| partitionR | R Documentation |
PartitionR() is a function used to partition the temporal coefficient of variation of a community
into the variability of the average species and three stabilizing effects: the dominance, asynchrony and averaging effects
(see Details).
partitionR(z, ny = 1)
z |
A |
ny |
Only species appearing more than |
The analytic framework is described in details in Segrestin et al. (2024). In short, the partitioning relies on the following equation:
CV_{com} = CV_e \Delta \Psi \omega
where CV_{com} is the community coefficient of variation (reciprocal of community stability),
CV_e is the expected community CV when controlling for the dominance structure and species temporal synchrony,
\Delta is the dominance effect, \Psi is the asynchrony effect, and \omega is the averaging effect.
Returns an object of class 'comstab'.
An object of class 'comstab' is a list containing the following components:
'CVs' a named vector of calculated coefficient of variations. CVe is the CV of an average species,
CVtilde is the mean of species CVs weighted by their relative abundances, CVa is the expected community CV if
the community was stabilized by species asynchrony only, and CVc is the observed community CV.
'Stabilization' a named vector of the stabilizing effects. tau is the total stabilization, Delta is
the dominance effect, Psi is the asynchrony effect, and omega is the averaging effect.
'Relative' a named vector of the relative contributions of each stabilizing effect to the total stabilization.
Delta_cont, Psi_cont, and omega_cont are the relative contribution of respectively, the dominance, asynchrony, and averaging effects to the total stabilization.
Returns a vector of NAs if any Stabilizing effect is higher than 1.
Jules Segrestin, jsegrestin@gmail.com
Segrestin et al. (2024) A unified framework for partitioning the drivers of stability of ecological communities. Global Ecology and Biogeography, 33(5), e13828. https://doi.org/10.1111/geb.13828
require(stats)
# Simulates a custom community time series using 'comTS()':
z <- comTS(nsp = 10, ny = 30, even = 0.6, mvs = 1.5, sync = "0")
# Runs the partitioning of the community coefficient of variation:
partitionR(z)
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