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1,133 results for “wetlands”
FIGURE 1 in Two new brackish-water species of Macrostomum (Platyhelminthes, Macrostomida) from mangrove wetland in southern China
FIGURE 1. The locations of the two new species of Macrostomum in Guangdong Province, China.
FIGURE 1 in Three new free-living marine nematode species of Subsphaerolaimus Lorenzen, 1978, Halichoanolaimus de Man, 1886 and Belbolla Andrássy, 1973 from mangrove wetlands in Taiwan
FIGURE 1. The junction between China and the Pacific Ocean (showing the studied sampling stations).
REORGANIZING THE USE OF DEGRADATED WETLANDS
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Figure 6 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 6 Fifth instar (presumptive macropterous form) Pissonotus paraguayensisA dorsal view B ventral view.
Figure 8 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 8 Forest plot of Cox proportional hazard analysis for Pissonotus paraguayensisA females and B males survival where hazard ratio (HR) > 1 means that exposure to the factor increases the rate of occurrence of the event (cavity lost), and HR < 1 decreases the rate. If the HR = 1 the factor does not influence survival (Kleinbaum and Klein 2012). HR are depicted by box symbols with confidence bands and parenthetical values representing 95% confidence intervals. The p values represent Wald test of significance and magnitude of significance is denoted with asterisks (*). AIC, Akaike information criterion. Small letters next to plant species indicate sectional classification of Ludwigia: aJussiaea, bMacrocarpon, cMyrtocarpus based on Raven (1963) and Wagner et al. (2007).
Figure 7 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 7 APissonotus paraguayensis eggs oviposited in Ludwigia grandiflora subsp. hexapetalaB damage observed on L. g. subsp. hexapetala produced by ovipositions C oviposition marks.
Figure 4 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 4 Pissonotus paraguayensis female A abdomen, genital region, ventral view B pregenital sternite C gonapophysis IX D apex of gonapophysis IX E apex of hind leg (post tibial spur and tarsi) of adults.
Figure 3 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 3 Pissonotus paraguayensis. Habitus. Brachypter female A dorsal view B ventral view. Macropter female C dorsal view D ventral view.
Figure 5 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 5 Immature stages in dorsal view of Pissonotus paraguayensisA first instar C second instar E third instar G fourth instar I fifth instar (presumptive brachypter form). Apical portion (from tibiae) of hind leg B first instar D second instar F third instar H fourth instar J fifth instar (presumptive brachypter form). Scale bars: 0.5 mm (A); 1mm (C, E, G. I).
Figure 1 from: Marino de Remes Lenicov AM, Faltlhauser AC, Foieri A, Salinas NA, Hernández MC, Sosa AJ (2024) New morphological and biological contributions to adults and immature forms of Pissonotus paraguayensis (Fulgoromorpha, Delphacidae) in wetlands of Argentina. ZooKeys 1188: 227-250. https://doi.org/10.3897/zookeys.1188.113350
Figure 1 Different rearing and test devices for Pissonotus paraguayensisA device for aquatic plants utilised for rearing and host range tests B device for rearing and tests with plants in water-pics C small petri dishes with L. g. susp. hexapetala utilised in life cycle study D hanging plastic cylinders utilised in host range test for terrestrial plants.
Coastal Wetland Crab Image Dataset and Image Processing Model Weights
<p>This data was collected from the distribution range of mangroves along the coast of China and has been randomly selected and manually corrected to be labeled as a crab image analysis dataset. The weights of deep learning models trained on YOLOv5/v8 and EfficientNet are also uploaded simultaneously. Additionally, it includes the necessary test datasets and some test results. Please cite when using this dataset, and contact the administrator if you need help. The specific directories are as follows:</p> <p>- R-crab: Contains code needed to test model performance, with the subfolder data containing the test dataset required. It includes (1) crab-man as human-marked references, crab-ref as model detection results. (2) luoyuan-burrow for the detection results of crab burrows in the Luoyuan area case study, and luoyuan-crab for crab detection results, including crab classification, localization, and carapace width information. (3) method-test for testing different methods, i.e., whether to use a two-stage detection model. (4) size-conf-test records the model detection results under different image input sizes and confidence threshold levels. (1), (3), and (4) are completed in Out-of-sample data, while (2) is completed in Luoyuan. fig_attr.csv records the test results of image attributes on detection accuracy. label_results.csv records the comparison results between traits measured manually using ImageJ software and our designed model for detecting crab carapace width. luoyuan_list.csv records the numbering information of Luoyuan sampling plots.<br>- Aiweights: Contains model weights trained based on Object detection data, with cpm-model under v8n-seg-crab.pt for YOLOv8 trained to extract crab carapace width. Sfc-model under adam20.pth is a two-stage detection model trained based on EfficientNet. Trained_weights under burrow-baseline is a crab burrow detection model trained based on our improved YOLOv5 (improvements stored at https://github.com/GuuX29/crab-yolo-add, same below); frame-s6-2560.pt is a plot frame detection model for obtaining standard 50*50cm plot images; s-simam-20.pt and x-simam-20.pt are both for crab detection and classification models, with x having higher accuracy.<br>- Luoyuan: crop stores standardized processed Luoyuan image data, numbered as above, test stores detection results, same as R-crab.<br>- Object detection: Contains cropped 640 pixels crab field sampling images, with images in the images folder and bounding box labels in the labels folder, divided into training and testing at a 9:1 ratio, train.txt and val.txt record the allocation information.<br>- Out-of-sample: Records 100 independent test images not used to train the model, stored in images, crab-man, and crab-ref are the same as in R-crab.<br>- Segmentation: Stores image data used to test model detection of crab carapace width, with results also stored in R-crab.</p> <p>We hope this data and method will benefit the progress of research in this field. For any suggestions for improvement and help with usage, please contact the administrator. guuxuan1994@gmail.com </p>
