
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39245678
71763
10.1038/s41598-024-71763-y
Article
Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region
http://orcid.org/0000-0002-4507-7293
Shedrawi George georges@spc.int
gs855@uowmail.edu.au

12
Magron Franck 1
Vigga Bernard 1
Bosserelle Pauline 1
Gislard Sebastien 1
Halford Andrew R. 1
Tiitii Sapeti 3
Fepuleai Faasulu 3
Molai Chris 4
Rota Manibua 5
Jalam Shivam 6
Fatongiatau Viliami 7
Sami Abel P. 8
Nikiari Beia 5
Sokach Ada H. M. 8
Joy Lucy A. 8
Li Owen 2
Steenbergen Dirk J. 2
Andrew Neil L. 2
1 https://ror.org/05ewdm369 grid.33997.37 0000 0000 9500 7395 Pacific Community, Noumea, 98848 New Caledonia
2 https://ror.org/00jtmb277 grid.1007.6 0000 0004 0486 528X Australian National Centre for Ocean Resources and Security, University of Wollongong, Wollongong, 2522 Australia
3 https://ror.org/045kyae46 grid.494018.0 Ministry of Agriculture and Fisheries, Apia, Samoa
4 National Fisheries Authority, Port Moresby, Papua New Guinea
5 https://ror.org/04tm3b065 grid.511282.d Ministry of Fisheries and Marine Resources Development, Tarawa, Kiribati
6 Ministry of Fisheries, Suva, Fiji
7 Ministry of Fisheries, Nukualofa, Tonga
8 Vanuatu Fisheries Department, Ministry of Agriculture, Livestock, Forestry, Fisheries and Biosecurity, Port Vila, Vanuatu
8 9 2024
8 9 2024
2024
14 2091512 4 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes. The system leverages YoloV4, OpenCV, and ResNet101 to extract information from images of fish and invertebrates collected as part of in-country monitoring programs overseen by national fisheries authorities. As of December 2023, the system has facilitated automated identification of over six hundred nearshore finfish species, and automated length and weight measurements of more than 80,000 specimens across the Pacific. The system integrates other key fisheries monitoring data such as catch rates, fishing locations and habitats, volumes, pricing, and market characteristics. The collection of these metrics supports much needed rapid fishery assessments. The system’s co-development with national fisheries authorities and the geographic extent of its application enables capacity development and broader local inclusion of fishing communities in fisheries management. In doing so, the system empowers fishers to work with fisheries authorities to enable data-informed decision-making for more effective adaptive fisheries management. The system overcomes historically entrenched technical and financial barriers in fisheries management in many Pacific island communities.

Keywords

Artificial intelligence
Monitoring
Coastal fisheries management
Co-management
Artisanal fisheries
Data poor fisheries
Small Island Developing States
Subject terms

Ecosystem services
Marine biology
Conservation biology
Department of Foreign Affairs and Trade, Australian Government (Department of Foreign Affairs and Trade)Department of Foreign Affairs and Trade | Australian Centre for International Agricultural Research (ACIAR)FIS/2020/172 Ministry of Foreign Affairs and Trade, New Zealand (MFAT)Pacific European Union Marine Partnership Programme and the Government of Sweden FED/2018/397-941European Union Marine Partnership Programme and the Government of Sweden FED/2018/397-941issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Although diverse, Small Island Developing States (SIDS) share many challenges in their journeys toward sustainable development1. Inherent constraints like remoteness and small size amplify the impacts from natural disasters2, disease outbreaks3, and climate change4–8. The unique and disproportionate nature of these vulnerabilities was internationally recognized in 19929, which has driven considerable efforts to develop solutions in the decades since10–12.

These vulnerabilities are apparent in all Pacific SIDS13–15, where coastal fisheries are central to sustainable development for food security16. Fish, broadly defined and include invertebrates, provide critical livelihoods and food in the 22 countries and territories in the region17. In all Pacific SIDS, but particularly in the atoll countries of Micronesia, fish consumption rates are among the highest in the world and fish are the dominant animal source food, providing essential macro- and micronutrients18. Pacific Island peoples acquire fish from a variety of sources and with a diverse array of exchange mechanisms, ranging from home production through to cash-based markets12,19.

The central role of coastal fisheries in the lives and economies of Pacific peoples, which include nearshore commercial and subsistence fisheries, means that sustainability of fish catches is an important national policy priority in all jurisdictions in the region20,21. The importance of ensuring the sustainable supply of fish is also reflected in several international instruments22, including the Sustainable Development Goals of the United Nations (UN) 2030 Agenda23,24.

Agencies and organizations tasked with ensuring the sustainability of small-scale coastal fisheries often face historically insurmountable challenges25,26. Many of the constraints recognized in oceanic fisheries in Pacific SIDS have present day analogues in their coastal fisheries27–29, making conventional fisheries management challenging30–32. These are exacerbated by the lack of centralized and mandated coordination of scientific investigation and the diversity of coastal fisheries themselves in terms of individual stocks and fishing methods33. Consequently, Pacific coastal fisheries are usually categorized as data deficient despite many initiatives aimed at improving management and research to overcome these constraints34,35.

A major obstacle in addressing these challenges is the high cost of scalable methods to monitor the status of fisheries and performance of management36,37. Many of the methods used in the region were developed using twentieth century approaches for large scale fisheries38,39. The absence of affordable and accessible methods and centralized coordinated approaches creates a disconnect between local fisheries management, national policy development and global understandings of fishery status40,41.

The persistence of technical and financial barriers regularly inhibits expansion of science-based governance of fisheries in the Pacific. The real-world costs of collecting data in difficult, dynamic field conditions, means such science-based programs that depend on methodological rigor are often unattainable without highly trained personnel. The reliance on complex technical approaches limits the ability of people to adopt and action management independently, so that general involvement and acceptance of fisheries management remains poor42. In practice, resource-constrained fisheries managers often grapple with balancing the collection of reliable data with the human and financial resources required to effectively collect, curate and analyze data to manage their fishery43. Compounding this are the considerable time lags between data collection, analysis, reporting and application, which mean data are often not available, or made available too late, for it to effectively inform adaptive management44.

The high demand for rapid, easy-to-implement assessments that inform management of small-scale fisheries, particularly subsistence and artisanal fisheries, is reflected in many available toolkits, statistical methods, and field approaches41,45,46. These methods rely on relatively simple yet informative data with which to make broad management inferences, for example, comparing mean catch length with information on a species’ size at maturity. The utility of many such ‘rule of thumb’ methods however, still rely on the capacity of agencies and communities to collect accurate data at scales needed to make meaningful management decisions.

Digital technologies hold potential for transforming fishery livelihoods47, particularly in the culturally diverse and dispersed island context of the Pacific, where telecommunications infrastructure rollout is extending to ever more remote places. Policymakers, the private sector, scientists, and international development partners are increasingly co-leveraging digital technologies in the Pacific socio-economic development sector48. When these advances are paired with rapid evolution of computational, computer vision and artificial intelligence (AI) technologies there is an opportunity to automate repetitive time-consuming aspects of fishery evaluation. In doing so, information gathering exponentially expands and catalyzes a revolution in how coastal fisheries are managed49.

AI applications in fisheries have to date been predominantly applied in large-scale commercial fisheries or aquaculture, where machine learning approaches are used to identify growth or disease in products50,51, predict population connectivity patterns52, track oceanic vessel movements53, identify species from commercial fishing catches54–58, or extract classifications from images uploaded by enlisted recreational fishers59,60. In these cases the scale and resolution at which AI is applied is such that these systems are not suited to monitoring subsistence and artisanal fisheries in small islands developing states61. For example, AI technologies designed to track large commercial vessels or automatically identify fish species based on commercial purse-seine catches are not designed to handle the diverse species, fishing methods, and landing practices characteristic of small-scale fisheries. These programs, while appropriate and useful within their respective domains, must include the simultaneous capture of information needed to inform management of complex multispecies coastal fisheries, including method-disaggregated catch per unit effort, specimen length, market or landing dynamics, catch methods, and other fishery associated socio-economic data. Considering the above, most AI applications in recreational or coastal fisheries are primarily serving as a tool to describe or characterize fisheries, rather than utilizing it as an integrated part of comprehensive analysis platforms that fisheries require in order to adapt and adjust management according to change.

In this paper we describe the development of state-of-the-art digital fishery monitoring processing system for Pacific SIDS powered by AI and computer vision technology. This system provides an opportunity to design and build solutions that bridge conventional data collection programs and governance structures with people’s local (traditional) use of fishery resources62. While AI’s transformative impacts are growing exponentially across diverse fields63, its application in coastal fisheries is in its infancy. AI offers promising avenues to address current limitations in management of small-scale coastal fisheries. AI-powered systems demonstrate the potential to automate data collection and analysis, improving efficiency and cost-effectiveness64. The work presented here builds on this emerging trend, introducing an AI-based system specifically tailored for coastal fisheries monitoring. Unlike existing approaches, this system prioritizes affordability, versatility, and accessibility, to ensure usability in (and by) remote Pacific communities. Moreover, the system distinguishes itself in its capacity to process and integrate diverse monitoring data from multispecies coastal fisheries, including catch rates, fishing locations, and socio-economic information. Its additional compatibility with smaller existing programs currently using computer vision technologies, offers growth for learning and expansion of application as technology advances. The platform is managed by the peak regional technical agency the Pacific Community (SPC) headquarters, with access by national fisheries agencies, non-government organizations, and communities in SPC member countries (Fig. 1).Fig. 1 The basic construct of the AI-enabled coastal fisheries monitoring system behind Ikasavea, showing flow of raw data from various field contexts and countries (e.g. landing sites, communities, and/or markets) to a central computing facility hosted at SPC. Automated analyses of imagery extract information that is then fed back to inform management practice.

Structural architecture of the AI fisheries monitoring system

The architecture of the AI monitoring system is comprised of four main stages: data acquisition (photo imagery and survey data) and upload, AI enabled image processing and data extraction, data analysis, and reporting. CV technology facilitates the extraction of data from images, and deep learning algorithms were applied to train the system to make decisions based on extracted information (e.g., image standardization, image and specimen orientations, specimen taxonomic classifications, and measurement types). These processes are integrated and advanced through an automated software pipeline to ensure data consistency and an efficient workflow.

