary of EM data that we currently have in TUBS….
So in total we have just a bit over 200 EM trips.
Fiji being having the most number of trips as they started EM well before the rest of the countries with the first batch of their EM data dating back to 2015.
We also have some trips from Fiji which have some issues that are related to the data and I am sure the EM analysts in Fiji are working hard to solve this issues on their SVM, which is the video reviewing software.
The biggest challenge faced are the long analysis time that is needed to review the footages of the fishing trips. And our team is working hard by introducing new initiatives to improve this. A good example is our data scientists are working on an algorithm that will estimate the number of hooks, without the need for the EM analysts to count them one by one. So that is good example of how we are trying to fasten the work done by the EM analysts.
Another main challenge are the data errors that is stopping data being imported into TUBS. And the team is working closely with our tech service provider to build routines in the videos review software that are able to detect the errors when they are made during review time, thus clean data coming across the countries.
Also a debriefing tool in the near-future to be used for improved data accuracy.

4. Aggregated Catch Summary- Tropical Longline Fisheries
EM covers ALL species catch
EM has better coverage than on-board observers
On this slide I have an aggregated catch summary for our Tropical Longline fisheries in other words from Marshall and Palau, showing total number of catch in EM and compared to Longline and OnBoard observer data. You can find some interesting observations
EM(in blue) basically is covering for all catch species, whereas Logbooks and Onboard observers are only reporting for some the species.
Apologies if this slide is not visible from the back of the room, but try to get this presentation after this.
Purpose of this slide is not to make any bad impression on any mean of data collection but rather to see EM as a way you can verify data collected from Logsheets, Unloadings, Port sampling, etc.

5. EM data in Dorado Report categories: List of all EM trips(summary)
List of EM trips with corresponding Logsheets/Observer/Unloading/Port sampling
Species composition EM vs Logsheets/Observer/etc
Fate composition
Length Estimates
Just like for any other tuna fisheries data that we have, we are using Dorado to present your EM data back to you. I would like to show how to get to these EM reports.

6. Species Composition
EM vs Logsheet vs Observer

7. Species Composition
EM vs Logsheet vs Observer

8. Trip-Level Species Composition: EM vs Observer vs Logsheet
YFT-BET Species composition problem on logsheets
Observer data cover minor bycatch (not reported on logbooks)

9. EM trials – Preliminary findings
EM provides better coverage (100%) than on-board observers
EM data used to validate logbook data
Some challenges remain (e.g. video review time)
... but ongoing focus to improvements in system/SOPs…
EM reports available in Dorado
Potential to supplement On-board observer coverage
Compared to Onboard observers, EM camera are expected to capture everything given there are no technical complications whereas for Onboard observers, you will have to consider observers are likely to miss out on some fishing activities eg when observer getting sick during the trip on taking a nap during hauling. So talking about human limitations there.
EM to be only used a a mean of validating your logbook data.
Some challenges remain and improvements are ongoing. The major improvements are being directed towards getting timely data from the EM analysts and to improve standard operating procedures.
EM reports are available in Dorado and please do not hesitate to send us your reporting requirements.
Lastly we are seeing a potential in EM to supplement On-board observer coverage.
With that point I would like to end this presentations. And happy to answer if there are any questions.

10. Thank You
Any questions?