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25 results for “nearshore fishes”
Santa Barbara Channel Marine BON: Nearshore kelp forest integrated fish, 1981-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains counts of fish (including cryptic fish, which are deliberately sought out) produced by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. The four contributing projects are two research projects, the Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), and the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1980 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. This dataset includes five entities, three data tables and two R scripts. The main data table contains counts of organisms, the bottom-area and height of the water column over which the fish were surveyed. The column labeled “count” records the number of organisms found in each plot/transect at a given timestamp. A second data table contains place names and geolocation for sampling sites. Information is sufficient for the calculation of fish density, which is left to the user. The third data table contains the depths of each transect the for the fish survey. Sample R script is included to illustrate generation of a basic table of areal density by taxa and sampling site. A second R script is included to convert these data from their primary MBON structure to a Darwin Core Archive (and available through multiple sources). MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). For users who are i
Data package supporting manuscript "Widespread Heterogeneity in Density-Dependent Mortality of Nearshore Fishes"
This repository contains the complete data synthesis and analysis pipeline for a global meta-analysis on density-dependent mortality in reef fishes. We estimated mortality parameters (α and β) from >30 ecological studies and explored how ecological traits, experimental methods, and phylogenetic history explain variation in density dependence. It comprises eight data tables in csv format, three .tre files for phylogenetic trees (see method document for data sources), and the zipped code folder (including 12 R scripts) to ensure transparent, end-to-end reproducibility of data processing, analysis, and visualization. This package supports the manuscript “Widespread Heterogeneity in Density-Dependent Mortality of Nearshore Fishes” by Stier & Osenberg (Ecology Letters).
FIGURE 1 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 1 | A. Map of the study area, nearshore waters in front of the Huizache-Caimanero estuarine system, located in the southeast Gulf of California, Sinaloa, Mexico. Sampling sites are indicated by black circles. B. Detail of the study area and sampling sites located in front of the Presidio River inlet.
FIGURE 4 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 4 | Boxplots for diversity and evenness for the sampled months from September 1994 to June 1995. A. Diversity, B. Evennes. Line: median; box: 25th to 75th percentiles; whiskers: minimum to maximum value range. Differences in GLM p-values are shown by lowercase letters, different letters mean significant differences for the indexes (p-value <0.05).
FIGURE 7 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 7 | Vertical distribution of the abundance of the species found in the study. Bars indicate the mean value (individuals m-3) and error bars the Standard Error.
FIGURE 3 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 3 | Water temperature and salinity profiles at each sampling site, for each sampled month from September 1994 to June 1995.
FIGURE 5 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 5 | Correlation triplot of the db-RDA showing the relationship between explanatory (temperature and salinity) and response variables (species). Black labels indicate the position of species in the ordination; their size increase according to the abundance of the species to achieve a better visualization. Samples are coded by depths and months. The angles between species and explanatory variables reflect their correlations; a small angle implies a positive correlation, a large one suggests a negative correlation, and a 90° angle indicates no correlation between two variables.
FIGURE 2 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 2 | A. Box plots showing temporal variations in temperature and salinity during the sampling period (1994–1995). Line: median; box: 25th to 75th percentiles; whiskers: minimum to maximum value range. Months with the same letters do not significantly differ using Dunn's test. B. Monthly variability of salinity from October 2011 to August 2014, dots indicate the mean value and error bars the Standard Deviation.
FIGURE 6 in Larval fish assemblages in nearshore waters of southeast Gulf of California: vertical and temporal patterns
FIGURE 6 | Temporal and vertical variations of the abundance of the most abundant species found in the study. Dots indicate the mean value and error bars the Standard Error. SEP: September, DEC: December, Apr: April, JUN: June.
Fig. 1 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 1. Stylised maps of Singapore and Raffles Marina (inset). Dotted lines indicate areas outside Raffles Marina where fish traps were deployed.
Fig. 3. Average a in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 3. Average a) Species richness, b) catch abundance, and c) species diversity (Shannon Wiener index) of fish communities within and outside Raffles Marina before and after a harmful algal bloom event in February 2014 (all means ± SE). DJF-13: December 2013– February 2014; MAM-14: March 2014–May 2014; JJA-14: June 2014–August 2014; SON-14: September 2014–November 2014; DJF-14: December 2014–February 2015. Seasons that are not significantly different are denoted by the same letter (lower case – within marina; upper case – outside marina).
Fig. 2 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 2. Principal coordinates analysis of fish communities within and outside Raffles Marina before the harmful algal bloom event in February 2014. The two principal coordinates explained 55.1% of total variation. Factors shown within the circle correlate with PCO1 or PCO2 with a factor of at least 0.5.
Fig. 5 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 5. Principal coordinates analysis of fish communities outside Raffles Marina before and after a harmful algal bloom event in February 2014. The two principal coordinates explained 43.2% of total variation. Factors shown within the circle correlate with PCO1 or PCO2 with a factor of at least 0.5. (DJF-13: December 2013–February 2014; MAM-14: March 2014–May 2014; JJA-14: June 2014–August 2014; SON-14: September 2014– ovember 2014; DJF-14: December 2014–February 2015).
Fig. 4 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 4. Principal coordinates analysis of fish community within Raffles Marina before and after a HAB event in February 2014. The two principal coordinates explained 38.1% of total variation. Factors shown within the circle correlate with PCO1 or PCO2 with a factor of at least 0.5. (DJF-13: December 2013–February 2014; MAM-14: March 2014–May 2014; JJA-14: June 2014–August 2014; SON-14: September 2014–November 2014; DJF-14: December 2014–February 2015).
Fig. 7 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 7. Results from LDA analysis showing (a) the distribution and overlap of groups of clusters (ellipsoid) and (b) the contribution of parameters to F1 and F2.
Fig. 4 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 4. Boxplots showing (a) abundance (log10-transformed) and (b) diversity index (H′ index) of fish samples in each habitat. Note: The same letter(s) in each box indicates values that are not significantly different when applying the Duncan's post-test, p-value> 0.05.
Fig. 5 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 5. Nodal diagram showing species and sample groups and abundance (log10-transformed) of fish samples per cluster.
Fig. 1 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 1. Location of the sampling habitats at Had Khanom Mu Ko Thale Tai National Park, Thailand. Note: the sampling sites; ▲ seagrass beds; ■ mudflats; ● sandy beaches; ★ mangroves.
Fig. 3 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 3. Species richness of fish samples in each habitat during the study period.
Shellfish aquaculture farms as foraging habitat for nearshore fishes and crabs
<p>This is the dataset used for the analysis in our paper. The file contains species counts and behavior classifications of nearshore fish and crabs taken from time-lapse underwater video. Also included are habitat transect data from each site. Presence/absence of zostera marina and zostera japonica eelgrass, algae, as well as eelgrass metrics were quantified in the transects.</p> <p>The metadata spreadsheet contains descriptions of each column in the datasheet.</p> <p>This data was collected at shellfish aquaculture farm sites across Puget Sound, Washington, USA in 2017, 2018, and 2019 using underwater timelapse video cameras and habitat transects.</p> <p>Transects were done with 1/4 meter^2 quadrat divided into 25 10x10cm^2 squares, each quadrat was measured 5 times along a 30 meter line parallel to shore.</p> <p>Video was recorded in 2 minute lengths every 10 minutes starting 1 hour before high tide, going to 1 hour after high tide, using cameras positioned approximately 18 inches off the substrate. PVC stakes were used to mark a 1 meter square of camera visibility and organisms observed within that 1 meter square were recorded. </p> <p>For more information, see my chapter 1 thesis.</p>
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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.