Dynamic Habitat Mapping of Pelagic Tuna and Shark Species for Improved Fisheries Management

System Science Applications (D. Kiefer), Interamerican Tropical Tuna Commission (M. Hinton),

Southwest Fisheries Science Center (S. Kohin), JPL (E. Armstrong)

Dynamic Habitat Mapping of Pelagic Tuna and Shark Species for Improved Fisheries Management

Decision Support

• Improve and automate IATTC’s determination of Eastern Pacific Ocean tuna habitat and recruitment as inputs to it stock assessment model.

• Improve and automate SWFSC Pelagics Group’s determination of California Current albacore and blue, thresher, and mako shark habitat for stock assessment and conservation.

•GIS Data Services•Satellite/Model Data Integration•FishTracker Algorithm•Temporal & Spatial Data Matching- drifter algorithm

•GIS Data Services•Satellite/Model Data Integration•FishTracker Algorithm•Temporal & Spatial Data Matching- drifter algorithm

UserFisheries Management

DecisionsStock Assessment

Closures

UserFisheries Management

DecisionsStock Assessment

Closures

Fishery Data SetsSatellite Imagery

GHRSST (SST) 28kmAVISO (SSH) 28km

GLOBE COLOR (Chl-a) 9km

QUICKSCAT (Wind) 28km&

Ocean ModelsNASA ECCO2 18km

Fishery Data SetsSatellite Imagery

GHRSST (SST) 28kmAVISO (SSH) 28km

GLOBE COLOR (Chl-a) 9km

QUICKSCAT (Wind) 28km&

Ocean ModelsNASA ECCO2 18km

•Functional & statistical relationships between environmental variables and species presence and abundance

•Functional & statistical relationships between environmental variables and species presence and abundance

PHAM Habitat Analysis Process Flow

•Statistical Tools•Analysis of Variance•Cross Correlation•EOF Analysis•Regression Analysis

•Statistical Tools•Analysis of Variance•Cross Correlation•EOF Analysis•Regression Analysis

Import Data

Use oceanographic data to predict species distributions

Determine quantitative relationships between oceanographic variables and species distribution

Dynamic Maps of Distribution

Dynamic Maps of Distribution

Export / Interpret Data

Visualization and preliminary selection of habitat variables

Distribution of purse seine catches

BET

YFT

SKJ

PHAM screen of habitat analysis interface, map of calculated spawning sites, and graphical results of analysis

GHRSST Sea Surface Temperature

SST Frontal Probability Index: June 1988Data source: GHRSST Level 4 .25deg AVHRR_OI SST (Reynolds)

Velocity Vector Image

PHAM screen of NASA’s ECCO-2 global circulation model and graphical results of analysis. Drifter algorithm for spawning sites.

NASA ECCO2 Global Circulation Model – Mixed Layer Depth

-1.5 -1 -0.5 0.5 1LogChl

0.01

0.02

0.03

0.04

Frequency

17.5 20 22.5 25 27.5 30 32.5T

0.01

0.02

0.03

0.04

Frequency

20 25 30T

0.1

0.2

0.3

0.4

0.5

0.6

0.7

SkjFreq

-2 -1.5 -1 -0.5 0.5 1 1.5LogChl

0.1

0.2

0.3

0.4

0.5

0.6

FreqSkjPres

Distributions generated from PHAM output

Chlorophyll GHRSST

Bigeye

20

25

30T-1.5

-1

-0.5

0

0.5

LogChl05

10

15

CatchSet20

25

30T

Yellowfin

20

25

30T

-1.5

-1

-0.5

0

0.5

LogChl

00.20.40.60.8

Freq

20

25

30T

Skipjack

20

25

30

T

-1.5

-1

-0.5

0

0.5

LogChl

00.20.4

0.6Freq

20

25

30

T

0.2 0.4 0.6 0.8 1Predicted

0.2

0.4

0.6

0.8

1Observed Skipjack Presence

0.2 0.4 0.6 0.8 1Predicted

0.2

0.4

0.6

0.8

1

Observed Yellowfin Presence

PHAM screen of critical habitat of skipjack tuna as calculated from habitat analysis and current satellite imagery.

Juvenile Drift 7/1/2003

PHAM map of skipjack spawning sites (7/21/03) calculated from NASA’s ECCO-2 global circulation model and fisheries age structure analysis of catch.

A Simple Loophole Algorithm of Transforming SOI into SS2 Calculations of Bigeye Recruitment

reversal

Predicated

Bigeye Recruits