R/INDperform.R

In INDperform: Evaluation of Indicator Performances for Assessing Ecosystem States

#' INDperform: Performance validation of ecological state indicators
#'
#' INDperform provides an implementation of the 7-step approach suggested by
#' Otto \emph{et al.} (2018) to validate ecological state indicators
#' and to select a suite of complimentary and well performing indicators.
#' This suite can be then used to assess the current state of the system
#' in comparison to a reference period.
#'
#' The package builds upon the tidy data principles and offers functions to
#' \itemize{
#'   \item identify temporal indicator changes,
#'   \item model relationships to pressures while taking non-linear responses
#'         and temporal autocorrelation into account, and to
#'   \item quantify the robustness of these models.
#' }
#'
#' These functions can be executed on any number of indicators and pressures.
#' Based on these analyses and a scoring scheme for selected criteria the
#' individual performances can be quantified, visualized, and compared. The
#' combination of tools provided in this package can significantly help making
#' state indicators operational under given management schemes such as the
#' EU Marine Strategy Framework Directive.
#'
#' @section Usage:
#' INDperform offers function that can be applied individually to some
#' extent but mostly build upon each other to follow the 7-step approach.
#' They can be grouped into 3 broad categories. For demonstration purposes
#' the package provides a dataset of food web indicators and pressure
#' variables in the Central Baltic Sea (modified from Otto \emph{et al.},
#' 2018).
#'
#' @section 1. Validation of IND performances:
#' The following functions implement the first five steps of the 7-step
#' validation approach and model each IND as a function of time or a
#' single pressure variable using Generalized Additive Models (GAMs)
#' (based on the \code{\link{mgcv}} package):
#' \itemize{
#'   \item \code{\link{model_trend}}
#'   \item \code{\link{ind_init}}
#'   \item \code{\link{model_gam}}
#'   \item \code{\link{model_gamm}}
#'   \item \code{\link{select_model}}
#'   \item \code{\link{merge_models}}
#'   \item \code{\link{calc_deriv}}
#'   \item \code{\link{select_interaction}}
#'   \item \code{\link{test_interaction}}
#' }
#'
#' To show the model diagnostics or complete model results use the functions:
#' \itemize{
#'   \item \code{\link{plot_diagnostics}}
#'   \item \code{\link{plot_trend}}
#'   \item \code{\link{plot_model}}
#' }
#'
#'
#' @section 2. Scoring IND performance based on model output:
#' Among the 16 common indicator selection criteria, five criteria relate
#' to the indicators` performances and require time series for their
#' evaluation, i.e.
#'
#' 8. Development reflects ecosystem change caused by variation in
#'  manageable pressure(s)
#'
#' 9. Sensitive or responsive to pressures
#'
#' 10. Robust, i.e. responses in a predictive fashion, and statistically sound
#'
#' 11. Links to management measures (responsiveness and specificity)
#'
#' 12. Relates where appropriate to other indicators but is not redundant
#'
#' In this package, the scoring scheme for these criteria as proposed by
#' Otto \emph{et al.} (2018) serves as basis for the quantification
#' of the IND performance (see the scoring template table
#' \code{\link{crit_scores_tmpl}}). Sensitivity (criterion 9) and
#' robustness (criterion 10) are specified into more detailed sub-criteria
#' to allow for quantification based on statistical models and rated
#' individually for every potential pressure that might affect the IND
#' directly or indirectly.
#'
#' However, the scoring template can easily be adapted to any kind of state
#' indicator and management scheme by modifying the scores, the weighting
#' of scores or by removing (sub)criteria.
#'
#' The following functions relate to the indicator performance scoring
#' (used in this order):
#' \itemize{
#'   \item \code{\link{scoring}}
#'   \item \code{\link{expect_resp}}
#'   \item \code{\link{summary_sc}}
#'   \item \code{\link{plot_spiechart}}
#' }
#'
#' For examining redundancies and selecting robust indicator suites use
#' (in that order):
#' \itemize{
#'   \item \code{\link{dist_sc}}
#'   \item \code{\link{clust_sc}}
#'   \item \code{\link{plot_clust_sc}}
#' }
#'
#'
#' @section 3. Assessment of current state status:
#' Two approaches based on trajectories in state space to determine
#' the current state of the system in comparison to an earlier period
#' as reference using the selected IND suite (state space =
#' n-dimensional space of possible locations of IND variables)
#'
#' 1. Calculation of the Euclidean distance in state space of any
#' dimensionality between each single year (or any other time step
#' used) and a defined reference year:
#' \itemize{
#'   \item \code{\link{statespace_ed}}
#'   \item \code{\link{plot_statespace_ed}}
#' }
#'
#' 2. Given the identification of a reference domain in state space,
#' more recent observations might lie within or outside this domain.
#' The convex hull is a multivariate measure derived from
#' computational geometry representing the smallest convex set
#' containing all the reference points in Euclidean plane or space.
#' For visualization, only 2 dimensions considered (dimension
#' reduction through e.g. Principal Component Analysis suggested).
#' \itemize{
#'   \item \code{\link{statespace_ch}}
#'   \item \code{\link{plot_statespace_ch}}
#' }
#'
#'
#' @references
#' To learn more about the framework, see
#'
#' Otto, S.A., Kadin, M., Casini, M., Torres, M.A., Blenckner, T. (2018)
#' A quantitative framework for selecting and validating food web indicators.
#' \emph{Ecological Indicators}, 84: 619-631,
#' doi: https://doi.org/10.1016/j.ecolind.2017.05.045
#'
#'
"_PACKAGE"