Vegetation resilience and sensitivity in complex dynamic vegetation models

Resilience is typically defined as the ability of vegetation to recover from external perturbations such as fires or droughts, and it can be quantitatively measured by the rate of recovery following such events. Resilience can also be assessed indirectly, even in the absence of large perturbations. One key metric for this is autocorrelation. A loss of resilience over time, often referred to as "slowing down," can be detected as an increase in autocorrelation. In simple one-dimensional dynamical systems, a reduction in resilience is also associated with increased sensitivity of the system's stable state to external conditions.

Recent studies, using indicators such as the Normalized Difference Vegetation Index (NDVI) and Vegetation Optical Depth (VOD), have found that resilience tends to be higher in wetter regions of tropical forests compared to drier regions, and that resilience has been decreasing across large parts of the Amazon rainforest. Additionally, empirical recovery rates after disturbances have been found to correlate with autocorrelation, supporting the practical relevance of theoretical expectations. However, it remains unclear which specific vegetation properties and processes determine the observed patterns.

Here we use idealized simulations with the state-of-the-art dynamic vegetation model LPJmL and explore how the resilience of natural forests and its indicators depend on (i) climate, (ii) vegetation composition (i.e., the mix of plant functional types), (iii) the vegetation property (variable) being considered, and (iv) the nature of the perturbation(s). We find that autocorrelation qualitatively aligns with the recovery time from large, negative perturbations that affect all tree types similarly.

However, there are exceptions where the factors listed above can influence the relationship in unexpected ways. Specifically, for some tree types and climate regimes, recovery rates and autocorrelation do not align with each other, nor with the forest's sensitivity to climate change. For example, perturbations that alter the relative abundance of tree types can lead to different recovery rates compared to those affecting all tree types uniformly. Moreover, vegetation variables that recover quickly when perturbed in isolation (e.g., fluxes like net primary productivity) may still co-evolve with slower variables they depend on (e.g., carbon stored in trees). We identify key mechanisms behind these features in the model and test their relevance by simulating a more realistic setup, using observed climate data within a geographically realistic domain. We also discuss the relevance of these mechanisms in the real world.

Our findings highlight the need to better understand the nature of disturbances and trends in ecosystems, as well as the mechanisms captured by satellite-derived indicators. This knowledge, along with improved resilience monitoring, will be crucial for making reliable predictions about how ecosystems will respond to human-induced changes.