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476 results for “Erosive”
Data for: Coral adaptive capacity insufficient to halt global transition of coral reefs into net erosion under climate change
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Microevolutionary change in mimicry? Erosion of rattling behaviour among nonvenomous snakes on islands lacking rattlesnakes
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Data from: Reducing soil erosion by improving community functional diversity in semi-arid grasslands
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Data from: Hybridization drives genetic erosion in sympatric desert fishes of western North America
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Data from: Within- and among-population impact of genetic erosion on adult fitness-related traits in the European tree frog Hyla arborea
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Data from: A regime shift from erosion to carbon accumulation in a temperate northern peatland
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Long-term data from field erosion plot studies in eastern Austria
<p>The data include measured data for three different soil tillage practices from three experimental sites in eastern Austria. The Tables 1-3 include for each site and each year the period of operation, the planted crop, the annual precipitation, the surface runoff in mm, the soil loss in t/ha and the surface losses of total nitrogen, total phosphorus and soil organic carbon in kg/ha. Crop yields were determined from 45 m² large plots with one or two replications. Yields in kg/ha are not available for all years, but relative yield in % are.</p> <p>The experiments were conducted at three sites in eastern Austria: in Mistelbach, Pixendorf and Pyhra. The soils in Mistelbach and Pyhra are classified as Typic Argiudolls while the soil in Pixendorf is an Entic Hapludoll. Soil textures range from silt loam to loam. Average annual rainfall (1994-2018) at the sites amounts from 621 to 916 mm with average annual air temperatures between 9.4 and 10.4 °C.</p> <p>Following soil tillage treatments were investigated: (1) conventional tillage system with ploughing in fall (CT), (2) mulch tillage with cover crops during winter (MT) and (3) no-till with cover crops during winter (NT). The study design was a randomized block and each treatment was replicated twice. Each treatment was equipped with one field runoff plot. The experimental plots were 6 m wide and – depending on site conditions - between 40 and 80 m long. The study design consisted of 3 (Mistelbach) and 4 m wide (Pixendorf, Pyhra) and 15 m long runoff plots for each management variation. Each plot was bordered by stainless steel metal sheets. At the lower end of the plot surface runoff and soil loss were collected in a trough and then diverted by a 100-mm PVC pipe to an Automated Erosion Wheel (AEW). The design of this AEW is similar to a tipping bucket and consists of four equal sections of five liters resulting in a resolution of each tip of 0.08 mm for 60 m² plots. A magnetic sensor system was used for continuous runoff measurement.</p> <p>Soil-water-suspension was divided by an adapted multi-tube divisor taking 3.3% of the sample that is collected in a 60 L collection tank. After each erosive storm the collection tank was emptied and the runoff sample was brought to the laboratory, weighed and dried until constant mass was achieved to determine sediment concentration. Based on the continuously measured runoff data from the data logging system and the sediment concentration the amount of soil loss from the plots was calculated for each erosive event.</p> <p>Throughout the investigation period soil erosion, surface runoff and nutrient and carbon losses and partly also pesticide losses due to erosion processes were determined for all sites and tillage systems. Immediately after planting/seeding of summer crops the erosion plots were installed and then operated until harvest. After the harvest the equipment was removed. No measurements were performed during winter due to limited accessibility and frost damages to the equipment. At each site an automatic tipping bucket rain gauge was placed to measure rainfall in 5-min intervals.</p> <p>Crop yield was determined from each treatment with three replications.</p> <p>Total nitrogen and total carbon in sediment samples was analysed by dry combustion [3](OeNorm L1080, 1989) using a C/N Analyzer (Vario Max CN, Elementar). Soil organic carbon content was obtained by subtracting inorganic carbon content measured volumetrically by the Scheibler method with a Calcimeter (OeNorm L1084, 1989). Total phosphorus of the sediment was determined using a UV/VIS spectral photometer (DU-640 Beckmann).</p> <p>Pesticides were extracted from water by solid phase extraction and from sediments using distilled water or organic solvent [6]. After shaking for several hours and centrifugation, the sample passed through a solid phase extraction. After evaporation of the extract the pesticide residues were redissolved in another solvent and analysed with high performance liquid chromatography (HPLC).</p>
Dataset for ``Valley formation on early Mars by subglacial and fluvial erosion´´
