Effect of Organic Layer Thickness on Black Spruce Aging Mistakes in Canadian Boreal Forests

Boreal black spruce (Picea mariana) forests are prone to developing thick organic layers (paludification). Black spruce is adapted to this environment by the continuous development of adventitious roots, masking the root collar and making it difficult to age trees. Ring counts above the root collar...

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Bibliographic Details
Published in:Forests Vol. 7; no. 3; p. 69
Main Authors: Ahmed Laamrani, Annie DesRochers, Line Blackburn
Format: Journal Article
Language:English
Published: MDPI AG 15-03-2016
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Summary:Boreal black spruce (Picea mariana) forests are prone to developing thick organic layers (paludification). Black spruce is adapted to this environment by the continuous development of adventitious roots, masking the root collar and making it difficult to age trees. Ring counts above the root collar underestimate age of trees, but the magnitude of age underestimation of trees in relation to organic layer thickness (OLT) is unknown. This age underestimation is required to produce appropriate age-correction tools to be used in land resource management. The goal of this study was to assess aging errors that are done with standard ring counts of trees growing in sites with different degrees of paludification (OLT; 0–25 cm, 26–65 cm, >65 cm). Age of 81 trees sampled at three geographical locations was determined by ring counts at ground level and at 1 m height, and real age of trees was determined by cross-dating growth rings down to the root collar (root/shoot interface). Ring counts at 1 m height underestimated age of trees by a mean of 22 years (range 13–49) and 52 years (range 14–112) in null to low vs. moderately to highly paludified stands, respectively. The percentage of aging-error explained by our linear model was relatively high (R2adj = 0.71) and showed that OLT class and age at 0-m could be used to predict total aging-error while neither DBH nor geographic location could. The resulting model has important implications for forest management to accurately estimate productivity of these forests.
ISSN:1999-4907
DOI:10.3390/f7030069