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76 results for “behavioral state”
Sex- and state-dependent covariation of risk-averse and escape behavior in a widespread lizard
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Data from: Whole-organism behavioral profiling reveals a role for dopamine in state-dependent motor program coupling in C. elegans.
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Behavioral responses across a mosaic of ecosystem states restructure a sea otter-urchin trophic cascade
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On the Understandability of Language Constructs to Structure the State and Behavior in Abstract State Machine Specifications: A Controlled Experiment
<p>Data-Set and Artifacts: Documents, Forms, and R Scripts for Reproducibility of the Empirical Study</p>
Data from: Resource levels and prey state influence antipredator behavior and the strength of nonconsumptive predator effects
The risk of predation can drive trophic cascades by causing prey to engage in antipredator behavior (e.g. reduced feeding), but these behaviors can be energetically costly for prey. The effects of predation risk on prey (nonconsumptive effects, NCEs) and emergent indirect effects on basal resources should therefore depend on the ecological context (e.g. resource abundance, prey state) in which prey manage growth/predation risk tradeoffs. Despite an abundance of behavioral research and theory examining state-dependent responses to risk, there is a lack of empirical data on state-dependent NCEs and their impact on community-level processes. We used a rocky intertidal food chain to test model predictions for how resources levels and prey state (age/size) shape the magnitude of NCEs. Risk cues from predatory crabs (Carcinus maenas) caused juvenile and sub-adult snails (Nucella lapillus) to increase their use of refuge habitats and decrease their growth and per capita foraging rates on barnacles (Semibalanus balanoides). Increasing resource levels (high barnacle density) and prey state (sub-adults) enhanced the strength of NCEs. Our results support predictions that NCEs will be stronger in resource-rich systems that enhance prey state and suggest that the demographic composition of prey populations will influence the role of NCEs in trophic cascades. Contrary to theory, however, we found that resources and prey state had little to no effect on snails in the presence of predation risk. Rather, increases in NCE strength arose because of the strong positive effects of resources and prey state on prey foraging rates in the absence of risk. Hence, a common approach to estimating NCE strength – integrating measurements of prey traits with and without predation risk into a single metric – may mask the underlying mechanisms driving variation in the strength and relative importance of NCEs in ecological communities.
Data from: Behavioral evidence for fruit odor discrimination and sympatric host races of Rhagoletis pomonella flies in the western United States
The recent shift of Rhagoletis pomonella (Diptera: Tephritidae) from its native host downy hawthorn, Crataegus mollis, to introduced domesticated apple, Malus domestica, in the eastern U.S. is a model for sympatric host race formation. However, the fly is also present in the western U.S., where it may have been introduced via infested apples within the last 60 years. In addition to apple, R. pomonella also infests two hawthorns in the West, one the native black hawthorn, C. douglasii, and the other the introduced English ornamental hawthorn, C. monogyna. Here, we test for behavioral evidence of host races in the western U.S. through flight tunnel assays of western R. pomonella flies to host fruit volatile blends. We report that western apple, black hawthorn, and ornamental hawthorn flies showed significantly increased levels of upwind directed flight to their respective natal compared to non-natal fruit volatile blends, consistent with host race status. We discuss the implications of the behavioral results for the origin(s) of western R. pomonella, including the possibility that western apple flies were not introduced, but may represent a recent shift from local hawthorn fly populations.
Data from: On the scent of standing variation for speciation: behavioral evidence for native sympatric host races of Rhagoletic pomonella (Diptera: Tephritidae) in the southern United States
Standing variation can be critical for speciation. Here, we investigate the origins of fruit odor discrimination for Rhagoletis pomonella underlying the fly's sympatric shift in the northeastern U.S. from downy hawthorn (Crataegus mollis) to apple (Malus domestica). Because R. pomonella mate on host fruit, preferences for natal fruit volatiles generate prezygotic isolation. Apples emit volatiles that appear to be missing from gas chromatography/electroantennographic detection profiles for flies infesting downy hawthorns, raising the question of how R. pomonella evolved a preference for apple. In the southern U.S., R. pomonella attacks several native hawthorns. Behaviorally active volatile blends for R. pomonella infesting southern hawthorns contain the missing apple volatiles, potentially explaining why downy hawthorn flies could have evolved to be sensitive to a blend of apple volatiles. Flight tunnel assays imply that southern hawthorn populations were not the antecedent of a preassembled apple race, as southern flies were not attracted to the apple volatile blend. Instead, behavioral evidence was found for southern host races on native hawthorns, complementing the story of the historical sympatric shift to introduced apple in the North and illustrating how R. pomonella may evolve novel combinations of agonist and antagonist responses to volatiles to utilize new fruit resources.
