iNEXT.4steps (version 1.0.1)

Evenness: Main function for STEP 4: Assessment of evenness

Description

Evenness computes standardized and observed evenness of order q = 0 to q = 2 in increments of 0.2 (by default) and depicts evenness profiles based on five classes of evenness measures developed in Chao and Ricotta (2019). Note that for q = 0 species abundances are disregarded, so it is not meaningful to evaluate evenness among abundances specifically for q = 0. As q tends to 0, all evenness values tend to 1 as a limiting value.

Usage

Evenness(
  data,
  q = seq(0, 2, 0.2),
  datatype = "abundance",
  method = "Estimated",
  nboot = 30,
  conf = 0.95,
  nT = NULL,
  E.class = 1:5,
  SC = NULL
)

Value

A list of several tables containing estimated (or observed) evenness with order q.

Each tables represents a class of evenness.

Order.q

the order of evenness

Evenness

the computed evenness value of order q.

s.e.

standard error of evenness value.

Even.LCL, Even.UCL

the bootstrap lower and upper confidence limits for the evenness of order q at the specified level (with a default value of 0.95).

Assemblage

the assemblage name.

Method

"Estimated" or "Observed".

SC

the standardized coverage value under which evenness values are computed (only for method = "Estimated")

Arguments

data

(a) For datatype = "abundance", data can be input as a vector of species abundances (for a single assemblage), matrix/data.frame (species by assemblages), or a list of species abundance vectors.
(b) For datatype = "incidence_raw", data can be input as a list of matrix/data.frame (species by sampling units); data can also be input as a matrix/data.frame by merging all sampling units across assemblages based on species identity; in this case, the number of sampling units (nT, see below) must be input.

q

a numerical vector specifying the orders of evenness. Default is seq(0, 2, by = 0.2).

datatype

data type of input data: individual-based abundance data (datatype = "abundance") or species by sampling-units incidence matrix (datatype = "incidence_raw") with all entries being 0 (non-detection) or 1 (detection).

method

a binary selection of method with "Estimated" (evenness is computed under a standardized coverage value) or "Observed" (evenness is computed for the observed data).

nboot

a positive integer specifying the number of bootstrap replications when assessing sampling uncertainty and constructing confidence intervals. Enter 0 to skip the bootstrap procedures. Default is 30.

conf

a positive number < 1 specifying the level of confidence interval. Default is 0.95.

nT

(required only when datatype = "incidence_raw" and input data is matrix/data.frame) a vector of nonnegative integers specifying the number of sampling units in each assemblage. If assemblage names are not specified, then assemblages are automatically named as "Assemblage1", "Assemblage2",..., etc.

E.class

an integer vector between 1 to 5 specifying which class(es) of evenness measures are selected; default is 1:5 (select all five classes).

SC

(required only when method = "Estimated") a standardized coverage value for calculating estimated evenness. If SC = NULL, then this function computes the diversity estimates for the minimum sample coverage among all samples extrapolated to double reference sizes (Cmax).

References

Chao, A. and Ricotta, C. (2019). Quantifying evenness and linking it to diversity, beta diversity, and similarity. Ecology, 100(12), e02852.

Examples

## Evenness for abundance data
# The observed evenness values for abundance data
data(Data_spider)
Even_out1_obs <- Evenness(data = Data_spider, datatype = "abundance", 
                          method = "Observed", E.class = 1:5)
Even_out1_obs

# \donttest{
# Estimated evenness for abundance data with default sample coverage value
data(Data_spider)
Even_out1_est <- Evenness(data = Data_spider, datatype = "abundance", 
                          method = "Estimated", SC = NULL, E.class = 1:5)
Even_out1_est
# }

## Evenness for incidence raw data
# The observed evenness values for incidence raw data
data(Data_woody_plant)
Even_out2_obs <- Evenness(data = Data_woody_plant, datatype = "incidence_raw", 
                          method = "Observed", E.class = 1:5)
Even_out2_obs

# \donttest{
# Estimated evenness for incidence data with user's specified coverage value of 0.98
data(Data_woody_plant)
Even_out2_est <- Evenness(data = Data_woody_plant, datatype = "incidence_raw", 
                          method = "Estimated", SC = 0.98, E.class = 1:5)
Even_out2_est
# }