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

Autores: Schröder, W. (Autor de correspondencia); Nickel, S.; Schönrock, S.; Schmalfub, R.; Wosniok, W.; Meyer, M.; Harmens, H.; Frontasyeva, M. V.; Alber, R.; Aleksiayenak, J.; Barandovski, L.; Blum, O.; Carballeira, A.; Dam, M.; Danielsson, H.; De Temmermann, L.; Dunaev, A. M.; Godzik, B.; Hoydal, K.; Jeran, Z.; Karlsson, G. P.; Lazo, P.; Leblond, S.; Lindroos, J.; Liiv, S.; Magnússon, S. H.; Mankovska, B.; Núñez-Olivera, E.; Piispanen, J.; Poikolainen, J.; Popescu, I. V.; Qarri, F.; Santamaría Ulecia, Jesús Miguel; Skudnik, M.; Špiri¿, Z.; Stafilov, T.; Steinnes, E.; Stihi, C.; Suchara, I.; Thöni, L.; Uggerud, H. T.; Zechmeister, H. G.
Título de la revista: ANNALS OF FOREST SCIENCE
ISSN: 1286-4560
Volumen: 74
Número: 31
Fecha de publicación: 2017
Key message: 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. Context: For enhancing the spatial resolution of measuring and mapping atmospheric deposition by technical devices and by modelling, moss is used complementarily as bio-monitor. Aims: 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). Methods: 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.