The concept of ecological stability occupies a prominent place in both fundamental and applied ecological research. We review decades of work on the topic and examine how our understanding has progressed. We show that our understanding of stability has remained fragmented and is limited largely to simple or simplified systems. There has been a profusion of metrics proposed to quantify stability, of which only a handful are used commonly. Furthermore, studies typically quantify one to two metrics of stability at a time and in response to a single perturbation, with some of the main environmental pressures of today being the least studied. We argue that we need to build on the existing consensus and strong theoretical foundation of the stability concept to better understand its multidimensionality and the interdependencies between metrics, levels of organization and types of perturbations. Only by doing so can we make progress in the quantification of stability in theory and in practice and eventually build a more comprehensive understanding of how ecosystems will respond to ongoing environmental change.
README
Description of the different files submitted.
Data base of papers
This data base is composed of 459 empirical and theoretical papers quantifying stability in an ecological system and published between 1950 January 2018 in the journals Ecology, American Naturalist, Oikos, Ecology Letters, Science, Proceedings of the National Academy of Sciences of the United States of America (PNAS), Scientific Reports, Nature and Nature Communications. It can be visualized and edited using http://jsoneditoronline.org/. More information about the content of the data base can be found in the article and in guidelines_literature-analysis.docx.
merged_db_final.json
Guidelines for the content of the data base
Information about the content of the data base merged_db_final.json
guidelines_literature-analysis.docx
Python code for figures 1a and 2
Python code allowing to generate figure 1a and the data for figure 2 of the paper. It requires the package tethne: https://diging.github.io/tethne/ (works with python 2). It uses the data base of the paper "merged_db_final.json" and generates 4 output files: figure_1a.pdf and perturbation/metric_table.png (i.e. the tables used to generate figure 2).
Plot_fig_1a-2.py
R code for figure 1b, 3, S2 and S3
R code used to generate figure 1b, 3, S2 and S3. It uses the files: papers.csv, pert.csv and fig_hist_metricsfreq.csv which were extracted from the data base "merged_db_final.json".
Plot_fig_1b-3-S2-S3.R
fig_hist_metricsfreq csv file
Data file extracted from the data base containing the names of all the metrics (first column) and the number of times each of these metrics was used in the papers from the data base. This file is called by the code Plot_fig_1b-3-S2-S3.R and is used to generate fig 1b.
fig_hist_metricsfreq.csv
papers csv file
Data file extracted from the data base containing all the elements (presented in guidelines_literature-analysis.docx
) that have been recorded for each paper of the data base. This is called by the code Plot_fig_1b-3-S2-S3.R and is used to generate fig. S2 and S3
papers.csv
pert csv file
Data file extracted from the data base containing information about all the perturbations studied in the papers of the data base. It is called by the code Plot_fig_1b-3-S2-S3.R and is used to generate fig. 3 and S3.
pert.csv