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Create observation data from trees

Source: R/createInputs.R
makeObsData.Rd

Derives stand metrics by site/patch/species/year from tree measurements, filters sites, harmonizes years, optionally aggregates by site.

Usage

makeObsData(
  tree_dt,
  plotsize,
  aggregate_by_site = T,
  minNyears = 2,
  fix_period_length = NULL,
  dbh_growth_thresh = c(-10, 50),
  Npatches = NULL,
  Nspecies = NULL,
  NspeciesQuantile = NULL
)

Arguments

tree_dt

data.table of tree records with siteName, patchName, year, treeName, species_name, dbh, status (one of "alive", "new", "dead") and living. Mortality is derived from status; a mort column, if present, is ignored.

plotsize

numeric plot area used to scale recruitment.

aggregate_by_site

logical. Aggregate patches to site level. Default TRUE.

minNyears

integer or NULL. Keep sites with at least this many years and equal counts across patches. Default 2.

fix_period_length

integer or NULL. If set, drop sites whose inventory interval differs. Default NULL.

dbh_growth_thresh

length-2 numeric or NULL. Drop sites where any tree dbh change falls outside this range. Default c(-10, 50).

Npatches

integer or NULL. If set, keep only sites with exactly this many patches.

Nspecies

integer or NULL. Cap species to the top N (others merged to "other").

NspeciesQuantile

numeric in (0,1] or NULL. Choose the smallest N covering this fraction of individuals; overrides Nspecies if supplied.

Value

A list with:

  • obs_dt: observations at site or patch level. growth is the mean relative diameter increment (dbh/dbh_before - 1), and growth_n is how many trees that mean rests on. Both matter: means are aggregated across patches TREE-WEIGHTED (by growth_n), because that is the quantity the model predicts - it simulates sum(g*trees)/sum(trees) over the whole site, so an unweighted mean of patch means would be a different number. growth_n also travels with the response so fit can weight the likelihood by it: 34% of patch-level growth observations rest on a single tree while others average 30+, and the variance of a mean is \(\sigma^2/n\). Mortality comes back as a closed-cohort pair of counts, n_at_risk (trees alive at the start of the interval) and n_died (how many of them were dead at the end), plus the derived rate mort = n_died / n_at_risk (NA where no cohort was at risk). The counts are the binomial response; pass them to fit with mortality = "binomial".

  • tree_dt: input trees with added growth fields and species recode.