Temporal Scenario Research Articles

Temporal Scenarios-based Assessment of Maximum Hosting Capacity of Renewable Generation in Distribution Systems Considering ANM Techniques

Abstract Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

Semi-Device-Independently Characterizing Quantum Temporal Correlations.

We develop a framework for characterizing quantum temporal correlations in a general temporal scenario, in which an initial quantum state is measured, sent through a quantum channel, and finally measured again. This framework does not make any assumptions on the system nor on the measurements, namely, it is device independent. It is versatile enough, however, to allow for the addition of further constraints in a semi-device-independent setting. Our framework serves as a natural tool for quantum certification in a temporal scenario when the quantum devices involved are uncharacterized or partially characterized. It can hence also be used for characterizing quantum temporal correlations when one assumes an additional constraint of no-signalling in time, there are upper bounds on the involved systems' dimensions, rank constraints-for which we prove genuine quantum separations over local hidden variable models-or further linear constraints. We present a number of applications, including bounding the maximal violation of temporal Bell inequalities, quantifying temporal steerability, and bounding the maximum successful probability in quantum randomness access codes.

S2O: A smart objects orchestrator framework based on the web of things

The Web of Things (WoT) is a new paradigm in which everyday objects are connected to the Internet using popular Web technologies. The smart things are abstracted into RESTful Web services to facilitate their manipulation. The composition of these objects within the same business process creates an automated, time-synchronized routine that can perform even the most complex tasks. BPEL is an orchestration language that defines the process responsible for coordinating the Web services involved. However, this language presents some limitations in this context. BPEL 2.0 does not support RESTful Web services; its specification is incompatible with this architectural style of services. Moreover, its temporal expressivity is insufficient to cover all the constraints that may arise when composing services. This work aims to adapt BPEL to the requirements of the WoT environment, enabling it to create processes that invoke the smart things in precise time intervals. The solution is to exploit one of BPEL's strengths: its extensibility. The BPEL specification is enriched with four activities that reflect the REST uniform interface. They include the necessary attributes to send the request to the target object and receive the response in convened format representation. Also, temporal attributes are added to BPEL elements to schedule their start, end, and duration of execution. The manual addition of these temporal values requires a verification of their accuracy. The BPEL process must be reviewed to ensure its validity before its execution. A temporal Petri Net is proposed to detect any conflicts or inconsistencies between BPEL activities. As a result, associating the formal model with the extension allows BPEL to orchestrate smart things represented by RESTful Web services according to a well-defined temporal scenario. They respect the REST constraints and provide the BPEL activities with temporal attributes for efficient time management. The approach can be applied in all application areas to create temporal scenarios.

Self-care and types of temporality of women during parental leave

Introduction. During the period of parental leave, the mother's self-care is an indicator of her well-being, satisfaction with herself. Also undergoing changes are the temporal features of the mother, such as her time scenario, focus and attitude to time. The problem of self-relationship and temporal characteristics of women on parental leave needs further empirical analysis and generalization. The aim of the study is to identify the relationship of the temporal scenario, the relation to time and focus with the self-relationship parameters of mothers on parental leave, as well as to establish differences in the types of temporality of women with different attitudes towards themselves. Materials and Methods. The respondents were 207 women aged 25 to 31 years on parental leave (the age of the child is from 7 to 9 months). A set of author's methods was used: the questionnaire "Attitude to oneself," "Subjective assessment of time orientation," "Time scenario of personality," "Scale of subjective attitude to time." Methods of mathematical statistics were primary descriptive statistics, comparison criterion, correlation analysis. Results. The results of empirical studies confirmed our assumption that there are differences in self-attitudes in women on parental leave, as well as the hypothesis that attitudes to time, along with a permissive or prohibitive time scenario, are an indicator of constructive (destructive) self-exposure of women on maternity leave. Discussion and Conclusions. Women with an active-positive attitude to themselves have a resource type of temporality – a resolving time scenario, a resource time focus (for the present and future) in combination with a flexible attitude to time; in respondents with unstable self-reaction, a neurotic-fixed type of temporality is expressed - prohibiting the time scenario, fixation on the past and future, in combination with time dependence as the leading type of relation to time; examined women with passive-positive self-reaction are characterized by infantile-fixed temporality with a pronounced indifferent attitude to time, infantile-resolving time scenario and fixation on the present.

