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105 results for “movement patterns”
CBM01 Plains bison movement patterns in an experimental heterogeneous landscape at Konza Prairie
This GPS-collar data set was used to evaluate the factors that influence where bison choose to graze and how grazing and space use patterns affect ecosystem function and structure. Our objectives were to quantify space use and movement patterns of adult female Plains bison in the context of selection for specific prescribed burn frequencies and topographical features in the bison-grazed watersheds at Konza Prairie. We hypothesized bison would track post-prescribed burn forage productivity and we predicted watersheds burned for the first time in several years would be used to a greater extent than watersheds burned more frequently.
Fig. 1 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 1. Mean activity density of Carabus scheidleri (a) and C. coriaceus (b) per sampling plot in different for- estry treatments (C = control, CC = clear-cutting, P = preparation cut- ting) between 2014 and 2018. Verti- cal lines represent a 95% confidence interval and capital letters above bars indicate significant differences based on Tukey's multiple compari- sons of means
Fig. 2 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 2. Movements of Carabus scheidleri (a) within and between forestry treatments (C = control, CC = clear-cutting, P = preparation cutting) based on CMR. The number next to the arrow corresponds with the number of recorded movements. Individual trajectories of radio-tracked C. coriaceus (b) in the experimental area, black dots represent the first release point for each trajectory
Fig.1 in Home range, movements and activity patterns of an exceptionally large male Brown Bear (Ursus arctos L.) in the area of the Bulgarian-Greek border (Western Rhodope Mts.)
Fig.1. Dominant male (supposed age of at least 15 years old) with scars of battles with rivals. Hunting Forestry Adjilarska (photo from a kamera-trap, D. Bukovsky, 17.03.2014 at a game-feeding station).
Fig 2. A in Home range, movements and activity patterns of an exceptionally large male Brown Bear (Ursus arctos L.) in the area of the Bulgarian-Greek border (Western Rhodope Mts.)
Fig 2. A century-old bear marking tree just on the Bulgarian/Greek border. The tree is marked also with white paint as a border pillar (photo N. Spassov).
Figure 4 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 4. Multiple comparisons of the moving distance among the four RCC breeding subseasons (M: mating, B: brooding, W: wading, G: growing).
Figure 3 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 3. Principal component analysis for the climatic variables and the moving distance. The first axis (PC1) explains 31.44% of total variation, and the second axis (PC2) accounts for 22.44% of total variation.
Fig. 3 in Diel movement patterns of Pacific sugar limpet, Patelloida saccharina (Gastropoda: Patellogastropoda: Lottiidae) in response to semi-diurnal tides of Samal Island, Philippines
Fig. 3. Movement patterns of Patelloida saccharina limpets in Catagman, Samal Island, during various lunar phases. A, New Moon, 27–28 March 2017; B, First Quarter, 4–5 April 2017; C, Full Moon, 9–10 April 2017; D, Last Quarter, 19–20 April 2017. The left and right y-axes correspond to the percentage of moving limpets and tidal height measurements in metres above chart datum (C.D.), respectively. Grey vertical bars represent the percentage of actively moving limpets during each lunar phase. The bar at the bottom corresponds to the day-night cycle: (from left to right) white = daytime, grey = transitioning to sunset, black = night-time, grey = sunrise. Note that the activity of P. saccharina limpets was observed during dark periods only, from sunset to night-time until before sunrise, as long as they were covered by the tide.
Fig. 2 in Diel movement patterns of Pacific sugar limpet, Patelloida saccharina (Gastropoda: Patellogastropoda: Lottiidae) in response to semi-diurnal tides of Samal Island, Philippines
Fig. 2. Patelloida saccharina clamped on: A, bare rock face; B, rock covered with sand and turf algae. C, portion of the 5 × 5 cm grid quadrat laid on a reef patch with three P. saccharina individuals. Apices of limpet shells were painted with nail polish. Photographs by EY Zapanta and MA Fortaleza.
Annotated Video Dataset of Fencing Movements and Corresponding Error Patterns
<p>Video dataset containing 1289 videos and their augmentation of four fencing movements, performed by a variety of fencers. The main actions included are counterattack, lunge, flèche and parry. For each movement samples with typical error patterns are included and annotated. Additionally labels for mulit-labelling are documented. The corresponding paper "Mastering Fencing Techniques with Machine Learning: A Video-Based Classification and Correction System" is published at the 10th IEEE Swiss Conference on Data Science (SDS 2023)</p>
Data from: Environmental conditions and individual characteristics influence movement patterns of juvenile Passerculus sandwichensis (Savannah sparrow) throughout the post-fledging period
Open the record for dataset details and reuse information.
Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups
<p>All data for the article "Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups", published in Scientific Reports.</p> <p>Both the raw data (csv files labelled with individual ID) and the collated data (allData.csv, data5min.csv, furSeals_finalData.csv) are provided. Please see the “README.txt” file for more information on what each raw data file contains. The RMarkddown.pdf includes all code used in the manuscript. If you have any further questions, please don’t hesitate to contact me (Rebecca Nagel) at renagel2@gmail.com.</p>
Individual and temporal variation in movement patterns of wild alpine reindeer and implications for disease management
<p>Animal behaviour is important for prevalence and outbreaks of infectious diseases, for instance by affecting individual interactions. Increasing the knowledge of individual movement patterns can provide better insight into disease prevalence and spread, helping to target efforts to minimise disease outbreaks. Chronic wasting disease (CWD) is a fatal prion disease affecting cervids. CWD is transmitted by animal-to-animal contact and through the environment, thus individual variation in space use and social associations may influence disease transmission patterns and infection risk. CWD was detected in Norwegian alpine reindeer (<em>Rangifer tarandus</em>) in 2016, and eradication of the infected population was implemented. A 3:1 infection rate between males and females suggests sex-specific behavioural drivers. We utilised an extensive individual-based dataset of 149 GPS-marked wild reindeer to investigate individual variation in movement patterns in terms of inter- and intra-annual home range size and site fidelity, and variation in home range overlap and distances between individuals. We aimed to identify patterns which could indicate higher potential disease risk. Females had larger annual and seasonal home ranges than males, except during calving and rut. Greater home range overlaps and shorter between-individual distances were found between same-sex individuals than different-sex individuals, except during the rut. Accordingly, the rut season stands out with greater male home ranges, greater home range overlap and shorter distances between males and between males and females, which could indicate that this season is critical for disease transmission. Measures to prevent disease spread should lower contact rates, e.g., by reducing the abundance of adult males before they mix with other groups during the rut. This can be achieved for instance by allowing earlier hunt on adult males when they are distributed in small male groups, to reduce the transmission risk and keep disturbance of other individuals low.</p>
Camera trap data used for assessment of movement patterns by injured moose
<p>The data were collected from camera trap recordings of animals on a remote trail in northern British Columbia, Canada, from 2017-2020. The data were used for an assessment of the general fauna composition of the area; and also for assessing movement patterns of moose - especially those with obvious leg injuries. The data set includes 4562 observations - that is, instances of an animal recorded on a given video. For further description see the paper "<strong><span>Performance of Wild Animals with "Broken" Traits: </span></strong><strong><span>Movement Patterns in Nature of Moose That Have Leg Injuries" published in Ecology and Evolution.</span></strong></p>
Fig. 2 in Movement and activity pattern of a brown bear (Ursus arctos L.) tracked in Central Balkan Mountain, Bulgaria
Fig. 2. Minimum, maximum and average speed of the bear in different habitats.
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
Movement patterns of invertebrates in tropical rainforest
<b>Description: </b><p>Community Data for directional malaise trapping across and along rivers from around the SAFE landscape</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/20"><b>Movement patterns of invertebrates in tropical rainforest</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=266">here</a></p><p><b>Files: </b>This consists of 1 file: Invert_Movement_Dataset_JW.xlsx</p><p><b>Invert_Movement_Dataset_JW.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Community Data for directional malaise trapping</b> (described in worksheet DF)</p><p>Description: Community Data for directional malaise trapping above rivers</p><p>Number of fields: 60</p><p>Number of data rows: 82</p><p>Fields: </p><ul><li><b>SAFESiteCode</b>: location code (Field type: Location)</li><li><b>Orientation</b>: trap orientation (Field type: Categorical)</li><li><b>Date_Out</b>: trap set date (Field type: Date)</li><li><b>Date_In</b>: trap collection date (Field type: Date)</li><li><b>Width_River_Along</b>: width of river at along trap (Field type: Numeric)</li><li><b>Width_River_Across</b>: width of river at across trap (Field type: Numeric)</li><li><b>Width_River_Middle</b>: width of river at the midpoint between the traps (Field type: Numeric)</li><li><b>Width_River_Average</b>: average river width (of three previous columns) (Field type: Numeric)</li><li><b>Height_Along</b>: height of across trap above the river (Field type: Numeric)</li><li><b>Height_Across</b>: height of along trap above the river (Field type: Numeric)</li><li><b>Rainfall</b>: rainfall collected (Field type: Numeric)</li><li><b>Forest_Type</b>: Surrounding forest type (Field type: Categorical)</li><li><b>Canopy_Across_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_River</b>: canopy cover (% from densiometer) above the river at the across trap (Field type: Numeric)</li><li><b>Canopy_Along_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_River</b>: canopy cover (% from densiometer) above the river at the along trap (Field type: Numeric)</li><li><b>Canopy_Middle_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_River</b>: canopy cover (% from densiometer) above the river at the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Average_L</b>: left bank canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_R</b>: right bank canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_LR</b>: both banks canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_River</b>: river canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Basal_Area_Across_L</b>: across trap, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Across_R</b>: across trap, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Across</b>: across trap, average of both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along_L</b>: along trap, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along_R</b>: along trap, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along</b>: along trap, average of both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle_L</b>: midpoint, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle_R</b>: midpoint, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle</b>: midpoint,both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area</b>: average tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Eph_Ab</b>: Ephemeroptera (Field type: Abundance)</li><li><b>Odo_Ab</b>: Odonata (Field type: Abundance)</li><li><b>Ple_Ab</b>: Plecoptera (Field type: Abundance)</li><li><b>Ort_Ab</b>: Orthoptera (Field type: Abundance)</li><li><b>Der_Ab</b>: Dermaptera (Field type: Abundance)</li><li><b>Dic_Ab</b>: Blattodea (Field type: Abundance)</li><li><b>Iso_Ab</b>: Isoptera (Field type: Abundance)</li><li><b>Hem_Ab</b>: Hemiptera (Field type: Abundance)</li><li><b>Thy_Ab</b>: Thysanoptera (Field type: Abundance)</li><li><b>Col_Ab</b>: Coleoptera (Field type: Abundance)</li><li><b>Mec_Ab</b>: Mecoptera (Field type: Abundance)</li><li><b>Dip_Ab</b>: Diptera (Field type: Abundance)</li><li><b>Lep_Ab</b>: Lepidoptera (Field type: Abundance)</li><li><b>Tri_Ab</b>: Trichoptera (Field type: Abundance)</li><li><b>Hym_Ab</b>: Hymenoptera (Field type: Abundance)</li><li><b>Uni_Ab</b>: Unidentified individuals (Field type: Numeric)</li><li><b>Abundance</b>: Total Abundance (Field type: Numeric)</li><li><b>AbundanceUni</b>: Total Abundance without unidentified individuals (Field type: Numeric)</li><li><b>Staph_Ab</b>: subset of coleoptera that were staphylinids (Field type: Abundance)</li><li><b>Richness</b>: species richness (Field type: Numeric)</li><li><b>Mass</b>: total mass (Field type: Numeric)</li><li><b>Mass_Outlier</b>: total mass without individuals weighing over 0.25g (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2015-05-18 to 2015-07-14</p><p><b>Latitudinal extent: </b>4.6358 to 4.7342</p><p><b>Longitudinal extent: </b>117.4576 to 117.6411</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Blattodea<br> -  -  - Coleoptera<br> -  -  -  - Staphylinidae<br> -  -  - Dermaptera<br> -  -  - Diptera<br> -  -  - Ephemeroptera<br> -  -  - Hemiptera<br> -  -  - Hymenoptera<br> -  -  - Isoptera<br> -  -  - Lepidoptera<br> -  -  - Mecoptera<br> -  -  - Odonata<br> -  -  - Orthoptera<br> -  -  - Plecoptera<br> -  -  - Thysanoptera<br> -  -  - Trichoptera<br></div><p></p>
Figure 1 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 1. The study area of Zhalong Reserve. Inset shows its location in northeastern China.
Experimental light at night of different spectra alters movement patterns of bats along ecological corridors
<p>Data used for the study. </p> <p>Meaning of codes for "spectra" and "lamppost_side" variables:</p> <p>- C = control</p> <p>- G = green</p> <p>- R = red</p> <p>- W = white</p> <p>- L = left</p> <p>- R = right</p>
Movement patterns and habitat use for the sympatric species: Gambelia wislizenii and Aspidoscelis tigris
<p><strong>Background</strong>: Movement is an important characteristic of an animal's ecology, reflecting perception of and response to environmental conditions. To effectively search for food, movement patterns likely depend on habitat characteristics and the sensory systems used to find prey. We examined movements associated with foraging for two sympatric species of lizards inhabiting the Great Basin Desert of southeastern Oregon. The two species have largely overlapping diets but find prey via different sensory cues, which link to their differing foraging strategies — the long-nosed leopard lizard, <em>Gambelia wislizenii</em>, is a visually-oriented predator, while the western whiptail, <em>Aspidoscelis tigris</em>, relies more heavily on chemosensory cues to find prey.</p> <p><strong>Methods</strong>: Using detailed focal observations, we characterized the habitat use and movement paths of each species. We placed markers at the location of focal animals every minute for the duration of each 30-min observation. Afterwards, we recorded whether each location was in the open or in vegetation, as well as the movement metrics of step length, path length, net displacement, straightness index, and turn angle, and then made statistical comparisons between the two species.</p> <p><strong>Results</strong>: The visual forager spent more time in open areas, moved less frequently over shorter distances, and differed in patterns of plant use compared to the chemosensory forager. Path characteristics of step length and turn angle differed between species.</p> <p><strong>Conclusions</strong>: The visual predator moved in a way that was consistent with the notion that they require a clear visual path to stalk prey whereas the movement of the chemosensory predator increased their chances of detecting prey by venturing further into vegetation. Sympatric species can partition limited resources through differences in search behavior and habitat use. </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.