Atmospheric nitrogen (N) deposition is a global and increasing threat to biodiversity and ecosystem function. Much of our current understanding of N deposition impacts comes from field manipulation studies, although interpretation may need caution where simulations of N deposition (in terms of dose, application rate and N form) have limited realism. Here, we review responses to simulated N deposition from the UKREATE network, a group of nine experimental sites across the UK in a diversity of heathland, grassland, bog and dune ecosystems which include studies with a high level of realism and where many are also the longest running globally on their ecosystem type. Clear responses were seen across the sites with the greatest sensitivity shown in cover and species richness of bryophytes and lichens. Productivity was also increased at sites where N was the limiting nutrient, while flowering also showed high sensitivity, with increases and declines seen in dominant shrub and forb species, respectively. Critically, these parameters were responsive to some of the lowest additional loadings of N (7.7–10 kg ha−1 yr−1) showing potential for impacts by deposition rates seen in even remote and ‘unpolluted’ regions of Europe. Other parameters were less sensitive, but nevertheless showed response to higher doses. These included increases in soil %N and ‘plant available’ KCl extractable N, N cycling rates and acid–base status. Furthermore, an analysis of accumulated dose that quantified response against the total N input over time suggested that N impacts can ‘build up’ within an ecosystem such that even relatively low N deposition rates can result in ecological responses if continued for long enough. Given the responses have important implications for ecosystem structure, function, and recovery from N loading, the clear evidence for impacts at relatively low N deposition rates across a wide range of habitats is of considerable concern.

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Preface 54There is much interest in using Earth Observation (EO) technology to track biodiversity, 55 ecosystem functions, and ecosystem services, understandable given the fast pace of 56 biodiversity loss. However, because most biodiversity is invisible to EO, EO-based 57 indicators could be misleading, which can reduce the effectiveness of nature 58 conservation and even unintentionally decrease conservation effort. We describe an 59 approach that combines automated recording devices, high-throughput DNA Meeting the Aichi Biodiversity Targets 64From Google Earth to airborne sensors, the Copernicus Sentinels, and cube satellites, 65Earth Observation is undergoing a rapid expansion in capacity, accessibility, resolution, 66and signal-to-noise ratio, resulting in a recognised shift in our capability for using 67 remote-sensing technologies to monitor biophysical processes on land and water [1][2][3] . 68These advances are motivating calls to use Earth Observation products to manage our 69 natural environment and to track progress toward global and national policy targets on 70 biodiversity and ecosystem services [4][5][6] . Foremost among these policies are the Strategic 71Plan for Biodiversity and the Aichi Biodiversity Targets, which were adopted in 2010 by products (net primary productivity and fire incidence) that could serve as Essential 108Biodiversity Variables for the Sahara, despite this biome's suitability for remote sensing 109 due to its visible biodiversity hotspots, remoteness, and availability of long time series. 110Many of the Aichi Targets require data with species-level resolution, either because some 111 species are direct policy targets (e.g. Target 9: "invasive species controlled or eradicated") 112 or because species compositional data define the metric (e.g. Target 11: "protected areas 113 are ecologically representative and conserved effectively"). species, but information could be 'borrowed' from data-rich species to increase the 294 precision of predictions for rare species. These procedures were able to compensate for 295 the fact that only 134 total bird species had been detected in the survey, which is less The GDM was parameterised with a training dataset of 2280 surveys and fourteen 303 environmental variables and explained 57% of the variation in beta diversity. In addition, for linking pure-EO data to biodiversity. 382The major remaining components of uncertainty relate to generalisability, because only a 383 single FSC-certified reserve was sampled; the applicability of results to arboreal species, 384 which tend to be detected more frequently in forests with disturbed canopy but are not 385 necessarily more widespread in these forests; and wide confidence intervals around 386 parameter estimates for some species as a consequence of sparse data and a fairly 394Another example of the CEOBE approach is the use of Generalised Dissimilarity 395Modelling to connect EO-derived metrics of habitat degradation and fragmentation 89,90 396 to over 300 million records of more ...

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ABSTRACT1. Coastal sand dunes are widespread worldwide, including around the coasts of the British Isles and Europe, providing a wide range of functions some of which are recognized for their socio-economic benefits.2. In some localities, their contribution to coastal defence and to tourism and regional character have been acknowledged in local plans, but this is far from ubiquitous.3. A rapid assessment was undertaken of the range of ecosystem services provided by coastal sand dune systems, using the Millennium Ecosystem Assessment ecosystem services classification augmented with habitatand locally-appropriate additions.4. Sand dunes were shown to provide a wide range of provisioning, regulatory, cultural and supporting services, many of which remain substantially overlooked.5. Although the importance of coastal sand dune for a diversity of characteristic and often rare organisms from a variety of taxa has been addressed, many of the broader ecosystem services that these habitats provide to society have been overlooked. This suggests that coastal sand dune systems are neglected ecosystems of significant and often under-appreciated societal value.

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21A variety of tools have emerged with the goal of mapping the current delivery of ecosystem services 22 and quantifying the impact of environmental changes. An important and often overlooked question 23 is how accurate the outputs of these models are in relation to empirical observations. In this paper 24 we validate a hydrological ecosystem service model (InVEST Water Yield Model) using widely 25 available data. We modelled annual water yield in 22 UK catchments with widely varying land cover, 26 population and geology, and compared model outputs with gauged river flow data from the UK 27 National River Flow Archive. Values for input parameters were selected from existing literature to 28 reflect conditions in the UK and were subjected to sensitivity analyses. We also compared model 29 performance between precipitation and potential evapotranspiration data sourced from global-and 30 UK-scale datasets. We then tested the transferability of the results within the UK by additional 31 validation in a further 20 catchments. 32Whilst the model performed only moderately with global-scale data (linear regression of modelled 33 total water yield against empirical data; slope = 0.763, intercept = 54.45, R 2 = 0.963) with wide 34 variation in performance between catchments, the model performed much better when using UK-35 scale input data, with closer fit to the observed data (slope = 1.07, intercept = 3.07, R 2 = 0.990). With 36 UK data the majority of catchments showed less than 10% difference between measured and 37 modelled water yield but there was a minor but consistent overestimate per hectare (86 38 m 3 /ha/year). Additional validation on a further 20 UK catchments was similarly robust, indicating 39 that these results are transferable within the UK. These results suggest that relatively simple 40 models can give accurate measures of ecosystem services. However, the choice of input data is 41 critical and there is a need for further validation in other parts of the world. 42Keywords 43 UK, mapping, rainfall, evapotranspiration, river flow, land cover 44 45

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