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89 results for “variable environment”
Influence of Titan's Variable Electromagnetic Environment on the Global Distribution of Energetic Neutral Atoms
<p>Data for the manuscript "Influence of Titan's Variable Electromagnetic Environment on the Global Distribution of Energetic Neutral Atoms" by Tippens et al., (2022). See README.txt for a description of the data files included here.</p>
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Figure 8 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 8. Relationships between values of distribution coefficients (Kd Shell/Water Zn (A), Kd Shell/Water Sr (B) and Kd Shell/Water Al (C)) and principal component 1, revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 6. Normed PCA factorial graph F1 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 6. Normed PCA factorial graph F1 × F2 of two revealed geographical groups of Beringiana beringiana samples (blue circles indicate samples from Primorsky Krai, Kunashir, Sakhalin, and Iturup islands; red circles indicate samples from Kamchatka Peninsula). Ellipses show 95 % confidence interval.
Figure 5 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 5. Relationships between Kd Shell/Sediment Al and Kd Shell/Water Al: (1) Peschanoye Lake, Kunashir Island, (2) Bolshoye Vavayskoye Lake, Sakhalin Island, (3) Vaskovskoye Lake, Primorsky Krai, (4) Lebedinoe Lake, Iturup Island, (5) Kurazhechnoye Lake, Kamchatka Peninsula, (6) Khalaktyrskoye Lake, Kamchatka Peninsula, (7) Chernoye Lake, Sakhalin Island.
Figure 4 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 4. Median joining networks of Beringiana beringiana (N = 50) based on the COI gene fragment. The circle size is proportional to the number of available sequences belonging to the given haplotype (the smallest = 1 sequence). The red numbers near branches indicate the number of nucleotide substitutions between haplotypes.
Figure 2 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 2. Shells of Beringiana beringiana specimens, collected from studied waterbodies: A – Kurazhechnoye Lake, Kamchatka Peninsula (voucher number RMBH biv1212/2), B – Vaskovskoye Lake, Primorsky Krai (RMBH biv1181/1), C – Lebedinoye Lake, Iturup Island (RMBH biv1254/2), D – Bolshoye Vavayskoye Lake, Sakhalin Island (RMBH biv1250/1), E - Peschanoye Lake, Kunashir Island (RMBH biv1251/1), F – Khalaktyrskoye Lake, Kamchatka Peninsula (RMBH biv1213/3). Scale bar = 50 mm.
Figure 1 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 1. Map of the studied region. Lakes: (1) Kurazhechnoye, Kamchatka Peninsula (n = 5), (2) Khalaktyrskoye, Kamchatka Peninsula (n = 5), (3) Lebedinoye, Iturup Island (n = 3), (4) Peschanoye, Kunashir Island (n = 3), (5) Chernoye, Sakhalin Island (n = 3), (6) Bolshoye Vavayskoye, Sakhalin Island (n = 3), (7) Vaskovskoye, Primorsky Krai (n = 3). n reveals the number of collected shells of Beringiana beringiana.
Figure 3. Principal components 1 and 2 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 3. Principal components 1 and 2 visualized by drawing synthetic outlines of extreme (±2SD) and mean shell shapes of Beringiana beringiana. The umbo position marked by star.
Figure 11 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 11. Relationships between values of KAl and longitudinal cross-sectional area of shell (mm2), d Shell/Sediment revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 7 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 7. Relationships between Ca-normalized concentration of zinc in shells and principal component 1, (A), copper in shells and principal component 1 (B), revealed from the shell shape analysis of Beringiana beringiana: (1) Peschanoye Lake, Kunashir Island, (2) Lebedinoe Lake, Iturup Island, (3) Bolshoye Vavayskoye Lake, Sakhalin Island, (4) Kurazhechnoye Lake, Kamchatka Peninsula, (5) Vaskovskoye Lake, Primorsky Krai, (6) Khalaktyrskoye Lake, Kamchatka Peninsula.
Figure 10 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 10. Relationships between values of Kd Shell/Water Al (A), Kd Shell/Water P (B), Kd Shell/Water Fe (C) and longitudinal cross-sectional area of shell (mm2), revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 9 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 9. Relationships between Zn: Ca – ratio in Beringiana beringiana shells and longitudinal cross-sectional area of shell (mm2), revealed from shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Data for: Positive impact of postfire environment on bumble bees not explained by habitat variables in a remote forested ecosystem
<p>Bumble bees are important pollinators in temperate forested regions where fire is a driving force for habitat change, and thus understanding how these insects respond to fire is critical. Previous work has shown bees are often positively affected by the post-fire environment, with burned sites supporting greater bee abundance and diversity, and increased floral resources. The extent to which fire impacts variation in bumble bee site occupancy is not well understood, especially in higher latitude regions with dense, primarily coniferous forests. Occupancy models are powerful tools for biodiversity analyses, as they separately estimate occupancy probability (likelihood that a species is present at a particular location) and detection probability (likelihood of observing a species when it is present). Using these models, we tested whether bumble bee site occupancy is higher in burned locations as a result of the increase in canopy openness, floral species richness, and floral abundance. We quantified the impact of fire, and associated habitat changes, on bumble bee species' occupancy in an area with high wildfire frequency in British Columbia, Canada. The burn status of a site was the only significant predictor for determining bumble bee occurrence (with burned sites having higher occupancy); floral resource availability and canopy openness only impacted detection probability (roughly, sample bias). These findings highlight the importance of controlling for the influence of habitat on species detection in pollinator studies and suggest that fire in this system changes the habitat for bumble bees in positive ways that extend beyond our measurements of differences in floral resources and canopy cover.</p>
Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment
<p>The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.</p>
Data from: The influence of temperature on courtship and mate choice in a wolf spider: Implications for mating success in variable environments
Open the record for dataset details and reuse information.
