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11 pages, 1014 KiB  
Data Descriptor
Biodiversity of Coleoptera (Insecta) in Central European Russia
by Leonid V. Egorov, Aleksey S. Sazhnev, Sergey V. Dedyukhin, Alexander B. Ruchin, Olga S. Trushitsyna, Anna M. Nikolaeva, Mikhail N. Esin and Evgeniy A. Lobachev
Diversity 2024, 16(12), 740; https://doi.org/10.3390/d16120740 - 29 Nov 2024
Viewed by 488
Abstract
(1) Background: Beetles (Insecta: Coleoptera) are the most diverse order of insects. The beetle species live in many ecosystems around the globe and their roles in ecosystems are very diverse; therefore, it is important to know the local and regional biodiversity. Conservation of [...] Read more.
(1) Background: Beetles (Insecta: Coleoptera) are the most diverse order of insects. The beetle species live in many ecosystems around the globe and their roles in ecosystems are very diverse; therefore, it is important to know the local and regional biodiversity. Conservation of the entomofauna in individual macroregions requires effort to study the distribution and abundance of insects. To this end, databases are being created to record this information so that the status of a species can be objectively assessed and, if necessary, measures taken to protect it. (2) Methods: The materials were collected from the territory of eleven regions of European Russia (Ryazan, Tambov, Penza, Voronezh, Lipetsk, Moscow (including the city of Moscow), Vladimir, Kursk, Tula and Kaluga Oblasts and the Republic of Mordovia), mainly during the last approximately 20 years (2005–2024). The beetles were collected by different means (manual collection; the use of soil traps, fermental crown traps, and Malaise traps; light fishing; sweeping with an entomological net on plants and under water, etc.). (3) Results: The dataset presents data on 1310 species and subspecies of Coleoptera from 74 families found in the Eastern Part of the Eastern European Plain. In total, there are 65,100 samples and 10,771 occurrences in the dataset. (4) Conclusions: The largest families in terms of species diversity are Curculionidae (198 species), Carabidae (183 species), Staphylinidae (121 species) and Chrysomelidae (120 species). Full article
(This article belongs to the Section Animal Diversity)
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<p>Map of the region studied.</p>
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21 pages, 7687 KiB  
Article
Hydrological Regime of Rivers in the Periglacial Zone of the East European Plain in the Late MIS 2
by Aleksey Sidorchuk, Andrei Panin and Olga Borisova
Quaternary 2024, 7(3), 32; https://doi.org/10.3390/quat7030032 - 19 Jul 2024
Viewed by 753
Abstract
At the end of the Pleniglacial and the first half of the Late Glacial period, approximately between 18 and 14 ka BP, rivers of the central and southern parts of the East European Plain had channels up to 10 times as large as [...] Read more.
At the end of the Pleniglacial and the first half of the Late Glacial period, approximately between 18 and 14 ka BP, rivers of the central and southern parts of the East European Plain had channels up to 10 times as large as the present day channels of the same rivers. These ancient channels, called large meandering palaeochannels, are widespread in river floodplains and low terraces. The hydrological regime of these large rivers is of great interest in terms of the palaeoclimatology of the late Marine Isotope Stage 2 (MIS 2). In this study, we aimed at quantitative estimation of maximum flood discharges of rivers in the Dnepr, Don and Volga basins in the late MIS 2. To approach this, we used massive measurements of the morphometric characteristics of large palaeochannels on topographic maps and remote sensing data—palaeochannel width, meander wavelength and their relationships with river flow parameters. The runoff depth of the maximum flood, which corresponds to the maximum depth of daily snow thaw during the snowmelt period, was obtained for unit basins with an area of <1000 km2. The mean value for the southern megaslope of the East European Plain was 44.2 mm/day (6 times the modern value), with 46 mm/day for the Volga River (5.5 times), 45 mm/day (6.3 times) for the Don River and 39 mm/day (8 times the modern value) for the Dnepr River basins. In general, the Dnepr basin was drier than the Don and Volga basins, which corresponds well to the modern distribution of humidity. At the same time, the westernmost part of the Dnepr River basin was relatively wet in the past, and the decrease in humidity from the past to the modern situation was greater there than in the eastern and central regions. The obtained results contradict the prevailing ideas, based mainly on climatic modeling and palynological data, that the climate of Europe was cold and dry during MIS 2. The reason is that palaeoclimatic reconstructions were made predominantly for the LGM epoch (23–20 ka BP). On the East European Plain, the interval 18–14 ka BP is rather poorly studied. Our results of paleoclimatological and palaeohydrological reconstructions showed that the Late Pleniglacial and the first half of the Late Glacial period was characterized by a dramatic increase in precipitation and river discharge relative to the present day. Full article
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<p>Contemporary vegetation in the East European Plain (adapted from [<a href="#B13-quaternary-07-00032" class="html-bibr">13</a>], simplified): 1—taiga, 2—coniferous-broadleaved forests, 3—broadleaved forests, 4—forest–steppe, 5—steppe, 6—semidesert and desert, 7—mountain taiga, sub-Alpine and Alpine vegetation, 8—boundaries of the zones, 9—boundaries of the river basins, 10—the main rivers (arrows show flow direction), 11—marine basins, 12—continental part outside the main river basins.</p>
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<p>Examples of the large meandering paleochannels: (<b>A</b>) the Volga River basin, the Sok River near Sergievsk (53°56′56.67″ N, 51°10′3.65″ E) with ancient meander wavelength <span class="html-italic">L<sub>pas</sub>t</span> = 1600 m and channel width <span class="html-italic">W<sub>pas</sub>t</span> = 170 m, modern meander wavelength <span class="html-italic">L<sub>mo</sub>d</span> = 240 m and width <span class="html-italic">W<sub>mo</sub>d</span> = 20 m; (<b>B</b>) the Don River Basin, the Medveditsa River near Lysye Gory (51°32′43.44″ N, 44°50′2.86″ E), <span class="html-italic">L<sub>pas</sub>t</span> = 2200 m, <span class="html-italic">W<sub>pas</sub>t</span> = 210 m, <span class="html-italic">L<sub>mo</sub>d</span> = 520 m, <span class="html-italic">W<sub>mo</sub>d</span> = 40 m; and (<b>C</b>) the Dnepr River basin, the Psel River at Nizhnya Manuilivka (49°22′17.30″ N, 33°43′16.66″ E), <span class="html-italic">L<sub>pas</sub>t</span> = 2600 m, <span class="html-italic">W<sub>pas</sub>t</span> = 135 m, <span class="html-italic">L<sub>mo</sub>d</span> = 600 m, <span class="html-italic">W<sub>mo</sub>d</span> = 25–35 m.</p>
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<p>The distribution of well-preserved fragments of large meandering paleochannels on the southern megaslope of the East European Plain: 1—in the Volga River basin, 2—in the Don River basin, 3—in the Dnepr River basin, 4—the river net, 5—the main rivers, 6—the ancient extensions of the rivers on the Black Sea shelf, 7—river flow direction, 8—the main water divides, 9—modern sea coastlines, 10—sea basins during the Late Pleniglacial, 11—continental part outside the main river basins.</p>
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<p>The main landscapes on the southern megaslope of the East European Plain in the Late Pleniglacial. 1—Scandinavian ice sheet at the Pomeranian stage (~20 ka BP); 2—the ice-sheet boundary at the LGM; 3—the Pomeranian stage boundary; 4—the Luga stage boundary; 5—tundra and cold-tolerant xerophyte communities, locally with birch and larch open woodlands; 6—tundra and steppe communities in combination with pine and birch open woodlands; 7—meadow steppe with birch and pine forests, tundra, and halophilic communities; 8—herb and grass steppe; 9—meadow steppe with birch and pine forests; 10—southern herb and grass steppe without permafrost; 11—coniferous forests; 12—zonal boundaries; 13—southern boundary of continuous permafrost; 14—mountain glaciers. Adapted from [<a href="#B16-quaternary-07-00032" class="html-bibr">16</a>], simplified, with additions. For other elements of the legend, see <a href="#quaternary-07-00032-f003" class="html-fig">Figure 3</a>.</p>
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<p>The histogram of the ratios of channel bankfull widths of the ancient (<span class="html-italic">W<sub>past</sub></span>) and modern (<span class="html-italic">W<sub>mod</sub></span>) rivers in the Volga, Don, and Dnepr River basins. The nearest approximation is log-normal function. The <span class="html-italic">W<sub>mod</sub></span> of the rivers with broad floodplains were not used.</p>
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<p>Distribution of the values of coefficient <span class="html-italic">a</span> in Equation (3) on the southern megaslope of the East European Plain, which shows the geographical patterns of the data scatter when using this Equation. For other elements of the legend, see <a href="#quaternary-07-00032-f003" class="html-fig">Figure 3</a>.</p>
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<p>The distribution of the maximum flood runoff depth <span class="html-italic">X<sub>u_mod_max</sub></span> (in mm/day) for the unit catchment (<span class="html-italic">F</span> ≤ 1000 km<sup>2</sup>) calculated from the modern hydrological measurements (before 1970s) in the basins of the Volga, Don, and Dnepr Rivers. For other elements of the legend, see <a href="#quaternary-07-00032-f003" class="html-fig">Figure 3</a>.</p>
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<p>The distribution of the maximum flood runoff depth <span class="html-italic">X<sub>u_past_max</sub></span> for the unit catchment (<span class="html-italic">F</span> ≤ 1000 km<sup>2</sup>) in the basins of the Volga, Don, and Dnepr Rivers for the period 18–14 cal ka BP, calculated with Equations (4) and (9). For other elements of the legend, see <a href="#quaternary-07-00032-f003" class="html-fig">Figure 3</a>.</p>
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<p>The ratio <span class="html-italic">R</span> of the anomalies of the maximum flood runoff depths for the basins of the Volga, Don, and Dnepr Rivers, calculated as <span class="html-italic">X<sub>u_past_max</sub></span>/44.2 divided on <span class="html-italic">X<sub>u_mod_max</sub></span>/7.4. For other elements of the legend, see <a href="#quaternary-07-00032-f003" class="html-fig">Figure 3</a>.</p>
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<p>The climatic characteristics of the period 21–14 ka BP (within MIS 2), including the time of the large river formation (18–14 ka BP). (<b>A</b>) NGRIP oxygen isotope curve [<a href="#B43-quaternary-07-00032" class="html-bibr">43</a>], (<b>B</b>–<b>D</b>) anomalies of the air temperatures for January (<b>B</b>) and June (<b>C</b>), and for annual precipitation (<b>D</b>) in the upper Volga (green boxes) and upper Dnepr (yellow boxes) basins. Adapted from [<a href="#B47-quaternary-07-00032" class="html-bibr">47</a>], with additions.</p>
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17 pages, 7194 KiB  
Article
Vegetation Influences on Cloud Cover in Typical Plain and Plateau Regions of Eurasia: 2001–2021
by Tianwei Lu, Yong Han, Qicheng Zhou, Li Dong, Yurong Zhang, Ximing Deng and Danya Xu
Remote Sens. 2024, 16(12), 2048; https://doi.org/10.3390/rs16122048 - 7 Jun 2024
Viewed by 1217
Abstract
The feedback of vegetation on cloud cover is an important link in the global water cycle. However, the relative importance of vegetation and related factors (surface properties, heat fluxes, and environmental conditions) on cloud cover in the context of greening remains unclear. Combining [...] Read more.