Data acquisition (field methods)

Data and imagery are collected and uploaded in a variety of ways during fishery surveys in the region. Although the system can accommodate manual entry of data from paper records and images uploaded to a web browser from, for example, a camera SD card, the focus of data acquisition is through a smartphone or tablet-based application. The Android application, Ikasavea, was developed in collaboration with national agency partners (ika-savea means ‘fish-survey’ in Polynesian languages). Ikasavea can be customized by users through a web portal to adapt the survey design to their needs. Users can, for example, set administrative regions, assign spatial management areas to sampling hierarchies (e.g., locally managed marine areas in Fiji), and set specific input metrics and data fields used for a wide range of fishery assessments. This information is then synchronized to Ikasavea to facilitate structured flows of data input.

Given the diversity in artisanal and subsistence fishing practices, the system allows for various forms of data input through a range of modules in the tablet application and on the web-accessible portal, including data collection from landing sites, community fisheries, and fish markets. Enumerators intercepting fish catches in any of these contexts use Ikasavea to collect information on a range of catch attributes, such as fishing methods, market details, and socio-economic dimensions (See Fig. S1 Supplementary materials). A central attribute of the system is the use of photographs taken of the catch, which are then processed centrally. The use of photographs significantly reduces the level of taxonomic knowledge and time required at the point of data acquisition and reduces disruption to fishers and retailers, thereby reducing refusals.

Two categories of photograph or ‘measurement type’ can be processed – either a single specimen on a calibrated measuring board, with or without a digital scale (hereafter ‘board’), or multiple specimens arranged on a standardized, calibrated mat (hereafter ‘mat’). Specific identifier patterns on boards (numbers) and mats (symbols) were used to calibrate measurements and orient images (see Fig. S2 Supplementary materials). Mats proved more appropriate for fishers taking part in community-based fisheries monitoring programs or by fisheries officers when expediting landing surveys. Once data are uploaded to a central online database, automated image processing and data analyses derive information useful for management (See Fig. S3 in Supplementary material). This information is then sent back to the field for application by managers, in near real-time.

In instances when both length and weight were required, the board was attached to a scale system. Length and weight information allowed the development of length–weight relationships useful in quantitative fishery assessments. In this paper, weight was estimated to the nearest gram. Once sufficient length–weight information is gathered for each species, the relationship may be used to estimate weight from lengths measured in the photographs.

Fulton’s condition factor (K) and Tukey’s outlier detection on K methods were used in combination to remove outliers in body condition to improve LWR estimates65–67 e.g., Fig. 7. Fulton’s condition factor is a standard method to identify individuals that deviate significantly from a base population55. K was calculated using Eq. (1).1 K=100∗W/L3

Where 100 is scaling constant to increase K to manageable units, L is length in mm and W is weight in grams. Once K was calculated, Tukey’s IQR method68,69 was used to detect outliers. The calculation of K and evaluation of outliers served three purposes. Firstly, it identified individuals that had a disproportionate influence on the predictive model over and above what a healthy individual would have for that species’ population. Secondly, it identified specimens that were unsuitable for inclusion in AI model training or for further data analysis. Thirdly, calculation of K allowed tracking of body condition of fished species populations between geographic areas and through time70. Once local LWR models are established, predicted specimen weights can be used to estimate production volumes of a fishery. Allometric relationships for many coastal fisheries species can vary seasonally and spatially, so using locally developed LWRs rather than those from global data repositories such as FishBase71 increase the accuracy of length-based stock assessments72.73. All analyses were implemented in R core program and RStudio using a range of plotting and statistics packages74–79.

AI enabled image processing and data extraction

Raw images are processed through a series of AI-enabled steps via an automated image processing pipeline (Fig. 2), each involving model trainings prior to model deployment for consequent data extraction. As images are uploaded, they undergo automated corrections, are classified, and partitioned into various categories. Every subsequent step through the processing phases uses a model developed specifically for the classification level of partitioned imagery (stored in a model library). This method of multi-stage classification decreased the number of distinct types of imagery that each model must classify, which helped avoid overfitting the models. Below we summarize this workflow and the model’s validation and evaluation processes used to assess each of the models’ performance.Fig. 2 The multistage process for analyzing and classifying specimen images using convolutional neural network algorithms. Custom C# application accesses and updates the SQL database, fetches original images, creates image outputs, and runs YOLOv4/Darknet53 models through a wrapper and C +  + /CUDA implementation of YOLOv4 darknet and OpenCV libraries. It also runs ResNet101 models using Microsoft ML.NET. In Step 1, YOLOv4 is used to identify images, categorize them, and correct orientation. In Step 2, multiple YOLOv4 and OpenCV models adjust image properties and calibrate pixels to known dimensions. In Step 3, multiple YOLOv4 models detect and classify specimens based on a specimen's visible attributes. In Step 4, YOLOv4 extracts measurements for each taxonomic subclassification. In Step 5, a dual-stage process comparing YOLOv4 and ResNet101 detects the species name from a species detection model library, where the most accurate output is used.

Storage, analysis, and validation of specimen images are integrated into an ASP.NET web application and SQL Server backend. When photos are uploaded manually or during synchronization from Ikasavea, they are stored on the web server and registered for processing in the SQL Server database as photo tables. Using a custom C-sharp (C#) application on a machine equipped with a GPU, the application triggers the automated processing of images every 15 min. The AI detection and automatic measurement system drew from open-source libraries and models such as Open-Source Computer Vision (OpenCV)80, ResNet10181, and YOLOv482,83. OpenCV provides tools and algorithms for image processing, including image enhancement and feature detection59. This open-source package offered a comprehensive set of functions for image processing, such as contrast enhancement, thresholding, histogram equalization, and adaptive histogram equalization84. These functions, triggered by the C# application when images are uploaded, were used to preprocess images to improve quality before being transferred to deep learning models to perform further analysis. ResNet101 is a deep convolutional neural network architecture that is widely used for image classification tasks. It is a variant of the Residual Network (ResNet) architecture, which involves the training of very deep networks, and is widely used as a backbone in CV classification tasks. YOLOv4, also widely used in CV applications, is a faster single-pass object detection algorithm, which divides the input image into a grid, and predicts bounding boxes and class probabilities for each grid cell.

The C# application processes images by initiating YOLOv4/Darknet53 models via a C +  + /CUDA implementation for object detection, and ResNet101 models via Microsoft ML.NET for tasks like image classification or feature extraction. It generates image outputs, stores them on an SQL file server, and updates the SQL Server database with processing status and results. The application uses flags and output data fields, such as image type, predicted species, and bounding box coordinates, to filter images for further processing. For instance, fish-on-mat detection is triggered by the C# application once the OpenCV model has successfully calibrated an image containing a mat photo. Model training and validation involved just over 32 000 images of reef associated finfish and invertebrates from 13 Pacific SIDS and territories. The initial stages of development used images from Samoa, New Caledonia, Tonga, Papua New Guinea, Fiji, Kiribati, and Vanuatu. As images were uploaded and additional photographs were validated by trained observers, the models underwent retraining through a process of transfer learning85. This involved using validated, corrected, and ‘failed’ images (where ‘failed’ images were corrected and annotated) to retrain the models. Adding failed images to the training dataset improved fish detection or calibration models on a range of images of varying quality. Data augmentation was used to accelerate model training, minimize overfitting, and enhance accuracy for species with fewer photographs63. This method of periodic training and re-evaluation was needed to maintain accuracy and precision when dealing with highly variable imagery from the countries and programs using the system.

Preprocessing phase

The pre-processing phase proceeds as a series of steps powered by YOLOv4 and OpenCV to prepare images for data extraction. First, YOLOv4’s object detection capability is used to identify whether the image was from a board or a mat, and to categorize it accordingly. Using a combination of multiple YOLOv4 and OpenCV models, each image went through quality enhancement and calibration to normalize image projection to known real-world dimensions.

Lastly, a library of YOLOv4 models were used to identify, correct, and classify specimens based on their position, orientation, and broad taxonomic type. The ‘taxonomic type’ classification is used to group morphologically similar species into a single classification as either finfish or for grouping invertebrates into lobsters, crabs, bivalves, or gastropods. This step provides critical information about specimen characteristics, aiding subsequent data extraction processes. Once the taxonomic type of the specimen is identified, the images are placed in their respective categories.

Data extraction phase

This two-step phase utilizes deep learning to build algorithms to automate data extraction. First, YOLOv4 is used to extract accurate lengths from a calibrated image after detecting the snout and fork or caudal fin margin of fishes. For invertebrates, learned morphometric features are used. For photographs with an electronic scale, digits are recognized and recorded to provide a weight of the specimen, while for photographs using mats, only the lengths of each of the specimens are estimated.

For the calibrated measuring board method, images were standardized in size and position, starting at zero, vertically centered, and scaled to 2 pixels per mm. The red lines on the image correspond to the theoretical position of the center line and black lines every 10 cm to check that calibration is correct (see Fig. S4 in supplementary material). The fish’s fork position on the board was used to determine its length. A YOLOv4 model, trained on 1600 images, normalized to 416 × 416 input for 80 epochs, was used to detect the fork, providing a bounding box centered on the fork. The vertical position of this bounding box center yielded the fork length of the fish.

For the calibrated mat method, the size and position of specimen images were standardized. A YOLOv4 model, trained on 1334 images and normalized as 512 × 512 input for approximately 287 epochs, was first applied to detect fish bounding boxes on the mat. Each detected specimen’s image was cropped according to the bounding box with an extra 10% margin, then processed through a YOLOv4 fish orientation detection model to standardize the orientation of the fish. This model was trained on 30,528 images, normalized as 416 × 416 input for approximately 33 epochs.

Measurement of the specimen was done using a YOLOv4 fish snout/fork detection model (Fig. 2), using the distance between the center of the snout bounding box and the center of the fork bounding box of each detected specimen (see Fig. S5 in supplementary material). This model was trained on 4818 specimens, normalized as 512 × 512 input for approximately 80 epochs. During the processing, the system may detect multiple potential snouts or forks within a single image so a final step to disambiguate multiple specimens in the image processing pipeline ensured each detected fish was associated with only one snout and one fork. This process continues until each specimen is associated with a single snout and fork, a crucial step for ensuring the accuracy of measurements and overall effectiveness of the AI measurement system. The results are displayed to the user for validation or correction if required, with the user interface allowing editing of the measurement line and adding segments for length measurement along a curve. In the next step, pre-trained models for each measurement type and taxonomic group (e.g., mat or board having either a fish or invertebrate) extract a species classification of a specimen using both ResNet101 and YOLOv4 architecture. The species detection model library is used to assign a species name. This involves a dual-stage parallel process where the C# coded system compares both the YOLOv4 and ResNet101 outputs and reports only the most accurate classification in the user interface. This is achieved by comparing the model’s confidence score from 0 to 100, with zero having no confidence and 100 being highly confident.