Plot level biomass by components for small black spruce trees growing on the wetlands of the Boreal and Taiga Plains of Canada
<p>Tree mensuration data and biomass component data, collected from destructive sampling of 679 trees (DOI: 10.5281/zenodo.10632743), were scalled-up to the plot level, resulting in 58 black spruce boreal wetland plots within the Boreal Plains and Taiga Plains of Canada. This dataset contains plot level biomass by components for small black spruce trees growing on the wetlands of the Boreal and Taiga Plains of Canada.</p> <p>This dataset has been used to develop a new set of yield curves (i.e., total volume as a function of total stand age) as well as novel total volume-to-biomass conversion models to predict stem wood, stem bark, branch, and foliage biomass of the small black spruce trees. </p> <p>The novel equations are key to modelling treed peatlands in the Canadian Model for Peatlands (CaMP) and thus contribute to improving accuracy in modelled C stocks and flux estimates for Canada’s treed peatlands.</p>
Individual tree biomass by components for small black spruce trees growing on the wetlands of the Boreal and Taiga Plains of Canada
<p>New tree mensuration data and biomass component data from destructive sampling of 679 trees, were collected at 58 black spruce boreal wetland sites within the Boreal Plains and Taiga Plains of Canada. These data have been used to:</p> <p> Develop individual small black spruce trees allometric models. These models will focus on relationships between basal and breast height diameter, years-to-breast height, and height as function of basal diameter or breast height diameter, taper equation, and individual tree volume compilation.<br> Develop individual small black spruce trees biomass models. These equations will focus on estimating biomass of single trees based on diameters (basal and breast height diameter) and/or height). </p> <p> </p>
Patterns and drivers of soil organic carbon fractions and persistence in coastal wetlands in China
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Effect of soil stoichiometry mediated by vegetation on carbon emissions in a large wetland under hydrological stress
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Methane mean emissions from wetlands in Mexico for the period 2000-2017
<p>Metane emissions from wetlands from Mexico, using the for the period 2000-2017 at a resolution of 0.1º, using the output of 13 land surface models of global CH<sub>4</sub> emissions from wetlands published and used in the current Global Methane Budget (Table 2 in Saunois et al., 2020). The Global Methane Budget did not include emissions at the individual country scale, thus, CH<sub>4</sub> emissions from Mexican wetlands were calculated by selecting the Mexican territory from the global output, superimposing a mask of Mexican wetland distribution produced by INEGI (https://www.inegi.org.mx/temas/humedales/), and interpolating at a resolution of 0.01º.</p> <p>The data base (NetCDF) has four dimensions: latitud, longitud, time and the mean of the 13 models and their versions. It is already published in: </p>
Dataset for Prolonged storage of bound organic carbon in wetland but not upland soils: A 13C and 14C perspective
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CYGNSS-based inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019
<p>Inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019 at 0.5<sup>o</sup> x 0.5<sup>o </sup>resolution developed using microwave remote sensing data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation (https://cygnss.engin.umich.edu/), L1 v2.1.</p> <p>CYGNSS data used to produce these maps is available from NASA: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_V2.1</p> <p>A complete description of the method developed to obtain these maps is available in the following preprint: https://www.essoar.org/doi/10.1002/essoar.10504845.1</p> <p> </p> <p> </p>
Figure 7 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 7 Protoparmeliopsis crystalliniformis morphology (BDNA-L-0000349, holotype) A–C habitus and apothecia, areolate to squamulose thallus in white to whitish gray color D–E apothecia in vertical section F–G well-developed thalline margin H large crystals present in the thalline margin, not dissolving in KOH I clavate ascus J ascospores constantly simple and ellipsoid, often biguttulate in the beginning. Scale bars: 1 mm (A–C); 200 μm (D–E); 100 μm (F–G); 50 μm (H); 10 μm (I–J).
Figure 6 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 6 Lecanora parasymmicta morphology (BDNA-L-0001235, paratype in A BDNA-L-0001220, holotype in B–M) A–C habitus and apothecia, thalline margin of apothecia consistently absent from the beginning D blackish hypothallus (red arrows) E apothecia in vertical section F biatorine apothecia without thalline margin G–J clavate asci with eight spores K ascospores constantly simple but rarely 1-septate L immersed pycnidia M thread-like, curved pycnoconidia. Scale bars: 1 mm (A–D); 200 μm (E); 50 μm (F); 10 μm (G–K); 100 μm (L); 10 μm (M).
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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.