Understanding Vessel Activity in Marine Protected Areas Using Passive Acoustic Monitoring

Marine Policy 120 (2020) 104138

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Marine Policy

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Sleuthing with sound: Understanding vessel activity in marine protected areas using passive acoustic monitoring

Logan R. Kline a,*, Annamaria I. DeAngelis b, Candace McBride c, Giverny G. Rodgers c, Timothy J. Rowell b, Jeremy Smith c, Jenni A. Stanley b,d, Andrew D. Read e, SofieM. Van Parijs b a Under Contract to Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 166 Water Street, Woods Hole, MA, 02543, USA b Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 166 Water Street, Woods Hole, MA, 02543, USA c Parks Australia, GPO Box 858, Canberra, ACT, 2601, Australia d Woods Hole Oceanographic Institution, 360 Woods Hole Road, Woods Hole, MA, 02543, USA e National Marine Science Centre, Southern Cross University, 2 Bay Drive, Coffs Harbour, NSW, 2450, Australia

ARTICLE INFO ABSTRACT

Keywords: Monitoring compliance and enforcing laws are integral to ensuring the success of marine protected areas (MPAs), Marine protected areas but traditional monitoring techniques are costly and resource demanding. Three SoundTrap 300 recorders were Marine parks deployed for one month between 1 July and September 12, 2018 to collect acoustic data in two marine parks off Passive acoustic monitoring southeastern Australia: one recorder in Cod Grounds Marine Park (CGMP) and two in the Solitary Islands Marine Compliance Park National Park Zone (SIMP NPZ). Extractive activities such as fishingare not permitted in these zones. Raven Enforcement Surveillance Pro 2.0 was used to analyze data for vessel presence. Transmission loss equations for each site were generated using patrol boat GPS tracks and used to predict if acoustically recorded vessels were inside park boundaries based on received sound levels. In CGMP, 41 vessels were predicted within the park during the recording period; 34 vessels were predicted within the SIMP NPZ. Thursdays and Saturdays were identifiedas peak days for vessel presence in CGMP while Thursdays were the peak day in the SIMP NPZ. Most vessel activity at both locations took place between 06:00 and 17:00 AEST. Peak vessel presence in CGMP occurred at 09:00 AEST while the peak vessel presence in the SIMP NPZ occurred at 16:00 AEST. Approximately 12.7 h of vessel sounds were recorded within CGMP; approximately 3.8 h of vessel noise were recorded within the SIMP NPZ. Passive acoustic monitoring of vessel patterns in Australian Marine Parks has provided valuable insight to redirect compliance decisions on how to focus surveillance efforts.

1. Introduction health [4], and protecting endangered or commercially viable species [5,6]) and are principal to international efforts to improve MPA man Marine protected areas (MPAs) are recognized global conservation agement [7–9]. This is particularly true of large and/or remote MPAs, tools developed with the goal of conserving natural and cultural oceanic which present considerable challenges for ensuring effective surveil resources.1 The quantity and size of these areas have grown considerably lance [10]. Australian waters contain a handful of the largest MPAs (e.g. over the last few decades; as of 2018, MPAs constituted approximately Coral Sea Marine Park and Great Barrier Reef Marine Park) and repre 6.6% of the global ocean, with 13,000 MPAs worldwide averaging 2.5 sent some of the most thoroughly monitored regions of ocean on the km2 in size [1]. Effective compliance and enforcement of management planet [7,9,11]. rules are essential for meeting the ecological goals of MPAs (e.g. In Australia, the management of MPAs is shared between state and conserving species diversity and richness [2,3], promoting ecosystem Australian government agencies. Australian Marine Parks

* Corresponding author. 102 Kenduskeag Avenue, Apt 1, Bangor, ME, 04401, USA. E-mail addresses: [email protected] (L.R. Kline), [email protected] (A.I. DeAngelis), [email protected] (C. McBride), giverny. [email protected] (G.G. Rodgers), [email protected] (T.J. Rowell), [email protected] (J. Smith), [email protected] (J.A. Stanley), andrew. [email protected] (A.D. Read), [email protected] (S.M. Van Parijs). 1 https://oceanservice.noaa.gov/facts/mpa.html. https://doi.org/10.1016/j.marpol.2020.104138 Received 22 November 2019; Received in revised form 2 July 2020; Accepted 13 July 2020 Available online 27 July 2020 0308-597X/Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). L.R. Kline et al. Marine Policy 120 (2020) 104138

(Commonwealth reserves proclaimed under the Environment Protection acoustic signature [42]. In lieu of satellite-derived spatial information, and Biodiversity Conservation Act 1999) are located at the outer edge of PAM can provide estimations of presence and characteristics of motor state and territory waters, generally begin 3 nm from shore, and extend ized vessels with unknown spatial attributes, as well as continuous to the outer boundary of Australia’s exclusive economic zone (200 nm) monitoring of remote locations for months at a time. Additionally, PAM [12]. A total of 58 marine parks are currently managed by Parks can enhance management understanding and improve decision-making Australia, part of the federal environment portfolio, and were estab by unveiling human and biological presence patterns in the acoustic lished with the objective of protecting and conserving biodiversity and soundscape (see Refs. [24,42,43] for examples). Given that vessels other natural, cultural, and heritage values whilst allowing for ecolog without AIS technology comprise most anthropogenic noise contribu ically sustainable use of natural resources. Characterized by large, tions to shallow water soundscapes [44], PAM has the potential to be a highly biodiverse, and often remote ocean habitat that is difficult to powerful management tool. monitor, Australian Marine Parks are susceptible to a host of non We present the use of PAM to develop a replicable methodology by compliant activities, notably illegal fishing and pollution [13]. Strong which temporal patterns of unknown motorized vessels near or within enforcement of MPA laws has shown the potential to rapidly increase the two Australian Marine Parks, Cod Grounds and Solitary Islands Marine number and density of target species in protected areas [5,6], and Parks, could be characterized over a one month recording period. The compliance alone has a strong positive effect on species biomass [14]. primary aim of this study was to optimize the effectiveness of future Optimizing compliance programs to protect MPA values ensures surface and aerial patrols by presenting a novel, low-cost technique for enforcement activities are effective and targeted toward observed estimating and understanding vessel presence and implications for characteristics of noncompliance [9]. A major challenge facing Austra compliance in these highly protected areas. lian Marine Park managers is establishing effective and efficient tech niques to optimize enforcement activities. 2. Materials and methods Manned surface patrols are the most common method for monitoring and enforcement in coastal MPAs [11], however they are 2.1. Study sites cost-prohibitive and resource demanding [7]. Furthermore, surface pa trols have limitations in their scope of coverage and cannot realistically Australian Marine Parks are divided into zones that determine where detect all noncompliant activities. Studies have shown success in using certain activities can occur. National Park Zones (International Union automated identification systems (AIS) to monitor noncompliance of for Conservation of Nature Category II; NPZs) offer a high level of pro commercial vessels in MPAs [15]. However, AIS data are limited by tection, with the goal of protecting and conserving ecosystems, habitats, small fleet coverage, the inability to identify vessel type, lowered ca and native species [12]. This designation prohibits all extractive activ pacity for data analysis by management agencies, and the ability to turn ities, such as fishing, aquaculture, and mining, and only permits off AIS at any time, thus masking criminal activities [16]. Vessel non-extractive activities unless authorized for research and monitoring. monitoring systems (VMS) have also been utilized to successfully deter Three bottom-mounted acoustic recorders (SoundTrap 300s2) were noncompliance events and have been used to analyze fishingactivity in deployed in two NPZs located off the southeastern coast of Australia: one MPAs [17]. Yet, similar to AIS, VMS is limited by fleet coverage; in in Cod Grounds Marine Park (CGMP) at a depth of 37 m, deployed from Australia, less than 15% of the Australian commercial vessel fleet has July 1, 2018 to August 4, 2018, and two 140 m apart within the NPZ of access to VMS that alerts users to their presence in MPAs [7]. Notably, Solitary Islands Marine Park (SIMP NPZ) at a depth of 42 m, deployed recreational vessels are not equipped with VMS and are not required to from August 10, 2018 to September 12, 2018 (Fig. 1). The two recorders have AIS, creating a significant gap in MPA user understanding and in the SIMP NPZ were placed on opposite sides of a seamount, Pimpernel awareness for managers. Satellite imagery provides a means of identi Rock, and are referred to as East (EP) and West Pimpernel (WP) fying vessels unequipped with VMS or AIS, or vessels that have shut off respective to their positions. A popular fishing spot known as Banana their transponders to avoid detection [18]. However, this imagery is Rock is also located 1.5 km northeast of the SIMP NPZ hydrophones, limited by a satellite’s scope of coverage, image resolution, and various outside of the NPZ. Vessels typically detected within these NPZs are atmospheric and lens effects. small, motorized, recreational fishing craft. Recreational fishers often A motorized vessel within an MPA that cannot be detected through use hooks and lines and may fishstationary, drift, or troll. Species which surface patrols or satellite data can still be detected through other may be illegally targeted at CGMP and SIMP primarily include snapper means. Passive acoustic monitoring (PAM) is a powerful tool that can be (Pagrus auratus), pearl perch (Glaucosoma scapulare), and yellowtail utilized to improve domain awareness and monitor noncompliance kingfish (Seriola lalandi). where other technologies have limitations. Traditionally, PAM has been The entire 4 km2 of CGMP is designated as an NPZ; the MPA is used to explore spatiotemporal presence and acoustic behaviors of approximately 30 km south from Port Macquarie3 and its westernmost soniferous marine mammal [19–23], fish [24–28], and invertebrate boundary is approximately 8 km offshore. The SIMP NPZ is a 1 km2 species [29–32]. Recently, PAM has been used to monitor compliance section of a 152 km2 MPA that consists of NPZs, a Multiple Use Zone, and with laws in protected areas [33,34]. This nascent technique uses Special Purpose Zones differing in usage restrictions.4 The SIMP NPZ is acoustic signatures of illegal activity (e.g. gunshots from poaching approximately 12 km southeast from Brooms Head and its westernmost events) to inspect spatiotemporal patterns of noncompliance. Analyzing boundary is approximately 7 km offshore. Both NPZs were established these trends through PAM has considerable potential to enhance primarily to protect endangered species such as grey nurse sharks enforcement and compliance in protected areas by informing park (Carcharias taurus) [12]. The CGMP and SIMP NPZ recording sites were rangers of when and where illegal activities are most likely to occur. For chosen as initial sites for this study due to their relative accessibility for instance, time of day and seasonality are notable factors that influence deploying passive acoustic recorders compared to more remote parks, the likelihood of aerial and vessel patrols recording illegal fishing in their proximity to boat ramps leading to significant vessel use, and the cidents [35]. In the case of Australian Marine Park monitoring, PAM ongoing high risk of noncompliant fishing activity in violation of MPA bypasses the clandestine nature of illegal fishingactivities as motorized laws. vessels have detectable acoustic signatures made by the cavitation of propeller blades and engine noise [36–38]. Previous studies have demonstrated the potential of utilizing PAM to estimate vessel use of 2 http://www.oceaninstruments.co.nz/. marine habitats through quantificationand analysis of vessel signatures 3 https://parksaustralia.gov.au/marine/parks/temperate-east/cod-grounds/. [39–41]. In addition, a vessel with an engine can be roughly charac 4 https://parksaustralia.gov.au/marine/parks/temperate-east/solitary-is terized by size class and behavior according to the properties of its lands/.

