Finding missing links in interaction networks

Documenting which species interact within ecological communities is challenging and labour-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, but that the predictive power of structural models varies with the underlying network structure. Predictions can be improved by ensembling multiple models. Sample-coverage estimators of the number of missed interactions are highly correlated with the number of missed interactions, but strongly biased towards underestimating the true number of missing links. 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 prioritisation of research effort and more robust assessment of network properties.

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