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4,243 results for “seasonality”
Fig. 4 in Gape size influences seasonal patterns of piscivore diets in three Neotropical rivers
Fig. 4. Relationship between estimated predator gape width (from Fig. 3) and measured prey length size. Significant relationships are represented by solid lines and non-significant relationships with dotted lines. a) C. temensis and C. orinocensis; b) B. lucius and B. cuvieri.
Fig. 3 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 3. Partial regressions testing the effects of water depth (left) and distance from colonizing source (right) on fish species richness collected in 22 plots in Site of Long-Term Sampling (SLTS). Only statistically significant relationships are shown.
Fig. 1 in Cynolebias parnaibensis, a new seasonal killifish from the Caatinga, Parnaíba River basin, northeastern Brazil, with notes on sound producing courtship behavior (Cyprinodontiformes: Rivulidae)
Fig. 1. Cynolebias parnaibensis, Jacobina do Piauí, Piauí, Brazil. (a) UFPB 6719, holotype, male, 54.5 mm SL. (b) UFPB 6709, paratype, female, 46.2 mm SL.
Fig. 1 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 1. Geographical location of the study area and the Site of Long-Term Sampling (in the area). The system is installed in the Pantanal, Brazil.
Fig. 2 in Cynolebias parnaibensis, a new seasonal killifish from the Caatinga, Parnaíba River basin, northeastern Brazil, with notes on sound producing courtship behavior (Cyprinodontiformes: Rivulidae)
Fig. 2. General thump sequence produced by the male of Cynolebias parnaibensis during the courtship behavior (a), and a single thump expanded (the second one above) (b). Oscilogram above and spectogram below (window function Hann, overlap 99%, FFT size 1,200 points).
Fig. 4. Fish assemblage ordination resulting from a in Flow seasonality and fish assemblage in a tropical river, French Guiana, South America
Fig. 4. Fish assemblage ordination resulting from a CA analysis using species (a), family (b), trophic guild (c), and MOS (d) descriptors in the downstream site, Comté River. Bold text indicates the species, family, trophic guild or MOS which contributes most to axes. Dots = samples taken during high waters; triangles = samples takes during low waters. Numbers correspond to fish species in Table 1. Axis scales are indicated in the small box.
Fig. 2. Water level oscillation measured between August 1998 and July 2000 in Flow seasonality and fish assemblage in a tropical river, French Guiana, South America
Fig. 2. Water level oscillation measured between August 1998 and July 2000 at the Hydrological station on the Comté River. The numbers indicate the mean water level during the month of sampling.
Fig. 1 in Flow seasonality and fish assemblage in a tropical river, French Guiana, South America
Fig. 1. Localization of the sampling sites on the Comté River, French Guiana. A = upstream site; B = downstream site; HS = Hydrological station.
Fig. 4 in Spatial, seasonal and ontogenetic variation in the diet of Astyanax aff. fasciatus (Ostariophysi: Characidae) in an Atlantic Forest river, Southern Brazil
Fig. 4. Representation of the similarity patterns (Euclidean distances) in the seasonal and spatial diet composition of Astyanax aff. fasciatus at two sites on the rio das Pedras, Guarapuava, PR, Brazil.
Fig. 3 in Spatial, seasonal and ontogenetic variation in the diet of Astyanax aff. fasciatus (Ostariophysi: Characidae) in an Atlantic Forest river, Southern Brazil
Fig. 3. Diet composition of Astyanax aff. fasciatus at two sites on the rio das Pedras, according to season. Categories: plants, invertebrates, terrestrial invertebrates, aquatic invertebrates, terrestrial vegetation, aquatic vegetation and sediments and detritus.
Fig. 6 in Spatial, seasonal and ontogenetic variation in the diet of Astyanax aff. fasciatus (Ostariophysi: Characidae) in an Atlantic Forest river, Southern Brazil
Fig. 6. Ordination of individuals of Astyanax aff. fasciatus of different sizes according to the higher feeding preference. (SL1:> 50 mm; SL2: 51-75 mm and SL3: <76 mm).
Fig. 2 in Spatial, seasonal and ontogenetic variation in the diet of Astyanax aff. fasciatus (Ostariophysi: Characidae) in an Atlantic Forest river, Southern Brazil
Fig. 2. Diet composition of Astyanax aff. fasciatus at sites A (a) and B (b) on the rio das Pedras, according to the origin of the ingested items (value between parentheses corresponds to the feeding index of each category).
Fig. 5 in Spatial, seasonal and ontogenetic variation in the diet of Astyanax aff. fasciatus (Ostariophysi: Characidae) in an Atlantic Forest river, Southern Brazil
Fig. 5. Diet composition of Astyanax aff. fasciatus among the three established standard length classes, and according to the plant or animal origin of the item. (SL1:> 50 mm; SL2: 51-75 mm and SL3: <76 mm; value between parentheses corresponds to the feeding index of each category).