2 L.R. Kline et al. Marine Policy 120 (2020) 104138

Fig. 1. A map of the three sites where acoustic recorders were deployed for approximately one month in the Cod Grounds Marine Park and Solitary Islands Marine Park National Park Zone, both off the southeastern coast of Australia, between July 1, 2018 and September 12, 2018. Marine park boundaries are outlined in the insets and a local fishing spot, Banana Rock, is shown north of the Solitary Islands Marine Park National Park Zone.

2.2. Data collection recorders were set to sample at 48 kHz with the high gain calibration setting, providing an effective recording bandwidth of 20 Hz–24 kHz. 2.2.1. Passive acoustic data The recorders were also programmed to measure and record an Each of the three hydrophones recorded acoustic data between 32 instantaneous temperature every 10 s. and 35 days during the interval of July 1, 2018 through September 12, A second deployment was undertaken on December 12, 2018 in 2018 (Table 1). These dates corresponded with one public New South CGMP. This study does not include an analysis on vessel acoustic data Wales holiday on 11 July (the Queen’s birthday). An instrument error from the second round of recordings; however, acoustic data from the resulted in the CGMP hydrophone losing 2 h of data (07:00 and 08:00 second deployment of the CGMP recorder were used for transmission AEST) on July 18, 2018. Aside from this malfunction, data were loss modelling calculations as data were not available to use from the collected continuously over the study period for each recorder. All first deployment. During the second deployment, the CGMP recorder

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Table 1 Summary of passive acoustic recording effort across three sites, one site in the Cod Grounds Marine Park and two sites (East and West Pimpernel) in the Solitary Islands Marine Park National Park Zone, off the southeastern coast of Australia for approximately one month between July 1, 2018 and September 12, 2018. Site Location Depth Substrate Sampling rate, recording Recording dates Recording days High gain calibration (dB re (m) schedule (n) 1 μPa)

Cod Grounds 31⁰ 40.87 S 152⁰ 37 Sand 48 kHz, continuous Jul 1, 2018–Aug 4, 35 173.0 (CGMP) 54.61 E 2018 East Pimpernel 29⁰ 41.90 S 153⁰ 42 Soft, non- 48 kHz, continuous Aug 10, 2018–Sep 12, 33 172.7 (EP) 23.89 E reef 2018 West Pimpernel 29⁰ 41.88 S 153⁰ 42 Hard, reef 48 kHz, continuous Aug 10, 2018–Sep 11, 32 172.4 (WP) 23.81 E 2018 was set to record a continuous 30 min of data for every 1 h. The recorder very large vessels, with tones only below 500 Hz and no broadband was again set to sample at 48 kHz with the high gain calibration setting energy, were excluded from analysis. and recorded an instantaneous temperature every 1 h. Discrete vessel events were assigned a category of relative distance upon being selected. Vessels fell into three distinct categories based on 2.2.2. Patrol boat tracks the received intensity of their acoustic signatures: 1) closest point of During the deployment of the recorders in the SIMP NPZ on August approach (CPA) where a vessel approached and passed the hydrophone 10, 2018, the New South Wales (NSW) Department of Primary Industries at a close enough distance to produce a Doppler effect (see Ref. [46]); 2) (DPI) Fisheries Patrol Vessel (FPV) Peter Angel (a 6.8-m rigid-hulled transit level A (TA) where a vessel did not exhibit a CPA (thus the inflatable boat (RHIB) with twin F200 Yamaha 4-stroke engines, absence of a Doppler effect) and was thus presumed distant but still henceforth Peter Angel) conducted three circular passages at a nominal produced broadband energy; and 3) transit level B (TB) where a vessel speed of 15 km/h at distances of approximately 200, 500, and 1000 m was detectable above 500 Hz but distant enough to lack consistent around the SIMP NPZ recorders. Timestamped GPS locations were broadband tones. CPAs and TAs were further categorized into CPA with provided for these tracks and used in transmission loss modelling. a maneuver (CPA þ M) and TA with a maneuver (TA þ M), where Systematic GPS timestamps were not taken during the July 1, 2018 maneuvers indicated a change in vessel behavior, such as shifting gears deployment in CGMP. During the second deployment on December 12, or idling, within the duration of the event (Supplemental Fig. 1). Ma 2018, however, the patrol boat NSW DPI FPV Watonga (a 6.1-m RHIB neuvers are displayed in Raven Pro 2.0 as increases and decreases in the with twin Suzuki 90 horsepower engines, henceforth Watonga) con intensity or frequency of the tones emitted from the vessel engine and ducted two semi-concentric passages at a nominal speed of 21 km/h at were detectable for CPAs and TAs but too faint to conclusively detect on distances of approximately 200 and 1000 m around the CGMP recorder. TB signals. These distinct acoustic patterns were designated as a sepa Timestamped GPS locations were provided for these tracks and used in rate component in our analysis as maneuvering vessels (e.g. vessels transmission loss modelling. In-situ conditions, such as temperature (as mooring, changing gears, shutting down an engine) within the MPAs measured by the SoundTrap) and sound speed profile (taken from the were suspected to be more likely to exhibit extractive behaviors than Global Ocean Sound Speed ProfileLibrary [45]), did not differ between transiting vessels. While extractive behaviors, such as fishing, are not the firstand second deployments, and the environment (i.e. bathymetry permitted, transiting through the MPA is allowed. Designating CPAs and and seafloor properties) remained constant. Thus, these data were TAs as either maneuvering or non-maneuvering vessels allowed for determined to be appropriate for modelling transmission loss at the site separate analyses of vessels that may be engaged in illegal activity and interpreting data collected in the first deployment. versus transiting through the MPA. Overall presence of these categories and all detected vessels were plotted and reviewed for patterns using R 2.3. Data analyses 3.5.2 (R Core Team, Vienna, Austria).

Once acoustic fileswere extracted from each recovered recorder, the 2.4. Transmission loss modelling resulting recordings were analyzed manually using the acoustic software Raven Pro 2.0 (Center for Conservation Bioacoustics, Cornell University, An empirical transmission loss (TL) model was used to understand Ithaca, NY). Utilizing the spectrogram within Raven Pro 2.0, data were the TL of vessel sound as a function of range at CGMP and the SIMP NPZ. reviewed for the presence of vessel noise from 0 to 10 kHz using a 3-min When timestamped GPS tracks of a vessel are known and the corre viewing window, a fast Fourier transform (FFT) of 4096 points with a sponding received level (RL) of a vessel sound measured, these tracks 50% overlap, brightness of 44, and contrast of 71. Each site was visually can be used in conjunction with site-specificTL models and the modified and aurally reviewed across the entirety of its recording period passive sonar equation to estimate the source level (SL) of the vessel (including the partial days in which the recorders were deployed and given specific distances from recorders (Eq. (1); see Ref. [47] for more recovered; only partial hours were excluded from analysis). details). As the locations of acoustically detected vessels are unknown in The presence of discrete vessel events and their durations were this study’s dataset, we used information from the Watonga and Peter measured using the Raven Pro 2.0 selection tool. A discrete vessel event Angel GPS tracks to estimate the relationship between the RL of a vessel was defined as having a unique acoustic signature that could span sec and the TL incurred with sound traveling over a known distance. onds to hours. Acoustic signatures of vessels could be interrupted with RL ¼ SL À TL (1) periods of lowered or no vessel noise if the vessel switched gears or the engine was turned off. If the interval of lowered or no vessel noise lasted The GPS locations taken around the CGMP and SIMP NPZ recorders more than 3 min, the two events were classified as two discrete vessel by the patrol boats allowed an estimation of SL and TL based on the events; if the interval was less than 3 min, the event was considered a known range of the vessel to the recorders at particular times. For both single discrete vessel event. If two different acoustic signatures were the Watonga and Peter Angel, these locations were partitioned into main identified within the same timeframe, they were separated as two sub-tracks and transit sub-tracks that contained points which corre discrete vessel events based on differences in their frequency bands. sponded to times in the recordings. The Watonga GPS locations were Upon finding a vessel signature, a selection box was drawn around the divided into two inner and outer sub-tracks at roughly 200 m and 1000 entire event so that duration could be calculated. To reduce the number m from the CGMP recorder as well as an inner-outer transit sub-track of very distant, large vessels analyzed, acoustic signatures indicating designated where the Watonga was moving from the end of the inner

