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53 results for “Animal movements”
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
Examination of head versus body heading may help clarify the extent to which animal movement pathways are structured by environmental cues?
<p>Understanding the processes that determine how animals allocate time to space is a major challenge, althoughit is acknowledged that summed animal movement pathways over time must define space-time use. The criticalquestion is then, what processes structure these pathways? Following the idea that turns within pathways mightbe based on environmentally determined decisions, we equipped Arabian oryx with head- and body-mounted tagsto determine how they orientated their heads – which we posit is indicative of them assessing the environment– in relation to their movement paths, to investigate the role of environment scanning in path tortuosity. Aftersimulating predators to verify that oryx look directly at objects of interest, we recorded that, during routinemovement, > 60% of all turns in the animals' paths, before being executed, were preceded by a change in headheading that was not immediately mirrored by the body heading: The path turn angle (as indicated by the bodyheading) correlated with a prior change in head heading (with head heading being mirrored by subsequent turnsin the path) twenty-one times more than when path turns occurred due to the animals adopting a body headingthat went in the opposite direction to the change in head heading. Although we could not determine what theobjects of interest were, and therefore the proposed reasons for turning, we suggest that this reflects the use ofcephalic senses to detect advantageous environmental features (e.g. food) or to detect detrimental features (e.g.predators). The results of our pilot study suggest how turns might emerge in animal pathways and we proposethat examination of points of inflection in highly resolved animal paths could represent decisions in landscapesand their examination could enhance our understanding of how animal pathways are structured.</p>
Dataset for Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies
<h1>Abstract </h1> <p>Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of one week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.</p> <p> </p>
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Replication data in the form of simulation outputs for "The joint evolution of animal movement and competition strategies"
<p>This version of the data is a re-upload of an identical dataset uploaded to DataverseNL. The data are being uploaded here for uniformity of archiving with the other supplementary materials accompanying the manuscript, "The joint evolution of animal movement and competition strategies", which is accepted for publication in <em>The American Naturalist</em>.</p> <p>The dataset consists of a single zipped folder, data/, which contains subfolders with the simulation specific data. These subfolders are named 'sim_SCENARIO_rep_NNN_gro_RMAX', where 'SCENARIO' refers to the three scenarios of the model described in our manuscript, 'NNN' is the replicate number, and 'RMAX' is the maximum cell productivity in that simulation.</p> <p>Each of the subfolders consists of the following:</p> <ol> <li>The directory ‘depends/’, which holds the ‘extract.exe’ program. This program is used to extract specific data from the stored simulation output.</li> <li>The ‘sourceMe.R’ file allows the ‘extract.exe’ program to be linked to R, delivering the required data as a list object that can be handled in R.</li> <li>The stored simulation data on agents, as a series of ‘.arc’ and ‘.bin’ files.</li> <li>The ecological snapshots of the landscape, with prey items, foragers, kleptoparasites, and handlers (depending on the scenario), which are stored as PNG files named ‘00NNN.png’, where NNN is a three digit representation of the generation number (e.g. 001 for generation 1).</li> <li>Cumulative sums of the numbers of prey items, foragers, kleptoparasites, the intake from foraging (searching for prey), and the intake from the kleptoparasitic strategy (searching for handlers), on each cell of the landscape, for each of the last 9 generations of the simulation, as ‘NNN.txt’, where LAYER may be ‘items’ (prey items), ‘foragers’ (foragers), ‘klepts’ (kleptoparasites), ‘foragers_intake’ (the intake from the foraging strategy), or ‘klepts_intake’ (the intake due the kleptoparasite strategy. NB: These files are not used in our analyses, and may be ignored.</li> </ol> <p>'SCENARIO' may be one of “foragers” (scenario 1), “obligate” (scenario 2), or “facultative” (scenario 3).</p> <p>'NNN' may be one of “001”, “002”, or “003”.</p> <p>'RMAX' may be one of “0.001”, “0.005”, “0.01”, “0.02”, “0.03”, “0.04”, or “0.05”. The manuscript presents results for 'RMAX' = 0.01.</p>
Reference data from the Pathomove simulation, for the manuscript "Novel pathogen introduction triggers rapid evolution in animal social movement strategies"
<p>This is a reference dataset of multiple runs of the 'Pathomove' simulation, to accompany the manuscript "Novel pathogen introduction rapidly alters the evolution of movement, restructuring animal societies". The datasets are in the form of R data objects saved as Rds files.</p> <p>This version of the data is intended to accompany a resubmission to <em>eLife</em>.</p>
Examination of head versus body heading may help clarify the extent to which animal movement pathways are structured by environmental cues?
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Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
Dataset of how animal movement paths in a landscape vary due to wildfire
<p>Animals navigate landscapes based on perceived risks vs. rewards, as inferred from features of the landscape. In the wild, knowing how strongly animal movement is directed by landscape features is difficult to ascertain, but widespread disturbances such as wildfires can serve as natural experiments. We tested the hypothesis that wildfires homogenize the risk/reward landscape, causing movement to become less directed, given that fires reduce landscape complexity as habitat structures (e.g., tree cover, dense brush) are burned<em>. </em>We used satellite imagery of a research reserve in Northern California to count and categorize paths made primarily by mule deer (<em>Odocoileus hemionus</em>) in grasslands. Specifically, we compared pre-wildfire (August 2014) and post-wildfire (September 2018) image history layers among locations that were or were not impacted by wildfire (i.e., a Before/After Control/Impact design). Wildfire significantly altered spatial patterns of deer movement: more new paths were gained and more old paths were lost in areas of the reserve that were impacted by wildfire; movement patterns became less directed in response to fire, suggesting that the risk/reward landscape became more homogenous, as hypothesized. We found evidence to suggest that wildfire affects deer populations at spatial scales beyond their scale of direct impact and raises the interesting possibility that deer perceive risks and rewards at different spatial scales. In conclusion, our study provides an example of how animals integrate spatial information from the environment to make movement decisions, setting the stage for future work on the broader ecological implications for populations, communities, and ecosystems, an emerging interest in ecology.</p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
<p>Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies -- individual animals observed at specific locations and times -- can provide a rich source of information about how these processes relate to demographic rates. We developed SCR models that integrate complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using Bayesian analysis methods. We evaluated their performance through a simulation study, where we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T=25 to T=15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but movement models developed here could be extended to open SCR models. The movement process models could also be extended to accommodate additional "building blocks'' of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.</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>
Impact of climate change on alpine mass movement - animated drawing
<p>Observed and expected changes of climate-change driven alpine mass movements in the European Alps between colder and warmer conditions. Rising temperatures primary cause for the observed increase of rockfall in alpine regions. Above treeline, increased sediment availability and increased intense precipitation lead to increased debris-flow activity, potentially in locations without historical precedence. Warmer winters are leading scarcer snow conditions at low elevations and a transient state of increased snowfall at high elevations. This shift is leading to more wet snow avalanches, fewer and smaller avalanches at low and mid elevations, and increased avalanche activity at high elevations. No trends have been detected for rockfall below treeline, and findings for debris flows are highly variable. Expected, but not yet visible in observations, are more rock avalanches (despite a measurable increase of rock temperatures), changing ice avalanche activity (despite higher ice temperatures), deteriorating protection forests. Elements not related to mass movements (e.g., human infrastructure, ecosystems) are shown to remain constant, though in reality they may evolve faster than the discussed mass movements.</p>
Using a low-cost trackball system to assess repeatability of movement in small animals
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Analysis of temporal patterns in animal movement networks
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Dataset of how animal movement paths in a landscape vary due to wildfire
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How spatiotemporal cognition and movement of seed-dispersing animals influence plant distribution
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
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Data from: Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests
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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.