CHAID Models on boundary conditions of metal accumulation in mosses collected in Germany in 1990, 1995 and 2000
ABSTRACT The European heavy metals in mosses surveys allow mapping the metal accumulation in mosses indicating atmospheric deposition. Yet, there is still great uncertainty on how local and regional phenomena influence the atmospheric metal bioaccumulation. Therefore, the presented study aims at ranking factors that affect the spatial patterns of the metal concentrations in the mosses. Applying chi-square automatic interaction detection (CHAID) to the German moss measurements and related sampling site-specific descriptions taken from the surveys in 1990, 1995 and 2000 and supplementary land cover data, the spatial variation in metal concentrations in mosses were proved to depend mostly on different moss species, canopy drip and distance to the sea. Most of these findings could be corroborated by classification tree analyses on the same data as presented in another study. The results of both the studies should be verified by applying the same methodology using additional emission and deposition data and monitoring information from other countries participating in the UNECE moss surveys.
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ABSTRACT: Currently, the most important guideline for the application of the moss technique to monitor the atmospheric deposition of heavy metals is the "Heavy metals, nitrogen and POPs in European mosses: 2015 survey" published by the UNECE ICP Vegetation. Two main problems have been identified with this guideline: i) some of the recommendations regarding the methodological aspects involved in the application of the moss technique are not based on scientific criteria; and, ii) some recommendations in the manual are very vague and some aspects are even left out (e.g., elevation, distance to the coast). As a result there exists a high variability in the application of the protocol and many scientists adapt it to the specific conditions in the studied areas without evaluating how changes affect the results obtained. Therefore, in this article a total of 369 studies were reviewed including both methodological and application studies of the passive biomonitoring of the atmospheric deposition of heavy metals with terrestrial mosses. The results of this review have shown on the one hand, that none of the articles completely accomplished the ICP-Vegetation protocol suggestions, either because the information regarding some aspects was lacking or simply because the authors did not follow the manual suggestions. On the other hand, it was found that the results of methodological studies sometimes contradicted the ICP Vegetation manual recommendations. Thus, a new protocol in which each suggestion has been carefully and rigorously contrasted with the available literature has been proposed in this paper. In addition, practical and economic issues have also been considered and much more concise suggestions have been proposed which would facilitate its fulfilment in a more objective way. Copyright © 2015 Elsevier B.V. All rights reserved.Science of The Total Environment 02/2015; 517C:132-150. DOI:10.1016/j.scitotenv.2015.02.050 · 3.16 Impact Factor
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ABSTRACT: Context The establishment of seedlings after regeneration fellings is key to guaranteeing the development and persistence of the forest. Depending on the objective pursued, data available or type of forest, a number of different methods have been employed to assess the relationship between seedling establishment and both environmental and stand factors. Most authors have conducted their analyses using parametric regression or point pattern analysis. & Aim We analysed the way in which light, stand conditions, edaphic and topographic variables affect the regeneration of Pinus sylvestris L. in Central Spain. We used different methods to analyse the same data set. The strengths and weaknesses of each method were discussed. & Methods We used two parametric approaches: generalized linear mixed model regression using a negative binomial followed by the variant explanatory variables reduction prior to regression as well as three nonparametric approaches not commonly employed in forest regeneration: nonmetric multi-dimensional scaling, regression trees and random forests algorithm. & Results The parametric regression identified a larger number of variables associated with the regeneration process and the inclusion of a random effect in the model allowing the consideration of the spatial variability among plots. However, decision trees captured the complex interaction among variables, which typical parametric methods were unable to detect. & Conclusion Different statistical methods gave similar insights into the underlying ecological process. However, different statistical premises with inference implications can be noticed. This may give misinterpretation of the model depending on the nature of the data. The choice of a given method should be made according to the nature of the data and the achievement of desirable results.Annals of Forest Science 04/2015; DOI:10.1007/s13595-015-0479-4 · 1.54 Impact Factor
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ABSTRACT: Purpose Assessing effects of air pollution needs the monitoring of atmospheric deposition. At least for enhancing the spatial resolution of measuring deposition by use of technical devices and of deposition modeling, mosses are used complementarily as biomonitors. In Norway, since 1985, nationwide surveys have been carried out every 5 years. This study aimed at investigating statistical relationships between heavy metal concentrations in samples of moss and natural surface soil, collected in spatial dense networks covering Norway, and regional factors. Materials and methods Heavy metal (HM) concentrations in moss samples collected in 1990, 1995, 2000, 2005, and 2010 and in natural surface soil specimens sampled in 1995 and 2005 across Norway were assessed statistically. Classification and regression trees were computed in order to uncover multivariate relationships between HM concentrations in moss and natural surface soil and potential influencing environmental factors that were integrated into the multivariate analyses. Results and discussion Atmospheric deposition of HM could be proved as the strongest predictor for HM concentrations in moss and natural surface soil samples. Land use within a 5-km radius and population density around the sampling sites were identified as further predictors of HM concentrations in moss and natural surface soil. Conclusions HM monitoring with moss and natural surface soil samples indicates complementarily atmospheric deposition and thus should be carried out as long-term observation.Journal of Soils and Sediments 10/2014; 14(11):1-15. DOI:10.1007/s11368-014-0999-9 · 2.11 Impact Factor