Biodiversity faces unprecedented threats from rapid global change¹. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets^{2,3,4,5,6,7,8,9,10,11} that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

生物多样性面临着全球快速变化所带来的前所未有的威胁¹。生物多样性变化的信号来自于大地理和时间尺度上数千个物种的时间序列丰度数据集。对这些生物多样性数据集的分析指出了丰度的不同趋势，包括增加和减少。然而，这些分析并未完全解释数据中的空间、时间和系统发育结构。在这里，使用新的统计框架，我们展示了十个备受瞩目的生物多样性数据集^{2,3,4,5,6,7,8,9,10,11}，一旦空间、时间和系统发育消失，现有方法下的增加和减少就会消失结构已被考虑。这是现有方法严重低估趋势不确定性并且有时错误估计趋势方向的结果。根据我们适当识别不确定性的修订后的平均丰度趋势，我们未能在十个数据集中以 95% 的可信区间观察到单一的增加或减少趋势。这强调了人们对广阔的空间和分类尺度上的生物多样性变化知之甚少。尽管在大尺度上存在这种不确定性，但我们通过考虑空间、时间和系统发育结构，揭示了局部尺度预测准确性的提高。改进的预测为在政策相关规模上估计生物多样性变化提供了希望，从而指导适应性保护应对措施。