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118 results for “Eutrophication”
Data for the analysis from "Evidence for positive priming of leaf litter decomposition by contact with eutrophic pond sediments"
<p>These are the data files used in the analysis of the results of the experiments that are reported in the manuscript "Evidence for positive priming of leaf litter decomposition by contact with eutrophic pond sediments". More details on the analysis can be found in at: https://github.com/KennyPeanuts/sediment_priming</p>
Large projected decline in dissolved oxygen in a eutrophic estuary due to climate change
<p>This data includes the model codes and input files for the paper "Large projected decline in dissolved oxygen in a eutrophic estuary due to climate change" submitted to Journal of Geophysical Research-Oceans.</p> <p>It includes the input files and source code for ROMS and RCA model to produce simulations of Chesapeake Bay hypoxia during 1989-1998 and 2047-2098.</p> <p>ROMS (Regional Ocean Modeling System) model used in this study is version 3.4.</p> <p>RCA (Row-Column AESOP) water quality model used in this study is coupled with ROMS output, by UMCES group.</p> <p>For more details, please see the future publication.</p>
Eutrophication indicators in the Baltic Sea 1970-2100 and nutrient loads to three coastal systems. BALTSEM model simulations and observations.
<p>Dataset and model code accompanying manuscript: </p> <p>Ehrnsten, E. Humborg C., Gustafsson, E. and Gustafsson B. G. 2024. Disaster avoided: current state of the Baltic Sea without human intervention to reduce nutrient loads. Resubmitted to Limnology & Oceanography Letters 2024-09-13. </p> <p> </p> <p>This repository contains the following files:</p> <p> </p> <p>1_Data_description.pdf</p> <p>Description of data sets and details on model forcing and data collection methods.</p> <p> </p> <p>Eutrophication_indicators1970-2021_BALTSEM_and_observations.xlsx</p> <p>Eutrophication indicators in the Baltic Sea: BALTSEM model simulation output from real load and no reduction scenarios as well as observations 1970-2021.</p> <p> </p> <p>BALTSEM_output_future_1970-2100.xlsx</p> <p>BALTSEM model simulation output 1970-2021 with observed nutrient loads (Real loads scenario) and statistics of 100 model runs 2022-2100 with present (2021) nutrient loads. The 100 runs represent statistical variations in forcing and boundary conditions to account for uncertainty in future weather and sea level conditions.</p> <p> </p> <p>NPloads_BS_M_C.xlsx</p> <p>Nitrogen and phosphorus loads from the Baltic Sea, Mississippi and Changjiang catchments 1950-2021 collected from several published sources.</p> <p> </p> <p>baltsem9.5_carbon.tar.gz</p> <p>Copressed folder with model code for BALTSEM 9.5 as well as forcing data used in the simulations. Information on folder contents and a user guide to run the model simuations can be found in the file BALTSEMGettingStartedCarbon.pdf</p>
Dataset: The aftermath of a trophic cascade: Increased anoxia following invasive species introduction of a eutrophic lake
<p>This repository includes the setup and output from the analysis ran on Lake Mendota to explore the trophic cascade caused by invasion of spiny water flea in 2010. Scripts to run the model are located under /src, and the processed results for the discussion of the paper are located under /data_processed.</p>
Using Monitoring and Mechanistic Modeling to Improve Understanding of Eutrophication in a Shallow New England Estuary
<p>This data repository contains the names and description of data files used in the “Using Monitoring and Mechanistic Modeling to Improve Understanding of Eutrophication in a Shallow New England Estuary” manuscript by Cashel et al (2023). These data files, formatted as .txt files, include simulated and observed data of various water quality components with time. Time is always provided as the Julian Day in the first column (left). The type of data in each file is indicated by a parameter code. Parameter codes are defined below.