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ORIGINAL RESEARCH published: 21 February 2020 doi: 10.3389/fmars.2020.00086

Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

Samantha Cope1,2*, Ellen Hines2,3*, Roger Bland2,4, Jerry D. Davis3, Brendan Tougher1 and Virgil Zetterlind1

1 ProtectedSeas, Anthropocene Institute, Palo Alto, CA, United States, 2 Estuary & Ocean Science Center, San Francisco State University, Tiburon, CA, United States, 3 Department of Geography & Environment, San Francisco State University, San Francisco, CA, United States, 4 Department of Physics & Astronomy, San Francisco State University, San Francisco, CA, United States

Vessel traffic management systems can be employed for environmental management where vessel activity may be of concern. One such location is in San Francisco Bay where a variety of vessel types transit a highly developed urban estuary. We analyzed vessel presence and speed across space and time using vessel data from the Marine Monitor, a vessel tracking system that integrates data from the Automatic Identification System and a marine-radar sensor linked to a high-definition camera. In doing so, we Edited by: provide data that can inform collision risk to cetaceans who show an increased presence Annette Breckwoldt, Leibniz Centre for Tropical Marine in the Bay and evaluation of the value in incorporating data from multiple sources when Research (LG), Germany observing vessel traffic. We found that ferries traveled the greatest distance of any Reviewed by: vessel type. Ferries and other commercial vessels (e.g., cargo and tanker ships and Andrew M. Fischer, tug boats) traveled consistently in distinct paths while recreational traffic (e.g., motorized University of Tasmania, Australia Kristian Metcalfe, recreational craft and sailing vessels) was more dispersed. Large shipping vessels often University of Exeter, United Kingdom traveled at speeds greater than 10 kn when transiting the study area, and ferries traveled *Correspondence: at speeds greater than 30 kn. We found that distance traveled and speed varied by Samantha Cope [email protected] season for tugs, motorized recreational and sailing vessels. Distance traveled varied Ellen Hines across day and night for cargo ships, tugs, and ferries while speed varied between day [email protected] and night only for ferries. Between weekdays and weekends, distance traveled varied

Specialty section: for cargo ships, ferries, and sailing vessels, while speed varied for ferries, motorized This article was submitted to recreational craft, and sailing vessels. Radar-detected vessel traffic accounted for 33.9% Marine Conservation of the total track distance observed, highlighting the need to include data from multiple and Sustainability, a section of the journal vessel tracking systems to fully assess and manage vessel traffic in a densely populated Frontiers in Marine Science urban estuary. Received: 01 July 2019 Accepted: 03 February 2020 Keywords: vessel management, coastal monitoring, Automatic Identification System (AIS), marine radar, Published: 21 February 2020 cetaceans, San Francisco Bay Citation: Cope S, Hines E, Bland R, INTRODUCTION Davis JD, Tougher B and Zetterlind V (2020) Application of a New Shore-Based Vessel Traffic Monitoring Vessel traffic monitoring systems are often used for maritime surveillance and traffic control in System Within San Francisco Bay. coastal areas (e.g., Wahab et al., 2016; Zhen et al., 2017) but can also be employed for environmental Front. Mar. Sci. 7:86. protection purposes. Monitoring vessel activity can help local management institutions assess doi: 10.3389/fmars.2020.00086 compliance with protected area and/or fisheries regulations (Bergseth et al., 2015; Elahi et al., 2018;

