generic_sews: Generic Spatial Early-Warning signals
In spatialwarnings: Spatial Early Warning Signals of Ecosystem Degradation

generic_sews

R Documentation

Generic Spatial Early-Warning signals

Description

Computation, significance assessment and display of spatial generic early warning signals (Moran's I, variance and skewness)

Usage

generic_sews(
  mat,
  subsize = 4,
  abs_skewness = FALSE,
  moranI_coarse_grain = FALSE
)

Arguments

`mat`	A matrix (quantitative data), a binary matrix (which contains `TRUE` or `FALSE` values) or a list of those
`subsize`	The subsize used for the coarse-graining phase (see Details)
`abs_skewness`	Should the absolute skewness be used instead of its raw values ?
`moranI_coarse_grain`	Should the input matrix be coarse-grained before computing the Moran's I indicator value ?

Details

The Generic Early warning signal are based on the property of a dynamical system to "slow down" when approaching a critical point, that is, take more time to return to equilibrium after a perturbation. This is expected to be reflected in several spatial characteristics: the variance, the spatial autocorrelation (at lag-1) and the skewness. This function provides a convenient workflow to compute these indicators, assess their significance and display the results.

Before computing the actual indicators, the matrix can be "coarse-grained". This process reduces the matrix by averaging the nearby cells using a square window defined by the subsize parameter. This makes spatial variance and skewness reflect actual spatial patterns when working with binary (TRUE/FALSE data), but is optional when using continuous data. Keep in mind that it effectively reduces the size of the matrix by approximately subsize on each dimension.

The significance of generic early-warning signals can be estimated by reshuffling the original matrix (function indictest). Indicators are then recomputed on the shuffled matrices and the values obtained are used as a null distribution. P-values are obtained based on the rank of the observed value in the null distribution. A small P-value means that the indicator is significantly above the null values, as expected before a critical point.

The plot method can displays the results graphically. A text summary can be obtained using the summary method.

Value

generic_sews returns an object of class simple_sews_single (a list) if mat is a single matrix or an object of class simple_sews_list if mat is a list. You probably want to use some of the methods written for these complicated objects instead of extracting values directly (they are displayed using print(<object>)).

indictest returns an object of class generic_test (a data.frame).

plot methods return ggplot objects, usually immediately displayed when plot is being called at the R prompt.

References

Kefi, S., Guttal, V., Brock, W.A., Carpenter, S.R., Ellison, A.M., Livina, V.N., et al. (2014). Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns. PLoS ONE, 9, e92097.

Dakos, V., van Nes, E. H., Donangelo, R., Fort, H., & Scheffer, M. (2010). Spatial correlation as leading indicator of catastrophic shifts. Theoretical Ecology, 3(3), 163-174.

Guttal, V., & Jayaprakash, C. (2008). Spatial variance and spatial skewness: leading indicators of regime shifts in spatial ecological systems. Theoretical Ecology, 2(1), 3-12.

Examples


data(serengeti)
gen_indic <- generic_sews(serengeti, subsize = 5, 
                           moranI_coarse_grain = TRUE)

# Display results
summary(gen_indic)

# Display trends along the varying model parameter
plot(gen_indic, along = serengeti.rain)

# Compute significance (long)

gen_test <- indictest(gen_indic, nulln = 199)

print(gen_test)

# Display the trend, now with a grey ribbon indicating the 5%-95% quantile
# range of the null distribution
plot(gen_test, along = serengeti.rain)

# Display the effect size compared to null distribution 
plot(gen_test, along = serengeti.rain, what = "z_score")

# Note that plot() method returns a ggplot object that can be modified
# for convenience
if ( require(ggplot2) ) { 
  plot(gen_test, along = serengeti.rain) + 
    geom_vline(xintercept = 733, color = "red", linetype = "dashed") +
    xlab('Annual rainfall') + 
    theme_minimal()
}

spatialwarnings documentation built on Sept. 11, 2024, 8:55 p.m.