Taxonomic resolution and treatment effects – alone and combined – can mask significant biodiversity reductions

Taxonomic resolution or uncertainty poses an important problem in biodiversity research. Assessment of biodiversity at the species level is most informative and preferred, but requires effort and expertise. Alternatively, researchers often bin species into higher taxa because they are unable to recognize them, or to save money and time. Here we analyse, by simulation and analytical modelling, the combined effects of dose-dependent mortality and taxonomic binning on biodiversity indices for a fictitious community of organisms. We asked (1) how binning species in a sample into higher taxa significantly affects biodiversity measures, and (2) whether dose-dependent mortality effects, alone or in combination with taxonomic uncertainty, are duly captured by classic biodiversity indices. Our study shows that haphazard binning into various taxonomic levels is legitimate and preferable to orderly binning (all taxa binned at the same level), because it provides the best resolution. We further show that binning will regularly obscure statistical detection of biodiversity differences, if only due to scaling of mean and variance. Also, treatment effects in combination with taxonomic uncertainty can introduce estimation biases of at times complex nonlinear and non-intuitive nature under any taxonomic resolution scenario, potentially including relative increases in the biodiversity index when intuitively decreases would be expected. We recommend being specific about the expected qualitative and quantitative effects of any treatment or natural comparison before formulating a hypothesis regarding biodiversity reductions. Our theoretical study should aid in this endeavour.<b>EDITED BY</b> Isabelle Durance <b>EDITED BY</b> Isabelle Durance

分类学分辨率（taxonomic resolution）与分类学不确定性是生物多样性研究领域的重要问题。以物种水平开展生物多样性评估信息量最大且最受青睐，但该工作需要投入大量精力并具备专业知识。为此，研究者常因无法辨识物种，或是为节省经费与时间，将物种归并到高阶分类单元（higher taxa）中。本研究通过模拟与解析建模（analytical modelling），分析了剂量依赖性死亡率（dose-dependent mortality）与分类归并（taxonomic binning）对虚构生物群落（fictitious community）生物多样性指数（biodiversity indices）的联合影响。本研究旨在解答两个核心问题：（1）将样本中的物种归并为高阶分类单元，会对生物多样性测度产生何种显著影响；（2）经典生物多样性指数能否充分捕捉到单独存在或与分类学不确定性共同作用的剂量依赖性死亡率效应。研究结果表明，随机归并至不同分类学水平的做法是合理的，且优于有序归并（即所有分类单元统一归并至同一分类水平），因为前者能获得最佳分辨率。此外，本研究还发现，仅受均值与方差缩放效应影响时，分类归并通常会掩盖生物多样性差异的统计检测（statistical detection）。同时，在任意分类学分辨率场景下，处理效应与分类学不确定性的共同作用，可能会引入性质复杂的非线性、非直觉性估计偏差（estimation biases）——甚至可能出现预期生物多样性指数应下降时，其相对值反而上升的情况。我们建议，在提出关于生物多样性降低的研究假设前，需明确阐明任意处理或自然对照的预期定性与定量效应。本理论研究可为该研究方向提供助力。<b>编辑：</b> Isabelle Durance <b>编辑：</b> Isabelle Durance