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81 results for “saltmarsh”

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edi52/100

Decomposition and lignin content of wooden dowels deployed in a saltmarsh for 3, 5, or 7 years at Goat Island, North Inlet, Georgetown SC, 2017-2024.

Wooden dowels were used to examine organic matter decomposition in saltmarsh soils. Dowels were inserted to a depth of 45cm in control and fertilized marsh plots. They were harvested after 3, 5 or 7 years and sectioned into 5-cm segments. Weight loss of the dowel segments was used to quantify the fate of labile organic matter, while change in lignin content was used to quantify the fate of refractory organic matter.

openCC0Jan 2026View details →

edi52/100

A grazing crab drives saltmarsh carbon storage and recovery in VA, SC, and GA

Consumers can directly (e.g., consumption) and indirectly (e.g., trophic cascades) influence carbon cycling in blue carbon ecosystems. Previous work found that large grazers have nuanced effects on carbon stocks, yet, small, bioturbating-grazers, which remove plant biomass and alter sediment properties, remain an understudied driver of carbon cycling. We used field-derived and remote sensing data to quantify the grazing effects of the purple marsh crab, Sesarma reticulatum, on carbon stocks, flux, and recovery in salt marshes. Sesarma fronts led to a carbon loss of around 40-70% and Sesarma front migration rates accelerated over time. Despite latitudinal differences, front migration rate had no effect on carbon stocks, flux, or time to replacement. When we included Sesarma disturbance in carbon flux calculations, we found it may take 5-100 years for marshes to replace lost carbon, if at all. Combined, we show that small grazers cause a net loss in carbon stocks as they move through the landscape, and irrespective of migration rate, these grazer-driven impacts persist for decades. This work showcases the significant role of consumers in carbon storage and flux, challenging the classic paradigm of plant-sediment feedbacks as the primary ecogeomorphic driver of carbon cycling in blue carbon ecosystems.

openCustomMay 2024View details →

zenodo48/100

Dataset: Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery

<div> <div> <div> This repository containts the datasets from the article "Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery" (Submitted). The dataset was used in a workflow used to create saltmarsh maps for the Algarve region (South Portugal), focused on the 4 main costal systems of the region: Alvor, Arade, Ria Formosa and Guadiana.   For a description of the methodology see the article [link] and Github repo [link].   <h1>Repository content</h1> <h2>1. system-masks.zip</h2> Contains 4 <code>geojson</code>files with a polygon which delimits the areas included in the study. The files are named after the respective systems that they delimit. Any region outside of these polygons were not used in the analysis. CRS - EPSG:4326 <h2>2. manual-clean-up-masks.gpkg</h2> Polygons which were manually created to mask out (exclude) pixels which were classified as saltmarsh, but are clearly not. File contains a single layer with 52 polygons and one variable. Variables: <ul> <li>system [string] - Which system the polygon delimits</li> </ul> <h2>3. saltmarsh-training-data.gpkg</h2> Data used for supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, target classes, or predictor classes. File contains a single layer with 2448 points and 18 variables. Variables: <ul> <li>water_system [string] - Study system in which the quadrat was sampled</li> <li>transect [string] - Name of transect in which the quadrat was sampled</li> <li>quad_id [integer] - Unique identifier per quadrat</li> <li>cluster [integer] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [string] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>train [boolean] - Was quadrat used in the train (TRUE) or test (FALSE) stage of model training?</li> <li>ndvi [numerical] - Normalized Difference Vegetation Index, calculated from the satellite image mosaic as (nir – red) / (nir + red).</li> <li>ndwi_high [numerical] - Normalized Difference Water Index estimated from images at high tide, calculated as (green – nir) / (green + nir)</li> <li>ndwi_low [numerical] - Normalized Difference Water Index estimated from images at low tide, calculated as (green – nir) / (green + nir)</li> <li>subtime [numerical] - Fraction of time that a cell is estimated to be submerged in water over one year.</li> <li>coastal_blue [numerical] - Surface reflectance values at 443 nm.</li> <li>blue [numerical] - Surface reflectance values at 490 nm.</li> <li>green_i [numerical] - Surface reflectance values at 531 nm.</li> <li>green [numerical] - Surface reflectance values at 565 nm.</li> <li>yellow [numerical] - Surface reflectance values at 610 nm.</li> <li>red [numerical] - Surface reflectance values at 665 nm.</li> <li>rededge [numerical] - Surface reflectance values at 705 nm.</li> <li>nir [numerical] - Surface reflectance values at 865 nm.</li> </ul> <h2>4. saltmarsh-transect-metadata.csv</h2> Comma-delimited file with information about vegetation sampling transects. Each row represents one transect. File contains 6 variables. Variables: <ul> <li>water_system [string] - Study system in which the transect was sampled</li> <li>transect_set [string] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>site [string] - Name of the site within the study system. This was used exclusively to plan transects.</li> <li>transect [string] - Name of transect in which the quadrat was sampled</li> <li>date [date yyyy-mm-dd] - Date of transect sampling.</li> <li>notes [string] - Notes taken during transect sampling and which might be relevant to understand data issues.</li> </ul> <h2>5. saltmarsh-vegetation-quadrats.gpkg</h2> Data used for to create vegetation clusters (<code>cluster</code>) and saltmarsh community types (<code>marsh_type</code>). The later was used as the target class in the supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, or presence/absence of species. File contains a single layer with 2448 points and 32 variables. Variables: <ul> <li>water_system [string] - Study system in which the transect was sampled</li> <li>transect [string] - Name of transect in which the quadrat was sampled</li> <li>transect_set [string] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>quad_id [integer] - Unique identifier per quadrat</li> <li>distance_from_water [integer] - Distance from start of quadrat, which was the point closes to the water where saltmarsh was found for that transect.</li> <li>cluster [integer] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [string] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>Arthrocaulon.macrostachyum [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Tripolium.pannonicum [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.halimus [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Cistanche.phelypaea [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.portulacoides [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limbarda.crithmoides [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Juncus.effusus [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limoniastrum.monopetalum [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Myriolimon.ferulaceum [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limonium.vulgare [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Phragmites.australis [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Polygonum.maritimum [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Puccinellia.maritima [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.procumbens [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.europaea [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Caroxylon.vermiculatum [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.fruticosa [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.perennis [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Bolboschoenus.maritimus [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.maritimus [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Spergularia.bocconei [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Suaeda.vera [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Triglochin.maritima [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.montevidensis [boolean] - Presence (1) or absence (0) of the species with the variable name.</li> </ul> <h2>6. predicted-map.tif</h2> Geotiff file with a single layer for predicted saltmarsh community. Values are:   - no data - Not saltmarsh   - 1 - Low saltmarsh   - 2 - Middle saltmarsh   - 3 - High saltmarsh CRS - EPSG:32629 </div> </div> </div>

