Gradient analysis of old spruce – fir forests of the Great Smoky Mountains circa 1935

The response of old-growth spruce – fir vegetation to environmental gradients was investigated using 1930s plot data from the Great Smoky Mountains. Gradients related to forest composition and position of the ecotone with the deciduous forest were identified using canonical correspondence analysis (CCA) and their role in vegetation response to climate change was considered. The data were subsequently stratified into three elevation classes and ordinated separately using CCA to identify gradients at various elevations. The effect of elevation on tree stratum composition and structure was profound. Secondary gradients influencing the tree stratum included slope aspect, potential solar radiation, and topographic position. Abies fraseri basal area and density were high above 1800 m elevation. Comparable basal area levels of Picea rubens were attained at elevations ranging from 1400 to 1900 m. Total stand basal area and density increased with elevation. The importance of topographic position increased with elevation, while that of slope aspect and potential solar radiation decreased. Presumably, the increasing incidence of cloud cover with elevation diminished the effect of slope aspect and potential solar radiation at higher elevations. The transition from deciduous forest occurred in the 1300 – 1600 m elevation range. A substantial proportion (24%) of plots had mixed composition (30 – 70% spruce – fir by basal area), suggesting that the ecotone is not abrupt in old-growth forest. Environmental variables other than elevation did not have a strong effect on ecotone position. Attempts to infer long-term ecotone dynamics along the elevation gradient based on species size-class data were inconclusive. Key words: Abies fraseri, gradient analysis, Great Smoky Mountains, old-growth forest, Picea rubens, spruce – fir forest. </jats:p

Quantification of neighbourhood-dependent plant growth by Bayesian hierarchical modelling

1. The effects of neighbours on the growth of individual plants are fundamental to dynamics in plant populations and can be described by means of mathematical functions, so-called competition kernels, in formal spatiotemporal models. Little is known about the form and components such functions should have. 2. We evaluate some properties of kernel functions using data on the growth of Arabidopsis thaliana plants in replicated, even-aged stands of many individuals. Because of the essential non-independence of plant growth in stands, we employed a Bayesian hierarchical modelling approach to estimate values and uncertainties of kernel parameters in location-dependent models of interacting plants. 3. During the experiment plant size and a simple measure of neighbourhood crowding became strongly correlated, plants tending to be small where local crowding was intense, indicating that local competition was an important process in the growth of the plants. 4. Competitive interactions between plants of different sizes were strongly asymmetric, the larger individual acquiring a disproportionately greater share of resources. Competition increased with plant size and attenuated rapidly at distances of a few centimetres, but the exact shape of the attenuation function was less important. 5. Kernel functions with the same kind of structural features were similar in their predictive ability. However, a simple zone-of-influence model, based on overlap of pairs of individuals, with competition favouring the larger individual, was arguably the most parsimonious. 6. Neighbourhood competition in stands of even-aged plants may be successfully captured with relatively simple kernel functions. The results should inform and enhance the formal theory of spatiotemporal plant population and community dynamics. Bayesian hierarchical modelling is a powerful tool with which to analyse complex, spatially dependent data, and has potential as a widely applicable statistical approach for plant ecology