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Elvidge et al., 2018; Longépé et al., 2018; Hsu et al., 2019; Bay area and Costa Rica report that more complex radar systems Kurekin et al., 2019). Many marine protected areas (MPAs) utilizing this type of equipment for vessel monitoring in protected fail to meet thresholds for effective management due to staff areas have a cost on the order of millions (Prudente, 2014; and budget capacity (Gill et al., 2017). Vessel traffic monitoring Costa Rican Times, 2014). The M2 keeps costs low (<$100,000 systems can provide an unbiased and data-based solution as an for hardware, software, and deployment and $2,500–$5,000/year alternative to traditional onboard observers or manned patrol for ongoing maintenance and support) by utilizing commercial activity (Bartholomew et al., 2018). marine radar systems and custom software (ProtectedSeas, 2019). Vessel tracking technologies, such as the Automatic It utilizes solar panels for its power supply and directs all Identification System (AIS), inform an understanding of data through a local computer which transmits over wireless vessel use of the marine environment through time and space. internet to an online portal where users can view both live and The International Maritime Organization (IMO) requires all historic data via an internet web browser. The M2 can provide transiting ships of greater than 300 gross tonnage, and all managers with live situational awareness of their area without commercial passenger vessels, to broadcast static information needing to be physically present on the water, and historical data on a vessel and its voyage and dynamic information on the can facilitate vessel activity analysis over time to inform future vessel’s geolocation, heading, and speed throughout the voyage management decisions. (International Maritime Organization [IMO], 2018). AIS San Francisco Bay (SFB) in central California is the second data has helped detect illegal fishing (Longépé et al., 2018; largest estuary in the contiguous United States, is heavily Kurekin et al., 2019), model underwater noise pollution (Erbe urbanized, and is an ideal location for evaluating a vessel et al., 2014; Redfern et al., 2017; Cholewiak et al., 2018), and monitoring system, like the M2, that integrates data from inform risk assessments of collisions between ships and large different vessel tracking technologies as both commercial whales (Williams and O’Hara, 2010; Wiley et al., 2011; van der shipping, destined for major ports, heavy recreational vessel Hoop et al., 2012; Conn and Silber, 2013; Redfern et al., 2013; traffic filling over 80 marinas (Zabin et al., 2014), and high- Jensen et al., 2015). Since AIS was not originally designed for speed ferries traverse its waters. Ships access six of California’s environmental management, there are limitations to the use of eleven major seaports via SFB, including Oakland, Richmond, AIS in this capacity (Robards et al., 2016). San Francisco, Sacramento, Stockton, and Redwood City AIS may not provide a complete picture of vessel activity (California Department of Fish and Wildlife [CDFW], 2008). In since vessel operators can turn off onboard AIS transponders 2016, the Port of Oakland was ranked sixth in total volume of (McCauley et al., 2016), it relies on user input and onboard cargo traffic in the United States (United States Army Corps of equipment functioning (Schwehr and McGillivary, 2007), and Engineers [USACE], 2018) and 60th in the world (Association of most small-scale fisheries or recreational vessels may not meet American Port Authorities [AAPA], 2016). There are few MPAs the size at which AIS data transmission is required by law. within SFB (covering approximately 12 km2 total across 8 unique Solutions have been proposed to fill these data gaps including sites, or <1% of the total SFB area) (California Department of ship- and land-based radar, and air- and space-born radar (Bloisi Fish and Wildlife [CDFW], 2008), however further study on et al., 2015). Radar technology uses electromagnetic pulses to human use impacts is necessary to fully understand their effect determine a vessel’s geolocation and speed (Calvo-Gallego and on wildlife (Stern et al., 2017). Pérez-Martínez, 2012; Bole et al., 2013). Radar systems with an In addition to vessels, cetaceans also frequent SFB seasonally, Automatic Radar Plotting Aid track unique live targets whose in particular humpback (Megaptera novaeangliae) and gray attributes can later be stored for post hoc analysis (Nohara whales (Eschrichtius robustus)(National Marine Fisheries Service et al., 2008). Satellite images, digital photographs, and video [NMFS], 2017). Harbor porpoises (Phocoena phocoena) are in the can also be used to supplement vessel and activity detection Bay year round (Stern et al., 2017). Collisions with vessels of all and tracking (e.g., Taborda and Silva, 2012; Bloisi et al., 2017; sizes are a direct threat to cetacean species along the California Bartholomew et al., 2018; James et al., 2019). The Marine coast (Dransfield et al., 2014; Irvine et al., 2014; Calambokidis Monitor (M2) is a low-cost proprietary coastal monitoring et al., 2015). When an animal is struck, either by the vessel’s system developed by ProtectedSeas(2019) that integrates these bow or by its propeller (Knowlton et al., 1995; Silber et al., multiple components including AIS, off-the-shelf marine radar, 2010), the outcome can be death or serious injury (Moore et al., and a high-definition camera. 2005; Campbell-Malone et al., 2008). In the SFB area, cetacean The M2 was initially designed to assist in vessel monitoring strandings and mortalities are often accompanied by evidence of remote MPAs. The ecological benefits of MPAs, like fish stock of vessel collisions (Barcenas-De la Cruz et al., 2017; Scordino replenishment, habitat protection, and anticipated increased et al., 2017). In May 2019, the National Oceanic and Atmospheric biodiversity, are dependent on implementation and enforcement Administration declared an Unusual Mortality Event for gray of restrictions on activities (Edgar et al., 2014). The M2 whales due to elevated strandings off the U.S. west coast, autonomously monitors vessel traffic in coastal areas in place some found within SFB (National Oceanic and Atmospheric of physically manned patrols that require resources like boat- Administration [NOAA], 2019). use, fuel, and human effort (ProtectedSeas, 2019). In addition Common ship-strike mitigation efforts include vessel route to lowering these operational costs, the M2 provides a more adjustments and speed restrictions. The likelihood of a lethal affordable tool than military-grade radar equipment often strike is significantly reduced at speeds below 10 kn (Laist et al., utilized by law enforcement. Local sources in the Chesapeake 2001; Vanderlaan and Taggart, 2007; Wiley et al., 2011) due to

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FIGURE 1 | San Francisco Bay Area (left) with study area in the Central Bay (right) indicated by the solid black line. Locations of major ports, marinas, protected areas (California Department of Fish and Wildlife [CDFW], 2008), and ferry terminals (Golden Gate Bridge, Highway and Transportation District, 2017; San Francisco Bay Ferry, 2018) are also shown. San Francisco Bay Regulated Navigation Areas (RNAs) were digitized according to 33 CFR 165.1181. Those lying within study area boundaries are the North Ship Channel RNA and the Richmond Harbor RNA.

