This paper examines the effectiveness of 17 well-known biodiversity indices in measuring biodiversity in forest ecosystems. The authors used data on tree diameters, heights, and volumes from forest plots to assess the robustness of these indices in pairwise comparisons (e.g., comparing the same region at two different times or two different regions at the same time). Out of the 17 indices, 8 were found to be robust, consistently showing an increase, decrease, or no change in biodiversity. These robust indices include Shannon entropy, Shannon equitability, Simpson dominance, Gini-Simpson, Simpson dominance unbiased (finite samples), Gini-Simpson unbiased (finite samples), Berger-Parker Index, and Gini-Simpson equitability. The paper also discusses the importance of selecting robust diversity indices that align with changes in richness and abundance, ensuring accurate capture of biodiversity changes across different aspects. The authors provide a supplementary Excel template for calculating these indices, allowing readers to choose the most appropriate indices for their specific datasets. The study contributes to the literature by assessing the feasibility and applicability of diversity indices as proxies for biodiversity in forest management.This paper examines the effectiveness of 17 well-known biodiversity indices in measuring biodiversity in forest ecosystems. The authors used data on tree diameters, heights, and volumes from forest plots to assess the robustness of these indices in pairwise comparisons (e.g., comparing the same region at two different times or two different regions at the same time). Out of the 17 indices, 8 were found to be robust, consistently showing an increase, decrease, or no change in biodiversity. These robust indices include Shannon entropy, Shannon equitability, Simpson dominance, Gini-Simpson, Simpson dominance unbiased (finite samples), Gini-Simpson unbiased (finite samples), Berger-Parker Index, and Gini-Simpson equitability. The paper also discusses the importance of selecting robust diversity indices that align with changes in richness and abundance, ensuring accurate capture of biodiversity changes across different aspects. The authors provide a supplementary Excel template for calculating these indices, allowing readers to choose the most appropriate indices for their specific datasets. The study contributes to the literature by assessing the feasibility and applicability of diversity indices as proxies for biodiversity in forest management.