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

Relationships between the species composition of forest field-layer vegetation and environmental drivers, assessed using a national scale survey

Simulation models of forest stand dynamics have increased understanding of over-storey vegetation functioning, and have facilitated the development of tools capable of assessing possible successional trajectories. However, few models incorporate the response of the field layer vegetation despite it being another key component of forest ecosystems. Our main objective was to assess the degree to which field-layer vegetation composition in forests is determined by variables operating at different scales, from regional (e.g. climate, location) to local factors (e.g. basal area of canopy trees, management). We used data gathered during a nationwide forest survey to assess the relative effects of a broad spectrum of environmental variables on species composition. Variation partitioning was used to examine the relative contribution of subsets of environmental variables such as site spatial variation, boundary type and presence of herbivores. Ordination confirmed hypotheses that field layer vegetation is primarily structured by two composite geo-climatic gradients. However, variation partitioning demonstrated that site- and plot-scale management factors also strongly influence the floristic composition of forest patches. Disturbance variables (site boundary type/regional presence of deer) accounted for considerable species variation, exceeding that due to either site spatial variation or forest structure. This is the first time variation attributable to such a comprehensive range of environmental variables has been quantified for forests surveyed at a national scale. We thus provide a context within which regional studies, or analyses considering a more limited range of factors, can be viewed, and a framework from which robust models of floristic response to gradual and episodic natural and anthropogenic disturbances may be developed. The methodology we present, including a novel technique for the identification and removal of outliers in large data sets, provides a unique and standardized means of assessing the relative importance of diverse environmental drivers across a range of habitat types at the landscape scale, and is readily applicable elsewher