A transect of 68 acid grasslands across Great Britain, covering the lower range of ambient annual nitrogen deposition in the industrialized world (5 to 35 kg Nha-1 year-1), indicates that long-term, chronic nitrogen deposition has significantly reduced plant species richness. Species richness declines as a linear function of the rate of inorganic nitrogen deposition, with a reduction of one species per 4-m2 quadrat for every 2.5 kg Nha-1 year-1 of chronic nitrogen deposition. Species adapted to infertile conditions are systematically reduced at high nitrogen deposition. At the mean chronic nitrogen deposition rate of central Europe (17 kg Nha-1 year-1), there is a 23% species reduction compared with grasslands receiving the lowest levels of nitrogen deposition.

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45Evidence from an international survey in the Atlantic biogeographic region of Europe 46indicates that chronic nitrogen deposition is reducing plant species richness in acid 47 grasslands. Across the deposition gradient in this region (2 to 44 kg N ha -1 yr -1 ) species 48 richness showed a curvilinear response, with greatest reductions in species richness when 49 deposition increased from low levels. This has important implications for conservation 50 policies, suggesting that to protect the most sensitive grasslands resources should be 51 focussed where deposition is currently low. Soil pH is also an important driver of species 52 richness indicating that the acidifying effect of nitrogen deposition may be contributing to 53 species richness reductions. The results of this survey suggest that the impacts of nitrogen 54 deposition can be observed over a large geographical range.

1In this study we investigate the impact of nitrogen (N) deposition on the diversity of 2 three different vegetation functional groups -forbs, grasses and mosses -using a field 3 survey of acid grasslands across Great Britain. Our aim is to identify the vegetation 4 types that are most vulnerable to enhanced N deposition, and to shed light on the 5 mechanisms that may be driving N-initiated species changes in the UK. Sixty-eight 6 randomly selected grasslands belonging to the U.K. National Vegetation 7Classification group U4 (Festuca ovina-Agrostis capillaris-Galium saxatile grassland) 8were studied along a gradient of atmospheric N deposition ranging from 6 to 36 kg N 9 ha -1 yr -1 . At each site, vegetation was surveyed and samples were taken from the 10 topsoil and subsoil. Above-ground plant material was collected from 3 species: a 11 forb, grass and moss. 12Both the species richness and cover of forbs declined strongly with increasing 13 N deposition, from greater than 8 species/20% cover per m 2 quadrat at low levels of N 14 to fewer than 2 species/5% cover at the highest N deposition levels. Grasses showed 15 a weak but significant decline in species richness, and a trend toward increasing cover 16 with increasing N input. Mosses showed no trends in either species richness or cover. 17

Summary1. Ellenberg's indicator values scale the¯ora of a region along gradients re¯ecting light, temperature, continentality, moisture, soil pH, fertility and salinity. They can be used to monitor environmental change. 2. Ellenberg values can be extended from central Europe, for which they were de®ned, to nearby parts of Europe. Given a database of quadrat samples, they can be repredicted by a simple algorithm consisting of two-way weighted averaging, followed by local regression. 3. A database of British samples was assembled from two large surveys. Ellenberg values were repredicted. 4. Except for the indicator of continentality, the correlation of repredicted and original values was in the range 0´72 (light) to 0´91 (moisture). The continentality indicator could not be adequately repredicted by the algorithm, and is unusable in Britain. Discrepancies between original and repredicted values can be attributed to various causes, including wrong original values, diering ecological requirements inBritain and central Europe, biased sampling of the British range of habitats, and the occurrence of small plants in shaded or basic microhabitats within well illuminated or predominantly acid quadrats. 6. The repredicted values were generally reliable, but a small proportion was clearly wrong. Wrong values were due to either inadequate sampling of species' realized niches in Britain or sampling with quadrats that were too large and included species that were not close associates.

Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. 1The following paper is the final version prior to publication on 22 September 2015. are proposed, the way in which indicators could contribute to classification is discussed. All of the methods described in Table 1 consider a hierarchy of spatial units, but the degree to which they develop the other aspects of the conceptual approach proposed by Frissell et al.(1986) varies widely.2. Many of the frameworks focus entirely on hydromorphological processes and forms that are either directly measured or inferred. This is because interactions between processes and forms control the dynamic morphology or behaviour of rivers and their mosaics of habitats.Hydromorphological processes drive longitudinal and lateral connectivity within river networks and corridors, the assemblage and turnover of physical habitats, and the sedimentary and vegetation structures associated with those habitats.3. Some frameworks are conceptual, providing a way of thinking about or structuring analyses of river systems, and interpreting their processes, morphology and function (e.g. Frissell et al., 1986;Habersack, 2000;Fausch et al., 2002;Thorp et al., 2006;Beechie et al., 2010;McCluney et al., 2014). Some frameworks are more quantitative, generating one or more indices or classifications of spatial units that support assessment of river systems (e.g. Rosgen, 1994;González del Tánago and García de Jalón, 2004;Merovich et al., 2013;Rinaldi et al., 2013Rinaldi et al., , 2015a MacDonald, 2002;Brierley and Fryirs, 2005;Beechie et al., 2010; Rinaldi et al., 2013a Rinaldi et al., , 2015.In some cases, theoretical or historical analyses or consideration of specific future scenarios are used to develop space-time understanding that can support management decisionmaking (e.g. Buffington, 1997, 1998;Montgomery and MacDonald, 2002;Benda et al., 2004;Brierley and Fryirs, 2005;McCluney et al., 2014 , 1997, 1998Montgomery and MacDonald, 2002;Benda et al., 2004;Brierley and Fryirs, 2005;Merovich et al., 2013;Rinaldi et al., 2013Rinaldi et al., , 2015a. Furthermore, some of the frameworks include indicators of human pressures and their impacts (e.g. Merovich et al., 2013;McCluney et al., 2014;Rinaldi et al., 2013Rinaldi et al., , 2015a.6. Finally, although most frameworks could be described as incorporating processes to some degree, some methods are particularly process-based, even when processes are inferred from forms and associations rather than being quantified by direct measurements.Frameworks that consider temporal dynamics and trajectories of historical change (see point 4, above) are particularly effective in developing understanding of processes and the impacts of changed processes cascading through time and across spatial scales.Although the list of frameworks presented in Table 1 is far from comprehensive, ...

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