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26 results for “Multistability”
Dataset supplementing the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19.
<p>These files supplement the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19. The data are free for scientific use, provided this reference is appropriately cited.</p> <p>exp1_data.mat contains all the data of experiment 1 as cell arrays of size 8x16x8 (subject x block x trial) or 8x16 (subject x block). Specifically:<br> xEye: the horizontal eye position in raw (pixel coordinates)<br> gain: the OKN slow phase gain computed from the xEye data as described in the paper; in audio-visual blocks the sign is chosen such that positive gain corresponds to the direction of the grating associated with the low tone; in unambiguous visual blocks (1,16) positive sign corresponds to the direction of the grating.<br> ixLow, ixHigh, ixNone, ixBoth: indices for xEye and gain of the same subject and block for which the button corresponding to the low tone, the high tone, both buttons or no button was pressed.</p> <p>exp2_data.mat and exp3_data.mat contain the data of experiment 2 and experiment 3, respectively, and are organized analogously to exp1_data.mat.</p> <p>figure3.m through figure6.m use these data to plot the respective paper figures to exemplify usage of the data.</p> <p> </p>
Multistate modeling of Florida scrub-jay adult survival and breeding transitions
<p><strong>Metadata for data file “MS4APR2020 ALL ENTER 2 STATES.txt” for the research published in Ecosphere Article ECS21-0698</strong></p> <p>Multistate modeling of Florida scrub-jay adult survival and breeding transitions</p> <p>David R. Breininger<sup>1</sup>†, Geoffrey M. Carter<sup>1</sup>, Stephanie A. Legare<sup>1</sup> William V. Payne<sup>1</sup>, Eric D. Stolen<sup>1</sup>, Daniel J. Breininger<sup>2</sup>, James E. Lyon<sup>3</sup></p> <p><sup>1</sup>Herndon Solutions Group, LLC, NASA Environmental and Medical Contract, NEM-022, Kennedy Space Center, FL 32899, U.S.A.</p> <p><sup>2</sup>Department of Mathematics, Florida Institute of Technology, Melbourne FL, 32899 U.S.A.</p> <p><sup>3</sup>Merritt Island National Wildlife Refuge, Titusville FL 32901, U.S.A.</p> <p>†<em> Corresponding author: </em>e-mail: <a href="mailto:david.r.breininger@nasa.gov">david.r.breininger@nasa.gov</a></p> <p>The data uses input file format described in the Program MARK manual (White and Burnham 1999, Cooch and White 2006). Each record (row) starts with an adult bird’s capture history, followed by group variables (each bird belongs to only one group) and then covariates, which here are all time specific. Time starts with 2001 and ends with 2015. Periods “.” in the capture history represented birds that were censored because the study sites were discontinued. </p> <table> <tbody> <tr> <td> <p> </p> <p>The first 15 columns represent states from 2001 to 2015 where “I” refers to nonbreeder, “2” refers to breeder and “0” occurs when the bird is not observed.</p> </td> </tr> <tr> <td> <p>Spaces occur after the capture history and between group variables and covariates.</p> </td> </tr> <tr> <td> <p>The first group variable identifies males if given a "1".</p> </td> </tr> <tr> <td> <p>The second group variable identifies females if given a :1".</p> </td> </tr> <tr> <td> <p>The third and final group membership identifies birds of unknown sex if given a "1".</p> </td> </tr> <tr> <td> <p>All following data refer to time-specific covariates except for the semicolon at the end of each individual's record.</p> </td> </tr> <tr> <td> <p>The first 14 covariates refer to whether a bird resided on the study site edge (0) or interior (1).</p> </td> </tr> <tr> <td> <p>The 2nd set of 14 covariates refer density (number of pairs/potential pairs) within the local population.</p> </td> </tr> <tr> <td> <p>The 3rd set of 14 covariates refer to population breeder mortality rates (number of banded breeders that died/number of banded breeders).</p> </td> </tr> <tr> <td> <p>The final set of 14 covariates refer to mean family group size of adults in each local population.</p> </td> </tr> </tbody> </table>
Know what you don't know: Embracing state uncertainty in disease-structured multistate models
<p>Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark-recapture and dynamic multistate occupancy models are examples of HMMs that are frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity in state assignment. However, state uncertainty is a pervasive feature of many ecological systems, and multievent models were developed to explicitly account for it.</p> <p>We developed a novel extended multievent mark-recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease-structured example, both false-negative and false-positive state assignment errors are modeled at two levels of state assignment---the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as survival, and the pathogen detection processes. We provide code to simulate and analyze datasets with various underlying ecological processes and fit our model to a mark-recapture dataset of <em>Mixophyes fleayi</em> (Fleay's barred frog) infected with the amphibian chytrid fungus (<em>Batrachochytrium dendrobatidis</em>, <em>Bd</em>).</p> <p>In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting <em>Bd</em>, pathogen detection was highly dependent on infection intensity, and false-positives were non-negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4--5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model.</p> <p>Our results highlight that incorporating state assignment errors improves inference on the ecological state process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modeling state uncertainty in a range of related models. --</p>
