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1,188 results for “Deltas”
Fig. 1 in Biozonation And Correlation Of Two Wells In Niger Delta Using Calcareous Nannofossils
Fig. 1 Geologic map of the Niger Delta region (after Correldor et al. 2005)
Figure 1 in Trichodinid fauna of freshwater fishes with infestation indices in the Lower Kızılırmak Delta in Turkey and a checklist of trichodinids (Ciliophora: Trichodinidae) in Turkish waters
Figure 1. Sampling area and all geographical regions of Turkey.
Figure 5 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 5. Laying time of the red-backed shrikes.
Figure 2 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 2. Nest plants of the red-backed shrikes (n = 108) in the Kızılırmak Delta during 2011–2012.
Figure 6 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 6. Clutch size distribution of the red-backed shrikes.
Figure 3 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 3. Distribution of the orientations of the red-backed shrike nests in the supporting plants.
Figure 4 in The monitoring of feather mites (Acari, Astigmata) of the Warbler (Aves: Sylviidae) species in the Kızılırmak delta, Samsun, Turkey
Figure 4. Monthly and annual comparison of the number of feather mite individuals.
Figure 2 in Assessment of the zooplankton community structure of the coastal Uzungöl Lagoon (Kızılırmak Delta, Turkey) based on community indices and physicochemical parameters
Figure 2. PCA results for environmental variables (number 1-5 represented sampling sites).
Figure An1. Distribution of salinity (a), temperature (b), dissolved oxygen (c), and AOU (d). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An1. Distribution of salinity (a), temperature (b), dissolved oxygen (c), and AOU (d).
Figure An3. Distribution of pCO2 (a), Chl-a (b), and Pheo (c). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An3. Distribution of pCO2 (a), Chl-a (b), and Pheo (c).
FIGURE 4 in Foraminifera biostratigraphy and paleoenvironment of Well 5, OML 34, Niger Delta, Nigeria
FIGURE 4. Sequence Stratigraphy chart showing paleobathymetry of deposition of studied well.
FIGURE 3 in Foraminifera biostratigraphy and paleoenvironment of Well 5, OML 34, Niger Delta, Nigeria
FIGURE 3. Ranges of planktonic foraminifera showing biozones for the studied Well 5.
FIGURE 1 in Foraminifera biostratigraphy and paleoenvironment of Well 5, OML 34, Niger Delta, Nigeria
FIGURE 1. Location of studied well (modified after Odemerho; Urhobo Historical Society, 2008).
Three-dimensional soil organic carbon density by logarithmic function and coefficient scaling in Yangtze River Delta, China
<h3>Three-dimensional soil organic carbon density (SOCD) dataset with 90-m resolution generated by Lin, S., Zhu, Q., Yin, B., Yang, G., Liao, K., Lai, X., Guo, C., 2025. Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China. Environmental Modelling & Software, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2025.106582" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.envsoft.2025.106582.</span></span></a></h3> <h3>Here, based on the best performance, the three-dimensional SOCD generated by LF corrected with coefficient scaling method were provided. The accurate SOCD maps with the spatial resolution of 90-m at any specific depth interval can be generated by our method. This dataset includes:</h3> <ul> <li>Spatial distribution map of parameter 1 (p1) of LF (LF_p1.tif)</li> <li>Spatial distribution map of parameter 2 (p2) of LF (LF_p2.tif)</li> <li>The calculation code and fitted functions of scaling coefficient a, k of LF (fitted_fx_scalingcoff.m)</li> <li>Readme.docx</li> </ul> <p>Note: the unit of SOCD is kg m-2; the spatial distribution maps provided by this dataset does not mask any water bodies.</p> <p><strong>How to use our dataset? Please refer to our article and Readme.docx for more details.</strong></p> <p> </p> <p> </p>
Wave-influenced Delta Morphodynamics, Long-term Sediment Bypass and Trapping Controlled by Relative Magnitudes of Riverine and Wave-driven Sediment Transport
