@article{SmithZottaBoultonetal.2023,
  author    = {Smith, Taylor and Zotta, Ruxandra-Maria and Boulton, Chris A. and Lenton, Timothy M. and Dorigo, Wouter and Boers, Niklas},
  title     = {Reliability of resilience estimation based on multi-instrument time series},
  series = {Earth System Dynamics},
  volume    = {14},
  journal   = {Earth System Dynamics},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {2190-4987},
  doi       = {10.5194/esd-14-173-2023},
  pages     = {173 -- 183},
  year      = {2023},
  abstract  = {Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process - rather than actual changes in the dynamical properties of the system - is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.},
  language  = {en}
}
@article{SmithTraxlBoers2022,
  author    = {Smith, Taylor and Traxl, Dominik and Boers, Niklas},
  title     = {Empirical evidence for recent global shifts in vegetation resilience},
  series = {Nature climate change},
  volume    = {12},
  journal   = {Nature climate change},
  number    = {5},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {1758-678X},
  doi       = {10.1038/s41558-022-01352-2},
  pages     = {477 -- 484},
  year      = {2022},
  abstract  = {The authors demonstrate that a vegetation system's ability to recover from disturbances-its resilience-can be estimated from its natural variability. Global patterns of resilience loss and gains since the early 1990s reveal shifts towards widespread resilience loss since the early 2000s. 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.},
  language  = {en}
}