Vessel Monitoring System Data Research Articles

Identifying Fishing Trip Behavior from Vessel Monitoring System (VMS) Data Using Machine Learning Models

Illegal fishing in Indonesian waters poses a serious challenge that requires innovative solutions. This research offers an advanced technological approach by applying the Hidden Markov Model (HMM) in Machine Learning to address this issue. Data from the Vessel Monitoring System (VMS) is utilized to efficiently identify fishing vessel activities. By involving a dataset that encompasses various vessel activities, this model can detect suspicious fishing practices in real-time. The research findings demonstrate that this model consistently identifies fishing vessel activities with a high level of accuracy. This study makes a significant contribution to efforts in preventing Illegal, Unreported, and Unregulated (IUU) Fishing and supports marine resource sustainability initiatives.

Speaking Deads: Sea Turtle Mortality Areas and Fisheries Overlaps Identified Through Backtracking of Stranded Carcasses in the Adriatic Sea

ABSTRACT Interaction with fishing gears represents the main anthropogenic threat at sea for sea turtles worldwide, and identifying the hotspots of turtle bycatch is a priority knowledge gap. Turtle stranding data represent a source of information about mortality areas at sea that are not fully exploited. This study aims to infer turtle mortality areas of turtles stranded along the Italian Adriatic Coast in the period 2019–2021 (1432 records), through backtrack modelling of carcasses. Specifically, the decomposition process of eight loggerhead carcasses was monitored, and the relationship between floating period (FP), turtle size and sea temperature was modelled through a generalized additive model. Oceanographic information was then used to track the routes of floating carcasses back, knowing their size and decomposition stage, and finally estimate the likely area of mortality. A complementary numerical experiment of connectivity between coastal and offshore areas gave indication that areas of potential mortality are relatively close to the coast, particularly in the northern Adriatic. Stranded turtles probably represent just a small fraction (17%–25%) of total at‐sea mortalities in the study area (Italian Adriatic waters), with decomposition rates, season and distance from shore influencing their stranding likelihood. Hence, strandings can inform only about spatio‐temporal variability of coastal mortality hotspots. Inferred areas of turtle mortality were most likely located in the North and Central Adriatic all over the year and overlap with heatmaps of fishing effort, obtained from vessel monitoring system (VMS) and automatic identification system (AIS) data, in the Gulf of Manfredonia and in the North‐West Adriatic in the cold (September–December) and warm (May–August) periods, respectively.

Fishing Vessel Type Recognition Based on Semantic Feature Vector

Identifying fishing vessel types with artificial intelligence has become a key technology in marine resource management. However, classical feature modeling lacks the ability to express time series features, and the feature extraction is insufficient. Hence, this work focuses on the identification of trawlers, gillnetters, and purse seiners based on semantic feature vectors. First, we extract trajectories from massive and complex historical Vessel Monitoring System data that contain a large amount of dirty data and then extract the semantic features of fishing vessel trajectories. Finally, we input the semantic feature vectors into the LightGBM classification model for classification of fishing vessel types. In this experiment, the F1 measure of our proposed method on the East China Sea fishing vessel dataset reached 96.25, which was 6.82% higher than that of the classical feature-modeling method based on fishing vessel trajectories. Experiments show that this method is accurate and effective for the classification of fishing vessels.

Estimating emissions from fishing vessels: a big Beidou data analytical approach

Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the emissions from fishing vessels on fishing grounds. In this study, a framework for estimating fishing vessel emissions, using a bottom-up dynamic method based on the big data from the Beidou VMS (vessel monitoring system) of fishing vessels, is proposed and applied to a survey of fishing vessel emissions in the East China Sea. The results of the study established a one-year emission inventory of fishing vessels in the East China Sea. This study was the first to use VMS data to estimate fishing vessel emissions in a fishing area, and the results will help to support the management of their carbon emissions.

