The editor’s choice for our October issue is ‘Using machine learning to link climate, phylogeny and leaf area in eucalypts through a 50-fold expansion of leaf trait datasets‘, by Karina Guo et al.
Sheet ID NSW370638, collected in 1992 in Queensland, Australia. National Herbarium of NSW (CC-BY 4.0)
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, Karina Guo et al. 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.
The researchers were able to confirm current positive global relationships between leaf area and mean annual temperature and precipitation. Additionally, they took this a step further and examined how it changes across time. 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.
Read the full article online: Using machine learning to link climate, phylogeny and leaf area in eucalypts through a 50-fold expansion of leaf trait datasets
