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40 results for “depth gradient”
Data from: Plasticity contributes to a fine-scale depth gradient in sticklebacks’ visual system
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Maximum soil depth measurements from the Terrestrial Gradient plots at the Coweeta Hydrologic Lab in Macon County, NC.
Maximum soil depths were assessed as part of the soil chemistry studies at the gradient plots at the Coweeta Hydrologic Laboratory in Macon County, North Carolina. There were 5 sites representing an elevational gradient and different forest types. A soil probe was used to estimate the maximum soil depth of each of the 80 x 80 -m plots at each site.
Data from: Phenotypic, ecological and genomic variation in common bully (Gobiomorphus cotidianus) populations along depth gradients in New Zealand's Southern Great Lakes
<p>Depth gradients in lakes are often key drivers of population divergence and speciation in fishes. New Zealand has many deep lakes but no known profundal specialist fishes or cases of intralacustrine speciation. We sampled a native benthic fish, the common bully, from 5–90 m depth in four South Island lakes, to test for morphological, ecological, or genetic differentiation associated with depth. Deeper fish consistently had narrower bodies, while other morphological traits showed variable relationships with depth. Carbon and nitrogen stable isotope values of fish increased with depth, largely tracking isotopic trends with depth of benthic invertebrate prey. Genotyping-by-sequencing showed some genome-wide differentiation between two of the lakes, but no evidence for within-lake genetic structuring along depth gradients. These results indicate that individual bullies associate with shallower or deeper habitats within their lifetimes, but we found no evidence of progress toward genetic divergence within lakes. The apparent lack of intralacustrine genetic divergence in New Zealand's fishes may be explained by a combination of environmental factors and constraints intrinsic to its marine-derived freshwater fish fauna.</p>
Depth and latitudinal diversity gradients in seamount benthic communities of the South Atlantic
Aim <p>Although latitudinal and bathymetric species diversity gradients in the deep sea have been identified and investigated, studies have rarely considered these gradients across seamount and oceanic island ecosystems. This study aimed to identify whether the current understanding of latitudinal and bathymetric gradients in α-diversity (species richness) apply to South Atlantic seamount ecosystems, as well as ascertaining whether identifiable trends were present in seamount β-diversity along a bathymetric gradient.</p> Location <p>The South Atlantic Ocean.</p> Time period <p>2013-2019</p> Major taxa studied <p>Benthic communities.</p> Methods <p>Drop camera images from 39 transects, collected between 250 m and 950 m, were used to characterise species richness from within the Exclusive Economic Zones of Ascension Island, Saint Helena and Tristan da Cunha, spanning 8°S to 40°S. We subsequently applied linear modelling to test possible environmental drivers across latitudinal and bathymetric ranges (see transect metadata for variables). An Analysis of Similarity was employed to investigate the beta-diversity gradient, and the level of species turnover with depth.</p> Results <p>Surface primary productivity and substrate hardness both had significant positive effects on species richness, and there was significantly higher species richness at temperate latitudes. No significant relationship between species richness and depth was detected, but there was a significant species turnover with depth.</p> Main conclusions <p>Seamounts and oceanic islands do not conform to established depth-diversity relationships within the depth range studied. However, despite their isolation and small sizes, seamounts and oceanic islands in the South Atlantic appear to follow latitudinal patterns of deep-sea species richness established for specific taxonomic groups in different ecosystems.</p>
Data from: Phenotypic, ecological and genomic variation in common bully (Gobiomorphus cotidianus) populations along depth gradients in New Zealand's Southern Great Lakes
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Data from: Digging for DNA at depth: rapid universal metabarcoding surveys (RUMS) as a tool to detect coral reef biodiversity across a depth gradient
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Data from: Adaptive responses along a depth and a latitudinal gradient in the endemic seagrass Posidonia oceanica
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Data from: Molecular assessment of Pocillopora verrucosa (Scleractinia; Pocilloporidae) distribution along a depth gradient in Ludao, Taiwan
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Depth and latitudinal gradients of diversity in seamount benthic communities
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Data from: Highly contrasted responses of Mediterranean octocorals to climate change along a depth gradient
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Data from: Context-dependent evolution of ostracod morphology along the ecogeographical gradient of ocean depth
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Figure 3 in Terrestrial isopods and myriapods in a forested scree slope: subterranean biodiversity, depth gradient and annual dynamics
Figure 3. Activity dynamics of isopod Ligidium germanicum (LIGE); diplopods Mecogonopodium carpathicum (MECA), Polydesmus denticulatus (PODE), Trachysphaera acutula (TRAC); and centipedes Harpolithobius anodus (HAAN), Lithobius forficatus (LIFO), during the period November 2008– November 2009.
