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The Broken Window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes

View ORCID ProfileChristie A. Bahlai, View ORCID ProfileEaston R. White, Julia D. Perrone, View ORCID ProfileSarah Cusser, View ORCID ProfileKaitlin Stack Whitney
doi: https://doi.org/10.1101/2020.07.07.192211
Christie A. Bahlai
1Department of Biological Sciences and Environmental Science and Design Research Institute, Kent State University
2Kellogg Biological Station Long Term Ecological Research Site, Michigan State University
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  • For correspondence: cbahlai@kent.edu
Easton R. White
3Department of Biological Sciences, University of New Hampshire
4Gund Institute for Environment, University of Vermont
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Julia D. Perrone
1Department of Biological Sciences and Environmental Science and Design Research Institute, Kent State University
5School of Information, Kent State University
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Sarah Cusser
2Kellogg Biological Station Long Term Ecological Research Site, Michigan State University
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Kaitlin Stack Whitney
6Science and Technology Studies Department, Rochester Institute of Technology
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Abstract

A core issue in temporal ecology is the concept of trajectory—that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitudinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create ‘broken windows,’ that is, reanalyze long-term studies from short-term observational perspectives to examine discontinuities in trends at differing temporal scales.

The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly ‘sample’ moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the ‘broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions.

Highlights Trends identified in short-term ecology studies can be misleading.

Non-random resampling can show how prone different systems are to misleading trends

The Broken Window algorithm is a new tool to help synthesize temporal data

This tool helps to understand how much data is needed for forecasting to be reliable It can also be used to quantify how likely it is that an observed trend is spurious.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Minor changes to text to clarify awkward sentences, several citations added.

  • https://github.com/cbahlai/broken_window

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 01, 2021.
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The Broken Window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes
Christie A. Bahlai, Easton R. White, Julia D. Perrone, Sarah Cusser, Kaitlin Stack Whitney
bioRxiv 2020.07.07.192211; doi: https://doi.org/10.1101/2020.07.07.192211
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The Broken Window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes
Christie A. Bahlai, Easton R. White, Julia D. Perrone, Sarah Cusser, Kaitlin Stack Whitney
bioRxiv 2020.07.07.192211; doi: https://doi.org/10.1101/2020.07.07.192211

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