</p> <ul> <li>SAL = salinity (ppt)</li> <li>WT = water temperature (℃)</li> <li>PAR = photosynthetically active radiation (W/m<sup>2</sup>)</li> <li>TN = total nitrogen (mg/L as N)</li> <li>NH3 = ammonium (mg/L as N)</li> <li>NO3 = nitrate + nitrite (mg/L as N)</li> <li>TP = total phosphorus (mg/L as P)</li> <li>DIP = orthophosphate (mg/L as P)</li> <li>CBOD = carbonaceous biological oxygen demand (mg/L)</li> <li>CHL = phytoplankton as chlorophyll <em>a </em>(µg/L)</li> <li>MACRO = macroalgae biomass (gDW/m<sup>2</sup>)</li> <li>DO = dissolved oxygen (mg/L)</li> </ul> <p><strong><em>1 - Simulated Data</em></strong></p> <p>1.1 – PRE Model Data</p> <p> The primary simulated data for this study includes model output on an interval of 0.05 days. Columns two through eight contain the average concentration of each WASP Segment per each time step. Data presented in these columns from left to right are from WASP Segments 8, 9, 10, 12, 14, 16, and 17.</p> <ul> <li>WASP_SAL.txt </li> <li>WASP_WT.txt </li> <li>WASP_PAR.txt</li> <li>WASP_TN.txt</li> <li>WASP_NH3.txt</li> <li>WASP_NO3.txt</li> <li>WASP_TP.txt</li> <li>WASP_DIP.txt</li> <li>WASP_CBOD.txt</li> <li>WASP_CHL.txt</li> <li>WASP_MACRO.txt</li> <li>WASP_DO.txt</li> </ul> <p>1.2 – Macroalgae Scenario Data</p> <p> Simulated data files also include a model scenario evaluating the impact of macroalgae as a state variable. This set of model output comes from simulations with macroalgae removed as a state variable. These files have a model output of 0.05 days with time in the first column, and the second column contains the average concentration within WASP segment 17.</p> <ul> <li>MACRO_PAR.txt</li> <li>MACRO_NH3.txt</li> <li>MACRO_NO3.txt</li> <li>MACRO_DIP.txt</li> <li>MACRO_CBOD.txt</li> <li>MACRO_CHL.txt</li> </ul> <p>1.3 – Dissolved Oxygen Parameter Analysis Data</p> <p> Simulated data from model scenarios evaluating the impact of parameterization on dissolved oxygen concentrations are listed below. These model simulations have an output of 0.05 days. Rows two through ten contain average DO concentration (mg/L) for WASP Segments 9, 10, 11, 12, 13, 14, 15, 16, and 17. Files containing “1” indicate the high condition of each parameter analysis, and those with a “2” indicate the low condition.</p> <ul> <li>CBOD1_DO.txt</li> <li>CBOD2_DO.txt</li> <li>Phyto1_DO.txt</li> <li>Phyto2_DO.txt</li> <li>SOD1_DO.txt</li> <li>SOD2_DO.txt</li> </ul> <p>1.4 – Heatmap Simulations</p> <p> Simulated data for heatmaps presents average concentrations from noon of each day. The first row is time, and the following rows of two through nine have simulated data for WASP Segments 10, 11, 12, 13, 14, 15, 16, and 17.</p> <ul> <li>WASP_TN2.txt</li> <li>WASP_TP2.txt</li> <li>WASP_DO2.txt</li> <li>WASP_CHL2.txt</li> </ul> <p><strong><em>2 – Observed Data </em></strong></p> <p>2.1 – Sonde Data</p> <p> Observed sonde data is presented in .txt files from various years and location. Each data file has the first column of time, and the second column as concentration per each time step. File names begin with the site name, followed by an “S” or “B” (surface and bottom respectively, if applicable), the year, and the parameter code. Site names are: LNB (Little Narragansett Bay), Pawcatuck (Pawcatuck Point), Avondale (Avondale Marina), Greenhaven (Greenhaven Marina), WYC (Westerly Yacht Club), PR (Pawcatuck Rock), Viking (Viking Marina), and R1 (Route 1). File names per location are provided in the two tables below. Row 1 of each table has the site name, and the corresponding files are listed below.</p> <table align="center"> <tbody> <tr> <td> <p><strong>LNB</strong></p> </td> <td> <p><strong>Pawcatuck</strong></p> </td> <td> <p><strong>Avondale </strong></p> </td> <td> <p><strong>Greenhaven </strong></p> </td> </tr> <tr> <td> <p>LNB_SAL.txt</p> </td> <td> <p>Pawcatuck2018_ SAL.txt</p> </td> <td> <p>AvondaleS2019_SAL.txt</p> </td> <td> <p>GreenhavenS2018_SAL.txt</p> </td> </tr> <tr> <td> <p>LNB_WT.txt</p> </td> <td> <p>Pawcatuck2018_WT.txt</p> </td> <td> <p>AvondaleB2019_SAL.txt</p> </td> <td> <p>GreenhavenB2018_SAL.txt</p> </td> </tr> <tr> <td> <p>LNB_DO.txt</p> </td> <td> <p>Pawcatuck2018_DO.txt</p> </td> <td> <p>AvondaleS2019_WT.txt</p> </td> <td> <p>GreenhavenS2018_WT.txt</p> </td> </tr> <tr> <td> <p>LNB_CHL.txt</p> </td> <td> <p>Pawcatcuk2018_CHL.txt</p> </td> <td> <p>AvondaleB2019_WT.txt</p> </td> <td> <p>GreenhavenB2018_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_SAL.txt</p> </td> <td> <p>AvondaleS2019_CHL.txt</p> </td> <td> <p>GreenhavenS2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_WT.txt</p> </td> <td> <p>AvondaleB2019_CHL.txt</p> </td> <td> <p>GreenhavenB2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_DO.txt</p> </td> <td> <p>AvondaleS2019_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_CHL.txt</p> </td> <td> <p>AvondaleB2019_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_SAL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_SAL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_WT.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_WT.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_CHL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_CHL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_DO.txt</p> </td> <td> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <table align="center"> <tbody> <tr> <td> <p><strong>WYC</strong></p> </td> <td> <p><strong>PR</strong></p> </td> <td> <p><strong>Viking</strong></p> </td> <td> <p><strong>R1</strong></p> </td> </tr> <tr> <td> <p>WYC2018_SAL.txt</p> </td> <td> <p>PRS2018_SAL.txt</p> </td> <td> <p>Viking2018_SAL.txt</p> </td> <td> <p>R1S2018_SAL.txt</p> </td> </tr> <tr> <td> <p>WYC2018_WT.txt</p> </td> <td> <p>PRB2018_SAL.txt</p> </td> <td> <p>Viking2018_WT.txt</p> </td> <td> <p>R1B2018_SAL.txt</p> </td> </tr> <tr> <td> <p>WYC2018_CHL.txt</p> </td> <td> <p>PRS2018_WT.txt</p> </td> <td> <p>Viking2018_CHL.txt</p> </td> <td> <p>R1S2018_WT.txt</p> </td> </tr> <tr> <td> <p>WYC2018_DO.txt</p> </td> <td> <p>PRB2018_WT.txt</p> </td> <td> <p>Viking2018_DO.txt</p> </td> <td> <p>R1B2018_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2018_DO.txt</p> </td> <td> <p>Viking2019_SAL.txt</p> </td> <td> <p>R1S2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2018_DO.txt</p> </td> <td> <p>Viking2019_WT.txt</p> </td> <td> <p>R1B2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_SAL.txt</p> </td> <td> <p>Viking2019_CHL.txt</p> </td> <td> <p>R1S2020_SAL.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_SALtxt</p> </td> <td> <p>Viking2019_DO.txt</p> </td> <td> <p>R1B2020_SAL.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_WT.txt</p> </td> <td> <p>Viking2020_SAL.txt</p> </td> <td> <p>R1S2020_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_WT.txt</p> </td> <td> <p>Viking2020_WT.txt</p> </td> <td> <p>R1B2020_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_DO.txt</p> </td> <td> <p>Viking2020_CHL.txt</p> </td> <td> <p>R1S2020_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_DO.txt</p> </td> <td> <p>Viking2020_DO.txt</p> </td> <td> <p>R1B2020_DO.txt</p> </td> </tr> </tbody> </table>
Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Beaverdam Reservoir forecasting data assimilation experiments using the FLARE (Forecasting Lake And Reservoir Ecosystems) system: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations and meteorological data, forecasts.zip contains forecast parquet files generated from the BVR FLARE DA experiment workflow, and scores.zip contains forecast skill metrics required for analysis. Within the forecasts and scores folders, there are four runs that were conducted with different parameter tuning and uncertainty quantification. The "all_UC" folder includes forecasts run with process, driver, parameter, and initial condition uncertainty quantification. The "IC_off" folder includes forecasts run without initial conditions uncertainty included (i.e., only process, driver, and parameter uncertainty). The "constant_bad_pars" folder includes forecasts run with constant parameters (but daily updating of initial conditions) that were not tuned for Beaverdam Reservoir before forecasts were generated. Finally, the "tuned_bad_pars" folder includes forecasts that were run with daily updating of initial conditions and parameters, but the parameters started out at random values that were not tuned for Beaverdam Reservoir.</p>
Declining bivalve species and functional diversity along a coastal eutrophication-deoxygenation gradient in the northern Gulf of Mexico
Open the record for dataset details and reuse information.
