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593 results for “fish distribution”
Distribution and habitat use of juvenile steelhead and other fishes of the lower Feather River
Understanding how fish presence is related to habitat features is useful in restoration planning and monitoring as better information about how fish use habitat may lead to more impactful restoration projects. The California Department of Water Resources (DWR), conducted a two-year study of microhabitat and mesohabitat in Feather River. The goal of this study was to identify relationships between habitat conditions (depth, substrate, velocity, and cover) and where juvenile Chinook salmon and steelhead occur. Snorkel surveys were conducted monthly March through August in 2001 and 2002 across 29 different sites, which were selected at random (13 in Low Flow Channel, and 16 in High Flow Channel). Each sampling section covered an area 25 meters long by 4 meters wide, running parallel to riverbank. These data were published to support the Healthy Rivers and Landscapes Science Program.
Modelled distributions of fish and epibenthic invertebrates in the southern North Sea
<p>These data include distribution maps of fish and invertabrate species in the southern North Sea from 2014 until 2023. The maps are modelled using point data of presence/absence and biomass (per trawled km²) from scientific fisheries surveys to estimate the distribution of the probability of occurrence (POC) or biomass (kg per km²), respectively. Also included are forecasts of species' distributions assuming increasing water temperatures in the southern North Sea according to the ICCP scenario RCP8.5.</p> <p>Each files contains a raster stack with layers for each species. The data can be read into the R using the 'stack'-command from the 'raster'-package. The raster stacks contain layers with headers, which code the species and size group. For some species of relevance to fisheries managment, Numbers behind the latin names of the species give information on the included size classes in cm with 'no' indicating no size class information was available.</p> <p>The file names are composed of the follwing elements:</p> <p>'bio' = biomass</p> <p>'poc' = probability of occurrence</p> <p>'emp' = observed occurrence/abundance data from fisheries surveys with employed spatial smoother</p> <p>'sdm' = modelled distributin data from random forests</p> <p>'fc' = forecast distributions based on temperature predictors according to RCP8.5</p> <p>'rel.ca2' = core areas (CA) of distribution representing values > then the mid-point of modelled POC value range</p> <p>Year numbers give the time frame of empirical data or model predictions. </p> <p> </p> <p><strong>You can access the .tiff-files with the following R-commands using the directory path where you have stored the files:</strong></p> <p><em><strong>library(raster)</strong></em></p> <p><em><strong>poc<-stack("your_path/poc.sdm.2014_2023.tiff")</strong></em></p> <p><em><strong>poc$gadus.morhua_5_113 </strong># Plots distribution of Atlantic cod as probability of occurrence observed at a size range from 5 - 113 cm tail length</em></p>
Detailed insight into gillnet catches: fish directivity and micro distribution
<p>This dataset contains data for gillnets that were deployed in Římov reservoir, South Bohemia, Czech Republic (48°50'55.0"N 14°29'14.0"E). The sampling dates were recorded from July 30 to August 2, 2019. This experiment was conducted to test the bias of gillnets in relation to fish direction capture. To determine if this is a random pattern or if it follows a directional pattern. The dataset includes various terms such as eventID, eventDate, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, habitat, waterBody, locality, DEIMS.iD, basisOfRecord, minimumDepthInMeters, maximumDepthInMeters, samplingEffort, samplingProtocol, dynamicProperties, occurrenceStatus, organismQuantity, organismQuantityType, measurementValue, measurementUnit, measurementType, measurementRemarks, organismRemarks, acceptedNameUsageID, scientificName, taxonRank, class, order, family.</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Fig. 2 in Movement and longitudinal distribution of a migratory fish (Salminus brasiliensis) in a small reservoir in southern Brazil
Fig. 2. Total time spent by dourados (Salminus brasiliensis) in the reservoir (Tin), total time spent in an unknown location (Tun) and total time spent outside of the reservoir in a stretch of the upstream Erechim river (Tout). ANOVA with permutation tested for differences among groups (F(2,54) = 87.17; p <0.0002). The numbers above the plots represent the number of individuals. Different letters above the plots represent significant differences according to Tukey's test. Circles represent outlier values; the heavy horizontal line crossing the box is the median; the bottom and top of the box are the lower and upper quartiles, respectively; and the whiskers are the minimum and maximum values.
