DOI: 10.5061/dryad.c0t1892
title: Data from: Advancing our understanding of ecological stability

description: 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.
description: READMEDescription of the
 different files submitted.Data base of
 papersThis 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.jsonGuidelines for the content of the data baseInformation about the content of the data base merged_db_final.jsonguidelines_literature-analysis.docxPython code for figures 1a and 2Python 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.pyR code for figure 1b, 3, S2 and S3R 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.Rfig_hist_metricsfreq csv fileData 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.csvpapers csv fileData 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 S3papers.csvpert csv fileData 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