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566 results for “ponds”
Figure 3 in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 3. Seasonal variation of total taxa richness (A), mean density (A), and relative contribution of biomass (B) of most abundant groups of aquatic invertebrates at three ponds in a Patagonian wetland (Mallín Crespo) during the study period (May 2008 to April 2009). Livestock stocking period is indicated by the black bar.
PASS Survey - Brasside Pond, Durham
<p>PASS Survey recorded on the south-side of Brasside Pond SSSI (Durham). Perimeter shading and macrophyte coverage given based on area of survey, as this is a large pond. </p>
Pond-based production of oyster spat (Ostrea edulis)
<p>Aim : Understanding how temperature affects the reproduction cycle of Flat oysters (<i>Ostrea edulis</i>) and development of protocol for pond production of oyster spat to ensure a reliable supply of high-quality oyster seed.</p><p>1) Mature oysters from Galway Bay, Ireland, were placed in land-based ponds for spawning. The amount of larvae in the ponds as well as the water temperature in the ponds were measured daily during the study. Daily temperature means in Galway Bay, Carton point New Quay, Ireland were extracted from online databasis for use in a temperature-maturation model. The data was collected during the summers 2019 to 2021 at Galway Bay, Ireland.</p><p> 2) Oyster broodstock were placed in land based tanks and allowed to spawn. Abiotic parameters (temperature, salinity/alkalinity, oxygene and pH) as well as biotic parameters (microalgal concentration and species composition, and oyster larval density), were monitored during the study. Data was collected during June 2021.The study was performed in Fiskebäckskil, on the Swedish west coast. </p>
Fig. 3a-j in Physico-chemical characteristics of habitats colonized by the pond snail Radix labiata (Gastropoda, Basommatophora, Lymnaeidae): a model approach
Fig. 3a-j: Graphical presentation of essential parameters associated with logistic regression: white vertical line: position of the maximum probability of occurrence (xmax), black bar: optimum range of the given variable, grey-shaded area: range of the given variable that is still tolerated by the species; a) water temperature, b) pH, c) electric conductivity, d) oxygen content in the water, e) nitrate concentration in the water, f) water depth, g) biological oxygen demand within five days, h) content of ammonium nitrogen, i) geographic altitude, j) current velocity.
Fig. 1 in Physico-chemical characteristics of habitats colonized by the pond snail Radix labiata (Gastropoda, Basommatophora, Lymnaeidae): a model approach
Fig. 1: General habitus of the shell of R. labiata as well as the living animal: a) Front view of the shell (height: 1.4 cm, width: 0.75 cm), b) back view of the shell, c) living animal with its typical triangular tentacles.
Fig. 2a-j in Physico-chemical characteristics of habitats colonized by the pond snail Radix labiata (Gastropoda, Basommatophora, Lymnaeidae): a model approach
Fig. 2a-j: Results of the logistic regression procedure carried out for ten environmental variables: a) water temperature, b) pH, c) electric conductivity, d) oxygen content in the water, e) nitrate concentration in the water, f) water depth, g) biological oxygen demand within five days, h) content of ammonium nitrogen, i) geographic altitude, j) current velocity.
Year 1 Tanzanian ponds snail-parasite dynamics
<div> <div> <div> <div> <p>Different populations of hosts and parasites experience distinct seasonality in environmental factors, depending on local-scale biotic and abiotic factors. This can lead to highly heterogeneous disease outcomes across host ranges. Variable seasonality characterizes urogenital schistosomiasis, a neglected tropical disease caused by parasitic trematodes (<em>Schistosoma</em> <em>haematobium</em>). Their intermediate hosts are aquatic <em>Bulinus</em> snails that are highly adapted to extreme rainfall seasonality, undergoing prolonged dormancy yearly. While <em>Bulinus</em> snails have a remarkable capacity for rebounding following dormancy, we investigated the extent to which parasite survival within snails is diminished. We conducted an investigation of seasonal snail-schistosome dynamics in 109 ponds of variable ephemerality in Tanzania from August 2021 to July 2022. First, we found that ponds have two synchronized peaks of schistosome infection prevalence and observed cercariae, though of lower magnitude in the fully-desiccating than non-desiccating ponds. Second, we evaluated total yearly schistosome prevalence across an ephemerality gradient, finding ponds with intermediate ephemerality to have the highest infection rates. We also investigated dynamics of non-schistosome trematodes, which lacked synonymity with schistosome patterns. We found peak schistosome transmission risk at intermediate pond ephemerality, thus the impacts of anticipated increases in landscape desiccation could result in increases or decreases in transmission risk with global change.</p> </div> </div> </div> </div>
