American Mammals Susceptibility to Dengue According to Geographical, Environmental, and Phylogenetic Distances

Interaction network of Phoradendron and its hosts and the influence of phylogenetic, geographic, and environmental factors on the probability of interaction

PREMISE

Phoradendron (Santalaceae) mistletoes consist of approximately 230 hemiparasitic species across the Americas. As obligate hemiparasites, their distribution depends partly on the distribution of their hosts, reflecting a shared evolutionary history.

METHODS

Using network analysis and statistical modeling, we explored the bipartite host-parasite network, focusing on species organization and the likelihood of hosts sharing a Phoradendron species based on phylogenetic, geographic, and environmental factors.

RESULTS

Our analysis suggests a modular and highly specialized interaction network influenced by evolutionary and biogeographical factors. Phoradendron parasitized hosts in genera from various families and orders, primarily Fabales, Malpighiales, and Sapindales. The network exhibited modularity and connectivity, with hosts sharing a higher likelihood of being parasitized by the same Phoradendron species when they were closer in phylogeny, geography, and environment, while phylogenetic distance emerged as the most relevant determinant. Moreover, we established a link between Phoradendron within-module connectivity and host phylogenetic distance, providing an evolutionary framework for understanding ecological patterns and network connectance.

CONCLUSIONS

The network of Phoradendron-host interactions is highly specialized, and the structure of this network depends mainly on the phylogenetic distance of the hosts.

Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: A Case Study with a Bivalve Data Set

Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of 2 deep learning methods-supervised classification approaches and unsupervised similarity learning-to infer organism relationships from specimen images. As a basis, we assembled an image data set covering 4144 bivalve species belonging to 74 families across all orders and subclasses of the extant Bivalvia, with molecular phylogenetic data being available for all families and a complete taxonomic hierarchy for all species. The suitability of this data set for deep learning experiments was evidenced by an ablation study resulting in almost 80% accuracy for identifications on the species level. Three sets of experiments were performed using our data set. First, we included taxonomic hierarchy and genetic distances in a supervised learning approach to obtain predictions on several taxonomic levels simultaneously. Here, we stimulated the model to consider features shared between closely related taxa to be more critical for their classification than features shared with distantly related taxa, imprinting phylogenetic and taxonomic affinities into the architecture and training procedure. Second, we used transfer learning and similarity learning approaches for zero-shot experiments to identify the higher-level taxonomic affinities of test species that the models had not been trained on. The models assigned the unknown species to their respective genera with approximately 48% and 67% accuracy. Lastly, we used unsupervised similarity learning to infer the relatedness of the images without prior knowledge of their taxonomic or phylogenetic affinities. The results clearly showed similarities between visual appearance and genetic relationships at the higher taxonomic levels. The correlation was 0.6 for the most species-rich subclass (Imparidentia), ranging from 0.5 to 0.7 for the orders with the most images. Overall, the correlation between visual similarity and genetic distances at the family level was 0.78. However, fine-grained reconstructions based on these observed correlations, such as sister-taxa relationships, require further work. Overall, our results broaden the applicability of automated taxon identification systems and provide a new avenue for estimating phylogenetic relationships from specimen images.

Predicting the dispersal and invasion dynamics of ambrosia beetles through demographic reconstruction and process-explicit modeling

Evaluating potential routes of invasion of pathogens and vectors of sanitary importance is essential for planning and decision-making at multiple scales. An effective tool are process-explicit models that allow coupling environmental, demographic and dispersal information to evaluate population growth and range dynamics as a function of the abiotic conditions in a region. In this work we simulate multiple dispersal/invasion routes in Mexico that could be taken by ambrosia beetles and a specific symbiont, Harringtonia lauricola, responsible for a severe epiphytic of Lauraceae in North America. We used Xyleborus bispinatus Eichhoff 1868 as a study subject and estimated its demography in the laboratory in a temperature gradient (17, 20, 26, 29, 35 °C), which we then used to parameterize a process-based model to estimate its metapopulation dynamics. The maximum intrinsic growth rate of X. bispinatus is 0.13 with a thermal optimum of 26.2 °C. The models suggest important regions for the establishment and dispersal the states of Veracruz, Chiapas and Oaxaca (high host and secondary vectors diversity), the Isthmus of Tehuantepec (connectivity region), and Michoacán and Jalisco (important avocado plantations). The use of hybrid process-based models is a promising tool to refine the predictions applied to the study of biological invasions and species distributions.

Wildlife susceptibility to infectious diseases at global scales

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., [Formula: see text] 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.

Climate predictors and climate change projections for avian haemosporidian prevalence in Mexico

Long-term, inter-annual and seasonal variation in temperature and precipitation influence the distribution and prevalence of intraerythrocytic haemosporidian parasites. We characterized the climatic niche behind the prevalence of the three main haemosporidian genera (Haemoproteus, Plasmodium and Leucocytozoon) in central-eastern Mexico, to understand their main climate drivers. Then, we projected the influence of climate change over prevalence distribution in the region. Using the MaxEnt modelling algorithm, we assessed the relative contribution of bioclimatic predictor variables to identify those most influential to haemosporidian prevalence in different avian communities within the region. Two contrasting climate change scenarios for 2070 were used to create distribution models to explain spatial turnover in prevalence caused by climate change. We assigned our study sites into polygonal operational climatic units (OCUs) and used the general haemosporidian prevalence for each OCU to indirectly measure environmental suitability for these parasites. A high statistical association between global prevalence and the bioclimatic variables ‘mean diurnal temperature range’ and ‘annual temperature range’ was found. Climate change projections for 2070 showed a significant modification of the current distribution of suitable climate areas for haemosporidians in the study region.

Flavivirus Persistence in Wildlife Populations

A substantial number of humans are at risk for infection by vector-borne flaviviruses, resulting in considerable morbidity and mortality worldwide. These viruses also infect wildlife at a considerable rate, persistently cycling between ticks/mosquitoes and small mammals and reptiles and non-human primates and humans. Substantially increasing evidence of viral persistence in wildlife continues to be reported. In addition to in humans, viral persistence has been shown to establish in mammalian, reptile, arachnid, and mosquito systems, as well as insect cell lines. Although a considerable amount of research has centered on the potential roles of defective virus particles, autophagy and/or apoptosis-induced evasion of the immune response, and the precise mechanism of these features in flavivirus persistence have yet to be elucidated. In this review, we present findings that aid in understanding how vector-borne flavivirus persistence is established in wildlife. Research studies to be discussed include determining the critical roles universal flavivirus non-structural proteins played in flaviviral persistence, the advancement of animal models of viral persistence, and studying host factors that allow vector-borne flavivirus replication without destructive effects on infected cells. These findings underscore the viral–host relationships in wildlife animals and could be used to elucidate the underlying mechanisms responsible for the establishment of viral persistence in these animals.