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73 results for “Coastal ecology”
Variation in Landsat 8-estimated land surface temperature with elevation from Spartina alterniflora marsh cross sections in the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site and Virginia Coast Reserve (VCR) LTER sites for winter and summer observations spanning 2013-2018
We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, w
Event logs from Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) cruises to the Martha's Vineyard Coastal Observatory (MVCO) ongoing since 2017
This package provides a table of cruises to the Martha's Vineyard Coastal Observatory for Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER). The majority of events are single day cruises, however, samples missing an MVCO Event Number were collected on multi-day NES-LTER transect cruises aboard larger research vessels. The same sampling protocols for CTD and bongo collection are used on both cruise types. Sampling frequency is approximately monthly, with NES-LTER sampling ongoing since 2017. Cruises involve collection of water column bottle samples, surface bucket samples, and zooplankton net tow samples, as well as ship-provided data. NES-LTER transect cruises will have more extensive underway and acoustic data which can be found by searching by cruise at https://www.rvdata.us/data. The event number for each cruise is provided, along with date, vessel name, cruise identifier where applicable, link to data location (for CTD, ADCP, and other underway data), and checklist of six data types.
Continuous soil temperature measurements at 10 cm depth from 3 month-long deployments in summer and winter within the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site in 2017 and 2018
To understand the influence of marsh elevation and flooding on soil temperature in Spartina alterniflora marsh, we measured soil temperature at 10 cm depth along two transects that spanned a marsh edge to interior gradient. We then associated those measurements with elevation, creek water height and vegetation characteristics. Soil temperature was logged every 15 min with a Hobo Onset Tidbit Pendant Temperature probe in Spartina alterniflora-dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected along two transects of approximately 250 m in length from 18 plots (transect 1) or 20 plots (transect 2) and over 3 sample deployments of approximately 1 month in length: 27 Jul – 31 Aug 2017 (transect 1), 8 Jan – 13 Feb 2018 (transect 1) and 23 Aug – 18 Sep 2018 (transect 2). Plot elevations along each transect were measured with a Trimble R6 RTK after probes were installed in the marsh. Creek water heights were estimated with the pressure transducer associated with the GCE-LTER eddy covariance flux tower data. Spartina alterniflora height forms were measured for each sample station during August as the mean of all stem heights within 0.25 m quadrants centered over each soil probe location. While these data are 24 hr soil temperature measurements, during all three deployments, we found that daily mean soil temperature was negatively correlated with marsh elevation on the marsh platform during low tide conditions, which represented the majority of the observations.
Coastal and Marine Ecological Classification Standard (CMECS) Catalog
<p>The <strong>Coastal and Marine Ecological Classification Standard (CMECS) Catalog</strong> is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).</p> <p>The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, <a href="https://www.go-fair.org/fair-principles/" rel="nofollow">https://www.go-fair.org/fair-principles/</a>). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via <a href="https://github.com/NOAA-OCM/cmecs" target="_blank" rel="noopener">the NOAA-OCM/cmecs GitHub</a> as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the <a href="https://github.com/NOAA-OCM/cmecs/wiki/CMECS-Thesaurus-Quick-Link"><strong>CMECS Thesaurus</strong></a>, is also available in PDF and MD formats.</p> <p>This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the <a href="https://github.com/NOAA-OCM/cmecs/releases/tag/v1.1.1" target="_blank" rel="noopener">CMECS GitHub v1.1.1 Release Page.</a></p> <p><strong>Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov</strong></p> <p>For more information about the CMECS Catalog, see the <a href="https://github.com/NOAA-OCM/cmecs/wiki">https://github.com/NOAA-OCM/cmecs/wiki.</a></p> <p>For more information about CMECS, including technical guidance and classification examples, visit the <a href="https://iocm.noaa.gov/standards/cmecs-home.html" rel="nofollow">NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage</a>.</p> <p>CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the <a href="https://www.ncei.noaa.gov/products/coastal-marine-ecological-classification-standard" rel="nofollow">NOAA National Centers for Environmental Information (NCEI) CMECS webpage</a> under the <strong>Vocabulary Maintenance</strong> section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.</p>
Dataset from Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194.
<p>Data and its metadata used in the analysis from the publication: Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194. <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678">https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678</a> </p>
Fig. 7 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 7. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about fishing season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 6 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 6. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory routes of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 4 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 4. Trophic chain based on those food items and predators most cited by fishermen in the southeastern Brazilian coast for a) reef fishes and b) pelagic fishes. Numbers are percent of interviewed fishermen who mentioned each feeding interaction. Fish sizes are not in scale. Those feeding interactions that agree with reported feeding habits of these fishes in the biological literature are marked *(Randall, 1967; Berkeley & Houde, 1978; Menezes & Figueiredo, 1980; Sazima, 1986; Pipitone & Andaloro, 1995; Barreiros & Santos, 1998; Vasconcellos & Gasalla, 2001; Silvano, 2001; Silvano & Güth, 2006; Figueiredo & Vieira, 2005; Gibran, 2007).
