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Abstract Broad‐scale assessments of plant–frugivore interactions indicate the existence of a latitudinal gradient in interaction specialization. The specificity (i.e. the similarity of the interacting partners) of plant–frugivore interactions could also change latitudinally given that differences in resource availability could favour species to become more or less specific in their interactions across latitudes.Species occurring in the tropics could be more taxonomically, phylogenetically and functionally specific in their interactions because of a wide range of resources that are constantly available in these regions that would allow these species to become more specialized in their resource usage.We used a data set on plant–avian frugivore interactions spanning a wide latitudinal range to examine these predictions, and we evaluated the relationship between latitude and taxonomic, phylogenetic and functional specificity of plant and frugivore interactions. These relationships were assessed using data on population interactions (population level), species means (species level) and community means (community level).We found that the specificity of plant–frugivore interactions is generally not different from null models. Although statistically significant relationships were often observed between latitude and the specificity of plant–frugivore interactions, the direction of these relationships was variable and they also were generally weak and had low explanatory power. These results were consistent across the three specificity measures and levels of organization, suggesting that there might be an interplay between different mechanisms driving the interactions between plants and frugivores across latitudes. 

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. 

Abstract Spatial biases are an intrinsic feature of occurrence data used in species distribution models (SDMs). Thinning species occurrences, where records close in the geographic or environmental space are removed from the modeling procedure, is an approach often used to address these biases. However, thinning occurrence data can also negatively affect SDM performance, given that the benefits of removing spatial biases might be outweighed by the detrimental effects of data loss caused by this approach. We used real and virtual species to evaluate how spatial and environmental thinning affected different performance metrics of four SDM methods. The occurrence data of virtual species were sampled randomly, evenly spaced, and clustered in the geographic space to simulate different types of spatial biases, and several spatial and environmental thinning distances were used to thin the occurrence data. Null datasets were also generated for each thinning distance where we randomly removed the same number of occurrences by a thinning distance and compared the results of the thinned and null datasets. We found that spatially or environmentally thinned occurrence data is no better than randomly removing them, given that thinned datasets performed similarly to null datasets. Specifically, spatial and environmental thinning led to a general decrease in model performances across all SDM methods. These results were observed for real and virtual species, were positively associated with thinning distance, and were consistent across the different types of spatial biases. Our results suggest that thinning occurrence data usually fails to improve SDM performance and that the use of thinning approaches when modeling species distributions should be considered carefully. 

Abundance–occupancy relationships predict that species that occupy more sites are also more locally abundant, where occupancy is usually estimated following the assumption that species can occupy all sampled sites. Here we use the National Ecological Observatory Network small-mammal data to assess whether this assumption affects abundance–occupancy relationships. We estimated occupancy considering all sampled sites (traditional occupancy) and only the sites found within the species geographic range (spatial occupancy) and realized environmental niche (environmental occupancy). We found that when occupancy was estimated considering only sites possible for the species to colonize (spatial and environmental occupancy) weaker abundance–occupancy relationships were observed. This shows that the assumption that the species can occupy all sampled sites directly affects the assessment of abundance–occupancy relationships. Estimating occupancy considering only sites that are possible for the species to colonize will consequently lead to a more robust assessment of abundance–occupancy relationships. 

Abstract The large spatial scale, geographical overlap, and similarities in transmission mode between the 1918 H1N1 influenza and 2020 SARS-CoV-2 pandemics together provide a novel opportunity to investigate relationships between transmission of two different diseases in the same location. To this end, we use initial exponential growth rates in a Bayesian hierarchical framework to estimate the basic reproductive number, R0, of both disease outbreaks in a common set of 43 cities in the United States. By leveraging multiple epidemic time series across a large spatial area, we are able to better characterize the variation in R0 across the United States. Additionally, we provide one of the first city-level comparisons of R0 between these two pandemics and explore how demography and outbreak timing are related to R0. Despite similarities in transmission modes and a common set of locations, R0 estimates for COVID-19 were uncorrelated with estimates of pandemic influenza R0 in the same cities. Also, the relationships between R0 and key population or epidemic traits differed between diseases. For example, epidemics that started later tended to be less severe for COVID-19, while influenza epidemics exhibited an opposite pattern. Our results suggest that despite similarities between diseases, epidemics starting in the same location may differ markedly in their initial progression. 

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.