Типы темпоральности личности: оценка и перспективы использования

Introduction. During a person’s life, a "type of temporality of his personality" is formed (by himself and/or by circumstances), which determines everyday and work activity. The problem of the study is the answers the questions: is it possible to describe the types of temporality of personality based on temporal orientation and temporal scenario? Are there some differences in time attitude, its functions, and the dominant orientation of adaptive activity in subjects with different types of temporality?
 Research methods. The respondents were 355 representatives of socionomic professions, aged 27 to 35 years (180 women and 175 men). Psychodiagnostic methods were the author’s developments: the questionnaire "Subjective assessment of temporal orientation," the scale "Temporal scenario of personality," the methodology "Scale of subjective attitude to time," the questionnaire "Temporality of personality," the questionnaire "Subjective assessment of adaptive activity of personality."
 Results. Resourceful, neurotic-fixed and infantile-fixed types of personality temporality are revealed. "Resourceful type" - a combination of a resolving temporal scenario with a focus on the present and future, with a flexible attitude to time, which performs a mobilizing function and a pragmatic orientation of adaptive activity as well. "Neurotic-fixed type" is a combination of a forbidding temporal scenario, with fixation on the past, a temporal dependence, where time has subject-limiting and suppressing functions, with a homeostasis orientation of adaptive activity. "Infantile-fixed type" is a combination of infantile-resolving temporal scenario with a fixed focus on the present, indifferent attitude to time, performing a limiting function for the subject and hedonism as the leading orientation of adaptive activity.

Deadly mushrooms of the genus Galerina found in Antarctica colonized the continent as early as the Pleistocene

Abstract Fungi are probably the most diverse group of eukaryotic organisms in the Antarctic continent and nearby archipelagos, and they dominate communities in either mild or harsh habitats. However, our knowledge of their global distribution ranges and the temporal origins of their Antarctic populations is rather limited or almost absent, especially for species that do not lichenize. We focused for the first time on elucidating the taxonomic identity and phylogenetic relationships of several Antarctic collections of the deadly fungal Basidiomycota genus Galerina. By using molecular sequence data from the universal fungal barcode and a dataset encompassing 178 specimens, the inferred phylogeny showed that the Antarctic specimens corresponded with the sub-cosmopolitan species Galerina marginata, Galerina badipes and Galerina fallax, and their most closely related intraspecific genetic lineages were from northern Europe and North America. We found that these species probably host Antarctic-endemic intraspecific lineages. Furthermore, our dating analyses indicated that their Antarctic populations originated in the Pleistocene, a temporal frame that agrees with that proposed for the Antarctic colonization of plants such as the grass Deschampsia antarctica, mosses and some amphitropical lichens. Altogether, these findings converge on the same temporal scenario for the assembly of the most conspicuous terrestrial Antarctic plant and fungal communities.

Anticipating food price crises by reservoir computing

Anticipating price crises in the market of agri-commodities is critical to guarantee both the sustainability of the food system and to ensure food security. However, this is not an easy task, since the problem implies analyzing small and very volatile time series, which are highly influenced by external factors. In this paper, we show that suitable reservoir computing algorithms can be developed that outperform traditional approaches, by reducing the Mean Absolute Error and, more importantly, increasing the Market Direction Accuracy. For this purpose, the applicability of five variants of such method to forecast this market is explored, and their performance evaluated by comparing the results with those obtained with the standard LSTM and SARIMA benchmarks. We conclude that decomposing the time series and modeling each component with a separate RC is essential to successfully anticipate price trends, and that this method works even in the complex changing temporal scenario of the Covid-19 pandemic, when part of the data were collected.