opencc-by-4.0Sep 2024View details →

zenodo48/100

Indicative distribution map for Ecosystem Functional Group MFT1.3 Coastal saltmarshes and reedbeds

This archive contains indicative distribution maps and profiles for MFT1.3 Coastal saltmarshes and reedbeds, 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 et al. (2020) for details. 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).

opencc-by-4.0Jul 2021View details →

zenodo48/100

Moita-Montijo Bay saltmarsh and tidal flat change rates (1958-2018)

Saltmarsh and tidal flat change rates were determined for the period of 1958 to 2018, for the Moita-Montijo Bay (Tagus Estuary, Portugal). Rates were determined only for the exposed saltmarsh. No rates were calculated for sheltered saltmarsh (saltmarsh inside abandoned saltpans and similar structures). Saltmarsh margins were delineated in ArcMap, using, as the carthographic basis, orthorectified aerial photgraphs from 1958 and orthophotomaps from 1995 and 2018 (from Direção Geral do Território (DGT)). Tidal flat change rates were determined for the period of 1977 to 2018, in the north and south banks of the Montijo Channel (Moita-Montijo Bay, Tagus Estuary, Portugal). Rates were determined only for the Montijo Channel margins. The tidal flat margins were delineated in ArcMap, using, as the carthographic basis, orthorectified aerial photgraphs from 1977 and orthophotomaps from 1995 and 2018 (from DGT). The Digital Shoreline Analysis System (DSAS) in ArcMap was used to calculate the End Point Rates (EPR) between the margins (See Himmelstoss et al. (2018) for details on how to the EPR is calculated using DSAS). This dataset was produced in the scope of the Project Planta II*. Further details can be consulted in Martins et al. (2023).   Dataset: <ul> <li>Saltmarsh change rates in the Moita-Montijo Bay (1958 to 1995) (Tagus Estuary, Portugal) = rates_smarsh_moitamontijobay_1958_1995.shp</li> <li>Saltmarsh change rates in the Moita-Montijo Bay (1995 to 2018) (Tagus Estuary, Portugal) = rates_smarsh_moitamontijobay_1995_2018.shp</li> <li>Tidal flat change rates in the north bank of the Montijo Channel (1977 to 1995) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_nb_moitamontijobay_1977_1995.shp</li> <li>Tidal flat change rates in the north bank of the Montijo Channel (1995 to 2018) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_nb_moitamontijobay_1995_2018.shp</li> <li>Tidal flat change rates in the south bank of the Montijo Channel (1977 to 1995) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_sb_moitamontijobay_1977_1995.shp</li> <li>Tidal flat change rates in the south bank of the Montijo Channel (1995 to 2018) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_sb_moitamontijobay_1995_2018.shp</li> </ul> Please cite the paper where this dataset is discussed as Martins, D., Alves da Silva, A., Duarte, J., Canário, J., & Vieira, G. (2023). Changes in vessel traffic disrupt tidal flats and saltmarshes in the Tagus Estuary, Portugal. Estuaries and Coasts. DOI: <a href="https://doi.org/10.1007/s12237-023-01198-7">https://doi.org/10.1007/s12237-023-01198-7</a> *Project Planta II - PTDC/CTA-GQU/31208/2017, co-funded by the European Regional Development Fund (ERDF), through the financing Regional Operational Program of Lisbon, and by the Portuguese Science and Technology Foundation (FCT), through the national funds (PIDDAC).

