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1,133 results for “wetlands”
Figures 8, 9 in Six freshwater microturbellarian species (Platyhelminthes) in permanent wetlands of the Coastal Plain of southern Brazil: new records, abundance, and distribution
Figures 8, 9. Photograph of specimen in vivo after squeeze preparation (8) and diagrammatic reconstruction (9) in dorsal view of Stenostomum hemisphericum recorded for the Coastal Plain of southern Brazil.
Figures 2–7 in Six freshwater microturbellarian species (Platyhelminthes) in permanent wetlands of the Coastal Plain of southern Brazil: new records, abundance, and distribution
Figures 2–7. Photographs of specimens in vivo after squeeze preparation (2, 4, 6) and diagrammatic reconstructions in dorsal view (3, 5, 7) of species of Catenula recorded in the Coastal Plain of southern Brazil. 2, 3. Catenula evelinae. 4, 5. C. leuca. 6, 7. C. turgida. Scale bars = 100 µm.
Figure 1 in Six freshwater microturbellarian species (Platyhelminthes) in permanent wetlands of the Coastal Plain of southern Brazil: new records, abundance, and distribution
Figure 1. Study areas in the Coastal Plain of the southern Brazilian state of Rio Grande do Sul (white area): Terra de Areia (1 = 29°29'05" S, 049°52'21" W), Osório (2 = 29°53'20" S, 050°08'09" W, and 3 = 29°52'02" S, 050°05'16" W), Tramandaí (4 = 30°05'09" S, 050°10'24" W), and Capivari do Sul (5 = 30°10'22" S, 050°23'10" W).
Data from: Unveiling the landscape predictors of resilient vegetation in coastal wetlands to inform conservation in the face of climate extremes
<div> <p>Unveiling spatial variation in vegetation resilience to climate extremes can inform effective conservation planning under climate change. Although many conservation efforts are implemented on landscape scales, they often remain blind to landscape variation in vegetation resilience. We explored the distribution of drought-resilient vegetation (i.e., vegetation that could withstand and quickly recover from drought) and its predictors across a heterogeneous coastal landscape under long-term wetland conversion, through a series of high-resolution satellite image interpretations, spatial analyses, and nonlinear modelling. We found that vegetation varied greatly in drought resilience across the coastal wetland landscape and that drought-resilient vegetation could be predicted with distances to coastline and tidal channel. Specifically, drought-resilient vegetation exhibited a nearly bimodal distribution and had a seaward optimum at ~2 km from coastline (corresponding to an inundation frequency of ~30%), a pattern particularly pronounced in areas further away from tidal channels. Furthermore, we found that areas with drought-resilient vegetation were more likely to be eliminated by wetland conversion. Even in protected areas where wetland conversion was slowed, drought-resilient vegetation was increasingly lost to wetland conversion at its landward optimum in combination with rapid plant invasions at its seaward optimum. Our study highlights that the distribution of drought-resilient vegetation can be predicted using landscape features but without incorporating this predictive understanding, conservation efforts may risk failing in the face of climate extremes.</p> <p> </p> <p>This ZIP file contains the following datasets and code for the above paper:</p> <p>1. data_cheng_et_al_GCB_2024.zip This file contain a total of three files.</p> <p>(1) analys_grid_50_m.shp This file is a shapefile for the 50-by-50 m grid cells analyzed in the focal study area and their IDs.</p> <p>(2) veg_change.shp This file contains the spatial distribution of vegetation before and after the 2011 drought. </p> <p> Variable list:</p> <p> change: Changes of vegetation after the 2011 drought.</p> <p> vegBefore: Distribution of vegetation before the drought.</p> <p> vegAfter: Distribution of vegetation after the drought.</p> <p>(3) tidalchannel.shp This file contains manually digitized tidal channels (and water surfaces).</p> <p>2. gridfeatures.xlsx This file contains data on the landscape features of each analysis grid.</p> <p>3. analyses.R This file contains the R code used for data analysis.</p> </div>
Dataset for "Technical Note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models "
<p>This record contains data used in Peltola et al. (2018): Technical Note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models, Biogeosciences.</p> <p>The record contains model input data, as well as CH<sub>4</sub> flux data used to validate the modelling results.</p> <p>Please see the read me file for more information.</p> <p>For further information contact Olli Peltola (olli.peltola@helsinki.fi)</p>
Supplementary material 4 from: Theissinger K, Kästel A, Elbrecht V, Makkonen J, Michiels S, Schmidt S, Allgeier S, Leese F, Brühl C (2018) Using DNA metabarcoding for assessing chironomid diversity and community change in mosquito controlled temporary wetlands. Metabarcoding and Metagenomics 2: e21060. https://doi.org/10.3897/mbmg.2.21060
OTU ID, taxonomy of identified species, BOLD Bin, sequence abundancies per site an time and OTU sequences
Supplementary material 3 from: Theissinger K, Kästel A, Elbrecht V, Makkonen J, Michiels S, Schmidt S, Allgeier S, Leese F, Brühl C (2018) Using DNA metabarcoding for assessing chironomid diversity and community change in mosquito controlled temporary wetlands. Metabarcoding and Metagenomics 2: e21060. https://doi.org/10.3897/mbmg.2.21060
We pooled the library according to the number of specimens per sample and could show that our read abundance highly correlates with specimen abundance. Thus, we could use the read abundancies as surrogates for relative species abundancies.
