Utilizing Remote Sensing, AUV’s and
Acoustic Biotelemetry to Create Dynamic
Single SpeciesDistribution Models
the Mid-AtlanticMatthew Breece, Matt Oliver, Danielle
Haulsee University of Delaware
Dewayne Fox, Lori BrownDelaware State University
Josh Kohut, Dave Aragon, Chip Haldeman
Rutgers UniversityBrad Wethebee
University of Rhode IslandJohn Manderson
NOAA NMFS
Atlantic Sturgeon
• Large range • Anadromous • Broad coastal
movements• Vulnerable to impacts
– During migrations • Over exploited
Project Significance • Limited understanding of
adult movements – Migration routes and timing – Environmental drivers
• Increased understanding will help reduce impacts– Fisheries – Shipping traffic – Habitat degradation
• Establish a quantitative link between ocean conditions and occurrence
Methods • 195 adults telemetered
– 90 days (March-May)• 2009-2012
– ~700km net hauled – 532 captures
• Mean weight 40kg• Max ~135kg and 230cm FL
Delaware River Receivers• 42 Receivers
– C & D Canal - Trenton, NJ
2009 2010 2011Distribution of Spawning Atlantic Sturgeon
Contribution to Model
• Salt front• Mixed
reworking
Historic Drought Contemporary Future 0
10
20
30
40
50
60
Salt front
Mixed reworking
Nondepositional
Bedload
Uniform reworking
Fine deposition
Coarse reworking
Con
tribu
tion
(%)
Philadelphia
C & D Canal
Chester, PA
Wilmington, DE
10km
N
Training Model
Historic
• MaxEnt – Similar
estimates – Shows
capabilities of the model
Historic Drought Current Future
Philadelphia
C & D Canal
Chester, PA
Wilmington, DE
10km
N
Projections
Passive Receiver Array
• > 150 Stationary Receivers
• Our focus – Delaware Bay– Atlantic Ocean
Atlantic Sturgeon Maximum Likelihood Model
• Matching detections with SST – Delaware Bay/Coastal ocean
detections• 2009-2011
– Developed Maximum Likelihood model to estimate presence on basis of SSTs
• Extrapolate data – Areas lacking receiver
coverage
Sea Surface Temperature
Maximum Likelihood Model
• Telemetry/SST inputs • Estimate mean and
standard deviation • Model Fitting
– Strong seasonal component
– Included a time dependent negative cosine (seasonal cycle)
Maximum Likelihood Model
Moving Forward
• Expand coverage – Include ACT data– Additional years
• Increase factors – Derived from Satellites
• Salinity • Chlorophyll • Dissolved organics
• Add East/West component
• Mission run from Tuckerton, NJ to Chincoteague, VA – Telemetry data– Temperature– Salinity– Productivity
• Found 4 sturgeon – All in the same
water mass
Test Run November 2011 on RU15
Understanding of Movements • Link movements to oceanographic conditions
– Determine patterns/associations – Identify important water properties/types
• Facilitate Management – Minimize incidental take of Endangered Species – Not only helps sturgeon but allows fisheries to keep
fishing • Enables more efficient management • Limit the impact on fisheries
• OTIS (Oceanographic Telemetry Identification Sensor)– Autonomous Underwater Vehicle (AUV)
• Teledyne/Webb Research G2 Slocum Electric Glider • Acoustic Integration!
• Mission Plan
– Zig-zag the coastal ocean
– Measure in situ oceanographic conditions
– Monitor in near-real time for acoustic transmitters associated with telemetered fish
– Focus efforts on areas with high concentrations of telemetered fish
• Mission
– October 5th – 23rd 2012
– 337km traveled
– On-the-fly mission changes when fish are detected
• Sand Tiger Sharks (25)
• Atlantic Sturgeon (4)
• Further proof of concept: – AUVs are an effective tool for detecting telemetered fish in more
remote locations
• Real time data observing allows for on-the-fly mission changes to adapt to oceanic conditions and presence of fish
• Science data collected by glider allows us to begin to make associations between the vertical structure of water column and the presence of different fish species.
