Marine Environmental Data Research Articles

Effects of Climate Change on the Distribution of Scomber japonicus and Konosirus punctatus in China’s Coastal and Adjacent Waters

Climate change has increasingly impacted the marine environment, with various marine environmental factors interacting to influence fish distribution. Assessing the impact of climate change on the future distribution of fish depends on understanding how biological responses interact with environmental conditions. Enhancing our understanding of the potential impacts of climate change is crucial for the sustainable development of marine fisheries. In this study, we analyzed the habitat suitability of Scomber japonicus and Konosirus punctatus in the coastal waters of China (17°–41° N, 107°–130° E) using marine environmental data, including, as follows: sea surface temperature (SST); sea surface salinity (SSS); pH; and biological occurrence data from 2000 to 2010. A maximum entropy (MaxEnt) model was employed to predict the future distribution of these two species based on the shared socioeconomic pathway (SSP) scenarios for 2040–2050 and 2090–2100. The results indicate that climate change will significantly affect the future habitat distribution of S. japonicus and K. punctatus, leading to a decrease in suitable habitat areas offshore China and a gradual shift northeastward in the center of these habitats. These findings are essential for understanding the impacts of climate change on the distribution of S. japonicus and K. punctatus, with significant implications for fisheries resource assessment and management.

A new ship tracing technology from oil spills based on multi-source data

Oil spill from ship can cause serious pollution to the Marine environment, but it is very difficult to find and confirm the troublemaker. In order to determine the oil spill ship, this paper proposes a new method to trace the source of ship oil spills and find the suspected ship that spills oil based on SAR imagery, AIS data and related marine environment data. First, we filter AIS data based on position of oil spill areas on remote sensing imagery and convert oil spill areas into trajectory points. Secondly, based on the Lagrangian particle motion model, a bidirectional drift model is proposed to calculate the average similarity between the forward and backward drift results. Finally, the most likely oil spill ship is determined according to the average similarity results. The results of the case study show that the method is effective and practical.

Spatiotemporal analysis of marine environmental influence on the distribution of chub mackerel in the Northwest Pacific Ocean based on geographical and temporal weighted regression

In order to further analyze the impact of the marine environment factors in the Northwest Pacific Ocean on the distribution of chub mackerel Scomberjaponicus resources, a spatio-temporal weighted regression model was constructed based on the marine environmental and fishery data of China's light purse seine production in the high seas of the Northwest Pacific Ocean. Six environmental factors, including sea surface temperature (SST), chlorophyll a concentration (Chl-a), eddy kinetic energy (EKE), SST gradient intensity (GSST), longitudinal SST gradient (xGSST) and latitudinal SST gradient (yGSST) in spring, summer and autumn were used to analyze the impact of the temporal and spatial environmental factors on chub mackerel. The results show that the statistically significant variables (p < 0.05) were different in seasonal models. The SST, Chl-a, yGSST and EKE were statistically significant in the spring model, SST, yGSST and GSST in the summer model, and EKE, yGSST and GSST in the autumn model. The adjusted coefficients of determination (R2adj.) of the spring, summer and autumn models were 0.488, 0.378 and 0.475, respectively. The results suggest that the coefficients of various environmental factors changed not only with time, but also with geographical location. Through this study, we can provide methodological reference for the study of resource change and fishery formation mechanism of chub mackerel fishery in the Northwest Pacific Ocean.

Environmental Influences on Illex argentinus Trawling Grounds in the Southwest Atlantic High Seas

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI &gt; 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations.

Prediction on the changes in potential suitable areas for mangroves along the coast of Guangxi and the threat from Spartina alterniflora invasion.

