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221 results for “multi-scales”
Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California
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Data from: Multi-scale spatial genetic structure within and between populations of wild cherry trees in nuclear genotypes and chloroplast haplotypes
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Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data
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Disentangling effects of climate and land use on biodiversity and ecosystem services – a multi-scale experimental design
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Data from: Multi-scale structure of chromatin condensates explains phase separation and material properties
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Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
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Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
Extreme model exploration of a multi-scale simulation of tumor growth
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different simulations trajectories obtained for alternative parameter values. The parameter explored are: i) the decay rate of the TNF after it binds the cell; ii) the TNF binding rate; and iii) the TNF secretion rate by NFkB activated cells.</p> <p>The dataset includes 48 different combinations of parameters. Each simulation is stored in a folder instance_[0-9]+ which includes the PhysiBoSS standard output files (<a href="https://github.com/gletort/PhysiBoSS/wiki">https://github.com/gletort/PhysiBoSS/wiki</a>). The root folder also includes other settings, logs, and outputs as well as the binary used to run the simulation.</p>
Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Multi-scale habitat assessment of pronghorn migration routes
<p>We studied the habitat selection of pronghorn (<i>Antilocapra americana</i>) during seasonal migration; an important period in an animal's annual cycle associated with broad-scale movements. We further decompose our understanding of migration habitat itself as the product of both broad- and fine-scale behavioral decisions and take a multi-scale approach to assess pronghorn spring and fall migration across the transboundary Northern Sagebrush Steppe region. We used a hierarchical habitat selection framework to assess a suite of natural and anthropogenic features that have been shown to influence selection patterns of pronghorn at both broad (migratory neighborhood) and fine (migratory pathway) scales. We then combined single-scale predictions into a scale-integrated step selection function (ISSF) map to assess its effectiveness in predicting migration route habitat. During spring, pronghorn selected for native grasslands, areas of high forage productivity (NDVI), and avoided human activity (i.e., roads and oil and natural gas wells). During fall, pronghorn selected for native grasslands, larger streams and rivers, and avoided roads. We detected avoidance of paved roads, unpaved roads, and wells at broad spatial scales, but no response to these features at fine scales. In other words, migratory pronghorn responded more strongly to anthropogenic features when selecting a broad neighborhood through which to migrate than when selecting individual steps along their migratory pathway. Our results demonstrate that scales of migratory route selection are hierarchically nested within each other from broader (second-order) to finer scales (third-order). In addition, we found other variables during particular migratory periods (i.e., native grasslands in spring) were selected for across scales indicating their importance for pronghorn. The mapping of ungulate migration habitat is a topic of high conservation relevance. In some applications, corridors are mapped according to telemetry location data from a sample of animals, with the assumption that the sample adequately represents habitat for the entire population. Our use of multi-scale modelling to predict resource selection during migration shows promise and may offer another relevant alternative for use in future conservation planning and land management decisions where telemetry-based sampling is unavailable or incomplete.</p>
Data from: Multi-driver and multi-scale assessment of vine community structure and composition across a complex tropical environmental matrix
Ecological communities are structured by multiple processes operating at multiple scales yet understanding the scale-dependency of these processes remains an open challenge. This might be particularly true for parasites, for which biotic rather than abiotic processes may play a primary role in structuring communities. Focusing on vines, a group of structural parasites that gain access to the canopy using different climbing mechanisms, we examined the influence of abiotic factors in tandem with host-parasite and parasite-parasite interactions in the assembly of tropical vine communities. Two synthetic variables, namely Climate1 and landscape Variety, were consistently important in explaining variation in species richness and diversity, as well as species composition, but their importance varied with scale. Whereas Climate1 summarizes the largest variability among climatic variables, landscape Variety expresses landscape heterogeneity within a neighborhood. Significant patterns of species co-occurrences suggest that vine-vine interactions also contribute to vine community assembly. Our results may be critical to understand vine proliferation and help design management strategies for their control.
Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
1. Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. 2. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time scales, e.g. step lengths obtained from GPS tags every hour, dive depths obtained from time-depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi-stream and multi-scale data is lacking. 3. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple, connected behavioural processes, each of which operates at the time scale at which the corresponding variables were observed. 4. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time scales, and in particular help to draw a much more comprehensive picture of an animal's movement patterns, e.g. with regard to long-term vs. short-term movement strategies. 5. The suggested approach is illustrated in two real-data applications, where we jointly model i) coarse-scale horizontal and fine-scale vertical Atlantic cod (Gadus morhua) movements throughout the English Channel, and ii) coarse-scale horizontal movements and corresponding fine-scale accelerations of a horn shark (Heterodontus francisci) tagged off the Californian coast.
Data from: Multi-scale drivers of community diversity and composition across tidal heights: an example on temperate seaweed communities
1. Despite recent advances in understanding community assembly processes, appreciating how these processes vary across multiple spatial scales and environmental gradients remains a crucial issue in ecology. 2. This study aimed to disentangle the drivers of diversity and composition of seaweed communities through a gradient of spatial scales based on a hierarchical sampling design consisting of 19 sites distributed in four sectors along the Brittany coastline. Using randomised community matrices and Moran's eigenvector maps (MEMs), we compared i) the relative importance of deterministic and stochastic processes, ii) the environmental correlates of community composition and iii) the scale of variation in community composition for seaweed communities located at two different tidal heights. 3. Processes shaping community patterns are expected to vary along a gradient of tidal heights. Therefore, we specifically examined the following hypotheses: the contribution of deterministic over stochastic processes as well as the relative importance of environmental filtering over biotic interactions should be enhanced for seaweed communities of the infralittoral fringe compared to subtidal ones, whereas dispersal of propagules in the water column should be more restricted resulting in finer-scale variation in community composition for seaweed communities of the infralittoral fringe compared to subtidal communities. 4. Seaweed communities were largely shaped by deterministic processes, although the relative importance of deterministic processes was greater for communities of the infralittoral fringe than for subtidal communities. Sea surface temperature and geophysical variables were correlates of community composition at the two tidal heights; additionally, waves and current were correlated with the composition of the communities of the infralittoral fringe while kelp density was correlated with the composition of subtidal communities. Variation in community composition was observed at a finer scale for infralittoral fringe than for subtidal communities. Synthesis. Our results suggest that the relative importance of deterministic and stochastic processes in structuring seaweed communities varies across tidal heights. Furthermore, the MEMs framework highlights that the nature of environmental correlates and the spatial scale at which they were good correlates of community composition also vary across tidal heights and may therefore be useful to broaden our understanding of community assembly across vertical gradients.
Data from: Multi-scale landscape and wetland drivers of lake total phosphorus and water color
We quantified relationships between local wetland cover in the riparian lake buffer and lake total phosphorus (TP) and water color (color) using multilevel mixed-effects models that also incorporate landscape features such as hydrogeomorphology and land use at broad regional scales to determine 1) Within regions, are local wetland relationships with TP and color affected by interactions with local land use or hydrogeomorphic variables? 2) Across regions, are local wetland relationships with TP and color different? And if so, 3) Are differences in local wetland relationships with TP and color a result of cross-scale interactions? We answered these questions by analyzing TP, color, and multi-scaled landscape data for 1790 North temperate lakes. We found that local wetland-TP and wetland-color relationships were not affected by local-scale interactions; we found that local wetland-TP and wetland-color relationships were different across regions; and these differences were related to cross-scale interactions with regional landscape characteristics. For example, regional human land-use affected local wetland-TP relationships such that in regions with high amounts of agriculture, local wetlands were associated with decreased lake TP. However, in regions with low amounts of agriculture, local wetlands were associated with increased lake TP. In contrast, regional hydrogeomorphic characteristics influenced local wetland-color relationships such that in regions with high groundwater contribution, the strength of local wetland relationships were weak. Regional landscape setting influences local wetland relationships with TP and color through cross-scale interactions and lake TP and color are controlled by both local-scale wetland extent and regional-scale landscape variables.
Multi-scale landscape genetics of American marten at their southern range periphery
<p>American marten (<i>Martes americana</i>) are a conservation priority in many forested regions of North America. Populations are fragmented at the southern edge of their distribution due to suboptimal habitat conditions. Facilitating gene flow may improve population resilience through genetic and demographic rescue. We used a multi-scale approach to estimate the relationship between genetic connectivity and landscape characteristics among individuals at three scales in the northeastern United States: regional, subregional, and local. We integrated multiple modeling techniques and identified top models based on consensus. Top models were used to parameterize resistance surfaces at each scale, and circuit theory was used to identify potential movement corridors.</p> <p>Regional gene flow was affected by forest cover, climate, developed land cover, and slope. At subregional and local scales, effects were site-specific and included subsets of climate, developed land cover, and slope. Developed land cover significantly affected gene flow in all sites examined at each scale. The effect of slope on gene flow was positive or negative depending on the site examined. At finer scales, reduced variance in forest cover may have limited the ability to detect a relationship with gene flow. Occupancy probability was a relatively poor predictor and we caution its use as a proxy for landscape resistance. Our results underscore the importance of replication and multi-scale approaches in landscape genetics. Climate warming and landscape conversion may reduce the genetic connectivity of marten populations in the northeastern United States and represent the primary challenges to marten conservation at the southern periphery of their range.</p>
Data from: A novel multi-scale assessment of community assembly across time, space, and functional niche
A basic ecological tenet is that organisms in a community occupy different niches and have different traits, but how consistently competition, selection, and phylogenetic effects structure communities remains uncertain. Are all communities created equal? We examine how mammalian carnivoran communities are assembled with regard to mass, diet, and locomotion. Here, we use a multivariate nearest-neighbor framework to examine multiple North American localities spanning 3 million years to determine whether community assembly is consistent through time and four modern localities around the world to assess the effects of habitat. Additionally, we examined how trait patterns differ among families and how family-level evolutionary effects affect them. We found some broadly consistent patterns, although differences are more pronounced than similarities. Diet is more affected by evolutionary constraints than by time or place. Locomotion is most affected by habitat, and the ability to partition niches is related to habitat heterogeneity. Mass is influenced by family, but also by habitat and the mass-selective extinction events at the end-Pleistocene. These findings indicate that assembly patterns are not largely determined by within-community interactions but instead show that each community is a product of its independent variables.
