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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). Decision Support. - PowerPoint PPT Presentation
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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