##Overview As long-term datasets increase in scope and
length, new analytical tools are being developed to capture patterns of
species interactions over time. The package codyn includes
recently developed metrics of ecological community dynamics. Functions
in codyn implement metrics that are explicitly temporal,
and include the option to calculate them over multiple replicates.
Functions fall into two categories: temporal diversity indices and
community stability metrics.
##Temporal Diversity Indices Many traditional measure of community
structure represent a ‘snapshot in time’ whereas ecological communities
are dynamic and many are experiencing directional change with time. The
diversity indices in codyn are temporal analogs to
traditional diversity indices such as richness and rank-abundance
curves. They include:
turnover calculates total turnover as well as the
proportion of species that either appear or disappear between
timepoints.
mean_rank_shift quantifies relative changes in
species rank abundances by taking the sum difference of species ranks in
consecutive time points. This metric goes hand-in-hand with “rank
clocks,” a useful visualization tool for shifts in species
ranks.
rate_change analyzes differences in species
composition between samples at increasing time lags. It reflects the
rate of directional change in community composition.
rate_change_interval produces a data frame
containing differences in species composition between samples at
increasing time intervals.
##Community Stability Metrics Ecologists have long debated the
relationship between species diversity and stability. Unstable species
populations may stabilize aggregate community properties if a decrease
in one species is compensated for by an increase in another. In a time
series, this should be reflected by a pattern in which species
negatively covary or fluctuate asynchronously while total community
stability remains relatively stable. codyn includes a
function to characterize community stability,
community_stability, and three metrics to characterize
species covariance and asynchrony:
variance_ratio characterizes species covariance
(Schluter 1984; Houlahan et al. 2007), and
includes a null-modeling approach to test significance (Hallett et al. 2014). Null modeling is built-in
to the variance_ratio function. Two additional functions,
cyclic_shift and confint.cyclic_shift, allow
this method to be generalized to other test statistics.
synchrony has two options. The first compares the
variance of the aggregated community with the variance of individual
components (Loreau and Mazancourt 2008).
The second compares the average correlation of each individual species
with the rest of the aggregated community (Gross
et al. 2014).
##Citations