Environmental, spatial and temporal drivers of plant community composition in British forest habitat

Broadleaved forest habitat is important for a number of ecosystem functions and as a refuge for many rare plant species in human-modified landscapes. It is however, threatened by global change drivers such as deforestation and the associated fragmentation of remaining habitat areas, along with increased disturbance and exposure to nutrient inputs from surrounding intensive agriculture. This thesis uses a unique combination of data on plant species occurrence, local environmental conditions and forest spatial extent in order to investigate the ways in which species richness and functional diversity in forest communities are dependent upon local and landscape scale drivers, and to quantify the strength of these relationships. This provides novel understanding of the response of forest plants with different life history traits to the configuration and quality of available habitat, and therefore the way in which understorey assemblages are likely to alter over time following landscape change. Results highlight the importance of local environmental conditions within forest patches but also suggest that patch area and landscape connectivity have an important effect on the trait composition of communities. Preserving large, well connected areas of habitat is therefore likely to be key for the conservation of many species, particularly rarer forest specialists which often possess traits linked to low dispersal ability. Furthermore, there is evidence that species are slow to respond to changes in the spatial extent of habitat. As such, considering the history of forest patches is necessary in order to explain present day patterns in plant species occurrence and to devise effective conservation measures. This highlights the need to integrate understanding of local and landscape scale processes with temporal data in order to properly understand the way in which forest communities are formed and to predict ongoing change under expected global change scenarios