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 elsewhere.