4 L.R. Kline et al. Marine Policy 120 (2020) 104138

sub-track into the beginning of the outer sub-track (Fig. 2A). Due to the the signature of the vessel was greater than that of surrounding back duty-cycling period of 30 min/h for the second phase of the CGMP re ground and biological sounds. Vessels often did not have consecutive cordings, only approximately half of the outer sub-track was within the spans of clean data; in these cases, multiple selections greater than 1 s duty-cycle; the acoustic data for the Watonga ended at 09:34 AEST while were made. When a vessel did not have any periods of clean data, or the the GPS locations continued until 09:48 AEST. The Peter Angel GPS lo acoustic signature was hard to distinguish from ambient or biological cations were separated into three inner, middle, and outer sub-tracks at sounds, selections were not made and that vessel was eliminated from roughly 200 m, 500 m, and 1000 m, respectively, from the recorders in range and size class analysis. Selections were made where it was the SIMP NPZ (Fig. 2B). Two transit sub-tracks of inner-middle and assumed the vessel was making its closest point of approach (i.e. at the middle-outer were specified.As the recorder data for the SIMP NPZ were time of the lowest frequency for CPAs and times of greatest acoustic continuous, all GPS locations provided for the Peter Angel were used in pressure for other distance classes). These selections were exported as the following analysis. sound clips. Using ArcGIS 10.3.1 (ESRI, Redlands, CA) the ranges between each Each vessel sample sound clip was run through custom-made MAT GPS location for the Watonga and the CGMP recorder were calculated; LAB scripts that calculated peak frequency and RL at peak frequencies the same was done between the Peter Angel GPS locations and the two (as was done in the patrol boat analysis). Again, peak frequencies were SIMP NPZ recorders. For each main and transit sub-track at each site, 15 manually checked in Raven Pro 2.0 to ensure that the script was not to 20 GPS patrol boat locations were selected at intervals respective to selecting biological sources (or, in the case of CGMP, high-frequency each sub-track’s sample size (i.e. the number of GPS points in each sub- signatures of the nearby mooring). In some instances, the script was track). This resulted in 69 location selections for CGMP and 98 location unable to generate accurate peak frequencies for any of the samples selections for the SIMP NPZ; the smaller sample size for CGMP was a taken of a vessel’s spectrogram; these vessels were eliminated from result of the duty-cycling on the site’s second deployment. Raven Pro 2.0 further range and size class analyses. Valid samples with the highest was then used to view spectrograms of recordings concurrent with each peak RLs were selected for each vessel to be used for range and size class of the sampled GPS locations. Using a padding of 2.5 s on either side of analyses. each location’s timestamp in the acoustic data, sound clips lasting 5 s As the distance from the acoustic recorder to a vessel is difficult to were generated, annotated with the calculated ranges of each sample estimate with high accuracy with fewer than three receivers, simulated track location to the recorder, and exported. Sample locations that vessels of different size classes were modelled using custom MATLAB overlapped the data captured in another sound clip were removed from scripts at ranges every 10 m from 1 to 2000 m, encompassing ranges analysis (e.g. if one GPS location was taken at 08:07:02 AEST and the both inside and outside of the NPZs to determine whether RLs of vessel next sample was less than 2.5 s away, the second sample would be recordings were indicative of vessels inside the MPAs based on SLs for skipped). For the SIMP NPZ recorders, locations recorded when Pim different vessel types. The calculated RLs were inserted into the pernel Rock was between the vessel and the hydrophones were elimi modelled TL equations and expanded passive sonar equation, and SL nated from analysis as the physical presence of the seamount obscured was solved for at ranges from 1 to 2000 m. This generated estimates of broadband energy of the passing patrol boat. the SL (n ¼ 200) required to result in the measured RL at each 10 m The software MATLAB R2017a (The MathWorks Inc., Natick, MA) range increment following range-dependent TL. Estimated SLs at each was then used to calculate the RL of each sound selection at the spectrum range were separated into three categories based on SL ranges reported level. Mean-square sound pressure spectral density (PSD) levels in units in the literature [41,48]: small (vessels such as RHIBs and small of dB re 1 μPa2/Hz were calculated (FFT length ¼ 48,000 points, Hann outboard boats), medium (vessels such as trawlers and yachts), and large window length ¼ 48,000, FFT overlap of 0%), and the maximum PSD (vessels such as cargo and cruise ships) (Table 2, Supplemental Fig. 2). level was identified to estimate the peak frequency and corresponding The ranges of estimated SLs attributable to each size class were binned RL over a 1 Hz-wide band for each patrol boat sound clip. Peak fre and used to determine the probability of an unknown vessel being inside quencies were manually verifiedin Raven Pro 2.0 to ensure that RLs at each NPZ if it were a small, medium, or large vessel based on the number peak frequencies were produced by the patrol boat. A predominant of predicted SLs at ranges less and greater than park boundaries. feature of the CGMP recordings was a high-frequency repetitive noise Evaluations of vessel sizes that may be near the recorders suggest attributed to a nearby mooring moving in the current. In cases where the that they were likely to be predominately medium and small vessel peak frequency was attributable to a biological sound (e.g. cetacean calls classes (DPI patrols, unpublished data). Using the probability of all three or fishgrunts) or, in the CGMP selections, mooring noise, the sample was size classes inflates the likelihood that a vessel will be categorized as removed from analysis. outside of the NPZ, as the large vessel class with higher SLs is almost Plots of RL versus range of the patrol boat to the recorder were always found beyond park boundaries. Therefore, the probability of a generated for valid patrol boat locations for CGMP and the SIMP NPZ, vessel being inside of CGMP or the SIMP NPZ was calculated only with and lines were fittedusing generalized linear model (GLM) regressions. small and medium size class probabilities. In order to determine whether Data were fittedwith the modifiedpassive sonar equation after TL term a discrete vessel event of small or medium size would be inside of the expansion with SL, b, and α as unknowns, where b is a coefficient for NPZ, the probability (Pinside) was computed using Eq. (3), where N is the geometric spreading, α is an absorption coefficient (dB/m), and r is number of vessels defined by size class and inside or outside the park range (Eq. (2)). The resulting equations provided estimates of the SL of boundary. the patrol boats and empirically derived models of TL in the environ Nsmall inside þ Nmedium inside ments of CGMP and the SIMP NPZ that were used to estimate probable Pinside ¼ (3) Nsmall inside þ Nsmall outside þ Nmedium inside þ Nmedium outside ranges of discrete vessel events based on size classes and simulated SLs. � � � � r Poutside would be calculated as 1 – Pinside. Each possible vessel range RL ¼ SL À log þ αðrÞ 1m (2) was ascribed as inside or outside of the park according to whether the simulated range was inside or outside of the average radius, calculated as the average distance from an acoustic recorder to the NPZ border by � 2.5. Range and size class estimation for unknown vessels taking the distance every 45 (1218 m for CGMP, 762 m for the SIMP NPZ). Discrete vessel events that were identified as having a 75% or Once TL equations were generated for CGMP and the SIMP NPZ, greater chance of being within CGMP or the SIMP NPZ were selected, probabilities of vessel range and size class could be determined. For then plotted and analyzed for daily, diel, duration, hourly, and weekday discrete vessel events detected by the recorders at each site, around 10 s patterns using R 3.5.2. of relatively “clean” selections of vessel noise were made at times where

5 L.R. Kline et al. Marine Policy 120 (2020) 104138

Fig. 2. Maps of the systematic, partitioned patrol sub-tracks taken by the (A) NSW DPI FPV Watonga in Cod Grounds Marine Park on December 12, 2018 and (B) NSW DPI FPV Peter Angel in the Solitary Islands Marine Park National Park Zone on August 10, 2018. Tracks in (A) were partitioned into two main inner and outer sub-tracks as well as an inner-outer transit sub-track. The black bar in the outer sub-track represents the end of the acoustic data collected for the patrol. Tracks in (B) were partitioned into three main inner, middle, and outer sub-tracks as well as two inner-middle and middle-outer transit sub-tracks.

