Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e. time between original survey and re-survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility) and spatial sampling biases (i.e. site-selection biases). We use PREDICTS model-based estimates as a proxy for the real-world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30-year period, the number of sites for detecting trends doubled for a decade, increased 10-fold within three years and yearly trends were undetectable. Measurement errors had a non-linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.

中文翻译：

---

使用当地物种丰富度的全球生物多样性趋势的不可检测性

尽管物种正在以惊人的速度消失，但先前的研究已经就当地范围内全球生物多样性变化的程度甚至方向提供了相互矛盾的结果。在这里，我们评估了使用当地物种丰富度检测全球生物多样性趋势的能力，以及它如何受到监测站点数量、采样间隔（即原始调查和重新调查站点之间的时间）、测量误差（误差当地物种丰富度的测量），监测的空间粒度（分类群流动性的代表）和空间采样偏差（即选址偏差）。我们使用基于 PREDICTS 模型的估计作为现实世界生物多样性分布的代理，并随机选择监测点来计算当地物种丰富度趋势。我们发现，虽然拥有数百个站点的监测网络可以检测 30 年内物种丰富度的全球变化，但检测趋势的站点数量在十年内翻了一番，在三年内增加了 10 倍，并且无法检测到年度趋势。测量误差对统计功效具有非线性影响，1% 的误差会略微降低统计功效，5% 的误差会大大降低可靠检测任何趋势的功效。检测当地物种丰富度的全球变化的能力也与空间粒度有关，这使得更难检测以较小地块采样的地点的趋势。空间抽样偏差不仅降低了检测全球生物多样性负面趋势的能力，而且有时会产生积极趋势。我们的结论是，使用当地的丰富度来检测准确的全球生物多样性趋势可能对于当前的方法来说根本不可行。我们建议，监测在国家层面实施的具有代表性的站点网络，并结合考虑错误和偏差的模型，可以帮助提高我们对全球生物多样性变化的理解。