Jesper Riis Christiansen

The natural abundance of 15N in litter and soil profiles under six temperate tree species: N cycling depends on tree species traits and site fertility

AimsWe investigated the influence of tree species on the natural 15N abundance in forest stands under elevated ambient N deposition.MethodsWe analysed δ15N in litter, the forest floor and three mineral soil horizons along with ecosystem N status variables at six sites planted three decades ago with five European broadleaved tree species and Norway spruce.ResultsLitter δ15N and 15N enrichment factor (δ15Nlitter–δ15Nsoil) were positively correlated with N status based on soil and litter N pools, nitrification, subsoil nitrate concentration and forest growth. Tree species differences were also significant for these N variables and for the litter δ15N and enrichment factor. Litter from ash and sycamore maple with high N status and low fungal mycelia activity was enriched in 15N (+0.9 delta units) relative to other tree species (European beech, pedunculate oak, lime and Norway spruce) even though the latter species leached more nitrate.ConclusionsThe δ15N pattern reflected tree species related traits affecting the N cycling as well as site fertility and former land use, and possibly differences in N leaching. The tree species δ15N patterns reflected fractionation caused by uptake of N through mycorrhiza rather than due to nitrate leaching or other N transformation processes.

To replicate, or not to replicate – that is the question: how to tackle nonlinear responses in ecological experiments

A fundamental challenge in experimental ecology is to capture nonlinearities of ecological responses to interacting environmental drivers. Here, we demonstrate that gradient designs outperform replicated designs for detecting and quantifying nonlinear responses. We report the results of (1) multiple computer simulations and (2) two purpose-designed empirical experiments. The findings consistently revealed that unreplicated sampling at a maximum number of sampling locations maximised prediction success (i.e. the R² to the known truth) irrespective of the amount of stochasticity and the underlying response surfaces, including combinations of two linear, unimodal or saturating drivers. For the two empirical experiments, the same pattern was found, with gradient designs outperforming replicated designs in revealing the response surfaces of underlying drivers. Our findings suggest that a move to gradient designs in ecological experiments could be a major step towards unravelling underlying response patterns to continuous and interacting environmental drivers in a feasible and statistically powerful way.