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PeskAAS: A near-real-time, open-source monitoring and analytics system for small-scale fisheries
bioRxiv - Scientific Communication and Education Pub Date : 2020-09-22 , DOI: 10.1101/2020.06.03.131656 Alexander Tilley , Joctan Dos Reis Lopes , Shaun P. Wilkinson
bioRxiv - Scientific Communication and Education Pub Date : 2020-09-22 , DOI: 10.1101/2020.06.03.131656 Alexander Tilley , Joctan Dos Reis Lopes , Shaun P. Wilkinson
Small-scale fisheries are responsible for landing half of world's fish catch, yet there are very sparse data on these fishing activities and associated fisheries production in time and space. Fisheries-dependent data underpin scientific guidance of management and conservation of fisheries systems, but it is inherently difficult to generate robust and comprehensive data for small-scale fisheries, particularly given their dispersed and diverse nature. In tackling this challenge, we use open source software components including the Shiny R package to build PeskAAS; an adaptable and scalable digital application that enables the collation, classification, analysis and visualisation of small-scale fisheries catch and effort data. We piloted and refined this system in Timor-Leste; a small island developing nation. The features that make PeskAAS fit for purpose are that it is: (i) fully open-source and free to use (ii) component-based, flexible and able to integrate vessel tracking data with catch records; (iii) able to perform spatial and temporal filtering of fishing productivity by fishing method and habitat; (iv) integrated with species-specific length-weight parameters from FishBase; (v) controlled through a click-button dashboard, that was co-designed with fisheries scientists and government managers, that enables easy to read data summaries and interpretation of context-specific fisheries data. With limited training and code adaptation, the PeskAAS workflow has been used as a framework on which to build and adapt systematic, standardised data collection for small-scale fisheries in other contexts. Automated analytics of these data can provide fishers, managers and researchers with insights into a fisher's experience of fishing efforts, fisheries status, catch rates, economic efficiency and geographic preferences and limits that can potentially guide management and livelihood investments.
更新日期：2020-09-22
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