lower impact forces (Silber et al., 2010) and increased time for temporal or seasonal differences in vessel presence or speed? A collision avoidance (Laist et al., 2001; Williams et al., 2016). On baseline understanding of vessel traffic provides a foundation the U.S. east coast, these methods have been effective in reducing for traffic management and future cetacean risk assessments in collisions (Laist et al., 2014; van der Hoop et al., 2015). On SFB. The methods used also provide insight on utilizing M2 for the U.S. west coast, a joint recommendation from the Greater vessel monitoring. Farallones and Cordell Bank National Marine Sanctuaries and The United States Coast Guard modified shipping lanes passing through sanctuary boundaries to improve navigational safety and MATERIALS AND METHODS avoid overlap with whale habitat (Joint Working Group on Vessel Strikes, and Acoustic Impacts [JWG], 2012). During peak whale Data Collection migration season in 2017, the National Marine Fisheries Service We gathered vessel data for 1 year (13 February 2017–12 February issued a voluntary speed reduction to 10 kn in the shipping lanes 2018) using the M2 located at the Estuary & Ocean Science (United States Coast Guard [USCG], 2017). SFB lies outside of Center in Tiburon, California (Figure 1). The study area was these managed area boundaries. defined by the maximum range of the marine radar sensor, With the growing presence of cetaceans in SFB and gray approximately 60 km2 in the northern portion of the Central whales at a heightened risk, it is critical that we understand both Bay, as this was smaller in spatial extent than the range of the spatial and temporal trends in vessel traffic. The spatial and AIS detection points. AIS data are transmitted over very-high- temporal extent of vessel use is vital to evaluating overlap with frequency (VHF) channels (United States Coast Guard [USCG], cetacean habitat (Redfern et al., 2013). This research utilized a 2016), while the radar relies on electromagnetic pulse reflections new technology, the M2, to track major vessel classes within to identify solid objects, limiting the range of target detection a portion of SFB using data collected via AIS broadcasts and due to the presence of land, bridges, and dock structures within radar combined with photographs for those vessels that did the area. The M2 radar system tracks vessels within a range not broadcast AIS data. Results shed light on potential threats of 5 nmi (ProtectedSeas, 2019) but can regularly track targets to cetaceans by answering the following questions: (1) how at greater distances depending on sea state. Conditions in SFB do vessel presence and speed vary spatially? and (2) are there allowed for vessels to be tracked across the study area defined