Data from: State-dependent behavior alters endocrine-energy relationship: implications for conservation and management
Glucocorticoids (GC) and triiodothyronine (T3) are two endocrine markers commonly used to quantify resource limitation, yet the relationships between these markers and the energetic state of animals has been studied primarily in small-bodied species in captivity. Free-ranging animals, however, adjust energy intake in accordance with their energy reserves, a behavior known as state-dependent foraging. Further, links between life-history strategies and metabolic allometries cause energy intake and energy reserves to be more strongly coupled in small animals relative to large animals. Because GC and T3 may reflect energy intake or energy reserves, state-dependent foraging and body size may cause endocrine-energy relationships to vary among taxa and environments. To extend the utility of endocrine markers to large-bodied, free-ranging animals, we evaluated how state-dependent foraging, energy reserves, and energy intake influenced fecal GC and fecal T3 concentrations in free-ranging moose (Alces alces). Compared with individuals possessing abundant energy reserves, individuals with few energy reserves had higher energy intake and high fecal T3 concentrations, thereby supporting state-dependent foraging. Although fecal GC did not vary strongly with energy reserves, individuals with higher fecal GC tended to have fewer energy reserves and substantially greater energy intake than those with low fecal GC. Consequently, individuals with greater energy intake had both high fecal T3 and high fecal GC concentrations, a pattern inconsistent with previous documentation from captive animal studies. We posit that a positive relationship between GC and T3 may be expected in animals exhibiting state-dependent foraging if GC is associated with increased foraging and energy intake. Thus, we recommend that additional investigations of GC- and T3-energy relationships be conducted in free-ranging animals across a diversity of body size and life-history strategies before these endocrine markers are applied broadly to wildlife conservation and management.
Data from: A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates
Integrating behavior and physiology is critical to formulating new hypotheses on the evolution of animal life-history strategies. Migratory capital breeders acquire most of the energy they need to sustain migration, gestation and lactation before parturition. Therefore, when predicting the impact of environmental variation on such species, a mechanistic understanding of the physiology of their migratory behavior is required. Using baleen whales as a model system, we developed a dynamic state variable model that captures the interplay among behavioral decisions, energy, reproductive needs and the environment. We applied the framework to blue whales (Balaenoptera musculus) in the Eastern North Pacific Ocean, and explored the effects of environmental and anthropogenic perturbations on female reproductive success. We demonstrate the emergence of migration to track prey resources, enabling us to quantify the trade-offs among capital breeding, body condition, and metabolic expenses. We predict that periodic climatic oscillations affect reproductive success less than unprecedented environmental changes do. The effect of localized, acute anthropogenic impacts depended on whales' behavioral response to the disturbance; chronic, but weaker, disturbances had little effect on reproductive success. Because we link behavior and vital rates by modeling individuals' energetic budgets, we provide a general framework to investigate the ecology of migration and assess the population consequences of disturbance, while identifying critical knowledge gaps.
