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Multi Species Stock Assessment Framework for Great Slave Lake

Regional Peer Review - Ontario and Prairie Region

March 14-15, 2023
Virtual Meeting

Chairperson: Joclyn Paulic


The fisheries of Great Slave Lake (GSL) are dominated by three species, Lake Whitefish (Coregonus clupeaformis), Lake Trout (Salvelinus namaycush) and Inconnu (Stenodus leucichthys). Expansion of the fisheries is expected in the near future and the development of an Integrated Fisheries Management Plan (IFMP) is critical for the long-term management of lake fisheries. There are ongoing pressures on DFO Science to provide robust advice on the stock status for Great Slave Lake fisheries to support the establishment of sustainable harvest levels and limit reference points for each of the three dominant species. A previous assessment of Lake Whitefish for GSL was not definitive due to missing data at the time of the assessment (DFO 2015). Multiple stocks of Inconnu exist within GSL, however only one stock, the Buffalo River, was assessed within a precautionary approach framework (PA) with limit reference points established (DFO 2013). Lake Trout has not been assessed in recent decades.

Due to challenges in the acquisition of data (e.g., funding, sufficient personnel, logistics) and data management, the quantitative approach to conducting a stock assessment is uncertain. Additionally, there has been a rapid development of alternative quantitative modeling techniques over the last ten years, especially in the area of data limited assessment that could provide new opportunities. As a first step to the stock assessments, Science will explore the available data and modeling approaches for each stock with the aim of using the highest level possible in quantitative analysis. The recommendations from this meeting will provide direction for quantitative analysis (i.e., models) for the upcoming (2023/24) stock assessments of these species.


The objectives of this peer-review meeting are to:

  1. Compile Lake Whitefish, Lake Trout and Inconnu harvest, abundance indicators, and biological/demographic parameters from Great Slave Lake.
  2. Peer review compiled data and make decisions on datasets which are potentially able to support data requirements for full or data-limited quantitative assessments based on data quality.
  3. Explore the potential to apply various quantitative assessment models ranging from indicator-based assessments to full data rich stock assessment options.
  4. Select the appropriate models that will be used for the full stock assessment of each species. List assumptions and caveats with the use of the models.

Expected Publications

Expected Participation



Participation to CSAS peer review meetings is by invitation only.

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