ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology.
ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology.
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DOI：
10.1111/2041-210x.13857
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发表时间：
2022-07
影响因子：
6.6
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生态学家越来越多地可以获得来自不同来源的物种出现情况和丰度数据，以分析生物多样性的时间趋势。然而，与任何特定研究问题相关的抽样偏差往往没有得到很好的探索和报道；这可能会破坏统计推断。在其他学科中，研究人员通常会完成“偏见风险”评估，以揭露和记录偏见破坏结论的可能性。现有数据的巨大增长，以及最近围绕其用于推断时间趋势的争议，表明生态学迫切需要类似的评估。我们介绍了ROBITT，这是一个结构化的工具，用于评估“生态学时间趋势研究中的偏差风险”。ROBITT与其他学科的同行有类似的形式：它包括信号性问题，旨在引出关于关键研究领域潜在偏见的信息。在回答这些问题时，用户将定义研究推理目标(S)和相关的统计目标人群。这些信息被用来评估与研究问题相关的领域(例如地理、分类、环境)的潜在抽样偏差，以及这些偏差如何随时间变化。如果评估表明存在偏见，则用户必须清楚地描述它们和/或解释将采取哪些缓解措施。提供了用户完成ROBITT评估所需的一切：工具、指导文档和工作示例。根据其他学科，该工具和指导文件是在生态学和证据合成相关领域的专家之间形成共识的过程中制定的。我们建议，应大力鼓励研究人员在发表生物多样性趋势研究报告时纳入ROBITT评估，特别是在使用汇总数据时。这将帮助研究人员构建他们的思维结构，清楚地认识到潜在的抽样问题，强调需要专家咨询的地方，并提供一个机会来描述可能没有报告的数据检查。ROBITT还将使审查者、编辑和读者能够确定在给定数据集和一些分析方法的情况下，研究结论得到了多大程度的支持。反过来，它应该加强基于证据的政策和实践，减少对数据的不同解释，并提供与我们对现实的理解相关的不确定性的更清晰的图景。
Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete ‘risk‐of‐bias’ assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology. We introduce ROBITT, a structured tool for assessing the ‘Risk‐Of‐Bias In studies of Temporal Trends in ecology’. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken. Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus‐forming process across experts working in relevant areas of ecology and evidence synthesis. We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence‐based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.