Metabolomics in the wild: A scoping review of non-invasive methods for understanding vertebrate wildlife health and success

An increase in metabolomics research over the last decade has expanded species-specific knowledge of biological processes, but has primarily focused upon humans, domesticated and model animal species. Recent advances, particularly around data management pipelines for untargeted studies, make the field more accessible to disciplines outside those embedded in analytical chemistry, including wildlife conservation. To establish the breadth of existing wild vertebrate animal metabolomic research, specifically utilising non-invasive sampling, this scoping review establishes: (1) general trends in publication numbers; (2) coverage of wildlife taxonomic groups and species; (3) subject numbers and sample size; (4) non-invasive sample types; (5) metabolomic techniques utilised; (6) the context of current literature and (7) conservation implications. This scoping review was conducted following the JBI methodology and reported in accordance with PRISMA-ScR; searches were conducted using PubMed, Scopus, Web of Science and supplemented with Google Scholar; following screening, 83 peer-reviewed original research papers were identified using predefined criteria and data extracted. The most popular areas of research were physiology and reproduction (30%) and diet and nutrition (27%). Although most publications focused on a single species, there were 11 multi-species publications, resulting in metabolomics data for 93 species. A large proportion of which, 72%, were mammalian, and of these, 52% are currently listed at risk of extinction on the IUCN Red List. With over 60% of all publications published between 2022–2025, non-invasive vertebrate wildlife metabolomics appears to be gaining traction as a tool in wildlife physiology research. However, future research would benefit from alignment with model animal metabolome research experimental design, as few studies included the consideration of metabolite extraction optimisation, storage conditions, the use of integrated omics platforms beyond microbiome analysis and consideration of statistical power, particularly in respect to intra-individual variation and the characterisation of species-specific metabolomes.