Lake Ecosystems Group, UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Bailrigg, UK.
School of the Environment, Washington State University, Pullman, WA, USA.
Glob Chang Biol. 2020 Jun;26(6):3230-3240. doi: 10.1111/gcb.15045. Epub 2020 Mar 29.
Freshwater ecosystems are heavily impacted by multiple stressors, and a freshwater biodiversity crisis is underway. This realization has prompted calls to integrate global freshwater ecosystem data, including traditional taxonomic and newer types of data (e.g., eDNA, remote sensing), to more comprehensively assess change among systems, regions, and organism groups. We argue that data integration should be done, not only with the important purpose of filling gaps in spatial, temporal, and organismal representation, but also with a more ambitious goal: to study fundamental cross-scale biological phenomena. Such knowledge is critical for discerning and projecting ecosystem functional dynamics, a realm of study where generalizations may be more tractable than those relying on taxonomic specificity. Integration could take us beyond cataloging biodiversity losses, and toward predicting ecosystem change more broadly. Fundamental biology questions should be central to integrative, interdisciplinary research on causal ecological mechanisms, combining traditional measures and more novel methods at the leading edge of the biological sciences. We propose a conceptual framework supporting this vision, identifying key questions and uncertainties associated with realizing this research potential. Our framework includes five interdisciplinary "complementarities." First, research approaches may provide comparative complementarity when they offer separate realizations of the same focal phenomenon. Second, for translational complementarity, data from one research approach is used to translate that from another, facilitating new inferences. Thirdly, causal complementarity arises when combining approaches allows us to "fill in" cause-effect relationships. Fourth, contextual complementarity is realized when together research methodologies establish the wider ecological and spatiotemporal context within which focal biological responses occur. Finally, integration may allow us to cross inferential scales through scaling complementarity. Explicitly identifying the modes and purposes of integrating research approaches, and reaching across disciplines to establish appropriate collaboration will allow researchers to address major biological questions that are more than the sum of the parts.
淡水生态系统受到多种胁迫因素的严重影响，一场淡水生物多样性危机正在发生。这一认识促使人们呼吁整合全球淡水生态系统数据，包括传统分类学数据和新型数据（如 eDNA、遥感），以更全面地评估系统、区域和生物群体之间的变化。我们认为，数据整合不仅具有填补空间、时间和生物代表性差距的重要目的，而且具有更具野心的目标：研究跨尺度的基本生物学现象。这种知识对于辨别和预测生态系统功能动态至关重要，在这个研究领域，概括可能比依赖分类特异性更可行。整合可以使我们不仅仅停留在记录生物多样性的丧失，而是更广泛地预测生态系统的变化。基本生物学问题应该成为综合的、跨学科的因果生态机制研究的核心，将传统的测量方法和生物学前沿的新方法结合起来。我们提出了一个支持这一愿景的概念框架，确定了与实现这一研究潜力相关的关键问题和不确定性。我们的框架包括五个跨学科的“互补性”。首先，当研究方法提供对同一焦点现象的独立实现时，它们可能提供比较互补性。其次，对于转化互补性，一种研究方法的数据被用于转化另一种方法的数据，从而促进新的推断。第三，当结合方法可以使我们“填补”因果关系时，就会出现因果互补性。第四，当研究方法共同建立焦点生物反应发生的更广泛的生态和时空背景时，就会出现上下文互补性。最后，通过尺度互补性，整合可能允许我们交叉推断尺度。明确识别整合研究方法的模式和目的，并跨越学科建立适当的合作，将使研究人员能够解决不仅仅是各部分总和的重大生物学问题。
