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512 results for “Activity pattern”
When death comes: Linking predator-prey activity patterns to timing of mortality to understand predation risk
<p>The assumption that activity and foraging are risky for prey underlies many predator-prey theories and has led to the use of predator-prey activity overlap as a proxy of predation risk. However, the simultaneous measures of prey and predator activity along with timing of predation required to test this assumption have not been available. Here, we used accelerometry data on snowshoe hares (<em>Lepus</em> <em>americanus</em>) and Canada lynx (<em>Lynx canadensis)</em> to determine activity patterns of prey and predators and match these to precise timing of predation. Surprisingly, we found that lynx kills of hares were as likely to occur during the day when hares were inactive as at night when hares were active. We also found that activity rates of hares were not related to the chance of predation at daily and weekly scales, whereas lynx activity rates positively affected the diel pattern of lynx predation on hares and their weekly kill rates of hares. Our findings suggest that predator-prey diel activity overlap may not always be a good proxy of predation risk, and highlight a need for examining the link between predation and spatiotemporal behavior of predator and prey to improve our understanding of how predator-prey behavioral interactions drive predation risk.</p>
Data from: Fission-fusion dynamics in sheep: The influence of resource distribution and temporal activity patterns
<p><span>Fission-fusion events, i.e. changes to the size and composition of animal social groups, are a mechanism to adjust the social environment in response to short-term changes in the cost-benefit ratio of group living. Furthermore, the time and location of fission-fusion events provide insight into the underlying drivers of these dynamics. Here, we describe a method for identifying group membership over time and for extracting fission-fusion events from animal tracking data. We applied this method to high-resolution GPS data of free-ranging sheep (<em>Ovis aries</em>). Group size was highest during times when sheep typically rest (mid-day and at night), and when anti-predator benefits of grouping are high while costs of competition are low. Consistent with this, fission and fusion frequencies were highest during early morning and late evening, suggesting that social restructuring occurs during periods of high activity. However, fission and fusion events were not more frequent near food patches and water resources when adjusted for overall space use. This suggests a limited role of resource competition. Our results elucidate the dynamics of grouping in response to social and ecological drivers, and we provide a tool for investigating these dynamics in other species.</span></p>
Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation
<p>Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation</p> <p>Megan E. Young, Camille Spencer-Salmon, Clayton Mosher, Sarita Tamang, Kanaka Rajan, and Peter H. Rudebeck</p> <p>This dataset contains peripheral physiology (heart rate) and single neuron activity data from the paper entitled “Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation” by Young, Spencer-Salmon and colleagues.</p> <p>The study investigated how neurons in macaque subcallosal anterior cingulate cortex, basolateral amygdala, and rostromedial striatum encoded anticipated reward during Pavlovian and instrumental tasks.</p> <p>Heart rate data were pre-processed using methods described in the paper and were downsampled to 50 Hz for analysis. Neural activity data were pre-processed using steps as described in the paper.</p> <p>The dataset is saved as .MAT files.</p> <p>Files included:</p> <p>‘Pavlovian_task_neurons.mat’ – single neuron data from the Pavlovian task. Each of the 656 rows represents a single neuron and its associated information.</p> <p>‘Instrumental_task_neurons.mat’ – single neuron data from the instrumental task. Each of the 425 rows represents a single neuron and its associated information.</p> <p>“heart_rate.mat” – heart rate data from monkeys D and H.</p> <p><br> File structure and information:</p> <p>Pavlovian_task_neurons.mat</p> <p>Structure “Pavlovian_task_neurons”<br> - “Pavlovian_task_neurons.unit_name” – neuron specific identifier<br> - “Pavlovian_task_neurons.monkeynumber” – subject specific #<br> - “Pavlovian_task_neurons.monkeyname” – subject specific name<br> - “Pavlovian_task_neurons.date” – date on which data were recorded<br> - “Pavlovian_task_neurons.session” – session identifier from date (a-d)<br> - “Pavlovian_task_neurons.channel” – recording channel data recorded from<br> - “Pavlovian_task_neurons.wavemark” – waveform number (a-e)<br> - “Pavlovian_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “Pavlovian_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “Pavlovian_task_neurons.condition” – condition # from Monkey Logic for each of the trials (1 by n trials)<br> - “Pavlovian_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “Pavlovian_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)<br> - “Pavlovian_task_neurons.stimID” – stimulus shown on that trial (1 = neutral, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).</p> <p><br> Instrumental_task_neurons.mat</p> <p>Structure “Instrumental_task_neurons”<br> - “instrumental_task_neurons.unit_name” – neuron specific identifier<br> - “instrumental_task_neurons.monkeynumber” – subject specific #<br> - “instrumental_task_neurons.monkeyname” – subject specific name<br> - “instrumental_task_neurons.date” – date on which data were recorded<br> - “instrumental_task_neurons.session” – session identifier from date (a-d)<br> - “instrumental_task_neurons.channel” – recording channel data recorded from<br> - “instrumental_task_neurons.wavemark” – waveform number (a-e)<br> - “instrumental_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “instrumental_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “instrumental_task_neurons.condition” – condition 7-18 from Monkey Logic for each of the trials (1 by n trials). CNDs 7,8,13,14= CS+ juice vs CS+ water; CNDs 9,10,15,16= CS+ juice vs CS-; CNDs 11,12,17,18= CS+ water vs CS-.<br> - “instrumental_task_neurons.choice” – outcome associated with chosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.unchosen” - outcome associated with unchosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.chosenside” – side of the screen chosen (left [0] or right [1]) (1 by n trials)<br> - “instrumental_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “instrumental_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)</p> <p>heart_rate.mat</p> <p>Structures – “monkey_d_hr” – monkey D heart rate data<br> – “monkey_h_hr” – monkey H heart rate data</p> <p>Structure of monkey_d/h_hr<br> - “monkey_d/h_hr.trial_type” – trial type presented (1 = neutral, 2 = unsignaled, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).<br> - “monkey_d/h_hr.trials” – number of trials in each session by trial type<br> - “monkey_d/h_hr.session – percent change in heart rate for each trial from -200 ms to 3500ms after stimulus onset. Column 1 = session; Column 2 = trial type; Column 3 = trial number; Columns 4 – 3703 = percent change in heart rate.</p> <p> </p>
Fig. 8. A in Small carnivores (Mammalia: Carnivora) in the Wonorejo Mangroves, Jawa Timur, Indonesia: habitat use and activity patterns
Fig. 8. A, domestic cat on a dyke on 15 November 2018; B, activity pattern of the domestic cat.
