Industry-led data collection Stephen Mangi and Thomas Catchpole Applied Fisheries Science and Technology Group (AFST)
• Focus on self-sampling • Appl...
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
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
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
Types of data used in stock assessment Stock assessment 80 40 60 Spawning/R 20
150 100 50
Gear
Yield/Recruit
200
100
250
Vessel
Scientific survey data
0
0
Life history parameters 0
Catch
20
40
60
80
100
Effort
Fishery dependent data
Socio-economic data
• Variation in stock biomass = (recruitment + growth) – (natural death + fishing removal) • Fishing removal = landings + discards • We need a better estimate of proportion discarded • Discards come from observer programmes – low coverage
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
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
Willingness and capacity for collecting data
Main result: Strong preference for recording of activity, little support for size measurement
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
Create incentives Strong support for compensation (monetary, quota) for time required to make financially viable Trust and improved management also highlighted
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
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
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
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
Number of species in observer and skipper’s data Species in discarded catch
Main result Number of species recorded by skippers and observers is largely the same
Species in retained catch
Variability in species abundance (Species diversity index) Skipper
Observer
Discards
0.89
0.88
Retained
0.90
0.91
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
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
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
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
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 collection needs to make commercial sense at vessel level
• Data and its use should be communicated with the fishermen