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TABLE 1 | Vessel types and vessel type classes. vessel type, and start date and time. For vessels transmitting AIS data, vessel type was reported in the static data via an AIS vessel Cargo Miscellaneous type number. For vessels detected by radar, the camera linked to Cargo (AIS) Wing in Ground (AIS) the radar sensor output, part of the M2 system, archived digital Cargo Wet Hazard A (Major) (AIS) Pilot Vessel (AIS) photographs of these vessels. We manually determined vessel Cargo Wet Hazard B (AIS) Mobile Offshore Drilling Units (AIS) type for all radar-detected vessels using these photographs which Cargo Wet Hazard C (Minor) (AIS) Offshore Supply Vessels (AIS) allowed us to include those vessel types that did not transmit AIS Cargo Wet Hazard D (Recognizable) (AIS) Processing Vessels (AIS) data. Interpretation of photographs was limited by daylight and Research (AIS) weather conditions, so we consider our radar-detected vessel data Tanker Governmental (AIS) to be an underestimate of total traffic. Tanker (AIS) Autonomous Craft (AIS) Since the accuracy of AIS data is dependent on vessel Tanker Hazard A (Major) (AIS) Dredger (AIS) user input and onboard equipment and transmitter functioning Tanker Hazard B (AIS) Dive Vessel (AIS) (Schwehr and McGillivary, 2007), we removed tracks from Tanker Hazard C (Minor) (AIS) Military Ops (AIS) consideration that were likely erroneous or duplicates. Vessels Tanker Hazard D (Recognizable) (AIS) Search and Rescue (AIS) transmitting AIS data with an invalid or missing MMSI number Special Craft (AIS) (<201000000 or >775999999) or AIS vessel type number (<10 Tug Local Vessel (AIS) or >100) were removed (Jensen et al., 2015; Redfern et al., 2017) Tug (AIS) Medical Transportation (AIS) since they could not be accurately identified (607 of 37,500 total Pushboat (AIS) Anti-Pollution (AIS) AIS tracks observed, or 1.6%). We calculated the maximum speed Law Enforcement (AIS) statistic for all vessel tracks using the speed over ground attribute Ferry Military or Law Enforcement (radar) provided in the detection points, and removed any track with an Passenger (AIS) Port Tender (AIS) unreasonable maximum speed (Rycyk et al., 2018). Due to the High-Speed Craft (AIS) Fishing (AIS) geographic complexity and heavy vessel use of SFB, we attributed Commercial Fishing Boat (radar) maximum speeds greater than 50 kn to onboard equipment error Motorized recreational Other Commercial (radar) or the detection of an aircraft by radar, and these tracks were Small Pleasure Craft (radar) Other Recreational (radar) removed from analysis (1,243 of 37,500 total AIS tracks observed, Large Pleasure Craft (radar) Non-Motorized (radar) or 3.3%; 26 of 184,712 total radar tracks observed, or <0.01%). Recreational Fishing Boat (radar) Other (AIS) The radar sensor tracked all vessels in its range regardless Pleasure Craft (AIS) Unspecified (AIS) of whether vessels were transmitting AIS data, so the M2 Sailing vessel system identified and removed radar-detected vessel tracks that Sailing Vessel (radar) duplicated AIS tracks. This is accomplished by associating detection points from the marine radar sensor with AIS detection Primary data sources noted for each. In the case that a vessel typically transmitting Automatic Identification System (AIS) data failed to do so, vessel data were points using the following qualifications: points were within acquired using the marine radar sensor. AIS classifications determined using 100 m in geolocation, were detected less than 15 s apart, had a the United States Coast Guard AIS Encoding Guide (available at https://www. difference in speed less than 1.5 kn, and had less than a 10-degree navcen.uscg.gov/pdf/AIS/AISGuide.pdf) with additional updates from a US Coast Guard Special Notice (available at https://www.uscg.mil/d13/dpw/docs/LNM- difference in heading. If two points met all requirements, one SpecialNotice-AIS.pdf). Radar classifications determined by observed vessel use association was created. Radar-detected vessel tracks with more of study area. Due to a low frequency of observation, the “miscellaneous” vessel than 20 points associated with an AIS track were removed from classes were not included in our analysis. consideration. Those vessel tracks with less than 20 associated detection points that duplicated AIS tracks were discovered in the photo-identification process and removed from consideration in Figure 1. While the full area of SFB and its tributaries is as well. Of the total 184,712 radar-detected tracks, 125,680, or large (approximately 1,400 km2), vessels must pass through the 68.0%, had 20 or more AIS associations and were removed study area to access the major ports of Richmond, Sacramento, as a result of AIS track duplication; 671, or <0.01%, had less and Stockton, and the area is surrounded by ferry terminals and than 20 associations but were discovered and removed during marinas on all sides. Therefore, the study area is representative of photo-identification; and 37,232, or 20.2%, were removed due to a range of vessel classes observed in SFB. inconclusive photos (including those at night). The M2 system provided spatial data in two formats. Positional error of radar data (dependent on range) may First, individual detection points reported dynamic information, have impacted radar and AIS track associations since one including a vessel’s geolocation, range and bearing from the requirement is that both detection points be within 100 m sensor, speed over ground, heading, unique voyage number, in geolocation. Since AIS data is self-reported and relies on and vessel identification number. For vessels transmitting AIS onboard equipment functioning, the positional error of this data data, this identification number was the Maritime Mobile Service is unknown. To rectify this error, we identified and removed Identity (MMSI). Second, vessel tracks provided lines connecting false negatives in photo-identification, and we considered false a vessel’s detection points joined in chronological order (Jensen positives to be highly unlikely due to ad hoc observation and the et al., 2015; Redfern et al., 2017) and reported static information, other requirements necessary for association, including similar including the unique voyage and vessel identification number, speed and heading.

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FIGURE 2 | Total distance traveled in the study area (in nmi) across 175 days of 24-h data acquisition for all major vessel classes. Distance traveled is separated by data source (AIS or radar). Motorized recreational and sailing vessel distance calculation did not include vessel transits at night.

Data Analysis Temporal Differences in Vessel Traffic Using a geographic information system (GIS), we projected Using the start date and time of each vessel track, we analyzed (UTM Zone 10) and clipped vessel detection points and tracks daily vessel presence and speed across three temporal resolutions: to the extent of the study area (Environmental System Research season, day/night using local Pacific Time (PST/PDT), and Institute [ESRI], 2016). Both radar-detected vessels and vessels weekday/weekend. We quantified vessel presence by summing transmitting AIS data were reclassified into six major vessel the length of all tracks per day over the study area for each vessel classes that reflected the primary vessel types that used the study class (Jensen et al., 2015). The speed over ground for all detection area: cargo, tanker, tug, ferry, motorized recreational vessels, points within the study area was averaged for each day and for and sailing vessels (Table 1). Due to occasional M2 outages, each vessel class. Seasons were assigned using commonly defined we only considered data from days with a full 24-h period of calendar quarters in the region: winter was assigned to January– data acquisition in our analysis (January–March: n = 32 days, March, spring to April–June, summer to July–September, and April–June: n = 45 days, July–September: n = 51 days, October– autumn to October–December (Forney and Barlow, 1998; Becker December: n = 47 days). et al., 2014; Jensen et al., 2015). Tracks were assigned to day/night using the StreamMetabolism package in R (Sefick, 2016), which Spatial Distribution of Vessel Traffic utilized the NOAA Solar Calculator to provide past sunrise and First, we generated a grid across the study area in a GIS. To sunset times for Tiburon, California. If a vessel track began determine an appropriate cell-size we created a positional error during the day (after sunrise and before sunset), we assigned model using the reported bearing accuracy of the radar sensor a day/night attribute of 1. We assigned all other vessel tracks (±1◦)(Furuno, 2016). With the formula, (those beginning at night) a day/night attribute of 0. Since those vessels solely detected by the marine radar sensor could not be θ = arc length ◦ 2πr, identified in photographs at night, we restricted our day/night 360 analysis to those vessels required to transmit AIS data (cargo and we calculated the 2◦ arc length (corresponding to the reported tanker ships, tugs, and ferries). Finally, if a vessel track began accuracy of ±1◦), where r was equal to the range under which Monday-Friday, we assigned a weekday/weekend attribute of 1. 95% of the radar-detected points fell and used the resulting length We assigned all other vessel tracks (those beginning Saturday– (266 m) as the cell size. Only cells completely contained within Sunday) a weekday/weekend attribute of 0. the study area were used. We used a Shapiro–Wilk test to find that median total track Next, we calculated density (trips per day) for each vessel class length and average speed across 175 days of data collection by counting the number of unique voyages within a cell and were not normally distributed across most temporal resolutions dividing by the total number of days observed. We also calculated (Supplementary Tables 1–3). Consequently, we used the non- median speed over ground using all detection points within a cell parametric Mood’s median test to compare the daily distances for each vessel class. Finally, we overlaid the density and speed and speeds (Jensen et al., 2015). For those samples with a normal maps to create a bivariate choropleth map for each vessel class to distribution, the test was less powerful, so we defined significance facilitate viewing both simultaneously. as p < 0.01 to ensure fewer type I errors across all vessel

