Although interfertility is the key criterion upon which Mayr's biological species concept is based, it has never been applied directly to delimit species under natural conditions. Our study fills ...this gap. We used the interfertility criterion to delimit two closely related oak species in a forest stand by analyzing the network of natural mating events between individuals. The results reveal two groups of interfertile individuals connected by only few mating events. These two groups were largely congruent with those determined using other criteria (morphological similarity, genotypic similarity and individual relatedness). Our study, therefore, shows that the analysis of mating networks is an effective method to delimit species based on the interfertility criterion, provided that adequate network data can be assembled. Our study also shows that although species boundaries are highly congruent across methods of species delimitation, they are not exactly the same. Most of the differences stem from assignment of individuals to an intermediate category. The discrepancies between methods may reflect a biological reality. Indeed, the interfertility criterion is an environment-dependant criterion as species abundances typically affect rates of hybridization under natural conditions. Thus, the methods of species delimitation based on the interfertility criterion are expected to give results slightly different from those based on environment-independent criteria (such as the genotypic similarity criteria). However, whatever the criterion chosen, the challenge we face when delimiting species is to summarize continuous but non-uniform variations in biological diversity. The grade of membership model that we use in this study appears as an appropriate tool.
The analysis of complex networks is a rapidly growing topic with many applications in different domains. The analysis of large graphs is often made via unsupervised classification of vertices of the ...graph. Community detection is the main way to divide a large graph into smaller ones that can be studied separately. However another definition of a cluster is possible, which is based on the structural distance between vertices. This definition includes the case of community clusters but is more general in the sense that two vertices may be in the same group even if they are not connected. Methods for detecting communities in undirected graphs have been recently reviewed by Fortunato. In this paper we expand Fortunato’s work and make a review of methods and algorithms for detecting essentially structurally homogeneous subsets of vertices in binary or weighted and directed and undirected graphs.
The increasing needs of humanity for food supply, the need to reduce fertilizer and pesticide use to protect human and environmental health, and the threats of climate change and disease emergence ...all provide incentives to use microorganisms to promote crop growth and health (Busby et al. 2017; D'Hondt et al. 2021; Toju et al. 2018). One of the challenges currently facing us is discovering and identifying microbial strains or consortia capable of alleviating biotic and abiotic stresses, and integrating them into crop management (Berg et al. 2017; Poudel et al. 2016). Addressing this challenge is crucial in the case of European cultivated grapevine (Vitis vinifera L.) because this emblematic crop is a very heavy user of phytosanitary products (mainly copper, sulfur and synthetic chemical fungicides targeting leaf diseases). Strengthening microbial biocontrol of grapevine leaf diseases by stimulating the microbiota naturally present in vineyards or by inoculating new microorganisms (Bartoli et al. 2020) could reduce viticulture reliance on chemical fungicides. However, this nature-based solution (Maes and Jacobs 2017) will only be effective and sustainable if microbial antagonisms are resilient to microclimatic and climatic variations and associated changes in vine physiology. This is why vine-pathogen-microbiota interactions should be studied under a range of abiotic conditions. Powdery mildew is one of the grapevine leaf diseases for which the use of chemical fungicides must be reduced. It is caused by the ascomycete fungus Erysiphe necator
Introduction
New tools have been developed to distinguish the COVID‐19 diagnosis from other viral infections presenting similar symptomatology and mitigate the lack of sensitivity of molecular ...testing. We previously identified a specific “sandglass” aspect on the white blood cells (WBC) scattergram of COVID‐19 patients, as a highly reliable COVID‐19 screening test (sensitivity: 85.9%, specificity: 83.5% and positive predictive value: 94.3%). We then decided to validate our previous data in a multicentric study.
Methods
This retrospective study involved 817 patients with flu‐like illness, among 20 centers, using the same CBC instrument (XN analyzer, SYSMEX, Japan). After training, one specialist per center independently evaluated, under the same conditions, the presence of the “sandglass” aspect of the WDF scattergram, likely representing plasmacytoid lymphocytes.