Biological and physical controls on multidecadal acidification in a eutrophic estuary
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Agricultural land use and ensuing eutrophication both shape parasitic trematode communities in rural African lakes
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Paleolimnological assessment of a hyper-eutrophic lake (Nowlans Lake, N.S., Canada) Cladoceran communities
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Data from: Cities can grow without harming lakes: Lake Washington has become less eutrophic despite rapid population growth
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Temporal and spatial dynamics of Synechococcus clade II and other microbes in the eutrophic subtropical San Diego Bay
This data set comprises amplicon sequence variants (ASVs) from the nutrient-replete waters of San Diego Bay (SDB). Using 16S and 18S rRNA gene and internal transcribed spacer (ITS) region sequencing for Synechococcus, we analyzed multiple locations in San Diego Bay monthly for over a year (2021-2022) with additional samples dating back to 2015. San Diego Bay ITS region sequences were compared to nearby coastal samples collected from the Scripps Pier at Scripps Institution of Oceanography.
Fast and furious: Early differences in growth rate drive short-term plant dominance and exclusion under eutrophication
<p>1. The reduction of plant diversity following eutrophication threatens many ecosystems worldwide. Yet, the mechanisms by which species are lost following nutrient enrichment are still not completely understood, nor are the details of when such mechanisms act during the growing season, which hampers understanding and the development of mitigation strategies.</p> <p>2. Using a common garden competition experiment, we found that early-season differences in growth rates among five perennial grass species measured in monoculture predicted short-term competitive dominance in pairwise combinations and that the proportion of variance explained was particularly greater under a fertilisation treatment.</p> <p>3. We also examined the role of early-season growth rate in determining the outcome of competition along an experimental nutrient gradient in an alpine meadow. Early differences in growth rate between species predicted short-term competitive dominance under both ambient and fertilized conditions and competitive exclusion under fertilized conditions.</p> <p>4. The results of these two studies suggests that plant species growing faster during the early stage of the growing season gain a competitive advantage over species that initially grow more slowly, and that this advantage is magnified under fertilisation. This finding is consistent with the theory of asymmetric competition for light in which fast-growing species can intercept incident light and hence outcompete and exclude slower-growing (and hence shorter) species. We predict that the current chronic nutrient inputs into many terrestrial ecosystems worldwide will reduce plant diversity and maintain a low biodiversity state by continuously favouring fast-growing species. Biodiversity management strategies should focus on controlling nutrient inputs and reducing the growth of fast-growing species early in the season.</p>
Data from: Herbivory and eutrophication mediate grassland plant nutrient responses across a global climatic gradient
Plant stoichiometry, the relative concentration of elements, is a key regulator of ecosystem functioning and is also being altered by human activities. In this paper we sought to understand the global drivers of plant stoichiometry and compare the relative contribution of climatic vs. anthropogenic effects. We addressed this goal by measuring plant elemental (C, N, P and K) responses to eutrophication and vertebrate herbivore exclusion at eighteen sites on six continents. Across sites, climate and atmospheric N deposition emerged as strong predictors of plot‐level tissue nutrients, mediated by biomass and plant chemistry. Within sites, fertilization increased total plant nutrient pools, but results were contingent on soil fertility and the proportion of grass biomass relative to other functional types. Total plant nutrient pools diverged strongly in response to herbivore exclusion when fertilized; responses were largest in ungrazed plots at low rainfall, whereas herbivore grazing dampened the plant community nutrient responses to fertilization. Our study highlights (1) the importance of climate in determining plant nutrient concentrations mediated through effects on plant biomass, (2) that eutrophication affects grassland nutrient pools via both soil and atmospheric pathways and (3) that interactions among soils, herbivores and eutrophication drive plant nutrient responses at small scales, especially at water‐limited sites.
