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The existence of patterns in population dynamics across species geographic ranges and climatic niches is a pervasive idea in ecology. Population variability (i.e. temporal variability in population density) should hypothetically increase near range edges or niche limits because of less suitable environments in these areas, but the occurrence of such patterns remains largely unexplored. Further, fluctuations in temperature could pose demographic constraints on populations and also influence their variability. We used Breeding Bird Survey data to show that the population variability of 97 resident North American birds consistently increases towards their niche limits and in areas with more variable temperatures, but not towards their geographic range edges. However, our model has limited explanatory power, and phylogenetic history and species traits could not explain these results. These findings suggest that other factors, such as biotic interactions and resource availability, might be more important drivers of population variability in resident North American birds. 

Communities that are farther away from one another in distance or time tend to be more dissimilar. These relationships are often referred to as ‘distance–decay' relationships, relating compositional dissimilarity of communities to geographic distance or exploring compositional shifts through time at a single site. The data required to explore both relationships simultaneously – and their potential interactions – require standardized sampling through time across a set of geographically unique sites. We used data on five taxonomic groups sampled between 2013 and 2021 as part of the National Ecological Observatory Network (NEON) to explore evidence for geographic and temporal distance–decay relationships. Links between these relationships were explored by estimating the temporal consistency of geographic distance–decay relationships and estimating the strength of geographic patterns in temporal distance–decay relationships. Overall, we found evidence for geographic and temporal distance–decay relationships across the five studied taxa, but detected no temporal signal in geographic distance–decay relationships and no spatial signal in temporal distance–decay relationships. Together, this highlights that community composition changes across geographic and temporal gradients, but that the drivers of these changes may depend on different drivers at different scales. 

The scaling relationship observed between species richness and the geographical area sampled (i.e. the species-area relationship (SAR)) is a widely recognized macroecological relationship. Recently, this theory has been extended to trophic interactions, suggesting that geographical area may influence the structure of species interaction networks (i.e. network-area relationships (NARs)). Here, we use a global dataset of host–helminth parasite interactions to test existing predictions from macroecological theory. Scaling between single locations to the global host–helminth network by sequentially adding networks together, we find support that geographical area influences species richness and the number of species interactions in host–helminth networks. However, species-area slopes were larger for host species relative to their helminth parasites, counter to theoretical predictions. Lastly, host–helminth network modularity—capturing the tendency of the network to form into separate subcommunities—decreased with increasing area, also counter to theoretical predictions. Reconciling this disconnect between existing theory and observed SAR and NAR will provide insight into the spatial structuring of ecological networks, and help to refine theory to highlight the effects of network type, species distributional overlap, and the specificity of trophic interactions on NARs. 

Abstract Identifying the factors that structure host–parasite interactions is fundamental to understand the drivers of species distributions and to predict novel cross-species transmission events. More phylogenetically related host species tend to have more similar parasite associations, but parasite specificity may vary as a function of transmission mode, parasite taxonomy or life history. Accordingly, analyses that attempt to infer host−parasite associations using combined data on different parasite groups may perform quite differently relative to analyses on each parasite subset. In essence, are more data always better when predicting host−parasite associations, or does parasite taxonomic resolution matter? Here, we explore how taxonomic resolution affects predictive models of host−parasite associations using the London Natural History Museum's database of host–helminth interactions. Using boosted regression trees, we demonstrate that taxon-specific models (i.e. of Acanthocephalans, Nematodes and Platyhelminthes) consistently outperform full models in predicting mammal-helminth associations. At finer spatial resolutions, full and taxon-specific model performance does not vary, suggesting tradeoffs between phylogenetic and spatial scales of analysis. Although all models identify similar host and parasite covariates as important to such patterns, our results emphasize the importance of phylogenetic scale in the study of host–parasite interactions and suggest that using taxonomic subsets of data may improve predictions of parasite distributions and cross-species transmission. Predictive models of host–pathogen interactions should thus attempt to encompass the spatial resolution and phylogenetic scale desired for inference and prediction and potentially use model averaging or ensemble models to combine predictions from separately trained models. 

ABSTRACT Data that catalogue viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open sharing, posing challenges for research on macroecology, evolution, and public health. Here, we solve this problem by establishing a dynamically maintained database of vertebrate-virus associations, called The Global Virome in One Network (VIRION). The VIRION database has been assembled through both reconciliation of static data sets and integration of dynamically updated databases. These data sources are all harmonized against one taxonomic backbone, including metadata on host and virus taxonomic validity and higher classification; additional metadata on sampling methodology and evidence strength are also available in a harmonized format. In total, the VIRION database is the largest open-source, open-access database of its kind, with roughly half a million unique records that include 9,521 resolved virus “species” (of which 1,661 are ICTV ratified), 3,692 resolved vertebrate host species, and 23,147 unique interactions between taxonomically valid organisms. Together, these data cover roughly a quarter of mammal diversity, a 10th of bird diversity, and ∼6% of the estimated total diversity of vertebrates, and a much larger proportion of their virome than any previous database. We show how these data can be used to test hypotheses about microbiology, ecology, and evolution and make suggestions for best practices that address the unique mix of evidence that coexists in these data. IMPORTANCE Animals and their viruses are connected by a sprawling, tangled network of species interactions. Data on the host-virus network are available from several sources, which use different naming conventions and often report metadata in different levels of detail. VIRION is a new database that combines several of these existing data sources, reconciles taxonomy to a single consistent backbone, and reports metadata in a format designed by and for virologists. Researchers can use VIRION to easily answer questions like “Can any fish viruses infect humans?” or “Which bats host coronaviruses?” or to build more advanced predictive models, making it an unprecedented step toward a full inventory of the global virome. 

Understanding the role of biotic interactions in shaping natural communities is a long-standing challenge in ecology. It is particularly pertinent to parasite communities sharing the same host communities and individuals, as the interactions among parasites—both competition and facilitation—may have far-reaching implications for parasite transmission and evolution. Aggregated parasite burdens may suggest that infected host individuals are either more prone to infection, or that infection by a parasite species facilitates another, leading to a positive parasite–parasite interaction. However, parasite species may also compete for host resources, leading to the prediction that parasite–parasite associations would be generally negative, especially when parasite species infect the same host tissue, competing for both resources and space. We examine the presence and strength of parasite associations using hierarchical joint species distribution models fitted to data on resident parasite communities sampled on over 1300 small mammal individuals across 22 species and their resident parasite communities. On average, we detected more positive associations between infecting parasite species than negative, with the most negative associations occurring when two parasite species infected the same host tissue, suggesting that parasite species associations may be quantifiable from observational data. Overall, our findings suggest that parasite community prediction at the level of the individual host is possible, and that parasite species associations may be detectable in complex multi-species communities, generating many hypotheses concerning the effect of host community changes on parasite community composition, parasite competition within infected hosts, and the drivers of parasite community assembly and structure.