Peskas: Automated analytics for small-scale, data-deficient fisheries

doi:10.21203/rs.3.rs-4386336/v1

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Peskas: Automated analytics for small-scale, data-deficient fisheries

https://doi.org/10.21203/rs.3.rs-4386336/v1

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Small-scale fisheries account for almost 90 percent of global fisheries employment and are responsible for landing more than 40% of the world’s fish catch. Yet their importance to livelihoods and food and nutrition security in Least Developed Countries are only recently emerging due to the logistical, financial, and capacity challenges of gathering and interpreting data in this diverse, dispersed and informal sector. Peskas was designed as a low-cost solution to tackle this problem, providing a template workflow for ingestion and analysis to a decision dashboard, which can be adapted to different contexts and needs.

Marine and Freshwater Ecology

Agricultural Economics & Policy

Near-real-time

small-scale fisheries

dashboard

stock assessment

IUU fishing

Nr	Code metadata description	Please fill in this column
C1	Current code version	peskas.timor.portal v1.0.0 peskas.timor.data.pipeline v3.1.0
C2	Permanent link to code/repository used for this code version	portal: https://github.com/WorldFishCenter/peskas.timor.portal pipeline: https://github.com/WorldFishCenter/peskas.timor.data.pipeline
C3	Permanent link to reproducible capsule	portal: https://doi.org/10.5281/zenodo.10906764 pipeline: https://doi.org/10.5281/zenodo.10906697
C4	Legal code licence	Apache License
C5	Code versioning system used	git
C6	Software code languages, tools and services used	R, HTML, python
C7	Compilation requirements, operating environments and dependencies	portal: https://github.com/WorldFishCenter/peskas.timor.portal/blob/main/DESCRIPTION pipeline: https://github.com/WorldFishCenter/peskas.timor.data.pipeline/blob/main/DESCRIPTION
C8	If available, link to developer documentation/manual	https://worldfishcenter.github.io/peskas.timor.data.pipeline/
C9	Support email for questions	[email protected]

Small-scale fisheries (SSFs) lack a clear global definition, but are characterised by low-impact fishing methods, their importance to local and coastal economies and cultures, and their reliance on local ecological knowledge and sustainable practices in nearshore and coastal environments. These fisheries contrast with large-scale, industrial operations in terms of the technology used, their ecological impact, and economic motivations. Small-scale fisheries account for more than 40% of the world’s fish catch, and (in 2016) were estimated to employ 1.9 percent of the globally employed population (1 out of every 50 jobs worldwide) [1]. Yet their importance to livelihoods and food and nutrition security in Least Developed Countries are only recently and slowly emerging due to the logistical, financial, and capacity challenges of gathering and interpreting data in this diverse, dispersed and informal sector [2–4]. More than 90 percent of small-scale fishers and fish workers are in low- and middle-income countries of the global south [5], and this corresponds to a lack of institutional capacity and low political will to prioritise small-scale fisheries [4]. Only recently, targeted research has illuminated the value and contributions of this mostly invisible sub-sector in dimensions of food and nutrition security [6], production and economic value and livelihoods, gender inclusion and equality, and governance [1]. There has also been a concerted advocacy to promote inclusion of small-scale fishers and fisheries in global agendas and dialogues around the blue economy, initiated through the creation of the Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines) [7], the International Year of Artisanal Fisheries (2022), and the COFI subcommittee on Fisheries Management focusing on the importance of fisheries in ending poverty, hunger and malnutrition [8].

The SSF Guidelines state that “All parties should promote the availability, flow and exchange of information, including on aquatic transboundary resources, through the establishment or use of appropriate existing platforms and networks at community, national, subregional and regional level, including both horizontal and vertical two-way information flows. Considering the social and cultural dimensions of SSFs, appropriate approaches, tools and media should be used for communication with and capacity development for small-scale fishing communities.” [7]. Sustainable Development Goal 9c calls to “significantly increase access to information and communications technology and to strive to provide universal and affordable access to the Internet in least developed countries by 2020” [5].

