Twenty years with harvest control rules in ICES - what now? Dankert W. Skagen

Transcription

1 Twenty years with harvest control rules in ICES - what now? Dankert W. Skagen

2 Started with North Sea herring 1997: F-rule with trigger-point F decided according to risk-evaluations Separate Fs for adults and juveniles Unspecified below the trigger point - low F agreed ad hoc. Since then, what has happened? Gradual development in ICES: More stocks included, often recovery plans at first, various tools for evaluation were developed, inspired by what was done elsewhere. ICES developed standards for evaluation: SGMAS , WKGMSE 2013.

3 Definitions of concepts: Harvest control rule: Rule for deciding on exploitation next year according to an estimate of the state of the stock. Management plan is wider: Objectives Data infrastructure (sampling, surveys etc.) Assessment method Harvest control rule Additional rules (access control etc.) Implementation and enforcement Legislation/ international agreements But management plan is often used for what is a harvest control rule.

4 Status ICES gives advice for approximately 200 stocks (2015) Management plan mentioned in the advice for 43 stocks Approved and used 21 Under revision 6 Used, not approved 2 Exist, not used 1 Failed 6 Under development 7 Sum 43 For some stocks the status is unclear - for example approved but not agreed between parties, or rule applied by some, but not all nations Two stocks are managed according to local plans - not evaluated by ICES, accounted as used, not approved.

5 21 approved plans - What do they say? Fixed F or HR with trigger 14 With stabilizer 9 Escapement 3 TAC directly 1 Others 3 Others are Follow ICES advice (greenland halibut in the West - essentially FMSY) Similar to ICES C3 rule (boarfish) Open and close fishing grounds (sandeel)

6 Revisions At present, six plans are in various stages of revision. Two stocks because of 'formal' changes (stock definition, revised reference points) The others because stock dynamics and/or assessment has changed from what was assumed when the plan was developed. Revisions in the past have not been explored systematically here. Likely reasons: Revision clause - new ideas Altered stock dynamics/assessments Unwanted results, like being trapped with low TAC by a stabilizing rule

7 Failures: Slightly subjective, but rules that have not worked as intended: 6 failures - what went wrong? No reliable assessment any more (two stocks) Rule did not lead to reduced F Some were covered by an EU cod management plan in 2008: Reduce F compared to year before. If target F was exceeded, you can have a higher F. That may be because of retro or over-fishing. For one stock: Existing plan is not used - managers set higher TAC. So, failures mostly occurred because of poorly designed rules that did not prevent high fishing mortality

8 Lessons learned: Mostly simple straight-forward plans (perhaps with extensions). Almost true: Most are simple Fixed F plans, with reduction below a trigger and often a stabilizer, but often, some added complexity. Short lived species have escapement rules. Revisions are most often triggered by changes in stock productivity or because of unwanted results like being trapped with low TAC. Failures i.e. stock collapse despite the rule, typically occur because of poorly designed rules, for example where F does not get reduced as intended. In some cases, assessment breakdown. Stock dynamics outside the assumed range have occurred, for example recruitment failure. Management plans have been revised accordingly, no disasters. Evaluation methods and criteria for approval have varied a good deal, but faulty uncertainty assumptions have not been the cause of breakdown of rules. We are very keen on getting the uncertainty right, and to communicate it - are we barking up the wrong tree?

9 Design of harvest rules A general design of harvest rules could be: Basis: Information about the state and well-being of the stock Rule: 'Formula': Basis -> measure of exploitation Translation into something operational (e.g. F -> TAC) Additional rules (e.g. stabilizers) Is a simple F- rule a good way to manage a stock? Perhaps yes, but perhaps not always. We may want: Even simpler rules o Fixed TAC, only change if absolutely needed o Harvest rate rather than F, filter rules rather than percentage stabilizers etc. Rules that are less dependent on precise assessments. Rules that adapt to fluctuations in stock productivity, either by following fluctuations or by stabilizing TAC despite fluctuations More experience: Adding complexity creates problems, and does not always solve problems Some rule elements do not work. Should not be repeated. Stabilizer getting trapped. Reduction in F relative to this years estimate of F in the year before

10 Can we simplify the process for simple rules? The F-rule is close to how ICES will advice in the absence of a management plan. The standard ICES practice (PA-advice and MSY advice) have never been systematically evaluated. We have two separate issues: Find a feasible 'true' F Find out how the perceived F from our assessments deviates from the true F. In Management Strategy Evaluations we consider these together. It may sometimes be simpler to separate them. To find a good level of the true F and set a sensible breakpoint, we can consider the deterministic production curve: We want near maximum catch, so the F should be at the plateau. We want safety, so the F should be at the left hand side of the plateau, for example near F0.1. We may set a breakpoint below which we reduce F just for extra safety, at an SSB that we normally will not reach.

11 We may then apply a simple, stepwise procedure: 1) Decide an F-value at the left hand part of the plateau in a deterministic production curve 2) Derive the range of variation of SSB as function of F-level by translating variability in Recruitment, growth and maturity into variability in SSB. Find that the F where the range starts to include the SSB limit. 3) Consider the uncertainty (confidence intervals) in the assessments for the stock to ensure that the assessed F usually is close to where it should be and not at a level that brings SSB down towards the limit. For 2 and 3, simulations may be a handy tool, but not the only way. Simulations is still the major tool, as a final confirmation and for more complex situations. But if simulations give other conclusions than the simple process outlined here, has something gone wrong in the simulations?

12 Simulations: Testing HCRs. A great advantage with harvest rules is that they can be formally tested. Make a test-bench: A range of artificial populations, covering what is plausible for the stock. Project the stock forward in time, apply the rule and see how it performs. Can be made for single stocks or for multiple stocks. Critical factors: Biology o Assumptions about future recruitments o Future changes in growth and maturity o Changes in natural mortality Quality of future assessments Assumptions about future implementation (but it is not a job for science to model how managers implement their rules(?)) Performance criteria: Average long term yield. Interannual variability (Relative change in TAC) Risk: Probability of unwanted events (e.g. SSB < Blim)

13 Real world External factors Population model True stock Real removals Observation model Implementation model Perceived stock Decided removals Decision model Managers world

14 Simulation tools, what has happened? Large development, Handle more elaborate rules Better(?) tools for presenting results More sophisticated handling of uncertainty Integration into assessment tools Many tools have been developed, with slightly different solutions. Time for harmonizing? Approval of a rule should not depend on the choice of software. More elaborate rules include transfer of TACs over years, multiple fleets with different selections at age, and sometimes experiments with other kinds of rules than the standard F rule. All this requires coding, and some of the options make very little difference (Banking and Borrowing, for example) Presenting results in an understandable way is still a challenge. Scientists would like graphs showing tradeoffs and explaining relations and dynamics Stakeholders want a small number of choices tabulated, and clear demonstration of differences. We are not there yet. Better software allows producing enormous amounts of fancy graphics. Should we rather involve computer game programmers?

15 Handling uncertainty. We want a plausible range of realities - the rule should work well within that range. We have two kinds of uncertainty in harvest rule simulations: Biological properties, including present state and future dynamics Assessment uncertainty: How our basis for decisions deviates from reality. Development has been towards: Redefined range of realities: Bootstrap assessments, derive SR-functions, initial numbers, selections at age etc. for each replica (next slide). More sources of uncertainty included, for example: o Growth variation o Density dependent growth and maturity o Natural mortality o Structural uncertainty in assessments. More elaborate models for assessment uncertainty, as 'short cut' alternative to doing assessments as part of the simulation: Include covariances, autocorrelations or imitating retrospective errors. Assessments 'in the loop' is used less often nowadays. They are problematic with time consuming assessment methods, and it is hard to design adequate errors in input data. Still, the choice of sources of uncertainty varies, depending on interest and cleverness of analysts. Time for cleaning up?

16 The range of realities: Previously, probability distributions were derived for recruitment and initial numbers from one standard assessment. Bootstrap replicas were generated from these distributions. New alternative: o Generate a set of histories by bootstrapping assessments from data with errors. o Derive a set of 'stocks' with stock-recruitment functions, variances, initial numbers, selections at age etc. and examine the response to the rule for each of these. The development should be welcome, but will we end up with so much uncertainty that we cannot fish? Some of the 'stocks' from a bootstrap may be impossible. For example, the stock-recruit function may be almost a straight line, which is not viable. If this were an actual assessment it would have required some rethinking Another way of looking at this development: Rather than finding a plan for one stock with properties that we think we know, we develop a plan that shall be valid for a range of stock properties, which we make very wide to be sure that our stock is included in the range. Is that asking too much? After all, a rule can be revised, and failures do not seem to be caused by including too little uncertainty.

17 Risk in harvest rule simulations A basic requirement is to have a low risk of depleting the stock. Formally, risk is the product of probability and cost. We use it for probability of something unwanted happening, in practice that SSB<Blim. Recent improvement: Agreed on exactly risk of what (highest annual risk in a period) Typical field where everybody made their own definitions. Some paradoxes: Mix of realities: o We treat a Blim as a fixed value, which presumably was created by a perfect assessment. But our perception of the state of the stock is conditional on assessment method and assumptions. So we don t know the true Blim. o In modern simulation tools, we examine a range of realities, but we compare all with a Blim that was derived from one particular reality. Should we have one Blim for each reality? o In simulations, we consider the probability that the 'true' SSB is below Blim. In practical management we see a perceived SSB, which can be quite different. We take action according to how that uncertain SSB is related to the fixed Blim value. Blim shall represent the point where recruitment gets impaired. Is there such a point? With Blim as Bloss, the value depends on how heavily the stock has been exploited in the past.

18 Some unresolved SR function problems. The exact shape of the SR function will have a considerable impact, out of proportion with the predictive power of the function. Only in very few cases does a SR function explain a significant part of the recruitment variation, and even if so, we do not know whether we are faced with a SR function or a RS relation Recruit B-H We can have large, more or less periodic variations in recruitment, which have a large impact on stock productivity. Except for including some autocorrelation, the sensitivity to such variations is rarely examined In some cases we get stock-recruit functions that are not viable, in particular if it is almost a straight line. Most likely, this is caused by good year classes producing a large SSB. So one should rather concentrate on regimes than on finding a stationary SR relation.

19 Some suggestions for risk and uncertainties. Rather than using SSB<Blim as criterium of failure, use something signaling reduced stock productivity, that may be improved by altering exploitation. Recruitment impairment is one example. The criterium may be represented as an SSB value, but not necessarily so, and it should be valid across assessments. Exactly what such a criterium shall be remains to be explored and decided. It may be an SSB in a year in the past, but expressed as a distribution. Consider also the cost aspect - in practice what kind of recovery to expect. That requires some opinion of the dynamic behavior below the limit. If we are in unknown territory of SSB (Blim=Bloss), a hockey stick with breakpoint at the limit is a quite conservative assumption. Consider other ways of modeling recruitment than as a function of SSB. In particular, consider changing recruitment regimes over time. Underlying all this: Better understanding of why productivity in general and recruitment in particular varies. This also leads into ecosystem aspects of management

20 Towards ecosystem management: What do we want? Mining: Catch all, invest somewhere else Rational use of a self-renewing resource Preserve nature The mining strategy is perhaps not outspoken in public, but is probably not unknown in parts of the world. The preservation strategy is often encountered, and has created debate and criticism. With the rational use strategy, we need to: Know the interactions between members of the system. At present such knowledge is still fragmentary Understand influences on the productivity of a stock, i.e. how o recruitment o growth o natural mortality are decided Know what we want to achieve.

21 Ecosystem simulation tools Several model framework for simulating dynamics of a complex system (Atlantis, Ecosim). Perhaps these are more complex than they need to since we are not sure what really matters. But to examine the effect of management actions, we know of no better way. Some food for future thought: Influences can be of many kinds predator-prey predation on early life stages competition on habitats aggression In metapopulations, different components may have slightly different properties, that fit different conditions. Hence, a shift in say, a hydrographic regime may be handled by the component that is best suited for that. Perhaps this is a field worth exploring further. Theory for dynamic interactions goes back to the 1920ies, and has been extended since then. It tells about what kinds of dynamic one can encounter in for example predator - prey systems with delay. Should not be quite forgotten, in particular to remind us that it can be quite hard to foresee intuitively the dynamics of interactions in an ecosystem.

22 Where are we with ecosystem management of fisheries? Long way to go. We understand some interactions, predator - prey in particular, but very much lacking. In a few cases we take such interactions into account. Apparently, the occurrence of a strong or weak year class is hard to foresee from what we know about the members in the ecosystem We see members expanding or contracting, like pelagics in the Norwegian sea, none of these fluctuations were expected. We do not know the effect of exploiting or protecting any of the species in the system. For example, what would happen if we fished out the mackerel? - We don't know. Perhaps the best thing at present is to have rules that can adapt to changing productivity. People have been able to agree on some priorities, for example: Ensure that there is enough capelin for the cod, we do not try to promote capelin by fishing out the cod. But do we want cod or haddock?

23 What now? - some suggestions for future development Most rules are constant F-rules. Perhaps ok, but should we consider alternatives? Simple F-rules may perhaps be evaluated with simple tools, without complex simulations. Most failures occur because the rule does not deliver the intended fishing mortality. Some rules have to be revised because biology changes The risk to Blim is a paradox when we admit uncertain stock dynamics Modern tools to incorporate uncertainty may produce unduly much uncertainty. Simulations can be made more spothisticated, but perhaps also simpler and more targeted. In the wider perspective: Our understanding of how and why productivity in general and recruitment in particular varies, is still insufficient. Standard assumptions that recruitment depends on SSB are probably not adequate. Ecosystem models: o The understanding of interactions that we need is still fragmentary. o We will need agreement on priorities. Management plans were a major step forward. They have come to stay and can still be improved.