Managing Data-Poor Fisheries: Case Studies, Models & Solutions 1:159–184, 2010
Copyright: California Sea Grant College Program 2010
ISBN number 978-1-888691-23-8
[Article]
From Rags to Fishes: Data-Poor Methods for Fishery Managers
KRISTEN T. HONEY*
Stanford University,
Emmett Interdisciplinary Program in Environment and Resources (E-IPER),
School of Earth Sciences and Hopkins Marine Station,
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JERRY H. MOXLEY
Environmental Defense Fund, Oceans Program,
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Abstract.—Fishery managers often must make decisions regardless of data availability or completeness of
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consider and choose appropriate analytical methods and alternative management approaches, based on availDEOHGDWDW\SHTXDQWLW\DQGTXDOLW\DQGIHDVLELOLW\FRQVWUDLQWVVFDOHYDOXHDQGLPSOHPHQWDWLRQFRVWV:H
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research funding and lack of support for monitoring
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Fortunately, there are methods for assessing stock
status and identifying management reference points for
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development of alternative assessment methods that
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not because they are unimportant economically and
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our overall intent is to make data-poor methods more
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Approach and Key Terms
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methods more accessible to those managing data-poor
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a “stock assessment” as a multistage process that uses
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Various types of data are combined to predict rates of
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of a synthesis of “data-poor methods” and alternatives
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A Framework for Existing Data-poor Methods
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analysis — involving the evaluation of alternative
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TEXT BOX 1.—Key terms
alternative method: (see data-poor method).
conventional method: A management approach, often standard in industrialized commercial fisheries today that synthesizes available data and evaluates
scientific knowledge with a stock assessment (or other single-species population model) to understand individual stock dynamics and estimate sciencebased reference points for a fishery. In response to science-based expectations, trends, and uncertainty, managers adjust fishing pressure for singlestock management (e.g., with quotas, effort reduction, partial seasonal closures, gear restrictions, and/or other harvest rules and regulations).
data poor: A condition to describe a fishery that lacks sufficient information to conduct a conventional stock assessment; this includes fisheries with few
available data, as well as fisheries with copious amounts of data but limited understanding of stock status due to poor data quality or lack of data
analysis.
data-poor method (also called “alternative method”): A method for fisheries data analysis and/or for informing a control rule or management action, which
can be a qualitative or quantitative method, but is not a full, stage-structured stock assessment that requires data-rich or data-moderate conditions.
data richness: A relative index, combining data type, quantity, and quality, in order to qualitatively measure the amount of information that exists for a
stock or fishery.
meta-analysis: A suite of quantitative techniques to statistically combine information across related, but independent, studies that can be field,
experimental, or other information sources.
small scale: A small fishery (either in landings or value) that typically employs traditional gear and targets nearshore stocks. Most typically, small-scale
fisheries harvest inshore stocks, often through traditional, artisanal, and/or subsistence methods without commercial sale or large-scale resale of
harvest (Berkes et al. 2001).
stock assessment: A “stock assessment” is a multistage process that uses (often sophisticated Bayesian) modeling approaches to combine various types of
data and predict rates of change in stock biomass and productivity based on information about yield from fisheries, and the rates at which fish enter the
harvestable population (recruitment), growth in size, and exit the population (natural and fishing mortality). As defined by (NRC 1998) the multiple
steps for a stock assessment are: (1) definition of the geographic and biological extent of the stock; (2) choice of data collection procedures and
collection of data; (3) choice of an assessment model and its parameters and conduct of assessments; (4) specification of performance indicators and
evaluation of alternative actions; and (5) presentation of results.
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Step 1 — Gauge Data-richness.
Step 2 — Data-richness Informs Analytical Options.
Step 3 — Apply Fishery-evaluation Methods:
(ordered from high to low information requirements)
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Step 1 — Gauge Data-richness
Sources of available information determine the
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number of other constraints, such as those itemized in
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assess data-richness by itemizing and organizing all
available information sources into one intuitive and
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both be incorporated into the scorecard since they both
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TABLE 1.—Categories of data-poor methods
We broadly group data-poor methods (i.e., alternative methods) into two general sets or categories to help make the full range of
evaluation options visible and of use to managers who must manage stocks without the benefit of fishery stock assessments:
Less data and resource intensive.
Fishery-Evaluation Methods
Often requires only one or two data types.
Data-driven techniques to identify changes, trends, or priorities.
Types of fishery-evaluation methods:
(i) Sequential trend analysis (index indicators).
(ii) Vulnerability analysis.
(iii) Extrapolation.
More data and resource intensive.
Decision-Making Methods
Often requires several data types, plus assumptions about how to balance these different data inputs.
Creates a framework for decision making.
Types of decision-making methods:
(i) Decision trees.
(ii) Management strategy evaluation (MSE).
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A manager must consider feasibility constraints and evaluative criteria to appropriately identify and implement assessment methods,
especially when managing data-poor fisheries.
Feasibility constraints
Scale of the fishery
Stakeholder incentives
Value-cost of new method
Index of fishery size, based on geographic range and/or economic importance.
Index to characterize the degree of incentives alignment (between industry profits and
biological conservation), as well as stakeholder interest and co-management
capacity.
Index of cost feasibility (time, money, scalable/replicable), based on the fishery’s
present condition, existing resources/infrastructure, and specific context.
Evaluative criteria
Nature of data available
Ranges from zero to perfect information.
162
Data-rich or data-medium status
Fishery-dependent
(by sector & gear type)
Recreational
Commercial
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Monitoring
& Governance
Life History
Socio-Economic
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environmental
index or
timeseries
Management Strategy Evaluation
(simulation, more resource intensive)
Extrapolation
supplemental
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parameters
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Fishery-Evaluation Methods
supplemental
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Existing stock assessment
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age
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or
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series series
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Scorecard to evaluate relative data-richness in a data-poor fishery
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Other prior analyses or assessments
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(e.g., Productivity-Susceptibility
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this volume)
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Smith et al. 2009, this volume)
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see Rago 2008).
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(e.g., In-season Depletion Estimator;
see Maunder 2010, this volume).
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(e.g., Fractional change in
lifetime egg production, FLEP;
see O’Farrell and Botsford 2005).
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(e.g., Multivariate ENSO Index,
MEI, as a biophysical indicator;
see Aburto et al. 2008).
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T R E N D S A N A LY S E S
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SCORECARD
Environmental Proxy — Case-Study Example
Multivariate El Niño Southern Oscillation (ENSO) Index (MEI) and leopard grouper (Mexico)
In the Gulf of California, the El Niño Southern Oscillation
(ENSO) strongly affects the physical environment. Fluctuations in
this climatic driver can have large effects on the Gulf’s habitats
and fish populations. Because of the strong influence of ENSO on
the rocky reef habitat where larval and juvenile leopard grouper
(Mycteroperca rosacea) live, environmental linkages exist
between the Multivariate ENSO index (MEI), kelp-bed habitat
cover, leopard grouper larval recruitment, and leopard grouper
adult abundance (Aburto-Oropeza et al. 2007).
Scientists are now quantifying these relationships and determining
their predictability, using visual survey data of kelp abundance,
larval recruitment, and juvenile and adult abundances (Ballantyne
et al., in review; Aburto-Oropeza et al. 2007). By accounting for
the MEI index at the time of larval recruitment, model
performance improves by more accurately predicting the
abundance of juvenile or adult fish (Ballantyne et al., in review;
Aburto-Oropeza et al. 2007). Environmental relationships, such as
the one between fish abundance and the MEI index, can lead to
general predictions of fishery yield on the basis of juvenile
abundance, natural mortality, fishing mortality and catchability.
This MEI example has been confirmed by model testing and
verification for over a decade, but scientists continue to collect
data and test models. On-going testing and updating is important
because environmental relationships that influence fish
productivity can change over time (e.g., ocean regime shifts).
Minimum Data Needs:
Environmental time series.
Abundance index time series (from CPUE or fishery-independent
surveys, preferably with age-length information to estimate juvenile and
adult abundances).
Optimal Data Needs:
Underlying population model with known life-history data.
The longer the time series, the better, especially if data-collection
protocols remain consistent through time.
Tight coupling (i.e., low noise) between environmental time-series data
and catch/abundance time-series data.
Mechanistic understanding of the process(es), which couple
environmental parameters and fish production.
Cautions and Caveats:
Vulnerable to misspecification of variable recruitment typical at low
population sizes as environmentally driven.
Past conditions assumed similar to current and future conditions (i.e.,
unchanged system dynamics).
Multi-state systems or changes in system dynamics (e.g., regime shifts)
can mask or misrepresent trends detected by this method. Said another
way, the underlying relationship between environmental indices and
indices can change as a result of regime shifts or oscillation changes in
systems with multiple states.
Relationships should be tested and updated regularly (e.g., annually) as
new data emerge.
Managers still must decide how detected trends affect harvest control
rules. References for environmental proxies:
(Ballantyne et al., in preparation; Vance et al. 1998; Aburto-Oropeza et al. 2007)
occur, given a detected change in the environment
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Sequential Trends Analysis (1b): Per-recruit Methods
Example technique (see Scorecard 2): fractional
change in lifetime egg production²)UHTXHQWO\
long-term time series lack catch-at-age data or even
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of demographic properties may be available, such as
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and biomass-per-recruit models, these life-history
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SCORECARD
Per-Recruit Method — Case-Study Example
Stocks with “medium” quality data, including many temperate rocky reef fishes
(e.g., Pacific rockfish, USA) and tropical reef fishes (e.g., Emperor red snapper, Seychelles, Africa)
Many reef fishes fit a description of “medium” data
richness with quality time-series data, so per-recruit
methods are viable management options (Grandcourt et al.
2008). O’Farrell and Botsford (2005, 2006) applied these
methods to rocky reef fish in nearshore California waters.
Per-recruit methods can also apply to tropical reef fish and
other stocks.
Reef fisheries are important resources and protein sources
for many developing countries, regardless of whether or not
a commercial industry exists. In many such fisheries, for
example the Seychelles fishery for Emperor red snapper
(Lutjanus sebae) in East Africa, scientific support for
managers is lacking or nonexistent. Often, however, fishery
managers can access basic life-history information. They
also have time-series data from reef surveys or local catch.
