traitscrape is an R package designed to rapidly retrieve categorical coral trait information from the CoralTrait Database (www.coraltraits.org) and summarise traits at both the species and genus levels. It is built to support ecological, demographic, and resilience-based analyses where trait data must be extracted consistently and reproducibly across species lists or entire genera. This release (v0.1.0) provides a stable version of the package intended for scientific use and long-term reproducibility. Installation install.packages("remotes") remotes::install_github("dohertyml/traitscrape") Core functionality 1. spp_scrape() — Species-level trait extraction spp_scrape() accepts any character vector of coral binomials and performs the following steps: Cleans and standardises species names Matches them to CoralTraits species records Retrieves selected categorical traits for each species via CoralTraits’ public endpoints Returns a tidy tibble with: species ID species name genus trait ID trait name trait value the CoralTraits URL used HTTP status indicators cleaned species names for matching This is the recommended function when users have a verified species list for their region, ensuring that only locally present species are used in any trait analysis. 2. genus_scrape() — Genus-level retrieval genus_scrape() retrieves all species belonging to specified genera in CoralTraits, then extracts trait observations for every species × trait combination. This function is intended for use cases where: species-level identification is incomplete or uncertain researchers only have a genus list from a survey traits must be inferred at genus level broad structural or life-history patterns are being analysed This avoids the need to manually compile global species lists for each genus, and guarantees consistent pulling of all available CoralTraits trait entries. 3. dominant_traits() — Genus-level trait summarisation Trait values vary across species within a genus. To support higher-level trait analyses, dominant_traits() collapses species-level trait observations to a single representative value per genus × trait. It provides: modal trait value per genus optional tie handling ("first" or "NA") counts of species contributing to each modal category This produces a clean genus × trait table suitable for demographic modelling, resilience comparisons, or trait-based community analyses. Trait set included By default, traitscrape extracts the most widely used categorical coral traits: Life history strategy (Darling et al. 2012) Sexual system Mode of larval development The life history strategy trait in particular follows the Darwinian / functional ecology framework introduced by Darling et al. (2012), which classifies corals into competitive, stress-tolerant, weedy, or generalist strategies. This classification underpins much modern resilience, demographic, and recovery modelling. Users may optionally supply custom trait IDs or additional traits to the scraper. Workflow overview Prepare a vector of species names or genus names Call spp_scrape() or genus_scrape() Inspect or clean the resulting trait table Optionally call dominant_traits() to obtain a genus-level summary Use resulting tables in demographic models, Bayesian analyses, community comparisons, or trait-based resilience assessments All functions return clean, analysis-ready data frames and require minimal additional processing. Example workflow 1. Scrape traits for a list of species spp <- c("Acropora cervicornis", "Porites porites", "Favia fragum") traits_spp <- spp_scrape(spp, verbose = TRUE) head(traits_spp) 2. Scrape all species within selected genera genera <- c("Porites", "Acropora") traits_gen <- genus_scrape(genera, verbose = TRUE) 3. Summarise genus-level dominant traits genus_traits <- dominant_traits(traits_gen, tie_method = "first") genus_traits Credits and required citations traitscrape depends entirely on the availability and open architecture of the CoralTrait Database. Users must cite: Madin, J. S., Anderson, K. D., Andreasen, M. H., Bridge, T. C. L., Cairns, S. D., Connolly, S. R., Darling, E. S., Díaz, M., Dornelas, M., Doughty, C., et al. (2016).The Coral Trait Database. Scientific Data, 3, 160017.https://doi.org/10.1038/sdata.2016.17 Trait classifications for life history strategy derive from: Darling, E. S., Alvarez-Filip, L., Oliver, T. A., McClanahan, T. R., & Côté, I. M. (2012).Evaluating life-history strategies of reef corals using species traits. Ecology Letters, 15(12), 1378–1386.https://doi.org/10.1111/j.1461-0248.2012.01861.x Please include both citations in any publications making use of traitscrape outputs. Authors and versioning This Zenodo archive represents version v0.1.0, corresponding to the initial public beta release. The intention is to support reproducibility for coral demographic and trait-based studies across the globe.