management of datapoor stocks in mixed fisheries the good...

Helena Geromont

Management of datapoor stocks in mixed fisheries The good, the adequate and the arbitrary

Marine Resource Assessment and Management Group (MARAM), Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701, South Africa

Introduction

Mixed fishery and bycatch management

Defining realistic metrics

Data-poor methods

MSE: evaluating trade-offs

Some conclusions

Stocks with quantitative assessments: 10% High-value Data-rich

Expertise-rich

Unassessed stocks: 90% Low-value, often inshore, often mixed fisheries

Limited funding, limited monitoring =>Data-poor

Often limited numerical expertise and limited infrastructure

Need for simple quantitative management

Current state of world fisheries

Data-rich assessment methods are not applicable to data-poor fisheries: Lack of funds for extensive data collection/data analyses Insufficient data to estimate model parameters reliably Lack of expertise to apply complex statistical methods annually Complex assessments not always that realiable anyway Require simple approaches: Generic rather than stock-specific (no annual species-specific assessments) Broad-brush rather than optimal (overall multi-stock rebuilding goals) Easily understood by fishery stakeholders (need buy-in to work) Rely on few data (start simple and move towards data-moderate methods) Robust to high levels of uncertainty (precautionary but economically viable)

Keep it simple stupid!

Problems with management system:

Pre-2010: Days-At-Sea (DAS)

Post-2010: Hard Annual Catch Limits (ACL)

Many groundfish stocks remain overfished

Age-based models give biased estimates of abundance (retrospective patterns)

Catches well below optimum yield (OY)

Single species interpretation of multispecies yield

Rothschild (2013):

Define clear and realistic management goals

Adopt simpler stock assessment approaches

Use simpler metrics to measure performance

US: New England groundfish mixed-fishery

Rothschild, BJ, Keiley, EF, and Jiao, Y. 2013. Failure to eliminate overfishing and attain optimum yield in the New England groundfish fishery. ICES Journal of Marine Science. DOI:10.1093/icesjms/fst118

Reducing risk (overall, or a specific species?) Annual catch limits( ACLs) are set much lower than OFLs for each species Choice of precautionary buffer somewhat arbitrary (75% FMSY)

Single-species assessments questionable => unreliable F estimates Continued overfishing for some species despite buffers: biological goals not met

Achieving OY (overall, or a specific species?)

Total landings of all stocks substantially less sum of ACLs

Once species-specific ACLs are reached, the fishery starts choking Loss of revenue: economic goals of fishery not met

“Weakest link” management Multi-species fishery management goals not met

Example: New England groundfishRisk/yield tradeoffs

Cy << ACLy

s

s∑ << OFLy

s

s∑

ACL: Annual Catch Limit < OFL: Overfishing Limit

Directed catch: anchovy, adult sardine

Bycatch: juvenile sardine, horse mackerel

Aim: Limit juvenile sardine and horse mackerel bycatch in small pelagics fishery

=>impacts adult sardine and horse mackerel directed catches

Trade-off: cannot optimise directed sardine and anchovy simultaneously

Joint Management Procedure (JMP):

1) Total Annual Catch for anchovy (TACA)and adult sardine (TACS), and

2) Total Annual Bycatch (TABS) for juvenile sardine

Simple empirical MP based on bi-annual hydroacoustic surveys (data-rich)

Nov: initial TACS/TACA/TABS -> May mid-year update: revise TACA/TABS

Example: SA small pelagics fishery

De Moor CL and DS Butterworth. Incorporating technological interactions in a joint MP for SA sardine and anchovy. In Management Science in Fisheries. An introduction to simulation-based methods. Ed: Edwards, TT and Dankel, DJ. 2016. Routledge. 460pp

Aim: Limit juvenile horse mackerel bycatch in small pelagics fishery

=>impacts adult horse mackerel fishery: threefold Y/R gain

1) Precautionary Upper Catch Limit (PUCL)

Fixed PUCL: Choking of anchovy fishery:

=>avoid areas with high horse mackerel bycatch

=>loss of anchovy catch

2) Flexible PUCL3: three-year allocation

allocationy=PUCL3-bycatchy-1-bycatchy-2

Add restriction to limit annual take:

bycatchy<0.5PUCL3

Need restrictions, with flexibility to deal with high R fluctuations

Example: SA small pelagics: horse mackerel bycatch

Furman, L. B. 2014 Using Management Strategy Evaluation to address problems arising as a result of competing users of the South African horse mackerel resource. MSc Thesis 132 pp.

