Addressing the Impact of Atmospheric Nitrogen Deposition on Western European Grasslands.

There is a growing evidence base demonstrating that atmospheric nitrogen deposition presents a threat to biodiversity and ecosystem function in acid grasslands in Western Europe. Here, we report the findings of a workshop held for European policy makers to assess the perceived importance of reactive nitrogen deposition for grassland conservation, identify areas for policy development in Europe and assess the potential for managing and mitigating the impacts of nitrogen deposition. The importance of nitrogen as a pollutant is already recognized in European legislation, but there is little emphasis in policy on the evaluation of changes in biodiversity due to nitrogen. We assess the potential value of using typical species, as defined in the European Union Habitats Directive, for determining the impact of nitrogen deposition on acid grasslands. Although some species could potentially be used as indicators of nitrogen deposition, many of the typical species do not respond strongly to nitrogen deposition and are unlikely to be useful for identifying impact on an individual site. We also discuss potential mitigation measures and novel ways in which emissions from agriculture could be reduced.

Mechanistic analytical models for long-distance seed dispersal by wind.

We introduce an analytical model, the Wald analytical long-distance dispersal (WALD) model, for estimating dispersal kernels of wind-dispersed seeds and their escape probability from the canopy. The model is based on simplifications to well-established three-dimensional Lagrangian stochastic approaches for turbulent scalar transport resulting in a two-parameter Wald (or inverse Gaussian) distribution. Unlike commonly used phenomenological models, WALD's parameters can be estimated from the key factors affecting wind dispersal--wind statistics, seed release height, and seed terminal velocity--determined independently of dispersal data. WALD's asymptotic power-law tail has an exponent of -3/2, a limiting value verified by a meta-analysis for a wide variety of measured dispersal kernels and larger than the exponent of the bivariate Student t-test (2Dt). We tested WALD using three dispersal data sets on forest trees, heathland shrubs, and grassland forbs and compared WALD's performance with that of other analytical mechanistic models (revised versions of the tilted Gaussian Plume model and the advection-diffusion equation), revealing fairest agreement between WALD predictions and measurements. Analytical mechanistic models, such as WALD, combine the advantages of simplicity and mechanistic understanding and are valuable tools for modeling large-scale, long-term plant population dynamics.