<p>This repository contains supporting data for *Wave-influenced Delta Morphodynamics, Long-term Sediment Bypass and Trapping Controlled by Relative Magnitudes of Riverine and Wave-driven Sediment Transport,* submitted to Geophysical Research Letters.</p> <p>Delft3D Config for Q1000Qs100Hs15.docx → contains configuration file info for running the Delft3d simulation Q1000Qs100Hs15.</p> <p>Matlab 2022b was used to load Delft3D output files and process the data.</p> <p>WaveDelta_Depth&DepositThick.mat Contains:</p> <p>Depth → timesteps x 322 x 142. Timesteps are variable for each simulation.</p> <p>MSEDRiverSum → 4 x 322 x 142. Mass of sediments from riverine source at four intermediate timesteps. </p> <p>MSEDWaveSum → 4 x 322 x 142. Mass of sediments from longshore transport (LSTUp) source at four intermediate timesteps.</p> <p>VolRiverSum → 322 x 142. Final Volume of sediments from riverine source.</p> <p>VolWaveSum → 322 x 142. Final Volume of sediments from longshore transport (LSTUp) source.</p> <p>XCOR & YCOR are the X and Y grid coordinate locations for maps (322 x 142).</p> <p>Shift2&4km are used for cases highly skewed downdrift to correct for 0 location at the river mouth.</p> <p>Cerc_formula.mlx Computes CERC longshore transport for the simulation conditions.</p> <p>J.mlx calculates river mouth balance (J) values (Nienhuis et al. (2016)) presented in Figure S4. </p> <p>Additional 114 Figures are provided in Data_Visualization_Figures.rar to show Depth and thickness; subaqueous morphodynamics and depth and sediment transport (quiver).</p>
Delta GUI change detection using inferred models replication package
<p><strong>TESTAR </strong>is a scriptless automated open-source tool developed by the Universitat Politècnica de València and the Open University of the Netherlands.</p> <p><strong>TESTAR Change detection .NET</strong> is an open-source tool that simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. This tool is also developed by the Universitat Politècnica de València and the Open University of the Netherlands.</p> <p>This replication package contains:</p> <ul> <li>The Excel file that was used to perform and store the systematic mapping of the literature.</li> <li>The TESTAR Change detection .NET version that was used to compare the state models inferred from the OBS, Calibre, and MyExpenses applications. </li> <li>The state models that were inferred from the OBS, Calibre, and MyExpenses applications. These are stored in the OrientDB graph database. </li> <li>Three documents (OBS, Calibre, MyExpenses) that detail with images the GUI changes results detected using the TESTAR Change detection .NET tool.</li> <li>A video demo that shows how to use the TESTAR Change detection .NET tool with the inferred models from the Calibre web system.</li> </ul>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</p>
Data from: Developing and applying a macroinvertebrate‐based multimetric index for urban rivers in the Niger Delta, Nigeria
<p class="MsoCommentText">Urban pollution of riverine ecosystem is a serious concern in the Niger Delta region of Nigeria. No biomonitoring tool exists for the routine monitoring of effects of urban pollution on riverine systems within the region. Therefore, the aim of this study was to develop and apply a macroinvertebrate-based multimetric index for assessing water quality condition of impacted urban river systems in the Niger Delta area of Nigeria. Macroinvertebrate and physico-chemical samples were collected from 11 stations in eight river systems. Based on the physico-chemical variables, the stations were categorised into three impact categories namely; least impacted stations (LIS), moderately impacted stations (MIS) and heavily impacted stations (HIS). Seventy seven (77) candidate metrics were tested and only five: Hemiptera abundance, %Coleoptera+Hemiptera, %Chironomidae+Oligochaeta, Evenness index and Logarithm of relative abundance of very large body size (>40-80 mm) were retained and integrated into the final Niger Delta urban multimetric index (MINDU). The validation data set showed a correspondence of 83.3% between the index result and the physico-chemically-based classification for the LIS and a 75% correspondence for the MIS. A performance of 22.2% was recorded for the HIS. The newly developed MINDU proved useful as a biomonitoring tool in the Niger Delta region of Nigeria, and can thus be used by environmental managers and government officials for routine monitoring of rivers and streams subjected to urban pollution.</p>
Molecular Dynamics of SARS-CoV-2 Delta Variant Receptor Binding Domain in Complex with ACE2 Receptor