Vessel Monitoring System (VMS) Technology as a Management Strategy for Longline Tuna Capture Fisheries in Benoa

Benoa Port has fishing vessel activities that are the foundation of Bali's tuna swamp fisheries (longlines). Sustainable tuna capture fisheries will be impossible to attain if the number of infractions in the marine and fisheries sectors continues to rise. The government's efforts to reduce infractions in marine and fisheries are achieved through tightening the oversight system. The Vessel Monitoring System (VMS) surveillance system was created to reduce breaches in the marine and fishery sectors. The goal of this study is to determine the monitoring and suspected indication of violations of longlines using the VMS at the Benoa PSDKP as well as to learn about the capture fisheries management strategy using VMS data on longlines at Benoa Port using an analytic hierarchy process (AHP) approach. This study used a qualitative-quantitative descriptive technique with AHP analysis to determine the value of the management priority scale. Based on the findings of monitoring longlines with web tracks from January to December 2022, the most targeted fishing area was the Indian Ocean High Seas, and the most prominent suspected indication of violation was the inactivity of VMS when the vessel was operating. The main approach for managing capture fisheries utilizing VMS data on longlines in Benoa Port with the AHP analysis is for the government to offer subsidies for installing VMS on all fishing vessels, particularly those under 30 GT.

High resolution assessment of commercial fisheries activity along the US West Coast using Vessel Monitoring System data with a case study using California groundfish fisheries

Commercial fisheries along the US West Coast are important components of local and regional economies. They use various fishing gear, target a high diversity of species, and are highly spatially heterogeneous, making it challenging to generate a synoptic picture of fisheries activity in the region. Still, understanding the spatial and temporal dynamics of US West Coast fisheries is critical to meet the US legal mandate to manage fisheries sustainably and to better coordinate activities among a growing number of users of ocean space, including offshore renewable energy, aquaculture, shipping, and interactions with habitats and key non-fishery species such as seabirds and marine mammals. We analyzed vessel tracking data from Vessel Monitoring System (VMS) from 2010 to 2017 to generate high-resolution spatio-temporal estimates of contemporary fishing effort across a wide range of commercial fisheries along the entire US West Coast. We identified over 247,000 fishing trips across the entire VMS data, covering over 25 different fisheries. We validated the spatial accuracy of our analyses using independent estimates of spatial groundfish fisheries effort generated through the NOAA’s National Marine Fisheries Service Observer Program. Additionally, for commercial groundfish fisheries operating in federal waters in California, we combined the VMS data with landings and ex-vessel value data from California commercial fisheries landings receipts to generate highly resolved estimates of landings and ex-vessel value, matching over 38,000 fish tickets with VMS data that included 87% of the landings and 76% of the ex-vessel value for groundfish. We highlight fisheries-specific and spatially-resolved patterns of effort, landings, and ex-vessel value, a bimodal distribution of fishing effort with respect to depth, and variable and generally declining effort over eight years. The information generated by our study can help inform future sustainable spatial fisheries management and other activities in the marine environment including offshore renewable energy planning.

Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China

Measuring the distribution of the fishing effort of trawlers is of great significance for describing marine fishery activities, quantifying fishing systems in terms of marine ecological pressure, and revising the regulations of fishing. The purpose of this paper is to develop an efficient learning algorithm to detect the fishing behavior of trawlers to analyze the distribution of fishing effort. The vessel-monitoring system data of more than 4600 trawlers from September 2019 to April 2023 were used for feature extraction. According to the spatiotemporal information provided by the vessel position data, 11-dimensional features were extracted to form the feature vectors. A Slime Mould Algorithm-optimized Light Gradient-Boosting Machine (SMA-LightGBM) algorithm was proposed to classify the feature vectors to recognize fishing behavior. The presented method showed a remarkable generalization ability and high accuracy, sensitivity, specificity, and Matthews correlation coefficient in the test results, with scores of 98.23%, 98.75%, 97.75%, and 0.9646, respectively. Subsequently, the trained model was used to identify the fishing behavior of trawlers belonging to the coastal provinces of the Bohai Sea and the Yellow Sea in the sea area of 117° E~132° E, 26° N~41° N. The fishing effort was calculated and evaluated according to the fishing behavior recognition results. The mean absolute error was 0.3031 kW·h, and the coefficient of determination score was 0.9772. The thermal map of the fishing effort of the trawler was mapped, and the spatiotemporal characteristics were estimated in the region of interest from 2019 to 2023 with a spatial resolution of 18 degree × 18 degree. This method is an efficient way of analyzing the spatiotemporal characteristics of the fishing effort of trawlers. It provides a quantitative basis for the assessment of fishery resources and can inform fishing policies.