Model validation and addressing bias

Performance of AI length and weight detection

We tested the accuracy and precision of 869 AI-measured lengths from a suite of randomly selected specimen species and sizes sold at markets in Tonga. Depending on the species’ morphology either the total or fork length of a specimen was used to validate the AI measurements and collected in situ using the same board. An observer recorded the measurement beneath the posterior margin of the intersecting lobes of the caudal fin (fork length) or furthest margin of a straight-line distance from snout to tail (total length).

A linear regression model was used to evaluate the AI-enabled system’s ability to replicate a human measurement. The model was fitted between paired AI (independent variable) and human (dependent variable) measured lengths. We chose the dependent variable based on the need to evaluate the accuracy of the AI measurement (the predictor) to predict a real-world human measurement. The dataset covered lengths ranging from 130 to 720 mm. Results of this analysis indicated that AI measurements were consistent with human measurements for both measurement types. The near one to one accuracy is verified by a strong positive linear relationship (R2 = 0.99) (Fig. 3a). In six cases during this validation assessment the AI detection demonstrated that it could detect erroneous measurements by human observers, which were corrected post-hoc.Fig. 3 (a) Comparison of fish length measurements estimated by an AI system and those by a human observer. Each point (n = 869) represents an individual fish, with the x-coordinate being the AI measurement and the y-coordinate being the human measurement. The blue line represents the fitted linear regression and the orange shaded area represents the bootstrapped 95% confidence interval for the fit (please note due to the extremely close relationship the 95% CI is narrow). (b) Bland–Altman plot showing the agreement between fish length measurements taken by an AI system and a human observer for the mat measurement type. The x axis shows the mean of the AI and human measurements, and the y axis shows the difference between the two measurements. The solid blue line indicates the mean difference (bias), while the dashed gray lines represent the upper and lower 95% confidence interval and the grey shaded region indicates the 95% confidence interval of the fitted linear model, (c) the comparable Bland–Altman plot for the board measuring type.

A Bland–Altman plot was constructed to assess size-specific and measurement type bias that was not detected by the linear regression86,87. Overall, 92% of the AI measured lengths were within 5 mm and 98 percent of lengths were within 10 mm of the human measured lengths (Fig. 3b). The overall mean absolute error was 3.4 mm and the root mean square error was 4.9 mm. The mean square error for the board and mat measurement types was 2.7 and 4.2 mm, which equates to a 1.2 and 1.8% error, respectively. There was minimal positive bias apparent with increasing length. The mean bias was approximately 5 mm for specimens greater than 600 mm on both measurement types (slope coefficient b = 0.01 for both mat and board), which was considered acceptable.

We assessed how often enumerators either validated or changed the length estimated by the AI system in a random selection of 80,446 finfish (Fig. 4). The Studentized deleted residuals method69 was used to identify outliers in both x and y variables. Observer validated AI produced length estimates in all but 0.4% of instances. The relationship between these values showed a near perfect fit, where the model coefficients were near zero or one for the intercept and slope, respectively. This high degree of agreement indicated that the AI length measurement system was robust over a range of lengths, species and contexts. We identified 278 (0.35%) instances where lengths were changed on specimens, mostly because the AI failed from poor quality images (e.g., poor color, lighting, and/or orientation—three dimensionality). In addition, observers either changed a correct AI identification or length measurement (See Fig. S6 in Supplementary material).Fig. 4 Human validation of AI length estimates. Blue circles indicate validated length estimates and red circles indicate estimates identified as outliers using Studentized deleted residuals method. The orange dashed line indicates the 1:1 relationship and the black dashed line indicates the fitted linear model.

Performance of AI species identification

Correctly identifying species in multi-species fisheries requires specialist knowledge that is rarely available in the context of communities in the Pacific region. This limitation is more evident in SIDS where species names are often in local languages, and where many species are grouped into a single local name. Automating the identification process will significantly improve the likelihood that a sampling program will correctly identify species88.

We evaluated four computer vision models (m20, m21, m22, and m23) developed in successive years on their ability to identify species from 51,800 specimen images. The models’ performance was measured by their recall accuracy89,90, which is the rate of correctly identifying a species (true positives) and the rate of missing a species that was present (false negatives).

Each model was trained in a different year, with an increasing number of species and images. Specifically, m20 was trained on 21 species using 1,817 images, m21 on 64 species using 5210 images, m22 on 111 species using 10,191 images, and m23 on 264 species using 32,818 images. The training data for each model included images from its respective year and all preceding years, with the training datasets comprising 90% of the testing dataset.

Each model was tasked with classifying images of each species and the number of correct classifications were tallied and the proportion of correct classifications were calculated. To maintain a balanced learning system and to make meaningful assessments of each model’s performance, only species with more than twenty images were included. This criterion reduced the total number of finfish species that have been identified by the AI classification system from 612 to 264. The number of training images were capped at two hundred specimens per species to reduce the risk of overfitting. We found that models tend to perform better when trained on 150 to 200 unique specimens (Fig. S7 in supplementary material). When a model is overfitted, it may misclassify images by focusing on characteristics that are common between species but not related to the specimen’s taxonomy, such as the presence of a red pectoral fin margin, which therefore could lead to an increase in misclassifications.

Each model was able to recall species classifications with high accuracy, but only when applied to images taken in its development and preceding years (Fig. 5). New species or conspecifics with varied colorations or markings that were uploaded, resulted in a decline in performance. For example, model m22 (trained on imagery up to December 2022) was able to recall and accurately classify species 94%, 95%, 91%, and 35% of the time when assessed against images from 2019 to 2023, respectively (Fig. 5). Model m23 had a 79% successful recall rate on 264 species as opposed to m22’s recall rate of approximately 30% on the same dataset. Model m22 correctly identified species in images uploaded in 2022 at a rate 97%, a drastic improvement over Model m21’s accuracy of 35% when evaluated against the same imagery (Fig. 5).Fig. 5 Recall performance (accuracy) of four models (m20 to m23) over 4 years (2020–2023). Each model was trained on imagery collected preceding 31st December of that year and preceding years. Individual data points within each panel represent the proportion of correct classifications (i.e., the number of correct specimen classifications/total number of specimens) for each species in the training dataset for that year. Only species that were represented by twenty or more images were included. Grey points represent the mean ± standard error of the mean number of correct classifications for each model. The number of species (n) in the training and performance evaluation datasets are annotated on each panel.

Model m23’s recall performance over imagery uploaded in its development year was lower than expected at 79% but when evaluated against preceding years imagery (2020 to 2022) the average recall rate of m23 was over 91% (Fig. 5). In contrast, the recall score of models trained on imagery in their same or preceding years was over 90%. Model m23’s lower recall rate is due to the upload of 153 new species with only 20 to 40 specimen images, whereas a maximum of only 47 new species were introduced in 2022 and 43 new species in 2021. The m23 model therefore had to learn to classify over three times as many species with too few images. This indicates that when a model was applied to new imagery over time, they were exposed to new species that were not part of their training, or distinct species that shared common characteristics leading to misidentification. This signals that as the number of species increased, which included distinct species with similar characteristics, a resulting increase in misclassifications occurred (i.e., false positives).

Application and uptake across Pacific SIDS

The Ikasavea monitoring program has seen rapid uptake in the region. Following the first upload of images collected in Kiribati as part of landing surveys, other countries’ fisheries management authorities expressed interest. This initiated a deliberate effort to apply modifications to accommodate the tailored needs of Pacific SIDS programs. As programs were integrated, the volume of images increased. By March 2021, new and established fisheries monitoring programs across all three Pacific subregions of Micronesia, Melanesia, and Polynesia were integrated, thereby accelerating uptake towards what is now a regional AI-supported monitoring system. Larger countries like Papua New Guinea (PNG) have more recently contributed to the data pool, emphasizing the system’s usability, utility, and performance in diverse and extensive geographical contexts. Active participation by diverse national fisheries management agencies demonstrates the system’s ability to manage large-scale data, thereby validating its scalability. As of March 2024, 11 national fisheries authorities are using the system, with the Cook Islands, Nauru and Palau currently being onboarded for their national programs (Fig. 6). By December 2023, over 80,000 images had been uploaded, containing over 180,000 specimens (Fig. 7).Fig. 6 Extent of uptake by Pacific Island Countries and Territories (PICTs) of the Ikasavea system, allowing AI-supported data collection of key invertebrate and finfish landed by fishers or sold at markets. Three letter ISO codes as: American Samoa (ASM), Cook Islands (COK), Federated States of Micronesia (FSM), Fiji (FJI), French Polynesia (PYF), Guam (GUM), Kiribati (KIR), Marshall Islands (MHL), Nauru (NRU), New Caledonia (NCL), Niue (NIU), Northern Mariana Islands (MNP), Palau (PLW), Papua New Guinea (PNG), Pitcairn Islands (PCN), Samoa (WSM), Solomon Islands (SLB), Tokelau (TKL), Tonga (TON), Tuvalu (TUV), Vanuatu (VUT), and Wallis and Futuna (WLF). Extent of EEZs indicative only.

Fig. 7 Cumulative count of fish species identifications and length measurements made by the AI system using images uploaded to the SPC web portal by national fisheries authorities. Red points indicate when a new country first uploaded images to the SPC web portal. Count does not include images from mats obtained by SPC partners. Three letter ISO codes for PICTs as indicated in Fig. 6.

In the four years since the system became operational, over 50 finfish and invertebrate species have sufficient data for length–weight predictions across the region. This is a critical metric that can be used in analyses of life histories, population growth, and in comparative analyses between different populations from different regions, habitats and/or environmental conditions. Three species were selected here to demonstrate how the LWR models were developed using AI detection (Fig. 8). Of the 7885 measurements made, 313 (not all shown) were detected as outliers using Tukey’s IQR method on K, and were the result of deficient images (e.g., gutted, damaged or malformed specimens) or incorrect lengths or weight validations by observers (e.g., AI’s detected weight being overridden by a human observer) (Fig. 8a). As the number of uploaded images with boards and scales increase, the number of locally derived allometric LWRs are also expected to increase, providing much needed allometric data across the region. Generalized linear model regression on log10 transformed data was used to build predictive LWR models91.Fig. 8 Allometric length–weight relationships of three species from Samoa (WS) and Papua New Guinea (PNG). These relationships were derived from automated length classification and weight readings using CV technologies. Outliers (shown for Lutjanus gibbus as red circles) were identified and removed using Tukey’s outlier method on Fulton’s condition factor (K). The predicted weights, represented by red lines, were obtained through generalized linear regression on log-transformed data.

Implications for fisheries management

The system serves as a comprehensive platform that integrates the collection of multiple lines of fisheries data necessary for informing the sustainable management of fisheries, rather than as a single-purpose tool for automated species identification and/or length measurements. The unique ability of this system to collect and integrate AI-automated morphometric data simultaneously with other relevant fisheries data, over a broad range of coastal fisheries, enhances monitoring beyond conventional paper-based or single species stock assessment approaches92. As such, the system facilitates data collection to estimate volumes and pricing of fish products traded across market networks, volumes of landed catches, and catch per unit effort that is appropriately categorized for artisanal and commonly used subsistence fishing methods. In addition to data collection from fish markets and landings, the system integrates monitoring programs from coastal communities implementing community-based fisheries management93. Together these data support a broad range of fishing activities94, economic dynamics of the sector18, and management needs within coastal Pacific communities13.

In the absence of tailored fisheries management tools, often broader standardized tools and indices are applied43. Data for context-specific determination of LWR are, for the first time, being collected at regional, national, and subnational scales, thereby explicating the spatial and temporal variabilities in complex multispecies coastal fisheries. Such measures of LWR can be used to more accurately assess local population condition and stock status. The temporal and spatial scales of data collected, including both length and weight, have important implications for the management of fish populations in and across Pacific SIDS. The continuous and autonomous tracking of body condition, using indices such as Fulton’s Factor (K) and Le Cren’s modification on K to relative Kn together with other length-based stock assessment indices (e.g., Length-Based Spawning Potential Ratio—LBSPR), can provide an indication of changes in stock status67,72,73,95. Collecting weight data in addition to length data can further improve these LBSPR indices. It can also help track changes in body condition and detect spawning individuals55.

The platform’s principal function is to assist with ‘data poor’ fisheries management, specifically in Pacific SIDS. It responds to the need for access, compatibility and functionality, both within the region and between regional and global systems. Regarding the former, it offers authenticated users with tailored data exports, while for the latter it offers options for customized Application Programming Interfaces (APIs) to connect with alternative platforms aligned with SDG 14.4.1. Among others, this includes, for example the United Nations, Food and Agriculture Organization’s Virtual Research Environment (VRE)96. Such interfaces allow state of the art length-based stock assessment methods for SSF97 to integrate into the AI data collection system, generating models and predictive models as needed and addressing the enduring and critical challenge of timely stock assessment reporting in coastal fisheries98. These efficiencies thus enable better data collection and promote integrated assessment and advice in small-scale fisheries42,99.

While scientific support for decisions concerning highly migratory fish stocks in the Pacific SIDS region has expanded since 2004 under the formal mandate provided by the Western and Central Pacific Fisheries Commission (WCPFC) Convention, a critical gap remains. Unlike the WCPFC’s managed operations in the broader Pacific region, which drive informed policy development through science100, there exists no comparable regional framework for the science and management of coastal fisheries in Pacific SIDS101. Despite the latter being a critical global indicator of improved fisheries management102. This institutional void extends to the literature on applied science guiding national and regional policy development on artisanal and coastal fisheries103. Consequently, there is relatively limited attention from national and international policymakers and funding bodies directed toward the national and regional challenges faced by Pacific coastal fisheries104. The efficient means of collecting scientific evidence for fisheries management offered through this regional system, stands to decrease the uncertainty in the status and dynamics of Pacific coastal fisheries47.

The co-design of the monitoring system has strengthened collaborations among communities and national and regional organizations engaged in Pacific coastal fisheries100. The development and rollout of the monitoring system has strengthened decentralization ambitions within national programs for data collection, while also integrating them into regional coordination efforts. Domestically, such collaborations can be leveraged to tailor harvesting strategies as part of national requirements for coastal fisheries management104–106. Applying information, they collect and own, leveraged through regional partnerships, enables greater collaboration between fisheries authorities and communities107. This allows national fisheries agencies, for example, to more efficiently gauge the effectiveness of their strategies within the context of local practices and traditions108. Particularly in the context of community fisheries, where since 2015 regional policy and management has focused on enabling scaling community-based fishery management through cost-effective support measures109, this system functions to catalyze the kind of decentralized management solution that is required93. Uninterrupted data flows between the centralized support platform and remote fisheries offices and/or villages and fishers, enables the essential rapid return of results that has thus far challenged local adaptive management13.

Limitations in practice

A noteworthy limitation in later models trained with more than 140 new species, was the reduction in maximum recall. This is a common occurrence in CV deep learning applications in large datasets110,111. While further training is expected to overcome this decline in precision, caution is needed as ‘excessive’ training can also lead to specificity and misclassifications (false positives), especially when species share similar characteristics. These issues can be overcome112–115 as the system and AI technologies rapidly develop. For example, current trials in integrating CSPDarkNet-53 backbone architecture for species detection116,117 promises to improve recall, and thus accuracy in species detection. Recognizing the need for retraining, models in the system underwent multiple retraining following large influxes of new species or those conspecifics with varied visible characteristics. Detection rates were further improved by using multiple model algorithms simultaneously (e.g., YOLOv4, ResNet101). Other measures that integrated multiple approaches to preprocessing, like splitting classifications into grouped classification chains (Fig. 2) were also applied to improve recall and accuracy. This included, for example, partitioning input imagery into morphologically or geographically partitioned groups and trainings and comparing multiple model architectures in each group. As the system matures over time it is critical that new model capabilities are continually integrated into the system to further enhance its applicability, efficiency and useability118.

A major obstacle to any such system is the need to continuously upgrade software and hardware. Both these needs have resourcing implications (e.g. budget and technical skills) that must be recognized for these endeavors to continuously deliver capacity enhancements. The growing magnitude of data input from users into the system, for example, resulted in the need to upgrade the infrastructure and invest in new and more powerful computing capacity. While these costs were low relative to those incurred from manual methods at this scale, implementation and support to these systems must consider ongoing costs, and with that have sustainable resourcing mechanisms in place.

This system has created much needed efficiencies in data collection and has closed a critical capacity gap by improving access to technology and monitoring science in artisanal and subsistence fisheries. However, there remains the need for continued investment in national fisheries monitoring budgets and the human resources to collect these data on the ground119. Good data collection is quite simply contingent on the time and effort invested by fisheries authorities and community enumerators, so without such functional programs in countries monitoring could not occur, regardless of the improvement in technology. The projected upshift in the frequency of assessment and reporting of the status of key coastal fisheries and ensuring provision of regular informed policy briefs to decision makers, should help strengthen investment in these areas.

Conclusions

This paper describes the evolution, structural components and functionality of a comprehensive AI-enabled monitoring system that serves national and regional coastal fishery management needs. With the system’s development being integrated in national fisheries programs, it is making significant contributions to adaptive management cycles at various scales and demonstrates the value of digital and AI technology in addressing the enduring challenge of delivering scientific evidence for fisheries managers of tropical SSF. The system’s ability to incorporate a broad range of approaches in the fisheries management cycle and its versatility to integrate other programs, promises to revolutionize how institutions and communities communicate and use the information collected120.

The AI-enabled fisheries monitoring system represents a first on several fronts. Firstly, it enables near real time transfer of data to diverse, data-poor coastal fisheries management contexts for evidence-based adaptive management. Secondly, it allows the application of state-of-the-art technology in remote fisheries contexts, empowering local actors with information needed to made good management decisions. Thirdly, it supports the development of management metrics and tools that are tailored by and for Pacific people to specific conditions and geographies (e.g., country specific LWRs). Fourthly, the system’s compatibility to interface with other learning platforms facilitates the continued improvement and refinement of supporting models, thereby keeping up with technical advances in the field. In addition to these benefits, the experience of co-developing and implementing the system with multiple stakeholders has strengthened cross border collaboration of Pacific SIDS in the Western, Central and South Pacific Region.

Commitments by national fisheries authorities and stakeholders engaging with the system demonstrate the level of buy-in into the fisheries monitoring support platform. Its evolution and integration at scale serves to illustrate how this approach can be replicated among SIDS globally, thereby addressing critical barriers to achieving adaptive management44. The development of this innovation is a result of a technology implemented, tested, and refined as part of policy and practice—this ensures institutional fit by embeddedness as it matures, that it responds to existing and emerging needs, that it challenges, with evidence, entrenched management practices and approaches121, and that Pacific Island nations are on the forefront of the rapid advances in AI technologies.

The strategic design and execution of the Ikasavea system responds to an urgent need for timely management of fish stocks in a changing world44,122. Fish remain a critical resource of people in SIDS, many of whom live in remote communities far from capital cities and national fishery agencies. Projecting into the future, Ikasavea offers potential to develop into an exchange platform for fishery-related knowledge among connected but geographically distant communities and their supporting agencies123.

Supplementary Information

Supplementary Figures.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71763-y.

Acknowledgements

We are grateful to the many fishers and retailers that allowed their fish to be sampled, and the fisheries officers and enumerators that recorded information that fed into the development and implementation of the monitoring system. We acknowledge support from the European Union and the Government of Sweden under the Pacific European Union Marine Partnership programme through the Pacific Community (SPC), the Australian Department of Foreign Affairs and Trade, and the New Zealand Foreign Affairs and Trade Aid Program. NLA, OL, BN, AS, and DJS were supported by the Australian government through ACIAR project FIS/2020/172. GS acknowledges a University of Wollongong postgraduate scholarship. We are grateful to Kristel Steenbergen for Figure 1 and Eleanor McNeill for edits to graphics.

Author contributions

GS, FM, PB, AH, BV, NA, DS conceptualized the program. All authors collected and/or curated the data. GS, FM, SG, DS, NA conducted the formal analysis. All authors contributed to the methodology. FM and BV developed the software. GS, FM, SG validated the study. GS, FM, DS, NA visualized the data. GS, FM, DS, NA wrote the manuscript. All authors reviewed and approved the manuscript.

