codyn (version 2.0.5)

rate_change: Rate of community change over successive time intervals

Description

Calculates the slope of the differences in species composition within a community over increasing time intervals, which provides a measures of the rate of directional change in community composition. Differences in species composition are characterized by Euclidean distances, which are calculated on pair-wise communities across the entire time series. For example, a data set with six time intervals will have distance values for five one-year time lags (year 1 vs year 2, year 2 vs year 3 ...), four two-year time lags (year 1 vs year 3, year 2 vs year 4 ...) and so forth. These distance values are regressed against the time lag interval. The slope of the regression line is reported as an indication of the rate and direction of compositional change in the community.

Usage

rate_change(df, time.var, species.var, abundance.var, replicate.var = NA)

Arguments

df

A data frame containing time, species and abundance columns and an optional column of replicates

time.var

The name of the time column

species.var

The name of the species column

abundance.var

The name of the abundance column

replicate.var

The name of the optional replicate column

Value

The rate_change function returns a numeric rate change value unless a replication column is specified in the input data frame. If replication is specified, the function returns a data frame with the following attributes:

• rate_change: A numeric column with the synchrony values.

• replicate.var: A column that shares the same name and type as the replicate.var column in the input data frame.

Details

The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.

The rate_change function uses linear regression to relate Euclidean distances to time lag intervals. It is recommended that fit of this relationship be verified using rate_change_interval, which returns the full set of community distance values and associated time lag intervals.

References

Collins, S. L., Micheli, F. and Hartt, L. 2000. A method to determine rates and patterns of variability in ecological communities. - Oikos 91: 285-293.

Examples

# NOT RUN {
data(knz_001d)
rate_change(knz_001d[knz_001d$subplot=="A_1",],
                       time.var = "year", 
                       species.var = "species",
                       abundance.var = "abundance") # for one subplot
                       
rate_change(knz_001d, 
             time.var = "year", 
             species.var = "species",
             abundance.var = "abundance",
             replicate.var = "subplot") # across all subplots
# }