Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R-and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.

A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study.

Summary The identification of traits that influence the responses of the species to environmental variation provides a mechanistic perspective on the assembly processes of ecological communities. While much research linking functional ecology with assembly processes has been conducted with animals and plants, the development of predictive or even conceptual frameworks for fungal functional community ecology remains poorly explored. Particularly, little is known about the contribution of traits to the occurrences of fungal species under different environmental conditions. Wood‐inhabiting fungi are known to strongly respond to habitat disturbance, and thus provide an interesting case study for investigating to what extent variation in occurrence patterns of fungi can be related to traits. We apply a trait‐based joint species distribution model to a data set consisting of fruit‐body occurrence data on 321 wood‐inhabiting fungal species collected in 22 460 dead wood units from managed and natural forest sites. Our results show that environmental filtering plays a big role on shaping wood‐inhabiting fungal communities, as different environments held different communities in terms of species and trait compositions. Most importantly, forest management selected against species with large and long‐lived fruit‐bodies as well as late decayers, and promoted the occurrences of species with small fruit‐bodies and early decayers. A strong phylogenetic signal in the data suggested the existence of also some other functionally important traits than the ones we considered. We found that those species groups that were more prevalent in natural conditions had more associations to other species than species groups that were tolerant to or benefitted from forest management. Therefore, the changes that forest management causes on wood‐inhabiting fungal communities influence ecosystem functioning through simplification of interactive associations among the fungal species. Synthesis. Our results show that functional traits are linked to the responses of wood‐inhabiting fungi to variation in their environment, and thus environmental changes alter ecosystem functions via promoting or reducing species with different fruit‐body types. However, further research is needed to identify other functional traits and to provide conclusive evidence for the adaptive nature of the links from traits to occurrence patterns found here.

Ecological factors, host characteristics and/or interactions among microbes may all shape the occurrence of microbes and the structure of microbial communities within organisms. In the past, disentangling these factors and determining their relative importance in shaping within-host microbiota communities has been hampered by analytical limitations to account for (dis)similar environmental preferences (‘environmental filtering’). Here we used a joint species distribution modelling (JSDM) approach to characterize the bacterial microbiota of one of the most important disease vectors in Europe, the sheep tick Ixodes ricinus, along ecological gradients in the Swiss Alps. Although our study captured extensive environmental variation along elevational clines, the explanatory power of such large-scale ecological factors was comparably weak, suggesting that tick-specific traits and behaviours, microhabitat and -climate experienced by ticks, and interactions among microbes play an important role in shaping tick microbial communities. Indeed, when accounting for shared environmental preferences, evidence for significant patterns of positive or negative co-occurrence among microbes was found, which is indicative of competition or facilitation processes. Signals of facilitation were observed primarily among human pathogens, leading to co-infection within ticks, whereas signals of competition were observed between the tick endosymbiont Spiroplasma and human pathogens. These findings highlight the important role of small-scale ecological variation and microbe-microbe interactions in shaping tick microbial communities and the dynamics of tick-borne disease.

Key Points• KIF23/MKLP1 mutation found in the CDA III patients causes cytokinesis failure.Haplotype analysis and targeted next-generation resequencing allowed us to identify a mutation in the KIF23 gene and to show its association with an autosomal dominant form of congenital dyserythropoietic anemia type III (CDA III). The region at 15q23 where CDA III was mapped in a large Swedish family was targeted by array-based sequence capture in a female diagnosed with CDA III and her healthy sister. Prioritization of all detected sequence changes revealed 10 variants unique for the CDA III patient. Among those variants, a novel mutation c.2747C>G (p.P916R) was found in KIF23, which encodes mitotic kinesin-like protein 1 (MKLP1). This variant segregates with CDA III in the Swedish and American families but was not found in 356 control individuals. RNA expression of the 2 known splice isoforms of KIF23 as well as a novel one lacking the exons 17 and 18 was detected in a broad range of human tissues. RNA interference-based knock-down and rescue experiments demonstrated that the p.P916R mutation causes cytokinesis failure in HeLa cells, consistent with appearance of large multinucleated erythroblasts in CDA III patients. We conclude that CDA III is caused by a mutation in KIF23/MKLP1, a conserved mitotic kinesin crucial for cytokinesis. (Blood. 2013;121(23):4791-4799)