Results
Overall, this approach showed sensitivity: 59.0%, specificity: 72.9% and positive predictive value: 77.7%. Sensitivity improved with subgroup analysis, including in patients with lymphopenia (65.2%), patients presenting symptoms for more than 5 days (72.3%) and in patients with ARDS (70.1%). COVID‐19 patients with larger plasmacytoid lymphocyte cluster (>15 cells) more often have severe outcomes (70% vs. 15% in the control group).
Conclusion
Our findings confirm that the WBC scattergram analysis could be added to a diagnostic algorithm for screening and quickly categorizing symptomatic patients as either COVID‐19 probable or improbable, especially during COVID‐19 resurgence and overlapping with future influenza epidemics. The observed large size of the plasmacytoid lymphocytes cluster appears to be a hallmark of COVID‐19 patients and was indicative of a severe outcome. Furthers studies are ongoing to evaluate the value of the new hematological parameters in combination with WDF analysis.
We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing ...of DNA sampled from the Earth’s environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth’s major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.
Next-generation sequencing (NGS) can used to sample nucleic acids in the environment for the presence of species and ecological functions.
Machine-learning software can search for ‘the ghosts of interactions past’ in the raw NGS data to reconstruct the networks of ecological interactions.
NGS data and machine-learning in the cloud could be combined in the next generation of global biomonitoring. Autonomous NGS samplers would sequence and upload data for ecological network reconstruction, to detect ecosystem change accurately, cheaply and generically.
Reconstruction of highly replicated networks of ecological interaction, using this next generation of biomonitoring, would provide general ecological information for ecosystem comparison and a revolution in the breadth of our understanding of the ecology of ecosystem change.
One major challenge in parasitology and epidemiology is determining whether the richness of parasitic and infectious diseases simply tracks host diversity or is largely determined by exogenous ...factors, such as climate-forced variables. We addressed this issue by analysing a 30-year survey of fungal diseases in French forests. We first combined generalized linear models and stepwise analyses to select the habitat descriptors that may account for variations in parasitic fungal species richness. Our results suggest that host species diversity is not a major determinant of parasite richness. Temperature seasonality, host abundance, and the composition of host species assemblages may play a greater role. Then we used structural equation modelling to investigate the links between these habitat descriptors and parasitic fungal species richness. Our results showed that climatic and host species descriptors had not only direct effects on species richness, but also indirect effects (via host species and sampling effort, respectively). Our results also showed that the direct effects of climate and host species were roughly equal. We therefore conclude that it is important to take into account exogenous factors when investigating the potential causes of spatial variation in the richness of parasitic diseases, in particular for plant diseases.
Fungal communities associated with plants and soil influence plant fitness and ecosystem functioning. They are frequently studied by metabarcoding approaches targeting the ribosomal internal ...transcribed spacer (ITS), but there is no consensus concerning the most appropriate bioinformatic approach for the analysis of these data. We sequenced an artificial fungal community composed of 189 strains covering a wide range of Ascomycota and Basidiomycota, to compare the performance of 360 software and parameter combinations. The most sensitive approaches, based on the USEARCH and VSEARCH clustering algorithms, detected almost all fungal strains but greatly overestimated the total number of strains. By contrast, approaches using DADA2 to detect amplicon sequence variants were the most effective for recovering the richness and composition of the fungal community. Our results suggest that analyzing single forward (R1) sequences with DADA2 and no filter other than the removal of low-quality and chimeric sequences is a good option for fungal community characterization.
•A mock community of 189 fungal strains was used to compare 360 pipelines.•Observed fungal richness depended strongly on the bioinformatic approach.•Community richness and composition were well recovered by DADA2.•Single forward (R1) reads often gave better results than assembled reads.•Bioinformatic approach selection should be based on study goals.
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