R/data-mortaforest.r

In biometrics: A Package for Biometrics and Modelling

#' Climatic, forest structure and forest mortality variables in California (USA)
#'
#' @description
#' The data file contains one row per unique 3.5km grid cell by year
#' combination. The data frame covers
#' all grid cells within the state of California where at least one Aerial
#'  Detection Survey (ADS) flight
#' was taken between 2009 and 2015, so each grid cell position has between
#' 1 and 7 years of data
#' (reflected as 1 to 7 rows in the data file per grid cell position).
#' The main response variables
#' are `mort.bin` (presence of any mortality) and `mort.tph` (number of dead
#' trees/ha within the given
#' grid cell by year).
#' @usage
#' data(mortaforest)
#' @format The data frame contains four variables as follows:
#' \describe{
#' \item{live.bah}{Live basal area from the GNN dataset}
#' \item{live.tph}{Live trees per hectare from the GNN dataset}
#' \item{pos.x}{rank-order x-position of the grid cell (position `1` is
#'  western-most)}
#' \item{pos.y}{rank-order y-position of the grid cell (position `1` is
#'  northern-most)}
#' \item{alb.x}{x-coordinate of the grid cell centroid in California
#' Albers (EPSG 3310)}
#' \item{alb.y}{y-coordinate of the grid cell centroid in California
#' Albers (EPSG 3310)}
#' \item{mort.bin}{`1`= dead trees observed in grid cell. `0`= no dead
#' trees observed}
#' \item{mort.tph}{Dead trees per hectare from the aggregated ADS dataset}
#' \item{mort.tpa}{Dead trees per acre from the aggregated ADS dataset}
#' \item{year}{Year of the ADS flight. Most flights occurred from May-August.}
#' \item{Defnorm}{Mean annual climatic water deficit for the grid cell, for
#' Oct 1-Sept 31 water year, averaged from 1981-2015}
#' \item{Def0}{Climatic water deficit for the grid cell during the Oct-Sept
#' water year overlapping the summer ADS flight of the given year}
#' \item{Defz0}{Z-score for climatic water deficit for the given grid
#' cell/water year. Calculated as (`Def0`--`Defnorm`)/(standard deviation in
#'  deficit among all years 1981-2015 for the given grid cell)}
#' \item{Defz1}{Z-score for climatic water deficit for the given grid cell
#'  in the preceeding water year.}
#' \item{Defz2}{Z-score for climatic water deficit for the given grid cell
#' two water years prior.}
#' \item{Tz0}{Z-score for temperature for the given grid cell/year.}
#' \item{Pz0}{Z-score for precipitation for the given grid cell/year.}
#' \item{Defquant}{FDCI variable. Quantile of `Defnorm` of the given grid cell,
#'  relative to the `Defnorm` of all other grid cells with a basal area
#'   within 2.5 m\eqn{^{2}}{^2}/ha of the given cell is basal area.}
#'  }
#' @source
#' The data were obtained from the DRYAD repository \doi{10.5061/dryad.7vt36}
#' @references
#' - Young DJN, Stevens JS, Earles JM, Moore J, Ellis A, Jirka AM,
#' Latimer ML. 2017. Long-term climate and competition explain forest
#' mortality patterns under extreme drought. Ecology Letters
#' 20(1):78-86. \doi{10.1111/ele.12711}
#'
#' - Salas-Eljatib C, Fuentes-Ramírez A, Gregoire TG, Altamirano A,
#' Yaitul V. A study on the effects of unbalanced data when fitting
#' logistic regression models in ecology. Ecological Indicators
#' 85:502-508. \doi{10.1016/j.ecolind.2017.10.030}
#'
#' @examples
#' data(mortaforest)
#' head(mortaforest)
'mortaforest'