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1. Detecting interaction between species is notoriously difficult, and disentangling species associations in host-related gut communities is especially challenging. Nevertheless, due to contemporary methods, including metabarcoding and 16S sequencing, collecting observational data on community composition has become easier and much more common. 2. We studied the previously collected datasets of intestinal bacterial microbiota and parasite compositions within longitudinally followed mouse lemurs by analysing the potential interactions with diversity metrics and novel joint species distribution modelling. 3. Both methods showed statistical association between certain parasite species and bacterial microbiota composition. Unicellular Eimeria sp. had an effect on diversity of gut microbiota. The cestode Hymenolepis diminuta had negative associations with several bacterial orders, whereas closely related species Hymenolepis nana had positive associations with several bacterial orders. 4. Our results reveal potential interactions between some, but not all, intestinal parasites and gut bacterial microbiota. Host variables contributed over half of the total variation explained with the model, and sex was the most important single host variable; especially with microbiota, there were sex-related differences in the community composition. 5. This study shows how joint species distribution modelling can incorporate both within-host dynamics of several taxa and host characteristics to model potential interactions in intestinal community. These results provide new hypothesis for interactions between and among parasites and bacterial microbiota to be tested further with experimental studies.

Infection by multiple pathogens of the same host is ubiquitous in both natural and managed habitats. While intraspecific variation in disease resistance is known to affect pathogen occurrence, how differences among host genotypes affect the assembly of pathogen communities remains untested. In our experiment using cloned replicates of naive Plantago lanceolata plants as sentinels during a seasonal virus epidemic, we find non-random co-occurrence patterns of five focal viruses. Using joint species distribution modelling, we attribute the non-random virus occurrence patterns primarily to differences among host genotypes and local population context. Our results show that intraspecific variation among host genotypes may play a large, previously unquantified role in pathogen community structure. The factors that determine whether pathogens co-occur in a host are poorly understood, especially for plant viruses. Here the authors conduct field experiments with the plant Plantago lanceolata and its viruses, showing that viral co-occurrences are driven predominantly by environmental context and host genotype rather than viral interactions.

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.

© 2019 The Authors. Ecological Monographs published by Wiley Periodicals, Inc. on behalf of Ecological Society of America This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

While the negative effects that pathogens have on their hosts are well-documented in humans and agricultural systems, direct evidence of pathogen-driven impacts in wild host populations is scarce and mixed. Here, to determine how the strength of pathogen-imposed selection depends on spatial structure, we analyze growth rates across approximately 4000 host populations of a perennial plant through time coupled with data on pathogen presence-absence. We find that infection decreases growth more in the isolated than well-connected host populations. Our inoculation study reveals isolated populations to be highly susceptible to disease while connected host populations support the highest levels of resistance diversity, regardless of their disease history. A spatial eco-evolutionary model predicts that non-linearity in the costs to resistance may be critical in determining this pattern. Overall, evolutionary feedbacks define the ecological impacts of disease in spatially structured systems with host gene flow being more important than disease history in determining the outcome. The ecological and evolutionary impacts of disease vary in spatially structured populations. Here, the authors study ~4000 populations of Plantago lanceolata and find that resistance evolution depends on both disease history and population structure, with isolated populations more susceptible to fungal disease.

The telomerase enzyme is essential for telomere maintenance. Pathogenic variants in telomere-associated genes have been associated with critical telomere shortening, resulting in telomere biology disorders (TBD) such as bone marrow failure, idiopathic pulmonary fibrosis, and dyskeratosis congenita. The TBDs are clinically heterogeneous and families with TBD often experience an earlier onset and increased symptom severity for each generation. Consensus guidelines have identified certain genetic variants as pathogenic or likely pathogenic, but many are classified as variants of uncertain significance (VUS) in the absence of additional supporting evidence. The pathogenicity of a VUS in genes encoding the telomerase complex could be evaluated by in vitro telomerase activity (TA) measurement. We have developed a functional TA assay in patient-derived T-cells based on the Telomeric Repeat Amplification Protocol (TRAP) combined with qPCR. TA was significantly lower in six TBD patients with a TERT or TERC variant compared to controls (0.11 versus 0.54, p  < 0.001). Four patients had a TA of more than three standard deviations below the mean of controls, strongly supporting pathogenicity of the variants. In summary, functional analysis of TA in patient-derived cells could support pathogenic evaluation in clinical diagnostics and reduce the number of reported VUS for TBD patients.

