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Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was r

24 februari 2023, Item does not contain fulltext

Growing productive wetland species on rewetted peatland (paludiculture) is a promising solution to offset carbon loss from drained peatlands. The inlet of nitrogen (N) rich surface water, a proposed method to improve productivity of vegetation, may affect methane (CH4) emissions. This study aims to compare initial CH4 emissions from newly rewetted peat with different types of vegetation and N loading simulating diffuse N inlet. Diffusive CH4 emissions were measured in peat mesocosms during one growing season. Peat cores were either planted with Typha latifolia or Phragmites australis or they were left bare. Mesocosms received 0, 50, 150 or 450 kg ha−1 year−1 N. Plants affected CH4 emissions from rewetted peat soil, leading to stable fluxes over time of 133 mg m−2 day−1 CH4 at 20 °C. Biomass harvesting lead to a 153% increase of CH4 emissions. With increasing N load, CH4 emissions from mesocosms with Typha and Phragmites decreased up to a load of 150 kg ha−1 N, but this was only significant for the Phragmites treatment. Emissions of unvegetated mesocosms increased with increasing N load but not significantly. In conclusion, our mesocosm study suggests that vegetation can reduce or prevent an increase in CH4 emissions from rewetted peatlands compared to only rewetting, possibly due to an increased oxygenation of the sediments by macrophyte roots preventing excessive CH4 formation, while added N does not provoke great changes in emissions at N concentrations up to 150 kg ha−1.

Item does not contain fulltext

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.

25 februari 2022, Contains fulltext : 248658.pdf (Publisher’s version ) (Closed access)

20 april 2021, Contains fulltext : 235958.pdf (Publisher’s version ) (Open Access)

Radboud University, 2 februari 2021, Promotor : Huijbregts, M.A.J. Co-promotor : Santini, L., Contains fulltext : 229059.pdf (publisher's version ) (Open Access)