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512 results for “Activity pattern”
Linked collectors and determiners for: Faunal study of velvet ants (Hymenoptera: Mutillidae) and their activity patterns and habitat preference at Ash Meadows National Wildlife Refuge, Nye County, Nevada, USA.
Natural history specimen data linked to collectors and determiners held within, "Faunal study of velvet ants (Hymenoptera: Mutillidae) and their activity patterns and habitat preference at Ash Meadows National Wildlife Refuge, Nye County, Nevada, USA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/20d58797-2815-434b-a9c5-5786e926af9d">https://bionomia.net/dataset/20d58797-2815-434b-a9c5-5786e926af9d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/20d58797-2815-434b-a9c5-5786e926af9d">https://gbif.org/dataset/20d58797-2815-434b-a9c5-5786e926af9d</a>. Formatted as a Frictionless Data package.
Figure 1 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 1. Mean monthly temperature (°C, bars) and rainfall (mm, line) at the study area in north-eastern Algeria.
Figure 3 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 3. Daily activity patterns of Timon pater at the study area, expressed as the mean number of individuals seen at each daytime interval along several independent 1000-m-long transects.
Figure 2 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 2. Monthly variation in the number of faecal pellets collected (A) (total sample, n5164) and in the mean volume of each pellet (B) (total sample, n5139) of Timon pater at the study area.
Fig. 6 in Activity pattern and resource use of two Callosciurus species in different habitats in northeastern Thailand
Fig. 6. Proportion of food items consumed by (a) Callosciurus finlaysonii (n = 143) and (b) C. caniceps (n = 35).
Fig. 2 in Activity pattern and resource use of two Callosciurus species in different habitats in northeastern Thailand
Fig. 2. Frequencies of detection of active (a) Callosciurus finlaysonii and (b) C. caniceps in each survey time. Data are displayed as mean ± SD. Different letters in the figure indicate a significant difference (Steel-Dwass test; P <0.05).
Fig. 1 in Activity pattern and resource use of two Callosciurus species in different habitats in northeastern Thailand
Fig. 1. Location of the study site in the headquarters (HQ) of the Sakaerat Environmental Research Station and census routes. Upper figure shows the location of Sakaerat Biosphere Reserve and the bottom figure shows an enlarged view of the HQ. Solid lines show the census route of the present study, broken lines show paved survey route of the previous study (Kobayashi et al., 2019b), broken line shows non-paved survey route of the previous study (Kobayashi et al., 2019b) in natural forests (DDF: dry dipterocarp forest in light grey; DEF: dry evergreen forest in dark grey). Dotted areas in the bottom figure are relatively open spaces with few trees, white square is the nursery, and striped squares are buildings.
Fig. 2 in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. 2. Daily activity patterns of Sunda stink-badger within Lots 5 and 7 of the Lower Kinabatangan Wildlife Sanctuary, Sabah, Borneo. The grey areas represent an extension of the activity pattern to depict its circular nature, and 'carpet' marks along the x-axis represent individual photographic events. Vertical dashed red lines indicate either the end or beginning of the diurnal phase of the diel (0700–1659 hours). Vertical blue lines indicate either the end or beginning of the nocturnal phase of the diel (1900–0459 hours). Regions between red and blue lines represent the crepuscular regions of the diel (0500–0659 hours and 1700–1859 hours).
Fig. A3 in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. A3. Co-occurrence of Sunda stink-badger Mydaus javanensis and Malay civet Viverra tangalunga photo-captured in the Lower Kinabatangan Wildlife Sanctuary, Sabah, Malaysian Borneo on 13 June 2013.
Fig. A2 in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. A2. Kernel density modelled activity patterns for the Sunda stink-badger during different periods; overlap between activity patterns is indicated by the shaded regions. Vertical dashed red lines indicate either the end or beginning of the diurnal phase of the diel (0700–1659h). Vertical blue lines indicate either the end or beginning of the nocturnal phase of the diel (1900–0459h). Regions between red and blue lines represent the crepuscular regions of the diel (0500–0659h and 1700–1859h). 'Carpet' marks along the x-axis represent individual photographic events, and are colour co-ordinated to their respective activity pattern. Top (a): Overlap in kernel density modelled activity pattern in the wet season (November–February) compared to the dry season (March–October). Middle (b): Overlap in kernel density modelled activity pattern on full moon nights compared to new moon nights. Bottom (c): Overlap in kernel density modelled activity pattern on 'bright' nights (full moon, waxing gibbous, and waning gibbous) compared to 'dim' nights (new moon, new crescent, and old crescent).
