Nematode communities, plant nutrient economy and life-cycle characteristics jointly determine plant monoculture performance over 12 years

Knowledge from agriculture and ecological field studies suggests that plant monocultures lose productivity over time, but the drivers underlying the long-term performance of monocultures of grassland species are not completely understood. We examined the performance of 60 grassland species growing in monoculture for 12 years in a biodiversity experiment (Jena Experiment) and studied three groups of biotic drivers potentially affecting plant performance in monocultures over time: 1) soil biota (nematode communities, arbuscular mycorrhizal fungi), 2) leaf traits related to leaf economics spectrum, and 3) plant life-cycle characteristics related to buffered population growth (viable seeds in topsoil, seedling density, seed survival). Monocultures of 15 out of 60 species increased productivity, while monocultures of the remaining 45 species showed slight to strong losses of productivity over time, resulting in zero biomass in 15 species. All three biotic drivers were related to the varying long-term performance of monocultures. Their combined influence on monoculture performance could be interpreted as a tradeoff between 'fast' versus 'slow' life strategies. 'Fast' species showed rapid resource use and little buffering of population growth through a viable seed bank, which led to high biomass production in young monocultures but a consecutive loss of biomass production over time. 'Slow' species were characterized by positive nematode effects (high abundance of predatory nematodes), conservative use of resources, and a viable seed bank with high recruitment success resulting in gradually increasing productivity over time. In summary, our study highlights the importance of studying long-term field monocultures to investigate the complex role of different biotic drivers responsible for productivity changes over time. These insights provide an essential baseline for estimating biodiversity effects on productivity as well as to understand and predict long-term performance of plant populations.