Fig. 1 in Small carnivores (Mammalia: Carnivora) in the Wonorejo Mangroves, Jawa Timur, Indonesia: habitat use and activity patterns
Fig. 1. Map of the study area in Wonorejo Mangroves east of Surabaya, Jawa Timur, Indonesia.
Data from: Dynamic shifts in predator diel activity patterns across landscapes and threat levels
<p>Understanding the constraints dominant predators impose on subordinate species is important for predicting ecosystem dynamics and anticipating outcomes of predator management. Subordinate predators may avoid dominant predators in time or space, making it difficult to quantify antipredator behaviours unless joint spatiotemporal analyses are used. Here, we tested whether an invasive dominant predator (red fox <em>Vulpes vulpes</em>) alters the spatiotemporal activity of an invasive subordinate predator (feral cat <em>Felis catus</em>). We collated records of both species from 3,667 camera-traps deployed experimentally across two regions of south-eastern Australia with simplified predator guilds. Foxes were poison-baited in some landscapes within each region. We used generalised additive models to quantify changes in predator spatiotemporal activity across geographic space, vegetation types, human footprint and (artificially manipulated) gradients of dominant predator activity. Foxes and cats had similar diel activity patterns when averaged across all sites, however there was important differentiation at a finer scale cats did not reduce their spatial activity but shifted diel patterns when localised fox activity was high. Cats were crepuscular on average. However, across dry vegetation types of both regions (where foxes were nocturnal), cats shifted to diurnal behaviour with increasing fox activity. In contrast, fox activity was relatively consistent throughout the daily cycle in the wet forest; here cats avoided dawn when fox activity was high. Changes in cat diel activity patterns may facilitate spatial coexistence between these two invasive predators, potentially shifting feral cat impacts onto different native prey. It is well-appreciated that predator activity varies spatially and fluctuates throughout the daily cycle. However, our study demonstrates that diel activity patterns also vary across space, likely mediated by both landscape-context and fear. Dominant predator avoidance in time appears to be spatially dynamic a key nuance which is overlooked when simply comparing the average activity overlap between two species.</p>
Differences in Muscle Activity Patterns and Graphical Product Quality in Children With Graphomotor Impairment
ClinicalTrials.gov study NCT02501590. IPD Sharing: Not stated. Countries: 1. Publications: 5.
The Impact of Real-time Feedback on Physical Activity Patterns in Flemish Employees
ClinicalTrials.gov study NCT01432327. IPD Sharing: Not stated. Countries: 1. Publications: 1.
EMG Training for Altering Activation Patterns After Stroke
ClinicalTrials.gov study NCT03619772. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Altering Activation Patterns Post-stroke
ClinicalTrials.gov study NCT02418949. IPD Sharing: NO. Countries: 1. Publications: 5.
Contrasting patterns of risk from human and non-human predators shape temporal activity of prey
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Temporal mismatches in flight activity patterns between Pipistrellus kuhlii and Prays oleae in Mediterranean olive farms: Implications for biocontrol services potential
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Dual loop active learning of hydrophobicity of patterned SAMs
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Assessing the potential of camera traps for estimating activity pattern compared to collar-mounted activity sensors: A case study on Eurasian lynx (Lynx lynx) in South-Eastern Norway
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Host spatial structure and disperser activity determine mistletoe infection patterns
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Data from: fission-fusion dynamics in sheep: the influence of resource distribution and temporal activity patterns
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Artificial supplementary food influences hedgehog occupancy and activity patterns more than predator presence or natural food availability
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Data from: Dynamic shifts in predator diel activity patterns across landscapes and threat levels
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Age-specific activation patterns and inter-subject similarity during verbal working-memory maintenance and Cognitive Reserve
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Three-dimensional stratification pattern in an old-growth lowland forest: how does height in canopy and season influence temperate bat activity?
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