HiTrip: Historical trajectory interpolation for trawlers via deep learning on multi-source data

The Vessel Monitoring System (VMS) on trawlers has revolutionized our understanding of spatiotemporal fishing activities. However, the low temporal resolution of historical VMS datasets complicates a precise analysis of fishing effort distribution. One inherent challenge for precise interpolation is the stark contrast between trawler movement patterns during steaming, characterized by straight lines, and fishing, which often involves consecutive turns. In this study, we introduce HiTrip, a deep learning approach that interpolates historical VMS data from two-hour intervals down to three minutes by harnessing both VMS and marine hydrological datasets. The proposed deep learning model, integrating ResNet, LSTM, and MLP, seamlessly synthesizes spatial features from coarse fishing effort distributions, sea surface factor fields, and current fields, while accounting for the temporal relationships within trajectory segments. Evaluated on 1855 East China Sea trawler VMS records and Copernicus Climate Data Store hydrological factor data, HiTrip achieves a 0.20 km interpolation error, meeting a finery 0.005° × 0.005°spatial resolution demand for fishing effort distribution analysis. Ablation study validates the efficacy of our deep learning model integrating multi-source datasets. Moreover, when evaluated on a diverse Global Fishing Watch dataset, which includes 45 trawlers spanning various global maritime regions, HiTrip maintains a 0.40 km error, emphasizing its broad generalization ability.

Spatial effort displacement in Florida commercial reef fisheries after red tides

Red tides have affected marine and coastal communities for centuries. The effects of red tides on commercial capture fisheries have rarely been evaluated, owing in part to a lack of appropriate data. Leveraging modeled maps of red tide severity, vessel monitoring systems data, and logbook records, we investigated the commercial fisheries impacts of red tide events in Florida during the period 2008–2019. We focused on the bottom longline and vertical line fisheries targeting reef fishes. Red tides were punctuated events peaking at the end of summer, with 2018 and 2019 the most intense years. The total area affected by intense red tides (concentrations> 1,000 cells/L) was 103,483 km2, about the size of the state of Kentucky. Red tides did not have negative effect on fleet-level fisheries metrics such as number of fishing vessels, trips, fishing effort or revenue (generalized mixed effect models, p > 0.5). Fishing effort was, however, displaced from the red tide areas, and fishers avoided areas characterized by intense blooms (zero-inflated negative binomial model, p < 0.001). Follow up of the long-term likely responses of the resource and the catch are needed to fully understand the consequences of red tide events in commercial fisheries in the Gulf of Mexico.

Methods to get more information from sparse vessel monitoring systems data

Vessel Monitoring Systems (VMS) and other vessel tracking data have been used for many years to map the distribution of fishing activities. Mapping areas with low levels of fishing activity can be of particular interest; for example to avoid conflicts between fishing and other ocean uses like offshore renewable energy or to protect relatively pristine ecosystems from increasing fishing pressure. A particular problem when trying to delineate areas that are lightly fished, is the relative sparsity of vessel monitoring data in these areas. This paper explores three novel methods for estimating the distribution of fishing activity from VMS data, with particular focus on lightly impacted areas. The first new method divides the area of interest into a nested grid with varying cell sizes (depending on the density of data at each location); the second new method uses Voronoi diagrams to define polygons around observations and the third method applies a local regression to generate a smooth map of fishing intensity. The new methods are compared with two established methods: applying spatial grids and interpolating fishing tracks. The track interpolation method generally performs better than any of the new methods, however it is not always possible or appropriate to apply track interpolation; in those cases the local regression method is the best alternative.

Linking real world fisheries datasets for mapping of revenue from fishing grounds to dependent communities

Mapping the economic value of the ocean is pivotal to understand how marine ecosystems contribute to human well-being and to support fisheries management. We present a framework to analyse fisheries data and map fishing revenues by linking Vessel Monitoring System (VMS) information to logbooks and observer data. We provide a detailed step-by-step methodology and describe different approaches available to fulfill each step, with special notes for the processing of real-world messy data. The framework consists of six processing steps: (1) identifying the target fishery and subsetting VMS data, (2) extracting relevant variables, (3) linking observer and VMS data, (4) identifying fishing activity, (5) linking VMS and logbook data, and (6) extracting derived variables and mapping revenue back to the communities that extracted the resources. Building this framework opens a broad range of applications including marine spatial planning, rapid response analyses, high-resolution stock assessments, and spatially explicit-socioeconomic analyses. We demonstrate the framework in the reef fishery of the Gulf of Mexico, where spatial planning for aquaculture is currently underway.

