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26 results for “spatiotemporal evolution”
Character displacement in the midst of background evolution in island populations of Anolis lizards: a spatiotemporal perspective
<p>Negative interactions between species can generate divergent selection that causes character displacement. However, other processes cause similar divergence. We use spatial and temporal replication of island populations of <i>Anolis </i>lizards to assess the importance of negative interactions in driving trait shifts. Previous work showed that the establishment of <i>Anolis sagrei </i>drove resident <i>Anolis carolinensis </i>to perch higher and evolve larger toepads. To further test the interaction's causality and predictability, we resurveyed a subset of islands nine years later. <i>Anolis sagrei</i> had established on one island between surveys. We found that <i>A. carolinensis </i>on this island now perch higher and have larger toepads. However, toepad morphology change on this island was not distinct from shifts on six other islands whose <i>Anolis </i>community composition had not changed. Thus, the presence of <i>A. sagrei </i>only partly explains <i>A. carolinensis </i>trait<i> </i>variation across space and time<i>. </i>We also find that <i>A. </i><i>carolinensis</i> on islands with previously established <i>A. sagrei</i> now perch higher than a decade ago, and that current <i>A. carolinensis </i>perch height is correlated with <i>A. sagrei</i> density. Our results suggest that character displacement likely interacts with other evolutionary processes in this system, and that temporal data are key to detecting such interactions.</p>
Spatiotemporal-evolution-of-Ebola: First release
<p>Data and code for "Spatiotemporal evolution of Ebola virus disease at sub-national level during the 2014 West Africa epidemic: model scrutiny and data meagreness"</p>
Spatiotemporal evolution of a controlled forest fire near Torre do Pinhão (Portugal)
<p>This dataset represents part of the propagation of a controlled forest fire on March 1, 2019, near Torre do Pinhão, Portugal. The data was generated from a 15-minute video captured using a UAV. The video's description, frame selection and segmentation process are available at https://doi.org/10.5281/zenodo.7944963.<br>The data represents the evolution of the burned region divided into 170 slices. Each slice is represented by a source polygon (S), a target polygon (T) and a one-to-one mapping of the vertices of S in T. Each polygon represents the burned region at a given time instant and each slice represents the evolution of the burned region during a time interval. These data are the inputs of interpolation methods to create a continuous representation of the fire spread, even when the original video frames are not good, for instance, due to occlusion of the area of interest by smoke. Figure <em>goodCorrespondences.png</em> shows an example of the correspondences between two polygons and the <a title="Interpolation" href="https://tinyurl.com/y4xwvwsb" target="_blank" rel="noopener">video</a> presents the evolution of the burned region obtained using a simple linear interpolation method. The dataset was created using a supervised method. The source code and method description are available on <a href="https://github.com/josemoreiraUA/EES_dataLab" target="_blank" rel="noopener">gitHub</a>.</p> <ul> <li><em>source</em>: the url of the original video (raw data) in zenodo.</li> <li><em>eventData</em>: the date and time of the prescribed forest fire.</li> <li><em>location</em>: the name of the place of the prescribed fire.</li> <li><em>coordinates </em>and <em>coordinatesDMS</em>: the coordinates of the prescribed fire in WSG84. The later represents the coordinates in degrees, minutes and seconds.</li> <li>numberOfFrames: the number of frames extracted from the video.</li> <li><em>correspondences</em>: this is a data structure to represent the correspondences between the polygons delimiting the extent of the burned area in frames (1, 2), (2, 3), … , (169, 170). The key is the number of the slice in [1, 170] and the value is a dictionary with the following keys: <ul> <li><em>frameNbInVideo_source</em>: the number of the frame corresponding to the source polygon for the slice.</li> <li><em>elapsedTimeInVideo_source</em>: the elapsed time in milliseconds since the beginning of the video.</li> <li><em>frameNbInVideo_target</em>: the number of the frame corresponding to the source polygon for the slice.</li> <li><em>numberOfVertices</em>: the number of vertices of the source and target polygons.</li> <li><em>vertexMappings</em> versus <em>sourceCoords </em>and <em>targetCoords</em>: The correspondences between the source and target vertices in each slice are represented in two distinct but equivalent formats. In <strong><em>data_fmtA</em></strong>, the list <em>sourceCoords </em>holds the coordinates (x,y) of the source vertices and the list <em>targetCoords </em>holds the coordinates of the target vertices. The two lists have the same length and the correspondence is given by the position in the list. In <em><strong>data_fmtB</strong></em>, the correspondences are represented in the list <em>vertexMapings </em>where each entry holds the coordinates of the source and corresponding target vertices.</li> </ul> </li> </ul> <p>Note that the target polygon in slice <em>i</em> and the source polygon in slice <em>i+1</em> are geometrically identical but they are topologically distinct because the number of vertices differs. This is to ensure that there is a one-to-one correspondence between the vertices of the source and target polygons in each slice. It is up to the vertex correspondence algorithm to add vertices to the source and target polygons to obtain a one-to-one correspondence between those polygons, as described on github. Click here to display a figure with an example of correspondences between the vertices of a source and a target polygons representing the extent of the burned area at two times.</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