The challenge of measuring and understanding the dynamics of an underwater and highly mobile resource (fish), to achieve sustainable management outcomes places demands on fishers and vessels for data. The development of Peskas was primarily driven by the critical need to address the data scarcity concerning SSF, which are instrumental in global fish production yet lack comprehensive data collection and analysis mechanisms. Peskas emerges as a solution to this challenge, offering a digital application that facilitates the collection, classification, analysis, and visualisation of SSF catch and effort data, marking a significant step towards leveraging digital technology for environmental and financial sustainability.

The Peskas software was initially piloted and refined in Timor-Leste, a small island developing nation, showcasing its adaptability and scalability in a real-world context. The design principles of Peskas — open-source accessibility, component-based flexibility, integration of vessel tracking with catch records, and the capability for detailed spatial and temporal analysis — make it uniquely suited for enhancing SSF data management. Additionally, its user-friendly dashboard, co-designed with fisheries scientists and government managers, allows for straightforward data interpretation, aiding decision-making processes for fishers, managers, and researchers alike.

Fisheries-dependent data (catch volume, composition and fishing effort) are the foundation of environmental (and fisheries) sustainability, and on which rests the decision-making of stakeholders at various levels of influence and knowledge. Yet, the data and the systems to support informed decision-making are absent from the small-scale fisheries sectors in least-developed countries. We addressed this issue by utilising open-source software tools to develop Peskas; a digital platform that is both adaptable and scalable, facilitating the aggregation, categorization, analysis, and visualisation of data related to the catch and effort of small-scale fisheries.

Peskas is a software designed for the comprehensive management of small-scale fisheries data, from data collection to data visualisation and publication. It utilises an automated digital infrastructure to process and visualise fisheries data, ensuring that data-driven decisions can be made for sustainable fishery management. The following subsections describe the Peskas’s architecture and functionalities in detail.

2.1 Software architecture

Peskas is composed of two core R packages, one dedicated to the data workflow, peskas.timor.pipeline [9] and one intended for visualisation, peskas.timor.portal [10]. The R language was selected for its powerful statistical computation and graphical capabilities conducive to data analysis tasks. Docker containerization is a cornerstone of the architecture, ensuring platform independence and streamlined deployment across diverse environments. Version control and automated workflows are managed through GitHub and GitHub Actions, supporting continuous integration and deployment practices, while Google Cloud Platform is used for data storage. For interactive data visualisation, a Shiny web application based on a Bootstrap 5 UI kit (Tabler) and running on Google Cloud Run is employed to construct a dynamic, multilingual web application, whereas Rmarkdown is utilised to generate detailed reports integrating both analysis and outputs. The dissemination of data is carried out through the Dataverse API, enabling open access to the data sets curated by Peskas.

The architecture of Peskas was designed to minimise costs as far as possible, and to be adaptable and scalable, allowing for flexibility and ease of maintenance. From data collection and preprocessing to analysis, validation, and dissemination, each step runs within a Docker environment, ensuring consistency and reproducibility. The use of GitHub Actions automates the entire pipeline, enabling daily updates to the data and visualisations provided by the portal. Figure illustrates this architecture of the Peskas platform which ensures all components work together seamlessly, providing reliable, scalable, and insightful analytics for fisheries management.

2.2 Software functionalities

The Peskas platform is engineered to deliver a comprehensive array of functionalities that streamline the collection, processing, analysis, and presentation of fisheries data. This multifaceted approach not only enhances the utility of the platform for various stakeholders but also ensures the integrity and accessibility of the data, as the following subsections describe. Peskas consists of six core modules, each dedicated to a particular data flow step and consisting in turn of a series of functions:

Data Collection: KoboToolbox surveys and continuous, solar-powered GPS vessel trackers to collect and send data in near real-time, alongside fishery metadata for a thorough data-gathering process.
Pre-processing: Data formatting, shaping, and standardisation to prepare the raw data for analysis.
Validation: Outlier detection and error identification, and includes an alert system to maintain data quality.
Analytics: Modelling fisheries indicators, nutritional characterization, and data mining to extract valuable insights.
Data export: Automated dissemination of processed and analysed fisheries data to ensure accessibility and comprehension. This involves restructuring data for dashboard integration and open publication.
Visualisation: Tools for data reporting and sharing of insights through a comprehensive dashboard.

