Spatial variance and spatial skewness as leading indicators of regime shifts in global forests

Forests have considerable potential to influence the stability of the Earth system and mitigate climate change by influencing biogeochemical and biophysical processes. Tree cover, as the primary layer of exchange for carbon, energy and water cycles, play a critical role in such dynamics. However, the persistence and functionality of forests are highly dependent on their resilience to the ongoing rapid changes in natural and anthropogenic pressures. Experimental evidence of a sudden increase in tree mortality across different biomes is rising concerns about the ongoing changes in forest resilience and the associated risks to the climate mitigation potential of forests. Previous global-scale assessments of forest resilience have focused on the use of critical slowing down indicators, such as temporal autocorrelation and variance. These studies have provided important insights, but they can only partially capture the effects of stochastic disturbances and forest management.  

In this study, we explore the potential of spatial statistical indicators (SSI), such as spatial variance and skewness, as early warning signals of regime shifts in global forests. To this aim, we first derive tree cover values for the 2000-2023 period at 0.05-degree spatial resolution for the whole globe by combining multiple satellite observations. We then, develop a machine learning model to disentangle the climate effects on tree cover distributions and elucidate the underlying mechanisms. SSI are ultimately computed on the residuals of the machine learning model and their spatial and temporal variations analysed.

Results show, along with a widespread erosion of tree cover, an increase in both SSI prominently in tropical and boreal forests over the observational period. According to the stability theory, the simultaneous increase in these metrics indicates a rising instability of the system by reflecting an alteration of the shape of the basin of attraction. Such patterns appear largely driven by the increase in stochastic perturbations and human pressures which are not detected using traditional critical slowing down indicators. Overall, this study contributes to better understand the recent dynamics in forest resilience and its underlying mechanisms that can lead to critical transitions. Considering the expected intensification of natural pressures in view of climate change, it is becoming urgent to identify adaptation measures to preserve the long-term stability of global forests and the provision of their ecosystem services.