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Figure 8 in Morphology and composition of the internal axis in two morphologically contrasting deep-water sea pens (Cnidaria: Octocorallia)
Figure 8. Axis area and relative weight in relation to distance from base in five colonies of Umbellula encrinus (a–b), and in eight colonies of Anthoptilum grandiflorum (c–d). The vertical arrows indicate maximum peduncle length for the studied colonies.
Figure 4 in Morphology and composition of the internal axis in two morphologically contrasting deep-water sea pens (Cnidaria: Octocorallia)
Figure 4. Umbellula encrinus. (a) colony in situ standing in a ~90° position (Qikiqtarjuaq, Baffin Bay, at 680 m, estimated to be ~2 m tall); (b) twisting of axis; (c) colony in situ (Scott Inlet, Baffin Bay, at 520 m) with inset showing twisting of stalk. Photo credits: ArcticNet-CSSF-DFO (a, c).
Figure 2 in Urbanization impacts on the trophic guild composition of bird communities
Figure 2. Exploratory boxplot showing the richness of trophic guilds (number of trophic guilds per transect) at each urban stratum of the urban gradient. References: urban (CENTER), suburban (SUB), periurban (PERI), forested (PARK) and natural (NAT) sectors.
Figure 5 in Urbanization impacts on the trophic guild composition of bird communities
Figure 5. Biplot of redundancy analysis (RDA) showing the association between trophic guild abundance and significant habitat structure variables along the urban gradient. Arrow length indicates the importance of the variable in the model and arrow orientation, the direction of the variable increase. References: percentage of area covered by herbaceous vegetation (GRASS); pavement (PAVEM); high buildings (BHIGH) and water bodies (WATERB). Guilds: aquatic diving carnivore (AquaDivCar); aquatic diving omnivore (AquaDivOmn); aquatic filter (AquaFil); aquatic striding carnivore (AquaStrCar); aquatic striding omnivore (AquaStrOmn); bark insectivore (BarkIns); coursing aerial insectivore (CourAerIns); foliage granivore (FoliGra); foliage insectivore (FoliIns); foliage omnivore (FoliOmn); generalized granivore (GeneGra); generalized insectivore (GeneIns); generalized omnivore (GeneOmn); hawking aerial carnivore (HawkAerCar); hawking aerial insectivore (HawkAerIns); nectarivore (Nect); terrestrial granivore (TerrGra); terrestrial insectivore (TerrIns); and terrestrial omnivore (TerrOmn).
Figure 1 in Urbanization impacts on the trophic guild composition of bird communities
Figure 1. Santa Fe city and strata of the urban gradient indicated by circles. The urban matrix is represented by white and water bodies and forested areas with shades of grey. References: urban (CEN), suburban (SUB), periurban (PERI), forested (PARK) and natural (NAT) sectors.
Figure 3 in Urbanization impacts on the trophic guild composition of bird communities
Figure 3. Biplot of redundancy analysis (RDA) showing the association between the richness of each trophic guild and significant habitat structure and human disturbance variables along the urban gradient. Arrow length indicates the importance of the variable in the model and arrow orientation, the direction of the variable increase. References: percentage of area covered by trees/shrubs (TREESHRUB); herbaceous vegetation (GRASS); water bodies (WATERB); pavement (PAVEM); pedestrian rate (PEDESR); and vehicle rate (VEHICLR). Guilds: aquatic diving carnivore (AquaDivCar); aquatic diving omnivore (AquaDivOmn); aquatic filter (AquaFil); aquatic striding carnivore (AquaStrCar); aquatic striding omnivore (AquaStrOmn); bark insectivore (BarkIns); coursing aerial insectivore (CourAerIns); foliage granivore (FoliGra); foliage insectivore (FoliIns); foliage omnivore (FoliOmn); generalized granivore (GeneGra); generalized insectivore (GeneIns); generalized omnivore (GeneOmn); hawking aerial carnivore (HawkAerCar); hawking aerial insectivore (HawkAerIns); nectarivore (Nect); terrestrial granivore (TerrGra); terrestrial insectivore (TerrIns); and terrestrial omnivore (TerrOmn).
Figure 4 in Urbanization impacts on the trophic guild composition of bird communities
Figure 4. Proportion of variance of the purely environmental (Env), spatially structured environmental (EnvSpa) and purely spatial components (Spa) which explain the richness (left) and abundance (right) of trophic guilds across the Santa Fe city.