2.2.1 Data Collection and Integration

A key functionality of Peskas is its capability to automate the retrieval of data from a wide range of sources. Peskas leverages APIs such as the one provided by KoBoToolbox to automate the retrieval of survey data. While KoBoToolbox is a key tool for field data collection in diverse environments, Peskas is designed to work with any similar XForms or data collection platform. For tracking vessel movements, Peskas integrates with Pelagic Data Systems (PDS), which offers a vessel tracking system (hardware) and data-as-a-service solution for monitoring fishing vessels. This yields high-resolution data on vessel movements. However, Peskas's architecture is built to accommodate data from any tracking system that provides compatible data formats, ensuring flexibility in sourcing vessel movement data. The automated integration of tracking data into Peskas enriches the dataset with high-resolution geolocation and movement information, enabling the calculation of fishing effort per boat and extrapolation across municipal and national fleets. Beyond these specific integrations, Peskas currently utilises Airtable¹ as the preferred platform for its metadata registry, including static tables with vessels, catch, and regional information used for the subsequent data processing steps. This approach ensures that Peskas remains a versatile tool for data collection and integration, capable of working with a broad spectrum of data sources and types.

2.2.2 Preprocessing

Once the data is collected, Peskas initiates a preprocessing phase where the raw data undergoes cleaning, normalisation, and transformation. This phase standardises data formats, ensuring uniformity and converts data into tidy formats. It also involves a preliminary quality check for inconsistencies and missing values. One of the challenges addressed during this phase is the integration of data collected via three distinct survey versions of KoboToolbox. These versions, each with its unique structure, necessitate being merged to achieve a unified data framework. After the merging of the KoboToolbox data, the pipeline triggers two pivotal data-mining functions: get_weight and, subsequently calculate_nutrients. These functions extract information regarding the weight of each catch and its nutrient composition, respectively. These functions pull data from FishBase [11], a comprehensive external resource, by employing an API. This integration allows for the dynamic inclusion of length-weight relationships and nutrient concentration information based on the most updated information. Moreover, this stage involved the alignment with the established aquatic foods ontology [12], where variables are not only renamed for enhanced clarity but are also aligned with a recognized and controlled vocabulary where applicable.

2.2.3 Validation

In the validation module of Peskas, data undergoes a rigorous validation process to ensure its accuracy and consistency for subsequent analyses; it involves the validation of both catch and vessel movement data. For catch data, the process involves the examination of outliers and anomalous values, which when identified are excluded from further analysis. The validation procedure is organised through a systematic labelling process, wherein each data entry is assigned a specific code that reflects a particular validation status. For instance, data entries without outliers are tagged with the code "0," indicating "no alerts." Conversely, a code "5" signals that the "Trip duration is too long," and a code "7" denotes that the "Recorded length is too large for the catch type," among others. In total, 21 distinct alert flags have been established, each addressing a specific and critical dimension of the data to facilitate a comprehensive quality assessment. The decisions on how to structure data alerts took place through dialogue with local stakeholders and fisheries experts to ensure they were context-specific.

Validation employs both univariate and multivariate methods to detect outliers and assess the data precision. Univariate outlier detection is conducted using the median absolute deviation (MAD) method, in the “univOutl” package [13]. Multivariate approaches are employed to verify the accuracy of specific variables, such as catch weight, where outliers are identified using thresholds based on Cook's Distance of the residuals between catch weight and catch value.

To refine outlier detection accuracy and fine-tune detection parameters, entries flagged as potential outliers undergo manual scrutiny on a specialised validation platform. This internal tool, a Google Sheet integrated with Kobotoolbox via Google Apps Script, streamlines the review process. This setup facilitates a more efficient and effective manual validation step, especially from the stakeholder's perspective, ensuring that outlier detection is both precise and adaptable to the nuances of the data. The final product of this validation module is a dataframe that has been meticulously cleaned and validated, ensuring its readiness for further analysis and processing in modelling and metric extraction phases.

