C. Wood

Gully hotspot contribution to landscape methane (CH4) and carbon dioxide (CO2) fluxes in a northern peatland

Peatlands are long term carbon catchments that sink atmospheric carbon dioxide (CO(2)) and source methane (CH(4)). In the uplands of the United Kingdom ombrotrophic blanket peatlands commonly exist within Calluna vulgaris (L.) dominated moorland ecosystems. These landscapes contain a range of topographical features that influence local hydrology, climate and plant community composition. In this study we examined the variation in ecosystem CO(2) respiration and net CH(4) fluxes from typical plant-soil systems in dendritic drainage gullies and adjacent blanket peat during the growing season. Typically, Eriophorum spp., Sphagnum spp. and mixed grasses occupied gullies while C. vulgaris dominated in adjacent blanket peat. Gross CO(2) respiration was highest in the areas of Eriophorum spp. (650+/-140 mg CO(2) m(-2) h(-1)) compared to those with Sphagnum spp. (338+/-49 mg CO(2) m(-2) h(-1)), mixed grasses (342+/-91 mg CO(2) m(-2) h(-1)) and C. vulgaris (174+/-63 mg CO(2) m(-2) h(-1)). Measurements of the net CH(4) flux showed higher fluxes from the Eriophorum spp (2.2+/-0.6 mg CH(4) m(-2) h(-1)) locations compared to the Sphagnum spp. (0.6+/-0.4 mg CH(4) m(-2) h(-1)), mixed grasses (0.1+/-0.1 mg CH(4) m(-2) h(-1)) and a negligible flux detected from C. vulgaris (0.0+/-0.0 mg CH(4) m(-2) h(-1)) locations. A GIS approach was applied to calculate the contribution of gullies to landscape scale greenhouse gas fluxes. Findings from the Moor House National Nature Reserve in the UK showed that although gullies occupied only 9.3% of the total land surface, gullies accounted for 95.8% and 21.6% of the peatland net CH(4) and CO(2) respiratory fluxes, respectively. The implication of these findings is that the relative contribution of characteristic gully systems need to be considered in estimates of landscape scale peatland greenhouse gas fluxes.

Vegetation database of Great Britain: Countryside Survey

This paper describes the vegetation database created as part of the Countryside Survey (CS) of Great Britain (GIVD ID EU-GB-003) which was established to monitor ecological and land use change in 1978 (http://www.countrysidesurvey.org.uk). The sample design is based on a series of stratified, randomly selected 1 km squares, which numbered 256 in the 1978 survey, 500 in the 1990 survey, 569 in the 1998 survey and 591 in the 2007 survey. Stratification of sample squares was based on predefined strata (called land classes) which have been derived from a classification of all 1 km squares in Britain based on their topographic, climatic and geological attributes obtained from published maps. A series of vegetation plots were located within each 1 km square using a restricted randomisation procedure designed to reduce aggregation. Linear features (road verges, watercourse banks, hedges, arable margins and field boundaries) and areal features (fields, unenclosed land and small semi-natural biotope patches) were sampled. Linear plots were 1 x 10 m laid out along a feature whilst unenclosed land and small biotopes were sampled using 2 m x 2 m plots. Larger randomly-placed plots were nested 14 m² plots with an inner nest of 2 m x 2 m. Within each 1 km Countryside Survey sample square the land cover and all landscape features were mapped and each parcel of land (and vegetation plot) has been assigned to a Broad Habitat/EUNIS habitat type. This database of vegetation plots is a very useful resource. The data is freely available from the website, however, there are restrictions on the release of the spatial location of the plots. There is now a considerable time-series of plots within the database going back to 1978 representing different habitat types and landscape features that can be analysed to determine changes in vegetation metrics (e.g. Ellenberg scores) and individual species. Vegetation changes can be linked to environmental drivers and the spatial scale (across GB) is sufficiently large to analyse gradients in most driving variables.