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All organisms have a position within an ecosystem. The complete set of living and nonliving factors—including food resources, climate, and terrain—that define the position of a given organism are collectively referred to as the organism’s ecological niche.Multiple species cannot occupy the exact same niche within their habitat. If the niches of two or more species overlap to a large extent, the competitive exclusion principle dictates that one species will outcompete the other, forcing it to...

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Related Experiment Video

Updated: Jun 20, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

Googling food webs: can an eigenvector measure species' importance for coextinctions?

Stefano Allesina¹, Mercedes Pascual

¹National Center for Ecological Analysis and Synthesis, Santa Barbara, California, United States of America. allesina@nceas.ucsb.edu

Plos Computational Biology

|September 5, 2009

Summary

Predicting species extinctions is crucial. An algorithm, similar to Google's page ranking, identifies species whose loss causes the fastest ecosystem collapse due to coextinctions.

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Area of Science:

Ecology
Network Theory
Conservation Biology

Background:

Forecasting the ecological impact of species extinctions is a significant challenge.
Species interactions form complex networks, where the loss of one species can trigger cascading secondary extinctions.

Purpose of the Study:

To develop a method for ordering species by their importance in coextinction cascades.
To identify the extinction sequence leading to the most rapid ecosystem network collapse.

Main Methods:

Adapted Google's page ranking algorithm to analyze ecological networks.
Identified species crucial for coextinction sequences based on network structure.

Main Results:

The adapted algorithm effectively orders species by their importance for coextinctions.
This method provides a sequence of species loss leading to the fastest network collapse.
The approach bridges qualitative and quantitative food web descriptions.

Conclusions:

The proposed algorithm offers a robust method for assessing extinction risk.
It outperforms traditional measures like 'hub' or centrality analysis in identifying critical species for coextinction.
This provides a foundation for advanced analysis of ecosystem stability and biodiversity loss.