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Herpes simplex virus 1 (HSV-1) infection and stress responses disrupt transcription termination by RNA Polymerase II (Pol II). In HSV-1 infection, but not upon salt or heat stress, this is accompanied by a dramatic increase in chromatin accessibility downstream of genes. Here, we show that the HSV-1 immediate-early protein ICP22 is both necessary and sufficient to induce downstream open chromatin regions (dOCRs) when transcription termination is disrupted by the viral ICP27 protein. This is accompanied by a marked ICP22-dependent loss of histones downstream of affected genes consistent with impaired histone repositioning in the wake of Pol II. Efficient knock-down of the ICP22-interacting histone chaperone FACT is not sufficient to induce dOCRs in ΔICP22 infection but increases dOCR induction in wild-type HSV-1 infection. Interestingly, this is accompanied by a marked increase in chromatin accessibility within gene bodies. We propose a model in which allosteric changes in Pol II composition downstream of genes and ICP22-mediated interference with FACT activity explain the differential impairment of histone repositioning downstream of genes in the wake of Pol II in HSV-1 infection.
Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and metabarcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures – an acoustic index model and a bird community composition derived from an independently developed Convolutional Neural Network - correlated well with restoration (adj-R² = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via metabarcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data.
Neural processing of a desired moving direction requires the continuous comparison between the current heading and the goal direction. While the neural basis underlying the current heading is well-studied, the coding of the goal direction remains unclear in insects. Here, we used tetrode recordings in tethered flying monarch butterflies to unravel how a goal direction is represented in the insect brain. While recording, the butterflies maintained robust goal directions relative to a virtual sun. By resetting their goal directions, we found neurons whose spatial tuning was tightly linked to the goal directions. Importantly, their tuning was unaffected when the butterflies changed their heading after compass perturbations, showing that these neurons specifically encode the goal direction. Overall, we here discovered invertebrate goal-direction neurons that share functional similarities to goal-direction cells reported in mammals. Our results give insights into the evolutionarily conserved principles of goal-directed spatial orientation in animals.
Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.
Recently, we have shown that C6-ceramides efficiently suppress viral replication by trapping the virus in lysosomes. Here, we use antiviral assays to evaluate a synthetic ceramide derivative α-NH2-ω-N3-C6-ceramide (AKS461) and to confirm the biological activity of C6-ceramides inhibiting SARS-CoV-2. Click-labeling with a fluorophore demonstrated that AKS461 accumulates in lysosomes. Previously, it has been shown that suppression of SARS-CoV-2 replication can be cell-type specific. Thus, AKS461 inhibited SARS-CoV-2 replication in Huh-7, Vero, and Calu-3 cells up to 2.5 orders of magnitude. The results were confirmed by CoronaFISH, indicating that AKS461 acts comparable to the unmodified C6-ceramide. Thus, AKS461 serves as a tool to study ceramide-associated cellular and viral pathways, such as SARS-CoV-2 infections, and it helped to identify lysosomes as the central organelle of C6-ceramides to inhibit viral replication.
Infected wounds pose a major mortality risk in animals. Injuries are common in the ant Megaponera analis, which raids pugnacious prey. Here we show that M. analis can determine when wounds are infected and treat them accordingly. By applying a variety of antimicrobial compounds and proteins secreted from the metapleural gland to infected wounds, workers reduce the mortality of infected individuals by 90%. Chemical analyses showed that wound infection is associated with specific changes in the cuticular hydrocarbon profile, thereby likely allowing nestmates to diagnose the infection state of injured individuals and apply the appropriate antimicrobial treatment. This study demonstrates that M. analis ant societies use antimicrobial compounds produced in the metapleural glands to treat infected wounds and reduce nestmate mortality.
Seasonal plasticity in insects is often triggered by temperature and photoperiod changes. When climatic conditions become sub-optimal, insects might undergo reproductive diapause, a form of seasonal plasticity delaying the development of reproductive organs and activities. During the reproductive diapause, the cuticular hydrocarbon (CHC) profile, which covers the insect body surface, might also change to protect insects from desiccation and cold temperature. However, CHCs are often important cues and signals for mate recognition and changes in CHC composition might affect mate recognition. In the present study, we investigated the CHC profile composition and the mating success of Drosophila suzukii in 1- and 5-day-old males and females of summer and winter morphs. CHC compositions differed with age and morphs. However, no significant differences were found between the sexes of the same age and morph. The results of the behavioral assays show that summer morph pairs start to mate earlier in their life, have a shorter mating duration, and have more offspring compared to winter morph pairs. We hypothesize that CHC profiles of winter morphs are adapted to survive winter conditions, potentially at the cost of reduced mate recognition cues.
