Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

doi:10.21203/rs.3.rs-3317182/v1

Download PDF

Systematic Review

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

https://doi.org/10.21203/rs.3.rs-3317182/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The value of fish resources to socioeconomic development is immense. However, there is growing evidence that continuous fishing-both at sea and in freshwater sources is threatening the availability of fish resources evidenced by the dwindling fisheries resources. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable fishing practices. Owing to this we use a systematic literature review to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems. The review documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change and implement new policies and regulations thus promoting sustainable fisheries management.

Marine and Freshwater Ecology

Renewable Resources

Artificial Intelligence and Machine Learning

Geographic Information Systems

Information Retrieval and Management

Fisheries

Sustainable Fisheries Management

Data Analytics

Data-driven Decision Making

Global fish and aquaculture resources are critically vital for ensuring food security, health, and employment [1]. Fish for instance is an inexpensive source of protein and micronutrients for millions of people [2]. Around 17% of the world's total animal protein consumption is made up of fish, crustaceans, and mollusks, most of which are caught in the ocean [3], [4]. Average intake of aquatic food climbed from 9.9 kilograms per person in the 1960s to a record-breaking 20.5 kilograms in 2019, then slightly decreased to 20.2 kilograms in 2020. [3]. As a result of rising affluence, urbanization, improved post-harvest practices, and dietary trends, it is predicted that by 2030, global consumption of aquatic food would increase by 15%, reaching an average of 21.4 kg per person. (www.unctad.org). Therefore, in low-income countries where overall protein intake is low and diets are less varied, fish and shellfish are particularly crucial for reducing malnutrition defects [2]–[4]. In addition, fishing is an indispensable livelihood source [5]; with about 660 million people depending directly or at least partially on fishing, aquaculture, or fish-related activities including those workers in subsistence and secondary sectors, and their dependents [3]. Increased engagement in fishing and fish-related activities has ballooned international trade in fisheries and aquaculture products thus boosting socioeconomic development (www.wef.org). According to the 2022 FAO report, fishing and aquaculture trade generated a record high of 165 billion USD in 2018 and 151 billion USD in 2020. With increased aquaculture production and an emphasis on sustainable fisheries, fishing activities, and fisheries resources are thus projected to be a key component in reaching UN Sustainable Development Goals [6] thus a need for continuous management of fisheries resources, fishing zones, and the value chains [7].

1.1 Sustainable Fisheries Management and Emerging Sustainability Issues

According to the 2021 International Resource Panel Report, sustainable management of biotic resources such as marine fish is a crucial pivot in Agenda 2030; especially SDGs 1, 2, 3, 5, 10, 12, 13, and 14 [6]. A study by Nguyen and Tran [8] in Vietnam observed that integrated sustainable fisheries management not only helps monitor migratory species such as oceanic tunas but also aids in the optimization of multispecies ecosystems and the management of multi-gear fisheries [9]. This supports a plethora of literature that sustainable fisheries management-encompassing inter alia maintaining and balancing fish stocks, reducing ecosystem pollution, and Illegal, Unreported, and Unregulated (IUU) fishing [3]; not only offsets fishing-related trade-offs [9] but also balances sustainability benefits in world fisheries [8], [10], [11]. Unfortunately, recent studies have documented that the increasing quest for and trade in fisheries resources beyond sustainable limits is threatening livelihoods and development targets [12]. The abundance of fisheries resources is still dwindling as a result of overfishing, pollution, poor management, and other factors. In 2019, 64.6 percent of fishery stocks were within biologically sustainable limits, which is 1.2 percent less than in 2017 [3].

Studies have recommended that to reverse this trend, concerted efforts are needed to bring all stakeholders and actors; especially the big fishing companies and dealers into global sustainability frameworks [5], [12]. However, big fishing companies and dealers are increasingly placing unprecedented pressure on freshwater and marine ecosystems in their effort to catch enough fish to meet the surging demand [1]. In addition, efforts to manage sustainable fishing and promote ocean stewardship are still hindered by a number of issues, such as a lack of accurate and actionable information [3], and a lack of resources on the part of many governments to process and analyze data on where, when, and what kinds of fish are being caught by fishing boats, as well as other human activity at sea [13]. It takes five times as much work (measured in kilowatt-hours) to catch the same number of fish as it did in 1950 since the targeted species are currently in short supply [4]. This deficiency gravely jeopardizes the capacity of endangered ocean species to reproduce and maintain their population, as well as the financial viability of fishing operations [14]. In addition, establishing effective management practices and systems in the management of fisheries and the lack of data to quantify catches and determine the best fisheries practices, especially in developing countries [1], [15]. Practices such as reduction in the fish fleet; especially in Asia have further not translated into more sustainable outcomes but rather proliferated into Illegal, Unreported, and Unregulated (IUU) fishing [3].

1.2 Why Data Analytics in Sustainable Fisheries Management Now and Not Later

The complexity in managing fisheries resources both at micro and macro levels requires the adoption of new fisheries management, technologies, and data reporting pathways; because current methods such as fish stock assessments that often use catch and landings records have proved ineffective and problematic in reporting and representing the extent of fish effort, removal, and sustainability in diverse fishing zones with many users, species, and fishing zones [2]. In addition, existing fisheries data and the developed advanced tools (such as ocean digital tools, and remote sensing platforms and vehicles) are mainly used as small-scale pilots and experiments [5].

We argue that these gaps in fisheries management could be leveraged by the use of data analytics. Several studies contend that even though balancing fishing interest and environmental sustainability; especially in the ocean fisheries and in poor fishing countries-where the appetite for marine fishes and creatures such as tuna, salmon, anchovy, and shrimp has ballooned [5] at a compound annual growth rate of 3.2 percent between 1961 and 2016 is complex [1], [4], the use of advanced data collection, processing, interpretation techniques presents an untapped potential to the management of both inland and marine fishing resources and effort [16]. According to studies, stakeholders in the fishing industry in USA and Europe are already reaping socioeconomic benefits by integrating data analytics into all aspects of the value chain, encompassing fishery management, detection and capture, processing, reporting, and surveillance and control [1]. According to the 2019 Annual Economic Report on the EU Fishing Fleet, the application of advanced analytics in fish operations and management has resulted in a reduction in operational variable costs for labor, repair, maintenance, running fish related businesses, and fuel from 77.2 to 66 billion USD since 2017. In reality, large-scale fishing enterprises globally might save more than $11 billion as a result of advanced analytics [4]. This impetus has partly led to the initiation of the 60 million USD five-year project named The Audacious Project to inter alia catalyze and apply big satellite data and Artificial Intelligence to publicly map and visualize over one million industrial fishing vessels in near real-time the ocean [13].

In developing countries, most fisheries managers and people are also increasingly becoming tech-savvy, and during the recent years, as technology advancements have improved information deployment efficiency, increased data intake capabilities, and increased data availability, positive attitudes toward algorithms have grown in popularity across industries [10], [17]–[22]. There is also agreement that fisheries need to adopt cutting-edge technical solutions to maintain a sustainable trajectory while minimizing their environmental impact since rules alone cannot eliminate overfishing and emerging marine ecosystem concerns such as pollution, and climate change [11], [23]. Most concerns, including catch reporting, trade information sharing, subsidies, tariff policies, and regulation enforcement, will benefit from increased national and international collaboration using modern data analytics. [24]. The rising use of advanced data analytics may also be advantageous to the public and the fisheries sector [4].

Based on the insights above, it is noteworthy to contend that the value of data analytics and its application in fisheries management not only focuses on the primary sources of fish extraction in the marine and freshwater ecosystem but further extends to other components of the fish value chain including traceability of supply and demand chains, markets, restaurants and individual consumption patterns, behavior, and environmental concerns to manage the fisheries systems-which are novel approaches to holistic fisheries management [4], [5]. The only lacuna is the lack of crisp studies; especially on where specific data analytics tools and methods could be applied in a given fishing ecosystem [13] – which aspect/gap we aim to fill. To support this argument, we delve into the literature to identify the recent advances in data analytics in the fisheries sector, highlight the recent data analytics tools, and show how they are used in fisheries, where, the importance of informing fisheries managers on the best actions or interventions for sustainable resource extraction.

2.1 Inclusion/Exclusion Criteria

Systematic literature reviews (SLR) engage repeatable analytical methods for secondary data collection and analysis [25]. SLR synthesize published literature and describe its cutting-edge [25], [26]. Systematic literature reviews originally known for medical research published in core clinical journals [26], are growing exponentially in other fields such as Merkert and Bushell [27]. Although this is a secondary research on computer-assisted searches, the exhibition of rigor is paramount in SLR [25]. During the search for articles, we predominantly focused on articles from high-impact journals. We used Scopus and Science Direct databases.