Relative measures (no absolute numbers) can be sufficient
to detect a time-series trend and gauge population stock,
structure, or change over space and/or time (Grandcourt et
al. 2008).
Minimum Data Needs:
Life-history data:
o Mortality estimate.
o Age-length relationship (e.g., von-Bertalanffy growth estimate).
o Length-egg production relationship.
Size-frequency distribution of virgin conditions (estimated from MPAs or
other unfished areas, the earliest period of fishing exploitation known, or
models).
Size-frequency distribution of a recent population state.
Optimal Data Needs:
Detailed knowledge of life-history parameters (ideally, informed with
fishery-independent sources) to increase accuracy and confidence in model
estimates and stock productivity.
Known size-frequency distribution of unfished state.
Cautions and Caveats:
Outputs sensitive to life-history assumptions (e.g., mortality particularly
difficult to estimate).
Uses the earliest catch distribution as an estimate of the unfished state.
Past conditions should be similar to current and future.
Managers still must decide how detected trends affect harvest control rules.
References for FLEP:
(O’Farrell and Botsford 2005, 2006; Grandcourt et al. 2008).
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be used as a limit reference point in order to maintain
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information is also available for many tropical reef
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egg production relationship, and some estimate of
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no-take marine reserves, these populations may serve
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populations have achieved a steady state inside the no¿VKLQJDUHD:HQGWDQG6WDUUWKLVYROXPH/(3
is calculated as the sum across size classes of each size
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they offer little or no understanding into mechanistic
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Per-recruit methods are most useful if the historical
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Sequential Trends Analysis (1c): Population and
Length-Based Indices
Example technique (see Scorecard 3): In-season
depletion estimator²7KHGHSOHWLRQHVWLPDWRUFDQEH
used to calculate near-real time stock abundance by
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survival parameters, the depletion estimator compares
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assessment abundance estimates, the depletion estimaWRULVDFFXUDWHOHVVWKDQHUURU0DXQGHUWKLV
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Example technique (see Scorecard 4): An-indexmethod (AIM)²$Q,QGH[0HWKRG $,0 LV XVHG WR
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can use catch data to infer stock status and estimate a
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stock assessments, correctly tracks population trends
but is still susceptible to misspecifying stock status
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appropriately captures underlying population dynamLFVZLWKOLQHDUDVVXPSWLRQV$VLVWUXHZLWKRWKHULQGH[
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series data strongly affect the value of this data-poor
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Example technique: Depletion-corrected average
catch (DCAC).²7KH GHSOHWLRQFRUUHFWHG DYHUDJH
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an easy-to-use method that uses only catch time-series
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VSHFLHV HJ QDWXUDO PRUWDOLW\ M 7KH DELOity of this method to identify sustainable yields from
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It has long been recognized that a simple average
catch taken during some time period is probably susWDLQDEOHLIWKHXQGHUO\LQJDEXQGDQFHKDVQRWFKDQJHG
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biomass as the stock transitions from higher abundance
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and imprecise probability distributions of M, the ratio
RI )06< WR M W\SLFDOO\ LQ WKH UDQJH RI WR and the suspected relative change in abundance from
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Fishery-evaluation Methods — Type 2
Vulnerability Analysis
A variety of methods make use of general lifehistory characteristics and general species understandLQJ WR HYDOXDWH SRVVLEOH VWRFN UHVSRQVHV WR ¿VKLQJ
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SCORECARD
Population and Length-Based Indices — Case-Study Example (within-season)
In-Season Depletion Estimator: Stocks Driven by Variable Recruitment
(e.g., salmonids, squids, and yellowfin tuna)
Many species have highly uncertain abundance
estimates due to natural variability in annual
recruitment, so new recruits sometimes comprise a
substantial portion of total stock abundance.
Frequently, this occurs for short-lived, fastgrowing species and for species with highly
variable and stochastic recruitment dynamics. Such
stocks include salmonids, squids and fast-growing
tuna.
Minimum Data Needs:
Life-history data.
In-season catch time series (e.g., hourly, daily, or weekly data, depending on
management goals and resources).
In-season CPUE or effort time series, at frequent intervals, using consistent
data-collection protocols.
Detailed life-history knowledge to correspond with the resolution of data
collected.
Several decades ago, Walters and Buckingham
(1975) proposed quantitative methods for inCautions and Caveats:
season adjustments for the management of British
CPUE does not always track actual population because of effort creep,
Columbia salmon (Oncorhynchus spp.). Since
targeting behavior of fishermen, and/or stock dynamics.
then, these ideas have been refined and
Use with caution for schooling species or aggregate spawners.
implemented for in-season management.
Within-season comparisons assume consistency across a fishing season:
Successful examples include fisheries for Alaska
o Method may not be appropriate (requires correction) for fisheries
salmon (Walters 1996; Hilborn 2006) and Falkland
with effort or regulatory changes within one fishing season.
Islands’ squid (Loligo gahi; McAllister et al. 2004;
o Method may not be appropriate (requires correction) if anomalous
Roa-Ureta and Arkhipkin 2006). Within this
conditions differentially impact different times within one season
volume, Maunder (2010) presents a depletion
(e.g., algal blooms or hypoxic zones).
estimator, using growth, recruitment, and survival
Managers still must decide how detected trends affect harvest control rules.
estimates of eastern Pacific yellowfin tuna
(Thunnus albacares) with in-season data to guide
within-season management.
References for depletion estimator for in-season management:
(Hilborn and Walters 1992; Maunder 2010, this volume).
SUHVVXUH.QRZOHGJHRIEDVLFYLWDOVWDWLVWLFVHJVXUYLYDOJURZWKLQWULQVLFUDWHRILQFUHDVHDJHRIPDWXULW\IHFXQGLW\FDQLQIRUPVLPSOHSRSXODWLRQG\QDPLF
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can be used to assess species vulnerability and prioriWL]HVWRFNVIRUUHVHDUFKDQGPDQDJHPHQW:LQHPLOOHU
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Example technique (see Scorecard 5): Productivity
and susceptibility analysis of vulnerability.²7KH3URductivity and Susceptibility Analysis of vulnerability
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life-history characteristics — “productivity” — and
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make a preliminary characterization of overall risk of
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SCORECARD
Population and Length-Based Indices — Case-Study Example
AIM: Atlantic Northeast groundfish (USA)
AIM estimates biological reference points, using a linear
model of population growth to fit a time-series
relationship between catch data (fishery-dependent data)
and a relative abundance index (from CPUE or fisheryindependent data). If the underlying population model is
valid, AIM enables managers to identify levels of relative
fishing mortality that will achieve a stable or targeted
stock size. This data-poor method continues to undergo
additional peer review (Palmer 2008; Miller et al. 2009).
To date, it has been applied to U.S. Atlantic stocks, such
as haddock (Melanogrammus aeglefinus). The New
England groundfish case study demonstrates the
application of AIM to stock complexes where certain
species may be overlooked and unassessed, despite the
large amounts of data and stock assessments focused on a
few well-studied species in the same complex.
Minimum Data Needs:
Life-history data.
Catch time series.
Abundance index time series (from CPUE or fishery
independent surveys).
Optimal Data Needs:
Detailed population data and length-age information, in order
to calibrate the AIM index with independent quantitative
assessments.
Cautions and Caveats:
Underlying linear model assumptions are not appropriate for
all stocks.
Susceptible to problems with misspecification (i.e., use of
catch series from long overfished periods, recruitment pulses,
or noisy data), resulting in bias.
Past conditions should be similar to current and future
conditions.
Managers still must decide how detected trends affect harvest
control rules.
References for AIM:
(Palmer 2008; Rago 2008; Miller et al. 2009).
AIM model downloadable from NOAA Fisheries Toolbox Version 3.0, 2008.
AIM Internet address: http://nft.nefsc.noaa.gov/AIM.html.
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recommendations from a life-history vulnerability
analysis can only be as good as the life-history science
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life-history parameters used, and check assumptions
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SCORECARD Productivity & Susceptibility Analysis — Case-Study Example
Bycatch and Other Species of Low Value or Low Priority to Management
(e.g., Atlantic pelagic sharks and rays, USA)
Bycatch species are all too often unassessed. Given some
basic life history and fishing mortality estimates,
however, methods like Productivity and Susceptibility
Analysis (PSA) can estimate individual species
vulnerability to fishing pressure, relative to other species.
By simultaneously accounting for estimated stock
productivity and stock susceptibility to fishing effort, PSA
produces estimates of overexploitation risk. Target and
nontarget species can all be evaluated. Therefore, PSA
can be a good choice for managers with bycatch concerns
(e.g., in fisheries that are managed to protect weak stocks
or stock complexes mixed with productive stocks).
Minimum Data Needs:
Life-history data to estimate stock productivity.
o Typically, done with estimates of von Bertalanffy growth
coefficient, natural mortality rate, and mean age at 50%
maturity.
Fishing mortality data to estimate stock susceptibility.
o Typically, done with estimates of the catchability coefficient
and bycatch rates, which may be affected by fleet dynamics
and/or fish behavior like schooling.
Optimal Data Needs:
Detailed and accurate knowledge of life-history parameters to increase
accuracy and confidence in model estimates of stock productivity.
Detailed and accurate knowledge of fishing mortality.
Multiple independent data sources and/or expert knowledge to
corroborate estimates for calculated parameters (see above).
A recent evaluation of Atlantic pelagic sharks and rays
shows the advantage of life-history vulnerability analyses
when managing bycatch species. Simpfendorfer et al.
(2008) assess the risk of overexploitation for
elasmobranchs caught as bycatch in the Atlantic
Cautions and Caveats:
swordfish fishery (longline). To evaluate population
Only assesses expected, relative vulnerability to fishing pressure (not
vulnerability, they used multivariate statistics to analyze
absolute population numbers).
information using three primary data metrics: (1)
Will not specify harvest guidelines (i.e., Total Allowable Catch [TAC]
Ecological Risk Assessment, (2) the inflection point of
or target catch limits).
the population growth curve (a proxy for Bmsy), and (3)
Managers still must decide how detected trends affect harvest control
the World Conservation Union (IUCN) Red List status
rules.
(Simpfendorfer et al. 2008). Integrated results provide a
relative measure of overexploitation risk for each
nontarget species of management concern.
References for PSA:
(Simpfendorfer et al. 2008; Patrick et al. 2009; Field et al. 2010, this volume)
Fishery-evaluation Methods — Type 3
Extrapolation (“Robin Hood” Management)
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understanding from “sister” systems thought to be
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“sister” species may offer an informed starting point
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behavior, and other information can be gleaned from
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SCORECARD 6.
Extrapolation Method — Case-Study Example
Species of Low Value or Low Priority to Management (e.g., trochus fishery, Vanuatu, Pacific Islands)
An example of the extrapolation method comes from the Pacific
Islands country of Vanuatu, where a sea snail — the trochus
(Trochus niloticus) — is fished. Despite its “data-less” status, for
years Vanuatu officials have successfully managed the trochus
fishery for years (Johannes 1998). To do this, fishery officials use
generalized growth rates determined by the government. The fishery
managers emphasize one central principle: trochus stocks should be
harvested about once every three years (Johannes 1998). After
successes in villages that initially implemented this three-year rule,
neighboring villages extrapolated this control rule for managing
their own local stocks (Johannes 1998). Ultimately, many
communities implemented similar control rules based on
generalized growth knowledge for stocks beyond the Trochidae
family, including octopus, lobster and other species (Johannes
1998).
Minimum Data Needs:
Anecdotal observations or related “sister” species
and systems with similar characteristics that are
known.
Optimal Data Needs:
The more information from sources above, the better.
Life-history data to supplement observations and test
underlying assumptions in the extrapolations.
Cautions and Caveats:
Large scientific and management uncertainty.
Risk of misspecifying stock status, vulnerability,
sustainable harvest levels; this risk can be potentially
serious and lead to overfishing, especially (e.g., if the
data-poor stock of interest is less productive than its
data-rich “sister” species; Clark 1991, 1993, 1999).
Not a replacement for quantitative management.
Managers still must decide how detected trends
affect harvest control rules.
While the lack of explicit conservation goals in such traditional
management systems has been criticized, more recent empirical
evaluations suggest that the application of traditional knowledge,
marine tenure, and other traditional management techniques can
result in sustainable conservation outcomes (Cinner et al. 2005a,
2005b).
References for extrapolation and “Robin Hood” methods:
(Johannes 1998; Prince 2010, this volume; Smith et al. 2009, this volume).
Step 4—Apply Decision-making Methods
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deemed appropriate, based on the data richness, availDEOH UHVRXUFHV DQG ¿VKHU\ FRQWH[W +HUH LQ 6WHS ZHUHYLHZWZRJHQHUDOFDWHJRULHVRI'HFLVLRQ0DNLQJ
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full stock assessment:
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capable of incorporating diverse sources and types of
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data needs, and important cautions and caveats for the
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Decision-making Methods — Type 1
Decision Trees (Analytical Approach)
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of reference points based on stock characteristics and
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value species because their catch records tend to have
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are often aggregated over space and time at a scale that
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tree approach may fail to detect trends and can mask
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use of data from no-take marine reserves to improve
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Length-Based Decision Tree — Case-Study Example
Australia’s Eastern Tuna and Billfish Fishery
Australia’s Eastern Tuna and Billfish Fishery (ETBF) is a highvalue but low-information fishery for bigeye and yellowfin
stocks near Tasmania (Prince 2010, this volume). Regional data
exist, particularly fishery-dependent time series, but information
is insufficient for a stock assessment. ETBF data-poor challenges
arise from unresolved population structure and resulting
implications for quantitative stock assessment techniques, since
the population connectivity is unknown between tuna stocks in
the Tasman Sea and the Western Central Pacific Ocean (Prince
2010, this volume).
A decision-tree approach to management is appropriate for this
kind of fishery, especially if used in conjunction with other datapoor methods. For example, CUSUM and other FisheryEvaluation methods (see Step 3) can be used to verify the
statistical significance of detected change in time-series data.
To develop a formal harvest policy for ETBF under Australia’s
Harvest Strategy Guidelines (DAFF 2007), fishery managers
adopted an innovative empirical strategy for regional stock
management in the Tasman Sea (Prince 2010, this volume). They
use regional fisheries data as “local” indicators for stock status.
Time-series data from regional fleet effort, landings, and length
distributions of catch are quantitative inputs into a decision tree,
designed to inform ETBF management in a manner responsive to
local change in stock status and fishing trends. Inputs are sizebased indicators of stock status with a determined level of
*
Spawning Potential Ratio (SPR) for local stocks. Then, relative
to local SPR, managers can evaluate and adjust the proportion of
the catch comprised of “mature” (small), “optimum” (medium),
and “megaspawner” (large) fish. Using detected trends over
time, this decision tree organizes information and frames
expected outcomes to recommend action for local management.
Minimum Data Needs:
Life-history data.
Catch time series.
Length-age data for catch size distribution to estimate:
o
Size-frequency distribution of virgin conditions (i.e.,
unfished state).
o
Size-frequency distribution for the fished population.
Optimal Data Needs:
Detailed knowledge of life-history parameters (ideally, informed with
fishery-independent sources) to increase accuracy and confidence in
model estimates and stock productivity.
Scientific knowledge of critical size classes with detailed length-age data
(e.g., length at maturity, megaspawning size).
Known size-frequency distribution of unfished state.
Cautions and Caveats:
Depletion of megaspawners from fishing can falsely suggest the fishery
is catching “optimum” (medium) individuals.
May not be appropriate for stocks with low steepness** because of little
difference between “mature” (small) and “optimum” (medium)
individuals.
Simple assessment methods like this (based on maintaining conservative
levels of spawning biomass, SPR*) are helpful, but do not directly
translate to harvest control rules.
The accuracy and statistical power of decision-tree outputs depends
strongly on the quality of data inputs and underlying model assumptions.
*
SPR is the number of eggs that could be produced by an average recruit in a fished stock, divided by the number of eggs that could be
produced by an average recruit in an unfished stock.
**
Steepness is conventionally defined as the proportion of unfished recruitment produced by 20% of unfished spawning biomass.
References for length-based reference points set by decision tree:
(Froese 2004; Cope and Punt 2009, this volume).
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reserves might also be used to inform season length
and other effort controls through a reserve-based “denVLW\UDWLR´PHWKRG%DEFRFNDQG0DF&DOOLQUHYLHZ
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stakeholders are engaged in collaborative efforts to
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populations and assess marine reserve performance
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Management Strategy Evaluation
(Simulation Approach)
0DQDJHPHQW 6WUDWHJ\ (YDOXDWLRQ 06( LV D JHQHUDOPRGHOLQJIUDPHZRUNGHVLJQHGIRUWKHSUREDELOLVtic evaluation of the performance of alternative management strategies for pursuing different management
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models to collectively and systematically evaluate the
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MPA-Based Decision Tree — Case-Study Example
Nearshore stick fishery in Southern California (USA)
The California stick fishery is a part of California’s
nearshore finfish fishery (further discussed below, see box
with the MSE case-study example). The stick fishery in
Southern California targets cabezon (Scorpaenichthys
marmoratus) and grass rockfish (Sebastes rastrelliger),
using selective “stick” gear that minimizes bycatch of
nontarget species. In the Santa Barbara Channel Islands
region, local fishermen and researchers are collaborating to
collect spatially explicit data, inside and outside of marine
reserves (Wilson et al. 2010, this volume). Currently, they
are evaluating the management potential for establishing an
experimental program for the Santa Barbara Channel
Islands, based on a framework using an innovative marine
reserve-based decision tree (Wilson et al. 2010, this
volume).
This decision tree uses a data-driven model that is spatially
scale-less and based on length-age catch data, inside and
outside of the reserves (Wilson et al. 2010, this volume).
The reserve-based decision tree is integrated within a MSE
simulation, so it responds dynamically to simulated
changes in environmental conditions. For the basic decision
tree, data inputs are life-history parameters: species growth
rates, size/age at maturity, fecundity at age, and selectivity
to fishing. Conditions inside the reserves serve as a proxy
for virgin conditions and are used to estimate unfished
biomass. This decision tree uses similar size-based criteria
and spawning biomass targets, as described above (see
previous scorecard with the decision-tree case-study
example, highlighting Prince 2010, this volume). Time
series of individual length data are compared with current
catch and effort data. Using these relationships, the
decision-tree makes recommended harvest adjustments,
either up or down, to maintain the spawning biomass and
stock at specified levels (Wilson et al. 2010, this volume).
(for this MPA-based decision tree)
Minimum Data Needs:
Life-history data.
CPUE time series.
Length-age data, inside/outside MPAs, to estimate:
o Size-frequency distribution of virgin conditions (i.e., unfished
state). If estimated from MPAs, this often requires 5 to 10 years
(minimum) of data without fishing, although it varies widely by
species and species-specific life histories. In the absence of notake MPAs, models can estimate virgin conditions.
o Size-frequency distribution for the fished population.
Environmental time series.
Optimal Data Needs:
The longer the time series for inside/outside reserves, the better, because
it improves virgin estimates.
Detailed knowledge of life-history parameters (ideally, informed with
fishery-independent sources) to increase accuracy and confidence in
model estimates.
Scientific knowledge of critical size classes with detailed length-age data
(e.g., length at maturity, mega-spawning size).
Known size-frequency distribution of unfished state.
Cautions and Caveats:
Assumes reserve conditions as an appropriate proxy for the unfished
state.
Requires enforcement of reserves.
Interpretation of CPUE can be difficult, if standardized methods are not
used inside/outside reserves.
Depletion of megaspawners from fishing can falsely suggest the fishery
is catching “optimum” (medium) individuals.
May not be appropriate for stocks with low steepness** because of the
lack of contrast between “mature” (small) and “optimum” (medium)
individuals.
Simple assessment frameworks, like this (based on maintaining
conservative levels of spawning biomass, SPR*) are helpful, but do not
directly inform harvest control rules.
The accuracy and statistical power of decision-tree outputs depends
strongly on the quality of data inputs and underlying model assumptions.
Outputs from combined methods, such as a decision tree and MSE (as
used here), are powerful but fairly data intensive. This decision-treeMSE approach is suited for “data-medium” and “data-rich” fisheries. It
is not recommended for “data-poor” stocks on the low end of the datarichness spectrum.
