Spatial Early Warning Signals of Ecosystem Degradation
Matrix coarse-graining
Convert an object to a matrix
Custom Spatial Early-Warning signals
Plot a matrix
The Lifshitz-Slyozov-Wagner distribution
Extract the r-spectrum from objects
extract_variogram() method for variogram_sews objects
Flowlength connectivity indicator (uniform topography)
Generic Spatial Early-Warning signals
Power-law range indicator
Change in patch-size distributions types
Significance-assessment of spatial early-warning signals
Indicator based on Kolmogorov Complexity
Labelling of unique patches and detection of percolation.
Indicators based on the LSW distribution
Early-warning signals based on patch size distributions
predict method for patchdistr_sews objects
Early-warning signals based on patch size distributions
Get patch sizes.
Distribution-fitting functions
Display the r-spectrum of a spectral_sews object
Moran's Index at lag of 1
Skewness indicator
Spatial variance indicator
Clustering of pairs
Flow length (uniform slope)
Kolmogorov complexity of a matrix
Spatial correlation at lag 1
Power-law range indicator
Spectral Density Ratio (SDR) indicator
Structural variance
Variogram parameters
Objects exported from other packages
r-spectrum
Spatial early-warning signals: display of trends
simple_sews objects
Early Spatial-Warnings of Ecosystem Degradation
Spectrum-based spatial early-warning signals.
Early-warning signals based on variograms
predict() method for variogram_sews objects
Early-Warning signals based on variograms (EXPERIMENTAL)
Estimate the minimum patch size of a power-law distribution
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Useful links