对生物多样性变化进行可靠的估计需要高分辨率时间序列数据

1. 信息

2. 摘要

Robust estimates of biodiversity change are essential to inform management and conservation, and to address global biodiversity loss. However, common temporal data limitations may compromise trend accuracy. Here, we test how temporal resolution influences biodiversity trends using 1,353 river invertebrate time series collected annually for ≥ 10 years across 18 European countries. We simulate reduced sampling frequencies and durations and compare these trends to the complete time series. Reducing frequency from annual to every 2–6 years resulted in 87–73% of sites matching in trend directions, but only 78–39% matching in magnitude. Reducing duration from 10 to 9–2 years resulted in 88–52% direction matches and 86–8% magnitude matches. Similar results were observed for longer time series ( ≥ 20 years). Additionally, a comparison of two real-world monitoring datasets with different temporal resolutions shows that 53% of sites matched in direction, but only 12% in magnitude. Our findings indicate that biodiversity change magnitude is more sensitive to temporal resolution than direction. Consequently, accurate estimates of both direction and magnitude require high-resolution time series, whereas lower-resolution data may only reliably capture direction. These results highlight the value and limitations of temporal biodiversity data, and help plan future monitoring.

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4. 引用

@article{cortsguzmn2026,
  author = {Daniela Cortés-Guzmán and James S. Sinclair and Jukka Aroviita and Iker Azpiroz and Milo L. de Baat and Ignacio Bañares and Elmar Becker and Miguel Cañedo-Argüelles and Eddy Cosson and David Cunillera-Montcusí and Rémi Escaffre and Martial Ferréol and Marie Anne Eurie Forio and Peter Goethals and Alexia M. González-Ferreras and Kaisa-Leena Huttunen and Aitor Larrañaga and Eva S. López and Manu Rubio and Rudy Vannevel and Martin Wilkes and Peter Haase},
  title = {Robust estimates of biodiversity change require high-resolution time series},
  journal = {Nature Communications},
  year = {2026},
  volume = {17},
  number = {1},
  pages = {5941},
  doi = {10.1038/s41467-026-75321-0},
  publisher = {Springer Science and Business Media LLC},
}