Resolving ecological questions through meta-analysis: goals, metrics, and models.

We evaluate the goals of meta-analysis, critique its recent application in ecology, and highlight an approach that more explicitly links meta-analysis and ecological theory. One goal of meta-analysis is testing null hypotheses of no response to experimental manipulations. Many ecologists, however, are more interested in quantitatively measuring processes and examining their systematic variation across systems and conditions. This latter goal requires a suite of diverse, ecologically based metrics of effect size, with each appropriately matched to an ecological question of interest. By specifying ecological models, we can develop metrics of effect size that quantify the underlying process or response of interest and are insensitive to extraneous factors irrelevant to the focal question. A model will also help to delineate the set of studies that fit the question addressed by the meta-analysis. We discuss factors that can give rise to heterogeneity in effect sizes (e.g., due to differences in experimental protocol, parameter values, or the structure of the models that describe system dynamics) and illustrate this variation using some simple models of plant competition. Variation in time scale will be one of the most common factors affecting a meta-analysis, by introducing heterogeneity in effect sizes. Different metrics will apply to different time scales, and time-series data will be vital in evaluating the appropriateness of different metrics to different collections of studies. We then illustrate the application of ecological models, and associated metrics of effect size, in meta-analysis by discussing and/or synthesizing data on species interactions, mutual interference between consumers, and individual physiology. We also examine the use of metrics when no single, specific model applies to the synthesized studies. These examples illustrate that the diversity of ecological questions demands a diversity of ecologically meaningful metrics of effect size. The successful application of meta-analysis in ecology will benefit by clear and explicit linkages among ecological theory, the questions being addressed, and the metrics used to summarize the available information.

本研究首先评述元分析（meta-analysis）的核心目标，批判性审视其在生态学领域的近期应用，并提出一种可更清晰联结元分析与生态学理论的研究范式。元分析的目标之一，是针对实验操控无响应的原假设开展检验。然而，多数生态学家更倾向于通过定量方式刻画生态学过程，并探究其在不同生态系统与环境条件下的系统变异规律。达成后一类研究目标，需要一套多样化、基于生态学原理的效应量指标体系，且每一项指标需与对应的核心生态学研究问题精准匹配。通过构建明确的生态学模型，我们可开发出可量化目标生态学过程或响应特征的效应量指标，且该类指标不受与核心研究问题无关的外源因素干扰。同时，生态学模型可辅助界定符合元分析研究问题的研究文献范围。本研究将探讨可能引发效应量异质性的各类因素（例如实验方案差异、参数取值不同，或是描述系统动态的模型结构差异），并通过若干简单的植物竞争模型阐释这类变异规律。时间尺度差异是引发效应量异质性的最常见因素之一，进而对元分析结果产生影响。不同的效应量指标适用于不同的时间尺度，而时间序列数据对于评估不同指标适配各类研究数据集的合理性至关重要。随后，本研究将通过讨论/整合物种相互作用、消费者间相互干扰以及个体生理学相关数据，阐释生态学模型及其配套效应量指标在元分析中的具体应用场景。此外，本研究还将探讨当不存在单一普适模型可适配整合的研究文献时，效应量指标的选用策略。上述案例表明，生态学研究问题的多样性，亟需与之匹配的、具备生态学意义的多样化效应量指标体系。若能在生态学理论、待解决的研究问题以及用于整合现有数据的效应量指标三者之间建立清晰明确的联结，将有助于元分析在生态学领域的更高效应用。