*
SPR is the number of eggs that could be produced by an average recruit in a fished stock, divided by the number of eggs that could be
produced by an average recruit in an unfished stock.
**
Steepness is conventionally defined as the proportion of unfished recruitment produced by 20% of unfished spawning biomass.
References for MPA-based decision tree:
(Prince 2010, this volume; Wilson et al. 2010, this volume).
ous data inputs, ranging from biological to socioecoQRPLFFULWHULDDORQJWKHHQWLUHGDWDULFKQHVVVSHFWUXP
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Management Strategy Evaluation (MSE) — Case-Study Example
California’s nearshore finfish fishery (USA)
California’s nearshore finfish fishery is a multi-species
(for this example MSE application)
fishery with 19 nearshore species, collectively managed by
Minimum Data Needs:
a complex process with overlapping jurisdictions between
Life-history data.
federal and state regulatory agencies (Weber and Heneman
Catch time series.
2000). The Marine Life Management Act (MLMA) of
Length-age data for catch size distribution to estimate:
1999 is a precautionary law to manage state fisheries and
o Size-frequency distribution of virgin conditions (i.e., unfished state).
achieve multiple objectives, including: conservation,
o Size-frequency distribution for the fished population.
habitat protection, the prevention of overfishing and the
Defined goals and objective functions (with known constraints and metrics to
rebuilding of depressed stocks. Current regulatory efforts
evaluate success, whether defined by biological, social and/or economic goals).
use total allowable catch (TAC), limited entry, trip limits,
Clear rules about how these various goals and objective functions interact. This
gear restrictions, fish-size limits, seasonal closures and area
requires some understanding of regulatory options, societal preferences and/or
closures (e.g., no-take marine reserves). Most stocks,
regulatory priorities to define acceptable trade-offs and recognize unacceptable
however, are data-poor; only five species in this fishery are
outcomes.
formally assessed with a stock assessment (CDFG 2002;
Field et al. 2010, this volume). Regulatory consequences of
Optimal Data Needs:
data-poor status are large cuts in allowable catch (Kaufman
Detailed knowledge of life-history parameters (ideally, informed with fisheryet al. 2004). This approach to precautionary management
independent sources) to increase accuracy and confidence in model estimates
makes California’s nearshore finfish fishery of great
and stock productivity.
political interest.
Detailed socioeconomic knowledge, potentially with high-resolution, spatial
data.
For unassessed species, several data-poor methods are
The more data and longer the time series, the better, especially if dataavailable for use by California fishery managers (see Field
collection protocols remain consistent through time.
et al. 2010, this volume). With minimal information, data The higher the quality of input data, the better.
poor methods rely on general life-history understanding to
inform vulnerability assessments and extrapolation
Cautions and Caveats:
methods (see Life-History Vulnerability Analyses in the
The statistical power of MSE outputs depends strongly on the quality of data
Fishery-evaluation Methods section, above). On the other
inputs and underlying model assumptions.
side of the “data-richness” spectrum, a limited number of
Choice about objective function(s) and evaluation criteria drive outcomes.
nearshore species have full stock assessments. Given the
MSE outputs rely on multiple sub-model assumptions and assumptions about
uneven distribution of data and information constraints, a
sub-model integration, which potentially compounds modeling errors and
MSE provides a method to organize and synthesize the
uncertainty.
multi-species and patchy data-rich information.
Assumptions about how to link MSE sub-models with one another are
critically important, but potentially hidden from view. Be wary of “black box”
Recently, such a MSE effort was initiated for nearshore
outputs.
fisheries management in Port Orford, Oregon (see Bloeser
MSE is powerful but data intensive. It requires careful design and appropriate
et al. 2010, this volume). In the future, outputs will likely
development to individually match the MSE with stock characteristics, fishery
help to define priorities and guide management decisions.
context and management goals.
MSE is suited for “data-medium” and “data-rich” fisheries, yet often limited by
the method’s need for large time investment and highly skilled technical work.
It is not recommended for “data-poor” stocks on the low end of the datarichness spectrum.
References for MSE:
(Sainsbury et al. 2000; Gunderson et al. 2008; Bentley and Stokes 2009, this volume; Butterworth et al. 2010, this volume; Dewees 2010, this
volume; Dichmont and Brown 2010, this volume; Smith et al. 2009, this volume).
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182
183
References for extrapolation and “Robin Hood” methods:
(Johannes 1998; Prince 2010, this volume; Smith et al. 2009, this volume)
Extrapolation methods
or “Robin Hood”
management (in Australia)
Ecological understanding of stock from:
Anecdotal observations.
Local knowledge.
Related “sister” species and systems with
similar characteristics that are known.
The more information, the better, from
observations or similar systems.
Life-history data.
Detailed and accurate knowledge of lifehistory parameters.
Life-history data to estimate stock
Detailed and accurate knowledge of fishing
Life-history vulnerability
productivity.
mortality.
analyses
Fishing mortality data to estimate stock
Multiple data sources, fishery independent
susceptibility.
sources of information, and/or expert
knowledge to corroborate estimates for
calculated parameters.