Dynamic valuation of the provisioning services of marine fisheries ecosystem based on BeiDou VMS data: A case study of TACs project for Acetes chinensis in the Yellow Sea

The refined management of the total allowable catch system (TACs) project is a form of marine fishery spatial planning (MFSP). With the decline of global fishery resources, exploring the dynamic valuation of marine fisheries ecosystem provisioning services (MFEPS) in the TACs project and its application has been of great significance to the scientific formulation of MFSP and the sustainable development of marine resources. In this study, we take the TACs project for Northern mauxia shrimp (Acetes chinensis) in Haizhou Bay of the Yellow Sea from 2020 to 2022 as a case study. First, we constructed a driver–behavior–pressure–state–impact–response (DBPSIR) framework to explain the relationship between fishing activities and MFEPS. We then used BeiDou vessel monitoring system data, sampled catches, and fishing logs to extract the fishing effort and major catch species of each netting during the TACs project period. The values of catches, cost of bycatch resource restoration, and profit per unit of effort (PPUE) of each netting were calculated to dynamically assess the MFEPS value at different stages of the fishing period in this sea area. The fishing effort of the vessels showed an evident multi-core aggregation distribution pattern. The catch compositions were different at different fishing stages, but A. chinensis was the dominant species at all times. The area of high PPUE distribution was different from that of fishing effort, mainly distributed in 119°46′–119°55′E, 34°38′–34°42′N and 120°08′–120°15′E, 34°41′–34°45′N. The high values of PPUE in the early stage of the fishing period were significantly larger than those in the middle and late stages, and the return on effort gradually decreased. Therefore, it is reasonable to move the permitted fishing time forward. The reasonableness of the permitted fishing area setting in 2021–2022 was significantly higher than in 2020. In addition, fishers can be guided to increase their fishing efforts in waters with high PPUE values during the corresponding fishing stage. These results provide a scientific basis for the refined management of the TACs project in the context of fishing moratoriums. Future studies on the valuation of MFEPS should pay more attention to long-term dynamic ecosystem monitoring. It is necessary to regulate fishing behavior by combining measures such as the scientific formulation of fishing quotas and bycatch rates. Furthermore, elastic control of the MFSP could be improved to protect and utilize marine biological resources more reasonably.

Potential cooperation of RFMOs for the integrity of MCS: Lessons from the three RFMOs in the Asia-Pacific

Enhanced monitoring, control, and surveillance (MCS) of illegal, unreported, and unregulated (IUU) vessels is key to sustainable fisheries. This study examined relevant conservation and management measures (CMMs) in three regional fishery management organizations (RFMOs) of the Asia-Pacific to identify their challenges. We focused on two CMMs: vessel monitoring systems (VMS) and IUU vessel lists. We also conducted two qualitative analyses to gain an in-depth understanding of registered vessels, including the fishing capacity of member states in the three RFMOs and the relationships between the fishing capacity and catch of flag states. Consequently, we identified several challenges, including VMS data gaps and difficulties in applying sanctions to different IUU entities. While these challenges may be common across RFMOs, there are still limited inter-RFMO cooperation to address them. Also, our quantitative analyses revealed that coastal developing states with limited capacity have a high number of recorded vessels and that a large fishing capacity (especially that of large distant water fishing nations) does not necessarily indicate a large catch. Our analyses highlight the need to improve vessel management capacity and ensure the accuracy of vessel related information. Overall, our findings indicate the need for collaborative work among RFMOs in the form of, for example, a formal communication platform, which would provide RFMOs opportunities to promote knowledge or technology transfer. Inter-RFMO cooperation can be particularly useful for the youngest RFMO managing the North Pacific as its enhancement of CMMs is essential to achieve the integrity of MCS for IUU vessels in the Asia-Pacific.

Evaluating the dependence of opportunistic Yellow‐legged Gulls (Larus michahellis) on marine habitat and fishing discards

