fisheries models: methods, data requirements, environmental linkages richard methot noaa fisheries...

Fisheries Models:Methods, Data Requirements,

Environmental Linkages

Richard MethotNOAA Fisheries

Science & Technology

Stock Assessment Overview 2

PRESENTATION OUTLINE

• Assessment Goals• What is a “Stock Assessment”?• Data Inputs• Assessment Methods• Role of Environmental Data


Stock Assessment

• Collecting, analyzing, and reporting demographic information for the purpose of determining the effects of fishing on fish populations.

• Key Concepts / Jargon– Stock; Population; Unit– Abundance; Biomass; Spawning

Biomass– Recruitment; Yearclass; Cohort– Fishery– Fishing mortality (F); Exploitation Rate


POPULATION MODEL:(Abundance, mortality)

POPULATION MODEL:(Abundance, mortality)

STOCK STATUSSTOCK STATUS OPTIMUM YIELD

OPTIMUM YIELD

CATCHLOGBOOKS,OBSERVERS,

AGE/SIZE DATA

BIOLOGY AGE,

GROWTH, MATURITY

ABUNDANCE TREND RESOURCE SURVEY,

FISHERY CPUE, AGE/SIZE DATA

ADVANCED MODELS HABITAT CLIMATE ECOSYSTEM MANMADE STRESS

SOCIOECONOMICS

STOCK ASSESSMENT PROCESS


OPTIMUM YIELD

OPTIMUM YIELD

SINGLE SPECIESASSESSMENT

MODEL

TIME SERIESOF RESULTS

BIOMASS,RECRUITMENT,

GROWTH,MORTALITY

STOCK ASSESSMENT & ECOSYSTEM

HOLISTIC ECOSYSTEM

MODEL CUMULATIVE

EFFECTS OF ALL FISHERIES AND

OTHER FACTORS

INDICATORS ENVIRONMENTAL,ECOSYSTEM,OCEANOGRAPHIC

RESEARCH ONINDICATOR

EFFECTS


Assessment ResultsUsed in Fishery Management

• Monitoring / Reactive– Exploitation rate is higher than a maximum limit:

• overfishing is occurring and must be eliminated;

– biomass is below a minimum level:• the stock is overfished (depleted). A rebuilding

plan must be prepared to rebuild the stock in as short a time as possible;

• Proactive– Assessment forecasts provide the technical

basis (operational model) for setting and adjusting fishery quotas and other management measures to:

• implement harvest policies• Rebuild depleted stocks


HARVEST CONTROL RULE= OPERATIONAL MODEL

0.0 0.2 0.4 0.6 0.8 1.0

RELATIVE ABUNDANCE

CA

TC

H

What is the current stock abundance relative to historical and target levels?

What level of fishing mortality (F) is the limit (RED) and target (GREEN)?

What level of short-term future catch would achieve target?


FISHING REDUCES LIFETIME EGG PRODUCTION

0 5 10 15 20 25 30 35 40

AGE

Tota

l Eg

gs

Unfished

40% of Unfished Egg Production is Obtained with F=0.14


DIRECT FISHING EFFECTSYield per Recruit and Eggs (Spawning Biomass) per Recruit

0%

20%

40%

60%

80%

100%

Exploitation Rate (Fishing Mortality)

Biomass per Recruit

Yield per Recruit


Assessment Inputs

• STOCK STRUCTURE: Spatial limits of demographic unit

• TOTAL CATCH: total removals due to human activities (due to fishery landings, discarded bycatch, and cryptic mortality due to encounters with fishing gear);

• SURVEYS: the relative or absolute magnitude of a fish population (by age);

• LIFE HISTORY: growth, maturation, fecundity, natural mortality, and other characteristics of individual fish.


What is a “Stock”?

• A group of individuals of the same species that:– inhabit the same

geographic region;

– interbreed when mature;

– have sufficiently high levels of diffusion/mixing

Northern Stock

Southern Stock

High mixing within

Low mixing between


• Commercial fishing effort, catch, and value– Dealer reports – Vessel trip reports

• Recreational fishing effort and catch– Telephone surveys– Shoreside sampling surveys

• Size and age structure of catch– Commercial catch sampling surveys– Recreational catch sampling surveys

• Electronic dissemination of data• Serves stock assessment, economic

analysis, and fishery monitoring needs

Pillar I - Catch DataFisheries Information System


Fishery ObserversSince 1972 NOAA Fisheries has deployed fishery observers to collect catch and bycatch data from US and foreign commercial fishing and processing vessels.Today, 42 fisheries all around the nation are monitored by observer programs logging over 60,000 observer days at sea.Data support fish stock assessment, fishery monitoring, protected species mortality monitoring, and other conservation and management programs.


Pillar II - Abundance IndexFishery-Independent Surveys

• Catch/Effort = q * Abundance– Survey sampling units (effort) is

highly standardized;– Sampling follows a statistical design;– Assert that q is sufficiently constant;– Sometimes, q can be measured

directly, so survey catch rate can be transformed directly to measure of abundance


Fishery-Independent Surveys

10 NOAA Ships

Plus 1768 charter DAS


Fishery CPUE as Abundance Index

• Fishery Catch = q * Effort * Abundance– So

• Catch/Effort = q * Abundance

• Unfortunately,– Fishing effort is very hard to

standardize, so the effective q may not be constant;

– Fishing tends to occur where abundance is high, not where abundance is average.


Autonomous Underwater Vehicle Contains cameras, sensors, acoustics Reach into habitats inaccessible to

other survey tools

Advanced Technology


Pillar III - Fish Biology / Life History

0 5 10 15 20 25

AGE

Length

Weight

Eggs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 5 10 15 20 25

AGE

% Mature

Natural Mortality

Ease: Length > Weight >> Age > Eggs & Maturity >>> Mortality


STOCK ASSESSMENT LOGICEstimating Abundance

• How big must stock have been if:– We saw a relative decline of X% per

year in the survey index;– While Y tons of catch were removed

per year;– And the stock’s biology indicates

that natural changes in abundance are only +/-Z% per year


BASIC ASSESSMENT APPROACHES

• Index Methods– Is stock abundance:

• Increasing, decreasing, or stable?

• Equilibrium Methods– On average, is fishing mortality:

• too high, too low, or just right?

• Dynamic Population Methods– Estimates time series of stock abundance and

mortality– Forecast stock abundance and catch level that

maintains mortality target– Can be biomass-based, but age & size structure

provide more detail, especially for forecasting

• Choice depends on data availability and complexity of management questions


Trend in Survey Abundance Index

• Lack of fit due to:– Sampling variability

of the observations• Environmental

data can improve stratification and adaptive sampling

– Unknown changes in the calibration, q

• Environmental data can inform about changes in availability of fish to the survey

0

20000

40000

60000

80000

100000

120000

140000

1975 1980 1985 1990 1995 2000 2005YEAR

CP

UE

OBSEXP

• Other Data in Model:– Recruitment index for some

years– Proportion at each age in the

fishery– Total catch


INTEGRATED ANALYSIS

• Ability to use various age, length, abundance data to calibrate model

• Smoothly transitions from pre-data era, to data-rich era, to forecast.

• Produces estimates of model uncertainty

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

YEAR

AB

UN

DA

NC

E

100

1000

10000

100000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

YEAR

AN

NU

AL

RE

CR

UIT

ME

NT

DATA ERA


MODEL PROCESSES

CONSTANT

• Assert, Believe!, Hope!! To Be Stable Over Time– Traditional Data Provide

Little Information To Estimate Variability

• Examples:– Natural Mortality– Survey Catchability– Average Spawner-

Recruitment Relationship

VARIABLE

• Expected To Vary Over Time– Data Are

Informative About Fluctuations

• Examples:– Fishing Mortality– Annual Recruitment– Growth and

Maturity Changes


PRODUCTIVITYHigh productivity stocks maintain high recruitment levels even as stock abundance declines. They rebuild quickly as fishing mortality is reduced.

