Data and code of the study "Three decades of butterfly–plant interaction turnover explained by climate and species loss"

This repository contains the datasets and R scripts used in the analyses for the manuscript: Colom, P., Stefanescu, C., Corbera, J. & Lázaro, A. (2025).Three decades of butterfly–plant interaction turnover explained by climate and species loss. The study uses long-term (13–29 years) standardized data on butterfly–plant interactions from seven Mediterranean communities to investigate how inter-annual climate variability and species loss shape temporal interaction turnover and its components — species turnover and rewiring. 📁 Repository contents Data files (CSV format) File Description annual_climate_data.csv Mean annual temperature and precipitation per site and year. butterfly_season_climate_data.csv Climate data corresponding to the butterfly flight season. butterfly_trait_data.csv Species-level ecological and morphological traits. interaction_turnover_novotny.csv Interaction turnover and its components calculated following Fründ (2021) and Novotny (2009). persistence.csv Species persistence in interaction networks across years by sites. phenology_abundance_change.csv Interannual changes in butterfly abundance and phenology phenology_abundance_variables.csv Phenological and abundance descriptors derived from GAMs (peak day, flight length, total abundance). rewiring_binomial.csv Dataset for the binomial GLMM testing rewiring probability. rewiring_frequency.csv Dataset for the GLMM testing species-level rewiring frequency. R scripts Script Description abundance_phenology_change.R Computes interannual changes in abundance and phenology. abundance_phenology_variables.R Fits GAMs to derive phenological metrics for each species–site–year combination. butterfly_richness_trends.R Fits GLMMs and site-level models of butterfly species richness over time. interaction_turnover_components_novotny.R Partitions interaction turnover into species turnover and rewiring. interaction_turnover_models.R GLMMs testing temporal and climatic effects on turnover components. persistence.R Calculates species persistence across years. persistence_rewiring_freq_models.R GLMMs linking persistence and rewiring frequency to species traits. rewiring_freq_binomial.R Prepares data for binomial models of rewiring. rewiring_probability_model.R Binomial GLMM testing effects of abundance and phenology variation on rewiring probability. RLQ_fourth_corner.R Performs RLQ and fourth-corner analyses linking traits to temporal and climatic gradients. *Raw data on butterfly abundance and butterfly–plant interactions are not publicly available due to data volume and data-sharing restrictions from the Catalan Butterfly Monitoring Scheme. These data can be obtained from the corresponding author upon reasonable request. 📊 Data provenance and acknowledgements Butterfly and interaction data: Collected by the authors in the frame of the Catalan Butterfly Monitoring Scheme (www.catalanbms.org). Climate data: downscaled using ClimateDT (Marchi et al. 2024) and CHELSA v2.1 (Karger et al. 2017). Trait data: compiled from Vila et al. (2018), García-Barros et al. (2013), and own measurements. García-Barros, E., Munguira, M.L., Stefanescu, C. & Vives-Moreno, A. (2013). Lepidoptera: Papilionoidea. Museo Nacional de Ciencias Naturales-CSIC, Madrid. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., et al. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific data, 4(1), 1-20. Marchi, M., Bucci, G., Iovieno, P. & Ray, D. (2024). ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data. Environments, 11(4), 82. Vila, R., Stefanescu, C. & Sesma, J.M. (2018). Guia de les papallones diürnes de Catalunya. Lynx Edici. Barcelona. 📬 Contact Pau ColomDepartment of Evolutionary Biology, Ecology and Environmental Sciences, University of BarcelonaBiodiversity Research Institute (IRBio), Spain📧 [e-mail] ; [e-mail]