Abstract
Aboveground biomass (AGB) of short-stature shrubs and trees contain a substantial part of the total carbon pool within boreal ecosystems. These ecosystems, however, are changing rapidly due to climate-mediated atmospheric changes, with overall observed decline in woody plant AGB in boreal northwestern Canada. Allometric equations provide a means to quantify woody plant AGB and are useful to understand aboveground carbon stocks as well as changes through time in unmanaged boreal ecosystems. In this paper, we provide allometric equations, regression coefficients, and error statistics to quantify total AGB of shrubs and short-stature trees. We provide species- and genus-specific as well as multispecies allometric models for shrub and tree species commonly found in northwestern boreal forest and peatland ecosystems. We found that the three-dimensional field variable (volume) provided the most accurate prediction of shrub multispecies AGB (R2 = 0.79, p < 0.001), as opposed to the commonly used one-dimensional variable (basal diameter) measured on the longest and thickest stem (R2 = 0.23, p < 0.001). Short-stature tree AGB was most accurately predicted by stem diameter measured at 0.3 m along the stem length (R2 = 0.99, p < 0.001) rather than stem length (R2 = 0.29, p < 0.001). Via the two-dimensional variable cross-sectional area, small-stature shrub AGB was combined with small-stature tree AGB within one single allometric model (R2 = 0.78, p < 0.001). The AGB models provided in this paper will improve our understanding of shrub and tree AGB within rapidly changing boreal environments.
Highlights
Ecosystems in northwestern Canada are changing rapidly due to a warming climate, drier conditions, extended growing season, and climate-mediated increases in frequency and intensity of disturbances, such as wildfire, permafrost thaw, insect and pathogen outbreaks, and anthropogenic natural resource extraction (e.g., [1,2,3])
We have provided regression coefficients and error statistics for our best models based on Nonlinear least squares regression (NLS) and for our Linear logarithmic regression with correction (LLRC) models
Our analysis has shown that aboveground biomass (AGB) of shrubs can be modeled with higher accuracies when using a 3D field variable, such as volume
Summary
Ecosystems in northwestern Canada are changing rapidly due to a warming climate, drier conditions, extended growing season, and climate-mediated increases in frequency and intensity of disturbances, such as wildfire, permafrost thaw, insect and pathogen outbreaks, and anthropogenic natural resource extraction (e.g., [1,2,3]). One of the significant outcomes of climate-mediated change in these environments is the increased abundance of short-stature vegetation, such as shrubs [4,5] and low productive and juvenile trees, in particular where wildfire disturbance sets back ecosystems to an early successional stage post fire [4] or in the rapidly changing transition zones between elevated forests and adjacent peatlands due to permafrost thaw [6]. Shrubification in permafrost environments has been reported [5,8,11,12,13], spatially explicit quantities of shrub and short-stature tree AGB remain largely unknown for boreal northwestern
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