R/EDR_data.R

In ecoregime: Analysis of Ecological Dynamic Regimes

#' Ecological Dynamic Regime data
#'
#' @description
#' Example datasets to characterize and compare EDRs, including abundance data,
#' state, segment, and trajectory dissimilarity matrices for 93 artificial communities
#' belonging to three different EDRs.
#'
#' @format
#' List of four nested sublists. Each element of `"EDR1"`, `"EDR2"`, and `"EDR3"`
#' is associated with one EDR and includes the following elements:
#' * `abundance`: Data table with 15 columns and one row for each community state:
#'     + `EDR`: Integer indicating the identifier of the EDR.
#'     + `traj`: Integer containing the identifier of the trajectory for each
#'     artificial community in the corresponding EDR. Each trajectory represents
#'     a different sampling unit.
#'     + `state`: Integer indicating the observations or states of each community.
#'     The sequence of states of a given community forms a trajectory.
#'     + `sp1, ..., sp12`: Vectors containing species abundances for each community
#'     state.
#' * `state_dissim`: Object of class `dist` containing Bray-Curtis dissimilarities
#' between every pair of states in `abundance`.
#' * `segment_dissim`: Object of class `dist` containing the dissimilarities
#' between every pair of trajectory segments in `abundance`.
#' * `traj_dissim`: Object of class `dist` containing the dissimilarities
#' between every pair of community trajectories in `abundance`.
#'
#' The element `EDR3_disturbed` represents the dynamics of three disturbed communities
#' originally associated with EDR3. It includes an abundance matrix with 16 columns
#' and one row for each community state. The column `disturbed_states` is a numeric
#' vector indicating whether the corresponding state represents a state before the
#' disturbance (0), during or immediately after the release of the disturbance (1),
#' or a post-disturbance state (> 1).
#'
#' @details
#' Artificial data was generated following the procedure explained in Box 1 in
#' Sánchez-Pinillos et al. (2023). The initial state of each community was
#' defined using a hypothetical environmental space with optimal locations for
#' 12 species. Community dynamics were simulated using a general Lotka-Volterra model.
#'
#' Abundances for `EDR3_disturbed` were generated following the procedure explained
#' in Sánchez-Pinillos et al. (2024) for ecological systems affected by pulse
#' disturbances.
#'
#' State dissimilarities were calculated using the Bray-Curtis metric. Segment and
#' trajectory dissimilarities were calculated using the package 'ecotraj'.
#'
#' @references
#' Sánchez-Pinillos, M., Kéfi, S., De Cáceres, M., Dakos, V. 2023. Ecological Dynamic
#' Regimes: Identification, characterization, and comparison. *Ecological Monographs*.
#' <doi:10.1002/ecm.1589>
#'
#' Sánchez-Pinillos, M., Dakos, V.,  Kéfi, S. 2024. Ecological Dynamic
#' Regimes: A key concept for assessing ecological resilience. *Biological Conservation*.
#' <doi:10.1016/j.biocon.2023.110409>
#'
#'
"EDR_data"