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82 results for “coastal change”
Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
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Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years
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Is climate change to blame? Increased rainfall reduces emergence of Taiwanosemia hoppoensis (Hemiptera: Cicadidae) in coastal windbreak forests
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Cretaceous coastal mountain building and potential impacts on climate change in East Asia
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Data for: Dynamic coastal pelagic habitat drives rapid changes in growth and condition of juvenile sockeye salmon (Oncorhynchus nerka) during early marine migration
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Coastal SEES Collaborative Research: Non-Market Value Meta-Data on Willingness to Pay for Coastal Marsh Habitat Change
These data represent a meta-dataset of observations on per household economic value - represented by per household willingness-to-pay (WTP) - for improvements in coastal marsh habitat, drawn from stated-preference studies in the research literature. The metadata allow estimation of benefit transfer functions via meta-regression modeling. Within these econometric functions, the dependent variable is a comparable estimate of economic value (e.g., WTP) drawn from extant primary valuation studies. Independent variables represent observable factors hypothesized to explain variation in this value measure across observations. These functions can be used to produce out-of-sample predictions of WTP for coastal marsh habitat improvements at sites for which no primary valuation studies have been conducted. They can also be used to understand the factors associated with systematic variations in marsh habitat values across different sites and studies. These data are described in Vedogbeton, H. and R.J. Johnston. 2020. Commodity Consistent Meta-Analysis of Wetland Values: An Illustration for Coastal Marsh Habitat. Environmental and Resource Economics 75(4), 835-865, and allow replication of the results presented therein. The metadata are extracted from primary studies that estimate total (use and nonuse) per household WTP for changes in the quantity or quality of coastal marsh wildlife habitats or their services, in US and Canada. These studies were identified via a systematic review of the literature. The metadata combine information provided by these primary non-market valuation studies with publicly available external data extracted from sources such as the US Census, US National Historical GIS (https://www.nhgis.org/), and US Fish and Wildlife Service National Wetlands Inventory (https://www.fws.gov/wetlands/Data/Mapper.html). Studies included in the metadata are restricted to those that estimate total per household WTP for coastal wetland habitat changes using generally acce
Back into the past: Resurveying random plots to track community changes in Italian coastal dunes
<p>This dataset includes two excel sheets. The first contains vegetation data ("species_data", a matrix of 668 plots x 213 species) and the second contains plant functional traits data ("traits_data") that were used to evaluate temporal changes in taxonomic and functional diversity of Mediterranean coastal dune habitats.</p> <p><span><span><span><span><span><span><span><span><span><span><span>As to the first sheet ("species_data"): vegetation data were collected at two points in time (Time 0, hereafter T<sub>0</sub>: 2002-2007, and Time 1, herafter T<sub>1</sub>: 2017-2018) in 334 randomly-sampled, georeferenced, standardized (4 m<sup>2</sup>) plots. Historical data used for the resurveying study were extracted from RanVegDunes (Sperandii et al. 2017). Details on the resurveying protocol can be found in Sperandii et al. (2019), but in short: resampling activities took place during the same months in which the original sampling was done, and plot positions were relocated using a GPS unit on which historical geographic coordinates were stored. Plots are located in coastal dune sites along the Tyrrhenian and Adriatic coasts of Central Italy, and belong to herbaceous communities classified into the following EU Habitats (sensu Annex I 92/43/EEC): upper beach (Habitat 1210), embryo dunes (Habitat 2110), shifting dunes (Habitat 2120), fixed dunes (Habitat 2210), and dune grasslands (Habitat 2230). A subset of plots could not be classified into an EU Habitat because they were highly disturbed or invaded by alien species ("NC-plots"). The matrix includes cover data, expressed as percentage (%) cover. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>As to the second sheet ("traits_data"): this sheet includes data on 3 plant functional traits, two of them quantitative (plant height, specific leaf area - SLA) and one qualitative (plant lifespan). Data for the quantitative traits represent species-level average trait values and were extracted from "TraitDunes", a database registered on the global platform TRY (Kattge et al., 2020). Functional trait data were collected in the same sites covered by the resurveying study. Functional trait data were originally measured on the most abundant species, and are available for a varying number of species depending on the trait.</span></span></span></span></span></span></span></span></span></span></span></p> <p>References:</p> <p>Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., ... & Wirth, C. (2020). TRY plant trait database–enhanced coverage and open access. Global Change Biology.</p> <p>Sperandii, M.G., Prisco, I., Stanisci, A., & Acosta, A.T.R (2017). RanVegDunes-A random plot database of Italian coastal dunes. Phytocoenologia, 47(2), 231-232.</p> <p>Sperandii, M.G., Bazzichetto, M., Gatti, F., & Acosta, A.T.R. (2019). Back into the past: Resurveying random plots to track community changes in Italian coastal dunes. Ecological Indicators, 96, 572-578.</p>
Data from: Phylogeography, population genetics, and distribution modeling reveal vulnerability of Scirpus longii (Cyperaceae) and the Atlantic Coastal Plain Flora to climate change.
A proactive approach to conservation must be predictive, anticipating how habitats will change and which species are likely to decline or prosper. We use composite species distribution modeling to identify suitable habitats for 18 members of the North American Atlantic Coastal Plain Flora (ACPF) since the Last Glacial Maximum and project these into the future. We then use Scirpus longii (Cyperaceae), a globally imperiled ACPF sedge with many of the characteristics of extinction vulnerability, as a case study. We integrate phylogeographic and population genetic analyses and species distribution modeling to develop a broad view of its current condition and prognosis for conservation. We use genotyping-by-sequencing to characterize the genomes of 142 S. longii individuals from twenty populations distributed throughout its range (New Jersey to Nova Scotia). We measure the distribution of genetic diversity in the species and reconstruct its phylogeographic history using SNAPP and RASE. Extant populations of S. longii originated from a single refugium south of the Laurentide ice sheet around 25 thousand years ago. The genetic diversity of S. longii is exceedingly low, populations exhibit little genetic structure, and the species is slightly inbred. Projected climate scenarios indicate that nearly half of extant populations of S. longii will be exposed to unsuitable climate by 2070. Similar changes in suitable habitat will occur for many other northern ACPF species – centers of diversity will shift northward and Nova Scotia may become the last refuges for those species not extinguished.
Data from: How sea-level change mediates genetic divergence in coastal species across regions with varying tectonic and sediment processes
Plate tectonics and sediment processes control regional continental shelf topography. We examine the genetic consequences of how glacial-associated sea-level change interacted with variable near-shore topography since the last glaciation. We reconstructed the size and distribution of areas suitable for tidal estuary formation from the Last Glacial Maximum, ~20 thousand years ago, to present from San Francisco, California, USA (~38 °N) to Reforma, Sinaloa, Mexico (~25 °N). We assessed range-wide genetic structure and diversity of three co-distributed tidal estuarine fishes (California Killifish, Shadow Goby, Longjaw Mudsucker) along ~4,600 km using mitochondrial control region and cytB sequence, and 16–20 microsatellite loci from a total of 524 individuals. Results show that glacial-associated sea-level change limited estuarine habitat to few, widely separated refugia at glacial lowstand, and present-day genetic clades were sourced from specific refugia. Habitat increased during postglacial sea-level rise and refugial populations admixed in newly formed habitats. Continental shelves with active tectonics and/or low sediment supply were steep and hosted fewer, smaller refugia with more genetically differentiated populations than on broader shelves. Approximate Bayesian computation favored the refuge-recolonization scenarios from habitat models over isolation by distance and seaway alternatives, indicating isolation at lowstand is a major diversification mechanism among estuarine (and perhaps other) coastal species. Because sea-level change is a global phenomenon, we suggest this top-down physical control of extirpation-isolation-recolonization may be an important driver of genetic diversification in coastal taxa inhabiting other topographically complex coasts globally during the Mid- to Late Pleistocene and deeper timescales.
Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland
<p>Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVI<sub>max</sub>) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.</p>
Data from: Rapid climate change increases diversity and homogenizes composition of coastal fish at high latitudes
<p>Rapid warming at high latitudes triggers poleward shifts of species' distributions that impact marine biodiversity. In the open sea, the documented redistributions of fish lead to a borealization of Arctic fauna. A climate driven borealization and increased species diversity at high latitudes is also expected in coastal fish communities, but it has not been previously documented on a large, biogeographic scale. Here, we investigate the impact of temperature change over the last 25 years on fish communities along the coast of Norway. The study area, spanning different ecoclimatic zones between 62° and 71° N, harbors over 200 species of boreal and Arctic fish. Several of these fish species are harvested by coastal and indigenous communities, influencing settlements geography and livelihood. The long-term data on coastal water temperatures and fish species were obtained from monitoring stations and scientific surveys. Water temperature measured at three fixed sampling stations distributed along the coast show increased temperatures during the study period. The fish species distribution and abundance data were obtained from the annually standardized scientific bottom trawl survey program. Fish species richness, which was highest in the south, increased with warming first in the south and then, gradually, further north, eventually affecting biodiversity in the whole study area. Fish community composition showed a distinct latitudinal pattern early in the study, with Arctic fish species confined north and boreal species dominating south. The poleward shifts eventually eroded this zoogeographic pattern, resulting in more boreal fish species in the north and an increased homogenization of species composition along the Norwegian coast. The climate-driven reorganization of fish communities affects coastal ecosystems that are exposed to fisheries, aquaculture and other rapidly expanding human activities, stressing the urgent need for a climate adaptation of integrated coastal management.</p>
Changes in phytoplankton size-structure alter trophic-transfer in a temperate, coastal planktonic food-web
<p>Datasets, Metadata, Figures and Matlab codes used to produce the figures used in "Changes in phytoplankton size-structure alter trophic-transfer in a temperate, coastal planktonic food-web"</p>
Local reflects global: Life-stage dependent changes in the phenology of coastal habitat use by North Sea herring
<p>Climate warming is affecting the suitability and utilisation of coastal habitats by marine fishes around the world. Phenological changes are an important indicator of population responses to climate-induced changes but remain difficult to detect in marine fish populations. The design of large-scale monitoring surveys does not allow fine-grained temporal inference of population responses, while the responses of ecologically and economically important species groups such as small pelagic fish are particularly sensitive to temporal resolution. Here, we use the longest, highest-resolution time series of species composition and abundance of marine fishes in northern Europe to detect possible phenological shifts in the small pelagic North Sea herring. We detect a clear forward temporal shift in the phenology of nearshore habitat use by small juvenile North Sea herring. This forward shift can best be explained by changes in water temperatures in the North Sea. We find that reducing the temporal resolution of our data to reflect the resolution typical of larger surveys makes it difficult to detect phenological shifts and drastically reduces the effect sizes of environmental covariates such as seawater temperature. Our study therefore shows how local, long-term, high-resolution time series of fish catches are essential to understand the general phenological responses of marine fishes to climate warming and to define ecological indicators of system-level changes.</p>
Dispersal and coastal geomorphology limit potential for mangrove range expansion under climate change
<p>Latitudinal range limits for mangroves on high-energy, wave-dominated coasts are controlled by geomorphological features and estuarine dynamics. Mangroves reach a southern global range limit along the South African coastline, but the distribution is patchy, with stands occurring in only 16% of the estuaries in the region. Yet, the persistence of forests planted >50 years ago beyond the natural distribution limit suggests that additional estuaries could support mangroves. Understanding regional drivers is necessary to inform global scale estimates for how this important ecosystem is predicted to respond to climate change.</p> <p>Here, we combine species distribution modeling (MaxEnt), Lagrangian particle tracking using an eddy- and tide-resolving numerical ocean model, and connectivity matrices, to identify suitable mangrove habitats at present, as well as under the IPCC RCP4.5 and RCP8.5 climate scenarios.</p> <p>Within the current South African distribution range (± 900 km), eight more estuaries were identified as suitable under contemporary conditions. When considering potential range extension (± 110 km), an additional 14 suitable estuaries were identified. Connectivity matrices suggest limited long-distance dispersal, stranding mostly at or near the release location, and a decreased probability of connectivity towards the range limit. Under both future climate scenarios, 30% of estuaries currently supporting mangroves are predicted to become unsuitable, while an additional six estuaries beyond the current distribution are predicted to become suitable. However, there is limited connectivity between these new sites and established forests.</p> <p><em>Synthesis</em>: This study shows that dispersal substantially limits mangrove distribution at the southern African range limit and highlights the importance of including this process in species distribution models. Ultimately, our results provide new insight for mangrove conservation and management at range limits that are not controlled predominantly by temperature, as it has been assumed that mangroves will largely expand to higher latitudes under climate change.</p>
Figure 2 in Structural and diversity changes in coastal dunes from the Mexican Caribbean: the case of the invasive Australian pine (Casuarina equisetifolia)
Figure 2. Height patterns of the coastal dune vegetation in the sampling plots.