The feedback of vegetation on cloud cover is an important link in the global water cycle. However, the relative importance of vegetation and related factors (surface properties, heat fluxes, and environmental conditions) on cloud cover in the context of greening remains unclear. Combining the Global Land Surface Satellite (GLASS) leaf area index (LAI) product and the fifth-generation reanalysis data of the European Centre for Medium-Range Weather Forecasts (ERA5), we quantified the relative contribution of vegetation and related factors to total cloud cover (TCC) in typical regions (Eastern European Plain, Western Siberian Plain, Mongolian Plateau, and Northeastern China Plain) of Eurasia over 21 years, and investigated how vegetation moderated the contribution of the other factors. Here, we show that the relative contribution of different factors to TCC was closely related to the climate and vegetation characteristics. In energy-limited (moisture-limited) areas, temperature (relative humidity) was more likely to be the factor that strongly contributed to TCC variation. Except for sparsely vegetated ecosystems, the relative contribution of LAI to TCC was stable within a range of 8–13%. The case study also shows that vegetation significantly modulated the contribution of other factors on TCC, but the degree of the regulation varied among different ecosystems. Our results highlight the important influence of vegetation on cloud cover during greening, especially the moderating role of vegetation on the contribution of other factors. Full article
(This article belongs to the Section Ecological Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>Study area. (<b>a</b>) is the elevation map of the four typical regions in Eurasia, (<b>b</b>–<b>e</b>) represent the percentage of different ecosystems of EEP, WSP, MGP, and NEP, respectively, where the gray color represents other ecosystems with less area, which are not considered in this study.</p>
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<p>Spatial and temporal variation of TCC and LAI from 2001–2021 (JJA). Where (<b>a</b>–<b>h</b>) are the spatial distributions of the multi-year mean (JJA) of TCC and LAI, respectively, with black numbers representing the mean of the corresponding region. Columns 1–4 represent EEP, WSP, MGP, and NEP, respectively. (<b>i</b>,<b>j</b>) are ridge plots characterizing the trend distributions of TCC and LAI (JJA), respectively, where the black dashed lines and numbers represent the median of the trends and the black solid lines represent the 25% and 75% quantiles of the trend, respectively.</p>
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<p>The averaged relative contribution of the leaf area index (LAI), surface albedo (Albedo), surface air temperature at 2 m (T2M), relative humidity (RH), surface sensible heat flux (SSH), surface latent heat flux (SLH), soil water content (SW), and forecast surface roughness (FSR) to total cloud cover (TCC), where (<b>a</b>–<b>d</b>) represent EEP, WSP, MGP, and NEP, respectively. The numbers represent the percentage of the relative contribution. Note that when the averaged relative contribution exceeds 15%, it is highlighted with a black outline.</p>
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<p>The correlation coefficient between LAI and the contribution of other factors to TCC, where (<b>a</b>,<b>c</b>) represent the farm and BDF ecosystems, respectively. The black circle indicates <span class="html-italic">p</span> &lt; 0.1, and (<b>b</b>,<b>d</b>) are the clustering results of the farmland and BDF based on the K-means clustering method. The black <b>×</b> marks centroids of the three clusters. Note the full names of these abbreviations: LAI (leaf area index), Albedo (surface albedo), T2M (surface air temperature at 2 m), RH (relative humidity), SSH (surface sensible heat flux), SLH (surface latent heat flux), SW (soil water content), and FSR (forecast surface roughness).</p>
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<p>The changes in heat fluxes with LAI, where (<b>a</b>,<b>b</b>) are SSH of the farm and BDF ecosystems, while (<b>c</b>,<b>d</b>) are SLH.</p>
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19 pages, 4863 KiB  
Article
Consistency Analysis and Accuracy Evaluation of Multi-Source Land Cover Data Products in the Eastern European Plain
by Guangmao Jiang, Juanle Wang, Kai Li, Chen Xu, Heng Li, Zongyi Jin and Jingxuan Liu
Remote Sens. 2023, 15(17), 4254; https://doi.org/10.3390/rs15174254 - 30 Aug 2023
Cited by 3 | Viewed by 1817
Abstract
Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were [...] Read more.
Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products. Full article
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<p>Topographic map of the Eastern European Plain (except the Franks Joseph Land archipelago).</p>
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<p>Spatial distribution of three land cover data products for the Eastern European Plain: (<b>a</b>) GlobeLand30, (<b>b</b>) GLC_FCS30, and (<b>c</b>) FROM_GLC.</p>
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<p>Spatial distribution of validation sample points.</p>
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<p>Area share of different land cover data products feature types. (CRP: cropland; FST: forest; GRS: grassland; SHR: shrubland; WET: wetland; WAT: water; TUN: Tundra; IMP: impervious surface; BAL: bare land; PSI: permanent ice/snow).</p>
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<p>Degree of confusion for different land cover data product types. (<b>a</b>) GlobeLand30 = ft (GLC_FCS30). (<b>b</b>) GlobeLand30 = ft (FROM_GLC). (<b>c</b>) GLC FCS30 = ft (FROM_GLC).</p>
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<p>Degree of confusion for different land cover data product types. (<b>a</b>) GlobeLand30 = ft (GLC_FCS30). (<b>b</b>) GlobeLand30 = ft (FROM_GLC). (<b>c</b>) GLC FCS30 = ft (FROM_GLC).</p>
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<p>Distribution of spatial consistency among major land cover data types.</p>
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<p>Distribution of spatial consistency among major land cover data types.</p>
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<p>Overall spatial consistency of the three land cover data products.</p>
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19 pages, 17237 KiB  
Article
Long-Range Transport Analysis Based on Eastern Atmospheric Circulation and Its Impact on the Dust Event over Moldavia, Romania in August 2022
by Diana-Corina Bostan, Ingrid-Mihaela Miclăuș, Cosmina Apetroaie, Mirela Voiculescu, Adrian Timofte and Marius-Mihai Cazacu
Atmosphere 2023, 14(9), 1366; https://doi.org/10.3390/atmos14091366 - 30 Aug 2023
Viewed by 1224
Abstract
During the second half of August 2022, a dust intrusion event occurred when dust that originated in the dry regions of the Kalmyk steppe (located in Russia, northeast of the Black Sea, north of Georgia, and northwest of the Caspian Sea) and the [...] Read more.