Fig. 6 in Effect of abiotic variables on fish eggs and larvae distribution in headwaters of Cuiabá River, Mato Grosso State, Brazil
Fig. 6. Temporal (a) and spatial (b) frequency of occurrence of the seven most abundant taxa of fish larvae captured in the headwaters of the Cuiabá River between November 2007 and March 2008.
Fig. 2 in Effect of abiotic variables on fish eggs and larvae distribution in headwaters of Cuiabá River, Mato Grosso State, Brazil
Fig. 2. Temporal (a) and spatial (b) distribution of density (individuals/10m3) of fish eggs and larvae captured in the headwaters of the Cuiabá River, in all the collection sites, between November 2007 and March 2008.
Fig. 1. A in Distribution of Fish Parasites Argulus japonicus and Argulus coregoni (Crustacea: Branchiura: Argulidae) in the Lake Biwa Basin, Central Japan
Fig. 1. A map of Shiga Prefecture, central Japan, to show the distribution of Argulus japonicus (closed circles, in Lake Biwa; closed triangle, in Chirinsan-no-ike Pond) and A. coregoni (open circles) in the Lake Biwa Basin. Only large rivers are shown. Dashed lines indicate the borders of Shiga and neighboring prefectures. 1, Katayama; 2, Onoe; 3, near the mouth of the Uso River; 4, off Omi-maiko; 5, Wani-Imajuku; 6, Akanoi; 7, Shina; 8, Hiei-tsuji; 9, Chirinsan-no-ike Pond; 10, Otsu; 11, Harihata River; 12, lower Ado River; 13, Kawachidani Stream; 14, upper Ane River; 15, Oike River; 16, Kanzaki River. See Tables 1 and 2 for detailed information on the collection localities of A. japonicus and A. coregoni, respectively.
F I G U R E 3 in Biological information on a rare pelagic fish, black ruff Centrolophus niger, caught in Icelandic waters: Distribution, feeding, and otoliths
F I G U R E 3 Images of the proximal and distal sides of the right and left otoliths from black ruff Centrolophus niger (Gmelin, 1789). Scale bar and the plane at which the length and width of the otolith were measured are shown.
F I G U R E 1 in Biological information on a rare pelagic fish, black ruff Centrolophus niger, caught in Icelandic waters: Distribution, feeding, and otoliths
F I G U R E 1 Three specimens of black fish (Centrolophus niger) caught during the International Ecosystem Summer Survey of the Nordic Seas in 2021. Specimens were photographed prior to freezing. Photograph by James Kennedy.
F I G U R E 8 Total length v in Biological information on a rare pelagic fish, black ruff Centrolophus niger, caught in Icelandic waters: Distribution, feeding, and otoliths
F I G U R E 8 Total length v. (a) total weight, (b) fork length, and (c) standard length for black ruff Centrolophus niger (Gmelin, 1789) from the current and previous studies. The origin of the previous data is indicated in the legend. (a) Nonlinear and (b, c) linear regression models are shown. Note that total weight corresponds to frozen weight for measurements in the current study, whereas for previous studies, corresponds to the weight given in the respective study.
F I G U R E 2 in Biological information on a rare pelagic fish, black ruff Centrolophus niger, caught in Icelandic waters: Distribution, feeding, and otoliths
F I G U R E 2 Location of sampling stations of the Icelandic component of the International Ecosystem Summer Survey of the Nordic Seas 2009–2021. Stations where black ruff Centrolophus niger (Gmelin, 1789) were caught are shown in Black. The main surface currents in the Northeast Atlantic are shown in the final panel; the cold East Greenland current (green) and the warm Atlantic current (red) (Blindheim & Østerhus, 2005).
