Fisheries 101: Modeling and assessments to achieve sustainability

Training Module July 2013

Outline• Small Scale Unassessed Fisheries• Fishery assessments

– How they can be used– What are we trying to assess and why?

• Projection Modeling Overview – What are projection models and why should we use

them?– Inputs and outputs

2

The problem is most acute in small scale, coastal fisheries

Global Fisheries

Costello et al., 2012, Science 3

Small Scale Unassessed Fisheries• Small scale, but collectively

responsible for 40% of global catch• Account for 90% of all fishermen• Millions of jobs in fish processing,

marketing, etc.• Many appear to be overfished and not

producing as much food/money as possible

• So what do we do?4

Fishery DependentInformation (logbookdata, discards, etc.)

Landings by Gear Type 1. commercial 2. recreational

Fishery IndependentSurveys (e.g., trawlsurveys)

Life History Information growth, maturity, etc.

Stock Assessment(statistical model)

Biomass + Fishing Mortality

Management Decision

Catch Demographic Data 1. age composition 2. length composition

Stock Assessments: The Foundation of Fisheries Management

Courtesy of S. Ralston 5

Fishery DependentInformation (logbookdata, discards, etc.)

Landings by Gear Type 1. commercial 2. recreational

Fishery IndependentSurveys (e.g., trawlsurveys)

Life History Information growth, maturity, etc.

Stock Assessment(statistical model)

Biomass + Fishing Mortality

Management Decision

Catch Demographic Data 1. age composition 2. length composition

Stock Assessments: The Foundation of Fisheries Management

> 80% of fisheries are unassessed

Courtesy of S. Ralston 6

Data Poor Assessments• Methods are less costly and data intensive

than traditional assessments• Goal of the assessment is to make

management decisions using only readily available information

• A variety of approaches – we will highlight three of them

7

Length

Young

Prime

Old

Length Frequency

Wilson et al. 2010

1. Marine Reserve-Based Decision Tree• This tool examines the length

frequency and density of scientifically sampled fish inside and outside of marine reserves as well as trends in the catch

• The model can then be used to adjust last years total allowable catch in order to achieve a target reference point

8

Compare CPUE of prime sized fish inside and outside of reservesLevel 1

Compare CPUE and proportion of old fish in catch to SPR40 levelsLevel 3

Evaluate CPUE of young fish over past 5

years

Evaluate CPUE of young fish over past 5

years

Level 4 Compare CPUE of young fish to SPR50 levels

1. Marine Reserve-Based Decision Tree

Output:

Adjustment to Total Allowable Catch (TAC)

Evaluate CPUE of prime sized fish in fished area over previous 3-5 years

rising stable fallingLevel 2

9

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

1.40

1.45

1.50

-

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Spaw

ning

Pot

entia

l Rati

o

Fishing Mortality

2. Spawning Potential Ratio (SPR) methodsSPR = A measure of current egg production relative to unfished levels

Lightly Fished

Spawning = 50% of unfished levels

Spawning = 10% of unfished levelsHeavily

Fished

No Fishing

target

10

3. Catch Curve Analysis: usingNo-take Zones as Reference Areas Total Mortality (Z) = Natural (M) + Fishing (F) Mortality

F = Z - M

ReserveNon-Reserve

M = natural mortality

Z = M + F

Wilson et al. in review 11

Outline• Small Scale Unassessed Fisheries• Fishery assessments• How they can be used• What are we trying to assess and why?

• Projection Modeling Overview • What are projection models and why should we

use them?• Inputs and outputs

12

What is a “projection model”?• A way to combine essential elements of a system

to answer specific questions about management outcomes

• Critical concepts• Level of detail required depends on question• General principles don’t answer specific

questions• Assumptions must be clear, can be challenged

• No model is exact to reality

13

Why use projection models?• Models can include more information

than any individual can consider• Helps to organize thinking• Often reveal counterintuitive results• Models move from simple to complex

based on the type of question you are addressing

14

Population Dynamics• One species• Few parameters• One area

Bioeconomic simulation models:Example of a simple simulation model

Courtesy of S. Valencia and J. WilsonFish Icon courtesy of L. Allen

15

Population Dynamics• Age • Recruitment• Growth• Movement (larval and adult)• Mortality (natural and fishing)

Bioeconomic simulation modeling: Moving toward more complex models

Courtesy of S. Valencia and J. WilsonFish Icon courtesy of L. Allen

16

Incorporate into the model:• Fishing effort – fleet dynamics• Habitat quality• Adult emigration• Larval spillover

Bioeconomic simulation modelingMoving toward more complex models (cont.)

Courtesy of S. Valencia and J. WilsonFish Icon courtesy of L. Allen

17

Complex Model inputs• Combines• Habitat (where are productive reefs)• Life history (reproduction, growth, migration)• Human behavior (where/how much they fish)• Community objectives (profit, sustainability,

ecological outcomes, local employment)

• Used for:• Scenario evaluation (what happens if …?)• Optimization (what’s the best place for ….?)

18

Example Outcomes

Decision Table- Compared with long term status quo

% Change

Yield% Change Biomass P<0.1B0 P>0.4B0

Scenario 1 20 14 .37 .44Scenario 2 15 17 .25 .63Scenario 3 10 12 .14 .79Scenario 4 12 22 .28 .72Scenario 5 18 4 .21 .68

Biomass Profits

TimeTime

19

Summary• Stock assessments are costly and data

intensive• Data poor stock assessments and projection

models are ways to make predictions using basic/incomplete/imperfect data

• No tool is perfect, but if you manage adaptively you can reconsider decisions over time

20

Thank you!