A method to detect discontinuities in census data. Issue 19 (20th September 2018)

• Record Type: Journal Article
• Authors: Barichievy, Chris; Angeler, David G.; Eason, Tarsha; Garmestani, Ahjond S.; Nash, Kirsty L.; Stow, Craig A.; Sundstrom, Shana; Allen, Craig R.
• Is Part Of: Ecology and evolution. Volume 8:Issue 19(2018)
• Page Start: 9614
• Page End: 9623
• Publication Date: 2018-09-20
• Subjects: discontinuities -- discontinuity detector -- ecosystem management -- resilience; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.4297
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: 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. Abstract : 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. In this study, we present a method to detect discontinuities.