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87 results for “coastal wetland”
Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.
This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.
Soil respiration rates, biogeochemical pools, and mineral-associated organic matter from high organic matter and high mineral content coastal wetland soils in Apalachicola, Florida, 2022
This data set was used to observe how the application of dredged sediment would impact soil respirations rates, biogeochemical pools, mineral associated organic matter of coastal wetland soils from Apalachicola, Florida. To achieve this, a combination of intact core and bottle incubations were used, comparing a high organic matter coastal wetland soil to a high mineral content wetland soil which were collected in June, 2022. All laboratory analysis was conducted at the University of Central Florida in Orlando, Florida.
Examination of protein-like fluorophores in chromophoric dissolved organic matter (CDOM) in a wetland and coastal environment for the wet and dry seasons of the years 2002 and 2003 (FCE)
Water samples are collected at the end of the dry and the wet season from all LTER sites and stored on ice until return to the lab. They are pre-filtered through pre-combusted GF/F filters and ultrafiltered and concentrated with a Pellicon 2 Mini tangential flow ultrafiltration system.Concentrated samples were then analyzed using fluorescence and SEC-HPLC. This CDOM optical study revealed the presence of two classes of compounds associated with the protein-like peak (peak T; excitation/emission (Ex/Em) maxima at around 280 nm/325 nm), which have very different chemical structures and ecological roles. In addition to proteins, we propose phenolic compounds as possible origins of peak T in coastal and wetland environments. In this study, natural water samples were obtained from subtropical rivers and estuarine environments within the Florida Coastal Everglades (FCE) ecosystem. The samples were ultra-filtered and excitation-emission fluorescence matrices (EEMs) were obtained. The EEMs showed the presence of four peaks with Ex/Em maxima at around 280 nm/325 nm (T), less than 260 nm/460 nm (A), 300 nm/412nm (M), and 350 nm/470 nm (C). To better understand the nature of peak T, the components originating this peak were separated using size exclusion chromatography (SEC) and detected by fluorescence emission at Ex/Em = 280 nm/325 nm. The elution curves revealed the presence of two elution peaks at a molecular weight of greater than 50K (void volume; T1) and around 7.6K (T2). This result suggested the need of cautious interpretation in the use of peak T as a proxy for the detection of proteinaceous materials in wetland and estuarine environments, since significant amounts of potentially interfering phenolic compounds are leached from senescent biomass in wetland and coastal ecosystems. As such EEM spectra of gallic acid an important component of hydrolysable tannins, and condensed tannins extracted from red mangroves (Rhizophora mangle) showed the presence of a peak maxima
Biomarker assessment of spatial and temporal changes in the composition of flocculent material (floc) in the subtropical wetland of the Florida Coastal Everglades (FCE) from May 2007 to December 2009
Flocculent material (floc) is an important energy source in wetlands. In the Florida Everglades, floc is present in both freshwater marshes and coastal environments and plays a key role in food webs and nutrient cycling. However, not much is known about its environmental dynamics, in particular its biological sources and bio-reactivity. We analysed floc samples collected from different environments in the Florida Everglades and applied biomarkers and pigment chemotaxonomy to identify spatial and seasonal differences in organic matter sources. An attempt was made to link floc composition with algal and plant productivity. Spatial differences were observed between freshwater marsh and estuarine floc. Freshwater floc receives organic matter inputs from local periphyton mats, as indicated by microbial biomarkers and chlorophyll-a estimates. At the estuarine sites, the floc is dominated by mangrove as well as diatom inputs from the marine end-member. The hydroperiod (duration and depth of inundation) at the freshwater sites influences floc organic matter preservation, where the floc at the short-hydroperiod site is more oxidised likely due to periodic dry-down conditions. Seasonal differences in floc composition were not consistent and the few that were observed are likely linked to the primary productivity of the dominant biomass (periphyton in the freshwater marshes and mangroves in the estuarine zone). Molecular evidence for hydrological transport of floc material from the freshwater marshes to the coastal fringe was also observed. With the on-going restoration of the Florida Everglades, it is important to gain a better understanding of the biogeochemical dynamics of floc, including its sources, transformations and reactivity.
