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428 results for “zooplankton”
Figure 6 in Does the location of coastal brackish waters determine diversity and abundance of zooplankton assemblages?
Figure 6. Seasonal (monthly) changes in the total zooplankton biomass (mean, minimum, and maximum values) (mg. dm–3) in the Vistula Lagoon and Lake Łebsko in 2010–2011.
Figure 5 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 5. Station-wise variation in the 0–500 m column integrated mesozooplankton abundance/density and biomass in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 6 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 6. Depth-wise variation in the number of zooplankton groups at each station in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 1 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 1. Map of the sampling site in the Bay of Bengal. Stations CB1 to CB5 are located along the central (88°E) and WB1 to WB4 along the western margin of the bay.
Figure 13 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 13. Multivariate cluster analysis of the data of all 129 copepod species combined from all the stations and depths in the central and western bay using the 30% cut-off level of Bray–Curtis similarity. Cluster/Group I are assemblages mostly from the mixed layer (M) and thermocline (T) from central and western transects. Group II comprises assemblages found between the thermocline and 500 m and Group III includes only a few species found exclusively from 200–300 m depth at stations CB3–CB5.
Figure 4 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 4. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the western Bay of Bengal during spring intermonsoon. ng: negligible biovolume; NO DATA is where the net failed to open/close. *At WB3, medusae (100 mL 100 m–3) and at WB4 salps (200 mL 100 m–3) were observed at the surface during the day.
Figure 9 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 9. Vertical distribution of abundance (log number 100 m–3) of the major copepod species in the central Bay of Bengal during spring intermonsoon.
Figure 3 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 3. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the central Bay of Bengal during spring intermonsoon. ng: Negligible biovolume; NO DATA is where the net failed to open/close. *Swarms of medusae were observed at CB3 (their biovolume 90 mL 100 m–3) and CB4 (200 mL 100 m–3) at the surface at night.
Figure 8 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 8. Vertical distribution of the various types (orders) of copepods in the central (a) and western (b) Bay of Bengal during the spring intermonsoon. The percentages at every depth are averages from 5 stations in the central and 4 stations in the western bay. Data are unavailable at 300–500 m in the central bay due to negligible abundance.
Figure 12 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 12. Variation in multivariate dispersion (MVDISP) indices between different depth strata (9 stations data combined) and between the central and western transects in the Bay of Bengal.
Figure 11 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 11. Variation in Shannon diversity (H'), species richness (d), and evenness (J') of copepods in different depth strata in the upper 500 m of the central (a) and western (b) Bay of Bengal.
Figure 1 in Assessment of the zooplankton community structure of the coastal Uzungöl Lagoon (Kızılırmak Delta, Turkey) based on community indices and physicochemical parameters
Figure 1. Geographical location of study area, coordinates of sampling points. Station 1: 41°32'33.85"N - 36°04'56.80"E; Station 2: 41°33'36.66"N - 36°05'22.24"E; Station 3: 41°34'11.10"N - 36°05'40.67"E; Station 4: 41°34'44.82"N - 36°06'0.14"E; Station 5: 41°35'7.57"N - 36° 06'20.14"E.
Figure 6 in Assessment of the zooplankton community structure of the coastal Uzungöl Lagoon (Kızılırmak Delta, Turkey) based on community indices and physicochemical parameters
Figure 6. Zooplankton community indices (Shannon Diversity, Pielou evenness and Species richness) during the study period.
Figure 3 in Assessment of the zooplankton community structure of the coastal Uzungöl Lagoon (Kızılırmak Delta, Turkey) based on community indices and physicochemical parameters
Figure 3. Seasonal density (ind. m -3) changes of nauplii larvae and copepodit individuals in Uzungöl Lagoon.
Fig. 3 in Zooplankton associated with phytotelms and treefrogs in a neotropical forest
Fig. 3. Non-metric multidimensional scaling (NMDS) ordination of the zooplankton species showing differences in composition between frogs' skin (grey circles) and bromeliad phytotelms (black circles). Dashed lines indicate the range of each community dispersion and solid lines indicate the distance of each sample from centroid.
Fig. 2 in Zooplankton associated with phytotelms and treefrogs in a neotropical forest
Fig. 2. Rarefaction curve considering zooplanktonic species frequency from both bromeliad tanks and frogs' skin in a Semideciduous Stationary Forest remnant, Pernambuco, Brazil.
Fig. 1 in Zooplankton associated with phytotelms and treefrogs in a neotropical forest
Fig. 1. Location of the conservation unit in the municipality of São LourenÇo da Mata, eastern region of Pernambuco, Brazil. In green, the forest was a sample of the study.
Fig. 4 in Quantifying zooplankton species: use of richness estimators
Fig. 4. Species accumulation curves, uniques and duplicates for the BA1 station of Furnas reservoir, state of Minas Gerais, Brazil, from March 2011 to February 2012.
Fig. 6 in Quantifying zooplankton species: use of richness estimators
Fig. 6. Species accumulation curves, uniques and duplicates for the BA3 station of Furnas reservoir, state of Minas Gerais from March 2011 to February 2012.
Fig 1 in Quantifying zooplankton species: use of richness estimators
Fig 1. Sampling stations in the Hydroelectric Power Plant of Furnas reservoir, state of Minas Gerais, Brazil (A, Barranco Alto region; B, junction of rivers Verde and Sapucai - VSJ).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.