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21 results for “spatiotemporal structure”
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Figure 6 in Spatiotemporal distribution and population structure of Clibanarius symmetricus (Randall, 1840) (Crustacea, Diogenidae) in an Amazon estuary
Figure 6. Frequency distribution of cephalothoracic shield length (in mm) classes of the Clibanarius symmetricus specimens collected in the Marapanim estuary, Pará, Brazil. The vertical line represents the onset of sexual maturity (3.6 mm).
Figure 2 in Spatiotemporal distribution and population structure of Clibanarius symmetricus (Randall, 1840) (Crustacea, Diogenidae) in an Amazon estuary
Figure 2. Median Clibanarius symmetricus density by season (a), sector (b), site (c), and midlittoral zone (d) in the Marapanim River estuary (PA), between August 2006 and July 2007, based on the results of the PERMANOVA.
Figure 1 in Spatiotemporal distribution and population structure of Clibanarius symmetricus (Randall, 1840) (Crustacea, Diogenidae) in an Amazon estuary
Figure 1. Geographical location of the study area, showing the 4 sampling sites in the Marapanim estuary: A1 and A2 (western margin), B1 and B2 (eastern margin).
Figure 4 in Spatiotemporal distribution and population structure of Clibanarius symmetricus (Randall, 1840) (Crustacea, Diogenidae) in an Amazon estuary
Figure 4. Frequency of occurrence of Clibanarius symmetricus specimens of nonovigerous females (F), ovigerous females (OF), males (M), and intersex individuals (INT) collected each month between August 2006 and July 2007, in the Marapanim estuary, Pará, Brazil.
Figure 3 in Spatiotemporal distribution and population structure of Clibanarius symmetricus (Randall, 1840) (Crustacea, Diogenidae) in an Amazon estuary
Figure 3. Mean Clibanarius symmetricus density, and the temperature and salinity recorded in the Marapanim estuary, Pará, Brazil, between August 2006 and July 2007.
Data & Figures on the Multiscale Spatiotemporal Characterization of Embodied Environmental Efficiency of Building Structures in Geneva from 1850 to 2018
<p>Process data, figures, and figure data resulting from the study on Multiscale Spatiotemporal Characterization of Embodied Environmental Efficiency of Building Structures in Geneva from 1850 to 2018.</p>
Climate and plant structure determine the spatiotemporal butterfly distribution in a tropical mountain
Mountains are among the most powerful natural gradients for testing ecological and evolutionary responses of biota to environmental influences because differences in climate and plant structure occur over short spatial scales. We describe the spatiotemporal distribution patterns and drives of fruit-feeding butterfly diversity on the mountaineous region of Serra do Cipó, Minas Gerais, Brazil. Seven elevations from 822 to 1388 m were selected for evaluating the effects of abiotic factors and vegetation characteristics on butterfly diversity. A total of 44 fruit-feeding butterfly species were recorded in a two-years study. Species richness (local and regional) of fruit-feeding butterflies decreased with increasing elevation. The interaction between temperature or humidity and precipitation influenced the abundance and β-diversity of butterflies in the altitudinal gradient, while β-diversity decreased with increasing plant richness. Butterfly richness (local and regional) and β-diversity varied with the sampling period, with fewer species in July (2012 and 2013), dry period, as expected for Neotropical insects. β-diversity in space and time was due to species replacement (turnover), indicating that butterfly composition differs throughout the mountain and over time. In summary, climate and plant richness largely influenced butterfly diversity in the altitudinal gradient. Climatic changes in conjunction with increasing anthropic impacts in mountainous regions of southeast Brazil will likely influence the community of mountaintop butterflies in Espinhaço Mountain Range.
