Despite intensive research on species dissimilarity patterns across communities (i.e. beta-diversity), we still know little about their implications for variation in food-web structures. Our analyses of 50 lake and 48 forest soil communities show that, while species dissimilarity depends on environmental and spatial gradients, these effects are only weakly propagated to the networks. Moreover, our results show that species and food-web dissimilarities are consistently correlated, but that much of the variation in food-web structure across spatial, environmental, and species gradients remains unexplained. Novel food-web assembly models demonstrate the importance of biotic filtering during community assembly by (1) the availability of resources, and (2) limiting similarity in species’ interactions to avoid strong niche overlap and thus competitive exclusion. This reveals a strong signature of biotic filtering processes during local community assembly, which constrains the variability in structural food-web patterns across local communities despite substantial turnover in species composition.

This archive contains the data and scripts necessary to reproduce the statistical analyses of the study Biotic filtering by species’ interactions constrains food-web variability across spatial and abiotic gradients.

• Food-web and environmental data were obtained from the GATEWAy (1.0) database: https://idata.idiv.de/ddm/Data/ShowData/283?version=3.
• Food-web topolgical metrics were calculated using the script allMetBB.R. 
• Jaccard dissimilarity was calculated using the R package vegan.
• Structural Equation Models (SEMs) were performed using the R package lavaan.

This archive contains also the script src/sampling-error.R, which reproduce potential sampling errors in species composition through a Monte Carlo approach. The data file data/samling-error.csv contains the raw data generated from the Monte Carlo and can be loaded to reproduce the sensitivity analyses. The script SEM_bootstrap.R reproduces the bootstrap analyses for the SEMs.

Data files have .csv extensions or .RData extension.

All R scripts have .R extension.

To save all plots, first create the subfolder plots in this directory.

The script simulations.R is very resource intensive and requires a long time to complete; to skip this, simply load the results already saved in simluations.csv.