lecture 4 topics stock assessment i depletion or removal models
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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

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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
  • 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
parameter estimation and state reconstruction for dynamic models
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

slide3
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
slide4
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)
slide5
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
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