During the second half of August 2022, a dust intrusion event occurred when dust that originated in the dry regions of the Kalmyk steppe (located in Russia, northeast of the Black Sea, north of Georgia, and northwest of the Caspian Sea) and the Precaspian plain was transported over the eastern region of Romania. The arid soil found in these areas can be attributed to an extended period of intense drought, with notable instances occurring in 2002, 2003, 2015, and 2018. This situation was further intensified by heatwaves experienced in May and June of 2022. The dust event was captured in MODIS images. In addition, smoke trains originating from fires in the north of the Azov were detected, but these did not reach Romania. Optical parameters from AERONET were used to confirm the dust event. To determine the trajectory of the particles, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in this paper. The ensemble median model was used to highlight the presence and concentration of dust in the eastern part of Romania. Aerosols were detected between 0 and 4 km, according to radar and ceilometer data from the REXDAN cloud remote sensing facility in Galați, Romania. This dust intrusion event was the result of the dominant easterly circulation caused by the extension of the East European High to the northeast of the continent, which transported the dust towards the eastern part of Romania for more than 2 days. Moreover, the torrential rains between 22 and 24 August did not clear the atmosphere of dust, since the intense easterly circulation kept carrying the dust into the Moldavian area. Full article
(This article belongs to the Section Aerosols)
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<p>Transported dust origin regions; data source: Esri, Maxer, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGrid, ING, GIS User Community.</p>
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<p>Map of the studied area. Elevation model data source: Shuttle Radar Topography Mission geospatial.org.</p>
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<p>Synoptic maps at ground level. Black lines represent isobars (hPa) for 23 (<b>a</b>) and 22 (<b>b</b>) of August 2022 [<a href="#B14-atmosphere-14-01366" class="html-bibr">14</a>] (closed contour lines: isobars (i.e., lines of equal pressure); curved line with triangles: cold atmospheric front; curved line with black semicircles: warm atmospheric front; curved line with semicircles and triangles: occluded atmospheric front; letter H: symbol for the atmospheric high pressure field, letter D: symbol for the atmospheric low pressure field; values below 1015 mb: cyclonic field (low pressure); values above 1015 mb: anticyclonic field (high pressure); values and small symbols scattered on the map in the background: Bjerknes scheme).</p>
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<p>Synoptic maps at 500 hPa level for 22 August (<b>a</b>) and 23 August (<b>b</b>) 2022. Black lines represent isohypses (gpdam) and white lines represent isobars (hPa) at ground level; Romania’s borders are outlined in red [<a href="#B14-atmosphere-14-01366" class="html-bibr">14</a>]. (thick black lines: surfaces with equal geopotential values; thin white lines: isobars (i.e., lines of equal pressure); letter H: symbol for the atmospheric high pressure field; letter D: symbol for the atmospheric low pressure field; color palette: interpolated geopotential values).</p>
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<p>Streamlines for (<b>a</b>) 22 August 2022 (500 hPa level—above, ground level—below) and (<b>b</b>) 23 August 2022 (500 hPa level—above, ground level—below). (more intensely colored areas: landforms area; curved lines: streamlines (these vectors describes the flow of air masses); thick black arrows: movement direction of the currents).</p>
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<p>Vertical wind profile (wind bars—speed in m/s and direction, Y axis—pressure (mb), X axis—time—every 3 h) for 22 August (<b>left</b>) and 23 August (<b>right</b>) 2022. (the gray boxes: group the heights between which the wind has a southerly direction).</p>
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<p>MODIS satellite images for 21 August (<b>a</b>), 22 August (<b>b</b>), 23 August (<b>c</b>), and 24 August (<b>d</b>) 2022 (dust from the Russian steppes is highlighted inside the circles).</p>
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<p>Dust surface mass (μg/m<sup>3</sup>) for 22 August (<b>a</b>) and 23 August (<b>b</b>) 2022 (Modern-Era Retrospective Analysis for Research and Application, Version 2).</p>
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<p>European ensemble model—dust surface concentration (μg/m<sup>3</sup>) for 22 August (<b>a</b>) and 23 August (<b>b</b>) 2022 (Run 12h)—median (upper left), mean (upper right), stdev (bottom left), range (bottom right).</p>
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<p>Backward trajectories ending at 12:00 UTC for 23 August 2022 (duration: 72 h) ((<b>a</b>)—for Târgu Neamț observation point, (<b>b</b>)—for Iași observation point).</p>
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<p>Backward trajectories ending at 12:00 UTC for 23 August 2022 (duration: 120 h) for the REXDAN-Galați observation point ((<b>a</b>)—altitudes: 500, 1500, 3000 m, (<b>b</b>)—altitudes: 500, 1500, 5000 m).</p>
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<p>Backward trajectories ending at 12:00 UTC for 23 August 2022 (duration: 120 h) ((<b>a</b>)—for Târgu Neamț observation point, (<b>b</b>)—for Iași observation point).</p>
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<p>Bulletin of air quality in Neamț county—general air quality index for 23 August 2022 (1—good, 2—acceptable, 3—moderate, 4—bad, 5—very bad, 6—extremely bad).</p>
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<p>Bulletin of air quality in Bacău county—general air quality index for 23 August 2022 (1—good, 2—acceptable, 3—moderate, 4—bad, 5—very bad, 6—extremely bad).</p>
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<p>Bulletin of air quality in Botoșani county—general air quality index for 23 August 2022 (1—good, 2—acceptable, 3—moderate, 4—bad, 5—very bad, 6—extremely bad).</p>
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<p>Bulletin of air quality in Vaslui county—general air quality index for 23 August 2022 (1—good, 2—acceptable, 3—moderate, 4—bad, 5—very bad, 6—extremely bad).</p>
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<p>Absorption Ångström exponent [AAE at 440–675 nm] vs. scattering Ångström exponent [SAE at 440–675 nm] in the number density plot from Republic of Moldova (Kishinev) monitoring site: (<b>a</b>) August of 2022 (<b>b</b>) 22–23 August 2022.</p>
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<p>Combined results of radar and lidar data for 22 August (<b>a</b>) and 23 August (<b>b</b>) (Cloudnet image from UGAL–REXDAN).</p>
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15 pages, 4857 KiB  
Article
Long-Term Variability of the Hydrological Regime and Its Response to Climate Warming in the Zhizdra River Basin of the Eastern European Plain
by Bing Bai, Qiwei Huang, Ping Wang, Shiqi Liu, Yichi Zhang, Tianye Wang, Sergey P. Pozdniakov, Natalia L. Frolova and Jingjie Yu
Water 2023, 15(15), 2678; https://doi.org/10.3390/w15152678 - 25 Jul 2023
Cited by 1 | Viewed by 1381
Abstract
Climate warming globally has a profound effect on the hydrological regime, amplifying evapotranspiration and precipitation and accelerating the processes of snow melt and permafrost thaw. However, in the context of small river basins—those encompassing less than 10,000 km2—the response of the [...] Read more.
Climate warming globally has a profound effect on the hydrological regime, amplifying evapotranspiration and precipitation and accelerating the processes of snow melt and permafrost thaw. However, in the context of small river basins—those encompassing less than 10,000 km2—the response of the hydrological regime to climate change is intricate and has not yet been thoroughly understood. In this study, the Zhizdra River Basin, a typical small river basin in the eastern European plain with a total drainage area of 6940 km2, was selected to investigate the long-term variability of the hydrological regime and its responses to climate warming. Our results show that during the period of 1958–2016, the average runoff in the Zhizdra River Basin was approximately 170 mm, with significant fluctuations but no trend. Sensitivity analysis by the Budyko framework revealed that the runoff was more sensitive to changes in precipitation (P) compared to potential evapotranspiration (E0), implying that the Zhizdra River Basin is limited by water availability and has a slightly dry trend. A comprehensive analysis based on the seasonality of hydrometeorological data revealed that temperature predominantly affects spring runoff, while P mainly controls autumn runoff. Both factors make significant contributions to winter runoff. In response to climate change, the nonuniformity coefficient (Cv) and concentration ratio (Cn) of runoff have noticeably declined, indicating a more stabilized and evenly distributed runoff within the basin. The insights gleaned from this research illuminate the complex hydrological responses of small river basins to climate change, underlining the intricate interrelation among evapotranspiration, precipitation, and runoff. This understanding is pivotal for efficient water resource management and sustainable development in the era of global warming. Full article
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<p>Geographic location of the Zhizdra River Basin (EEP refers to the East European Plain).</p>
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<p>Time series of (<b>a</b>) precipitation (<span class="html-italic">P</span>), (<b>b</b>) temperature (T), (<b>c</b>) potential evapotranspiration (<span class="html-italic">E</span><sub>0</sub>), and (<b>d</b>) evapotranspiration (<span class="html-italic">E</span>). The time series for (<b>a</b>–<b>d</b>) spans 1958 to 2016. The black solid line represents the annual average value of meteorological elements. (<b>e</b>) The monthly average temperature and precipitation in the Zhizdra River Basin from 1958 to 2016.</p>
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<p>(<b>a</b>) Annual average runoff depth (mm) and 11-year moving average for the Zhizdra River Basin from 1958 to 2016 and (<b>b</b>) monthly average runoff depth (mm) for 1958–2016 in the Zhizdra River Basin.</p>
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<p>Nonuniformity index (<b>a</b>) and concentration ratio (<b>b</b>) of the annual runoff distribution.</p>
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<p>Sliding <span class="html-italic">t</span>-test for (<b>a</b>) runoff (<span class="html-italic">Q</span>), (<b>b</b>) temperature (<span class="html-italic">T</span>), (<b>c</b>) potential evapotranspiration (<span class="html-italic">E</span><sub>0</sub>), (<b>d</b>) evapotranspiration (<span class="html-italic">E</span>), and (<b>e</b>) precipitation (<span class="html-italic">P</span>). The time series spans 1958 to 2016. The red line represents 0.05 significance level. The blue line represents the <span class="html-italic">t</span>-value of 0.</p>
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<p>Relationships among the aridity ratio, evapotranspiration index, and underlying surface parameter (n).</p>
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<p>Contour plots of seasonal changes as a function of annual variations in precipitation (<span class="html-italic">P</span>) and potential evapotranspiration (<span class="html-italic">E</span><sub>0</sub>). Q<sub>spr</sub> is the average spring runoff from 1958 to 2016; Q<sub>sum</sub>, Q<sub>aut</sub>, and Q<sub>win</sub> are the average values of summer, autumn, and winter runoff, respectively. ΔE<sub>0</sub> and std(ΔE<sub>0</sub>) are <span class="html-italic">E</span><sub>0</sub> departure from the average annual <span class="html-italic">E</span><sub>0</sub> and its standard deviation; ΔP and std(ΔP) are the relative changes in annual <span class="html-italic">P</span> to the average annual <span class="html-italic">P</span> and its standard deviation; ΔQ is the relative changes in seasonal runoff to their average annual values.</p>
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20 pages, 3969 KiB  
Article
Freshwater Landscape Reconstruction from the Bronze Age Site of Borsodivánka (North-Eastern Hungary)
by Angel Blanco-Lapaz, Klára P. Fischl, Astrid Röpke, Tanja Zerl, Nadine Nolde, Michael Schmid and Tobias L. Kienlin
Diversity 2023, 15(3), 340; https://doi.org/10.3390/d15030340 - 27 Feb 2023
Viewed by 2213
Abstract
This multiproxy work presents the archeozoological analysis of fish and microvertebrate remains from the Middle Bronze Age tell site of Borsodivánka (Borsod Plain, North-eastern Hungary). The fish faunal assemblage provides valuable data on the choice of exploited consumption patterns, taphonomy, and aquatic paleoenvironmental [...] Read more.
