1. The exponential growth of scientific literature-which we call the "big literature" phenomenonhas created great challenges in literature comprehension and synthesis. The traditional manual literature synthesis processes are often unable to take advantage of big literature due to human limitations in time and cognition, creating the need for new literature synthesis methods to address this challenge.2. In this paper, we discuss a highly useful literature synthesis approach, Automated Content Analysis (ACA), which has not yet been widely adopted in the fields of ecological and evolutionary biology. ACA is a suite of machine-learning tools for the qualitative and quantitative synthesis of big literature commonly used in the social sciences and in medical research.3. Our goal is to introduce ecologists and evolutionary biologists to ACA and illustrate its capacity to synthesize overwhelming volumes of literature. First, we provide a brief history of the ACA method and summarize the fundamental process of ACA. Next, we present two ACA studies to illustrate the utility and versatility of ACA in synthesizing ecological and evolutionary literature.Finally, we discuss how to maximize the utility and contributions of ACA, as well as potential research directions that may help to advance the use of ACA in future ecological and evolutionary research.4. Unlike manual methods of literature synthesis, ACA is able to process high volumes of literature at substantially shorter timespans, while helping to mitigate human biases. The overall efficiency and versatility of this method allows for a broad range of applications for literature review and synthesis, including both exploratory reviews and systematic reviews aiming to address more targeted research questions. By allowing for more extensive and comprehensive review of big literature, ACA has the potential to fill an important methodological gap and to therefore contribute to the advancement of ecological and evolutionary research.
The physical structure of vegetation is thought to be closely related to ecosystem function, but little is known of its pertinence across geographic regions. Here, we used data from over three million trees in continental North America to evaluate structural diversity – the volumetric capacity and physical arrangement of biotic components in ecosystems – as a predictor of productivity. We show that structural diversity is a robust predictor of forest productivity and consistently outperforms the traditional measure – species diversity – across climate conditions in North America. Moreover, structural diversity appears to be a better surrogate of niche occupancy because it captures variation in size that can be used to measure realized niche space. Structural diversity offers an easily measured metric to direct restoration and management decision making to maximize ecosystem productivity and carbon sequestration.
In the academic libraries’ efforts to support digital humanities and social science, GIS service plays an important role. However, there is no general service model existing about how libraries can develop GIS services to best engage with digital humanities and social science. In this study, we adopted the action research method to develop and improve our service model. Our results suggested that a library’s GIS service can support humanities and social science from the research collaboration, learning support, and outreach perspectives, with different focuses according to the stages of learning and research. The research framework adopted in this study not only can serve as an efficient tool for developing GIS services but also can be expanded to other library service areas.
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