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Aquaculture conference Technical Day, Nelson 2015

13c1e126e91944499df10d649c4aeec9?s=47 jatalah
September 17, 2015

Aquaculture conference Technical Day, Nelson 2015

13c1e126e91944499df10d649c4aeec9?s=128

jatalah

September 17, 2015
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  1. BLUE MUSSEL OVER-SETTLEMENT PREDICTIVE MODEL JAVIER ATALAH CAWTHRON INSTITUTE, NELSON

    Fisheries & Aquaculture research series 16th of September 2015, Nelson
  2. NEW ZEALAND'S GREEN MUSSEL AQUACULTURE • The most important aquaculture

    sector $US218M pa (73% of NZ aquaculture revenue) • The Marlborough Sounds the largest farming area • Reliant on wild caught juveniles (spat) Photo: Kevin Heasman
  3. BIOFOULING AND THE MUSSEL INDUSTRY • Significant threat to the

    industry • High biomass and density of biofouling • Strong competitors for food and space • Blue mussel (Mytilus galloprovincialis) major problem
  4. BLUE MUSSEL OVER-SETTLEMENT IMPATCS • Spat lines heavily over-settled are

    dumped • Blues carried through 1-2 grow-out stage to “final seeding” • Removed by size grading or optical sorting • Crop loss when lines lifted from water • Increased weight and extra floatation • Increased factory processing costs • ~NZ$20 million p.a. (ca. 12% of regional revenue)
  5. IMPACTS OF BLUES ON ANNUAL PERNA YIELD

  6. RATIONALE AND OBJECTIVES • Few management options available • Considerable

    operational problems • How can be avoid? Long term and seasonal patterns Model response to drivers Forecast and inform farmers Blue = Bad Green = Good
  7. SPAT MONITORING HISTORICAL MFA SPAT DATASET Blue and green spat

    monitoring ~40 yr Collector rope deployed for 2 wk 2, 4, 10 and 15 m depth Overall >50 sites
  8. SAMPLING SITES

  9. La niña El niño SOUTHERN OSCILLATION INDEX

  10. BMPO historical dataset Environmental data BMPO + environmental data data

    Predictive model Web app to forecast blue and green spat settlement
  11. LONG-TERM TEMPORAL TREND PELORUS Blues Greens

  12. PELORUS SEASONAL RECRUITMENT PATTERNS Blues Greens

  13. RECRUITMENT AND TEMPERATURE Blues Greens Temperature (° C)

  14. DEPTH PATTERNS Greens Blues

  15. PREDICTIVE MODEL • Spatio-Temporal Bayesian Models • Latent Gaussian models

    using integrated nested Laplace approximations • INLA – R package
  16. http://www.r-inla.org/ SPATIOTEMPORAL STRUCTURE

  17. THE WEB APP https://cawthron.shinyapps.io/BMOP/

  18. None
  19. THE BLUES RADAR

  20. FUTURE WORK • Refine web app and launch it •

    Select and validate predictive mode • Input live data into model and app • Spat counts using imagery analysis • Feedback from end-users shiny@cawthron.org.nz • Mobile devices app
  21. THANKS Marine Farming Association Hayden Rabel, Biosecurity team, Modelling team

    Sustainable Farming Fund (MBIE) Culture Shellfish Programme (MBIE) NIWA, MSQP