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274 results for “baltic sea”
Figure 1 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 1: Map of sampling sites in Northern Germany. Insets (a–c) provide higher resolution. Sampling areas: 1 – Glücksburg; 2 – Glücksburg Estuary; 3 – Bockholmwik; 4 – Neukirchen; 5 – Norgaardholz; 6 – Falshoft; 7 – Maasholm, Schlei; 8 – SchÖnhagen; 9 – Fischleger; 10 – Karlsminde; 11 – EckernfÖrde, port; 12 – EckernfÖrde, Kiekut; 13 – Aschau, sea; 14 – Aschau, lagoon; 15 – Kiel-Bülk; 16 – Kiel-Schilksee, marina; 17 – Kiel-Friedrichsort; 18 – Kiel-Holtenau, Tonnenhof; 19 – Kiel-Düsternbrook; 20 – Kiel-MÖnkeberg; 21 – Kiel-Heikendorf, Hafen; 22 – Kiel-Laboe; 23 – Kiel-Marina Wendtorf; 24 – Kiel-Brasilien; 25 – Hohwacht; 26 – Weissenhäuser Strand; 27 – Heiligenhafen, sea; 28 – Heiligenhafen, Binnensee; 29 – Heiligenhafen, marina; 30 – Grossenbroderfahre; 31 – Strukkamphuk, Fehmarn; 32 – Westerberg, Fehmarn; 33 – Flügge, Orther Bucht, Fehmarn; 34 – Gruner Brink, Fehmarn; 35 – Burgtiefe, Fehmarn; 36 – Burger Binnensee, Fehmarn; 37 – Wulfen, Fehmarn; 38 – Marina Grossenbrode; 39 – Süssau; 40 – Kellenhusen; 41 – Neustadt, Binnenwasser; 42 – Brodtener Ufer; 43 – Rosenhagen; 44 – Steinbeck; 45 – Boltenhagen; 46 – Wohlenberg; 47 – Hohen-Wieschendorf; 48 – Zierow; 49 – Redentin; 50 – Bridge to Poel, S side; 51 – Bridge to Poel, N side; 52 – Kirchdorf; 53 – Timmendorf, Poel; 54 – Gollwitz, Poel.
Figure 4 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 4: Number of species from different phytogeographical elements in each macrophyte community of the SW Baltic Sea. Phytogeographical elements are indicated in accordance with Cormaci et al. (1982), supplemented by data from Zinova (1962) and Kalugina-Gutnik (1975): C – Cosmopolitan, SC – Sub-cosmopolitan, AP – Atlanto-Pacific, IP – Indo-Pacific, CB – Circumboreal, IA – Indo-Atlantic, Abt – Boreo-tropical Atlantic, CT – Circumtropical, Ab – Boreo-Atlantic, Aba – Boreo-Arctic Atlantic, Pb – Boreo-Pacific.
Figure 3 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 3: Distribution of the macrophyte communities of the SW Baltic Sea in habitats with different exposure. The y-axis shows the proportion of habitats with different exposure grades in which the different communities were found.
Figure 2 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 2: Maximum likelihood phylogram based on tufA sequence data, showing the phylogenetic relationships of 12 Ulvales samples from the Baltic Sea (bold) identified in this study. Numbers after species names indicate collection sites (see Figure 1). Numbers below branches are bootstrap values; poorly supported nodes (>0.70) are not labelled. Branch lengths are proportional to sequence divergence.
Figure 5 in Seaweed resources of the Baltic Sea, Kattegat and German and Danish North Sea coasts
Figure 5: Different views of macroalgal blooms on German Baltic Sea coasts. (A) Beach wrack dominated by Ceramium virgatum, Hohwacht, 16.8.2012 (Photo © F. Weinberger). (B) Mat of Pylaiella littoralis covering a meadow of eelgrass, Mönckeberg, 15.5.2013 (Photo © C. Lieberum). (C) Beach wrack dominated by Cladophora sp., Stein, 12.4.2014 (Photo © M. Hammann). (D) Beach wrack composed of various red algae and eelgrass, Neukirchen, 30.4.2012 (Photo © F. Weinberger).
