Confronting an individual-based simulation model with empirical community patterns of grasslands

Abstract

Grasslands contribute to global biogeochemical cycles and can host a high number of plant species. Both-species dynamics and biogeochemical fluxes-are influenced by abiotic and biotic environmental factors, management and natural disturbances. In order to understand and project grassland dynamics under global change, vegetation models which explicitly capture all relevant processes and drivers are required. However, the parameterization of such models is often challenging. Here, we report on testing an individual- and process-based model for simulating the dynamics and structure of a grassland experiment in temperate Europe. We parameterized the model for three species and confront simulated grassland dynamics with empirical observations of their monocultures and one two-species mixture. The model reproduces general trends of vegetation patterns (vegetation cover and height, aboveground biomass and leaf area index) for the monocultures and two-species community. For example, the model simulates well an average annual grassland cover of 70% in the species mixture (observed cover of 77%), but also shows mismatches with specific observation values (e.g. for aboveground biomass). By a sensitivity analysis of the applied inverse model parameterization method, we demonstrate that multiple vegetation attributes are important for a successful parameterization while leaf area index revealed to be of highest relevance. Results of our study pinpoint to the need of improved grassland measurements (esp. of temporally higher resolution) in close combination with advanced modelling approaches.

Fig 1. Aboveground biomass dynamics for three monocultures.

Black dots show observed values and colored lines represent the simulated dynamics of (A) F. pratensis, (B) P. pratensis and (C) P. lanceolata using the model GRASSMIND (annual average and vertical grey lines/polygons denote the intra-annual range of two censuses per year).

Fig 2. Comparison of observed and simulated patterns in terms of aboveground biomass, vegetation height, leaf area index and vegetation cover for three monocultures.

Each dot reflects a yearly value. Colors mark results for the different species. In (A) all compared patterns for the three species are combined. Different patterns are normalized by the maximum value (of observations and simulations for all species). The black solid line shows the linear regression line for which the R² and nrmse is displayed. In (B-E) each pattern is displayed without normalization. Note that axes for AGB are logarithmic.

Fig 3. Comparison of observed and simulated patterns for the two-species-mixture.

In (A-B) annual dynamics of AGB and LAI are shown. In (C-E) each dot represents an annual value compared between observation and simulation. In (A, D-E) species are marked by different colors (yellow and blue). In (C) all results are combined and normalized by the maximum value (of observations and simulations per selected pattern). In (D) the observed AGB and vegetation cover is compared with the simulated values only for P. pratensis within the mixture, while in (E) both patterns are shown only for P. lanceolata’s contribution in the mixture (again normalized by their respective maximum values).

Fig 4. Comparison of different observed vegetation attributes included as single attribute in the inverse model parameterization.

The calibrated vegetation pattern is framed by a blue rectangle while the other vegetation patterns are shown for evaluation purposes (example of F. pratensis monoculture, using MAPE as cost function). Green lines (and shaded polygons) describe simulations (yearly mean and range) while black dots and grey lines describe the observations (yearly mean and range). All four vegetation patterns are normalized and summarized in a 1:1 plot (right panel). See methods for details.