Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: A pilot study in Germany

2019

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Background: The collection of atmospheric deposition by technical samplers and validated deposition modelling using chemical transport models is spatially complemented by using mosses as bioindicator: since 1990, the European moss survey has been providing data on element concentrations in moss every 5 years at up to 7300 sampling sites. In the moss specimens, heavy metals (since 1990), nitrogen (since 2005) and persistent organic pollutants (since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. In this study, the spatial structures of element concentrations in moss collected in Germany between 1990 and 2015 were comparatively investigated by using Moran's I statistics and Variogram analysis and mapped by use of Kriging interpolation. This is the precondition to spatially join the moss survey data with data collected at other locations within different environmental networks and to validate spatial patterns of atmospheric deposition as derived by technical sampling and modelling. Results: The calculated maps reveal a clear and statistically significant decrease of most heavy metals, but not of nitrogen, in moss. Due to decreasing element concentrations and the unchanged application of the element concentration classification for the mapping, the heavy metal maps for the survey 2015 do no longer depict much spatial variation. Conclusions: Therefore, in an upcoming study, this analysis needs to be complemented for the heavy metals by calculating maps that depict the spatial structure of survey-specific percentile statistics

Section: Introductionmentioning

confidence: 99%

Spatial structures of heavy metals and nitrogen accumulation in moss specimens sampled between 1990 and 2015 throughout Germany

2019

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“…Accordingly, ecosystem condition should be measured using indicators and specified for the national level of the EU member states [15]. For Germany, a spatially explicit and nationally applicable concept for the classification of changes in ecosystem integrity was developed [10,12,16,17,19,21]. A fundamental component of the methodology is the classification of Germany's semi-natural ecosystems.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the developed ecosystem classification is connectable, and the systematizations associated with it gain in ecologically founded interpretability and spatial differentiation. For 61 ecosystem types (40 semi-natural, 21 intensively managed), a historical reference condition was quantified based on data from the period 1961-1990 [10,12,19,20]. The reference condition was quantitatively described as a type-specific condition of ecosystems, the characteristics of which are characterized by intervals of historical condition variables ) that are less affected by substance inputs and climate change than those of subsequent periods.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of the present study was first to test the quality of the map of Germany's current semi-natural ecosystem types (cEsT) on a scale of 1:500,000 [10,19] ("Methodology and results of the quality check" section). For observations of ecosystem integrity at the regional level (e.g., for nature conservation and forestry), the nationwide map of cEsT is only of limited use due to its small map scale-dependent spatial resolution and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Validating the map of current semi-natural ecosystem types in Germany and their upscaling using the Kellerwald-Edersee National Park as an example

Völksen³

2019

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Background: Implementation Action 5 of the EU Biodiversity Strategy to 2020 includes that Member States map and assess the state of ecosystems and their services in their national territory. A fundamental component of the respective methodology developed in Germany is the classification of semi-natural ecosystems. In this context, this study aims to examine the quality and re-usability of the map of current semi-natural ecosystem types (cEsT) in Germany (1:

“…For As, Cd, Ni, Pb and N, there is good evidence that atmospheric deposition is one of the main factors for the accumulation of substances in mosses [12,26,40]. The level of these correlations varies from element to element depending on the reference areas studied, such as participating states or ecological spatial units [41][42][43][44][45][46][47][48]. For the variance not explained by simple bivariate regression (deposition, accumulation of substances in mosses), further significant predictors could be identified and should be regarded: The spatial density of various land-use classes around the moss sampling sites (agricultural, forestry, urban-industrial, respective areal percentage), the distance and height of adjacent tree stands, the distance to the sea as an indicator of the sea spray effect, the population density, the altitude above sea level as well as the precipitation amount [11,23,24,27,31,52].…”

Section: Introductionmentioning

confidence: 99%

Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019

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Background: The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results: The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013-2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100-300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions: For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.