Several bird species benefit from anthropogenic food subsidies, such as landfills and fishing discards, that are being reduced owing to introduced legislation. For instance, since 2019, Europe has passed legislation to reduce dramatically the amount of fisheries discards through the Landing Obligation (LO), which states that all fish species under Total Allowable Catch (TAC) should not be discarded at sea. This European discard ban is expected to impact some gulls, as several gull populations have been observed to be heavily reliant on this resource. In this work, GPS tracking data from adult Yellow‐legged Gulls Larus michahellis and Vessel Monitoring System (VMS) data (i.e. detailed data of the spatial distribution of fishing vessels) are used to determine the utilization of the marine habitat and the level of interaction of gulls with fishing activity offshore and discards in the south‐eastern part of the Bay of Biscay. We found that these gulls had a low use of the marine habitat (ca. 15%) compared with terrestrial habitats, in contrast to the ca. 40% of the diet of this population which comprises marine prey. Furthermore, we detected that most of the gull tracking positions at sea did not interact with the fishing vessels (an overlap of 21.5% between gulls and VMS data). Conversely, other gull populations of the Iberian Peninsula were found to depend much more strongly on fishing activity. Therefore, the Yellow‐legged Gull population of the SE Bay of Biscay did not obtain most of its fish prey from interactions with offshore fishing activity but most possibly by taking fish remains in harbours and by feeding themselves. Consequently, we expect a minimal effect of the European policies oriented to ban fishing discards offshore on this population, which would be more affected by policies directed towards the reduction of food availability in landfills or fish landing management in harbours.

Estimating hidden fishing activity hotspots from vessel transmitted data

Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch.

Validating the fishing locations reported in the logbooks using the positional data of vessel monitoring systems in the multi-day fishery of Sri Lanka

As part of monitoring, control and surveillance of marine fisheries, a logbook monitoring and vessel monitoring system (VMS) were introduced for multi-day fishing. The present study investigates the possibility of using VMS data to validate logbook records of fishing locations of multi-day fishing vessels. The data from 1,424 multiday vessels were fitted with VMS, which was operated from May 2017 to April 2018. During the period, there were 17,626 fishing trips and 66,717 fishing occasions. In the VMS tracks where the locations were flooded, drift gillnet-operated vessels were seen as a condensed zig-zag pattern, while longline fishing vessels as curve-shaped tracks, and ring net fishing vessels as condensed locations had irregular-shaped clumps. The differences in coordinates of fishing locations in the logbook records matched with the tracks predicted by VMS in multi-day boats of three gear types indicating that almost all fishing operations were within ±10º of both latitudes and longitudes and further around 70% of fishing locations had less than 1 arcminute of a difference. Therefore, VMS track data can be used to verify and validate logbook records of fishing locations in multi-day vessels.

Estimation of the spatiotemporal distribution of fish and fishing grounds from surveillance information using machine learning: The case of short mackerel (Rastrelliger brachysoma) in the Andaman Sea, Thailand

Rapid and accurate data gathering on fish catches and the geographical distribution of fishing activities is essential in fish stock assessments and fisheries management. Surveillance systems like the Vessel Monitoring System (VMS) have become popular globally, providing accurate data on fishing activities and fish abundance in near real-time. Our study demonstrates a procedure to find the best method in machine learning algorithms for detecting fishing operations from surveillance data. VMS data of 3,237 trips conducted by 91 purse seiners targeting short mackerel (Rastrelliger brachysoma) in Thai waters of the Andaman Sea during 2020 were used for the study. Twenty-five spatiotemporal, environmental, and fisheries-related parameters were investigated as predictors to detect and map the fishing activity. Eight algorithms were used to compare the model performance using cross-validation. The total catch per trip was reallocated to the predicted fishing locations of the trip from the best algorithms to depict the fishing ground. The results show that operation time and vessel speed are essential elements for determining the fishing operation of purse seiners. The most-relevant predictors are the calculated speed, operation time, and instantaneous speed. The Random Forest (RF) algorithm performed the best, with 86% accuracy, and therefore was used as the final model to detect the fishing operations. However, all reallocation approaches performed similarly well in depicting fish distribution. The allocation of catch data equally to the fishing position is recommended since it is the most straightforward technique. In contrast, reallocation catch using the RF algorithm prediction ratio for visualising the CPUE distribution achieved the best performance (with a slope of 1) compared with the training data. This study provides the first decent procedures for using surveillance information to detect and identify the prospective fishing operation of purse seiners—an approach that allows for a better understanding of short mackerel distribution. The proposed procedures should help with time–area-based management of intensively exploited migratory fish species, which requires rapid and accurate information.