We limited all the searches to 2021 and beyond for papers retrieved from Scopus and 2023 for research articles retrieved from Science Direct. All the search was limited to English in all databases and no subject area limitations were put on results from Scopus and Science Direct. Exclusion criteria included non-English articles, studies unrelated to fisheries data analytics, and non-peer-reviewed sources. The search terms used with the main keywords in Scopus were “data AND analytics AND fisheries AND management”, which generated 16 articles. In addition, the search term “how AND to AND collect AND fisheries AND management AND data” was used to retrieve 82 articles from Scopus. A total of 98 articles were generated from the Scopus database. Furthermore, the search term “methods for collecting fisheries data in India” generated 3564 searches without limitations. When the search was limited to only research articles and to articles published in 2023 and beyond, a total of 201 documents were retrieved from the Science Direct database. In total, we retrieved 299 documents.

We downloaded these articles as Scopus.ris and ScienceDirect.ris and uploaded them in Covidence – a software for managing and streamlining systematic reviews. We examined and screened the articles in Covidence. The screening of process entailed removing duplicated copies of articles, and for this, one document was automatically removed. Two reviewers independently screened articles and consensus was achieved in case of conflicting article choices. Of the 298 screened articles, 163 were excluded at the title and abstract screening stage, 12 studies excluded because they were not available in full text and 18 articles were excluded at the full text review stage. A total of 105 articles passed the eligibility criteria as shown in the PRISMA chart as presented in Fig. 1, and were included in the systematic review and subjected to data extraction using a predefined data extraction excel form (supplementary material - “Data Extraction”).

The extracted data included study characteristics, for instance, title, author(s), publication year, goal/objective of the study, non-digital and digital fisheries data collection methods from the resource, data manipulation/management methods, country/area where the method was used as well as data analytics techniques employed and key findings in relevance of data analytics column (supplementary material - “Data extraction”). The quality and risk of bias of included studies were assessed by reviewers to evaluate their methodological rigor and any potential biases/disagreements were resolved through discussion.

2.2 Data Synthesis

The finding from the selected studies were synthesized using a narrative approach, the key themes, and applications for data analytics in fisheries. The synthesis aimed to provide a comprehensive overview of the current state of data analytics in fisheries and highlight its implications for sustainable fisheries management.

As noted in section 2.2, here we discuss the review findings in a coherent manner. We begin by giving an insightful description of data analytics, and the different data analytics methods. Tools and software used in different fishing zones; highlighting their feasibility in sustainable fisheries management. We then show the spatial distribution of the main fishing countries using data analytics and lastly we discuss the relevance of the different data analytics tools and how they are relevant in sustainable fishing with a focus on different fishing zones, areas, and ecosystems.

3.1 Data Analytics in Fisheries

According to different scholars and literature, data analytics is the process of gathering, transforming [5] and organizing data in order to make deductions, forecasts, and guide well-informed decisions [28]. Data analysis and data analytics are sometimes conflated [11]. These words are not precisely the same, but they are related. In actuality, data analysis is a branch of data analytics [18] that focuses on gleaning insights from data [29]. Data science (using data to theorize and forecast) and data engineering (creating data systems) are two more processes that are included in data analytics as a whole [4]. In the context of fisheries management therefore, we can coalesce these definitions to denote that advancing data analytics could help both fisheries and fishery resource users symbiotically benefit [24] through increased data availability [10], better tools for communicating and deploying fishery-related information [1], improved and efficient fisheries data ingestion [23], better decision-making on fisheries complexities on sustainability vis-à-vis profitability [16], biological uncertainties, better monitoring, surveillance and reporting to fisheries managers [17].

In fact, several studies reveal that since the 1990s there has been increased focus on the need to science fishery-based management and stock assessments to promote good fishery performance but the lack of data [5]; especially in small fisheries impeded this target [1]. Additionally, a dearth of fishery data results in confusion about stock status, which raises the risk of overfishing and threatens the economic and food security of users who depend on that stock [5], [13]. This brought to the fore the need for data analytics as an integral part in fisheries management [11]. To leverage these benefits, a series of data analytics techniques and tools are used [30]. Here, we document some of the advanced techniques and tools used in the context of sustainable fisheries management.

3.2 Main techniques/tools and how they are used

Some of the unique data analytics tools include:

3.2.1. Drones

Also known as unmanned aerial vehicle (UAV), a drone is any aerial vehicle that can be flown remotely by a pilot or that uses software to fly autonomously [31]. Numerous unmanned aircraft have cameras for gathering visual data and propellers to steady flight patterns [11]. Drone technology is progressively being incorporated into sectors such as videography, search and rescue, agriculture, and transportation [32], [33]. In the context of fisheries management, drones that are outfitted with cameras or other sensing equipment are being utilized more and more to study the ocean. Some even have the ability to navigate underwater [34]. Drones are more adaptable and less expensive than oceanographic vessels [35]. They can also offer a more thorough sampling of the surroundings when delivered in groups [36]. In fact, despite covering a smaller area than satellites, drones can produce more precise photos that enable the detection of tiny objects or events [17].

In addition, given that various drones can fly at various elevations and distances, small scale fisheries and near shore marine coastal fisheries can utilize very close-range drones, which can often reach up to three miles [4]. Close-range UAVs have a 30 mile maximum range. Short-range drones, which can fly up to 90 miles, are typically utilized for espionage and intelligence gathering [31]. Mid-range unmanned aerial vehicles (UAVs) have a 400-mile range and can be used for meteorological research [32], marine scientific studies on species such as dolphins and sharks [30], and intelligence gathering [37].

3.2.2. Satellites

According to NASA [37], a satellite is a moon, planet, or object in orbit around a planet or star that serves primarily as a communications tool, transmitting things like phone calls and TV broadcasts around the globe (www.nasa.gov). Satellites have potential to see vast areas of Earth at once due to their eagle's eye viewpoint [31]. Satellites can therefore acquire information more rapidly and effectively than devices on the ground [38]. In the sustainable fisheries management domain, we relate that Satellites equipped with optical and radar sensors can provide an unprecedented level of spatial and temporal resolution [17], which makes them particularly useful for monitoring [4], [39]. The electromagnetic spectrum's whole range of light reflected by the earth's surface is measured by optical sensors [32]. These data can be used to derive crucial oceanic metrics [40] like water temperature and turbidity [8]. Microwave radiation is emitted by radar sensors, which measure the amount that is reflected back to the device [32]. They can offer information about the topography of the water [41], winds, target species detection [42], and vessel movement [1]. Radar devices, unlike optical sensors, can gather data even in bad weather and lighting situations, such as when the sky is overcast or dark [18].

3.2.4. Vessel onboard and underwater surveillance devices

These devices are mostly utilized in the detection, control and monitoring of potential risks in marine areas [30]. Unmanned robots, autonomous underwater vehicles, and unmanned underwater vehicles (UUVs) are a few of the equipment that can locate and identify dangerous and unsafe behaviors in the marine environments [4] such as the seabed [30]. According to the US National Oceanic and Atmospheric Administration [32] traditional ocean monitoring mechanisms were mainly laborious involving the use of observers and manual recording onboard vessels and new devices have bridged these gaps.

For instance, onboard sensors have the ability to automate and simplify this time-consuming procedure [43] while also producing more thorough and trustworthy data that may be included into platforms known as electronic monitoring systems (EMSs) [30]. Additionally, tools like Vehicle Monitoring Systems (VMS) [17] and the Automatic Identification System (AIS) can gather data on a vessel's position [5], speed, and direction [38], supplement radar systems [1], and reduce the likelihood of marine collisions [4].

Based on the evidence of the myriad benefits of data analytics tools and techniques shown above, we contend that data analytics has innumerable benefits related to sustainable fisheries management. One pertinent aspect that needs to be focused on; as data analytics is a new paradigm shift [28], is the spatial spread related to where or which countries and regions use some of these technologies in fisheries practices [3], [4]; which trend we explore in the next section.

3.3 Scope of Spatial Use of Data Analytics in the Fisheries Domain

To underscore the value of data analytics, we visualized from the sourced literature which continents are increasingly adopting data analytics techniques in fisheries management and operations (Fig. 2). Extracted results showed that Asian fishing countries are the most dominant (43%), North America (33%), Europe (9.2%), Australia /Oceania (6.4%) South America and Caribbean (5%) and African (2.7%) and Antarctica fisheries (0.9%). We contend that the lower ranking of Antarctica is negligible due to less human settlement and fishing activity [44].

The data shown in Fig. 2 is pertinent as it lays bare what is at stake in terms of fisheries management benefits to countries and countries-especially if equated to the global fisheries report. A review of the 2022 FAO report documents that Asian countries such as China have not only continuously led global receipts emanating from fisheries; but also demonstrate sustained fisheries and aquaculture production compared to their peers in Africa. For instance, despite the devastating impacts of COVID-19 [45]; Asian countries predominate fish and aquaculture production as depicted in Table 1; estimated at 70 percent [3].

