570 Biowissenschaften; Biologie
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Highlights
• Northern and eastern grassland-savanna boundary defined by minimum temperature.
• Dynamics of fire, frost and growing season temperatures combine to produce this limit.
• Western limit is related to moisture availability.
• Modern, high-resolution climate data enables refinement of bioclimatic limits.
• Reparameterisation improves global model performance at regional scale.
Abstract
Understanding the controls of biome distributions is crucial for assessing terrestrial ecosystem functioning and its response to climate change. We analysed to what extent differences in climate factors (minimum temperatures, water availability, and growing season temperatures (degree days above 5 °C (GDD5)) might explain the poorly understood borders between grasslands, savannas and shrublands in eastern South Africa. The results were used to improve bioclimatic limits in the dynamic global vegetation model (DGVM) LPJ-GUESS. The vegetation model was also used to explore the role of fire in the biome borders. Results show no clear differences between the adjacent biomes in water availability. Treeless grasslands primarily occur in areas with minimum temperatures and GDD5 values below that of savannas. The standard fire module in LPJ-GUESS is not able to reproduce observed burned area patterns in the study region, but simulations with prescribed fire return intervals show that a combination of low temperatures and fire can explain the treeless state of the grassland biome. These results confirm earlier hypotheses that a combination of low winter temperatures, causing frost damage to trees, and low growing season temperatures that impede tree sapling growth and recruitment, particularly under re-occurring fires, drive the grassland-savanna border. With these insights implemented, the LPJ-GUESS simulation results substantially improved grass distribution in the grassland biome, but challenges remain concerning the grassland-shrubland boundary, tree-grass competition and prognostic fire modelling.
Protection against pathogens is a major function of the gut microbiota. Although bacterial natural products have emerged as crucial components of host-microbiota interactions, their exact role in microbiota-mediated protection is largely unexplored. We addressed this knowledge gap with the nematode Caenorhabditis elegans and its microbiota isolate Pseudomonas fluorescens MYb115 that is known to protect against Bacillus thuringiensis (Bt) infection. We find that MYb115-mediated protection depends on sphingolipids that are derived from an iterative type I polyketide synthase (PKS), thereby describing a noncanonical pathway of bacterial sphingolipid production. We provide evidence that MYb115-derived sphingolipids affect C. elegans tolerance to Bt infection by altering host sphingolipid metabolism. This work establishes sphingolipids as structural outputs of bacterial PKS and highlights the role of microbiota-derived sphingolipids in host protection against pathogens.
In the deep-sea, the interaction between benthic fauna and substrate mainly occurs through bioturbational processes which can be preserved as traces (i.e., lebensspuren). Lebensspuren are common features of deep seafloor landscapes and usually more abundant than the organism that produce them (i.e., tracemakers), rendering them promising proxies to infer biodiversity. The density and diversity relationships between lebensspuren and benthic fauna are to the present day unclear and contradicting hypotheses have been proposed suggesting negative, positive, or even null correlations. To test these hypotheses, in this study lebensspuren, tracemakers (specific epibenthic fauna that produce these traces), degrading fauna (benthic fauna that can erase lebensspuren), and fauna in general were characterized taxonomically at eight deep-sea stations in the Kuril Kamchatka Trench area. No general correlation (over-all study area) could be observed between diversities of lebensspuren, tracemakers, degrading fauna and fauna. However, a diversity correlation was observed between specific stations, showing both negative and positive correlations depending on: 1) the number of unknown tracemakers (especially significant for dwelling lebensspuren); and 2) the lebensspuren with multiple origins; and 3) tracemakers that can produce different lebensspuren. Lebensspuren and faunal density were not correlated. However, lebensspuren density was either positively or negatively correlated with tracemaker densities, depending on the lebensspuren morphotypes. A positive correlation was observed for resting lebensspuren (e.g., ophiuroid impressions, Actinaria circular impressions), while negative correlations were observed for locomotion-feeding lebensspuren (e.g., echinoid trails). In conclusion, lebensspuren diversity may be a good proxy for tracemaker biodiversity when the lebensspuren-tracemaker tandem can be reliable characterized; and lebensspuren-density correlations vary depending the specific lebensspuren residence time, tracemaker density and associated behaviour (rate of movement), but on a global scale abiotic and other biotic 42 factors may also play an important role.
In the deep sea, interactions between benthic fauna and seafloor sediment primarily occur through bioturbation that can be preserved as traces (i.e. lebensspuren). Lebensspuren are common features of deep-sea landscapes and are more abundant than the organisms that produce them (i.e. tracemakers), rendering lebensspuren promising proxies for inferring biodiversity. The density and diversity relationships between lebensspuren and benthic fauna remain unclear, and contradicting correlations have been proposed (i.e. negative, positive, or even null correlations). To approach these variable correlations, lebensspuren and benthic fauna were characterized taxonomically at eight deep-sea stations in the Kuril-Kamchatka Trench area, together with two novel categories: tracemakers (specific epibenthic fauna that produce these traces) and degrading fauna (benthic fauna that can erase lebensspuren). No general correlation (overall study area) was observed between diversities of lebensspuren, tracemakers, degrading fauna, and fauna. However, a diversity correlation was observed at specific stations, showing both negative and positive correlations depending on: (1) the number of unknown tracemakers (especially significant for dwelling lebensspuren); (2) the lebensspuren with multiple origins; and (3) tracemakers that can produce different lebensspuren. Lebensspuren and faunal density were not correlated. However, lebensspuren density was either positively or negatively correlated with tracemaker densities, depending on the lebensspuren morphotypes. A positive correlation was observed for resting lebensspuren (e.g. ophiuroid impressions, Actiniaria circular impressions), while negative correlations were observed for locomotion-feeding lebensspuren (e.g. echinoid trails). In conclusion, lebensspuren diversity may be a good proxy for tracemaker biodiversity when the lebensspuren–tracemaker relationship can be reliable characterized. Lebensspuren–density correlations vary depending on the specific lebensspuren residence time, tracemaker density, and associated behaviour (rate of movement). Overall, we suggest that lebensspuren density and diversity correlations should be studied with tracemakers rather than with general benthic fauna. On a global scale, abiotic (e.g. hydrodynamics, substrate consistency) and other biotic factors (e.g. microbial degradation) may also play an important role.