Character displacement in the midst of background evolution in island populations of Anolis lizards: a spatiotemporal perspective
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The spatiotemporal evolution and influencing factors of hotel industry in the metropolitan area: an empirical study based on China
Through the online booking platform, 10,543 big data of spatial and temporal distribution of Beijing hotel industry has been obtained in this paper. Then, the methods of GIS and the geographical detector are used to study the spatiotemporal evolution process and the influencing factors of Beijing hotel industry during 2003-2018. The results are as follows: a. During the period of 2003-2018, the hotel industry in Beijing maintained a high growth rate and had three growth peaks in 2008, 2010 and 2014. Meanwhile, major historical events, such as the Olympic Games had a significant influence on the development of the hotel industry. b. Between 2003 and 2018, the hotel industry in Beijing gradually developed from the centripetal agglomeration to aggregation + diffusion, and also from the single center to the multi-center. Besides, various hotels presented two characteristics of city orientation and scenic orientation. c. The natural geographical environment had shaped the overall pattern and characteristics of the spatial distribution of the hotel industry in Beijing, and the socio-economic factors such as commercial activities, public facilities, tourism services and traffic conditions significantly influenced the location selection of the hotel industry. Therefore, the urban center is the ideal area for the spatial layout of the hotel industry. d. Geographical detector research showed that the factors, such as administrative organs, road network density, leisure and recreational facilities, and companies have strong explanatory power for hotel location selection, which is an important reference index for hotels to select the micro location. This paper is a beneficial supplement to the existing research and has certain guiding significance for the sustainable development of Beijing hotel industry.
Data from: The spatiotemporal evolution and formation mechanism of the digital economic gap: Based on the case of China
<p>We analyzed the formation mechanism of digital economic gap (DEG), measured the DEGs at four levels (the gaps in information and communication technology accessibility, application skill, digital economic outcome, and efficiency), and explored its spatiotemporal evolution in China by using DEA–Malmquist index method, Gini Coefficent method, Kernel density, and Geodetector. Data from 263 cities in China between 2011 and 2019 were collected. The results demonstrated that (1) The four levels of DEGs showed different trends. The first-, second- and third- level DEGs showed ceiling effects, and the fourth-level DEG oscillated upward. (2) The distribution location of the four levels of DEGs varied. The first- and second-level DEGs shifted at a stable low degree. The third-level DEG increased steadily and polarized. The fourth-level DEG increased steadily and formed a multi-polarization trend, with one strong polar. (3) The long-term transfer trend of the DEGs at four levels changed little, and showed a phenomenon of "club convergence". (4) As for the formation of DEGs, the first-level DEG was influenced by most factors and was education- and policy-driven; the second-level DEG was profit-driven; the third-level DEG was profit- and education-driven; the fourth-level DEG was human resource-driven.</p>
Fig. 6 in How many taxa? Spatiotemporal evolution and taxonomy of Amphoricarpos (Asteraceae, Carduoideae) on the Balkan Peninsula
Fig. 6 Morphological variation in Amphoricarpos on the Balkan Peninsula based on 17 metric characters and six ratios. a, c Principal component analysis. b, d Canonical discriminant analyses. Labelling and grouping in a and b follow Blečić and Mayer (1967), in c and d they reflect the five BAPS clusters shown in Fig. 5b–e
Fig. 2 in How many taxa? Spatiotemporal evolution and taxonomy of Amphoricarpos (Asteraceae, Carduoideae) on the Balkan Peninsula
Fig. 2 Relationships of Amphoricarpos from the Balkan Peninsula inferred from phylogenetic analyses of Internal Transcribed Spacer (ITS) sequences. a Bayesian consensus phylogram; numbers above branches are bootstrap values>50 %, those below branches PP values>0.50. b Bayesian consensus chronogram (obtained
Fig. 1 in How many taxa? Spatiotemporal evolution and taxonomy of Amphoricarpos (Asteraceae, Carduoideae) on the Balkan Peninsula
Fig. 1 Sampled populations of Amphoricarpos on the Balkan Peninsula. The inserts show the position of the sampled area in southeastern Europe and a plant from population 26. The taxonomic assignment follows Blečić and Mayer (1967)
Fig. 4 in How many taxa? Spatiotemporal evolution and taxonomy of Amphoricarpos (Asteraceae, Carduoideae) on the Balkan Peninsula
