The first arriving virus shapes within-host viral diversity during natural epidemics.

Viral diversity has been discovered across scales from host individuals to populations. However, the drivers of viral community assembly are still largely unknown. Within-host viral communities are formed through co-infections, where the interval between the arrival times of viruses may vary. Priority effects describe the timing and order in which species arrive in an environment, and how early colonizers impact subsequent community assembly. To study the effect of the first-arriving virus on subsequent infection patterns of five focal viruses, we set up a field experiment using naïve Plantago lanceolata plants as sentinels during a seasonal virus epidemic. Using joint species distribution modelling, we find both positive and negative effects of early season viral infection on late season viral colonization patterns. The direction of the effect depends on both the host genotype and which virus colonized the host early in the season. It is well established that co-occurring viruses may change the virulence and transmission of viral infections. However, our results show that priority effects may also play an important, previously unquantified role in viral community assembly. The assessment of these temporal dynamics within a community ecological framework will improve our ability to understand and predict viral diversity in natural systems.

Mixed-species groups in bats: non-random roost associations and roost selection in neotropical understory bats.

BACKGROUND: Mixed-species groups in animals have been shown to confer antipredator, foraging and other benefits to their members that may provide selective advantages. In most cases, however, it is unclear whether functional benefits are a principal driver of heterospecific groups, or whether groups simply result from simultaneous exploitation of common resources. Mixed-species groups that form independently of environmental conditions may, however, evidence direct benefits of species associations. Bats are among the most gregarious mammals, with sometimes thousands of individuals of various species roosting communally. Despite numerous potential functional benefits of such mixed-species roosting groups, interspecific attraction has never been shown. To explore alternative explanations for mixed-species roosting, we studied roost selection in a speciose neotropical understory bat community in lowland rainforest in Costa Rica. Long term roost data were recorded over 10 years in a total of 133 roosts comprising both natural roosts and structurally uniform artificial roosts. We modelled bat roost occupancy and abundance in each roost type and in forest and pasture habitats to quantify the effects of roost- and environmental variability. RESULTS: We found that bat species presence in natural roosts is predictable from habitat and structural roost parameters, but that the presence and abundance of other bat species further modifies roost choice. One third of the 12 study species were found to actively associate with selected other bat species in roosts (e.g. Glossophaga commissarisi with Carollia sowelli). Other species did not engage in communal roosting, which in some cases indicates a role for negative interspecific interactions, such as roost competition. CONCLUSIONS: Mixed-species roosting may provide thermoregulatory benefits, reduce intraspecific competition and promote interspecific information transfer, and hence some heterospecific associations may be selected for in bats. Overall, our study contributes to an improved understanding of the array of factors that shape diverse tropical bat communities and drive the dynamics of heterospecific grouping in mammals more generally.

Explaining variation in tropical plant community composition: influence of environmental and spatial data quality.

The degree to which variation in plant community composition (beta-diversity) is predictable from environmental variation, relative to other spatial processes, is of considerable current interest. We addressed this question in Costa Rican rain forest pteridophytes (1,045 plots, 127 species). We also tested the effect of data quality on the results, which has largely been overlooked in earlier studies. To do so, we compared two alternative spatial models [polynomial vs. principal coordinates of neighbour matrices (PCNM)] and ten alternative environmental models (all available environmental variables vs. four subsets, and including their polynomials vs. not). Of the environmental data types, soil chemistry contributed most to explaining pteridophyte community variation, followed in decreasing order of contribution by topography, soil type and forest structure. Environmentally explained variation increased moderately when polynomials of the environmental variables were included. Spatially explained variation increased substantially when the multi-scale PCNM spatial model was used instead of the traditional, broad-scale polynomial spatial model. The best model combination (PCNM spatial model and full environmental model including polynomials) explained 32% of pteridophyte community variation, after correcting for the number of sampling sites and explanatory variables. Overall evidence for environmental control of beta-diversity was strong, and the main floristic gradients detected were correlated with environmental variation at all scales encompassed by the study (c. 100-2,000 m). Depending on model choice, however, total explained variation differed more than fourfold, and the apparent relative importance of space and environment could be reversed. Therefore, we advocate a broader recognition of the impacts that data quality has on analysis results. A general understanding of the relative contributions of spatial and environmental processes to species distributions and beta-diversity requires that methodological artefacts are separated from real ecological differences.