Figure 2 in Terrestrial isopods and myriapods in a forested scree slope: subterranean biodiversity, depth gradient and annual dynamics
Figure 2. The non-metric multidimensional scaling ordination analysis (NMS) diagram of Isopoda and Myriapoda collected during both sampling periods; variance explained by axes 1 and 2 as 87.0% and 8.7%, respectively (triangles – depths, dots – species). Abbreviations: i – Isopoda: LIGE – Ligidium germanicum, MEGR – Mesoniscus graniger, TRCA – Trichoniscus carpaticus; d – Diplopoda: JUCU – Julus curvicornis, LEMA – Leptoiulus mariae, LETR – L. trilobatus, MECA – Mecogonopodium carpathicum, POCO – Polydesmus complanatus, PODE – Polydesmus denticulatus, TRAC – Trachysphaera acutula, STST – Strongylosoma stigmatosum; c – Chilopoda: HAAN – Harpolithobius anodus, LI - Lithobius sp. juv., LIFO – Lithobius forficatus, LILU – Lithobius lucifugus; STAC – Strigamia acuminata, STTR – Strigamia transsilvanica.
Figure 3 from: Jakšová P, Ľuptáčik P, Miklisová D (2019) Distribution of Oribatida (Acari) along a depth gradient in forested scree slopes. Subterranean Biology 31: 29-48. https://doi.org/10.3897/subtbiol.31.36241
Figure 3 Monthly temperature fluctuations along the depth gradient of the investigated screes. Abbreviations: AJ – Ardovská jaskyňa Cave, BS – Belinské skaly Rocks, DK – Drienčanský kras Karst, MR – Malý Ružínok Valley, SL – Silická ľadnica Cave.
Figure 4 from: Jakšová P, Ľuptáčik P, Miklisová D (2019) Distribution of Oribatida (Acari) along a depth gradient in forested scree slopes. Subterranean Biology 31: 29-48. https://doi.org/10.3897/subtbiol.31.36241
Figure 4 Distribution of Oribatida along the vertical profile of the screes expressed as a total number of trapped individuals and species richness. Abbreviations: AJ – Ardovská jaskyňa Cave, BS – Belinské skaly Rocks, DK – Drienčanský kras Karst, MR – Malý Ružínok Valley, SL – Silická ľadnica Cave, *Number of individuals = 661.
Figure 1 from: Jakšová P, Ľuptáčik P, Miklisová D (2019) Distribution of Oribatida (Acari) along a depth gradient in forested scree slopes. Subterranean Biology 31: 29-48. https://doi.org/10.3897/subtbiol.31.36241
Figure 1 Localities of samplings. 1 – Ardovská jaskyňa Cave, 2 – Belinské skaly Rocks, 3 – Drienčanský kras Karst, 4 – Malý Ružínok Valley, 5 – Silická ľadnica Cave.
Figure 4 in Depth-related gradient of soft-bottom crustacean distribution along the Cilician shelf*
Figure 4. nMDS (A; see Figure 1 for depth code) and biplot of CCA (axes 1–2) performed on log-transformed (log 10 × [N + 1]) density values (N) of the crustacean taxa and environmental variables (arrows) at each replicate (B; see Figure 1 for depth and transect codes around the symbols) and at 7 depths of a combination of 3 transects each, including 7 stations (C) in each season. Arrows refer to the direction and relative importance of environmental variables in the ordination. Symbol size is proportional to depth of sampling sites variable, i.e. the largest symbol corresponds to the maximum depth. See Table 3 for the abbreviations of the environmental variables.
Figure 1 in Depth-related gradient of soft-bottom crustacean distribution along the Cilician shelf*
Figure 1. Study area (framed in rectangle) and sampling stations located on soft bottoms (1: 10 m, 2: 25 m, 3: 50 m, 4: 75 m, 5: 100 m, 6: 150 m, and 7: 200 m) across 3 transects, Erdemli (E), Limonlu (L), and Kumkuyu (K), in the Cilician shelf in 2000.
eDNA metabarcoding reveals submarine canyon biodiversity across depth gradient
<p>Dataset for thesis Chapter 5</p>
Data from: Phenotypic plasticity drives a depth gradient in male conspicuousness in threespine stickleback, Gasterosteus aculeatus
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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.