Indici per la valutazione della qualit? ecologica dei laghi

Collection of methods to evaluate lake quality using biological element

Meta-analysis of multidecadal biodiversity trends in Europe

Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15-91 years) collected across Europe, using a comprehensive dataset comprising similar to 6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe. The global biodiversity decline might conceal complex local and group-specific trends. Here the authors report a quantitative synthesis of longterm biodiversity trends across Europe, showing how, despite overall increase in biodiversity metric and stability in abundance, trends differ between regions, ecosystem types, and taxa.peerReviewe

Spatially valid data of atmospheric deposition of heavy metals and nitrogen derived by moss surveys for pollution risk assessments of ecosystems

For analysing element input into ecosystems and associated risks due to atmospheric deposition, element concentrations in moss provide complementary and time-integrated data at high spatial resolution every 5 years since 1990. The paper reviews (1) minimum sample sizes needed for reliable, statistical estimation of mean values at four different spatial scales (European and national level as well as landscape-specific level covering Europe and single countries); (2) trends of heavy metal (HM) and nitrogen (N) concentrations in moss in Europe (1990–2010); (3) correlations between concentrations of HM in moss and soil specimens collected across Norway (1990–2010); and (4) canopy drip-induced site-specific variation of N concentration in moss sampled in seven European countries (1990–2013). While the minimum sample sizes on the European and national level were achieved without exception, for some ecological land classes and elements, the coverage with sampling sites should be improved. The decline in emission and subsequent atmospheric deposition of HM across Europe has resulted in decreasing HM concentrations in moss between 1990 and 2010. In contrast, hardly any changes were observed for N in moss between 2005, when N was included into the survey for the first time, and 2010. In Norway, both, the moss and the soil survey data sets, were correlated, indicating a decrease of HM concentrations in moss and soil. At the site level, the average N deposition inside of forests was almost three times higher than the average N deposition outside of forests

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

Estimating aquatic invertebrate diversity in the southern Alps using data from Biodiversity Days

High biodiversity is a prerequisite for the integrity, stability, and functioning of global aquatic ecosystems, but it is currently subject to anthropogenic threats. Small freshwater bodies with high habitat diversity are essential to sustain regional biodiversity, but species inventory and biodiversity are largely overlooked, especially in mountainous regions. In the Italian Alps, obligate assessments of freshwater biota (e.g., for the European water framework directive, WFD) are usually done in larger rivers or lakes only, which is why many taxa from small freshwater habitats might have been overlooked so far. Here we summarize and discuss the efforts to record aquatic invertebrates within the framework of so-called "Biodiversity Days", organized since 2001 at 13 different sites located across the North Italian province of South Tyrol. These events with voluntary participation of scientists and naturalists from universities and environmental agencies led to the detection of 334 benthic invertebrate taxa in streams and lakes (mostly species or genus level), whereby higher taxa richness was found in streams. The overall hierarchy of species numbers within invertebrate orders or families corresponded to that of other Alpine regions (groups richest in taxa were Chironomidae and Trichoptera) and these Biodiversity Days contributed to biodiversity research of that region in detecting 167 additional taxa. Besides analyzing yearly gains in the regional taxa inventory, we predict that future surveys will lead to new discoveries of aquatic taxa for that province (i.e., current modeling estimates a regional inventory of more than 600 taxa). However, specific surveys in hitherto unconsidered habitats, such as morphologically modified or urban waters, might reveal even more taxa than currently estimated. Besides characterizing the invertebrate fauna of this region and providing a first reference list for future monitoring projects in the same region, this work demonstrates that such Biodiversity Days can contribute to biodiversity research

Metal accumulation in mosses across national boundaries: uncovering and ranking causes of spatial variation.

This study aimed at cross-border mapping metal loads in mosses in eight European countries in 1990, 1995, and 2000 and at investigating confounding factors. Geostatistics was used for mapping, indicating high local variances but clear spatial autocorrelations. Inference statistics identified differences of metal concentrations in mosses on both sides of the national borders. However, geostatistical analyses did not ascertain discontinuities of metal concentrations in mosses at national borders due to sample analysis in different laboratories applying a range of analytical techniques. Applying Classification and Regression Trees (CART) to the German moss data as an example, the local variation in metal concentrations in mosses were proved to depend mostly on different moss species, potential local emission sources, canopy drip and precipitation

