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Mediterranean oak woodlands are currently facing unprecedented degradation threats from oak decline. The Iberian oak decline "Seca", related to Phytophthora infection, causes crown defoliation that may adversely affect ecosystem services (ESs). We aim to improve our understanding of how Seca-induced declines in crown foliation affect the provision of multiple ecosystem services from understory vegetation. We selected holm (Quercus ilex) and cork oak (Q. suber) trees in a Spanish oak woodland and evaluated three proxies of canopy effects. One proxy (crown defoliation) solely captured Seca-dependent effects, one proxy solely captured Seca-independent effects (tree dimensions such as diameter and height), while the third proxy (tree vigor) captured overall canopy effects. We then used the best-performing proxies to assess canopy effects on key ecosystem services (ESs) such as aboveground net primary production (ANPP), grass and legume biomass, species diversity, litter decomposition rates, and a combined index of ecosystem multifunctionality. <br /> We found that both types of canopy effects (i.e. Seca-dependent and Seca-independent effects) were related, indicating that ANPP was disproportionally more affected by Seca when defoliated trees were large. Responses of other ESs were mostly not significant, although lower species diversity was found under trees with intermediate vigor. Our results underline that a Seca-related decline in canopy density triggered a homogenization of ecosystem service delivery on the ecosystem scale. The ecosystem functions (EFs) under trees of low vigor are similar to that in adjacent open microsites indicating that the presence of vigorous (i.e. old and vital) trees is critical for maintaining EFs at a landscape level. Our results also highlight the importance of quantifying not only defoliation but also tree dimensions as both factors jointly and interactively modify canopy effects on ecosystem multifunctionality.
Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time-intensive and not well-suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant-pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep-learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA- or enzyme-activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.
Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014-2018) were from the Dedelow (Dd), Bad Lauchstadt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil-crop-climate interrelations in changing climatic conditions.
Moderate and temporary heat stresses prime plants to tolerate, and survive, a subsequent severe heat stress. Such acquired thermotolerance can be maintained for several days under normal growth conditions, and can create a heat stress memory. We recently demonstrated that plastid-localized small heat shock protein 21 ( HSP21) is a key component of heat stress memory in Arabidopsis thaliana. A sustained high abundance of HSP21 during the heat stress recovery phase extends heat stress memory. The level of HSP21 is negatively controlled by plastid-localized metalloprotease FtsH6 during heat stress recovery. Here, we demonstrate that autophagy, a cellular recycling mechanism, exerts additional control over HSP21 degradation. Genetic and chemical disruption of both metalloprotease activity and autophagy trigger superior HSP21 accumulation, thereby improving memory. Furthermore, we provide evidence that autophagy cargo receptor ATG8-INTERACTING PROTEIN1 (ATI1) is associated with heat stress memory. ATI1 bodies co-localize with both autophagosomes and HSP21, and their abundance and transport to the vacuole increase during heat stress recovery. Together, our results provide new insights into the module for control of the regulation of heat stress memory, in which two distinct protein degradation pathways act in concert to degrade HSP21, thereby enabling cells to recover from the heat stress effect at the cost of reducing the heat stress memory.
Zoosporic fungi of the phylum Chytridiomycota (chytrids) regularly dominate pelagic fungal communities in freshwater and marine environments. Their lifestyles range from obligate parasites to saprophytes. Yet, linking the scarce available sequence data to specific ecological traits or their host ranges constitutes currently a major challenge. We combined 28 S rRNA gene amplicon sequencing with targeted isolation and sequencing approaches, along with cross-infection assays and analysis of chytrid infection prevalence to obtain new insights into chytrid diversity, ecology, and seasonal dynamics in a temperate lake. Parasitic phytoplankton-chytrid and saprotrophic pollen-chytrid interactions made up the majority of zoosporic fungal reads. We explicitly demonstrate the recurrent dominance of parasitic chytrids during frequent diatom blooms and saprotrophic chytrids during pollen rains. Distinct temporal dynamics of diatom-specific parasitic clades suggest mechanisms of coexistence based on niche differentiation and competitive strategies. The molecular and ecological information on chytrids generated in this study will aid further exploration of their spatial and temporal distribution patterns worldwide. To fully exploit the power of environmental sequencing for studies on chytrid ecology and evolution, we emphasize the need to intensify current isolation efforts of chytrids and integrate taxonomic and autecological data into long-term studies and experiments.
