Comparing species interaction networks along environmental gradients
|Author(s)||Pellissier Loic1, 2, Albouy Camille1, 2, 3, Bascompte Jordi4, Farwig Nina5, Graham Catherine2, Loreau Michel6, 7, Maglianesi Maria Alejandra8, 9, 10, Melian Carlos J.11, Pitteloud Camille1, 2, Roslin Tomas12, Rohr Rudolf13, Saavedra Serguei14, Thuiller Wilfried15, Woodward Guy16, Zimmermann Niklaus E.1, 2, Gravel Dominique17|
|Affiliation(s)||1 : ETH, Inst Terr Ecosyst, Landscape Ecol, Zurich, Switzerland.
2 : Swiss Fed Res Inst WSL, CH-8903 Birmensdorf, Switzerland.
3 : IFREMER, Unite Ecol & Modeles Halieut, Rue Ile dYeu,BP21105, F-44311 Nantes 3, France.
4 : Univ Zurich, Dept Evolutionary Biol & Environm Studies, CH-8057 Zurich, Switzerland.
5 : Philipps Univ Marburg, Conservat Ecol, Fac Biol, Karl von Frisch Str 8, D-35032 Marburg, Germany.
6 : CNRS, Ctr Biodivers Theory & Modelling, Theoret & Expt Ecol Stn, F-09200 Moulis, France.
7 : Paul Sabatier Univ, F-09200 Moulis, France.
8 : Univ Estatal Distancia, Vicerrectoria Invest, San Jose 2050, Costa Rica.
9 : Biodivers & Climate Res Ctr BiK F, D-60325 Frankfurt, Germany.
10 : Senckenberg Gesell Naturforsch, D-60325 Frankfurt, Germany.
11 : Eawag Swiss Fed Inst Aquat Sci & Technol, Dept Fish Ecol & Evolut, CH-6047 Kastanienbaum, Switzerland.
12 : Swedish Univ Agr Sci, Dept Ecol, Uppsala, Sweden.
13 : Univ Fribourg, Dept Biol Ecol & Evolut, Fribourg, Switzerland.
14 : MIT, Dept Civil & Environm Engn, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
15 : Univ Grenoble Alpes, CNRS, LECA Lab Ecol Alpine, F-38000 Grenoble, France.
16 : Imperial Coll London, Dept Life Sci, Silwood Pk Campus, Ascot SL5 7PY, Berks, England.
17 : Univ Sherbrooke, Dept Biol, Fac Sci, Canada Res Chair Integrat Ecol, 2500 Blvd Univ, Sherbrooke, PQ J1K 2R1, Canada.
|Source||Biological Reviews (1464-7931) (Wiley), 2018-05 , Vol. 93 , N. 2 , P. 785-800|
|WOS© Times Cited||140|
|Keyword(s)||network, metaweb, motif, rarefaction analysis, null model, environmental gradient, network comparison, network properties|
Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis-based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.