Diversity and coexistence are influenced by time‐dependent species interactions in a predator–prey system

Abstract

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. Here, we addressed this challenge in experimental microbial microcosms by employing empirical dynamic modelling tools to: (1) detect causal interactions between prey species in the absence and presence of a predator; (2) quantify the time-varying strength of these interactions and (3) explore stability in the resulting communities. Our findings show that predators boost the number of causal interactions among community members, and lead to reduced dynamic stability, but higher coexistence among prey species. These results correspond to time-varying changes in species interactions, including emergence of morphological characteristics that appeared to reduce predation, and indirectly facilitate growth of predatorsusceptible species. Jointly, our findings suggest that careful consideration of both context and time may be necessary to predict and explain outcomes in multi-trophic systems.

gauseR: Simple methods for fitting Lotka‐Volterra models describing Gause’s “Struggle for Existence”

“…Importantly, many modern modeling tools exist that are potentially more suitable for fitting these data than are the simple Lotka-Volterra methods that we present here (e.g., Karakoç et al, 2020).…”

Resilience of rhizosphere microbial predators and their prey communities after an extreme heat event

“…Moreover, bacterial resilience only varied among predators in the last week of recovery also when predator‐specific resilience differences disappeared. This temporal mismatch in bacterial and protist resilience highlights the importance of time‐dependent differential responses in predator–prey interactions (Karakoç et al., 2020; Thakur, 2020). Finally, our results indicate thermal acclimation in one (predator 3) among three predator species in terms of their size reduction.…”

Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data

“…The distribution of organisms and their functions are not homogeneous and species interactions are not constant in time due to abiotic forces and adaptive mechanisms [72][73][74]. Therefore, to validate patters of microbial interactions one need not only to follow the community over time, but also to have appropriate number of samples in time and space [66,75]. Finally, the time frame for collection of samples and numbers of biological replicates will depend on the biodiversity of a give ecosystem and how dynamic the given process is [76].…”