Diatom-inferred depth models in 8 Canadian boreal lakes: inferred changes in the benthic:planktonic depth boundary and implications for assessment of past droughts

Assessment of past drought in boreal regions, a region predicted to be strongly affected by climate warming, can provide insights into future availability of water. However, limited instrumental data and paleoclimatic data are available for this assessment. To address this lack of data in the boreal region of northwest Ontario, a regional study of lakes in the Winnipeg River Drainage Basin was initiated. Diatom-inferred (D-I) depth models were developed based on surface samples collected along a depth gradient within 8 small boreal lakes. Weighted-averaging and modern analog approaches provided robust within-lake depth models for each of the study lakes, with bootstrapped r(2) values ranging from 0.90 to 0.98, and root-mean-squared-errors of prediction (RMSEP) between 1.1 and 2.5 m. Large differences in the estimated depth optima for three representative, but common diatom species across our 8 study lakes suggested that within-lake calibration datasets are more appropriate for inferring past drought based in depth models than a regional multi-lake calibration dataset, and that light and related variables are controlling factors governing the maximum depth of benthic taxa. A down-core application of the D-I depth models on a near-shore core from Meekin Lake, retrieved near the present-day ecotone between the benthic and planktonic diatom assemblages indicated highly similar trends in inferred depth (r = 0.96). The models have significant correlations with other metrics of changes in depth including diatom species richness (r = 0.74-0.78) and evenness (r = 0.76-0.8), thereby allowing a check on the strength and direction of the depth inferences down-core. Near-shore cores located near the benthic: planktonic transition is a sensitive region that can provide estimates of past droughts in lakes where such inferences have been difficult to estimate. (C) 2011 Elsevier Ltd. All rights reserved.