BIOMASS

RE

CR

UIT

ME

NT

High Productivity

Low Productivity

Low productivity stocks can sustain only low fishing mortality rates. They require multiple generations to rebuild from low biomass levels.

Short-term (annual) environmental variability obscures these ecological relationships

Long-term (decadal) environmental and ecosystem shifts are confounded with relationships


ENVIRONMENTAL DATA &“VARIABLE” PROCESSES

• Including environmental component in model can:– Reduce alias in estimate of

biomass linkage caused by long-term environmental patterns;

– Provide additional information on historical fluctuations during data-poor periods;

– Provide early indicators of upcoming fluctuations.

• Similar situation for environmental effects on body growth

• Ecosystem effects are harder!

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

0 100000 200000 300000 400000

SSBR

ecru

irts

Expect-mean

Time_series

Virgin & Init

)( yIyyenv eRR

Recruitment = f(biomass, environment, ecosystem) + e


ENVIRONMENTAL DATA &CONSTANT PROCESSES

• New Information About Changes In “Constant” Processes– Need Validation Outside Model

• EXAMPLES:– Predators Affect Natural Mortality– Spatial Distribution Affects Catchability– Thermocline Depth Affects Catchability– PDO Regime Affects Average

Recruitment

Fisheries And The Environment

FATE

A NOAA Fisheries Oceanographic Program

Supporting NOAA’s mission to ensure the sustainable use of US fishery resources under a

changing climate


Standardized Copepod Anomalies

0.00 0.25 0.50 0.75 1.00 1.25 1.50Hatchery coho salmon survival (%)0

2

4

6

8

10

12

14

A FATE Ecosystem IndicatorA FATE Ecosystem Indicator Peterson et al.; Northwest Fisheries Science Center

This function can be used to predict returns of salmon the following year; copepod anomalies from 2001 predict that about 10% of the juvenile salmon that went to sea in spring 2001 will return to spawn in fall 2002.


Sablefish Recruitment VariabilityMichael J. Schirripa and Jim J. Colbert

Northwest Fisheries Science Center, Oregon State University

Recruitment is fit to stock biomass as well as annual deviations in the Spring sea level anomalies. This made

possible estimates of current year-class strengths

016.0)393.0*( SLI y

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

0 100000 200000 300000 400000

SSB

Rec

ruir

tsExpect-mean

Time_series

Virgin & Init

)( yIyyenv eRR Iy = (SL*0.393)-0.016

-3

-2

-1

0

1

2

-2 -1 0 1 2 3 4

Q2 North Sea Level (Z -score)

Rec

Dev

P < 0.0001R-squared = 0.436

1997 El Nino2003

1999

Stock Assessment Overview 30Evan Howell and Jeff Polovina, Pacific Islands Fishery Science Center


-0.4

-0.2

0

0.2

1981 1982 1983 1984

1981 cohort

ST

D G

row

th r

ate

Growth year

Using variables related to oceanic conditions* we can fit growth rates for individual California cohorts and the probability that a cohort will mature after the third ocean winter at sea.

*e.g. Wind Turbulence,Upwelling, Sea Level Height, Sea Surface Temperature. 0.25

0.5

0.75

1

1975 1980 1985 1990 1995 2000

Pro

po

rtio

n m

atu

rin

g a

fter

3 O

W

Brood year (cohort)

CA Chinook Growth and Maturation Vary with the Environment

B. Wells, C. Grimes, J. Field, C. Reiss; Southwest Fisheries Science Center


CONCLUSIONS

• Environmental information can improve precision and accuracy of fish assessments by providing:– Info on large scale changes in spatial distribution;– Info on factors affecting fish behavior and

availability to surveys;– Info of factors affecting spatial distribution in

fishing effort;– Indicators to adjust mortality and growth factors

otherwise held constant;– Indicators to forecast upcoming fluctuations in

highly variable recruitment

fisheries models: methods, data requirements, environmental linkages richard methot noaa fisheries...

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