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1,133 results for “wetlands”
Figure 5 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 5 Phylogenetic relationships amongst available species in the genus Protoparmeliopsis based on a Maximum Likelihood analysis of the dataset of the mitochondrial small subunit (mtSSU) sequences. The tree was rooted with three sequences of the genus Polyozosia. Maximum Likelihood bootstrap values ≥ 70% and posterior probabilities ≥ 95% are shown above internal branches. Branches with bootstrap values ≥ 90% are shown in bold. The new species Protoparmeliopsis crystalliniformis is presented in bold, and all species names are followed by the GenBank accession numbers. Reference Table 1 provides the species related to the specific GenBank accession numbers and voucher information.
Figure 3 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 3 Phylogenetic relationships amongst available species in the Lecanora symmicta group based on a Maximum Likelihood analysis of the dataset of the mitochondrial small subunit (mtSSU) sequences. The tree was rooted with four sequences of the Lecanora subfusca group. Maximum Likelihood bootstrap values ≥ 70% and posterior probabilities ≥ 95% are shown above internal branches. Branches with bootstrap values ≥ 90% are shown in bold. The new species Lecanora parasymmicta is presented in bold, and all species names are followed by the GenBank accession numbers. Reference Table 1 provides the species related to the specific GenBank accession numbers and voucher information.
Figure 4 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 4 Phylogenetic relationships amongst available species in the genus Protoparmeliopsis based on a Maximum Likelihood analysis of the dataset of ITS sequences. The tree was rooted with five sequences of the genus Polyozosia. Maximum Likelihood bootstrap values ≥ 70% and posterior probabilities ≥ 95% are shown above internal branches. Branches with bootstrap values ≥ 90% are shown in bold. The new species Protoparmeliopsis crystalliniformis is presented in bold, and all species names are followed by the GenBank accession numbers. Reference Table 1 provides the species related to the specific GenBank accession numbers and voucher information.
Figure 2 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 2 Phylogenetic relationships amongst available species in the Lecanora symmicta group based on a Maximum Likelihood analysis of the dataset of ITS sequences. The tree was rooted with five sequences of the Lecanora subfusca group and Tephromela. Maximum Likelihood bootstrap values ≥ 70% and posterior probabilities ≥ 95% are shown above internal branches. Branches with bootstrap values ≥ 90% are shown in bold. The new sequences of Lecanora parasymmicta and Lecanora symmicta produced from this study are presented in bold, and all species names are followed by the GenBank accession numbers. Reference Table 1 provides the species related to the specific GenBank accession numbers and voucher information.
Figure 1 from: Lee BG, Hur J-S (2021) Two new lecanoroid lichen species from the forested wetlands of South Korea, with a key for Korean Protoparmeliopsis species. MycoKeys 84: 163-183. https://doi.org/10.3897/mycokeys.84.70798
Figure 1 Specific collection sites for two new species A habitat/landscape of Lecanora parasymmictaB habitat/landscape of Protoparmeliopsis crystalliniformisC locations of Lecanora parasymmicta (black circle) and Protoparmeliopsis crystalliniformis (two black stars) on the map.
