INDperform-package: INDperform: Performance validation of ecological state...

In saskiaotto/INDperform: Evaluation of Indicator Performances for Assessing Ecosystem States

Description

INDperform provides an implementation of the 7-step approach suggested by Otto 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. See also the website: https://saskiaotto.github.io/INDperform/

Details

The package builds upon the tidy data principles and offers functions to

• identify temporal indicator changes,

• model relationships to pressures while taking non-linear responses and temporal autocorrelation into account, and to

• 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.

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 et al., 2018).

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 mgcv package):

• model_trend

• ind_init

• model_gam

• model_gamm

• select_model

• merge_models

• calc_deriv

• select_interaction

• test_interaction

To show the model diagnostics or complete model results use the functions:

• plot_diagnostics

• plot_trend

• plot_model

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 et al. (2018) serves as basis for the quantification of the IND performance (see the scoring template table 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):

• scoring

• expect_resp

• summary_sc

• plot_spiechart

For examining redundancies and selecting robust indicator suites use (in that order):

• dist_sc

• dist_sc_group

• clust_sc

• plot_clust_sc

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:

• statespace_ed

• 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).

• statespace_ch

• plot_statespace_ch

Author(s)

Maintainer: Saskia A. Otto [e-mail] (ORCID)

Authors:

• Rene Plonus

• Steffen Funk

• Alexander Keth

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. Ecological Indicators, 84: 619-631, doi: https://doi.org/10.1016/j.ecolind.2017.05.045

See Also

Useful links:

• https://saskiaotto.github.io/INDperform/

• Report bugs at https://github.com/saskiaotto/INDperform/issues

saskiaotto/INDperform documentation built on Oct. 27, 2021, 10:33 p.m.