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Virginia Beach, VA, United States

Barco S.G.,Research and Conservation Section | Lockhart G.G.,Research and Conservation Section | Swingle W.M.,Research and Conservation Section
OCEANS 2012 MTS/IEEE: Harnessing the Power of the Ocean | Year: 2012

We used a combination of AIS and RADAR to characterize large (≥ 65' long) vessel traffic in the Chesapeake Bay ocean approach along the US eastern seaboard from May 2008 through April 2009. During the 60 days of monthly surveys, we recorded over 2.6×106 records of data with ship position information. There were 1181 hours of AIS and 540 hours of RADAR data collected for a monthly mean of 98 and 45 hours for AIS and RADAR respectively. These data represented 1411 transits by vessels broadcasting AIS for a total of 69,606km of track line, and vessels acquired with RADAR represented a total of 506 transits for a total of 8,702km of track line. AIS and RADAR data resulted in 1.2 and 0.9 transits per hour respectively. We corrected the AIS data to match RADAR effort, and, when effort was equal, AIS data represented only 49.7% of the total large vessel transits observed, and transits per hour were equal for corrected AIS and RADAR data. We recorded more inbound vessels using AIS and more outbound vessels with RADAR. The density pattern of vessels recorded using AIS differed from the RADAR pattern. There was a very discrete pattern for vessels broadcasting an AIS signal that corresponded with the shipping lane buoys. Vessels recorded using RADAR, on the other hand, were more dispersed and displayed a less discrete pattern covering a large area outside of the ship buoys. Winter was the season with the fewest vessel transits for both data collection methods (23.8% AIS; 17.0% RADAR), though there was not a substantial decrease over other seasons in the AIS data. Spring, summer and fall were similar for AIS (24.2%, 25.7%, 26.3% respectively), but fall (31.4%) had considerably more vessels recorded by RADAR than spring and summer (26.5%, 25.1% respectively). When examining vessel type data, more than two thirds of the vessel transits recorded using AIS were cargo vessels (68%; n=337). Excluding unknown vessels, military vessels made up 47% (n=179) of the vessels we identified with RADAR, followed by fishing (32%; n=120) and federal law enforcement (USCG) vessels (10%, n=38). Spatial analysis was necessary to accurately evaluate ship speed over the entire study area. Spatial analyses of these data allowed us to examine speed behavior in a grid format, reducing the effect of directional bias from an overrepresentation of records from relatively slow moving vessels. Without utilizing spatial analysis, one could draw the conclusion that vessels were transiting the area more slowly than they actually were because AIS speed records are skewed by the greater number of speed points for slower vessels. There was an obvious reduction in speed of vessels transmitting AIS in the shipping lanes from before to after the Ship Strike Rule that was enacted in December 2008 to protect the western North Atlantic right whale (Eubalaena glacialis). The Ship Strike Rule identified a Seasonal Management Area (SMA) within which commercial ships ≥65 ft. in length were required to slow to 10kts or less for part of the year (50 CFR224.105). Although vessels slowed, many were not in compliance with the mandatory speed limit of the Rule. Speed restrictions had no effect on vessels observed using RADAR, most of which were not required to comply with the regulations. Cargo ships can be quite large and travel at speeds in excess of 20kt, and were required to broadcast AIS signals, so their behavior was relatively easy to assess. Few cargo vessels traveled 10kt or less for an entire transit through the SMA. Thus, we observed an apparent reaction to the speed restrictions in the SMA, but did not observe strict compliance. Recently the US Coast Guard has begun issuing speeding citations to vessels that have been recorded blatantly disregarding the speed restrictions in SMAs. We applaud these efforts and suspect that if the effort continues consistently it will encourage compliance. Our data suggest that AIS traffic levels were relatively constant (∼1.2 transits/hr) around the clock. For this project, we chose to operate the RADAR unit during daylight hours in order to assess vessel type and approximate length from the shore-based platform. In the future, we need to assess RADAR vessel traffic during all hours in order to better compare it to AIS traffic. In 2007, Virginia ports were listed as the third busiest port on the U.S. Atlantic coast for vessel calls. A large portion of the vessels calls for the port of Baltimore also enter at Chesapeake Bay. The addition of commercial Baltimore traffic as well as federal and fishing vessels not broadcasting AIS very likely makes the Chesapeake Bay approach one of the two busiest coastal port approaches and may make it the busiest in the western North Atlantic. This project raises numerous questions about vessel traffic patterns, vessel speeds, risks to whales and data incorporated into marine spatial planning efforts. There is a definite need for both longer-reaching ship RADAR data and more detailed whale presence data in the mid-Atlantic region, especially off its busiest port. Without data on whale presence, it is nearly impossible to answer questions about the level of risk that the vessel traffic patterns present for whales and on how installations of offshore wind and other energy ventures may affect whales and ships. This project brings into question the use of AIS data alone as an accurate representation of large vessel traffic in coastal regions of the United States, especially in areas with high fishing and military traffic. In Virginia, during daylight hours, AIS data represent a maximum of two thirds, and, as little as one half, of the large vessel (>65 feet) traffic in the Chesapeake Bay approach. Further research is needed to address the accurate determination of vessel size when using RADAR. A mobile research platform with the capability of assessing RADAR targets on the water or an offshore platform with a greater range and use of night vision technology may provide solutions. © 2012 IEEE. Source