State-dependent mortality, not behavior, fragments population distribution of a long-lived mammal after ecological disturbance
<p>Data and code used to produce figures and analyses for the peer-reviewed paper "State-dependent mortality, not behavior, fragments population distribution of a long-lived mammal after ecological disturbance". All demographic and GPS data were collected by the authors as part of the Wyoming Range Mule Deer Project, and spatial data were derived from publicly available GIS platforms. </p> <p>Questions about data and code can be directed to rraffer1@uwyo.edu and tlasharr@uwyo.edu.</p> <p> </p>
Data for behavioral state-dependent habitat selection analysis of translocated female greater sage-grouse, North Dakota 2018-2020
<p>This dataset is associated with the article, "Behavioral state-dependent habitat selection and implications for animal translocations" (Picardi et al., 2021, Journal of Applied Ecology).</p> <p>Post-release monitoring of translocated animals can be used to inform future translocation protocols. In particular, quantifying habitat selection of translocated individuals may help identify features that characterize suitable settlement habitat and inform the choice of future release sites; however, because translocated animals undergo post-release behavioral modification, the underlying behavioral state needs to be taken into account.</p> <p>We analyzed behavioral state-dependent habitat selection in female greater sage-grouse (<i>Centrocercus urophasianus</i>) translocated from Wyoming to North Dakota, USA, using Hidden Markov Models in combination with Integrated Step Selection Analysis. This dataset includes GPS tracks (resampled at a 6-hour resolution using a Continuous Time Movement Model) for 48 individuals translocated between 2018 and 2020, along with environmental variables associated with each location. We segmented each track into behavioral phases corresponding to an exploratory state, characterized by broad and directed movements, and a restricted state, characterized by short and tortuous movements. Then, we quantified habitat selection in each state while also accounting for seasonality and individual reproductive status.</p> <p>Habitat selection of translocated sage-grouse differed between the post-release exploration and the settlement phase. While in the exploratory state, sage-grouse exhibited natal habitat preference induction by selecting for high sagebrush cover, typical of their natal area but not of the release area. In the restricted state, sage-grouse selected for gentle topography and also adjusted their habitat selection based on the season and their reproductive needs.</p>
Active neural coordination of motor behaviors with internal states
<p>The brain must continuously coordinate skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time-series, we found that information flowing from the brain to movements and heart rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brain's dynamics actively and efficiently coordinate motor behavior with internal physiology.</p>
Caution is warranted when using animal space-use and movement to infer behavioral states
<p><strong>Background:</strong> Identifying the behavioral state for wild animals that can't be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals.</p> <p><strong> Methods:</strong> Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events.</p> <p><strong> Results:</strong> For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual's UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events.</p> <p><strong> Conclusions:</strong> Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.</p>
Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality [Dataset]
<p>The calcium imaging data of the telencephalon of head-tethered adult zebrafish during GO/NOGO tasks in the virtual reality environment and the behavior data were deposited.</p> <p>The codes to process the neural activity data by calcium imaging to perform Non-negative Matrix Factorization </p> <p>For details, see "Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality" Torigoe et al., Nature Communications in press.</p>
A dataset on the physiological state and behavior of drivers in conditionally automated driving
<p>A detailed description of the data collected, the experimental design, materials and methods used in each experiment, and the references associated with this work can be found in the manuscript published in the journal Data in Brief, available in Open Access <a href="https://doi.org/10.1016/j.dib.2023.109027">here</a><strong>.</strong> <strong>Please cite this publication if you use the dataset.</strong></p> <p><strong>Reference : </strong>Quentin Meteier, Marine Capallera, Emmanuel de Salis, Leonardo Angelini, Stefano Carrino, Marino Widmer, Omar Abou Khaled, Elena Mugellini, Andreas Sonderegger. A dataset on the physiological state and behavior of drivers in conditionally automated driving, Data in Brief, 2023, 109027, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2023.109027">https://doi.org/10.1016/j.dib.2023.109027</a>.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This is a set of physiological and behavioural data collected in 6 fixed-base driving simulator experiments from 346 drivers. The data was collected as part of the AdVitam project (for Adaptive Driver-Vehicle Interaction to Make future driving safer), a joint research project funded by the Hasler Foundation, led by the Human Tech Institute (HEIA-FR), the He-Arc, the EPFL+ECAL Lab, and the University of Fribourg (Switzerland).