The Robustness of Structural Features in Species Interaction Networks

• URL: http://arxiv.org/abs/2502.16778v1
• Date: Mon, 24 Feb 2025 02:14:17 GMT
• Title: The Robustness of Structural Features in Species Interaction Networks
• Authors: Sanaz Hasanzadeh Fard, Emily Dolson,
• Abstract summary: Species interaction networks are a powerful tool for describing ecological communities.<n>We analyzed 148 real-world bipartite networks representing four different types of species interactions.<n>We found substantial variation in how robust different properties were to the missing data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Species interaction networks are a powerful tool for describing ecological communities; they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of drawing abstract inferences about groups of similar networks, ecologists often use graph topology metrics to summarize structural features. However, gathering the data that underlies these networks is challenging, which can lead to some interactions being missed. Thus, it is important to understand how much different structural metrics are affected by missing data. To address this question, we analyzed a database of 148 real-world bipartite networks representing four different types of species interactions (pollination, host-parasite, plant-ant, and seed-dispersal). For each network, we measured six different topological properties: number of connected components, variance in node betweenness, variance in node PageRank, largest Eigenvalue, the number of non-zero Eigenvalues, and community detection as determined by four different algorithms. We then tested how these properties change as additional edges -- representing data that may have been missed -- are added to the networks. We found substantial variation in how robust different properties were to the missing data. For example, the Clauset-Newman-Moore and Louvain community detection algorithms showed much more gradual change as edges were added than the label propagation and Girvan-Newman algorithms did, suggesting that the former are more robust. Robustness also varied for some metrics based on interaction type. These results provide a foundation for selecting network properties to use when analyzing messy ecological network data.

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