Fig. 4 in Diet seasonality and food overlap of the fish assemblage in a pantanal pond
Fig. 4. Niche breadth values to fish assemblage in the Sinhá Mariana pond (Mato Grosso State, Brazil) using Levin's stan- dardized index, during rainy and dry seasons.
Fig. 5 in Diet seasonality and food overlap of the fish assemblage in a pantanal pond
Fig. 5. Values of trophic niche breadth (mean ± standard error) of fish species in the Sinhá Mariana pond (Mato Grosso State, Brazil) during rainy and dry seasons.
Fig. 3 in Diet seasonality and food overlap of the fish assemblage in a pantanal pond
Fig. 3. Dendrogram of diet similarity of the fish assemblage in the Sinhá Mariana pond (Mato Grosso State, Brazil) showing the trophic guilds during rainy (A) and dry (B) seasons. Abbreviations of the species names are showing in table 1.
Fig. 2 in Diet seasonality and food overlap of the fish assemblage in a pantanal pond
Fig. 2. Water level in the in the studied region, Sinhá Mariana pond, Mato Grosso State, Brazil, from March/2000 to February/2001, showing the wet and dry seasons. These dates were provided by Agência Nacional de Águas (ANA).
Data from: All-season space use by non-native resident Mandarin Ducks (Aix galericulata) in northeastern Germany
<p>Data from article "All-season space use by non-native resident Mandarin Ducks (<em>Aix galericulata</em>) in northeastern Germany", accepted for publication in Journal of Ornithology in 2021.</p> <p><strong>Article abstract:</strong></p> <p>Patterns of space use are often subject to large temporal and individual-level variation, due to seasonality in behaviour and environmental conditions as well as age- or sex-specific needs. Especially in temperate regions, seasonality likely influences space use even in non-migratory birds. In waterfowl of the family <em>Anatidae</em>, however, few studies have analyzed space use of the same individuals across the full annual cycle. We used a resident population of Mandarin Ducks (<em>Aix galericulata</em>) in northeast Germany to study their year-round space use in relation to season, sex, and age. We marked 172 birds with colour rings and surveyed relevant water bodies for re-encounters for several years. As space-use patterns we derived home ranges from minimum convex polygons and the number of water bodies used by individual birds. Our analysis revealed that individuals shifted their space use between seasons, in particular extending their home ranges during the non-breeding season. Between years, in contrast, birds tended to show season-specific site fidelity. Sex differences were apparent during both breeding and non-breeding season, male consistently having larger home ranges and using slightly more water bodies. No difference was found between first-year and adult birds. Our study demonstrates that mark-resighting can provide valuable information about space use in species with suitable behaviour and readily accessible habitat. In such cases, it may be a valid alternative to more expensive GPS-tracking or short-term manual radio telemetry, particularly within citizen-science projects.</p> <p><strong>Data description:</strong></p> <p>Mark-resight data on Mandarin ducks in the Potsdam/Berlin region, Germany. Each sighting (including capture and ringing event) contains information about:</p> <p>- individual with sex and age specification </p> <p>- date, season, sub-season as defined in article</p> <p>- coordinates of sighting location: Gauss-Krüger coordinate system with right (R; Rechtswert) and high (H; Hochwert) value. For distinction between "fine" and "coarse" coordinates, see article. Water body names refer to the "coarse" site specification. </p> <p> </p> <p> </p>
A field experiment reveals seasonal variation in the Daphnia gut microbiome
<p>The gut microbiome is increasingly recognized for its impact on host fitness, but it remains poorly understood how naturally variable environments influence gut microbiome diversity and composition. We studied changes in the gut microbiome of ten genotypes of water fleas (<em>Daphnia magna</em>) in submerged mesocosm enclosures in a eutrophic lake over a period of 16 weeks, from early summer to autumn. The microbial diversity increased when <em>Daphnia</em> were reintroduced from the laboratory to the lake, and the composition of gut microbes drastically changed. Both gut microbiome diversity and composition continued to change over the 16-week period, with alpha diversity peaking in late summer. The gut microbiome community was clearly distinct from that of the surrounding water, and temporal changes in the two communities were independent of each other. There were no consistent differences in the gut microbiomes among <em>Daphnia</em> genotypes in the lake environment. The change in gut microbiome over the season was accompanied by a decline in reproductive output and survival. There were weak, but statistically supported, effects of microbiota composition on<em> Daphnia </em>fitness, but there was no evidence that natural variation in microbiome diversity or composition was associated with tolerance to the cyanotoxin microcystin. We conclude that the gut microbiome of <em>Daphnia</em> is highly dynamic in a natural lake environment, but that host genetic effects on microbiome diversity and composition between genotypes within a population can be vanishingly small. These results emphasize that establishing the ecological effects of gut microbiota will require largescale experiments under natural conditions.</p>
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </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.