T cell exhaustion is a hallmark of cancer and persistent infections, marked by inhibitory receptor upregulation, diminished cytokine secretion, and impaired cytolytic activity. Terminally exhausted T cells are steadily replenished by a precursor population (Tpex), but the metabolic principles governing Tpex maintenance and the regulatory circuits that control their exhaustion remain incompletely understood. Using a combination of gene-deficient mice, single-cell transcriptomics, and metabolomic analyses, we show that mitochondrial insufficiency is a cell-intrinsic trigger that initiates the functional exhaustion of T cells. At the molecular level, we find that mitochondrial dysfunction causes redox stress, which inhibits the proteasomal degradation of hypoxia-inducible factor 1α (HIF-1α) and promotes the transcriptional and metabolic reprogramming of Tpex cells into terminally exhausted T cells. Our findings also bear clinical significance, as metabolic engineering of chimeric antigen receptor (CAR) T cells is a promising strategy to enhance the stemness and functionality of Tpex cells for cancer immunotherapy.
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
Highlights
• Data awareness and data integrity underpins the trustworthiness of results and subsequent further analysis.
• Big data and bioinformatics enable efficient resource use by repurposing publicly available RNA-Sequencing data.
• Manual checks of data quality and integrity are insufficient due to the overwhelming volume and rapidly growing data.
• Automation and artificial intelligence provide cost-effective and efficient solutions for data integrity and quality checks.
• FAIR data management, various software solutions and analysis tools assist metadata maintenance.
Infection research largely relies on classical cell culture or mouse models. Despite having delivered invaluable insights into host-pathogen interactions, both have limitations in translating mechanistic principles to human pathologies. Alternatives can be derived from modern Tissue Engineering approaches, allowing the reconstruction of functional tissue models in vitro. Here, we combined a biological extracellular matrix with primary tissue-derived enteroids to establish an in vitro model of the human small intestinal epithelium exhibiting in vivo-like characteristics. Using the foodborne pathogen Salmonella enterica serovar Typhimurium, we demonstrated the applicability of our model to enteric infection research in the human context. Infection assays coupled to spatio-temporal readouts recapitulated the established key steps of epithelial infection by this pathogen in our model. Besides, we detected the upregulation of olfactomedin 4 in infected cells, a hitherto unrecognized aspect of the host response to Salmonella infection. Together, this primary human small intestinal tissue model fills the gap between simplistic cell culture and animal models of infection, and shall prove valuable in uncovering human-specific features of host-pathogen interplay.
CRISPR/Cas9 gene editing has revolutionised loss-of-function experiments in Leishmania, the causative agent of leishmaniasis. As Leishmania lack a functional non-homologous DNA end joining pathway however, obtaining null mutants typically requires additional donor DNA, selection of drug resistance-associated edits or time-consuming isolation of clones. Genome-wide loss-of-function screens across different conditions and across multiple Leishmania species are therefore unfeasible at present. Here, we report a CRISPR/Cas9 cytosine base editor (CBE) toolbox that overcomes these limitations. We employed CBEs in Leishmania to introduce STOP codons by converting cytosine into thymine and created http://www.leishbaseedit.net/ for CBE primer design in kinetoplastids. Through reporter assays and by targeting single- and multi-copy genes in L. mexicana, L. major, L. donovani, and L. infantum, we demonstrate how this tool can efficiently generate functional null mutants by expressing just one single-guide RNA, reaching up to 100% editing rate in non-clonal populations. We then generated a Leishmania-optimised CBE and successfully targeted an essential gene in a plasmid library delivered loss-of-function screen in L. mexicana. Since our method does not require DNA double-strand breaks, homologous recombination, donor DNA, or isolation of clones, we believe that this enables for the first time functional genetic screens in Leishmania via delivery of plasmid libraries.
