- Terrestrial:
- Gross primary productivity (2001:2018)
- Ecosystem respiration (2001:2018)
- Leaf area index (1994:2017)
- Marine:
- Chlorophyll A (1998:2018)
- >1M pixels, weekly obs, 0.25 degree grid resolution
Ecosystems are showing symptoms of resilience loss
Juan C. Rocha
Enviromental Research Letters (2022)
Forest to savanna
Regime shifts are large, abrupt and persistence critical transitions in the function and structure of (eco)systems
Coral transitions
Regime shifts are large, abrupt and persistence critical transitions in the function and structure of (eco)systems
Fisheries collapse
Regime shifts are large, abrupt and persistence critical transitions in the function and structure of (eco)systems
Where are regime shifts likely to occur?
Depends on our ability to observe and measure resilience
Data & methods
Critical slowing down
- \(\uparrow\) Variance and autocorrelation
- \(\Delta\) skewness and kurtosis
Dakos et al. 2012. PLoS ONE
Kéfi et al. 2014. PLoS ONE
- \(\downarrow\) Variance and autocorrelation
Titus & Watson 2020 J Theor Ecol
Fractal dimension
- \(\uparrow\) adaptive capacity
- Measure of self-similarity across scales
West, Geoffrey. 2017. Scale
Gneiting et al. 2012. Statistical Science.
Analysis: one pixel
The generic resilience indicators do not necessarily align with critical slowing down or speeding up theories
Detection
In the absence of ground truth, if \(\Delta\) is > 95% or < 5% of the distribution is considered a signal of resilience loss
Gross primary productivity 
~30% of ecosystem show symptoms of resilience loss, boreal forest and tundra particularly strong signals
Chlorophyll A 
~25% of ecosystem show symptoms of resilience loss, Easter Indo-Pacific and Tropical Eastern Pacific Oceans particularly strong signals
Robustness tests: permutations, auto-regressive models
Lessons
- CSD vs CSU: contradiction? Multiple drivers at different time scales
- Patterns detected align with previous studies
- Underestimation: limitations of the data resolution
- No ground truth: distribution matters
- Identify areas for management
Tack | Gracias
Questions?
email: juan.rocha@su.se
twitter: @juanrocha
slides: juanrocha.se/presentations/resilience_loss_2022
paper: Rocha, JC. 2022. Ecosystems are showing symptoms of resilience loss. ERL
Stockholm Resilience Centre
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Resilience indicators
Critical slowing down
- \(\uparrow\) Variance and autocorrelation
- \(\Delta\) skewness and kurtosis
- Model-based indicators:
- Diffusion jump models
- Time varying AR(p) models
- Threshold AR(p) models
- Potential analysis
- Spatial indicators:
- Fourier transforms
- Power spectrum
- Patch-size distributions
Critical slowing down
Resilience ~ slowness
- Variance and autocorrelation
- NDVI: normalized difference vegetation index
- VOD: vegetation optical depth
- Limited spatial and temporal resolution
- Confirm a threshold: 1500mm
Verbesselt J, et al. Remotely sensed resilience of tropical forests. 2016.
Limitations: fail when dynamics are driven by stochastic processes or when signals have too much noise
Resilience indicators
Resilience indicators
Fractal dimension
- \(\uparrow\) adaptive capacity
- Measure of self-similarity across scales
- Fractal geometry:
- Bounded
- Magnitudes do not depend on scale
- Clear interpretation
- Applications in medicine
West, Bruce. 2010. Frontiers Physiology
Gneiting et al. 2012. Statistical Science.
Terrestrial ecosystem respiration 
~30% of ecosystem show symptoms of resilience loss, boreal forest and tundra particularly strong signals
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Ecosystems are showing symptoms of resilience loss
Juan C. Rocha Enviromental Research Letters (2022)