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DJDankel CI-HCR Hjort Symposium

Bergen, October 9, 2014

Dorothy Dankel

on 21 June 2016

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Transcript of DJDankel CI-HCR Hjort Symposium

low risk of collapse?
stable yield?
robust against uncertainties?
Uncertainty has consequences: the case for the Confidence Interval Harvest Control Rule (CI-HCR)
Why a CI-HCR?
What's the issue?
Dorothy J. Dankel, Sondre Aanes, Jon Helge Vølstad
Centre for the Study of the Sciences & the Humanities, Univ. of Bergen;Institute of Marine Research, Bergen;
Norwegian Computing Center (Norsk Regnesentral)
Prediction errors
Independent identically distributed log normal errors
i.e. Myrseth, Enberg, Heino & Fiksen (2011)
Empirical Error
contains both the systematical error and the random error
This is probably the most realistic situation.

Compared 1 year prediction from AFWG reports 1991-2014 with converged VPA. Used empirical data & found that 1 year prediction is closest associated with converged VPA the year before.
= a systematic time lag in predictions
(causes retrospective bias in prediction)
RSE can be controlled by sampling!
(Aanes, Subbey, Vølstad in prep.)
a result of the assessment process itself
low risk of collapse?
stable yield?
robust against uncertainties?
new formulations of the traditional harvest control rule:
Interval & Confidence Interval HCRs
tested with random error and systematic error
although sensitive to assumptions, the new formulations are more robust to realistic (systematic) uncertainty
The advantage of CI-HCR is that the advised quota will depend on the quality of the assessments. Also, the adequate level of monitoring for advice support can be determined based on what science can actually provide.
% loss Yield
CV Yield
Independent identical error
Systematic error
Relative standard error
least loss of Y
stable Y
least risk
CI-HCR has
Fish are normal, fisheries are post-normal
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