Temperate semi-natural grassland plant communities are expected to shift under global change, mainly due to land use and climate change. However, the interaction of different drivers on diversity and the influence of diversity on the provision of ecosystem services are not fully understood. To synthesize the knowledge of grassland dynamics and to be able to predict community shifts under different land-use and climate change scenarios, we developed the GrasslandTraitSim.jl model. In contrast to previously published grassland models, we link morphological plant traits to species-specific processes via transfer functions, thus avoiding a large number of species-specific parameters that are difficult to measure and calibrate. This allows any number of species to be simulated based on a list of commonly measured traits: specific leaf area, maximum height, leaf nitrogen per leaf mass, leaf biomass per plant biomass, above-ground biomass per plant biomass, root surface area per below-ground biomass, and arbuscular mycorrhizal colonization rate. For each species, the dynamics of the above- and below-ground biomass and its height are simulated with a daily time step. While the soil water content is simulated dynamically, the nutrient dynamics are kept simple, assuming that the nutrient availability depends on total soil nitrogen, yearly fertilization with nitrogen and the total plant biomass. We present a model description – which is complemented by online documentation with tutorials, flow charts, and interactive graphics – and calibrate and validate the model with two different datasets. We show that the model replicates the seasonal dynamics of productivity for experimental sites of the grass species Lolium perenne across Europe satisfactorily well. Furthermore, we demonstrate that the model can be used to simulate the productivity and functional composition of grassland sites with different numbers of mowing events and grazing intensity in three regions in Germany. Therefore, the GrasslandTraitSim.jl model is presented as a useful tool for predicting the plant biomass production and plant functional composition of temperate grasslands in response to management under climate change.
