References
Baruah, G. et al. 2020. Eco-evolutionary processes underlying early
warning signals of population declines. - Journal of Animal Ecology 89:
436–448.
Bury, T. M. et al. 2021. Deep learning for early warning signals of
tipping points. - Proceedings of the National Academy of Sciences 118:
e2106140118.
Cailleret, M. et al. 2019. Early-warning signals of individual tree
mortality based on annual radial growth.
Capdevila, P. et al. 2022. Global patterns of resilience decline in
vertebrate populations. - Ecology Letters 25: 240–251.
Carpenter, S. R. and Brock, W. A. 2006. Rising variance: a leading
indicator of ecological transition. - Ecology Letters 9: 311–318.
Carpenter, S. R. et al. 2011. Early warnings of regime shifts: A
whole-ecosystem experiment. - Science 332: 1079.
Chen, S. et al. 2019. Eigenvalues of the covariance matrix as early
warning signals for critical transitions in ecological systems. -
Scientific Reports 9: 2572.
Cheng, Z. et al. 2008. Robustness analysis of cellular memory in an
autoactivating positive feedback system. - FEBS Lett 582: 3776–3782.
Clements, C. F. and Ozgul, A. 2016. Including trait-based early warning
signals helps predict population collapse. - Nature Communications 7:
10984.
Clements, C. F. et al. 2017. Body size shifts and early warning signals
precede the historic collapse of whale stocks. - Nature Ecology &
Evolution 1: 188.
Clements, C. F. et al. 2019. Early warning signals of recovery in
complex systems. - Nature Communications 10: 1681.
Dakos, V. 2018. Identifying best-indicator species for abrupt
transitions in multispecies communities. - Ecological Indicators 94:
494–502.
Dakos, V. et al. 2012a. Methods for detecting early warnings of critical
transitions in time series illustrated using simulated ecological data.
- PLOS ONE 7: e41010.
Dakos, V. et al. 2012b. Robustness of variance and autocorrelation as
indicators of critical slowing down. - Ecology 93: 264–271.
Dakos, V. et al. 2015. Resilience indicators: prospects and limitations
for early warnings of regime shifts. - Philosophical Transactions of the
Royal Society B: Biological Sciences 370: 20130263.
Dale, V. H. and Beyeler, S. C. 2001. Challenges in the development and
use of ecological indicators. - Ecological Indicators 1: 3–10.
De Gooijer, J. G. and Hyndman, R. J. 2006. 25 years of time series
forecasting. - International Journal of Forecasting 22: 443–473.
Deb, S. et al. 2022. Machine learning methods trained on simple models
can predict critical transitions in complex natural systems. - Royal
Society Open Science 9: 211475.
Enni, S. A. and Herrie, M. B. 2021. Turning biases into hypotheses
through method: A logic of scientific discovery for machine learning. -
Big Data & Society 8: 20539517211020776.
Folke, C. et al. 2010. Resilience thinking. - Ecology and Society in
press.
Forzieri, G. et al. 2022. Emerging signals of declining forest
resilience under climate change. - Nature 608: 534–539.
Fraedrich, K. 1978. Structural and stochastic analysis of a
zero-dimensional climate system. - Quarterly Journal of the Royal
Meteorological Society 104: 461–474.
Gsell, A. S. et al. 2016. Evaluating early-warning indicators of
critical transitions in natural aquatic ecosystems. - Proceedings of the
National Academy of Sciences 113: E8089 LP-E8095.
Held, H. and Kleinen, T. 2004. Detection of climate system bifurcations
by degenerate fingerprinting. - Geophysical Research Letters in press.
Humphries, G. et al. 2018. Machine learning for ecology and sustainable
natural resource management.
Kéfi, S. et al. 2013. Early warning signals also precede
non-catastrophic transitions. - Oikos 122: 641–648.
Kleinen, T. et al. 2003. The potential role of spectral properties in
detecting thresholds in the Earth system: application to the
thermohaline circulation. - Ocean Dynamics 53: 53–63.
Kuehn, C. 2011. A mathematical framework for critical transitions:
Bifurcations, fast–slow systems and stochastic dynamics. - Physica D:
Nonlinear Phenomena 240: 1020–1035.
Lenton, T. M. et al. 2022. Operationalising positive tipping points
towards global sustainability. - Global Sustainability 5: e1.
Lever, J. J. et al. 2020. Foreseeing the future of mutualistic
communities beyond collapse. - Ecology Letters 23: 2–15.
Ludwig, D. et al. 1978. Qualitative analysis of insect outbreak systems:
the spruce budworm and forest. - Journal of Animal Ecology 47: 315–332.
McSharry, P. E. et al. 2003. Prediction of epileptic seizures: are
nonlinear methods relevant? - Nature Medicine 9: 241–242.
Medeiros, L. P. et al. 2022. Ranking species based on sensitivity to
perturbations under non-equilibrium community dynamics. - Ecology
Letters in press.
Oliver, T. H. et al. 2015. Biodiversity and resilience of ecosystem
functions. - Trends in Ecology & Evolution 30: 673–684.
Patterson, A. C. et al. 2021. When and where we can expect to see early
warning signals in multispecies systems approaching tipping points:
insights from theory. - The American Naturalist 198: E12–E26.
Quax, R. et al. 2013. Information dissipation as an early-warning signal
for the Lehman Brothers collapse in financial time series. - Scientific
Reports 3: 1898.
Scheffer, M. et al. 2009. Early-warning signals for critical
transitions. - Nature 461: 53–59.
Scheffer, M. et al. 2012. Anticipating critical transitions. - Science
338: 344 LP – 348.
Schreuder, M. J. et al. 2020. Early warning signals in psychopathology:
what do they tell? - BMC Medicine 18: 269.
Southall, E. et al. 2022. How early can an upcoming critical transition
be detected? - medRxiv: 2022.05.27.22275693.
Strogatz, S. H. 2015. Nonlinear dynamics and chaos: with applications to
physics, biology, chemistry, and engineering. - CRC Press.
Suweis, S. and D’Odorico, P. 2014. Early warning signs in
social-ecological networks. - PLOS ONE 9: e101851.
Ushey, K. et al. 2022. reticulate: Interface to “Python.”
Weinans, E. et al. 2019. Finding the direction of lowest resilience in
multivariate complex systems. - Journal of The Royal Society Interface
16: 20190629.
Weinans, E. et al. 2021. Evaluating the performance of multivariate
indicators of resilience loss. - Scientific Reports 11: 9148.
Wickham, H. 2016. ggplot2: elegant graphics for data analysis. -
Springer-Verlag New York.