According to this decomposition, variation in species composition and environmental conditions were much more important than variation in stand structure in determining geographical patterns in plot-averaged variation in mortality.
We again found that variation in environmental conditions resulted in higher variation in mortality than variation in stand structure (Fig. S5).
Since different species responded in different ways to changes in environment and stand structure, we also calculated variation in mortality due to these factors but using Pinus taeda (the most common gymnosperm species) instead of A. rubrum.
Since the early applications of LiDAR to forestry management (Nilsson 1996), the technology has been widely used to spatially quantify variation in stand structure at a range of resolutions (e.g. Holmgren et al. 2003; Hyyppä et al. 2001; Lim and Treitz 2004; Popescu et al. 2004; Watt et al. 2013a).
The model also predicts much of the variation in these metrics for a tree of a given species and size, resulting solely from deterministic responses to variation in stand structure.
High plot-averaged mortality in the west is driven primarily by species composition (Fig. 3B); whilst variation in stand structure has relatively little impact on plot-averaged across the region (Fig. 3A).
We also wanted to create maps showing how mortality varies regionally in response to variation in species identity, stand structure (stem size and plot basal area) and environmental conditions, whilst controlling for variation in the other factors.
Old-growth boreal forests display high within-stand structural variation, with large variation in tree sizes and age structure, a heterogeneous horizontal spatial pattern with gaps of different sizes and multi-layered canopies.
Since the model reproduced geographical variation well, we were able to decompose the variation into the separate effects of stand structure (stem-size distributions and plot basal area), environment and species (Fig. 3A C).
This may be simply because stand structure does not vary systematically across the region, otherwise, geographical variation in size distributions would result in geographical variation in plot-averaged mortality.
In step-wise linear regression models, 13 52% of the variation in damage frequency was accounted for by windspeed and stand structure.