Data availability

The datasets analyzed during the current study are not publicly available due to privacy policy agreements between the Pacific Community (SPC) and its member Countries and Territories but are available from the corresponding author on reasonable request. Main models that are used as part of an image processing chain to calibrate images, detect, measure and identify specimens can be found here https://github.com/PacificCommunity/cfap-ai-models.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Béné C Contribution of fisheries and aquaculture to food security and poverty reduction: Assessing the current evidence World Dev. 2016 79 177 196 10.1016/j.worlddev.2015.11.007
Béné, C. et al. Contribution of fisheries and aquaculture to food security and poverty reduction: Assessing the current evidence. World Dev. 79, 177–196 (2016).10.1016/j.worlddev.2015.11.007
2. Lowitt K Ville AS Lewis P Hickey GM Environmental change and food security: the special case of small island developing states Reg. Environ. Change 2015 15 1293 1298 10.1007/s10113-015-0849-3
Lowitt, K., Ville, A. S., Lewis, P. & Hickey, G. M. Environmental change and food security: the special case of small island developing states. Reg. Environ. Change 15, 1293–1298 (2015).10.1007/s10113-015-0849-3
3. Bennett NJ The COVID-19 Pandemic, small-scale fisheries and coastal fishing communities Coast. Man. 2020 48 336 347 10.1080/08920753.2020.1766937
Bennett, N. J. et al. The COVID-19 Pandemic, small-scale fisheries and coastal fishing communities. Coast. Man. 48, 336–347 (2020).10.1080/08920753.2020.1766937
4. Gillett R Cartwright I The Future of Pacific Island Fisheries 2010 Pacific Community
Gillett, R. & Cartwright, I. The Future of Pacific Island Fisheries (Pacific Community, 2010).
5. Bell, J. D. et al. Adapting tropical Pacific fisheries and aquaculture to climate change: management measures, policies and investments. in Vulnerability of Tropical Pacific Fisheries and Aquaculture to Climate Change (eds. Bell, J. D., Johnson, J. E. & Hobday, A. J.) 803–876 (Secretariat of the Pacific Community, Noumea, New Caledonia, 2011).
6. de Suarez JM Cicin-Sain B Wowk K Payet R Hoegh-Guldberg O Ensuring survival: Oceans, climate and security Ocean Coast. Manag. 2014 90 27 37 10.1016/j.ocecoaman.2013.08.007
de Suarez, J. M., Cicin-Sain, B., Wowk, K., Payet, R. & Hoegh-Guldberg, O. Ensuring survival: Oceans, climate and security. Ocean Coast. Manag. 90, 27–37 (2014).10.1016/j.ocecoaman.2013.08.007
7. Bahri T Adaptive Management of Fisheries in Response to Climate Change: FAO Fisheries and Aquaculture Technical Paper No. 667 2021 FAO
Bahri, T. et al. Adaptive Management of Fisheries in Response to Climate Change: FAO Fisheries and Aquaculture Technical Paper No. 667 (FAO, 2021).
8. Leal Filho W Climate change adaptation on small island states: An assessment of limits and constraints J. Mar. Sci. Engin. 2021 9 602 10.3390/jmse9060602
Leal Filho, W. et al. Climate change adaptation on small island states: An assessment of limits and constraints. J. Mar. Sci. Engin. 9, 602 (2021).10.3390/jmse9060602
9. Report of the United Nations Conference on Environment and Development. (United Nations, Rio de Janeiro, Brazil, 1992).
10. Report of the Global Conference on the Sustainable Development of Small Island Developing States. (United Nations, Bridgetown, Barbados, 1994).
11. Report of the International Meeting to Review the Implementation of the Programme of Action for the Sustainable Development of Small Island Developing States. (United Nations, Port Louis, Mauritius, 2005).
12. Friedman RS Scanning Models of Food Systems Resilience in the Indo-Pacific Region Front. Sustain. Food Syst. 2022 10.3389/fsufs.2022.714881
Friedman, R. S. et al. Scanning Models of Food Systems Resilience in the Indo-Pacific Region. Front. Sustain. Food Syst.10.3389/fsufs.2022.714881 (2022).10.3389/fsufs.2022.714881
13. Andrew NL Evans L Pomeroy RS Approaches and frameworks for management and research in small-scale fisheries Small-scale fisheries management: frameworks and approaches for the developing world 2011 CABI
Andrew, N. L. & Evans, L. Approaches and frameworks for management and research in small-scale fisheries. In Small-scale fisheries management: frameworks and approaches for the developing world (ed. Pomeroy, R. S.) (CABI, 2011).
14. Thomas A Climate change and small island developing states Annu. Rev. Environ. Resour. 2020 45 1 27 10.1146/annurevenviron-012320-083355
Thomas, A. et al. Climate change and small island developing states. Annu. Rev. Environ. Resour. 45, 1–27. 10.1146/annurevenviron-012320-083355 (2020).10.1146/annurevenviron-012320-083355
15. Campbell JR Development, global change and traditional food security in Pacific Island countries Reg. Environ. Change 2015 15 1313 1324 10.1007/s10113-014-0697-6
Campbell, J. R. Development, global change and traditional food security in Pacific Island countries. Reg. Environ. Change 15, 1313–1324 (2015).10.1007/s10113-014-0697-6
16. Gillett R. & Fong M. Fisheries in the economies of Pacific Island countries and territories (Benefish Study 4). Noumea, New Caledonia: Pacific Community. 704 p. https://purl.org/spc/digilib/doc/ppizh. (2023).
17. Bell JD Planning the use of fish for food security in the Pacific Mar. Policy 2009 33 64 76 10.1016/j.marpol.2008.04.002
Bell, J. D. et al. Planning the use of fish for food security in the Pacific. Mar. Policy 33, 64–76 (2009).10.1016/j.marpol.2008.04.002
18. Gillett, R. E. Fisheries in the economies of Pacific Island countries and territories. Noumea, New Caledonia: Pacific Community 1–684 (2016).
19. Vaughan MB Vitousek PM Mahele. Sustaining Communities through Small-Scale Inshore Fishery Catch and Sharing Networks Pac. Sci. 2013 67 329 344 10.2984/67.3.3
Vaughan, M. B., Vitousek, P. M. & Mahele.,. Sustaining Communities through Small-Scale Inshore Fishery Catch and Sharing Networks. Pac. Sci. 67, 329–344 (2013).10.2984/67.3.3
20. Gillett R Lightfoot C The Contribution of Fisheries to the Economies of Pacific Island Countries: A Report Prepared for the Asian Development Bank, the Forum Fisheries Agency, and the World Bank 2002 ADB
Gillett, R. & Lightfoot, C. The Contribution of Fisheries to the Economies of Pacific Island Countries: A Report Prepared for the Asian Development Bank, the Forum Fisheries Agency, and the World Bank (ADB, 2002).
21. Govan, H. & Lalavanua, W. The “Pacific Way” of Coastal Fisheries Management: Status and Progress of Community-Based Fisheries Management. 64 https://purl.org/spc/digilib/doc/ocw6w (2022).
22. Rice J Evolution of international commitments for fisheries sustainability ICES J. Mar. Sci. 2014 71 157 165 10.1093/icesjms/fst078
Rice, J. Evolution of international commitments for fisheries sustainability. ICES J. Mar. Sci. 71, 157–165 (2014).10.1093/icesjms/fst078
23. United Nations. Transforming our world: the 2030 Agenda for Sustainable Development Department of Economic and Social Affairs. https://sdgs.un.org/2030agenda (2015).
24. United Nations. Sustainable Development Goals: 17 Goals to Transform Our World. https://sdgs.un.org/ (2023).
25. Hoelting, R. A. After Rio: The Sustainable Development Concept Following the United Nations Conference on Environment and Development. Ga. J. Intl. Comp. Law 24, 117 (1994).
26. FAO and SPC. Report of the FAO/SPC Regional Workshop on Improving Information on Status and Trends of Fisheries in the Pacific Region. Apia, Samoa, 22–26 May 2006. FAO Fisheries and Aquaculture Report. No. 920. Rome, FAO. 2010. 70p
27. Dalzell, P Adams T. J. H. & Polunin N. V. C. Coastal fisheries in the Pacific Islands. Oceanogr. Mar. Biol. 34, 395–531 (1996).
28. Barclay K Cartwright I Governance of tuna industries: The key to economic viability and sustainability in the Western and Central Pacific Ocean Mar. Policy 2007 31 348 358 10.1016/j.marpol.2006.09.007
Barclay, K. & Cartwright, I. Governance of tuna industries: The key to economic viability and sustainability in the Western and Central Pacific Ocean. Mar. Policy 31, 348–358 (2007).10.1016/j.marpol.2006.09.007
29. Cánovas-Molina A García-Frapolli E A review of vulnerabilities in worldwide small-scale fisheries Fish. Manage. Ecol. 2022 29 491 501 10.1111/fme.12538
Cánovas-Molina, A. & García-Frapolli, E. A review of vulnerabilities in worldwide small-scale fisheries. Fish. Manage. Ecol. 29, 491–501 (2022).10.1111/fme.12538
30. Cochrane KL Andrew NL Parma AM Primary fisheries management: a minimum requirement for provision of sustainable human benefits in small-scale fisheries Fish Fish. 2011 12 275 288 10.1111/j.1467-2979.2010.00392.x
Cochrane, K. L., Andrew, N. L. & Parma, A. M. Primary fisheries management: a minimum requirement for provision of sustainable human benefits in small-scale fisheries. Fish Fish. 12, 275–288 (2011).10.1111/j.1467-2979.2010.00392.x
31. Govan, H. The Pacific Islands and Biodiversity Beyond National Jurisdiction: Briefing Note of the Council of Regional Organisations in the Pacific Members of the Marine Sector Working Group, 10.13140/RG.2.1.1247.9527. (2014).
32. Keen MR Schwarz A-M Wini-Simeon L Towards defining the blue economy: Practical lessons from Pacific Ocean governance Mar. Policy 2018 88 333 341 10.1016/j.marpol.2017.03.002
Keen, M. R., Schwarz, A.-M. & Wini-Simeon, L. Towards defining the blue economy: Practical lessons from Pacific Ocean governance. Mar. Policy 88, 333–341 (2018).10.1016/j.marpol.2017.03.002
33. Anon. A new song for coastal fisheries pathways to change: the Noumea strategy. Future of coastal/Inshore fisheries management (Pacific Community (SPC), Noumea, New Caledonia, 2015). 639. 2099597
