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4,243 results for “seasonality”
Figure 5 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)
Figure 5. Seasonal dynamics of parameters: a – relative biomass of diatoms (1) and weighted average volume of phytoplankton cells (2), b – relative biomass of dinoflagellates (1) and coccolithophores (2), c – molar ratios N/P (1) and Si/N (2), d – net phytoplankton growth rate (1) and ratio g/µ (2) in station 2.
Figure 4 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)
Figure 4. Seasonal dynamics of parameters: a – intensity of solar radiation (1) and water temperature (2), b – nitrates (1) and ammonium (2), c – silicates (1) and phosphates (2), c – net primary production (1) and chlorophyll a concentration (2) in station 2.
Figure 3 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)
Figure 3. Seasonal dynamics of parameters: a – relative biomass of diatoms (1) and weighted average volume of phytoplankton cells (2), b – relative biomass of dinoflagellates (1) and coccolithophores (2), c – molar ratios N/P (1) and Si/N (2), d – net phytoplankton growth rate (1) and ratio g/µ (2) in station 1.
Figure 2 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)
Figure 2. Seasonal dynamics of parameters: a – intensity of solar radiation (1) and water temperature (2), b – nitrates (1) and ammonium (2), c – silicates (1) and phosphates (2), c – net primary production (1) and chlorophyll a concentration (2) in station 1.
Figure 5 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 5. Principal component analysis of the samples collected from two sites, alfalfa field and boundary zone based on nine soil properties.
Figure 4 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 4. Seasonal changes in total precipitation and mean monthly temperature in Kopaida valley during the period April 2021 – March 2022.
Figure 3 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 3 depicts the monthly changes of the mean soil moisture of all three soil depths and the instant soil temperature at the sampling time at 10 cm depth. It is obvious that these two parameters altered identically in the two fields and only small differences can be detected, e.g. the rise in soil moisture in July in the alfalfa field due to the application of irrigation water.
Figure 3 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 3. Seasonal fluctuations of the mean soil moisture up to 15 cm depth and of the soil temperature at 10 cm depth in the alfalfa plantation and its boundary zone in Kopaida valley.
Figure 1 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 1. Left: Extraction efficiency (enchytraeid individuals m-2) of sucrose centrifugation, wet extraction with filter and wet extraction without filter. Bars and error bars denote means and 95% confidence intervals respectively. Means that are significantly different in multiple comparisons using Wilcoxon test are represented by different letters above bars (P <0.05). Right: Relationship between Enchytraeidae populations extracted with wet extraction without filter and a) sucrose centrifugation, and b) wet extraction with filter.
Fig. 3 in Spatial and seasonal patterns in fish assemblage in Córrego Rico, upper Paraná River basin
Fig. 3. Non-metric multidimensional scaling (NMDS) ordination based on fish abundance data from Córrego Rico: a) Stretches grouped in upper, middle and lower (S1 to S7; d- dry season and r- rainy season). b) Stretches (S1 to S7; d- dry season and r- rainy season); fish species with highest correlations with the axis (Ser-het: Serrapinnus heterodon; Par-oxy: Paravandellia oxyptera; Pia-arg: Piabina argentea; Geo-bra: Geophagus brasiliensis; Ser-not: Serrapinnus notomelas; Che-est: 'Cheirodon' stenodon); environmental variables with highest correlations with the fish assemblage (Dis: discharge; Wid: width; O%: dissolved oxygen; Vel: water velocity; Am: ammonia; Nit: Nitrate). Vectors show the direction and magnitude of correlations.
Fig. 2 in Rainfall as a driver of seasonality in parasitism
Fig. 2. Relationship between rainfall measured two months prior to parasitological sampling (Rt-2) and the probability of Trichuris egg presence. Points (triangles = territorial males, circles = bachelor males) and the line (with 95% confidence intervals) are predicted values from a binomial generalized linear mixed model.
