Stock assessment Stock assessment Multiple aspects: K - PDF document

Stock assessment Stock assessment Multiple aspects: K ������������������������������ � History of stock abundance and exploitation – ���������������������������� experience and present – assessment in the narrow sense ����������������� � Standards for exploitation � Design and evaluation of management strategies We shall look briefly at each of these Assessment is to translate information from How this is done � Catches (numbers at age, i.e. by year class) Two almost equivalent approaches: � Survey measurements 1 . Count the number of fish that has been taken from each year class into Add loss because of other causes (natural mortality) Add what remains at present past and present � Stock abundance That gives the history backwards. � Exploitation (e.g. Fishing mortality) Fine for 'old' year classes but you do not know how much is still present 2 . Make a model population with assumed parameters: Find out how much fish there must have been to: � Account for the catches that have been taken � Recruitments (and initial values) � Mortalities (annual level & selection at age) (that fish must have been there) � Account for additional mortality Derive expected observations (catches and survey results) � The amount still present Find the parameters that give expected observations as close as possible to the real observations. Data quality: What matters for the result? Catches : � Catch statistics Catches: Tell the history back in time � Sampling for age reading, individual weights and maturities Typical problems: � Misreporting Survey data: Present compared to past � Unaccounted discards � Non-random sampling Surveys usually are relative measures – � Age reading calibrate with the past which is known from the catches. Surveys: � Consistency � Coverage (right place at right time – every year) � Sampling for age composition – trawl hauls representative for the stock Typical problems Year, weather, vessel, equipment, interpretation, migrating fish, partial coverage Trawling on registrations, depth stratification, different ages in different areas. 1

Does it matter? Another example with artificial data - The effect of one wrong survey measurement on TAC at Fstatus quo how survey noise propagates to the TAC advice and predicted SSB after the TAC is taken. Artificial data, all perfect except for that survey. Perfect catch data The effect on the TAC advice of a wrong survey index in one year Noisy survey data (random noise + year effects) – CV 20% Index 50% too high in all years. 150 Different assessment methods Percent 100 50 30 0 25 True Last yr 2 yrs ago 4 yrs ago 20 SSB after the TAC is taken D_all_Sep 15 D_all_VPA 1400 D_even_Sep 10 D_even_VPA 1200 1000 5 800 600 0 400 -50 - -40 -30 - -20 -10 - 0 10 - 20 30 - 40 > 50 200 <-50 -40 - -30 -20 - -10 0 - 10 20 - 30 40 - 50 0 True Last yr 2 yrs ago 4 yrs ago Advised increase in TAC – correct is 11% Yield and SSB per recruit: Standards for exploitation Long term equilibrium when everything is constant Two approaches: We look at how the yield and the SSB depend on the fishing mortality. Artificial data. Equilibrium considerations Yield per recruit and SSB – recruit relation Simulations taking into account variation in � Recruitment � Growth � Maturation For F above F0.1, the curve is almost flat. Some stocks will have a distinct maximum, others not Depends on growth rate, selection at age and natural mortality Messages from the yield per recruit Stock-recruit functions How does the recruitment depend on the spawning biomass (SSB)? Above F0.1, it does not pay much in the long term to increase the fishing mortality. Many variants, often difficult to choose Same yield with more work For some stocks the yield will go down with increasing F – the growth potential is not utilized As F increases, SSB goes down. At some F, SSB will reach a level that leads to impaired recruitment. Often, that is the limiting factor. Important: � The SSB where the recruitment gets impaired This is all about long term average, not what you get � What happens if the SSB becomes very large next year � What happens at very low SSB 2

When discussing long term strategies, keep in mind that Combining yield per recruit and stock-recruit and the ecosystems are not static. taking the variations in recruitment into account For large pelagic stocks periods with high and low we get a more complete basis for decisions: productivity is very common. These are processes in nature, but the fishery has to adapt to that. The figure shows the annual catch of some important pelagic stocks. Assumed Hockey stick stock recruitment function. The scale is not the same for all the stocks. Risk is the probability of falling below the break-point. Simulation of management rules To do this kind of study requires : Make a test-bench that is a plausible set of artificial � Very careful consideration on how the population model is populations conditioned. We apply proposed harvest rules to them, to see � Insight in the strengths and weaknesses in the data that how they perform. go into the assessment The harvest rules are applied to a perceived stock, which � Interpretation of the management rule - it must be comes out of assessments. programmable Population model � Insight in priorities and preferences � Experience in how rules can be made workable. True s tock Actual removal Observation model by the fis hery These processes take time. Apparent s tock Communication between stakeholders is essential: Decision rule Implementation � Explore ideas from all parties – this is a creative process. TAC � Mutual understanding Model seque nce � Confidence in the process and results Data flow Some final words about management plans What matters most at the end is the average fishing mortality that the plan leads to. How you get to that level of fishing mortality is a matter of taste For the safety, the plan must allow for sufficient action sufficiently early to cope with unexpected changes in nature. Nature sets limitations to what you can get. Science can tell about that, but it cannot negotiate nature. 3