This multiproxy work presents the archeozoological analysis of fish and microvertebrate remains from the Middle Bronze Age tell site of Borsodivánka (Borsod Plain, North-eastern Hungary). The fish faunal assemblage provides valuable data on the choice of exploited consumption patterns, taphonomy, and aquatic paleoenvironmental conditions at the site during the Bronze Age. Only freshwater taxa are present in the assemblage, for example, northern pike (Esox lucius); cyprinids: roach (Rutilus rutilus), common carp (Cyprinus carpio), common chub (Squalius cephalus) and common nase (Chondrostoma nasus); and percids: European perch (Perca fluviatilis) and pikeperch (Sander lucioperca). Herpetofaunal and micromammal remains are also part of this study, improving our knowledge of the site’s freshwater ecosystem. The grass snake (Natrix cf. natrix) and the European pond terrapin (Emys orbicularis), typical of aquatic ecosystems, are associated with the Aesculapian ratsnake (Zamenis longissimus), more typical of forest, shrubland, and grassland. The presence of amphibians such as toads (Bufo/Bufotes sp.) and frogs (Rana sp.) complete the herpetofaunal list. The microvertebrates also support a mature fluvial system, as represented by taxa like the European water vole (Arvicola amphibius). Other micromammals are present, such as the wood mouse (Apodemus sylvaticus), the group of the common/field vole (Microtus arvalis/agrestis), the European mole (Talpa europaea), and the house mouse (Mus musculus). All of them are common in forests, shrubland, and grassland. However, the commensal house mouse is more commonly associated with anthropogenic areas. In conclusion, Borsodivánka is characterized by a diverse landscape mosaic, displayed by the co-existence of a well-developed forest and a freshwater inland ecosystem with agricultural land in the wider area. Finally, the Tisza River and its flood plain represented the main water source close to the site, distinguished by the dominance of fish species from deep and slow-flowing waters. Full article
(This article belongs to the Special Issue The Environment and Climate during Pleistocene and Holocene)
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<p>Borsodivánka location on the Borsod plain and the foothill zone of the Bükk mountains. Modified from <a href="http://www.pinterest.com" target="_blank">www.pinterest.com</a> (accessed on 2 February 2023).</p>
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<p>(<b>a</b>). The mound of Borsodivánka-Marhajárás-Nagyhalom, surrounded by water, on the old Austrian–Hungarian maps of the First and Second Military Surveys (1806–1869). (<b>b</b>). Borsodivánka-Marhajárás. The tell part of the site seen from the south-east with surface survey in progress on the surrounding outer settlement [<a href="#B1-diversity-15-00340" class="html-bibr">1</a>]. The red cicle indicate the location of the site of Borsodivánka.</p>
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<p>(<b>a</b>). Geodetic survey map of Borsodivánka-Marhajárás-Nagyhalom. Both tops belong to the tell, and it was only disturbed and divided into two in early modern times. (<b>b</b>). Profile with the layer sequence of the tell set.</p>
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<p>(<b>a</b>). Reconstructed layout of house A with the marks of the profiles. (<b>b</b>). Harris matrix of the house A layer sequence. (<b>c</b>). Profile of house A with two main occupation periods. From North (A,B) and South (C,D). Modified from Fischl et al. 2022 [<a href="#B11-diversity-15-00340" class="html-bibr">11</a>].</p>
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<p>Some examples of fish from Borsodivánka 2020. (<b>a</b>). <span class="html-italic">Chondrostoma nasus</span> (Probe 49, Layer 7), left pharyngeal arch. (<b>b</b>). <span class="html-italic">Cyprinus carpio</span> (Probe 44, Layer 7), left 2nd pharyngeal tooth. (<b>c</b>). <span class="html-italic">Rutilus rutilus</span> (Probe 40, Layer 7), left pharyngeal arch. (<b>d</b>). <span class="html-italic">Esox lucius</span> (Probe 48, Layer 7), precaudal vertebra. (<b>e</b>). <span class="html-italic">Esox lucius</span> (Probe 48, Layer 7), a fragment of the left dentary. (<b>f</b>). <span class="html-italic">Perca fluviatilis</span> (Probe 44, Layer 7), scale. (<b>g</b>). <span class="html-italic">Sander lucioperca</span> (Probe 49, Layer 7), left dentary incomplete. Scale 5 mm.</p>
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<p>Some examples of microvertebrate from Borsodivánka 2020. (<b>a</b>). <span class="html-italic">Zamenis longissimus</span> (Probe 6, Layer S1101), trunk vertebra. (<b>b</b>). <span class="html-italic">Natrix</span> cf. <span class="html-italic">natrix</span> (Probe 29, Layer S55), trunk vertebra. (<b>c</b>). <span class="html-italic">Apodemus sylvaticus</span>, right maxillary (left, Probe 13, Layer S1097), and mandibular tooth rows (right, Probe 16, Layer S1097). (<b>d</b>). <span class="html-italic">Mus musculus</span> (Probe 16, Layer S1097), right m1. (<b>e</b>). <span class="html-italic">Arvicola amphibius</span> (Probe 48, Layer S1100), left M2. (<b>f</b>). <span class="html-italic">Microtus arvalis</span> (Probe 18, Layer 1066), right m1. (<b>g</b>). <span class="html-italic">Talpa europaea</span> (Probe 29, Layer S55), lumbar vertebrae region. (<b>h</b>). <span class="html-italic">Rana</span> sp. (Probe 16, Layer S1097), right premaxilla. (<b>i</b>). <span class="html-italic">Bufo/Bufotes</span> sp. (Probe 42 Layer S7), left humerus of male. (<b>j</b>). <span class="html-italic">Emys orbicularis</span> (Probe 44, Layer S7), left humerus in dorsal (left) and lateral (right) views.</p>
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<p>Based on NMI, results of the habitat weighting method for the microvertebrate assemblage at Borsodivánka (2020 campaign): Forest (Fo), Shrubland (Sh), Grassland (Gr), Wetland (We), and Rocky (Ro).</p>
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15 pages, 3793 KiB  
Article
Manifestation of the Early 20th Century Warming in the East-European Plain: Atmospheric Circulation Anomalies and Its Connection to the North Atlantic SST and Sea Ice Variability
by Valeria Popova, Tatiana Aldonina and Daria Bokuchava
Atmosphere 2023, 14(3), 428; https://doi.org/10.3390/atmos14030428 - 21 Feb 2023
Cited by 1 | Viewed by 1426
Abstract
A study of the climatic characteristics and annual runoff of the Volga and Severnaya Dvina rivers demonstrates that, on the East European Plain (EEP), Early Twentieth Century Warming (ETCW) manifested in a multiyear drought between 1934 and 1940; this drought has no analogues [...] Read more.