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FIGURE 3 | (a-f) Spatial distribution of trips per day and median speed for cargo (a), tanker (b), tug (c), ferry (d), motorized recreational (e), and sailing vessels (f). Cells colored blue indicate an average speed less than 10 kn, the voluntary speed restriction used in National Marine Sanctuaries outside SFB (United States Coast Guard [USCG], 2017). Locations of major ports, marinas, protected areas (California Department of Fish and Wildlife [CDFW], 2008), and ferry terminals (Golden Gate Bridge, Highway and Transportation District, 2017; San Francisco Bay Ferry, 2018) are also shown. San Francisco Bay Regulated Navigation Areas (RNAs) were digitized according to 33 CFR 165.1181.

classes (Mood, 1950). We conducted post hoc analysis for vessel AIS, and data on recreational vessels were primarily provided by classes with a significant difference in daily distance or average radar (Figure 2). AIS data provided track data across 64,281 nmi, speed across seasons. or 66.1% of the total vessel distance tracked, while radar data provided track data across 33,008 nmi, or 33.9%. Ferries traveled the greatest total distance through the study area while cargo RESULTS ships traveled the least total distance (Figure 2).

After the filtering process, 34,814 total tracks (AIS tracks: Spatial Distribution of Vessel Traffic n = 19,626; radar-detected tracks: n = 15,188) remained for Commercial vessel traffic (cargo and tanker ships, tugs, and analysis. Data on commercial vessels were primarily provided by ferries) was most dense in distinct paths across the study

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distance existed for tugs, motorized recreational, and sailing vessels (Table 2). Ferries traveled the fastest and sailing vessels the slowest of all vessel classes in all seasons (Figure 4). Significant differences in average speed between seasons existed for tugs, motorized recreational, and sailing vessels (Table 2). See Supplementary Tables 4–6 for detailed numeric results.

Day/Night During the day, ferries traveled the farthest of all vessel classes while tugs traveled farthest at night (Figure 5). Both tugs and ferries traveled significantly less distance from day to night (Table 3). Ferries traveled the fastest of all vessel classes during the day and at night. Cargo ships traveled significantly faster at night, and there were no significant differences in daily distance or average speed for tanker ships.

Weekday/Weekend Median daily distance was greatest on weekdays for ferries and greatest on weekends for sailing vessels (Figure 6). Significant differences in daily distance between weekdays and weekends existed for cargo ships, ferries, motorized recreational, and sailing vessels (Table 4). Motorized recreational and sailing

FIGURE 4 | Daily distance (nmi/day) (top) and average speed (kn) (bottom) vessels traveled significantly greater distance from weekdays to by season for each vessel class. Box and whisker plots show sample weekends. Ferries traveled the fastest of all vessel classes during minimum, maximum, median, and first and third interquartile values. Outliers both weekdays and weekends, but average speed decreased are not shown. Statistically significant differences in median daily distance significantly from weekdays to weekends. Sailing vessels traveled between seasons denoted by † (p < 0.01). Statistically significant differences significantly more slowly on weekends, although both weekday in median speed between seasons denoted by ‡ (p < 0.01). Dashed line indicates 10-kn voluntary speed restriction used in National Marine and weekend median values fell between 4.16 and 4.48 kn. Sanctuaries outside San Francisco Bay (United States Coast Guard [USCG], 2017). Sample sizes: n1 = 32 days (winter), n2 = 45 days (spring), n3 = 51 days (summer), n4 = 47 days (autumn). DISCUSSION

