Tracking of Marine Vertebrates:Overview & Fishtracker Algorithm

by

Dale Kiefer1

F. J. O’Brien1

M. Domeier2

1System Science ApplicationsPacific Palisades, California

kiefer@runeasy.com

2 Pfleger Institute of Environmental ResearchOceanside, California

November 30, 2004Ocean Biodiversity Informatics

Hamburg, Deutschland

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.

Mirounga leonina: Antarctic Elephant Seal elephant seal

Laysan albatross: tracking and GIS

Great White Shark

• 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

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)}

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

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

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)

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

Fig.5. A typical display showing, simulation control, path, superimposed on satellite imagery, time series from tag

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-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.

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

Demonstration