Inferring coexistence likelihood in changing environments from ecological time series.

Inferring coexistence metrics, such as niche and fitness differences, in changing environments is key for understanding the mechanism behind species coexistence and predicting its likelihood. However, it first requires estimating the per capita interactions between organisms and their intrinsic growth rates-parameters that are typically measured by isolating organisms from their natural context. Here, we first use weighted multivariate regression on the per capita growth rates of populations to estimate these key ecological parameters directly from time-series data of species-rich communities. Second, we infer niche differences and species resistance, which are two important metrics for understanding species coexistence. Our approach allows these metrics to vary over time and under different environmental conditions. We validate our approach using synthetic data and apply it to both experimental and observational data as a proof of concept. Experimental results show an expected allocative trade-off between grazing resistance and rapid growth in algae. Moreover, coexistence likelihood decreases, and coexistence balance is disturbed under stressful environmental conditions. Observational data suggests variations in intrinsic growth rates and per capita interactions among autotrophic guilds with respect to seasonal patterns. In addition, interactions between cyanobacteria with green algae and chrysophytes might indicate a potential cause for bloom development. Our approach offers a powerful toolbox to gain insight into the mechanisms underlying ecological dynamics, species coexistence, and community structures under varying environments. Such an understanding will help us address important ecological and evolutionary questions, such as explaining biodiversity patterns and solving the problem of cyanobacteria bloom.