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Highlights
• Three ecological groups were identified based on distributional patterns.
• Old assessments were confirmed with the latest occurrence data.
• For each group, we derived different population trends in times of global change.
• Global change elevates importance of vector-borne diseases.
• Our results serve as base for effective Simuliidae monitoring.
Abstract
The black fly genus Simulium includes medically and ecologically important species, characterized by a wide variation of ecological niches largely determining their distributional patterns. In a rapidly changing environment, species-specific niche characteristics determine whether a species benefits or not. With aquatic egg, larval and pupal stages followed by a terrestrial adult phase, their spatial arrangements depend upon the interplay of aquatic conditions and climatic-landscape parameters in the terrestrial realm. The aim of this study was to enhance the understanding of the distributional patterns among Simulium species and their ecological drivers. In an ecological niche modelling approach, we focused on 12 common black fly species with different ecological requirements. Our modelling was based on available distribution data along with five stream variables describing the climatic, land-cover, and topographic conditions of river catchments. The modelled freshwater habitat suitability was spatially interpolated to derive an estimate of the adult black flies' probability of occurrence. Based on similarities in the spatial patterns of modelled habitat suitability we were able to identify three biogeographical groups, which allows us to confirm old assessments with current occurrence data: (A) montane species, (B) broad range species and (C) lowland species. The five veterinary and human medical relevant species Simulium equinum, S. erythrocephalum, S. lineatum, S. ornatum and S. reptans are mainly classified in the lowland species group. In the course of climatic changes, it is expected that biocoenosis will slightly shift towards upstream regions, so that the lowland group will presumably emerge as the winner. This is mainly explained by wider ecological niches, including a higher temperature tolerance and tolerance to various pollutants. In conclusion, these findings have significant implications for human and animal health. As exposure to relevant Simulium species increases, it becomes imperative to remain vigilant, particularly in investigating the potential transmission of pathogens.
Highlights
• We study dormancy in the ‘rare mutation’ regime of stochastic adaptive dynamics.
• We first derive the polymorphic evolution sequence, based on prior work.
• Our evolutionary branching criterion extends a result by Champagnat and Méléard.
• In a classical model dormancy can favour evolutionary branching.
• Dormancy also affects several more population characteristics.
Abstract
In this paper, we investigate the consequences of dormancy in the ‘rare mutation’ and ‘large population’ regime of stochastic adaptive dynamics. Starting from an individual-based micro-model, we first derive the Polymorphic Evolution Sequence of the population, based on a previous work by Baar and Bovier (2018). After passing to a second ‘small mutations’ limit, we arrive at the Canonical Equation of Adaptive Dynamics, and state a corresponding criterion for evolutionary branching, extending a previous result of Champagnat and Méléard (2011).
The criterion allows a quantitative and qualitative analysis of the effects of dormancy in the well-known model of Dieckmann and Doebeli (1999) for sympatric speciation. In fact, quite an intuitive picture emerges: Dormancy enlarges the parameter range for evolutionary branching, increases the carrying capacity and niche width of the post-branching sub-populations, and, depending on the model parameters, can either increase or decrease the ‘speed of adaptation’ of populations. Finally, dormancy increases diversity by increasing the genetic distance between subpopulations.
Measurements of charged-particle production in pp, p−Pb, and Pb−Pb collisions in the toward, away, and transverse regions with the ALICE detector are discussed. These regions are defined event-by-event relative to the azimuthal direction of the charged trigger particle, which is the reconstructed particle with the largest transverse momentum (ptrigT) in the range 8<ptrigT<15 GeV/c. The toward and away regions contain the primary and recoil jets, respectively; both regions are accompanied by the underlying event (UE). In contrast, the transverse region perpendicular to the direction of the trigger particle is dominated by the so-called UE dynamics, and includes also contributions from initial- and final-state radiation. The relative transverse activity classifier, RT=NTch/⟨NTch⟩, is used to group events according to their UE activity, where NTch is the charged-particle multiplicity per event in the transverse region and ⟨NTch⟩ is the mean value over the whole analysed sample. The energy dependence of the RT distributions in pp collisions at s√=2.76, 5.02, 7, and 13 TeV is reported, exploring the Koba-Nielsen-Olesen (KNO) scaling properties of the multiplicity distributions. The first measurements of charged-particle pT spectra as a function of RT in the three azimuthal regions in pp, p−Pb, and Pb−Pb collisions at sNN−−−√=5.02 TeV are also reported. Data are compared with predictions obtained from the event generators PYTHIA 8 and EPOS LHC. This set of measurements is expected to contribute to the understanding of the origin of collective-like effects in small collision systems (pp and p−Pb).
Controlling and understanding electron correlations in quantum matter is one of the most challenging tasks in materials engineering. In the past years a plethora of new puzzling correlated states have been found by carefully stacking and twisting two-dimensional van der Waals materials of different kind. Unique to these stacked structures is the emergence of correlated phases not foreseeable from the single layers alone. In Ta-dichalcogenide heterostructures made of a good metallic “1H”- and a Mott insulating “1T”-layer, recent reports have evidenced a cross-breed itinerant and localized nature of the electronic excitations, similar to what is typically found in heavy fermion systems. Here, we put forward a new interpretation based on first-principles calculations which indicates a sizeable charge transfer of electrons (0.4-0.6 e) from 1T to 1H layers at an elevated interlayer distance. We accurately quantify the strength of the interlayer hybridization which allows us to unambiguously determine that the system is much closer to a doped Mott insulator than to a heavy fermion scenario. Ta-based heterolayers provide therefore a new ground for quantum-materials engineering in the regime of heavily doped Mott insulators hybridized with metallic states at a van der Waals distance.