<p>These datasets contain:</p> <p>-Morphometrical data for 66 Martian valley networks, as described in the study ``Valley formation on early Mars by subglacial and <br> fluvial erosion´´.</p> <p>- Shape files and raw data text files with MOLA altimetry data extracted from high resolution valley streamlines, for the purpose of longitudinal profile analysis. </p> <p>- Longitudinal profile observations, including the presence and interpretation of undulating sections. </p> <p>You are free to use and modify this data. If you do so, please cite the study </p> <p>Grau Galofre et al., 2020, ``Valley formation on early Mars by subglacial and fluvial erosion´´</p>
Supporting Information for "How erosion influence fault segmentation and earthquakes in thrust belts: Low-temperature thermochronology and fluvial shear stress analyses on the southern Longmen Shan, eastern Tibet"
<p>Supporting Information for</p> <p>How erosion influence fault segmentation and earthquakes in thrust belts: Low-temperature thermochronology and fluvial shear stress analyses on the southern Longmen Shan, eastern Tibet</p> <p>Yijia Ye<sup>1</sup>, Xibin Tan<sup>1, </sup>*, Yiduo Liu<sup>2</sup>, Feng Shi<sup>1</sup>, Yuan-Hsi Lee<sup>3</sup>, Michael A. Murphy<sup>2</sup>, Xiwei Xu<sup>4</sup></p> <ol> <li>State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, 100029, China</li> <li>Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, 77204-5007, USA</li> <li>Department of Earth and Environmental Sciences, National Chung-Cheng University, Chia-Yi, 62102, Taiwan</li> <li>Institute of Crustal Dynamics, China Earthquake Administration, Beijing, 100085, China</li> </ol> <p><em>* </em>Corresponding author. E-mail address: <a href="mailto:tanxibin@sina.com">tanxibin@sina.com</a></p> <p> </p> <p><strong>Contents of this file </strong></p> <p>Figures S1 and Table S1</p>
Data from: Local biodiversity erosion in South Brazilian grasslands under moderate levels of landscape habitat loss
1.Habitat loss is one of the greatest threats to biodiversity, exerting negative effects on the ecological viability of natural vegetation remnants. The South Brazilian grasslands belong to one of the largest temperate grassland regions in the world, but have lost 50% of their natural extent in the past 35 years. To date, there is no empirical evidence for the effects of habitat loss on these grasslands' biological diversity, undermining their conservation. 2.Using data from a large-scale biodiversity survey, we asked if local plant communities respond to levels of habitat loss representative of the entire region (≤50%). Vegetation in grassland remnants was sampled in 24 landscapes at three localities each, using 9 plots per locality. To investigate whether species losses were a consequence of stochastic or nonrandom local extinctions and whether plant communities became more homogenized, we evaluated species richness, beta-diversity components (spatial turnover and nestedness), and phylogenetic diversity, in respect to landscape change. In part of the landscapes, arthropods were sampled to investigate if loss of plant diversity had a cascading effect on other trophic levels. We evaluated generic richness of ants, an omnivore group with high levels of plant associations, in respect to a plant community's phylogenetic diversity. 3.Local plant communities in landscapes with less grassland cover had fewer species, less spatial turnover, increased nestedness and lower phylogenetic diversity. Our results suggest that the observed species loss can be linked to taxonomic homogenization and is nonrandom, decreasing evolutionary diversity within the community. Furthermore, ant richness declined by 50% in plant communities with the lowest phylogenetic diversity, suggesting that effects of habitat loss propagate to higher trophic levels. 4.Policy implications. We conclude that the biological diversity of South Brazilian grasslands, at the producer and consumer level, is at risk under the current rate of land use conversion, even at habitat losses below 50%. To avoid substantial biodiversity loss, conservation and more restrictive policies for conversion of native grasslands to different land uses in South Brazil are urgent.
Data from: Virus adaptation to quantitative plant resistance: erosion or breakdown?
Adaptation of populations to new environments is frequently costly due to trade-offs between life history traits, and consequently, parasites are expected to be locally adapted to sympatric hosts. Also, during adaptation to the host, an increase of parasite fitness could have direct consequences on its aggressiveness (i.e. the quantity of damages caused to the host by the virus). These two phenomena have been observed in the context of pathogen adaptation to host qualitative and monogenic resistances. However, the ability of pathogens to adapt to quantitative polygenic plant resistances and the consequences of these potential adaptations on other pathogen life history traits remain to be evaluated. Using Potato virus Y and two pepper genotypes (one susceptible and one with quantitative resistance), experimental evolutions showed that adaptation to a quantitative resistance was possible and resulted in resistance breakdown. This adaptation was associated to a fitness cost on the susceptible cultivar, but had no consequence neither in terms of aggressiveness, which could be explained by a high tolerance level, nor in terms of aphid transmission efficiency. It results that quantitative resistances are not necessarily durable but management strategies mixing susceptible and resistant cultivars in space and/or in time should be useful to preserve their efficiency.
