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22 results for “Sediment detection”
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
<p>Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa. </p>
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
Open the record for dataset details and reuse information.
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin & Chlorophyll a
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p> <p>Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section.</p> <p>Hyperspectral data are submitted separately due to their size; see the Related works.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Datasets from "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks". JGR Biogeosciences.
<p>This repository contains the datasets linked to "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks" (Journal of Geophysical Research: Biogeosciences, <a href="https://doi.org/10.1029/2024JG008047">https://doi.org/10.1029/2024JG008047</a>). This includes:</p> <ul> <li>The images and annotation* text files (in YOLO format) used for the detection of siliceous microfossils. </li> <li>The models and training results for the trainings presented in the main text and supporting information.</li> </ul> <p>*Note that while annotations were attributed to 14 general categories, only twelve of these were used during training (<em>i.e. </em>Pennate, Centric, Silicoflagellate, Centric_debris, Spore, Other_Biomin, Cocco, Silicoflagellate_debris, Undetermined_silica, Chateoceros_Bacteriastrum, Calcispheres, Foraminifer), and all were pooled into a single "Microfossil" category for the purpose of the training.</p>
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
<p>The European weather loach (<i>Misgurnus fossilis</i>) is classified as highly endangered in several countries of Central Europe. Populations of <i>M. fossilis</i> are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, <i>M. fossilis</i> was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the Threshold cycle (Ct) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of <i>M. fossilis</i>. We tested several method combinations for long-term sample preservation, DNA capture and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural <i>M. fossilis</i> population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of <i>M. fossilis </i>did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of <i>M. fossilis</i>, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of <i>M. fossilis</i>.</p>
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
<p>Climate warming will impact the sustainability of arid and semi-arid zone environments so we need to understand the influence of changes in arid lands on vertebrate populations. However, biomonitoring and biodiversity assessment in arid environments can be prohibitively time-consuming, expensive, and logistically challenging due to their often remote and inhospitable nature. Sampling of environmental DNA (eDNA) coupled with high-throughput sequencing is an emerging biodiversity assessment method. Here we explore the application of eDNA metabarcoding and various sampling approaches to estimate vertebrate richness and assemblage at human-constructed and natural water sources in a semi-arid region of Western Australia. Three sampling methods: sediment samples, filtering through a membrane with a pump, and membrane sweeping in the water body, were compared using two eDNA metabarcoding assays, 12S-V5 and 16smam, for 120 eDNA samples collected from four gnammas (gnamma: Australian Indigenous Noongar language term – granite rock pools) and four cattle troughs in the Great Western Woodlands, Western Australia. We detected higher vertebrate richness in samples from cattle troughs and found differences between assemblages detected in gnammas (more birds and amphibians) and cattle troughs (more mammals, including feral taxa). Total vertebrate richness was not different between swept and filtered samples, but all sampling methods yielded different assemblages. Our findings indicate that eDNA surveys in arid lands will benefit from collecting multiple samples at multiple water sources to avoid underestimating vertebrate richness. The high concentration of eDNA in small, isolated water bodies permits the use of sweep sampling which simplifies sample collection, processing, and storage, particularly when assessing vertebrate biodiversity across large spatial scales.</p>
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
Open the record for dataset details and reuse information.
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
Open the record for dataset details and reuse information.
Data from: Detection of tephra layers in Antarctic sediment cores with hyperspectral imaging
Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras.
Supplementary data for "Validation of a quantitative PCR assay for detection of sugar kelp (Saccharina latissima) in coastal marine sediments"
<p>Raw data for a paper in preparation: "Validation of a quantitative PCR assay for detection of sugar kelp (Saccharina latissima) in coastal marine sediments".</p><p>Contains:</p><ol><li>Spreadsheet of curated raw data used to calculated <i>S. latissima</i> COI gene copy number per gram of dry sediment for a biomass spiking experiment to determine how gene copy number correlates with known amounts of added kelp biomass.</li><li>Spreadsheet of curated raw data from a field study looking at <i>S. latissima</i> gene copy numbers in field samples from the Damariscotta River estuary in Maine, from three sites, one under a small kelp farm, one under a site with a single kelp line, and another in a control site with no known influence of kelp farming activity.</li></ol>
CNN for image-based sediment detection applied to a large terrestrial and airborne dataset
<p>This repository contains data sets and model used in the manuscript "CNN for image-based sediment detection applied to a large terrestrial and airborne dataset" submitted to Earth Surface Dynamics (2021) by Xingyu Chen, Marwan A. Hassan, and Xudong Fu.</p>
Data from: Detection of tephra layers in Antarctic sediment cores with hyperspectral imaging
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Data from: Detecting copper toxicity in sediments: from the sub-individual level to the population level
1.Sediments accumulate chemicals that can be toxic to biota and often contribute to aquatic ecosystem decline. Measuring mortality in laboratory-bred organisms is a common way to assess sediment toxicity. However, mortality-based responses of resilient laboratory organisms may not reflect indigenous macroinvertebrate responses, which can be relatively more sensitive to sediment toxicants. A possible solution is to also measure responses at the sub-individual level. 2.Several organism responses to sediment copper toxicity were assessed in a field-based microcosm. Responses of laboratory-bred chironomids and snails deployed in microcosms were compared at sub-individual (metabolomic and gene expression), individual (survival and dry weight) and population (reproduction) levels, and contrasted to the abundance of colonizing macroinvertebrates in the microcosms. 3.Colonizing macroinvertebrate abundance showed a range of sensitivities based on EC50 (effect dose 50% change). Chironomidae made up 94.5% of the microcosm macroinvertebrates, with Paratanytarsus the most sensitive genus (EC50: 89 mg/kg copper) and Procladius the least sensitive (EC50: 681 mg/kg). 4.Survival of laboratory-bred organisms was the least sensitive response, comparable to decreased abundance of the least sensitive macroinvertebrate. Juvenile production in the snail, Potamopyrgus antipodarum, was the most sensitive population level response (EC50: 121 mg/kg), in contrast the snail Physella acuta was relatively more tolerant (EC50: 298 mg/kg). 5.Changes in sub-individual responses (gene expression and metabolite abundance) in laboratory-bred chironomid, Chironomus tepperi, were evident at 60 mg/kg. These changes likely reflect the direct effects of copper exposure and represent metal-specific responses. 6.Synthesis and applications. We showed that copper toxicity in sediments could be readily detected through changes in gene expression and metabolites in laboratory-bred chironomids exposed in field-based microcosms. These responses were more sensitive than mortality, and detected copper levels that caused microcosm chironomid populations to decline. These novel approaches will provide managers with new tools to better assess sediment toxicity.
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