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Species invasions and range shifts can lead to novel host–parasite communities, but we lack general rules on which new associations are likely to form. While many studies examine parasite sharing among host species, the directionality of transmission is typically overlooked, impeding our ability to derive principles of parasite acquisition. Consequently, we analysed parasite records from the non-native ranges of 11 carnivore and ungulate species. Using boosted regression trees, we modelled parasite acquisition within each zoogeographic realm of a focal host's non-native range, using a suite of predictors characterizing the parasites themselves and the host community in which they live. We found that higher parasite prevalence among established hosts increases the likelihood of acquisition, particularly for generalist parasites. Non-native host species are also more likely to acquire parasites from established host species to which they are closely related; however, the acquisition of several parasite groups is biased to phylogenetically specialist parasites, indicating potential costs of parasite generalism. Statistical models incorporating these features provide an accurate prediction of parasite acquisition, indicating that measurable host and parasite traits can be used to estimate the likelihood of new host–parasite associations forming. This work provides general rules to help anticipate novel host–parasite associations created by climate change and other anthropogenic influences. 

We develop a novel method that provides theoretical guarantees for learning from weak labelers without the (mostly unrealistic) assumption that the errors of the weak labelers are independent or come from a particular family of distributions. We show a rigorous technique for efficiently selecting small subsets of the labelers so that a majority vote from such subsets has a provably low error rate. We explore several extensions of this method and provide experimental results over a range of labeled data set sizes on 45 image classification tasks. Our performance-guaranteed methods consistently match the best performing alternative, which varies based on problem difficulty. On tasks with accurate weak labelers, our methods are on average 3 percentage points more accurate than the state-of-the-art adversarial method. On tasks with inaccurate weak labelers, our methods are on average 15 percentage points more accurate than the semi-supervised Dawid-Skene model (which assumes independence). 

While many viruses of wild mammals are capable of infecting humans, our understanding of zoonotic potential is incomplete. Viruses vary in their degree of generalism, characterized by the phylogenetic relationships of their hosts. Among the dimensions of this phylogenetic landscape, phylogenetic aggregation, which is largely overlooked in studies of parasite host range, emerges in this study as a key predictor of zoonotic status of viruses. Plausibly, viruses that exhibit aggregation, typified by discrete clusters of related host species, may (i) have been able to close the phylogenetic distance to humans, (ii) have subsequently acquired an epidemiologically relevant host and (iii) exhibit relatively high fitness in realized host communities, which are frequently phylogenetically aggregated. These mechanisms associated with phylogenetic aggregation may help explain why correlated fundamental traits, such as the ability of viruses to replicate in the cytoplasm, are associated with zoonoses. 

While vector-borne parasite transmission often operates via generalist-feeding vectors facilitating cross-species transmission in host communities, theory describing the relationship between host species diversity and parasite invasion in these systems is underdeveloped. Host community composition and abundance vary across space and time, generating opportunities for parasite invasion. To explore how host community variation can modify parasite invasion potential, we develop a model for vector-borne parasite transmission dynamics that includes a host community of arbitrary richness and species' abundance. To compare invasion potential across communities, we calculate the community basic reproductive ratio of the parasite. We compare communities comprising a set of host species to their subsets, which allows for flexible scenario building including the introduction of novel host species and species loss. We allow vector abundance to scale with, or be independent of, community size, capturing regulation by feeding opportunities and non-host effects such as limited oviposition sites. Motivated by equivocal data relating host species competency to abundance, we characterize plausible host communities via phenomenological relationships between host species abundance and competency. We identify an underappreciated mechanism whereby changes to communities simultaneously alter average competency and the vector to host ratio and demonstrate that the interaction can profoundly influence invasion potential.