Fig. 1 in Seasonal prevalence of queens and males in colonies of tawny crazy ants (Hymenoptera: Formicidae) in Florida
Fig. 1. Mean ± SE (n = 3–11) number of queens (including female dealates), volume of brood (mL), and number of male alates per colony, collected monthly in Gainesville (Alachua County), Florida, USA, to show monthly fluctuations within seasons designated as winter (Dec–Feb), spring (Mar–May), summer (Jun–Aug), and fall (Sep–Nov).
Fig. 2 in Seasonal parasitism of the leaf-cutting ant Atta sexdens Linnaeus (Hymenoptera: Formicidae) by phorid flies (Diptera: Phoridae) in a Brazilian Cerrado-Atlantic Forest ecotone
Fig. 2. Correlation between temperature and the number of leaf-cutting ants Atta sexdens parasitized by Apocephalus attophilus (r = −0.722; df = 9; P <0.05).
Fig. 1 in Seasonal parasitism of the leaf-cutting ant Atta sexdens Linnaeus (Hymenoptera: Formicidae) by phorid flies (Diptera: Phoridae) in a Brazilian Cerrado-Atlantic Forest ecotone
Fig. 1. Number of leaf-cutting ants Atta sexdens parasitized by Apocephalus attophilus and Eibesfeldtphora tonhascai in a Brazilian Cerrado-Atlantic Forest ecotone. The seasons are as follows: spring (Sep–Nov), summer (Dec–Feb), fall (Mar–May), and winter (Jun–Aug).
Fig. 2 in Survival and development of fall armyworm (Lepidoptera: Noctuidae) in weeds during the off-season
Fig. 2. Insect survival (mean ± SE) (a), biomass (mean ± SE) (b), and injury level (mean ± SE) (c) of Spodoptera frugiperda fed with 6 weeds and maize in the greenhouse for 21 d. Means capped with the same letter do not differ significantly.
Fig. 1 in Survival and development of fall armyworm (Lepidoptera: Noctuidae) in weeds during the off-season
Fig. 1. Mean (± EP) of larval survival and pre-imaginal survival (%) (a), larval development time and pre-imaginal development time (b), and biomass of larvae and pupae of surviving individuals of Spodopterafrugiperda fed with 6 weeds and maize in laboratory conditions (c). Uppercase letters are used to compare larval stage, while lowercase letters are used to compare pupal stage. Means capped with the same letter do not differ significantly. Means followed by an asterisk (*) were zero (0), and were not used in the analysis.
Fig. 2 in Infestation and seasonal fluctuation of chigger mites on the Southeast Asian house rat (Rattus brunneusculus) in southern Yunnan Province, China
Fig. 2. Seasonal fluctuation of infestations of the Southeast Asian house rat (R. brunneusculus) with Walchia (W.) micropelta at Jingha, southern Yunnan of China (April 2016–March 2017).
Fig. 5 in Infestation and seasonal fluctuation of chigger mites on the Southeast Asian house rat (Rattus brunneusculus) in southern Yunnan Province, China
Fig. 5. Seasonal fluctuation of infestations of the Southeast Asian house rat (R. brunneusculus) with Walchia (W.) turmalis at Jingha, southern Yunnan of China (April 2016–March 2017).
Fig. 1 in Infestation and seasonal fluctuation of chigger mites on the Southeast Asian house rat (Rattus brunneusculus) in southern Yunnan Province, China
Fig. 1. Seasonal fluctuation of overall infestations of the Southeast Asian house rat (R. brunneusculus) with chiggers at Jingha village in southern Yunnan of China (April 2016–March 2017).
Fig. 4 in Infestation and seasonal fluctuation of chigger mites on the Southeast Asian house rat (Rattus brunneusculus) in southern Yunnan Province, China
Fig. 4. Seasonal fluctuation of infestations of the Southeast Asian house rat (R. brunneusculus) with Leptotrombidium (L.) deliense at Jingha, southern Yunnan of China (April 2016–March 2017).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.