Finding missing links in interaction networks

Journal article

Documenting which species interact within ecological communities is challenging and labor intensive. As a result, many interactions remain unrecorded, potentially distorting our understanding of network structure and dynamics. We test the utility of four structural models and a new coverage‐deficit model for predicting missing links in both simulated and empirical bipartite networks. We find they can perform well, although the predictive power of structural models varies with the underlying network structure. The accuracy of predictions can be improved by ensembling multiple models. Augmenting observed networks with most‐likely missing links improves estimates of qualitative network metrics. Tools to identify likely missing links can be simple to implement, allowing the prioritization of research effort and more robust assessment of network properties.

Authors: Terry, JCD; Lewis, OT

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Ecological Society of America
• Journal: Ecology
• Volume: 101
• Issue: 7
• Article number: e03047
• Publication date: 2020-05-04
• Acceptance date: 2020-02-25
• DOI: 10.1002/ecy.3047
• EISSN: 1939-9170
• ISSN: 0012-9658
• Language: English
• Keywords: bipartite network; FFR; network metrics; missing links; prediction; ecological network; sampling completeness