Inshore/Offshore trawl management:

Hake directed TAC and effort limitation (DAS) to curb bycatch targeting

PUCLs for kingklip and monkfish bycatch

In addition: Limit juvenile kingklip bycatch

Implement time/area restrictions:

Moratorium on trawling in some months in some areas to reduce catch

of species that are known to aggregate in those areas

“kingklip box”: seasonal area closure

Avoid large spawning aggregations of kingklip

Simple solution that works => compliance by industry

Example: SA demersal fishery

PUCL: Precautionary Upper Catch Limit

ePUCLs (experimental Preliminary Catch Limits):

Voluntary limits for minor bycatch species (eg kob, carpenter, panga)

Pilot project: Industry body manages ePUCLs to avoid dumping

Catch limits based on annual stock assessments (ASPM) for key bycatch species

Data: 1995-2011 survey index (government run observer program)

standardised CPUE (likely biased)

catch time series (total landings not well known)

Not so much data-poor but management-poor (lack of government funding/

monitoring/compliance)

Voluntary ePUCLs: Hake trawl industry taking initiative with pressure from MSC: manage bycatch or lose hake certification!

Example: SA inshore trawl fishery

Greenston, JD and Attwood CG. 2013. Assessing the feasibility of a bycatch co-management programme in South Africa’s inshore trawl fishery. Report for he Responsible Fisheries Alliance.

Different productivity levels

Low M stocks more vulnerable to overfishing,

Need more precaution/constraints for vulnerable stocks

Large fluctuations in stock size for shorter-lived species

Different depletion levels

Need rebuilding strategies for depleted stocks

but not waste potential yield from healthy stocks

Different economic value

Catch directed according to market value and availability

Different objectives, different performance indicators

Challenges specific to mixed fisheries

Balance management trade-offs: Maximise future catch (OY) of priority stocks

Reduce risk of overfishing/ undue depletion Avoid underfishing Maintain economic stability: avoid choking

Need a balanced management approach: Select realistic objectives: set attainable targets and limits for each stock Choose simple metrics to measure performance Prioritise stocks/species Balance overall trade-offs and performance of stock group. Need management approach that addresses bioeconomic concerns

Need to formalise an approach to do all that

Aim

OY: Optimum Yield

Metrics to measure performance Example: MSC certification criteria and performance indicators

Certification scores are based on:

Sustainability of stock

Environmental impact of fishery

Effective management of fishery Focus MSE on Principle 1 performance indicators. Principle 3 implicit when adopting an MP approach.

MSC performance indicators

Principle1

Principle 3

Principle 2

SG60: stock biomass “likely” above point where recruitment becomes impaired (PRI ) 30%-ile of probability interval above PRI

SG80: stock biomass ”highly likely” above PRI and fluctuating about MSY level 20%-ile of probability interval above PRI median of probability interval above BMSY

SG100: “certain” that stock above PRI and fluctuating about or above MSY level 5%-ile of probability interval above PRI median of probability interval above BMSY

Limit Reference Point (LRP): PRI=0.5BMSY

Target Reference Point (LRP): BMSY

Apply scoring system to multi-species fishery: SG60 for incidental bycatch, SG80 for

important bycatch species and SG100 for high-value target species

MSC stock status scores

SG60: Rebuilding time twice the generation time, but not longer than 20 years. Monitor to check that rebuilding strategies are effective

SG80: Some evidence (high likelihood) of recovery within generation time

SG100: Short rebuilding time period of between 5 years and one generation time for stock Strong evidence (high likelihood) of recovery within time

Generation time: tgen= t0-1/k ln(1-Lopt/Linf)

Shortcut method: tgen = amat+1/M Select MP and buffer to achieve biomass recovery in a pre-specified time

MSC rebuilding time frames (PI 1.1.2)

Where are we now? Don’t know Data poor, no assessment, or biased

Where do we go? Move stock towards target (BMSY)

Maintain stock above limit

How do we get there?