Spatiotemporal variation of the 2010 Yushu Mw 6.9 earthquake sequence: Insight from the 3-D electrical resistivity structure
<p>This dada set will be available in GRL, it was support by the Chinese Earthquake Administration, and it was also supported by the National Natural Science Foundation of China (Grant No. 41674081).</p>
Climate and plant structure determine the spatiotemporal butterfly distribution in a tropical mountain
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Spatiotemporal distributions of the thunderstorm and lightning structures over the Qinghai–Tibet Plateau
<p>This dataset is for the article titled "<span>Spatiotemporal distributions of the thunderstorm and lightning structures over the Qinghai–Tibet Plateau</span>" which is being submitted to the Remote Sensing for review. </p> <p><strong><span>Abstract: </span></strong><span>Utilizing data from the Tropical Rainfall Measuring Mission (TRMM) satellite's Precipitation Radar (PR) and Lightning Imaging Sensor (LIS), our study explores the previously uncharted spatiotemporal distributions of thunderstorm and lightning structures over the Qinghai-Tibet Plateau (QTP). The structural aspects are crucial when considering the impact of thunderstorm and lightning activity in the atmospheric processes. The vertical development height and horizontal extension area of QTP thunderstorms exhibit distinct spatial patterns, with greater heights in the south and larger areas in the east. The spatial distribution of thunderstorm vertical development height relative to the surface aligns more closely with the horizontal extension, indicating stronger convection in the eastern QTP. Thunderstorm lightning frequency shows an eastward and northward prevalence, while lightning density peaks in the western and northeastern QTP, with a minimum in the southeast. Additionally, lightning duration, spatial expansion, and radiance are more pronounced in the eastern QTP than the western part. Monthly variations reveal heightened values during the summer season for thunderstorm vertical extension, areas with reflectivity greater than 30dBZ, and lightning frequency. Diurnal variations highlight an afternoon increase in thunderstorm vertical and horizontal extension, lightning frequency, duration, and spatial scale. From a statistical perspective, under weak convective conditions, lightning length exhibits a positive correlation with thunderstorm convection intensity, contrasting with the opposite relationship suggested by previous studies. The article further analyzes and discusses the correlations between various thunderstorm and lightning structural parameters, enhancing our understanding of the distinctive features of thunderstorm and lightning activities in the QTP.</span></p>
Spatiotemporal connectivity dynamics in spatially structured populations
<p>Connectivity is a fundamental concept linking dispersal to the emergent dynamics and persistence of spatially structured populations. Functional measures of connectivity typically seek to integrate aspects of landscape structure and animal movement to describe ecologically meaningful connectedness at the landscape and population scale.</p> <p>Despite this focus on function, traditional measures of landscape connectivity assume it is a static property of the landscape, hence abstracting out the underlying spatiotemporal population dynamics. Connectivity is, arguably, a dynamic property of landscapes, and is inherently related to the spatial distribution of individuals and populations across the landscape. Static representations of connectivity potentially overlook this variation and therefore adopting a dynamic approach should offer improved insights about connectivity and associated ecological processes.</p> <p>Using a large scale, long-term time-series of occupancy data from a metapopulation of water voles (Arvicola amphibius), we tested competing hypotheses about how considering the dynamic nature of connectivity improves the ability of spatially-explicit occupancy models to recover population dynamics. Iteratively relaxing standing assumptions of connectivity metrics, these models ranged from spatially and temporally fixed connectivity metrics that are widely applied, to the more flexible, but lesser used model that allowed temporally-varying connectivity measures that incorporate spatiotemporally dynamic patch occupancy states.</p> <p>Our results provide empirical evidence that demographic-weighting using patch occupancy dynamics and temporal variability in connectivity measures are important for describing metapopulation dynamics.</p> <p>We highlight the implications of commonly held assumption in connectivity modeling and demonstrate how they result in different and highly variable predictions of metapopulation capacity. Thus, we argue that the concept of connectivity and its potential applications would benefit from recognizing inherent spatiotemporal variation in connectivity that is explicitly linked to underlying ecological state variables. </p>
Data from: Spatiotemporal variability in the structure of seagrass meadows and associated macrofaunal assemblages in southwest England (UK): using citizen science to benchmark ecological pattern
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Spatiotemporal connectivity dynamics in spatially structured populations
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Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks
<p>Files for reproducing results from Kelkar et al. (JPCB 2020) - Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks</p> <p> </p> <p>This folder contains simulations starter files and also plug-and-play datasets to test ML algorithms on molecular dynamics (MD) simulation data.</p> <p> </p> <p>All analysis scripts can also be found on GitLab on this link: https://gitlab.com/atharva-kelkar/kelkar_et_al_jpcb_2020</p>
Data from: Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red-listed grasshopper
Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we employ genomic data (ddRADSeq) to test biologically-informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross-backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species, (b) distributional shifts resulted from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles, and (c) contemporary distribution of suitable habitats after extensive land clearing for agriculture. Spatiotemporally-explicit simulations under different scenarios considering these aspects and statistical evaluation of competing models within an Approximate Bayesian Computation (ABC) framework supported spatial configuration of topographic barriers to dispersal and human-driven habitat fragmentation as the main factors explaining the geographical distribution of genomic variation in the species, with no apparent impact of hypothetical distributional shifts linked to Pleistocene climatic oscillations. Collectively, this study supports that both historical (i.e., topographic barriers) and contemporary (i.e., anthropogenic habitat fragmentation) aspects of landscape composition have shaped major axes of genomic variation in the studied species and emphasizes the potential of model-based approaches to gain insights into the temporal scale at which different processes impact the demography of natural populations.