34. Gillett R Marine fishery resources in the Pacific islands 2011 FAO Fisheries and Aquaculture Reviews and Studies
Gillett, R. Marine fishery resources in the Pacific islands (FAO Fisheries and Aquaculture Reviews and Studies, 2011).
35. Adams TJH Nicholas VC Modern institutional framework for reef fisheries management reef fisheries 1996 Springer
Adams, T. J. H. Modern institutional framework for reef fisheries management. In reef fisheries (ed. Nicholas, V. C.) (Springer, 1996).
36. Punt, A. E. & Nolan, C. P. Evaluating the costs and benefits of alternative monitoring programmes for fisheries management. Proc. International Conference on Integrated Fisheries Monitoring, Sydney, Australia, 1–5 February 1999 (1999).
37. Hartill BW Payne GW Rush N Bian R Bridging the temporal gap: Continuous and cost-effective monitoring of dynamic recreational fisheries by web cameras and creel surveys Fish. Res. 2016 183 488 497 10.1016/j.fishres.2016.06.002
Hartill, B. W., Payne, G. W., Rush, N. & Bian, R. Bridging the temporal gap: Continuous and cost-effective monitoring of dynamic recreational fisheries by web cameras and creel surveys. Fish. Res. 183, 488–497 (2016).10.1016/j.fishres.2016.06.002
38. Honey KT Moxley JH Fujita RM From rags to fishes: data-poor methods for fishery managers Managing Data-Poor Fish. Case Stud. Models Solut. 2010 1 159 184
Honey, K. T., Moxley, J. H. & Fujita, R. M. From rags to fishes: data-poor methods for fishery managers. Managing Data-Poor Fish. Case Stud. Models Solut. 1, 159–184 (2010).
39. Pons M Cope JM Kell LT Comparing performance of catch-based and length-based stock assessment methods in data-limited fisheries Can. J. Fish. Aquat. Sci. 2020 77 1026 1037 10.1139/cjfas-2019-0276
Pons, M., Cope, J. M. & Kell, L. T. Comparing performance of catch-based and length-based stock assessment methods in data-limited fisheries. Can. J. Fish. Aquat. Sci. 77, 1026–1037 (2020).10.1139/cjfas-2019-0276
40. Chrysafi A Kuparinen A Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment Environ. Rev. 2016 24 25 38 10.1139/er-2015-0044
Chrysafi, A. & Kuparinen, A. Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment. Environ. Rev. 24, 25–38 (2016).10.1139/er-2015-0044
41. Cope JM The stock assessment theory of relativity: Deconstructing the term “data-limited” fisheries into components and guiding principles to support the science of fisheries management Rev Fish Biol. Fisheries 2023 10.1007/s11160-022-09748-1
Cope, J. M. et al. The stock assessment theory of relativity: Deconstructing the term “data-limited” fisheries into components and guiding principles to support the science of fisheries management. Rev Fish Biol. Fisheries10.1007/s11160-022-09748-1 (2023).10.1007/s11160-022-09748-1
42. Salpin C Onwuasoanya V Bourrel M Swaddling A Marine scientific research in Pacific Small Island Developing States Mar. Policy 2018 95 363 371 10.1016/j.marpol.2016.07.019
Salpin, C., Onwuasoanya, V., Bourrel, M. & Swaddling, A. Marine scientific research in Pacific Small Island Developing States. Mar. Policy 95, 363–371 (2018).10.1016/j.marpol.2016.07.019
43. Parks J Pomeroy RS Adaptive management in small-scale fisheries: a practical approach Small scale fisheries management: frameworks and approaches for the developing world 2011 CAB International
Parks, J. Adaptive management in small-scale fisheries: a practical approach. In Small scale fisheries management: frameworks and approaches for the developing world (ed. Pomeroy, R. S.) (CAB International, 2011).
44. Edmondson E Fanning L Implementing adaptive management within a fisheries management context: A systematic literature review revealing gaps, challenges, and ways forward Sustainability 2022 14 7249 10.3390/su14127249
Edmondson, E. & Fanning, L. Implementing adaptive management within a fisheries management context: A systematic literature review revealing gaps, challenges, and ways forward. Sustainability 14, 7249 (2022).10.3390/su14127249
45. Chong L Performance evaluation of data-limited, length-based stock assessment methods ICES J. Mar. Sci. 2020 77 97 108 10.1093/icesjms/fsz212
Chong, L. et al. Performance evaluation of data-limited, length-based stock assessment methods. ICES J. Mar. Sci. 77, 97–108 (2020).10.1093/icesjms/fsz212
46. Castello L An approach to assess data-less small-scale fisheries: Examples from Congo rivers Rev Fish Biol Fisheries 2023 33 593 610 10.1007/s11160-023-09770-x
Castello, L. et al. An approach to assess data-less small-scale fisheries: Examples from Congo rivers. Rev Fish Biol Fisheries 33, 593–610 (2023).10.1007/s11160-023-09770-x
47. Harden-Davies HR Research for regions: strengthening marine technology transfer for Pacific Island Countries and biodiversity beyond national jurisdiction Intl. J. Mar. Coast. Law 2017 32 797 822 10.1163/15718085-13204023
Harden-Davies, H. R. Research for regions: strengthening marine technology transfer for Pacific Island Countries and biodiversity beyond national jurisdiction. Intl. J. Mar. Coast. Law 32, 797–822 (2017).10.1163/15718085-13204023
48. UNCTAD Digital Economy Report Pacific Edition 2022: Towards Value Creation and Inclusiveness 2022 United Nations Publications
UNCTAD. Digital Economy Report Pacific Edition 2022: Towards Value Creation and Inclusiveness (United Nations Publications, 2022).
49. Grosz BJ Stone P A century-long commitment to assessing artificial intelligence and its impact on society Commun. ACM 2018 61 68 73 10.1145/3198470
Grosz, B. J. & Stone, P. A century-long commitment to assessing artificial intelligence and its impact on society. Commun. ACM 61, 68–73 (2018).10.1145/3198470
50. Zion B The use of computer vision technologies in aquaculture–a review Comput. Electron. Agricult. 2012 88 125 132 10.1016/j.compag.2012.07.010
Zion, B. The use of computer vision technologies in aquaculture–a review. Comput. Electron. Agricult. 88, 125–132 (2012).10.1016/j.compag.2012.07.010
51. Aftab K Intelligent fisheries: Cognitive solutions for improving aquaculture commercial efficiency through enhanced biomass estimation and early disease detection Cogn. Comput. 2024 10.1007/s12559-024-10292-2
Aftab, K. et al. Intelligent fisheries: Cognitive solutions for improving aquaculture commercial efficiency through enhanced biomass estimation and early disease detection. Cogn. Comput.10.1007/s12559-024-10292-2 (2024).10.1007/s12559-024-10292-2
52. Lopez-Marcano S Brown CJ Sievers M Connolly RM The slow rise of technology: Computer vision techniques in fish population connectivity Aquatic Conser. 2021 31 210 217 10.1002/aqc.3432
Lopez-Marcano, S., Brown, C. J., Sievers, M. & Connolly, R. M. The slow rise of technology: Computer vision techniques in fish population connectivity. Aquatic Conser. 31, 210–217 (2021).10.1002/aqc.3432
53. Signaroli M Lana A Alós J Novel computer vision tools applied to marine recreational fisheries spatial planning Fish Res 2024 271 106924 10.1016/j.fishres.2023.106924
Signaroli, M., Lana, A. & Alós, J. Novel computer vision tools applied to marine recreational fisheries spatial planning. Fish Res 271, 106924 (2024).10.1016/j.fishres.2023.106924
54. Bradley D Opportunities to improve fisheries management through innovative technology and advanced data systems Fish Fish. 2019 20 564 583 10.1111/faf.12361
Bradley, D. et al. Opportunities to improve fisheries management through innovative technology and advanced data systems. Fish Fish. 20, 564–583 (2019).10.1111/faf.12361
55. Vilas C Use of computer vision onboard fishing vessels to quantify catches: The iObserver Mar. Policy 2020 116 103714 10.1016/j.marpol.2019.103714
Vilas, C. et al. Use of computer vision onboard fishing vessels to quantify catches: The iObserver. Mar. Policy 116, 103714 (2020).10.1016/j.marpol.2019.103714
56. Ovalle JC Vilas C Antelo LT On the use of deep learning for fish species recognition and quantification on board fishing vessels Mar. Policy 2022 139 105015 10.1016/j.marpol.2022.105015
Ovalle, J. C., Vilas, C. & Antelo, L. T. On the use of deep learning for fish species recognition and quantification on board fishing vessels. Mar. Policy 139, 105015 (2022).10.1016/j.marpol.2022.105015
57. Palmer M Álvarez-Ellacuría A Moltó V Catalán IA Automatic, operational, high-resolution monitoring of fish length and catch numbers from landings using deep learning Fish. Res. 2022 246 106166 10.1016/j.fishres.2021.106166
Palmer, M., Álvarez-Ellacuría, A., Moltó, V. & Catalán, I. A. Automatic, operational, high-resolution monitoring of fish length and catch numbers from landings using deep learning. Fish. Res. 246, 106166 (2022).10.1016/j.fishres.2021.106166
58. Ovalle JC Vilas C Antelo LT On the use of deep learning for fish species recognition and quantification on board fishing vessels Mar. Policy 2022 139 105015 10.1016/j.marpol.2022.105015
Ovalle, J. C., Vilas, C. & Antelo, L. T. On the use of deep learning for fish species recognition and quantification on board fishing vessels. Mar. Policy 139, 105015 (2022).10.1016/j.marpol.2022.105015
59. Barbedo JGA A Review on the Use of Computer Vision and Artificial Intelligence for Fish Recognition, Monitoring, and Management Fishes 2022 7 335 10.3390/fishes7060335
Barbedo, J. G. A. A Review on the Use of Computer Vision and Artificial Intelligence for Fish Recognition, Monitoring, and Management. Fishes 7, 335 (2022).10.3390/fishes7060335
60. Silva CNS Dainys J Simmons S Vienožinskis V Audzijonyte A A Scalable Open-Source Framework for Machine Learning-Based Image Collection, Annotation and Classification: A Case Study for Automatic Fish Species Identification Sustainability 2022 14 14324 10.3390/su142114324
