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181 results for “Coastal Forest”
Coastal Forest Aboveground Biomass Data at six sites in the Chesapeake Bay and Delaware Bay region, 2021
This dataset contains aboveground biomass measurement and vegetation inventory of 17 coastal forest sites collected during June 1-8 of 2021 across Virginia (n = 6 in Goodwin Island and Phillips Creek), Maryland (n = 4, Monie Bay and Moneystump Swamp) and Delaware (n = 7, Milford Neck and Donas landing). The aboveground biomass was computed with allometric equations and all study sites were located within a narrow elevation range of 0-5m above sea level.
Reciprocal Transplant Experiment in a Coastal Forest, Nassawadox, VA
In the Mid-Atlantic region, accelerated sea-level rise is provoking rapid shifts in many coastal areas. As salt water intrusion occurs, tidal wetland species are colonizing space opened up by upland dieback and can even invade coastal forest understory prior to said dieback. One such species is the high marsh grass Spartina patens, which is known to exhibit divergent growth forms under the differing conditions of marsh, dune, and swale habitats. It is now colonizing the understory of coastal pine forests experiencing salt water intrusion. I established plots at three sites on the Delmarva peninsula in adjacent marsh and forest areas and conducted a reciprocal transplant experiment to address the following questions: a) How do environmental conditions for S. patens differ between marsh and forest habitats? b) How do S. patens traits differ between these habitats and are these differences a product of plasticity? Our results showed lower salinity and light availability in the forest which corresponded to greater height and leaf area of S. patens plants. Though some traits showed an effect of their habitat of origin, many functional traits exhibited strong phenotypic plasticity in response to environment. This plasticity may prove crucial to the species' resilience to future change.
Invertebrates in a retreating coastal forest near Nassawadox, VA , 2019-2020
Sampling was conducted on the Eastern Shore of Virginia at Brownsville Preserve, part of the Virginia Coast Reserve LTER and adjacent to Upper Phillips Creek (37.463, -75.835). Plots were established in five zones along the forest-to-salt marsh gradient based on vegetation communities and seedling recruitment. The high forest (HF) is characterized by both coniferous and deciduous tree species, with maximum seedling recruitment. The mid forest (MF) contains primarily coniferous trees and shrubs and shows reduced seedling recruitment. The low forest (LF) canopy shows partial mortality due to salt stress and no tree recruitment; the understory contains primarily invasive (Phragmites australis) but also salt marsh (Spartina patens, Distichlis spicata) plant species. The transition zone (TZ) contains mostly dead mature trees, with its understory dominated by S. patens, D. spicata, and P. australis. The high marsh (HM) does not contain trees and is dominated by S. patens and D. spicata. 2019 data was collected September 16-18, 2019. 2020 data was collected September 8-9, 2020.
Forest Transition Experiment - Soil Pore Water Salinity in a Coastal Virginia Forest
A pore water sipper was be used to collect a pore water sample from the top 15 cm of the soil, and was read using a portable refractometer in the field.
Forest Transition Experiment - Vegetation Monitoring on a Coastal Virginia Forest, 2019-2023
This dataset contains data on vegetation (shrubs, trees, non-woody vegetation, seedlings and Phragmites occurrence in permanent plots at the Brownsville Forest near Nassawadox, VA.
Vegetation and physical characteristics of Chesapeake Bay retreating Coastal Forests 2022-2024
This data set contains biomass and physical data across an upland forest to marsh transition. These measurements are taken at 5 sites around the Chesapeake and Delaware Bays. Data is collected at up to 5 ectones across the upland to marsh (High Marsh, Transition Zone, Low, Mid and High Forest). These ecotone definitions follow Smith et al. 2019, https://doi.org/10.6073/pasta/4524c22708628eb7f06d174edae89ff2).
