DOI: 10.5061/dryad.stqjq2cd6
title: Data and code from: Network theory predicts ecosystem robustness across environmental conditions

description: Network theory quantifies how changes in species richness, S, lead to
 changes in the number of interactions (or links) between species, L.
 Networks with a steep relationship between L and S have a high number of
 links per species, making the network resistant to collapse and therefore
 more robust. However, changes in S often coincide with environmental
 shifts, which can lead to impacts on L that are not expected from network
 theory. In this paper, we constructed relationships between L and S for 18
 ecosystems using 1081 observations collected across 420 environmental
 conditions. We found that environmental noise (unspecified spatiotemporal
 variation) and environmental gradients (directional environmental change)
 commonly affected ecological network size (S and L), community
 composition, and also induced network rewiring, which means that species
 changed interaction partners as the environment changed. Yet, we found the
 log(L) ~ log(S) relationship to be remarkably constant across
 environmental conditions. Specifically, the slope of this relationship
 remained constant across conditions, implying consistency in how species
 loss proportionally affects L. Our results therefore show that network
 theory predicts ecosystem robustness across environmental conditions.
 These results suggest generality to how environmental drivers operate at
 the level of ecological networks, which is encouraging for conservation.
description: We searched the literature to collect ecological network datasets
 with at least eight observations per ecosystem (to allow for meaningful
 linear regressions), ensuring that these datasets were based on field
 observations rather than simulations. Network observations were coded as
 undirected adjacency matrices, where 0 and 1 indicated the absence or
 presence of an interaction, respectively. For each network observation, we
 recorded the number of species (S), the number of interactions (or links,
 L), and the composition of observed species and links. In cases where
 organisms were not identified at the species level, the term
 "species" referred to taxa.
description: # Data and code from: Network theory predicts ecosystem robustness across
 environmental conditions Title: Network theory predicts ecosystem
 robustness across environmental conditions Authors: Germain Agazzi,
 Camille Carpentier, Olivia Bleeckx, and Frédérik De Laender Contact:
 [germain.ag@gmail.com](mailto:germain.ag@gmail.com) or
 [frederik.delaender@unamur.be](mailto:frederik.delaender@unamur.be) ###
 Introduction In this paper, we reused datasets from previously published
 papers in the literature. The zip file
 "Datasets_NetworkPredictRobustnessEnvironmentalConditions"
 contains these datasets, which have been transformed from their original
 to fit to the format used in this study. All of these datasets are
 described in more detail in their original publications (references are in
 the following text). The "dataset" section summarizes them, and
 the "code" section explains the code. ### Dataset All datasets
 contain common columns: * A and B: names of the two interacting species
 (sometimes represented by codes) * inter: name of the interaction between
 A and B * category: category of the network (antagonistic or mutualistic)
 * partition: partition type of the network (unipartite or bipartite) All
 of them also contain other columns that are detailed below. File Alcantara
 (from Julio M. Alcántara and Pedro J. Rey. Linking Topological Structure
 and Dynamics in Ecological Networks. The American Naturalist, August
 2012): * web: code for the web (location_code) * location: location name *
 code: treatment code File Chiu (from Chun-Huo Chiu, Anne Chao, Sebastian
 Vogel, Peter Kriegel, and Simon Thorn. Quantifying and estimating
 ecological network diversity based on incomplete sampling data.
 Philosophical Transactions of the Royal Society B: Biological Sciences,
 378(1881):20220183, May 2023): * plot: code referring to the plot *
 treatment: treatment abbreviation, "G" for naturally shaded,
 "N" for artificially shaded, and "O" for open * year:
 sampling year File David (from Jeanne David and Renate A. Wesselingh.
