This article examines 17 biodiversity indices to determine which are most effective for assessing biodiversity in forest ecosystems. The study uses data on tree diameters, heights, and volumes from forest plots to evaluate how well each index captures changes in biodiversity across different regions or time periods. Out of the 17 indices, 8 were found to be robust, consistently indicating changes in biodiversity over pairwise comparisons. These 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 remaining seven indices showed inconsistent or inconclusive results, highlighting the importance of selecting appropriate indices based on the type of data and the specific biodiversity aspects being measured. The study also provides an Excel template for calculating these indices, allowing users to input their own data and assess the robustness of each index. The findings suggest that robust indices are essential for accurately capturing biodiversity patterns in forested areas, ensuring reliable assessments for conservation and management decisions.This article examines 17 biodiversity indices to determine which are most effective for assessing biodiversity in forest ecosystems. The study uses data on tree diameters, heights, and volumes from forest plots to evaluate how well each index captures changes in biodiversity across different regions or time periods. Out of the 17 indices, 8 were found to be robust, consistently indicating changes in biodiversity over pairwise comparisons. These 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 remaining seven indices showed inconsistent or inconclusive results, highlighting the importance of selecting appropriate indices based on the type of data and the specific biodiversity aspects being measured. The study also provides an Excel template for calculating these indices, allowing users to input their own data and assess the robustness of each index. The findings suggest that robust indices are essential for accurately capturing biodiversity patterns in forested areas, ensuring reliable assessments for conservation and management decisions.