Sensitivity of functional diversity metrics to sampling intensity

1. Functional diversity (FD) metrics are increasingly used in ecological research, particularly in studies of community assembly and ecosystem functioning. However, studies using FD metrics vary greatly in the intensity by which ecological communities were sampled and it is largely unknown how sensitive these metrics are to low sampling intensity (undersampling).
2. Here, we used a combination of simulations with theoretically assembled communities and three comprehensive, independent, empirical datasets on plant, ground beetle and bird communities to investigate the sensitivity of nine commonly used FD metrics to undersampling.
3. Simulations with both theoretical communities and empirical data showed that in a wide range of contexts, the measurement of various FD metrics requires a much higher sampling effort to reach an ‘adequate’ precision (defined as an r2 of at least 0.7 between different subsets of the same population), than that required for commonly used taxonomic diversity metrics (e.g. species richness), although the ‘accuracy’ (their deviation from the diversity value of a completely sampled community) of their measurements is not more sensitive to undersampling than species richness. We also found that some FD metrics (e.g. Functional Dispersion) are consistently less sensitive to undersampling than others (e.g. nearest neighbour distance-metrics). Problems of undersampling were generally most severe in datasets with high overall species richness and low overall abundances.
4. We found that the precision of many FD metrics is highly sensitive to undersampling, and more so than commonly used taxonomic diversity metrics. Therefore, to ensure reproducible results in functional biodiversity research, we recommend that thorough sampling designs are used to sample communities and that datasets originally collected for studying taxonomic diversity should only be used for FD when it can be shown that undersampling is not a major issue. In cases where undersampling is suspected or logistically unavoidable, FD metrics that are relatively insensitive to its effects (e.g. Functional Dispersion) should be prioritized.

1. 功能多样性（FD）指标在生态学研究中的应用愈发广泛，尤其常用于群落组装与生态系统功能相关研究。不过，采用FD指标的相关研究在生态群落采样强度上差异显著，目前学界仍不清楚这些指标对低采样强度（即欠采样，undersampling）的敏感程度。
2. 本研究结合理论组装群落的模拟实验，以及三套覆盖植物、步甲与鸟类群落的全面独立实测数据集，探究了9种常用FD指标对欠采样的敏感程度。
3. 基于理论群落与实测数据的模拟结果显示，在绝大多数场景下，各类FD指标要达到「可接受」的精度（定义为同一种群不同子集间的决定系数R²≥0.7），所需的采样工作量远高于常用分类多样性指标（如物种丰富度）；不过，其测量的「准确度」（即与完全采样群落的多样性数值的偏差）对欠采样的敏感程度并不高于物种丰富度。此外，本研究还发现，部分FD指标（如功能离散度（Functional Dispersion））对欠采样的耐受程度始终高于其他指标（如最近邻距离类指标）。总体而言，欠采样带来的问题在总物种丰富度较高、总个体丰度较低的数据集中最为突出。
4. 本研究证实，多数FD指标的精度对欠采样极为敏感，且敏感程度高于常用分类多样性指标。因此，为保障功能生物多样性研究的结果可复现，我们建议采用严谨的采样方案开展群落采样；若需使用原本为分类多样性研究收集的数据集开展FD分析，则需先验证欠采样不会对结果造成显著影响。当怀疑存在欠采样问题或受后勤条件限制无法避免时，应优先选用对欠采样耐受度较高的FD指标（如功能离散度）。