Data from: Clinical spectrum and risk factors associated with asymptomatic erosive esophagitis as determined by Los Angeles classification: a cross-sectional study
Background: Gastro esophageal reflux disease (GERD) is a chronic and recurrent disease, and it varies in regions. However, to date, there are no reports available on clinical features and the risk factors for the asymptomatic reflux esophagitis in Nepalese adults. Methods: Data were gathered from 142 erosive patients who had undergone endoscopy at Bir Hospital, Kathmandu. Los Angeles classification was used to grade the severity of the disease. Patients were interviewed to find out the presence of various reflux symptoms. Results: Based on the Los Angeles classification, the severity of the disease assessed was; grade A 31.8% (31/142), grade B 39.4 % (56/142), grade C 33.8% (48/142), and grade D 4.9% (7/142). One hundred and twenty six (88.7%) subjects had reflux symptoms. Prevalence of asymptomatic esophagits was 16(11.3%). Age was independently linked to asymptomatic esophagitis (P<0.05), and the odd of being asymptomatic appeared lower in younger adults (P<0.05; OR: 0.118; CI: 0.014-.994). Conclusion: A low prevalence of asymptomatic reflux esophagits (RE) was seen. Most subjects experienced mild to moderate RE. Age remained an independent factors associated with reflux esophagitis, and the odd of being asymptomatic was lower in younger age.
Data from: Metabolic erosion primarily through mutation accumulation, and not tradeoffs, drives limited evolution of substrate specificity in Escherichia coli
Evolutionary adaptation to a constant environment is often accompanied by specialization and a reduction of fitness in other environments. We assayed the ability of the Lenski Escherichia coli populations to grow on a range of carbon sources after 50,000 generations of adaptation on glucose. Using direct measurements of growth rates, we demonstrated that declines in performance were much less widespread than suggested by previous results from Biolog assays of cellular respiration. Surprisingly, there were many performance increases on a variety of substrates. In addition to the now famous example of citrate, we observed several other novel gains of function for organic acids that the ancestral strain only marginally utilized. Quantitative growth data also showed that strains with higher mutation rate exhibited significantly more declines, suggesting that most metabolic erosion was driven by mutation accumulation and not by physiological tradeoffs. These reductions in growth by mutator strains were ameliorated by growth at lower temperature, consistent with the hypothesis that this metabolic erosion is largely caused by destabilizing mutations to the associated enzymes. We further hypothesized that reductions in growth rate would be greatest for substrates used most differently from glucose, and we used flux balance analysis to formulate this question quantitatively. To our surprise, we found no significant relationship between decreases in growth and dissimilarity to glucose metabolism. Taken as a whole, these data suggest that in a single resource environment, specialization does not mainly result as an inevitable consequence of adaptive tradeoffs, but rather due to the gradual accumulation of disabling mutations in unused portions of the genome.
Dataset: Modelling Seepage Erosion in Porous Media with a Linear Decay Function
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PROTECTION OF IRRIGATED AREAS FROM WATER EROSION
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Supplementary Information for "Deciphering controls of pore-pressure evolution on sediment bed erosion by debris flows"
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Alpine meadow patch erosion data
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Mitigating Rainfall Induced Soil Erosion through Bio-approach: From Laboratory Test to Field Trail
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STR data from: Temporal analysis shows relaxed genetic erosion following improved stocking practices in a subarctic transnational brown trout population
<p><span>Maintaining standing genetic variation is a challenge in human-dominated landscapes. We used genetic (i.e., 16 short tandem repeats) and morphological (i.e., length and weight) measurements of 593 contemporary and historical brown trout (<i>Salmo trutta</i>) samples to study fine-scale and short-term impacts of different management practices. These had changed from traditional breeding practices, using the same broodstock for several years, to modern breeding practices, including annual broodstock replacement, in the transnational subarctic Pasvik River. Using population genetic structure analyses (i.e., Bayesian assignment tests, DAPCs, and PCAs), four historical genetic clusters (E2001A-D), likely representing family lineages resulting from different crosses, were found in zone E. These groups were characterized by consistently lower genetic diversity, higher within-group relatedness, lower effective population size, and significantly smaller body size than contemporary stocked (E2001E) and wild fish (E2001F). However, even current breeding practices are insufficient to prevent genetic diversity loss and morphological changes as demonstrated by on average smaller body sizes and recent genetic bottleneck signatures in the modern breeding stock compared to wild fish. Conservation management must evaluate breeding protocols for stocking programs and assess if these can preserve remaining natural genetic diversity and morphology in brown trout for long-term preservation of freshwater fauna. </span></p>
