SWFSC, FRD

July 2013

3.2 Overview of Assessment Modeling

Based on Data Availability Kevin Piner

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 2

Stock Assessment Topics

1. How data controls model complexity data

2. Model complexity and management uncertainty

3. Strengths, Challenges and Strategies

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 3

What is a stock assessment?

Synthesis of existing knowledge and data

recreating past population dynamics

to learn about current status and future

sustainable catch levels.

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 4

Population/stock

Define what is a population/stock?

Common biological processes

Spawning grouping with young contributing

Common growth and death patterns

Exploited by defined groups

No emigration or immigration

Attributes Biomass

Numbers

B/N at age

B/N at age and sex

B/N at age and sex by area….etc.

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 5

Population

additions subtractions

Stock Assessments are Population Models

Processes controls

dynamics

Parameters control

process

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 6

Population

The role of Data

observation

prediction

compare

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 7

Population

Dynamic Population Models

year 1 year 2 year 3 year 4………………….year n

observation

expectation

compare

dynamics

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 8

The Basics…….

Catch

and

Index of Abundance

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 9

Catch : what have we removed from the population

Population

Catch=Kills Includes fish not kept!

Blue Shark “catch”

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 10

Indices of abundance: Tell us how population abundance changes

Population

Fishery Independent vs Fishery Dependent

Absolute vs Relative

Pacific Bluefin Tuna CPUE

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 11

Population Intrinsic rate of

increase catch

• Data (minimal): catch and index of abundance

• Complexity/Realism: Incorporates all processes

(recruitment, growth, and natural mortality) into a single

aggregate function of production

• Few but Strong assumptions

Biomass Dynamic (production models)

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 12

Additional Data

Nt+1 = Nt e -Zt

age

num

ber

0 5 10 15 20 0

10

20

30

40

50

60

70

80

leng

th

age

Population

Life History:

How fast do they grow

when are they mature

How quickly do they die naturally

Assume this process!

Spawners

Rec

ruits

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 13

Age-structured production models (inclusion of S/R relationship)

Data (moderate): Uses more biological detail than a surplus production model (keep track of

age-specific quantities)

• Data: catch, index and life history (growth and M)

• Complexity: adds all relevant processes, but simplified

• More assumptions but explicit and testable

Population (made up of numbers at age)

age

1977

0.01

0.02

0.03

0.04

0.05

0.06

1980

0.02

0.04

0.06

0.08

0.10

1983

0.02

0.04

0.06

0.08

0.10

0.12

1989 0.02

0.04

0.06

0.08

1992

0.02

0.04

0.06

0.08

0.10

0.12

0.14

1995

0.01

0.02

0.03

0.04

0.05

0.06

2001

0 5 15 25

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Age compositions

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 14

Biological data: demographics of the population

Population

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 15

• Data (rich)- catch, index, life history and composition

• Complexity: With appropriate data, almost all processes can be modeled explicitly

• Assume that the processes match reality

• Full dynamics can be estimated- e.g. Year-specific births

Age Structured Models

Population (made up of numbers at age

Spawners

Rec

ruits

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 16

Truly data intensive models!

Spatial patterns and movement

Multi-species and Ecosystem

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• TRULY Data intensive- Need all the information of previous models and MOVEMENT between areas

Spatially Structured models

Population Population movement

Things common to areas

Area 1 Area 2

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 18

• Amazingly Data intensive- Need linkages between species

Multi-species Models

Population Population

Population

Linkage

predation

competition

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 19

Population models are a tradeoff of complexity and realism

Simple models

Complex models

Data needed Model realism Assumptions

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 20

ADDED INFORMATION (Complexity and Costs)

RE

CO

MM

EN

DE

D

CA

TC

H L

EV

EL

^ ^

^ ^

^

Why does assessment complexity/realism matter…………

^ ^

Strengths

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | NOAA Fisheries | Page 21

Challenges

Strategies

Range of models to match data complexity

Models contain enough process to match most situations

Increasing our understanding of how to use model process

to emphasize important data types and minimize others- e.g. time varying selection

Data for important model processes still missing- e.g. movement

Improve understanding of biology and improved data collection to better incorporate

relevant model process