Alpine bumble bees are the most important pollinators in temperate mountain ecosystems. Although they are used to encounter small-scale successions of very different climates in the mountains, many species respond sensitively to climatic changes, reflected in spatial range shifts and declining populations worldwide. Cuticular hydrocarbons (CHCs) mediate climate adaptation in some insects. However, whether they predict the elevational niche of bumble bees or their responses to climatic changes remains poorly understood. Here, we used three different approaches to study the role of bumble bees’ CHCs in the context of climate adaptation: using a 1,300 m elevational gradient, we first investigated whether the overall composition of CHCs, and two potentially climate-associated chemical traits (proportion of saturated components, mean chain length) on the cuticle of six bumble bee species were linked to the species’ elevational niches. We then analyzed intraspecific variation in CHCs of Bombus pascuorum along the elevational gradient and tested whether these traits respond to temperature. Finally, we used a field translocation experiment to test whether CHCs of Bombus lucorum workers change, when translocated from the foothill of a cool and wet mountain region to (a) higher elevations, and (b) a warm and dry region. Overall, the six species showed distinctive, species-specific CHC profiles. We found inter- and intraspecific variation in the composition of CHCs and in chemical traits along the elevational gradient, but no link to the elevational distribution of species and individuals. According to our expectations, bumble bees translocated to a warm and dry region tended to express longer CHC chains than bumble bees translocated to cool and wet foothills, which could reflect an acclimatization to regional climate. However, chain lengths did not further decrease systematically along the elevational gradient, suggesting that other factors than temperature also shape chain lengths in CHC profiles. We conclude that in alpine bumble bees, CHC profiles and traits respond at best secondarily to the climate conditions tested in this study. While the functional role of species-specific CHC profiles in bumble bees remains elusive, limited plasticity in this trait could restrict species’ ability to adapt to climatic changes.
Xiphophorus fish exhibit a clear phenotypic polymorphism in puberty onset and reproductive strategies of males. In X. nigrensis and X. multilineatus, puberty onset is genetically determined and linked to a melanocortin 4 receptor (Mc4r) polymorphism of wild-type and mutant alleles on the sex chromosomes. We hypothesized that Mc4r mutant alleles act on wild-type alleles by a dominant negative effect through receptor dimerization, leading to differential intracellular signaling and effector gene activation. Depending on signaling strength, the onset of puberty either occurs early or is delayed. Here, we show by Förster Resonance Energy Transfer (FRET) that wild-type Xiphophorus Mc4r monomers can form homodimers, but also heterodimers with mutant receptors resulting in compromised signaling which explains the reduced Mc4r signaling in large males. Thus, hetero- vs. homo- dimerization seems to be the key molecular mechanism for the polymorphism in puberty onset and body size in male fish.
Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.
Highlights
• The integrated stress response leads to a general ATF4-dependent activation of NRF2
• ATF4 causes a CHAC1-dependent GSH depletion, resulting in NRF2 stabilization
• An elevation of NRF2 transcript levels fosters this effect
• NRF2 supports the ISR/ATF4 pathway by improving cystine and antioxidant supply
Summary
The redox regulator NRF2 becomes activated upon oxidative and electrophilic stress and orchestrates a response program associated with redox regulation, metabolism, tumor therapy resistance, and immune suppression. Here, we describe an unrecognized link between the integrated stress response (ISR) and NRF2 mediated by the ISR effector ATF4. The ISR is commonly activated after starvation or ER stress and plays a central role in tissue homeostasis and cancer plasticity. ATF4 increases NRF2 transcription and induces the glutathione-degrading enzyme CHAC1, which we now show to be critically important for maintaining NRF2 activation. In-depth analyses reveal that NRF2 supports ATF4-induced cells by increasing cystine uptake via the glutamate-cystine antiporter xCT. In addition, NRF2 upregulates genes mediating thioredoxin usage and regeneration, thus balancing the glutathione decrease. In conclusion, we demonstrate that the NRF2 response serves as second layer of the ISR, an observation highly relevant for the understanding of cellular resilience in health and disease.
Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C\(_1\)) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus.
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R
2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R
2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
Aim
Global warming is assumed to restructure mountain insect communities in space and time. Theory and observations along climate gradients predict that insect abundance and richness, especially of small‐bodied species, will increase with increasing temperature. However, the specific responses of single species to rising temperatures, such as spatial range shifts, also alter communities, calling for intensive monitoring of real‐world communities over time.
Location
German Alps and pre‐alpine forests in south‐east Germany.
Methods
We empirically examined the temporal and spatial change in wild bee communities and its drivers along two largely well‐protected elevational gradients (alpine grassland vs. pre‐alpine forest), each sampled twice within the last decade.
Results
We detected clear abundance‐based upward shifts in bee communities, particularly in cold‐adapted bumble bee species, demonstrating the speed with which mobile organisms can respond to climatic changes. Mean annual temperature was identified as the main driver of species richness in both regions. Accordingly, and in large overlap with expectations under climate warming, we detected an increase in bee richness and abundance, and an increase in small‐bodied species in low‐ and mid‐elevations along the grassland gradient. Community responses in the pre‐alpine forest gradient were only partly consistent with community responses in alpine grasslands.
Main Conclusion
In well‐protected temperate mountain regions, small‐bodied bees may initially profit from warming temperatures, by getting more abundant and diverse. Less severe warming, and differences in habitat openness along the forested gradient, however, might moderate species responses. Our study further highlights the utility of standardized abundance data for revealing rapid changes in bee communities over only one decade.
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.