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Dataset results
365 results for “Spatial modeling”
Modeling human migration across spatial scales in Colombia
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Data from: Modeling spatial expansion of invasive alien species: relative contributions of environmental and anthropogenic factors to the spreading of the harlequin ladybird in France
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Integrating stakeholders’ perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
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Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus
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Data from: How does spatial resolution affect model performance? A case for ensemble approaches for marine benthic mesophotic communities
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Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved numerical integration technique
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Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
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Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
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Data from: Separating mortality and emigration: modelling space use, dispersal and survival with robust-design spatial-capture-recapture data
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Data from: The ecology of spider sociality – A spatial model
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A spatially explicit model to simulate the population dynamics of gypsy moth (Lymantria dispar)
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Data from: The evolution of marine larval dispersal kernels in spatially structured habitats: analytical models, individual-based simulations, and comparisons with empirical estimates
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Data from: A spatially explicit hierarchical model to characterize population viability
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Data from: Simulation-based validation of spatial capture-recapture models: a case study using mountain lions
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Data from: Improving public safety through spatial synthesis, mapping, modeling, and performance analysis of emergency evacuation routes in California localities
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Data for modelling spatial patterns and determinants of atmospheric carbon dioxide concentrations in Phoenix metro area
The purpose of this work is to describe determinants and spatial patterns of atmospheric carbon dioxide (CO2) in Phoenix, Arizona. Specifically, we use geographic information systems (GIS) and regression-based analyses to identify the human and biological factors that contribute to spatial and temporal variations in near-surface atmospheric CO2 levels. We use these factors to create estimated surfaces of CO2 for the urban area. We validate our surfaces using independently collected records of CO2 from several monitoring stations and transects. To investigate the temporal patterns and variations of CO2, we were able to generate CO2 surfaces for the early mornings and the afternoons, and on weekdays when traffic is heavy and spatially focused and on weekends when it is lighter and more spatially dispersed. Our findings suggest there is a distinct relationship between the structure of Phoenix CO2 levels and spatial patterns of human activities and vegetation densities. Morning CO2 levels are higher than afternoon levels and correspond closely to the density of traffic, population, and employment. The spatial structure of human activity explains the pattern of CO2 better on weekdays than on weekends. CO2 surfaces reflect declining densities of human activity with distance from the city center, the pattern of irrigated agriculture in the Phoenix area, and riparian habitats on the urban fringe. Spatial and temporal patterns of CO2 are useful in understanding urban climate and ecosystem processes.
A Spatially Explicit Model of Vegetation-Habitat Interactions on Barrier Beaches
ISLAND is a model designed to simulate annual changes in vegetation, geomorphology, water table depth, and average ground water salinity on a cross-sectional transect, running from ocean to lagoon, on a barrier beach. The model is composed of three submodels; (1) a vegetation submodel which simulates the development of a four "species" plant community consisting of two grasses, an annual and a perennial shrub, (2) a geomorphology submodel which simulates the redistribution of sand by water and wind, and (3) a ground water submodel which simulates the average depth of the water table and the salinity of the ground water. Information calculated in each of the submodels is reevaluated and made available to the other submodels after each time iteration. The model works on a one year time step and acts on five meter sections along the transect. It is assumed that each five meter section is homogeneous with respect to vegetation, height above sea level, water table depth, and ground water salinity.
Synthetic soil crusts against green-desert transitions: a spatial model
<p>Semiarid ecosystems are threatened by global warming due to longer dehydration times and increasing soil degradation. Mounting evidences indicate that, given the current trends, drylands are likely to expand and possibly experience catastrophic shifts from vegetated to desert states. Here we explore a recent suggestion based on the concept of ecosystem terraformation, where a synthetic organism is used to counterbalance some of the nonlinear effects causing the presence of such tipping points. Using an explicit spatial model incorporating facilitation and considering a simplification of states found in semiarid ecosystems i.e., vegetation, fertile and desert soil, we investigate how engineered microorganisms can shape the fate of these ecosystems. Specifically, two different, but complementary, terraformation strategies are proposed: Cooperation-based: C-terraformation; and Dispersion-based: D-terraformation. The first strategy involves the use of soil synthetic microorganisms to introduce cooperative loops (facilitation) with the vegetation. The second one involves the introduction of engineered microorganisms improving their dispersal capacity, thus facilitating the transition from desert to fertile soil. We show that small modifications enhancing cooperative loops can effectively change the location of the critical transition found at increasing soil degradation rates, also identifying a stronger protection against soil degradation by using the D-terraformation strategy. The same results are found in a mean field model providing insights into the transitions and dynamics tied to these terraformation strategies. The potential consequences and extensions of these models are discussed.</p>
Data from: Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics
<p>1. Assessing the source-sink status of populations and habitats is of major importance for understanding population dynamics and for the management of natural populations. Sources produce a net surplus of individuals (per capita contribution to the metapopulation >1) and will be the main contributors for self-sustaining populations, whereas sinks produce a deficit (contribution < 1). However, making these types of assessments is generally hindered by the problem of separating mortality from permanent emigration, especially when survival probabilities as well as moved distances are habitat-specific.<br> 2. To address this long-standing issue, we propose a spatial multi-event Integrated Population Model (IPM) that incorporates habitat-specific dispersal distances of individuals. Using information about local movements, this IPM adjusts survival estimates for emigration outside the study area.<br> 3. Analyzing 24 years of data on a farmland passerine (the northern wheatear Oenanthe oenanthe) we assessed habitat-specific contributions, and hence the source-sink status and temporal variation of two key breeding habitats, while accounting for habitat- and sex-specific local dispersal distances of juveniles and adults. We then examined the sensitivity of the source-sink analysis by comparing results with and without accounting for these local movements.<br> 4. Estimates of first-year survival, and consequently habitat-specific contributions, were higher when local movement data were included. The consequences from including movement data were sex specific, with contribution shifting from sink to likely source in one habitat for males, and previously noted habitat differences for females disappearing.<br> 5. Assessing the source-sink status of habitats is extremely challenging. We show that our spatial IPM accounting for local movements can reduce biases in estimates of the contribution by different habitats, and thus reduce the overestimation of the occurrence of sink habitats. This approach allows combining all available data on demographic rates and movements, which will allow better assessment of source-sink dynamics and better informed conservation interventions.</p>
Data from: Spatially structured statistical network models for landscape genetics
A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape-genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non-binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape-genetic data. We further explore a set of spatial-statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape-genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape-genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.
ScienceDex guides
Understand access before you commit
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.