Estimating biodiversity using symbolic meta analysis

Global species richness is a key biodiversity metric. Concerns continue to grow over its decline due to overexploitation and habitat destruction by humans. Despite recent efforts to estimate global species richness, the resulting estimates have been highly uncertain and often logically inconsistent. Estimates lower down either the taxonomic or geographic hierarchies are often larger than those above. Further, these estimates have been typically represented in a wide variety of forms, including intervals (a, b), point estimates with no uncertainty, and point estimates with either symmetrical or asymmetrical bounds, making it difficult to combine information across different studies. Here, we develop a Bayesian hierarchical approach to estimate global species richness (we estimate 22.02m species; 95% HPD interval (10.43m, 35.28m)) that combines 50 estimates from published studies. The data mix of intervals and point estimates are reconciled using techniques from symbolic data analysis. This approach allows us to recover interval estimates at each species level, even when data are partially or wholly unobserved, while respecting logical constraints, and to determine the effects of estimation on the whole hierarchy of obtaining future estimates for particular taxa at various levels in the hierarchy.

Methods
Meta analysis

全球物种多样性是衡量生物多样性的关键指标。由于人类过度开发和栖息地破坏，对其下降的担忧持续加剧。尽管近年来人们致力于估算全球物种多样性，但所得估计结果高度不确定，且往往存在逻辑上的不一致。在分类学或地理学等级较低的估计通常大于等级较高的估计。此外，这些估计通常以多种形式呈现，包括区间（a, b）、无不确定性的点估计以及具有对称或非对称界限的点估计，这使得难以将不同研究中的信息结合起来。在此，我们开发了一种贝叶斯层次方法来估算全球物种多样性（我们估计有2202万种物种；95% HPD区间为（1043万，3528万）），该方法结合了50项已发表研究的估计。通过符号数据分析技术，我们调和了区间估计和点估计的数据混合。这种方法使我们能够在数据部分或全部未观察到的情况下，在每个物种级别恢复区间估计，同时尊重逻辑约束，并确定估计对整个等级的影响，从而为在等级中不同级别获取特定税级的未来估计确定效应。