<p>Molecular dynamics simulation for 10 ns at 37 C degrees of SARS-CoV-2 delta variant. Performed with NAMD and visualized/analyzed in ChimeraX software using Frontera supercomputer from Texas Advanced Computing Center. By Victor Padilla-Sanchez, PhD.</p> <p>https://www.youtube.com/watch?v=8N_MjWwxbMQ</p>
Delta-15N values for leaf and soil samples from a mesocosm experiment looking at dung beetle presence and the movement of dung-derived nitrogen (DDN)
<b>Description: </b><p>We deployed 18 mesocosms into each of the ecosystem types (logged forest and oil palm) in mid-May 2016, to give the soil one month to recover from the disturbance. We constructed mesocosms from black plastic containers, 40 cm diameter and 25 cm high after removing the base. We dug mesocosms 20 cm into the ground leaving 5cm above the surface. We arranged them in a 6 x 3 grid with a minimum of 3 m between each mesocosm to minimise interaction between the soil nutrient cycling in each mesocosm. As proximity of the seedlings to mature trees may increase competition for nitrogen and other nutrients we recorded the distance of each mesocosm to the nearest mature tree (any species with diameter at breast height > 30 cm) for inclusion in our analyses. <br>We randomly selected 12 of the mesocosms, to receive 15N-labelled dung patties weighing 300 ± SD 2.27 g in logged forest and 410 ± SD 2.04 g in oil palm. We populated six randomly selected mesocoms from within those 12 treated with dung with a standardised dung beetle communities (Fig. 1, Table S2). The remaining six mesocosms were left as soil only controls. We covered each mesocosm with a fine nylon mesh secured with a rubber belt to prevent beetles leaving or colonising the mesocosms, and to standardise any microclimatic effects between treatments. However, after 48 hours we opened the dung beetle treatments for a 24 hours period to allow the beetles to emigrate rather than forcing them to artificially stay in the same pat (cf. Roslin 2000; Slade et al. 2017), and then re-covered the mesocosms with netting.<br>We sampled soil seven times from logged forest over the course of the experiment. Any remaining surface dung was removed prior to soil sampling, and replaced thereafter, in order to reduce the possibility of contamination. If a soil core was unsuccessful (most likely due to beetle channels) a second core was taken directly beside. On each sample day, a core of 10cm depth was taken and split into vertical horizons 0- 2cm, 2-5 cm and 5-10cm. <br>We sampled leaves eight times over the eight-month duration of the experiment, with high frequency during the first month, aimed to capture the initial assimilation of DDN into the plants. We collected one leaf from the Dipterocarpaceae or palm seedlings for each sample event. For dipterocarp seedlings we alternated collection of the terminal leaf from top and bottom (leaving the topmost, newest leaf) between consecutive sample days, and for palm seedlings we sampled the two penultimate leaflets from alternating sides of the mid-stem, from the youngest fully formed frond. As assimilated 15N did not plateau in logged forest during the 8-month timeframe of the experiment, we took a sample after 21 months in order to determine whether all DDN had been turned over in the plant biomass after this time.<br>The leaf and soil samples were dried at 60°C for a minimum of 48 hours. We then ground samples to a fine powder using a ball mill (Retsch UK Ltd., Hope, UK). We weighed ground samples into 6 x 4 mm ultraclean tin capsules (Elemental Microanalysis Ltd., Okehampton, UK) using an ultra-microbalance with readability 1 μg (Mettler-Toledo, Greifensee, Switzerland) to provide sufficient elemental carbon and nitrogen for analysis by continuous flow isotope ratio mass spectrometry (SERCON, Crewe, UK). <br>Isotope ratios are expressed in per mil (‰) relative to international reference standards (Rstandard), which are Atmospheric Nitrogen and Vienna PeeDee Belemnite (VPDB) for nitrogen and carbon, respectively. The delta value describes the isotopic composition of each sample, which signifies a measurement of difference relative to laboratory standards. The calculation of δ values is given by: <br>δHX = [(RSAMPLE /RSTANDARD −1)]*1000</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/11"><b>Using stable isotopes to link biogeochemical processes to biodiversity of conservation concern</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Research grant, NE/K016148/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 (374) )</li><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 JLD.4 (41))</li><li>Sabah Biodiversity Centre (SABC) (Research licence JKM.1000-2/2 JLD.5 (153))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5113431">here</a></p><p><b>Files: </b>This consists of 1 file: 3_Kemp_15N_mesocosms_data.xlsx</p><p><b>3_Kemp_15N_mesocosms_data.