The spatial variability of forest structure (for example, tree sizes, distribution of stems and foliage, dead wood) is the result of multiple factors such as disturbances, succession, topography, and soil properties. However, the explicit scales at which these variation patterns occur are often described only qualitatively.
We studied three 2 km × 2 km landscapes in northeastern Finland and two in eastern Canada. We estimated canopy cover in contiguous 0.1-ha cells from aerial photographs and used scale-derivative analysis to identify characteristic scales of variation in the canopy cover data. We analyzed the patterns of variation at these scales using Bayesian scale space analysis.
We identified structural variation at three spatial scales in each landscape. Among landscapes, the largest scale of variation showed the greatest variability (20.1–321.4 ha), related to topography, soil variability, and long-term disturbance history. Superimposed on this large-scale variation, forest structure varied at similar scales (1.3–2.8 ha) in all landscapes. This variation correlated with recent disturbances, soil variability, and topographic position. We also detected intense variation at the smallest scale analyzed (0.1 ha, grain of our data), partly driven by recent disturbances.
Except for the large-scale variation, the identified scales were remarkably similar among the landscapes. This suggests that boreal forests may display characteristic scales of variation that occur somewhat independent of the tree species characteristics or the disturbance regime.
The study is published in Ecosystems, and lives here.