Fig. 1 in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. 1. Location of the camera trap stations in Lots 5 and 7 of the LKWS along the northern bank of the Kinabatangan River, Sabah, Borneo.
Fig. 2 in Home range and activity patterns of Sunda scops owl in Peninsular Malaysia
Fig. 2. Home range pattern generated by each of the six radiotracked owls, based on 95% MCP (shaded area) and HM (nonshaded area) methods.
Fig. 2 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 2. Density estimates of daily activity patterns of six felid species in Thailand. Solid lines are kernel-density estimates; dashed lines are trigonometric sum distributions. The short vertical lines above the x-axis indicate the times of individual photographs.
Fig. 1. Camera trap data was collected from 14 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 1. Camera trap data was collected from 14 protected areas within Thailand. NP = national park; WS = wildlife sanctuary; NH = non-hunting area.
Fig. 4 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 4. Daily activity patterns of tigers and leopards in five study areas in Thailand. Individual photograph times are indicated by the short vertical lines above the x-axis. The overlap coefficient is the area under the minimum of the two density estimates, as indicated by the shaded area in each plot.
Fig. 3 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 3. Daily activity patterns of and overlap of Asiatic golden cat compared to leopard cat and clouded leopard in Khao Yai National Park, Thailand. Individual photograph times are indicated by the short vertical lines above the x-axis. The overlap coefficient is the shaded area under the two density estimates.
Data from: Non-breeding sites, loop migration and activity patterns over the annual cycle in the Lesser Grey Shrike Lanius minor from a western edge of its range
<p>Raw data from three tracked individuals. Two were tracked with light geolocators (22UL and an incomplete track of 22UH) and one (16KN) with GDL3-PAM multi-sensor logger. All produced by Swisss Ornithological Insitute.</p>
Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats
<p class="MsoNormal"><span>The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; <em>Myotis</em> spp., <em>Eptesicus nilssonii</em> or <em>Pipistrellus nathusii</em> and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for <em>E. nilssonii</em> and <em>Myotis </em>spp. For <em>E. nilssonii</em> and <em>Myotis</em> spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of <em>P. nathusii</em> increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring </span><span>network to be an effective and cost-efficient method for gathering b</span><span>at activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of cli</span><span>mate change.</span></p>
Deep Representation Learning of Physical Activity and Sleep Patterns During Pregnancy Identifies post-hoc Inferences Associated with Prematurity
<p><strong>Running title</strong>: series2signal gestational age "clock" for pregnancy monitoring</p> <p><strong>Summary</strong>: </p> <p>Preterm birth (PTB) is the leading cause of infant mortality globally. While research has focused on the development of predictive models for PTB, cost-effective interventions have remained understudied. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations. However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy that decays over time. In this study, we use physical activity data collected using a wearable device comprising over 181, 944 hours of data across N = 1, 083 patients. Using a new state-of-the art deep learning time-series classification architecture, we first develop a ”clock” of healthy dynamics in physical activity patterns during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also developed a novel interpretability algorithm that integrates unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and static clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy based on measures of physical activity and sleep.</p> <p>Importantly, we found that deviations from this normal ”clock” of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21). Combining prediction with interpretability allows us to robustly signal when activity behaviors increase or decrease the likelihood of preterm birth and advocates for the future development of clinical decision support through passive monitoring and suggestions around exercise habits and sleep patterns, which are easily implemented in low- and middle-income countries (LMICs). Beyond this particular application, the presented pipeline can be used to analyze high-fidelity time-series data in other translational studies utilizing wearable devices.</p> <p> </p> <p><strong>Data description (brief)</strong>: the raw wearables data is available as .mtn files with the GA encoded in the filename after the underscore. The processed data with sleep annotations can be loaded using the pickle module for serialized objects in python. See https://github.com/nealgravindra/wearables for examples.</p>
Data from: Global change in brain state during spontaneous and forced walk in Drosophila is composed of combined activity patterns of different neuron classes
<p><span>Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole-brain lightfield imaging in adult <em>Drosophila </em>to investigate the relationship between walk and brain-wide neuronal activity. We observed a global change in activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual candidate neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to global activity of all major neuronal classes.</span></p>
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