Vessel traffic was dominated by commercial high-speed ferry use area (Figures 3a–d). Cargo and tanker ships traveled almost in SFB, likely a response to growing congestion on roadways exclusively through the North Ship Channel and Richmond (Merrick et al., 2003; Weinrich, 2004). According to published Harbor Regulated Navigation Areas (RNAs) (Figures 3a,b). schedules by the Golden Gate Ferry (Golden Gate Bridge, These vessels traveled at speeds greater than 10 kn while Highway and Transportation District, 2017) and San Francisco transiting through the study area but under 10 kn while transiting Bay Ferry (San Francisco Bay Ferry, 2018) services, ferries near the Port of Richmond. Tug and ferry traffic was also most regularly operate throughout the day almost every day of the dense within the RNAs but was more dispersed overall than the year and more frequently during the day than at night, which larger ship traffic (Figures 3c,d). A third distinct path for ferries was reflected in our results. The increase we observed in ferry outside the RNAs was evident in the northwest corner of the traffic on weekdays was also reflected in these schedules as service study area, likely traffic to the nearby ferry terminal. becomes more frequent on weekdays due to commuter traffic as Recreational vessel traffic was dispersed across much of the opposed to more recreational use on weekends. This may also study area (Figures 3e,f). In the eastern portion, motorized explain why we detected significantly faster speeds on weekdays. recreational vessels traveled at speeds below 10 kn while some Slower speeds at night, which we also observed, are likely less vessels in the western portion traveled at speeds greater than influenced by commuter traffic and more by restricted visibility 10 kn (Figure 3e). Sailing vessel traffic was most dense across the (Merrick et al., 2003). center of the study area (Figure 3f). These vessels traveled almost For cargo and tanker ships, there were few significant temporal exclusively under 10 kn across the entire study area. differences, suggesting that other drivers influence when these vessels use the study area and how fast they travel. The significant Temporal Differences in Vessel Traffic differences in cargo ship speeds between day/night and distance Seasons traveled between weekdays/weekends may be explained by the Across vessel classes, median daily distance was greatest for presence or absence of other vessel types. Large cargo ships may ferries and lowest for cargo and tanker ships in all seasons more easily transit the study area during times of reduced ferry (Figure 4), and no significant differences existed for these traffic at night and reduced recreational traffic on weekdays. Tug vessel classes between seasons. Significant differences in daily boats typically escort these large vessels to assist with docking

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TABLE 2 | Mood’s median test results (p-values) comparing daily total distance traveled (nmi/day) and daily average speed (kn) across seasons and post hoc analysis between seasons.

Daily total distance (nmi/day)

All seasons Winter vs. Winter vs. Winter vs. Spring vs. Spring vs. Summer vs. Spring Summer Autumn Summer Autumn Autumn

Cargo 0.05 – – – – – – Tanker 0.87 – – – – – – Tug <0.01* 0.01 0.04 <0.01* 0.68 0.4 0.04 Ferry 0.03 – – – – – – Motorized recreational <0.01* 0.25 <0.01* 0.01 <0.01* 1 0.01 Sailing vessel <0.01* 0.49 0.5 0.65 1 <0.01* <0.01*

Daily average speed (kn)

All seasons Winter vs. Winter vs. Winter vs. Spring vs. Spring vs. Summer vs. Spring Summer Autumn Summer Autumn Autumn

Cargo 1 – – – – – – Tanker 0.59 – – – – – – Tug <0.01* 0.36 0.66 <0.01* 0.68 <0.01* <0.01* Ferry 0.78 – – – – – – Motorized recreational <0.01* <0.01* <0.01* 0.07 0.04 0.21 <0.01* Sailing vessel <0.01* 0.01 <0.01* 1 <0.01* 0.04 <0.01*

Significance defined as p < 0.01*. Dashes indicate unnecessary post hoc comparisons due to non-significant p-values across seasons.