One like all? Behavioral response range of native and invasive amphipods to neonicotinoid exposure
(2024)
Highlights
• Short-time neonicotinoid exposure causes behavioral responses in non-target species.
• Environmentally relevant concentrations can induce changes in invertebrate behavior.
• Different baseline activity of ecological similar crustacean amphipods.
• Species respond specifically to thiacloprid exposure.
• Acantocephalan infection affects locomotion of intermediate host Gammarus roeselii.
Abstract
Native and invasive species often occupy similar ecological niches and environments where they face comparable risks from chemical exposure. Sometimes, invasive species are phylogenetically related to native species, e.g. they may come from the same family and have potentially similar sensitivities to environmental stressors due to phylogenetic conservatism and ecological similarity. However, empirical studies that aim to understand the nuanced impacts of chemicals on the full range of closely related species are rare, yet they would help to comprehend patterns of current biodiversity loss and species turnover. Behavioral sublethal endpoints are of increasing ecotoxicological interest. Therefore, we investigated behavioral responses (i.e., change in movement behavior) of the four dominant amphipod species in the Rhine-Main area (central Germany) when exposed to the neonicotinoid thiacloprid. Moreover, beyond species-specific behavioral responses, ecological interactions (e.g. parasitation with Acanthocephala) play a crucial role in shaping behavior, and we have considered these infections in our analysis. Our findings revealed distinct baseline behaviors and species-specific responses to thiacloprid exposure. Notably, Gammarus fossarum exhibited biphasic behavioral changes with hyperactivity at low concentrations that decreased at higher concentrations. Whereas Gammarus pulex, Gammarus roeselii and the invasive species Dikerogammarus villosus, showed no or weaker behavioral responses. This may partly explain why G. fossarum disappears in chemically polluted regions while the other species persist there to a certain degree. But it also shows that potential pre-exposure in the habitat may influence behavioral responses of the other amphipod species, because habituation occurs, and potential hyperactivity would be harmful to individuals in the habitat. The observed responses were further influenced by acanthocephalan parasites, which altered baseline behavior in G. roeselii and enhanced the behavioral response to thiacloprid exposure. Our results underscore the intricate and diverse nature of responses among closely related amphipod species, highlighting their unique vulnerabilities in anthropogenically impacted freshwater ecosystems.
Beside mosquitoes, ticks are well-known vectors of different human pathogens. In the Northern Hemisphere, Lyme borreliosis (Eurasia, LB) or Lyme disease (North America, LD) is the most commonly occurring vector-borne infectious disease caused by bacteria of the genus Borrelia which are transmitted by hard ticks of the genus Ixodes. The reported incidence of LB in Europe is about 22.6 cases per 100,000 inhabitants annually with a broad range depending on the geographical area analyzed. However, the epidemiological data are largely incomplete, because LB is not notifiable in all European countries. Furthermore, not only differ reporting procedures between countries, there is also variation in case definitions and diagnostic procedures. Lyme borreliosis is caused by several species of the Borrelia (B.) burgdorferi sensu lato (s.l.) complex which are maintained in complex networks including ixodid ticks and different reservoir hosts. Vector and host influence each other and are affected by multiple factors including climate that have a major impact on their habitats and ecology. To classify factors that influence the risk of transmission of B. burgdorferi s.l. to their different vertebrate hosts as well as to humans, we briefly summarize the current knowledge about the pathogens including their astonishing ability to overcome various host immune responses, regarding the main vector in Europe Ixodes ricinus, and the disease caused by borreliae. The research shows, that a higher standardization of case definition, diagnostic procedures, and standardized, long-term surveillance systems across Europe is necessary to improve clinical and epidemiological data.
Background: Alternative splicing is a key mechanism in eukaryotic cells to increase the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied both across human tissues and in genetically diverse individuals. This has identified disease-relevant splicing events, as well as associations between splicing and genomic variations, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue and its determinants remain poorly understood.
Results: We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results shows that splicing rates in single cells can be accurately predicted based on sequence composition and other genomic features. We also identified a moderate but significant contribution from DNA methylation to splicing variation across cells. By combining sequence information and DNA methylation, we derived an accurate model (AUC=0.85) for predicting different splicing modes of individual cassette exons. These explain conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.
Conclusions: Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated component of DNA methylation variation on splicing.
Background: Alternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic variations, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remain poorly understood.
Results: We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results shows that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on sequence as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons (AUC=0.85). These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.
Conclusions: Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.
Background: Alternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic features, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remains poorly understood.
Results: We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results show that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on genomic features as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons. These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.
Conclusions: Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.