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Figure 4 in Population dynamics of the copepod invader Oithona davisae in the Black Sea
Figure 4. Seasonal dynamics of the mean (2013–2016) shares of males among adult males and females (А) and dead individuals (B) among females () and males (◊).
Figure 5 in Population dynamics of the copepod invader Oithona davisae in the Black Sea
Figure 5. Seasonal temperature deviations in the Sevastopol Bay during the period 2014–2016 compared with the seasonal temperature trend in 2013. The circle marks an abnormal water temperature in June 2016.
Figure 3 in Population dynamics of the copepod invader Oithona davisae in the Black Sea
Figure 3. Seasonal dynamics of total population abundance (), number of orthonauplii (), and copepodites (■) during 2014–2016.
Figure 8 in Population dynamics of the copepod invader Oithona davisae in the Black Sea
Figure 8. Relationships between the mean generation time and the mean development temperature in Sevastopol Bay () and Fukuyama Harbor (----) (Uye and Sano, 1998).
Figure 4 in Genetic structure and population dynamics of the silver pheasant (Lophura nycthemera) in southern China
Figure 4. Bayesian tree based on mitochondrial haplotypes (1053 bp, selected model of HKY+I+G). It exhibits the phylogenetic relationships of silver pheasant, and three species (L. hatinhensis, L. leucomelanos, and L. swinhoii) are noticeable outgroups. Above branches there are numbers indicating Bayesian posterior probabilities, whereas below branches there are bootstrap values produced by ML. Each colored line represents a geographic population, while the line in black represents the shared haplotype.
Figure 3 in Genetic structure and population dynamics of the silver pheasant (Lophura nycthemera) in southern China
Figure 3. mtDNA MJN for silver pheasant. Circle size represents proportion of haplotype. Circles with single color indicate a private haplotype, whereas circles with two or more colors represent a shared haplotype.
Figure 1 in Genetic structure and population dynamics of the silver pheasant (Lophura nycthemera) in southern China
Figure 1. According to geodistance and topographic characters, we sorted sampling sites into geographic populations. The sampling sites including 7 provinces: Sichuan, Anhui, Jiangxi, Fujian, Zhejiang, Hubei, and Hunan. The abbreviation are as follows: Sichuan (SC), Anhui (AH), Jiangxi (JX), Fujian (FJ), Zhejiang (ZJ), Hubei (HB), Hunan (HN).
Figure 2 in Genetic structure and population dynamics of the silver pheasant (Lophura nycthemera) in southern China
Figure 2. Bayesian skyline plot of whole population of silver pheasant. The expansion time was computed by tau = 2µkt. The solid line means the estimated average effective population size and the dashed line represents 95% confidence interval.
Figure 1 in Exploring the dynamics of small pelagic fish catches in the Marmara Sea in relation to changing environmental and bio-optical parameters
Figure 1. Time series of deseasonalised Chl-a, net primary productivity (NPP), and sea surface temperature (SST). Solid lines show the time series, dash-dot lines show the deseasonalised time series, dashed lines indicate their respective nonlinear trends, and flat solid lines show linear trend components.
Figure 2 in Exploring the dynamics of small pelagic fish catches in the Marmara Sea in relation to changing environmental and bio-optical parameters
Figure 2. Time series of fisheries catches (tons) and fishing effort in the Marmara Sea between 2000 and 2019. Flat solid lines indicate linear trends.
Figure 1 in Dynamics of a Western Anatolian population ofNatrix natrix and Natrix tessellata (Serpentes: Natricidae)
Figure 1. Mean temperature and monthly average precipitation of Denizli according to the data of the General Directorate of Meteorology (1957–2018).
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 Range dynamics of some nemoral species of Lepidoptera in the Russian Far East due to climate change
Figure 2. Some species of Lepidoptera from Amur region (Russia): A – Lobocla bifasciata, 1.07.2021; B, C – Chrysozephyrus brillantinus, 16.07.2021; D – Clanis undulosa, 30.06.2021; E – Acosmeryx naga, 3.07.2021; F – Ambulyx tobii, 1.07.2021; G – Rhagastis mongoliana, 3.07.2021; H, I, J – Siglophora sanguinolenta (H, I –25– 27.07.2021, J – live specimen, 9.09.2021). A, B, D–J – upperside, C – underside. A–H, J – males, I – female. Localities: A – 7 km N Tarmanchukan; B, C – 2 km S Voronezhskoe–1; D, F, H–J – 8 km SE Boitsovo; E – Mokhovaya Pad'; G – 4.5 km NW Rachi.
Figure 1 in Range dynamics of some nemoral species of Lepidoptera in the Russian Far East due to climate change
Figure 1. Distribution records of the some nemoral species of Lepidoptera in the southern part of the Amur region (Russia).
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.
ScienceDex guides
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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.