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Tracking of Marine Vertebrates:Overview & Fishtracker Algorithm
by
Dale Kiefer1
F. J. O’Brien1
M. Domeier2
1System Science ApplicationsPacific Palisades, California
2 Pfleger Institute of Environmental ResearchOceanside, California
November 30, 2004Ocean Biodiversity Informatics
Hamburg, Deutschland
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Statement of the Challenge
Effective conservation of most marine vertebrate populations requires an assessment the life history of the species. Electronic tags offer the promise of filling some of the missing gaps. The community of scientists and resource managers using such tags have great need of an information system that fully integrates data they have acquired from tagged marine organisms with environmental information such as satellite imagery and data streams from weather buoys and drifters.
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Mirounga leonina: Antarctic Elephant Seal elephant seal
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Laysan albatross: tracking and GIS
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Great White Shark
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• 4 dimensional system for marine applications WGS 84/geodetic representation •interfaces for models, spreadsheets, databases, and Internet • PC Desktop & Web-enabled GIS applications
Models
EASY software architecture
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Technical Challenge of Tracking Archival Tags:spatial/temporal matching of sst from tag and satellite image
sunSatellite SST sensor
clouds
Tag time series = {time i, temperature i, depth i, irradiance i}
Imagery time series = {time i, temperature (latitude j, longitude k)}
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Start End
Range
max fish range tolocation bars
northern limit of habitat
central & lateraltransects of
location bar t1
candidatepixel for
location bar t1
southern limit of habitat
central & lateraltransects of
location bar t2central & lateral
transects oflocation bar t3
candidatepixel for
location bar t2
candidatepixel for
location bar t3
arc to determinenorthern extent of
location bar t2
arc to determinesouthern extent of
location bar t2
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Figure 3. Step 5: Costing the arcs: a function of temperature match for candidate pixels and distance between consecutive pairs of candidate
pixels
Start End
Enumerateall possible
arcs
Estimateliklihood/costfor each arc
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Fig 4. Step 6: calculating the best path by summing the cost of cost of arcs for all possible paths
Start End
Sum arccosts for all
paths
Select lowestcost path(s)
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Fig 1. Fish Tag Options Window
Unique features of the Fishtracker (O’Brien) Algorithm:
• includes a consideration of maximum swimming speed of the fish
• costs the distance to swim around land obstacles
• calculates the most likely path as a global feature of the time series (analyzes thousands of possible paths) rather than a serial solution that is prone to much greater error
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Fig.5. A typical display showing, simulation control, path, superimposed on satellite imagery, time series from tag
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-40
-30
-20
-10
0
10
20
30
40
50
Date
Nor
th L
atitu
de
FishTracker Latitude
Wildlife Computer Latitude
Microw ave Telemetry Latitude
Fish 19203 Fish 19368
8/4/
00
8/28
/00
9/21
/00
10/1
5/00
11/8
/00
12/2
/00
12/2
6/00
1/19
/01
8/20
/02
9/13
/02
10/7
/02
FishTracker SST-based latitude solutions vs. Wildlife Computers and Microwave Telemetry light-based latitude estimate False color contours illustrating relative importance of the range juvenile bluefin tuna occupy in the eastern Pacific; each color represents a relative importance increase of 20%. The polygon encloses 100% of position estimates for fish 159, 233, and 441
combined.
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False color contours of seasonal spatial use and movement pattern for fish 159 and 233 combined. The smaller total range of fish 441 is illustrated
by polygon.
Jan-June
Oct-Nov
Dec
July-Sept
441 rangeX deployment pointX recapture point
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Demonstration