A study of the climatic characteristics and annual runoff of the Volga and Severnaya Dvina rivers demonstrates that, on the East European Plain (EEP), Early Twentieth Century Warming (ETCW) manifested in a multiyear drought between 1934 and 1940; this drought has no analogues in this region in terms of intensity and duration according to Palmer’s classification, and caused extreme hydrological events. The circulation conditions during this event were characterized by an extensive anticyclone over Eastern Europe, combined with a cyclonic anomaly in the circumpolar region. An analysis of the spatial features of sea surface temperature (SST) anomalies indicate that the surface air temperature (SAT) anomalies in July on the EEP during ETCW were related not only to the North Atlantic (NA) warming and positive AMO phase, but also to a certain spatial pattern of SST anomalies characteristic of the 1920–1950 period. The difference between the SST anomalies of the opposite sign in the different NA zones, used as the indicator of the obtained spatial pattern, shows the quite close relations between the July SAT anomalies on the EEP and the atmospheric circulation patterns responsible for them. The positive phase of the Atlantic Multidecadal Oscillation (AMO) and the expansion of the subtropical high-pressure belt to the north and to the east can be considered as global-scale drivers of this phenomenon. The AMO also impacts the sea ice cover in the Barents–Kara Sea region, which, in turn, could have led to specific atmospheric circulation patterns and contributed to droughts on the EEP in the 1930s. Full article
(This article belongs to the Special Issue Advances in Atmospheric Sciences ‖)
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<p>Location of the hydrological stations (red triangles).</p>
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<p>(<b>a</b>) Annual runoff, percent when compared with 1961–1990, for the rivers Severnaya Dvina, Volga, Vyatka, Belaya, and Oka; dotted line shows 5-year running averages. (<b>b</b>) Spatially averaged (20–60 E; 45–70 N) variations in the SAT anomalies, °C, 1, GISSTEMP, 2, CRUTEM, 3, ERA20C, 4, CERA20C, and precipitation; percent when compared with 1961–1990, for summer (JJA), autumn (SON), and cold period (November–April).</p>
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<p>Spatially averaged (20–60 E; 45–70 N) PDSI, for JJA and SON seasons (upper panel) and spatial distribution of the JJA PDSI for “wet” (1923, 1926, 1927, 1928), (left bottom panel) and “dry” (1936, 1938, 1939, 1940) (right bottom panel) years.</p>
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<p>(<b>a</b>) Averaged SLP, gPa patterns for the years of “hot” (1931, 1936, 1938) and “cold” (1923, 1926, 1928) SAT anomalies on EEP; the difference between them and the EOF3 of SLP variability in July–August. (<b>b</b>) SLP PC3 and AMO(×4) (upper panel) and correlation coefficients between (blue) SLP PC3 and July–August SAT anomalies on EEP, (red) AMO and SLP PC3, calculated over 15-year running periods; horizontal dotted lines indicate the threshold level of statistically significant values (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) Variation in the AMO, SST anomalies, °C, averaged in sector S1 (20–50 W, 40–50 N), SST<sub>S1</sub>, and SST difference, °C, between S1 and S2 (65–80 W, 20–30 N), ΔSST<sub>S1-S2</sub>, March–April (upper panel); thin (thick) lines show annual values (15-year running averages). Correlation coefficients between July SAT on the EEP (20–60 E; 45–70 N) and detrended timeseries of AMO, SST<sub>S1</sub>, ΔSST<sub>S1-S2</sub> (March–April), calculated over 15-year running periods (bottom panel); horizontal dotted lines indicate the threshold level of statistically significant values (<span class="html-italic">p</span> &lt; 0.05). (<b>b</b>) Difference between SST, °C, (March–April) averaged for the years of “hot” (1931, 1936, 1938) and “cold” (1923, 1926, 1928) July SAT anomalies on the EEP (upper panel), and correlation between SST and July SAT on the EEP, detrended, in 1923–1950; white and red dashed lines indicate the borders of S1 and S2, respectively (bottom panel).</p>
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<p>Linear regression coefficients of timeseries of AMO and ΔSST<sub>S1-S2</sub> (March–April), detrended and normalized onto SLP anomalies: July, August, April, in 1923–1950.</p>
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<p>Sea ice area (×10<sup>3</sup> km<sup>2</sup>) in the Barents Sea in (<b>a</b>) May and (<b>b</b>) August; Blue (red) dots indicate years of positive, red, and negative, blue, July SAT anomalies on the EEP.</p>
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<p>Difference in SIC (%) between the years of negative and positive July SAT anomalies on the EEP in (<b>a</b>) May and (<b>b</b>) August.</p>
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<p>Correlation coefficients between SIA in the Barents Sea in May and SLP anomalies in July for 1924–1950.</p>
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16 pages, 2311 KiB  
Article
Distribution of Four Vole Species through the Barn Owl Tyto alba Diet Spectrum: Pattern Responses to Environmental Gradients in Intensive Agroecosystems of Central Greece
by Vasileios Bontzorlos, Konstantinos Vlachopoulos and Anastasios Xenos
Life 2023, 13(1), 105; https://doi.org/10.3390/life13010105 - 30 Dec 2022
Cited by 6 | Viewed by 2241
Abstract
Voles are the most common vertebrate pests in European agriculture. Identifying their distribution and abundance patterns provides valuable information for future management. Barn Owl diet analysis is one of the optimum methods used to record small mammal distribution patterns on large spatial scales. [...] Read more.
Voles are the most common vertebrate pests in European agriculture. Identifying their distribution and abundance patterns provides valuable information for future management. Barn Owl diet analysis is one of the optimum methods used to record small mammal distribution patterns on large spatial scales. From 2003 to 2005, a total of 10,065 Barn Owl pellets were collected and analyzed from 31 breeding sites in the largest agroecosystem in Greece, the Thessaly plains. A total of 29,061 prey items were identified, offering deep insight into small mammal distribution, specifically voles. Four discrete vole species (Harting’s vole Microtus hartingi, East European vole Microtus levis, Thomas’s pine vole Microtus thomasi, and Grey dwarf hamster Cricetulus migratorius) comprised 40.5% (11,770 vole prey items) of the total Barn Owl prey intake. The presence and abundance of the voles varied according to underlying environmental gradients, with soil texture and type playing a major role. M. levis showed no significant attachments to gradients, other than a mild increase in Mollisol soils. It was syntopic in all sites with M. hartingi, which was the dominant and most abundant small mammal species, preferring non-arable cultivated land, natural grasslands, set-aside fields, and fallow land. M. thomasi was strictly present in western Thessaly and strongly associated with a sandy-clay soil texture and Alfisol soils. C. migratorius was the least represented vole (162 items), exclusively present in eastern Thessaly and demonstrating a stronger association with cereals, Mollisol soils, and an argillaceous-clay soil texture. This is the first study in Greece at such a large spatial scale, offering insights for pest rodents’ distribution in intensive agroecosystems and their response to environmental gradients including soil parameters. Full article
(This article belongs to the Special Issue Abundance and Dynamics of Small Mammals and Their Predators)
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<p>Distribution patterns of four vole species (<span class="html-italic">M. hartingi</span>, <span class="html-italic">M. levis</span>, <span class="html-italic">M. thomasi</span>, and <span class="html-italic">C. migratorius</span>) in the agroecosystems of Thessaly, central Greece, based on the spatial interpolation of values from all 31 Barn Owl nesting sites, from which pellet samplings and pellet analysis were carried out. Interpolation was based on the inverse distance weight model (IDW), which determines cell values using a linearly weighted combination of a set of measured values of sample points, where the weight is a function of the inverse distance from the output cell location. Upper left panel, <span class="html-italic">M. thomasi</span> distribution patterns; upper right panel, <span class="html-italic">C. migratorius</span> distribution patterns; lower right panel, <span class="html-italic">M. hartingi</span> distribution patterns; lower right panel, <span class="html-italic">M. levis</span> distribution patterns.</p>
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<p>Individual response patterns for each vole species to the six discrete environmental gradients. Only responses that fitted a first- or second-order polynomial model are included in the graphs. (<b>a</b>) Vole response curves for the “Industrial Cultivations Gradient”; (<b>b</b>) vole response curves for the “Land Uses Gradient”; (<b>c</b>) vole response curves for the “Arable Land Gradient”; (<b>d</b>) vole response curves for the “Soil Texture Gradient”; (<b>e</b>) vole response curves for the “Soil Types EM and V Gradient”; (<b>f</b>) vole response curves for the “Soil Types I and V Gradient”.</p>
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17 pages, 1126 KiB  
Article
Features of the Territorial Distribution, Composition and Structure of Phytocenoses with the Participation of Fraxinus excelsior, Their Resource Qualities, Ecological and Economic Importance (Southeastern Part of the East European Plain)
by Maxim Viktorovich Larionov, Alexey Anatolievich Volodkin, Olga Alexandrovna Volodkina, Evgeny Valentinovich Lebedev, Olga Evgenievna Khanbabayeva, Svetlana Vitalievna Tazina, Elena Anatolyevna Kozlova, Elena Evgenievna Orlova, Inna Nikolaevna Zubik, Varvara Dmitrievna Bogdanova, Mikhail Vladimirovich Vorobyev, Alena Pavlovna Demidova, Liliya Rafisovna Akhmetova, Yulia Igorevna Kondratenko, Ivan Ivanovich Goloktionov, Ekaterina Vladislavovna Soboleva and Karina Mikirtichevna Gordyushkina
Life 2023, 13(1), 93; https://doi.org/10.3390/life13010093 - 28 Dec 2022
Cited by 3 | Viewed by 2338
Abstract
At present, the distribution area of Fraxinus excelsior L. in the forest ecosystems of the Volga Region is rather low and ranges from 0.01% to 2.5%. In the Middle Volga Region, using the example of the Penza region, five types of deciduous forests [...] Read more.