The dataset and model code pertinent to the Everglades Peat Elevation Model (EvPEM): The salinity and inundation mesocosm experiment in freshwater and brackish water sawgrass wetlands in Florida Coastal Everglades (2015-2017).
This is an assembled data and Everglades Peat Elevation Model (EvPEMv1.0) Stella code used to estimate and simulate net ecosystem carbon balance (NECB) and peat elevation change in response to saltwater intrusion and level of inundations. Data from several studies were combined for the estimation of NECB, model parameterization, and calibration (Wilson, 2018; Wilson et al., 2018, 2019; Charles et al., 2019; Servais et al., 2020). The reported data includes aboveground net primary productivity (ANPP), belowground net primary productivity (BNPP), peat elevation change, and decomposition rates that were collected from outdoor laboratory mesocosm experiments conducted at the Florida Bay Interagency Science Center in Key Largo, Florida during 2015-17. The plant-soil monoliths were obtained from a freshwater peat and a brackish water peat marsh located within the Florida Coastal Everglades and transported to the Key Largo facility for the experimental manipulations. In experiments focused on the brackish water marsh, three experiments were carried out reflecting the combined effect of salinity, inundation, and peat exposure to air. The brackish water experiments characterized submerged (SUB), exposed (EXP), and extended depth of exposure of peat surface (EXTEXP) conditions, as we varied water depth relative to the peat surface. Each experiment was subjected to two salinity manipulations: (1) ambient (~10 ppt) porewater salinity (AMB) and (2) elevated (~20 ppt) salinity (SALT). The experimental design included six (2 X 3) treatments: (1) submerged ambient salinity (AMB.SUB), (2) submerged elevated salinity (SALT.SUB.), (3) exposed ambient salinity (AMB.EXP), (4) exposed elevated salinity (SALT.EXP), (5) exposed with extended exposure/dry-down ambient salinity (AMB.EXTEXP), and (6) exposed with extended exposure/dry-down elevated salinity (SALT.EXTEXP). The water level was kept 4 cm above the peat surface for the brackish water SUB treatments. Exposure for the EXP treatment
Periphyton, hydrological and environmental data in a coastal freshwater wetland (FCE), Florida Everglades National Park, USA (2014-2015)
The characteristic, calcareous periphyton mats of the Everglades, and particularly their diatom assemblages, provide an ideal community to study the patterns and mechanisms of community assembly along environmental gradients with ecotones. Understanding patterns and mechanisms of diatom community assembly along salinity and P gradients can be incorporated into tools for predicting changes in these gradients, and the location and movement of the "white zone" ecotone, caused by saltwater intrusion and water management outcomes in the Southern Everglades. Patterns of environmental variation and periphytic-diatom community structure along the freshwater-marine gradient of Everglades National Park, FL., USA were examined by sampling along a series of 7 transects extending from oligotrophic, freshwater marshes through the ecotone and down to the northern edge of the fringing mangrove forests. Seven transects spanning the west-east extent of the southeast Everglades, from the Main Park Road in the west to the Model Lands in the east, were sampled once in the dry season (May) and once in the wet season (November) of 2014 and 2015. These data are published in "Mazzei and Gaiser. 2018. Diatoms as tools for inferring ecotone boundaries in a coastal freshwater wetland threatened by saltwater intrusion. Ecological Indicators. 88:190-204."
Environmental and periphyton composition data from Biscayne Bay Coastal Wetlands, Florida, USA, July 2022 - November 2022
Environmental and periphyton data were collected from transects in the Biscayne Bay Coastal Wetlands (BBCW) during the wet and dry seasons of 2022 to investigate the rate of carbonate sediment production by periphyton. Environmental data include surface water metrics (pH, salinity, conductivity, and water depth) and soil depths. Periphyton data include nutrient, production, and diatom species composition in samples collected from artificial substrates (periphytometers) placed in the field. Data collection for this project is complete, although the South Florida Management District continues to monitor these transects for a larger ongoing BBCW project.