Figure 3 in Fucus vesiculosus adapted to a life in the Baltic Sea: impacts on recruitment, growth, re-establishment and restoration
Figure 3: Boxplot showing number of Fucus vesiculosus juveniles per dm2 surviving from mid-July to early November, 1994 in Askö. Treatments in (A) "Manipulated Fucus" without understorey (diagonal stripes) and "Natural Fucus (control)" with understorey (white) and (B) "Cladophora-covered substratum" (chequered) and "Cleaned substratum" cleared from both Fucus and understorey vegetation (vertical stripes). Note major difference in scales for y-axes in each panel. n = 6 for all treatments. *indicates significant difference in density of juveniles between two treatments at one date (p <0.05). One-way ANOVA of treatments at each date showed a higher number of juveniles in treatments containing Fucus (i.e. Figure 3A) on all dates compared to treatments without Fucus (i.e. Figure 3B; p <0.001).
Figure 1 in Fucus vesiculosus adapted to a life in the Baltic Sea: impacts on recruitment, growth, re-establishment and restoration
Figure 1: Maps showing areas and sites for field experiments presented in this paper. (A) Map of the Baltic Sea showing areas 1–5. (B) Area 1 with Sites A–G outside Trosa town (black) and Area 2 with control Site H near Askö laboratory on Askö island. (C) Area 5 with Sites I–J in Gdansk Bay.
Figure 2 in Fucus vesiculosus adapted to a life in the Baltic Sea: impacts on recruitment, growth, re-establishment and restoration
Figure 2: Fucus vesiculosus increase in size over time during 4 years. Fucus vesiculosus: volume (calculated as a cone from thallus height and circumference) plotted against biomass (g dry weight) with linear regression for (A) 1-year-old, (B) 2-year-old, (C) 3-year-old and (D) 4-year-old thalli grown in the field at Askö during 1991–1994.
Figure 4 in Seaweed resources of the Baltic Sea, Kattegat and German and Danish North Sea coasts
Figure 4: Interannual variation (2006–2017) of the share of Furcellaria lumbricalis and Coccotylus truncatus in the loose-lying red algal community biomass (BM) in the Kassari Bay, West Estonian Archipelago Sea. Compiled results of annual monitorings 2006–2017; database of the Estonian Marine Institute.
Figure 1 in Seaweed resources of the Baltic Sea, Kattegat and German and Danish North Sea coasts
Figure 1: Types of coastlines, annual average sea surface salinities, and species numbers of algal macrophytes that have been recorded in different sea areas of the Baltic Sea and the German and Danish North Sea. Modified from Rönnbäck et al. (2007); species numbers are from HELCOM (2012) for the Baltic Sea and from Schories et al. (2009a,b) for the North Sea.
Figure 5 in Fucus vesiculosus adapted to a life in the Baltic Sea: impacts on recruitment, growth, re-establishment and restoration
Figure 5: Boxplot showing germination (%) of Fucus vesiculosus zygotes after 7 days of exposure under laboratory conditions to exudates from Cladophora glomerata, Ulva intestinalis, Pylaiella littoralis, Ceramium tenuicorne and Hildenbrandia rubra. All treatments differed (p <0.001) from the control (one way ANOVA). n = 6 for all treatments.
Figure 4 in Fucus vesiculosus adapted to a life in the Baltic Sea: impacts on recruitment, growth, re-establishment and restoration
Figure 4: Boxplot showing number of Fucus vesiculosus juveniles per dm2 on five different types of substratum. (A) Askö, after 5 months, n = 10 except ceramic tile (n = 7) and Hildenbrandia treatment (n = 9). (B) Räfsnäs after 4 months, sample size n = 6. Note major difference in scales for y-axes in each panel. Letters (A–E) above bars show groups that differ significantly (at p = 0.05) within each site according to Tukey HSD post hoc test.
Figure 2 in Seaweed resources of the Baltic Sea, Kattegat and German and Danish North Sea coasts
Figure 2: Loose-lying Furcellaria lumbricalis-Coccotylus truncatus community in the Kassari Bay, West Estonian Archipelago Sea (Photo: K. Kaljurand).