An example of fishery yield predictions from VMS-based navigational characteristics applied to double trawlers in China

Vessel monitoring systems (VMSs) provide information on the spatial and temporal distribution of fishing vessels operating at sea and are mostly used for fisheries enforcement and vessel surveillance. VMS has potential shortcomings in fisheries resource assessment due to its inability to acquire information on catch yields. This study proposed a method for predicting fishing vessel yield based on VMS data. Speed, heading difference and track characteristics were used to determine the state of VMS data for three double trawlers (Zhepuyu 71319, 71528 and 71568) in 2021. Then, fishing effort and temporal and spatial features were calculated for each trip based on the VMS data of the dragging state. Finally, six machine learning (ML) models (k-Nearest Neighbor[KNN], Random Forest[RF], Support Vector Machine[SVM], eXtreme Gradient Boosting[XGBoost], Gradient Boosting Decision Tree[GBDT] and Ridge Regression[RR]) were used to predict the yield of the double trawlers per trip. Two of the three double trawlers were used in turn as the training set to construct the prediction model, and the other one was utilised as the test set to evaluate the model performance. The prediction results were evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of determinant (R2) and model prediction error per trip. The prediction error of the total yield per year was calculated. Results showed that the six ML models exhibited high prediction performance and affirmed the accuracy of yield prediction, and the performance of the models varied with the dataset. XGBoost, GBDT and KNN had the best prediction performance when Zhepuyu 71319, 71528 and 71568 were used as the test set, respectively. Moreover, the total yield errors of all models were less than 10 %. This study selected multiple features to construct ML models to predict the yield of double trawlers based on the navigation characteristics of double trawlers and further explored the VMS data to improve the assessment methods of fishery resources, enabling the VMS data to play a greater role in aiding scientific decisions.

CPUE Estimation and Standardization Based on VMS: A Case Study for Squid-Jigging Fishery in the Equatorial of Eastern Pacific Ocean

Different fisheries, even the same fishery, use different ways of quantifying fishing efforts such as the number of vessels, days, voyages, and hooks. In squid-jigging fisheries, fishing hours, fishing days, and the number of vessels are valid units for calculating the catch per unit effort (CPUE). A vessel monitoring system (VMS) provides vessel position data with high spatial and temporal resolution and offers the possibility to quantify the CPUE at a finer scale. Using the squid fishery in the equatorial waters of the eastern Pacific as a case study, the CPUE was evaluated and standardized based on VMS data. The drifting operating points of the squid fishing vessels were filtered by the speed threshold, solar radiation, and operating time setting methods, leading to the number of fishing hours per day, and the nominal CPUE was calculated by combining the catch data obtained from logbooks. Then, the generalized linear model (GLM) and generalized additive model (GAM) were applied to conduct CPUE standardization considering spatiotemporal factors and environmental variables including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and chlorophyll-a (Chl_a). The results showed that month, latitude, SST, SSH, and Chl-a all have a high significant effect on CPUE as demonstrated through the significance test conducted by GLM. The GAM including the significant factors was judged to be the best model according to the AIC guidelines. The latitude range for high CPUE in the fishery was 3°S~0°S, SST range 24~25 °C, SSH range 4~8 m, and Chl_a range 0.15~0.20 mg/m3. In addition, the nominal and standardized CPUEs were compared based on fishing hours and fishing days. The results indicated that the two types of CPUEs were highly related hence there was no significant difference.

Spatial–temporal distribution of large-size light falling-net fisheries in the South China Sea

With the rapid modernization of methods for monitoring fishing vessels, different ways of obtaining data on large-size light falling-net vessels have developed. In particular, the vessel monitoring system (VMS) may be used to track fishing activities in the South China Sea (SCS). In this study, we used VMS data to show the characterize and differences in the information transmitted when monitoring large-size falling-net fishing vessels, as well as their advantages and disadvantages. We investigated the spatial distribution of large-size light falling-net fisheries in the SCS using Beidou VMS data from 2017 to 2020. We also compared trajectories of fishing vessels from various provinces. According to these data, vessels operate in the Nansha Islands and the waters of the Pearl River Estuary, Beibu Gulf, Zhongsha, and Xisha Islands, and there is a clear seasonal pattern of fishing in the SCS. There are the differences of vessels in terms of distribution areas and ports of entry and exit. Additionally, we found the operation area of the large-size falling-net fishing vessels divided into four main fishing zones. The analysis compares the number of days of operation in the different fishing areas as well as seasonal variations. Our study shows that the operating area of large-size light falling-net vessels has extended southwards and that the number of days for which fishing vessels operate has increased compared with the last decade. Using the Beidou VMS to study the large-size light falling-net vessels in the SCS can provide more detailed scientific reference data for the SCS fisheries.