Empirical evidence for recent global shifts in vegetation resilience

Abstract

The character and health of ecosystems worldwide is tightly coupled to changes in Earth’s climate. Theory suggests that ecosystem resilience—the ability of ecosystems to resist and recover from external shocks such as droughts and fires—can be inferred from their natural variability. Here, we quantify vegetation resilience globally with complementary metrics based on two independent long-term satellite records. We first empirically confirm that the recovery rates from large perturbations can be closely approximated from internal vegetation variability across vegetation types and climate zones. On the basis of this empirical relationship, we quantify vegetation resilience continuously and globally from 1992 to 2017. Long-term vegetation resilience trends are spatially heterogeneous, with overall increasing resilience in the tropics and decreasing resilience at higher latitudes. Shorter-term trends, however, reveal a marked shift towards a global decline in vegetation resilience since the early 2000s, particularly in the equatorial rainforest belt.

Global vegetation resilience linked to water availability and variability

“…Importantly, we consider the relationships between the different climatic predictors and vegetation resilience separately for each land-cover type; this assures that differences in resilience indicators caused by different land-cover types are not mistaken for differences in their actual resilience. Our analysis extends the discussion of vegetation resilience and its dependence on long-term precipitation characteristics to the global scale and uncovers succinct and variable relationships between water availability and both theoretical and recently introduced empirical 4 measures of vegetation resilience.…”

“…A wide body of previous research 16 , 17 has proposed that the capacity of a system to recover from external shocks, and hence the system’s resilience, is closely tied to both the variance and the lag-one autocorrelation (AC1) of time series encoding the dynamics of the system in question 1 , 18 – 22 ; higher values of AC1 and variance are associated with lower resilience (see Methods). Under some assumptions, it can indeed be shown analytically that the variance and AC1 are related to the recovery rate and hence the resilience of the system in question; an empirical confirmation of these relationships—and thereby an empirical justification for the use of variance and AC1 as proxies for vegetation resilience—has recently been provided using global-scale satellite data 4 . Based on the results from the latter study, we will in the following focus on three different ways of estimating vegetation resilience.…”

“…The potential for abrupt transitions between alternative stable states—and corresponding risks of further carbon emissions—is not only confined to the tropics 12 , 13 , making identifying controls on ecosystem resilience a global concern. Indeed, recent work 4 , 5 , 14 , 15 has shown that many regions are losing vegetation resilience; however, the drivers of spatial heterogeneity in resilience and resilience trends remain unconstrained. In this work, we therefore aim to improve our understanding of possible climatic drivers of vegetation resilience.…”

Catchment water storage dynamics and its role in modulating streamflow generation in spectral perspective: a case study in the headwater of Baiyang Lake, China

“…It reminds us that hydrological resilience (the ability to recover from anthropogenic disturbances) is variable along a longterm rainfall variability, with higher resilience in the wet phase and lower resilience in the dry phase. It is a hot topic in recent years (Helman et al, 2017;Ahlström et al, 2017;Yi and Jackson, 2021;Smith et al, 2022). But the mechanisms related to the resilience variability are still not fully understood.…”

Vegetation disturbances characterization in the Tibetan Plateau from 1986 to 2018 using Landsat time series and field observations

“…Change in vegetation structure and function could infer the effects of climate change and human interventions because vegetation is highly sensitive to various natural and anthropogenic factors (Parmesan and Yohe 2003). To tackle urgent issues related to global climate change, carbon emissions and losses of biodiversity, detailed information about long-term vegetation dynamics over a large spatial extent and a long temporal scale is urgently needed (Dixon et al 1994, DeFries et al 1999, Brun et al 2019, Chen et al 2021, Smith et al 2022. In the past several decades, considerable attention has been dedicated to the changes in vegetation at regional to global scales using forest inventory and satellite observation data (Hansen et al 2013, Ceccherini et al 2020, Piao et al 2020.…”