This report and data set provides baseline data of the types of boats, the location of boats, the abundance of boats, and the activities of the people on the boats. The report is largely descriptive, including boat count and boat densities. Basic patterns of the distribution of boats by time, location, or types are described, and statistical analysis of the differences in boating patterns are provided when appropriate. Aerial surveys were conducted at altitudes between 500 and 1000 feet, at a speed of approximately 100 knots to characterize boating patterns throughout the Florida Keys National Marine Sanctuary (FKNMS).

Aerial Vessels Detection Dataset: The dataset construction involved manually collecting all aerial images of vessels using UAV drones and manually annotated into three classes 'Person', 'Ship', and ''Boat'. The aerial images were collected through manual flights above Cyprus Coasts in Limassol, Famagusta and Larnaca areas. The main purpose of this dataset is to be used for marine monitoring. Capturing footage over large areas and localizing any unwanted vessels entering an area of interest, can aid in localizing refugees that illegally enter a country or manage marine traffic for commercial use.

The images are collected in 720p and Full HD (1080p) but are usually resized before training.

All images were manually annotated and inspected afterward with the vessels that indicate 'Person' for people detection, 'Boat' for small to medium-sized boats, and 'Ship' for large ships or commercial ships. All annotations were converted into VOC and COCO formats and initially labeled in YOLO, for training in numerous frameworks. The data collection took part in different periods.

The dataset includes a total of 10252 images of which 1024 are split for validation, 1025 for testing, and the rest 8203 for training.

    Subset
    Images
    Person
    Boat
    Ship


    Training
    8203
    219
    48550
    920


    Validation
    1024
    7
    5890
    143


    Testing
    1025
    13
    5247
    109

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

NOTE If you use this dataset in your research/publication please cite us using the following :

Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Vessels Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7076145

The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) oversees more than 250 state parks, historic sites, recreational trails, golf courses, boat launches and more, encompassing nearly 350,000 acres, that are visited by 74 million people annually. These facilities contribute to the economic vitality and quality of life of local communities and directly support New York’s tourism industry. Parks also provide a place for families and children to be active and exercise, promoting healthy lifestyles. The agency is responsible for the operation and stewardship of the state park system as well as advancing a statewide parks, historic preservation, and open space mission. The New York State Office of Parks, Recreation, and Historic Preservation operates marinas and boat launching sites across the state. For more information about boating in New York State parks, visit http://nysparks.com/recreation/boating/

This publication contains data from a two-phase 1998-1999 study in the Frank Church Wilderness which surveyed visitors about the use of motorboats and jet boats on the Salmon River. This study was done in coordination with the Western Whitewater Association (WWA) and Northwest River Runners (NRR), which are jet boat organizations based in Boise and Lewiston, ID, respectively. Information was gathered regarding visitor demographics and opinions on river use. Phase I included interviews with six leadership members of the WWA. Included in this data publication are notes from two of those interviews and excerpts from all six interviews. Phase II included sending surveys to jet boat users. Data include the results of these mailback surveys. Follow-up telephone surveys were conducted if the mailback surveys were not returned and those results have also been included.

Analysis of ‘Boat Launch Sites by State Parks or Marine Facility’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ab17cc49-40ac-442e-95c2-522c8d793008 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) oversees more than 250 state parks, historic sites, recreational trails, golf courses, boat launches and more, encompassing nearly 350,000 acres, that are visited by 74 million people annually. These facilities contribute to the economic vitality and quality of life of local communities and directly support New York’s tourism industry. Parks also provide a place for families and children to be active and exercise, promoting healthy lifestyles. The agency is responsible for the operation and stewardship of the state park system as well as advancing a statewide parks, historic preservation, and open space mission. The New York State Office of Parks, Recreation, and Historic Preservation operates marinas and boat launching sites across the state. For more information about boating in New York State parks, visit http://nysparks.com/recreation/boating/

--- Original source retains full ownership of the source dataset ---

This dataset is a list of all individuals and corporate entities that hold a valid Party and Charter Boat (P/C) license for the current calendar year and their general locations. The P/C license is issued by the Department and allows the license holder (or employees of the license holder) to carry recreational fishing passengers for hire on a registered vessel or to land fish taken by recreational fishing passengers outside the territorial waters of the state. The P/C license can be issued to a business (corporate entity) or to an individual and can be issued to both residents of New York State and non-residents. The license expires on December 31st of the year it is issued, and license holders must apply annually in order to be issued the license. License holders are required to associate a vessel with their license; if they have multiple vessels they use for party/charter trips, they must purchase a license for each vessel. This dataset includes name (either corporate or an individual person), city, county, state, county FIPS code if in New York, vessel name, and vessel registration number.

