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

Variation of compounds in leaves of susceptible and resistant alternate hosts of Cronartium pini and C. ribicola

Abstract Leaf compounds may contribute to plant defense against Cronartium rusts. Secondary compounds are either natural or induced in leaves. We studied the variation of compounds in leaves of six alternate hosts of Cronartium pini and two of C. ribicola that represented either susceptible or resistant species to these rusts. Extracts from the plant leaves were analyzed using LC-MSMS (liquid chromatography tandem mass spectrometry) and compounds were compared between susceptible and resistant species of the same plant genera to identify significant differences between resistant and susceptible species. Also, LC–MS (liquid chromatography mass spectrometry) with external calibration was used to quantify 12 candidate compounds known from the literature. Among these compounds, the most abundant significant ones in C. pini -resistant Melampyrum pratense were chlorogenic acid and quercitrin, in Veronica chamaedrys ferulic acid, quercitrin and luteolin and in Impatiens glandulifera quercitrin, ferulic acid, kaempferol, rutin and hyperoside. In C. ribicola -resistant Ribes rubrum the most abundant significant compounds were caffeic acid, p-coumaric acid and quercitrin. Among all extracted leaf compounds, concentrations of three compounds were over 1000 times greater in rust-resistant M. pratense, three compounds in V. chamaedrys, eight compounds in I. glandulifera, and one compound in R. rubrum than in rust-susceptible species. Among the compounds, the most promising possibly linked to rust resistance were chlorogenic acid and quercitrin

Variation of compounds in leaves of susceptible and resistant alternate hosts of Cronartium pini and C. ribicola

Abstract Leaf compounds may contribute to plant defense against Cronartium rusts. Secondary compounds are either natural or induced in leaves. We studied the variation of compounds in leaves of six alternate hosts of Cronartium pini and two of C. ribicola that represented either susceptible or resistant species to these rusts. Extracts from the plant leaves were analyzed using LC-MSMS (liquid chromatography tandem mass spectrometry) and compounds were compared between susceptible and resistant species of the same plant genera to identify significant differences between resistant and susceptible species. Also, LC–MS (liquid chromatography mass spectrometry) with external calibration was used to quantify 12 candidate compounds known from the literature. Among these compounds, the most abundant significant ones in C. pini -resistant Melampyrum pratense were chlorogenic acid and quercitrin, in Veronica chamaedrys ferulic acid, quercitrin and luteolin and in Impatiens glandulifera quercitrin, ferulic acid, kaempferol, rutin and hyperoside. In C. ribicola -resistant Ribes rubrum the most abundant significant compounds were caffeic acid, p-coumaric acid and quercitrin. Among all extracted leaf compounds, concentrations of three compounds were over 1000 times greater in rust-resistant M. pratense, three compounds in V. chamaedrys, eight compounds in I. glandulifera, and one compound in R. rubrum than in rust-susceptible species. Among the compounds, the most promising possibly linked to rust resistance were chlorogenic acid and quercitrin

First thorough identification of factors associated with Cd, Hg and Pb concentrations in mosses sampled in the European Surveys 1990, 1995, 2000 and 2005

The aim of this study was, for the first time ever, to thoroughly identify the factors influencing Cd, Hg and Pb concentrations in mosses sampled within the framework of the European Heavy Metals in Mosses Surveys 1990–2005. These investigations can be seen as a follow up of a previous study where only the moss data recorded in the survey 2005 was included in the analysis (Schröder et al. 2010). The analyses of this investigation give a complete overview on the statistical association of Cd, Hg and Pb concentrations in mosses and sampling site-specific and regional characteristics, encompassing data from 4661 (1990), 7301 (1995), 6764 (2000) and 5600 (2005) sampling sites across Europe. From the many metals monitored in the European moss surveys, Cd, Hg and Pb were used as examples, since only for these three metals deposition measurements are being recorded in the framework of the European Monitoring and Evaluation Programme (EMEP). As exemplary case studies revealed that other factors besides atmospheric deposition of metals influence the element concentrations in mosses, the moss datasets of the above mentioned surveys were analysed by means of bivariate statistics and decision tree analysis in order to identify factors influencing metal bioaccumulation. In the analyses we used the metadata recorded during the sampling as well as additional geodata on, e.g., depositions, emissions and land use. Bivariate Spearman correlation analyses showed the highest correlations between Cd and Pb concentrations in mosses and EMEP modelled total deposition data (0.62 ≤ rs ≤ 0.73). For Hg the correlations with all the tested factors were considerably lower (e.g. total deposition r s  ≤ 0.24). Decision tree analyses by means of Classification and Regression Trees (CART) identified the total deposition as the statistically most significant factor for the Cd and Pb concentrations in the mosses in all four monitoring campaigns. For Hg, the most significant factor in 1990 as identified by CART was the distance to the nearest Hg source recorded in the European Pollutant Emission Register, in 1995 and 2000 it was the analytical method, and in 2005 it was the sampled moss species. The strong correlations between the Cd and Pb concentrations in the mosses and the total deposition can be used to calculate deposition maps with a regression kriging approach on the basis of surface maps on the element concentrations in the mosse

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