OPUS: CRS--Experiments, Meta-analysis and Big Data in Advancing Ecological Understanding

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Science relies on both theory and data. Data provide the information that guides and tests theory. Together, theory and data advance our understanding of the natural world. Ecological data once emphasized the observations and controlled environment experiments of individual scientists, but progressed to field experiments in natural systems, then to multi-investigator studies and research synthesis, and more recently, to the analysis of "big data," studies based on large electronic databases that include climate, geography, land use, and ecological data. Experiments are the most rigorous way to test hypotheses and disentangle potential confounding factors, but face challenges and limitations. For example, experiments in natural populations have the great advantage of realism, but are limited because they are almost always conducted at local spatial scales. This project will examine how individual experiments, the synthesis of data from many experiments using meta-analysis, and analyses of "big data" at large spatial scales, have brought different perspectives and understanding to ecology over the past 50 years, and changed our understanding of the natural world. Broader impacts of this research will include seminars and training for freshman, graduate students and professionals, presentations, and a YouTube video on how to record, save, organize, and make scientific data in ecology available and transparent.We have witnessed and will continue to experience a major shift in emphasis in how we gather information used to understand the natural world. Can we still answer important fundamental and applied questions through experiments carried out by individual researchers, or is ecology best situated within multi-investigator research programs? Are questions different in individual experiments, large group studies, meta-analyses and analysis of large databases? This study will examine five hypotheses, focusing on plant population, community and macro-ecology. H1: The role of individual investigator field experiments in advancing ecological understanding ecology, once central, is now more secondary; H2: Field experiments have declined from a peak from the mid-1970s to 1990s, to the present; H3: Observational macroecological observational studies using "big data" have concordantly increased in frequency, funding, visibility and prestige; H4: The role of meta-analysis and research synthesis has increased exponentially from the late 1990s to the present; and H5: The focus of work addressed by individual experiments, large group experiments, meta-analysis and the analysis of observational big data differ systematically in spatial scale, topics, questions asked, and insights and understanding gained.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.