Matthew Breece, Matt Oliver, Danielle Haulsee University of Delaware Dewayne Fox, Lori Brown

1. Utilizing Remote Sensing, AUV’s and Acoustic Biotelemetry to Create Dynamic Single SpeciesDistribution Models the Mid-Atlantic Matthew Breece, Matt Oliver, Danielle Haulsee University of Delaware Dewayne Fox, Lori Brown Delaware State University Josh Kohut, Dave Aragon, Chip Haldeman Rutgers University Brad Wethebee University of Rhode Island John Manderson NOAA NMFS

2. Large range Anadromous Broad coastal movements Vulnerable to impacts During migrations Over exploited Atlantic Sturgeon

3. 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

4. Methods • 195 adults telemetered • 90 days (March-May) • 2009-2012 • ~700km net hauled • 532 captures • Mean weight 40kg • Max ~135kg and 230cm FL

5. Delaware River Receivers • 42 Receivers • C & D Canal - Trenton, NJ

6. Distribution of Spawning Atlantic Sturgeon 2009 2010 2011

7. Contribution to Model Salt front Mixed reworking

8. Training Model Philadelphia Chester, PA Wilmington, DE N C & D Canal 10km

9. Historic • MaxEnt • Similar estimates • Shows capabilities of the model

10. Projections Philadelphia Chester, PA Wilmington, DE N C & D Canal Historic Drought Current Future 10km

11. Passive Receiver Array • > 150 Stationary Receivers • Our focus • Delaware Bay • Atlantic Ocean

12. 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

13. Maximum Likelihood Model • Telemetry/SST inputs • Estimate mean and standard deviation • Model Fitting • Strong seasonal component • Included a time dependent negative cosine (seasonal cycle)

14. Maximum Likelihood Model

27. Moving Forward • Expand coverage • Include ACT data • Additional years • Increase factors • Derived from Satellites • Salinity • Chlorophyll • Dissolved organics • Add East/West component

28. Test Run November 2011 on RU15 • Mission run from Tuckerton, NJ to Chincoteague, VA • Telemetry data • Temperature • Salinity • Productivity • Found 4 sturgeon • All in the same water mass

29. 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

30. OTIS (Oceanographic Telemetry Identification Sensor) • Autonomous Underwater Vehicle (AUV) • Teledyne/Webb Research G2 Slocum Electric Glider • Acoustic Integration!

31. 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

32. Mission • October 5th – 23rd 2012 • 337km traveled • On-the-fly mission changes when fish are detected

33. Atlantic Sturgeon (4) Sand Tiger Sharks (25)

35. 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.