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FIGURE 2 in A cascade of dams affects fish spatial distributions and functional groups of local assemblages in a subtropical river
FIGURE 2 | Ordination plots produced by the non-metric multidimensional scaling analysis (NMDS) using the Bray-Curtis index, considering the abundance (CPUEN) of movement/reproductive/size (A) and trophic categories (B), and biomass (CPUEB) of movement/ reproductive/size categories (C) and trophic categories (D), among survey locations. [DU (red triangle), DD (yellow triangle)] = Sampling points away from the dams; [R1 (red circle), R2 (green circle), R3 (orange circle)] = sampling points located in the reservoirs; [DR1 (green square), DR2 (orange square), DR3 (red square)] = locations downstream from dams. Labels are for Detritivores, Invertivores, Carnivores, Omnivores, and Piscivores; S/SM = sedentary/short migration, NPC = no parental care, PC = parental care, IF = internal fertilization, and LM = long migration; and Small, Medium, and Large body sizes.
FIGURE 1 in A cascade of dams affects fish spatial distributions and functional groups of local assemblages in a subtropical river
FIGURE 1 | Sampling locations in the Upper Uruguai River, Brazil. The symbols indicate survey sites: circles represent sites within the reservoirs (R1 = site 3 to 5; R2 = site 7 to 9; R3 = site 11 to 14); squares indicate sites located just below dams [Downstream R1 (DR1) = 6, Downstream R2 (DR2) = 10, Downstream R3 (DR3) = 15], and triangles indicate sites that are most distant from dams [Distant Upstream (DU) = 1 and 2; Distant Downstream (DD) = 16 and 17].
Spatial distribution of cattle, sheep and goat density, and grazed areas for the European Union and the United Kingdom
<p>To improve the sustainability of the European livestock sector we need improved knowledge on livestock density, and also on the grazing patterns. Here we provide spatially explicit data on the distribution of cattle, sheep and goats, developed by combining agricultural and veterinary statistics, in-situ data, expert surveys and machine learning. The data allow for the differentiation between livestock that are grazing on semi-natural areas and managed grasslands, versus those that do not graze and are kept indoors. </p> <p>This dataset covers all European Union Member States and the United Kingdom, and presents the spatial distribution of cattle, sheep and goat density for approximately the year 2020. Livestock density was allocated on the Corine Land Cover data, resulting in a data-set with a 100 m resolution (EPSG: 3035 - ETRS89-extended / LAEA Europe).</p> <p>Together with the livestock density maps, we also provide spatial data on the probability for grazing, and allocated grazed and non grazed areas.</p> <p><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <p> </p> <ul> <li><strong>clc_forage_mask.tif</strong> , forage areas mask for EU, based on selected Corine Land Cover classes (not including seminatural land cover areas such as natural grasslands...). This was developed by surveying grazing, grassland and livestock experts from all EU Member States and the United Kingdom. More info in the upcoming paper and in the linked paper below (Malek et al. 2024). Values are the same as in the Corine Land Cover data.</li> <li><strong>grazing_probability.tif</strong> , grazing probability map, indicating how likely each location in the EU+UK is grazed</li> <li><strong>allocated_grazing.tif</strong> , allocated grazing map, indicating which areas are grazed and which are not</li> </ul> <p> </p> <ul> <li>cattle density maps: <ul> <li><strong>cattle_grazing.tif</strong> , cattle grazing on managed forage areas</li> <li><strong>cattle_other.tif</strong> , cattle kept indoors, receiving feed from managed forage areas</li> <li><strong>cattle_seminatural.tif</strong> , cattle grazing in semi-natural areas</li> <li><strong>cattle_mosaic_categorical.tif</strong> (with a legend file) , combined categorical map for all cattle types.</li> </ul> </li> </ul> <p> </p> <ul> <li>sheep and goat density maps: <ul> <li><strong>sheep_goat_other.tif</strong> , sheep and goat density</li> <li><strong>sheep_goat_seminatural.tif </strong>, sheep and goat grazing in seminatural areas</li> </ul> </li> </ul>
