species_summary: Create A Cover-Weighted Summary of Species

In fqacalc: Calculate Floristic Quality Assessment Metrics

Create A Cover-Weighted Summary of Species

Description

species_summary produces a table summarizing species' frequency, total cover, relative frequency, relative cover, and relative importance.

Usage

species_summary(
  x,
  key = "name",
  db,
  cover_class = "percent_cover",
  allow_no_c = TRUE,
  allow_non_veg = TRUE,
  plot_id = NULL
)

Arguments

x	A data frame containing a list of plant species. This data frame must have one of the following columns: name or acronym. For cover-weighted or relative functions, this data frame must also have a column called cover containing cover values and ideally a column containing plot IDs.
key	A character string representing the column that will be used to join the input data frame x with the regional FQA database. If a value is not specified, the default is "name". "name" and "acronym" are the only acceptable values for key.
db	A character string representing the regional FQA database to use. See db_names for a list of potential values and the fqadata R package where the databases are hosted.
cover_class	a character string representing the cover classification used. Acceptable cover classes are: "percent_cover", "carolina_veg_survey", "braun-blanquet", "daubenmire", and "usfs_ecodata". "percent_cover" is the default.
allow_no_c	Boolean (TRUE or FALSE). If TRUE, allow species that are found in the regional FQA database but have not been assigned a C Values. If FALSE, omit species that have not been assigned C Values.
allow_non_veg	Boolean (TRUE or FALSE). If TRUE, allow input to contain un-vegetated ground and un-vegetated water.
plot_id	A character string representing the column in x that contains plot identification values. plot_id is a required argument in plot_summary, where it acts as a grouping variable. plot_id is optional but highly recommended for cover-weighted functions and relative functions. If plot_id is set in a cover-weighted function or a relative function, it only prevents duplicates from occurring in the same plot. It does not act as a grouping variable.

Value

A data frame where each row is a species and each column is information about that species based on the input data frame.

Examples

transect <- data.frame(
acronym  = c("ABEESC", "ABIBAL", "AMMBRE", "ANTELE", "ABEESC", "ABIBAL", "AMMBRE"),
cover = c(50, 4, 20, 30, 40, 7, 60),
quad_id = c(1, 1, 1, 1, 2, 2, 2))

species_summary(transect, key = "acronym", db = "michigan_2014")

#can also include bare ground and unveg water
transect_unveg <- data.frame(acronym  = c("GROUND", "ABEESC", "ABIBAL", "AMMBRE",
"ANTELE", "WATER", "GROUND", "ABEESC", "ABIBAL", "AMMBRE"),
cover = c(60, 50, 4, 20, 30, 20, 20, 40, 7, 60),
plot_id = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2))

species_summary(transect_unveg, key = "acronym", db = "michigan_2014",
plot_id = "plot_id")

fqacalc documentation built on Sept. 26, 2023, 5:10 p.m.