We built a database of morphometric and ecological data on a representative taxon sample of the order Araneae. We followed the taxon sample of the Araneae Tree of Life project (AToL) 2, which contains 932 terminals of all at that time valid families except Synaphridae (corresponds to ~2% of described species). This sample is representative of the phylogenetic and morphological diversity of spiders. AToL terminals that were not identified to species level and for which no image material was available were replaced with described species with a type locality close to the collection site (26.3% of the used sample; details in main article 1). 11% of the AToL terminals were omitted as there was not enough information to determine a suitable replacement, resulting in a total of 828 included species. The morphological data were assembled by extracting data from taxonomic descriptions using the WSC database 3, and measurements on images published in articles or online repositories (including Morphbank :: Biological Imaging, http://www.morphbank.net, where images of AToL specimens were deposited), with one to seven sources combined per species (for statistics of used sources see main article 1, and for a list see file “Dataset_morphometric-data-raw.xlxs”). As many spiders exhibit a significant sexual dimorphism, only data of adult females were used. We included only general traits, i.e., ones that were assumed to be affected by more than one niche property. For instance, body shape may be under selection from a mix of abiotic (e.g., temperature and microhabitat structure) and biotic (e.g., prey spectrum and predation) factors. The following measurements were recorded: body length; cephalothorax (prosoma) length; cephalothorax width; height of cephalothorax (carapace); length of mouth parts (i.e. cheliceral base segment); diameter of each eye type; length of front leg (excl. coxa, trochanter and pretarsus). From these the following six traits were calculated: (1) body size (=body length); (2) body shape (cephalothorax width / cephalothorax length); (3) relative cephalothorax height (cephalothorax height / (cephalothorax length + width)); (4) size of mouth parts (paturon length / cephalothorax height); (5) eye size (sum of diameters of all eye types / cephalothorax width); (6) relative leg length (length of front leg / cephalothorax width). From each trait the species mean was calculated (i.e., from the 1-7 data sources, for details see main article 1 and file “Dataset_morphometric-data-raw.xlsx”) and log-transformed, to build the species matrix for further analysis (file “Dataset_combined-trait-matrix.csv”). The ecological data matrix was built by assessing the literature on same or closely related species, and in few cases complemented by personal observations (for details, see notes in file “Dataset_ecological-data-raw.xlsx”). We used a binary coded category: state 0, non-builder; state 1, builder. We defined a species as a ‘builder’ (1), if individuals spend most of their life in a self-constructed web or burrow, i.e. foraging and reproduction takes place on, in or from the artefact, and the artefact aids in prey capture, signalling and/or defence. In contrast, a ‘non-builder’ (0) does not build a capture web or a burrow, it may build a retreat, which, however, is only used in periods of inactivity and does not aid in prey capture. Method References 1 Wolff, J. O., Wierucka, K., Uhl, G., & Herberstein, M. E. Building behavior does not drive rates of phenotypic evolution in spiders. Proc Natl Acad Sci 118, e2102693118 (2021). 2 Wheeler, W. C. et al. The spider tree of life: phylogeny of Araneae based on target‐gene analyses from an extensive taxon sampling. Cladistics 33, 574-616 (2017). 3 Nentwig, W., Gloor, D. & Kropf, C. Taxonomic database: Spider taxonomists catch data on web. Nat Cell Biol 528, 479 (2015).

This data set contains raw data tables, scripts and supplemental figures supporting the article Wolff et al. (2021, PNAS 118: e2102693118). In our study we assembled morphometric and ecological trait data of spiders from literature and de novo measurements and observations and used this data to infer the rates of morphological change over deep time in relation to web building behaviour.

For a description of files and data please refer to the README file.