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The inland waters around southern Vancouver Island and northern Washington State, known as the Salish Sea, host critical habitat for endangered southern resident killer whales (SRKW). This is, however, a highly traversed area, with approaches to industrial ports and coastal cities, international shipping lanes, ferry routes, and considerable recreational vessel traffic. Vessel noise is a key threat to SRKW prosperity, and so conservation measures directed to mitigate its effects have been explored annually since 2017. Here, we describe trials undertaken in 2020, which included spatially limited slowdown zones, exclusion areas as Interim whale Sanctuary Zones (ISZs), and a lateral displacement of tug transits to increase the distance between their route and SRKW foraging areas. To assess each of the measures we first considered the level of mariner participation using data from the Automated Identification Systems (AIS), mandatory for commercial vessels. Knowing this, the changes in soundscape were examined, focused on impacts on broadband (10 Hz to 100 kHz) ambient noise and the frequencies used by SRKW for communication (500 Hz to 15 kHz) and echolocation (15 to 100 kHz). A control period of two-months prior to trial initiation was used to quantify the changes. High levels (> 80%) of compliance were found for each measure, except ISZs, where observance was low. Median reduction in speeds ranged from 0.2-3.5 knots. Resulting sound reductions were most notable in the lower frequencies, although reductions were also recorded in SRKW pertinent ranges. Tug displacement also reduced ambient noise in these frequencies, despite making up a small portion of the overall traffic. The management trials were effective in reducing potential impacts singularly and in concert. Greater awareness and stakeholder engagement may increase compliance and, therefore, the efficacy of measures in the future.

Chronic low-frequency noise from commercial shipping is a worldwide threat to marine animals that rely on sound for essential life functions. Although the U.S. National Oceanic and Atmospheric Administration recognizes the potential negative impacts of shipping noise in marine environments, there are currently no standard metrics to monitor and quantify shipping noise in U.S. marine waters. However, one-third octave band acoustic measurements centered at 63 and 125 Hz are used as international (European Union Marine Strategy Framework Directive) indicators for underwater ambient noise levels driven by shipping activity. We apply these metrics to passive acoustic monitoring data collected over 20 months in 2016-2017 at five dispersed sites throughout the U.S. Exclusive Economic Zone: Alaskan Arctic, Hawaii, Gulf of Mexico, Northeast Canyons and Seamounts Marine National Monument (Northwest Atlantic), and Cordell Bank National Marine Sanctuary (Northeast Pacific). To verify the relationship between shipping activity and underwater sound levels, vessel movement data from the Automatic Identification System (AIS) were paired to each passive acoustic monitoring site. Daily average sound levels were consistently near to or higher than 100 dB re 1 μPa in both the 63 and 125 Hz one-third octave bands at sites with high levels of shipping traffic (Gulf of Mexico, Northeast Canyons and Seamounts, and Cordell Bank). Where cargo vessels were less common (the Arctic and Hawaii), daily average sound levels were comparatively lower. Specifically, sound levels were ∼20 dB lower year-round in Hawaii and ∼10-20 dB lower in the Alaskan Arctic, depending on the season. Although these band-level measurements can only generally facilitate differentiation of sound sources, these results demonstrate that international acoustic indicators of commercial shipping can be applied to data collected in U.S. waters as a unified metric to approximate the influence of shipping as a driver of

Seamounts are prominent features of the seafloor that are often located in Areas Beyond National Jurisdiction (ABNJs). Whilst comprehensive biological information is lacking on most of these features, they have been recognised for hosting high biodiversity across multiple trophic levels. Technological advancements have enabled greater exploitation of biological resources further offshore with increasing concern over the long-term impacts of anthropogenic activities on vulnerable distant and deep-sea habitats. Analysis of ex situ vessel tracking technologies such as Automatic Identification Systems (AIS) have enabled spatial patterns of fishing activity to be monitored over large geographical areas. In this study, analysis of fishing activity within 30 km of seamount summits at the global scale found that these features within the waters of the Pacific Island Group and the Mediterranean Sea were subject to the highest levels of longlining and trawling activities respectively. Fishing in proximity to seamounts is dominated by the flag states of Taiwan, China, Japan, South Korea and Spain. Furthermore, our results reveal that the majority of sea areas managed by many Regional Fishery Management Organisations (RFMOs) have experienced increased fishing activity at seamounts compared to areas in the same ocean basin without management. This study demonstrates how free web-accessible data can be used to gain insights into remote areas where in situ research is prohibitively expensive and logistically challenging.

Many marine megafauna taxa are tied to the sea surface for breathing which makes them vulnerable to vessel collisions. Sea turtles have developed efficient mechanisms to reduce surface time for breathing to a few seconds, but they can extend their surface periods to rest or to rewarm after diving into deep and colder waters. However, knowledge of collision occurrences is limited to data of turtles stranded along the coastline worldwide, whereas events occurring offshore go likely underestimated due to the sinking of carcasses. Here we performed a spatially explicit assessment to identify, for the first time, oceanic areas of higher exposure for sea turtles from maritime traffic in the Tyrrhenian Sea, Western Mediterranean. Satellite-tracking data were used to estimate utilization distributions of loggerhead turtles using Brownian bridge kernel density estimation. Maritime traffic density maps based on Automatic Identification System (AIS) data were extracted from open-access data layers, provided by the European Maritime Safety Agency, summarized, and used for the exposure analysis. Turtle occurrences were also investigated in response to vessel densities and seasonal patterns by fitting a generalized additive model to the data. Our results demonstrated that loggerhead turtles are potentially exposed to maritime traffic across the entire basin, especially in the easternmost part. The exposure varies among spring/summer and autumn/winter months. Highest turtle occurrences were found in regions primarily subjected to cargo, tanker, and passenger transportation. This study represents the first-ever effort to characterize the exposure of oceanic loggerhead turtles to maritime traffic and highlights oceanic areas of higher exposure where research and conservation efforts should be directed to understand the effective impact of this stressor on the species.

Monitoring marine use is essential to effective management but is extremely challenging, particularly where capacity and resources are limited. To overcome these limitations, satellite imagery has emerged as a promising tool for monitoring marine vessel activities that are difficult to observe through publicly available vessel-tracking data. However, the broader use of satellite imagery is hindered by the lack of a clear understanding of where and when it would bring novel information to existing vessel-tracking data. Here, we outline an analytical framework to (1) automatically detect marine vessels in optical satellite imagery using deep learning and (2) statistically contrast geospatial distributions of vessels with the vessel-tracking data. As a proof of concept, we applied our framework to the coastal regions of Peru, where vessels without the Automatic Information System (AIS) are prevalent. Quantifying differences in spatial information between disparate datasets-satellite imagery and vessel-tracking data-offers insight into the biases of each dataset and the potential for additional knowledge through data integration. Our study lays the foundation for understanding how satellite imagery can complement existing vessel-tracking data to improve marine oversight and due diligence.