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450 results for “Spatio-Temporal”
Spatio-temporal analysis of remotely sensed forest loss data in the Cordillera Administrative Region, Philippines
<p>The Cordillera Administrative Region (CAR) in the Philippines is among the last forest frontiers in the country and is also home to 13 major watersheds in Northern Luzon that supply irrigation and hydroelectricity to other regions. However, it is faced with the deterioration of the quality of its watersheds due to forest loss driven mainly by agricultural expansion and illegal logging. Thus, this study was conducted to analyze the spatial and temporal patterns of forest loss that could serve as a basis for policy decisions. Also, this paper determined the strength of relationships using Pearson's correlation coefficient (<i>r</i>) between forest loss and seven independent variables, which includes forest cover, agricultural areas, built-up, road network, and socio-economic data. This study utilized the Hansen Global Forest Change (HGFC), a Landsat-derived dataset from 2001 to 2019. Results revealed that 70,925 hectares (ha) of forest loss were detected with an annual deforestation rate of 3,744 ha/year across the region. Based on the validation, the accuracy of the HGFC data is 72%, but great caution should be observed when using the data with less than 0.2 ha due to very low accuracy. On a region-wide analysis, only the forest cover had a strong association with the forest loss with a computed <i>r-value</i> of 0.78. Conversely, on a provincial level, the explanatory variables had a strong to moderately strong correlation with deforestation. Hence, immediate, science-based, and sustained regional and multi-stakeholder efforts are necessary to conserve and protect the remaining forest cover in the region.</p>
Spatio-temporal variation in dry season determines the Amazonian fire calendar
<p><strong>Spatio-temporal variation in dry season determines the Amazonian fire calendar</strong></p> <p><strong>Contact:</strong> nsc.nathaliacarvalho@gmail.com</p> <p><strong>Data repository for the paper:</strong> Carvalho et al. Spatio-temporal variation in dry season determines the Amazonian fire calendar. Environmental Research Letters (2021).</p> <p><strong>Background: </strong>Fire is one of the main anthropogenic drivers that threatens the Amazon. Despite the clear link between rainfall and fire, the spatial and temporal relationship between these variables is still poorly understood in the Amazon. We stratified the Amazon basin according to the dry season onset/end and investigated its relationship with the spatio-temporal variation of fire. We found well-defined seasonal fire patterns related to variation of the dry season end. </p> <p><strong>Fire Amazonian Calendar:</strong> Our results and maps are also available in a user-friendly interface (<a href="http://amazonianfirecalendar.shinyapps.io/fire_amazon/">amazonianfirecalendar.shinyapps.io/fire_amazon/</a>). </p> <p><strong>Dataset:</strong> Rasters files of the dry season (onset, end, length) and fire dynamics (Monthly percentage of fire in the peak month, Fire peak month and Critical Fire Period).</p> <p><strong>Coverage:</strong> Amazon basin</p> <p><strong>Spatial resolution:</strong> 10km</p> <p><strong>Coordinate reference system:</strong> South America Albers Equal Area Conic with Datum SAD69</p> <p><strong>For the use of this dataset, please cite:</strong> Carvalho, N. S.; Anderson L. O.; Nunes C. A.; Pessôa, A. C.M.; Silva Junior, C. H. L., Reis, J.B.C.; Shimabukuro, Y. E.; Berenguer E.; Barlow J. and Aragão, L. E. O. C. Spatio-temporal variation in dry season determines the Amazonian fire calendar. Environmental Research Letters (2021). <a href="https://doi.org/10.1088/1748-9326/ac3aa3">https://doi.org/10.1088/1748-9326/ac3aa3</a></p>
Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"
<p><strong>Abstract</strong></p> <p><strong>Aim:</strong> In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends.<br> <br> <strong>Location:</strong> Switzerland (Europe)<br> <br> <strong>Taxon:</strong> Birds</p> <p><strong>Methods: </strong>We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time.</p> <p><strong>Results: </strong>Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation.<br> Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.</p> <p> </p> <p> </p>
Risky business: how an herbivore navigates spatio-temporal aspects of risk from competitors and predators
<p>Understanding factors that influence animal behavior is central to ecology. Basic principles of animal ecology imply that individuals should seek to maximize survival and reproduction, which means carefully weighing risk against reward. Decisions become increasingly complex and constrained, however, when risk is spatiotemporally variable. We advance a growing body of work in predator-prey behavior by evaluating novel questions where a prey species is confronted with multiple predators and a potential competitor. We tested how fine-scale behavior of female mule deer (Odocoileus hemionus) during the reproductive season shifted depending upon spatial and temporal variation in risk from predators and a potential competitor. We expected female deer to avoid areas of high risk when movement activity of predators and a competitor were high. We used GPS data collected from 65 adult female mule deer, 35 adult female elk, 33 adult coyotes, and six adult mountain lions. Counter to our expectations, female deer exhibited selection for multiple risk factors, however, selection for risk was dampened by the exposure to risk within deer home ranges, producing a functional response in habitat selection. Furthermore, temporal variation in movement activity of predators and elk across the diel cycle did not result in a shift in movement activity by female deer. Instead, the average level of risk within their home range was the predominant factor modulating the response to risk by female deer. Our results counter prevailing hypotheses of how large herbivores navigate risky landscapes, and emphasize the importance of accounting for the local environment when identifying effects of risk on animal behavior. Moreover, our findings highlight additional behavioral mechanisms used by large herbivores to mitigate multiple sources of predation and potential competitive interactions.</p>
Supplementary Information: Human populations in the world's mountains: spatio-temporal patterns and potential controls
<p>Supplementary Information (data, code, figures) for "Human populations in the world’s mountains: spatio-temporal patterns and potential controls" (Thornton et al., 2022)</p>
Spatio-temporal dynamics of genetic variation at the quantitative and molecular levels within a natural Arabidopsis thaliana population
<p><span>Evolutionary change begins at the population scale. Therefore, understanding adaptive variation requires the identification of the factors maintaining and shaping standing genetic variation at the within-population level. Spatial and temporal environmental heterogeneity represent ecological drivers of within-population genetic variation, determining the evolutionary trajectory of populations along with random processes. Here, we focused on the effects of </span><span>spatio-temporal heterogeneity on quantitative and molecular variation in a natural population of the annual plant <em>Arabidopsis thaliana</em>.</span></p> <p><span>We sampled 1,093 individuals from a Spanish <em>A. thaliana </em>population across an area of 7.4 ha for 10 years (2012-2021). Based on a sample of 279 maternal lines, we estimated spatio-temporal variation in life-history traits and fitness from a common garden experiment. We genotyped 884 individuals with nuclear microsatellites to estimate spatio-temporal variation in genetic diversity. We assessed spatial patterns by estimating spatial autocorrelation of traits and fine-scale genetic structure. We analyzed the relationships between phenotypic variation, geographic location and genetic relatedness, as well as the effects of environmental suitability and genetic rarity on phenotypic variation. </span></p> <p><span>The common garden experiment indicated that there was more temporal than spatial variation in life-history traits and fitness. Despite the differences among years, genetic distance in ecologically relevant traits (e.g. flowering time) tended to be positively correlated to genetic distance among maternal lines, whilst isolation by distance was less important. Genetic diversity exhibited significant spatial structure at short distances, which were consistent among years. Finally, genetic rarity, and not environmental suitability, accounted for genetic variation in life-history traits.</span></p> <p><span>Synthesis. Our study highlighted the importance of repeated sampling to detect the large amount of genetic diversity at the quantitative and molecular levels that a single <em>A. thaliana</em> population can harbor. Overall, population genetic attributes estimated from our long-term monitoring scheme (genetic relatedness and genetic rarity), rather than biological (dispersal) or ecological (vegetation types and environmental suitability) factors, emerged as the most important drivers of within-population structure of phenotypic variation in <em>A. thaliana.</em></span></p>
FIGURE 35 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURE 35. Number of known specimens of Feltia carolia (Schaus, 1929) comb. nov. (n=53) by month of collection, from 1922 to 2014; light gray: records from the literature and specimens in entomological collections; dark gray: specimens collected in light traps from 1998 to 1999 (see text for details).
FIGURES 13–17 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 13–17. Feltia carolia (Schaus, 1929) comb. nov., labial palpus, thorax and appendages. 13. Labial palpus, lateral. 14. Thorax, dorsal. 15. Forewing, upper side. 16. Male wing coupling. 17. Female wing coupling. Arrows point out frenula, retinacula and patches of enlarged scales. Scale bars: Fig. 13 = 0.5 mm, Fig. 14= 2 mm, Fig. 15= 5 mm, Figs. 16–17 = 0.5 mm.
FIGURES 7–12 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 7–12. Feltia carolia (Schaus, 1929) comb. nov., head and appendages. 7. Head, anterior. 8. Frons tubercle, anterior. 9. Head, lateral. 10. Male antenna in its widest part, ventral. 11. Male antenna distal segments, lateral. 12. Female antenna, lateral. Scale bars: Fig. 7 = 1 mm, Fig. 8 = 0.2 mm, Figs 9–11 = 0.5 mm, Fig. 12 = 0.2 mm.
FIGURES 26–27 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 26–27. Feltia carolia (Schaus, 1929) comb. nov., male genitalia, lateral. 26. genitalia with right valva removed. 27. Valva. Scale bars: Fig. 26 = 1 mm, Fig. 27 = 0.5 mm.
FIGURES 1–6 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 1–6. Feltia carolia (Schaus, 1929) comb. nov., dorsal and ventral habitus.1–2. Male, Lagoa Vermelha, Rio Grande do Sul, Brazil (MCTP 9536). 3–4 Male, Lagoa Vermelha, Rio Grande do Sul, Brazil (MCTP 9538). 5–6. Female, Cambará do Sul, Rio Grande do Sul, Brazil (CLAM 00021). Scale bar = 1 cm.
FIGURE 34 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURE 34. Feltia carolia (Schaus, 1929) comb. nov., spatial distribution. Black dots indicate localities of standardized samplings; yellow stars, the presence of F. carolia comb. nov., and the question mark, the state of the type locality; green shaded areas indicates roughly areas of southern Brazilian Campos, according to Overbeck et al. (2007).
FIGURES 32–33 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 32–33. Holotype of Porosagrotis carolia Schaus, 1929. 32. Reproduction from Schaus' original description plates. 33. Holotype located by J. Bolling Sullivan (see text for details). Arrows point out features indicating that this is the same specimen illustrated by Schaus (1929).
FIGURES 18–23 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 18–23. Feltia carolia (Schaus, 1929) comb. nov., labial palpus and legs. 18. Labial palpus, lateral. 19. Foreleg, lateral. 20. Mid leg, lateral. 21. Hind leg, lateral. 22. Foreleg tarsus, anterior. 23. Foreleg epiphysis, lateral.
FIGURES 28–31 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 28–31 Feltia carolia (Schaus, 1929) comb. nov., male and female genitalia, lateral. 28–29. Male genitalia. 28. Male genitalia with aedeagus and right valva removed. 29. Aedeagus with vesica everted, posterior side up. 30–31. Female genitalia. 30. Ventral. 31. Lateral.
Spatio-temporal plant hormonomics: From tissue to subcellular resolution
<p>Repository files for the review publication "Spatio-temporal plant hormonomics: From tissue to subcellular resolution".</p> <p>Repository_File_1: Statistical methods</p> <p>Repository_File_2: Raw data from Web of Science for targeted plant hormone analysis</p> <p>Repository_File_3: Raw data from Web of Science for untargeted plant hormone analysis</p>
Spatio-temporal dataset - Exposure data
<p>XLSX sheet with exposure data to be used for the practical Assignment in Aggregated exposure Pathway (AEP) development. The data set contains one xlsx file, with several data sheets:</p> <p>README: contain informtion about the data set and columns. </p> <p>EXPOSURE_DATA: Spatio-temporal exposure data (266 rows) from a 6-location monitoring study in Norway (2019)</p> <p>SITES: Data for geopositioning of the the sample locations/Sites (SITE_CODE) </p>
Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction
<p>This Zenodo repository provides comprehensive resources for the paper titled "Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction" published on <a href="https://academic.oup.com/bioinformatics/article/41/8/btaf429/8238154">Bioinformatics</a>. We created a dataset of 63,000 molecular dynamics simulations by performing 10 simulations of 10 ns on 6,300 complexes. Neural networks were developed to learn from this data in order to predict the binding affinities of protein-ligand complexes. The implementation of these neural networks are available on <a href="https://github.com/ICOA-SBC/MD_DL_BA" target="_blank" rel="noopener">github</a>. Our collection includes training/benchmark datasets, trained statistical models, and results on test sets (CSV & PDF files).</p> <p> </p> <p><strong>Training/benchmark datasets:</strong></p> <p>Training, validation and test sets are provided to train and evaluate the following neural networks:</p> <ul> <li>Pafnucy, Proli and Densenucy without MD data augmentation (dataset file names contain "initial")</li> <li>Pafnucy, Proli and Densenucy with MD data augmentation (dataset file names contain "MDDA")</li> <li>Pafnucy with/without MD data augmentation and Proli and Densenucy with MD data augmentation were also evaluated on the fep test set (test set file name contain "fep")</li> <li>Timenucy and Videonucy using spatiotemporal learning methods (dataset file names contain "4D")</li> <li>Pafnucy without MD data augmentation and on a reduced training set (dataset file names contain "reduced")</li> </ul> <p>For each training methodology (MD data augmentation and spatiotemporal learning), we provide the data for the whole complex, only the ligand or only the protein. Additionally for spatiotemporal learning, we provide the data with only the ligand using the tracking mode.</p> <p> </p> <p><strong>Statistical models:</strong></p> <p>We provide the models trained with Pafnucy, Proli, Densenucy, Timenucy and Videonucy. Each models were trained in 10 replicates. </p> <p>For Pafnucy, Proli, Densenucy, we provide the models trained with random and systematic rotations, as well as with or without MD data augmentation.</p> <p>For Proli, Densenucy, Timenucy and Videonucy, we provide the models trained on the whole complex, only the ligand or only the protein.</p> <p>For Pafnucy we also provide the models trained on the reduced set (5932 complexes).</p> <p> </p> <p><strong>Results on test sets (CSV & PDF files):</strong></p> <p>We provide the predictions on the PDBbind v.2016 core set.</p> <ul> <li>For spatiotemporal learning methods (Timenucy and Videonucy), there are predictions for only 83 complexes, as we did not perform simulations on the whole test set.</li> <li>For models trained with MD DA, predictions were carried on the crystallographic structures as well as on the frames extracted from the simulations performed on the test set (augmented test).</li> </ul> <p>Results on the FEP dataset are also provided for Pafnucy, Proli and Densenucy.</p> <p> </p> <p>The Raw MD data (~4.5 To) are stored, and can be visualized/downloaded, on the <a href="https://mdposit.mddbr.eu/#/browse?search=MDBind">MDDB</a>.</p> <p>This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-A0100712496 & 2022-AD011013521) and CRIANN (Grant 2021002).</p>
Spatio-temporal Characterization of Coherent Structures in Simulated Tropical Cyclone Boundary Layer
<p>Jupyter-notebooks to read and analyze processed outputs from three Tropical Cyclone (TC) simulations (CONTROL, LOWOR, and NONPARAM) and plot figures. The datasets are large and can be made available by the authors upon request.</p>
Data from: Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944
<p><strong>Paper Abstract:</strong></p> <p>Urban expansion, especially suburbanization, represents a major social, economic and environmental shift that has escalated since the mid-1900s in North America. Suburban development leads to corresponding changes in the treed environment of urban-rural fringes. It is important to understand where, when and why trees change in response to development over many decades, but this is difficult since long-term data are scarce. We used 70+ years (1944–2017) of leaf-off aerial photographs, often representing the only long-term landscape record, to quantify and map spatio-temporal changes in tree density through the entirety of the agricultural-suburban transitional period. Photo-interpretation of individual tree locations, along with recording observable drivers of change, was completed across six different modern landscapes in Mississauga, Ontario, Canada. Results indicate that tree density often recovers, or even increases, post-development. There are differences between landscapes, but most tree density gains are associated with forest expansion and tree planting, while most losses are associated with building and road construction. The influence of these drivers, along with the temporal trajectory of tree density changes, are shaped by a landscape’s socioecological legacy and the length, scope and intensity of development (as decided by decision makers). Processes include initial tree losses followed by recovery from tree planting, and forest succession in abandoned fields after land purchase and nearby development. We assert that the spatio-temporal changes in tree density and related drivers quantified here can be generalized to gain knowledge on how tree density and distribution across agricultural landscapes will change under different development scenarios.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper: <a href="https://www.sciencedirect.com/science/article/pii/S0169204619301914?via%3Dihub">Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944 - ScienceDirect</a></p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/SuburbanizingTreeDensity_1944to2017">ZZMitch/SuburbanizingTreeDensity_1944to2017: Code from "Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944" (L&UP, 2019) (github.com)</a></p> <p>- Note: High resolution imagery is not included in this repository. </p> <p> </p> <p><strong>If you use these data, please reference: </strong></p> <p>Bonney, M.T., He, Y., 2019. Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944. Landscape and Urban Planning 192, https://doi.org/10.1016/j.landurbplan.2019.103652. </p>
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International Brain Laboratory public data
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OpenNeuro
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