Fig. 2 in Spatial distribution of gastropods (Mollusca: Gastropoda) from the Kaliningrad Region (Russia) water bodies
Fig. 2. Freshwater habitats types of Gastropoda species of Kaliningrad Region classified by main ecological factors. Stagnant permanent freshwater bodies (sites of rich thanatocoenosis with rare mollusk's species): A – Melnichny Pond (Kaliningrad); Б – Polessky Canal (Krasnoe settlement); В – Western Canal (Zalivnoye settlement, Curonian Lagoon); Г – the mouth of the Guryevka River (Zalivnoye settlement, Curonian Lagoon); temporary water bodies and bogs (sites of rare Gastropoda species): Д – bogged polder near the shoreline of the Curonian Lagoon, Zalivnoye settlement (mouth of the Western Canal) where Bathyomphalus contortus (L., 1758) lives; E – ditch on the pasture of the Zalivnoye settlement populated by Omphiscola glabra (Müller, 1774) (in puddles); Ж – wet meadow swamp in the Polessk Town (near Slepenkov Str., «Veterinary Clinic», biotope was destroyed in 2014); З – a large bomb crater on the pasture of the Rybachiy Village (Curonian Spit) inhabited by Anisus sp.; watercourses with intensive currents are the main habitats of Ancylus fluviatilis Müller, 1774: И –«Ledyanaya» River above the Devil Bridge («Chortov most», «Berlinka», pre-war highway); К – Kornevka River above Vysokoje Village (mouth of a secondary stream); Л – Krasnaja River (Rominta, Tokarevka settlement, depth about 0.2–0.3 m, boulders river ground); М – Angerapp River (Ozersk Town, near stadium); intertidal zone (Curonian Lagoon): H, O – Zalivnoye settlement on the southern coastline of the Curonian Lagoon near the mouth of Western Canal; П – mouth of the Deima River (Zalivino settlement), eroded floodplain deposits of peat.
Linked collectors and determiners for: First records of Leucania rawlinsi Adams and L. senescens M ̂ schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution.
Natural history specimen data linked to collectors and determiners held within, "First records of Leucania rawlinsi Adams and L. senescens M ̂ schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4c9c5163-7fce-43f3-be05-c64780788e96">https://bionomia.net/dataset/4c9c5163-7fce-43f3-be05-c64780788e96</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4c9c5163-7fce-43f3-be05-c64780788e96">https://gbif.org/dataset/4c9c5163-7fce-43f3-be05-c64780788e96</a>. Formatted as a Frictionless Data package.
Figure 2 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches
Figure 2. Mean slope (°, ¡SE) of the five studied beaches calculated using three replicate random samples in each site. Identical letters indicate non-significant differences in the post hoc Scheffé's test for multiple pair-wise comparisons.
Figure 5 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches
Figure 5. Comparison of the mean number of burrows per m2 (¡SE) of Ocypode quadrata among beaches and zones. Numbers in brackets represent the numbers of squares sampled. Numbers at the left side of the bars indicate the results of Scheffé's test for multiple comparisons of zones among beaches. Letters at the right side of the bars indicate the results of Scheffé's test for multiple comparisons of zones within beaches. Identical labels (numbers or letters) indicate non-significant differences in the post hoc Scheffé's test. See Table III for the results of the ANOVA.
Figure 4 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches
Figure 4. Schemes of zonation of Ocypode quadrata in the study areas. Horizontal dotted lines indicate heights of mean number of individuals per m2 for each 1 m interval estimated using five randomized replicated samples. This the same site were equivalent in length (x-axis): Segredo, 21 m; Cabelo Gordo, 21 m; Pitangueiras, 24 m; Zimbro
Figure 1 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches
Figure 1. Map of the São Sebastião Channel, south-eastern Brazil, illustrating the five study beaches.
Figure 6 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches
Figure 6. Comparison of the mean burrow diameter (¡SE) of Ocypode quadrata among beaches and zones. Numbers in brackets represent the numbers of burrows sampled. Numbers at the left side of the bars indicate the results of the non-parametric Tukey-type test for multiple comparisons of zones among beaches. Letters at the right side of the bars indicate the results of the non-parametric Tukey-type test for multiple comparisons of zones within beaches. Identical labels (numbers or letters) indicate non-significant differences in the post hoc Scheffé's test. See Table IV for the results of the non-parametric Kruskal–Wallis tests.
Figure 3 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches
Figure 3. Mean sand grain size (phi, ¡SE) and mean sorting coefficient (phi, ¡SE) for each zone and beach calculated using five replicate measures in each zone. Mi, medium intertidal; Ui, upper intertidal; Sf, subterrestrial fringe.
Figure 6 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 6. Average profile of the southern and northern areas in Caraguatatuba Bay. The distance of 0 m (x-axis) corresponds to the beginning of the vegetation in the dry sand zone (southern) and at the berm (northern).
Figure 1 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 1. Location of Caraguatatuba Bay in the state of São Paulo, Brazil, and of the study areas (southern and northern). The numbers (1–14) indicate the stations sampled only in the along-shore study.
Figure 4 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 4. Mean number of individuals per quadrat (¡SE; n56) and mean shell length (mm; ¡SE; n is variable) in the two areas (southern and northern) and in the different tidal levels (in relation to MLW) in Caraguatatuba Bay. The comparisons (one-way ANOVA) among tidal levels within each area are presented in Table I. The letters (southern) and the numbers (northern) represent the results of the a posteriori SNK test. The horizontal bar indicates non-significant differences in the SNK test.
Figure 5 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 5. Mean number of individuals (fourth-root transformed; ¡SE; n55) and mean shell length (mm; ¡SE; n is variable) in the two areas (southern and northern) and at different subtidal depths in Caraguatatuba Bay. The comparisons (two-way ANOVA) between areas and among depths are presented in Table II. The letters (southern) and the numbers (northern) represent the results of the a posteriori SNK test, and the horizontal bar indicates non-significant differences in the SNK test.
Figure 3 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 3. Zonation (mean number of individuals per quadrat ¡SE; n56) of Tivela mactroides in the intertidal region of the southern and northern areas of Caraguatatuba Bay in relation to MLW. Tidal levels correspond to different distances from 20.2 m in the two areas.
Figure 9 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 9. Mean number of individuals (fourth-root transformed;+SE; n54) and mean shell length (mm;+SE; n5variable) in the four sub-areas in the southern and northern areas in Caraguatatuba Bay and in 0.0 and 0.4 m (in relation to MLW). The comparisons (two-way ANOVA) between areas and between tidal levels are presented in Table IV. The horizontal bars indicate non-significant differences in the SNK test.
Figure 8 in Along- and across-shore components of the spatial distribution of the clam Tivela mactroides (Born, 1778) (Bivalvia, Veneridae)
Figure 8. Mean grain size (phi;+SE; n54) and sorting coefficient (phi;+SE; n54) in the four sub-areas in the southern and northern areas in Caraguatatuba Bay and in the 0.0 and 0.4 m tidal levels (in relation to MLW). Data are presented for each area and sub-area. The results of the nested three-way ANOVA are presented in Table III. The horizontal bars indicate non-significant differences in the SNK test.
Figure 1 in Spatial and temporal distribution of breeding anurans in streams in southeastern Brazil
Figure 1. Location of (A) the RPPN Santuário do Caraça, Minas Gerais state, southeastern Brazil, and (B) the eight streams sampled within the reserve.
Figure 3 in Spatial and temporal distribution of breeding anurans in streams in southeastern Brazil
Figure 3. Relationship between estimated volume of stream sampled sections (m3) and (A) anuran species richness and (B) number of anuran species with calling males at the RPPN Santuário do Caraça, southeastern Brazil. Numbers in the graphs correspond to streams 1–8.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.