Management plans Dankert W. Skagen
A management plan includes: ● A rule (Harvest Control Rule) for deciding exploitation from year to year, according to the state of the stock. ● A system for monitoring the fishery and the stock (catch statistics, sampling, surveys, assessment) ● Rules and instruments for implementation and enforcement ● Instruments for revisions
Harvest control rules (HCRs) Can be rules for setting TACs, effort, area closures etc. Most often a rule for setting a TAC adapted to the estimated stock abundance. A well designed rule should: ● Cope with likely variations in nature ● Give satisfactory conditions for the industry ● Be adapted to the quality of the information about stock status that is available
Some examples of types of rule for setting TAC: 1: F-rules: Set TAC according to a fixed target F Reduce the target F if SSB is below some trigger level. 1a: As 1, but do not allow more than xx% change in TAC from year to year 1b: Derive the TAC-value according to rule 1, and set the final TAC midway between that and last years TAC 1c: As 1, but also allow a higher F if the SSB is above some level. 2: Apply a fixed TAC, and keep it unless there is sufficient evidence that it needs to be changed. Evidence can be full stock assessments, environment indicators, survey results, or trends in fishing mortality or SSB estimates. Variants: ● Multiannual decisions ● Separate rules for separate fleets ● Supplemented with effort regulations, area/seasonal closures etc.
Reference points: Most rules specify action in terms of targets , depending on where we are compared to trigger levels. For example: Apply a fishing mortality of 0.2 when the SSB is greater than 2.5 million tonnes. If it is lower, reduce the fishing mortality proportionally to the SSB. Here, there is a target F of 0.2, which can be reduced, and a trigger biomass of 2.5 million tonnes. These triggers and targets determine the performance of the rule in terms of yield and risk of depleting the stock, and should be evaluated by simulations. They are conceptually different from precautionary reference points, and should be set independently of the pa-values.
Some experience with rule performance: ● What matters mostly at the end of the day is the effective F that the rule leads to. ● The trigger level for reducing F has little influence if it is large enough. ● Rules to stabilize TACs (“15% rules”) can have strange side effects e.g. if the stock is increasing. (examples: North Sea herring, North-East Arctic cod) ● Very complicated rules usually create unforeseen problems. ● The performance of a rule is basically a question of how well it copes with changes in nature.
What is a feasible Fishing mortality? What effective F should the rule lead to? Yield at Target F 600 1.200 Y Fix R 500 1.000 Y S_R s 400 0.800 e Y mean i t i d l Y10 i 300 0.600 l b e a Y50 i Y b 200 0.400 o Y90 r P 100 0.200 Risklim F0.1 0 0.000 0 0.1 0.2 0.3 0.4 0.5 Target F A feasible range would be from about 0.18 (=F0.1) to where the risk starts to increase But the point where the risk increases is a function of the assumed recruitment level
Some experience with the process: Essential factors: ● Very careful consideration to how the population model is conditioned. ● Insight in the strengths and weaknesses in the data that go into the assessment ● Interpretation of the management rule - it must be programmable ● Insight in priorities and preferences ● Experience in how rules can be made workable. These processes take time. Communication between stakeholders is essential: ● Explore ideas from all parties – this is a creative process. ● Mutual understanding ● Confidence in the process and results
Testing of rules: Population model External factors True stock Real removals Implementation Observation model model Percieved stock Decided removals Decision model ● Create a workbench with elements with plausible properties. ● Run it numerous times with random numbers for uncertain values ● Look at the range of performance measures
Testing of rules: Conditioning of the simulations. Biology and fishery ● Recruitment scenarios ● Growth-maturity ● Selection at age ● Natural mortality Assessment: How good performance can we expect? Communication: ● Performance criteria ● How to make results understandable.
Further on conditioning Recruitment Blue whiting - 2010 assessment 80000 8000 70000 7000 60000 6000 Recruitment 50000 5000 Recruit SSB 40000 4000 SSB 30000 3000 Mean 20000 2000 10000 1000 0 0 1980 1985 1990 1995 2000 2005 2010 Year ● Periods with different recruitment ● No clear dependence on SSB ● Spikes ● Last period still uncertain Scenarios?
Weight at age Weight at age 0.3 0.25 1 2 0.2 3 Weight (kg) 4 0.15 5 6 0.1 7 8 0.05 9 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year ● Downward trend at least since 2000 ● Density dependence? ● Competition in the environment?? Worth modelling?
Natural mortality One clue may be the proportion of 10+ in the catches in the first years. We do not know the exploitation before 1981. The table shows the equilibrium F giving the historic percentages with different Ms. Percent M=0.10 M=0.15 M=0.20 10+ 1981 25 0.13 0.09 0.05 1982 20 0.16 0.12 0.08 1983 7 0.33 0.29 0.24 Hard to draw conclusions, but food for thought. A change in M will alter the assessment as well!
Selection at age Selection at young age Estimated with F at age relative to F3-7 AMCI 0.9 0.8 Poor estimates 0.7 0.6 1 of most recent Selection 0.5 2 0.4 3 year classes 0.3 0.2 0.1 is excluded 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year ● Less targeting now of juveniles – why? ● No sign of increased targeting of the big year classes
Assessment error Quite severe for Blue whiting Can we expect improvement?
Possible managment rules Still very open Key point: What to do when productivity changes?
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