An MP that requires few but (reliable) data to give directional advice. Dynamic system, fluctuations in stock size unavoidable

Three questions, not so many answers…

Time period

BMSY

BLIM

Account for high levels of uncertainty

Keep control rules simple

Lower expectations

Balance trade-offs between species

Management Strategy Evaluation

Poor Biased Unrealistic data assessments expectations

Step 5: Simulations

MSE: Evaluate performance

Step 3: OPERATING

MODELS

Suite of age/length-structured population

models

Step 4: MPs

Simple harvest control rules that incorporate

stock-specific triggers, target, limits and

buffers

Step 1: Define key objectives

for mixed-fishery

Step 2: Decide on appropriate performance

metrics

TAC/TAE/TAB

DATA

Step 6: Summary statistics: examine

mixed fishery trade-offs

Step 7: Mixed fishery

stakeholders to select best

compromise

Input and output controls: TAC/TAE

Empirical HCR: Life-History parameters Length-based Catch-based Abundance index based

Output: MSY,B, F

Assessment model: estimate model parameters from data

Input: data

HCRs (based on assessment output): B/K-based

F/FMSY-based

Datapoor methods: Model-based vs Empirical

Typical data-poor assessment methods DATA OUTPUT ASSESSMENT HCR

HCRs (based on output from assessment): LB-SPR Target DACS, DCAC B/BMSY, F/FMSY HCRs

Length composition

Life-history parameters

Qualitative Semi-

quantitative

Index of abundance

PSA, SAFE, Caddy’s traffic light

Beverton-Holt, Per-recruit

LB-SPR

AIM, Production models (Schaefer, Fox, Pella)

Reference points

Stock ranking, risk analysis

SPR

Catch

Output: TAC/TAE

Catch-MSY, COM, SSCOM, DB-SRA, CC-SRA

RY/MSY, B/K

RY/MSY, B/K

Data-moderate

Data-poor

Typical empirical methods

Minimum length L-Threshold L-Target, E-target LF=M

DACS, DCAC (no, not really) I-Ratio I-Slope I-Target

Life-history data

L-H+Length

L-H+Catch

Catch+Index Output:

TAC/TAE or Minimum length

DATA OUTPUT HCR

minlen Etarget

Example methods: input controls

Lmin = xLopt 0.7 ≤ x ≤1

Life-history parameters (M, k, b)

Ey+1 = Ey w+ (1− w)

Lyrecent

Lopt

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣⎢⎢

⎤

⎦⎥⎥

Advantages: Simple input control rules can be applied when historic time-series data are lacking. Life-history parameters are widely available. Cost effective management

Lopt is the length at which the biomass of cohort is maximised

Lopt = b / (M /κ + b)

Disdvantages: No historical trend information to track changes in biomass. Does not take dynamic effects into account

0 ≤ w ≤1Length data

Example methods: catch based

DACS DCAC

Catch time series (Cy)

TACy+1 = DCAC =Cy∑

n + Δ / (MSYL × c ×M )

TACy+1 = s (1 / n Cy∑ ) = 2B /K (1 / n Cy∑ )

Catch time-series shown as a percentage of the maximum catch to illustrate the transition phases of a typical fishery (Froese and Kesner-Reyes, 2002).

Advantages: Catch time-series data are available for many fisheries. Disadvantages: Catches are affected by effort regulations, catchability, etc. An estimate of current depletion is not available for most data-poor fisheries.

Example methods: length based Lstep Ltarget

TACy+1 = TACy ± step if Lyrecent > Lupper threshold

< Llower threshold

Mean length index (Ly)

TACy+1 = TAC target w+ (1− w)

Lyrecent − L0

Ltarget − L0

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣⎢⎢

⎤

⎦⎥⎥

Advantages: data easy and cheap to collect. Intuitive HCRs=> Stakeholder participation. Disadvantages: Mean length is an indirect index - not directly proportional to abundance! Delay in feed-back (worse for longer-lived stocks with lower M). Recruitment pulses affect catch profile

Equilibrium mean length in catch as a function of spawning biomass for age-independent natural mortality rates, M, of 0.2, 0.3 and 0.4 yr-1.

Example methods: Index based 8) Islope 9) Itarget 10) Iratio

TACy+1 = TAC target w+ (1− w)

I yrecent − I 0

I target − I 0

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣⎢⎢

⎤

⎦⎥⎥

TACy+1 = TACy(1+ λ slope(Iy ))

CPUE or survey index (Iy)

Iy=qBy

Advantages: Direct index of abundance. Track trends in B. Disadvantages: Scientific surveys can be costly. CPUE data much easier/cheaper to collect, but undetected bias (changes in q) could be problematic.

