Predicting missing links in global host–parasite networks

Abstract

1. Parasites that infect multiple species cause major health burdens globally, but for many, the full suite of susceptible hosts is unknown. Predicting undocumented host–parasite associations will help expand knowledge of parasite host specificities, promote the development of theory in disease ecology and evolution, and support surveillance of multi‐host infectious diseases. The analysis of global species interaction networks allows for leveraging of information across taxa, but link prediction at this scale is often limited by extreme network sparsity and lack of comparable trait data across species. 2. Here we use recently developed methods to predict missing links in global mammal‐parasite networks using readily available data: network properties and evolutionary relationships among hosts. We demonstrate how these link predictions can efficiently guide the collection of species interaction data and increase the completeness of global species interaction networks. 3. We amalgamate a global mammal host–parasite interaction network (>29,000 interactions) and apply a hierarchical Bayesian approach for link prediction that leverages information on network structure and scaled phylogenetic distances among hosts. We use these predictions to guide targeted literature searches of the most likely yet undocumented interactions, and identify empirical evidence supporting many of the top ‘missing’ links. 4. We find that link prediction in global host–parasite networks can successfully predict parasites of humans, domesticated animals and endangered wildlife, representing a combination of published interactions missing from existing global databases, and potential but currently undocumented associations. 5. Our study provides further insight into the use of phylogenies for predicting host–parasite interactions, and highlights the utility of iterated prediction and targeted search to efficiently guide the collection of information on host–parasite interactions. These data are critical for understanding the evolution of host specificity, and may be used to support disease surveillance through a process of predicting missing links, and targeting research towards the most likely undocumented interactions.

Mussel parasite richness and risk of extinction

“…However, it remains the most suitable curve for using host richness to estimate parasite richness and is relevant for a wide variety of systems, including human viruses, vertebrate trematodes, mammalian nematodes, and plant pollinators (Carlson et al., 2019 , 2020a ). Second, given the undersampling present in the mussel‐parasite system (Brian & Aldridge, 2019 ) (Figure 2 ), it is likely that parasites in the data set have associations with more host species than have currently been recorded (“missing links” [Dallas et al., 2017 ; Farrell et al., 2022 ]). This would cause an overestimation of host specificity and make the estimated curve steeper (i.e., increase the value of z in Equation 1 ), which would lead to unduly high estimates of richness.…”

Fish–parasite interaction networks reveal latitudinal and taxonomic trends in the structure of host–parasite associations

“…Solutions to this problem exist (e.g. Farrell et al , 2022 ) but are yet to be widely implemented. Nevertheless, considering host–parasite communities as bipartite interaction networks remains the most holistic approach currently available to tackle not only unresolved questions about the structure of particular communities (Runghen et al , 2021 ), but also to identify the main drivers of variation in key properties across different communities (Pellissier et al , 2018 ; Xing and Fayle, 2021 ).…”

Data Proliferation, Reconciliation, and Synthesis in Viral Ecology

“…Some of these datasets were validated against each other during production and others have been used for cross-validation in analytical work (Albery et al 2020), and certain studies have generated a study-specific ad hoc reconciled dataset (Farrell et al 2020, Gibb et al 2020). However, no work has been published with the primary aim of reconciling them as correctly, comprehensively, and reproducibly as possible.…”

“…For example, the field of biodiversity studies has adopted the concept of Essential Biodiversity Variables, which can be updated when the underlying data change (Pereira et al 2013, Fernández et al 2019, Jetz et al 2019. Having the ability to revisit predictions about the host-virus network could improve models that assess zoonotic potential of wildlife viruses (Farrell et al 2020, generate priority targets for wildlife reservoir sampling , Babayan et al 2018, Plowright et al 2019, and help benchmark model performance related to these tasks. Beyond training and validation, link prediction models built on these reconciled databases may be used to target future literature searches, shifting from systematic literature searches to a model-based approach to database updating.…”