Vessel movement data validation focuses on the unique challenges associated with global position data. Issues such as undetected trips, merged trips, or split trips, as well as potential delays or losses of information due to poor network coverage, necessitate a tailored validation approach. The validate_pds_data function is instrumental in addressing the complexities associated with GPS tracking data, by evaluating each vessel trip for its duration and the distance covered. This evaluation utilises specified parameters to assess the durations and distances, along with the distance between the start and end points of a fishing trip. Importantly, it also incorporates quality metrics to refine data quality. Among these metrics, “outlier limits” identify and exclude data points that markedly deviate from expected patterns, such as anomalously high speeds, indicating potential inaccuracies or anomalies in the data. Similarly, “signal trace dispersion” measures the consistency and reliability of GPS signal locations over time, where a high dispersion level could suggest issues like poor GPS signal quality or errors in data transmission.

2.2.4 Analytics

In the analytics module of Peskas, following data validation, the focus shifts towards quantifying fisheries indicators. This module is tasked with estimating the average catch per trip as well as the average revenue per trip across municipalities. Also, we estimate the number of landings per fisher by month derived from a generalised linear mixed model. At the heart of this module, the estimate_fishery_indicators function orchestrates the workflow, beginning with the ingestion of trip data, which is then enriched with metadata on registered boats. The inclusion of registered boat numbers is particularly essential as it underpins the calculation of total catch and revenue within each region. Once the average catch per trip is determined, and the frequency and duration of trips each fisherman undertakes in a month are established, the number of registered boats allows for an extrapolation of these averages to a regional scale. Notably, the estimation of catch is performed not only on aggregated values but also across different fish groups and important species. This approach adds a critical layer of granularity; by dissecting the catch data into specific taxa, the module enhances the understanding of species-specific trends and their implications for fisheries management.

2.2.5 Data export

The export module in Peskas disseminates the processed and analysed fisheries data to a wider audience, ensuring both accessibility and usability. This module undertakes a data restructuring process aimed at fulfilling dual objectives: firstly, to align with the dashboard framework for visualisation and user interactions, and secondly, to prepare the data for open publication. For open publication, both raw and aggregated (national and municipal) data undergo conversion from RDS file format to CSV. This transformation caters to the needs of a general audience, facilitating broader access and understanding. Accompanying the data, an informative README document is automatically generated, offering detailed descriptions of the data content and fields. Data is uploaded to the Harvard Dataverse portal under the CC BY-NC-SA 4.0 licence through the portal API service, automatically, every month. This automated process ensures that the latest fisheries data are consistently made available to researchers, policymakers, and the public.

2.2.6 Visualisation and Reporting

To make the insights accessible and actionable, Peskas boasts an advanced visualisation feature through an interactive Shiny dashboard. This dashboard serves as the primary interface for users to explore, analyse, and interpret the data in real time. It is complemented by the capability to generate detailed reports using Rmarkdown, which allows for the dynamic incorporation of analysis results, including charts and tables, into comprehensive documents

2.3 User Interface

The Peskas portal dashboard (https://timor.peskas.org/) is a robust web application hosted on Google Cloud Run, ensuring scalability and reliability. It leverages a suite of R packages to deliver a dynamic, interactive user experience, particularly in visualising fisheries data. The dashboard is updated daily with fresh data from the peskas.timor.pipeline, orchestrated through GitHub Actions, ensuring that the displayed information is real-time and actionable. Developed using the Shiny framework, the portal incorporates advanced visualisation tools such as “kepler.gl”, a powerful Javascript library for geospatial data analysis, which is used to provide stakeholders with a visual understanding of fishing activity distributions across Timor-Leste.

The user interface is designed to be intuitive and accessible, featuring a multilingual option that currently includes English, Portuguese, and Tetum. This feature is crucial for engaging local communities in sustainable fisheries management by allowing them to access and analyse data in their preferred language. Data interaction is facilitated by various R packages integrated into the portal, such as “reactable” for interactive tables [14], and “apexcharter” for responsive charting solutions [15]. These tools enable users to drill down into specifics such as catch volumes, species distribution, and fishing efforts, with the flexibility to customise views and download data according to their needs. The portal's backend is supported by Google services, with authentication managed via “googleAuthR” [16] and data storage solutions provided through “googleCloudStorageR” [17], ensuring secure and scalable cloud storage options.

The illustrative examples are available in the Figures carousel as Figure 3.