Metallakkumulation in Moosen: Standörtliche und regionale Randbedingungen des Biomonitoring von Luftverunreinigungen [Metal accumulation in mosses: local and regional boundary conditions of biomonitoring air pollution]

Metal accumulation in mosses: Local and regional boundary conditions of biomonitoring air pollution Goal and Scope. Several studies show that the concentration of metals in mosses depends not only on metal deposition but also on factors such as moss species, canopy drip, precipitation, altitude, distance to the sea and the analytical technique used. However, contrasting results have been reported and the interpretation of the spatial variability of the metal accumulation in mosses remains difficult. In the presented study existing monitoring data from the European Heavy Metals in Mosses Surveys together with surface data on precipitation, elevation and land use are statistically analysed to assess factors other than emissions that have an influence on the metal accumulation in the mosses. Main Features. Inference statistics and Spearman correlation analysis were applied to examine the association of the metal accumulation and the distance of the monitoring sites to the sea as well as the altitude. Whether or not significant differences of the metal loads in the mosses exist at national borders was examined with help of the U-test after Mann and Whitney. In order to identify and rank the factors that are assumed to have an influence on the metal uptake of the mosses Classification and Regression Trees (CART) were applied. Results. No clear tendency could be derived from the results of the inference statistical calculations and the correlation analyses with regard to the distance of the monitoring site to the sea and the altitude. According to the results of the CART-analyses mainly the moss species, potential emission sources around the monitoring sites, canopy drip and precipitation have an effect on the metal bioaccumulation. Assuming that each participating country followed strictly the manual for sampling and sample preparation the results of the inference statistical calculations furthermore suggest that in most cases different techniques for digestion and analysis bias the measurements significantly. Discussion. For the first time a national monitoring data base consisting of measurement data and metadata as well as surface information on precipitation, land use and elevation was applied to examine influence factors on the metal bioaccumulation in mosses. The respective results mirror existing knowledge from other national studies to a large extend, although further analyses are necessary to affirm the findings. These analyses should include data from other national monitoring programmes and should additionally be carried out with other decision tree algorithms than CART. Conclusions. The local variability in the metal concentration in mosses can be uncovered in terms of predictors or underlying hidden causes by using CART. Ideally, such an approach should be applied across the whole of Europe. This will only be feasible if all participating countries provide additional information about site characteristics as currently is done in for example the German moss surveys. Recommendations. The UNECE Metals in Mosses Survey experimental protocol should be improved in order to reduce the observed influences, to enhance standardisation, and to strengthen the quality control. This implies the integration of sampling site describing metadata into the assessment. Furthermore, basis research is needed to test the hypothesis concerning moss speciesspecific accumulation of depositions. Perspectives. Provided that the presented results hold true in further analyses correction factors should be applied on the moss data in order to get the depicted spatial patterns and temporal trends of metal bioaccumulation unbiased. Such factors should be calculated for natural landscape units or ecoregions that are homogeneous with regard to climate, vegetation and altitud

First Europe-wide correlation analysis identifying factors best explaining the total nitrogen concentration in mosses

In this study, the indicative value of mosses as biomonitors of atmospheric nitrogen (N) depositions and air concentrations on the one hand and site-specific and regional factors which explain best the total N concentration in mosses on the other hand were investigated for the first time at a European scale using correlation analyses. The analyses included data from mosses collected from 2781 sites across Europe within the framework of the European moss survey 2005/6, which was coordinated by the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation). Modelled atmospheric N deposition and air concentration data were calculated using the Unified EMEP Model of the European Monitoring and Evaluation Programme (EMEP) of the Convention on Long-range Transboundary Air Pollution (CLRTAP). The modelled deposition and concentration data encompass various N compounds. In order to assess the correlations between moss tissue total N concentrations and the chosen predictors, Spearman rank correlation analysis and Classification and Regression Trees (CART) were applied. The Spearman rank correlation analysis showed that the total N concentration in mosses and modelled N depositions and air concentrations are significantly correlated (0.53 ≤ rs ≤ 0.68, p < 0.001). Correlations with other predictors were lower than 0.55. The CART analysis indicated that the variation in the total N concentration in mosses was best explained by the variation in NH4+ concentrations in air, followed by NO2 concentrations in air, sampled moss species and total dry N deposition. The total N concentrations in mosses mirror land use-related atmospheric concentrations and depositions of N across Europe. In addition to already proven associations to measured N deposition on a local scale the study at hand gives a scientific prove on the association of N concentration in mosses and modelled deposition at the European scale