F I G U R E 7 Total length v in Biological information on a rare pelagic fish, black ruff Centrolophus niger, caught in Icelandic waters: Distribution, feeding, and otoliths
F I G U R E 7 Total length v. (a) frozen weight, (b) fork length, (c) and standard length and frozen weight v. (d) thawed weight for black ruff Centrolophus niger (Gmelin, 1789). (a) Nonlinear and (b–d) linear regression models are shown (a–d) as well as x = y line (d).
F I G U R E 4 in Biological information on a rare pelagic fish, black ruff Centrolophus niger, caught in Icelandic waters: Distribution, feeding, and otoliths
F I G U R E 4 Temperature profiles from the CTD probe at each station of the Icelandic part of the International Ecosystem Summer Survey of the Nordic Seas (IESSNS) where black ruff Centrolophus niger (Gmelin, 1789) were caught.
Fig. 1 in New Record of a Marine Fish Parasite Nerocila trichiura (Crustacea: Isopoda: Cymothoidae) from Japan, with its Confirmed Distribution in the Western North Pacific Ocean
Fig. 1. Nerocila trichiura, ovigerous female, NSMT-Cr 26315. A, Cypselurus hiraii (252 mm in total length) infested with N. trichiura (arrow); B, skin wound at attachment site; C, N. trichiura, dorsal view, fresh specimen. Scale bars: A, 50 mm; B, C, 10 mm.
Fig. 2 in New Record of a Marine Fish Parasite Nerocila trichiura (Crustacea: Isopoda: Cymothoidae) from Japan, with its Confirmed Distribution in the Western North Pacific Ocean
Fig. 2. Nerocila trichiura, ovigerous female, NSMT-Cr 26315. A, dorsal view; B, lateral view; C, cephalon and pereonite 1, dorsal view; D, pleotelson and right uropod, dorsal view; E, pereopod 7. Scale bars: A, B, 10 mm; C, E, 2 mm; D, 5 mm.
Fig. 3 in New Record of a Marine Fish Parasite Nerocila trichiura (Crustacea: Isopoda: Cymothoidae) from Japan, with its Confirmed Distribution in the Western North Pacific Ocean
Fig. 3. Map showing the localities where Nerocila trichiura was collected in the previous (circles) and present (star) studies. 1, 31°N, 76°W (Schioedte and Meinert 1881); 2, the West Indies (Trilles 1979); 3, Dakar Harbor, Senegal (Bruce and Harrison-Nelson 1988); 4, Banana and an unknown locality, Congo (Nierstrasz 1918; Monod 1931); 5, Durban, South Africa (Barnard 1955; Kensley 1978); 6, Comoro Islands (Kensley 2001); 7, Mauritius (type locality, Mier 1877; Bruce and Harrison-Nelson 1988); 8, 10°20′S, 70°00′E (Bruce and Harrison-Nelson 1988); 9, Great Chagos (Stebbing 1910); 10, Tamil Nadu coast, India (Trilles et al. 2013; Rameshkumar et al. 2013); 11, Zamboanga, Philippines (Schioedte and Meinert 1881); and 12, Kowaura Bay, Japan (this paper).
Figure 89 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 89: Reconstruction of the maximum glacial extent during the high point of the last ice-age (26 – 20 thousand years ago). Figure adapted from the publication Geologische Bundesanstalt (Hrsg.) (2013): Der Alpenraum zum Höhepunkt der letzten Eiszeit – Posterkarte. Geologische Bundesanstalt, Wien.
Figure 90 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 90: Representation of the seasonal cycle of stratification and mixing in a dimictic lake throughout a year.
Figure 88 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 88: Water temperature in lakes of the Po river catchment (orange) were generally warmer than in lakes of the Rhone (green) and Rhine (red) catchments. The lakes on the northern edge of the Alps remain cooler in summer and the lakes in the south remain warmer in winter. Upper panel shows 95 % quartile and lower panel shows 5% quartile of monthly mean lake surface water temperatures based on remote sensing (Advanced Very High Resolution Radiometer; 1989 – 2014 [214]). Quar- tiles were used rather than true minimum/maximum to avoid the effects of outliers.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.