codyn (version 2.0.6)

abundance_difference: Abundance Differences

Description

Calculates the abundance difference for species between two samples. Differences are on abundance values provided, if relative data is used, then differences in relative abundance will be calculated. There are three ways differences can be calculated. 1) Between treatments within a block (note: block.var and treatment.var need to be specified). 2) Between treatments, pooling all replicates into a single species pool (note: pool = TRUE, treatment.var needs to be specified, and block.var = NULL). 3) All pairwise combinations between all replicates (note: block.var = NULL, pool = FALSE and specifying treatment.var is optional. If treatment.var is specified, the treatment that each replicate belongs to will also be listed in the output).

Usage

abundance_difference(
  df,
  time.var = NULL,
  species.var,
  abundance.var,
  replicate.var,
  treatment.var = NULL,
  pool = FALSE,
  block.var = NULL,
  reference.treatment = NULL
)

Value

The `abundance_difference()` function returns a data frame with a subset of the following columns: - **species.var**: A column that has same name and type as the `species.var` column. - **difference**: A numeric column of the abundance differences between the two samples being compared (replicates or treatments). A numeric column of the change in abundance between consecutive timepoints. A positive value occurs when a species has greater abundance in `replicate.var2` than in `replicate.var` and/or in `treatment.var2` than in `treatment.var`. - **replicate.var**: A column that has same name and type as the `replicate.var` column, represents the first replicate being compared. Note, a replicate column will be returned only when `pool = FALSE` or `block.var = NULL`. - **replicate.var2**: A column that has the same type as the `replicate.var` column, and is named `replicate.var` with a 2 appended to it, represents the second replicate being compared. Note, a `replicate.var` column will be returned only when `pool = FALSE` and `block.var = NULL`. - **time.var**: A column that has the same name and type as the `time.var` column, if `time.var` is specified. - **treatment.var**: A column that has same name and type as the `treatment.var` column, represents the first treatment being compared. A `treatment.var` column will be returned when `pool = TRUE`, `block.var` is specified, or `treatment.var` is specified. - **treatment.var2**: A column that has the same type as the `treatment.var` column, and is named `treatment.var` with a 2 appended to it, represents the second treatment being compared. A `treatment.var` column will be returned when `pool = TRUE`, `block.var` is specified, or `treatment.var` is specified. - **block.var**: A column that has same name and type as the `block.var` column, if `block.var` is specified.

Arguments

df

A data frame containing a species, abundance, and replicate columns and optional time, treatment, and block columns.

time.var

The name of the optional time column.

species.var

The name of the species column.

abundance.var

The name of the abundance column.

replicate.var

The name of the replicate column. Replicate identifiers must be unique within the dataset and cannot be nested within treatments or blocks.

treatment.var

The name of the optional treatment column.

pool

An argument to allow abundance values to be pooled within a treatment. The default value is "FALSE", a value of "TRUE" averages abundance of each species within a treatment at a given time point.

block.var

The name of the optional block column.

reference.treatment

The name of the optional treatment that all other treatments will be compared to (e.g. only controls will be compared to all other treatments). If not specified all pairwise treatment comparisons will be made.

References

Avolio et al. Submitted

Examples

data(pplots)
# With block and no time
df <- subset(pplots, year == 2002 & block < 3)
abundance_difference(df = df,
                     species.var = "species",
                     abundance.var = "relative_cover",
                     treatment.var = "treatment",
                     block.var = "block",
                     replicate.var = "plot")

# With blocks and time
df <- subset(pplots, year < 2004 & block < 3)
abundance_difference(df = df,
                     species.var = "species",
                     abundance.var = "relative_cover",
                     treatment.var = "treatment",
                     block.var = "block",
                     replicate.var = "plot",
                     time.var = "year")

# With blocks, time and reference treatment
df <- subset(pplots, year < 2004 & block < 3)
abundance_difference(df = df,
                     species.var = "species",
                     abundance.var = "relative_cover",
                     treatment.var = "treatment",
                     block.var = "block",
                     replicate.var = "plot",
                     time.var = "year",
                     reference.treatment = "N1P0")

# Pooling by treatment with time
df <- subset(pplots, year < 2004)
abundance_difference(df = df,
                     species.var = "species",
                     abundance.var = "relative_cover",
                     treatment.var = "treatment",
                     pool = TRUE,
                     replicate.var = "plot",
                     time.var = "year")

# All pairwise replicates with treatment
df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32))
abundance_difference(df = df,
                     species.var = "species",
                     abundance.var = "relative_cover",
                     replicate.var = "plot",
                     time.var = "year",
                     treatment.var = "treatment")

# All pairwise replicates without treatment
df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32))
abundance_difference(df = df,
                     species.var = "species",
                     abundance.var = "relative_cover",
                     replicate.var = "plot",
                     time.var = "year")