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Lecture 4 topics (Stock assessment I: depletion or removal models)

Lecture 4 topics (Stock assessment I: depletion or removal models). Stock assessment aims to reconstruct historical abundance change as a basis for prediction of future changes

tatyana-clemons
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Lecture 4 topics (Stock assessment I: depletion or removal models)

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  1. Lecture 4 topics(Stock assessment I: depletion or removal models) • Stock assessment aims to reconstruct historical abundance change as a basis for prediction of future changes • Reconstruction involves three fundamental model components: state dynamics, observation process, and statistical criterion • If a removal of X fish causes abundance to drop by 20%, stock must have been X/0.2

  2. Parameter estimation and state reconstruction for dynamic models Observation errors Process errors Parameters Observation Model (predicted y) Data (observed y) State dynamics Model N Statistical criterion y N Historical inputs (catch, effort yt=qNt Nt+1=Nt-Ct Log-likelihood function Parameter No Parameter q

  3. There is a serious and unresolved issue about how to model the process and observation errors; options include: • Observation error (mean trajectory) approach: ignore process errors in making state predictions over time • Process error approach: explicitly estimate some process error values as arbitrary historical “inputs”, e.g. recruitment anomalies • State space approach: integrate over all process errors in calculating likelihood of the data

  4. There is also a serious and unresolved issue about how to represent catch (harvest process) in state dynamics; two simple options are: • “condition on catch”: simply subtract observed catches from model abundances without modeling harvesting process, ie treat catch as disturbing “input” and relative abundance change as predicted “output” (Leslie method) • “condition on effort”: model (predict) catches as function of abundance and fishing activity as an “input”, predict catch as “output” (Delury method)

  5. The single most powerful assessment method is to use u=C/N in reverse:N=C/uwhat are your options for estimating u? • Catch curves (Z-M) • Recapture rates from tagging studies • Swept area methods U=qE, q=a/A • Depletion models • Complex assessment models using multiple information sources about changes in relative abundance and age/size composition

  6. This is what you don’t want to happen!

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