Surv

ey/C

PUE

1 3

1 12 5

1/ 2 / 1/ 3y y

y y y yy y

TAC TAC I I− −

+ −− −

⎛ ⎞ ⎛ ⎞= ⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠∑ ∑

Management Strategy Evaluation (MSE): Simulation test 7 datapoor MPs using MSC performance metrics

Group stock-types with similar characteristics in depletion/

productivity baskets. Bayes-like approach to encompass uncertainty

Simulation test MPs for each stock group/basket

Construct Operating Model (OM) baskets

Selection of MPs and precautionary buffers to simulation tested

Data MPs Buffers

minlen0,…,minlen5 0%, 10%, 20%, 30%, 40%, 50%

Lstep0,…,Lstep5 0%, 10%, 20%, 30%, 40%, 50%

Etarget0,…,Etarget5 0%, 10%, 20%, 30%, 40%, 50%

Ltarget0,…,Ltarget5 0%, 10%, 20%, 30%, 40%, 50%

DACS0,…,DACS5 0%, 10%, 20%, 30%, 40%, 50%

Islope0,…,Islope5 0%, 10%, 20%, 30%, 40%, 50%

Itarget0,…Itarget5 0%, 10%, 20%, 30%, 40%, 50%

Catch time series (Cy)

Mean length index (Ly)

CPUE or survey index (Iy)

Life-history pars (L-H)

Automate MSE with DLM Toolkit package developed by Tom Carruthers (2015)

Toolkit V3.2.1 now available on Cran repository

Software:

Carruthers, T. R., Kell, L. T., Butterworth, D. D. S., Maunder, M. N. Geromont, H. F., Walters, C., McAllister, M. K., Hillary, R. Levontin, P., Kitakado, T., and Davies, C. R. Performance review of simple management procedures. – ICES Journal of Marine Science, doi: 10.1093/icesjms/fsv212.

HG_DAC

S0HG_

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AAV

(d)

MP summary statistics

OM1a (moderately depleted/productivity): 10-year projections Boxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

1.0 1.5 2.0

2030

4050

6070

lower 20%−ile B/BMSY

median

average

Yield

HG_DACS0

HG_DACS1

HG_DACS2

HG_DACS3

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HG_DACS5

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HG_minlenLopt0HG_minlenLopt1

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Risk/Yield plot:

OM1a: moderately depleted/medium productivity Risk/Yield tradeoffs after 10 years

0.0 0.5 1.0 1.5 2.0

2040

6080


median

averag

e Yield

HG_DACS0

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Risk/Yield plot:

OM1a: moderately depleted/medium productivity Risk/Yield tradeoffs after 20 years

HG_DAC

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5 year B

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HG_DAC

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(c)

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HG_Islop

e4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


OM1b (severely depleted/medium productivity): 10-year projections Boxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

0.0 0.5 1.0 1.5

1020

3040

5060

70


median

average

Yield

HG_DACS0

HG_DACS1

HG_DACS2

HG_DACS3

HG_DACS4

HG_DACS5



HG_Etarget4

HG_Etarget5

HG_minlenLopt0

HG_minlenLopt1


HG_minlenLopt4

HG_minlenLopt5HG_Lstep0

HG_Lstep1

HG_Lstep2

HG_Lstep3

HG_Lstep4

HG_Lstep5

HG_Ltarget0

HG_Ltarget1

HG_Ltarget2

HG_Ltarget3

HG_Ltarget4

HG_Ltarget5

HG_Islope0HG_Islope1HG_Islope2


HG_Islope5

HG_Itarget0

HG_Itarget1

HG_Itarget2

HG_Itarget3

HG_Itarget4

HG_Itarget5

Risk/Yield plot:

Mor

e yi

eld

OM1b: severely depleted/medium productivity Risk/Yield tradeoffs after 20 years

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

5 year B

/BMSY

(a)

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0

1

2

3

final B/B

MSY

(b)

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0

200

400

600

800

1000

1200

average

TAC

(c)

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


OM2a (moderately depleted/low productivity): 20-year projections Boxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

0.5 1.0 1.5

100200

300400

500600


median

averag

e Yield

HG_DACS0

HG_DACS1

HG_DACS2

HG_DACS3

HG_DACS4

HG_DACS5

HG_Etarget0

HG_Etarget1

HG_Etarget2

HG_Etarget3

HG_Etarget4

HG_Etarget5


HG_minlenLopt2

HG_minlenLopt3

HG_minlenLopt4

HG_minlenLopt5

HG_Lstep0

HG_Lstep1

HG_Lstep2

HG_Lstep3

HG_Lstep4

HG_Lstep5

HG_Ltarget0

HG_Ltarget1

HG_Ltarget2

HG_Ltarget3

HG_Ltarget4

HG_Ltarget5

HG_Islope0

HG_Islope1

HG_Islope2

HG_Islope3

HG_Islope4

HG_Islope5

HG_Itarget0

HG_Itarget1

HG_Itarget2

HG_Itarget3

HG_Itarget4

HG_Itarget5

Risk/Yield plot:

