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Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Highlights
• We propose a framework to address landscape effects on ecosystem services.
• We expect ecosystem service flows to be modulated by the amount and configuration of supply and demand areas.
• We stress the role of neutral areas in facilitating or hindering ecosystem service flows.
• Supply/demand ratios, spatial overlap, and ES characteristics need to be accounted for when assessing flows.
• We propose a research agenda with challenges to couple the effects of landscape configuration on ES flow.
Abstract
Despite advances in understanding the effects of landscape structure on ecosystem services (ES), many challenges related to these complex spatial interactions remain. In particular, the integration of landscape effects on different components of the service provision chain (supply, demand, and flow) remains poorly understood and conceptualized. Here we propose a theoretical framework to further explore how the spatial flow of ES can vary according to landscape structure (i.e. composition and configuration) emphasizing the role played by the configuration of supply, demand, and neutral areas, as well as individual characteristics of ES (e.g., service rivalry). For this, we expand the discussion on how landscape changes can affect ES flows and propose a theoretical representation of ES flows variation led by different supply-demand ratios. Additionally, we expand this discussion by integrating the potential effects of neutral areas in the landscape as well as of supply/demand spatial overlap. This novel approach links the spatial arrangement (e.g. fragmentation, network complexity, matrix resistance) usually captured by landscape metrics, and ratios of ES supply and demand areas to potential effects on spatial flows of ES. We discuss the application of this model using widely studied ES, such as pollination, pest control by natural enemies, and microclimate regulation. Finally, we propose a research agenda to connect the presented ideas with other prominent research topics that must be further developed to support landscape management targeting ES provision. The prominence of ES science calls for contributions such as this to give the scientific community the opportunity to reflect on the underlying mechanisms of ES and avoid oversimplified spatial assessments.