Data from: Multi-scale model of regional population decline in little brown bats due to white-nose syndrome
The introduced fungal pathogen Pseudogymnoascus destructans is causing decline of several species of bats in North America, with some even at risk of extinction or extirpation. The severity of the epidemic of white-nose syndrome caused by P. destructans has prompted investigation of the transmission and virulence of infection at multiple scales, but linking these scales is necessary to quantify the mechanisms of transmission and assess population-scale declines. We build a model connecting within-cave disease dynamics of little brown bats to regional scale dispersal, reproduction, and disease spread, including multiple plausible mechanisms of transmission. We parameterize the model using the approach of plausible parameter sets, by comparing stochastic simulation results to statistical probes from empirical data on within-cave prevalence and survival, as well as between-cave spread across a region. Our results are consistent with frequency-dependent transmission between bats, support an important role of environmental transmission, and show very little effect of dispersal among colonies on metapopulation survival. The model also offers a generalizable method to assess hypotheses about cave-to-cave transmission and to identify gaps in knowledge about key processes, and could be expanded to include additional mechanisms or bat species as research on this detrimental fungus progresses.
Data from: Spatiotemporal patterns of duck nest density and predation risk: a multi-scale analysis of 18 years and more than 10 000 nests
Many avian species are behaviorally-plastic in selecting nest sites, and may shift to new locations or habitats following an unsuccessful breeding attempt. If there is predictable spatial variation in predation risk, the process of many individuals using prior experience to adaptively change nest sites may scale up to create shifting patterns of nest density at a population level. We used 18 years of waterfowl nesting data to assess whether there were areas of consistently high or low predation risk, and whether low-risk areas increased, and high-risk areas decreased in nest density the following year. We created kernel density maps of successful and unsuccessful nests in consecutive years and found no correlation in predation risk and no evidence for adaptive shifts, although nest density was correlated between years. We also examined between-year correlations in nest density and nest success at three smaller spatial scales: individual nesting fields (10–28 ha), 16-ha grid cells and 4-ha grid cells. Here, results were similar across all scales: we found no evidence for year-to-year correlation in nest success but found strong evidence that nest density was correlated between years, and areas of high nest success increased in nest density the following year. Prior research in this system has demonstrated that areas of high nest density have higher nest success, and taken together, our results suggest that ducks may adaptively select nest sites based on the local density of conspecifics, rather than the physical location of last year's nest. In unpredictable environments, current cues, such as the presence of active conspecific nests, may be especially useful in selecting nest sites. The cues birds use to select breeding locations and successfully avoid predators deserve continued attention, especially in systems of conservation concern.
Data from: Multi-scale effects of habitat structure and landscape context on a vertebrate with limited dispersal ability (the brown-throated sloth, Bradypus variegatus)
As human population, food consumption, and demand for forest products continue to rise over the next century, the pressures of land use change on biodiversity are projected to intensify. In tropical regions, countryside habitats that retain abundant tree cover and structurally complex canopies may complement protected areas by providing suitable habitats and landscape connectivity for a significant portion of the native biota. Species with low dispersal capabilities are among the most at risk of extinction as a consequence of land use change. We assessed how the spatial distribution of the brown-throated sloth (Bradypus variegatus), a model species for a vertebrate with limited dispersal ability, is shaped by differences in habitat structure and landscape patterns of countryside habitats in north-central Costa Rica using a multi-scale framework. We quantified the influence of local habitat characteristics and landscape context on sloth occurrence using mixed-effects logistic regression models. We recorded 27 sloths within countryside habitats and found that both local and landscape factors significantly influenced their spatial distribution. Locally, sloths favored structurally complex habitats, with greater canopy cover and variation in tree height and basal area. At the landscape scale, sloths demonstrated a preference for habitats with high proportions of forest and nearly large tracts of forest. Although mixed-use areas and tree plantations are not substitutes for protected forests, our results suggest they provide important supplemental habitats for sloths. To promote the conservation and long-term viability of sloth populations in the tropical countryside, we recommend for land managers to retain structurally complex vegetation and large patches of native habitat.
Data from: Nest fidelity is driven by multi-scale information in a long-lived seabird
Although the reproductive success of most organisms depends on factors acting at several spatial scales, little is known about how organisms are able to synthesize multi-scale information to optimize reproduction. Using longitudinal data from a long-lived seabird, Monteiro's storm-petrel, we show that average breeding success is strongly related to oceanic conditions at the population level, and we postulate that (i) individuals use proximal information (their own reproduction outcome in year t) to assess the qualities of their mate and nest and to decide to retain them or not in year t + 1; (ii) the intensity of these responses depends on the quality of the oceanic environment in year t, which affects the predictability of reproduction outcome in year t + 1. Our results confirm that mate and nest fidelities are higher following successful reproduction and that the relationship between the success of a given pair and subsequent nest fidelity is stronger in years with unfavourable oceanic conditions, suggesting that individuals rely on distant information to modulate their use of proximal information and adjust their breeding strategy.
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.