W.K. Kellogg Biological Station, Department of Integrative Biology, Michigan State University, Hickory Corners, MI, USA.
Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA.
Ecol Lett. 2021 May;24(5):1103-1111. doi: 10.1111/ele.13710. Epub 2021 Feb 22.
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.
我们利用长达 40 年的长期生态研究(LTER)网络获取的丰富数据,来研究研究环境或分类群的某些方面是否会改变检测一致结果所需的研究持续时间。为此,我们使用了移动窗口算法。我们将分析仅限于长期(>10 年)的压力实验,记录生物数量。我们发现,与在较稳定环境中进行的实验相比,在动态非生物环境中进行的实验需要更长的研究时间才能得出一致的结果。与动物相比,植物的研究更容易出现虚假结果。我们研究的近一半实验需要 10 年或更长时间才能变得一致,即所有显著趋势的方向都一致,其中四项研究(占 100 项)需要的时间超过 20 年。在这里,我们倡导长期数据的重要性,并支持多十年实验在理解、解释和预测长期趋势方面的价值。