Abstract

To protect ecosystems and their services, the critical load concept has been implemented under the framework of the Convention on Long-range Transboundary Air Pollution (UNECE) to develop effects-oriented air pollution abatement strategies. Critical loads are thresholds below which damaging effects on sensitive habitats do not occur according to current knowledge. Here we use change-point models applied in a Bayesian context to overcome some of the difficulties when estimating empirical critical loads for nitrogen (N) from empirical data. We tested the method using simulated data with varying sample sizes, varying effects of confounding variables, and with varying negative effects of N deposition on species richness. The method was applied to the national-scale plant species richness data from mountain hay meadows and (sub)alpine scrubs sites in Switzerland. Seven confounding factors (elevation, inclination, precipitation, calcareous content, aspect as well as indicator values for humidity and light) were selected based on earlier studies examining numerous environmental factors to explain Swiss vascular plant diversity. The estimated critical load confirmed the existing empirical critical load of 5–15 kg N ha−1 yr−1 for (sub)alpine scrubs, while for mountain hay meadows the estimated critical load was at the lower end of the current empirical critical load range. Based on these results, we suggest to narrow down the critical load range for mountain hay meadows to 10–15 kg N ha−1 yr−1.

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