Restored off-channel pond habitats create thermal regime diversity and refuges within a Mediterranean-climate watershed
<p>Cool-water habitats provide increasingly vital refuges for cold-water fish living on the margins of their historical ranges; consequently, efforts to enhance or create cool-water habitat are becoming a major focus of river restoration practices. However, the effectiveness of restoration projects for providing thermal refuge and creating diverse temperature regimes at the watershed scale remains unclear. In the Klamath River in Northern California, the Karuk Tribe Fisheries Program, the Mid-Klamath Watershed Council, and the U.S. Forest Service constructed a series of off-channel ponds that recreate floodplain habitat and support juvenile coho salmon (<em>Oncorhynchus kisutch</em>) and steelhead (<em>Oncorhynchus mykiss</em>) along the Klamath River and its tributaries. We instrumented these ponds and applied multivariate auto-regressive time series models of fine-scale temperature data from ponds, tributaries, and the mainstem Klamath River to assess how off-channel ponds contributed to thermal regime diversity and thermal refuge habitat in the Klamath riverscape. Our analysis demonstrated that ponds provide diverse thermal habitats that are significantly cooler than creek or mainstem river habitats, even during severe drought. Wavelet analysis of long-term (10 years) temperature data indicated that thermal buffering (i.e. dampening of diel variation) increased over time but was disrupted by drought conditions in 2021. Our analysis demonstrates that in certain situations, human-made off-channel ponds can increase thermal diversity in modified riverscapes even during drought conditions, potentially benefiting floodplain-dependent cold-water species. Restoration actions that create and maintain thermal regime diversity and thermal refuges will become an essential tool to conserve biodiversity in climate-sensitive watersheds. </p>
Data for: Morphological species delimitation in the Western Pond Turtle (Actinemys): Can machine learning methods aid in cryptic species identification?
<p>As the discovery of cryptic species has increased in frequency, there has been interest in whether geometric morphometric data can detect fine-scale patterns of variation that can be used to morphologically diagnose such species. We used a combination of geometric morphometric data and an ensemble of five supervised machine learning methods to investigate whether plastron shape can differentiate two putative cryptic turtle species, <em>Actinemys marmorata</em> and <em>Actinemys pallida</em>. <em>Actinemys</em> has been the focus of considerable research due to its biogeographic distribution and conservation status. Despite this work, reliable morphological diagnoses for its two species are still lacking. We validated our approach on two datasets, one consisting of eight morphologically disparate emydid species, and the other consisting of two subspecies of <em>Trachemys</em> (<em>T. scripta scripta</em>, <em>T. scripta elegans</em>). The validation tests returned near-perfect classification rates, demonstrating that plastron shape is an effective means for distinguishing taxonomic groups of emydids via machine learning methods. By contrast, the same methods did not return high classification rates for a set of alternative phylogeographic and morphological binning schemes in <em>Actinemys</em>. All classification hypotheses performed poorly relative to the validation datasets and no single hypothesis was unequivocally supported for <em>Actinemys</em>. Two hypotheses had machine learning performance that was marginally better than our remaining hypotheses. In both cases, those hypotheses favored a two-species split between <em>A. marmorata</em> and <em>A. pallida</em> specimens, lending tentative morphological support to the hypothesis of two <em>Actinemys</em> species. However, the machine learning results also underscore that <em>Actinemys</em> as a whole have lower levels of plastral variation than other turtles within Emydidae, but the reason for this morphological conservatism is unclear.</p>
Data from: Is exposure to chytrid fungus and ranavirus higher in ponds invaded by American bullfrogs?