</p> <p>All experiments simulated conditional automation (Level 3 of automation according to the taxonomy released by the <a href="https://www.sae.org/standards/content/j3016_202104/">Society of Automotive Engineers</a>), except for experiment 1 (Level 0 - manual driving).</p> <p>Each folder contains raw and preprocessed data collected in each experiment:</p> <ul> <li>Exp1: Experimental manipulation of relaxation before driving and presence of passenger while driving (manual driving)</li> <li>Exp2: Experimental manipulation of cognitive workload at 2 levels using a verbal task (backwards counting)</li> <li>Exp3: Experimental manipulation of cognitive workload at 3 levels using visual and auditory tasks (N-back task)</li> <li>Exp4: Experimental manipulation of fatigue (sleep deprivation) and driving environment (rural vs. urban scenario)</li> <li>ExpTOR: Multiple takeovers requested through different modalities (visual, auditory, haptic), while performing different non-driving related tasks</li> <li>ExpFinal: Testing a contextual multimodal system for maintaining situation awareness and takeover quality in conditionally automated driving</li> </ul> <p>Physiological data (Electrocardiogram, electrodermal activity and respiration) were collected in all experiments. They were preprocessed in Python with the <a href="https://github.com/neuropsychology/NeuroKit">Neurokit</a> library. It was also used to process the physiological raw signals and calculate a large range of physiological indicators.</p> <p>Some of these measures were also collected in the various experiments: situation awareness, takeover performance (reaction, time, maximum steering wheel angle, ..), affective state, mental workload, non-driving-related task performance, sleepiness..</p> <p>More details on each experiment are provided in the ExpX_README.md of each folder.</p>
Mouse spontaneous behavior reflects individual variation rather than estrous state
<p>Repository containing datasets obtained for Levy et al. 2023, available online here: https://doi.org/10.1016/j.cub.2023.02.035</p> <p>Github link to related analysis code: <a href="https://github.com/dattalab/spontaneous-behavior-reflects-individuality-not-estrous">https://github.com/dattalab/spontaneous-behavior-reflects-individuality-not-estrous</a></p> <p><strong>Abstract</strong></p> <p>Behavior is shaped by both the internal state of an animal and its individual behavioral biases. Rhythmic variation in gonadal hormones during the estrous cycle is a defining feature of female internal state, one that regulates many aspects of sociosexual behavior. However, it remains unclear whether estrous state influences spontaneous behavior, and if so how these effects might relate to individual behavioral variation. Here we address this question by longitudinally characterizing the open field behavior of female mice across different phases of the estrous cycle, using unsupervised machine learning to decompose spontaneous behavior into its constituent elements1-4. We find that each female mouse exhibits a characteristic pattern of exploration that uniquely identifies it as an individual across many experimental sessions; in contrast, estrous state only negligibly impacts behavior, despite its known effects on neural circuits that regulate action selection and movement. Like female mice, male mice exhibit individual specific patterns of behavior in the open field; however, the exploratory behavior of males is significantly more variable than that expressed by females both within and across individuals. These findings suggest an underlying functional stability to the circuits that support exploration in female mice, reveal a surprising degree of specificity in individual behavior, and provide empirical support for the inclusion of both sexes in experiments querying spontaneous behaviors.</p> <p><strong>Behavioral DataFrames:</strong> holds the behavioral syllable data used for the analysis described in the manuscript:</p> <ul> <li>mean_df_female / scalar_df_female - main female dataset</li> <li>mean_df_male / scalar_df_male - main male dataset</li> <li>mean_df_female_control / mean_df_male_control - datasets for male/female control experiment presented in Fig S3.</li> </ul> <p><strong>Model files:</strong></p> <p>Additional MoSeq model files used for generating FigS2A and FigS2H. </p>
Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023</p> <p>Please download the data_v2.zip file, which contains the most updated and complete version of the data.</p> <p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Will a Real Time Advisory for Double Low State Change Clinical Behavior and Impact Outcomes
ClinicalTrials.gov study NCT01545596. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Role of Short-chain Fatty Acids in Malnutrition on Stress, Eating Behavior, and Nutritional State
ClinicalTrials.gov study NCT06064201. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: State-dependent behavior alters endocrine-energy relationship: implications for conservation and management
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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.