Know what you don't know: Embracing state uncertainty in disease-structured multistate models
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Monitoring animal populations with cameras using open, multistate, N-mixture models
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Crop performance, aerial, and satellite data from multistate maize yield trials
<p>Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening hundreds and thousands of varieties, and policymakers in decisions contributing to the overall improvement of agriculture and food production systems. Effective, generalizable approaches to track plant growth and predict yield at the individual plot level require large matched datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional and field scales. Despite their lower spatial resolution, satellite platforms exhibit multiple logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments integrated with the ground truth measurements are not yet publicly available. Here we generated, described, and evaluated a set of more than 20,000 plot-level images of over 80 hybrid maize (Zea mays) varieties grown in six locations across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth measurements of crop yield. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing data collected from drones for both within-environment and cross-environment yield prediction. Large, multimodal, multi-environment, genetically diverse training datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important new addition to the tool of farmers, plant geneticists, crop breeders, and policymakers.</p>
Crop performance, aerial, and satellite data from multistate maize yield trials
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Assessing the effects of changes in reproductive condition on the survival of anadromous Dolly Varden (<em>Salvelinus malma</em>) using Bayesian multistate capture-recapture modelling
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Data and code for figures: Formation and Collision of Multistability-Enabled Composite Dissipative Kerr Solitons
<p>This dataset contains the data presented in the Figures of the paper <Formation and Collision of Multistability-Enabled Composite Dissipative Kerr Solitons>.</p>
Data from: Applying the multistate capture-recapture robust design to characterize metapopulation structure
1. Population structure must be considered when developing mark-recapture (MR) study designs as the sampling of individuals from multiple populations (or subpopulations) may increase heterogeneity in individual capture probability. Conversely, the use of an appropriate MR study design which accommodates heterogeneity associated with capture-occasion varying covariates due to animals moving between 'states' (i.e. geographic sites) can provide insight into how animals are distributed in a particular environment and the status and connectivity of subpopulations. 2. The Multistate Closed Robust Design was chosen to investigate: 1) the demographic parameters of Indo-Pacific bottlenose dolphins (Tursiops aduncus) subpopulations in coastal and estuarine waters of Perth, Western Australia; and 2) how they are related to each other in a metapopulation. Using four years of year-round photo-identification surveys across three geographic sites, we accounted for heterogeneity of capture probability based on how individuals distributed themselves across geographic sites and characterized the status of subpopulations based on their abundance, survival and interconnection. 3. MSCRD models highlighted high heterogeneity in capture probabilities and demographic parameters between sites. High capture probabilities, high survival and constant abundances described a subpopulation with high fidelity in an estuary. In contrast, low captures, permanent and temporary emigration and fluctuating abundances suggested transient use and low fidelity in an open coastline site. 4. Estimates of transition probabilities also varied between sites, with estuarine dolphins visiting sheltered coastal embayments more regularly than coastal dolphins visited the estuary, highlighting some dynamics within the metapopulation. 6. Synthesis and applications. To date, bottlenose dolphin studies using mark-recapture approach have focussed on investigating single subpopulations. Here, in a heterogeneous coastal-estuarine environment, we demonstrated that spatially structured bottlenose dolphin subpopulations contained distinct suites of individuals and differed in size, demographics and connectivity. Such insights into the dynamics of a metapopulation can assist in local-scale species conservation. The MSCRD approach is applicable to species/populations consisting of recognizable individuals and is particularly useful for characterizing wildlife subpopulations that vary in their vulnerability to human activities, climate change or invasive species.
Data from: Applying the multistate capture-recapture robust design to characterize metapopulation structure
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Data from: A multistate dynamic site occupancy model for spatially aggregated sessile communities
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Data from: How Ebola impacts social dynamics in gorillas: a multistate modelling approach.
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Multistate hybrid time-dependent density functional theory with surface hopping accurately captures ultrafast thymine photodeactivation
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Spatially anonymized data for: Multistate Ornstein-Uhlenbeck approach for practical estimation of movement and resource selection around central places
<p>1. Home range dynamics and movement are central to a species' ecology and strongly mediate both intra- and interspecific interactions. Numerous methods have been introduced to describe animal home ranges, but most lack predictive ability and cannot capture effects of dynamic environmental patterns, such as the impacts of air and water flow on movement.</p> <p>2. Here, we develop a practical, multi-stage approach for statistical inference into the behavioral mechanisms underlying how habitat and dynamic energy landscapes---in this case how airflow increases or decreases the energetic efficiency of flight---shape animal home ranges based around central places. We validated the new approach using simulations, then applied it to a sample of 12 adult golden eagles (Aquila chrysaetos) tracked with satellite telemetry. </p> <p>3. The application to golden eagles revealed effects of habitat variables that align with predicted behavioral ecology. Further, we found that males and females partition their home ranges dynamically based on uplift. Specifically, changes in wind and sun angle drove differential space use between sexes, especially later in the breeding season when energetic demands of growing nestlings require both parents to forage more widely. </p> <p>4. This method is easily implemented using widely available programming languages and is based on a hierarchical multistate Ornstein-Uhlenbeck space use process that incorporates habitat and energy landscapes. The underlying mathematical properties of the model allow straightforward computation of predicted utilization distributions, permitting estimation of home range size and visualization of space use patterns under varying conditions.</p>
Data from: Life-history multistability caused by size-dependent mortality
Body size is a key determinant of mortality risk. In natural populations, a broad range of relationships are observed between body size and mortality, including positive and negative correlations. Previous evolutionary modelling has shown that negatively size-dependent mortality can result in life-history bistability, with early maturation at small size and late maturation at large size representing alternative fitness optima. Here we present a general analysis of conditions under which such life-history bistabilities can occur, reporting the following findings. First, alternative fitness optima can be found for any arbitrarily chosen forms of mortality functions, including functions according to which mortality smoothly declines with size. Second, while bistabilities occur more readily under negatively size-dependent mortality, our analysis reveals that they can also emerge under positively size-dependent mortality, a feature missed in earlier work. Third, any sharp drop of mortality with size facilitates bistability. Fourth, if the mortality regime involves more than one such sharp drop, multistable life histories can occur, with alternative fitness optima straddling each of the drops. Paradoxically, our findings imply that, fifth, a species-poor predator community capable of creating a 'rugged' mortality regime is conducive to evolutionary multistability, which could act as a stepping stone toward prey life-history diversification, whereas a species-rich predator community that results in a smoothly varying mortality regime may prevent diversification through this pathway.
Data from: Generalized Frequency Coding: A Method of Preparing Polymorphic Multistate Characters for Phylogenetic Analysis
A new method of coding polymorphic multistate characters for phylogenetic analysis is presented. By dividing such characters into subcharacters, their frequency distributions can be represented with discrete states. Differential weighting is employed to counter the effect of using multiple characters to represent one character. The new method, termed generalized frequency coding (GFC), is potentially superior to previously used methods in that it incorporates more information and can be applied to both qualitative and quantitative characters. The method was applied to a previously published data set that includes both types of polymorphic multistate characters, and performed well according to congruence with other studies and the g1 and nonparametric bootstrap statistics. The data set was also used to compare GFC to both gap-weighting and Manhattan distance step matrix coding. On these grounds and for philosophical reasons, GFC was found to be a better estimator of phylogeny.
Data from: Generalized Frequency Coding: A Method of Preparing Polymorphic Multistate Characters for Phylogenetic Analysis
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Spatially anonymized data for: Multistate Ornstein-Uhlenbeck approach for practical estimation of movement and resource selection around central places
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Data from: Life-history multistability caused by size-dependent mortality
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Allen Brain Atlas
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International Brain Laboratory public data
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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.