Population Genomics of Eucalypts

Jordan, Rebecca; Prober, Suzanne M.; Andrew, Rose L.; Freeman, Jules S.; Kerr, Richard J.; Steane, Dorothy A.; Vaillancourt, René E.; Potts, Brad M.

doi:10.1007/13836_2023_107

Cited by 2 publications

(1 citation statement)

References 318 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, species evolution does not appear to be monophyletic in this group, signalling incomplete lineage sorting or reticulate evolution [56]. Reticulate evolution is possible at lower phylogenetic levels in the uniformly diploid eucalypts, where morphological species often form complex multispecies networks involving nuclear and chloroplast gene exchange among recognised taxa [118,119], as also noted in other forest tree genera [14,15]. While artificial hybridisation studies of eucalypts indicate post-zygotic barriers occur [120,121], these do not appear to be strong among closely related species [122,123], including those crossed with maternal E. gunnii [124].…”

Section: Chloroplast Haplotype Sharing Implicates Hybridisationmentioning

confidence: 96%

Evolutionary Processes Shaping Postglacial Gene Pools of High-Altitude Forests: Evidence from the Endemic Eucalypts of Tasmania

Jones

Harrison

Hudson

et al. 2023

Forests

View full text Add to dashboard Cite

Climatic changes during the Pleistocene were responsible for dramatic redistributions of plant species worldwide. On the rugged southern hemisphere island of Tasmania, temperature increases following the last glaciation saw upslope migration of climatically suitable species from lowland refugia and the expansion of eucalypt-dominated forests and woodlands in the Central Highlands. We integrate multiple lines of evidence (chloroplast and nuclear DNA markers, seedling morphology, and survival in common garden experiments) from a group of closely related endemic eucalypts (the alpine white gums) to argue that (i) the Central Highlands of the island were colonised by multiple glacial refugia with hybridisation among species and previously separated populations, and (ii) natural selection has filtered the admixed populations, resulting in local adaptation to the harsh sub-alpine environment. Chloroplast haplotype diversity decreased and nuclear microsatellite diversity increased with altitude, chloroplast sharing among taxa was common, and nuclear DNA differentiation of morphologically distinct taxa was lower in the Central Highlands compared with lowland regions. Local adaptation in the highlands was signalled by evidence from (i) a glasshouse trial in which directional selection (QST > FST) had shaped seedling morphological trait variation and (ii) population survival differences in 35-year-old reciprocal plantings along the major environmental gradients. We conclude that the evolutionary response of these island endemic trees to past climate change has involved the interplay of both hybridisation and natural selection, highlighting the importance of maintaining species interactions under future climate change.

show abstract

Section: Chloroplast Haplotype Sharing Implicates Hybridisationmentioning

confidence: 96%

Evolutionary Processes Shaping Postglacial Gene Pools of High-Altitude Forests: Evidence from the Endemic Eucalypts of Tasmania

Jones

Harrison

Hudson

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

Using machine learning to link climate, phylogeny and leaf area in eucalypts through a 50‐fold expansion of leaf trait datasets

Guo,

Cornwell,

Bragg

2024

Journal of Ecology

View full text Add to dashboard Cite

Leaf area varies within and between species, and previous work has linked this variation to environment and evolutionary history. However, many previous studies fail to examine both these factors and often are data‐limited. To address this, our study developed a new workflow using machine learning to automate the extraction of leaf area from herbarium collections of Australian eucalypts (Eucalyptus, Angophora and Corymbia). This dataset included 136,599 measurements, expanding existing data on this taxon's leaf area by roughly 50‐fold. Our methods were validated using field standard metrics of accuracy, and comparisons to manual measurements both from the present study and existing datasets. With this dataset for the eucalypt clade, we observed positive relationships between leaf area and mean annual temperature and precipitation similar to those reported for the global flora. However, these relationships were not consistently observed within species, potentially due to gene flow suppressing local adaptation. When we examined these relationships at different phylogenetic levels, the slope of trait–climate associations within lineages converged towards the overall eucalypt slope at shallow phylogenetic scales (5–12 MY), suggesting that effects of gene flow relax just above the species level. The strengthening of trait–climate correlations at evolutionary scales just beyond the intraspecific level may represent a widespread phenomenon across various traits and taxa. Future studies can unveil these relationships with the larger sample sizes of new trait datasets generated through machine learning. Synthesis. Using machine learning, researchers are able to confirm current positive global relationships between leaf area and mean annual temperature and precipitation. Additionally, they were able to take this a step further and examine how it changes across time. Here they saw that at roughly 5–12 million years ago in the phylogenetic tree, the trait–climate slope begins to show significantly less variation. Overall, the study shows the potential of machine learning in ecology, with exciting new potential findings with its use.

show abstract

Population Genomics of Eucalypts

Cited by 2 publications

References 318 publications

Evolutionary Processes Shaping Postglacial Gene Pools of High-Altitude Forests: Evidence from the Endemic Eucalypts of Tasmania

Evolutionary Processes Shaping Postglacial Gene Pools of High-Altitude Forests: Evidence from the Endemic Eucalypts of Tasmania

Using machine learning to link climate, phylogeny and leaf area in eucalypts through a 50‐fold expansion of leaf trait datasets

Contact Info

Product

Resources

About