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

Co

ntr

ibu

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.