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Figure 2 in Seasonal population fluctuation and spatial distribution of Orthoptera in two grassland areas of Attica - Greece
Figure 2. Mean number of individuals per sample of Acrididae, Tettigoniidae and Gryllidae families in the mountain station through a 2-year study.
Figure 2 in Population dynamics, seasonality and sex ratio of twig-girdling beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) of an Atlantic rain forest in south-eastern Brazil
Figure 2. Mean (± SE) number of adult beetles of the tribe Onciderini found at the Serra do Japi during the period of adult activity (between October and May) from 2002 to 2006. (A) Species with peak abundance between December and February. (B) Species with peak abundance between January and March. (n = 5). An asterisk indicates significance among monthly abundances after Bonferroni correction (Friedman's ANOVA; p ≤ 0.013).
Figure 1 in Population dynamics, seasonality and sex ratio of twig-girdling beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) of an Atlantic rain forest in south-eastern Brazil
Figure 1. Rank-abundance pattern of adult beetles of the tribe Onciderini found at the Serra do Japi between December 2002 and December 2006. (n = 1113 individuals).
Figure 3 in Population dynamics, seasonality and sex ratio of twig-girdling beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) of an Atlantic rain forest in south-eastern Brazil
Figure 3. (A) Population fluctuation (number of individuals) of adult beetles of the tribe Onciderini found at the Serra do Japi between December 2002 and December 2006 (n = 1113 individuals). (B) Climate diagram for the Biological Reserve of the Serra do Japi at the same time period (data from a meteorological station approximately 8 km from the study site). The mean monthly temperature (◦ C) and rainfall (mm) are scaled to represent the potential evapotranspiration. Values inside each diagram indicate mean temperature (◦C) and total rainfall (mm) for each sampling year. Dry months are represented by dotted areas; humid months by the vertical lines and months with rain in excess of 100 mm are in solid black.
Supporting Data for Hahn et al. J. Climate: Seasonality in Arctic Warming Driven By Sea Ice Effective Heat Capacity
<p>This dataset includes CESM model experiment output for Hahn et al.: “Seasonality in Arctic Warming Driven By Sea Ice Effective Heat Capacity” submitted to Journal of Climate. Here we provide monthly climatologies averaged over the last thirty years for the Ice, No ice, and No ice, set albedo experiments with preindustrial and doubled CO<sub>2</sub> forcing. The variables hyam, hybm, and P0, useful for interpolating to pressure levels, are included in the FlatSOM1850.Q.nc file.</p>
Figure 2 in Anurans of Turvo State Park: testing the validity of Seasonal Forest as a new biome in Brazil
Figure 2. Ordination analysis of Seasonal Forest anuran communities using the index of geographic similarity coefficient (CGR) and non-metric multidimensional scaling (NMDS). For abbreviations see Table 1. 1 = group formed by the sites in the mid-west and south-eastern Brazil, 2 = group formed by sites in the Atlantic Forest, 3 = group formed by sites in southern Brazil.
Figure 1 in Anurans of Turvo State Park: testing the validity of Seasonal Forest as a new biome in Brazil
Figure 1. Map of South America highlighting the geopolitical division of Brazil. States of Rio Grande do Sul, (RS), Santa Catarina (SC), Paraná (PR), São Paulo (SP) and Mato Grosso do Sul (MS) are represented in grey. The 19 locations of Seasonal Forest (grey points) and TSP anuran communities (black point) are shown. For abbreviations see Table 1.
Figure 5 in Foraging mode of Australolacerta rupicola (FitzSimons, 1933) (Sauria: Lacertidae): evidence of seasonal variation in an extremely active predator?
Figure 5. Australolacerta rupicola feeding on a spider (a) and a grasshopper (b). Credit: S. Kirchhof.
Figure 1 in Clues supporting photoperiod as the main determinant of seasonal variation in amphibian activity
Figure 1. Path diagram of structural equation model, evaluating 265 the hypotheses that anuran species respond to the month as a latent variable that is a construct of photoperiod, temperature and rainfall. The whole model is congruent with observed data as indicated by its non-significant probability. Paths values are standardized effects ± 1 standard error. Asterisks (*) denote significant coefficients (P <0.05) and "ns" denote non-significant coefficients (P> 0.05). Arrow width represents the strength of the causal link. Month, latent variable; S, number of species calling per month; P, photoperiod; T, mean monthly temperature; R, monthly rainfall; u1 to u4, associated error variable.
Figure 3 in Clues supporting photoperiod as the main determinant of seasonal variation in amphibian activity
Figure 3. Correlation between residuals of the regression between photoperiod and amphibian activity and the fit of the sinusoidal model.
Figure 2 in Clues supporting photoperiod as the main determinant of seasonal variation in amphibian activity
Figure 2. Linear regression of the number of species calling per month (S) between September 1998 and April 2000 with photoperiod (P).
Figure 2 in Anurans of a seasonally dry tropical forest: Morro do Diabo State Park, São Paulo state, Brazil
Figure 2. Historical rainfall distribution and minimum and maximum mean monthly temperatures recorded from 1977 to 2002 in Morro do Diabo State Park, São Paulo state, Brazil. Source: Faria (2006).
Figure 3 in Anurans of a seasonally dry tropical forest: Morro do Diabo State Park, São Paulo state, Brazil
Figure 3. Cumulative curve of species and richness estimators of anurans recorded in Morro do Diabo State Park, São Paulo state, Brazil, from September 2005 to March 2007 based on sampling at breeding sites. The dots show the mean cumulative curve, generated by 500 randomized additions of samples, and the vertical bars indicate possible variation around the medium curve (confidence interval of 95%).
Figure 1 in Anurans of a seasonally dry tropical forest: Morro do Diabo State Park, São Paulo state, Brazil
Figure 1. Phytogeographic units of Brazil, pointing out the state of São Paulo, and showing the location of Morro do Diabo State Park (MDSP).
Figure 3 in A unique seasonal cycle in a leaf gall-inducing insect: the formation of stem galls for dormancy
Figure 3. Light micrographs of the Pseudotectococcus rolliniae metathoracic leg distal portion. (A) Exuviae from the first moult. (B) Crawler (5first instar). (C) Leaf nymph (5secondinstar). Scale bars: 10 mm.
Figure 5 in A unique seasonal cycle in a leaf gall-inducing insect: the formation of stem galls for dormancy
Figure 5. (A,E) Rollinia laurifolia stems: scanning electron micrographs showing Pseudotectococcus rollinae nymphs. (B–D, F) Photomicrographs of transverse sections of dormancy galls. (A,B) Crawler (5first-instar nymph) inside a dormancy gall. (C) Sudan Red B test showing plant cuticle (arrow) delimiting the dormancy gall concavity. (D) Exuviae from the first moult left inside a dormancy gall. (E) Crawler (5first-instar nymph) before dormancy starting. (F) Stem nymph (5stationary crawler) with its stylets (arrow) inserted into stem tissues inducing a dormancy gall. Crawler (cr), exuviae (ex), phelloderm (pd). Scale bars: 100 mm (A,B, D–F), 50 mm (C).
Figure 4 in A unique seasonal cycle in a leaf gall-inducing insect: the formation of stem galls for dormancy
Figure 4. Rollinia laurifolia lenticel ontogenesis. (A) Young stem with epidermis covered by cuticle and adjacent cortical parenchyma. (B) Phellogen differentiation from the external cortical parenchyma layer. (C) Young lenticel with first divisions of phellogen and suber deposition. (D) Mature lenticel with complementary cells (white arrow) and closing layer (black arrow). Cortical parenchyma (cp), epidermis (ep), phellogen (pg). Scale bar: 25 mm (A– C), 100 mm (D).
Figure 2 in A unique seasonal cycle in a leaf gall-inducing insect: the formation of stem galls for dormancy
Figure 2. Pseudotectococcus rolliniae scanning electron micrographs (A–C, E) and photomicrographs (D,F). (A) Stem nymphs (5first instar5''stationary crawlers'') located into dormancy galls. (B) Detail of a stem nymph under dormancy, showing body wax exudation covering. (C,D) Leaf nymphs (5second-instar) collected from surfaces of young leaves. (E) Crawler, still inside leaf gall. (F) Exuviae, found in dormancy stem gall. Scale bars: 500 mm (A), 50 mm (B), 100 mm (C,D), 50 mm (E,F).
Figure 1 in A unique seasonal cycle in a leaf gall-inducing insect: the formation of stem galls for dormancy
Figure 1. Schematic representation of Pseudotectococcus rollinae annual life cycle on Rollinia laurifolia plants. Numbers from two to four represent nymphal instars.
Figure 5 in Anurans of a seasonally dry tropical forest: Morro do Diabo State Park, São Paulo state, Brazil
Figure 5. Dispersion diagram of the similarity matrix in the composition of the anuran assemblage (Coefficient of Geographic Resemblance; CGR) with the geographic distance matrix among the localities. p is the significance level to Mantel's test (r), using 5000 Monte Carlo permutations.
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.