Open AccessA method to detect discontinuities in census data
Author(s) -
Barichievy Chris,
Angeler David G.,
Eason Tarsha,
Garmestani Ahjond S.,
Nash Kirsty L.,
Stow Craig A.,
Sundstrom Shana,
Allen Craig R.
Publication year - 2018
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.4297
Subject(s) - classification of discontinuities , computer science , discontinuity (linguistics) , resampling , data mining , artificial intelligence , machine learning , mathematics , mathematical analysis
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.