Supplementary material 2 from: Miranda-García ML, Muñoz-Pedreros A, Norambuena HV (2021) Waterbird assemblages of inland wetlands in Chile: A meta-analysis. Nature Conservation 45: 41-61. https://doi.org/10.3897/natureconservation.45.74062
Supplementary material 2
Supplementary material 1 from: Miranda-García ML, Muñoz-Pedreros A, Norambuena HV (2021) Waterbird assemblages of inland wetlands in Chile: A meta-analysis. Nature Conservation 45: 41-61. https://doi.org/10.3897/natureconservation.45.74062
Supplementary material 1
Stopover use of a large estuarine wetland by dunlins during spring and autumn migrations: linking local refuelling conditions to migratory strategies
<p>1. Carbon and nitrogen stable isotope values measured in blood (plasma and red blood cells) and toenails of dunlins (<em>Calidris alpina</em>) captured at the Tagus estuary, Portugal, during spring and autumn migrations.</p> <p>2. Biometric and haematological parameters of dunlins (<em>Calidris alpina</em>) captured at the Tagus estuary, Portugal, during spring and autumn migrations.</p> <p>3. Biometric parameters and plasma metabolite concentrations of dunlins (<em>Calidris alpina</em>) captured at the Tagus estuary, Portugal, during spring and autumn migrations.</p>
National Wetlands Inventory of the United States - State and Substate Shapefiles
<p>Data downloaded from original source at <a href="https://www.fws.gov/wetlands/data/State-Downloads.html">https://www.fws.gov/wetlands/data/State-Downloads.html</a> by State. The data is downloaded as a <a href="https://en.wikipedia.org/wiki/ZIP_file_format">.zip</a> file that contains the following layers:</p> <ul> <li>Wetlands polygon data</li> <li>Wetlands Project Metadata (includes image dates and project information)</li> <li>Wetlands Historic Map Information*</li> <li>Riparian polygon data*</li> <li>Riparian Project Metadata (includes image dates and project information)*</li> <li>Historic Wetlands*</li> <li>Historic Wetlands Project Metadata (includes image dates and project information)*</li> </ul> <p> </p> <p>* If available at the requested location.</p> <p>Information about each of these layers can be found on the NWS <a href="https://fws.gov/wetlands/Data/Metadata.html">Metadata</a> page. </p> <p>Detailed documentation of the Cowardin Classification system can be found on the NWS <a href="https://fws.gov/wetlands/Data/Wetland-Codes.html">Wetland Code</a> page. There you can also download the <a href="https://fws.gov/wetlands/Data/Wetland-Codes.html">NWI Code Definitions Table</a> that provides users with the full wetland or deepwater habitat description within their own mapping application.</p> <table> <tbody> <tr> <td> <p>Please read the <a href="https://fws.gov/wetlands/Data/Disclaimer.html">Disclaimer</a>, <a href="https://fws.gov/wetlands/Data/Limitations.html">Data Limitations, Exclusions and Precautions</a>, and the <a href="https://fws.gov/wetlands/Data/Wetlands-Geodatabase-User-Caution.html">Wetlands Geodatabase User Caution</a>.</p> </td> </tr> </tbody> </table>
The evolution of hummock-depression micro-topography in an alpine marshy wetland in Sanjiangyuan as inferred from vegetation and soil characteristics
The hummock-depression micro-topography characteristics of the alpine marshy wetland in Sanjiangyuan are indicative of wetland degradation and the process by which healthy wetlands are transformed into flat grasslands. The aim of the present study was to examine changes in plant community structure and soil characteristics in a hummock-depression micro-topography along a degradation gradient. We observed that: (1) the height and cover of dominant hydrophytes decreased gradually with an increase in degradation severity, leading to replacement by xerophytes; (2) with the transition from healthy to degraded wetlands, hummocks became sparser, shorter, and broader and became merged with nearby depressions; water reserves in the depressions shifted from perennial to seasonal, until they dried out completely; (3) soil moisture content, porosity, hardness, and organic matter gradually decreased by 30.61%, 19.06%, 37.04%, and 73.27%, respectively, in hummocks and by 33.25%, 8.19%, 47.72%, and 76.79%, respectively, in depressions. Soil bulk density, soil electrical conductivity, and soil dry weight increased by 31%, 83.33%, and 105.44%, respectively, in hummocks, but by only 11.93%, 7.14%, and 97.72%, respectively, in depressions. The results show that hummock soils in healthy wetlands have strong water absorption properties, through which plant roots can penetrate easily. Wetland degradation reduces the water absorption capacity of hummock soil and soil saturation capacity of depressions, thus enhancing soil erosion potential and susceptibility to external factors. Soil moisture is a key environmental factor influencing wetland degradation, and grazing accelerates the process. Based on the changes observed in hummock morphology, vegetation, and soil properties along a degradation gradient, a conceptual model is proposed to illustrate the process of gradual degradation of marshy wetlands from healthy to transitional wetlands and finally to a degenerated state. Thus, our research provides insights into the degradation process of the alpine marshy wetland ecosystem in Sanjiangyuan.
Data from: Contrasting nitrogen cycling between herbaceous wetland and terrestrial ecosystems inferred from plant and soil nitrogen isotopes across China
<p><span>Understanding nitrogen (N) cycling in different ecosystems is crucial to predicting and mitigating the global effects of altered N inputs. Although wetlands have always been assumed to differ largely from terrestrial ecosystems in N cycling, evidence from direct comparison from the field along wide environmental gradients is lacking. Here, we hypothesized strong coupling of plant and soil δ<sup>15</sup>N in terrestrial ecosystems due to lower N inputs and losses but weak coupling of plant and soil δ<sup>15</sup>N in wetlands because of higher N inputs and losses.</span></p> <p><span>We performed a large-scale field investigation on 26 pairs of herbaceous wetland and terrestrial sites across China covering 21 degrees of latitude and determined natural abundance of nitrogen isotopes (δ<sup>15</sup>N) in soils and leaves of 346 dominant and subordinate plant species. We analysed the relationships between leaf and soil δ<sup>15</sup>N and their drivers including plant functional types in these two types of ecosystems.</span></p> <p><span>Plant functional types including mycorrhizal type and N2-fixing status had consistently significant influences on leaf δ<sup>15</sup>N in herbaceous wetland and terrestrial ecosystems. Leaf δ<sup>15</sup>N increased significantly with soil δ<sup>15</sup>N within and across mycorrhizal types in both ecosystems, and, as hypothesized, the relationships were stronger and steeper in terrestrial than in wetland ecosystems. Moreover, leaf and soil δ<sup>15</sup>N were positively and significantly correlated within both N<sub>2</sub>-fixers and non-fixers in terrestrial ecosystems and within only non-N<sub>2</sub>-fixers in wetlands. At the community level, we also found more highly significant relationships between leaf and soil δ<sup>15</sup>N in terrestrial than in wetland ecosystems. Besides plant functional types, climatic and soil factors contributed to the variation in leaf δ<sup>15</sup>N in both ecosystems.</span></p> <p><span><em>Synthesis.</em> Weaker relationships between plant and soil δ<sup>15</sup>N in wetlands at species and community levels supports the hypothesis that larger N inputs and losses lead to weaker coupling in the plant-soil systems in wetlands than in terrestrial ecosystems. This provides strong evidence from a large spatial scale for contrasting N cycling in these two types of ecosystems regardless of plant functional type in terms of nutrient uptake strategy. Our findings add to our predictive power of ecosystem N dynamics under environmental changes, e.g. land-use changes and elevated N inputs.</span></p>
Influence of water level management on vegetation and bird use of restored wetlands in the Montezuma Wetlands Complex
<p>Active water management of wetlands promotes seed and tuber production to feed migrating waterfowl, but few assessments exist to determine how management actions influence wetland structure, vegetation, and bird response throughout the year. We identified effects of full water drawdown, partial water drawdown and passive wetlands (no active dewatering during the growing season) on plant communities and bird abundance in wetlands of the Montezuma Wetlands Complex, New York, May–October 2016–2018 and February–April 2017–2019. We detected few differences in the plant community during June, but during September we detected greater vegetative forage quality index for waterfowl, annual plant cover and seed density in full and partial drawdowns than passive wetlands. Bird abundance was greater in June–July in passive wetlands and greater in September–October in full drawdowns. During spring migration, duck densities were greater in full and partial drawdowns. Our results indicate that wetland managers should use a mix of full drawdowns and passive wetlands to provide habitat for the greatest diversity and number of birds throughout the year.</p>
Tipping the balance: the role of seed density, abiotic filters, and priority effects in seed-based wetland restoration
<p class="MsoNormal"><a name="_Hlk98319335"></a>Sowing native seeds is a common approach to reintroduce native plants to degraded systems. However, this method is often overlooked in wetland restoration despite the immense global loss of diverse native wetland vegetation. Developing guiding principles for seed-based wetland restoration is critical to maximize native plant recovery, particularly in previously invaded wetlands. Doing so requires a comprehensive understanding of how restoration manipulations, and their interactions, influence wetland plant community assembly. With a focus on the invader <em>Phragmites australis, </em>we established a series of mesocosm experiments to assess how native sowing density, invader propagule pressure, abiotic filters (water and nutrients), and native sowing timing (i.e., priority effects) interact to influence plant community cover and biomass in wetland habitats. Increasing the density of native seeds yielded higher native cover and biomass, but <em>P. australis</em> suppression with increasing sowing densities was minimal. Rather, community outcomes were largely driven by invader propagule pressure—<em>Phragmites australis</em> densities of <span><span>≤ 500 seeds/m<sup>2</sup> maintained high native cover and biomass. Low-water conditions increased the susceptibility of <em>P. australis</em> to native competition. </span>Early sowing of native seeds showed a large and significant benefit to native cover and biomass, regardless of native sowing density, suggesting that priority effects can be an effective restoration manipulation to enhance native plant establishment.