PeskAAS: A near-real-time, open-source monitoring and analytics system for small-scale fisheries

Abstract

Small-scale fisheries are responsible for landing half of the 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.

Spatio-Temporal Determination of Small-Scale Vessels’ Fishing Grounds Using a Vessel Monitoring System in the Southeastern Gulf of Mexico

“…If well, we addressed only a part of the complexity associated with the SSF using the RF model to classify fishing and transit positions of vessels, it is still necessary to increase the number of trips with onboard observers to improve the analytical forecasting capacity of the approach used in this study. Lastly, it is important to indicate that from the pilot VMS data, we could not infer which species were caught or the fishing gear used; thus, incorporation of these data could help to analyze such relationships (fishing gears-target species) and add more detail to differentiate the potential fishing grounds according to different target species (e.g., D'Andrea et al, 2020;Tilley et al, 2020). Despite the limitations in our study, our results showed the usefulness of VMS for SSF to help understand the fleet dynamics and provided insights into the potential fishing grounds available in the SGoM region for the first time.…”

“…Nowadays, the implementation of a VMS in the region is challenging, from gaining the acceptance of fishers to issue of the costs involved in the implementation of the system and its maintenance, especially given the size of the fleet (in the region there are around 20,000 small-scale vessels; Coronado et al, 2020). Thereby, a VMS for SSF demands the development of a cost-effective platform for daily catch recording in an integrative way, including the type of gears and species captured whenever possible (e.g., D'Andrea et al, 2020;Tilley et al, 2020). This type of data could be coupled with that obtained from onboard observers, and a program of monitoring systems in the coast, thus the acquisition of explicit spatial data from the smallscale vessels (Russo et al, 2018).…”

“…However, VMS and AIS monitoring systems have been implemented in industrial fleets, and in limited cases in SSF in developed countries (James et al, 2018). More recently, some interactive platforms that combine tracking data and catch data have been developed to support fishers and managers decisions (e.g., D'Andrea et al, 2020;Tilley et al, 2020). Unfortunately, some regions lack logistic or technical capabilities to monitor small-scale fleets over a wide range of fishing areas to obtain explicit spatial data (catch and effort).…”

Untangling Tales of Tropical Sardines: Local Knowledge From Fisheries in Timor-Leste

“…in Bobonaro municipality or Amblygaster spp. in Dili-but not 'herring' from the 'Elopidae family, ' as currently recorded (Tilley et al, 2020;WorldFish and MAF, 2021). We cannot provide any insight on where this name may relate to fusiliers or oxeye herring.…”

“…Furthermore, Timorese common names listed on FishBase also include similar name 'kopi' for fusiliers of the Caesionidae family, as well as for Tropical Pilchard A. sirm (Froese and Pauly, 2020). National fisheries monitoring in Timor-Leste currently records landings at the family level due to the difficulties associated with accurately identifying mixed-species catches to species level across the country (Tilley et al, 2020). However, this example illustrates that the same or similar local name could relate to three different families.…”