期刊
ECOLOGY LETTERS
卷 24, 期 5, 页码 1103-1111出版社
WILEY
DOI: 10.1111/ele.13710
关键词
Data mining; isothermality; long‐ term; moving window; population dynamics; time series; trajectory
类别
资金
- NSF Long-term Ecological Research Program [DEB 1832042]
- National Science Foundation Directorate for Computer and Information Science and Engineering [OAC 1838807]
- USDA National Institute on Food and Agriculture
- Michigan State University AgBioResearch
- NSF [DEB 1637459, 1026843, 0423385, 9810222, 9211775, 8702328, DEB 1831944, DEB 9211768, 9810221, 0423259, DEB 0218039, 0620910, OPP 1637708, LTREB 9815519, LTREB 9527669, OCE 1637630, OCE 1232779, OCE 1831937, DEB 1655499, 1027319]
- Direct For Biological Sciences
- Division Of Environmental Biology [0620910, 9810221, 9211775, 9810222, 8702328] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Division Of Environmental Biology [0423259] Funding Source: National Science Foundation
The study finds that experiments conducted in dynamic abiotic environments require a longer period to reach consistent results compared to those in more stable environments, and plant studies are more prone to producing spurious results. Approximately half of the studies required 10 years or longer to achieve consistency, with some even extending beyond 20 years.
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.
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