Published February 8, 2023 | Version 2

Submerged aquatic vegetation biomass from the Saint Lawrence River (2006-2016) to compare estimation from quadrat-diver technique to rake collection and echosounding

  • 1. Université de Montréal
  • 2. Environment and Climate Change Canada
  • 3. Concordia University

Description

Here we provide 4 datasets that describes 1) the comparison between quadrat and rake collected biomass (QR), 2) the comparison of rake biomass and biovolume, a biomass proxy derived from echosounding (RE), 3) a validation dataset that confronts quadrat measurements to quadrat prediction measured from echosounding using two intercalibration equations (derived from QR and RE datasets), and 4) a whole-system biomass estimation comparing biomass predicted from echosounding and from rake.

Original data comes from the Saint Lawrence River, mainly from Lac Saint-Pierre, but for the QR dataset also from Lac Saint-François and Lac Saint-Louis. Data from the QR (2006-2009) and validation dataset (2016) were collected by Christiane Hudon, Environment and Climate Change Canada, while the RE dataset and part of the validation dataset were collected as part of a project led by the Groupe de recherche interuniversitaire en limnologie (GRIL, 2012-2015) and by Morgan Botrel Ph.D. candidate (2016-2017, Université de Montréal). Data were created for an article on a method to estimate SAV biomass, led by Morgan Botrel and supervisor Roxane Maranger, with co-supervisor Christiane Hudon and Pascale Biron.

For the second version, the data is more clearly organized in the four categories mentioned above. Additionally, revised prediction equations were applied which modifies results used in the validation and whole-system datasets (dataset 2 and 4). Equations are presented in the associated publication:

Botrel, M., C. Hudon, P.M. Biron, R. Maranger. Combining quadrat, rake and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale. Limnology & Oceanography: Methods. Accepted (as of 2023/02/08)

Files

1-QR.csv

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Additional details

Related works

Is derived from
Dataset: 10.5281/zenodo.6784490 (DOI)
Is described by
Preprint: 10.1101/2022.03.15.484486 (DOI)
Peer review: 10.1002/lom3.10539 (DOI)