The genus Microhyla Tschudi, 1838 includes 52 species and is one of the most diverse genera of the family Microhylidae, being the most species-rich taxon of the Asian subfamily Microhylinae. The recent, rapid description of numerous new species of Microhyla with complex phylogenetic relationships has made the taxonomy of the group especially challenging. Several recent phylogenetic studies suggested paraphyly of Microhyla with respect to Glyphoglossus Gunther, 1869, and revealed three major phylogenetic lineages of mid-Eocene origin within this assemblage. However, comprehensive works assessing morphological variation among and within these lineages are absent. In the present study we investigate the generic taxonomy of Microhyla-Glyphoglossus assemblage based on a new phylogeny including 57 species, comparative morphological analysis of skeletons from cleared-and-stained specimens for 23 species, and detailed descriptions of generalized osteology based on volume-rendered micro-CT scans for five speciesal-together representing all major lineages within the group. The results confirm three highly divergent and well-supported clades that correspond with external and osteological morphological characteristics, as well as respective geographic distribution. Accordingly, acknowledging ancient divergence between these lineages and their significant morphological differentiation, we propose to consider these three lineages as distinct genera: Microhyla sensu stricto, Glyphoglossus, and a newly described genus, Nanohyla gen. nov.
Tula orthohantavirus (TULV) is a rodent-borne hantavirus with broad geographical distribution in Europe. Its major reservoir is the common vole (Microtus arvalis), but TULV has also been detected in closely related vole species. Given the large distributional range and high amplitude population dynamics of common voles, this host-pathogen complex presents an ideal system to study the complex mechanisms of pathogen transmission in a wild rodent reservoir. We investigated the dynamics of TULV prevalence and the subsequent potential effects on the molecular evolution of TULV in common voles of the Central evolutionary lineage. Rodents were trapped for three years in four regions of Germany and samples were analyzed for the presence of TULV-reactive antibodies and TULV RNA with subsequent sequence determination. The results show that individual (sex) and population-level factors (abundance) of hosts were significant predictors of local TULV dynamics. At the large geographic scale, different phylogenetic TULV clades and an overall isolation-by-distance pattern in virus sequences were detected, while at the small scale (<4 km) this depended on the study area. In combination with an overall delayed density dependence, our results highlight that frequent, localized bottleneck events for the common vole and TULV do occur and can be offset by local recolonization dynamics.
The study of diamond frogs (genus Rhombophryne, endemic to Madagascar) has been historically hampered by the paucity of available specimens, because of their low detectability in the field. Over the last 10 years, 13 new taxa have been described, and 20 named species are currently recognized. Nevertheless, undescribed diversity within the genus is probably large, calling for a revision of the taxonomic identification of published records and an update of the known distribution of each lineage. Here we generate DNA sequences of the mitochondrial 16S rRNA gene of all specimens available to us, revise the genetic data from public databases, and report all deeply divergent mitochondrial lineages of Rhombophryne identifiable from these data. We also generate a multi-locus dataset (including five mitochondrial and eight nuclear markers; 9844 bp) to infer a species-level phylogenetic hypothesis for the diversification of this genus and revise the distribution of each lineage. We recognize a total of 10 candidate species, two of which are identified here for the first time. The genus Rhombophryne is here proposed to be divided into six main species groups, and phylogenetic relationships among some of them are not fully resolved. These frogs are primarily distributed in northern Madagascar, and most species are known from only few localities. A previous record of this genus from the Tsingy de Bemaraha (western Madagascar) is interpreted as probably due to a mislabelling and should not be considered further unless confirmed by new data. By generating this phylogenetic hypothesis and providing an updated distribution of each lineage, our findings will facilitate future species descriptions, pave the way for evolutionary studies, and provide valuable information for the urgent conservation of diamond frogs.
Simple Summary Gliomas are heterogenous types of cancer, therefore the therapy should be personalized and targeted toward specific pathways. We developed a methodology that corrected strong batch effects from The Cancer Genome Atlas datasets and estimated glioma grade-specific co-enrichment mechanisms using machine learning. Our findings created hypotheses for annotations, e.g., pathways, that should be considered as therapeutic targets. Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.