Highlights
• 153 chemicals of emerging concern detected in complex multi-component mixtures.
• 108 possible mixture risk assessment scenarios were investigated.
• Non-detects, QSARs, and experimental ecotoxicological data were integrated for risk assessment.
• 8 chemicals were the main risk drivers in at least one site across the River Aconcagua basin.
Abstract
Environmental risk assessments strategies that account for the complexity of exposures are needed in order to evaluate the toxic pressure of emerging chemicals, which also provide suggestions for risk mitigation and management, if necessary. Currently, most studies on the co-occurrence and environmental impacts of chemicals of emerging concern (CECs) are conducted in countries of the Global North, leaving massive knowledge gaps in countries of the Global South.
In this study, we implement a multi-scenario risk assessment strategy to improve the assessment of both the exposure and hazard components in the chemical risk assessment process. Our strategy incorporates a systematic consideration and weighting of CECs that were not detected, as well as an evaluation of the uncertainties associated with Quantitative Structure-Activity Relationships (QSARs) predictions for chronic ecotoxicity. Furthermore, we present a novel approach to identifying mixture risk drivers. To expand our knowledge beyond well-studied aquatic ecosystems, we applied this multi-scenario strategy to the River Aconcagua basin of Central Chile. The analysis revealed that the concentrations of CECs exceeded acceptable risk thresholds for selected organism groups and the most vulnerable taxonomic groups. Streams flowing through agricultural areas and sites near the river mouth exhibited the highest risks. Notably, the eight risk drivers among the 153 co-occurring chemicals accounted for 66–92 % of the observed risks in the river basin. Six of them are pesticides and pharmaceuticals, chemical classes known for their high biological activity in specific target organisms.
Highlights
• An airport can result in high particle concentrations in a distant residential area.
• The particle size distribution indicated the airport as the main source of particles.
• Lower air traffic during the COVID-19 pandemic lead to lower particle concentrations.
• The particle concentration showed high temporal variations.
Abstract
Exposure to ultrafine particles has a significant influence on human health. In regions with large commercial airports, air traffic and ground operations can represent a potential particle source. The particle number concentration was measured in a low-traffic residential area about 7 km from Frankfurt Airport with a Condensation Particle Counter in a long-term study. In addition, the particle number size distribution was determined using a Fast Mobility Particle Sizer.
The particle number concentrations showed high variations over the entire measuring period and even within a single day. A maximum 24 h-mean of 24,120 cm−3 was detected. Very high particle number concentrations were in particular measured when the wind came from the direction of the airport. In this case, the particle number size distribution showed a maximum in the particle size range between 5 and 15 nm. Particles produced by combustion in jet engines typically have this size range and a high potential to be deposited in the alveoli. During a period with high air traffic volume, significantly higher particle number concentrations could be measured than during a period with low air traffic volume, as in the COVID-19 pandemic.
A large commercial airport thus has the potential to lead to a high particle number concentration even in a distant residential area. Due to the high particle number concentrations, the critical particle size, and strong concentration fluctuations, long-term measurements are essential for a realistic exposure analysis.
Correlations in azimuthal angle extending over a long range in pseudorapidity between particles, usually called the "ridge" phenomenon, were discovered in heavy-ion collisions, and later found in pp and p−Pb collisions. In large systems, they are thought to arise from the expansion (collective flow) of the produced particles. Extending these measurements over a wider range in pseudorapidity and final-state particle multiplicity is important to understand better the origin of these long-range correlations in small-collision systems. In this Letter, measurements of the long-range correlations in p−Pb collisions at sNN−−−√=5.02 TeV are extended to a pseudorapidity gap of Δη∼8 between particles using the ALICE, forward multiplicity detectors. After suppressing non-flow correlations, e.g., from jet and resonance decays, the ridge structure is observed to persist up to a very large gap of Δη∼8 for the first time in p−Pb collisions. This shows that the collective flow-like correlations extend over an extensive pseudorapidity range also in small-collision systems such as p−Pb collisions. The pseudorapidity dependence of the second-order anisotropic flow coefficient, v2({\eta}), is extracted from the long-range correlations. The v2(η) results are presented for a wide pseudorapidity range of −3.1<η<4.8 in various centrality classes in p−Pb collisions. To gain a comprehensive understanding of the source of anisotropic flow in small-collision systems, the v2(η) measurements are compared to hydrodynamic and transport model calculations. The comparison suggests that the final-state interactions play a dominant role in developing the anisotropic flow in small-collision systems.
We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.
Previous phylogenetic analyses of the grass-specialist leafhopper tribe Chiasmini have resolved relationships among genera but have included few representatives of individual genera. Here the phylogeny of 20 Chinese species belonging to 8 chiasmine genera was investigated by combining DNA sequence data from two mitochondrial genes (COI, 16S) and two nuclear genes (H3, 28S). In both maximum likelihood (ML) and Bayesian inference (BI) analyses, relationships among genera were largely consistent with prior analyses, with most members of the tribe placed into two sister clades: (Exitianus + Nephotettix) and the remaining five sampled genera. To examine morphology-based species definitions in the taxonomically difficult genus Exitianus Ball, 1929, one mitochondrial gene (COI) and one nuclear gene (ITS2) were used to infer the phylogenetic relationships and status of two common and widespread species and compare the performance of different molecular species-delimitation methods. These analyses divide the included populations into two well-supported clades corresponding to current morphological species concepts but some inconsistencies occurred under the jMOTU, ABGD and bPTP methods depending on the which gene and analytical parameter values were selected. Considering the variable results yielded by methods employing single loci, the BPP method, which combines data from multiple loci, may be more reliable in Exitianus.