flowlength_sews: Flowlength connectivity indicator (uniform topography)
In spatialwarnings: Spatial Early Warning Signals of Ecosystem Degradation

flowlength_sews

R Documentation

Flowlength connectivity indicator (uniform topography)

Description

Measures the connectivity of runoff-source areas as determined by vegetation patterns and (uniform) topography

Usage

flowlength_sews(mat, slope = 20, cell_size = 1)

Arguments

`mat`	The input matrix (must be a logical matrix)
`slope`	The slope of the area documented by the matrix (in degree).
`cell_size`	The horizontal size of a cell in the matrix (as viewed from above).

Details

This function computes Flowlength, a simple metric that measures the potential hydrological connectivity of runoff-source areas (e.g., bare soil) considering vegetation cover, vegetation patterns and topography. Flowlength is defined as the average length of all the potential runoff pathways in the target area. Thus, a higher value of the index indicates a higher hydrologic connectivity of runoff source areas. This function is designed for an idealized uniform hillslope (e.g., with constant slope angle, the direction of maximum slope being from the top to the bottom of the input matrices).

The deviations of Flowlength from its expected values under random or aggregated-pattern null models can be used as an indicator of imminent transition to a degraded state (Rodriguez et al. 2017) in the context of arid drylands. An increased deviation of flowlength compared to its null values is expected as a possible transition gets closer. This deviation can be computed using any null model provided by spatialwarnings (see indictest for more details), but a specific null model is provided for Flowlength based on a much-faster analytical approximation, using the argument null_method = "approx_rand" when calling indictest (see examples below).

In general, Flowlength can be used as indicator of dryland functional status by assessing potential water and soil losses in patchy landscapes (Mayor et al. 2008, Moreno-de las Heras et al. 2012, Mayor et al. 2013, Okin et al. 2015). Finally, the combination of observed and expected Flowlength under null models for random or aggregated vegetation cover can be used for assessing the cover-independent role of bare- soil connectivity (Rodriguez et al. 2018).

Value

A 'simple_sews' object containing the flow length value, among other things, see simple_sews_object for more information.

References

Rodriguez, F., A. G. Mayor, M. Rietkerk, and S. Bautista. 2017. A null model for assessing the cover-independent role of bare soil connectivity as indicator of dryland functioning and dynamics. Ecological Indicators.

Mayor, A.G., Bautista, S., Small, E.E., Dixon, M., Bellot, J., 2008. Measurement of the connectivity of runoff source areas as determined by vegetation pattern and topography: a tool for assessing potential water and soil losses in drylands. Water Resour. Res. 44, W10423.

Mayor, A.G., Kefi, S., Bautista, S., Rodriguez, F., Carteni, F., Rietkerk, M., 2013. Feedbacks between vegetation pattern and resource loss dramatically decrease ecosystem resilience and restoration potential in a simple dryland model. Landsc. Ecol. 28, 931-942.

Moreno-de las Heras, M., Saco, P.M., Willgoose, G.R., Tongway, D.J., 2012. Variations in hydrological connectivity of Australian semiarid landscapes indicate abrupt changes in rainfall-use efficiency of vegetation. J. Geophys. Res. 117, G03009.

Okin, G.S., Moreno-de las Heras, M., Saco, P.M., Throop, H.L., Vivoni, E.R., Parsons, A.J., Wainwright, J., Peters, D.P.C., 2015. Connectivity in dryland landscapes: shifting concepts of spatial interactions. Front. Ecol. Environ. 13 (1), 20-27.

Examples


## Not run:  
fl_result <- flowlength_sews(arizona, slope = 20, cell_size = 1)

# Compute the Z-score (standardized deviation to null distribution) and plot 
#   its variations along the gradient. This Z-score is suggested by 
#   Rodriguez et al. (2017) as an indicator of degradation. 
fl_test <- indictest(fl_result, nulln = 19)
plot(fl_test, what = "z_score")

# Use the analytical approximation suggested in Rodriguez et al. (2017), 
# instead of permuting the original values in the matrix (much faster)
fl_test <- indictest(fl_result, null_method = "approx_rand")
plot(fl_test, what = "z_score")


## End(Not run)

spatialwarnings documentation built on March 21, 2022, 5:08 p.m.