Timor-Leste is one of the smallest, newest and poorest countries in the world, having regained independence from Indonesian occupation in 2001. Newly independent Timor-Leste emerged into a situation of chronic poverty and malnutrition, and extremely limited governance capacity and infrastructure [18, 19]. Yet, the country has garnered recent attention from researchers and fisheries stakeholders around the world as having adopted the most sophisticated, near-real-time monitoring system for small-scale fisheries [20].

In 2011, the Government of Timor-Leste signalled their intention to leverage the potential of fisheries in eliminating poverty and malnutrition [21], but the sustainable growth of the sector relied on information that was not available. Not only was there no data, but also no system or the capacity to collect, analyse or use it to make decisions. As such, Peskas was developed in response to a specific need. To ensure it was relevant, contextualised and fit for purpose, it was co-designed with fisheries officers and iteratively updated in response to targeted and anecdotal feedback from government officers, researchers, and fishers since 2017.

The results of pilot catch monitoring and analysis were used to guide the drafting of the National Fisheries Strategy in 2018 [22], and in 2020 the Government of Timor-Leste formally adopted Peskas as their national monitoring system and invested in new enumerators for every coastal municipality. This uptake effectively brought the ~ 16,000km² of Timor-Leste’s marine territory under improved, evidence-based management [23].

Four years on, where before almost no quantitative research could be undertaken due to the dearth of data, Peskas has now encouraged and enabled empirical and experimental research in fisheries in Timor-Leste [24, 25]. Peskas offers a near-real-time monitoring system that provides detailed insights into fishing trends over time and space, and this has brought answers to long-standing research questions about fishers’ activity patterns, seasonality, important fishing grounds, and safety at sea concerns. Furthermore, Peskas data was analysed to highlight the impact of COVID-19 on fisheries value chains and subsistence in the Timor-Leste context [24].

Specific research has highlighted priority areas and gaps in management capacity, errors in scientific knowledge [26] and development needs [27]. It has made possible the rigorous testing of fisheries technology like nearshore Fish Aggregating Devices (FADs). These are objects moored in coastal waters to attract fish and provide small-scale fishers access to a new and more sustainable source of fish, without requiring investment (or subsidies) for new equipment, fuel or techniques [28]. The analysis of habitat-specific catch rates (including FADs) demonstrated that social and ecological factors affect the rate of return on investment (costs of FAD equipment, deployment and maintenance), but that FADs have great potential as climate-smart technologies to enhance food security while improving environmental sustainability [29].

The aim of the government to decentralise fisheries management to municipal and local stakeholders [30] is slowed by broader infrastructure and capacity issues, but co-management, where the various stakeholders come together to design mutual goals and management mechanisms [31] has shown promise in Timor-Leste. Peskas was utilised in this case to illustrate how coastal fisheries and fishing patterns differ by location, and dissuade the one-size fits-all approach [32].

Peskas championed the sex disaggregation of fisheries data to understand the roles of women in Timor-Leste fisheries, and their contributions to household food and nutrition security [33, 34]. This has identified and prioritised data gaps, and has been leveraged to highlight and challenge gender and social norms in Timor-Leste [33] which is crucial for inclusive management practices.

The nutritional and developmental benefits of consuming fish are becoming increasingly clear, especially in lactating mothers and young children [6, 35, 36]. However, in Timor-Leste, where rates of child stunting malnutrition are the highest in the world [37], fish consumption in upland, inland areas are typically very low and approximately a quarter of consumption rates in coastal areas [38]. A randomised controlled trial has leveraged Peskas ongoing monitoring of catch rates and fishing revenue as part of a clinical trial in Timor-Leste to test the effects of increasing supply (FADs) and demand (behaviour change messaging) on the consumption of fish products in inland and upland communities [39]. A nascent research direction related to this, is the integration of nutrition-sensitive fisheries management approaches into Peskas, where the catches from different fishing patterns (gear, location, habitat) exhibit common nutrient concentration profiles, which can be utilised for management strategies that do not solely maximise production, but also address nutritional needs in the population [40].