OM2a: moderately depleted/low productivity Risk/Yield tradeoffs after 20 years

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0.0

0.5

1.0

1.5

2.0

2.5

5 year B

/BMSY

(a)

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

final B/B

MSY

(b)HG_

DACS0

HG_DAC

S1HG_

DACS2

HG_DAC

S3HG_

DACS4

HG_DAC

S5HG_

Etarget0

HG_Etar

get1HG_

Etarget2

HG_Etar

get3HG_

Etarget4

HG_Etar

get5HG_

minlenL

opt0HG_

minlenL

opt1HG_

minlenL

opt2HG_

minlenL

opt3HG_

minlenL

opt4HG_

minlenL

opt5HG_

Lstep0

HG_Lste

p1HG_

Lstep2

HG_Lste

p3HG_

Lstep4

HG_Lste

p5HG_

Ltarget0

HG_Ltar

get1HG_

Ltarget2

HG_Ltar

get3HG_

Ltarget4

HG_Ltar

get5HG_

Islope0

HG_Islo

pe1HG_

Islope2

HG_Islo

pe3HG_

Islope4

HG_Islo

pe5HG_

Itarget0

HG_Itarg

et1HG_

Itarget2

HG_Itarg

et3HG_

Itarget4

HG_Itarg

et5

0

200

400

600

800

1000

average

TAC

(c)

HG_DAC

S0HG_

DACS1

HG_DAC

S2HG_

DACS3

HG_DAC

S4HG_

DACS5

HG_Etar

get0HG_

Etarget1

HG_Etar

get2HG_

Etarget3

HG_Etar

get4HG_

Etarget5

HG_min

lenLopt0

HG_min

lenLopt1

HG_min

lenLopt2

HG_min

lenLopt3

HG_min

lenLopt4

HG_min

lenLopt5

HG_Lste

p0HG_

Lstep1

HG_Lste

p2HG_

Lstep3

HG_Lste

p4HG_

Lstep5

HG_Ltar

get0HG_

Ltarget1

HG_Ltar

get2HG_

Ltarget3

HG_Ltar

get4HG_

Ltarget5

HG_Islo

pe0HG_

Islope1

HG_Islo

pe2HG_

Islope3

HG_Islo

pe4HG_

Islope5

HG_Itarg

et0HG_

Itarget1

HG_Itarg

et2HG_

Itarget3

HG_Itarg

et4HG_

Itarget5

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


OM2b (severely depleted/low productivity): 20-year projections Boxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

0.0 0.2 0.4 0.6 0.8 1.0

1020

3040

5060


median

averag

e Yield

HG_DACS0

HG_DACS1

HG_DACS2

HG_DACS3

HG_DACS4

HG_DACS5


HG_Etarget2

HG_Etarget3

HG_Etarget4

HG_Etarget5


HG_minlenLopt2

HG_minlenLopt3

HG_minlenLopt4

HG_minlenLopt5

HG_Lstep0

HG_Lstep1

HG_Lstep2

HG_Lstep3

HG_Lstep4

HG_Lstep5

HG_Ltarget0

HG_Ltarget1

HG_Ltarget2

HG_Ltarget3

HG_Ltarget4

HG_Ltarget5


HG_Islope2

HG_Islope3

HG_Islope4

HG_Islope5

HG_Itarget0HG_Itarget1

HG_Itarget2

HG_Itarget3

HG_Itarget4

HG_Itarget5

Risk/Yield plot:

Mor

e yi

eld

OM2b: severely depleted/low productivity Risk/Yield tradeoffs after 20 years

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_DACS1

17.7% POF

66.6% FMSY yield

F/FMSY

2 4 6 8

0.00.5

1.01.5

2.0

Index

MSEobj@

B_BMSY

[1, mm, ]

39.4% < BMSY

3% < 0.5BMSY

0% < 0.1BMSY

B/BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Etarget1

39% POF

77.1% FMSY yield

2 4 6 8

0.00.5

1.01.5

2.0

Index

MSEobj@

B_BMSY

[1, mm, ]

