We utilise the wealth of data accessible through the 40‐year‐old Long‐Term Ecological Research (LTER) network to ask if aspects of the study environment or taxa alter the duration of research necessary to detect consistent results. To do this, we use a moving‐window algorithm. We limit our analysis to long‐term (> 10 year) press experiments recording organismal abundance. We find that studies conducted in dynamic abiotic environments need longer periods of study to reach consistent results, as compared to those conducted in more moderated environments. Studies of plants were more often characterised by spurious results than those on animals. Nearly half of the studies we investigated required 10 years or longer to become consistent, where all significant trends agreed in direction, and four studies (of 100) required longer than 20 years. Here, we champion the importance of long‐term data and bolster the value of multi‐decadal experiments in understanding, explaining and predicting long‐term trends.

中文翻译：

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人口水平的现场实验需要多长时间？利用来自40年历史的LTER网络的数据

我们利用通过40年历史的长期生态研究（LTER）网络可访问的大量数据来询问研究环境或分类单元的各个方面是否改变了检测一致结果所必需的研究持续时间。为此，我们使用移动窗口算法。我们将分析限于记录有机体丰度的长期（> 10年）新闻实验。我们发现，与在较温和的环境中进行的研究相比，在动态的非生物环境中进行的研究需要更长的研究时间才能达到一致的结果。与对动物的研究相比，对植物的研究通常具有虚假结果的特征。我们研究的近一半研究需要10年或更长时间才能保持一致，所有重要趋势都在方向上一致，而四项研究（每100项研究）则需要20年以上。这里，