Clinical and economic outcomes associated with use of anti-arrhythmic drugs versus ablation in atrial fibrillation.

Aim: To evaluate the clinical and economic impact of antiarrhythmic drugs (AADs) compared with ablation both as individual treatments and as combination therapy without/with considering the order of treatment among patients with atrial fibrillation (AFib). Materials & methods: A budget impact model over a one-year time horizon was developed to assess the economic impact of AADs (amiodarone, dofetilide, dronedarone, flecainide, propafenone, sotalol, and as a group) versus ablation across three scenarios: direct comparisons of individual treatments, non-temporal combinations, and temporal combinations. The economic analysis was conducted in accordance with CHEERS guidance as per current model objectives. Results are reported as costs per patient per year (PPPY). The impact of individual parameters was evaluated using one-way sensitivity analysis (OWSA). Results: In direct comparisons, ablation had the highest annual medication/procedure cost ($29,432), followed by dofetilide ($7661), dronedarone ($6451), sotalol ($4552), propafenone ($3044), flecainide ($2563), and amiodarone ($2538). Flecainide had the highest costs for long-term clinical outcomes ($22,964), followed by dofetilide ($17,462), sotalol ($15,030), amiodarone ($12,450), dronedarone ($10,424), propafenone ($7678) and ablation ($9948). In the non-temporal scenario, total costs incurred for AADs (group)+ablation ($17,278) were lower compared with ablation alone ($39,380). In the temporal scenario, AADs (group) before ablation resulted in PPPY cost savings of ($22,858) compared with AADs (group) after ablation ($19,958). Key factors in OWSA were ablation costs, the proportion of patients having reablation, and withdrawal due to adverse events. Conclusion: Utilization of AADs as individual treatment or in combination with ablation demonstrated comparable clinical benefits along with costs savings in patients with AFib.

Prediction of Kidney Transplantation Infection based on Traditional Machine Learning and Deep Learning

nfection prediction after kidney transplantation is significant. Most existing models for predicting kidney transplant infection are statistical, unintelligent, and straightforward. The foremost task of this paper is to analyze kidney transplantation data, introduce existing traditional machine learning and deep learning methods from non-temporal and temporal scenarios, respectively, and comprehensively evaluate the predictive power of the methods for kidney transplantation infection. Specifically, in the non-temporal scenario, we use Naïve Bayes, K-Nearest Neighbor, Support Vector Machines, and Random Forest models. In addition, in the temporal scenario, we propose an MTAN model based on a sliding window algorithm to exploit the hidden information of adjacent time series fully. Experimental results show that the kidney transplantation prediction models built by Naïve Bayes and Support Vector Machines have better stability than those constructed by K-Nearest Neighbor and Random Forest. The MTAN model with sliding windows can better mine the hidden temporal information.

Associations between testing and treatment pathways in lesional temporal or extratemporal epilepsy: A census survey of NAEC center directors.

