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Such forecasting can be categorized into three time scales: trait\u2010based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to\n                    <jats:italic>c<\/jats:italic>\n                    .\u00a020 generations, allele\u2010based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.\n                  <\/jats:p>","DOI":"10.1111\/nph.71370","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:11:33Z","timestamp":1786511493000},"page":"36-48","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive evolutionary genomics: principles, validation, and practice"],"prefix":"10.1111","volume":"252","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7493-416X","authenticated-orcid":false,"given":"Daniel","family":"Ortiz\u2010Barrientos","sequence":"first","affiliation":[{"name":"School of the Environment The University of Queensland  St Lucia QLD 4072 Australia"},{"name":"Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture The University of Queensland  St Lucia QLD 4072 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6296-0187","authenticated-orcid":false,"given":"Maddie E.","family":"James","sequence":"additional","affiliation":[{"name":"School of the Environment The University of Queensland  St Lucia QLD 4072 Australia"},{"name":"Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture The University of Queensland  St Lucia QLD 4072 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3479-9223","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture The University of Queensland  St Lucia QLD 4072 Australia"},{"name":"Queensland Alliance for Agriculture and Food Innovation The University of Queensland  St Lucia QLD 4072 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