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A Classification Framework to Assess Ecological, Biogeochemical, and Hydrologic Synchrony and Asynchrony

Ecosystems in the Anthropocene face pressures from multiple, interacting forms of environmental change. These pressures, resulting from land use change, altered hydrologic regimes, and climate change, will likely change the synchrony of ecosystem processes as distinct components of ecosystems are impacted in different ways. However, discipline-specific definitions and ad hoc methods for identifying synchrony and asynchrony have limited broader synthesis of this concept among studies and across disciplines. Drawing on concepts from ecology, hydrology, geomorphology, and biogeochemistry, we offer a unifying definition of synchrony for ecosystem science and propose a classification framework for synchrony and asynchrony of ecosystem processes. This framework classifies the relationships among ecosystem processes according to five key aspects: 1) the focal variables or relationships representative of the ecosystem processes of interest, 2) the spatial and temporal domain of interest, 3) the structural attributes of drivers and focal processes, 4) consistency in the relationships over time, and 5) the degree of causality among focal processes. Using this classification framework, we identify and differentiate types of synchrony and asynchrony, thereby providing the basis for comparing among studies and across disciplines. We apply this classification framework to existing studies in the ecological, hydrologic, geomorphic, and biogeochemical literature, and discuss potential analytical tools that can be used to quantify synchronous and asynchronous processes. Furthermore, we seek to promote understanding of how different types of synchrony or asynchrony may shift in response to ongoing environmental change by providing a universal definition and explicit types and drivers with this framework

Estimating the reproducibility of psychological science

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams

Data from: Estimating the reproducibility of psychological science

This record contains the underlying research data for the publication "Estimating the reproducibility of psychological science" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/5257Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams