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19 results for “Groundwater ecosystems”
Daily summer riverine and groundwater pCO2, dissolved oxygen, and ecosystem metabolism from sites along the Alaska Beaufort Sea coast, 2019-2022
Three riverine systems near Utqiaġvik, Alaska, are sampled regularly by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program in order to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Sampling includes one intermediate river, Avak Creek, and two smaller streams: the Mayoeak River (thermokarst stream) and a beaded stream. In 2019, 2021, and 2022, we performed additional sampling at these core program sites, with continuous monitoring of the streams between late June and August. Continuous measurements included riverine discharge, pCO₂, dissolved oxygen (DO), and ecosystem production, along with periodic grab samples of dissolved organic carbon (DOC). In 2022, there was also monitoring of groundwater wells at the two streams, capturing continuous hydraulic head and groundwater pCO₂, in addition to grab samples for DOC. Daily averages of continuous data and DOC laboratory results are presented for surface water and groundwater. This dataset is not part of the BLE Core Program and is not currently planned as an ongoing effort.
Indicative distribution map for Ecosystem Functional Group SF1.2 Groundwater ecosystems
<p>This archive contains indicative distribution maps and profiles for <strong>SF1.2 Groundwater ecosystems</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Baltimore Ecosystem Study: Long-Term Monitoring of Riparian Water Table Depth and Groundwater Chemistry
Long-term monitoring of riparian water tables and groundwater chemistry began in 2000 along four first or second order steams in and around the Gwynns Falls watershed in Baltimore City and County, MD. One site (Oregon Ridge) is in the completely forested Pond Branch catchment that serves as a ""reference"" study area for the Baltimore LTER (BES). Two sites (Glyndon, Gwynbrook) were in suburban areas of the watershed; one just upstream from the Glyndon BES long-term stream monitoring site in the headwaters of the Gwynns Falls, and one along a tributary that enters the Gwynns Falls just above the Gwynnbrook BES long-term stream monitoring site farther downstream. The final, urban site (Cahill) was along a tributary to the Gwynns Falls in Leakin Park in the urban core of the watershed. Water table data and more detailed descriptions of soils, vegetation, stream channel properties and microbial processes at these sites can be found in Groffman et al. (2002, Environmental Science and Technology 36:4547-4552) and Gift et al. (2010, Restoration Ecology 18:113-120).
Groundwater is a globally-threatened keystone ecosystem
<p>Global maps of groundwater diversity, interactions between groundwater and surface water and global surface diversity linked into two maps.</p>
Middle Rio Grande riparian plant cover sensitivity to variability in groundwater depth collected by the Bosque Ecosystem Monitoring Program
Determining the ecological consequences of interactions between slow changes in long-term climate means and amplified variability in climate is an important research frontier in plant ecology. We combined the recent approach of climate sensitivity functions with a revised hydrological ‘bucket model’ to improve predictions on how plant species will respond to future changes in both the mean and variance of groundwater resources. We leveraged spatiotemporal variation in a long-term dataset of riparian vegetation cover to build the first groundwater sensitivity functions (GSFs) for common plant species of dryland riparian corridors. Our results demonstrate the value of this approach to identifying which plant species will thrive (or fail) in an increasingly variable climate layered on top of declining groundwater stores. Riparian plant species differed in sensitivity to both the mean and variance in groundwater levels. Rio Grande cottonwood (Populus deltoides ssp. wislizenii) cover was predicted to decline with greater interannual groundwater variance, while coyote willow (Salix exigua) and other native wetland species were predicted to benefit from greater year-to-year variance. No non-native species were sensitive to groundwater variance, but patterns for Russian olive (Elaeagnus angustifolia) predict declines under deeper mean groundwater tables. Warm air temperatures modulated groundwater sensitivity for cottonwood, which was more sensitive to variability in groundwater in years/sites with warmer maximum temperatures than in cool sites/periods. Cottonwood cover declined most with greater intra-annual coefficients of variation (CV) in groundwater, but was not significantly correlated with inter-annual CV, perhaps due to the relatively short time series (16 y) relative to cottonwood lifespan. In contrast, non-native tamarisk (Tamarix chinensis) cover increased with both intra- and inter-annual CV in groundwater. Altogether, our results predict that changes in groundw
Fig. 5 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 5. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov. A. Holotype (ZMA V.Pl. 7264.1), microphotograph of gregarine protozoans in a gut diverticulum. B. Specimen from the Movile Cave, microphotograph of a nematode infecting the bulb of the adenodactyl (ZMA V.Pl. 7265.2). C. Holotype, microphotograph of the mass of sperm inside the copulatory bursa with the rod-like structures. D. Specimen from the Limanu well, microphotograph of the mass of sperm inside the copulatory bursa, with a multilayered concentric organization of the circular structures (ZMA V.Pl. 7269.1).
Fig. 3 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 3. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov., holotype (ZMA V.Pl. 7264.1). A. Sagittal reconstruction of the female copulatory apparatus (anterior to the left). B. Sagittal reconstruction of the male copulatory apparatus (anterior to the left). Only the terminal portions of the spermiducal vesicles are drawn. C. Sagittal reconstruction of the male copulatory apparatus with the cervix-like protrusion (anterior to the left).
Fig. 2 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 2. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov. Sketch of the ventral view of a preserved (Bouin's fluid) specimen from Movile Cave.
Fig. 1 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 1. Geographic distribution of freshwater planarians of the genus Dendrocoelum recorded from Romania. ▲: unspecified locality of D. lacteum Müller, 1774. Rectangular inset (bottom) corresponds with enlarged area, showing the collection sites of D. obstinatum Stocchino & Sluys, sp. nov. indicated by blue stars (see also Table 1).
Groundwater and surface water quantity and quality measurement results for groundwater dependent ecosystem in Kazu leja, Latvia in 2019 and 2020
<p>Groundwater and surface water quantity and quality measurement results and methodology of the investigations of the groundwater dependant ecosystem in Kazu leja, Latvia in 2019 and 2020 is presented. The data comprises:</p> <ol> <li>Methodology</li> <li>Measurement site data [2_observation_sites.csv]</li> <li>Field and laboratory measurement results [3_field_and_lab_results.csv]</li> <li>Measurement uncertainty [4_measurement_uncertainity.csv]</li> <li>Automated water level, temperature, and electrical conductivity measurements [5_level_temperature_electricConductivity_loggers.csv]</li> </ol>
Data for effects of multiple drivers of environmental change on native and invasive macroalgae in nearshore groundwater dependent ecosystems
<p><strong><em>Okuhata, B.K., Delevaux, J.M.S., Richards Donà, A., Smith, C.M., Gibson, V.L., Dulai, H., El-Kadi, A.I., Stamoulis, K., Burnett, K.M., Wada, C.A., Bremer, L.L., Effects of multiple drivers of environmental change on native and invasive macroalgae in nearshore groundwater dependent ecosystems</em></strong></p> <p>Environmental change scenarios, with a spatial extent of the Keauhou basal aquifer (Hawai‘i), were produced using a recharge coverage from Engott (2011), land use coverages from the State of Hawai‘i (2022) and National Oceanic and Atmospheric Administration (2006); climate change calculations based on Elison Timm et al. (2015), and native forest conversion calculations from Bremer et al. (2021). Scenarios were developed based on the following assumptions:</p> <p>Scenario 0 (Baseline) assumes current land use, groundwater recharge, and groundwater withdrawal rates (National Oceanic and Atmospheric Administration, 2006; State of Hawai‘i, 2022; Engott, 2011; Commission on Water Resource Management, unpublished data, 2018). Please see Okuhata et al. (2021) for more details regarding the scenario assumptions for the baseline groundwater model.</p> <p>Scenario 1 (Climate Change) assumes current land use, but with Representative Concentration Pathway (RCP) 8.5 mid-century rainfall projections (Elison Timm et al., 2015), where rainfall and recharge calculations were based on estimates from Giambelluca et al. (2013) and Engott (2011). </p> <p>Scenario 2 (Urban Development) assumes RCP 8.5 mid-century rainfall conditions along with future permitted development, which includes an increase in water demand (Fukunaga & Associates, Inc., 2017). </p> <p>Scenario 3 (Native Forest Conversion + Urban Development) assumes RCP 8.5 mid-century rainfall conditions and future permitted development, along with the assumption that native forests are not protected and converted to non-native forests (Bremer et al., 2021), therefore altering recharge estimates (Wada et al., 2017; Engott, 2011).</p> <p>Please note that scenario numbers listed in the groundwater model and marine water quality model shapefiles may differ from the manuscript scenario numbers. The following table assigns the scenario numbers to their respective scenarios in the manuscript, groundwater model, and marine water quality model.