Information about fish distribution in space and time is valuable both for understanding diverse ecological processes and for guiding applied fisheries management decisions. One important applied case is in quantifying how the relative biomass of a fish stock varies over time, known as an index of biomass or abundance.This project is aimed at developing abundance and distribution indices for a variety of fish species in the southern Gulf of St. Lawrence (sGSL) using a hydroacoustic data time series. Hydroacoustic data has been collected between 2005 and 2020 while an annual multispecies bottom trawl survey conducted its fishing and navigating activities. The annual multi-species bottom trawl survey, conducted each September since 1971, provides information on the September abundance and distribution of many marine fish species throughout the sGSL. Total bottom trawl catch weights and numbers, representative length frequency and representative individual length-weight data has been recorded for each fish species in each survey set since 1971.Ultimately, the results of the hydroacoustic data analysis will be used to develop species-specific spatial distribution time-series, and indices of abundance for individual stock assessments. Expected stock assessment inputs from this project are time series of spatially-aggregated biomass per year per species.The work will be divided in two distinct tasks. Task 1 will cover the hydroacoustic data processing, and task 2 will cover the spatiotemporal analysis of the hydroacoustic and trawl data to derive distribution and abundance indices. More specifically, task 1 consists of (1) exploring the appropriate analysis methods for the available data (multi-frequency classification), (2) developing an analysis method specific to the species targeted by the survey, (3) carrying out the analysis of a temporal series of hydroacoustic data using the Echoview software and (4) writing the associated reports. Task 2 will consist of (1) using the acoustic data and trawl data to develop abundance indices using statistical methods of spatiotemporal data analysis, and (2) writing associated reports.
