Is Evolution of Blind Mole Rats Determined by Climate Oscillations?

The concept of climate variability facilitating adaptive radiation supported by the ‘‘Court Jester’’ hypothesis is disputed by the ‘‘Red Queen’’ one, but the prevalence of one or the other might be scale-dependent. We report on a detailed, comprehensive phylo-geographic study on the ,4 kb mtDNA sequence in underground blind mole rats of the family Spalacidae (or subfamily Spalacinae) from the East Mediterranean steppes. Our study aimed at testing the presence of periodicities in branching patterns on a constructed phylogenetic tree and at searching for congruence between branching events, tectonic history and paleoclimates. In contrast to the strong support for the majority of the branching events on the tree, the absence of support in a few instances indicates that network-like evolution could exist in spalacids. In our tree, robust support was given, in concordance with paleontological data, for the separation of spalacids from muroid rodents during the first half of the Miocene when open, grass-dominated habitats were established. Marine barriers formed between Anatolia and the Balkans could have facilitated the separation of the lineage ‘‘Spalax’’ from the lineage ‘‘Nannospalax’’ and of the clade ‘‘leucodon’’ from the clade ‘‘xanthodon’’. The separation of the clade ‘‘ehrenbergi’’ occurred during the late stages of the tectonically induced uplift of the Anatolian high plateaus and mountains, whereas the separation of the clade ‘‘vasvarii’’ took place when the rapidly uplifting Taurus mountain range prevented the Mediterranean rainfalls from reaching the Central Anatolian Plateau. The separation of Spalax antiquus and S. graecus occurred when the southeastern Carpathians were uplifted. Despite the role played by tectonic events, branching events that show periodicity corresponding to 400-kyr and 100-kyr eccentricity bands illuminate the important role of orbital fluctuations on adaptive radiation in spalacids. At the given scale, our results supports the ‘‘Court Jester’’ hypothesis over the ‘‘Red Queen’’ one

An integrative systematic revision of the European southern birch mice (Rodentia: Sminthidae, Sicista subtilis group)

1. The systematics of the genus Sicista is unclear, mostly because of the high level of chromosomal variability within the genus. One of the most challenging groups for systematists is the steppic Sicista subtilis species group that extends from central Europe to Lake Baikal. We present a systematic review of these European southern birch mice using an integrative taxonomic approach. 2. In this review, we evaluate the degree of genetic and morphological differentiation of the Sicista subtilis complex by analysing 12 European populations, and propose a new taxonomic treatment for the subtilis group based on an integrative approach combining phylogenetic and morphometric analyses with a review on previously published cytogenetic and morphological data. 3. The phylogenetic relationship was reconstructed using sequences of the whole mitochondrial cytochrome b (CytB) and the nucleus-encoded interphotoreceptor binding protein (IRBP) under the maximum parsimony and maximum likelihood criteria. Based on whole CytB sequences, genetic distances were reconstructed and visualised among the taxa.These data were supplemented with multivariate analysis of the morphology of the baculum and of the penile spike of museum specimens. 4. Based on the genetic and the morphological data set, we suggest that the subspecies trizona and nordmanni should be raised to the species rank. We suggest Sicista trizona (Frivaldszky, 1865) and Sicista nordmanni (Keyserling and Blasius, 1840) as names for these species, respectively. 5. The species status of Sicista severtzovi is not supported by our results, so we recommend reclassifying it as a subspecies of Sicista subtilis. 6. Finally,the large genetic distance between the Hungarian and Romanian populations of Sicista trizona led us to describe the Romanian population as a new subspecies

The distribution of the common hamster (Cricetus cricetus) in western Ukraine

Abstract The aim of this study was to determine the current range of the common hamster (Cricetus cricetus) in western Ukraine by checking the points of occurrence known from literature, personal reports and museum collections. According to RUSIN et al. (2013) the common hamster was reported from 23 localities grouped in 12 areas in 7 oblasts of Western Ukraine. In total, we confirmed eight areas of hamster occurrence from RUSIN et al. (2013) and found one new locality. The highest densities of the common hamster occured around Hrymailiv, Ternopol oblast and Halych, Ivano- Frankovsk oblast. The areas located in the vicinity of Lutsk in Volyn oblast, Chernovtsy and between Sambir and Old Sambir in Lvov oblast represent medium density populations. Low and very low densities were found in areas close to Lvov and Kamieniec Podolski, and Khmelnitskiy oblast. In general, it can be stated that the Volyn Upland and Podolia are still inhabited by the common hamster. Moreover, habitat conditions that support the existence of the common hamster and possibilities of contact with hamster populations from neighboring countries are also discussed in this paper.</jats:p

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection&ndash;diffusion&ndash;reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms