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67 results for “movement modeling”
The role of chloroplast movement in C4 photosynthesis: A theoretical analysis using a 3-D reaction-diffusion model for maize
<p>Chloroplast movement within mesophyll (M) cells in C<sub>4</sub> plants is hypothesized to enhance the CO<sub>2</sub> concentrating mechanism (CCM), but this is difficult to verify experimentally. A three-dimensional (3-D) leaf model can help analyze how chloroplast movement influences the operation of CCM. The first volumetric reaction-diffusion model of C<sub>4</sub> photosynthesis that incorporates: detailed 3-D leaf anatomy, light propagation, ATP and NADPH production and CO<sub>2</sub>, O<sub>2</sub> and bicarbonate concentration driven by diffusional and assimilation/emission processes, was developed and implemented for maize leaves to simulate various chloroplast movement scenarios within M cells: the movement of all M chloroplasts towards bundle-sheath (BS) cells (aggregative movement) and movement of only those of interveinal M cells towards BS cells (avoidance movement). Light absorbed by bundle-sheath (BS) chloroplasts relative to M chloroplasts increased in both cases. Avoidance movement decreased light absorption by M chloroplasts considerably. Consequently, total ATP and NADPH production and net photosynthesis rate increased for aggregative movement and decreased for avoidance movement case compared to the default case of no chloroplast movement at high light intensities. Leakiness increased in both chloroplast movement scenarios due to the imbalance in energy production and demand in M and BS cells. These results suggest the need to design strategies for coordinated increases in electron transport and Rubisco activities for an efficient CCM at very high light intensities.</p>
Movements during sleep reveal the developmental emergence of a cerebellar-dependent internal model in motor thalamus
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The role of chloroplast movement in C4 photosynthesis: A theoretical analysis using a 3-D reaction-diffusion model for maize
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Data from: Climate-mediated hybrid zone movement revealed with genomics, museum collection and simulation modeling
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From pup to predator; generalized hidden Markov models reveal rapid development of movement strategies in a naïve long‐lived vertebrate
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Data from: Exploring movement decisions: can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)?
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
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Targeting fin whale conservation in the North-Western Mediterranean Sea: Insights on movements and behaviour from biologging and habitat modelling
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Modelling connectivity at a regional scale during seasonal movements of the greater horseshoe bat
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Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
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Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
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Data from: Accounting for movement in spatial surplus production models: A case study of redfish on the Eastern Grand Banks of Newfoundland
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Data from: Changing measurements or changing movements? Sampling scale and movement model identifiability across generations of biologging technology
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A reproducible model for magnetosensitivity: earthworms in transparent soil reduce their cumulative movement in extremely weak magnetic field
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Combining bioenergetics and movement models to improve understanding of the population consequences of disturbance
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Data from: Lessons from movement ecology for the return to work: modeling contacts and the spread of COVID-19
<p>Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts.</p>
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: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator
1.Time series of animal movement obtained from bio-loggers are becoming widely used across all taxa. These data are nowadays of high quality, combining high resolution with precision, as the tags are able to collect for longer times and store larger quantities of data. Due to their nature, high-frequency data sequences often pose non-trivial problems in time series analysis: non-linearity, non-Normality, non-stationarity, and long memory. These issues can be tackled by modelling the data sequence as a realization of a stochastic regime switching process. 2. We suggest a novel Markov switching autoregressive model where the hidden Markov chain is non-homogeneous, with time-varying transition probabilities, whose dynamics depend on the dynamics of some contemporary categorical covariates. 3. To illustrate the use of the method, we apply it to the depth profiles of four individuals of flapper skate (Dipturus cf. intermedia) in order to identify swimming behaviours. Individual time series were obtained from data storage tags that recorded pressure every two minutes. The environmental covariates used were lunar phase (a proxy for the spring-neap tidal cycle), lunar cycle, and diel cycle. For all individuals two states (or regimes) were always selected (the autoregressive order was either three or four), representing different regimes of animal activity, i.e., state 1 for resting or horizontal swimming or slow vertical movement; state 2 for fast ascending and descending. The cycle of the four lunar phases was the only environmental covariate that explained the hidden state dynamics in all individuals, whereas lunar cycle was selected for two individuals and diel cycle for one only. 4. The method is an efficient approach to fit one-dimensional tag data using categorical environmental covariates, and to classify the observations into a small number of states representing individual behaviours of tagged individuals.
Data from: Analyzing movement behavior and dynamic space-use strategies among habitats using multi-event capture-recapture modeling
1.The environment of most species is heterogeneous at different spatial and temporal scales; this heterogeneity can have a direct effect on various components of fitness. As a consequence, individual space-use and movement strategies are central issues in ecology and conservation and receive considerable attention from researchers. 2.In the last 30 years, this issue has led to the development of capture–recapture models that allow movement between sites to be quantified, while handling imperfect detection. For studies involving numerous recapture sites in which the emphasis is on dispersal or migration rather than movement between particular sites, Lagrange et al. recently proposed a parsimonious CR multi-event model that contrasts individuals that move and individuals that stay in place, irrespective of the sites involved. 3.In this study, we developed a generalized version of this model to allow survival probability and movement probability to differ for different types of habitat to which the individual sites may be assigned. We investigated the potential of this new parameterization by studying the movements of an amphibian, the yellow-bellied toad (Bombina variegata), in a set of breeding and resting/foraging ponds. 4.Our capture-recapture multi-event model provides a highly flexible tool allowing users to model movements within and between several habitats. This approach can be potentially used to study movement behavior and space-use strategies of a wide range of taxa.
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
<p>Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.</p>
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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.