EAGER: Managing our expectations: quantifying and characterizing misleading trajectories in ecological processes

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A fundamental problem in ecology is understanding how to scale discoveries: from patterns observed in the lab or the plot to the field or the region, or bridging between short term observations to long term trends and trajectories. The PIs propose a method to directly address the temporal aspects of scaling ecological observations, which involves reusing data from the two dozen Long Term Ecological Research (LTER) sites, an NSF program in place since the early 1980s. The PIs intend to bridge the gap between short-term observations and the long-term trends using an automated approach of repeatedly sampling moving windows of data from existing long-term time series, and analyzing these sampled data as if they represented the entire dataset. By compiling typical statistics used to describe the relationship in the sampled data and through repeated samplings, the results will provide insights to the questions, how often are the trends observed in short term data misleading, and can we use characteristics of these trends to predict our likelihood of being misled? The experiences in reusing the LTER data will be captured and shared with the ecology and open science community. This project is supported by the National Science Foundation's Public Access Initiative which is managed by the NSF Office of Advanced Cyberinfrastructure on behalf of the Foundation.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.