The evaluation to determine candidacy and treatment for epilepsy surgery in persons with drug-resistant epilepsy (DRE) is not uniform. Many non-invasive and invasive tests are available to ascertain an appropriate treatment strategy. This study examines expert response to clinical vignettes of magnetic resonance imaging (MRI)-positive lesional focal cortical dysplasia in both temporal and extratemporal epilepsy to identify associations in evaluations and treatment choice. We analyzed annual report data and a supplemental epilepsy practice survey reported in 2020 from 206 adult and 136 pediatric epilepsy center directors in the United States. Non-invasive and invasive testing and surgical treatment strategies were compiled for the two scenarios. We used chi-square tests to compare testing utilization between the two scenarios. Multivariable logistic regression modeling was performed to assess associations between variables. The supplemental survey response rate was 100% with 342 responses included in the analyses. Differing testing and treatment approaches were noted between the temporal and extratemporal scenarios such as chronic invasive monitoring selected in 60% of the temporal scenario versus 93% of the extratemporal scenario. Open resection was the most common treatment choice; however, overall treatment choices varied significantly (p < .001). Associations between non-invasive testing, invasive testing, and treatment choices were present in both scenarios. For example, in the temporal scenario stereo-electroencephalography (SEEG) was more commonly associated with fluorodeoxyglucose-positron emission tomography (FDG-PET) (odds ratio [OR] 1.85; 95% confidence interval [CI] 1.06-3.29; p=.033), magnetoencephalography (MEG) (OR 2.90; 95% CI 1.60-5.28; p=<.001), high density (HD) EEG (OR 2.80; 95% CI 1.27-6.24; p=.011), functional MRI (fMRI) (OR 2.17; 95% CI 1.19-4.10; p=.014), and Wada (OR 2.16; 95% CI 1.28-3.66; p=.004). In the extratemporal scenario, choosing SEEG was associated with increased odds of neuromodulation over open resection (OR 3.13; 95% CI 1.24-7.89; p=.016). In clinical vignettes of temporal and extratemporal lesional DRE, epilepsy center directors displayed varying patterns of non-invasive testing, invasive testing, and treatment choices. Differences in practice underscore the need for comparative trials for the surgical management of DRE.

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards.

In Search of Socio-Technical Congruence: A Large-Scale Longitudinal Study

We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular, we explore whether alignment or misalignment of social communication structures and technical dependencies in large software projects influences software quality. To this end, we have defined a quantitative and operational notion of socio-technical congruence, which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">socio-technical motif congruence</i> (STMC). STMC is a measure of the degree to which developers working on the same file or on two related files, need to communicate. As socio-technical congruence is a complex and multi-faceted phenomenon, the interpretability of the results is one of our main concerns, so we have employed a careful mixed-methods statistical analysis. In particular, we provide analyses with similar techniques as employed by seminal work in the field to ensure comparability of our results with the existing body of work. The major result of our study, based on an analysis of 25 large open-source projects, is that STMC is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> related to project quality measures—software bugs and churn—in any temporal scenario. That is, we find no statistical relationship between the alignment of developer tasks and developer communications on the one hand, and project outcomes on the other hand. We conclude that, wherefore congruence does matter as literature shows, then its measurable effect lies elsewhere.

Singularities of surface mixing activity in the Western Mediterranean influence bluefin tuna larval habitats

Understanding how the surface dynamics of the ocean influence the spawning and larval ecology of many large pelagic species, in particular tuna species, is a major challenge. For temperate tunas, the selection of geographically restricted spawning grounds is influenced by environmental conditions, but the influence of surface mixing properties on the early life stages of these species remains poorly understood. Here, based on ichthyoplankton samples collected over 4 yr and satellite-derived finite size Lyapunov exponents (FSLEs), we examined how horizontal mixing activity drives the probability of presence of Atlantic bluefin tuna Thunnus thynnus larvae. We further analyzed the spatial and temporal scales of the FSLE variability at which the relationship between larval presence and mesoscale activity is maximized. We found that moderate mixing activity strongly favors the spatial-temporal distribution of larval habitats, evidencing an optimal environmental window of bluefin tuna spawning and early life development within the mesoscale dynamics. During the spawning season, the Balearic Sea presents a unique spatial and temporal hydrodynamic scenario within the Western Mediterranean. These results can be used for developing oceanographic indicators and improving larval abundance indices that are currently used in Atlantic bluefin tuna stock assessments.