At present, the distribution area of Fraxinus excelsior L. in the forest ecosystems of the Volga Region is rather low and ranges from 0.01% to 2.5%. In the Middle Volga Region, using the example of the Penza region, five types of deciduous forests were identified in the composition with Fraxinus excelsior L.: oak forest aegopodium, oak forest nettle, oak forest hazel-linden, oak forest aegopodium-motley grass, oak forest carex-motley grass. In the forest phytocenoses of the Moksha River basin, the quality of Fraxinus excelsior L. is 1.5–1.7. In the forest phytocenoses of the Khoper River basin, the average quality value reaches 2.4–2.8, and in the forest tracts of the Sura river basin it is 2.8–3.2. In the western part of the study area, individuals of age class II–III (21–40, 41–60 years) predominate, in the central part—age class I (1–20 years), in the eastern part—age class V (81–100 years). This circumstance allows us to conclude that its populations in the western regions are represented by stands of different ages; the presence of young stands and middle-aged stands indicates the presence of conditions for reproduction and distribution. At the border of its range, Fraxinus excelsior L. grows in a stable population; in the western part of the Middle Volga Region, the number of species in forest stands with a predominance of Fraxinus excelsior L. is 26–30% higher than this indicator in more eastern regions. In the direction from east to west, the number of species in the composition of forest stands increases (up to 8.4), with a predominance of Fraxinus excelsior L. The number of plant associations increases in the direction from east to west. If in the east of the Penza region Fraxinus excelsior L. occurs in 6–7 plant associations, then in the west of the region—in 18–25 associations. The maximum timber stock for 100 years of Fraxinus excelsior L. stands reaches 380 m3/ha. Such a natural bioresource potential is of importance for the conduct of the national economy. Forest management in phytocenoses with the participation of this tree species is a strategic branch direction. It is expedient to restore populations of Fraxinus excelsior L. everywhere and to cultivate them in the territory of the East European Plain and especially in its south-eastern part. This is fully consistent with the principles of sustainable ecological and economic development against the background of local natural, climatic and geographical conditions. This type is necessary when solving environmental, resource-saving and economic problems in the territory under consideration. Full article
(This article belongs to the Special Issue State-of-Art in the Environmental Sciences and Human Ecology)
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<p>Percentage of forests dominated by <span class="html-italic">Fraxinus excelsior</span> L. in the forested area, %. 1—Krasnodar Territory; 2—Stavropol Territory; 3—Adygea, 4—Karachaevo–Cherkessia; 5—Kabardino–Balkaria; 6—North Ossetia; 7—Ingushetia; 8—Chechen Republic; 9—Nizhny Novgorod Region; 10—Mordovia; 11—Chuvashia; 12—Mari El; 13—Udmurtia.</p>
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<p>The number of plant associations in communities formed by the stands of <span class="html-italic">Fraxinus excelsior</span> L.</p>
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25 pages, 12071 KiB  
Article
Characteristics of Snow Depth and Snow Phenology in the High Latitudes and High Altitudes of the Northern Hemisphere from 1988 to 2018
by Shanna Yue, Tao Che, Liyun Dai, Lin Xiao and Jie Deng
Remote Sens. 2022, 14(19), 5057; https://doi.org/10.3390/rs14195057 - 10 Oct 2022
Cited by 11 | Viewed by 2355
Abstract
Snow cover is an important part of the Earth’s surface and its changes affect local and even global climates due to the high albedo and heat insulation. However, it is difficult to directly compare the results of previous studies on changes in snow [...] Read more.
Snow cover is an important part of the Earth’s surface and its changes affect local and even global climates due to the high albedo and heat insulation. However, it is difficult to directly compare the results of previous studies on changes in snow cover in the Northern Hemisphere mainland (NH) due to the use of different datasets, research methods, or study periods, and a lack comparison in terms of the differences and similarities at high latitudes and high altitudes. By using snow depth datasets, we analyzed the spatio-temporal distributions and variations in snow depth (SD) and snow phenology (SP) in the NH and nine typical areas. This study revealed that SD in the NH generally decreased significantly (p < 0.01) from 1988 to 2018, with a rate of −0.55 cm/decade. Changes in SD were insignificant at high altitudes, but significant decreases were found at high latitudes. With regard to SP, the snow cover onset day (SCOD) advanced in 31.57% of the NH and was delayed in 21.10% of the NH. In typical areas such as the Rocky Mountains, the West Siberian Plain, and the Central Siberian Plateau, the SCOD presented significant advancing trends, while a significant delay was the trend observed in the Eastern European Plain. The snow cover end day (SCED) advanced in 37.29% of the NH and was delayed in 14.77% of the NH. Negative SCED trends were found in most typical areas. The snow cover duration (SCD) and snow season length (SSL) showed significant positive trends in the Rocky Mountains, while significant negative trends were found in the Qinghai–Tibet Plateau. The results of this comprehensive comparison showed that most typical areas were characterized by decreased SD, advanced SCOD and SCED, and insignificantly increasing SCD and SSL trends. The SCD and SSL values were similar at high latitudes, while the SSL value was larger than the SCD value at high altitudes. The SD exhibited similar interannual fluctuation characteristics as the SCD and SSL in each typical area. The SCD and SSL increased (decreased) with advanced (delayed) SCODs. Full article
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Graphical abstract

Graphical abstract
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<p>Locations of (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau in the NH.</p>
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<p>(<b>a</b>) Variation slopes and (<b>b</b>) significance pattern (<span class="html-italic">p</span> &lt; 0.05) of SD in the NH.</p>
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<p>Variation slopes of the monthly average SD in the NH. (<b>a</b>–<b>h</b>) correspond to October, November, December, January, February, March, April, and May, respectively.</p>
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<p>Significance patterns (<span class="html-italic">p</span> &lt; 0.05) of monthly average SD in the NH. (<b>a</b>–<b>h</b>) correspond to October, November, December, January, February, March, April, and May, respectively.</p>
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<p>Interannual SD variations in the NH.</p>
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<p>Spatial distributions of the annual (<b>a</b>) SCOD, (<b>b</b>) SCED, (<b>c</b>) SSL, and (<b>d</b>) SCD in the NH. The bar charts on the right correspond to the (<b>a</b>) SCOD, (<b>b</b>) SCED, (<b>c</b>) SSL, and (<b>d</b>) SCD legends.</p>
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<p>Variation slopes of the annual (<b>a</b>) SCOD, (<b>b</b>) SCED, (<b>c</b>) SSL, and (<b>d</b>) SCD in the NH. The histograms on the right correspond to the (<b>a</b>) SCOD, (<b>b</b>) SCED, (<b>c</b>) SSL, and (<b>d</b>) SCD legends.</p>
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<p>Significance patterns (<span class="html-italic">p</span> &lt; 0.05) in the annual (<b>a</b>) SCOD, (<b>b</b>) SCED, (<b>c</b>) SSL, and (<b>d</b>) SCD. Significance was assessed at the 5% level.</p>
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<p>Interannual variations in SD and SP in (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau. The vertical axes corresponding to SCOD and SCED indicate the day in the snow hydrological year, while the vertical axes corresponding to the SSL and SCD indicate the number of days accumulated during the snow hydrological year.</p>
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<p>Comparison of the SCOD derived from NHSD and ground observation datasets in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau. The vertical axis labels in panels (<b>b</b>–<b>i</b>) are the same as those in panel (<b>a</b>). * and ** indicate that the correlation were significant at the 95% and 99% confidence level, respectively.</p>
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<p>Comparison of the SCED derived from NHSD and ground observation datasets in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau. The vertical axis labels in panels (<b>b</b>–<b>i</b>) are the same as those in panel (<b>a</b>). * and ** indicate that the correlation were significant at the 95% and 99% confidence level, respectively.</p>
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<p>Comparison of the SCOD derived from NHSD, IMS, and ERA-Interim in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau.</p>
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<p>Comparison of the SCED derived from NHSD, IMS, and ERA-Interim in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau.</p>
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<p>Comparison of the SSL derived from NHSD and ground observation datasets in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau. The vertical axis labels in panels (<b>b</b>–<b>i</b>) are the same as those in panel (<b>a</b>). * and ** indicate that the correlation were significant at the 95% and 99% confidence level, respectively.</p>
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<p>Comparison of the SCD derived from NHSD and ground observation datasets in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau. The vertical axis labels in panels (<b>b</b>–<b>i</b>) are the same as those in panel (<b>a</b>). * and ** indicate that the correlation were significant at the 95% and 99% confidence level, respectively.</p>
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<p>Comparison of the SSL derived from NHSD, IMS, and ERA-Interim in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau.</p>
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<p>Comparison of the SCD derived from NHSD, IMS, and ERA-Interim in typical areas. The typical areas are (<b>a</b>) the Alps, (<b>b</b>) the Rocky Mountains, (<b>c</b>) the Qinghai–Tibet Plateau, (<b>d</b>) Alaska, (<b>e</b>) the Eastern European Plain, (<b>f</b>) the Eastern Siberian Mountains, (<b>g</b>) Northern Canada, (<b>h</b>) the West Siberian Plain, and (<b>i</b>) the Central Siberian Plateau.</p>
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8 pages, 1111 KiB  
Article
Genotyping-by-Sequencing Analysis Shows That Siberian Lindens Are Nested within Tilia cordata Mill
by Sergei V. Shekhovtsov, Irina N. Shekhovtsova and Oleg E. Kosterin
Diversity 2022, 14(4), 256; https://doi.org/10.3390/d14040256 - 30 Mar 2022
Cited by 2 | Viewed by 2624
Abstract
Tilia sibirica and T. nasczokinii are considered to be endemic Siberian linden species. They have very small distributions located hundreds to thousands of kilometers away from other lindens. It is unclear how closely these species are related to the widespread Tilia cordata: [...] Read more.