Lockhart G.G.,Research and Conservation Section | Swingle W.M.,Research and Conservation Section | Bort J.,Research and Conservation Section | Lynott M.C.,Research and Conservation Section | And 2 more authors.
OCEANS 2012 MTS/IEEE: Harnessing the Power of the Ocean | Year: 2012

We have conducted collaborative research projects in coastal mid-Atlantic waters, including summer aerial surveys, sea turtle telemetry studies, opportunistic whale sightings data collection, and a ship traffic characterization study. Additionally, we respond to live and dead strandings of cetacean and pinniped species for which there are no density or tagging data in the area, but where we have regular documentation of occurrence in the stranding record. Compiling these data into a single spatial analysis allows us to better: 1) understand human and wildlife use of areas, 2) plan for development, and 3) predict potential conflicts. Our tagging and sighting projects can be used to understand species habitat use. For the tagging data, we included a maximum of one location data point per day from five satellite-tagged loggerhead sea turtles (Caretta caretta) from July-October in 2011. From this analysis, we calculated that 71% (64 out of 118) of the data points were within 30 nm of one or more mid-Atlantic Wind Energy Areas (MAWEAs). During 2011, a total of 14,576 km of aerial survey transits resulted in 1,572 sea turtle sightings, with 1,122 animals (71%) located within 30 nm of the Virginia WEA. In addition, 49% (227 out of 648) of bottlenose dolphin (Tursiops truncatus) group sightings, representing 57% (1471 out of 2588) individual animals, were within 30nm of MAWEAs. Using photo identification techniques, we documented a minimum of 57 humpback whales (Megaptera novaeangliae) and 5 fin whales (Balaenoptera physalus) in near-shore Virginia ocean waters from December 2011 through February 2012. One major challenge to applying this biological monitoring data in marine spatial planning efforts is the lack of spatial density data for marine mammals and sea turtles, particularly on a regional and seasonal scale. In the U.S. mid-Atlantic region, relatively few survey efforts have occurred, and those that were conducted have been of such broad scale that few, if any, of the less common species were sighted. These results have led to calculated densities of zero or near zero for some species in a region where anecdotally we know that they regularly occur. When these densities are applied to management documents, inaccurate conclusions may be made regarding environmental impact assessments. For example, the 2012 Draft Programmatic Environmental Impact Statement (DPEIS) for the Atlantic OCS Proposed Geological and Geophysical Activities Mid-Atlantic and South Atlantic Planning Areas used marine mammal density calculations to model marine mammal takes from seismic surveys and assign an environmental impact measure to the action. Densities were reported for all marine mammal species with habitat ranges in the area. The DPEIS identifies Zone 20 extending across the continental shelf from Cape Lookout to the Delaware Bay, including Virginia waters. Many of the species have zero or near zero reported densities, but have regular presence in Virginia stranding and sighting records (Table 1). Humpback whales and bottlenose dolphins (Tursiops truncatus) are of particular interest, because both our sighting reports and our stranding data are inconsistent with these densities. A similar problem has occurred in analyses of vertical line and whale co-occurrence that was conducted for the NOAA Fisheries Atlantic Large Whale Take Reduction Team. Lack of formal survey data from the mid-Atlantic region resulted in a model with large areas of zero density, despite extensive anecdotal stranding and sighting records. We propose that a method for determining 'minimum density' should be developed and utilized for species with verified anecdotal records, such as stranding data. Then we can better model animal presence, and potential impacts from human activities, for species with inadequate data from formal surveys. The monitoring of anthropogenic activity in marine habitats is also important to predict the cumulative impact of development to populations and species. During our 2008-2009 ship characterization study, we utilized AIS technology to record 323 vessels (18,473km of transits) traveling through the Virginia WEA, representing 30% of all AIS recorded large vessel traffic. Ship traffic rerouting as a result of wind energy development could increase the risk of interactions between wildlife and human activity by potentially displacing whales and ships into smaller, higher-density areas. Commercial fishing and Federal vessel activity are currently unrepresented in AIS datasets, and should be monitored and considered in spatial planning efforts in order to have a complete picture of how offshore development projects will affect all vessel activity. Marine spatial planning is an important tool to mitigate potential conflicts associated with ocean use issues, and both biological monitoring data and human use information are essential for the process to be successful. This analysis indicates that non-traditional sources of data, in addition to data from traditional density surveys, are being used to monitor endangered and protected species and should be included in the spatial planning process to inform management decisions. © 2012 IEEE. Source

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