AI Article Synopsis
- The study discusses the importance of recognizing patterns across different scales in analyzing complex systems, emphasizing that discontinuity approaches can help identify these scales and improve our understanding of resilience in various contexts.
- It highlights the current limitations of existing discontinuity methods, such as their subjectivity and computational challenges, which necessitate a simpler and clearer alternative.
- The researchers introduce a new method for detecting discontinuities in census data through resampling a neutral model, and they provide the relevant R code, aiming to enhance both fundamental and applied ecological research.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202717 | PMC |
| http://dx.doi.org/10.1002/ece3.4297 | DOI Listing |
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