Emerging signals of declining forest resilience under climate change

Abstract

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience)1. Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience2, yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators3–5, has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO2 fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans.

Half-millennium evidence suggests that extinction debts of global vertebrates started in the Second Industrial Revolution

“…Furthermore, we must note that there are inevitable issues of spatial and temporal autocorrelation in analyzing annual trends of forest cover at the global scale. Since we have attempted to model the influence of temporal patterns, we have considered dealing with spatial rather than temporal autocorrelation; the latter is more valuable in examining the signals of declining forest resilience under climate change 39 .…”

EWSmethods: an R package to forecast tipping points at the community level using early warning signals and machine learning models

“…Resilience-based approaches are critical for the management of globally imperilled systems (Folke et al 2010, Oliver et al 2015, Capdevila et al 2022 but are applicable in other disciplines. Remotely sensed data could allow global level tipping point assessments for example (Forzieri et al 2022), individual mortality risk may be detectable (Cailleret et al 2019) or positive thresholds can be encouraged (Lenton et al 2022). The low barrier to entry that EWSmethods provides for R users can aid the development of these developing research avenues.…”

C-band Scatterometer (CScat): the first global long-term satellite radar backscatter data set with a C-band signal dynamic

“…Using optical MODIS Leaf Area Index data, a recent study found that most of the world's vegetated areas are becoming greener, particularly in China and India (Chen et al, 2019). Using optical vegetation index NDVI, another recent research explored the long-term (2000-2020) resilience change of global forests (Forzieri et al, 2022).…”