Project Details
Projekt
Biodiversity effects on seasonality, trends, and events in highly resolved long-term time series of the N and P cycles: a synthesis
Subject Area
Ecology and Biodiversity of Plants and Ecosystems
Term
from 2016 to 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 163658437
Plant diversity influences many ecosystem variables, such as productivity and nutrient cycling. However, only a few studies differentiated between initial (short-term) and longer-term effects of plant diversity. In the few existing longer-term studies including the Jena Experiment, the relationship between plant diversity and ecosystem variables, mostly productivity, became stronger with time suggesting that short-term experiments underestimated biodiversity effects. Plant diversity effects on productivity can be expected to feed back on element cycling, and even more so with ongoing experimental duration. This may cause differently pronounced temporal trends along the plant diversity gradient and also may modify the size of (seasonal) variability and the effects of extreme events on ecosystem functioning. Our objectives are to test (i) whether the strength of plant diversity effects on element concentrations, fluxes, and (stoichiometric) ratios changes with season; (ii) if plant diversity controls variability in element concentrations, fluxes and ratios; (iii) whether the temporal course of monthly-resolved element concentrations, fluxes and ratios (as reflected by linear and non-linear trends, seasonal amplitudes, and breaking points) depend on plant community composition. Furthermore, we assess which role other plot properties and environmental conditions including particularly wet and dry phases, extreme temperatures, and cumulative meteorological indicators such as sum of precipitation or temperature during key periods in the vegetation period play; (iv) whether the plot-specific soil or plant histories in the delta-BEF experiment influence the biodiversity-nutrient cycling relationship. Our analyses will rely on time series of water fluxes and element concentrations, fluxes, and ratios in fortnightly resolution since 2003. To cope with measurement gaps, we will use Bayesian modeling for gap filling. The completed data sets will then be treated with methods of the mathematical time series analysis.
DFG Programme
Research Units