SIMP NPZ to minimize any overrepresentation of double-counting the Table 2 same vessel detected on both recorders. Overlapping vessels could be Vessel size classes (small, medium, and large) and length ranges with examples sequential within 2 min (e.g. a vessel that stopped being detected in WP of vessel types and recorded source level ranges attributed to each class. at 12:24 AEST and started being detected in EP between 12:24–12:26 μ 2 Class Size (m) Examples dB re 1 Pa /Hz Source AEST) or overlap in time (e.g. a vessel that was detected from Range 12:20–12:30 AEST in WP and 12:28–12:32 AEST in EP). If the acoustic Small �7 RHIBs, small outboard 125–150 [48] signatures matched in frequency in both sites and the presence of the engines event in one filealigned with the absence or decrease in amplitude in the Medium 7.1–40 Fishing vessels, trawlers, 151–170 [41] yachts, tugs other, or there was a temporal overlap of similar signatures, the earliest Large 40.1–250 Cruise ships, tankers, 171–180 [41] start and latest end time of the event was recorded into a new table as a cargo ships single discrete vessel event. Vessel distances for these combined discrete vessel events were ascribed according to the closest distance category assigned to the EP or WP recordings (e.g. a CPA in EP and TA in WP that 2.6. Time difference of arrival analysis for the SIMP NPZ were combined would become a CPA in the new selection table). When one of the categories had a maneuver, the distance category for the As the SIMP NPZ recorders were placed only 140 m apart, it was clear combined vessel would have a maneuver on the closest category (e.g. a that some vessels distinguished as being within the park in the EP and CPA in EP and TA þ M in WP that were combined would become a CPA WP datasets could be double-counted. A simple time difference of arrival þ M in the new selection table). In cases where a discrete vessel event in (TDOA) analysis was conducted on vessels identifiedas being within the

6 L.R. Kline et al. Marine Policy 120 (2020) 104138 one dataset was not present in another, the vessel start- and end-time recordings (50%), and 164 from the WP recordings (58%) were valid for information was unaltered and entered into the new selection table as range calculations. Of the vessel selections that had range estimations a single vessel. The resulting vessels were then plotted for daily, week successfully generated, 41 were determined to have a 75% or greater day, diel, and duration patterns and displayed for comparison alongside chance to be within the boundaries of CGMP (11% of the total sample) CGMP results. For diel analyses, hours considered as nighttime versus and 43 were determined to likely be within the SIMP NPZ boundaries; daytime were determined by averaging sunrise and sunset times across 15 discrete vessel events were detected from the EP recorder (6% of the the deployment taken from Port Macquarie (CGMP)5 and Coffs Harbour total EP sample) and 28 were detected from the WP recorder (10% of the (SIMP NPZ)6 weather stations. total WP sample). The CPA and CPA þ M distances had the highest probability of 3. Results occurring inside of CGMP and the SIMP NPZ (Table 3). In CGMP, CPAs were 95% likely to be within the park boundaries and CPA þ Ms had an 3.1. Summary of vessel presence inside and outside of the NPZs 85% chance of being within the park. For the SIMP NPZ, CPAs recorded by the EP recorder were 28% likely to be inside while CPAs recorded by Vessels were detected 34 out of 35 days by the CGMP recorder (97% the WP recorder were 75% likely to be within the SIMP NPZ. The CPA þ of the recording period) (Fig. 3). On the SIMP NPZ recorders, vessels M category in the SIMP NPZ was 43% likely to be inside for the EP were detected on 29 out of 33 days in the EP data (88% of the recording recorder and 50% likely to be inside the park for the WP recorder. The period) and 29 out of 32 days in the WP data (91% of the recording TA and TA þ M categories were variable and low in occurrence inside of period). In total, 364 discrete vessel events were detected by the CGMP the NPZ. Discrete TAs were 14% likely to be within CGMP while TA þ recorder, 240 by the EP recorder, and 283 by the WP recorder. Ms had a 12% chance of being inside the park. In the SIMP NPZ, TAs In the CGMP data, most vessel activity occurred between 15 and July were 28% (EP) and 3% (WP) likely to be within the park while TA þ Ms 28, 2018, with peak activity on July 26, 2018. Vessel activity in the had 8% (EP) and 3% (WP) likelihood of being within the park. The TB SIMP NPZ data clustered within three groups of dates: 10–19 August, 22 distance category had the lowest probability of occurring inside of either August – 3 September, and 5–11 September 2018. Peak vessel activity park. No TBs were found to be within CGMP; no TBs were detected by was on August 29, 2018 for both the EP and WP recorders. the EP recorder and only two were detected and determined as inside the park by the WP recorder. The TB category had only a 3% chance of being 3.2. Transmission loss modelling inside the SIMP NPZ. The 43 discrete vessel events recorded on either SIMP NPZ recorder Two regression lines were generated using patrol boat track ranges and classified to be inside of the SIMP NPZ were filtered to 34 discrete and RLs: one for CGMP with 62 valid patrol boat points and another for vessel events through the TDOA analysis. Vessels determined to be the SIMP NPZ with 67 valid patrol boat points. outside of the NPZ were not considered in the TDOA analysis. Nine The CGMP regression line (Eq. (4); Fig. 4) had a good model fit(r 2 ¼ vessels were determined to be overlapping while EP and WP recorded 6 0.89) and the SL of the Watonga was estimated at 143.9 dB re 1 μPa2/Hz and 19 additional discrete vessel events, respectively. All following centered on the peak frequency, which fell within the range proposed by analyses done on the SIMP NPZ dataset refer to post-TDOA vessel the literature [46]. The geometric spreading constant, b, of 19.5 in identifications. dicates that sound propagated more spherically than cylindrically at close ranges. The absorption value, α, of 0.009 dB/m represents a small 3.3.1. Daily vessel presence inside the NPZs but important amount of sound absorbed within the water column and Vessel activity within CGMP appeared more regular than within the sediment layers. SIMP NPZ, with estimated peak vessel presence inside CGMP occurring on 7, 18, and July 26, 2018 (n ¼ 4 discrete vessel events each) (Fig. 5). ¼ : À ð : ð Þ þ : ð ÞÞ RL 143 9 19 5log r 0 009 r (4) Weekends had slightly more vessel presence within CGMP than week The regression line developed for the SIMP NPZ using the Peter Angel days with an average of 1.4 boats per day (14 vessels over 10 weekend ranges revealed a good model fit(r 2 ¼ 0.79; Eq. (5); Fig. 4). The SL of the days). Weekdays averaged approximately 1.1 boats per day (27 vessels Peter Angel was estimated at 145.1 dB re 1 μPa2/Hz centered on the peak over 25 weekdays). Estimated peak vessel presence in the SIMP NPZ ¼ frequency, which also fell within the range proposed by the literature occurred on August 30, 2018 (n 10 discrete vessel events). Weekdays [48]. The difference in the estimated SLs for the two patrol boats is had more vessel presence within the SIMP NPZ than weekends with an congruent with the Peter Angel having a larger engine than the Watonga. average of 1.2 boats per day (28 vessels over 24 weekdays); weekend This equation shows that the SIMP NPZ also exhibited spherical days averaged 0.6 boats per day (6 vessels over 10 weekend days). Given spreading (b ¼ 17.7) at close ranges and an appreciable amount of sound that the small sample size, it is not possible to evaluate scientifically absorption (α ¼ 0.013). significant differences among weekdays versus weekends. For CGMP, Tuesdays and Fridays had low vessel presence (n ¼ 2 ¼ : À ð : ð Þ þ : ð ÞÞ RL 145 1 17 7log r 0 013 r (5) discrete vessel events each) while Thursdays and Saturdays had higher vessel presence (n ¼ 12 discrete vessel events and n ¼ 10 discrete vessel 3.3. Vessel presence inside of CGMP and the SIMP NPZ events, respectively) (Fig. 6). The CPA category was most common on Thursdays (n ¼ 6 discrete vessel events) while CPA þ M presence In total, 364 discrete vessel events were selected from the CGMP increased on weekends (n ¼ 3 discrete vessel events each on Saturdays recordings, 240 from the EP recordings, and 283 from the WP re and Sundays). Discrete TAs occurred most often on Thursdays (n ¼ 3 cordings. Of these, 285 discrete vessel events recorded from CGMP (78% discrete vessel events) while TA þ Ms took place mostly on Saturdays (n of the total sample), 149 vessels from EP (62%), and 200 vessels from ¼ 2 discrete vessel events). Discrete TBs were not recorded in CGMP. WP (71%) had acoustic signatures intense enough to be analyzed for In the SIMP NPZ, Sundays and Mondays had low vessel presence (n peak frequency. After removing events in which the script measured ¼ 1 discrete vessel event each) while Thursday was the peak day for biological or mooring sounds, a total of 208 discrete vessel events from vessel activity (n ¼ 12 discrete vessel events). Discrete CPAs peaked on the CGMP recordings (57% of the total sample), 119 vessels from the EP Thursdays (n ¼ 12 discrete vessel events) and CPA þ Ms occurred mostly on Saturdays (n ¼ 3 discrete vessel events). The TA category was distributed across Tuesdays, Fridays, and Saturdays (n ¼ 1 discrete 5 https://www.timeanddate.com/sun/australia/port-macquarie. vessel event each) and TA þ Ms were distributed across Wednesdays and 6 https://www.timeanddate.com/sun/australia/coffs-harbour. Sundays (n ¼ 1 discrete vessel event each). The two TBs recorded in the

7 L.R. Kline et al. Marine Policy 120 (2020) 104138

Fig. 3. Daily acoustic vessel presence derived from three recording sites, one in the Cod Grounds Marine Park and two in the Solitary Islands Marine Park National Park Zone (East and West Pimpernel). The total number of vessels present is plotted against recording date, with weekends shown in black and weekdays in grey. Partial recording days are denoted with asterisks beneath the bars.

Table 3 Sample size of discrete vessels per behavior per acoustic recorder that met or exceeded the 75% probability of being located within Cod Grounds Marine Park or the Solitary Islands Marine Park National Park Zone during the recording period.

