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Including climate into the assessment of future fish recruitment, using multiple regression models.

Including climate into the assessment of future fish recruitment, using multiple regression models. by Jan Erik Stiansen Bjarte Bogstad Asgeir Aglen Einar Svendsen Harald Loeng Sigbjørn Mehl Odd Nakken. Climate-fish stocks relations have, with a few exceptions,

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Including climate into the assessment of future fish recruitment, using multiple regression models.

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  1. Including climate into the assessment of future fish recruitment, using multiple regression models. by Jan Erik Stiansen Bjarte Bogstad Asgeir Aglen Einar Svendsen Harald Loeng Sigbjørn Mehl Odd Nakken

  2. Climate-fish stocks relations have, with a few exceptions, not been implemented in the assessments Climate information are mostly used as background information Present how multiple regression models can implement climate into assessment without having to make any large changes in the way the work is carried out today Illustrate by three models with predictive power

  3. Method procedure Method is simple: Decide what to model Use stock parameters and climate variables in a multiple regression It must have time lag, which gives the projective power There should have a plausible cause-and-effect mechanism (keyword: life cycle) Don’t use to many parameters in model (“rule of thumb” : 10 data points pr variable) Try and fail, try and fail,try and fail,try and fail,try and fail,try and fail, try and fail, ……….., Success ! ?

  4. Where can the method be implemented • - Short-medium term recruitment predictions • Growth predictions (length-at-age, weight-at-age) • other areas ?

  5. The choice of variables will always be a trade-off between • - best possible fit • - the presences of a time lag • updating as close to the working groups as possible • Trust in the chosen variables

  6. Three models presented: Barent Sea capelin Rec1 ~ Skintemp + 0group + SSB (1 year prognosis) t t-1 t-1 t-1 Norwegian spring spawning herring Rec3 ~ - Skintemp + 0group (3 year prognosis) t t-3 t-3 Northeast Arctic cod (2 year prognosis) Rec3 ~ Temp(Kola) + Rec1 + SSB(capelin) t t-3 t-2 t-2

  7. R2=0.72 n=22 all P < 0.03 Rec1 ~ Skintemp + 0group + SSB t t-1 t-1 t-1

  8. R2=0.85 n=35 all P < 0.02 Rec3 ~ - Skintemp + 0group t t-3 t-3

  9. R2=0.81 n=21 all P < 0.01 Rec3 ~ Temp(Kola) + Rec1 + Capelin(SSB) t t-3 t-2 t-2

  10. Red line: first year prognosis Blue line: second year prognosis

  11. ”Error” in prognoses made each year by official prognosis and regression model compared to latest assessment official 1 official 2 regression 1 regression 2

  12. NEA cod recruits (age 3) vs SSB adjusting for climate Recruits - temperature SSB (1983-2003) x 10 (1980-2000)

  13. Conclusions Regression models are easy to implement into some parts of the assessment This is a good approach for incorporating climate Need prognostic power in regression models (e.g. time lag) Have been successfully implemented into the Barents Sea Capelin prognosis at last assessment

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