Here are a few use cases for this project:

1. "Water Sports Analytics": This use case can involve the application of the model to analyze various water sports activities in the Aggertalsperre area. By identifying different classes such as swimmer, SUP, Canoe, and SailBoat, insights can be gathered about the popularity or frequency of different sports.

2. "Bird Migration Study": Given the ability of the model to identify birds, it can be put to use for avian research, observing bird species around the Aggertalsperre region, and possibly tracking migration patterns.

3. "Rescue Operations": This model could have use cases in safety and rescue operations, identifying people in distress in the water by recognizing swimmers or people, and facilitating a timely rescue.

4. "Marine Traffic Management": The ability of the model to distinguish between different kinds of watercraft (like boats, sailboats, canoes, etc.) makes it useful for marine traffic analysis and management around the Aggertalsperre locale.

5. "Recreational Monitoring": The model can support recreational site managers by providing insights into activities taking place in the reservoir, like paddle boating and canoeing, enabling better visitor management and planning.

This dataset is an extract and collation of 4 months of data from the Craft Tracking System run by the Australian Maritime Safety Authority (AMSA). This dataset shows the location of cargo ships, fishing vessels, passenger ships, pilot vessels, sailing boats, tankers and other vessel types at 1 hour intervals. The Craft Tracking System (CTS) and Mariweb are AMSA’s vessel traffic databases. They collect vessel traffic data from a variety of sources, including terrestrial and satellite shipborne Automatic Identification System (AIS) data sources. This dataset has been built from AIS data extracted from CTS, and it contains vessel traffic data for January - April 2023. The dataset covers the extents of Australia’s Search and Rescue Region. Each point within the dataset represents a vessel position report and is spatially and temporally defined by geographic coordinates and a Universal Time Coordinate (UTC) timestamp respectively. This dataset is a derivative of the monthly Craft Tracking System data available from https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData. As such this record is not authoritative about the source data. If you have any queries about the Craft Tracking System data please contact AMSA.
Description of the data: This data shows a high volume of cargo ships and tankers traveling between international destinations and the ports of Australia, as well as significant cargo traffic between domestic ports. These vessels tend to travel in straight lines along designated shipping lanes, or along paths that maximize their efficiency on route to their destination. Fishing activities are prominent in international waters, particularly in the Indian Ocean, Coral Sea, and Arafura Seas. The tracking of fishing vessels drops dramatically at the boundary of the Australian Exclusive Economic Zone (EEZ). Most domestic fishing activities appear to be closer to the Australian coast, often concentrating on the edge of the continental shelf. However, the data does not specifically indicate whether the vessels are domestic or international. Western Australia exhibits a great deal of vessel activity associated with the oil and gas industry. Each of these platforms is serviced by tugboats and tankers. At large ports, dozens of cargo ships wait in grid patterns to transit into the port. Shipping traffic in most of the Gulf of Carpentaria is relatively sparse, as the majority of cargo vessels travel from Torres Strait west into the Arafura Sea, bypassing the gulf. However, there is a noticeable concentration of fishing activity along the coast around Karumba and the Wellesley Islands, presumably associated with the prawning industry. Along the Queensland coastline, vessel traffic is dominated by cargo ships, which travel in designated shipping areas between the Great Barrier Reef and the mainland. There are three passages through the reef off Hay Point (Hydrographers Passage), north of Townsville (Palm Passage), and off Cairns (Grafton Passage). The Great Barrier Reef (GBR) region is frequented by pleasure crafts, sailing vessels, and passenger ships. Pleasure crafts mainly seem to visit the islands and outer reefs, while sailing vessels tend to stay within the GBR lagoon, traversing its length. Passenger ships ferry people to popular reef destinations such as reefs off the Whitsundays, Cairns, and Port Douglas, as well as Heron Island and Lady Musgrave Island. Many large passenger ships, presumably cruise vessels, travel between major ports and international destinations. These ships tend to travel 20 km further offshore than the majority of sailing boats. eAtlas Processing: The following is the processing that was applied to create this derivative dataset. This processing was functionally just a collation of 4 months of data, and a file format change (to GeoPackage) and a trimming of the length of the text attributes (which should not affect their values). Four months of data was used as this was the maximum practical limit of the rendering performance of QGIS and GeoServer.