Number of Total number of Percentage vessels inside vessels analyzed inside (%)

Cod Grounds CPA 20 21 95 (CGMP) CPA 11 13 85 þ M TA 6 43 14 TA þ 4 34 12 M TB 0 97 0 East CPA 5 18 28 Pimpernel CPA 3 7 43 (EP) þ M TA 5 18 28 TA þ 2 24 8 M TB 0 52 0 West CPA 21 28 75 Fig. 4. The regression lines of the NSW DPI FPV Watonga ranges versus Pimpernel CPA 3 6 50 received levels for sample locations taken from GPS points in Cod Grounds (WP) þ M Marine Park on December 12, 2018, and the NSW DPI FPV Peter Angel ranges TA 1 32 3 versus received levels for sample locations taken from GPS points in the Solitary TA þ 1 29 3 Islands Marine Park National Park Zone on August 10, 2018. M TB 2 69 3

SIMP NPZ both took place on Wednesdays. Vessel presence in CGMP largely took place between the hours of 3.3.2. Diel vessel presence inside the NPZs 05:00 and 20:00 AEST, with one vessel inside the NPZ at 23:00 AEST At both sites, most vessel activity took place between daylight hours (Fig. 7). The peak hour of vessel presence at CGMP occurred at 09:00 (from 07:00–17:00 AEST for CGMP and 06:00–17:00 AEST for the SIMP AEST (n ¼ 5 discrete vessel events). The CPA category peaked at 11:00 NPZ). AEST (n ¼ 4 discrete vessel events) and CPA þ Ms peaked at 14:00 AEST

8 L.R. Kline et al. Marine Policy 120 (2020) 104138

Fig. 5. Estimated daily acoustic inside-park vessel presence derived from three recording sites, one in the Cod Grounds Marine Park and two in the Solitary Islands Marine Park National Park Zone (East and West Pimpernel). East and West Pimpernel have been combined via a simple time dif ference of arrival analysis. The total number of vessels present is plotted against recording date, with weekends shown in black and weekdays in grey. Partial recording days are denoted with asterisks beneath the bars.

Fig. 6. The estimated number of discrete vessels inside Cod Grounds Marine Park and the Solitary Islands Marine Park National Park Zone plotted against day of the week. Vessel presence is subdivided into each vessel behavioral category definedas closest point of approach (CPA), CPA with a maneuver (CPA þ M), transit level A (TA), TA with a maneuver (TA þ M), and transit level B (TB).

9 L.R. Kline et al. Marine Policy 120 (2020) 104138

Fig. 7. The number of estimated discrete vessels inside Cod Grounds Marine Park and the Solitary Islands Marine Park National Park Zone plotted against hour of day. Vessel presence is subdivided into each vessel behavioral category defined as closest point of approach (CPA), CPA with a maneuver (CPA þ M), transit level A (TA), TA with a maneuver (TA þ M), and transit level B (TB). Average nighttime hours across respective recording periods are denoted for each site with translucent grey bars.

(n ¼ 3 discrete vessel events). The TA category peaked at 06:00 AEST (n only occurred within the NPZ on August 29, 2018 (n ¼ 0.05 h total daily ¼ 2 discrete vessel events) and TA þ Ms were evenly distributed across duration). 06:00, 07:00, 12:00, and 16:00 AEST (n ¼ 1 discrete vessel event each). The TB category was not recorded in CGMP. 4. Discussion In the SIMP NPZ, vessel presence took place between 05:00 and 18:00 AEST. Peak hours occurred at 16:00 AEST (n ¼ 9 discrete vessel In this study, we demonstrate the feasibility of using a single acoustic events). The CPA category appeared more skewed than at CGMP and device to explore motorized vessel activity trends within MPAs and to peaked at 16:00 AEST (n ¼ 9 discrete vessel events). CPA þ Ms peaked at inform compliance planning. We identifieddiscrete vessel events within 17:00 and 18:00 AEST (n ¼ 2 discrete vessel events each). The TA two Australian Marine Parks and explored diel and daily trends in vessel category was distributed evenly between 05:00, 07:00, and 17:00 AEST presence, providing a coarse spatial prediction of vessel presence within (n ¼ 1 discrete vessel event each) and TA þ Ms occurred at 08:00 and park boundaries. Through examining vessel behavior in these parks, we 17:00 AEST (n ¼ 1 discrete vessel event each). Both TBs recorded in the have laid the foundation for long-term PAM of compliance. Further SIMP NPZ took place at 08:00 AEST. more, this study provides higher resolution insight into MPA use than has been known previously. Continuous acoustic data collected from this 3.3.3. Duration of vessel presence inside the NPZs study complements the “snapshots” of activity that surface and aerial In CGMP, vessels were estimated to be within the NPZ for 12.7 h patrols have captured previously in identifying and apprehending non across the recording period (1 July – August 4, 2018). Peak vessel ac compliant park users. With on-going acoustic data collection, a pano tivity at this site, in terms of total duration, occurred on July 19, 2018 (n rama of vessel activity in MPAs can be revealed and further used for ¼ 1.8 h). Discrete CPAs averaged around 20 min in duration and cu compliance risk assessment and planning. mulative duration for CPAs peaked on 9 July and July 28, 2018 (n ¼ 0.8 The trends highlighted by acoustically monitoring CGMP and the h each). Discrete CPA þ Ms averaged less than 30 min and peaked in SIMP NPZ showcase important characteristics of the spatially, biologi duration on July 12, 2018 (n ¼ 1.4 h). Discrete TAs averaged under 45 cally, and geographically distinct MPAs. Both sites have clear differences min in duration and peaked in duration on July 19, 2018 (n ¼ 1.6 h). in the number of vessels entering the MPAs daily. Vessels traveling Discrete TA þ Ms averaged around 20 min in duration and peaked in through CGMP did not exhibit much variation throughout the recording duration on July 26, 2018 (n ¼ 0.5 h). The TB category was not recorded period in terms of the number of discrete vessel events present per day within CGMP. relative to the SIMP NPZ, for which the data displayed a comparatively Vessels within the SIMP NPZ occurred for 3.8 h across the recording higher level of variation in vessel presence. In CGMP, vessels were more period (10 August – September 12, 2018). The day with the longest likely to cross park boundaries during the morning hours (06:00–11:00 vessel duration was August 17, 2018 (n ¼ 1 h). Discrete CPAs averaged AEST). Vessels inside of the SIMP NPZ peaked in activity between 16:00 around 13 min and peaked in daily duration on August 16, 2018 (n ¼ and 18:00 AEST, possibly correlating with a nightly fishchorus observed 0.5 h). Discrete CPA þ Ms averaged under 40 min in duration and in the same hour; it has been shown previously that fishers in other lo peaked in daily duration on August 17, 2018 (n ¼ 1 h). Discrete TAs cations have exploited periods of heightened fishchorusing to maximize averaged around 3 min and peaked in daily duration on August 24, 2018 their take [49]. Vessels also spent more time within CGMP boundaries (n ¼ 0.1 h). Discrete TBs also averaged around 3 min in duration and than SIMP NPZ boundaries. The accessibility of CGMP to coastal boat