Humming-Query and Reinforcement-Learning based Modeling Approach for Personalized Music Recommendation

Abstract Music recommendation is a prominent application of recommender systems, which has been attracting more and more attentions. There are two research streams of music recommender systems: one is static recommendation based on learning user’s preference according to historical data, and the other is dynamic recommendation considering user’s feedback. But the individual music preference for a certain moment is closely related to personal experience of the music and music literacy, as well as temporal scenario with diversity. Thus, it’s necessary to design a new music recommendation framework by integrating static recommendation and dynamic recommendation. Therefore, we propose a novel approach for music recommendation HRRS (Humming-Query and Reinforcement-Learning based Recommender Systems) by integrating prior two research streams. This novel recommendation framework HRRS based on humming query and reinforcement learning is learning and adapting to user’s current preference continually by collecting interactive data in real time. This preliminary recommendation framework captures song characters, personal dynamic preferences, and yields a better listening experience with proper interaction.

High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery.

Bioethanol production obtained from cereal straw has aroused great interest in recent years, which has led to the development of breeding programs to improve the quality of lignocellulosic material in terms of the biomass and sugar content. This process requires the analysis of genotype–phenotype relationships, and although genotyping tools are very advanced, phenotypic tools are not usually capable of satisfying the massive evaluation that is required to identify potential characters for bioethanol production in field trials. However, unmanned aerial vehicle (UAV) platforms have demonstrated their capacity for efficient and non-destructive acquisition of crop data with an application in high-throughput phenotyping. This work shows the first evaluation of UAV-based multi-spectral images for estimating bioethanol-related variables (total biomass dry weight, sugar release, and theoretical ethanol yield) of several accessions of wheat, barley, and triticale (234 cereal plots). The full procedure involved several stages: (1) the acquisition of multi-temporal UAV images by a six-band camera along different crop phenology stages (94, 104, 119, 130, 143, 161, and 175 days after sowing), (2) the generation of ortho-mosaicked images of the full field experiment, (3) the image analysis with an object-based (OBIA) algorithm and the calculation of vegetation indices (VIs), (4) the statistical analysis of spectral data and bioethanol-related variables to predict a UAV-based ranking of cereal accessions in terms of theoretical ethanol yield. The UAV-based system captured the high variability observed in the field trials over time. Three VIs created with visible wavebands and four VIs that incorporated the near-infrared (NIR) waveband were studied, obtaining that the NIR-based VIs were the best at estimating the crop biomass, while the visible-based VIs were suitable for estimating crop sugar release. The temporal factor was very helpful in achieving better estimations. The results that were obtained from single dates [i.e., temporal scenario 1 (TS-1)] were always less accurate for estimating the sugar release than those obtained in TS-2 (i.e., averaging the values of each VI obtained during plant anthesis) and less accurate for estimating the crop biomass and theoretical ethanol yield than those obtained in TS-3 (i.e., averaging the values of each VI obtained during full crop development). The highest correlation to theoretical ethanol yield was obtained with the normalized difference vegetation index (R2 = 0.66), which allowed to rank the cereal accessions in terms of potential for bioethanol production.

Learning of Hierarchical Temporal Structures for Guided Improvisation

AbstractThis article focuses on learning the hierarchical structure of what we call a “temporal scenario” (for instance, a chord progression) to perform automatic improvisation consistently over several different time scales. We first present a way to represent hierarchical structures with a phrase structure grammar. Such a grammar enables us to analyze a scenario at several levels of organization, creating a “multilevel scenario.” We then develop a method to automatically induce this grammar from a corpus, based on sequence selection with mutual information. We applied this method to a corpus of transcribed improvisations based on the chord sequence, also with chord substitutions, from George Gershwin's “I Got Rhythm.” From these we obtained multilevel scenarios similar to the analyses performed by professional musicians. We then present a novel heuristic approach, exploiting the multilevel structure of a scenario to guide the improvisation with anticipatory behavior in an improvisation paradigm driven by a factor oracle. This method ensures consistency of the improvisation with regard to the global form, and it opens up possibilities when playing on chords that do not exist in memory. This system was evaluated by professional improvisers during listening sessions and received excellent feedback.

