Harvesting strategies and tactics The ecological basis of sustainability is compensatory improvement...
-
date post
20-Dec-2015 -
Category
Documents
-
view
216 -
download
2
Transcript of Harvesting strategies and tactics The ecological basis of sustainability is compensatory improvement...
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. 1997. Fisheries Oceanography 6:188-204
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?
STOCKSIZE
TIME
NOWWE 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=vnow+Vfuture
Vnow
Vfuture
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)
Sopt
Slope=U opt (Fmsy)
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
FMSY
Bmin Bmsy
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:ImplementMonitorAssessObjective
N
N
u(N)
u(N)
E Nsystem
monitoring
Assessment N
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 example
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
Fishing Capacity (C) and Stock (B) isoclines
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1 1.2
Biomass (B)
Fis
hin
g c
ap
ac
ity
(C
)
B isocline
C isocline
C
Learn to think in terms of state space changes, not time plots
Fishing Capacity (C) and Stock (B) isoclines
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1 1.2
Biomass (B)
Fis
hin
g c
ap
ac
ity
(C
)
B isocline
C isocline
C
Dynamics over time
0
0.2
0.4
0.6
0.8
1
1.2
0 20 40 60 80 100 120
Year
Biom
ass
(B),
Capa
city
(C),
Effo
rt (E
)
B
C
E
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
Florida pompano Recreational Effort and Catch
0
50000
100000
150000
200000
250000
300000
350000
1980 1985 1990 1995 2000 2005
Effort
Catch
Florida pompano recreational catch per effort
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1980 1985 1990 1995 2000 2005
CommercialNet ban
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. 2003 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”
Distribution of three stocks along an environmental gradient
0
0.01
0.02
0.03
0.04
0.05
0.06
0 20 40 60 80 100
gradient position
Ab
un
da
nc
e
Stock A
Stock B
Stock C
0
0.01
0.02
0.03
0.04
0.05
0.06
0 50 100
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Stock A
Stock B
Stock C
Optimum Effort
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 Ftarget,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=ΣiEi[ΣjqjjPjBij – ci](optimum Ei satisfies dV/dEi=0:ΣjqijPj∂Bij/∂Ei=ci (marginal income=cost)
• Subject to the constraint that no predicted F j exceeds Ftarget,j
Fj= ΣiqijEiBij/ ΣiBij ≤Ftarget,j for every j• You can let Solver find the optimum Ei 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 Ftarget,j
• Find the most profitable (maximum V) allocation of fishing effort over areas:
V=ΣiEi[ΣjqjPjBij – ci] - kΣj(Fj/Ftarget,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 Ei=0, i.e. to be closed.
• Each area i has a marginal penalty “cost” contribution equal to
pkΣjFjp-1/Ftarget,j
p∂Fj/∂Ei = ΣjKjBij
where Kj is large only if Fj>Ftarget,j
• That is, close those areas that have high abundances Bij of species with low Ftarget,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.