Table 1

Global fish and aquaculture production by region/continent-2022
Continental-wise Fisheries and Aquaculture Production	Percentage (%)
Asia	70
Americas (North and South and Caribbean)	12
Europe	10
Africa	07
Oceania	01
Total	100%
Source: [3]

At the same time however, increased fisheries effort and production could also act as a tipping point for increased need for data analytics based on the number of fleet, and increased need for fisheries food/resources [9], [24], [46]. Global estimates report an increase in fish effort with 75 percent of motorized and 95 percent of non-motorized fishing vessels operate in Asia and Africa respectively [3]. The increase in fish effort could partly scupper sustainable fisheries management due to increased effort [13]. Based on the complex evidence above, we hypothesize that countries/regions requiring sustainable fisheries production and at the same time negate the negative shocks resulting from sustained fishing effort could leverage the immense benefits availed by data analytics.

3.4 Relevance of Data Analytics

To show the relevance of data analytics in sustainable fisheries management, we breakdown each data analytics technique to show which fishing ecosystem/zone or value chain (Table 2) it can be applied to among others coastal coral reef and offshore zones [47], [48], high seas [49], and in marine [50] and freshwater species zones [51], [52]; so as to mitigate the myriad fisheries management challenges [3] such as marine pollution [53], fish supply chains [54], species stock assessments [55], IUU [56], and sea grass assessments [57] among others.

Table 2

Data analytics techniques, their relevance and where they are used
Data Analytic Method	Where is the method used	Relevance of Data Analytics	Author
Intelligent Analytics	River depth/River flow	Real time catchment regulation and water management	[58]
Big data, remote sensing, machine learning	Coastal and marine zones	Transform enormous amounts of data for decision making	[47]
Down scaling method; statistical methods and hybrid dynamical-statistical methods	Estuarine zones	Evaluation of salinity and estuarine surface temperature	[59]
Multiple simulation models and observational datasets	Red Sea	Ocean winds, weather forecasting,	[60]
Bayesian hierarchical methods	High sea	Analysis of trans boundary fisheries	[49]
DBSIRM model – Big data analytics technology	Yellow sea	Fish index assessment and sustainable growth/development	[55]
LADS surveys - Bathymetry	Marine benthic species	Swath mapping, quantitative framework for spatial data management	[61]
GPS tracking technology	Coral reef zones	Segmentation of vessel trajectories into fishing and non-fishing activities	[48]
PesKAAS application	Timor and Banda Sea	Gives near real time information on catch and effort	[62]
Predictive analysis, information fusion, and visual analytics	Dovar straight	Automatic identification of structural abnormalities, the prediction of vessel itineraries	[56]
Predictive analytics and time series analysis	North Atlantic and Labrador Sea	Ocean monitoring, fish food supply chain and spot trends/decision making	[54]
Principal component analysis (PCA), cluster analysis	Deep Sea, cold coral zones, canyons	Assessment of potential habitat extent in the deep sea	[63]
mKRISHI® Fisheries analytic system	Indian Ocean/ deep Sea-30km	Identification of risk zones in a fishery system	[15]
Geovisual analytics, Hybrid spatial temporal filtering (HSF) and automated behavioural change point analysis	Pacific and Atlantic ocean	Assess how well interactive and automated mapping solutions enhance fisheries enforcement efforts	[64]
Triplot of Redundancy Analyses (RDA) and Pearson correlation	Benthic zones	Assessment of morphological changes in benthic species	[65]
Multivariate statistical analysis, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA	Fresh water ecosystems	Assessment of marine pollution and eutrophication in aquatic systems. Identification of distinct groupings of variables and understand the correlations between them	[51]
AI to pattern analysis and information prediction	Fresh water ecosystems	Aquaculture improvement in small scale fisheries	[52]
Log transformation, R, SPSS	Deep sea	Determining micro plastic pollution in zoo plankton	[53]
Sensor information systems and data analysis using scatter plots for visualization	Arabian sea	To find enabling trends and insights in fish ecosystems and value chains	[66]
Google Earth Engine (GEE), Regression plots	Near shore, Sea grass ecosystems and Island zones, and Arabian Sea	Multi-temporal analysis of Sea grass losses	[57]
Satellite photos	Coastal zones of India	Assessment of ocean acidification/ environmental, social and governance	[39]
Non-parametric Kruskal-Wallis test, permutational multivariate analysis of variance (PERMANOVA)	Coastal mangrove ecosystems	Spatial temporal data on and quantification of marine litter/pollution in mangroves	[67]
Remote sensing and video surveillance devices	Indian coast/ Arabian Sea	To detect spatial and temporal changes in beach ecosystems	[68]
Multivariate techniques, such as PCO analysis	Estuaries	Demonstrates spatial temporal changes in micro plastics pollution in fishes	[69]
Statistical data analysis, Pearson Product Moment Correlation analysis	Arabian Sea, Indian coast, Marine fish products	Estimate micro plastic presence in fishes	[70]
SPSS software version 24 and Excel 2019, Shapiro-Wilk test, Pearson correlation tests	Persian Gulf	Understand the concentration and marine plastics in fishes	[71]
Machine learning methods Linear Regression, Random Forest (RF), and Support Vector Regression (SVR)	Benthic and coastal zones	Estimating water turbidity, chlorophyll (macro and micro algae)	[72]
Principal Component Analysis (PCA) using R,	Native coastal and fresh water ecosystems/zones	Climate change and species vulnerability assessment	[73]
Pearson correlation, cluster analysis, and principle component analysis (PCA). For instance, cluster analysis	Ghana/Gulf of Guinea	Radiological evaluation of beach sediments and species risk	[74]
Multivariate analysis e.g. Pearson correlation, principal component analysis, and cluster analysis. Principal component analysis (PCA) and cluster analysis	Southeast Bay of Bengal	Estimate the health risk posed by consuming tainted seafood by giving useful insights into the distribution of heavy metals in the seafood	[75]
RD-TFD and wordnet ontology features, tokenization, stopword removal, and stemming algorithms: Statistical metrics like precision, recall, F-measure, and accuracy	Marine ecosystems (Jelly fish clusters)	To analyse and group jelly fish clusters	[50]
Artificial Intelligence (AI) models e.g. tray based on a deep-learning, simple online and real-time tracking algorithms	Coastal aquaculture zones/China	Making best technology and data driven decisions for sustainable prawn farm management	[76]
Univariate and multivariate statistics e.g. fixed permutational analysis of variance (PERMANOVA), Canonical Analysis of Principal Coordinates (CAP)	MPAs, Coral zones and non-protected zones	Compare fish conservation and management policies in MPAs and non MPA zones	[77]
Deep learning models and dimensionality reduction methods	Marine/Deep sea	Precise and effective monitoring cetaceans (marine mammals)	[78]
Machine learning and data mining method	Deep sea fishing vessels	Creating decision support tools in marine spatial planning / sustainable /harvesting	[79]
Machine learning and computer vision techniques, Python, and Annotators	Marine ecosystems	Increasing understanding of long term stock fluctuations and environmental changes	[80]
R; REPHYTOX for data simulation	Marine and coastal areas	Informing management on scallops, effects of pollution, SST, and nutrient concentrations	[81]
Machine learning and statistical modelling solution - Haul biomass Index Estimator (HBIE)	High Sea	To fill gaps in trawl surveys	[82]
ArcGIS, Google Earth	Coastal marsh land zones	Analysis of microplastic characteristics and distribution	[83]
Visualization tools	USA coast	Obtain insights into spatial dynamics of anglers’ behaviour and fish populations	[84]
Isotope ratios, and Internet of Things (IoT)	Mediterranean coast	Identify patterns and insights of sea food/ fish supply chains	[85]
Remote sensing, modelling and sampling designs	Fresh water and riverine ecosystems	Shape fish populations and ecosystems dynamics	[86]
Logistic regression model	Migratory Salmon Species / River estuaries	Sex identification of returning / spawning salmons	[87]
Clustering analysis using R	East and South China Sea	Understand mixed trawl fisheries characteristics and fishing habits of target species.	[43]
Bhattacharya technique, FISAT software and Barefoot Ecologist Toolbox	Java Sea	Understand the spawning potential ratio of invasive species	[88]
Generalised linear mixed model (GLMM)	Fresh water ecosystems	Estimate fish effort from remote traffic encounters	[35]

3.4.1 Relevance in coastal and near-shore zones fisheries management

Coastal zones are a rich habitat for species and ecosystems [47], [61] such as mangroves [88], estuaries [59] sea grasses [57], and coral reefs [48], [87] among others which not only provide fish resources but are crucial management zones [3]. However, the increase in fish effort coupled with management gaps and IUU fishing [3], [46], [75] are threatening the sustainability of such ecosystems such as migratory species [87]. With this hindsight, there’s growing evidence that data analytics could be applied [5].

