Graph tools for analysing trophic assemblies in the isotopic space

Authors:
Open Access
Publisher website
Google Scholar
Abstract
A large amount of information on the trophic assembly of ecosystems is summarized in the isotopic space, and metrics for extracting this information are essential. Here, we show how the characterization of the isotopic space as an isotopic‐network of organisms linked by their trophic similarity significantly expands the isotopic toolbox for ecologists. Complete isotopic‐network metrics such as connectivity, modularity or nestedness can capture interrelationship, segmentation and gradients in community assembly. Node centrality metrics such as degree, closeness, eigenvector or betweenness can identify the interdependence of organisms with other components of the system. The topological roles derived from within‐ and between‐guild connectivity identify the importance of isotopic guilds for the structure of the network, with potential implications for ecosystem functioning. We report how these metrics were able to evidence features of the complete ecosystem, differences in the trophic properties of isotopic guilds and the role of particular species in ecosystem functioning; in a way that was not evident without them. Our results show that the isotopic‐network metrics explored here were not associated with the widely used Layman metrics, indicating that they capture complementary and novel features of the trophic arrangement of communities. In addition to these metrics, a huge number of graph methods are available that can be directly related to different biological processes. Consequently, graph theory can expand and complement the tools available for isotopic analyses, representing a valuable research programme to advance the extraction of biologically relevant information from isotopic data.
Funding Information
  • Ministerio de Ciencia, Innovación y Universidades (PID2020‐114440GB‐I00)
  • Comisión Sectorial de Investigación Científica (Grupos_657725)
  • Comisión Sectorial de Investigación Científica (I+D_2024_22520240100172UD)

This publication has 54 references indexed in Scilit: