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Spatiotemporal Data (spatiotemporal + data)
Selected AbstractsFrom segment to somite: Segmentation to epithelialization analyzed within quantitative frameworksDEVELOPMENTAL DYNAMICS, Issue 6 2007Paul M. Kulesa Abstract One of the most visually striking patterns in the early developing embryo is somite segmentation. Somites form as repeated, periodic structures in pairs along nearly the entire caudal vertebrate axis. The morphological process involves short- and long-range signals that drive cell rearrangements and cell shaping to create discrete, epithelialized segments. Key to developing novel strategies to prevent somite birth defects that involve axial bone and skeletal muscle development is understanding how the molecular choreography is coordinated across multiple spatial scales and in a repeating temporal manner. Mathematical models have emerged as useful tools to integrate spatiotemporal data and simulate model mechanisms to provide unique insights into somite pattern formation. In this short review, we present two quantitative frameworks that address the morphogenesis from segment to somite and discuss recent data of segmentation and epithelialization. Developmental Dynamics 236:1392,1402, 2007. © 2007 Wiley-Liss, Inc. [source] A new metric for evaluating the correspondence of spatial patterns in vegetation modelsGLOBAL ECOLOGY, Issue 4 2008Guoping Tang ABSTRACT Aim, To present a new metric, the ,opposite and identity' (OI) index, for evaluating the correspondence between two sets of simulated time-series dynamics of an ecological variable. Innovation, The OI index is introduced and its mathematical expression is defined using vectors to denote simulated variations of an ecological variable on the basis of the vector addition rule. The value of the OI index varies from 0 to 1 with a value 0 (or 1) indicating that compared simulations are opposite (or identical). An OI index with a value near 0.5 suggests that the difference in the amplitudes of variations between compared simulations is large. The OI index can be calculated in a grid cell, for a given biome and for time-series simulations. The OI indices calculated in each grid cell can be used to map the spatial agreement between compared simulations, allowing researchers to pinpoint the extent of agreement or disagreement between two simulations. The OI indices calculated for time-series simulations allow researchers to identify the time at which one simulation differs from another. A case study demonstrates the application and reliability of the OI index for comparing two simulated time-series dynamics of terrestrial net primary productivity in Asia from 1982 to 2000. In the case study, the OI index performs better than the correlation coefficient at accurately quantifying the agreement between two simulated time-series dynamics of terrestrial net primary productivity in Asia. Main conclusions, The OI index provides researchers with a useful tool and multiple flexible ways to compare two simulation results or to evaluate simulation results against observed spatiotemporal data. The OI index can, in some cases, quantify the agreement between compared spatiotemporal data more accurately than the correlation coefficient because of its insensitivity to influential data and outliers and the autocorrelation of simulated spatiotemporal data. [source] Dynamic models for spatiotemporal dataJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 4 2001Jonathan R. Stroud We propose a model for non-stationary spatiotemporal data. To account for spatial variability, we model the mean function at each time period as a locally weighted mixture of linear regressions. To incorporate temporal variation, we allow the regression coefficients to change through time. The model is cast in a Gaussian state space framework, which allows us to include temporal components such as trends, seasonal effects and autoregressions, and permits a fast implementation and full probabilistic inference for the parameters, interpolations and forecasts. To illustrate the model, we apply it to two large environmental data sets: tropical rainfall levels and Atlantic Ocean temperatures. [source] Testing coevolutionary hypotheses over geological timescales: interactions between Cretaceous dinosaurs and plantsBIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 1 2010RICHARD J. BUTLER Testing coevolutionary scenarios over extended geological timescales is fraught with difficulties. Most tests rely on comparisons of temporal variations in taxonomic diversity for the groups of interest: however, this approach typically excludes spatiotemporal data. Here, we apply a quantitative method that incorporates the spatiotemporal distributions of the proposed coevolving groups using a Geographical Information System. Distributional data for Cretaceous dinosaur and plant groups were mapped onto palaeogeographical reconstructions in a series of time-slices. Within each time-slice, palaeocontinental surfaces were divided into a series of grids, each of which was scored as present, absent or inapplicable (unsampled) for each group. Distributions were compared statistically to determine whether the putative coevolving groups co-occurred within grid squares more or less frequently than expected by chance. Pairwise comparisons were made between herbivorous dinosaur clades and major plant groups (e.g. cycads, angiosperms) on a global scale. Only three nonrepeated associations of marginal significance were recovered, demonstrating that, in general, current knowledge of the spatiotemporal distributions of these groups provides little support for coevolutionary hypotheses. The Geographical Information System methods used are readily applicable to many other questions whose answers are reliant on a detailed knowledge of organismal distributions in time and space. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 1,15. [source] | ||||||||||