Marcos, C.N.de Evan, T.Arroyo, J.M.Recalde, A.De las Heras Molina, AnaLópez Bote, Clemente JoséGonzález-Recio, O.Carro, M.D.2026-07-142026-07-142026Marcos, C. N., de Evan, T., Arroyo, J. M., Recalde, A., Heras-Molina, A., López-Bote, C., González-Recio, O., & Carro, M. D. (2026). Method: Analysing compositional data in animal science. Animal - Open Space, 5, 100148. https://doi.org/10.1016/j.anopes.2026.10014810.1016/j.anopes.2026.100148https://hdl.handle.net/20.500.14352/138531Author contributions CNM: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – Original Draft, Writing – Review & Editing; TdE: Formal analysis, Investigation, Resources, Writing – Review & Editing; JMA: Formal analysis, Investigation, Resources, Writing – Review & Editing; AR: Formal analysis, Investigation, Resources, Writing – Review & Editing; AHM: Data curation, Formal analysis , Investigation, Writing – Review & Editing; CLB: Funding acquisition, Project administration, Resources, Writing – Review & Editing; OGR: Conceptualisation, Supervision, Writing – Review & Editing; MDC: Conceptualisation, Funding acquisition, Project administration, Resources, Supervision, Writing – Review & Editing. All authors approved the final version to be published and agreed to be accountable for all aspects of the workCompositional data contain relative information and may have a constant sum constraint (i.e. 100%). In animal science, compositional data include, for example, relative abundances of pathogens in a sample, carcass or body measurements, chemical fractions expressed as proportions of total fresh or DM in feeds, volatile fatty acid (VFA) profiles expressed as molar proportions in digesta samples, or individual fatty acid (FA) expressed as proportions of total FA in meat or milk. Because compositional data are multivariate, the use of conventional univariate statistical analyses may lead to flawed or misleading interpretation of results. Although the analysis of compositional data (CoDA) is challenging, most researchers agree that logratio transformations are a valid analysis approach. This study shows that CoDA using simple logratios is more appropriate and may lead to results with greater biological relevance than conventional statistical analyses. Four previously published datasets were used. The first dataset comprised in vitro VFA profiles of industrial and craft brewer’s grains, the second included the chemical composition of almond hull silages with different treatments, and the third consisted of data on chemical composition and total VFA in vitro production of various agroindustrial by-products. The fourth dataset contained FA profiles of adipose tissue samples from barrows. Centred logratio transformations were applied to brewer’s grains VFA profiles, whereas additive logratio transformations were used for almond hull silages and FA tissue data, and pairwise logratios were applied to the agroindustrial by-products dataset. CoDA addressed common issues associated with compositional data and the interpretation of the results was more biologically relevant. In the brewer’s grain dataset, results and conclusions varied depending on how individual VFAs were expressed, whereas CoDA agreed with the expected ruminal fermentation stoichiometry and provided the same results regardless of how data were expressed. When using conventional statistical analysis, differences between almond hull silages in ashes and lignin content, which do not align with the expected silage dynamics, were detected, but no differences were observed when using CoDA. When correlating chemical fractions of agroindustrial by-products with in vitro total VFA production, some correlation coefficients could be misleading, whereas correlations obtained when using logratios agreed well with the relative fermentability of individual chemical fractions. Finally, in the FA tissue dataset, a variable with biological relevance (C16:0) was selected as a reference, providing insights supported by existing scientific literature. In conclusion, these results indicate CoDA is a valuable analytical approach for animal scientists working with compositional dataengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal article2772-6940https://doi.org/10.1016/j.anopes.2026.100148open access636.09636.084Chemical compositionFatty acidsLogratiosUnivariateVolatile fatty acidsNutrición y cuidado de los animales3109 Ciencias Veterinarias3109.06 Nutrición