Soil Erosion Modeling dataset
<p><strong><span>Descriptions of the soil erosion modeling dataset</span></strong></p> <p><span>Introduction</span></p> <p><span>The "Soil Erosion Modeling dataset.xlsx" file contains data on soil erosion modeling studies conducted in Ethiopia. It includes information about the study area, modeling methods, erosion agents, soil loss estimates, and model validation techniques. This dataset can be used to analyze trends in soil erosion modeling research in Ethiopia, identify the most used models and methods, and assess the effectiveness of different soil conservation practices. The dataset <span>focuses on studies related to soil erosion modeling and contains detailed information on various research articles. </span></span></p> <p><strong><span>Methodology</span></strong></p> <p><span>The data was collected from scientific peer-reviewed papers published between 2003 and 2023 in reputable<span>, Scopus-indexed international journals. We used the Scopus, ScienceDirect, and SpringerLink databases to retrieve and download all the articles in the present dataset. The dataset contains 38 attributes, 38 columns, and 371 rows, excluding the header. </span></span></p> <p><strong><span>Here's a brief description of the dataset:</span></strong></p> <p><span>Sheet Name: Sheet1</span></p> <p><span>Total Columns: 38</span></p> <p><span>Total Rows: Includes multiple rows<span>, each representing a distinct research study.</span></span></p> <p><strong><span>Column Descriptions</span></strong></p> <p><span>ID: Unique identifier for each study.</span></p> <p><span>Publication Year: The year the study was published.</span></p> <p><span>Publisher: The publishing entity.</span></p> <p><span>Journal: The journal where the study was published.</span></p> <p><span>DOI: Digital Object Identifier for the study.</span></p> <p><span>Authors: contains authors of each study.</span></p> <p><span>Title: Title of the research paper.</span></p> <p><span>Region of Study: Geographic region where the study was conducted.</span></p> <p><span>Name of the Study Area: Specific area within the region.</span></p> <p><span>Study Scale: The scale at which the study was conducted (e.g., Watershed, Plot).</span></p> <p><span>Erosion Agent: The agent at which the study was aimed</span></p> <p><span>Modeling Type: Modeled <span>erosion type (e.g., Rill and sheet, sediment yield)</span></span></p> <p><span>Quantification Synthesis: a <span>method of soil loss estimate (gross, net)</span></span></p> <p><span>The approach of soil loss estimate: The methodology (qualitative/quantitative)</span></p> <p><span>Average Soil loss (Mg ha-1 yr-1): The predicted average soil loss for each study</span></p> <p><span>Model Name: contains the name of all soil erosion models <span>applied in Ethiopia</span></span></p> <p><span>Modeling Aim: The aim at which the study was focused</span></p> <p><span>LUC/BMPs Status: The studies that focused on land use change or best management practices</span></p> <p><span>Modeled land use/cover (LU): The <span>land use/cover in which the study was modeled</span></span></p> <p><span>Land use/cover data sources: The sources of land use and land cover data for each study</span></p> <p><span>Modeled Period: The period when the study period was focused on</span></p> <p><span>Latitude/Longitude: The geographic coordinates of the study area</span></p> <p><span>Altmin/Altmax: The maximum and minimum altitude</span></p> <p><span>Area: Contains the area of each study</span></p> <p><span>Rainfall data indicative period: The period when the rainfall data was collected for each study</span></p> <p><span>Rainfall Data Type: <span>The type of rainfall data (point, gridded, etc.)</span></span></p> <p><span>Rainfall Time Resolution: Temporal resolution of the rainfall data (e.g., Daily, Event-based).</span></p> <p><span>Rainfall Amount (mm): Annual or seasonal rainfall amount.</span></p> <p><span>DEM Size for Topography: The size of Digital Elevation Model resolution.</span></p> <p><span>Fieldwork Activities: Whether fieldwork was conducted (Yes/No).</span></p> <p><span>Type of Fieldwork Activities: Specific types of fieldwork activities conducted.</span></p> <p><span>Soil Sampling Activities: <span>Was soil sampling conducted (Yes/No)?</span></span></p> <p><span>Type of Soil Information: <span>The soil information collected (e.g., Point, Maps).</span></span></p> <p><span>Validation of Model Results: Whether the model results were validated (Yes/No).</span></p> <p><span>Type of Validation: Method used for validation (e.g., Measured Sediment Yield).</span></p> <p><span>Model Calibration: Whether the model was calibrated (Yes/No).</span></p>
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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.