Habitat prediction for southern bluefin tuna spatial management

Habitat prediction for southern bluefin tuna spatial management

Alistair Hobday

Klaas Hartmann

Hobday and Hartmann (2006)

Pelagic Fisheries and EcosystemsCSIRO Marine & Atmospheric Research

Breakthrough Technology I “Physical Observations”

• In situ coverage is patchy in space and time– Climatologies, no interannual variation

• Satellite data provides surface features– Platform coverage, clouds, space

• Ocean models (3D, multivariable, space/time coverage)– Short-term– Season/annual– Long-term

• Allow new phase of fisheries oceanography

Bluelink product: synTS

• synTS (synthetic temperature and salinity) – statistical data product

– derived using SSH, SST & climatology for Australian region=> temperature at standard oceanographic depths

=> produced in near real time

• David Griffin and team at CSIRO

Breakthrough Technology II“Smart Tags”

• Tag technology is making a difference to understanding fish movements and habitat use

• New insight into the basic biology of many species

• Improved the advice we can provide for management

Pop-up Satellite Archival Tags

SBT track

Southern Bluefin Tuna “problem”

• World-wide stock at historical low (<10%)

• International catch agreement (quota)– Australia abides by this

`

Bluefin tuna on the east coast

• Bycatch in a longline fishery– Limited quota held on the east coast

– Fish are discarded if captured, because cannot be legally sold

• Management Goal– Avoid catching SBT (unless own

quota)

Minimize non-quota holders catching bluefin tuna

(Real-time spatial management)

• Zone east coast into 3 regions– Core SBT habitat: 4t quota required for access

– Marginal SBT habitat zone: 0.5t quota required for access

– Poor SBT habitat zone: no quota required for access

• Assist management by identifying present distribution of tuna habitat

• First example of using environment information for real-time “management” (in Australia, perhaps unique in world)

Method

Analysis and habitat prediction tools

Biological Data(tags)

Habitat Preferences

Physical Data (near-real time distribution of

environment)

Satellite datasynTS (SSH & SST)

Habitat Prediction Maps

Management Support(sustainable use)

Biological Data: Tag temperatures(based on 45 tags for 2006)

• Distribution of temperatures is fish “preference” (e.g. SST)

SST

1. Generate distribution of surface habitat(proportion of time fish spend in water colder than at each

pixel)

10 15 200

0.02

0.04

0.06

0.08

0.1

0.12

Surface

Temperature

Pro

po

rtio

n

150 155 160-42

-40

-38

-36

-34

-32

-30

-28

10 15 200

0.02

0.04

0.06

0.08

0.1

0.1250m

Temperature

Pro

po

rtio

n

150 155 160

-42

-40

-38

-36

-34

-32

-30

-28

10 15 200

0.02

0.04

0.06

0.08

0.1

0.12

100m

Temperature

Pro

po

rtio

n

150 155 160

-42

-40

-38

-36

-34

-32

-30

-28

10 15 200

0.02

0.04

0.06

0.08

0.1

0.12

200m

Temperature

Pro

po

rtio

n

150 155 160

-42

-40

-38

-36

-34

-32

-30

-28

2. ….then do for sub-surface habitats(using the near-real time ocean model)

3. Sum to create full habitat probability distribution(probability that fish are in water column “colder” than each spot)

Transferring predictions to management

• Management-selected habitat probabilities• Core SBT habitat: 80% probability• Marginal SBT habitat zone: 15% probability• Poor SBT habitat zone: 5% probability

• Turn continuous habitat probabilities into 3 zones– Each pixel is classified into one of 3 types

• Send reports every 1-2 weeks to fishery managers– Decide on lines to divide these zones

4. Convert to zones and add lines

Informing Stakeholders…Climatology

short

Fisheries managers place linesAccepted approach by stakeholders…but how is management doing?

• Raw zones are complex shapes

• Simple lines needed (1, 2 or 3 segments)

• Subjective approach….subject to pressure from stakeholders….

No quota

Limited quota

Quota zone

Misclassified pixels

Core: correct

Core: incorrectBuffer: correct

OK: correct

Buffer: incorrect

Buffer: incorrect

Core: incorrect

OK: incorrect

OK: incorrect

Objective function… Actual Classification from Analysis

(numbers in brackets indicate weights used in the objective function) OK Buffer Core

OK Correct Management (0) Non-Precautionary Management (1)

Non-Precautionary Management (2)

Buffer Precautionary

Management (1) Correct Management (0)

Non-Precautionary Management (1)

Man

aged

As

Core Precautionary

Management (2) Precautionary

Management (1) Correct Management (0)

…seeks to balance the contribution of non-precautionary (blue) and precautionary (orange) misclassifications to the score

lukluf

.

Line placement 7

AFMA Optimiser

Classification success

AFMA: Precautionary bias:red bar > aqua bar (n=2)

AFMA: Non-precautionary bias:aqua bar > red bar (n=5)

Optimizer: no biasyellow bar = brown bar (n=7)

1 2 3 4 5 6 7

Human vs Machine

• Correctly-classified habitat (~80% of habitat area)– Computer wins 5/7 placements– When management did better….strong bias to

precautionary or non-precautionary

• No bias in line placement?– Computer 7/7 placements– Managers 0/7

• Non-precautionary bias (disadvantage fish)– Managers: 5/7 times

• Precautionary bias (disadvantage fishers)– Managers: 2/7 times

Management Uptake

Year Reports Line Adjustments

Line Complexity

2003 18 5

2004 12 7

2005 10 5

2006 15 10

Summary

Effective management support tool– Using biology + physics => management– Real time, adaptive….

This season (ended Oct 2006, next start May 2007)

• Include more tags (57)

• Provide computer line placement as a guide

• Encourage more rapid response to predictions

Future

• Email daily habitat prediction (but lines can jump, QC)

• Continued validation (observer data in all zones)

• Habitat predictions for other species

Extra slides

Effect of delays in placement

Zones moving north(1 week delay)

Zones moving south(1 day delay)

Model date Implementation date Model date Implementation date

Disadvantage fish(by 7 days)

Disadvantage fisher(but only by 1-day)

SBT habitat can be fished SBT habitat with extra protection

Validation: How good is the prediction?Compare captures of SBT in each of the zones

Cumulative probability of SBT habitat: – E.g. 20% sets in waters

where cumulative prob of SBT presence is <70%

SBT sets– e.g. 50% of the SBT were

captured in waters where they are predicted to spend 50% of their time…..

Future: catch per zone (f, wt)

May 29, 2006 (Line 1)

July 13, 2006 (Line 2)

July 27, 2006 (Line 3)

August 10, 2006 (Line 4)

August 24, 2006 (Line 5)

September 7, 2006 (Line 6)

September 21, 2006 (Line 7)

September 29, 2006 (Line 8)

October 12, 2006 (Line 9)

October 27, 2006 (Line 10)

Management maps

Examples of maps distributed online to stakeholders