Silva, C. N. S., Dainys, J., Simmons, S., Vienožinskis, V. & Audzijonyte, A. A Scalable Open-Source Framework for Machine Learning-Based Image Collection, Annotation and Classification: A Case Study for Automatic Fish Species Identification. Sustainability 14, 14324 (2022).10.3390/su142114324
61. Lekunberri X Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning Ecol. Inform. 2022 67 101495 10.1016/j.ecoinf.2021.101495
Lekunberri, X. et al. Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning. Ecol. Inform. 67, 101495 (2022).10.1016/j.ecoinf.2021.101495
62. Atlas WI Wild salmon enumeration and monitoring using deep learning empowered detection and tracking Front. Mar. Sci. 2023 10.3389/fmars.2023.1200408
Atlas, W. I. et al. Wild salmon enumeration and monitoring using deep learning empowered detection and tracking. Front. Mar. Sci.10.3389/fmars.2023.1200408 (2023).10.3389/fmars.2023.1200408
63. Shorten C Khoshgoftaar TM A survey on image data augmentation for deep learning J Big Data 2019 6 60 10.1186/s40537-019-0197-0
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J Big Data 6, 60 (2019).10.1186/s40537-019-0197-0
64. Beyan C Browman HI Setting the stage for the machine intelligence era in marine science ICES J. Mar. Sci. 2020 77 1267 1273 10.1093/icesjms/fsaa084
Beyan, C. & Browman, H. I. Setting the stage for the machine intelligence era in marine science. ICES J. Mar. Sci. 77, 1267–1273 (2020).10.1093/icesjms/fsaa084
65. Nash R Valencia AH Geffen A The origin of Fulton’s condition factor - Setting the record straight Fisheries 2006 31 236 238
Nash, R., Valencia, A. H. & Geffen, A. The origin of Fulton’s condition factor - Setting the record straight. Fisheries 31, 236–238 (2006).
66. Tukey JW Exploratory Data Analysis 1997 Addison-Wesley Pub. Co., Reading Mass
Tukey, J. W. Exploratory Data Analysis (Addison-Wesley Pub. Co., Reading Mass, 1997).
67. Lloret, J., Shulman, G. & Love, R. M. Description of condition indicators. in Condition and Health Indicators of Exploited Marine Fishes 1–16 (John Wiley & Sons, Ltd, 2013). 10.1002/9781118752777.ch1.
68. Iglewicz B Hoaglin DC How to Detect and Handle Outliers 1993 Quality Press
Iglewicz, B. & Hoaglin, D. C. How to Detect and Handle Outliers (Quality Press, 1993).
69. Sullivan JH Warkentin M Wallace L So many ways for assessing outliers: What really works and does it matter? J. Bus. Res. 2021 132 530 543 10.1016/j.jbusres.2021.03.066
Sullivan, J. H., Warkentin, M. & Wallace, L. So many ways for assessing outliers: What really works and does it matter?. J. Bus. Res. 132, 530–543 (2021).10.1016/j.jbusres.2021.03.066
70. Sánchez-González JR Arbonés A Casals F Variation over time of length–weight relationships and condition factors for four exotic fish species from a restored shallow lake in NE Iberian Peninsula Fishes 2020 5 7 10.3390/fishes5010007
Sánchez-González, J. R., Arbonés, A. & Casals, F. Variation over time of length–weight relationships and condition factors for four exotic fish species from a restored shallow lake in NE Iberian Peninsula. Fishes 5, 7 (2020).10.3390/fishes5010007
71. Froese, R. and Pauly, D. Editors. FishBase 2000: concepts, design and data sources. ICLARM, Los Baños, Laguna, Philippines. 344 p (2000).
72. Prince J Hordyk A Valencia SR Loneragan N Sainsbury K Revisiting the concept of Beverton-Holt life-history invariants with the aim of informing data-poor fisheries assessment ICES J. Mar. Sci. 2015 72 194 203 10.1093/icesjms/fsu011
Prince, J., Hordyk, A., Valencia, S. R., Loneragan, N. & Sainsbury, K. Revisiting the concept of Beverton-Holt life-history invariants with the aim of informing data-poor fisheries assessment. ICES J. Mar. Sci. 72, 194–203 (2015).10.1093/icesjms/fsu011
73. Hordyk AR Ono K Prince JD Walters CJ A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: Application to spawning potential ratios for data-poor stocks Can. J. Fish. Aquat. Sci. 2016 73 1787 1799 10.1139/cjfas-2015-0422
Hordyk, A. R., Ono, K., Prince, J. D. & Walters, C. J. A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: Application to spawning potential ratios for data-poor stocks. Can. J. Fish. Aquat. Sci. 73, 1787–1799 (2016).10.1139/cjfas-2015-0422
74. Auguie B. Package ‘gridExtra’. Miscellaneous functions for “grid” graphics. https://CRAN.R-project.org/package=gridExtra (2017).
75. RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2023).
76. Attali, D. & Baker, C. ggExtra: Add Marginal Histograms to ‘ggplot2’, and More ‘ggplot2’ Enhancements. R package version 0.10.1. https://CRAN.R-project.org/package=ggExtra (2023).
77. R Core Team R: A Language and Environment for Statistical Computing 2023 Vienna, Austria R Foundation for Statistical Computing
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing). https://www.R-project.org/ (2023).
78. Robinson, D., Hayes, A. & Couch, S. Broom: Convert Statistical Objects into Tidy Tibbles. https://CRAN.R-project.org/package=broom (2023).
79. Wickham H 2023 Welcome to the tidyverse J. Open Source Softw. 2023 4 1686 10.21105/joss.01686
Wickham, H. et al. 2023 Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2023).10.21105/joss.01686
80. Bradski G Kaehler A Pisarevsky V Learning-based computer vision with Intel’s open source computer vision library Intel. Tech. J. 2005 9 119 130
Bradski, G., Kaehler, A. & Pisarevsky, V. Learning-based computer vision with Intel’s open source computer vision library. Intel. Tech. J. 9, 119–130 (2005).
81. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proc. IEEE conference on computer vision and pattern recognition. 770–778 (2016).
82. Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. Proc. of the IEEE conference on computer vision and pattern recognition 779–788 (2016).
83. Bochkovskiy, A., Wang, C.-Y. & Liao, H.-Y. M. YOLOv4: Optimal Speed and Accuracy of Object Detection. Preprint at http://arxiv.org/abs/2004.10934 (2020).
84. Pulli K Baksheev A Kornyakov K Eruhimov V Real-time computer vision with OpenCV Commun. ACM 2012 55 61 69 10.1145/2184319.2184337
Pulli, K., Baksheev, A., Kornyakov, K. & Eruhimov, V. Real-time computer vision with OpenCV. Commun. ACM 55, 61–69 (2012).10.1145/2184319.2184337
85. Brodzicki A Piekarski M Kucharski D Jaworek-Korjakowska J Gorgon M Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets Found. Comput. Decis. Sci. 2020 45 179 193 10.2478/fcds-2020-0010
Brodzicki, A., Piekarski, M., Kucharski, D., Jaworek-Korjakowska, J. & Gorgon, M. Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets. Found. Comput. Decis. Sci. 45, 179–193 (2020).10.2478/fcds-2020-0010
86. Bland JM Altman DG Measuring agreement in method comparison studies Stat. Methods Med. Res. 1999 8 135 160 10.1177/096228029900800204 10501650
Bland, J. M. & Altman, D. G. Measuring agreement in method comparison studies. Stat. Methods Med. Res. 8, 135–160 (1999).10501650 10.1177/096228029900800204
87. Monkman GG Hyder K Kaiser MJ Vidal FP Using machine vision to estimate fish length from images using regional convolutional neural networks Methods Ecol. Evol. 2019 10 2045 2056 10.1111/2041-210X.13282
Monkman, G. G., Hyder, K., Kaiser, M. J. & Vidal, F. P. Using machine vision to estimate fish length from images using regional convolutional neural networks. Methods Ecol. Evol. 10, 2045–2056 (2019).10.1111/2041-210X.13282
88. Wäldchen J Mäder P Machine learning for image based species identification Methods Ecol. Evol. 2018 9 2216 2225 10.1111/2041-210X.13075
Wäldchen, J. & Mäder, P. Machine learning for image based species identification. Methods Ecol. Evol. 9, 2216–2225 (2018).10.1111/2041-210X.13075
89. Blair JD Gaynor KM Palmer MS Marshall KE A gentle introduction to computer vision-based specimen classification in ecological datasets J. Anim. Ecol. 2024 93 147 158 10.1111/1365-2656.14042 38230868
Blair, J. D., Gaynor, K. M., Palmer, M. S. & Marshall, K. E. A gentle introduction to computer vision-based specimen classification in ecological datasets. J. Anim. Ecol. 93, 147–158 (2024).38230868 10.1111/1365-2656.14042
90. Khalid MM Karan O Deep learning for plant disease detection Int. J. Math. Comput. Sci. 2024 2 75 84
Khalid, M. M. & Karan, O. Deep learning for plant disease detection. Int. J. Math. Comput. Sci. 2, 75–84 (2024).
91. De Robertis A Williams K Weight-Length Relationships in Fisheries Studies: The Standard Allometric Model Should Be Applied with Caution Trans. Am. Fish. Soc. 2008 137 707 719 10.1577/T07-124.1
De Robertis, A. & Williams, K. Weight-Length Relationships in Fisheries Studies: The Standard Allometric Model Should Be Applied with Caution. Trans. Am. Fish. Soc. 137, 707–719 (2008).10.1577/T07-124.1
92. Evans K Optimising fisheries management in relation to tuna catches in the western central Pacific Ocean: A review of research priorities and opportunities Mar. Policy 2015 59 94 104 10.1016/j.marpol.2015.05.003
Evans, K. et al. Optimising fisheries management in relation to tuna catches in the western central Pacific Ocean: A review of research priorities and opportunities. Mar. Policy 59, 94–104 (2015).10.1016/j.marpol.2015.05.003
93. Steenbergen DJ Song AM Andrew N A theory of scaling for community-based fisheries management Ambio 2022 51 666 677 10.1007/s13280-021-01563-5 34085210
Steenbergen, D. J., Song, A. M. & Andrew, N. A theory of scaling for community-based fisheries management. Ambio 51, 666–677 (2022).34085210 10.1007/s13280-021-01563-5
94. Stewart KR Characterizing Fishing Effort and Spatial Extent of Coastal Fisheries PLoS One 2010 5 e14451 10.1371/journal.pone.0014451 21206903
Stewart, K. R. et al. Characterizing Fishing Effort and Spatial Extent of Coastal Fisheries. PLoS One 5, e14451 (2010).21206903 10.1371/journal.pone.0014451
95. Hordyk A Ono K Valencia S Loneragan N Prince J A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries ICES J. Mar. Sci. 2015 72 217 231 10.1093/icesjms/fsu004
Hordyk, A., Ono, K., Valencia, S., Loneragan, N. & Prince, J. A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries. ICES J. Mar. Sci. 72, 217–231 (2015).10.1093/icesjms/fsu004
96. Taconet M Taconet M Virtual Research Environments supporting sustainability of global fisheries FAO Fisheries and Aquaculture - Abstracts 2024 FAO Fisheries and Aquaculture - Abstracts
Taconet, M. et al. Virtual Research Environments supporting sustainability of global fisheries. In FAO Fisheries and Aquaculture - Abstracts (eds Taconet, M. et al.) (FAO Fisheries and Aquaculture - Abstracts, 2024).
97. Hordyk AR Carruthers TR A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the productivity susceptibility analysis (PSA) PLoS One 2018 13 e0198298 10.1371/journal.pone.0198298 29856869
Hordyk, A. R. & Carruthers, T. R. A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the productivity susceptibility analysis (PSA). PLoS One 13, e0198298 (2018).29856869 10.1371/journal.pone.0198298
98. Prince JD Dowling NA Davies CR Campbell RA Kolody DS A simple cost-effective and scale-less empirical approach to harvest strategies ICES J. Mar. Sci. 2011 68 947 960 10.1093/icesjms/fsr029
Prince, J. D., Dowling, N. A., Davies, C. R., Campbell, R. A. & Kolody, D. S. A simple cost-effective and scale-less empirical approach to harvest strategies. ICES J. Mar. Sci. 68, 947–960 (2011).10.1093/icesjms/fsr029
99. Garcia S Towards Integrated Assessment and Advice in Small-Scale Fisheries: Principles and Processes 2008 FAO Fisheries and Aquaculture
Garcia, S. et al. Towards Integrated Assessment and Advice in Small-Scale Fisheries: Principles and Processes (FAO Fisheries and Aquaculture, 2008).
100. Gillett R Tauati MI Fisheries of the Pacific Islands: Regional and National Information: (FAO Fisheries and Aquaculture 2018
Gillett, R. & Tauati, M. I. Fisheries of the Pacific Islands: Regional and National Information: (FAO Fisheries and Aquaculture (2018).
101. Govan, H., Kinch, J. & Brjosniovschi, A. Strategic Review of Inshore Fisheries Policies and Strategies in Melanesia-Fiji, New Caledonia, Papua New Guinea, Solomon Islands and Vanuatu-Part II: Country Reports. (Pacific Community, Noumea, New Caledonia, 2013).
102. Thomas Travaille KL Crowder LB Kendrick GA Clifton J Key attributes related to fishery improvement project (FIP) effectiveness in promoting improvements towards sustainability Fish Fish. 2019 20 452 465 10.1111/faf.12357
Thomas Travaille, K. L., Crowder, L. B., Kendrick, G. A. & Clifton, J. Key attributes related to fishery improvement project (FIP) effectiveness in promoting improvements towards sustainability. Fish Fish. 20, 452–465 (2019).10.1111/faf.12357
103. Batista VS Fabré NN Malhado ACM Ladle RJ Tropical artisanal coastal fisheries: Challenges and future directions Rev. Fish. Sci. Aqua. 2014 22 1 15 10.1080/10641262.2013.822463
Batista, V. S., Fabré, N. N., Malhado, A. C. M. & Ladle, R. J. Tropical artisanal coastal fisheries: Challenges and future directions. Rev. Fish. Sci. Aqua. 22, 1–15 (2014).10.1080/10641262.2013.822463
104. Ayilu RK Fabinyi M Barclay K Small-scale fisheries in the blue economy: Review of scholarly papers and multilateral documents Ocean Coast. Manage. 2022 216 105982 10.1016/j.ocecoaman.2021.105982
Ayilu, R. K., Fabinyi, M. & Barclay, K. Small-scale fisheries in the blue economy: Review of scholarly papers and multilateral documents. Ocean Coast. Manage. 216, 105982 (2022).10.1016/j.ocecoaman.2021.105982
105. Dowling NA Empirical harvest strategies for data-poor fisheries: A review of the literature Fish. Res. 2015 171 141 153 10.1016/j.fishres.2014.11.005
Dowling, N. A. et al. Empirical harvest strategies for data-poor fisheries: A review of the literature. Fish. Res. 171, 141–153 (2015).10.1016/j.fishres.2014.11.005
106. Carruthers TR Hordyk AR The Data-Limited Methods Toolkit ( DLM tool): An R package for informing management of data-limited populations Methods Ecol. Evol. 2018 9 2388 2395 10.1111/2041-210X.13081
Carruthers, T. R. & Hordyk, A. R. The Data-Limited Methods Toolkit ( DLM tool): An R package for informing management of data-limited populations. Methods Ecol. Evol. 9, 2388–2395 (2018).10.1111/2041-210X.13081
107. Steenbergen DJ Tracing innovation pathways behind fisheries co-management in Vanuatu Ambio 2022 51 2359 2375 10.1007/s13280-022-01788-y 36138263
Steenbergen, D. J. et al. Tracing innovation pathways behind fisheries co-management in Vanuatu. Ambio 51, 2359–2375 (2022).36138263 10.1007/s13280-022-01788-y
108. Kronen M Vunisea A Magron F McArdle B Socio-economic drivers and indicators for artisanal coastal fisheries in Pacific island countries and territories and their use for fisheries management strategies Mar. Policy 2010 34 1135 1143 10.1016/j.marpol.2010.03.013
Kronen, M., Vunisea, A., Magron, F. & McArdle, B. Socio-economic drivers and indicators for artisanal coastal fisheries in Pacific island countries and territories and their use for fisheries management strategies. Mar. Policy 34, 1135–1143 (2010).10.1016/j.marpol.2010.03.013
109. Pacific Community. Pacific Framework for Action on Scaling up Community-based Fisheries Management: 2021–2025. in 20 (Pacific Community, 2021).
110. Masarczyk W Tautkute I Masarczyk W Reducing catastrophic forgetting with learning on synthetic data 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2020 IEEE
Masarczyk, W. & Tautkute, I. Reducing catastrophic forgetting with learning on synthetic data. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (ed. Masarczyk, W.) (IEEE, 2020).
111. Tian, J., Mithun, N. C., Seymour, Z., Chiu, H.-P. & Kira, Z. Striking the Right Balance: Recall Loss for Semantic Segmentation. 2022 International Conference on Robotics and Automation (ICRA) 5063–5069, 10.1109/ICRA46639.2022.9811702. (2022).
112. Yao X Huang T Wu C Zhang R-X Sun L Adversarial Feature Alignment: Avoid catastrophic forgetting in incremental task lifelong learning Neural Comput. 2019 31 2266 2291 10.1162/neco_a_01232 31525313
Yao, X., Huang, T., Wu, C., Zhang, R.-X. & Sun, L. Adversarial Feature Alignment: Avoid catastrophic forgetting in incremental task lifelong learning. Neural Comput. 31, 2266–2291 (2019).31525313 10.1162/neco_a_01232
113. Chen, S. et al. Recall and learn: fine-tuning deep pretrained language models with less forgetting. in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics). 10.18653/v1/2020.emnlp-main.634. (2020).
114. Koike, T., Qian, K., Schuller, B. W. & Yamamoto, Y. Learning higher representations from pre-trained deep models with data augmentation for the COMPARE 2020 Challenge Mask Task. in Interspeech 2020 (ISCA, 2020). 10.21437/interspeech.2020-1552, (2020).
115. Maracani A Michieli U Toldo M Zanuttigh P Maracani A RECALL: Replay-based Continual Learning in Semantic Segmentation 2021 IEEE/CVF International Conference on Computer Vision (ICCV) 2021 IEEE
Maracani, A., Michieli, U., Toldo, M. & Zanuttigh, P. RECALL: Replay-based Continual Learning in Semantic Segmentation. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (ed. Maracani, A.) (IEEE, 2021).
116. Redmon, J. & Farhadi, A. YOLOv3: An Incremental Improvement. Arxiv abs/1804.02767, (2018).
117. Roy AM Bhaduri J Kumar T Raj K WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection Ecol. Inform. 2023 75 101919 10.1016/j.ecoinf.2022.101919
Roy, A. M., Bhaduri, J., Kumar, T. & Raj, K. WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection. Ecol. Inform. 75, 101919 (2023).10.1016/j.ecoinf.2022.101919
118. Zhang Q A novel ResNet101 model based on dense dilated convolution for image classification N Appl. Sci. 2021 4 9
Zhang, Q. A novel ResNet101 model based on dense dilated convolution for image classification. N Appl. Sci. 4, 9 (2021).
119. Agapito M Jentoft S Beyond the Basics: Improving Information About Small-Scale Fisheries Transdisciplinarity for Small-Scale Fisheries Governance 2019 Springer International Publishing
Agapito, M. et al. Beyond the Basics: Improving Information About Small-Scale Fisheries. In Transdisciplinarity for Small-Scale Fisheries Governance (ed. Jentoft, S.) (Springer International Publishing, 2019).
120. Mease LA Erickson A Hicks C Engagement takes a (fishing) village to manage a resource: Principles and practice of effective stakeholder engagement J. Environ. Manage. 2018 212 248 257 10.1016/j.jenvman.2018.02.015 29438930
Mease, L. A., Erickson, A. & Hicks, C. Engagement takes a (fishing) village to manage a resource: Principles and practice of effective stakeholder engagement. J. Environ. Manage. 212, 248–257 (2018).29438930 10.1016/j.jenvman.2018.02.015
121. Cvitanovic C Hobday AJ McDonald J Van Putten EI Nash KL Governing fisheries through the critical decade: the role and utility of polycentric systems Rev. Fish Biol. Fish. 2018 28 1 1 18 10.1007/s11160-017-9495-9
Cvitanovic, C., Hobday, A. J., McDonald, J., Van Putten, E. I. & Nash, K. L. Governing fisheries through the critical decade: the role and utility of polycentric systems. Rev. Fish Biol. Fish. 28(1), 1–18 (2018).10.1007/s11160-017-9495-9
122. Adams, T. J. H. Modern institutional framework for reef fisheries management. in Reef Fisheries (eds. Polunin, N. V. C. & Roberts, C. M.) 337–360 (Springer Netherlands, Dordrecht, 1996). 10.1007/978-94-015-8779-2_13, (1996).
123. Johannes RE The renaissance of community-based marine resource management in Oceania Annu. Rev. Ecol. Syst. 2002 33 317 340 10.1146/annurev.ecolsys.33.010802.150524
Johannes, R. E. The renaissance of community-based marine resource management in Oceania. Annu. Rev. Ecol. Syst. 33, 317–340 (2002).10.1146/annurev.ecolsys.33.010802.150524