Figure 1 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 1. Scheme of the study area and location of sampling points.
Armitage et al., 2024. Multidecadal changes in coastal benthic species (primary and secondary data: SOTEAG)
<p>The primary data forms part of an ongoing monitoring programme by the Shetland Oil Terminal Environmental Advisory Group (SOTEAG). SOTEAG has undertaken biennial benthic surveys in Sullom Voe and the surrounding areas since 1974 to determine the status of infaunal communities and seafloor sediments. For more information on SOTEAG and the types of monitoring they practice please see: <a href="https://soteag.org.uk/">https://soteag.org.uk/</a>. A full description of sampling methods can be found here: https://soteag.org.uk/environmental-monitoring/monitoring-reports/</p> <p>These published data are a subset of the original SOTEAG data. They were used in a 32-year analysis of benthic infaunal invertebrates to investigate changes in species and trait composition, as well as detect long-term temperature-related shifts in benthic species and communities. The manuscript is entitled “Multidecadal changes in coastal benthic species composition and ecosystem functioning occur independently of temperature-driven community shifts” by Armitage et al., 2024. It should be noted that many of these data are a subset of the original and may have been transformed or standardised for statistical analyses. Below is a description of each .csv file, and whether the data are primary or secondary data, and how they are used in the analyses for the published paper. For details on how the data were reduced, please see the publication. In summary, a total of 15 stations were selected with a biennial year range of 1986 – 2018. The depth range covered 4 – 58m, where stations were classified into shallow, intermediate, and deep depth groups. </p> <p><strong>Env.csv</strong></p> <p><em>Primary data</em>: The environmental data that were collected during sampling by SOTEAG. The data are in long format with each column denoting: Station, Year, Water depth (m), Gravel %, Sand %, Mud %, Grain size, Water temperature (ᵒC), and water salinity. </p> <p><strong>Indices.csv</strong></p> <p><em>Secondary data</em>: These data are the various species and functional indices created from an analysis using species and trait data. The data are in long format and have row names consisting of the station and year, as well as column headings species richness (sp.rich), total species abundance (abundance) species diversity: Shannon (shannon), species diversity: Simpson (simpson), species evenness (evenness), functional richness (FRic), functional evenneness (FEve), functional diversity, also knowns as dispersion (FDis), functional redundancy (Red) species vulnerability (Vul), sampled station (Station), year of sampling (Year), classified depth group (Depth_group), and the decade (Decade). </p> <p><strong>Species.csv</strong></p> <p><em>Primary data</em>: This is the SOTEAG species abundance/community list that has been reduced (rare species were removed, a subset of stations were selected, and data were averaged and standardised across replicates). </p> <p><strong>Species_classification_all.csv </strong></p> <p><em>Secondary data</em>: This is extra supporting information for the species list highlighting the taxonomic ranking (coloumns) and species (rows). </p> <p><strong>STI_and_EcoFun_per_sp.csv</strong></p> <p><em>Secondary data</em>: This is data produced from a combination of species abundance and traits. The first two columns are row ID and species names. Columns 3-12 describe five ecosystem functions with the calculations/methodology provided in the publication. These data show the mean and scaled (0-1) scores for each function. The column headings equate to bioturbation (Bio), sediment stability (Sed), nutrient recycling (NutR), low trophic position (lowTP), high trophic position (highTP). The following columns X0 – X100 represent the quantiles of temperature affinities, with 50 being the 50% mark. The last column is the number of records (nrec) related to the thermal affinity