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42 results for “demersal fish”
Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "
<p>Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.</p>
Fig. 3 in The demersal fish assemblages of the infra and circalittoral coastal rocky bottoms of the Aeo- lian Archipelago (Central Mediterranean Sea) studied by Remotely Operated Vehicle (ROV) Abstract
Fig. 3: Variation (mean ± S.E.) in species richness, diversity (H') and total density among sectors and depth ranges.
Fig. 1 in The demersal fish assemblages of the infra and circalittoral coastal rocky bottoms of the Aeo- lian Archipelago (Central Mediterranean Sea) studied by Remotely Operated Vehicle (ROV) Abstract
Fig. 1: Map of the study area in the southern Tyrrhenian Sea. The three sectors of the Aeolian Archipelago (1=Western sector; 2=Central sector; 3=Eastern sector) and locations of the ROV transects () and the main fishing ports () are indicated in the inset maps.
Fig. 2 in The demersal fish assemblages of the infra and circalittoral coastal rocky bottoms of the Aeo- lian Archipelago (Central Mediterranean Sea) studied by Remotely Operated Vehicle (ROV) Abstract
Fig. 2: Scatter plot of the canonical discriminant analysis on the effects of (a) sector and (b) depth range. Species contribution to the observed patterns is shown with directional vectors. Aant=Anthias anthias, Afil=Aulopus filamentosus, Crub=Callanthias ruber, Cchr=Chromis chromis, Cjul=Coris julis, Dgib=Dentex gibbosus, Dvul=Diplodus vulgaris, Gkol=Gobius kolombatovici, Hdac=Helicolenus dactylopterus, Mhel=Muraena helena, Scab=Serranus cabrilla, Teph=Thorogobius ephippiatus.
Global gridded fishing exploitation patterns (F/FMSY) of demersal and pelagic fish
<p>Global gridded fishing mortality (F) relative to the fishing mortality that supports maximum sustainable yield (FMSY) for three fish functional types: forage fish, large pelagic fish, and demersal fish. </p> <ul> <li>Years 1841-2004</li> <li>0.5-degree spatial resolution</li> </ul> <p>Outputs can be used to simulate historical fishing patterns of pelagic and demersal fish in ecosystem models. The F/FMSY needs to be multiplied with FMSY of each fish type in the model to obtain F. FMSY will depend on fish model specification and assumptions. Outputs are also provided as a time series per functional type and LME.</p> <p>The F/FMSY timeseries are estimated using reconstructed catch data and a data limited catch assessment model for all LME × functional type combinations with intermediate and high catches. For all remaining combinations and the high seas, F/FMSY timeseries are estimated by converting nominal effort timeseries per functional type to an F/FMSY using conversion factors. The estimated F/FMSY timeseries are allocated per functional type, ecosystem, and year across a 0.5-degree spatial grid in proportion to total gridded effort in each ecosystem.</p> <p>The gridded F/FMSY are used as input into the FEISTY model forced by outputs from GFDL’s ocean model (MOM6-COBALTv2) to generate historical time series of fish biomass and catch. The catch and biomass simulated FEISTY outputs are available between 1961 and 2004 for each LME and the High seas. </p> <p><strong>Reference to the paper with full description of the analysis: </strong></p> <ul> <li>van Denderen PD, N Jacobsen, KH Andersen, JL Blanchard, C Novaglio, CA Stock, CM Petrik (submitted) Estimating fishing exploitation rates to simulate global catches and biomass changes of pelagic and demersal fish <em>Earth's Future</em> </li> </ul> <p><strong>Additional data sources:</strong></p> <ul> <li>Reconstructed fisheries catches: Watson, R. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. <em>Sci Data</em> 4, 170039 (2017). <a href="https://doi.org/10.1038/sdata.2017.39">https://doi.org/10.1038/sdata.2017.39</a></li> <li>Novaglio, C., Rousseau, Y., Watson, R. A., & Blanchard J. L. (2024). ISIMIP3a reconstructed fishing activity data (v1.0) [data set]. ISIMIP Repository. <a href="https://doi.org/10.48364/ISIMIP.240282">https://doi.org/10.48364/ISIMIP.240282</a></li> <li>Global gridded fishing effort data reconstruction: Rousseau, Y., Blanchard, J. L., Novaglio, C., Pinnell, K., Tittensor, D. P., Watson, R. A., & Ye, Y. (2022). Global Fishing Effort [Data]. Institute for Marine and Antarctic Studies (IMAS), University of Tasmania (UTAS).</li> </ul> <p> </p>
Fig. 3 in Scientific Note Behavior of prey links midwater and demersal piscivorous reef fishes
Fig. 3. Results of hierarchical clustering using the group average linkage method. The dendrogram illustrates variable strengths in multi-species relationships and patterns of species groupings (species codes as in Table 2).
