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82 results for “coastal change”
Data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change
<p>This folder will include data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change.</p>
Gulf of Mexico Coastal Change Data
<p>Data set used to calculate the shoreline migration rates of 13 Gulf of Mexico barriers during the Holocene. Radiocarbon and OSL dates are from published peer-reviewed literature, cited at the bottom of the table. Dates missing a time marker were only used to validate chronologies. Note age units vary among sites.</p>
Data for the manuscript: Enabling Climate Change Adaptation in Coastal Systems. A Systematic Literature Review
<p>This dataset includes the list of publications, framework and dataset used for the paper "Enabling Climate Change Adaptation in Coastal Systems. A Systematic Literature Review"</p>
Health Impacts of a Climate Change Adaptation Strategy to Address Drinking-water Salinity in Coastal Bangladesh
ClinicalTrials.gov study NCT02746003. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Future changes in mangrove coastal water properties
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Data from: Effects of climate change on habitat availability and configuration for an endemic coastal alpine bird
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Data from: Secondary contact and changes in coastal hydrology influence the nonequilibrium population structure of a salmonid (Oncorhynchus keta)
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Cross-continental analysis of coastal biodiversity change
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Data from: The influence of spatially heterogeneous anthropogenic change on bill size evolution in a coastal songbird
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Shoreline change in coastal Natural World Hertiage Sites
<p>This repository contains the data associated with the paper: Multi-decadal shoreline change in coastal Natural World Heritage Sites - a global assessment (<a href="http://doi.org/10.1088/1748-9326/ab968f">http://doi.org/10.1088/1748-9326/ab968f</a>).</p> <p>It contains:</p> <p>- 2020_transects_data_shorelines_linearity_geomorphology_final.csv: a dataset of transect-based shoreline and geomorphological datasets in 67 coastal Natural World Heritage Sites with available shoreline data.</p> <p>-2020_transects_data_shorelines_strong_linear_geomorphology_final.csv: a dataset of transect-based strong linear trends (recessional, depositional and stable) and geomorphological datasets in 59 coastal Natural World Heritage Sites with strong linear behaviour.</p> <p>Shorelines were derived from a global assessment of derived Landsat images (Luijendijk, A. <em>et al.</em>,2018,<em> </em> https://doi.org/10.1038/s41598-018-24630-6, Hagenaars, G. <em>et al., </em>2018, <a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.coastaleng.2017.12.011?_sg%5B0%5D=e7jxl0sHv0Rk-vxjjQWhconm1RtF5itcOHzqmZiTCaB0Jn7eujgLvuHi94LPIFeL3E_yP8wB-_F1qUjL9eidIw8eJw.qTorN3HHl-zPzbBg0YIpOUil3-oAv0JrEaaHD7qmCOA89tuoCGo70cV3H6QHsTerBHY5M7tjQZebo2GBybINVw">10.1016/j.coastaleng.2017.12.011</a> and Hagenaars, G. <em>et al., </em>2017, <a href="http://resolver.tudelft.nl/uuid:34a0114b-5e39-4b52-9940-3a7e9f5a2982">http://resolver.tudelft.nl/uuid:34a0114b-5e39-4b52-9940-3a7e9f5a2982</a>). Conditional and outlier data cleaning were undertaken on the raw shoreline dataset for further analysis.</p> <p>The geomorphological data have been obtained from the following datasets:</p> <ul> <li><strong>Topography:</strong> Global Map DEM (2017) - <a href="https://globalmaps.github.io/">https://globalmaps.github.io/</a></li> <li><strong>Land cover:</strong> Global Land Cover by National Mapping Organisations - GLCNMO (Kobayashi, T. <em>et al.</em>, 2013, <a href="https://doi.org/10.5539/jgg.v9n3p1">10.5539/jgg.v9n3p1</a>)</li> <li><strong>Coastal type:</strong> Worldwide Typology of Nearshore Coastal Systems (Dürr, H. H. <em>et al.</em>, 2011, https://doi.org/10.1007/s12237-011-9381-y)</li> <li><strong>Lithology:</strong> Global Lithological Map - GliM (Hartmann, J. & Moosdorf, N., 2012, <a href="https://doi.org/10.1029/2012GC004370">https://doi.org/10.1029/2012GC004370</a>)</li> </ul> <p>For each perpendicular transect to the satellite-derived shorelines, a set of data is provided in the table columns:</p> <ul> <li>transect_id: unique identifier of the transect</li> <li>Continent: continent of the transect</li> <li>country_name: country of the transect</li> <li>Intersect_lon: the longitude of the middle point of the transect drawn between the first available shoreline (1984 or more) and the latter available shoreline (2016).</li> <li>Intersect_lon: the latitude of the middle point of the transect drawn between the first available shoreline (1984 or more) and the latter available shoreline (2016).</li> <li>NAME: name of the coastal Natural World Heritage Site</li> <li>ORIG_NAME: the original name of the coastal Natural World Heritage Site</li> <li>INT_CRIT: criteria of selection of the coastal Natural World Heritage Site</li> <li>dt_max: is the latest year for which SDS data points are available (2016)</li> <li>dt_min: is the first year for which SDS data points are available (starts from 1984)</li> <li>nb_shorelines: the number of shorelines available from dt_min to dt_max</li> <li>cor: Pearson’s correlation coefficient (<em>r</em>)</li> <li>p.value: Pearson’s correlation coefficient (<em>r</em>) p-value</li> <li>cor_fact: classification of Pearson’s correlation coefficient (<em>r</em>) <ul> <li>0: Non-linear (-0.3 to 0.3).</li> <li>1: Weak linear ( -0.7 to -0.3 or 0.3 to 0.7)</li> <li>2: Strong linear (less than -0.7 or greater than 0.7)</li> </ul> </li> <li>coeflm: Ordinary Least Square linear regression rate (m yr-1)</li> <li>std: standard deviation of the Ordinary Least Square linear regression rate (m yr-1)</li> <li>Mean_DEM_Class: topography <ul> <li>1: 0 ≤ elevation ≤ 1 m (extremely low-lying)</li> <li>2: 1 < elevation ≤ 10 m (low-lying)</li> <li>3: 10 < elevation ≤ 50 m (middle)</li> <li>4: 50 < elevation ≤ 400 m (high)</li> <li>5: No data (transects without available elevation)</li> </ul> </li> <li>GLIM_categories: lithology <ul> <li>ev: Evaporites</li> <li>ig: Polar ice and Glaciers</li> <li>pa: Acid Plutonic Rocks</li> <li>pb: Basic-Ultrabasic Plutonic Rocks</li> <li>pi: Intermediate Plutonic Rocks</li> <li>mt: Metamorphic Rocks</li> <li>sc: Carbonate Sedimentary Rocks</li> <li>sm: Mixed Sedimentary Rocks</li> <li>ss: Siliciclastic Sedimentary Rocks</li> <li>su: Unconsolidated Sediments</li> <li>py Pyroclastic</li> <li>va: Acid Volcanic Rocks</li> <li>vb: Basic Volcanic Rocks</li> <li>vi: Intermediate Volcanic Rocks</li> </ul> </li> <li>Land_Cover_Class: land cover <ul> <li>1: Coral reefs</li> <li>2: Mangroves</li> <li>3: Marshes</li> <li>4: Vegetated</li> <li>5: Non-vegetated</li> <li>6: Urban areas</li> </ul> </li> <li>Coastal_type_Class_1: coastal types <ul> <li>1: Small deltas</li> <li>2: Tidal systems</li> <li>3: Lagoons</li> <li>4: Fjords and fjärds</li> <li>5: Large rivers</li> <li>6: Large rivers with tidal influence</li> <li>7: Karst-dominated stretches of coasts</li> <li>8: Arheic (dry areas)</li> <li>9: Islands</li> </ul> </li> <li>cor_classes_1: classification of Pearson’s correlation coefficient (<em>r</em>) <ul> <li>StrongLin: Strong linear (less than -0.7 or greater than 0.7)</li> <li>WeakLin: Weak linear ( -0.7 to -0.3 or 0.3 to 0.7)</li> <li>NonLin: Non-linear (-0.3 to 0.3).</li> </ul> </li> <li>shoreline_trend_classes: Ordinary Least Square linear regression rate (m/y) for strong linear shoreline behaviours <ul> <li>StrongLinSta: stable - shoreline change rate between -0.5 and 0.5 m yr-1 </li> <li>StrongLinAcr: depositional - shoreline change rate >0.5 m yr-1</li> <li>StrongLinErr: recessional - shoreline change rate <-0.5 m yr-1</li> </ul> </li> </ul>
Data from: "Hot deals at sea": responses of a top predator (Bottlenose dolphin, Tursiops truncatus) to human-induced changes in the coastal ecosystem
The main response of top predators to human-induced environmental changes is often behavioural. Although human activities regularly impose a disturbance on top predators, they can also be a source of reliable and concentrated food resources for species with a high degree of behavioural plasticity. This study represents the first assessment of the influence of these resources on migratory patterns and social interaction of a marine top predator, the common bottlenose dolphin, Tursiops truncatus. Pollock's closed robust design models and association analyses were applied to data collected over nine consecutive years of research in a coastal area subject to significant use and pressure by humans. Photo-identification data were collected year-round during 955 boat-based surveys, resulting in 1 638 common bottlenose dolphin group encounters. Results of this study revealed a significant upward trend in density of bottlenose dolphins, preferences for a coastal area with higher human pressure, and a reduction of the social interactions associated to a temporal switch to the food sources provided by human activities. The observed link between human activities and changes in common bottlenose dolphin behaviour aim to contribute to a better understanding of the ecology of a marine top predator and provide some of the needed baseline data, from which effective management and conservation strategies can be designed.
