Angiosperms are the cornerstone of most terrestrial ecosystems and human livelihoods^1,2. A robust understanding of angiosperm evolution is required to explain their rise to ecological dominance. So far, the angiosperm tree of life has been determined primarily by means of analyses of the plastid genome^3,4. Many studies have drawn on this foundational work, such as classification and first insights into angiosperm diversification since their Mesozoic origins^5–7. However, the limited and biased sampling of both taxa and genomes undermines confidence in the tree and its implications. Here, we build the tree of life for almost 8,000 (about 60%) angiosperm genera using a standardized set of 353 nuclear genes⁸. This 15-fold increase in genus-level sampling relative to comparable nuclear studies⁹ provides a critical test of earlier results and brings notable change to key groups, especially in rosids, while substantiating many previously predicted relationships. Scaling this tree to time using 200 fossils, we discovered that early angiosperm evolution was characterized by high gene tree conflict and explosive diversification, giving rise to more than 80% of extant angiosperm orders. Steady diversification ensued through the remaining Mesozoic Era until rates resurged in the Cenozoic Era, concurrent with decreasing global temperatures and tightly linked with gene tree conflict. Taken together, our extensive sampling combined with advanced phylogenomic methods shows the deep history and full complexity in the evolution of a megadiverse clade.

Previous studies have found that introduced plants commonly experience more favorable microbial interactions in their non-native range, suggesting that changes to the microbiome could be an important contributor to invasion success. Little is known about microbiome variation across native and invading populations, however, and the potential sources of more favorable interactions are undescribed. Here, we report one of the first microbiome comparisons of plants from multiple native and invading populations, in the noxious weed yellow starthistle. We identify clear differences in composition and diversity of microbiome bacteria. Our findings raise new questions about the sources of these differences, and we outline the next generation of research that will be required to connect microbiome variation to its potential role in plant invasions.

Traits with intuitive names, a clear scope and explicit description are essential for all trait databases. The lack of unified, comprehensive, and machine-readable plant trait definitions limits the utility of trait databases, including reanalysis of data from a single database, or analyses that integrate data across multiple databases. Both can only occur if researchers are confident the trait concepts are consistent within and across sources. Here we describe the AusTraits Plant Dictionary (APD), a new data source of terms that extends the trait definitions included in a recent trait database, AusTraits. The development process of the APD included three steps: review and formalisation of the scope of each trait and the accompanying trait description; addition of trait metadata; and publication in both human and machine-readable forms. Trait definitions include keywords, references, and links to related trait concepts in other databases, enabling integration of AusTraits with other sources. The APD will both improve the usability of AusTraits and foster the integration of trait data across global and regional plant trait databases.

Accurate kinship estimation between close relatives is crucial in conservation and restoration but remains challenging in wild populations due to structure and inbreeding. The efficacy of kinship inference using reduced‐representation sequencing data (e.g., DArTseq, RADseq) is also uncertain. We evaluated the sensitivity and precision of six kinship methods (Goudet's beta dosage, KING Homo, KING Robust, PC‐Relate, PLINK, RelateAdmix) at detecting parent–offspring and sibling relationships. Analyses were conducted on 3395 individuals and 363 families from six non‐model Australian plant species: Acacia terminalis , Acacia suaveolens , Banksia serrata , Banksia aemula , Hakea sericea and Hakea teretifolia . Method performance varied across species and filtering parameters. Goudet's beta dosage and RelateAdmix performed well in low‐structure, noninbred species but were less reliable in structured or inbred contexts. PLINK offered a balance of sensitivity and precision but was sensitive to filtering and often underestimated relatedness. KING Robust was highly precise but missed many true relatives. PC‐Relate showed high false positives and is not recommended for similar applications. We recommend PLINK for general use, Goudet's beta dosage and RelateAdmix for low‐structure species, and KING Robust for high‐precision needs. Comparing multiple methods is advisable, as each has different assumptions and complementary strengths. Further theoretical development is needed for species with high inbreeding.