A systematic map of generative AI guidelines and reporting in ecology and evolutionary biology: towards the framework of AI disclosure for Improved Transparency (AIdIT)

Abstract Generative artificial intelligence (AI) is rapidly becoming embedded across scientific workflows, yet mechanisms for transparently documenting its use remain fragmented and weakly enforced. Focusing on ecology and evolutionary biology as a model discipline, we systematically mapped AI-related journal policies across 230 journals and assessed article-level compliance using a large sample of recent publications. To provide a reporting background, we also synthesised author contribution guidelines. Nearly half of journals provided no guidance on AI use, and where policies existed, they were largely generic, publisher-driven, and poorly translated into reporting practice. While author contribution statements were widely adopted, explicit AI disclosures appeared in fewer than 6% of papers, even in journals with formal AI policies. Text-mining of 124 guideline documents revealed highly standardised, precautionary language emphasising responsibility and prohibitions, with minimal operational guidance on acceptable uses or disclosure formats. To address this gap, we introduce AIdIT (AI disclosure for Improved Transparency), a standardised, taxonomy-based framework for reporting AI use across all stages of the research lifecycle. AIdIT integrates structured categories of AI use, human oversight statements, and machine-readable outputs to support reproducibility, accountability, and comparability. Together, our systematic evidence synthesis and proposed framework highlight an urgent need to normalise AI transparency as a core component of open research practice.