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>OP leaf</b> (described in worksheet OP_leaf)</p><p>Description: Details the δ15N of leaves sampled in oil palm from palm seedlings across eight sample days up to day 233. </p><p>Number of fields: 13</p><p>Number of data rows: 144</p><p>Fields: </p><ul><li><b>Name</b>: Code for date (ddmm), mesocosms ID and depth (00 = surface; 02 = 2 cm belowground; 05 = 5 cm belowground, and 10 = 10 cm belowground) (Field type: id)</li><li><b>Day</b>: Experimental day as the number of days since day zero (defined by the planting of the seedlings, either dipterocarps or palms) (Field type: id)</li><li><b>Day2</b>: Experimental day as a factor (Field type: id)</li><li><b>Mesocosm</b>: Unique identifier for each of the 18 mesocosms (Field type: id)</li><li><b>Treatment</b>: Treatment assignment (Field type: categorical)</li><li><b>DistMature</b>: Distance from the nearest mature tree (any species with diameter at breast height > 30 cm). (Field type: numeric)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>d15N</b>: the delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>d13C</b>: the delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li></ul></li><li><p><b>LFE leaf</b> (described in worksheet LFE_leaf)</p><p>Description: Details the δ15N of leaves sampled in logged forest, taken from dipterocarp seedlings across nine sample days, up to day 625.</p><p>Number of fields: 13</p><p>Number of data rows: 147</p><p>Fields: </p><ul><li><b>Name</b>: Code for date (ddmm), mesocosms ID and depth (00 = surface; 02 = 2 cm belowground; 05 = 5 cm belowground, and 10 = 10 cm belowground) (Field type: id)</li><li><b>Day</b>: Experimental day as the number of days since day zero (defined by the planting of the seedlings, either dipterocarps or palms) (Field type: id)</li><li><b>Day2</b>: Experimental day as a factor (Field type: id)</li><li><b>Mesocosm</b>: Unique identifier for each of the 18 mesocosms (Field type: id)</li><li><b>Treatment</b>: Treatment assignment (Field type: categorical)</li><li><b>DistMature</b>: Distance from the nearest mature tree (any species with diameter at breast height > 30 cm). (Field type: numeric)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>d15N</b>: the delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>d13C</b>: the delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li></ul></li><li><p><b>LFE soil</b> (described in worksheet LFE_soil)</p><p>Description: Details the δ15N of soil sampled in logged forest across seven sample days up to day 64</p><p>Number of fields: 14</p><p>Number of data rows: 375</p><p>Fields: </p><ul><li><b>Name</b>: Code for date (ddmm), mesocosms ID and depth (00 = surface; 02 = 2 cm belowground; 05 = 5 cm belowground, and 10 = 10 cm belowground) (Field type: id)</li><li><b>Day</b>: Experimental day as the number of days since day zero (defined by the planting of the seedlings, either dipterocarps or palms) (Field type: id)</li><li><b>day2</b>: Experimental day as a factor (Field type: id)</li><li><b>Mesocosm</b>: Unique identifier for each of the 18 mesocosms (Field type: id)</li><li><b>Treatment</b>: Treatment assignment (Field type: categorical)</li><li><b>DistMature</b>: Distance from the nearest mature tree (any species with diameter at breast height > 30 cm). (Field type: numeric)</li><li><b>Depth</b>: The depth which the soil sample was taken from, i.e. 0002 is the horizon between the ground surface and 2 cm belowground (Field type: numeric)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>d15N</b>: the delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>d13C</b>: the delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2016-05-01 to 2017-02-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
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