Summary



The effects of increased atmospheric nitrogen inputs, from both NOy and NHx, on diversity in various semi-natural and natural ecosystems are reviewed The severity of these impacts depends on abiotic conditions (eg buffering capacity, soil nutrient status and soil factors that influence the nitrification potential and nitrogen immobilization rate) in the particular system The sensitivity of fresh water ecosystems, wetlands and bogs, species-rich grasslands, heathlands and field layer of forests, all of which have conservational value, are discussed in detail




The most important effects of nitrogen deposition are: (i) accumulation of nitrogenous compounds resulting in enhanced availability of nitrate or ammonium; (ii) soil-mediated effects of acidification; and (iii) increased susceptibility to secondary stress factors




Long-term nitrogen enrichment has gradually increased the availability of nitrogen in several vegetation types, leading to competitive exclusion of characteristic species by more nitrophilic plants, especially under oligo- to mesotrophic soil conditions




Soil acidification (with losses of buffering capacity and increased concentrations of toxic metals) is especially important after nitrification of ammonium in weakly buffered environments: acid-resistant plant species then become dominant at the expense of the often rare plants typical of intermediate pH The related change in the balance between ammonium and nitrate may also affect the performance of several species




The susceptibility of plant species to secondary stress factors (pathogens; frost and drought) may be affected by air-borne nitrogen but data are only available for a few communities (eg dry heathlands)




Most global biodiversity is contained within natural and semi-natural vegetation It is thus crucial to control emissions of nitrogenous compounds to the atmosphere, in order to reduce or prevent effects on diversity in these systems Most research has focused on acidification in forestry stands and lakes and on the effects on trees We highlight serious gaps in knowledge of other ecosystems

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2745.1998.8650717.x

The effects of air‐borne nitrogen pollutants on species diversity in natural and semi‐natural European vegetation

This study explored reflectance indices sampled with a 'leaf reflectometer' as measures of pigment content for leaves of contrasting light history, developmental stage and functional type (herbaceous annual versus sclerophyllous evergreen). We employed three reflectance indices: a modified normalized difference vegetation index (NDVI), an index of chlorophyll content; the red/green reflectance ratio (R RED :R GREEN ), an index of anthocyanin content; and the change in photochemical reflectance index upon dark-light conversions (APRI), an index of xanthophyll cycle pigment activity. In Helianthus annuus (sunflower), xanthophyll cycle pigment amounts were linearly related to growth light environment; leaves in full sun contained approximately twice the amount of xanthophyll cycle pigments as leaves in deep shade, and at midday a larger proportion of these pigments were in the photoprotective, de-epoxidized forms relative to shade leaves. Reflectance indices also revealed contrasting patterns of pigment development in leaves of contrasting structural types (annual versus evergreen). In H. annuus sun leaves, there was a remarkably rapid increase in amounts of both chlorophyll and xanthophyll cycle pigments along a leaf developmental sequence. This pattern contrasted with that of Quercus agrifolia (coast live oak, a sclerophyllous evergreen), which exhibited a gradual development of both chlorophyll and xanthophyll cycle pigments along with a pronounced peak of anthocyanin pigment content in newly expanding leaves. These temporal patterns of pigment development in Q. agrifolia leaves suggest that anthocyanins and xanthophyll cycle pigments serve complementary photoprotective roles during early leaf development. The results illustrate the use of reflectance indices for distinguishing divergent patterns of pigment activity in leaves of contrasting light history and functional type.

https://nph.onlinelibrary.wiley.com/doi/pdf/10.1046/j.1469-8137.1999.00424.x

Assessing leaf pigment content and activity with a reflectometer

Vegetation stress : an introduction to the stress concept in plants

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: methods based on Additive Noise Models (ANMs) and Information Geometric Causal Inference (IGCI). We present the benchmark CAUSEEFFECTPAIRS that consists of data for 100 different causee ffect pairs selected from 37 data sets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the method based on Additive Noise Models that has originally been proposed by Hoyer et al. (2009), which obtains an accuracy of 63 ± 10 % and an AUC of 0.74 ± 0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.

/pdf/distinguishing-cause-from-effect-using-observational-data-39ffkai7u5.pdf

Distinguishing cause from effect using observational data: methods and benchmarks

http://directory.umm.ac.id/Data%20Elmu/jurnal/P/PlantScience/Plant%20Science_BioMedNet/321-340/340..pdf

Ozone: An abiotic elicitor of plant defence reactions

Forest Decline and Ozone

Forest decline and ozone. A comparison of controlled chamber and field experiments.

1 Introduction 2 Sulphur dioxide 3 Nitrogen dioxide 4 Ammonia and sulphides 5 Acid rain 6 Ozone, pan and photochemical smog 7 Stratospheric ozone depletion and enhanced UV-B Radiation 8 Global warming 9 Other pollutants 10 Recent forest decline 11 Interaction and integration Appendix 1: Free radicals 2: Acidic, pH, pKa and microequivalents 3: Glossary and units

Air Pollution and Climate Change: The Biological Impact

Air Pollution and Acid Rain. The Biological Impact.

Air Pollution and Acid Rain: The Biological Impact

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