10 L.R. Kline et al. Marine Policy 120 (2020) 104138 ramps, compared with the SIMP NPZ, is a possible driver for this dif TL modelling based on the patrol boats Watonga and Peter Angel and ference. Acoustic patterns identified in the data also draw similarities revealed important characteristics, such as sound attenuation and between the sites. Thursday, for instance, was the most common day in spreading, about each site’s respective acoustic properties at the time which vessels were inside MPA boundaries while Fridays and Sundays the patrol tracks were made. The equations suggest that TL occurred at a contained relatively low numbers of discrete vessel events in both sites. faster rate at close ranges in CGMP; however, TL at both CGMP and the A possible reason for this is that Thursdays are close to the cessation of SIMP NPZ tended to converge as a vessel moved further away from the the Australian workweek; persons traveling from outside the area (e.g. hydrophone. Distinctions between the environment and the engines of from capital cities) may choose to take an extended weekend due to the the patrol boats impact the extent of TL and caused differences in the distance travelled and wish to capitalize on opportune fishing times generated equations. The engine on the Watonga emitted higher fre upon arriving on a Thursday evening (L. Beckton, personal communi quency sounds than the Peter Angel, for instance; this would have cation, August 1, 2019). Coupling acoustic evaluation of vessel visitation resulted in the calculated TL values differing between the equations. It is trends with local knowledge regarding human dimensions of MPA use important to note that the TL equations utilized in this study were has the potential to greatly refine patrolling efforts in the future. simplifications of the passive sonar equation and were used predomi Although no clear short-term trends were statistically discernible in nantly to supply estimates of whether vessels were inside of the MPAs. vessel presence over the month of recording at these locations, the data This did not require precise estimates of sound attenuation and suggest that compliance efficiency may benefit from altering patrol spreading values, and other variables that are occasionally included in patterns to reflect patterns of MPA use noted in the data. Patrol quotas transmission loss modelling – such as mode-stripping [53]– were not are set per fiscalyear, subject to funding and risk, and the timing of these used for this analysis. Similarly, varying vessel speeds were not patrols aims to maximize vessel detection (e.g., by targeting holidays, considered. These equations are also applicable only to limited time weekends, lunar cycles, early mornings and evenings) in response to frames and to each respective site; differences in ocean temperature, intelligence reports. Early data suggests that in CGMP, a regular and bathymetry, and other parameters across time and space influence the more targeted patrol schedule incorporating weekends (especially Sat degree of transmission loss. Future data collection and analysis will be urdays), between the hours of 06:00 and 11:00 AEST, could increase needed to monitor vessel presence over the long-term and provide es detection of illegal activities and deter noncompliance. At the SIMP NPZ, timates of vessel presence for different seasons as well as new study sites. the data indicates that weekday patrols, prioritizing Thursdays, between Estimates of range and size class were derived for motorized vessels the hours of 15:00 and 18:00 AEST could optimize detection rates. transiting and maneuvering in CGMP and the SIMP NPZ. However, not Although further data collection is needed for both sites to examine all vessels have discernible acoustic signatures; thus, all possible vessel vessel patterns over longer time periods and statistically determine types are not represented in these analyses. Quiet vessels without en trends, these preliminary patterns contain promising information for gines are largely undetectable through PAM, although it is likely that improving compliance activities and achieving compliance outcomes. most illegal fishing activity occurring in Australian Marine Parks is Given changes made to patrol patterns based on this preliminary data, taking place on motorized vessels rather than more covert, silent future studies may assess the effectiveness of recommended changes and watercraft such as sailboats, catamarans, and canoes. Even so, it is confirmthe validity of using PAM in compliance planning. Additionally, possible that these vessel types are traversing through MPAs and studies covering a longer period may be able to address the influenceof participating in noncompliant activities, and our existing methods may public holidays and fishing seasons on vessel pattern shifts or under represent vessel activity. Future studies may utilize high- disruptions. resolution, remotely sensed images in order to visually identify acous In addition to analyzing daily and diel trends in vessel presence, this tically undetectable vessels that may be in MPAs. It is also important to study designated distance categories for acoustically recorded vessels in note that our modelling choices may under represent the number of order to aid park managers attempting to understand the habits of MPA discrete vessel events that passed within MPA boundaries during the users. Our analysis of vessels determined to be inside of CGMP or the study periods. Vessels that were unable to be analyzed for peak fre SIMP NPZ generally suggests that most vessels recorded were likely to be quency and RL due to interfering biological or mooring sounds were in compliance, meaning they fell into the CPA, TA, or TB categories and eliminated from analysis. Some of these vessels had strong enough did not acoustically show deviation from transiting. An important caveat acoustic signatures to be suspected as being inside of the park. The to this is that different mechanisms of fishing require different types of majority of omitted discrete vessel events, however, were those that had maneuvers; a vessel that is trolling, for instance, may not display a acoustic signatures less intense than ambient noise levels. These vessels maneuver as it continuously drags fishing line through the water. Ves are suspected to have been outside of the MPA regardless of whether sels within CGMP had a higher rate of maneuvering (33% of vessels they were able to be analyzed by the MATLAB scripts. Thus, any un inside the park) than within the SIMP NPZ (21% of vessels inside the derrepresentation from data processing is more likely to stem from the park), likely a result of the accessibility of CGMP to boaters compared infrequent times where biological sounds were of greater amplitude with the SIMP NPZ. When compared with repeat visitors, many firsttime than vessel noise. Finally, the threshold selected for determining visitors to MPAs are more likely to be unaware of zoning and other rules whether a vessel was inside or outside of park boundaries may over [50]. The relative accessibility of CGMP to the coast therefore may lead estimate the number of vessels determined to be inside of the park. At a to a greater number of transient visitors who are unaware of zoning rules 75% probability threshold, the possibility for a vessel to be determined and are more likely to be noncompliant. Other studies suggest that as inside by chance is smaller than at a lower threshold (e.g. a 50% where recreational fishersare aware of zoning rules, perceptions about probability). However, there is a chance that vessel presence is being the likelihood of detection and severity of penalties for noncompliance overestimated at the 75% margin. A 90% confidence threshold, for can influence decisions to fish illegally [51,52]. This may indicate a instance, would decrease the number of vessels identified as being difference in fisher perception of patrol frequency or enforcement within the park. severity between the two sites that may not have been noted through Although this current approach requires analyses of archival data, it manned surface and aerial patrolling. Acoustic analyses of vessel dis allows for information to be gathered over long time periods in remote tance categories provide unique insight for managers to understand areas that are otherwise difficult to access and monitor. Real-time anthropogenic influences and trends behind noncompliance. acoustic systems are in use globally and could potentially be adapted Empirical transmission loss equations developed for each of the two for the purpose or real-time reporting of vessel presence [7]. Current study sites provided estimates of vessel range given the assumption that applications of real-time acoustic monitoring include the detection of vessels traversing in Australian Marine Parks are mostly small and me endangered marine mammals [54–56], alerting mariners to their prox dium in size. These equations were customizable to each MPA through imity and requiring them to reduce their speed so as to minimize the risk

11 L.R. Kline et al. Marine Policy 120 (2020) 104138

of vessel strike7,.8 Real-time acoustic monitoring has also been utilized Supervision, Project administration, Funding acquisition. to alert park rangers to criminal activities with characteristic acoustic signatures, such as fish bombing blasts [34]. However, to develop a Acknowledgements real-time vessel reporting system for use in Australian Marine Parks, a better understanding is needed of what noncompliant vessel behavior This research was funded by the Director of National Parks, sounds like in order to develop reliable and suitable triggers. Australia. The authors would like to thank Alyssa Scott and Jessica To our knowledge, this is the firstscientific study of its kind that has McCordic for providing comments on an earlier version of this manu used a single recording unit to estimate the range and size class of vessels script as well as Genevieve Davis for providing support and critique on with unknown parameters as a monitoring tool for compliance. Prior mapping and figures.Thanks also go to New South Wales Department of research on acoustically monitoring vessels within MPAs has focused on Primary Industries staff at Coffs Harbour and Port Macquarie for their utilizing three or more hydrophones or an arrayed network to detect and assistance in the deployment and retrieval of acoustic recorders. locate vessels (however, see Ref. [46]). One drawback of using fewer recording units is the inability to determine the precise location of un Appendix A. Supplementary data known vessels through triangulation, a process that requires three or more recorders to pinpoint location. However, singular hydrophones Supplementary data to this article can be found online at https://doi. have been hypothesized to be adequate for determining the unique org/10.1016/j.marpol.2020.104138. acoustic signature and fundamental frequency of individual vessels [57], and our research supports this claim. This resolution of data is References sufficient for compliance decision-making and enforcement activities

