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67 results for “movement modeling”
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.
Predicting readers' prototypical eye-movement behavior using MASC, a model of Attention in the Superior Colliculus: Stimulus materials, model code, data, and statistical analyses.
<p>The goal of the present research was to determine the role of rudimentary visuo-motor pathways, from the retina and the primary visual cortex to the superior colliculus (SC), in the guidance of human eye movement during reading. To this end, we used MASC, our model of Attention in the Superior Colliculus (Adeli et al., Journal of Neuroscience 2017), a model that relies on well-established saccade-programming principles in the SC. MASC predicts sequences of fixations over an input image by spatially integrating incoming signals in the space of the SC.</p> <p>Here, MASC computed the distribution of luminance contrast over sentences' images (visual-saliency map), after blurring it proportional to retinal eccentricity (retina transformation). It then projected the visual-saliency map into SC space, using a logarithmic afferent-mapping function (magnification factor). Input signals were averaged over retinotopically organized populations of neurons (point images) of constant size, first in the visual map and then in a spatially-registered motor map. The most active population was identified through a winner-take-all process. After jitter applied to the winning population, the next fixation location was determined using inverse efferent mapping. This sequence of events was then repeated to predict following fixation locations, but inserting after each saccade an inhibitory spatial tag (Inhibition of Saccade Return; ISR -referred to as IOR in the uploaded files). All MASC's parameters, but one, were biologically determined, using electrophysiological data in macaque; the ISR window was the one fit parameter.</p> <p>MASC was tested by comparing its predicted sequences of fixations over sentences from the French-Sentence Corpus (FSC) to the eye-movement behavior of 40 French-native speakers reading the same sentences for comprehension (Albrengues et al., Plos One 2019). Then, MASC was dissected to determine the crucial processing steps enabling prediction of human behavior (10 comparison models -see the general README file). Finally, to address crucial issues in the reading literature, i.e., the role of inter-word spacing and character print size in eye-movement guidance, MASC was additionally tested in four additional display conditions: the same sentences from the FSC, but with blank spaces between words being either filled or removed, or with the screen width angle being multiplied by 2 or 4, such that characters were larger in angular size (0.5° and 1°) than in the original experiment (0.25°). MASC's predicted effects of inter-word spacing and print size were compared to previously published data.</p> <p>All material relevant to the project is reported here, including the FSC materials (bitmap and information text files), the Matlab code for our MASC model, raw simulation data for MASC and all our comparison models, as well as MASC's simulations in the different display conditions, the scripts we developed in R to transform raw simulation data into data matrices for statistical analyses of (word-based) eye-movement behavior, the resulting data matrices for all models as well as the data matrix for FSC readers, the R-scripts for statistical comparison of oculomotor behavior between data sets and conditions, literature-review tables of previously published data (for comparison with MASC's predictions), and the R-scripts generating the figures summarizing our results.</p> <p>Further information can be found in the general README file as well as in the README files attached to each folder. The authors' respective contributions to the project, the licence attached to the included materials and their condition of use are listed in the general README file.</p> <p>A manuscript reporting and discussing these modeling data is in preparation (Vitu, F., Adeli, H. & Zelinsky, G. J.); A reference will be provided here when the manuscript appears in a journal.</p> <p>Other references to be cited:</p> <p>- For the model code: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467. http://www.jneurosci.org/content/37/6/1453</p> <p>- For FSC materials and data: Albrengues, C., Lavigne, F., Aguilar, C., Castet, E., & Vitu, F. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE 14(7): e0219666. https://doi.org/10.1371/journal.pone.0219666<br> </p>
Model of a machine tool with relative movements
<p>Matrices for a numerical model of a machine tool with different components moving relatively to each other during a simulated work process.</p> <p>For usage see RUNME.m and the <a href="http://modelreduction.org/index.php/Machine_tool_with_relative_movements">MOR Wiki</a>.</p>
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
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Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
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Data from: Ultrasound evaluation of the combined effects of thoracolumbar fascia injury and movement restriction in a porcine model
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Data from: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator
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Modeling and forecasting art movements with CGANs
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Data from: Analyzing movement behavior and dynamic space-use strategies among habitats using multi-event capture-recapture modeling
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Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
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Data from: Modeling connectivity to identify current and future anthropogenic barriers to movement of large carnivores: a case study in the American Southwest
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Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19
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Data from: Modelling the functional link between movement, feeding activity and condition in a marine predator
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Monte-Carlo Simulation Models of Animal Movement
This dataset consists of a series of computer programs in the PASCAL language that implement a simple model of animal movement. Results of model use were published in: Porter, J.H. and J.L. Dooley, Jr. 1993. Animal dispersal patterns: a reassessment of simple mathematical models. Ecology 74:2436-2443. MOVESIM.PAS, MOVESV1.PAS and MOVESV21.PAS are the model. Input is a set of coordinates for sampling points. The model randomly selects a starting point, then selects a random distance and direction. The distance to the nearest sampling point is then calculated and the process is repeated. There are a number of associated programs. MOVESTAT.SAS - SAS program to list frequency histogram for MOVESIM output. RANDGEN.PAS - Generate a set of random sampling stations within a given area. FITIT.PAS - Estimate fit of different movement models to output. ADJUST1.PAS - Estimate the number of individuals that would have been caught at a given distance if sample points had been evenly distributed.
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: Space-use behavior of woodland caribou based on a cognitive movement model
1. Movement patterns offer a rich source of information on animal behaviour and the ecological significance of landscape attributes. This is especially useful for species occupying remote landscapes where direct behavioural observations are limited. In this study, we fit a mechanistic model of animal cognition and movement to GPS positional data of woodland caribou (Rangifer tarandus caribou; Gmelin 1788) collected over a wide range of ecological conditions. 2. The model explicitly tracks individual animal informational state over space and time, with resulting parameter estimates that have direct cognitive and ecological meaning. Three biotic landscape attributes were hypothesized to motivate caribou movement: forage abundance (dietary digestible biomass), wolf (Canis lupus; Linnaeus, 1758) density and moose (Alces alces; Linnaeus, 1758) habitat. Wolves are the main predator of caribou in this system and moose are their primary prey. 3. Resulting parameter estimates clearly indicated that forage abundance is an important driver of caribou movement patterns, with predator and moose avoidance often having a strong effect, but not for all individuals. From the cognitive perspective, our results support the notion that caribou rely on limited sensory inputs from their surroundings, as well as on long-term spatial memory, to make informed movement decisions. Our study demonstrates how sensory, memory and motion capacities may interact with ecological fitness covariates to influence movement decisions by free-ranging animals.
Data from: Density-dependent movement and the consequences of the Allee effect in the model organism Tetrahymena
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Data from: Space-use behavior of woodland caribou based on a cognitive movement model
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Data from: A generalized residual technique for analyzing complex movement models using earth mover's distance
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Data from: Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics
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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.