Fig. 4 NeighborNet diagram based on uncorrected P distances derived from AFLP data of Amphoricarpos from the Balkan Peninsula. Numbers positioned along the splits are bootstrap values derived from Neighbourjoining analysis (1,000 replicates). Populations are coded as in Fig. 1 and
Fig. 3 in How many taxa? Spatiotemporal evolution and taxonomy of Amphoricarpos (Asteraceae, Carduoideae) on the Balkan Peninsula
Fig. 3 Relationships of Amphoricarpos from the Balkan Peninsula inferred from phylogenetic analyses of plastid rps16–trnK sequences. a, Bayesian consensus phylogram; numbers above branches are bootstrap values>50 %, those below branches PP values>0.50. b, Statistical parsimony network. Small black dots represent unsampled haplotypes, numbers are population identifiers as in Fig. 1 and Table 1
FIGURE 3 in Spatiotemporal evolution of Reaumuria (Tamaricaceae) in Central Asia: insights from molecular biogeography
FIGURE 3. Reconstructions of ancestral areas performed with S-DIVA on the left, Lagrange on the right, for several nodes, and the most similar states (areas) with largest frequencies calculated by RASP, are illustrated on the right. Pie charts at the internal nodes represent the calculated probabilities (relative frequencies) of alternative ancestral area reconstructions. In the right figure, several dispersals are indicated by arrows on the branches. Node numbers (1~9) are in italics at the right of the node, the same as in Fig. 2. Area letters as stated in the text: A: Tianshan Mountains; B: Pamir-Alai mountains; C: eastern Central Asia; D: western Central Asia; E: Iran-Turkey; and F: Mediterranean. A detailed description is in the text.
FIGURE 2 in Spatiotemporal evolution of Reaumuria (Tamaricaceae) in Central Asia: insights from molecular biogeography
FIGURE 2. Phylogenetic tree and chronogram using BEAST Bayesian inference. Values at left of nodes on the tree are bootstrap support above, and posterior probability below. At the right of nodes are the estimated dating values and their 95% HPD from BEAST. Node numbers (1~9) are in italic at the right of nodes. The classification system derived from tree construction, including two sections and five series, is shown on the right of figure, and detailed in the Appendix.
FIGURE 1 in Spatiotemporal evolution of Reaumuria (Tamaricaceae) in Central Asia: insights from molecular biogeography
FIGURE 1. Distribution of Reaumuria, modified from Hao et al. (2013). Three species of series Kaschgaricae are detailed in Fig. 1c, six distribution areas, and westward and eastward idspersals conducted from biogeographical history reconstruction are illustrated in Fig. 1d.
FIGURE 4. For S in Spatiotemporal evolution of Reaumuria (Tamaricaceae) in Central Asia: insights from molecular biogeography
FIGURE 4. For S-DIVA results, the calculations of its biogeographical events including dispersal, vicariance and extinction, produced in RASP, are shown.
Data from: The spatiotemporal evolution and formation mechanism of the digital economic gap: Based on the case of China
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The spatiotemporal evolution and influencing factors of hotel industry in the metropolitan area: an empirical study based on China
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Spatiotemporal evolution of the global species diversity of Rhododendron
<p>Evolutionary radiation is a widely recognized mode of species diversification, but its underlying mechanisms have not been unambiguously resolved for species-rich cosmopolitan plant genera. In particular, it remains largely unknown how biological and environmental factors have jointly driven its occurrence in specific regions. Here we use <em>Rhododendron</em>, the largest genus of woody plants in the Northern Hemisphere, to investigate how geographic and climatic factors, as well as functional traits, worked together to trigger plant evolutionary radiations and shape the global patterns of species richness based on a solid species phylogeny. Using 3437 orthologous nuclear genes, we reconstructed the first highly supported and dated backbone phylogeny of <em>Rhododendron</em> comprising 200 species that represent all subgenera, sections, and nearly all multi-species subsections, and found that most extant species originated by evolutionary radiations when the genus migrated southwards from circumboreal areas to tropical/subtropical mountains, showing rapid increases of both net diversification rate and evolutionary rate of environmental factors in the Miocene. We also found that the geographically uneven diversification of <em>Rhododendron</em> led to a much higher diversity in Asia than in other continents, which was mainly driven by two environmental variables, i.e., elevation range and annual precipitation, and was further strengthened by the adaptation of leaf functional traits. Our study provides a good example of integrating phylogenomic and ecological analyses in deciphering the mechanisms of plant evolutionary radiations and sheds new light on how the intensification of the Asian monsoon has driven evolutionary radiations in large plant genera of the Himalaya-Hengduan Mountains.</p>
Phylogenomic and ecological analyses reveal the spatiotemporal evolution of global pines
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Allen Brain Atlas
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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.