Accurate and unbiased radiative energy transfer models are critical to our understanding of ecosystem primary productivity, carbon cycling, and climate change. Much of the current research in this area is based on models parameterized for grasslands and broadleaf forests. However, many temperate montane and boreal forests are dominated by conifers, which present unique challenges to modellers. We propose two fundamentally different strategies by which plant canopies optimize solar radiation interception. Laminar canopies (e.g., grasslands, broadleaf trees) are .solar panels. that directly intercept incoming radiant energy. By contrast, conifer canopies are conical anechoic (.without echo.) surfaces that intercept radiant energy by scattering it through the canopy. The properties of anechoic surfaces are well known in acoustical and electrical engineering, but have not been applied in environmental biophysics. We discuss the physical principles of anechoic surfaces, and demonstrate how these principles apply to conifer trees and canopies. A key feature of anechoic interception is low radiance over all wavelengths, which is an emergent property of the system. Using empirical data from boreal forest stands in Riding Mountain National Park (Manitoba, Canada), we demonstrate that conifer canopies have very low near-infrared radiance compared to laminar broadleaf canopies. Vegetation index values for conifers are thereby reduced, resulting in underestimates of primary productivity and other biophysical parameters. We also discuss the adaptive significance of boreal conifer geometry, and consider factors driving selection of laminar versus anechoic canopy architectures.
Landscape complexity in the boreal forest is a function of physiographic complexity (spatial processes) and post-fire successional (temporal) processes operating across scales. In this study we examine the role of succession and topographic complexity in determining the landscape complexity of Riding Mountain National Park, Manitoba, Canada. Landscape complexity is assessed by using multifractal analysis to quantify landscape patterns from Landsat TM imagery. To determine whether complexity changes with age, . young. sites (post-fire stand ages = 11 and 30 years) were matched with adjacent . old. sites (post-fire stand ages ≯ 95 years). The influence of physiography on landscape complexity is examined by comparing sites having . simple. and . complex. physiographies (as determined by fractal surface analysis). The scaling properties of landscape complexity are determined by calculating the multifractal spectrum (Dq) for each site. Landscape complexity increases during early succession; multifractal profiles of 11 year old sites are lower than those of adjacent older stands. However, the multifractal profiles of 30 year old and adjacent older stands are indistinguishable, suggesting that change in landscape complexity occurs within 30 years following fire. Physiographically . complex. sites have consistently greater landscape complexity than adjacent . simple. sites. We conclude that landscape complexity increases over time as succession proceeds, and in space along a gradient from . simple. to . complex. physiographies. It follows that landscape complexity is lowest in early-successional, physiographically . simple. sites and highest in late-successional, physiographically . complex. sites.