As one of the important blue carbon pools in tropical and subtropical intertidal zones, mangroves are widely distributed along the coast of Guangxi in China. To deeply explore the variations of potential suitable habitats for mangroves in China under the background of climate change, based on remote sensing interpretation data of coastal wetlands in Guangxi, global marine environment and bioclimatic environment data in 2021, we constructed a maximum entropy habitat distribution model to simulate the spatial distribution of potential suitable areas for mangroves and the invasive species, Spartina alterniflora, along the coast of Guangxi, and predicted the patterns under extreme climate change scenarios (SSP5-8.5). The results showed that the interpreted area of mangrove forests along the coastline of Guangxi was 9136.7 hm2 in 2021, while the predicted area of potential suitable habitat area was 55955.9 hm2. Current distribution area of mangroves had basically covered its potential high suitability area and nearly 10% of the moderate suitability area. The current area of S. alterniflora was 1320.4 hm2, and the predicted area of potential high suitability area was twice of current area, indicating that there was still a large proportion of high suitability area that was not occupied by S. alterniflora. The most important environmental factors driving the distribution of potential habitats in mangroves were offshore Euclidean distance (62.2%), terrain deviation index (8.7%), average sea surface temperature in the hottest season (6.1%), and seabed terrain elevation (5.6%). The contribution of geographical conditions on mangrove distribution was predominant. Under the climate change scenario (SSP5-8.5), potential suitable area for mangroves would increase by 5.3%, while that for S. alterniflora would decrease by 3.1%. The overlapping proportion of the potential suitable area for mangroves and S. alterniflora was similar under current and SSP5-8.5 scenarios, being 15.2% and 14.5%, respectively. In the future, it is necessary to strengthen the protection and ecological restoration of mangroves along the coast of Guangxi and there is great challenge for preventing further invasion of S. alterniflora.

Communication Management and Data Compression Algorithm Design of BeiDou Transparent Transmission Terminal for Argo Buoy

The Argo buoy detects marine environmental data by making profile movements in the ocean and transmits the profile detection data to the shore base through the communication terminal. However, due to the large volume of data collected from profile detections and the continuous operation of the terminal, the remote communication of buoys is characterized by lengthy communication times and significant power consumption. A low-power Beidou transparent transmission terminal is designed to solve these problems in this paper. The terminal performs low-power operation management and power management for terminal remote communication. After the end of a communication process, the microcontroller turns the Beidou module power off and enters STOP mode. Before the next communication process begins, the serial port wakes up the microcontroller, which powers up the Beidou module. Before the remote communication, the microcontroller compacts the profile detection data collected by the buoy to reduce the quantity of remote communication data. In this paper, a variety of data compression methods are used to compare the compression rate, and the best compression method is selected according to the format characteristics of the data. The results show that the Beidou transparent transmission terminal of the Argo buoy can realize low power consumption for remote communication in ocean exploration. The terminal reduces power consumption by 77.282% per communication, and the average number of remote communications for each profile detection data are reduced by 55 times. The low-power Beidou transparent transmission terminal improves the battery life and is conducive to the long-term operation of the buoy.

Spatio-Seasonal Habitat Suitability Model of Anisakis spp. in Chub Mackerel (Scomber Japonicus) as a Scientific-Based Approach to Provide Safety Assessment Policy of Fisheries Product in Indonesia

The infestation of Anisakis spp. in Chub mackerel (Scomber japonicus) may have significant health and socioeconomic implications. Understanding the spatio-seasonal occurrence of Anisakis spp. in its host, S. japonicus, is crucial ecologically and for effective management. This research aimed to develop a predictive map (spatial-seasonal patterns) for Anisakis’s infestation in S. japonicus using the maximum entropy (MaxEnt) algorithm. Anisakis spp. and S. japonicus occurrences were obtained through a comprehensive Bibliographic analysis of the Scopus database (2017-2022) and the Ocean Biodiversity Information System (OBIS) database to collect the necessary data. Environmental predictors were sourced from the Global Marine Environment Data. The resulting model demonstrated a reliable performance, as indicated by an Area Under Curve (AUC) value on the Receiver Operating Characteristic (ROC) chart exceeding 0.8. The findings of this study revealed that the infestation of Anisakis spp. in S. japonicus is projected to be more prevalent during the fourth quarter of each year. Furthermore, the environmental factors influencing the infestation were identified as diffuse attenuation, water depth, and distance from the coast. These research outcomes can be a foundational reference for developing an effective control system for inspecting fresh or frozen fish within the quarantine department. By utilizing the spatial-seasonal patterns and environmental predictors identified in this study, authorities can implement targeted measures to prevent and mitigate the infestation of Anisakis spp. in S. japonicus, safeguarding public health and maintaining the quality of fish products.

