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A method to detect discontinuities in census data
Citation:
Barichievy, C., D. Angeler, T. Eason, A. Garmestani, K. Nash, C. Stow, S. Sundstrom, AND C. Allen. A method to detect discontinuities in census data. Ecology and Evolution. Wiley-Blackwell Publishing, Hoboken, NJ, 8(19):9614-9623, (2018).
Impact/Purpose:
The distribution of pattern across scales has predictive power in the analysis of complex systems. Hence, discontinuity approaches remain a fruitful avenue of research in the quest for quantitative measures of resilience. Discontinuity analysis provides an objective means of identifying scales in complex systems (Sundstrom et al. 2014) and facilitates delineation of hierarchical patterns in processes, structure and resources (Angeler et al. 2015; Spanbauer et al. 2016).
Description:
The distribution of pattern across scales has predictive power in the analysis of complex systems. Discontinuity approaches remain a fruitful avenue of research in the quest for quantitative measures of resilience because discontinuity analysis provides an objective means of identifying scales in complex systems and facilitates delineation of hierarchical patterns in processes, structure, and resources. However, current discontinuity methods have been considered too subjective, too complicated and opaque, or have become computationally obsolete; given the ubiquity of discontinuities in ecological and other complex systems, a simple and transparent method for detection is needed. In this study, we present a method to detect discontinuities in census data based on resampling of a neutral model and provide the R code used to run the analyses. This method has the potential for advancing basic and applied ecological research.
URLs/Downloads:
https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.4297
Free access through PubMed Central