Relationship between site-specific nitrogen concentrations in mosses and measured wet bulk atmospheric nitrogen deposition across Europe

To assess the relationship between nitrogen concentrations in mosses and wet bulk nitrogen deposition or concentrations in precipitation, moss tissue and deposition were sampled within a distance of 1 km of each other in seven European countries. Relationships for various forms of nitrogen appeared to be asymptotic, with data for different countries being positioned at different locations along the asymptotic relationship and saturation occurring at a wet bulk nitrogen deposition of ca. 20 kg N ha−1 yr−1. The asymptotic behaviour was more pronounced for ammonium-N than nitrate-N, with high ammonium deposition at German sites being most influential in providing evidence of the asymptotic behaviour. Within countries, relationships were only significant for Finland and Switzerland and were more or less linear. The results confirm previous relationships described for modelled total deposition. Nitrogen concentration in mosses can be applied to identify areas at risk of high nitrogen deposition at European scale

Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010

In recent decades, naturally growing mosses have been used successfully as biomonitors of atmospheric deposition of heavy metals and nitrogen. Since 1990, the European moss survey has been repeated at five-yearly intervals. In 2010, the lowest concentrations of metals and nitrogen in mosses were generally found in northern Europe, whereas the highest concentrations were observed in (south-)eastern Europe for metals and the central belt for nitrogen. Averaged across Europe, since 1990, the median concentration in mosses has declined the most for lead (77%), followed by vanadium (55%), cadmium (51%), chromium (43%), zinc (34%), nickel (33%), iron (27%), arsenic (21%, since 1995), mercury (14%, since 1995) and copper (11%). Between 2005 and 2010, the decline ranged from 6% for copper to 36% for lead; for nitrogen the decline was 5%. Despite the Europe-wide decline, no changes or increases have been observed between 2005 and 2010 in some (regions of) countries

Growing stock monitoring by European National Forest Inventories: Historical origins, current methods and harmonisation

peer reviewedWood resources have been essential for human welfare throughout history. Also nowadays, the volume of growing stock (GS) is considered one of the most important forest attributes monitored by National Forest Inventories (NFIs) to inform policy decisions and forest management planning. The origins of forest inventories closely relate to times of early wood shortage in Europe causing the need to explore and plan the utilisation of GS in the catchment areas of mines, saltworks and settlements. Over time, forest surveys became more detailed and their scope turned to larger areas, although they were still conceived as stand-wise inventories. In the 1920s, the first sample-based NFIs were introduced in the northern European countries. Since the earliest beginnings, GS monitoring approaches have considerably evolved. Current NFI methods differ due to country-specific conditions, inventory traditions, and information needs. Consequently, GS estimates were lacking international comparability and were therefore subject to recent harmonisation efforts to meet the increasing demand for consistent forest resource information at European level. As primary large-area monitoring programmes in most European countries, NFIs assess a multitude of variables, describing various aspects of sustainable forest management, including for example wood supply, carbon sequestration, and biodiversity. Many of these contemporary subject matters involve considerations about GS and its changes, at different geographic levels and time frames from past to future developments according to scenario simulations. Due to its historical, continued and currently increasing importance, we provide an up-to-date review focussing on large-area GS monitoring where we i) describe the origins and historical development of European NFIs, ii) address the terminology and present GS definitions of NFIs, iii) summarise the current methods of 23 European NFIs including sampling methods, tree measurements, volume models, estimators, uncertainty components, and the use of air- and space-borne data sources, iv) present the recent progress in NFI harmonisation in Europe, and v) provide an outlook under changing climate and forest-based bioeconomy objectives

Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe

Background: This paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey. Results: Correlations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5 km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75–100 km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of < 40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (= above-average) or low (= below-average) correlation coefficients. Conclusions: LDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites

Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe

BackgroundThis paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey.ResultsCorrelations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75-100km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of <40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (=above-average) or low (=below-average) correlation coefficients.ConclusionsLDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites

Modelling and mapping heavy metal and nitrogen concentrations in moss in 2010 throughout Europe by applying Random Forests models

Objective: This study explores the statistical relations between the concentration of nine heavy metals(HM) (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb),vanadium (V), zinc (Zn)), and nitrogen (N) in moss and potential explanatory variables (predictors)which were then used for mapping spatial patterns across Europe. Based on moss specimens collected in 2010 throughout Europe, the statistical relation between a set of potential predictors (such as the atmospheric deposition calculated by use of two chemical transport models (CTM), distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils) and concentrations of HMs and nitrogen (N) in moss (response variables) were evaluated by the use of Random Forests (RF) and Classification and Regression Trees (CART). Four spatial scales were regarded: Europe as a whole, ecological land classes covering Europe, single countries participating in the European Moss Survey (EMS), and moss species at sampling sites. Spatial patterns were estimated by applying a series of RF models on data on potential predictors covering Europe. Statistical values and resulting maps were used to investigate to what extent the models are specific for countries, units of the Ecological Land Classification of Europe (ELCE), and moss species. Results: Land use, atmospheric deposition and distance to technical emission sources mainly influence the element concentration in moss. The explanatory power of calculated RF models varies according to elements measured in moss specimens, country, ecological land class, and moss species. Measured and predicted medians of element concentrations agree fairly well while minima and maxima show considerable differences. The European maps derived from the RF models provide smoothed surfaces of element concentrations (As, Cd, Cr, Cu, N, Ni, Pb, Hg, V, Zn), each explained by a multivariate RF model and verified by CART, and thereby more information than the dot maps depicting the spatial patterns of measured values. Conclusions: RF is an eligible method identifying and ranking boundary conditions of element concentrations in moss and related mapping including the influence of the environmental factors

Trace elements and nitrogen in naturally growing moss Hypnum cupressiforme in urban and peri-urban forests

We monitored trace metals and nitrogen using naturally growing moss Hypnum cupressiforme Hedw. in urban and peri-urban forests of the City Municipality of Ljubljana. The aim of this study was to explore the differences in atmospheric deposition of trace metals and nitrogen between urban and peri-urban forests. Samples were collected at a total of 44 sites in urban forests (forests within the motorway ring road) and peri-urban forests (forests outside the motorway ring road). Mosses collected in urban forests showed increased trace metal concentrations compared to samples collected from peri-urban forests. Higher values were significant for As, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Tl and V. Within the motorway ring road, the notable differences in element concentrations between the two urban forests were significant for Cr, Ni and Mo. Factor analysis showed three groups of elements, highlighting the contribution of traffic emissions, individual heating appliances and the resuspension of contaminated soils and dust as the main sources of trace elements in urban forests

Predicting phenology of European beech in forest habitats

Reliable phenological observations are important for studying the response of trees to climate and climate change. National phenological networks were not specifically established to monitor tree phenology within forests, yet they are often used to generalise tree phenological phases at national or regional scales. Our objective was to investigate whether a phenological monitoring network using trees in open areas can accurately predict phenology of European beech (Fagus sylvatica L.) located within forests by using two models: one with correlates of environmental variables and one with interpolated monthly air temperature and sun hours. The first leaf unfolding, general leaf colouring and leaf fall dates from 2004 through 2010 were modelled using data from 47 Slovene National Phenology Network (NPN) stations in open areas and tested on phenological observations within forests using data from the UNECE CRLTAP ICP Forests network. Good agreement was found between predicted and observed first leaf unfolding in the forest, while slightly lower agreement was detected for general leaf colouring and leaf fall. Suggestions for the improvement of national phenological network are discussed in order to better predict beech phenology in forest habitats