Community performance curves predict temporal stability despite interaction effects

Predicting whether aggregate community properties remain stable as environments fluctuate is a central challenge of ecological research. A critical open question is, however, how well the fundamental niche of species can predict this aspect of community stability. We conceptualised community performance (CP) curves by aggregating species-specific performance across environmental conditions and asked whether the variability of these curves explains community temporal variability. In Lotka-Volterra and in mechanistic consumer-resource simulations, CP variability explained on average 92% of the variation in total abundance variability. Explanatory power remained above 75% across gradients of interaction strength and coexistence conditions. Derivation of the expected community variability - CP variability relationship for the Lotka-Volterra case explains this high explanatory power despite interaction effects. In a purpose-designed microcosm experiment, CP variability explained 67% of variation in the temporal variability of total community biomass. Explanatory power of CP variability therefore retains substantial information about community stability even in strongly interacting systems, providing a tractable basis for forecasting stability when species interactions are unknown.