CSRcalculator: A reproducible framework for understanding plant ecological strategies

Societal Impact Statement Understanding how plants respond to environmental change is essential for conserving biodiversity and managing ecosystems under climate change. We developed CSRcalculator, an open‐source platform that brings together major competitor, stress‐tolerator, ruderal (CSR) models to make ecological strategy classification accessible, reproducible and scalable. The software enables users to rapidly translate plant trait data into standardised ecological strategy classifications through a simple, user‐friendly interface, promoting consistent and reproducible analyses across studies. By improving access to the CSR framework and supporting reproducible research, CSRcalculator strengthens ecological monitoring, informs conservation and land management and enables more robust evidence for environmental policy and practice. Summary The competitor, stress‐tolerator, ruderal (CSR) theory, first proposed by John Philip Grime, is a useful framework for understanding plant ecological strategies and predicting responses to environmental changes and pressures. However, current tools for assigning CSR strategies are limited to an Excel sheet format and have yet to be integrated into modern computational platforms that enable reproducible research. We present CSRcalculator ( https://github.com/TeddyGaskin/CSRcalculator ), an open‐source R package and shiny application that calculates CSR scores and assigns strategies based on user‐uploaded trait data. CSRcalculator supports CSR assignments according to the three most prominent models: the original soft approach , the global StrateFy and a morpho‐physiological model. The R package outputs a table including CSR scores, assigned strategies and intermediate traits for the selected model. The shiny application produces this same table alongside an interactive ternary plot to visualise the strategy distribution and an optional summary table describing the overall CSR strategy distribution, using metrics such as the modal strategy, axis means, standard deviations and ranges. Group‐level analyses calculate the same statistics across user‐defined categories. We demonstrate the utility and reliability of CSRcalculator by validating its outputs against a published dataset and original CSR tools, as well as benchmarking its performance across dataset sizes to confirm it scales efficiently to large datasets.