Data for: Development of a learning community focused on sea-level rise and coastal habitat change
<p>Rapid habitat changes are occuring in salt marshes located in the Northeastern United States, including expansion of ponded areas on the marsh platform, die off of coastal forests, and subsequent colonization of 'ghost forests' by marsh vegetation. This work focuses on two main areas: (1) environmental conditions along the marsh forest border undergoing rapid transitions; and (2) environmental conditions and plant stress in marsh platforms with extensive ponding, with three study sites: in Long Island and Southern New England, where there are often significant slope breaks along the upland (slope ~0.01), and in southern New Jersey on the Atlantic Coastal plan (slope ~0.003).</p> <p>To better understand drivers of environmental change in marsh-forest borders undergoing rapid transitions, we measured shallow groundwater levels, soil salinity, and forest health and structure along the salt marsh-upland border at three sites with varying slopes using installation of shallow groundwater wells, drone imagery and associated image processing, and geophysical methods. To better understand drivers of environmental change on the marsh platform, we measured used piezometers to understand vertical gradients in marsh groundwater levels, and measured photosynthesis and plant biomass and used drone imagery to map plant stress indices, as indicators of plant stress. While we anticipate that this data will be published in journal articles of the next 2 years, we archive collected data to facilitated data sharing, as required by NSF.</p>
Effects of biochar soil amendments on soil properties and plant recruitment in coastal climate change adaptation projects
<p>Biochar is a carbon-rich material produced through the pyrolysis of organic waste from the agricultural and forestry industries. Because black carbon is chemically stable, its application to agricultural fields is being touted as a method to mitigate greenhouse gas emissions as it has been shown to improve the fertility of the soil and crop yields as well as increase carbon sequestration. This data publication archives data from a set of paired restoration projects that incorporated biochar soil amendments to test the ability of biochar to provide positive ecosystem or carbon sequestration benefits.</p>
Climate change and coastal area
<p>Graph for paper</p>
Supplementary material 1 from: Shaffer JA, Parks D, Campbell K, Moragne A, Hueske B, Adams P, Bauman JM (2023) Coastal beaver, Chinook, coho, chum salmon and trout response to nearshore changes resulting from diking and large-scale dam removals: synergistic ecosystem engineering and restoration in the coastal zone. Nature Conservation 53: 61-83. https://doi.org/10.3897/natureconservation.53.85421
Mean woody plant abundance surveyed from the riparian and shrub-emergent marsh vegetation
Evaluating the effects of land-use change and future climate change on vulnerability of coastal landscapes to saltwater intrusion
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Data for: Development of a learning community focused on sea-level rise and coastal habitat change
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Data from: “Hot deals at sea”: responses of a top predator (Bottlenose dolphin, Tursiops truncatus) to human-induced changes in the coastal ecosystem
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Effects of biochar soil amendments on soil properties and plant recruitment in coastal climate change adaptation projects
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Data from: A boulder beach formed by waves from a calving glacier revisited: multidecadal -tsunami- controlled coastal changes in front of Eqip Sermia
<div>This study is a contribution to the National Science Centre project ‘GLAVE’ (Award No. UMO-2020/38/E/ST10/00042)</div> <div> </div>
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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.