NDLI: Bioindication and modelling of atmospheric deposition in forests enable exposure and effect monitoring at high spatial density across scales

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Bioindication and modelling of atmospheric deposition in forests enable exposure and effect monitoring at high spatial density across scales

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Annals of Forest Science : Volume 74, Issue 1, March 2017

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Bioindication and modelling of atmospheric deposition in forests enable exposure and effect monitoring at high spatial density across scales

Content Provider	Springer Nature Link
Author	Schröder, Winfried Nickel, Stefan Schönrock, Simon Schmalfuß, Roman Wosniok, Werner Meyer, Michaela Harmens, Harry Frontasyeva, Marina V. Alber, Renate Aleksiayenak, Julia Barandovski, Lambe Blum, Oleg Carballeira, Alejo Dam, Maria Danielsson, Helena Temmermann, Ludwig Dunaev, Anatoly M. Godzik, Barbara Hoydal, Katrin Jeran, Zvonka Karlsson, Gunilla Pihl Lazo, Pranvera Leblond, Sebastien Lindroos, Jussi Liiv, Siiri Magnússon, Sigurður H. Mankovska, Blanka Núñez Olivera, Encarnación Piispanen, Juha Poikolainen, Jarmo Popescu, Ion V. Qarri, Flora Santamaria, Jesus Miguel Skudnik, Mitja Špirić, Zdravko Stafilov, Trajce Steinnes, Eiliv Stihi, Claudia Suchara, Ivan Thöni, Lotti Uggerud, Hilde Thelle Zechmeister, Harald G.
Copyright Year	2017
Abstract	Moss surveys provide spatially dense data on environmental concentrations of heavy metals and nitrogen which, together with other biomonitoring and modelling data, can be used for indicating deposition to terrestrial ecosystems and related effects across time and areas of different spatial extension. For enhancing the spatial resolution of measuring and mapping atmospheric deposition by technical devices and by modelling, moss is used complementarily as bio-monitor.This paper investigated whether nitrogen and heavy metal concentrations derived by biomonitoring of atmospheric deposition are statistically meaningful in terms of compliance with minimum sample size across several spatial levels (objective 1), whether this is also true in terms of geostatistical criteria such as spatial auto-correlation and, by this, estimated values for unsampled locations (objective 2) and whether moss indicates atmospheric deposition in a similar way as modelled deposition, tree foliage and natural surface soil at the European and country level, and whether they indicate site-specific variance due to canopy drip (objective 3).Data from modelling and biomonitoring atmospheric deposition were statistically analysed by means of minimum sample size calculation, by geostatistics as well as by bivariate correlation analyses and by multivariate correlation analyses using the Classification and Regression Tree approach and the Random Forests method.It was found that the compliance of measurements with the minimum sample size varies by spatial scale and element measured. For unsampled locations, estimation could be derived. Statistically significant correlations between concentrations of heavy metals and nitrogen in moss and modelled atmospheric deposition, and concentrations in leaves, needles and soil were found. Significant influence of canopy drip on nitrogen concentration in moss was proven.Moss surveys should complement modelled atmospheric deposition data as well as other biomonitoring approaches and offer a great potential for various terrestrial monitoring programmes dealing with exposure and effects.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	12864560
Journal	Annals of Forest Science
Volume Number	74
Issue Number	2
e-ISSN	1297966X
Language	English
Publisher	Springer Paris
Publisher Date	2017-04-10
Publisher Institution	Institut National de la Recherche Agronomique (INRA)
Publisher Place	Paris
Access Restriction	Subscribed
Subject Keyword	Bioaccumulation Biomonitoring EMEP Environmental specimen bank ICP Forests Level II LOTOS-EUROS Forestry Wood Science & Technology Forestry Management Tree Biology Environment
Content Type	Text
Resource Type	Article
Subject	Ecology Forestry

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