Human activities are leading to a global mass extinction, and this biodiversity loss could lead to decreased ecosystem functioning, such as plant productivity and carbon storage, thereby diminishing the ecosystem services on which humanity depends. Most evidence for biodiversity-ecosystem function relationships come from experimentally manipulating plant diversity and measuring the resulting ecosystem functions, such as productivity. These highly controlled local experiments are informative, yet much simplified from natural systems. Isolating biodiversity effects in more complex natural systems has been difficult to date, thus limiting our ability to inform environmental policy. Recent analytical breakthroughs make it possible, for the first time ever, to detect and quantify causal relationships from correlative data and from there assess how the effect strength changes across time and conditions. Herein, using a 30+ year multi-field succession experiment, we develop a novel application of these dynamic modelling frameworks (convergent cross mapping and empirical dynamic modeling) to assess where and when biodiversity is most critical for ecosystem functioning in natural systems.
Results/Conclusions
Preliminary results suggest that plant species richness causally forces aboveground productivity across all fields and years. Additionally, results suggest that aboveground biomass also causally forces species richness, providing support for a bidirectional relationship, and providing insight into the complexity of the system. The analyses of the effect strength of diversity on productivity indicates a small positive impact of biodiversity on productivity in natural systems that is present over a variety of conditions, though there is large variation in the effect strength of diversity on productivity, especially in less productive plots and years, lending novel insight into why biodiversity effects have been tricky to detect in natural systems. We conclude that applying these methods to large observational datasets has the potential to unearth information regarding where and when biodiversity maintenance is most critical, providing a novel data source for sustainable conservation management planning.