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 processbased 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.

Impact of mowing frequency and temperature on the production of temperate grasslands: explanations received by an individual‐based model

“…Since the GRASSMIND model consists of many different processes, we describe here only those relevant for our study. A detailed description of the GRASSMIND model can be found in Taubert et al (2020a, b), under <http://www.formind.org/downloads> and <http://www.ufz.de/index.php?en=48444>. Note that in this study, belowground processes in soil such as competition for water and nitrogen are excluded (assumption that soil water and nitrogen do not limit plant growth (Schmid et al 2021)).…”

“…Orange boxes indicate competition processes leading to limiting growth. For a detailed description of variables and equations see Taubert et al (2020a, b)2020b, <https://www.formind.org/downloads> and <https://www.ufz.de/index.php?en=48444>.…”

Connecting CSR theory and LPJmL 5.3 to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions

“…These three strategy shares sum up to 100% and also encompass species that would be added to a sub-strategy in a less coarse approach. Consequently, our results show a very simplified representation of the different strategies within a community and across sites, which might be better represented using a small scale model such as IBC GRASS (May et al, 2009) or GRASSMIND (Taubert et al, 2020a(Taubert et al, , b, 2012. However, large-scale applications also benefit from the inclusion of universally applicable trade-offs between different ecological strategies and the improved representation of productivity changes.…”

Tackling unresolved questions in forest ecology: The past and future role of simulation models

“…To simplify the high species richness of these forests, tropical gap models typically simulate forest succession by grouping tree species that share similar ecological features into plant functional types (PFTs). The gap model approach was also extended beyond forests, for example, to grassland systems (Coffin & Lauenroth, 1990;Schmid et al, 2021;Taubert et al, 2020).…”