The monthly CTS data was downloaded from https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData and unzipped. This data was then loaded into QGIS.

The Vector / Data Management Tools / Merge Vector Layers... tool was used to combine the 4 months of data: Input layers: cts_srr_04_2023_pt, cts_srr_03_2023_pt, cts_srr_02_2023_pt, cts_srr_01_2023_pt Save to GeoPackage: AU_AMSA_Craft-tracking-system_Jan-Apr-2023 Layername: AU_AMSA_Craft-tracking-system_Jan-Apr-2023

To reduce the size of the dataset the text attributes were trimmed to the length needed to store the attribute data. Processing Toolbox > Vector table > Refactor fields Input layer: AU_AMSA_Craft-tracking-sytem_Jan-Apr-2023 Remove attributes: layer, path (these were created by the Merge Vector Layers tool) Change: Source Expression, Original Length, New Length TYPE, 254, 80 SUBTYPE, 254, 20 TIMESTAMP, 50, 25 Refactored: AU_AMSA_Craft-tracking-system_Jan-Apr-2023_Trim.gpkg Layer name: au_amsa_craft_tracking_system_jan_apr_2023

Data dictionary:

CRAFT_ID: Double Unique identifier for each vessel LON: Double Longitude in decimal degrees LAT: Double Latitude in decimal degrees COURSE: Double Course over ground in decimal degrees SPEED: Double Speed over ground in knots TYPE: Text Vessel type NULL 'Cargo ship - All' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category D' 'Cargo ship - No additional info' 'Cargo ship - Reserved 5' 'Cargo ship - Reserved 6' 'Cargo ship - Reserved 7' 'Cargo ship - Reserved 8' 'Engaged in diving operations' 'Engaged in dredging or underwater operations' 'Engaged in military operations' 'Fishing' 'HSC - All' 'HSC - No additional info' 'HSC - Reserved 7' 'Law enforcement' 'Local 56' 'Local 57' 'Medical transport' 'Other - All' 'Other - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Other - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Other - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Other - No additional info' 'Other - Reserved 5' 'Other - Reserved 6' 'Other - Reserved 7' 'Other - Reserved 8' 'Passenger ship - All' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category D' 'Passenger ship - No additional info' 'Passenger ship - Reserved 5' 'Passenger ship - Reserved 6' 'Passenger ship - Reserved 7' 'Pilot vessel' 'Pleasure craft' 'Port tender' 'Reserved' 'Reserved - All' 'Reserved - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Reserved - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Reserved - Reserved 6' 'Reserved - Reserved 7' 'Sailing' 'SAR' 'Ship according to RR Resolution No. 18 (Mob-83)' 'Tanker - All' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category D' 'Tanker - No additional info' 'Tanker - Reserved 5' 'Tanker - Reserved 6' 'Tanker - Reserved 7' 'Tanker - Reserved 8' 'Towing' 'Towing Long/Large' 'Tug' 'unknown code 0' 'unknown code 1' 'unknown code 100' 'unknown code 104' 'unknown code 106' 'unknown code 111' 'unknown code 117' 'unknown code 123' 'unknown code 125' 'unknown code 140' 'unknown code 150' 'unknown code 158' 'unknown code 2' 'unknown code 200' 'unknown code 207' 'unknown code 209' 'unknown code 223''unknown code 230' 'unknown code 253' 'unknown code 255' 'unknown code 4' 'unknown code 5' 'unknown code 6''unknown code 9' 'Vessel with anti-pollution facilities or equipment' 'WIG - All' 'WIG - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'WIG - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'WIG - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'WIG - No additional info' 'WIG - Reserved 6' 'WIG - Reserved 7' SUBTYPE: Text Vessel sub-type NULL 'Fishing Vessel' 'Powerboat' LENGTH: Short integer Vessel length in metres BEAM: Short integer Vessel beam in metres DRAUGHT: Double Draught of the vessel, in metres. TIMESTAMP: Text Vessel position report UTC timestamp in dd/mm/yyyy hh:mm:ss AM/PM format