without requiring precise locations for unknown vessels. Using singular [1] B.C. O’Leary, N.C. Ban, M. Fernandez, A.M. Friedlander, P. García-Borboroglu, hydrophones for analyzing vessel presence in MPAs offers a rapid, Y. Golbuu, P. Guidetti, J.M. Harris, J.P. Hawkins, T. Langlois, D.J. McCauley, E. low-cost, easily deployed mechanism that is simple to set up, recover, K. Pikitch, R.H. Richmond, C.M. Roberts, Addressing criticisms of large-scale – and analyze when compared to arrayed networks. These benefits are marine protected areas, BioScience 68 (2018) 359 370, https://doi.org/10.1093/ biosci/biy021. especially useful in characterizing ill-explored domains such as remote [2] S. Mangubhai, M. Saleh, Suprayitno, A. Muljadi, Purwanto, K.L. Rhodes, MPAs. In such cases where a high-resolution understanding of vessel K. Tjandra, Do Not Stop: the importance of seamless monitoring and enforcement – activity would take years to compile through manned surface and aerial in an Indonesian marine protected area, J. Mar. Biol. 2011 (2011) 1 11, https:// doi.org/10.1155/2011/501465. patrols, singular hydrophones offer the ability to gather data in a rela [3] N.J. Bennett, P. Dearden, From measuring outcomes to providing inputs: tively short period of time and examine short- and long-term patterns. governance, management, and local development for more effective marine protected areas, Mar. Pol. 50 (2014) 96–110, https://doi.org/10.1016/j. marpol.2014.05.005. 5. Conclusion [4] L.J. McCook, T. Ayling, M. Cappo, J.H. Choat, R.D. Evans, D.M.D. Freitas, M. Heupel, T.P. Hughes, G.P. Jones, B. Mapstone, H. Marsh, M. Mills, F.J. Molloy, Many MPAs are susceptible to illegal activities, such as fishing, in C.R. Pitcher, R.L. Pressey, G.R. Russ, S. Sutton, H. Sweatman, R. Tobin, D. R. Wachenfeld, D.H. Williamson, Adaptive management of the Great Barrier Reef: a violation of regulations designed to protect biodiversity and the natural, globally significant demonstration of the benefits of networks of marine reserves, cultural, and heritage values. Although large commercial vessels can be Proc. Natl. Acad. Sci. 107 (2010) 18278–18285, https://doi.org/10.1073/ monitored through AIS or VMS technology, recreational and smaller pnas.0909335107. [5] B. Kelaher, A. Page, M. Dasey, D. Maguire, A. Read, A. Jordan, M. Coleman, vessels are difficult to monitor using traditional boat or aerial patrols. Strengthened enforcement enhances marine sanctuary performance, Glob. Ecol. Using PAM as a surveillance tool offers a unique insight into the use of Conserv. 26 (2015), https://doi.org/10.1016/j.gecco.2015.02.002. � MPAs by smaller vessels and can provide long-term, baseline records of [6] P. Guidetti, M. Milazzo, S. Bussotti, A. Molinari, M. Murenu, A. Pais, N. Spano, R. Balzano, T. Agardy, F. Boero, G. Carrada, R. Cattaneo-Vietti, A. Cau, vessel activity without the need for manned patrols to be present on the R. Chemello, S. Greco, A. Manganaro, G. Notarbartolo di Sciara, G. Russo, water for long periods of time. Additionally, PAM provides park man L. Tunesi, Italian marine reserve effectiveness: does enforcement matter? Biol. agers with context for assessing the prevalence of noncompliance. This Conserv. 141 (2008) 699–709, https://doi.org/10.1016/j.biocon.2007.12.013. study demonstrates that PAM is a feasible and effective tool for park [7] A.D. Read, C. McBride, T. Spencer, P. Anderson, J. Smith, T. Costa, S. Clementz, A. Dowd, Preventing noncompliance in marine protected areas using a real-time managers through combining acoustic data with transmission loss alert system, Ocean Coast Manag. 173 (2019) 123–130, https://doi.org/10.1016/j. modelling to predict the likelihood that a vessel is inside of MPA ocecoaman.2019.03.001. boundaries. With this tool, park managers can optimize patrol efforts to [8] C.J. Brown, B. Parker, G.N. Ahmadia, R. Ardiwijaya, E.T. Game Purwanto, The cost of enforcing a marine protected area to achieve ecological targets for the recovery deter noncompliance and ensure the protection of MPA values. of fish biomass, Biol. Conserv. 227 (2018) 259–265, https://doi.org/10.1016/j. biocon.2018.09.021. CRediT authorship contribution statement [9] A.D. Read, R.J. West, B.P. Kelaher, Using compliance data to improve marine protected area management, Mar. Pol. 60 (2015) 119–127, https://doi.org/ 10.1016/j.marpol.2015.06.008. Logan R. Kline: Methodology, Investigation, Writing - original draft, [10] J. Lubchenco, S.R. Palumbi, S.D. Gaines, S. Andelman, Plugging a hole in the – Writing - review & editing, Visualization, Data curation. Annamaria I. ocean: the emerging science of marine reserves, Ecol. Appl. 13 (2003) S3 S7, https://doi.org/10.1890/1051-0761(2003)013[0003:PAHITO]2.0.CO;2. DeAngelis: Methodology, Software, Validation, Investigation, Writing - [11] K.L.F. Davis, G.R. Russ, D.H. Williamson, R.D. Evans, Surveillance and poaching on review & editing, Data curation, Supervision. Candace McBride: inshore reefs of the Great Barrier Reef Marine Park, Coast. Manag. 32 (2004) Conceptualization, Resources, Writing - review & editing, Project 373–387, https://doi.org/10.1080/08920750490487223. [12] Director of National Parks, Temperate East Marine Parks Network Management administration. Giverny G. Rodgers: Conceptualization, Resources, Plan 2018, Attorney General’s Department, 2018. http://www.legislation.gov. Writing - review & editing, Project administration. Timothy J. Rowell: au/Details/F2018L00321/Html/Volume_1. (Accessed 25 June 2020). Methodology, Software, Validation, Writing - review & editing. Jeremy [13] R.G. Smith, K. Anderson, Understanding non-compliance in the marine environment, in: Trends & Issues in Crime and Criminal Justice, Australian Smith: Conceptualization, Investigation, Resources. Jenni A. Stanley: Institute of Criminology, Canberra, 2004. https://www.aic.gov.au/publications/ta Conceptualization, Methodology, Resources, Supervision, Funding ndi/tandi275. acquisition. Andrew D. Read: Conceptualization, Writing - review & [14] B.J. Bergseth, G.R. Russ, J.E. Cinner, Measuring and monitoring compliance in no- take marine reserves, Fish Fish. 16 (2015) 240–258, https://doi.org/10.1111/ editing, Supervision, Project administration. Sofie M. Van Parijs: faf.12051. Conceptualization, Methodology, Resources, Writing - review & editing, [15] D.J. McCauley, P. Woods, B. Sullivan, B. Bergman, C. Jablonicky, A. Roan, M. Hirshfield, K. Boerder, B. Worm, Ending hide and seek at sea, Science 351 (2016) 1148–1150, https://doi.org/10.1126/science.aad5686. [16] D.C. Dunn, C. Jablonicky, G.O. Crespo, D.J. McCauley, D.A. Kroodsma, K. Boerder, 7 http://robots4whales.whoi.edu/. K.M. Gjerde, P.N. Halpin, Empowering high seas governance with satellite vessel 8 listenforwhales.org. tracking data, Fish Fish. 19 (2018) 729–739, https://doi.org/10.1111/faf.12285.

