1. Utilizing Remote Sensing, AUV’s and Acoustic Biotelemetry to Create Dynamic Single Species Distribution 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. Atlantic Sturgeon Large range Anadromous Broad coastal movements Vulnerable to impacts –During migrations Over exploited

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. 200920102011 Distribution of Spawning Atlantic Sturgeon

7. Contribution to Model Salt front Mixed reworking

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

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

10. HistoricDroughtCurrentFuture Philadelphia C & D Canal Chester, PA Wilmington, DE 10km N Projections

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. 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

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 5 th – 23 rd 2012 –337km traveled –On-the-fly mission changes when fish are detected

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

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.