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44 results for “Spatio-temporal model”
Replicate analysis from: tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models
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A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides
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Data from: Spatio-temporally explicit model averaging for forecasting of Alaskan groundfish catch
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Estimating spatio-temporal reproductive dynamics of fish populations with passive acoustic monitoring: A state-space model approach
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Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio-temporal ecosystem model that approximates coupled physical, biological, and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate-linked forecasts), primary producers (spring and fall size-fractionated chlorophyll-a), secondary producers (copepods), juveniles (age-0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort), and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom-up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's-D) and identify that age-0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher-processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age-0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher-processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate "ecological teleconnections" (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages.
Data from: Spatio-temporal modelling of auk abundance after the Erika oil spill and implications for conservation
Species distribution models are widely used in applied ecology and conservation. While accounting for spatial dependences is now the rule, temporal dependences have rarely been dealt with explicitly. In this study, we analyse wintering auk distribution in the Bay of Biscay and English Channel and estimate changes in abundance within and between years while accounting for space–time dependencies. We then propose a retrospective estimate of the impact of the Erika oil spill that occurred in December 1999. Two series of extensive aerial surveys, repeated at intervals of 1–2 months, were carried out at a 10-year interval off the French Atlantic coast (2001–2002 and 2011–2012). Spatially and temporally explicit Bayesian models were fitted to these data to provide spatio-temporal predictions of auk abundance. These were then used to compare abundances within the area affected by the Erika oil spill two and twelve years after the catastrophe. The results showed that 1·55 million auks wintered in the study area in 2011–2012. The main wintering area was the English Channel (more than one million auks) but the Bay of Biscay also became an important area in the middle of winter (470 000 auks) owing to a strong southward shift in auk distribution. Two years after the catastrophe (2001–2002), the area affected by the Erika oil spill hosted a small proportion of auks of the Bay of Biscay – about 80 000 individuals. This number increased by more than three times 10 years later and reached 270 000 individuals, whereas no significant change was detected elsewhere. We suggest that it could result from a recovery after the extra-mortality induced by the Erika oil spill. Policy implications. This study identified major auk wintering areas, with abundances much higher than previously realized. Oil spills have occurred regularly in these areas, with major delayed impacts on auk breeding populations. The worst case scenario would be if a major oil spill occurred in the English Channel in February, when abundance reaches one million auks. Although such a disaster has not so far occurred, stricter policies on the transport of hydrocarbons should be implemented to prevent such a possibility.
Field and Lab_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Field and Lab observations_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</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>
Data of Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction
<p>Using the dense ground-based GNSS receiver network and ionosonde data from East and Southeast Asia during 2010-2021, a novel Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction (STEP) was developed. The model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities.<br>This dataset includes both the model and the results generated by STEP. The parameters provided are: UT (hours), Latitude (°), Longitude (°), Date, Y_pred (TECU/min), and Y_true (TECU/min). The dimensions of Y_pred and Y_true are 10812 x 610, where 10812 represents the product of the number of date and the number of UT (minus 18), and 610 corresponds to the product of the number of Latitude and Longitude. The model with a .pth extension can be loaded using PyTorch.</p>
COMPOSITIONAL SPATIO-TEMPORAL PM2.5 MODELLING IN WILDFIRES (R SCRIPT AND DATASET)
<p>The present R script and dataset were used in the assessment of a spatio-temporal PM<sub>2.5</sub> model in wildfire events with a limited number of monitoring stations using a compositional approach (CoDa). </p>
Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model
<p>Composite data with NWM 2.0 streamflow, basin PET, drainage area and stoage.</p> <p>SAR data used in this study are downloaded from</p> <p><a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p> <p>The zip folder comprises 4 subfolders (DEM, WD, VX, VY), containing the elevation, water depths in time, and velocities (in x and y directions) in time for all training and testing simulations. The overview.csv file provides the runtime of the numerical model on each different simulation, identified by its id.</p> <p>The simulations ids are divided as follows:</p> <p>- 1-80: Training and validation</p> <p>- 501-520: Testing dataset 1</p> <p>- 10001-10020: Testing dataset 2</p> <p>- 15001-15020: Testing dataset 3</p>
Data from: Spatio-temporal modelling of auk abundance after the Erika oil spill and implications for conservation
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Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.