 Étude de la biodiversité des espaces vert gérés par l’UCLouvain sur le
 campus de Louvain-la-Neuve: type de gestion et réseaux
 plantes-pollinisateurs. Master thesis, Catholic university of Louvain La
 Neuve, 2023): * site: site name * gestion: gestion method,
 "Tonte" for mowing and "Fauche" for reaping * date:
 sampling month, 'Juin" for June "miJuillet" for
 "mid July" and "finJuillet" for "end July"
 File Guardiola (from Moisès Guardiola, Constanti Stefanescu, Ferran Roda,
 and Joan Pino. Do asynchronies in extinction debt affect the structure of
 trophic networks? A case study of antagonistic butterfly larvaeplant
 networks - Guardiola - 2018 - Oikos - Wiley Online Library. Oikos,
 November 2017): * web: Id of the web File Kemp (from Jurene E. Kemp,
 Darren M. Evans, Willem J. Augustyn, and Allan G. Ellis. Invariant
 antagonistic network structure despite high spatial and temporal turnover
 of interactions. Ecography, 40(11):1315–1324, 2017): * location: location
 name * date: season File Nielsen (from Anders Nielsen and Ørjan Totland.
 Structural properties of mutualistic networks withstand habitat
 degradation while species functional roles might change. Oikos,
 123(3):323–333, 2014): * site: site code * web: ID web (site_state) *
 state: gestion state, "CC" for clear cut, "YF" for
 young forest, and "OGF" for old growth forest File Olito (from
 Colin Olito and Jeremy W. Fox. Species traits and abundances predict
 metrics of plant–pollinator network structure, but not pairwise
 interactions. Oikos, 124(4):428–436, 2015.): * tsect: transect code *
 jdate: sampling day * meter: altitude File Saavedra(from Serguei Saavedra,
 Simone Cenci, Ek Del-Kal, Karina Boege, and Rudolf P. Rohr. Reorganization
 of interaction networks modulates the persistence of species in late
 successional stages. Journal of Animal Ecology, May 2017): * state:
 successional state * location: location code * date: date code File
 Saravia (from Leonardo A. Saravia, Tomás I. Marina, Nadiah P. Kristensen,
 Marleen De Troch, and Fernando R. Momo. Ecological network assembly: How
 the regional metaweb influences local food webs. Journal of Animal
 Ecology, 91(3):630-642, 2022): * foodweb.name: web name * study.site:
 ecosystem name File VanderZee (from Els M. van der Zee, Christine
 Angelini, Laura L. Govers, Marjolijn J. A. Christianen, Andrew H. Altieri,
 Karin J. van der Reijden, Brian R. Silliman, Johan van de Koppel, Matthijs
 van der Geest, Jan A. van Gils, Henk W. van der Veer, Theunis Piersma,
 Peter C. de Ruiter, Han Olff, and Tjisse van der Heide. How
 habitat-modifying organisms structure the food web of two coastal
 ecosystems. Proceedings of the Royal Society B: Biological Sciences,
 283(1826):20152326, March 2016): * site: site code, "BdA" for
 Africa and "RI" for America * treatment: treatment code,
 "B" for bare ground, "C" for cobblestone, and
 "E" for colonized * plot: plot code File Welti (from Ellen A.R.
 Welti, Fan Qiu, Hannah M. Tetreault, Mark Ungener, John Blair, and Anthony
 Joern. Fire, grazing, and climate shape plant–grasshopper interactions in
 a tallgrass prairie. Functional Ecology, December 2018.): * year: sampling
 year * plot: plot code File Wood (from Spencer A. Wood, Roly Russell,
 Dieta Hanson, Richard J. Williams, and Jennifer A. Dunne. Effects of
 spatial scale of sampling on food web structure. Ecology and Evolution,
 5(17):3769–3782, September 2015.): * WebID: web ID * WebScale: sampling
 scale, "Q" for quadrats, "L" for locales and
 "S" for sites ### Code The code
 (Code_NetworkPredictRobustnessEnvironmentalConditions.R) is run under R
 4.4.2 with the Following packages: * reshape2 1.4.4 * tidyverse 2.0.0 *
 car 3.1-3 * arm 1.14-4 * rstatix 0.7.2 * fitdistrplus 1.2-1 * lme4
 1.1-35.5 * ade4 1.7-22 * factoextra 1.0.7 * lmerTest 3.1-3 * MuMIn 1.48.4
 * performance 0.12.4 * stringr 1.5.1 * vegan 2.6-8 * ggpmisc 0.6.1 *
 cowplot 1.1.3 * patchwork 1.3.0 * ggpubr 0.6.0 The code is divided into
 several parts: * setup: setup, importations, commands, and packages *
 analyses: individual analyses for each dataset * all: merging of all
 datasets and analyses * graphs: code for generating figures * Diff: beta
 diversity analyses