Figure 3 in Seaweed resources of the Baltic Sea, Kattegat and German and Danish North Sea coasts
Figure 3: Interannual variation (1980–2017) of the total community biomass (BM), the total Furcellaria lumbricalis biomass and the area of the loose-lying red algal community in the Kassari Bay, West Estonian Archipelago Sea. Data after Martin et al. (2006a), updated with data of the Estonian Marine Institute on annual monitorings 2003–2017.
Chesapeake Bay and Baltic Sea phytoplankton sample metadata
<p>We analyze the relationship between species richness, salinity and resource use efficiency from 10712 summer (June to September) surface phytoplankton samples from the Chesapeake Bay (n=3967) and the Baltic Sea (n=6745). As sample species richness (alpha diversity) has a U-shape distribution along an estuarine salinity gradient and species richness is known to scale with resource use efficiency – an important ecosystem function, we hypothesized that the ecosystem function can be predicted from salinity.</p>
Thetis Baltic Sea simulation: model and observation data sets
<p>Model and observation data sets used in article "Adjoint-based optimization of a regional water elevation model".</p>
Niche partitioning between planktivorous fish in the pelagic Baltic Sea assessed by DNA metabarcoding, qPCR and microscopy: Data and Analyses
<p class="MsoNormal"><span>Marine communities undergo rapid changes because of human-induced ecosystem pressures. The Baltic Sea pelagic food web has experienced several regime shifts during the past century, resulting in a system where competition between planktivorous mesopredators is assumed to be high. While the two clupeids sprat and herring reveal signs of competition, the stickleback population has increased drastically during the past decades. Here, we investigate diet overlap between the three dominating planktivorous fish in the Baltic Sea, utilizing DNA metabarcoding on the <em>18S rRNA</em> gene and the <em>COI </em>gene, targeted qPCR, and microscopy. Our results show niche differentiation between clupeids and stickleback and that rotifers play an important function in niche partitioning of stickleback, as a resource that is not being used, neither by the clupeids nor by other zooplankton. <span>We further show that all the diet assessment methods used in this study are consistent but DNA metabarcoding describes the plankton-fish link at the highest taxonomic resolution. </span>This study suggests that rotifers and other understudied soft-bodied prey may have an important function in the pelagic food web and that the growing population of pelagic stickleback is supported by the unutilized feeding niche offered by the rotifers.</span></p>
Fig. 4 in A new species of the genus Cottus (Scorpaeniformes, Cottidae) from the Baltic Sea Basin and its phylogenetic placement
Fig. 4. The result of statistical analysis of morphometric characters of type and non-type specimens of Cottus cyclophthalmus sp. nov. from rivers Krasnaya, Neris, Šerkšnė, Siesartis, and Žeimena (method of principal components was used). The numbers correspond to the places where the sculpins were caught, as indicated on the map (Fig. 1).
Fig. 2 in A new species of the genus Cottus (Scorpaeniformes, Cottidae) from the Baltic Sea Basin and its phylogenetic placement
Fig. 2. Cottus cyclophthalmus sp. nov., holotype, ♂ (ZIN 56687), SL 83.3 mm, TL 99.0 mm, Krasnaya River, near Tokarevka village, 54º24'59.4" N 22º23'50.4" E. 3D scan images. a. Lateral view. b. Dorsal view. c. Ventral view.
Fig. 1 in A new species of the genus Cottus (Scorpaeniformes, Cottidae) from the Baltic Sea Basin and its phylogenetic placement
Fig. 1. The map of sampling sites showing the distribution of Cottus cyclophtalmus sp. nov. The numbers indicate sampling sites in various rivers: 1. Krasnaya River. 2. Neris River. 3. Žeimena River. 4. Siesartis River. 5. Šerkšnė River. The star marks the type locality of the new species; the circles mark sampling sites of non-type specimens; the triangle marks the locality where specimen of Cottus microstomus sp. nov. was caught.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.