WP 6 progress

Cefas, Imperial (IC), AZTI, JRC

(all participants)

Reminder objectivesReminder objectives

• Numerically describe fishers’ responses to (i) alternative enforcement regimes and (ii) changes in enforcement intensity

• Evaluate potential consequences of alternative regimes combined with other fisheries management methods

• Be used afterwards

TeamTeam

• CEFAS: overall responsibility for software and “architecture”

• IC: modules for calculating new functions (penalty prob.; enforcement cost)

• AZTI: links FLR to databases• JRC: testing solutions for less experienced

users (web access version)

Progress so farProgress so far

Version 1.1 (initial version as presented at the London progress meeting)

 

1. Input of enforcement effort – cost data and enforcement effort - probability of detecting infringement (π(e)) data, and fit appropriate models;

 

• Model selection criteria and multiple fitting methods are used to choose the most parsimonious of several different enforcement – effort and enforcement – π(e) relationships

 

• Users can define their own effort – cost, effort - π(e) relationships

 

• Graphical illustration of the fitted relationships and data / user defined relationships

Progress so far (2)Progress so far (2) 2. Investigate the quantitative relationships between costs and benefits (social

benefits, private profits, level of harvesting) with changing system parameters and variables (for example, the cost of fishing, price of fish, magnitude of fines, shadow value of biomass etc.)

 • Customise the COBECOS object to any given case study (e.g. fines, lambda)

 • Optimise for the most socially beneficial combination of enforcement efforts

 • Include stochasticity in the prediction of illegal harvest, social benefits and

private benefits that accounts for fit of effort – cost and effort – π(e) relationships

 • Visualise the effect of all model parameters on the level of social or private

benefits in more than one dimension (i.e. you can produce a surface of social benefits over a range of shadow value of biomass and enforcement effort)

Progress so farProgress so far

3. Compare a number of different scenarios (compare COBECOS objects) both graphically and numerically. (The storage of stochastic simulations in the COBECOS object coupled with the functions of R allow the user to evaluate useful summary statistics about different scenarios).

 4. Simulate data from hypothetical effort - cost and effort - π(e)

relationships to investigate the properties of the model 5. Follow a tutorial that guides users through the full range of

functions and capabilities of the COBECOS program

• TUTORIAL and USER GUIDE(can make changes ~ D6 fully functioning, full documented)

Suggested changesSuggested changes• Users can now specify an exponential relationship (e.g. increasing cost

efficiency with effort) between effort and cost that includes an intercept.

 • Multivariate effort – probability of detection modelling. Currently the user

must specify marginal relationships between each enforcement type and the probability that an infringement will be detected. This is unrealistic because in many cases there is a single recorded response variable (π(e)) for a combination of enforcement types at different levels. Modelling the surface of π(e) over multiple enforcement types could potentially resolve this(?)

• Or we use standardise description of enforcement ~ software can not solve what is in effect a conceptual issue (especially in the context of “risk-based” enforcement by agencies)(i.e strategies by enforcement agency to maximise detection at certain times)

?

Enforcement relationshipsEnforcement relationships

Enforcement relationshipsEnforcement relationships

??

? ?

Uptake: implementation Uptake: implementation Case study Cobecos software Bio-economic model

1 ?

?

2 ?

FLR object (FLRstock), projection of stock; via coded methods

3 ?

?

N= x

Need case study uptake – “helpful” if try to use it even if have other code

Uptake: implementation Uptake: implementation Case study Cobecos software Bio-economic model

1 ?

?

2 ?

FLR object (FLRstock), projection of stock; via coded methods

3 ?

?

N= x

Need case study uptake!

Shadow value of biomassSOCIAL BENEFITS

Cost versus prob of infringements

Uptake: implementation Uptake: implementation Case study Cobecos software Bio-economic model

1 ?

?

2 ?

FLR object (FLRstock), projection of stock; via coded methods

3 ?

?

N= x

Need case study uptake!

Case study specificity~ high