Design of Riser System Based on Target Oilfield and Platform

Abstract: Risers are an important component in the development of offshore oil and gas resources, but they are also one of the weakest components. Therefore, the safety of the design of deep-water riser systems plays a crucial role in the development of offshore oil and gas resources. This article focuses on the selection and initial design of riser systems based on the target oilfield and platform. Taking into account both the transportation capacity of oil and gas pipelines and the strength of casing, multiphase flow theory is used to calculate the pressure drop and transportation capacity of the riser. Based on this, the size of the oil pipe and casing is iteratively calculated. The riser design is based on the specifications and professional software Orcaflex, which is mainly used in the design process, is used for stress analysis of the riser, Taking into account the main scale of the FWPSO platform, reservoir and wellhead data, as well as the marine environment data where the riser is located, the selected oil and casing will be subjected to wall thickness design verification.

Evaluating the impacts of environmental and fishery variability on the distribution of bigeye tuna in the Pacific Ocean

Abstract Climate change-induced variabilities in the environment and fishing pressure affect the distribution and abundance of bigeye tuna in the Pacific Ocean. Understanding the causal relationships among these factors is complicated and challenging. We constructed a multi-output neural network model based on data from four types of bigeye tuna fisheries (longline and purse seine in the west-central and eastern Pacific Ocean, respectively) and marine environmental data, aiming to analyse the response of bigeye tuna to natural and anthropogenic factors from 1995 to 2019 in the Pacific Ocean. The input layer weights were used to explore the importance of environmental variable, while the output layer weights evaluated the contribution of fishing operations. These factors determined the final spatiotemporal distribution and abundance dynamics for bigeye tuna. The optimal model predicted a strong correlation between the locations of major habitats and El Niño southern Oscillation (ENSO) events, indicating that bigeye tuna abundance dynamics respond to the intensity of climate variability. During El Niño events, suitable conditions lead to an expansion of the main habitats east of 170°W, while during La Niña events, the strengthening of the westward advection leads to the contraction of major habitats west of 170°W. Furthermore, the resource abundance of bigeye tuna is predicted to be higher during moderate to weak El Niño events than during strong El Niño events. The abundances in purse seine and longline-dependent fisheries demonstrate significant different distribution patterns under different ENSO events, reflecting the unique environmental preferences at different life stages of bigeye tuna. Given the increasing frequency of climate variability and escalating fishing pressures, our findings provide beneficial insights for the sustainable development of bigeye tuna resource in the Pacific Ocean.

Evaluating Suitability of Fishing Areas for Squid-Jigging Vessels in the Northwest Pacific Ocean Derived from AIS Data

Understanding the spatial distribution of fishing activity and suitable fishing areas is important for improving sustainable fisheries management and protecting vulnerable fish stocks. To identify climate-related habitat changes and variations in the distribution of fishing activity for squid-jigging vessels in the Northwest Pacific Ocean, two types (weighted arithmetic mean method, weighted-AMM; weighted geometric mean method, weighted-GMM) of habitat suitability index (HSI) models were developed in this study with marine environmental data at different depths. The boosted regression tree (BRT) model was adopted to access the monthly important environmental variables and the relative influence of the corresponding variables. The results showed that the weighted-AMM has better prediction performance than the weighted-GMM. The suitable fishing areas showed significant seasonal changes in both spatial location and coverage area. The hotspot map showed that the suitable fishing area for squid-jigging vessels was located in the scope of 42∘ N∼44∘ N, 155∘ E∼170∘ E throughout the year during 2012∼2019, which suggests that high squid-jigging fishing pressure should be given more attention in fishery management. The HSI model also had good prediction performance for the fishery data of Chinese companies, except for June and July. Additionally, fishing efforts could be used as alternative data for fishery research. The study has also suggested that fishery data are restricted by spatial and temporal distribution and fishing experience, which probably biases the results of the research.