Fig. 3 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 3. Main habitats of fishes according to fishermen in the southeastern Brazilian coast: percentages of fishermen who mentioned each habitat category are in Appendix 1. Double-headed arrows indicate that fishes occur in both habitats in horizontal space (e.g. open ocean and reefs), up and down arrows indicate that fishes occur in both habitats in vertical space (e.g., near the bottom and at the surface). Fish sizes are not in scale.
Fig. 1 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 1. Ordination plot of the correspondence analysis (first two axes) based on fishermen answers about uses of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 2 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 2. Ordination plots of the correspondence analysis (first two axes) based on fishermen answers about fishing methods and baits of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 8 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 8. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about reproductive (spawning) season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 5 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 5. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory behavior of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Figure 5 in The ecological condition of coastal waters off the Heracles Peninsula (Crimea, the Black Sea)
Figure 5. Comparison of the H/L index in the mussel M. galloprovincialis with a shell length of 30 and 50 mm from various water areas.
Figure 4 in The ecological condition of coastal waters off the Heracles Peninsula (Crimea, the Black Sea)
Figure 4. Relationship between surface water parameters and seasonal temperature changes (Twater, ºС): A - primary production, B - concentration of total suspended matter, C - period of mass of suspension, D - maximum daylight illumination
Fig. 1 in Feeding ecology of Rivulus luelingi (Aplocheiloidei: Rivulidae) in a Coastal Atlantic Rainforest stream, southern Brazil
Fig. 1. Location of the study site in Guaratuba Munipality (black square), Paraná State coast (southern Brazil).
Texas Ecological Mapping Systems (EMS) for Southern Texas Coastal Counties
<p>We mapped 66 Ecological Mapping Systems (EMS) for eight coastal counties in south Texas, from Refugio and Aransas County south to the Mexican border. Land cover (LC), geophysical setting information, and woody vegetation height were all attributed to image objects derived from 10 m Sentinel-2 satellite imagery to model EMS type. A supervised process with training data collected from aerial photographs, aided by quantitative, species-specific, ground-collected virtual plot data, was used to classify LC in a RandomForest framework. Out of bag (OOB) error for LC was 15.24%. Recently collected LiDAR point cloud information was used to map height for woody vegetation, and the height was, in turn, used to distinguish between herbaceous, shrubland, and woodland/forest types via modification of LC results, and to define several canopy >10 m versions of forested EMS types. Geophysical settings were mapped based primarily on the distribution of soil Map Units (MUs) from the national digital soil survey (gSSURGO). Elevation and potential ponding information were derived from analysis of LiDAR-derived digital elevation models (DEMs) as an aid in mapping several EMS types. Heads-up modification of both LC and EMS modeling results using aerial photograph interpretation improved results. The agreement between EMS mapped type and field-collected data (most 10 years old or more) was >75%. The most abundant EMS types included Coastal and Sandsheet: Deep Sand Grassland (10.7% of the region), Native Invasive: Mesquite/Mixed Shrubland (5.0%), Gulf Coast: Coastal Prairie (4.6%), and South Texas: Sandy Mesquite Savanna Grassland (4.4%). The improved land cover, geophysical settings data, vegetation height data, and the use of finer-resolution image objects for modeling enabled mapping of all EMS types more accurately than previous datasets. The new EMS dataset will facilitate analysis and conservation of important habitats and modeling of species of concern that are tied to those habitats.</p>
Data from: Deep learning reveals hidden diversity of <em>Synechococcus</em> in the coastal water of China: Novel clades and their ecological insights
Open the record for dataset details and reuse information.
Common barriers, but temporal dissonance: genomic tests suggest ecological and paleo-landscape sieves structure a coastal riverine fish community
<p>Assessments of spatial and temporal congruency across taxa from genetic data provide insights into the extent to which similar processes structure communities. However, for coastal regions that are affected continuously by cyclical sea-level changes over the Pleistocene, congruent interspecific response will not only depend upon co-distributions, but also on similar dispersal histories among taxa. Here, we use SNPs to test for concordant genetic structure among four co-distributed taxa of freshwater fishes (Teleostei: Characidae) along the Brazilian Atlantic coastal drainages. Based on population relationships and hierarchical genetic structure analyses, we identify all taxa share the same geographic structure suggesting the fish utilized common passages in the past to move between river basins. In contrast to this strong spatial concordance, model-based estimates of divergence times indicate that despite common routes for dispersal, these passages were traversed by each of the taxa at different times resulting in varying degrees of genetic differentiation across barriers with most divergences dating to the Upper Pleistocene, even when accounting for divergence with gene flow. Interestingly, when this temporal dissonance is viewed through the lens of the species-specific ecologies, it suggests that an ecological sieve influenced whether species dispersed readily, with an ecological generalist showing the highest propensity for historical dispersal among the isolated rivers of the Brazilian coast (i.e., the most recent divergence times and frequent gene flow estimated for barriers). We discuss how our findings, and in particular what the temporal dissonance, despite common geographic passages, suggest about past dispersal structuring coastal communities as a function of ecological and paleo-landscape sieves.</p>
Figure 1 in The ecological condition of coastal waters off the Heracles Peninsula (Crimea, the Black Sea)
Figure 1. Map of the research area.
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Allen Brain Atlas
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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.
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