Variables related to nitrogen deposition improve defoliation models for European forests

Abstract

Contribution of the co-authors Marco Ferretti: co-designing and supervising the work, and co-writing the paper. Marco Calderisi: designing the statistical analyses. Peter Waldner: co-designing the work, compiling the data set in collaboration with national experts and revising the paper. Aldo Marchetto: co-designing the work, performing the quality control, running the statistical analyses and co-writing the paper. Anne Thimonier, Matthiew Jonard, Nathalie Cools, Pasi Rautio, Nicholas Clarke, Karin Hansen, Päivi Merilä and Nenad Potočić: contributing important parts of the discussion of the results. Science (2015) 72:897-906 DOI 10.1007 cation resulted important factors (although with possible diverse effect) in explaining the variability of F 25 among plots.

The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe

“…The combination of these soil data with long-term time series of various other forest ecosystem variables measured on the same plots makes this database an interesting source of information for integrated forest ecosystem studies (Ferretti et al 2014;De Vries et al 2014). The comprehensive picture of processes and state variables in a forest that may be provided this way is indispensable for a quantitative understanding of ecosystem processes based on ecosystem models: Statistical models can be validated with time series over more than 10 years in most cases.…”

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

“…For estimating atmospheric deposition we have to quantify the ratio between deposition values inside and outside of forests. Data to calculate these ratios should be collected in compliance with MSS and geostatistical criteria such as spatial auto-correlation which in general should be implemented in environmental monitoring schemes (Clarke et al 2010;Cools and de Vos 2011;Ferretti 2010Ferretti , 2011Ferretti et al 2014.…”

“…Currently, it comprises approximately 760 permanent plots in 30 participating countries, including 500 plots with atmospheric deposition and forest ecosystem impact monitoring . Comparability and meaningful interpretation of data derived from long-term monitoring across time require representative measurements based on harmonized methods and a statistically sound design (Clarke et al 2010;Cools and de Vos 2011;Ferretti 2010Ferretti , 2011Ferretti et al 2014. Within this context, the spatial representativity and comparability of atmospheric deposition measurements are crucial issues -for exposure and effect assessments Erisman et al 2003;Lorenz and Granke 2009;Žlindra et al 2011).…”

“…N may affect ecosystems through eutrophication, acidification and direct toxicity, each impacting several ecosystem services (Gaudio et al 2015;Jones et al 2014). Based on data collected within the ICP Forests Level II, Ferretti et al (2014) and Seidling et al (2014) investigated the relation between forest condition in Europe and potential predictors. For 71 ICP Forests Level II plots, the importance of throughfall N bulk deposition as predictor for the frequency of trees with defoliation > 25% was proved for beech and Norway spruce, while the opposite was observed for Scots pine.…”

Forest N Dynamics after 25 years of Whole Watershed N Enrichment: The Bear Brook Watershed in Maine

“…Some ecosystems, however, have exceeded the critical loads beyond which negative effects occur (McNulty et al, 2007; Pardo et al, 2011; Tietema et al, 1998). Environmental monitoring and experimental manipulation studies show that N‐enriched ecosystems have increased N losses via leaching (Lovett and Goodale, 2011; Lu et al, 2011; Templer et al, 2012a; Niu et al, 2016) and denitrification (Morse et al, 2015; Templer et al, 2012b), increased tree stress and fine root mortality (Ferretti et al, 2015; Gundersen et al, 1998; Minocha et al, 2015; Smithwick et al, 2013), and altered microbial community structure and function (Carrara et al, 2018; Kopáček et al, 2013; Morrison et al, 2018; Treseder, 2008). The responses varied with the magnitude of N inputs, vegetation type, disturbance history, geographic location, climate, and the duration of the experiments.…”