54.1% < BMSY

3.2% < 0.5BMSY

0% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_minlenLopt1

55.4% POF

88.4% FMSY yield

2 4 6 8

0.00.5

1.01.5

2.0

Index

MSEobj@

B_BMSY

[1, mm, ]

52.1% < BMSY

2.3% < 0.5BMSY

0% < 0.1BMSY

2 4 6 8

0.51.0

1.5Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Lstep1

33.3% POF

64.5% FMSY yield

2 4 6 8

0.00.5

1.01.5

2.0

Index

MSEobj@

B_BMSY

[1, mm, ]

51.7% < BMSY

9.3% < 0.5BMSY

1.3% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Ltarget1

33.4% POF

60.6% FMSY yield

2 4 6 8

0.00.5

1.01.5

2.0

Index

MSEobj@

B_BMSY

[1, mm, ]

51.3% < BMSY

9.6% < 0.5BMSY

2% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Islope1

49.9% POF

86.3% FMSY yield

2 4 6 8

0.00.5

1.01.5

2.0Index

MSEobj@

B_BMSY

[1, mm, ]

56.6% < BMSY

14.6% < 0.5BMSY

1% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Itarget1

38.3% POF

86.7% FMSY yield

2 4 6 8

0.00.5

1.01.5

2.0

Index

MSEobj@

B_BMSY

[1, mm, ]

49% < BMSY

5.8% < 0.5BMSY

0% < 0.1BMSY

Projection year

MSE

OM1a: Reference case basket 10yr projection plots with MPs using 10% buffers

OM1a: Kobe plots for10-year projections with 10% buffer MPs

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5

c(MSEobj@B_BMSY[1, mm, 1], MSEobj@B_BMSY[1, mm, 2])

c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

Start

End

14% 0%

4% 82%

HG_DACS1

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

29% 6%

17% 48%

HG_Etarget1

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

39% 15%

8% 38%

HG_minlenLopt1

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

23% 0%

17% 60%

HG_Lstep1

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

14% 0%

21% 65%

HG_Ltarget1

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

46% 3%

5% 46%

HG_Islope1

0.0 0.5 1.0 1.5 2.0

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

34% 11%

12% 43%

HG_Itarget1

B/BMSY

F/FMS

Y

MSE

OM1a: 10yr projection boxplots with MPs using 10% buffersBoxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

5 year B

/BMSY

(a)

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

final B/B

MSY

(b)

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0

50

100

150

average

TAC

(c)

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


MSY

Robustness tests: misclassification of stock in generic basket

Not risk prone

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_DACS1

83.9% POF

85.2% FMSY yield

F/FMSY

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

94% < BMSY

65.4% < 0.5BMSY

12.2% < 0.1BMSY

B/BMSY

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Etarget1

86.3% POF

91.2% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

98.2% < BMSY

67.4% < 0.5BMSY

0.1% < 0.1BMSY

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_minlenLopt1

91.6% POF

107.7% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

96.9% < BMSY

60.1% < 0.5BMSY

0.1% < 0.1BMSY

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Lstep1

63.2% POF

68.2% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

94.9% < BMSY

65.4% < 0.5BMSY

6.5% < 0.1BMSY

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Ltarget1

67.5% POF

65.4% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

94.8% < BMSY

66.7% < 0.5BMSY

8.6% < 0.1BMSY

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Islope1

77% POF

81% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

96.2% < BMSY

70.1% < 0.5BMSY

10.9% < 0.1BMSY

2 4 6 8

0.51.0

1.52.0

2.53.0

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Itarget1

66.2% POF

94.3% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.81.0

Index

MSEobj@

B_BMSY

[1, mm, ]

95.7% < BMSY

58% < 0.5BMSY

1.5% < 0.1BMSY

Projection year

MSE

OM1b: True B/K lower than expected 10yr projection plots with MPs using 10% buffers

OM1b: Kobe plots for10-year projections with 10% buffer MPs

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

Start

End

74% 0%

12% 14%

HG_DACS1

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

83% 0%

14% 3%

HG_Etarget1

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

91% 2%

3% 4%

HG_minlenLopt1

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

42% 0%

44% 14%

HG_Lstep1

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

46% 0%

39% 15%

HG_Ltarget1

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

71% 1%

21% 7%

HG_Islope1

0.0 0.2 0.4 0.6 0.8 1.0

0.51.0

1.52.0

2.53.0


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

67% 2%

23% 8%

HG_Itarget1

B/BMSY

F/FMS

Y

MSE

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0.0

0.5

1.0

1.5

2.0

5 year B

/BMSY

(a)