<em> </em></span><span><span>Given the urgent wetland restoration need combined with the limited studies on seed-based wetland restoration, these findings provide guidance on restoration manipulations that are grounded in ecological theory to improve seed-based wetland restoration outcomes.</span></span></p>
Baliles Center (Hull Springs) Restored Wetland Data from 2022-02-05 to 2022-03-08
<p>General Metadata for Hull Springs Restored Wetland 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>HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_Depth_YYYY-MM-DD_metadata.txt HS_wetland_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>2021-06-16 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor.</li> <li>2021-11-10 by KF - updated to include the depth calculations from the water level logger.</li> </ul> <p>Description</p> <p>These data are from the sampling station in the restored wetland at the Baliles Center for Environmetal Education at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252).</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 at each 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>
Concentrations and Yields of Total Hg and MeHg in Large Boreal Rivers Linked to Water and Wetland Coverage in the Watersheds
<p>Large rivers are major contributors of mercury (Hg) exports to the ocean, as they integrate processes of loading and loss occurring at the watershed level. Within a watershed, stream-scale studies have revealed that specific landscape features, such as wetlands or lakes, are hotspots for Hg and methylmercury (MeHg) loading, sinks and transformation, but we still do not know how these landscape features operate at the whole network scale and over large geographic gradients. In this study, we evaluate how landscape metrics (vegetation types, wetland and lake cover, climate, hydrology) are related to riverine concentrations and watershed yields of Hg and MeHg in 18 large boreal rivers draining watersheds that range from 44 km<sup>2</sup> to 209 453 km<sup>2</sup>, distributed along a 650 km latitudinal transect in the James Bay region of Québec. Our results reinforce the role of wetlands as sources of MeHg, but further show that surface coverage of water in the watershed is the major driver of both Hg and MeHg concentrations and exports to the coast at the whole network scale. Our findings also demonstrate that seasonality modulates the relationship between landscape features and the various Hg forms. Based on hydrometric data, we additionally estimate annual exports for the whole Eastern James Bay to 441 kg Hg and 15 kg MeHg, for an average landscape yield of 1.24 g Hg km<sup>-2</sup> y<sup>-1</sup> and 0.041 g MeHg km<sup>-2</sup> y<sup>-1</sup>. Our study provides tools to broadly predict riverine Hg concentrations and exports with only a few easily accessible landscape metrics.</p>
H-O isotopic compositions in China's wetlands
<p class="MsoNormal"><a name="OLE_LINK5"></a><a name="OLE_LINK4"></a><a name="OLE_LINK3"></a><span><span><span>Water oxygen isotopic analysis is a useful tool for tracing water cycle in aquatic ecosystems on the premise that it is no oxygen isotope effect from phytoplankton activity. We measured the water hydrogen and oxygen isotopic compositions, phytoplankton Fv/Fm and related environmental factors in China's wetlands.</span></span></span></p> <p class="MsoNormal"><span><span><span><a name="OLE_LINK31"></a><span>We found that phytoplankton photosynthesis can modify water oxygen isotopic signature (δ<sup>18</sup>O), based on a national-scale survey of wetlands and a culture experiment.</span><span> <a name="_Hlk102067137"></a>Phytoplankton photosynthesis can make water δ<sup>18</sup>O more negative, and this biological effect counteracts part of the evaporation effect on water δ<sup>18</sup>O. This effect depends on phytoplankton biomass and light utilization efficiency and could be overwhelmed by the evaporation effect when the biomass is small.</span></span></span></span><a name="OLE_LINK2"></a> <a name="_Hlk102067151"></a></p> <p class="MsoNormal"><span><span><span>The biological effect may be universal in aquatic ecosystems, and thus </span></span><span>this study refreshes the traditional understanding on water oxygen isotope biogeochemistry.</span></span></p>
Distribution. NE Argentina (Entre Rios and Corrientes S of Ibera Wetlands). in Didelphidae
Distribution. NE Argentina (Entre Rios and Corrientes S of Ibera Wetlands).
