2025

• Eckert, M., Boulle, M. J., Pryke, J. S., & Drew, D. M. (2025). Assessment of soil feeding activity using different bait materials for the bait-lamina test: A small-scale study in Eucalyptus blocks. Pedobiologia, 151074. https://doi.org/10.1016/j.pedobi.2025.151074
• Gama, G., Martin, K. C., & Drew, D. M. (2025). Wood formation of drought-resistant Eucalyptus cladocalyx under cyclical drought treatment. Quantitative Plant Biology, 6, e12. https://doi.org/10.1017/qpb.2025.7 
• Keret, R., Hills, P. N., & Drew, D. M. (2025). Xylem parenchyma anatomy and gene expression patterns indicate mechanisms of cavitation resistance in Eucalyptus grandis during drought. Plant-Environment Interactions. https://doi.org/10.1002/pei3.70068 
• Schoeman, L., & Drew, D. M. (2025). Influence of previous drought exposure on the 3D microstructure of the cambium and developing xylem in Eucalyptus clones: An X‑ray CT investigation. IAWA Journal, 46(2), 256–275. DOI 10.1163/22941932-bja10170

2024

• Erasmus, C. S., Booysen, M. J., & Drew, D. (2024). Dataset of dendrometer and environmental parameter measurements of two different species of the group of genera known as eucalypts in South Africa and Portugal. Data in Brief, 57, 111035. https://doi.org/10.1016/j.dib.2024.111035
• Keret, R., Drew, D.M., & Hills, P. N. (2024). Xylem cell size regulation is a key adaptive response to water deficit in Eucalyptus grandis. Tree Physiology, tpae068. https://doi.org/10.1093/treephys/tpae068.

2023

• Gakenou, O.F., Martin, K.C., Germishuizen, I. and Drew, D.M. (2023). Parameter testing and application of the 3PG model for Eucalyptus grandis x Urophylla in subtropical conditions in South Africa. CERNE, 29, pp.e-103236.
• Keret, R., Hills, P., Drew, D.M. (2023). The evolution of in vitro tracheary element systems from annual to perennial plant species. Plant Cell, Tissue and Organ Culture (PCTOC), 153, 257-271. The evolution of in vitro tracheary element systems from annual to perennial plant species | SpringerLink. https://doi.org/10.1007/s11240-023-02478-7
• Keret, R., Schliephack, P.M., Stangler, D.F., Seifert, T., Kahle, H.P., Drew, D.M. and Hills, P.N. (2023). An open-source machine-learning approach for obtaining high-quality quantitative wood anatomy data from E. grandis and P. radiata xylem. Plant Science, p.111970. https://doi.org/10.1016/j.plantsci.2023.111970.
• Schoeman, L., Drew, D.M. (2023). Advanced imaging and quantification of the cambium and developing xylem in eucalypts using X-ray micro- and nano-computed tomography. IAWA Journal. https://doi.org/10.1163/22941932-bja10135.

2022

• Drew, D.M., Downes, G.M., Seifert, T., Eckes-Shepard, A., Achim, A. (2022). A review of progress and applications in wood quality modelling. Current Forestry Reports, 8, 317-332. https://link.springer.com/content/pdf/10.1007/s40725-022-00171-0.pdf?pdf=button.
• Eckes-Shephard, A. H., Ljungqvist, F. C., Drew, D. M., Rathgeber, C. B., Friend, A. D. (2022). Wood formation modelling–a research review and future perspectives. Frontiers in Plant Science, 13, 1-21. https://doi.org/10.3389/fpls.2022.837648.
• Mandigora, B.G., Drew, D.M. (2022). Enhancing predictive precision of dominant height projection equations for eucalypts by incorporating rainfall and temperature terms. Southern Forests, 84, 271-282. Enhancing predictive precision of dominant height projection equations for eucalypts by incorporating (tandfonline.com).
• Saunders, A., Drew, D.M. (2022). Measurements done on excised stems indicate that hydraulic recovery can be an important strategy used by Eucalyptus hybrids in response to drought. Trees, 36, 139-151. https://link.springer.com/content/pdf/10.1007/s00468-021-02188-7.pdf.
• Saunders, A., Drew, D.M. (2022). Stomatal responses of Eucalyptus spp. under drought can be predicted with a gain-risk optimization model. Tree Physiology, 42, 815-830. https://doi.org/10.1093/treephys/tpab145.

2021

• Drew, D.M. (2021). Exploring new frontiers in forecasting forest growth, yield and wood property variation. Annals of Forest Science, 78, 29-30. https://link.springer.com/content/pdf/10.1007/s13595-021-01054-3.pdf.
• Saunders, A., Drew, D.M., Brink, W. (2021). Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes. Trees, Forests and People, 13, 1-12.  https://doi.org/10.1016/j.tfp.2021.100139.