Data from: Human eutrophication drives biogeographic saltmarsh productivity patterns in China
Saltmarshes are important natural carbon sinks with a large capacity to absorb exogenous nutrient inputs. The effects of nutrients on biogeographic productivity patterns, however, have been poorly explored in saltmarshes. We conducted field surveys to examine how complex environments affect productivity of two common saltmarsh plants, invasive Spartina alterniflora and native Phragmites australis, along an 18,000-km latitudinal gradient on the Chinese coastline. We harvested peak aboveground biomass as a proxy for productivity, and measured leaf functional traits (e.g., leaf area, specific leaf area [SLA], leaf nitrogen [N] and phosphorus [P]), soil nutrients (dissolved inorganic N (DIN) and available P (AP)), and salinity. We compiled data on mean annual temperature (MAT) and exogenous nutrients (both N and P). Then, we examined how these abiotic factors affect saltmarsh productivity using both linear mixed effect models and structural equation modelling. Using a trait-based approach, we also examined how saltmarsh productivity responds to changing environments across latitude. Exogenous nutrients (both N and P) compared with temperature and other variables (e.g., DIN, AP, salinity) were the dominant factors in explaining the biogeographic productivity patterns of both S. alterniflora and P. australis. Leaf size-related traits (e.g., leaf area), rather than leaf economic traits (e.g., SLA, leaf N and P), can be used to indicate the positive effects of exogenous nutrients on the productivity of these two species. Our results demonstrated that human eutrophication surpassed temperature as the major driver of biogeographic saltmarsh productivity pattern, challenging current models in which biogeographic productivity pattern is primarily controlled by temperature. Our findings have potential broad implications for the management of S. alterniflora, which is a global invader, as it has benefited from coastal eutrophication. Furthermore, exogenous nutrient availability and leaf size need to be integrated into earth system models that are used to predict global plant productivity in saltmarshes.
Wetlands as a potential multi-functioning tool to mitigate eutrophication and brownification
<p>Eutrophication and brownification are ongoing environmental problems affecting aquatic ecosystems. Due to anthropogenic changes, increasing amounts of organic and inorganic compounds are entering aquatic systems from surrounding catchment areas, increasing both nutrients, total organic carbon (TOC), and water color with societal, as well as ecological consequences. Several studies have focused on the ability of wetlands to reduce nutrients, whereas data on their potential to reduce TOC and water color is scarce. Here we evaluate wetlands as a potential multi-functional tool for mitigating both eutrophication and brownification. Therefore, we performed a study over 18 months in nine wetlands allowing us to estimate the reduction in concentrations of total nitrogen (TN), total phosphorus (TP), TOC, and water color. We show that wetland reduction efficiency with respect to these variables was generally higher during summer, but many of the wetlands were also efficient during winter. We also show that some, but not all, wetlands have the potential to reduce TOC, water color, and nutrients simultaneously. However, the generalist wetlands that reduced all four parameters were less efficient in reducing each of them than the specialist wetlands that only reduced one or two parameters. In a broader context, generalist wetlands have the potential to function as multi-functional tools to mitigate both eutrophication and brownification of aquatic systems. However, further research is needed to assess the design of the generalist wetlands and to investigate the potential of using several specialist wetlands in the same catchment.</p>
NIOO-QingZ/Geertruidenberg_Mesocosms: Towards climate-robust water quality management: testing the efficacy of different eutrophication control measures during a heat
<p>Data and Codes used in the following open-access publication: </p> <p>https://www.sciencedirect.com/science/article/pii/S0048969722015145</p>
Scrub encroachment promotes biodiversity in temperate European wetlands under eutrophic conditions
<p>Wetlands are important habitats, often threatened by drainage, eutrophication and suppression of grazing. In many countries, considerable resources are spent combatting scrub encroachment. Here, we hypothesize that encroachment may benefit biodiversity – especially under eutrophic conditions where asymmetric competition among plants compromises conservation targets. We studied the effects of scrub cover, nutrient levels and soil moisture on richness of vascular plants, bryophytes, soil fungi and microbes in open and overgrown wetlands. We also tested the effect of encroachment, eutrophication and soil moisture on indicators of conservation value (red-listed species, indicator species and uniqueness). Plant and bryophyte species richness peaked at low soil fertility, whereas soil fertility promoted soil microbes. Soil fungi responded negatively to increasing soil moisture. Lidar-derived variables reflecting degree of scrub cover had predominantly positive effects on species richness measures. Conservation value indicators had a negative relationship to soil fertility and a positive to encroachment. For plant indicator species, the negative effect of high nutrient levels was offset by encroachment, supporting our hypothesis of competitive release under shade. The positive effect of soil moisture on indicator species was strong in open habitats only. Nutrient poor mires and meadows host many rare species and require conservation management by grazing and natural hydrology. On former agricultural lands, where restoration of infertile conditions is unfeasible, we recommend rewilding with opportunities for encroachment towards semi-open willow scrub and swamp forest, with the prospect of high species richness in bryophytes, fungi and soil microbes and competitive release in the herb layer.</p>
Data from: Water level drawdown induces a legacy effect on the seed bank and retains sediment chemistry in a eutrophic clay wetland
<p>The lack of extreme water level fluctuations in managed, non-peat forming wetland ecosystems can result in decreased productivity through the loss of heterogeneity of these ecosystems. Stochastic disruption, such as a water level drawdown, can effectively reverse this effect and return the wetland to a more productive state, associated with higher biodiversity through new vegetation development. Yet, aside from the effect on vegetation dynamics, little is known about longer-term effects (30 years) of a water level drawdown, hereafter referred to as legacy effects, and how this may impact future water level drawdowns.</p> <p>Here, we aim to unravel the legacy effects of a water level drawdown, stand alone and along a water level gradient, on seed bank properties and nutrient availability in a eutrophic clay wetland. To identify these, we studied the hydrologically managed nature reserve Oostvaardersplassen in the Netherlands. Here, one section was subjected to a multi-year water level drawdown and another section was kept inundated. We determined seed bank properties in both areas, spatially and along a soil elevation gradient (20 cm). Nutrient availability was measured by taking sediment samples along the water level gradient and through experimental manipulation of the water level in an indoor mesocosm experiment.</p> <p>Germination was higher in locations with a water level drawdown history, especially at relatively high elevations. Additionally, the proportion of pioneer species in the seed bank was higher in the water level drawdown area. Overall, nutrient concentrations were higher compared to other systems. Nutrient availability was higher in the inundated area and did not respond to the water level gradient. We conclude that 30 years after an induced water level drawdown there is no depletion of nutrients, while we still observe a legacy effect in the number of viable seeds in the seed bank.</p>
Data for: Thirty-one years of warming and oxygen decline in Massachusetts Bay, a well-flushed non-eutrophic temperate coastal waterbody
<p><strong>This dataset consists of the primary data used in manuscript ("Thirty-one years of warming and oxygen decline in Massachusetts Bay, a well-flushed non-eutrophic temperate coastal waterbody") submitted to Journal of Geophysical Research Oceans.</strong></p> <p><strong>Description of the data and file structure</strong></p> <p>The primary data are in the file T-S-DO_1992-2022-20230907.csv, in columnar format, and the metadata, including column information, are in file ColumnAndCodeDescriptions.txt.</p> <p><strong>Sharing/Access information</strong></p> <p>The primary data were generated by the Massachusetts Water Resources Authority (MWRA) and provided in response to a data request. They are public data, available on request from MWRA by contacting <a href="mailto:sally.carroll@mwra.com" target="_blank" rel="noopener">sally.carroll@mwra.com (opens in new window)</a> or <a href="mailto:douglas.hersh@mwra.com" target="_blank" rel="noopener">douglas.hersh@mwra.com (opens in new window)</a>. The methods of data collection are detailed in the Quality Assurance Project Plan: </p> <p>Libby, P. S., Fitzpatrick, M. R., Willenberg, Z. J., Abramson Pala, S. L., Borkman, D. G., & Turner, J. T. (2024). Quality assurance project plan (QAPP) for water column monitoring 2024-2026: Tasks 4-8 and 11. Boston: Massachusetts Water Resources Authority. Report 2024-02. 73p. <a href="https://www.mwra.com/harbor/enquad/pdf/2024-02.pdf" target="_blank" rel="noopener">https://www.mwra.com/harbor/enquad/pdf/2024-02.pdf (opens in new window)</a></p> <p> </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.