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0.0

0.5

1.0

1.5

2.0

2.5

final B/B

MSY

(b)

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0

20

40

60

80

100

120

average

TAC

(c)

HG_DAC

S1

HG_Etar

get1

HG_min

lenLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islo

pe1

HG_Itarg

et1

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


OM1b: 10yr projection boxplots with MPs using 10% buffersBoxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_DACS1

61.1% POF

69.7% FMSY yield

F/FMSY

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6

Index

MSEobj@

B_BMSY

[1, mm, ]

61.7% < BMSY

4.2% < 0.5BMSY

0% < 0.1BMSY

B/BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Etarget1

45.6% POF

61.3% FMSY yield

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6

Index

MSEobj@

B_BMSY

[1, mm, ]

62.7% < BMSY

2.8% < 0.5BMSY

0% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_minlenLopt1

64.3% POF

74.3% FMSY yield

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6

Index

MSEobj@

B_BMSY

[1, mm, ]

62.2% < BMSY

2.5% < 0.5BMSY

0% < 0.1BMSY

2 4 6 8

0.51.0

1.5Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Lstep1

36.9% POF

48.6% FMSY yield

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6

Index

MSEobj@

B_BMSY

[1, mm, ]

61.2% < BMSY

4.4% < 0.5BMSY

0% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Ltarget1

37% POF

56.8% FMSY yield

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6

Index

MSEobj@

B_BMSY

[1, mm, ]

59.9% < BMSY

2.4% < 0.5BMSY

0% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Islope1

56.8% POF

72.7% FMSY yield

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6Index

MSEobj@

B_BMSY

[1, mm, ]

65.8% < BMSY

5.8% < 0.5BMSY

0.2% < 0.1BMSY

2 4 6 8

0.51.0

1.5

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Itarget1

37.9% POF

60% FMSY yield

2 4 6 8

0.40.6

0.81.0

1.21.4

1.6

Index

MSEobj@

B_BMSY

[1, mm, ]

59.4% < BMSY

2.7% < 0.5BMSY

0% < 0.1BMSY

Projection year

MSE

OM2a: True M lower than expected 10yr projection plots with MPs using 10% buffers

OM2a: True M lower than expected Kobe plots for10-year projections with 10% buffer MPs

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

Start

End

55% 9%

6% 30%

HG_DACS1

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

36% 3%

28% 33%

HG_Etarget1

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

44% 20%

15% 21%

HG_minlenLopt1

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

17% 0%

41% 42%

HG_Lstep1

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

26% 1%

30% 43%

HG_Ltarget1

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

48% 7%

21% 24%

HG_Islope1

0.4 0.8 1.2 1.6

0.51.0

1.5


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

29% 11%

26% 34%

HG_Itarget1

B/BMSY

F/FMS

Y

MSE

HG_DAC

S1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0.0

0.5

1.0

1.5

2.0

5 year B

/BMSY

(a)

HG_DAC

S1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0.5

1.0

1.5

2.0

2.5

final B/B

MSY

(b)HG_

DACS1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0

200

400

600

800

1000

average

TAC

(c)

HG_DAC

S1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


OM2a: True M lower than expected 10yr projection boxplots with MPs using 10% buffersBoxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_DACS1

99.8% POF

115.6% FMSY yield

F/FMSY

2 4 6 8

0.00.2

0.40.6

0.8

Index

MSEobj@

B_BMSY

[1, mm, ]

99.2% < BMSY

86.4% < 0.5BMSY

17.2% < 0.1BMSY

B/BMSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Etarget1

88.1% POF

81.2% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.8

Index

MSEobj@

B_BMSY

[1, mm, ]

99% < BMSY

77.2% < 0.5BMSY

0.1% < 0.1BMSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_minlenLopt1

92.7% POF

100.8% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.8

Index

MSEobj@

B_BMSY

[1, mm, ]

99% < BMSY

72.8% < 0.5BMSY

0.1% < 0.1BMSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Lstep1

76.5% POF

68.1% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.8

Index

MSEobj@

B_BMSY

[1, mm, ]

99% < BMSY

74.2% < 0.5BMSY

3% < 0.1BMSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Ltarget1

84.1% POF

82.3% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.8

Index

MSEobj@

B_BMSY

[1, mm, ]

99% < BMSY

75.8% < 0.5BMSY

2.6% < 0.1BMSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Islope1

90.2% POF

96% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.8Index

MSEobj@

B_BMSY

[1, mm, ]