</p> <table> <tbody> <tr> <td> <p><strong>Scenario Name</strong></p> </td> <td> <p><strong>Manuscript #</strong></p> </td> <td> <p><strong>Groundwater Model #</strong></p> </td> <td> <p><strong>Marine Water Quality Model #</strong></p> </td> </tr> <tr> <td> <p>Baseline</p> </td> <td> <p>Scenario 0</p> </td> <td> <p>Scenario 1</p> </td> <td> <p>Scenario 0</p> </td> </tr> <tr> <td> <p>Climate Change</p> </td> <td> <p>Scenario 1</p> </td> <td> <p>Scenario 2</p> </td> <td> <p>Scenario 1</p> </td> </tr> <tr> <td> <p>Urban Development</p> </td> <td> <p>Scenario 2</p> </td> <td> <p>Scenario 7</p> </td> <td> <p>Scenario 6</p> </td> </tr> <tr> <td> <p>Native Forest Conversion + Urban Development</p> </td> <td> <p>Scenario 3</p> </td> <td> <p>Scenario 5</p> </td> <td> <p>Scenario 4</p> </td> </tr> </tbody> </table> <p>The groundwater model results are in shapefile format and were produced using the program SEAWAT (Langevin et al., 2008). The spatial extent is the Keauhou basal aquifer, and the projection is NAD 1983 UTM Zone 4N. The two polygon shapefiles represent the first and second layers of the groundwater model, and include groundwater level (meters relative to mean sea level), salinity (parts per thousand), temperature (degrees Celsius), nitrogen (milligrams per liter), and phosphorus (milligrams per liter) results under the assumptions of each scenario. The point shapefile represents the simulated discharge at SGD plumes under the assumptions of each scenario.</p> <p>The marine water quality model results are in floating point TIFF format and were produced using the program R software. The spatial extent is the coastal area of the Keauhou aquifer system, and the geographic coordinate system is WGS 1984. The files include the groundwater discharge (cubic meters per month), salinity (parts per thousand), temperature (degrees Celsius), nitrogen (kilograms per month), and phosphorus (kilograms per month) results under the assumptions of each scenario.</p> <p>The limu model results are in shapefile format and were produced using the program R software. The spatial extent is the coastal area of the Keauhou aquifer system, and the geographic coordinate system is WGS 1984. The files include the increase and decrease in area (hectares) for <em>Ulva lactuca</em> and <em>Hypnea musciformis </em>under the assumptions of each scenario.</p> <p>The limu experiment results are derived from a csv file, which reports the <em>Ulva lactuca</em> and <em>Hypnea musciformis </em>measured weights (initial and final) for each growth run. These were used to calculate the weight difference. Included also in the dataset are the fixed and random effects used in the R script to run the model.</p> <p>Contact Leah Bremer (<a href="mailto:lbremer@hawaii.edu">lbremer@hawaii.edu</a>) or Brytne Okuhata (bokuhata@hawaii.edu) of the University of Hawaiʻi for more information on these files.</p>
Data from: A comprehensive occurrence dataset for European Ostracoda inhabiting groundwater and groundwater-dependent ecosystems
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Data from: Contrasting plant water-use responses to groundwater depth in coastal dune ecosystems
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Data from: Niphargus–Thiothrix associations may be widespread in sulphidic groundwater ecosystems: evidence from southeastern Romania
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Fig. 4 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 4. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov., holotype (ZMA V.Pl. 7264.1). A. Microphotograph of the copulatory apparatus. B. Microphotograph of the anterior adhesive organ with the associated ventral muscles. C. Microphotograph of the cervix-like protrusion of the male atrium, with the opening of the common oviduct.
Supplementary material 2 from: Horváthová E (2022) Analysis of Drinking Water treatment costs – with an Application to Groundwater Purification Valuation. One Ecosystem 7: e82125. https://doi.org/10.3897/oneeco.7.e82125
Ecosystem types
Supplementary material 1 from: Horváthová E (2022) Analysis of Drinking Water treatment costs – with an Application to Groundwater Purification Valuation. One Ecosystem 7: e82125. https://doi.org/10.3897/oneeco.7.e82125
Regression results
Borrego Springs groundwater dependent ecosystem identification
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Data, code, and outputs for: groundwater-dependent ecosystem map exposes global dryland protection needs
<p>Contains all data and code used to produce results for:</p> <p>Rohde, M.M.<strong>,</strong> C.M. Albano, X. Huggins, K.R. Klausmeyer, C. Morton, A. Sharman, E. Zaveri, L. Saito, Z. Freed, J.K. Howard, N. Job, H. Richter, K. Toderich, A. Rodella, T. Gleeson, J. Huntington, H.A. Chandanpurkar, A.J. Purdy, J.S. Famiglietti, M.B. Singer, D.A. Roberts, K. Caylor, J.C. Stella. 2024<em>.</em> Groundwater-dependent ecosystem map exposes global dryland protection needs. <em>Nature</em>, doi: 10.1038/s41586-024-07702-8.</p> <p>The resultant global groundwater-dependent ecosystem interactive webmap is available at <a href="https://codefornature.projects.earthengine.app/view/global-gde">https://codefornature.projects.earthengine.app/view/global-gde</a>.</p> <p>The code is also available at <a href="https://github.com/XanderHuggins/global-gde-map">https://github.com/XanderHuggins/global-gde-map</a>.</p>
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