12 L.R. Kline et al. Marine Policy 120 (2020) 104138

[17] G.I. Lambert, S. Jennings, J.G. Hiddink, N.T. Hintzen, H. Hinz, M.J. Kaiser, L. Cocos Island, Costa Rica, Oryx 50 (2016) 18–26, https://doi.org/10.1017/ G. Murray, Implications of using alternative methods of vessel monitoring system S0030605314000337. (VMS) data analysis to describe fishing activities and impacts, ICES J. Mar. Sci. 69 [36] A. Pollara, A. Sutin, H. Salloum, Modulation of high frequency noise by engine (2012) 682–693, https://doi.org/10.1093/icesjms/fss018. tones of small boats, J. Acoust. Soc. Am. 142 (2017) EL30–EL34, https://doi.org/ [18] R.B. Cabral, J. Mayorga, M. Clemence, J. Lynham, S. Koeshendrajana, 10.1121/1.4991345. U. Muawanah, D. Nugroho, Z. Anna, Mira, A. Ghofar, N. Zulbainarni, S.D. Gaines, [37] P. Simard, K.R. Wall, D.A. Mann, C.C. Wall, C.D. Stallings, Quantification of boat C. Costello, Rapid and lasting gains from solving illegal fishing, Nat. Ecol. Evol. 2 visitation rates at artificial and natural reefs in the Eastern Gulf of Mexico using (2018) 650–658, https://doi.org/10.1038/s41559-018-0499-1. acoustic recorders, PloS One 11 (2016), e0160695, https://doi.org/10.1371/ [19] J.E. Wingfield, M. O’Brien, V. Lyubchich, J.J. Roberts, P.N. Halpin, A.N. Rice, journal.pone.0160695. H. Bailey, Year-round spatiotemporal distribution of harbour porpoises within and [38] C. Erbe, S. Liong, M.W. Koessler, A.J. Duncan, T. Gourlay, Underwater sound of around the Maryland wind energy area, PloS One 12 (2017), e0176653, https:// rigid-hulled inflatable boats, J. Acoust. Soc. Am. 139 (2016) EL223–EL227, doi.org/10.1371/journal.pone.0176653. https://doi.org/10.1121/1.4954411. [20] M. Simon, H. Nuuttila, M.M. Reyes-Zamudio, F. Ugarte, U. Verfub, P.G.H. Evans, [39] M.O. Lammers, R.E. Brainard, W.W.L. Au, T.A. Mooney, K.B. Wong, An ecological Passive acoustic monitoring of bottlenose dolphin and harbour porpoise, in acoustic recorder (EAR) for long-term monitoring of biological and anthropogenic Cardigan Bay, Wales, with implications for habitat use and partitioning, J. Mar. sounds on coral reefs and other marine habitats, J. Acoust. Soc. Am. 123 (2008) Biol. Assoc. U. K. 90 (2010) 1539–1545, https://doi.org/10.1017/ 1720–1728, https://doi.org/10.1121/1.2836780. S0025315409991226. [40] L.M. Zurk, H.H. Ou, S. Schecklman, A. Lutwak, Acoustic monitoring of marine [21] G.E. Davis, M.F. Baumgartner, J.M. Bonnell, J. Bell, C. Berchok, J. Bort Thornton, conservation areas, Mar. Technol. Soc. J. 48 (2014) 21–32, https://doi.org/ S. Brault, G. Buchanan, R.A. Charif, D. Cholewiak, C.W. Clark, P. Corkeron, 10.4031/MTSJ.48.6.7. J. Delarue, K. Dudzinski, L. Hatch, J. Hildebrand, L. Hodge, H. Klinck, S. Kraus, [41] L. Hatch, C. Clark, R. Merrick, S. Van Parijs, D. Ponirakis, K. Schwehr, B. Martin, D.K. Mellinger, H. Moors-Murphy, S. Nieukirk, D.P. Nowacek, S. Parks, M. Thompson, D. Wiley, Characterizing the relative contributions of large vessels � A.J. Read, A.N. Rice, D. Risch, A. Sirovi�c, M. Soldevilla, K. Stafford, J.E. Stanistreet, to total ocean noise fields: a case study using the Gerry E. StuddsStellwagen Bank E. Summers, S. Todd, A. Warde, S.M. Van Parijs, Long-term passive acoustic National Marine Sanctuary, Environ. Manag. 42 (2008) 735–752, https://doi.org/ recordings track the changing distribution of North Atlantic right whales 10.1007/s00267-008-9169-4. (Eubalaena glacialis) from 2004 to 2014, Sci. Rep. 7 (2017) 13460, https://doi. [42] M.B. Kaplan, T.A. Mooney, M.O. Lammers, E. Zang, Temporal and spatial org/10.1038/s41598-017-13359-3. variability in vessel noise on tropical coral reefs, Proc. Meet. Acoust. 27 (2016), [22] D. Risch, M. Castellote, C.W. Clark, G.E. Davis, P.J. Dugan, L.E. Hodge, A. Kumar, 005002, https://doi.org/10.1121/2.0000250. K. Lucke, D.K. Mellinger, S.L. Nieukirk, C.M. Popescu, C. Ramp, A.J. Read, A. [43] S.M. Haver, J. Gedamke, L.T. Hatch, R.P. Dziak, S. Van Parijs, M.F. McKenna, N. Rice, M.A. Silva, U. Siebert, K.M. Stafford, H. Verdaat, S.M. Van Parijs, Seasonal J. Barlow, C. Berchok, E. DiDonato, B. Hanson, J. Haxel, M. Holt, D. Lipski, migrations of North Atlantic minke whales: novel insights from large-scale passive H. Matsumoto, C. Meinig, D.K. Mellinger, S.E. Moore, E.M. Oleson, M.S. Soldevilla, acoustic monitoring networks, Mov. Ecol. 2 (2014) 24, https://doi.org/10.1186/ H. Klinck, Monitoring long-term soundscape trends in U.S. Waters: the NOAA/NPS s40462-014-0024-3. ocean noise reference station network, Mar. Pol. 90 (2018) 6–13, https://doi.org/ [23] H. Heenehan, J.E. Stanistreet, P.J. Corkeron, L. Bouveret, J. Chalifour, G.E. Davis, 10.1016/j.marpol.2018.01.023. A. Henriquez, J.J. Kiszka, L. Kline, C. Reed, O. Shamir-Reynoso, F. V�edie, W. De [44] L. Hermannsen, L. Mikkelsen, J. Tougaard, K. Beedholm, M. Johnson, P.T. Madsen, Wolf, P. Hoetjes, S.M. Van Parijs, Caribbean sea soundscapes: monitoring Recreational vessels without Automatic Identification System (AIS) dominate humpback whales, biological sounds, geological events, and anthropogenic anthropogenic noise contributions to a shallow water soundscape, Sci. Rep. 9 impacts of vessel noise, Front. Mar. Sci. 6 (2019), https://doi.org/10.3389/ (2019) 10. fmars.2019.00347. [45] J. Barlow, Global Ocean Sound Speed ProfileLibrary (GOSSPL), an Rdata Resource [24] J.A. Stanley, S.M. Van Parijs, L.T. Hatch, Underwater sound from vessel traffic for Studies of Ocean Sound Propagation, U.S. Department of Commerce, NOAA reduces the effective communication range in Atlantic cod and haddock, Sci. Rep. 7 Technical Memorandum NMFS-SWFSC-612, 2019. (2017) 14633, https://doi.org/10.1038/s41598-017-14743-9. [46] M.F. McKenna, D. Ross, S.M. Wiggins, J.A. Hildebrand, Underwater radiated noise [25] I.R. Urazghildiiev, S.M. Van Parijs, Automatic grunt detector and recognizer for from modern commercial ships, J. Acoust. Soc. Am. 131 (2012) 92–103, https:// Atlantic cod (Gadus morhua), J. Acoust. Soc. Am. 139 (2016) 2532–2540, https:// doi.org/10.1121/1.3664100. doi.org/10.1121/1.4948569. [47] R.J. Urick, Principles of Underwater Sound, third ed., Peninsula Publishing, Los [26] K.M. Hernandez, D. Risch, D.M. Cholewiak, M.J. Dean, L.T. Hatch, W.S. Hoffman, Altos, Calif, 2013. Softcover edition. A.N. Rice, D. Zemeckis, S.M. Van Parijs, Acoustic monitoring of Atlantic cod [48] M.L. Barlett, G.R. Wilson, Characteristics of small boat acoustic signatures, (Gadus morhua) in Massachusetts Bay: implications for management and J. Acoust. Soc. Am. 112 (2002), https://doi.org/10.1121/1.4778778, 2221–2221. conservation, ICES J. Mar. Sci. 70 (2013) 628–635, https://doi.org/10.1093/ [49] T.J. Rowell, D.A. Demer, O. Aburto-Oropeza, J.J. Cota-Nieto, J.R. Hyde, B. icesjms/fst003. E. Erisman, Estimating fish abundance at spawning aggregations from courtship [27] B.E. Erisman, T.J. Rowell, A sound worth saving: acoustic characteristics of a sound levels, Sci. Rep. 7 (2017) 3340, https://doi.org/10.1038/s41598-017- massive fishspawning aggregation, Biol. Lett. 13 (2017), https://doi.org/10.1098/ 03383-8. rsbl.2017.0656. [50] C.B. Smallwood, L.E. Beckley, Spatial distribution and zoning compliance of [28] D.R. Zemeckis, M.J. Dean, A.I. DeAngelis, S.M. Van Parijs, W.S. Hoffman, M. recreational fishing in Ningaloo Marine Park, north-western Australia, Fish. Res. F. Baumgartner, L.T. Hatch, S.X. Cadrin, C.H. McGuire, Identifying the distribution 125–126 (2012) 40–50, https://doi.org/10.1016/j.fishres.2012.01.019. of Atlantic cod spawning using multiple fixed and glider-mounted acoustic [51] A. Arias, S. Sutton, Understanding recreational Fishers’ compliance with No-take technologies, ICES J. Mar. Sci. 76 (2019) 1610–1625, https://doi.org/10.1093/ zones in the Great Barrier Reef Marine Park, Ecol. Soc. 18 (2013), https://doi.org/ icesjms/fsz064. 10.5751/ES-05872-180418. [29] J. Butler, J.A. Stanley, M.J. Butler, Underwater soundscapes in near-shore tropical [52] B.J. Bergseth, D.H. Williamson, G.R. Russ, S.G. Sutton, J.E. Cinner, habitats and the effects of environmental degradation and habitat restoration, A social–ecological approach to assessing and managing poaching by recreational J. Exp. Mar. Biol. Ecol. 479 (2016) 89–96, https://doi.org/10.1016/j. Fishers, Front. Ecol. Environ. 15 (2017) 67–73, https://doi.org/10.1002/fee.1457. jembe.2016.03.006. [53] M.A. Ainslie, C.A.F. de Jong, H.S. Dol, C. Marasini, G. Blacquiere, Assessment of [30] S.N. Patek, L.E. Shipp, E.R. Staaterman, The acoustics and acoustic behavior of the Natural and Anthropogenic Sound Sources and Acoustic Propagation in the North California spiny lobster (Panulirus interruptus), J. Acoust. Soc. Am. 125 (2009) Sea, 2009. http://resolver.tudelft.nl/uuid:de8b2c98-635f-4988-89bf-d8d 3434–3443, https://doi.org/10.1121/1.3097760. 75202b3b3. (Accessed 25 June 2020). [31] A. Monczak, C. Mueller, M.E. Miller, Y. Ji, S.A. Borgianini, E.W. Montie, Sound [54] T.M. Aide, C. Corrada-Bravo, M. Campos-Cerqueira, C. Milan, G. Vega, R. Alvarez, patterns of snapping shrimp, fish, and dolphins in an estuarine soundscape of the Real-time bioacoustics monitoring and automated species identification, PeerJ. 1 southeastern USA, Mar. Ecol. Prog. Ser. 609 (2019) 49–68, https://doi.org/ (2013) e103, https://doi.org/10.7717/peerj.103. 10.3354/meps12813. [55] M.F. Baumgartner, D.M. Fratantoni, T.P. Hurst, M.W. Brown, T.V.N. Cole, S.M. Van [32] E. Staaterman, C. Clark, A. Gallagher, M. deVries, T. Claverie, S. Patek, Rumbling Parijs, M. Johnson, Real-time reporting of baleen whale passive acoustic detections in the benthos: acoustic ecology of the California mantis shrimp Hemisquilla from ocean gliders, J. Acoust. Soc. Am. 134 (2013) 1814–1823, https://doi.org/ californiensis, Aquat. Biol. 13 (2011) 97–105, https://doi.org/10.3354/ab00361. 10.1121/1.4816406. [33] C. Astaras, J.M. Linder, P. Wrege, R.D. Orume, D.W. Macdonald, Passive acoustic [56] M.F. Baumgartner, J. Bonnell, S.M.V. Parijs, P.J. Corkeron, C. Hotchkin, K. Ball, L.- monitoring as a law enforcement tool for Afrotropical rainforests, Front. Ecol. P. Pelletier, J. Partan, D. Peters, J. Kemp, J. Pietro, K. Newhall, A. Stokes, T.V. Environ. 15 (2017) 233–234, https://doi.org/10.1002/fee.1495. N. Cole, E. Quintana, S.D. Kraus, Persistent near real-time passive acoustic [34] R. Showen, C. Dunson, G.H. Woodman, S. Christopher, T. Lim, S.C. Wilson, monitoring for baleen whales from a moored buoy: system description and Locating fish bomb blasts in real-time using a networked acoustic system, Mar. evaluation, Methods Ecol. Evol. 10 (2019) 1476–1489, https://doi.org/10.1111/ Pollut. Bull. 128 (2018) 496–507, https://doi.org/10.1016/j. 2041-210X.13244. marpolbul.2018.01.029. [57] E. Sørensen, H.H. Ou, L.M. Zurk, M. Siderius, Passive acoustic sensing for detection � [35] A. Arias, R.L. Pressey, R.E. Jones, J.G. Alvarez-Romero, J.E. Cinner, Optimizing of small vessels, in: Oceans 2010 MTSIEEE Seattle, Institute of Electrical and enforcement and compliance in offshore marine protected areas: a case study from Electronics Engineers, Seattle, WA, 2010, pp. 1–8, https://doi.org/10.1109/ OCEANS.2010.5664542.