A simulation model for exploring the effects of plant-soil feedbacks on the resilience of plant communities

Abstract

Plant–soil feedbacks (PSFs) are plant-induced changes to the abiotic and/or biotic properties of soil that positively or negatively impact plant growth. Recently, PSFs have been shown to play a key role in both promoting and maintaining high levels of diversity within plant communities. There is mounting evidence that diversity loss leads to reduced resilience of a community, which can be defined as an ecosystem’s ability to recover following disturbance, and/or its ability to resist the disturbance’s effects completely. PSFs may also positively influence the resilience of a community by promoting diversity, however this relationship is poorly understood. This is largely due to the complex and uncertain nature of such processes in natural systems, which renders empirical experiments unfeasible. Therefore, we aimed to develop a model capable of capturing the most important processes involved in the interactions among PSFs, diversity and resilience in diverse plant communities undergoing disturbance, in order to investigate how PSFs may affect plant community resilience at a theoretical level. We used a cellular automata simulation model to simulate plant community dynamics over 1000 years. In order to observe community resilience within this time, communities were subjected to a range of disturbance regimes that consisted of multiple disturbance events which occurred at different frequencies and intensities within a 60 year period. Resilience was then quantified by comparing the trajectories of the communities based on their diversity (using inverse Simpson’s Diversity Index) over time and following disturbance. In particular, the degree of change from the pre-disturbance state to the state immediately following disturbance was used to quantify resistance, and the rate of return to the pre-disturbance state following disturbance was used to quantify recovery. The model was tested using four well-known and highly studied PSF scenarios that are observed in natural systems i.e. negative, positive and 2 types of no/neutral conspecific PSF. We found the PSF scenario involving negative conspecific PSFs to be the most resilient when subjected to the majority of the disturbance regimes, with a neutral scenario of no PSF and a slow growth rate being more resilient under high frequency disturbance regimes. Communities with positive conspecific feedbacks experienced the greatest loss of diversity following disturbance, which generally deteriorated with increasing frequency of disturbance. Positive conspecific communities also did not recover following disturbance and instead became less diverse as time went on. These results are consistent with expectations based on the literature, suggesting the model is appropriate for exploring the effects of PSFs on the resilience of plant communities. Such research promises to greatly contribute to our understanding of how resilience is built within communities, which in part may assist restoration efforts aiming to return degraded ecosystems back to resilience.