and steering but are also a multi-purpose platform, assisting collisions while maintaining these status quos, vessels regularly in mooring, salvage, and clean-up operations, and responding watch for and report cetacean presence in and around SFB quickly to vessels in distress (Couce et al., 2015). Despite (United States Coast Guard [USCG], 2018). One potential flaw consistent distance and speed traveled in the study area across in this system is the need for cetaceans to be seen. We found seasons for large ships, tugs traveled significantly less distance in that there were no significant differences in cargo or tanker autumn than in winter and significantly slower speeds in autumn ship presence between day and night, but cargo ships traveled than any other season. This suggests that tug distance and speed significantly faster at night with a median speed greater than in autumn may be driven by the varied uses of these vessels 10 kn. Despite ferries traveling significantly more slowly at night, outside of escorting large ships in the study area. median speeds across day and night were still greater than 30 kn. In a recent study of rescued cetaceans with anthropogenic Cetaceans, such as humpback whales, actively forage at night trauma in the SFB area, the primary source of trauma was (Baird et al., 2000) but would potentially be more difficult to spot vessel collision (Barcenas-De la Cruz et al., 2017). Since lethal during this time. Previous research on cetaceans in SFB has relied collisions are most common when vessels are traveling faster on visual observation (e.g., Stern et al., 2017), so it is currently than 10 kn (Laist et al., 2001; Vanderlaan and Taggart, 2007), unknown if and how cetaceans use SFB at night. If present, they cetaceans are most at risk from vessels traveling above this also may be more likely to incur a lethal vessel collision during speed. In the National Marine Sanctuaries outside SFB, vessels are this time since vessels routinely traveled with median speeds asked to voluntarily slow down to 10 kn during peak migration greater than 10 kn. season (United States Coast Guard [USCG], 2018). Commercial While traditional spatial risk assessments for collisions shipping vessels did not always extend this speed limit into SFB between vessels and cetaceans involve overlaying vessel traffic upon entering. In the study area, cargo and tanker ships, ferries, with species habitat (Williams and O’Hara, 2010; van der Hoop and motorized recreational vessels traveled at average daily et al., 2012; Redfern et al., 2013; Dransfield et al., 2014), this speeds greater than 10 kn. Median ferry speed across all temporal research analyzes a new technology for vessel distribution and resolutions was greater than 29 kn, and these vessels were the speed. More information on cetacean use of SFB is needed to most abundant of any vessel class in the study area. These high adequately assess risk. Shifting cetacean assemblages in response speeds make commercial ferries a pervasive potential threat to to changing climate variables (MacLeod, 2009) has been observed local cetaceans. Further, narrow inland ship passageways, like off the central California coast (Benson et al., 2002), and the SFB, increase the chance of collision (Webb and Gende, 2015). same may occur in SFB. We do know that commercial shipping In the intensely urbanized SFB area, high concentrations of traffic is increasing worldwide (United Nations Conference on commuters need to be transferred across SFB waters quickly. Trade and Development [UNCTAD], 2017) and that congested Commercial shipping traffic activity is likely driven by the needs roadways in the SFB area have spurred expansion of ferry traffic of the global economy (Cumming et al., 2006). To mitigate (National Marine Fisheries Service [NMFS], 2017). As conditions

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TABLE 3 | Mood’s median test results (p-values) comparing daily total distance (nmi/day) and daily average speed (kn) across day/night.

Daily total distance (nmi/day) Daily average speed (kn)

Day/night Day/night

Cargo 0.52 <0.01* Tanker 0.01 0.83 Tug <0.01* 0.39 Ferry <0.01* <0.01*

Significance defined as p < 0.01*.

with endangered Northern Atlantic right whales (Eubalaena glacialis) off the U.S. east coast thanks to a network of seasonal management areas overlapping important habitat or major ports where speeds were limited to a maximum of 10 kn (Laist et al., 2014). In southern California, the Santa Barbara Channel Vessel Speed Reduction Zone overlaps with established shipping lanes through the area and suggests limiting speeds to 10 kn due to the presence of cetaceans that commonly forage in the area (Croll et al., 1998). Although most ships have not significantly lowered their speeds at this request (McKenna et al., 2012), the designated zone establishes a framework with spatially explicit boundaries under which management action can occur and provides a defined context for evaluating compliance. Results of this research show vessels in SFB often travel at speeds greater than the limits imposed in these managed areas. For vessels FIGURE 5 | Daily distance (nmi/day) (top) and average speed (kn) (bottom) greater than 1,600 gross tons in SFB, there is a speed limit within by day/night for each vessel class using exclusively AIS data. Box and whisker the RNAs of 15 kn for vessel safety (Code of Federal Regulations plots show sample minimum, maximum, median, and first and third [CFR], 2015), but ferries and recreational traffic do not meet the interquartile values. Outliers are not shown. Statistically significant differences weight requirement for the speed limit. in median daily distance between day/night denoted by † (p < 0.01). Statistically significant differences in median speed between seasons denoted As more becomes known about the increasing presence of by ‡ (p < 0.01). Dashed line indicates 10-kn voluntary speed restriction used cetaceans in SFB, a need for additional managed areas could in National Marine Sanctuaries outside San Francisco Bay (United States become apparent. While harbor porpoises are present year- Coast Guard [USCG], 2017). Sample sizes: n1 = 175 days (day), round (Stern et al., 2017), humpback whales have consistently n2 = 175 days (night). returned to Bay waters during spring and summer months since 2016 (Keener et al., 2016, personal communication), and most gray whale strandings in SFB historically occur in spring change, more research is needed on drivers of cetacean presence (Scordino et al., 2017). This suggests that a seasonal speed in SFB. For example, harbor porpoises entering SFB, especially reduction framework, like those imposed on the U.S. east coast pregnant or lactating females, need to forage in Bay waters to or suggested in the marine sanctuaries outside SFB, has the account for energy lost while swimming (Gallagher et al., 2018). potential to be effective in minimizing risk to cetaceans in the Therefore, the density and spatial distribution of prey in SFB as Bay. Data from vessel traffic monitoring systems, like the M2, can it relates to the metabolic needs of harbor porpoises would likely inform management decisions and could evaluate compliance if help inform where these animals forage when inside SFB. Climate regulations were put in place. variables also influence humpback whale diet along the California Our results support the claim that a single data source/sensor coast (Fleming et al., 2015) which could determine whether these cannot reliably capture all vessel activity (Bloisi et al., 2015). animals will forage on prey found in SFB. Despite the limitation of identifying radar-detected vessels at When the results of this research are compared with known night, the M2 system provided valuable daytime-use data on habitat, risk assessments can identify areas and times of recreational crafts that would have been absent from our analysis potential overlap and ultimately determine if and what mitigation if AIS data had been used exclusively. This information on may be necessary to minimize collision risk. But unlike the the spatial extent of vessel traffic contributes to the overall marine sanctuaries along the central California coast, there are predictability of presence and speed as it informs where these few managed marine areas within SFB (California Code of factors most likely vary. While commercial vessel traffic was most Regulations [CCR], 2019) for which regulations could be adapted dense in defined paths through the study area (predictable by or added. Managers had success reducing vessel collisions the established RNAs for shipping activity and ferry terminal