Physical controls on the spatial and temporal biogenic gas dynamics in two subtropical wetland ecosystems in Florida
<p>The spatial and temporal distribution of biogenic gas accumulation and release within peatland soils and their controls (i.e. both physical and environmental) remain highly uncertain. While several recent studies show the importance of the pore structure when defining gas dynamics, and particularly when modeling rapid gas releases (i.e. ebullition), it is unclear how different ecosystems (and particularly for subtropical systems) may show differences on such dependence. The study presented here investigates the spatial and temporal variability in biogenic gas accumulation and release in two 38-liter peat monoliths from two different wetland ecosystems in central Florida (pine flatwoods and emergent wetlands) at the laboratory scale. An array of non-invasive hydrogeophysical methods (using ground-penetrating radar, GPR) was combined with gas traps, time-lapse cameras, and direct measurements (i.e. porosity and bulk density) to explore gas content variability (i.e. build-up and release) within the peat matrix over a period of five months. The results show that specific physical soil properties for different types of wetland ecosystems play a critical role at controlling the dynamics of gas accumulation and release from peat soils. Furthermore, these differences are consistent with results on soils from other studies within similar ecosystems. This work has implications for better understanding how different types of ecosystem in subtropical systems may contribute differently to the production, accumulation, and release of greenhouse gases.</p>
FIGURE 22 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 22. Stylissa carteri (Dendy, 1889). A–C. In situ; D & E. Skeletal architecture (D, x5; E, x 10); F. Style.
FIGURE 17 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 17. Mycale (Aegogropila) crassissima (Dendy, 1905). A. In situ; B, C, D. Skeletal architecture (B, x5; C, x 10; D, x 20); E. Styles (macalostyles); F. Anisochelae; G & H. Sigmas; I. Raphides.
FIGURE 16 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 16. Iotrochota sinki sp. nov. A & B. In situ; C & D. Skeletal architecture (C, x5; D, x 10); E & F. Styles; G. Birotulate chelae.
FIGURE 14 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 14. Chondropsis isimangaliso sp. nov. A-E. In situ; F & G. Skeletal architecture (F, x5; G, x 10); H-I. Spicule compliment of strongyles and sigmas.
FIGURE 31 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 31. Spongia (Heterofibria) cooki sp. nov. A. In situ; B-E. Skeletal architecture (B, x5; C, x5; D, x10; E, x10)
FIGURE 15. Iotrochota nigra. A in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 15. Iotrochota nigra. A. In situ; B. Skeletal architecture (B, x5); C. Styles; D. Birotulate chelae.
FIGURE 28 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 28. Ircinia lividus sp. nov. A & B. In situ; C–F. Skeletal architecture (C, x5; D, x10, E, 40; F, x65).
FIGURE 11 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 11. Clathria (Clathria) ramsayiensis sp. nov. A. In situ; B & C. Skeletal architecture (B, x5; C, x10); D. Styles; E. Subtylostyles; F. Acanthostyles; G. Toxas; H. Palmate isochelae.
FIGURE 19 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 19. Cymbastela sodwaniensis Samaai et al., 2009. A-D. In situ; E & F. Skeletal architecture (E, x5; F, x 10); G. Oxeas.
FIGURE 9 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 9. Theonella timmi sp. nov. A & B. In situ; C, D. Phyllotriaenes; E. Tetraclone desmas; F. Acanthorhabd; G. Skeletal architecture (G, x10).
FIGURE 13 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 13. Chrondropsis cf. lamella (Lendenfeld, 1888). A-D. In situ; E & F. Skeletal architecture (E, x5; F, x10); G-H. Spicule compliment of strongyles and sigmas.
FIGURE 8 in Shallow-water Demospongiae (Porifera) from Sodwana Bay, iSimangaliso Wetland Park, South Africa
FIGURE 8. Stellitethya incrustans sp. nov. A. Preserved sample; B & C. Skeletal architecture (B, x5; C, x10). Spicule compliment, D & E. Oxyspherasters; F. Strongyloxea.
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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.