Enhancing the reliability of shipborne INS/GNSS integrated navigation system during abnormal sampling periods using Bi-LSTM and robust CKF

Developing a reliable navigation system is a crucial challenge for intelligent ships. Integrating Inertial Navigation Systems (INS) with shipborne Global Navigation Satellite Systems (GNSS) provides a viable solution. However, the system's reliability heavily relies on GNSS availability. This paper proposes a method to enhance the reliability of shipborne INS/GNSS integrated navigation systems during GNSS abnormal sampling periods. The proposed method consists of two parts. Firstly, using specially designed input and output parameters, a pseudo-GNSS prediction model based on a Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) is constructed to generate predicted pseudo-GNSS measurements during periods of GNSS loss, effectively filling in the gaps left by the absence of actual GNSS data. Secondly, Huber's M-estimation-based Robust Cubature Kalman filter (RCKF) is integrated into the data fusion algorithm, and a switching mechanism between RCKF and the Cubature Kalman filter (CKF) is established based on the source of GNSS data utilized for measurement update, to mitigate the impact of non-Gaussian distribution prediction noise introduced by pseudo-GNSS prediction model. Experimental validation using real ship navigation data in a natural marine environment confirms the proposed method's capability to maintain navigation accuracy during GNSS loss for shipborne INS/GNSS integrated navigation systems. The algorithm's superiority becomes more evident as the duration of GNSS loss increases, underscoring its effectiveness in enhancing shipborne integrated navigation system reliability.

The environmental niche of the squid-jigging fleet in the North Pacific Ocean based on automatic identification system data

The vast number of fishing fleets and limited regulatory capacity pose challenges to the effective monitoring and management of marine fishery resources. To understand the fishing characteristics of spatial distribution for squid-jigging fleets in the North Pacific Ocean (NPO), satellite-based automatic identification system (AIS) data during 2016 ∼ 2020 and marine environmental data were applied to explore the drivers of fishing activities based on the boosted regression tree (BRT) and generalized additive model (GAM). The results showed that the BRT model has better performance than the GAM. The spatial distribution of squid-jigging fleets has a significant seasonality. Specifically, the spatial distribution of Fishing effort (FE) gradually shifted to the northwest from May to September, while gradually shifted to the southwest from October to November. The favourable area for focusing FE was the 150°E ∼ 165°E, 40°N ∼ 44°N. The latitudinal distribution of FE was not wide and the longitudinal distribution was long. Variations in the marine environment constantly influenced the FE of the squid-jigging fleets. Overall, the influence of sea surface temperature (T0) on FE was most significant. The favourable range of T0 for FE was 12 ∼ 20°C. Water temperature at the 300 m depth (T300) was also an important factor through the fishing season. The favourable range of T300 for FE was 3 ∼ 6°C. There were obvious differences in the influence of other environmental factors on FE in different months. Different environmental variables drive the spatial distribution of fishing effort in different months. This study may help to scientifically and effectively guide fishery management and sustainable development by evaluating the spatial variations in fishing activity.

A Case Study in Sharing Marine eDNA Metabarcoding Data to OBIS

Metabarcoding of DNA collected from an environmental sample (eDNA) is increasingly employed in marine biodiversity surveys, with the ability to target taxa from microbes to plankton to large vertebrates depending on the molecular markers used. These techniques are often the only viable method to detect certain taxonomic groups, and therefore provide observations that are currently under-represented on existing biodiversity data platforms, such as the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF). Some of the reasons for this disconnect include the unique data structures inherent to eDNA datasets, the complexities of combining marine observation data and environmental data (De Pooter et al. 2017), and the minimal availability of documented examples. Here we present a detailed case study on the preparation of marine metabarcoding survey data for publication to OBIS. This data comes from the 2021 Gulf of Mexico Ecosystems and Carbon Cycle (GOMECC) cruise led by the National Oceanic and Atmospheric Administration (NOAA), employing 17 coastal-offshore transects across the Gulf of Mexico and the Atlantic Ocean. Metabarcoding libraries targeted bacteria and archaea with the 16S rRNA marker and eukaryotes with the 18S rRNA marker. Amplicon sequence variants (ASVs) were inferred for each marker, then taxonomy was assigned to these ASVs using the open-sourced reference databases PR2 5.0.1 and SILVA 138.1. OBIS requires that taxonomic assignments are converted to the World Register of Marine Species (WoRMS) nomenclature, which can be particularly challenging for marine bacteria and archaea as these taxa are underrepresented on WoRMS. Three tables—the per-sample ASV observation counts, assigned taxonomy of the ASV sequences, and sample collection data—were then converted to the DNA derived extension for Darwin Core (Abarenkov et al. 2023) using a combination of new and OBIS-provided Python scripts (LaScala-Gruenewald et al. 2021). This workflow is available at the NOAA Omics Data Management Guide*1, which will also host links to NOAA Omics datasets. Some of the key challenges to navigate when preparing metabarcoding data for OBIS include: developing a sample collection data template, converting taxonomic assignments to WoRMS nomenclature while preserving the original assignments, and recording the complex methodological and bioinformatic processes involved in data generation in order to be reproducible. developing a sample collection data template, converting taxonomic assignments to WoRMS nomenclature while preserving the original assignments, and recording the complex methodological and bioinformatic processes involved in data generation in order to be reproducible. We describe our workflow for tackling these challenges, with the aim of fostering discussion on best practices for publishing marine eDNA data to biodiversity data platforms. This work is part of a larger effort across NOAA ’Omics to develop a comprehensive bioinformatics platform and data management framework for marine eDNA and microbiome data.

Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scales

Climate change has posed a great challenge to global fisheries harvesting. Purpleback flying squid (Sthenoteuthis oualaniensis) is a major economic cephalopod in the northwestern Indian Ocean waters, but how to choose the optimal spatiotemporal scales and models for constructing a prediction model of its fishery remains to be researched. Reasonable modeling is essential for predicting and managing the fishery of purpleback flying squid. Based on the statistical data of purpleback flying squid fishery and multi-variable marine environment data in the northwest Indian Ocean from 2015 to 2021, this paper used the gravity center of fishing grounds method, GAM, GBT, 2DCNN, and 3DCNN models to analyze. Results: 1) The catches of purpleback flying squid reached their peak in 2019, and the higher catch areas were concentrated at 15° ∼ 19°N and 61° ∼ 65°E. The annual gravity center of the fishing grounds as a whole moved northeast. 2) Different models have advantages and disadvantages on different scales; Overall, the optimal models were GBT and 3DCNN models; The Optimal spatiotemporal scale was three days (0.25° × 0.25°). 3)In the GBT model, the most critical variables were SST, Chla, 97 m-DO, and SSS; In the 3DCNN model, the relative importance ranking of variables depended on the training data set. The results of the study help us to select the optimal model and optimal spatial and temporal scales to construct a prediction model for the Northwest Indian Ocean purpleback flying squid fishing grounds, which will provide a scientific basis for the sustainable development and management of the purpleback flying squid fishery in the region.

Research on High-Resolution Reconstruction of Marine Environmental Parameters Using Deep Learning Model

The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.

Risk assessment of unmanned underwater platforms in the South China Sea marine environment based on two-dimensional density-weighted operator and cloud barycentric Bayesian combination

With the rise of unmanned combat in naval battles, military powers are paying increasing attention to unmanned underwater platforms. However, there are few studies on the interaction between unmanned underwater platforms and naval battlefield environments, and most of them provide qualitative descriptions, which are of little help in future combat deployment and the development and utilization of marine resources. To solve this problem, we constructed a natural environmental risk index system for unmanned underwater platform operations in a sea area using the marine environmental factor data of the South China Sea region. We used a two-dimensional density-weighted operator to obtain the weights of different impact factors and conducted a risk assessment of the South China Sea region leveraging the cloud barycenter-Bayesian network method. The overall risk probability of unmanned underwater platforms was 14.7%, with a higher risk of 19.0%, a medium risk of 26.0%, a lower risk of 21.2%, and a low risk of 19.1%. Additionally, the risks in the west and north of the South China Sea were larger than those in the south and east. Therefore, the risk in offshore areas was greater than that in the open sea. Consequently, the open sea is more suitable for operating the relevant unmanned underwater platforms. Overall, this study provides technical support for the risk assessment of unmanned underwater vehicles operating in the South China Sea and the development and utilization of marine resources.