Tilia sibirica and T. nasczokinii are considered to be endemic Siberian linden species. They have very small distributions located hundreds to thousands of kilometers away from other lindens. It is unclear how closely these species are related to the widespread Tilia cordata: according to the current hypotheses, they could be pre-Pleistocene relicts or remnants of the recent continuous range of T. cordata that existed during the Holocene climatic optimum. Earlier studies detected significant differences between T. sibirica, T. nasczokinii, and T. cordata in microsatellite loci, but not in plastid sequences. Here we performed a phylogenetic analysis of several linden species based on GBS data. The obtained GBS sequences were assembled to create phylogenetic trees based on 16,000–294,000 variable sites. We found that T. cordata and the two putative Siberian species formed a monophyletic group. It consisted of three clades: the basal clade containing specimens from the Caucasus, and two sister clades representing populations from the East European Plains+the Urals and Siberia, respectively. Neither of the Siberian species was related to the Far Eastern T. amurensis, as was hypothesized earlier. Our study suggests that the colonization of Europe and Siberia after the Last Glacial Maximum occurred from different glacial refugia. Full article
(This article belongs to the Special Issue Phylogeny and Phylogeography of the Holarctic)
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Figure 1

Figure 1
<p>Sampling locations of the studied <span class="html-italic">Tilia</span> specimens. Location numbers in circles refer to <a href="#diversity-14-00256-t001" class="html-table">Table 1</a>. Distributions of <span class="html-italic">T. sibirica</span> and <span class="html-italic">T. nasczokinii</span> are too small so they are only represented by location circles (locations 5, 6 and 7, 8, respectively). Distribution of <span class="html-italic">T. cordata</span> is shaded in green; distribution of <span class="html-italic">T. taquetii</span> and <span class="html-italic">T. amurensis</span> is shaded in red. Arrows indicate putative dispersal routes of <span class="html-italic">T. cordata</span> after the LGM.</p>
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<p>Phylogenetic tree of the studied specimens constructed using the ML method. Numbers in brackets after specimen names refer to location numbers (<a href="#diversity-14-00256-f001" class="html-fig">Figure 1</a>, <a href="#diversity-14-00256-t001" class="html-table">Table 1</a>). Numbers at the branches indicate ML bootstrap support/Bayesian bootstrap support.</p>
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17 pages, 1398 KiB  
Article
Phytoplankton Composition and Ecological Status of Lakes with Cyanobacteria Dominance
by Małgorzata Poniewozik and Tomasz Lenard
Int. J. Environ. Res. Public Health 2022, 19(7), 3832; https://doi.org/10.3390/ijerph19073832 - 23 Mar 2022
Cited by 11 | Viewed by 2477
Abstract
Phytoplankton is one of the five biological quality elements used in the assessment of the ecological status of surface waters according to the European Water Framework Directive established in 2000. In this study, we determined the ecological status of three small and shallow [...] Read more.
Phytoplankton is one of the five biological quality elements used in the assessment of the ecological status of surface waters according to the European Water Framework Directive established in 2000. In this study, we determined the ecological status of three small and shallow lakes in the Polesie Plain, Eastern Poland, by using indices based on phytoplankton assemblages. The predominant phytoplankton of all three lakes were filamentous cyanobacteria, both heterocystous and non-heterocystous, represented by the genera Aphanizomenon, Planktothrix, Limnothrix, and Planktolyngbya. We used the Hungarian Q index, German PSI (Phyto-See-Index), and recently developed PMPL (Phytoplankton Metrics for Polish Lakes) for Polish lakes. We compared the results from the calculation of the indices to physicochemical data obtained from the lake water and Carlson’s Trophy State Index (TSI). On the basis of TSI, Gumienek and Glinki lakes were classified as advanced eutrophic, whereas Czarne Lake had a better score and was classified as slightly eutrophic. The trophic state was generally confirmed by the ecological status based on phytoplankton indices and also showed the diverse ecological situation in the lakes studied. Based on the Polish PMPL, Gumienek Lake was classified as having bad status (ecological quality ratio (EQR) = 0.05), whereas Glinki and Czarne lakes were classified within the poor status range (EQR = 0.25 and 0.35, respectively). However, based on the German PSI, the lakes were classified in a different manner: the status of Gumienek and Czarne lakes was better, but unsatisfactory, because they were still below the boundary for the good status category recommended by the European Commission. The best ecological status for the studied lakes was obtained using the Q index: Gumienek Lake with EQR = 0.42 had a moderate status, and Czarne Lake with EQR = 0.62 obtained a good status. However, Glinki Lake, with EQR = 0.40, was classified at the boundary for poor and moderate status. Based on our study, it seems that the best index for ecological status assessment based on phytoplankton that can be used for small lakes is the Polish (PMPL) index. Full article
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Figure 1

Figure 1
<p>Percentage shares of numbers (left column) and wet biomass (right column) of phytoplankton in the studied lakes in the following month of study. Abbreviations: CHL–Chlorophyta, BAC–Bacillariophyceae, EUG–Euglenophyceae, CHR–Chrysophyceae, CRY–Cryptophyceae, DIN–Dinophyceae, CYA–Cyanophyceae, MAY–May, JUN–June, JUL–July, AUG–August, SEP–September.</p>
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<p>Percentage shares of functional (left column) and morpho-functional (right column) groups—FGs and MFGs in the studied lakes in the following month of study. Abbreviations: MAY–May, JUN–June, JUL–July, AUG–August, SEP–September.</p>
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<p>Percentage shares of functional (left column) and morpho-functional (right column) groups—FGs and MFGs in the studied lakes in the following month of study. Abbreviations: MAY–May, JUN–June, JUL–July, AUG–August, SEP–September.</p>
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<p><b>A</b>. Ecological Quality (EQ) values and status class assessment according to PMPL metrics of lakes ecological status assessment (Polish method). Abbreviations: MBm–Metric of total biomass of phytoplankton, MChl a–Metric of chl-<span class="html-italic">a</span> concentration, MCY–Metric of biomass of cyanobacteria. <b>B</b>. Ecological Quality (EQ) values and status class assessment according to German metrics of lakes ecological status assessment. Abbreviation: PTSI–Metric Phytoplankton Seen Index. <b>C</b>. Ecological Quality Ratio (EQR) based on Q, PSI, and PMPL indices calculated for the studied lakes. Abbreviations: Q–index for Hungarian lakes, PSI–index for German lakes, PMPL–index for Polish lakes.</p>
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<p><b>A</b>. Ecological Quality (EQ) values and status class assessment according to PMPL metrics of lakes ecological status assessment (Polish method). Abbreviations: MBm–Metric of total biomass of phytoplankton, MChl a–Metric of chl-<span class="html-italic">a</span> concentration, MCY–Metric of biomass of cyanobacteria. <b>B</b>. Ecological Quality (EQ) values and status class assessment according to German metrics of lakes ecological status assessment. Abbreviation: PTSI–Metric Phytoplankton Seen Index. <b>C</b>. Ecological Quality Ratio (EQR) based on Q, PSI, and PMPL indices calculated for the studied lakes. Abbreviations: Q–index for Hungarian lakes, PSI–index for German lakes, PMPL–index for Polish lakes.</p>
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13 pages, 8755 KiB  
Article
Triangulopteris lacunata gen. et sp. nov. (Centroplasthelida), a New Centrohelid Heliozoan from Soil
by Dmitry G. Zagumyonnyi, Liudmila V. Radaykina and Denis V. Tikhonenkov
Diversity 2021, 13(12), 658; https://doi.org/10.3390/d13120658 - 11 Dec 2021
Cited by 10 | Viewed by 3469
Abstract
A new genus and species of centrohelid heliozoans, Triangulopteris lacunata gen. et sp. nov. (Pterocystidae Cavalier-Smith and Heyden, 2007), from four geographically remote locations (the Crimean Peninsula, the Dnieper Lowland (the East European Plain), Franz Josef Land, and the Kolyma Lowland (North–Eastern Siberia) [...] Read more.
