Taking a moment to measure networks—an approach to species conservation

Kehinde R. Salau, Jacopo A. Baggio, David W. Shanafelt, Marco A. Janssen, Joshua K. Abbott, Eli P. Fenichel

Research output: Contribution to journalArticlepeer-review

Abstract

Context: Network-theoretic tools contribute to understanding real-world system dynamics, such as species survival or spread. Network visualization helps illustrate structural heterogeneity, but details about heterogeneity are lost when summarizing networks with a single mean-style measure. Researchers have indicated that a system composed of multiple metrics may be a more useful determinant of structure, but a formal method for grouping metrics is still lacking. Objectives: Our objective is to present a tool that can account for multiple properties of network structure, which can be related to model outcomes. Methods: We develop an approach using the statistical concept of moments and systematically test the hypothesis that this system of metrics is sufficient to explain variation in processes that take place on networks, using an ecological system as an example. Results: Our results indicate that the moments approach outperforms single summary metrics by adjusted-R2 and AIC model fit criteria, and accounts for a majority of the variation in process outcomes. Conclusions: Our scheme is helpful for indicating when additional structural information is needed to describe system process outcomes such as survival or spread.

Original languageEnglish (US)
Pages (from-to)2551-2569
Number of pages19
JournalLandscape Ecology
Volume37
Issue number10
DOIs
StatePublished - Oct 2022

Keywords

  • Dominant eigenvalue
  • Graph theory
  • Network theory
  • Species spread and survival
  • Statistical moments
  • Weighted networks

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecology
  • Nature and Landscape Conservation

Fingerprint

Dive into the research topics of 'Taking a moment to measure networks—an approach to species conservation'. Together they form a unique fingerprint.

Cite this