Peskas has changed the daily practice of key stakeholders, enabling new interactions between fishers, community enumerators, NGOs, and government officials [41]. The software facilitates co-management by providing a platform for improved collaboration and communication, thereby fostering cooperation. A study that evaluated the impact of Peskas on fisheries management objectives found that despite limited regulatory changes, Peskas had brought about significant national and international investment into the fisheries sector, had driven improved collaboration between government departments, as well as vertical communication channels from coastal fishing communities to national government officers [41]. Timor-Leste has one of the fastest growing populations in the world, so there are lots of young, digitally capable people leaving school and searching for meaningful work, and one impact from Peskas is the creation of new enumerator positions in each municipality, bringing much needed income into rural economies [41]. The impacts observed and stated by stakeholders around collaboration, transparency and communication [41] have spawned the development of new research questions related to the role of digital transformation on behaviour and preference in fisheries management and conservation. Currently in preparation is a protocol to test the effects of access to Peskas information at different resolutions on the perceptions and preferences of fisheries stakeholders in southern Kenya.

A specific indicator (14.7.1) of the sustainable development goals is “Sustainable fisheries as a percentage of Gross Domestic Product in [...] all countries”. Until now, mechanisms to measure this contribution effectively, in real time for SSF have not existed. Peskas brings rigorous, quantitative data from small-scale fisheries into ocean accounting for the first time, empowering small-scale fishers with a seat at the table in blue economy dialogues and negotiations, and enabling integration of SSF into national food balance sheets and sustainable development goals.

The Peskas dashboard has had ~ 1400 visitors from 86 countries, and has been cited in 31 publications since 2020. Peskas exemplifies how digital transformation can drive sustainable management of natural resources, support healthy communities and a thriving planet, and enable tracking of national and international development and conservation objectives. It is open-source, so highly adaptable to other fisheries, countries, languages and sectors. It is currently being adapted to the aquaculture sector in Timor-Leste, and scaled up to Kenya, Tanzania and Mozambique fisheries as part of a global project.

Peskas as an open source solution to facilitate and support adaptation and collaboration is the first of its kind and represents a purposeful proposition for sustainable digital transformation in one of the world’s poorest contexts. Too often low-income country communities are the testing ground for new technologies hailed as the next best thing, but which inevitably fail due to lack of contextualisation with end users, or unreasonable cost-recovery expectations. Peskas is free to use and adapt, and we believe the basis for a sustainable mechanism from which to build digital transformation not just in fisheries, but across small-scale production sectors. However, from here the process must be to continue to build the capacity to collect good data, develop and adapt the systems, to analyse and understand the data in social-ecological terms, and use the knowledge to make smart decisions based on management target reference points and scientifically evidenced thresholds.

Acknowledgements

The development and continued improvement of this software would not have been possible without the generous support of the Norwegian Embassy in Jakarta, who supported the piloting and scaling of Peskas in Timor-Leste. Thanks also to the Minderoo Foundation, which provided bridge funding to maintain data collection before full government adoption. We are grateful to WorldFish and the Government of Timor-Leste staff and consultants who contributed over time to the improvement, with particular thanks to Shaun P. Wilkinson, Joctan dos Reis Lopes, Mario Gomes, Pedro Rodrigues, Acasio Guterres and many data enumerators who collected data and tested systems. This publication and continued work and scaling of Peskas beyond Timor-Leste are supported by the Aquatic Foods Initiative supported by the CGIAR Trust fund, and the AABS project funded by the UK Government’s Blue Planet Fund.

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Airtable was chosen here because it allows us to retrieve data in a programmatic manner, while still allowing us to edit it without the need for another editing interface. Airtable could be substituted with a database, google sheets, or (e.g.) CSV files committed to GitHub.

The authors declare no competing interests.

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Peskas: Automated analytics for small-scale, data-deficient fisheries

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Abstract

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Metadata

1. Motivation and significance

2. Software description

2.1 Software architecture

2.2 Software functionalities

2.2.1 Data Collection and Integration

2.2.2 Preprocessing

2.2.3 Validation

2.2.4 Analytics

2.2.5 Data export

2.2.6 Visualisation and Reporting

2.3 User Interface

3. Illustrative examples

4. Impact

5. Conclusions

Declarations

Acknowledgements

References

Footnotes

Additional Declarations

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