Risk Estimation of Typhoon Disaster Based on Three-Dimensional Information Diffusion Method

In the context of the increasing frequency and intensity of natural disasters, assessing the risk of typhoon disasters can provide significant assistance for risk control and emergency management of typhoon disasters. In this paper, based on the three-dimensional information diffusion method, the formal expected loss model is transformed into a computable typhoon risk assessment model. The fuzzy information in the small sample data is deeply mined, and the typhoon disaster risk assessment with the expected loss as the connotation is carried out, and the probability density distribution estimation of disaster-causing factors at different levels and the functional relationship identification between disaster-causing factors at different levels and direct economic loss rate are realized by using the information matrix. At the same time, combined with the frequency of typhoon occurrence, the annual risk of disasters is predicted to make up for the problem of insufficient marine environmental data and improve the calculation accuracy of risk assessment models. Taking Guangdong Province as an example, a typhoon risk assessment was conducted, estimating the probability distribution, direct economic loss rate distribution, and annual loss expectation of typhoon disasters under different wind speed scales and extreme wave heights. The results indicate that the risk estimation value of the three-dimensional information diffusion model is higher than that of the traditional model, which weakens the limitations of the low-dimensional information diffusion model and makes the evaluation results more reasonable and reliable.

Forecasting Albacore (Thunnus alalunga) Fishing Grounds in the South Pacific Based on Machine Learning Algorithms and Ensemble Learning Model

To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM) for albacore fishing grounds forecasting was constructed based on six machine learning algorithms. The overall accuracy (ACC), fishing ground forecast precision (P) and recall (R) were used as model accuracy evaluation metrics, to compare and analyze the accuracy of different machine learning algorithms for fishing grounds forecasting. We also explored the forecasting capability of the ELM for different grades of fishing grounds. A quantitative evaluation of the effects of different marine environmental factors on the forecast accuracy of albacore tuna fisheries was conducted. The results of this study showed the following: (1) The ELM achieved high accuracy forecasts of albacore fishing grounds (ACC = 86.92%), with an overall improvement of 4.39~19.48% over the machine learning models. (2) A better forecast accuracy (R2 of 81.82–98%) for high-yield albacore fishing grounds and a poorer forecast accuracy (R1 of 47.37–96.15%) for low-yield fishing grounds were obtained for different months based on the ELM; the high-yield fishing grounds were distributed in the sea south of 10° S. (3) A feature importance analysis based on RF found that latitude (Lat) had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades from February to December (0.377), and Chl-a had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades in January (0.295), while longitude (Lon) had the smallest effect on the forecast of different grades of fishing grounds (0.037).

Habitat changes and catch rate variability for greater amberjack in the Taiwan Strait: The effects of El Niño–southern oscillation events

El Niño–Southern Oscillation (ENSO) is a crucial oceanographic phenomenon that leads to interannual fluctuations in the climate and ecosystem productivity of tropical and subtropical areas. These fluctuations affect the suitability of habitats for many commercial fish species. However, detailed information on the effects of this major phenomenon and the resulting environmental changes on the habitat and catch rates of the economically and ecologically crucial species of the greater amberjack (Seriola dumerili) in the Taiwan Strait (TS) is lacking. In this study, we employed a weighted habitat suitability index (HSI) modeling method and used remotely sensed marine environmental data as well as data from recorders in Taiwanese fishing vessels (in 2014–2019) to understand the effects of ENSO events on the habitat suitability and catch rates for greater amberjack in the TS. Analysis of variance revealed that environmental factors substantially influenced greater amberjack habitats and catch rates during ENSO events across seasons. The catch rates were high in spring and summer in the southern and northern TS and in autumn and winter in the southern TS. The catch rates were higher in spring, summer, and autumn (&amp;gt;9.0 kg/h) in El Niño years, and in winter, the catch rates were higher in normal years (&amp;gt;12.0 kg/h) and lower in La Niña years. The predicted HSI for the southern and northern TS revealed that greater amberjack populations were predominantly distributed at 20–24°N and 24–28°N, respectively. Opposite habitat suitability was synchronously found in spring and summer during ENSO events, with higher HSI values recorded in spring in El Niño and normal years and higher HSI values recorded in summer in La Niña years. In winter, the HSI values of the southern and northern TS were higher in El Niño and normal years and substantially lower in La Niña years. Habitat suitability was extremely low in autumn. These findings imply that ENSO events play a key role in regulating environmental conditions and affect the catch rates and habitat suitability for the greater amberjack in the TS.

Increasing Efficiency of Nautical Chart Production and Accessibility to Marine Environment Data through an Open-Science Compilation Workflow

Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The robustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures.