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3 results for “Spatio-temporal movement”
Data from: Insect communities under skyglow: diffuse night-time illuminance induces spatio-temporal shifts in movement and predation
<p>We conducted our experiment at the iDiv Ecotron experimental facility, which is an indoor mesocosm facility consisting of independent, experimental chambers called “EcoUnits”. The Ecotron is located in Bad Lauchstädt, Saxony-Anhalt, Germany, at the Experimental Research Station of the Helmholtz Centre for Environmental Research (UFZ, 51.3917° N, 11.8762° E). Multiple environmental conditions in the EcoUnits can be fully controlled (e.g., nutrient supply and irrigation). Each EcoUnit has internal dimensions of 1.46 m × 1.46 m × 1.50 m (L × W × H, aboveground) and 1.24 m × 1.24 m × 0.80 m (L × W × H, belowground) with the soil surface area measuring 1.54 m².</p> <p>To assess the interactive effects of diffuse nighttime illuminance and landscape structure on animal movement patterns, we established a patch-grassland system which consisted of four meadow patches within each of the corners of an EcoUnit, separated by an area of bare ground. The EcoUnits were filled with 1.23 m<sup>3</sup> of unsterilised and homogenised soil from the vicinity of the iDiv Ecotron, and plant communities of 16 plant species were sown on February 4th 2020. We allowed for a settlement phase of roughly 5 months before starting our measurements.</p> <p> </p> <p>Each heading below describes columns in the two datasets that describe the movement activity (RFID sensor detections) and predation (bite marks left on artificial caterpillar prey dummies) by the experimental insect communities.</p> <p> </p> <p><strong>timestamp:</strong> Continuous value indicating the time and date (format: DD/MM/YYYY hh:mm:ss, timezone: UTC+2) at which a tagged individual was detected by an RFID sensor. We defined detections as distinct and only counted them when they (1) occurred on different sensors or when (2) at least 10 seconds had elapsed (without detection on the same sensor) between two consecutive detections on the same sensor. This prevented the repeated detection of resting or dead animals.</p> <p><strong>block_ID: </strong>Categorical value indicating whether an observation was made within the first or second temporal experimental block (b1, b2). Each block corresponded to a period of approximately one lunar cycle (i.e., 28 days: experimental block b1: 21.07.2020 - 18.08.2020, experimental block b2: 15.09.2020 - 13.10.2020).</p> <p><strong>rep_ID: </strong>Categorical value indicating the replicate prey dummy exposures (r2, r3, r4, r5) that took place within each 28-day temporal experimental block: r2 and r3 took place successively within block b1; r4 and r5 took place successively within block b2. NAs identify periods during each experimental block where prey dummies had not yet been deployed or were in the process of being collected/re-deployed. </p> <p><strong>unit_ID: </strong>Categorical value indicating the identity of each of the 12 EcoUnits at the iDiv Ecotron experimental facility.</p> <p><strong>patch_ID: </strong>Categorical value indicating the identify of each of the four meadow patches within each EcoUnit. Patch identities correspond to their coordinates within the EcoUnit.</p> <p><strong>x: </strong>Continuous value indicating the X-coordinate position of an RFID sensor or prey dummy within the Ecotron unit with respect to the position of each EcoUnit’s control panel.</p> <p><strong>y: </strong>Continuous value indicating the Y-coordinate position of an RFID sensor or prey dummy within the Ecotron unit with respect to the position of each EcoUnit’s control panel. </p> <p><strong>day_night: </strong>Categorical value indicating whether an individual was detected during the day (treatment lights off) or during the night (treatment lights on). Night includes the periods of dawn and dusk where daylight was gradually (i.e. linearly) brightened or dimmed over the course of two hours before sunrise and sunset, respectively.</p> <p><strong>habitat: </strong>Categorical value indicating whether a tagged individual was detected within a meadow patch (Patch) or within the area of bare ground (Matrix) which separates individual patches.</p> <p><strong>tag: </strong>Categorical value indicating the unique serial number associated with an RFID-tagged individual. This column is used to estimate local densities (sum of unique tag IDs detected).</p> <p><strong>species: </strong>Categorical value indicating the scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2020). Note that <em>Harpalus rufipes </em>(De Geer) is referred to in the database by its synonym <em>Pseudoophonus rufipes</em> (De Geer). Poecilus includes the species <em>Poecilus cupreus</em> and<em> Poecilus versicolor</em>. , <em>Harpalus affinis</em>, <em>Harpalus latus</em>) are pooled together at the genus level. </p> <p><strong>bodymass_mg: </strong>Continuous value indicating the live body mass of each tagged individual, excluding the added mass of the RFID tag.</p> <p><strong>detection: </strong>Integer with a fixed value of 1 representing the detection of a unique RFID tag. This column is used to estimate movement activity (sum of detections).</p> <p><strong>bite_count: </strong>Integer value indicating the number of bite marks recorded on an individual prey dummy during a 14-day exposure. Two independent observers scored the prey dummies by identifying and counting the bite marks left by carabid predators.</p> <p><strong>treatment_lux: </strong>Continuous value indicating the treatment of diffuse nighttime illuminance in Lux.</p>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
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>
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