Recent studies in the review revealed that data analytics methods and techniques are relevant in coastal zones as they aid benthic species mapping using the Triplot of Redundnacy Analyses (RDA), Pearson correlation and big data, remote sensing, machine learning and Bathymetry LADS surveys in marine benthic zones respectively [61], [65], decision making on species conservation and management using artificial intelligence models such as tray-based on machine learning, simple online and real time tracking algorithms in coastal aquaculture zones of China, univariate and multivariate statistics such as fixed Permutational Analysis of Variance PERMANOVA, Canonical Analysis of Principal Coordinates (CAP) in Marine Protected Areas (MPAs), coral and non-protected zones, and big data, remote sensing, machine learning respectively [47], [76], [77], determination of salinity and surface temperatures in estuaries using the downscaling method, statistical and hybrid dynamical-statistical methods [59], segmentation of fishing vessel trajectories in coral reef zones using Global Positioning Systems (GPS) [48], determination of coastal fish vulnerability zones in the Indian Ocean using the mKRISHI Fisheries Analytic system and App (Singh, 2016), analysis of sea grass losses in near shore ecosystems, Island zones and the Arabian sea using Google Earth Engine (GEE) and regression plots [57], detection and quantification of marine litter in mangrove zones using the Non-Parametric Kruskal-Wallis test and PERMANOVA [67], identification of changes in micro plastics pollution in coastal fish species in estuaries using multivariate techniques such as PCO analysis [69] and microplastic distribution in coastal marshlands using ArcGIS and Google Earth [83], radiological evaluation of the risk of species habituating along and in beaches such as crabs along the Gulf of Guinea using Pearson correlation, cluster analysis and Principle Component Analysis (PCA) [74], long-term understanding of species shocks and fluctuations due to environmental change using machine learning, computer vision techniques, Python, and Annotators [80], and obtaining insights into the spatial dynamics of fish anglers behaviors to fish populations especially along the US coast using visualization tools [84].

Some of the major breakthroughs in managing coastal pollution have been reported by studies conducted in estuaries in Tamil Nadu in India [69], marshland zones [83], and in mangrove ecosystems in the Central West coast of India [67]. In the study, Jeyasenta [69], represented the geographic similarity patterns of micro plastics in the water and sediment of the assessment sites across both seasons using multivariate techniques, such as PCO analysis. The software tool PRIMER ver. 7.0 was used to carry out these investigations. Additionally, the SPSS 20.0 software Programme was used to do ANOVA and correlation statistical tests. As a result, the analysis and interpretation of the data gathered for this study heavily relied on data analytics. In addition, De et al. [67] analysis and interpretation of the data gathered during the fieldwork is important to the use of data analytics. The data were analyzed using a variety of statistical techniques, including the non-parametric Kruskal-Wallis test, paired Mann-Whitney tests, permutational multivariate analysis of variance (PERMANOVA), and homogeneity tests for multivariate dispersion. With the aid of these techniques, it was possible to assess the significance of spatial variation differences in litter abundance among mangrove stands based on habitat characteristics, concentration differences between rural, peri-urban, and megacity regions, and variation in litter abundance between mangrove floors and canopies based on location. The paper also discusses the techniques for data analysis used to evaluate the homogeneity and normality of variances. The findings of these analyses shed light on the effects of anthropogenic litter pollution on India's coastal mangroves and can be used to build efficient management measures to lessen these effects. With this insights, we can argue that a whole spectrum of sustainable fisheries complexities in coastal zones-a beehive to several environmental and anthropogenic shocks in fisheries management [3] could be mitigated via data analytics.

3.4.2 Relevance in deep sea fisheries management

Deep sea fisheries are some of the most complex in management partly due to their presence in the ‘global common’ and the technicalities in monitoring high seas [3]. Fortunately, recent advances in technology could help mitigate this gap [4]. Extracted documents revealed that data analytics is increasingly being used to tackle deep sea fishing challenges [17]. For instance, multiple simulation models and observational datasets have been used in the Red Sea to monitor ocean winds, and weather forecasting [60], Bayesian hierarchical methods are used in the analysis of transboundary fisheries [49], the DBSIRM model-a big data analytics technology is being used in the Yellow sea for fish index assessments, sustainable growth and development [55], the PesKAAS application has been used in the Timor and Banda sea to give near real-time information on fish catch and effort [62], in the Dover straight, Alessandrini [56] used predictive analysis, information fusion and visual analytics for automatic identification of structural abnormalities in fishes and prediction of vessel itineraries well as Coronado [54] used predictive analytics and time series analysis for ocean monitoring, food supply chain and spot trends in fisheries to inform decision making in Canada’s North Atlantic and Labrador sea. Other breakthroughs in the deep sea include the assessment of potential habitat extent in deep sea cold coral zones and submarine canyons using PCA and cluster analysis [63]. In the Pacific and Atlantic ocean, geo-visual analytics, Hybrid Spatial-temporal filtering (HSF) and automated behavioral change point analysis have been used to assess how well interactive and automated mapping solutions enhance fisheries enforcement efforts [64]. In this study, the comparison of various methods for visualizing and examining movement data collected from Vessel Monitoring System (VMS) data was crucially emphasized. Making sense of the complicated and enormous VMS data sets requires the use of geo-visual analytics and other data analysis techniques. This knowledge can help with decision-making and enhance the management of fisheries resources.

In addition, log transformations, R, and SPSS are increasingly being used in deep sea fishing to determine micro plastic pollution in zooplankton due to the increase in ocean gyres especially in the Indian Ocean and Arabian Sea [53]. Related studies on micro plastic presence in fishes and fish products in the Arabian Sea and Indian Ocean have used advanced methods such as statistical data analysis, Pearson Product Correlation Analysis [70] and SPSS software version 24, Excel 2019, Shapiro-Wilk tests and Pearson correlation tests to understand the concentration of micro plastics in fishes; especially in the Persian gulf [71]. In addition, sensor information systems and data analytics using scatter plots for visualization is used in the Arabian sea to find enabling trends and insights in fish ecosystems and value chains [66] and satellite photos are increasingly becoming relevant in the assessment of ocean acidification, environmental and social governance especially in the Indian ocean zones [39] as well as remote sensing and video surveillance devices to detect spatial and temporal changes in ecosystems [68].

In the high sea benthic zones, machine learning methods using linear regression, Random Forest (RF) and Support Vector Regression (SVR) are used to estimate water turbidity, and chlorophyll (mirco and macro algae blooms) [72]. In addition, deep learning models and dimensionality reduction methods are used to precisely and effectively monitor marine mammals-cetaceans especially in Canada [78]. To achieve this, a robust analysis of massive datasets of photographs of cetaceans combined deep learning models and dimensionality reduction methods is used. This is through the creation of a binary land cover map with human verification as well, which is utilized to keep out of further analysis photos completely engulfed in land. Additionally, in this study, there was avoiding of manual examination of redundant photos during a single iteration by using clustering methods to choose a variety of representative images to annotate. Overall, the researchers were able to create a more effective and precise method of monitoring cetaceans thanks to the application of data analytics.

In the management of scallops and informing management on the scallop diversity and shocks due to the effects of marine pollution, Sea Surface Temperature (SST) changes and nutrient concentrations, R and REPHYTOX for data simulation has come in handy [81]. In the study conducted around the French-English Channel, Chenouf [81] a dataset that was produced using an algorithm that translates the effects of harmful algal blooms (HABs) in terms of fishing area closures by fusing information on regulations, in situ data on phycotoxin concentrations in scallops, and data on fisheries management was created. This algorithm is useful for analyzing management approaches to HABs. This dataset can be further examined using data analytics to glean insights that can guide management and decision-making tactics. This new advance is relevant, for instance, to spot patterns and trends in HAB occurrence and their effects over time on scallop production regions. It can also be used to pinpoint elements like water temperature, nutrient concentrations, and environmental circumstances that influence the development of HABs and the degree of toxicity they exhibit. Overall, data analytics can aid in enhancing our comprehension of the intricate relationships that exist between HABs, scallop production, and human activities in marine and coastal areas and can help informing the creation of more efficient management strategies to lessen the effects of HABs on these activities.

In addition, to fill gaps in trawl surveys, machine learning and statistical modeling solution-Haul Biomass Index Estimator (HBIE) is used [82]. For instance, in a research conducted in the Adriatic Sea and around Italy, advanced spatiotemporal and environmental modelling tools were used to fill in the gaps in trawl survey data. These methods entail the evaluation of sizable data sets, including information on fish biomass, environmental factors, and survey hauls [82]. This data and methodology may be applied to data augmentation, re-application to data from other scientific surveys, and haul contribution analysis—all of which could profit from data analytics methods. Related to the above, a study conducted in the East and China Sea zone used clustering analysis using R to understand mixed trawl fisheries characteristics and fishing habits of targeted species [43]. For instance, in the area around Taiwan, The vast amount of data gathered from the mixed trawl fisheries in Taiwan the data was analysed and interpreted using clustering analysis to classify catch métiers and group fishing expeditions with comparable catch compositions. With this method, it is possible to comprehend mixed fisheries' characteristics and the fishing habits of various target species better. Additionally, a two-step methodology is employed to categorize pertinent clusters and pinpoint particular catch trends. The application of data analytics in this study offers insightful information about the fishery's structure and can guide sustainable management strategies.