eAtlas notes: Fishing vessels are encoded as, TYPE: Fishing or TYPE: NULL, SUBTYPE: Fishing Vessel or TYPE: unknown code X. A lot of the vessels with and unknown code appeared to be predominately fishing vessels based on their behaviour. Location of the data: This dataset is filed in the eAtlas enduring data repository at: data on-custodian\ongoing\AU_AMSA_Craft-tracking-system

Here are a few use cases for this project:

1. Shark Detection and Alert: The "marine-sharks" model can be used to automatically detect the presence of sharks in an area where people are swimming. This can trigger safety alerts to lifeguards or beachgoers, enabling them to take preventive measures.

2. Marine Wildlife Research: Researchers studying marine ecosystems and interactions between marine animals such as sharks, seals, and dolphins can benefit from this computer vision model to automatically track and analyze animal behaviors and movements across large image or video datasets.

3. Ecotourism and Wildlife Observation: Tour guides, marine parks, and ecotourism companies can use the "marine-sharks" model to locate and monitor marine animals in real-time, enhancing the experience of wildlife sightseeing for tourists while maintaining a safe distance from the animals.

4. Search and Rescue Operations: In the event of a missing person or boat in open water, search and rescue teams can utilize the "marine-sharks" model for efficient detection and tracking of individuals, boats, and potential marine threats such as sharks during search missions, improving response times and safety.

5. Monitoring Human and Wildlife Interactions: Environmentalists and conservationists can use the "marine-sharks" model to analyze images or videos captured in protected marine areas, studying the impact of human activity on wildlife and ensuring that regulations are being followed to minimize negative effects on marine habitats.

Original provider: Observatoire PELAGIS UAR 3462 University La Rochelle - CNRS

Dataset credits: Observatoire PELAGIS UAR 3462, University La Rochelle - CNRS

Abstract: Since 2003, the Observatoire PELAGIS (La Rochelle University) participated to annual halieutic surveys, led by the IFREMER, to collect data on the distribution of marine top predators in order to estimate their relative abundance and preferred habitats in the Bay of Biscay, the Channel and the North Sea. Each year, three observers take place on board the vessel “Thalassa” to record sightings of seabirds, marine mammals, large fish and fishing boats, from dawn to dusk. Following distance sampling methods, visual censuses are made by two observers (while the third observer is resting) from the upper platform of the boat (min. 14m above the sea level). They are placed on each side of the deck, looking ahead for marine predator with an angle of 180°. For each sighting, the species and the number of individuals are recorded, as well as the behaviour, distance and angle (upon request).

Purpose: One of the main advantages of this survey lays in its ecosystemic approach, which provides information on all the components of the food web (from planktonic organisms to predators) as well as data on the environmental parameters (sea surface temperature, salinity…). Interactions between prey and predators are complex in marine ecosystems, which are submitted to strong spatio-temporal variations. Thanks to ecosystem-based surveys, observed distributions and densities of marine predators can be related to the occurrence of their prey, providing to scientists and managers a better understanding of the marine ecosystems structure.

Supplemental information: [2022-10-20] The records on 2016-09-21 got the longitude sign swapped. they are corrected. [2022-01-18] Data in 2020 and 2021 were appended. [2018-04-26] Data in 2016 and 2017 were appended.

Time and group size of the sightings are not available online. They may be released upon request.

This layer depicts the estimated number of marinas serving the Northeast’s recreational boating community in coastal counties from New York to Maine. Results are based on research from the Center for the Blue Economy and the National Oceanic and Atmospheric Administration’s 2013 Economics: National Ocean Watch (ENOW) database. ENOW provides time-series data on the coastal and ocean economy from 2005 to 2013 derived from national accounts of the Bureau of Labor Statistics and the Bureau of Economic Analysis. ENOW’s four economic indicators are the number of business establishments, number of people employed, wages paid to employees, and contribution to gross domestic product. For more information, users are encouraged to consult the Northeast Ocean Planning Baseline Assessment report.View Dataset on the Gateway

Here are a few use cases for this project:

1. Maritime Surveillance and Security: The "Object Detection" model can classify different types of ships and boats to identify potential security threats, illegal activities, or unauthorized boats in monitored areas.

