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225 results for “Environmental variability”
Fig. 6 in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 6. Results of the PLS analysis, with Colombo (6), Guarapuava (4) and Ponta Grossa (5) showing negative values and Fênix (2), Jundiaí do Sul (3) and Mbaracayú (1) showing positive values.
Fig. 5 in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 5. Graphic reconstruction of the wing shape of individuals with (A) positive and (B) negative scores on the second canonical axis (CV2, increased 10 times). The lines in gray represent the average configuration, and those in black represent the second canonical variable.
Fig. 2 in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 2. Dorsal side of the right wing of P. orbitalis showing (A) the 15 (numbered points) anatomical landmarks and (B) the general shape of the wing based on their positions.
Fig. 1. Map showing sites where P in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 1. Map showing sites where P. orbitalis populations were collected: 1. Mbaracayú (25◦17Ɩ S, 54◦49Ɩ W) in Paraguay and 2. Fênix (23◦54Ɩ S, 51◦58Ɩ W), 3. Jundiaí do Sul (23◦26Ɩ S, 50◦14Ɩ W), 4. Guarapuava (25◦23Ɩ S, 51◦ 27Ɩ W), 5. Ponta Grossa (25◦ 05Ɩ S, 50◦ 09Ɩ W) and 6. Colombo (25◦17Ɩ S, 49◦13Ɩ W) in Brazil.
Fig. 3 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 3. Cumulative distribution plots divided by year for (A) T. alpinus and (B) T. speciosus. For each species 2013 is shown in red, 2014 in teal, 2015 in pink. The x-axis represents each host individual, ordered from least to most flea infested, and the y-axis shows the cumulative proportion of total flea counts. The dotted line indicates individuals in the 90th percentile of flea abundances, illustrating that the top 10% most infected chipmunks usually account for close to 50% of all counted fleas. The proportion of individuals without fleas in each year is represented graphically as the proportion at which each colored line departs from the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 4. Relationships between fecal glucocorticoid metabolite levels, sex, and flea abundance for (A) T. alpinus and (B) T. speciosus. Points show the mean ± S.E. number of fleas counted for female (white) and male (black) individuals within FGM categories (FGM values were rounded to the nearest 10). Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each sex.
Fig. 2 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 2. Patterns of flea abundance across years, hosts, and flea species. Overall average flea abundances (A–B) and abundances of each flea species (C–D) in each year for T. alpinus (A, C) and T. speciosus (B, D). Abundances of each flea species on hosts of each sex (Males: closed circles, Females: open circles) on T. alpinus (E) and T. speciosus (F).
Fig. 5 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 5. Relationships between flea abundances and (A) the second principal component of temperature data; or (B) elevation for T. alpinus (white) and T. speciosus (black). Points show the mean ± S.E. number of fleas counted for a given study site in a given year. For each study site in each year, a mean ± S.E. temperature or elevation value is shown. Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each species.
Fig. 1 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 1. Map showing study sites. Sites (see Supplementary Data S1 for more information) located in and around Yosemite National Park (green) were visited either in all three years (2013, 2014, and 2015; black), in two of the years (yellow), or in only one year (red). Yellow and black lines show significant roadways in the area. Lakes are shown in blue, including Mono Lake at top right. Inset shows Yosemite National Park (green) on a map of California. Site codes: AL: Arrowhead Lake; CL: Cathedral Lake (upper); GA: Glen Aulin; GL: Gaylor Lakes; HC: Hoffmann Creek; MA: Mammoth Lakes; ML: May Lake; PC: Porcupine Creek; SL: Saddlebag Lake; SLN: Saddlebag Lake, north-side (Greenstone and Steelhead Lakes); TM: Tuolumne Meadows. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Figure 3 in Influence of environmental variability on the body condition of the mangrove horseshoe crab Carcinoscorpius rotundicauda from Banyuasin Estuarine, South Sumatra, Indonesia
Figure 3. The relative condition factor (Kn) of C. rotundicauda from Banyuasin Estuary Waters. There was a significant difference between Kn values for males and females at a significant level of 0.05.
Figure 2 in Influence of environmental variability on the body condition of the mangrove horseshoe crab Carcinoscorpius rotundicauda from Banyuasin Estuarine, South Sumatra, Indonesia
Figure 2. The prosoma width-weight relationship of C. rotundicauda from Banyuasin Estuary Waters. There was a different growth pattern for both sexes where males indicated negative allometric and females indicated isometric.
Figs 4-8 in Influence of environmental variables on seasonal abundance and relative growth of Macrobrachium amazonicum (Crustacea: Decapoda: Caridea): variations of a continental population
Figs 4-8. Percentage distribution of the independent effect of the abiotic factor on the total abundance (Fig. 4), and on the abundance by demographic category (Figs 5-8) of Macrobrachium amazonicum (Heller, 1862). Grey bars indicate a significant effect (p<0.05), determined by the randomization test. Positive and relative relationships are shown by the bars above and under the horizontal aXis, respectively (EC, conductivity; DO, dissolved oXygen; PI, precipitation; T, water temperature).
Figs 2, 3 in Influence of environmental variables on seasonal abundance and relative growth of Macrobrachium amazonicum (Crustacea: Decapoda: Caridea): variations of a continental population
Figs 2, 3. Percentage of total abundance (Fig. 2) and juveniles, males, non-ovigerous females and ovigerous females (Fig. 3) of Macrobrachium amazonicum (Heller, 1862) along the study period (J, juveniles; M, males; NOF, non-ovigerous female; OF, ovigerous females).
Fig. 3 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 3. Venn diagrams representing the results of the variance partitioning with partial CCA (canonical correspondence analysis): percentage of variation in fish abundance (a) and incidence (b) explained by land use and land cover, site, and spatial variables, as well as that shared between the three sets of variables in the Upper Araguari River basin, Minas Gerais. See Table 4 for a list of all explanatory variables.
Fig. 1 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 1. Locations of the 38 randomly selected sites sampled in the Upper Araguari River basin, State of Minas Gerais, Brazil.
Fig. 2 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 2. Detrended correspondence analysis (DCA) of fish abundance (a) and incidence (b) along the sampling sites. The species are shown in triangle and sampling sites in X-mark.
FIGURE 1 in Taxonomic and functional turnover of Amazonian stream fish assemblages is determined by deforestation history and environmental variables at multiple scales
FIGURE 1 | Sampled sites and forest fragments in the Machado River basin, Brazil. The inset map of Brazil depicts the relative location of the study area (black) within the Madeira River basin (dark gray), inside the Amazon biome (light gray).
FIGURE 4 in Taxonomic and functional turnover of Amazonian stream fish assemblages is determined by deforestation history and environmental variables at multiple scales
FIGURE 4 | Explained variation of environmental contribution in turnover metrics partitioned by MRM and associated commonality analysis into pure local, shared and pure catchment components. RC = Raup-Crick; MPD = mean pairwise distance; MNTD = mean nearest taxon distance; all = all sampled streams; ref = streams with forested watersheds; new = streams with recently deforested watersheds; old = streams with historically deforested watersheds.
FIGURE 2 in Taxonomic and functional turnover of Amazonian stream fish assemblages is determined by deforestation history and environmental variables at multiple scales
FIGURE 2 | Distribution of sampling sites with (A) forest patches ranked according to the forest quality multimetric index and (B) effective forest cover. Non-forested area is white and is not included in the multimetric index calculation (or legend).
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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)
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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.