Fig. 2 in Scientific Note Behavior of prey links midwater and demersal piscivorous reef fishes
Fig. 2. Black sea bass Centropristis striata and bank sea bass Centropristis ocyurus (left) in typical orientation at edge of ledge before ambush feeding on prey fish. Two scamp Mycteroperca phenax (right, indicated by arrows) along ledge move towards high density aggregation of prey fishes. Mycteroperca phenax attacked prey fishes subsequent to this photograph.
Fig. 1. A in Scientific Note Behavior of prey links midwater and demersal piscivorous reef fishes
Fig. 1. A school of blue runner Caranx crysos (top left) and group of greater amberjack Seriola dumerili (right, one visible in photograph) drive prey fishes down to ledge habitat during predation events.
Dataset for Remarkable Hypoxia Tolerance in Two Demersal Fish Species in the Gulf of California
<p>R code and the required datafile are included in this contribution. Running the R code will allow interested readers to recreate Figure 2 and look at additional environmental parameters for the species' habitat. This supplements the manuscript Gallo, N.D., Levin, L.A., Beckwith, M., and Barry J.P. Home sweet suboxic home: Remarkable hypoxia tolerance in two demersal fish species in the Gulf of California. Ecology. </p>
Figure. Map of Turkey showing the sampling sites in the Black Sea. in Length-weight relationships of 28 fish species caught from demersal trawl survey in the Middle Black Sea, Turkey
Figure. Map of Turkey showing the sampling sites in the Black Sea.
Cross-regional drivers of demersal fish community biomass and catch
<p>Video presentation for the Ocean Sciences Meeting.</p>
A unique demersal fish fauna in the Chukchi Borderland, Central Arctic Ocean
Open the record for dataset details and reuse information.
Public information use – are invasive demersal fish species more effective than natives?
Open the record for dataset details and reuse information.
Data from: Effects of Poor Knights Islands Marine Reserve on demersal fish populations
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN SUBSEQUENT PUBLICATIONS. PLEASE SEE Anderson et al. (2019) at https://doi.org/10.1002/ece3.4948 FOR MORE INFORMATION. We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity-based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over-dispersion and/or zero-inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modelled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint-absences in the space of the original species variables. A permutation-based filter with exact family-wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail an MCEM algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity-based settings. Our proposed analysis pathway provides new opportunities to combine model-based approaches with dissimilarity-based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.
FIGURE 7. Jaydia novaeguineae. A. SMF 35008, 8.8 in Survey of demersal fishes from southern Saudi Arabia, with five new records for the Red Sea
FIGURE 7. Jaydia novaeguineae. A. SMF 35008, 8.8 cm SL, Jizan, Saudi Arabia. B. KAUMM 58, 7.2 cm SL, Jizan, Saudi Arabia.
FIGURE 5. Dactyloptena gilberti, SMF 35003, 6.6 in Survey of demersal fishes from southern Saudi Arabia, with five new records for the Red Sea
FIGURE 5. Dactyloptena gilberti, SMF 35003, 6.6 cm SL, Jizan, Saudi Arabia. A. Lateral view. B. Dorsal view.
FIGURE 2 in Survey of demersal fishes from southern Saudi Arabia, with five new records for the Red Sea
FIGURE 2. Species accumulation curve for the survey of trawled fishes in the Jizan area, southern Red Sea, Saudi Arabia. Numbers for trawls 1 to 11 correspond to actual numbers of cumulative species collected in St1 to St11 by bottom trawl with all species including pelagic species (grey solid line) and demersal species only (black solid line). Dashed line from trawls 12 to 50 corresponds to estimated cumulative species numbers for demersal species from extrapolation for additional samplings by bottom trawling with upper and lower 95% confidence intervals (dotted lines). The number of trawls at which 90% and 95% of total expected demersal species are collected (i.e. 23 and 30 trawls, respectively) is indicated by vertical lines that meet the respective horizontal lines at the extrapolated species accumulation curve.
FIGURE 1 in Survey of demersal fishes from southern Saudi Arabia, with five new records for the Red Sea
FIGURE 1. Map of the Red Sea with details of the shelf area between the city of Jizan and Farasan Island in southern Saudi Arabia (water depth of 0–200 m in light grey,>200–1,000 m in mid grey and>1,000 m in dark grey). Positions of trawling stations St1 to St11 (S1 to S11 in the map) are shown as lines between starting point and end point of the trawl.
Figure 2 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 2. Bathymetric distribution of the dominant fish species within the depth range studied (30–500 m). Black circles represent the centre of gravity (COG); thick lines correspond to the habitat width (HW). Black arrows indicate a displacement in real terms of the COG beyond the depth range sampled. Grey arrows indicate a small displacement in real terms of COG, but included in the depth range considered. Numbers 1–13 on the top axis correspond to the 13 sectors into which the sampled depth interval was divided.
Figure 5 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 5. Relationship between regression coefficients (slopes) describing the relationship between fish size and depth and maximum average length of the 15 species with positive sizedepth relationships.
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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)
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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
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