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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