Data for: Leaf N-S of wetland plants in western China
<p><span>Salinization</span><span> alters the elemental balance of wetlands and induces variations in plant survival strategies. Sulfur (S) plays vital roles in serving regulatory and catalytic functions in stress resistance of plants. Yet, how plant S and its relationships with nitrogen (N) vary across natural environmental gradients are not well documented. We collected </span><span>1366 plant samples and 230 water and sediment samples from 230 wetlands in Tibetan Plateau and adjacent arid regions of western China, to analyze the effects of environmental variables on plant S accumulation and N-S correlations. We found that plant</span><span> S correlated with N in unimodal patterns. </span><span>Salinity, rather than temperature or nutrient supply, promoted disproportionate accumulation of S but limited N uptake, inducing decoupling of N-S correlation in plants. Towards high salinity, the faster increasing rates of total S than that of glutathione, the most abundant organic-S compound in plant resistance, provided potential evidences explaining the decoupled plant N-S correlation. A salinity of 3.9‰ was calculated to be a threshold at which substantial changes in plant N-S correlation occurred. We designed a conceptual model to illustrate the mechanisms driving </span><span>variations<span> of N-S correlation in plants and environments along salinity gradient. In addition, salinity reduced species richness and drove community reassembly by filtering species with high S concentrations at community scale. Our study addressed the critical roles of S in plant resistance under adverse conditions. </span></span><span>Studies on biogeochemical cycles of S and N in wetland ecosystems will further enhance our understanding of plant responses to future climate change.</span></p>
Figures 5-8 from: Courtney Mustaphi CJ, Githumbi EN, Shotter LR, Rucina SM, Marchant R (2016) Subfossil statoblasts of Lophopodella capensis (Sollas, 1908) (Bryozoa, Phylactolaemata, Lophopodidae) in the Upper Pleistocene and Holocene sediments of a montane wetland, Eastern Mau Forest, Kenya. African Invertebrates 57(1): 39-52. https://doi.org/10.3897/afrinvertebr.57.8191
Figures 5-8 - Lophopodella capensis statoblasts and organic detritus, including charcoal at right, from the sieved sediment subsample from25–26 cm stratigraphic depth, dated to 220–230 yr BP (5). A well preserved statoblast observed at 41–42 cm, dated to 670–711 (6). Pleistocene-aged statoblast from 480–481 cm, dated to 15600–15700 (7). Same specimen as Fig. 6 showing the split layers of the polar spine and recurved hooks (8).
Figure 1 from: Courtney Mustaphi CJ, Githumbi EN, Shotter LR, Rucina SM, Marchant R (2016) Subfossil statoblasts of Lophopodella capensis (Sollas, 1908) (Bryozoa, Phylactolaemata, Lophopodidae) in the Upper Pleistocene and Holocene sediments of a montane wetland, Eastern Mau Forest, Kenya. African Invertebrates 57(1): 39-52. https://doi.org/10.3897/afrinvertebr.57.8191
Figure 1 - Location of the study site in Africa (A) and within Kenya (B). The location of the coring site (black circle) within Enapuiyapui (C). The red line represents a fire break cutline.
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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.