This set of data comprises substrate utilisation profiles for saprotrophic fungi (using the commercially available BIOLOG plate method) and moisture content data from soils sampled from experimental plots at Sourhope, Scotland. The data were collected in order to determine how the high species richness of decomposer (saprotrophic) fungi and their relative frequencies of occurrence influence the decomposition of organic matter. Data were collected during a project funded under the NERC Soil Biodiversity Programme, established in 1999 and centred upon the intensive study of a large field experiment located at the Macaulay Land Use Research Institute (now the James Hutton Institute) farm at Sourhope in the Scottish Borders (Grid reference: NT8545019630). During the experiment, the site was monitored to assess changes in above-ground biomass production (productivity), species composition and relative abundance (diversity). Full details about this dataset can be found at https://doi.org/10.5285/662b8cb3-afca-43c6-a6e8-e56fcf94626b

Vegetation plays an important role in landscapes that are shaped by wind-driven (aeolian) sand transport, such as coastal dunes and semi-arid regions. We have a good knowledge of how and why different types of desert dunes and dune fields form without the presence of vegetation, but our understanding of the effects of vegetation in the formation of coastal foredunes, parabolic dunes, blowouts, and nebkha's (shrub hummocks) is limited to descriptive observations and reasoning. This is especially true for vegetated dune fields on a landscape scale, and the effects of various plant species on the evolution and dynamics of such environments are not quantified. This research project aims to develop a computer simulation model based on moving around slabs of sand across a grid of cells that represents a landscape surface including varying amounts of vegetation in each cell. These movements are controlled by a set of simple rules that dictate interactions between the existing surface, the vegetation in each cell, and the propagation of the sand slabs. This allows simulating the evolution of aeolian landscapes through self-organisation into different types of dune fields without actually modelling the complex airflow dynamics and sand transport patterns. Simulations will be compared with our current descriptive understanding of vegetated aeolian landscape development to ensure that the model generates realistic results. The model is then used to systematically investigate exactly how and why various kinds of plant species and vegetation patterns influence the dynamics of dune development in aeolian environments.

The dataset contains three modelled estimates of global ammonia emissions from seabird colonies, at a spatial resolution of 0.1 degrees latitude/longitude. The model estimates were derived with a) detailed global seabird population data collated from a large number of sources (data sources date from 1980-2010 for different parts of the world) b) climate data (source: High-resolution Gridded Datasets, Climatic Research Unit, University of East Anglia, UK. http://www.cru.uea.ac.uk/cru/data/hrg/ last updated by Harris, I. (2007), date: 1995) c) emission model derived by Riddick et al. (2012) with funding for the project from the CEH Integrating Fund (NERC). A detailed description and discussion of the datasets, including methodology and uncertainties, can be found in the following peer-reviewed article: S. N. Riddick, U. Dragosits, T. D. Blackall, F. Daunt, S. Wanless and M. A. Sutton (2012) The global distribution of ammonia emissions from seabird colonies. Atmospheric Environment, 55 (2012), pp. 319-327 https://doi.org/10.1016/j.atmosenv.2012.02.052 Full details about this dataset can be found at https://doi.org/10.5285/c9e802b3-43c8-4b36-a3a3-8861d9da8ea9