Next Article in Journal
A Novel Method for PD Feature Extraction of Power Cable with Renyi Entropy
Next Article in Special Issue
Relative Entropy in Biological Systems
Previous Article in Journal
Neighborhood Approximations for Non-Linear Voter Models
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(11), 7680-7697; doi:10.3390/e17117680

Using Expectation Maximization and Resource Overlap Techniques to Classify Species According to Their Niche Similarities in Mutualistic Networks

1,†,* and 2,†
1
Complex Systems Group, Institute of Physics, Facultad de Ciencias, Universidad de la República, 11400 Montevideo, Uruguay
2
Physics Department, Boğaziçi University, Bebek, 34342 Istanbul, Turkey
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: John Baez, John Harte, Marc Harper and Raúl Alcaraz Martínez
Received: 27 August 2015 / Revised: 12 October 2015 / Accepted: 9 November 2015 / Published: 12 November 2015
(This article belongs to the Special Issue Information and Entropy in Biological Systems)
View Full-Text   |   Download PDF [376 KB, uploaded 12 November 2015]   |  

Abstract

Mutualistic networks in nature are widespread and play a key role in generating the diversity of life on Earth. They constitute an interdisciplinary field where physicists, biologists and computer scientists work together. Plant-pollinator mutualisms in particular form complex networks of interdependence between often hundreds of species. Understanding the architecture of these networks is of paramount importance for assessing the robustness of the corresponding communities to global change and management strategies. Advances in this problem are currently limited mainly due to the lack of methodological tools to deal with the intrinsic complexity of mutualisms, as well as the scarcity and incompleteness of available empirical data. One way to uncover the structure underlying complex networks is to employ information theoretical statistical inference methods, such as the expectation maximization (EM) algorithm. In particular, such an approach can be used to cluster the nodes of a network based on the similarity of their node neighborhoods. Here, we show how to connect network theory with the classical ecological niche theory for mutualistic plant-pollinator webs by using the EM algorithm. We apply EM to classify the nodes of an extensive collection of mutualistic plant-pollinator networks according to their connection similarity. We find that EM recovers largely the same clustering of the species as an alternative recently proposed method based on resource overlap, where one considers each party as a consuming resource for the other party (plants providing food to animals, while animals assist the reproduction of plants). Furthermore, using the EM algorithm, we can obtain a sequence of successfully-refined classifications that enables us to identify the fine-structure of the ecological network and understand better the niche distribution both for plants and animals. This is an example of how information theoretical methods help to systematize and unify work in ecology. View Full-Text
Keywords: mutualistic networks; expectation maximization algorithm; niche theory mutualistic networks; expectation maximization algorithm; niche theory
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fort, H.; Mungan, M. Using Expectation Maximization and Resource Overlap Techniques to Classify Species According to Their Niche Similarities in Mutualistic Networks. Entropy 2015, 17, 7680-7697.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top