Telomere Biology Disorders (TBDs) are characterized by mutations in telomere-related genes leading to short telomeres and premature aging but with no strict correlation between telomere length and disease severity. Epigenetic alterations are also markers of aging and we aimed to evaluate whether DNA methylation (DNAm) could be part of the pathogenesis of TBDs. In blood from 35 TBD cases, genome-wide DNAm were analyzed and the cases were grouped based on relative telomere length (RTL): short (S), with RTL close to normal controls, and extremely short (ES). TBD cases had increased epigenetic age and DNAm alterations were most prominent in the ES-RTL group. Thus, the differentially methylated (DM) CpG sites could be markers of short telomeres but could also be one of the mechanisms contributing to disease phenotype since DNAm alterations were observed in symptomatic, but not asymptomatic, cases with S-RTL. Furthermore, two or more DM-CpGs were identified in four genes previously linked to TBD or telomere length ( PRDM8, SMC4, VARS, and WNT6) and in three genes that were novel in telomere biology ( MAS1L , NAV2, and TM4FS1) . The DM-CpGs in these genes could be markers of aging in hematological cells, but they could also be of relevance for the progression of TBD.

Functional diversity (FD) is an important component of biodiversity that quantifies the difference in functional traits between organisms. However, FD studies are often limited by the availability of trait data and FD indices are sensitive to data gaps. The distribution of species abundance and trait data, and its transformation, may further affect the accuracy of indices when data is incomplete. Using an existing approach, we simulated the effects of missing trait data by gradually removing data from a plant, an ant and a bird community dataset (12, 59, and 8 plots containing 62, 297 and 238 species respectively). We ranked plots by FD values calculated from full datasets and then from our increasingly incomplete datasets and compared the ranking between the original and virtually reduced datasets to assess the accuracy of FD indices when used on datasets with increasingly missing data. Finally, we tested the accuracy of FD indices with and without data transformation, and the effect of missing trait data per plot or per the whole pool of species. FD indices became less accurate as the amount of missing data increased, with the loss of accuracy depending on the index. But, where transformation improved the normality of the trait data, FD values from incomplete datasets were more accurate than before transformation. The distribution of data and its transformation are therefore as important as data completeness and can even mitigate the effect of missing data. Since the effect of missing trait values pool-wise or plot-wise depends on the data distribution, the method should be decided case by case. Data distribution and data transformation should be given more careful consideration when designing, analysing and interpreting FD studies, especially where trait data are missing. To this end, we provide the R package “traitor” to facilitate assessments of missing trait data.

Our current economic and political structures have an increasingly devastating impact on the Earth’s climate and ecosystems: we are facing a biospheric emergency, with catastrophic consequences for both humans and the natural world on which we depend. Life scientists – including biologists, medical scientists, psychologists and public health experts – have had a crucial role in documenting the impacts of this emergency, but they have failed to drive governments to take action in order to prevent the situation from getting worse. Here we, as members of the movement Scientist Rebellion, call on life scientists to re-embrace advocacy and activism – which were once hallmarks of academia – in order to highlight the urgency and necessity of systemic change across our societies. We particularly emphasise the need for scientists to engage in nonviolent civil resistance, a form of public engagement which has proven to be highly effective in social struggles throughout history.

Species Distribution Models (SDMs) are vital tools for predicting species occurrences and are used in many practical tasks including conservation and biodiversity management. However, the expanding minefield of SDM methodologies makes it difficult to select the most reliable method for large co‐occurrence datasets, particularly when time constraints make designing a bespoke model challenging. To facilitate model selection for practical out‐of‐sample prediction, we consider three major challenges: (a) the difficulty of incorporating multiple functional forms for species associations; (b) the limited knowledge on how characteristics of co‐occurrence data impact model performance; and (c) whether individual model predictions could be combined to obtain optimised community predictions without the need for bespoke models. To address these gaps, we propose an ensemble method that uses descriptive features of binary co‐occurrence datasets to predict model weightings for a set of candidate SDMs. We demonstrate how this method may be applied through a simple case study that uses five independent Joint Species Distribution Models (JSDMs) and Stacked Species Distribution Models (SSDMs) to predict out‐of‐sample observations for a diversity of co‐occurrence datasets. Moreover, we introduce a novel SSDM that offers the potential to include multiple functional forms for each species while delivering robust community predictions. Our case study highlights two major findings. First, the ability for the feature‐based ensemble to offer more robust species co‐occurrence predictions compared to other candidate SDMs while providing insights into the data features that impact model performance. Second, the competitiveness of the novel SSDM method for forecasting species co‐occurrences, even when using a simple univariate generalised linear model (GLM) as the base model prior to stacking. We conclude that feature‐based ensembles can provide ecologists with a useful tool for generating species distribution predictions in a way that is reliable and informative. Moreover, the flexibility of the ensemble and the novel SSDM method both offer exciting prospects for incorporating a diversity of functional forms while prioritising out‐of‐sample prediction.