Benthic diatoms of the Ebro Delta coastal wetlands (NW Mediterranean)
<p>This dataset includes 24 sites encompassing diatom counts and associated water chemistry parameters from wide range of wetland habitat types (coastal lagoons, salt and brackish marshes, shallow bays, microbial mats and nearshore marine waters) from the Ebro Delta (Spain). Diatom data represents sediment surface samples collected over three seasons (winter, spring and summer) from 2012-2013 years. The dataset has been formatted following the standards of the Tropical South American Diatom Database (<a href="https://zenodo.org/records/5721364">https://zenodo.org/records/5721364</a>)--a database constituent of Neotoma (<a href="https://www.neotomadb.org/">www.neotomadb.org</a>), a global community-curated database by regional experts for multiple types of paleoecological data--and as part of the project "DiatomS mEEt Databases: resources and practices to enable large-scale ecological research (SEED)" funded by the International Society for Diatom Research (<a href="https://isdr.org/early-career-networking-award/">https://isdr.org/early-career-networking-award/</a>).</p> <p> </p>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Survey of North Carolina Coastal Plain Ditches for Wetland Characteristics, 2015
We surveyed 32 drainage ditch reaches in the North Carolina Coastal Plain in summer 2015, including forested, freeway (roadside), and agricultural ditches, for wetland structure. We surveyed vegetation, including herbaceous communities and the tree/shrub layer, soils for water and organic matter content, and some hydrology, including USACE hydrologic indicators. This dataset supports a forthcoming publication.
Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels
<p class="MsoNoSpacing"><span>Wetlands of the Laurentian Great Lakes of North America, i.e., lakes Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (<em>n </em>= 34, 48%) than negative trends (<em>n </em>= 5, 7%). </span><span>These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. <span>Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the </span>decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide <span>high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show </span>increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing water levels <span>by removing or preventing barriers to movement, </span>such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes.</span></p>
Fig. 3 in Impact Of Coastal Wetland Restoration Strategies In The Chongming Dongtan Wetlands, China: Waterbird Community Composition As An Indicator
Fig. 3. Densities of Charadriidae (a), Anatidae (b), Ardeidae (c), and Laridae (d) among autumn, winter and spring in four sites. Error bars represent ±1 SE.
Fig. 3 in Waterbird Distribution Patterns And Environmentally Impacted Factors In Reclaimed Coastal Wetlands Of The Eastern End Of Nanhui County, Shanghai, China
Fig. 3. Non-metricmulti-dimensionalscaling(NMDS) ordinationplotsshowingwaterbird communitystructurefromsixstudysites.
Supplementary materials for paper "Untangling the drivers of change and policy impact in coastal wetland area in the Yangtze Estuary using causal inference"
<h1>Annual coastal wetland vegetation maps of the Yangtze Estuary from 1986 to 2021</h1> <p> </p> <h2><strong>Basic information</strong></h2> <p>Using remotely sensed data from Google Earth Engine, we generated a 30 m resolution annual dataset of Yangtze Estuary wetland vegetation for the period 1986-2021. This dataset includes three dominant vegetation types (<em>Spartina alterniflora</em>, <em>Phragmites australis</em>, and <em>Scirpus mariqueter</em>) and tidal flat areas. We combined fieldwork data and high-resolution images for accuracy assessment, achieving an overall accuracy exceeding 80% in different years. Detailed information about the mapping methods can be found in our paper and accompanying supplementary materials.</p> <h2><strong>Notes:</strong></h2> <p>In the image classification scheme: 0-Tidal flats, 1-<em>Spartina alterniflora, </em>2-<em>Phragmites australis, </em>3-<em>Scirpus mariqueter.</em></p> <h2><strong>Usage Policy:</strong></h2> <p>This dataset is a collaborative effort between East China Normal University and Deakin University. If you plan to use our data in <strong>a scientific analysis paper or other research work</strong>, we strongly recommend contacting us in advance to seek our opinions, <strong>citing the unique DOI of this dataset</strong>, and considering acknowledging our contributions or including us as co-authors.</p>
Fig. 2. The most numerous RDB wetland birds species, recorded during August Counts 2018 and 2021 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 2. The most numerous RDB wetland birds species, recorded during August Counts 2018 and 2021 (bar chart — amount of birds, line chart — number of sites were species was recorded).
Fig. 1 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 1. Coverage of wetlands of Azov-Black Sea coast of Ukraine in 2018 and 2021 (yellow — 1 count, red — 2 counts). The numbers of the wetlands (1–40) correspond to those in table 3.
Fig. 3 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 3. The most important wetlands for RDB wetland bird species according to August Counts 2018 and 2021 (bar chart — average amount of birds, line chart — number of the species recorded at the site).
Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels
Open the record for dataset details and reuse information.
Coastal Wetland Basal Consumer and End Member C-13 and N-15 Isotopic Ratios Across a Mangrove Encroachment Gradient, 2019
Foundation species support highly productive and valuable ecosystems, but anthropogenic disturbances and environmental changes are increasingly causing foundation species shifts, where one foundation species replaces another. The consequences of foundation shifts are not well understood, as there is limited research on the equivalency of different foundation species and the functions they support. Here, we provide insight into community-level consequences of foundation shifts in the Gulf of Mexico, where the typical marsh foundation species (Spartina alterniflora) is being replaced with a mangrove foundation species (Avicennia germinans), forcing marsh fauna to rely on Avicennia for foundational support. We evaluated the interactions of two common and ecologically valuable basal consumers, fiddler crabs (Uca spp.) and marsh periwinkle snails (Littoraria irrorata), with both foundation species across sites with different levels of mangrove encroachment. By investigating both physical support, measured as habitat association and co-occurrence, and trophic support, as basal resource diet contributions, we found that Avicennia can physically replace Spartina for some consumers, but is not providing equivalent trophic support. Uca and Littoraria commonly occupy encroached sites and associate with mangroves but incorporate almost no mangrove plant matter into their diets. The ultimate consequences of a foundation shift in the case of mangrove encroachment may include shifting energy flows and resource use and decreased populations of basal consumers. Looking at interactions with foundation species from multiple perspectives is necessary to obtain a complete picture of the effects that foundational shifts are having, especially as such shifts are becoming increasingly common.
Monthly litterfall, monthly tree band, and annual tree growth of a South Carolina coastal wetland forest
In the southern United States, forested wetlands are of special interest because of the extent of these forests. One of the problems in developing management practices for these areas is the difficulty in adequately describing productivity relations and predicting how the structure and function of these communities might be affected by natural or anthropogenic disturbances. Community response to environmental change often occurs over a period of years, and the majority of reported studies are for 1–3 years in duration. This study was initiated in an attempt to examine long-term changes in forest systems in response to drought, flooding, hurricanes, and climate change. This study documents long-term changes in structure, composition, and growth along a gradient of high water table forested sites of an ancient beach ridge landscape in coastal South Carolina. Permanent study plots (20-m x 25-m) were established across a moisture gradient (Xeric, Mesic, and Hydric – 5 plots in each) within a longleaf pine-swamp blackgum forest system on the southern end of the Waccamaw Neck area of Georgetown County, SC in January 2000. Litterfall was measured monthly using five 0.25 m2 litter traps in each plot beginning in February to December 2018. Monthly circumference change was recorded on a subset of trees within each plot using stainless steel dendrometer bands. Diameter of all trees greater than or equal to 10 cm diameter at breast height in each plot was recorded annually at the end of the growing season.
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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.