<p>Data associated with manuscript: https://doi.org/10.1007/s10530-025-03648-8<br><br>The spread of emerging infectious diseases (EIDs) and non-native invasive species are interconnected processes driving biodiversity loss. A key example involves Batrachochytrium dendrobatidis (Bd) and ranavirus (Rv), pathogens contributing to global amphibian declines. Their occurrence has been linked to invasive American bullfrogs (Lithobates catesbeianus) serving as asymptomatic vectors. To determine the relationship between Bd and Rv exposure and bullfrogs, we investigated whether pathogen occurrence and environmental loads are correlated with the presence and density of bullfrogs and whether the seasonal variation in Bd exposure is related to bullfrog phenology. We sampled 157 ponds in Belgium, including four with known bullfrog and Bd presence that were monitored monthly for two years, using quantitative environmental DNA (eDNA) barcoding. We validated a duplexed assay that simultaneously targets Bd and Rv, and we quantified eDNA concentrations of bullfrogs, Bd, and Rv serving as proxies for density and pathogen loads. Bd was detected more frequently when bullfrogs were present and both the frequency of detection and environmental loads of Bd increased in ponds with high bullfrog densities. In contrast, Rv detection likelihood and load were not significantly related to bullfrog presence or density. Seasonal fluctuations in Bd loads varied between ponds with and without breeding activity. In the former, Bd was consistently detected, likely due to overwintering tadpoles. In the latter, Bd loads varied throughout the year, with peak loads coinciding with juvenile immigration. Collectively, our findings indicate that amphibian exposure to Bd, but not Rv, is higher in areas and periods where bullfrogs are present and occur at high densities</p>
Campus Pond Data from 2021-09-29 to 2021-11-02
<p>General Metadata for Campus Pond Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific sensors at the site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Campus Pond data from 2021-09-07 to 2021-09-28
<p>General Metadata for Campus Pond Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific sensors at the site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Campus Pond (Buffalo Creek Watershed) Water Data from 2021-11-03 to 2021-11-23
<pre># General Metadata for Campus Pond Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md ## File Modified ## Description These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific sensors at the site are: * Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland. The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2) </pre>
Fig. 1 in The Seasonal Population Dynamics Of The Cyclopoid Copepods (Cyclopoida, Cyclopidae) In Ponds Of Kyiv Region (Ukraine)
Fig. 1. Seasonal population dynamics of the cyclopids in the pond near the village Khotov: 1 — abundance of cyclopids; 2 — water temperature of pond near the village Khotov during the period of the study.
Campus Pond (Buffalo Creek Watershed) Water Data from 2022-02-17 to 2022-03-14
<p>General Metadata for Campus Pond Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific sensors at the site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Campus Pond (Buffalo Creek Watershed) Data from 2021-11-23 to 2022-02-16
<p>General Metadata for Campus Pond Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific sensors at the site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT - the temperature (dC) from the DO, conductivity, * WaterTemp - The water temperature from the pressure transducer under water (dC) * AirTemp - the air temperature from the pressure transducer (BP sensor) in the air (dC) * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Campus Pond (Buffalo Creek Watershed) Data from 2022-03-18 to 2022-05-11
<p># General Metadata for Campus Pond Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <p> BC_CampusPond_YYYY-MM-DD_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD_YYYY-MM-DD is the date range of the sampling period.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts).</p> <p>## File Created</p> <p> * 2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702).</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific sensors at the site are:</p> <p> * Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger<br> * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger<br> * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger<br> * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</p> <p>The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS)<br> * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor.<br> * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor.<br> * DO - the concentration of dissolved oxygen in the water (mg/L)<br> * Temp.DO, .CT - the temperature (dC) from the DO or conductivity.<br> * WaterTemp - the water temperature measured from the pressure transducer in the water (dC).<br> * AirTemp - the air temperature measured from the pressure transducer (BP sensor) in the air (dC).<br> * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor.<br> * Z - the depth of the water (cm).<br> * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm)<br> * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg)<br> * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2)<br> * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</p> <p> </p>
Biogeochemical distinctiveness of peatland ponds, thermokarst waterbodies and lakes
<p>This archive entry contains the original dataset used in the manuscript "Biogeochemical distinctiveness of peatland ponds, thermokarst waterbodies and lakes" as a CSV file (Arsenault-et-al_All.csv). The dataset is a global synthesis of the biogeochemical properties of lakes, peatland ponds and thermokarst waterbodies. It comprises a total of 12,475 observations (11,357 lakes worldwide, 827 thermokarst waterbodies from the Arctic circumpolar and the Himalaya regions and 291 peatland ponds from North America, Europe and Patagonia, from published and unpublished sources). The entry also includes subsets of the main dataset used to performed statistical analyses to compare and distinguish the biogeochemical properties of lakes, peatland ponds and thermokarst waterbodies (see file Tables_Statistical-analyses.pdf for details).</p> <p>The archive entry also contains the list of references from which we built the dataset, as a TXT file.</p>
Technical Reports: Methods - The application of temperature and light intensity as intermittency sensors in a temporary pond
<p>Dataset for Technical Reports: Methods - The application of temperature and light intensity as intermittent sensors in a temporary pond.</p>
Mining minerals and critical raw materials from bittern: Understanding metal ions fate in saltwork ponds
<p>Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral<br> extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and<br> Ge) are also contained in this “liquid mine”. Most of them are considered as Critical Raw Materials by the European<br> Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development<br> of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation<br> saltwork ponds.<br> However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge<br> could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic<br> strengths expected (0.5–7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical<br> codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution<br> can be adopted to provide valuable description of the systems.<br> In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and<br> trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity<br> in halite and the major impurities contained, mainly Ca,Mgand sulphate species; ii) the fate of minor components as B,<br> Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials<br> mining from seawater brine, for process intensification and combination with desalination.</p>
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