Museomics contributes to the spatiotemporal assessment of genetic diversity and structure in wild and ex situ conservation organisms: a case study of three endangered coastal plants in Japan
<p><span>Understanding </span><span>the extent to which </span><span>genetic diversity of wild populations in ex</span> <span>situ</span><span> conservation can be retained is </span><span>crucial</span><span> for the management of </span><span>such</span><span> populations. Wild individuals collected in the target area in the past </span><span>and</span><span> present can be used to estimate the number of alleles lost over time in wild populations and</span><span> thereby</span><span> the number of alleles whose loss could be </span><span>prevented</span><span> by ex</span> <span>situ</span><span> conservation. </span><span>Here</span><span>, we assessed the genetic diversity of wild and ex</span> <span>situ</span><span> conservation populations of three endangered coastal herb species</span><span>,</span><span> <em>Cirsium maritimum</em> Makino (Asteraceae), <em>Linaria japonica</em> Miq. (Plantaginaceae</span><span>) and</span><span> <em>Suaeda glauca</em></span><span><em> </em>(Bunge) Bunge (Amaranthaceae), which are endangered </span><span>species on</span><span> Awaji Island, Hyogo Prefecture, Japan, via multiplexed inter-simple sequence repeat genotyping by sequencing (MIG-seq). We </span><span>incorporated</span><span> the museomics approach, which </span><span>involves conducting</span><span> genetic analyses of museum specimens collected from the targeted wild populations in the past to estimate the temporal transition of genetic diversity in wild populations and the number of alleles maintai</span><span>ned in <em>ex situ</em></span><span> conservation. </span><span>Our </span><span>results </span><span>reveal</span><span> a declining trend in genetic diversity in the wild populations of all </span><span>investigated</span><span> species, although </span><span>this trend is </span><span>not significant. In all the species, </span><span>numerous</span><span> alleles were already lost in current wild populations, </span><span>whereas they</span><span> were </span><span>present</span><span> in the past wild and ex</span> <span>situ</span><span> conservation populations. Our study </span><span>indicates</span><span> that extinct alleles in current wild populations have been maintained in ex</span> <span>situ</span><span> conservation</span><span> by museomics approach. These </span><span>appro</span><span>aches </span><span>were effective in verifying</span><span> the genetic diversity retention effects of <em>ex</em></span><em> <span>situ</span></em><span> conservation populations.</span></p>
Figure 5 in Spatiotemporal distribution and population structure of Clibanarius symmetricus (Randall, 1840) (Crustacea, Diogenidae) in an Amazon estuary
Figure 5. Frequency of occurrence of Clibanarius symmetricus specimens of nonovigerous females (F), ovigerous females (OF), males (M), and intersex individuals (INT) by size class (cephalothoracic shield length, in mm), collected in the Marapanim estuary, Pará, Brazil.
Data from: Spatial and spatiotemporal variation in metapopulation structure affects population dynamics in a passively dispersing arthropod
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Data from: Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red-listed grasshopper
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