1. An accumulating number of studies are reporting severe insect declines. These studies aim to quantify temporal changes in invertebrate populations and community composition and attribute them to anthropogenic drivers.
2. Seibold et al. 2019 (Nature, 574, 671–674) analysed arthropod biomass, abundance and species richness from forest and grassland plots in a region of Germany and reported declines of up to 78% between 2008 and 2018. However, their analysis did not account for the confounding effects of temporal pseudoreplication.
3. We show that simply by including a year random effect in the statistical models and thereby accounting for the common conditions experienced by proximal sites in the same years, four of the five reported declines become non‐significant out of six tests overall.
4. To place recent estimates of insect trends in a broader context, we analysed invertebrate biomass, abundance and richness from 640 time series from 1167 sites around the world. We found that the average trends across the terrestrial and freshwater realms were not significantly distinguishable from no net change. Shorter time series that are likely most affected by sampling error variance – such as those in Seibold et al. 2019 (Nature, 574, 671–674) – yielded the most extreme decline and increase estimates.
5. We suggest that the media uptake of negative trends from short time series may be serving to exaggerate the ‘insect Armageddon’ and could undermine public confidence in research. We advocate that future research uses appropriate model structures to build a more robust understanding of biodiversity change.

中文翻译：

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在无脊椎动物种群和生物多样性变化的时间分析中考虑年份效应和抽样误差：Seibold等人的评论。2019年

1. 越来越多的研究报告说昆虫严重死亡。这些研究旨在量化无脊椎动物种群和群落组成的时间变化，并将其归因于人为驱动因素。
2. Seibold等。2019（Nature，574，671–674）分析了德国某个地区的森林和草地地段的节肢动物生物量，丰度和物种丰富度，并报告说2008年至2018年之间的下降幅度高达78％。但是，他们的分析并未考虑到时间伪复制的混杂效应。
3. 我们表明，仅通过在统计模型中包括一年的随机效应并由此解释近几年来同一地点经历的常见情况，就可以在五项测试中将五项中的四项变为无意义。
4. 为了更广泛地了解昆虫趋势的最新估计，我们分析了来自全球1167个地点的640个时间序列中的无脊椎动物生物量，丰度和丰富度。我们发现，陆地和淡水领域的平均趋势与净变化没有明显区别。较短的时间序列可能最受采样误差方差的影响-例如Seibold等人的序列。2019（Nature，574，671–674）–产生了最极端的下降和上升估计。
5. 我们建议媒体从短时间序列中获取负面趋势可能会夸大“世界末日昆虫”，并可能破坏公众对研究的信心。我们提倡未来的研究使用适当的模型结构来建立对生物多样性变化的更全面的了解。