Figure 1 in Composition and habitat use of small mammals in old-growth mountain forests
Figure 1. Location of the study area in the Aigüestortes i Estany de Sant Maurici National Park (in dark grey) and its peripheral area (in light grey), indicating the UTM (Universal Transverse Mercator) coordinates. White squares, Abies alba plots; black squares, Pinus uncinata plots. Contour lines represent different altitude levels. Darker areas within the National Park boundaries correspond to lakes.
Figure 2 in Spatial-temporal composition of Chrysomelidae (Insecta: Coleoptera) communities in southern Brazil
Figure 2. Species accumulation curves of Chrysomelidae at different sites in Paraná State, Brazil. ANT, Antonina; SJ, São José dos Pinhais; CO, Colombo; PG, Ponta Grossa; TB, Telêmaco Borba; GUA, Guarapuava; JU, Jundiaí do Sul; FE, Fênix.
Figure 3 in Spatial-temporal composition of Chrysomelidae (Insecta: Coleoptera) communities in southern Brazil
Figure 3. Cluster analyses of Chrysomelidae at different sites in Paraná State, Brazil (Jaccard coefficient; cc = 0.71).
Figure 1 in Spatial-temporal composition of Chrysomelidae (Insecta: Coleoptera) communities in southern Brazil
Figure 1. Paraná State, Brazil (modified from Riedel et al. 2008) showing the sampling sites in different geomorphological regions. ANT, Antonina; SJ, São José dos Pinhais; CO, Colombo; PG, Ponta Grossa; TB, Telêmaco Borba; GUA, Guarapuava; JU, Jundiaí do Sul; FE, Fênix; C, Costal region; P1, P2 and P3 show the First, Second and Third Plateaus respectively.
Data from: BIOVERA-Tree: tree diversity, community composition, forest structure and functional traits along gradients of forest-use intensity and elevation in Veracruz, Mexico
<p>Here, we describe BIOVERA-Tree, a database on tree diversity, community composition, forest structure, and functional traits collected in 120 forest plots distributed along an extensive elevational gradient in Veracruz State, Mexico. BIOVERA-Tree includes information on forest structure from three levels of forest-use intensity, namely old-growth, degraded, and secondary forest, replicated across eight elevations from sea-level to near the tree line at 3500 m and on size and location of 4549 tree individuals with a diameter at breast height ≥ 5 cm belonging to 216 species, 154 genera, and 80 families. We also report measurements of eight functional traits, namely wood density for 143 species, maximum height for 216 species and leaf traits including: specific leaf area, lamina density, leaf thickness, chlorophyll content, and leaf area for 148 species and leaf dry matter content for 145 species.</p>
Figure 2 in Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix
Figure 2. Non-metric multidimensional scaling (NMDS) ordination in two dimensions of the oribatid mite community inhabiting 16 plots in savanna and 38 plots in forest fragments. Ordination was based on abundance data.
Figure 1 in Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix
Figure 1. Map showing the Alter do Chão region, Pará, Brazil. White represents savanna vegetation; dark grey represents forest fragments and the surrounding continuous forest; light grey in the left side of the figure represents the Tapajós River. Triangles represent the 16 plots in forest fragments; black squares represent 38 plots in savanna (modified from Vasconcelos and Vilhena 2006).
Figure 3 in Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix
Figure 3. Whittaker plot of oribatid mites collected in (A) forest fragments and (B) savanna vegetation, near the village of Alter do Chão, in the Brazilian state of Pará, Brazil. Species are organized in order of decreasing relative abundance.
Figure 2 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 2. Non-metric multi-dimensional scaling (MDS) ordination showing hemipteran composition for all sampling periods with selected plant species superimposed.
Figure 5 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 5. Annual cyclic pattern of the proportion of the effectively specialized fauna (squares) and singleton species (circles) for the total number of hemipteran species from each sampling period.
Figure 3 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 3. Mean number of individuals (from SIMPER analysis) of dominant hemipteran species, during each sampling period, for most plant species.
Figure 1 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 1. Interactions between plant species sampled and sampling period for (A) abundance (number of individuals) per plant and (B) species richness per plant (standard error bars are shown).
Figure 6 in Multi-scale patterns in the host specificity of plant-dwelling arthropods: the influence of host plant and temporal variation on species richness and assemblage composition of true bugs (Hemiptera)
Figure 6. Relationship between the effectively specialized fauna (squares) and singleton species (circles) for the number of hemipteran species from each sampling period and for the entire collection. An exponential decay equation is fitted for effectively specialized fauna, y = 2.973∗ exp (−0.00575∗ x) + (−1.478), R2 = 0.7598, and for singleton species, y = 22.53∗exp (−0.08466∗x) + 0.2614, R2 = 0.9873.
ScienceDex guides
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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.