Although numerous studies show that communities are jointly influenced by predation and competitive interactions, few have resolved how temporal variability in these interactions influences community assembly and stability. Yet, temporal variability in species interactions might be caused, for instance, by stochastic extinctions or invasions, behavioral changes such as flexible foraging, or changes in prey traits over relatively short time periods, e.g. due to plasticity or evolution. Classical analytical tools such as Lotka-Volterra models are restricted in their ability to quantify the effects of these sorts of dynamic interactions, because they typically do not account for variability across space, time, or context. Here, we addressed this challenge in experimental microbial microcosms by employing empirical dynamic modelling tools to: (i) detect causal interactions between bacterial prey species in the absence and presence of a protozoan keystone predator using convergent cross mapping (CCM); (ii) quantify the time-varying strength and direction of these interactions by computing sequential Jacobian-like matrices, and (iii) explore stability in the resulting communities using several indicators such as local stability (λmax), coefficient of variation (CV), mean species interaction strength, and the prevalence of weak interactions using those matrices. Finally, we tested for causal associations among these stability indices and community diversity.
Results/Conclusions
Our findings show that predators boost the number of causal interactions among community members, and lead to reduced species-level stability, but higher coexistence among prey species. These results corresponded to time-varying changes in species interactions, including emergence of morphological characteristics that appeared to reduce predation, and indirectly facilitate growth of predator-susceptible species. Communities were stable (lower CV, λmax < 1) in the absence of predators and unstable when predators were present (higher CV, λmax > 1). However, systems also became more stable over time, corresponding to higher diversity and decreased mean interaction strength. We detected several causal interactions between these dynamics depending on predator presence. For example, there was bidirectional causation between stability and diversity when predator was present. While diversity causally forced mean interaction strength when predators were absent, mean interaction strength causally forced diversity when predators were present. These results suggest that combinations of multi-species microcosm experiments and observation-driven high-dimensional models might improve predictions of community dynamics and stability, especially in complex, multi-trophic systems. Also, describing the key components of ecosystems, and the consequences of rapid environmental change, is only possible with the appreciation of both context and time component.