Spatial Variability in Marsh Vulnerability and Coastal Forest Loss in Chesapeake Bay
Sea level rise (SLR) and saltwater intrusion are driving shifts in coastal ecosystems that must migrate to survive. Marsh migration into adjacent uplands is a primary mechanism for sustaining coastal marshes, but potentially limited by natural and anthropogenic barriers. In this study, we focus on the Chesapeake Bay as a case study and combine previous delineations of the marsh-forest boundary and high-resolution topobathymetric data with sea level rise predictions to uniquely assess marsh migration potential on the scale of U.S. Geological Survey HUC10 watersheds. Combining these predictions results in a high-resolution Chesapeake Bay-wide assessment of marsh migration potential through the end of the century. Additionally, we analyze high-resolution land use data within the potential migration area to assess what ecosystems are at risk of loss to marsh via salinization and what potential anthropogenic features exist in the marsh migration corridor. The data consists of 3 files created from analyses conducted during the study: 1) A table summarizing characteristics of the study sites, including elevation and land use, and 2) A zipped Shapefile containing the boundaries of the HUC10 watersheds, 3) A zipped raster (CB_MarshMigrationArea.tif) of elevation categories. Cell values indicate: 1 = area below threshold elevation 2 = area between threshold elevation and 0.5 m of SLR. 3 = area between 0.5 and 1 m of SLR. 4 = area between 1 and 1.5 m of SLR. 5 = area between 1.5 and 2 m of SLR. 6 = area between 2 and 2.5 m of SLR. 7 = area above 2.5 m of SLR. Additionally, uploaded are 10 additional files containing the exact copies of the publicly available data we analyzed to create the above files. To obtain these files from their original sources (i.e. USGS, NOAA, etc) please see the links provided in the Metadata-LO-Letters-dat-V3.rtf file. 1) Points at the marsh-forest boundary 2) Chesapeake Conservancy High-Resolution Land Use 3) Chesapeake Conservancy High-Resolution L
Forest Transition Experiment - Leaf Litter in a Coastal Virginia Forest
Leaf litter collected in basket-based litter traps in forest transition plots
Surface Elevation (SET) Data for Brownsville Forest at the Virginia Coastal Reserve, 2019-2025
This dataset contains data from Surface Elevation Tables (SETs) located in the Brownsville Forest at the Virginia Coastal Reserve. These research plots were set up in 2019 as a part of a larger forest disturbance project. Marker Horizons and Shallow SETs were installed in 2020 but have not yet been measured. Tyler Messerschmidt has made all measurements of these SETs
Characteristics of the Marsh-Forest Boundary within Chesapeake Bay Region Coastal Watersheds
Sea level rise is leading to the rapid landward migration of marshes into coastal forests and other terrestrial ecosystems. Although complex biophysical interactions likely govern these ecosystem transitions, projections of sea level driven land conversion commonly rely on a simplified delineation of the marsh-upland boundary based on tidal datums alone. To determine the influence of biophysical drivers on the elevation of the marsh-forest transition, and their implication for land conversion, we examined almost 100,000 high-resolution marsh-forest boundary elevation points, determined independently from tidal datums, alongside 14 environmental variables in the Chesapeake Bay, the largest estuary in the United States.
Figure 3 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 3. PCoA ordinal configuration of soil macrofaunal communities from different habitats by Euclidean distance similarity index. In the code of the samples, the prefix means the code of the habitat, and the suffix means the number of the sample.
Figure 2 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 2. One-way ANOVA of taxonomic richness and abundance (A) and Margalef 's richness index R and Shannon– Weaver diversity index H' (B) across different habitats (mean ± SE). Means with different scripts are significantly different by Dunnett's T3 test (A) and LSD test (B), α = 0.05.
Figure 4 in Impact of overwintering cormorants (Phalacrocorax carbo) on springtail (Hexapoda: Collembola) communities of the Azov and Black Sea coastal forests
Figure 4. Relative total abundance, genus richness, and abundance of the different functional groups of springtails (abundance in impact sites/abundance in controls ± SE) in forests with active overwintering cormorant colonies (Black Sea) and abandoned forests (Azov Sea). Totabu: Total abundance, Genrich: Genus richness, Epied: Epiedaphic, Hemied: Hemiedaphic, Eued: Euedaphic, EMC: Euedaphic microorganism consumers, HMC: Hemiedaphic microorganism consumers, EPMC: Epiedaphic plant and microorganism consumers, EAMC: Epiedaphic animal and microorganism consumers. *: Statistically significant differences between the control and impact sites according to the Tukey HSD test.
Figure 2 in Impact of overwintering cormorants (Phalacrocorax carbo) on springtail (Hexapoda: Collembola) communities of the Azov and Black Sea coastal forests
Figure 2. Abundance of different functional groups of springtails (ind. m–2 ± SE, n = 2) at the control and impact sites of forests abandoned by cormorants (Azov Sea). Epied: Epiedaphic, Hemied: Hemiedaphic, Eued: Euedaphic, EMC: Euedaphic microorganism consumers, HMC: Hemiedaphic microorganism consumers, EPMC: Epiedaphic plant and microorganism consumers, EAMC: Epiedaphic animal and microorganism consumers. *: Statistically significant differences between the control and impact sites according to the Tukey HSD test.
Figure 3 in Impact of overwintering cormorants (Phalacrocorax carbo) on springtail (Hexapoda: Collembola) communities of the Azov and Black Sea coastal forests
Figure 3. PCA of the relationship between springtail genera abundance (shown as gray lines and diamond-shaped symbols) and edaphic parameters (black lines and dots). AOC impact: Impact sites with active overwintering colonies (Black Sea), AOC control: Control sites close to active overwintering colonies (Black Sea), AF impact: Impact sites in abandoned forests (Azov Sea), AF control: Control sites in abandoned forests (Azov Sea). 1) Mesaphorura, 2) Pseudachorutes, 3) Xenylla, 4) Folsomia gr. quadrioculata, 5) Parisotoma, 6) Isotomiella, 7) Cryptopygus, 8) Isotoma, 9) Lepidocyrtus, 10) Pseudosinella, 11) Entomobrya.
Figure 1 in Impact of overwintering cormorants (Phalacrocorax carbo) on springtail (Hexapoda: Collembola) communities of the Azov and Black Sea coastal forests
Figure 1. Abundance of different functional groups of springtails (ind. m–2 ± SE, n = 2 at the control and impact sites of forests with active overwintering cormorant colonies (Black Sea). Epied: Epiedaphic, Hemied: Hemiedaphic, Eued: Euedaphic, EMC: Euedaphic microorganism consumers, HMC: Hemiedaphic microorganism consumers, EPMC: Epiedaphic plant and microorganism consumers, EAMC: Epiedaphic animal and microorganism consumers. *: Statistically significant differences between the control and impact sites according to the Tukey HSD test.
FIGURE 4 in Fish responses to multiple scales in coastal blackwater Atlantic Forest streams in Southeast Brazil
FIGURE 4 | Redundancy analysis (RDA) explaining the relationship between selected local, landscape, and spatial variables and fish species. Vector lines indicate the relationship of significant environmental variables to the ordination axis. TDS – Total Dissolved Solids; AR – Areas under Regeneration; PCNM1 – Spatial Component represented by the Principal Coordinates of Neighbor Matrices. Species are indicated by black dots. Acronyms according to Tab. 3
FIGURE 3 in Fish responses to multiple scales in coastal blackwater Atlantic Forest streams in Southeast Brazil
FIGURE 3 | Venn diagram showing the variance partition of local, regional and spatial variables in explaining the composition of fish communities. a = fraction of exclusive explanation of local variables; b = fraction of exclusive explanation of landscape variables; c = fraction of exclusive explanation of spatial variables; d = fraction of shared explanation between local and landscape variables; e = fraction of shared explanation between landscape and spatial variables; f = fraction of shared explanation between local and spatial variables; g = fraction of shared explanation among local, landscape and spatial variables.
FIGURE 1 in Fish responses to multiple scales in coastal blackwater Atlantic Forest streams in Southeast Brazil
FIGURE 1 | Location of the study area in the coastal region of the state of São Paulo, indicating the Baixada Santista and Northern Coast basins and the distribution of the sampling sites in the Itapanhaú (JP1, JP2, VA), Itaguaré (MP, PM, GU), Guaratuba (P1–P6), and Una (BB1, BB2) sub-basins (Adapted from Esteves et al., 2019). In detail, sites of the Guaratuba sub-basin and main landscape types.
FIGURE 2 in Fish responses to multiple scales in coastal blackwater Atlantic Forest streams in Southeast Brazil
FIGURE 2 | Nonmetric Multidimensional Scaling (NMDS) plot of Axis 1 and Axis 2 of the total fish density of 14 blackwater streams. Stream codes according to Tab. 1.
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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.