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We chose to use data collected by the M2 to create a holistic baseline understanding of vessel traffic in SFB across 1 year, but more specific use in other management areas may have a clearer target vessel type, activity, spatial extent, or time of interest for monitoring (i.e., illegal fishing vessel presence inside an MPA) that would narrow the data and time needed for analysis. Manually identifying radar-detected vessels via photographs was time-intensive for this comprehensive study (15,792 vessels positively identified), but the online portal where users can view and interact with data, the M2 Viewer, streamlines the photo review process as users can filter on specific dates and times and quickly click through photos associated with targets and consequently tag vessel type, activity, and make notes. The ports in SFB are also some of the busiest in the country (United States Army Corps of Engineers [USACE], 2018) with a dense human population in the surrounding area. Therefore, M2 sites monitoring coastal areas outside SFB would likely track far less vessels. Other limitations of the M2 included outages due to low power from the solar panels and the inability to identify radar-detected target identity through photographs at night. Continuing improvements of the M2 system include FIGURE 6 | Daily distance (nmi/day) (top) and average speed (kn) (bottom) improved power supply and thermal cameras for use at night by weekday/weekend for each vessel class. Box and whisker plots show sample minimum, maximum, median, and first and third interquartile values. (ProtectedSeas, 2019). Outliers are not shown. Statistically significant differences in median daily The use of this research and the M2 system extend beyond distance between seasons denoted by † (p < 0.01). Statistically significant informing cetacean risk assessment within the SFB study differences in median speed between weekday/weekend denoted by ‡ area. For example, research on non-indigenous marine species (p < 0.01). Dashed line indicates 10-kn voluntary speed restriction used in invasions via recreational vessels in the SFB region has relied National Marine Sanctuaries outside San Francisco Bay (United States Coast on boater questionnaires and vessel observation at local marinas Guard [USCG], 2017). Sample sizes: n1 = 118 days (weekdays), n2 = 57 days (weekends). (e.g., Zabin et al., 2014), leaving vessel activity on the water largely unknown. In other coastal areas with high recreational traffic, research has sought to assess the level of underwater TABLE 4 | Mood’s median test results (p-values) comparing daily total distance noise pollution from these vessels through manual tracking (Erbe, (nmi/day) and daily average speed (kn) across weekday/weekend. 2002; Hermannsen et al., 2019). Using the M2 to collect vessel tracks would likely provide a more robust dataset than relying on Daily total distance (nmi/day) Daily average speed (kn) physical human effort. Outside of environmental concerns, the Weekday/weekend Weekday/weekend M2 could also inform general traffic management or provide a resource for maritime law enforcement. Because of its integration Cargo <0.01* 0.04 of AIS and radar data, the M2 system is especially useful where Tanker 0.11 0.2 many vessels are not transmitting AIS, where activities such as Tug 0.11 0.52 recreational activity or illegal fishing are of concern. Ferry <0.01* <0.01* Motorized recreational 0.02 <0.01* Sailing vessel <0.01* <0.01* DATA AVAILABILITY STATEMENT Significance defined as p < 0.01*. The datasets generated for this study are available on request to the corresponding author. location and schedules), without the incorporation of radar, recreational traffic would have been largely absent from analysis AUTHOR CONTRIBUTIONS (Figure 2). Therefore, traffic would have primarily appeared to travel in paths defined by commercial vessels exclusively, which All authors listed have made a substantial, direct and intellectual would have ignored the more dispersed and likely less predictable contribution to the work, and approved it for publication. pattern of recreational traffic (Figure 3). Since roughly one- third of the vessel traffic we observed was recreational with some median speeds above 10 kn, it is important that future FUNDING analyses of environmental impacts from vessels in urban areas, like SFB, consider these vessel types in addition to vessels Funds for open access publication fees to be provided by transmitting AIS data. Anthropocene Institute.

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ACKNOWLEDGMENTS SUPPLEMENTARY MATERIAL

The authors wish to thank the Estuary & Ocean Science Center The Supplementary Material for this article can be found of San Francisco State University for housing the M2 system and online at: https://www.frontiersin.org/articles/10.3389/fmars. assisting with equipment maintenance. 2020.00086/full#supplementary-material

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Cope et al. Application of New Vessel Monitoring System

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