A new genus and species of centrohelid heliozoans, Triangulopteris lacunata gen. et sp. nov. (Pterocystidae Cavalier-Smith and Heyden, 2007), from four geographically remote locations (the Crimean Peninsula, the Dnieper Lowland (the East European Plain), Franz Josef Land, and the Kolyma Lowland (North–Eastern Siberia) was examined using light and electron microscopy. The novel centrohelid is characterized by round shape, 4.3–16.3 μm in diameter, covered with two types of scales: 1.06–4.54 μm long triangular spine scales and 1.22–2.05 μm oval plate scales. Studied centrohelid heliozoan possesses a unique spine scale morphology. The base of scales is represented by a horse hoof-shaped basal plate. The inner surface and lateral wings of spine scales have numerous radial ribs with two ‘pockets’ that are located on both sides of the spine shaft. These pockets are formed by the lateral wings and ends of the basal plate. The cyst formation and transition to a spicules-bearing stage were noted. Additionally, phylogenetic tree was constructed based on SSU rRNA sequences including the strain HF-25 from the permafrost of Kolyma Lowland. The resulting phylogeny recovered it within the clade Pterista, while forming a separate sister lineage to H2 clade, which only had included freshwater environmental sequences. Full article
(This article belongs to the Special Issue Aquatic Biodiversity: Evolution, Taxonomy and Conservation)
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Graphical abstract

Graphical abstract
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<p>Map of sampling sites. See <a href="#diversity-13-00658-t001" class="html-table">Table 1</a> for the description of the sampling sites (1–4).</p>
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<p><span class="html-italic">Triangulopteris lacunata</span> gen. et sp. nov., strain HF-25 from the Kolyma Lowland after the loss of silica scales, light microscopy. (<b>A</b>,<b>D</b>)—starving cells; (<b>B</b>,<b>C</b>,<b>E</b>)—well-fed cells; (<b>F</b>,<b>G</b>)—cysts. Abbreviations: a.k— axopodial kinetocysts; ax—axopodia; f.v—food vacuole; m.c—microtubule organizing center (MTOC). Scale bars: (<b>A</b>–<b>G</b>)—10 μm.</p>
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<p><span class="html-italic">Triangulopteris lacunata</span> gen. et sp. nov., strain HF-25 from the Kolyma Lowland, scanning electron microscopy. (<b>A</b>)—general view of the dried cell; (<b>B</b>–<b>D</b>)—spine scales; (<b>E</b>,<b>F</b>)—plate scales. Abbreviations: b.p—basal plate; l.w—lateral wing; m.th—medial thickening; p—pockets; r.r—radial ribs; sh—shaft. Scale bars: (<b>A</b>)—2 μm; (<b>B</b>–<b>F</b>)—0.5 μm.</p>
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<p><span class="html-italic">Triangulopteris lacunata</span> gen. et sp. nov., from Franz Josef Land archipelago. Electron microscopy (TEM). (<b>A</b>,<b>B</b>)—general view of the dried cell; (<b>C</b>,<b>F</b>)–spine scales; (<b>D</b>,<b>E</b>)–spine and plate scales. Abbreviations: ax–axopodia; b.p–basal plate; l.w–lateral wing; m.th–medial thickening; p–pockets; r.r–radial ribs; sh–shaft. Scale bars: (<b>A</b>)–2 μm; (<b>B</b>)–5 μm; (<b>C</b>–<b>F</b>)–1 μm.</p>
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<p>Spicule-bearing stage of <span class="html-italic">Triangulopteris lacunata</span> gen. et sp. nov., HF-25 strain from Kolyma Lowland. (<b>A</b>)—general view of the dried cell (SEM); (<b>B</b>)—general view of the dried cell (TEM); (<b>C</b>,<b>D</b>)—spicules on the peripheral part of cells (TEM). Abbreviations: sp—spicules. Scale bars: (<b>A</b>,<b>B</b>)—5 μm; (<b>C</b>,<b>D</b>)—1 μm.</p>
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<p>Phylogenetic tree generated from Bayesian analysis based on SSU rRNA gene sequences from 79 centrohelids. The sequence from this study is highlighted in red and marked with a star. Bayesian posterior probabilities (BPP) and Maximum Likelihood (ML, TN+F+R6 model) bootstrap values are indicated on branches (values &gt;0.5/&gt;30 are shown); filled circles indicate values of BPP = 1.00 and ML bootstrap = 100%; dt—different topology. Abbreviations: F—freshwater environment; M—marine environment; S—soil environment. Outgroup: <span class="html-italic">Clypifer cribrifer</span> MW700077; <span class="html-italic">Oxnerella micra</span> JQ245079; <span class="html-italic">Meringosphaera mediterranea</span> MZ240752; <span class="html-italic">Raphidiophrys drakena</span> KU178911; <span class="html-italic">R. heterophryoidea</span> KU178912; <span class="html-italic">Yogsothoth knorrus</span> MH445508; <span class="html-italic">Pinjata ruminata</span> MK641802; <span class="html-italic">Marophrys marina</span> AF534710; <span class="html-italic">M. marina</span> AY268041; <span class="html-italic">Raphidocystis contractilis</span> AB196984; <span class="html-italic">R. ambigua</span> AF534708; <span class="html-italic">Acanthocystis nichollsi</span> AY749632; <span class="html-italic">A. costata</span> KF990486; <span class="html-italic">A. amura</span> KX639994; <span class="html-italic">Choanocystis symna</span> KF990487; <span class="html-italic">Ch. curvata</span> AY749616; <span class="html-italic">Spiculophrys agregata</span> KU178913.</p>
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18 pages, 3077 KiB  
Article
The Influence of Relief on the Density of Light-Forest Trees within the Small-Dry-Valley Network of Uplands in the Forest-Steppe Zone of Eastern Europe
by Pavel Ukrainskiy, Edgar Terekhin, Artyom Gusarov, Eugenia Zelenskaya and Fedor Lisetskii
Geosciences 2020, 10(11), 420; https://doi.org/10.3390/geosciences10110420 - 24 Oct 2020
Cited by 5 | Viewed by 2265
Abstract
An active process of the invasion of woody vegetation, resulting in the formation of light forests, has been observed in predominantly herbaceous small dry valleys of the forest-steppe uplands of the East European Plain over the past two decades. This paper investigates the [...] Read more.
An active process of the invasion of woody vegetation, resulting in the formation of light forests, has been observed in predominantly herbaceous small dry valleys of the forest-steppe uplands of the East European Plain over the past two decades. This paper investigates the spatial features of the density of trees in such light forests and its relationship with relief parameters. The Belgorod Region, one of the administrative regions of European Russia, was chosen as a reference for the forest-steppe zone of the plain. The correlation between some relief characteristics (the height, slope, slope exposure cosine, topographic position index, morphometric protection index, terrain ruggedness index, and width and depth of small dry valleys) and the density of light-forest trees was estimated. The assessment was carried out at the local, subregional and regional levels of generalization. The relief influence on the density of trees in the small dry valley network is manifested both through the differentiation of moisture within the territory under study and the formation of various conditions for fixing tree seedlings in the soil. This influence on subregional and regional trends in the density is greater than on local trends. The results obtained are important for the management of herbaceous small-dry-valley ecosystems within the forest-steppe uplands in Eastern Europe. Full article
(This article belongs to the Section Biogeosciences)
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Graphical abstract

Graphical abstract
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<p>The territory under study (the Belgorod Region) with the location of registration plots.</p>
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<p>An example of light forests in satellite images and on the ground: (<b>A</b>) Small dry valley general view with a light forests. (<b>B</b>) Small dry valley fragment with a light forest. (<b>C</b>) Light forest’s ground photography (typical light forest near the village of L’vovka, east of the Belgorod Region).</p>
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<p>The density of trees in light forests at the local ((<b>A</b>), search radius 5 km), subregional ((<b>B</b>), search radius 15 km), and regional ((<b>C</b>), search radius 50 km) scale levels of generalization within the Belgorod Region, SW Russia.</p>
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<p>Changes in the correlation coefficient between the density of trees and the studied relief characteristics with an increase in the search radius with the smoothing interpolation (statistically significant (<span class="html-italic">p</span> &lt; 0.05) correlations are above and below the blue dotted line). MPI: morphometric protection index, TRI: terrain ruggedness index, and TPI: topographic position index.</p>
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<p>The relationship between the studied relief characteristics and the density of trees in light forests with a search radius of 25 km in a smoothing interpolation.</p>
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