2. Navigation Assistance: The model can be used in navigation systems to help sailors and captains identify other types of vessels in their proximity. This can help avoid collisions and provide safer navigation in crowded waters.

3. Search and Rescue Operations: During search and rescue operations, the identification and classification of objects like boats or buoys could help pinpoint the location of missing or stranded individuals.

4. Fishing Industry: The model can be used to monitor fishery areas, distinguish between different types of vessels, track movements, and enforce regulations in protected zones.

5. Water Sports and Recreation: Useful in managing water sports activities, like kayaking or sailing races, ensuring routes are clear and tracking participants for safety purposes.

Original provider: Observatoire PELAGIS UMS 3462 University La Rochelle - CNRS

Dataset credits: Observatoire PELAGIS UMS 3462, University La Rochelle - CNRS - Agence Française pour la Biodiversité - Direction de l'Eau et de la Biodiversité

Abstract: Since 2003, the Observatoire PELAGIS (La Rochelle University) participated to annual halieutic surveys, led by the IFREMER, to collect data on the distribution of marine top predators in order to estimate their relative abundance and preferred habitats in the Bay of Biscay, the Channel and the North Sea. Each year, three observers take place on board the vessel “Thalassa” to record sightings of seabirds, marine mammals, large fish and fishing boats, from dawn to dusk. Following distance sampling methods, visual censuses are made by two observers (while the third observer is resting) from the upper platform of the boat (min. 14m above the sea level). They are placed on each side of the deck, looking ahead for marine predator with an angle of 180°. For each sighting, the species and the number of individuals are recorded, as well as the behaviour, distance and angle (upon request).

Purpose: One of the main advantages of this survey lays in its ecosystemic approach, which provides information on all the components of the food web (from planktonic organisms to predators) as well as data on the environmental parameters (sea surface temperature, salinity…). Interactions between prey and predators are complex in marine ecosystems, which are submitted to strong spatio-temporal variations. Thanks to ecosystem-based surveys, observed distributions and densities of marine predators can be related to the occurrence of their prey, providing to scientists and managers a better understanding of the marine ecosystems structure.

Supplemental information: [2020-04-30] Data in 2019 were appended. [2019-04-15] Data in 2018 were appended. Before this update, "Limicole spp" was associated with Charadriiforms. In this update, is is now associated with Scolopacidae.

[2018-04-26] Data in 2016 and 2017 were appended.

Time and group size of the sightings are not available online. They may be released upon request.

Abstract [Related Publication]: Rising noise levels in marine ecosystems are due primarily to increased human activities and have been shown to negatively affect the physiology and behaviour of fishes. However, the nature of these effects depends on the sound source. Recreational boating and shipping are two of the most common sources of anthropogenic noise on coral reefs, however their effects remain largely unknown. We examined the effects of noise from 4-stroke outboard-powered boats and ships (bulk carriers > 50,000 tonnes) on the escape response and routine swimming of whitetail damselfish (Pomacentrus chrysurus). Both 4-stroke and ship noise playbacks affected the escape response and routine swimming of whitetail damselfish, however the magnitude of the effects differed. Fish exposed to ship noise responded more slowly (higher response latency) and moved shorter distances when exposed to the stimulus compared to individuals exposed to 4-stroke noise. Our study suggests that exposure to anthropogenic noise increases the vulnerability of individuals to predation and corroborates that the effects of noise on fish depend on the noise source. Our results highlight the need to consider the impact of anthropogenic noise in future marine management plans, particularly recreational boating and commercial shipping, which are two of the most common sources of anthropogenic noise on coral reefs.

Dataset consists of a spreadsheet recording the escape response and routine swimming variables of individuals exposed to three different acoustic treatments.

The full methodology is available in the Open Access publication shown in the Related Publications link below.

