PeskAAS

PeskAAS provides automated monitoring and analytics for small-scale fisheries to quantify catch, effort, and spatial-temporal patterns.


Key Features:

  • Component-Based Integration: Modular design integrates vessel tracking data and catch records for combined analyses.
  • Spatial and Temporal Filtering: Filters fishing productivity by fishing methods and habitats across time and space.
  • FishBase Length–Weight Parameters: Incorporates species-specific length-weight parameters from FishBase to convert length data to biomass estimates.
  • Automated Analytics: Produces metrics for fishing effort, catch rates, economic efficiency, and geographic patterns.
  • Implementation in Shiny R: Built using the Shiny R package for interactive analytical workflows.
  • Support for Sparse Data: Designed to operate with limited and sparse small-scale fisheries data.

Scientific Applications:

  • Standardized monitoring: Enables systematic and standardized data collection and aggregation for small-scale fisheries.
  • Catch and effort estimation: Quantifies catch rates and fishing effort for assessment of fisheries productivity.
  • Biomass estimation: Uses FishBase length-weight parameters to estimate population biomass from length measurements.
  • Spatio-temporal analysis: Identifies geographic preferences and temporal trends in fishing activity and habitat use.
  • Fisheries management support: Provides evidence for management decisions, conservation planning, and livelihood investment prioritization.

Methodology:

Implemented in R using the Shiny R package, integrating vessel-tracking data and catch records, applying spatial and temporal filtering, incorporating species-specific length-weight parameters from FishBase, and automating analytics.

Topics

Details

License:
GPL-3.0
Programming Languages:
R
Added:
1/18/2021
Last Updated:
1/23/2021

Operations

Publications

Tilley A, Lopes JDR, Wilkinson SP. PeskAAS: A near-real-time, open-source monitoring and analytics system for small-scale fisheries. Unknown Journal. 2020. doi:10.1101/2020.06.03.131656.

Links