ZonationR: An R interface to the Zonation software for reproducible spatial conservation prioritisation workflows

Systematic conservation planning provides a science‐based framework for defining conservation goals and supporting transparent spatial decision‐making under limited resources. Within this framework, spatial conservation prioritisation tools are widely used to identify areas of high biodiversity value by integrating information on species distributions, connectivity, costs, and other factors into spatially explicit recommendations. ‘Zonation' is one of the leading software tools in this field, producing hierarchical priority rankings of landscapes based on conservation value. However, its standard workflow typically relies on manual steps for data preparation, execution, and post‐processing, which can become inefficient and difficult to reproduce when multiple scenarios are analysed, limiting accessibility and broader uptake. We introduce ‘ZonationR', an R package that provides a streamlined interface to the ‘Zonation' software, enabling fully reproducible and automated spatial prioritisation workflows. The package integrates the entire analysis pipeline, encompassing input preparation, execution of ‘Zonation', and post‐processing, while supporting both single‐variant and multi‐variant workflows. ‘ZonationR' also provides tools to explore and interpret outputs, including priority maps, feature performance curves, cost summaries, feature representation metrics, and similarity assessments between prioritisation solutions. By linking directly to the original ‘Zonation' engine, the package enables users to benefit from ongoing methodological developments in ‘Zonation' and access its functionality through a transparent, script‐based workflow, thereby reducing technical barriers to running and understanding spatial prioritisation analyses. Beyond these advantages, its integration within the R environment supports iterative testing of conservation scenarios and more rigorous assessment of methodological decisions, while facilitating seamless connections with wider ecological workflows (e.g. species distribution modelling). As conservation planning increasingly relies on large, complex, multi‐source datasets and integrative approaches, such integration is essential for enabling robust, transparent, and reproducible decision‐making across spatial scales.