Aerial imagery surveys are commonly used in marine mammal research to determine population size, habitat distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms as an assistive technology to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in unmanned aerial vehicle (UAV) imagery. Computer-based object detection achieved an average precision of 61.17% for belugas, 98.58% for boats, and 95.97% for kayaks. We then tested the performance of computer vision tracking of belugas and manned watercraft in UAV videos using the DeepSORT tracking algorithm, achieving a multiple object tracking accuracy (MOTA) ranging from 37% – 88% and multiple object tracking precision (MOTP) between 63% – 86%. Results from this research indicate that deep learning technology can perform at a similar caliber as human annotators in beluga and watercraft detection and tracking, allowing for larger datasets to be processed within a fraction of the time.

Data within this layer was created through two different mapping effort workshops: 1) the Human Uses Mapping Workshops, held in St. Thomas, U.S. Virgin Islands (USVI), in June, 2014, and 2) the Human Uses Mapping Workshops, held in St. Croix, U.S. Virgin Islands (USVI), in April, 2015. A Coastal Use Mapping Project is designed to collect information on how a community is using a coastal or marine area. The data helps resource managers understand both the range and intensity of key activities as well as planners to identify and reduce conflicts among human uses, and between human uses and the environment. Representatives (see Credits) from different marine sectors (recreation, watersports, marina, charter boating, SCUBA diving, and fishing industries) as well as NGOs and territorial and federal governmental partners met to provide first-hand information on the spatial and temporal distribution of human use activities around the USVI. This information was captured using the method of “participatory mapping.” Participatory mapping provides participants a map on which to indicate the location of their human use activities, while moderators generate representative spatial data files in real time. Both workshops used E-Beam™ technology to aid the participatory mapping method. This work represents an ongoing effort by TNC, USVI DPNR, NOAA’s Coral Reef Conservation Program (CRCP), and members of the Caribbean Regional Ocean Partnership (CROP) to update human use data throughout USVI in support of resource managers.

This package contains data from a 2003 study of visitors to the Situk River in the Gulf of Alaska. A sample of recreation visitors at the Yakutat airport departure point were interviewed between April and September. Questions included travel methods, lodging, length of stay, and visitor usage of the Situk River. After conducting a short interview with these visitors a follow up questionnaire was mailed to each respondent. These data include more indepth questions related to non-local recreation use, recreation visitor attitudes, and perceptions of the Situk during the three fishing seasons (Steelhead, King/sockeye, and Coho season).At the Situk River Partners Meeting in October 2002, the Partners recommended that the Forest Service move ahead with a multi-faceted National Environmental Policy Act process for the Situk River. Simultaneously, the Forest Service, Alaska Department of Fish and Game (ADF&G), and the Alaska Department of Natural Resources agreed to review existing data and explore methods to assess visitor issues, perceptions, and encounter rates on the river. The overall goal of the Situk Recreation Visitor Study was to assess recreation angler experiences and perceptions of conditions on the Situk River, and to measure perceived impacts and tolerances for those impacts. In addition to this overall goal, five related objectives were identified in the study plan. 1) Assess existing and preferred types of experiences by river segment, season and by type of user. 2) Describe users by river segment and type of trip. 3) Assess impact levels and tolerances for those levels. 4) Assess opinions towards various management strategies that might be used to address impacts. 5) Compare results to other surveys and other available information, both looking at existing reported information on the Situk as well as other Alaskan Rivers.These data were published on 09/07/2017. Minor metadata updates were made on 01/17/2025.

B. schlosseri microsatellite dataGenepop-formatted file containing individual genotype data (10 microsatellite loci)B. schlosseri COI data (Arlequin)Arlequin-formatted file of COI data by populationB. schlosseri COI data (by individual)Phylip-formatted file of COI sequence data by individualBoat_connectivityThis file contains a record of the number of times boaters have mentioned they have visited two locations sampled in the study.Boat connectivity (updated).xls

E.coli is a type of bacteria commonly found in the intestines of warm–blooded animals (including people). When found in freshwater, it can indicate the presence of pathogens associated with faecal contamination, from sources such as waste from humans and farmed animals such as sheep and cows. E.coli concentrations can vary due to differences in land use, climate, elevation, and geology. High E. coli concentrations may cause illness in humans and animals if ingested. This is an important consideration for human health, particularly where people use the river for swimming or boating. This dataset relates to the ""River water quality: bacteria (Escherichia coli)"" measure on the Environmental Indicators, Te taiao Aotearoa website.