Data for: Positive impact of postfire environment on bumble bees not explained by habitat variables in a remote forested ecosystem
Open the record for dataset details and reuse information.
Data from: Diet variability among insular populations of Podarcis lizards reveals diverse strategies to face resource-limited environments
Access to resources is a dynamic and multi-causal process that determines the success and survival of a population. It is therefore often challenging to disentangle the factors affecting ecological traits like diet. Insular habitats provide a good opportunity to study how variation in diet originates, in particular in populations of mesopredators such as lizards. Indeed, high levels of population density associated with low food abundance and low predation are selection pressures typically observed on islands. In the present study, the diet of eighteen insular populations of two closely related species of lacertid lizards (Podarcis sicula and Podarcis melisellensis) was assessed. Our results reveal that despite dietary variability among populations, diet taxonomic diversity is not impacted by island area. In contrast, however, diet disparity metrics, based on the variability in the physical (hardness) and behavioral (evasiveness) properties of ingested food items, are correlated with island size. These findings suggest that an increase in intraspecific competition for access to resources may induce shifts in functional components of the diet. Additionally, the two species differed in the relation between diet disparity and island area suggesting that different strategies exist to deal with low food abundance in these two species. Finally, sexual dimorphism in diet and head dimensions is not greater on smaller islands, in contrast to our predictions.
Crustal thicknesses, Moho depths and 3-D density anomaly model for GJI paper: Crustal structure of onshore-offshore Atlantic Canada and environs from constrained 3-D gravity inversion using variable mesh depths by J. Kim Welford
<p>The files are provided as ascii text files in terms of both latitudes/longitudes and eastings/northings. For the 3-D density anomaly model, it is provided with columns of x, y, z, and absolute density. The conversions from latitudes/longitudes to eastings/northings for all of the models and maps in this work are computed with ellipsoid WGS-84 and UTM zone 19 using Generic Mapping Tools.</p>
Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments
<p>These are the data and code to go with "<strong>Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments</strong>"</p> <p><strong>Figure 01 takes the following data/scripts:</strong></p> <p><a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20210610_Thally02_Figure01_plot_and_stats.R">20210610_Thally02_Figure01_plot_and_stats.R </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/track_keeper.csv">track_keeper.csv </a> and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/corr_Response%20growth%20.csv">corr_Response growth .csv </a>. These files contain growth rates per transfers for all selection environments throughout the experiment and growth rates in correlated environments during reciprocal transplants, respectively. </p> <p><strong>Figure 02 takes the following data/scripts:</strong></p> <p> </p> <p><a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20210610_Thally02_Figure02_plot_and_stats.R">20210610_Thally02_Figure02_plot_and_stats.R </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20161120_res_logis_t000.csv">20161120_res_logis_t000.csv </a>and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20161123_resloglint300.csv">20161123_resloglint300.csv .</a> These files contain the output of the shapes of the growth curves (i.e. information on lag time , growth at µmax, K etc) for all samples in all selection environments at t0 and t300, respectively</p> <p><strong>The remaining figures - position not clear at time of submission - take the following data/script. </strong></p> <p>For plasticity in FRRF data, the script 20181204_FRRF_plasticity.R takes the FRRF raw data contained in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/allfvfmdata_thally_t300_t000.csv">allfvfmdata_thally_t300_t000.csv </a>. Extracted parameters are in files <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/CvaluesThally02.csv">CvaluesThally02.csv</a>, <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/psiPSI_slope_intercept.csv">psiPSI_slope_intercept.csv</a>, and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/rP_extracted_values.csv">rP_extracted_values.csv </a> and can be analysed using the R script <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/extracted%20parameter%20plots.R">extracted parameter plots.R .</a> R script <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/FRRF%20visualisation%20only%20.R">FRRF visualisation only .R </a> is for visualisation only, as the title suggests. </p> <p>For comparing plasticity/growth , the data are in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20170327_giantbigtable.csv">20170327_giantbigtable.csv </a>, and can be visualised/analysed in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/plast%20vs%20growth.R">plast vs growth.R </a></p> <p>In order to recreate the AMOVAS based on SNVs, use <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/all_variants_fixed-only_using_5x_depth_threshold.csv?versionId=01cbf6bf-fe16-4e7d-aae8-58c527596ebb">all_variants_fixed-only_using_5x_depth_threshold.cvs </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/amova%20thally02.R?versionId=e9c21378-ee37-4418-9ce1-76db821e645a">amova thally02.R </a></p> <p>For additional information, please contact elisa.schaum@uni-hamburg.de </p>
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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.