99% < BMSY

79.2% < 0.5BMSY

4.6% < 0.1BMSY

2 4 6 8

12

34

Index

MSEobj@

F_FMSY

[1, mm, ]

HG_Itarget1

74.9% POF

78.4% FMSY yield

2 4 6 8

0.00.2

0.40.6

0.8

Index

MSEobj@

B_BMSY

[1, mm, ]

99% < BMSY

72.8% < 0.5BMSY

0.9% < 0.1BMSY

Projection year

MSE

OM2b: True M and B/K lower than expected 10yr projection plots with MPs using 10% buffers

OM2b: True M and B/K lower than expected Kobe plots for10-year projections with 10% buffer MPs

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

Start

End

99% 1%

0% 0%

HG_DACS1

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

83% 0%

16% 1%

HG_Etarget1

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

95% 0%

4% 1%

HG_minlenLopt1

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

53% 0%

46% 1%

HG_Lstep1

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

75% 0%

24% 1%

HG_Ltarget1

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

86% 0%

13% 1%

HG_Islope1

0.0 0.2 0.4 0.6 0.8

12

34


c(MSEo

bj@F_F

MSY[1

, mm, 1],

MSEob

j@F_FM

SY[1, m

m, 2])

67% 0%

32% 1%

HG_Itarget1

B/BMSY

F/FMS

Y

MSE

HG_DAC

S1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0.0

0.5

1.0

1.5

5 year B

/BMSY

(a)

HG_DAC

S1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0.0

0.5

1.0

1.5

final B/B

MSY

(b)HG_

DACS1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0

200

400

600

800

1000

average

TAC

(c)

HG_DAC

S1

HG_Etar

get1

HG_minl

enLopt1

HG_Lste

p1

HG_Ltar

get1

HG_Islop

e1

HG_Itarg

et1

0.0

0.1

0.2

0.3

0.4

0.5

AAV

(d)


OM2b: True M and B/K lower than expected 10yr projection boxplots with MPs using 10% buffersBoxplots: whiskers:5%-iles (SG100), boxes:20%-iles (SG80), bars: medians

MSY

MP buffers required to achieve 60/80/100 pass score Medium productivity stock (10y) Low productivity stock (20y)

OM1a SG60 SG80 SG100 Minlen 0% 10% 10% Etarget 0% 10% 20% DACS 0% 0% 0% Lstep 0% 20% 50% Ltarget 0% 10% 30% Islope 10% 20% 50% Itarget 0% 10% 20%

OM1b SG60 SG80 SG100 Minlen 20% 40% 50% Etarget 30% NA NA DACS 40% 50% NA Lstep 30% 50% NA Ltarget 10% 30% NA Islope 50% NA NA Itarget 20% 40% NA

OM2a SG60 SG80 SG100 Minlen 0% 10% 10% Etarget 0% 20% 20% DACS 0% 20% 40% Lstep 0% 0% 10% Ltarget 0% 20% 40% Islope 10% 30% 50% Itarget 0% 10% 20%

OM2b SG60 SG80 SG100 Minlen 30% 50% NA Etarget 50% NA NA DACS NA NA NA Lstep 30% NA NA Ltarget 50% NA NA Islope NA NA NA Itarget 40% NA NA

Sev

erel

y de

plet

ed

Mod

erat

ely

depl

eted

S

ever

ely

depl

eted

M

oder

atel

y de

plet

ed Bycatch 1 Bycatch 2 Target Bycatch 1 Bycatch 2 Target

Bycatch 1 Bycatch 2 Target Bycatch 1 Bycatch 2 Target

Prioritise species and organise into stock groups or stock baskets

Define appropriate performance indicators for target and major and minor

bycatch stocks: Different precautionary levels for categories to avoid choking

Develop and tune MPs for target stocks

Incorporate flexible multi-annual allocations for bycatch,

time/area closures where appropriate, and

exceptional circumstance clauses for low/bumper years

Keep buffers within reason- too much precaution is wasteful

Not possible to optimise yield consistently / not possible to avoid risk

completely

=> Decide on acceptable trade-offs with stakeholders

Simulation test (joint) MPs to achieve acceptable performance overall

Some conclusions

Thank you

Many thanks to Doug Butterworth, Carryn de Moor, Jessica Greenstone and Colin Attwood for providing a South African

perspective of bycatch mitigation.

management of datapoor stocks in mixed fisheries the good...

Documents