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# Lecture 4 topics (Stock assessment I: depletion or removal models) - PowerPoint PPT Presentation

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

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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

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

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

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)

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