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3,673 results for “Temporal”
Figure 1 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 1. Study site and sampling stations (P1, P2, and P2) in Barbosa Lake (the circle shows the connection site between the lake and the river).
Figure 4 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 4. Means and standard deviations of root biomass of E. azurea (g.DW–1) in Barbosa Lake during the study period.
Figure 2 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 2. Scheme of an adult E. azurea individual showing the sampled root mass in sequential direction from the apical to the basal parts of the plant.
Figure 4 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 4. Multiple comparisons of the moving distance among the four RCC breeding subseasons (M: mating, B: brooding, W: wading, G: growing).
Figure 3 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 3. Principal component analysis for the climatic variables and the moving distance. The first axis (PC1) explains 31.44% of total variation, and the second axis (PC2) accounts for 22.44% of total variation.
Fig. 7 in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 7. Non-metric multidimensional scaling based on the Nymphalidae species composition captured in 4 climatic periods—wet (dark blue), dry (red), transition from wet to dry (T.wd, light blue), and transition from dry to wet (T.dw, black)—in 2 habitats, savanna (S) and gallery forest (F) in the Fazenda Água Limpa and the Reserva Ecológica do Roncador, Brasília, DF. A.dem, Archeoprepona demophon; M.hel, Morpho helenor; and P.pol, Pariphthimoides poltys best characterize the T.dw, whereas C.aco, Catonephele acontius; P.oci, Pareupthichia ocirrhoe; and T.lao, Temenis laothoe best characterize the T.wd. Stress: 0.15.
Fig. 6 in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 6. Pielou's evenness values of the Nymphalidae species in the dry and wet seasons and in the transitional periods between these 2 seasons,from wet to dry (T.wd) and from dry to wet (T.dw), in savanna (Cerrado sensu stricto, ss) and forest (gallery forest) habitats in the Fazenda Água Limpa and the Reserva Ecológica do Roncador, Brasília, DF. Different lowercase letters represent significant differences (P <0.05).
Fig. 4. A in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 4. A) Temporal variation in Nymphalidae abundance captured in the dry and wet seasons and in the 2 transitional periods between those seasons, from wet to dry (T.wd) and from dry to wet (T.dw), in savanna (Cerrado sensu stricto, ss) and forest (gallery forest) habitats of the Fazenda Água Limpa and the Reserva Ecológica do Roncador, Brasília, DF. The box plots show the differences between the 4 climatic periods, considering B) the total butterfly abundance (gallery forest + savanna), and that C) in the gallery forest and D) in the savanna separately. Different lowercase letters represent significant differences (P <0.05).
Fig. 3 in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 3. Circular analysis of the number of individuals reflecting the abundance of the different Nymphalidae subfamilies and tribes captured from Jul 2012 to Jun 2013 in the Fazenda Água Limpa and Reserva Ecológica do Roncador. In this analysis, each column's length represents the observed abundance and the arrow the tendency of the highest abundance.
Fig. 2 in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 2. Circular analysis of the number of individuals reflecting the abundance of Nymphalidae captured from Jul 2012 to Jun 2013 in the Fazenda Água Limpa and Reserva Ecológica do Roncador. In this analysis, each column's length represents the observed abundance and the arrow the tendency of the highest abundance.
Fig. 1. Rarefaction curves, generated with 1,000 in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 1. Rarefaction curves, generated with 1,000 randomizations without replacement, based on the MauTau (Sobs, black circles) and "total" estimated (Jackknife 1, gray circles) values. The data of Nymphalidae species richness was acquired from Jul 2012 to Jun 2013 in the Fazenda Água Limpa and the Reserva Ecológica do Roncador, Brasilia, DF.
Figure 2 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 2. Trend of sea turtle nesting in the past 40 years at APNP: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Figure 1 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 1. Study location in Alas Purwo National Park's Pancur-Cungur Coast with six hypothetical stations (dot: sector benchmark point).
Figure 4 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 4. Number of four sea turtles nesting in each month during survey period: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Figure 22 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 22. Outlines of 8 dipole structures that preceded all intense algal blooms in the central part of the South Caspian during the study period from 1999 to 2022. Different colors mark different years.
Figure 20 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 20 shows interannual variability of monthly averaged cloudiness for June, July, August and September in 2002-2022. One can see that all bloom events have occurred when monthly averaged cloudiness was in the range of 0.1-0.57. In general, absence of clouds should be favorable for algal bloom due to high level of insolation, but we can point to years 2006, 2012, 2014, 2016, and 2022 when monthly averaged cloudiness in August was less than 0.3 and no algal blooms were identified.
Figure 21 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 21. Interannual variability of photosynthetically active radiation (Einstein/m2day) in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 20 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 20. Interannual variability of cloudiness in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 17 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 17. Interannual variability of SST (0C) in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 19 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 19 shows interannual variability of monthly averaged wind speed for June, July, August and September in 2000-2022. We can see that all bloom events occurred when monthly averaged wind speed was in the range of 4.3-5.0 m/s. In general, low wind speed should be favorable for algal bloom due to absence of wind-wave mixing, but the problem is that this area of the Caspian is the calmest area of the sea (Rahimi et al. 2022). Only twice, in September 2016 and 2019, wind speed reached 5.5-5.75 m/s (Figure 19). Seemingly, every year should be favorable for algal bloom, but that is not true. Moreover, in August 2003, 2006, 2007, 2014, and 2016, wind speed was less than 4.3 m/s and no algal blooms were recorded in these years.
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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.