10 citations as recorded by crossref.

1. Characterizing height-diameter relationships for Caribbean trees using mixed-effects random forest algorithm S. Yang et al. https://doi.org/10.1016/j.foreco.2022.120507
2. Tree growth after a major hurricane reflects predisturbance vigor rather than canopy damage L. Boeschoten et al. https://doi.org/10.1073/pnas.2532451123
3. Future Hurricanes Will Increase Palm Abundance and Decrease Aboveground Biomass in a Tropical Forest J. Zhang et al. https://doi.org/10.1029/2022GL100090
4. A process-based framework for validating forest landscape modeling outcomes M. Wu et al. https://doi.org/10.1016/j.envsoft.2025.106327
5. A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. https://doi.org/10.5194/gmd-16-4017-2023
6. Mechanisms of fine-scale landscape heterogeneity in shaping eco-hydrological processes in a typical subtropical forest of Southeast China: Insights from ground observations and a fine-scale terrestrial biosphere model Y. Li et al. https://doi.org/10.1016/j.ecolind.2025.113986
7. Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery M. Shi et al. https://doi.org/10.1029/2023MS003679
8. Quantifying Stand-level Carbon Using an Alternative Method: A Case Study of National Forests and Parks in Southern Appalachian Mountains S. Yang et al. https://doi.org/10.1007/s44392-025-00044-x
9. Height–diameter allometry for a dominant palm to improve understanding of carbon and forest dynamics in forests of Puerto Rico P. Chatzopoulos et al. https://doi.org/10.1111/btp.13297
10. Mechanisms and scales in modeling forest responses to changing disturbance regimes X. Xu et al. https://doi.org/10.1111/nph.71384