recruitment_ctfs: Recruitment, mortality, and growth.
In forestgeo/fgeo.analyze: Analyze ForestGEO Data

Description Usage Arguments Details Value Author(s) Examples

View source: R/demography_ctfs.R

These functions are adapted from the CTFS-R package. Compared to the original functions, these ones have a similar interface but use more conservative defaults and allow suppressing messages. These functions also feature formal tests, bug fixes, additional assertions, and improved messages.

recruitment_ctfs(
  census1,
  census2,
  mindbh = NULL,
  alivecode = NULL,
  split1 = NULL,
  split2 = NULL,
  quiet = FALSE
)

mortality_ctfs(
  census1,
  census2,
  alivecode = NULL,
  split1 = NULL,
  split2 = NULL,
  quiet = FALSE
)

growth_ctfs(
  census1,
  census2,
  rounddown = FALSE,
  method = "I",
  stdev = FALSE,
  dbhunit = "mm",
  mindbh = NULL,
  growthcol = "dbh",
  err.limit = 1000,
  maxgrow = 1000,
  split1 = NULL,
  split2 = NULL,
  quiet = FALSE
)

`census1, census2`	Two census tables, each being a ForestGEO-like tree table (dataframe). A stem table won't fail, but you should use a tree table because demography analyses make more sense at the scale of trees than at the scale of stems.
`mindbh`	The minimum diameter above which the counts are done. Trees smaller than `mindbh` are excluded. By default all living trees of any size are included.
`alivecode`	Character; valid values of `status` indicating that a tree is alive. The default, 'A', is the standard CTFS designation for living trees or stems.
`split1, split2`	Optional vector(s) to aggregate results by. Each vector should be a column of either `census1` or `census2`. The default aggregates the result across the entire census datasets.
`quiet`	Use `TRUE` to suppress messages.
`rounddown`	If `TRUE`, all `dbh < 55` are rounded down to the nearest multiple of 5.
`method`	Either "I" or "E": Use "I" to calculate annual dbh increment as `(dbh2 - dbh1)/time` Use "E" to calculate the relative growth rate as `(log(dbh2) - log(dbh1)) / time`
`stdev`	Logical: `FALSE` returns confidence limits. `TRUE` returns the SD in growth rate per group.
`dbhunit`	"cm" or "mm".
`growthcol`	Either "dbh" or "agb" to define how growth is measured.
`err.limit, maxgrow`	A number. Numbers such as 10000 are high and will return all measures.

Survivors are all individuals alive in both censuses, with status == A in the first census, and a diameter greater than mindbh in the first census. The total population in the second census includes all those alive plus any other survivors. Individuals whose status is NA in either census are deleted from all calculations.

Metrics of recruitment: Similar to metrics of mortality.

Metrics of mortality:

N: the number of individuals alive in the census 1 per category selected.
D: the number of individuals no longer alive in census 2.
rate: the mean annualized mortality rate constant per category selected, calculated as (log(N)-log(S))/time.
upper: upper confidence limit of mean rate.
lower: lower confidence limit of mean rate.
time: mean time interval in years.
date1: mean date included individuals were measured in census 1, as julian object (R displays as date, but treats as integer).
date2: mean date in census 2.
dbhmean: mean dbh in census 1 of individuals included.

Metrics of growth:

rate, the mean annualized growth rate per category selected, either dbh increment, or relative growth.
N, the number of individuals included in the mean (not counting any excluded).
clim (or sd with stdev = TRUE), width of confidence interval; add this number to the mean rate to get upper confidence limit, substract to get lower.
dbhmean, mean dbh in census 1 of individuals included.
time, mean time interval in years.
date1, mean date included individuals were measured in census 1, as julian object (R displays as date, but treats as integer).
date2, mean date in census 2.

Richard Condit, Suzanne Lao.

assert_is_installed("fgeo.x")

census1 <- fgeo.x::tree5
census2 <- fgeo.x::tree6

as_tibble(
  recruitment_ctfs(census1, census2)
)

# Use `interaction(...)` to aggregate by any number of grouping variables
sp_quadrat <- interaction(census1$sp, census1$quadrat)

recruitment <- recruitment_ctfs(
  census1, census2,
  split1 = sp_quadrat,
  quiet = TRUE
)
as_tibble(recruitment)

mortality <- mortality_ctfs(
  census1, census2,
  split1 = sp_quadrat, quiet = TRUE
)
as_tibble(mortality)

growth <- growth_ctfs(census1, census2, split1 = sp_quadrat, quiet = TRUE)
as_tibble(growth)

# Easy way to separate grouping variables
tidyr_is_installed <- requireNamespace("tidyr", quietly = TRUE)
if (tidyr_is_installed) {
  library(tidyr)

  as_tibble(growth) %>%
    separate(groups, into = c("sp", "quadrat"))
}