Industry-led data collection

Transcription

1 Industry-led data collection Stephen Mangi and Thomas Catchpole Applied Fisheries Science and Technology Group (AFST) Focus on self-sampling Applicability of different approaches / technologies to catch documentation reviewed through FSP project

2 Outline Drivers of data collection Data used in stock assessment Strengths / opportunities for industry-led data collection Weaknesses / threats towards industry-led data collection Self-sampling in the Inshore sector (SESAMI) trial Recommendations on how to set up data collection

3 Drivers of data collection Effective fisheries management requires many different types of data Effective and affordable methods of collecting such data are needed to provide information for policy formulation and management plans 1. Industry themselves Demonstrate compliance with management measures greater degree of self-management access to improved fishing opportunities 2. New CFP Requirement for full reporting of fishing activity accountability for all catches and not just landings 3. Science Increase the quantity and quality of data for scientific assessments Improve stock assessments Using scientific observers to collect information on commercial catches is very expensive hence low coverage of observer programmes

4 Types of data used in stock assessment Vessel Stock assessment Gear Yield/Recruit Spawning/R Scientific survey data Life history parameters Catch Effort Fishery dependent data Variation in stock biomass = (recruitment + growth) (natural death + fishing removal) Fishing removal = landings + discards We need a better estimate of proportion discarded Socio-economic data Discards come from observer programmes low coverage

5 Industry-led data collection: Strengths / opportunities Industry-led data collection provides Continuous Broad area High-resolution sampling Large numbers of ships of opportunity Potentially, its an efficient way of collecting commercial fishery data Enables industry to work closely with scientists to improve stock assessments It is better to sample a few fish from many locations than to sample many fish at each of a few locations

6 Industry-led data collection: Good practice Clear objectives of data collection How the data will be used Confidentiality Good communication trust and transparency among the different parties manage expectations Quality control - cross-checking of data with VMS, logbooks and observers Training Adequate financing - quality control methods, scientific analysis

7 Willingness and capacity for collecting data Main result: Strong preference for recording of activity, little support for size measurement

8 Practicalities of data collection Advantages of data collection Disadvantages of data collection Weather considered to have an appreciable impact Additional effort required Practical considerations Time/ additional crew and space (fish handling, storage and quarters) also important

9 Create incentives Strong support for compensation (monetary, quota) for time required to make financially viable Trust and improved management also highlighted

10 Industry-led data collection: Weaknesses / threats Requires strict protocols for data collection Voluntary participation leads to concerns about sampling bias May not work well for contentious, rare or protected species where there might be an incentive to misreport Rapid decline of enthusiasm if expected benefits are not realised Requires verification and auditing to maintain data quality Extensive training for fishers may be required

11 SESAMI trial (1) To document fishing patterns and catch composition of under tens Skippers record fishing effort, retained and discarded proportions of catch specific gear used mesh and hook sizes, net length, soak time Weight and length of various species Reasons for discarding Data collection phase: August 2012 to August 2013; Extended to end of March 2014 for vessels in S East Direct payment

12 SESAMI trial (2) 2,567 days of data from 30 vessels Mainly netters and hand liners Conducted 58 observer trips to validate the data provided by skippers Issued hand-held cameras to 7 under 7 m vessels Feedback on the project from 23 participating skippers

13 SESAMI practical issues Different measures (stone, pounds, kg, counts) Weight Different gear descriptions Gurdy, jigs, mackerel feathers, squid jigs Mackerel drift, drift herring, drift bass Length measurements: Large, medium and small Fishing location: ICES sub-rectangles Comparison of the data collected by skippers and observers from the same fishing trips

14 Number of species in observer and skipper s data Species in discarded catch Species in retained catch Main result Number of species recorded by skippers and observers is largely the same Variability in species abundance (Species diversity index) Skipper Discards Retained Observer

15 Weight of catch from observer and skipper s data Weight of discarded catch Weight of retained catch Main result High variability in discards data by observers No significant difference between weight recorded by observer and skipper

16 Comparisons based on weight per species per day Proportion discarded Proportion retained Main result Weight of discards by skippers is twice that by observer Weight of retained catch collected by skippers and observers is same

17 Can self-sampling deliver the information required to fully-document fisheries? Provided all methods of fishing are sampled Yes, if everyone did this it would better track what is actually caught and where I would like to believe that the majority of fishermen would give a true representation of what they catch/discard when recording data Needs to go on over longer periods to monitor fishing patterns. This is the only way of recording variables in weather patterns. Unfortunately there will always be exceptions to that which is why I believe the data could never be fully accurate without observers Good reliable source of data collection which is easy for the fishermen to accommodate in their routine

18 Recommendations Main issue Needs Recommendation Objectives Clear objectives (e.g. to document catch, biological monitoring, enforce a discard ban) Agree on data required How the data will be used Project steering group with representatives from the key parties Logistics Time for skippers to collect data Appropriate paperwork / suitable for offshore conditions Training Appropriate incentives Data collection needs to be feasible as part of the skipper s workload Data collection needs to make commercial sense at vessel level Coverage Full or partial coverage Informed by cost Data Quality and reliability of data Data ownership Data control / validation Data storage/ use Sampling procedure must be scientifically robust Data and its use should be communicated with the fishermen