Phytosanitary agencies conduct plant biosecurity activities, including early detection of potential introduction pathways, to improve control and eradication of pest and pathogen incursions. For such actions, analytical tools based on solid scientific knowledge regarding plant-pest or pathogen relationships for pest risk assessment are needed. Recent evidence indicating that closely related species share a higher chance of becoming infected or attacked by pests has allowed the identification of taxa with different degrees of vulnerability. Here, we use information readily available online about pest-host interactions and their geographic distributions, in combination with host phylogenetic reconstructions, to estimate a pest-host interaction (in some cases infection) index in geographic space as a more comprehensive, spatially explicit tool for risk assessment. We demonstrate this protocol using phylogenetic relationships for 20 beetle species and 235 host plant genera: first, we estimate the probability of a host sharing pests, and second, we project the index in geographic space. Overall, the predictions allow identification of the pest-host interaction type (e.g., generalist or specialist), which is largely determined by both host range and phylogenetic constraints. Furthermore, the results can be valuable in terms of identifying hotspots where pests and vulnerable hosts interact. This knowledge is useful for anticipating biological invasions or spreading of disease. We suggest that our understanding of biotic interactions will improve after combining information from multiple dimensions of biodiversity at multiple scales (e.g., phylogenetic signal and host-vector-pathogen geographic distribution).
Many human emergent and re-emergent diseases have a sylvatic cycle. Yet, little effort has been put into discovering and modeling the wild mammal reservoirs of dengue (DENV), particularly in the Americas. Here, we show a species-level susceptibility prediction to dengue of wild mammals in the Americas as a function of the three most important biodiversity dimensions (ecological, geographical, and phylogenetic spaces), using machine learning protocols. Model predictions showed that different species of bats would be highly susceptible to DENV infections, where susceptibility mostly depended on phylogenetic relationships among hosts and their environmental requirement. Mammal species predicted as highly susceptible coincide with sets of species that have been reported infected in field studies, but it also suggests other species that have not been previously considered or that have been captured in low numbers. Also, the environment (i.e., the distance between the species' optima in bioclimatic dimensions) in combination with geographic and phylogenetic distance is highly relevant in predicting susceptibility to DENV in wild mammals. Our results agree with previous modeling efforts indicating that temperature is an important factor determining DENV transmission, and provide novel insights regarding other relevant factors and the importance of considering wild reservoirs. This modeling framework will aid in the identification of potential DENV reservoirs for future surveillance efforts.
Many human emergent and re-emergent diseases have a sylvatic cycle. Yet, little effort has been put into discovering and modeling the wild mammal reservoirs of dengue (DENV), particularly in the Americas. Here, we show a species-level susceptibility prediction to dengue of wild mammals in the Americas as a function of the three most important biodiversity dimensions (ecological, geographical, and phylogenetic spaces), using machine learning protocols. Model predictions showed that different species of bats would be highly susceptible to DENV infections, where susceptibility mostly depended on phylogenetic relationships among hosts and their environmental requirement. Mammal species predicted as highly susceptible coincide with sets of species that have been reported infected in field studies, but it also suggests other species that have not been previously considered or that have been captured in low numbers. Also, the environment (i.e., the distance between the species’ optima in bioclimatic dimensions) in combination with geographic and phylogenetic distance is highly relevant in predicting susceptibility to DENV in wild mammals. Our results agree with previous modeling efforts indicating that temperature is an important factor determining DENV transmission, and provide novel insights regarding other relevant factors and the importance of considering wild reservoirs. This modeling framework will aid in the identification of potential DENV reservoirs for future surveillance efforts.
Disease transmission prediction across wildlife is crucial for risk assessment of emerging infectious diseases. Susceptibility of host species to pathogens is influenced by the geographic, environmental, and phylogenetic context of the specific system under study. We used machine learning to analyze how such variables influence pathogen incidence for multihost pathogen assemblages, including one of direct transmission (coronaviruses and bats) and two vector-borne systems (West Nile Virus [WNV] and birds, and malaria and birds). Here we show that this methodology is able to provide reliable global spatial susceptibility predictions for the studied host–pathogen systems, even when using a small amount of incidence information (i.e., < 20 % of information in a database). We found that avian malaria was mostly affected by environmental factors and by an interaction between phylogeny and geography, and WNV susceptibility was mostly influenced by phylogeny and by the interaction between geographic and environmental distances, whereas coronavirus susceptibility was mostly affected by geography. This approach will help to direct surveillance and field efforts providing cost-effective decisions on where to invest limited resources.
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