Other advances in the deep sea domain have involved the use of Logistic regression model to identify the sex of returning and spawning migratory salmons [5], [87]. For instance, in the study conducted in and around the River Yukon estuary in Canada, a morphometric model using a logistic regression model to determine the sex of Chinook Salmon that were returning to spawn was created. The fish's standard length (SL) and maximum eye diameter (MEF), as well as other demographic information, were used to create the model. The model was then put through a cross-validation evaluation and contrasted with other sex identification techniques [87]. The R statistical software suite was used to carry out each of these studies. As a result, data analytics were essential to the creation and assessment of the morphometric model.

In addition, in the Java Sea, the Bhattacharya technique, FISAT software, and Barefoot Ecologist Toolbox have been used to understand the spawning potential of invasive species [88]. In a study off Indonesia, these data analytics tools are utilized to analyze the information gathered about the length, weight, fecundity, and gonadal maturity of invasive crayfish in Java. The length data is specifically analyzed using the Bhattacharya technique, and the length at first maturity, selectivity capture, mortality fishing, natural mortality, and spawning potential ratio (SPR) are specifically analyzed using FISAT software. The SPR is also examined using the programme from the Barefoot Ecologist Toolbox [88]. With the use of these data analytics techniques, it is possible to gain important insights on the biology and reproduction of invasive crayfish, which are crucial for foretelling how well they will integrate into Java's ecosystem and creating long-term management plans.

3.4.3 Relevance in fishing vessels management

In the context of coastal and high sea fishing vessels management, a combination of data analytics methods have proved crucial [4]. For instance, for deep sea fishing vessels, machine learning, and data mining methods have been used to create decision support tools in marine spatial planning and sustainable fish resource harvesting [79]. Several studies have reported that fishing fleet management requires the integration of substantial volumes of data from numerous sources, including vessel monitoring systems, automatic identification systems, and satellite photography [4] and data analytics plays a significant role in this perspective [17]. To find patterns and trends in fishing activity, stock abundance, and environmental conditions, the data can be examined using machine learning and data mining methods [79] including tools such as drones [31]. This data is then employed to create decision support tools that can help with marine spatial planning, sustainable stock harvesting, and extensive fishing activity monitoring. Data analytics can also aid in identifying knowledge gaps and prioritizing research efforts to close those gaps.

In addition, Singh [15] proposed the mKRISHI fish analytic and system App for small fishing vessels around the Indian Ocean zone that can identify vessels risks and vessels under stress to about 30 kilometers offshore. The system streamlines the distribution of information about vessels, and ocean conditions/forecasts on ocean winds and waves to mobile phones in local languages, assisting fishers, their families, and other stakeholders in identifying risk zones, their occurrence date and time, and responding appropriately [15]. Numerous fishermen are saved from death and risk exposure thanks to this data-driven strategy.

3.4.4 Relevance in freshwater ecosystems management

The relevance of data analytics transcends marine zones as research has demonstrated their application into freshwater fisheries and ecosystem zones [5]. For instance, Petri et al [58] recommended the use of intelligent analytics for real-time catchment regulation and water management in river depth and river flow assessments. This can be done through the utilization of various data analytics techniques such as drones, and satellites [32] to accurately predict river depth, river flow and rainfall up to five days in advance, which data is further analyzed to assess the risk and support informed decisions-making related to regulating the catchment ecosystem [58]. Based on this insight, we argue that data analytics plays a crucial role by providing the necessary insights and information to optimize catchment flow and conserve water resources.

In addition, freshwater ecosystems are increasingly being threatened by eutrophication and pollution [3] and in this case data analytics methods such as PCA, multivariate statistical analysis and Hierarchical Cluster Analysis (HCA) could be leveraged to identify distinct groupings of pollution variables and understand correlations among them [51]. For instance, in a study conducted to identify natural and/or anthropogenic sources of heavy metals and to aid in the interpretation of geochemical data in Lake Ahansar in India, multivariate statistical analysis was used. The correlations between several water quality metrics and heavy metal concentrations were specifically examined using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) [51]. In order to interpret the data and reach meaningful conclusions about the historical trends of heavy metal contamination and eutrophication in the Ahansar lake system, these data analytics techniques helped to identify distinct groupings of variables and understand the correlations between them.

In freshwater aquaculture zones and farms, Artificial Intelligence using pattern analysis and information prediction has been used to boost the performance of small scale aquaculture fisheries [52]. In a study conducted in backwater and freshwater aquaculture zones, the importance of data analytics in the context of aquaculture is mentioned. For instance, a study by Jaikumar [52] focused on the application of AI to pattern analysis and information prediction for fish growers based on information from surveys on aquaculture farms.

Freshwater ecosystems threatened by climate change and species vulnerability to climate change have also been monitored through data analytics methods such as PCA using R. A study by Lianthuamluaia et al. [73] in India proved that data analytics is quite important. Principal Component Analysis (PCA) was utilized in the study to analyze the data for water quality metrics using R software. Additionally, the analysis made use of information relevant to a certain region on the composition of inter-seasonal rainfall, yearly rainfall, and air temperature changes over the previous 35 years. The information was examined to determine how the study area's rainfall and temperature varied from the Long Period Average (LPA) kept by IMD. The results of this study can be utilized to create a database that will be used to create future conservation and exploitation plans for other small native freshwater fish in South Asia.

Along riverine ecosystem zones, data analytics tools such as Remote sensing, modeling and sample designs is relevant in understanding ecosystems dynamics and shaping of fish populations. For instance, in Riverscape study in USA by Torgersen [86], data analytics was very important because it's essential to the analysis stage. Data analysis is required to connect intricate patterns in heterogeneous data sets to ecological processes and forces that shape population and ecosystem dynamics. Data management (storage and geo-referencing, visualization, and processing (such as filtering, reduction, and manipulation) can be used to categorize the complexity of data analysis. This is relevant due to the fact that practitioners may need to acquire new skills or employ personnel with the necessary experience due to the particular challenges posed by enormous, geographically referenced data sets and sophisticated tools for statistical analysis and visualization. Data analytics is thus a crucial instrument for comprehending patterns and processes in a management environment as well as for making defensible choices regarding the kinds of data gathering that will be aimed at being studied.

Furthermore, in the review, we observed that data analytics methods such as Generalized Linear Mixed Models (GLMM) are relevant in estimating fish effort from remote traffic encounters; especially in over fished freshwater ecosystem zones. A study in Canada by Trudeau et al, [35], heavily relied on data analytics to estimate fishing activity throughout a fisheries landscape. To attain this, data from different sources, such as aerial surveys, creel surveys, and angler diary surveys was combined, using a generalized linear mixed model (GLMM). Trudeau et al [35] also evaluate the precision of their model predictions using cross-validation techniques. The most relevant recommendation from the study is that it is proposed that for estimating fishing effort based on lake features, nonparametric techniques such as random forests could be employed to account for nonlinear responses and interactions of lake characteristics. This overly proves our hypothesis that the effective integration and analysis of various data sources using data analytics is essential for estimating fishing effort in recreational fisheries

3.4.5 Other fisheries related management practices such as supply chain management and business

Since the 1990s global fisheries are increasingly being strained by the skyrocketing consumption and demand of both marine and freshwater species especially in western markets [3], [17]. This has not only led to increased fish effort but also complex fish value chains [4] dotted with IUU [3] and less regulation of fish trade value chains [16]. This requires the monitoring of value chains to trace the types of fish consumed, nature of processing, and general fish standards [3]. With this complex scenario, data analytics has emerged as a bridge. An insightful research by Palocci, [85] to identify patterns and insights of seafood and fish supply chains based on innovative data analytics tools supports this argument. In their study in the Mediterranean Sea zone in Europe, they significantly observed that isotope ratios, and Internet of Things (IoT) is a feasible option to identifying fish supply chains. They argued that, in order to manage, store, and analyze big data in a way that is useful for decision-making, new techniques are required using the ‘search engine concept’ as an alternative method for accessing and discovering data and metadata integration and analysis based on research inquiries about nutritional quality, food safety, authenticity, and transparency. Data analytics could therefore be very useful in this research because it can aid in the identification of patterns and insights from the vast volumes of data gathered from various food supply chains.

In the Bay of Bengal, methods such as multivariate analysis e.g. Pearson correlation, PCA, and cluster analysis have been used to estimate health risks posed by consuming contaminated seafood through giving insights into heavy metal pollution in species and seafood [50]. The authors suggested the use of a number of data analytics tools, including feature extraction using RD-TFD and wordnet ontology features, tokenization, stopword removal, and stemming algorithms. The documents are clustered using the specified clustering method after the retrieved features are used to identify key traits using COA. Additionally, statistical metrics like precision, recall, F-measure, and accuracy could be used to analyze the suggested methodology. In data analytics, these statistical metrics are frequently used to assess how well clustering algorithms function. Overall, data analytics is essential to this research because it offers the tools and methods required to efficiently analyze and group massive amounts of material.