opencc-by-4.0Apr 2023View details →

zenodo44/100

Data from: Saltmarsh vegetation and secured woody debris facilitate mangrove re-colonization

Does the presence of saltmarsh vegetation affect the long-term regeneration of the pioneer mangrove species Avicennia germinans in a degraded dwarf forest? Does immobilized coarse woody debris (CWD) affect regeneration similarly? Do larger trees suppress or facilitate intraspecific saplings? The study was conducted in a dwarf mangrove forest in the high intertidal zone on Bragança peninsula in northern Brazil. The spatial patterns of A. germinans, the herbaceous halophyte Sesuvium portulacastrum, and CWD were mapped in three sample plots (each 400 m2) during two consecutive vegetation surveys, conducted in 2011 and 2014. Inhomogeneous Poisson and Thomas point-process models were used to assess the distribution of A. germinans life-history stages (seedlings, saplings, and adult dwarf trees), conditioned on the presence of S. portulacastrum and CWD. In addition, intraspecific interactions between trees and regeneration were assessed based on crown projection mapping. Bivariate point pattern analyses were used to assess the dependence of advance regeneration on dwarf A. germinans trees and S. portulacastrum. A. germinans saplings and trees were positively associated with S. portulacastrum and CWD, whereas seedlings were located around tree crowns. The density of fruit-bearing trees was positively associated with sapling density, indicating that regeneration relied on locally dispersed propagules. Herbaceous vegetation and CWD have an important ecological function in degraded mangroves by retaining tidally dispersed propagules. Here, we show that herbaceous vegetation does not suppress the growth of seedlings but facilitates mangrove recolonization. Due to limited tidal dispersal, regeneration relies on local propagule supply. In addition to hydrological restoration, the observed vegetation patterns suggest that, in the absence of propagule-retaining vegetation, restoration of high-intertidal mangroves can be facilitated by establishing nuclei of planted trees and installing secured logs.

opencc-by-4.0Oct 2021View details →

zenodo44/100

Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)

This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to Limnology and Oceanography Letters. It contains all input and output files for the post-processing of all model results. To be able to run the scripts as is, the folder structure should be as follows: Runs (includes all model run folders from Part II and Part III) Post/Basic/Channels Post/Basic/Cross_sections Post/Basic/Integrals Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders) Post/Basic/Skeleton_clean Post/Basic/Skeleton_final Post/Basic/Skeleton_raw Post/Basic/Unchanneled_path_length Post/Basic/Watersheds Post/Basic/Scenarios.txt Post/Basic/TIGER_2km_5m.slf Post/Paper_1/Erosion-deposition Post/Paper_1/Fluxes Post/Paper_1/Profiles Post/Paper_1/Std

opencc-by-4.0Dec 2022View details →

zenodo44/100

Investigating sediment supply impact on centennial saltmarsh biogeomorphic development with Demeter

This repository contains a series of simulations to investigate the impact of (changing) sediment supply on the long-term biogeomorphic development (200 years) of saltmarshes (4 km2). The model used is Demeter (https://doi.org/10.5281/zenodo.7101535).  Each simulation output file contains: <ul> <li>yearly bottom elevation maps (resolution of 5 m);</li> <li>yearly vegetation coverage maps (resolution of 5 m);</li> <li>yearly hydroperiod maps (resolution of 5 m);</li> <li>yearly exceeding bottom shear impulse maps (resolution of 5 m);</li> <li>vegetation distribution maps every 5 years (resolution of 50 cm).</li> </ul> Each simulation is also processed to compute channel network characteristics with TidalGeoPro (https://doi.org/10.5281/zenodo.7153257), such as: <ul> <li>maximum upstream channel length along channel network skeleton every 20 years (resolution of 5 m);</li> <li>total upstream channel length within tidal watershed along channel network skeleton every 20 years (resolution of 5 m);</li> <li>tidal watershed surface area along channel network skeleton every 20 years (resolution of 5 m);</li> <li>mean unchanneled path length within tidal watershed along channel network skeleton every 20 years (resolution of 5 m);</li> <li>channel cross-section width, depth and area along channel network skeleton every 20 years (resolution of 5 m);</li> <li>yearly unchanneled path length maps (resolution of 5 m).</li> </ul> If you are interested to use this dataset, or collaborate with the authors, please contact Olivier Gourgue (ogourgue@gmail.com).

opencc-by-4.0Dec 2021View details →

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Calibration Saltmarsh Stem data 1996

First 3-5 year resurvey of permanent monitoring plots using essentially the same protocol as the intial survey of 1992-1993 except that: 1.) standing biomass of the herbaceous groundcover was added (including for new lower salt marsh plots) using clip plots at the subplot locations; and 2.) an estimate of landcover/habitat class aggregation was conducted surrounding each plot center out to 60m in the four cardinal directions. Extends baseline data useful for estimating landscape-scale vegetative productivity, mortality, and turnover; for establishing pre-disturbance conditions in the case of later stand- or island-wide disturbance; and for assisting in the ground-truthing of landcover and habitat classification using aerial or satellite remote-sensing imagry.

openCustomJan 1999View details →

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Calibration Saltmarsh Harvest data 1996

openCustomJan 1999View details →

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Calibration Saltmarsh Plot data 1996

openCustomJan 1999View details →

dryad40/100

Data from: Restoration of native saltmarshes can reverse arthropod assemblages and trophic interactions changed by a plant invasion

Plant invasions profoundly impact both natural and managed ecosystems, and removal of the invasive plants addresses only part of the problem of restoring impacted areas. The rehabilitation of diverse communities and their ecosystem functions following removal of invasive plants is an important goal of ecological restoration. Arthropod assemblages and trophic interactions are important indicators of the success of restoration, but have largely been overlooked in saltmarshes. We determined how arthropod assemblages and trophic interactions changed with the invasion of the exotic plant Spartina alterniflora and with the restoration of the native plant Phragmites australis following Spartina removal in a Chinese saltmarsh. We investigated multiple biotic and abiotic variables to gain insight into the factors underlying the changes in arthropod assemblages and trophic structure. We found that although Spartina invasion had changed arthropod diversity, community structure, feeding-guild composition, and the diets of arthropod natural enemies in the saltmarsh, these changes could be reversed by the restoration of native Phragmites vegetation following removal of the invader. The variation in arthropod assemblages and trophic structure were critically associated with four biotic and abiotic variables (aboveground biomass, plant density, leaf N, and soil salinity). Our findings demonstrate the positive effects of controlling invasive plants on biodiversity and nutrient cycling, and provide a foundation for assessing the efficacy of ecological restoration projects in saltmarshes.

opencc-zeroApr 2022View details →

zenodo40/100

Data from "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction"

Sediment data from Steart Marshes described in Mossman et al. "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction". Data are provided as a .xlsx file (Data package.xlsx) with four tabs. Tab 1 has column heading descriptions. Tab 2 has total carbon samples. Tab 3 has total organic carbon samples. Tab 4 has bulk density samples. Each tab is also provided as a seperate csv file.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part III: Extra runs)

This is Part III of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to Limnology and Oceanography Letters. It contains all input and output files for the extra simulations used in the paper (Figures S3, S8-S10). Each zip file corresponds to a model run.  TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100. TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200. TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY. Main scenarios: - 01: Spartina (Figures 1-5, S3-S10) - 02: Salicornia (Figures 1-5, S3-S10) - 83: No vegetation (Figures 1-5, S3, S8-S10) Additional scenarios: - 146: Spartina, low bulk drag coefficient (Figure S3) - 147: Spartina, very low bulk drag coefficient (Figure S3) - 148: Salicornia, low bulk drag coefficient (Figure S3) - 149: Salicornia, very low bulk drag coefficient (Figure S3) - 122: Spartina, low settling velocity (Figure S8) - 123: Spartina, high settling velocity (Figure S8) - 124: Salicornia, low settling velocity (Figure S8) - 125: Salicornia, high settling velocity (Figure S8) - 126: No vegetation, low settling velocity (Figure S8) - 127: No vegetation, high settling velocity (Figure S8) - 128: Spartina, low critical bed erosion shear stress (Figure S8) - 129: Spartina, high critical bed erosion shear stress (Figure S8) - 130: Salicornia, low critical bed erosion shear stress (Figure S8) - 131: Salicornia, high critical bed erosion shear stress (Figure S8) - 132: No vegetation, low critical bed erosion shear stress (Figure S8) - 133: No vegetation, high critical bed erosion shear stress (Figure S8) - 134: Spartina, low Partheniades constant (Figure S8) - 143: Spartina, high Partheniades constant (Figure S8) - 136: Salicornia, low Partheniades constant (Figure S8) - 144: Salicornia, high Partheniades constant (Figure S8) - 138: No vegetation, low Partheniades constant (Figure S8) - 145: No vegetation, high Partheniades constant (Figure S8) - 150: Spartina, low sediment dry bulk density (Figure S8) - 151: Spartina, high sediment dry bulk density (Figure S8) - 152: Salicornia, low sediment dry bulk density (Figure S8) - 153: Salicornia, high sediment dry bulk density (Figure S8) - 154: No vegetation, low sediment dry bulk density (Figure S8) - 155: No vegetation, high sediment dry bulk density (Figure S8) - 76: Spartina, replicate #1 (Figures S9-S10) - 77: Spartina, replicate #2 (Figures S9-S10) - 78: Spartina, replicate #3 (Figures S9-S10) - 88: Spartina, replicate #4 (Figures S9-S10) - 80: Salicornia, replicate #1 (Figures S9-S10) - 81: Salicornia, replicate #2 (Figures S9-S10) - 82: Salicornia, replicate #3 (Figures S9-S10) - 89: Salicornia, replicate #4 (Figures S9-S10) - 85: No vegetation, replicate #1 (Figures S9-S10) - 86: No vegetation, replicate #2 (Figures S9-S10) - 87: No vegetation, replicate #3 (Figures S9-S10) - 90: No vegetation, replicate #4 (Figures S9-S10)

opencc-by-4.0Dec 2022View details →

zenodo40/100

Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part V: Figures)

This is Part V of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to Limnology and Oceanography Letters. It contains all input and output files to generate the figures of the paper. To be able to run the scripts as is, the path (at the beginning of each script) to the following folders must be updated: Runs (includes all model run folders from Part II and Part III) Post (includes all post-processing folders from Part IV)

opencc-by-4.0Dec 2022View details →

zenodo40/100

Fig. 1 in Diptera communities from Ria de Aveiro saltmarshes, with new records for Portugal

Fig. 1 – Sampling area location; sampled sites: 1, Foz do Rio Novo, dominated by Spartina maritima; 2, Salinas, dominated by Halimione portulacoides, Juncus maritimus, Sarcocornia perennis and Phragmites australis; 3, Bico, dominated by H. portucaloides and J. maritimus; 4, Reboxo, dominated by H. portucaloides, S. perennis, S. maritima and Limonium vulgare; 5, Monte Farinha, dominated by J. maritimus and H. portucaloides; 6, Cambeia, dominated by H. portucaloides, Salicornia ramosissima and Tamarix africana; 7, Cacia, dominated by J. maritimus, H. portucaloides and Aster tripolium subsp. pannonicus.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Linked collectors and determiners for: Saltmarsh flies of the genus Scorpiurus Parent from New Zealand (Insecta: Diptera: Dolichopodidae).

Natural history specimen data linked to collectors and determiners held within, "Saltmarsh flies of the genus Scorpiurus Parent from New Zealand (Insecta: Diptera: Dolichopodidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9434dfd7-7234-456a-b3d8-0bf7effeedf8">https://bionomia.net/dataset/9434dfd7-7234-456a-b3d8-0bf7effeedf8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9434dfd7-7234-456a-b3d8-0bf7effeedf8">https://gbif.org/dataset/9434dfd7-7234-456a-b3d8-0bf7effeedf8</a>. Formatted as a Frictionless Data package.

opencc-zeroAug 2024View details →

zenodo40/100

Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation

Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia. IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of: <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as: <code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code>

opencc-by-4.0Feb 2023View details →

zenodo40/100

Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)

This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to Limnology and Oceanography Letters. It contains all input and output files to generate the simulation grids.

opencc-by-4.0Dec 2022View details →

dryad40/100

Data from: Restoration of native saltmarshes can reverse arthropod assemblages and trophic interactions changed by a plant invasion

Open the record for dataset details and reuse information.

publicApr 2022View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record