Morphogenetic diversity of the ectomycorrhizal genus Cortinarius section Calochroi in the Iberian Peninsula

The taxonomic and genetic diversity of Cortinarius section Calochroi in one of the most biodiversity-rich regions in Europe, the Iberian Peninsula, was investigated through morphological and phylogenetic methods. This combined methodological approach allowed the identification of 15 known species and one new species, Cortinarius ortegae, which is described here. A dichotomous key is provided for field recognition of Calochroi species inhabiting different Iberian and Balearic Island woodlands. Polymorphism analyses within some studied species showed that the distribution of intraspecific lineages is dependent on geography and, in some cases, tree host. Furthermore, the dating analysis suggested that diversification within Calochroi started in the Pliocene and most of the current genetic diversity originated in the Pleistocene. This temporal scenario supports hypotheses in which climatic oscillations in the Quaternary may have been driving the evolution of this group of ectomycorrhizal fungi.

NotifiVR: Exploring Interruptions and Notifications in Virtual Reality.

The proliferation of high resolution and affordable virtual reality (VR) headsets is quickly making room-scale VR experiences available in our homes. Most VR experiences strive to achieve complete immersion by creating a disconnect from the real world. However, due to the lack of a standardized notification management system and minimal context awareness in VR, an immersed user may face certain situations such as missing an important phone call (digital scenario), tripping over wandering pets (physical scenario), or losing track of time (temporal scenario). In this paper, we present the results of 1) a survey across 61 VR users to understand common interruptions and scenarios that would benefit from some form of notifications; 2) a design exercise with VR professionals to explore possible notification methods; and 3) an empirical study on the noticeability and perception of 5 different VR interruption scenarios across 6 modality combinations (e.g., audio, visual, haptic, audio + haptic, visual + haptic, and audio + visual) implemented in Unity and presented using the HTC Vive headset. Finally, we combine key learnings from each of these steps along with participant feedback to present a set of observations and recommendations for notification design in VR.

Information filtering in evolving online networks

Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.

Phylogenetic relationships of endemic bunting species (Aves, Passeriformes, Emberizidae, Emberiza koslowi) from the eastern Qinghai-Tibet Plateau

In this study we reconstructed the phylogenetic relationships of a narrow-range Tibetan endemic, Emberiza koslowi, to its congeners and shed some light on intraspecific lineage separation of further bunting species from Far East Asia and along the eastern margin of the Tibetan Plateau in China. The onset of the Old World bunting radiation was dated to the mid Miocene and gave rise to four major clades: i) one group comprising mainly Western Palearctic species and all high-alpine endemics of the Tibetan Plateau; ii) a clade including E. lathami, E. bruniceps and E. melanocephala; iii) one group comprising mainly Eastern Palearctic species and all insular endemics from Japan and Sakhalin; iv) an exclusively Afrotropic clade that comprised all African species except E. affinis, whose phylogenetic relationships were ambiguous and only poorly supported in all reconstructions. The Tibetan bunting, E. koslowi, turned out as an early offshoot of the Western Palearctic-Tibetan clade 1 and thus represents an ancient relic lineage that dates back to a mid Miocene colonization event of its ancestors to the alpine plateau habitats. This temporal scenario of an early Miocene origin of alpine Tibetan endemics coincides with recent results for two further species, the Tibetan ground tit, Pseudopodoces humilis, and the Tibetan rosefinch, Carpodacus roborowskii. The origin of extant intraspecific phylogeographic patterns and splits among sister species in Eastern Asia were dated back to the Pleistocene with earliest lineage splits occurring among taxa from the Japanese Archipelago including Sakhalin and their mainland counterparts. A similarly ancient split separated a southern clade of E. godlewskii yunnanensis from S Sichuan and Yunnan from a northern clade including populations from central and northeastern China, Mongolia and S Siberia. Ecological segregation among breeding habitats of southern E. g. yunnanensis at lower elevations and those of other conspecifics at high-alpine habitats might have played a key role in the spatial genetic diversification of this species.