In this paper we present the case for the relevance of data analytics in sustainable fisheries management based on the current gaps in the current ecosystem related to sustaining and monitoring fisheries. We explained the various data analytics methods and tools that are being advanced and can be leveraged in the fisheries management space. One notable aspect that came to the fore is that there is a discrepancy in the current use of data analytics where less developed regions are below par in adopting these technologies which grossly affects sustainable practices and is likely to threaten livelihoods relying on fishing.

We further described the relevance of data analytics in coastal zones, freshwater zones, along the fisheries value chain, on vessels, and deep sea by showing how each method can be used and the relevance of each method to the management of a given fish species, or a fishery zone. These evidences brought to the fore the case for data analytics in the fishing industry. We thus contend that by analyzing data from, for instance, using IoT sensors that offer information on vessel behavior, such as fuel consumption and navigational situations, data analytics could, for instance, give guidance on the efficiency use and management of vessel fuels.

One notable observation and takeaway, especially among small scale fisheries and less tech-savvy fishing zones is that though there are immense benefits of data analytics, but fishing organizations/managers will not reach them by merely implementing sophisticated analytics projects. Instead, they need to completely restructure their fisheries management ecosystems and management pathways [17]. For instance, employees who are involved in such transformations must possess the necessary skill sets in addition to using the proper tools, procedures, and interfaces (such as dashboards with easy access to data, for example). Additionally, in the fishing value chain, business entities and fisheries operations dealers should train staff and offer assistance to ensure they understand the importance of sophisticated/advanced data analytics techniques and methods in the domain of fisheries management.

Acknowledgments

This project has been funded by the E4LIFE International PhD Fellowship Program offered by Amrita Vishwa Vidyapeetham. I extend my gratitude to the Amrita Live-in-Labs® academic program for providing all the support.

Declaration of interest:

None.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. However, this project has been funded by an International PhD Fellowship Program offered by our university.

