COLLABORATIVE RESEARCH: META-ANALYSIS: EVALUATION AND IMPROVEMENT OF AN IMPORTANT SYNTHETIC TOOL

在环境生物学中，对同一主题的不同研究可能会得出截然不同的结论。有时这些分歧是因为不同的方法或回答的问题略有不同，但其他时候，它们是由于所研究的年份，地点或物种之间的真正差异。重要的是，科学家能够区分这些可能性并自信地得出结论。例如，一些研究表明，种植不同的作物会提高农民的总产量，而另一些研究表明，种植单一类型的作物会带来最高的产量。这些相互矛盾的结果是否反映了自然界中的一些随机性，科学家在两种类型的研究中使用的不同方法，或者其他原因？一种称为荟萃分析的统计方法已经被开发出来解决这个问题。元分析帮助了许多学科，因为它允许科学家将许多研究的结果联合收割机结合起来，考虑到它们之间的差异，并提供否则会隐藏的见解。该项目将使用先进的技术来改进元分析的完成方式。研究人员将专注于在生态学领域使用该技术。参与该项目的早期职业科学家将接受现代生态学方法的培训，包括荟萃分析。该项目还将开发公开的在线材料，从而帮助全国各地的生态学家学习元分析方法，并提高元分析在生态问题中的应用。为了提高元分析在生态学中的应用，该项目将：1）系统地回顾最近的生态元分析，以描述它们通常是如何进行的（即，统计模型和计算效应大小的方式）和数据集的特征（例如，研究的数量、每项研究的样本量、以及研究间和研究内变化的幅度）; 2）使用模拟来评估现有荟萃分析模型和所提出的替代方案的性能（例如，不同的加权方案，使用贝叶斯与频率论方法，或使用不同的非独立性调整），在生态数据集可能遇到的一系列条件下（例如，使用样本大小和生态学中观察到的不同变异来源的幅度）。这些结果将有助于提高国家汇集和准确解释生态研究复杂结果的能力。

In environmental biology, different studies on the same topic can reach very different conclusions. Sometimes those disagreements are because of different methods or slightly different questions being answered, but other times they're due to true differences between years, places or species being studied. It is important that scientists be able to distinguish between these possibilities and confidently reach conclusions. For example, some studies suggest that growing different crops together results in higher overall yields for farmers, whereas other studies suggest that growing a single type of crop results in the highest yields. Do these conflicting results reflect some randomness in nature, different methods used by scientists in the two types of studies, or some other reason? A statistical approach called meta-analysis has been developed to solve this problem. Meta-analysis has helped many disciplines because it allows scientists to combine results from many studies, taking into account how they differ and providing insights that would otherwise remain hidden. This project will use advanced techniques to improve how meta-analyses are done. Researchers will focus on using the technique in the field of ecology. Early career scientists engaged in this project will be trained in modern ecological methods, including meta-analysis. This project also will develop online materials that will be available publicly, thus helping educate ecologists across the nation in meta-analytic methods and improving the application of meta-analysis to ecological problems.To improve the application of meta-analysis in ecology, this project will: 1) systematically review recent ecological meta-analyses to describe how they are typically performed (i.e., the statistical model and the way the size of an effect is calculated) and the characteristics of the dataset (e.g., the number of studies, the sample sizes of each study, and the magnitude of among-study and within-study variation); 2) use simulations to evaluate the performance of existing meta-analysis models and proposed alternatives (e.g., differing in weighting schemes, using Bayesian vs. frequentist approaches, or using different adjustments for non-independence), under a range of conditions likely to be encountered in ecological datasets (e.g., using sample sizes and the magnitudes of different sources of variation observed in ecology). The results will be useful for improving the nation's ability to bring together and accurately interpret the complicated results of ecological studies.