Harvesting strategies and tactics The ecological basis of sustainability is compensatory improvement in recruitment and/or growth rates as abundance is reduced Management is required when fishing effort is decoupled from abundance, due to density- dependence in catchability and/or presence of other profitable fish, or would result in “sustainable overfishing” (persistent low abundance and production) “Strategies” are long-term rules for dealing with variation, and “tactics” are ways to implement those rules in the short term

Variability is a universal feature of fish population dynamics From P.D. Spencer and J.S. Collie Fisheries Oceanography 6:

Harvest management strategies How to cope with uncontrolled and unpredictable natural variation by varying harvest rates in response to such variation Types of strategies: –Incrementalist (seat of pants)--monitor trends, respond when necessary –Feedback--vary harvest with system state –Adaptive—vary harvest so as to probe for opportunity

Lecture 3 topics (Harvest management strategies) The first question to ask is when harvest management is needed at all (bionomic dynamics) Design of feedback harvest policies Design of closed loop harvest policies N u(N)

A “harvest strategy” is a relationship between abundance and target harvest CURRENT STOCK SIZE EXPECTED SURPLUS PRODUCTION AND TARGET HARVEST PRODUCTION (+) HARVEST (-) STOCK SIZE WILL TEND TO MOVE TOWARD AND AROUND BALANCE POINT WHERE PRODUCTION=HARVEST

Why does the optimum harvest depend only on the current stock, not on past stocks or trends? STOCK SIZE TIME NOW WE CANNOT CHANGE THE PAST; IT SHOULD ONLY INFLUENCE CHOICE TODAY INSOFAR AS IT INFORMS US ABOUT THE FUTURE OUR CHOICE NOW CAN INFLUENCE VALUE OBTAINED IN THE FUTURE: V=v now +V future V now V future

Optimum form of the strategy rule depends on management objective CURRENT STOCK SIZE TARGET HARVEST Max total harvest (fixed escapement) 1:1 Max log utility (fixed harvest rate) S opt Slope=U opt (F msy )

A POPULAR WAY TO SPECIFY HARVES MANAGEMENT STRATEGIES IN MARINE FISHERIES CURRENT STOCK/UNFISHED STOCK TARGET EXPLOITATION RATE (FISHING RATE) AN ARCANE TERMINOLOGY HAS DEVELOPED TO DESCRIBE SUCH STRATEGY RULES F MSY B min B msy

Such strategy rules assume a stationary (regular) relationship between stock size and production ONLY A FEW OF THESE 105 CASES SHOW A STATIONARY, DOME SHAPED RELATIONSHIP; MOST SHOW EVIDENCE OF “REGIMES”

This picture is wrong: (we do not control u directly, nor do we know N when specifying u(N) ) Closed loop control recognizes fishing, monitoring, and assessment dynamics: Failures: Implement Monitor Assess Objective N u(N) EN system monitoring Assessment

Dual effects of control: the adaptive management problem Harvest choices have two effects: –Immediate benefits to fishers –Information on stock size and production for future managers to use An “actively adaptive” strategy is one that considers both effects in prescribing current harvest policy A good example of dual effects is the Fraser sockeye fishery Run DP exampleexample

Does this look like a well-regulated fishery? (Global tuna catches by gear type) and by Species:

Harvest management tactics The first tactical question is whether fishing effort and/or catch can be directly controlled There is a fundamental choice between input (effort, fishing mortality rate) control versus output (catch) control For each of these choices, there is a hierarchy of tactical management options

Bionomic dynamics: some fisheries “manage themselves” Isoclines show B,C combinations with zero rate of change Isoclines partition “state space” into regions of similar qualitative behavior, e.g. both capacity C and biomass B increasing

Learn to think in terms of state space changes, not time plots These dynamics over time Can be represented more compactly and generally (eg for stability analysis) using state space graphs

Decision hierarchy showing alternative regulatory tactics

Lots of regulatory tactics are completely ineffective at reducing exploitation rates This is a case where: (1) Stock is highly aggregated (2) Much effort is there anyway (other fish, hatcheries) (3) The fish are big, hence prized even when cpue is very low (0.2 fish/day)

Watch out for how effort responses can cancel intended regulatory effects, lead to reallocation

Two ways to interpret this pattern: (1) to get rid of the effort, all you have to do is get rid of the fish; or (2) you’ll have an effort problem if the fish do come back. (Beard et al NAJFM 23)

A common feature of all multispecies/stock fisheries is that bionomic feedback between effort and abundance of any one stock is weakened by presence of other stocks that may still attract fishing if the one stock is overfished.

Absent selective fishing practices, multistock fisheries create severe tradeoffs between potential yield and biological diversity Variability among stocks in productivity: Cumulative impact on probability of extinction

There is a wide spectrum of situations in terms of opportunity to fish more selectively (avoid less productive stocks). At one extreme are cases like coho salmon, where many stocks are thoroughly mixed at all spatial scales, gear cannot be made more selective Other cases involve opportunity to be more selective by using micro-scale differences in behavior (eg tuna vs billfish in longline fishing— billfish are shallow) Still others involve highly selective targeting by space choice or gear, mixed fishery arises from how effort is allocated among target choices

Using spatial organization to create selective fishing: “mosaic closures”

Designing mosaic closures Divide management region into polygons or raster cells, use spatial catch rate or survey data to estimate relative abundance of all species in each area i (spatial statistics). Estimate allowable or target fishing rate F target,j for each species j (stock assessment). Use numerical methods to find optimum effort in each area I (nonlinear optimization, e.g. Solver).

Solving for optimum mosaic of closed areas The optimization problem can be stated as: Find the most profitable (maximum V) allocation of fishing effort over areas: V=Σ i E i [Σ j q jj P j B ij – c i ] (optimum E i satisfies dV/dE i =0: Σ j q ij P j ∂B ij /∂E i =c i (marginal income=cost) Subject to the constraint that no predicted F j exceeds F target,j F j = Σ i q ij E i B ij / Σ i B ij ≤ Ftarget,j for every j You can let Solver find the optimum E i subject to the F constraints

Solving for optimum mosaic of closed areas One way to solve this problem is to convert it into an unconstrained optimization by adding penalty terms for exceeding F target,j Find the most profitable (maximum V) allocation of fishing effort over areas: V=Σ i E i [Σ j q j P j B ij – c i ] - kΣ j (F j /F target,j ) p In successive numerical steps, increase k,p until constraints are all met (p>>1)

Solving for optimum mosaic of closed areas Using the penalty function approach allows us to see which areas are likely to have optimum E i =0, i.e. to be closed. Each area i has a marginal penalty “cost” contribution equal to pkΣ j F j p-1 /F target,j p ∂F j /∂E i = Σ j K j B ij where K j is large only if F j >F target,j That is, close those areas that have high abundances B ij of species with low F target,j

Implementing mosaic closures Centralized control approach: design closure pattern, impose by regulation, make large investment in enforcement Industry-based control approach: provide industry with suggested closure pattern, prohibit discarding, warn that fishery will close completely if/when any target F (or allowable catch) is exceeded Cost approach: impose economic charges/penalties for exceedances of allowable catch.