R. Fujita, “The assessment and management of data limited fisheries: Future directions,” Mar. Policy, vol. 133, pp. 1–4, 2021, doi: 10.1016/j.marpol.2021.104730.
S. Shephard et al., “Community-based monitoring, assessment and management of limited inland fish stocks in North Rupununi, Guyana,” Fish. Manag. Ecol., vol. 30, pp. 121–133, 2022, doi: 10.1111/fme.12604.
FAO, “The State of World Fisheries and Aquaculture,” 2022. [Online]. Available: https://www.fao.org/3/ca9229en/online/ca9229en.html#chapter-1_1.
P. Christiani, J. Claes, E. Sandnes, and A. Stevens, “Precision fisheries: Navigating a sea of troubles with advanced analytics,” McKinsey Insights, vol. 4, pp. 1–14, 2019, [Online]. Available: https://www.mckinsey.com/industries/agriculture/our-insights/precision-fisheries-navigating-a-sea-of-troubles-with-advanced-analytics.
D. Bradley, M. Merrifield, K. M. Miller, S. Lomonico, J. R. Wilson, and M. G. Gleason, “Opportunities to improve fisheries management through innovative technology and advanced data systems,” Fish Fish., vol. 20, no. 3, pp. 564–583, 2019, doi: 10.1111/faf.12361.
UN, “Sustainable Developement Report : Implementing the SDG Stimulus,” 2023. [Online]. Available: https://s3.amazonaws.com/sustainabledevelopment.report/2023/2023-sustainable-development-report.pdf.
International Resource Panel, “Governing Ocean Resources : Implications for a Sustainable Blue Economy,” UNEP, 2021. www.resourcepanel.org.
T. V. Nguyen and T. Q. Tran, “Management of multispecies resources and multi-gear fisheries: The case of oceanic tuna fisheries in Vietnam,” Reg. Stud. Mar. Sci., vol. 63, pp. 1–18, 2023, doi: 10.1016/j.rsma.2023.103021.
C. Willis and M. Bailey, “Tuna trade-offs: Balancing profit and social benefits in one of the world’s largest fisheries,” Fish Fish., vol. 21, no. 4, pp. 740–759, 2020, doi: 10.1111/faf.12458.
J. L. Payne, A. M. Bush, N. A. Heim, M. L. Knope, and D. J. McCauley, “Ecological selectivity of the emerging mass extinction in the oceans,” Science (80-. )., vol. 353, no. 6, pp. 1284–1286, 2016, doi: 10.1126/science.aaf2416.
V. W. Y. Lam, W. W. L. Cheung, G. Reygondeau, and U. R. Sumaila, “Projected change in global fisheries revenues under climate change,” Nature Publishing Group, 2016. doi: 10.1038/srep32607.
World Ocean Assessment, “Second World Ocean Assessment of the Marine Environment,” 2021. www.un.org.
Global Fish Watch, “Global Fishing Watch to reveal all human activity at sea with investment through The Audacious Project,” 2023. www.globalfishingwatch.org (accessed Jun. 26, 2023).
World Bank, “Global Economic Prospects: Weakening Growth, Financial Risks,” 2022. [Online]. Available: www.worldbank.org.
D. Singh, “mKRISHI ® Fisheries : A case study on Early Warning System (EWS) for Disaster Communication and Management,” in 2016 IEEE International Symposium on Technology and Society (ISTAS), 2016, no. October, pp. 1–6, doi: 10.1109/ISTAS.2016.7764280.
FishWise, “Advancing Traceability in the Seafood Industry: Assessing Challenges and Opportunities,” 2018. [Online]. Available: https://fishwise.org/wp-content/uploads/2018/04/2018.02.22_Trace-WP_February-2018-Update-1.pdf.
European Union, Scientific, Technical and Economic Committee for Fisheries (STECF): The 2012 Annual Economic Report on the EU Fishing Fleet (STECF-12-10). ACRE by McKinsey; McKinsey analysis, 2019.
D. Ricard, C. Minto, O. P. Jensen, and J. K. Baum, “Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database,” Fish Fish., vol. 13, no. 4, pp. 380–398, 2012, doi: 10.1111/j.1467-2979.2011.00435.x.
C. N. Rahul and S. Babu, “Vulnerability management (VM) analytics - A systematic review of literatures,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019, pp. 3624–3625.
R. F. Raj and S. Babu, “User-entity behavior analytics (UEBA) - A systematic review of literatures,” Proc. Int. Conf. Ind. Eng. Oper. Manag., vol. 2019, no. MAR, pp. 3620–3621, 2019, [Online]. Available: https://www.scopus.com/record/display.uri?eid=2-s2.0-85067246846&origin=resultslist&sort=plf-f&src=s&sid=3471ac407ec4d71d1e4c7b97bba9f822&sot=b&sdt=b&s=TITLE-ABS-KEY%28data+analytics+amrita+university%29&sl=47&sessionSearchId=3471ac407ec4d71d1e4c7b97bba9f822.
K. S. K. Krishna and S. T., “Prognostication of Students Performance and Suggesting Suitable Learning Style for under Performing Students,” 2018, doi: 10.1109/CSITSS.2017.8447824.
T. Sasikala, M. Rajesh, and B. Sreevidya, “Prediction of Academic Performance of Alcoholic Students Using Data Mining Techniques,” Adv. Intell. Syst. Comput. Int. Conf. Cogn. Informatics Soft Comput., vol. 1040, pp. 141–148, 2019, doi: 10.1007/978-981-15-1451-7_14.
J. R. JAMBECK et al., “Plastic waste inputs from land into the ocean,” Science (80-. )., vol. 347, no. 6223, pp. 768–771, 2015, doi: 10.1126/science.1260352.
R. Froese et al., “Status and rebuilding of European fisheries,” Mar. Policy, vol. 93, pp. 159–170, 2018, doi: 10.1016/j.marpol.2018.04.018.
A. R. J. Piper, “How to write a systematic literature review : a guide for medical students How to write a systematic literature review : a guide for medical students,” Natl. AMR, p. 8, 2013.
R. Ferrari, “Writing narrative style literature reviews,” Med. writing-American Med. Writ. Assoc., vol. 24, no. December, p. 7, 2015, doi: 10.1179/2047480615Z.000000000329.
R. Merkert and J. Bushell, “Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control.,” J. Air Transp. Manag., no. August, 2020, doi: https://doi.org/10.1016/j.jairtraman.2020.101929.
P. Amorim, P. Sousa, M. Westmeyer, and G. M. Menezes, “Generic Knowledge Indicator (GKI): A tool to evaluate the state of knowledge of fisheries applied to snapper and grouper,” Mar. Policy, vol. 89, no. 133, pp. 40–49, 2018, doi: 10.1016/j.marpol.2017.11.030.
J. A. Estes et al., “Trophic downgrading of planet Earth,” Science (80-. )., vol. 333, pp. 301–306, 2011.
D. S. Terracciano, L. Bazzarello, A. Caiti, R. Costanzi, and V. Manzari, “Marine Robots for Underwater Surveillance,” Curr. Robot. Reports, vol. 1, no. 4, pp. 159–167, 2020, doi: 10.1007/s43154-020-00028-z.
S. Daley and M. Urwin, “Drone Technology: What is a Drone? Built-In Brief,” 2023. [Online]. Available: https://builtin.com/drones.
NOAA, “New data give NOAA more extensive picture of global climate,” 2023. https://phys.org/news/2023-02-noaa-extensive-picture-global-climate.html.
I. Lukambagire, R. R. Bhavani, and J. S. Von Lieres, “Aerial Drone use for Sustainable Development in India – A Content Blog Analysis,” in 2022 IEEE Conference on Technologies for Sustainability (SusTech), 2022, pp. 31–38, doi: doi: 10.1109/SusTech53338.2022.9794169.
A. Mckee, J. Grant, and J. Barrell, “Mapping American lobster (Homarus americanus) habitat for use in marine spatial planning,” Can. J. Fish. Aquat. Sci., pp. 1–56, 2020.
A. Trudeau et al., “Estimating fishing effort across the landscape : A spatially extensive approach using models to integrate multiple data sources,” Fish. Res., vol. 233, pp. 1–12, 2021, doi: 10.1016/j.fishres.2020.105768.
S. C. Inman, J. Esquible, M. L. Jones, W. R. Bechtol, and B. Connors, “Opportunities and impediments for use of local data in the management of salmon fisheries,” Ecol. Soc., vol. 26, no. 2, p. 26, 2021, doi: 10.5751/ES-12117-260226.
NASA, “What is a setellite?: How do NASA setellites help scientists study earth?,” Institute for Global Environemtal Strategies, 2023. www.nasa.gov.
Copernicus Marine Environment Monitoring Service, “Data Repositry,” 2023. [Online]. Available: https://marine.copernicus.eu.
Y. Gu, J. Dai, and M. A. Vasarhelyi, “Audit 4.0-based ESG assurance : An example of using satellite images on GHG emissions,” Int. J. Account. Inf. Syst., vol. 50, pp. 1–17, 2023, doi: 10.1016/j.accinf.2023.100625.
A. Kemsley and C. Pukini, “Marine Protected Area Watch and Marine Monitor (M2) RADAR Technology : Case Studies in Anthropogenic Use Monitoring in California’ s Marine Protected Areas,” IEEE, pp. 1–5, 2021, doi: 10.23919/OCEANS44145.2021.9705745.
W. A. Larson, D. A. Isermann, and Z. S. Feiner, “Incomplete bioinformatic filtering and inadequate age and growth analysis lead to an incorrect inference of harvested- induced changes,” Evoloutionary Appl., no. August 2020, pp. 278–289, 2021, doi: 10.1111/eva.13122.
J. Gilbey et al., “Life in a drop : Sampling environmental DNA for marine fishery management and ecosystem monitoring,” Mar. Policy, vol. 124, pp. 1–9, 2021, doi: 10.1016/j.marpol.2020.104331.
Y. Lee, N. Su, H. Lee, W. W. Hsu, and C. Liao, “Application of Métier-Based Approaches for Spatial Planning and Management : A Case Study on a Mixed Trawl Fishery in Taiwan,” J. Mar. Sci. Eng., vol. 9, no. 480, pp. 1–14, 2021, doi: https://doi.org/10.3390/jmse9050480.
UN-Habitat, “UN-Habitat: United Nations Human Settlements Programme,” 2023. https://www.un.org/youthenvoy/2013/08/un-habitat-united-nations-human-settlements-programme/.
WHO, “Urgent action needed to tackle stalled progress on health-related Sustainable Development Goals,” 2023. https://www.who.int/news/item/19-05-2023-urgent-action-needed-to-tackle-stalled-progress-on-health-related-sustainable-development-goals.
FAO, “State of World Fisheries and Aquaculture,” FAO, 2017. www.fao.org.
R. S. G. Noelia et al., “Agroproductive Data Information by Remote Sensing: Applied to the Bahia Blanca Cereal and Products Exchange Region,” 2021 19th Work. Inf. Process. Control. RPIC 2021, pp. 1–6, 2021, doi: 10.1109/RPIC53795.2021.9648472.
F. fishing effort in small-scale fisheries using G. tracking data and random forests Behivoke, M.-P. Etienne, J. ˆ Guitton, R. M. Randriatsara, E. Ranaivoson, and M. L´eopold, “Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests,” Ecol. Indic., vol. 123, pp. 1–7, 2021, doi: 10.1016/j.ecolind.2020.107321.
S. R. Midway, T. Wagner, J. D. Zydlewski, B. J. Irwin, and C. P. Paukert, “Transboundary Fisheries Science : Meeting the Challenges of Inland Fisheries Management in the 21st Century,” Fisheries, vol. 41, no. 9, pp. 536–546, 2016, doi: 10.1080/03632415.2016.1208090.
P. Pitchandi and M. Balakrishnan, “Advances in Engineering Software Document clustering analysis with aid of adaptive Jaro Winkler with Jellyfish search clustering algorithm,” Adv. Eng. Softw., vol. 175, no. 103322, pp. 1–11, 2023, doi: 10.1016/j.advengsoft.2022.103322.
W. Muneer, D. Behera, Y. Ankit, and A. Anoop, “Historical trends of heavy metal contamination and eutrophication in an aquatic system from Kashmir Himalaya , India,” Environ. Challenges, vol. 12, pp. 1–12, 2023, doi: 10.1016/j.envc.2023.100721.
M. Jaikumar, D. Ramadoss, G. Bhavan, K. Smrithi, and R. Daman, “Regional impacts of COVID-19 pandemic on aquaculture and small-scale fisheries : Insights and recovery strategies in India,” Aquaculture, vol. 570, no. 739403, pp. 1–8, 2023, doi: 10.1016/j.aquaculture.2023.739403.
P. Goswami et al., “Science of the Total Environment Microplastic intrusion into the zooplankton , the base of the marine food chain : Evidence from the Arabian Sea , Indian Ocean,” Sci. Total Environ., vol. 864, pp. 1–14, 2023, doi: 10.1016/j.scitotenv.2022.160876.
A. E. C. Mondragon, C. E. C. Mondragon, E. S. Coronado, A. E. Coronado, and C. E. C. Mondragon, “The Management of Operations Managing the food supply chain in the age of digitalisation : a conceptual approach in the fisheries sector,” Prod. Plan. Control, vol. 32, no. 3, pp. 242–255, 2021, doi: 10.1080/09537287.2020.1733123.
J. X. Wang, Y. P. Jiang, and W. Y. Pei, “Research on regional fishery modernization model based on DPSIRM and big data analysis,” in The 10th International Conference on Information Technology: IoTand Smart City (ICIT2022), 2022, pp. 324–331, doi: 10.1145/3582197.3582251.
A. Alessandrini et al., “Mining Vessel Tracking Data for Maritime Domain Applications,” in 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016, pp. 361–367, doi: 10.1109/ICDMW.2016.0058.
T. Sebastian, K. R. Sreenath, M. Paul, and R. Ranith, “Ecological Informatics Dwindling seagrasses : A multi-temporal analysis on Google Earth Engine,” Ecol. Inform., vol. 74, no. 101964, pp. 1–10, 2023, doi: 10.1016/j.ecoinf.2022.101964.
I. Petri, B. Yuce, A. Kwan, and Y. Rezgui, “An intelligent analytics system for real-time catchment regulation and water management,” IEEE Trans. Ind. Informatics, vol. 14, no. 9, pp. 3970–3981, 2018, doi: 10.1109/TII.2017.2782338.
E. J. Drenkard et al., “Next-generation regional ocean projections for living marine resource management in a changing climate,” ICES J. Mar. Sci., vol. 78, no. 6, pp. 1969–1987, 2021, doi: 10.1093/icesjms/fsab100.
S. Afzal et al., “RedSeaAtlas : A Visual Analytics Tool for Spatio-temporal Multivariate Data of the Red Sea,” in Workshop on Visualisation in Environmental Sciences, 2019, pp. 1–8, doi: 10.2312/envirvis.20191101.
K. H. Wiltshire, J. E. Tanner, F. Althaus, S. J. Sorokin, and A. Williams, “Deep-Sea Research Part II Predicting environmental suitability for key benthic species in an ecologically and economically important deep-sea environment,” Deep. Res. Part II, vol. 157–158, pp. 121–133, 2018, doi: 10.1016/j.dsr2.2018.06.011.
A. Tilley, J. D. R. Lopes, and S. P. Wilkinson, “PeskAAS: A near-real-time, open-source monitoring and analytics system for small- scale fisheries,” PLoS One, vol. 15, no. 11, pp. 1–11, 2020, doi: 10.1371/journal.pone.0234760.
A. Bargain et al., “Progress in Oceanography Predictive habitat modeling in two Mediterranean canyons including hydrodynamic variables,” Prog. Oceanogr., vol. 169, pp. 151–168, 2018, doi: 10.1016/j.pocean.2018.02.015.
R. A. Enguehard, R. Devillers, and O. Hoeber, “Comparing interactive and automated mapping systems for supporting fisheries enforcement activities — a case study on vessel monitoring systems ( VMS ),” J. Coast. Conserv., vol. 17, pp. 105–119, 2013, doi: 10.1007/s11852-012-0222-3.
K. Balachandar et al., “Chemosphere Benthic foraminifera as an environmental proxy for pollutants along the coast of Chennai, India,” Chemosphere, vol. 310, no. 136824, pp. 1–13, 2023, doi: 10.1016/j.chemosphere.2022.136824.
A. Kishore, M. Aeri, A. Grover, J. Agarwal, and P. Kumar, “Measurement : Sensors Secured supply chain management system for fisheries through IoT,” Meas. Sensors, vol. 25, no. 100632, pp. 1–5, 2023, doi: 10.1016/j.measen.2022.100632.
K. De, S. Sautya, G. U. Dora, S. Gaikwad, D. Katke, and A. Salvi, “Science of the Total Environment Mangroves in the ‘ Plasticene ’ : High exposure of coastal mangroves to anthropogenic litter pollution along the Central-West coast of India,” Sci. Total Environ., vol. 858, pp. 1–18, 2023, doi: 10.1016/j.scitotenv.2022.160071.
M. Ramesh, P. S. S. Krishna, V. A. Raj, and L. S. Nair, “Coupled coastal monitoring framework for the analysis of beach stability and nearshore hydrodynamics of a structure influenced medium energy coast in India,” Ocean Coast. Manag., vol. 239, no. 106619, pp. 1–20, 2023, doi: 10.1016/j.ocecoaman.2023.106619.
K. I. Jeyasanta et al., “Science of the Total Environment Microplastic pollution and its implicated risks in the estuarine environment of Tamil Nadu , India,” Sci. Total Environ., vol. 861, no. 160572, pp. 1–15, 2023, doi: 10.1016/j.scitotenv.2022.160572.
R. Rukmangada, B. C. Naidu, B. B. Nayak, A. Balange, M. K. Chouksey, and K. A. M. Xavier, “Microplastic contamination in salted and sun dried fish and implications for food security – A study on the effect of location, style and constituents of dried fish on microplastics load,” Mar. Pollut. Bull., vol. 191, no. 114909, pp. 1–9, 2023, doi: 10.1016/j.marpolbul.2023.114909.
M. Esmaeilbeigi, A. Kazemi, M. Gholizadeh, and R. Dabbagh, “Microplastics and heavy metals contamination in Atropus atropos and associated health risk assessment in the northwest of the Persian Gulf, Iran,” Reg. Stud. Mar. Sci., vol. 57, no. 102750, pp. 1–10, 2023, doi: 10.1016/j.rsma.2022.102750.
M. Ashphaq, P. K. Srivastava, and D. Mitra, “Preliminary examination of influence of Chlorophyll, Total Suspended Material, and Turbidity on Satellite Derived-Bathymetry estimation in coastal turbid water,” Reg. Stud. Mar. Sci., vol. 62, no. 102920, pp. 1–12, 2023, doi: 10.1016/j.rsma.2023.102920.
L. Lianthuamluaia et al., “Ecological Informatics Improving approaches and modeling framework for assessing vulnerability of Asian leaf fish in the major river basin floodplains of India in changing climate,” Ecol. Inform., vol. 73, no. 101926, pp. 1–18, 2023, doi: 10.1016/j.ecoinf.2022.101926.
E. O. Akuo-ko, M. Adelikhah, E. Amponsem, T. Kov, and A. Csord, “Radiological assessment in beach sediment of coastline, Ghana,” Heliyon, vol. 9, pp. 1–14, 2023, doi: 10.1016/j.heliyon.2023.e16690.
K. Pandion et al., “Health risk assessment of heavy metals in the seafood at Kalpakkam coast, Southeast Bay of Bengal,” Mar. Pollut. Bull., vol. 189, no. 114766, pp. 1–10, 2023, doi: 10.1016/j.marpolbul.2023.114766.
M. Xi, A. Rahman, C. Nguyen, S. Arnold, and J. Mcculloch, “Aquacultural Engineering Smart headset , computer vision and machine learning for efficient prawn farm management,” Aquac. Eng., vol. 102, no. 102339, pp. 1–11, 2023, doi: 10.1016/j.aquaeng.2023.102339.
L. S. Hellmrich, B. J. Saunders, J. R. C. Parker, J. S. Goetze, and S. Harvey, “Estuarine , Coastal and Shelf Science Stereo-ROV surveys of tropical reef fishes are comparable to stereo-DOVs with reduced behavioural biases,” Estuar. Coast. Shelf Sci., vol. 281, no. 108210, pp. 1–10, 2023, doi: 10.1016/j.ecss.2022.108210.
J. Boulent et al., “Scaling whale monitoring using deep learning : A human-in-the-loop solution for analyzing aerial datasets,” Front. Mar. Sci., vol. 10, no. 1099479, pp. 1–13, 2023, doi: 10.3389/fmars.2023.1099479.
C. Kelly, F. A. Michelsen, K. J. Reite, J. Kolding, Ø. Varpe, and A. P. Berset, “Capturing big fi sheries data : Integrating fishers ’ knowledge in a web-based decision support tool,” Front. Mar. Sci., vol. 9, no. 1051879, pp. 1–11, 2022, doi: 10.3389/fmars.2022.1051879.
F. Bonofiglio, F. C. De Leo, C. Yee, D. Chatzievangelou, and J. Aguzzi, “Machine learning applied to big data from marine cabled observatories : A case study of sable fish monitoring in the NE Pacific,” Front. Mar. Sci., vol. 9, no. 842946, pp. 1–15, 2022, doi: 10.3389/fmars.2022.842946.
S. Chenouf, M. Merzereaud, and P. Raux, “Dataset for Estimated Closures of Scallop ( Pecten maximus ) Production Areas Due to Phycotoxin Contamination along the French Coasts of the Eastern English Channel,” Data, vol. 7, no. 103, pp. 1–8, 2022, doi: https:// doi.org/10.3390/data7080103.
G. Coro, P. Bove, E. N. Armelloni, F. Masnadi, M. Scanu, and G. Scarcella, “Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling,” Front. Mar. Sci., vol. 9, no. 919339, pp. 1–17, 2022, doi: 10.3389/fmars.2022.919339.
Y. Wu et al., “Science of the Total Environment Linking human activity to spatial accumulation of microplastics along mangrove coasts,” Sci. Total Environ., vol. 825, pp. 1–11, 2022, doi: 10.1016/j.scitotenv.2022.154014.
M. J. Lant, D. H. Ogle, Z. S. Feiner, and G. G. Sass, “A comparison of fish catch rates among local , non-local , and non-resident anglers of three northern Wisconsin lakes,” Fish. Res., vol. 250, pp. 1–9, 2022, doi: 10.1016/j.fishres.2022.106286.
C. Palocci, K. Presser, A. Kabza, and E. Pucci, “A Search Engine Concept to Improve Food Traceability and Transparency : Preliminary Results,” Foods, vol. 11, no. 989, pp. 1–13, 2022, doi: https://doi.org/10.3390/ foods11070989.
C. E. Torgersen et al., “Riverscape approaches in practice : perspectives and applications,” Biol. Rev., vol. 97, pp. 481–504, 2022, doi: 10.1111/brv.12810.
C. A. Bradley and R. J. Brown, “Development of a Simple Morphometric Model to Identify Sex in Chinook Salmon Returning to Spawn in the Yukon River,” North Am. J. Fish. Manag., vol. 41, pp. 1538–1548, 2021, doi: 10.1002/nafm.10669.
A. Santoso, Yonvitner, and S. G. Akmal, “Length based-spawning potential ratio ( LB-SPR ) model for estimating successful adaptation of invasive crayfish ( Cherax quadricarinatus , Morten ) in Java,” in IOP Conference Series: Earth and Environmental Science, 2021, pp. 1–6, doi: 10.1088/1755-1315/674/1/012028.

Supplementary Materials are not available with this version

Download PDF

Version 1

posted

You are reading this latest preprint version

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Status:

Version 1

Abstract

Figures

1.0 Introduction

1.1 Sustainable Fisheries Management and Emerging Sustainability Issues

1.2 Why Data Analytics in Sustainable Fisheries Management Now and Not Later

2.0 Methods

2.1 Inclusion/Exclusion Criteria

2.2 Data Synthesis

3.0 Results

3.1 Data Analytics in Fisheries

3.2 Main techniques/tools and how they are used

3.2.1. Drones

3.2.2. Satellites

3.2.4. Vessel onboard and underwater surveillance devices

3.3 Scope of Spatial Use of Data Analytics in the Fisheries Domain

3.4 Relevance of Data Analytics

3.4.1 Relevance in coastal and near-shore zones fisheries management

3.4.2 Relevance in deep sea fisheries management

3.4.3 Relevance in fishing vessels management

3.4.4 Relevance in freshwater ecosystems management

3.4.5 Other fisheries related management practices such as supply chain management and business

4.0 Conclusion

Declarations

References

Supplementary Materials

Status:

Version 1