search. </p> <p><strong>STImacroSoteag.csv </strong></p> <p><em>Secondary data</em>: Species thermal affinities (methods described in publication) with the first two columns as row ID and species names. The following columns X0 – X100 represent the quantiles of temperature affinities, with 50 being the 50% mark. The last column is the number of records (nrec) related to the thermal affinity search. </p> <p><strong>Traits.csv</strong></p> <p><em>Primary data</em>: Trait data where each species is listed as a row and traits as a coloumn heading. For trait column names please see publication. Note that the ranges of scores given to a species affinity to a trait is 0-3, which may be split depening on the affinity of the species (i.e., fuzzy coding). </p> <p><strong>Traits_EcoF.csv</strong></p> <p><em>Secondary data</em>: This is data produced from a combination of species abundance and traits. The first two columns are row ID and species names. Columns represent the trait used for the calculation of the Ecosystem Function (please see equations 2 – 6 in the publication). The column headings equate to bioturbation (Bio), sediment stability (Sed), nutrient recycling (NutR), low trophic position (lowTP), high trophic position (highTP), while the trait abbreviations can be found in Table 1 of the publication. The scores follow the fuzzy coding approach highlighted above (Traits.csv). </p> <p><strong>TrendsVsSTIHats.csv</strong></p> <p><em>Secondary data</em>: Results from species thermal index analysis and used to plot figure 4 of the main publication. Data is in long format. </p> <p><strong>tsSSTdata.csv</strong></p> <p><em>Primary data:</em> Sea surface temperatures used in the species distribution model to fit species thermal indices. See publication for details (section 2.3). </p> <p><strong>Vulnerability.csv </strong></p> <p><em>Secondary data:</em> A species index that was a result from the main species abundance/community data. Index values are given for each species (rows) by station/year (columns). Community means of the index can be found in “indices.csv”.</p>
The sensitivity of primary productivity in Disko Bay, a coastal Arctic ecosystem to changes in freshwater discharge and sea ice cover
<p>Data for figure 4 in <a href="https://doi.org/10.5194/egusphere-2022-916">https://doi.org/10.5194/egusphere-2022-916</a></p>
Potential adaptability of marine turtles to climate change may be hindered by coastal development in the USA
<p>Marine turtles may respond to projected climatic changes by shifting their nesting range to climatically suitable areas, which may result in either increased exposure to threats or fewer threats. Therefore, there is a need to identify whether the habitat predicted to be climatically suitable for marine turtle nesting in the future will be affected by future threats and hinder marine turtles' ability to adapt. We modelled the geographic distribution of climatically suitable nesting habitat for marine turtles in the USA under future climate scenarios, identified potential range shifts by 2050, determined impacts from sea-level rise, and explored changes in exposure to coastal development as a result of range shifts. Overall nesting ranges of marine turtle species were not predicted to change between the current and future time periods, except for the northern nesting boundaries for loggerhead turtles. However, declines in climatically suitable nesting grounds were predicted; loggerhead turtles will experience the highest decreases (10%) in climatically suitable habitat followed by green (7%) and leatherback (1%) turtles. However, sea-level rise is projected to inundate 78–81% of current habitat predicted to be climatically suitable in the future, depending on species and scenario. Nevertheless, new beaches will also form, and suitable nesting habitat could be gained, with leatherback turtles potentially experiencing the biggest percentage gain in suitable habitat.</p>
Dynamics of Shoreline Change in the Coastal Region of Rupat Island, Riau, Indonesia
<p>shoreline changes in coastal of Rupat Island</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.