References for productivity and susceptibility analysis (PSA), an example of life-history vulnerability analyses:
(Simpfendorfer et al. 2008; Patrick et al. 2009; Field et al. 2010, this volume)
References for CUSUM control charts and statistical methods:
(Manly and MacKenzie 2000; Manly 2001; Scandol 2003; Kelly and Codling 2006)
References for multivariate El Niño Southern Oscillation index (MEI), which is an example of an environmental proxy:
(Ballantyne et al., in review; Aburto-Oropeza et al. 2007)
References for fractional change in lifetime egg production (FLEP), which is a per-recruit method:
(O'Farrell and Botsford 2005, 2006)
References for depletion estimator for in-season management:
(Hilborn and Walters 1992; Maunder 2010, this volume)
References for an-index-method (AIM), which is a population and length-based index:
(Palmer 2008; Rago 2008; Miller et al. 2009)
Method Type
(Fishery-Evaluation Methods presented in order of high to low information requirements).
Minimum
Optimal
Data Needs
Data Needs
Sequential trend analysis
(index indicators)
The more data and longer the time series,
Series data (most commonly time-series
the better.
Sub-category types include:
data), although data requirements vary
The higher the input-data quality, the
o
Population and
by type of trend analysis.
better (e.g., consistent data-collection
length-based
Common requirements are:
protocols through time).
indices
o
Life-history data.
As a broad generalization, 30+ data points
o
Per-recruit
o
Catch time series.
in a series is preferred (e.g., 30+ years of
methods
o
Abundance index time series
time-series data for annual catch),
o
Environmental
(from CPUE or fishery
although this varies with system
proxies
independent surveys).
dynamics.
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Only assesses expected, relative vulnerability to
fishing pressure (not absolute population numbers).
Will not specify harvest guidelines (i.e., Total
Allowable Catch [TAC] or target catch limits).
Managers still must decide how detected trends
affect harvest control rules.
Trend indicator only.
Managers still must decide how detected trends
affect harvest control rules.
Assumes system dynamics and assumptions remain
constant for the period over which data are
analyzed (e.g., no environmental regime shifts; no
major change in fleet dynamics or technologies).
Relationships should be tested and updated
regularly (e.g., annually) as new data emerge.
Large scientific and management uncertainty.
Risk of misspecifying stock status, vulnerability, and
sustainable harvest levels if the data-poor stock of
interest differs from its data-rich “sister” population.
Not a replacement for quantitative management.
Managers still must decide how detected trends affect
harvest control rules.
Cautions and Caveats
+21(<(7$/
Clear fishery goals and metrics with evaluation
criteria.
Depending on decision-tree design (highly
flexible) and manager goals, exact metrics and
data requirements vary.
Common data inputs to evaluate metrics and
decision points are:
o
Life-history data.
o
Catch time series.
o
CPUE time series.
o
Socioeconomic context and data.
184
Highly skilled technical expertise, time, and
resources for advanced programming and
simulation modeling.
Clear fishery goals and metrics with evaluation
criteria.
Depending on MSE design (highly flexible)
and manager goals, exact metrics and data
requirements vary.
Common data inputs to evaluate metrics and
decision points are:
o
Life-history data.
o
Catch time series.
o
CPUE time series.
o
Environmental context and data.
o
Socioeconomic context and data.
Given defined metrics and evaluation
criteria, the biological, social and/or
economic data have:
Multiple information sources to
increase the robustness of and
confidence in information.
Long time series.
Quality data.
Given defined metrics and evaluation
criteria, the biological, social and/or
economic data have:
Multiple information sources to
increase the robustness of and
confidence in information.
Long time series.
Quality data.
Cautions and Caveats
MSE is powerful but data intensive. It requires careful
design and appropriate development to individually
match the MSE with stock characteristics, fishery
context and management goals.
MSE is suited for “data-medium” and “data-rich”
fisheries, yet often limited by the method’s need for
large time investment and highly skilled technical
work. It is not recommended for “data-poor” stocks on
the low end of the data-richness spectrum.
The statistical power of MSE outputs depends strongly
on the quality of data inputs and underlying model
assumptions.
Choice about objective function(s) and evaluation
criteria drive outcomes.
Assumptions about how to link MSE sub-models with
one another are critically important, but potentially
hidden from view. Be wary of “black box” outputs.
Decision trees are flexible tools, requiring appropriate
design to individually match the decision tree with
stock characteristics, fishery context and management
goals.
A decision tree can provide a simple assessment
method, helpful for management (e.g., to maintain
conservative levels of spawning biomass, SPR),
although it may not directly address harvest control
rules.
The accuracy and statistical power of decision-tree
outputs depends strongly on the quality of data inputs
and underlying model assumptions.
References for MSE:
(Sainsbury et al. 2000; Bentley and Stokes 2009, this volume; Butterworth et al. 2010, this volume; Dewees 2010, this volume; Dichmont and Brown 2010, this volume; Smith et al.
2009, this volume).
Management strategy
evaluation (MSE)
References for length-based reference points set by decision tree:
(Froese 2004; Cope and Punt 2010, this volume)
References for MPA-based decision tree:
(Prince 2010, this volume; Wilson et al. 2010, this volume)
Decision-tree approach
Method Type
(Decision-Making Methods presented in order of expected practical use for data-poor management).
Minimum
Optimal
Data Needs
Data Needs
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