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1,513 results for “disturbance”
Canopy Trimming Experiment (CTE) Canopy invertebrate responses to disturbance
Seven tree species were selected to represent early (Cecropia, Prestoea) and late (Dacryodes, Manilkara, Sloanea) successional, and overstory (Cecropia, Dacryodes, Manilkara, Sloanea) and understory (Prestoea, Miconia, Psychotria), species in forests at El Verde. These trees were sampled in all CTE plots. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: Coral Reef: Early life stage bottleneck determines rates of coral recovery following severe disturbance; Data for Speare et al., 2024, Ecology
The data included in this data package were collected on the north shore of Moorea, French Polynesia, from 2011-2018 to evaluate drivers of different recovery rates of corals at two depths (10m and 17m). Data on juvenile coral densities, growth, and mortality, were collected from annual time series photoquadrats. Data from two experiments on coral settlement tiles were used to evaluate how exclusion of fishes influences the density of coral recruits, and the survival of coral recruits at 10 and 17m. These data were used for analyses in the manuscript entitled "Early life stage bottleneck determines rates of coral recovery following severe disturbance". These data are in support of a publication Speare et al. (2024) Ecology. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024).
Small Mammal Exclosure Study (SMES) Surface Soil Disturbance in the Chihuahuan Desert Grassland and Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (1995-2005)
The purpose of this study is to determine whether or not the activities of small mammals regulate plant community structure, plant species diversity, and spatial vegetation patterns in Chihuahuan Desert shrublands and grasslands. What role if any do indigenous small mammal consumers have in maintaining desertified landscapes in the Chihuahuan Desert? Additionally, how do the effects of small mammals interact with changing climate to affect vegetation patterns over time? This is data for animal created soil surface disturbance measured from each of the SMES study plots. Soil surface disturbance was measured from each of the 36 one-meter2 quadrats twice each year when vegetation was measured.
Integrated disturbance mapping over the Tibetan Plateau based on multiple detection algorithms
<p>This dataset contains the mapped representation of vegetation disturbance across the Tibetan Plateau, rendered at a 30-meter spatial resolution. The dataset comprehensively illustrates the extent of vegetation disturbance on the Tibetan Plateau during the period spanning 1986 to 2020. Notably, the map employs a categorization system, with values assigned to distinct classes: 0 denotes areas of disturbed vegetation, 1 denotes regions characterized by undisturbed vegetation, and 2 denotes zones devoid of vegetation.</p>
Extracted raw data from: Global dominance of lianas over trees is driven by forest disturbance, climate, and topography
<p>In a meta-analysis, we use an unprecedented dataset, representing 556 unique locations worldwide, distributed across 44 countries and six continents to show for the first time that lianas (woody vines) thrive relatively better than trees when forests are disturbed, temperature increase, precipitation decrease, and particularly in tropical lowlands. We demonstrate that liana dominance can persist for decades post-disturbance and hinder the recovery of disturbed forests, especially when climate favours lianas. With implications for the global carbon sink, our findings suggest that degraded tropical forests with environmental conditions favouring lianas should be the highest priority to consider for restoration management.</p>
U-Pb on zircon data from 'Evidence for large disturbances of the Ediacaran geomagnetic field from West Africa'
<p>This dataset provides the tabular U-Pb data on zircon presented in Robert et al. 2023 'Evidence for large disturbances of the Ediacaran geomagnetic field from West Africa', Precambrian Research, 394, 107095.</p> <p>The data are provided both as an xls formatted as in the original publication (in Table 1), and in a condensed .csv. Please the README file for a description of the columns of the csv.</p>
Supplementary data for "Effect of Human Disturbance on Bird Telomere Length: An Experimental Approach"
<p><strong>Abstract</strong></p> <p>Human recreational activities increase worldwide in space and frequency leading to higher rates of encounter between humans and wild animals. Because wildlife often perceive humans as predators, this increase in human disturbance may have negative consequences for the individuals and also for the viability of populations. Up to now, experiments on the effects of human disturbance on wildlife have mainly focused on individual behavioral and stress-physiological reactions, on breeding success, and on survival. However, the effects on other physiological parameters and trans-generational effects remain poorly understood. We used a low-intensity experimental disturbance in the field to explore the impacts of human disturbance on telomere length in great tit (<em>Parus major</em>) populations and found a clear effect of disturbance on telomere length. Adult males, but not females, in disturbed plots showed shorter telomere lengths when compared to control plot. Moreover, variation in telomere length of adult great tits was reflected in the next generation, as we found a positive correlation between telomere length of the chicks and of their fathers. Given that telomere length has been linked to animal lifespan, our study highlights that activities considered to be of little concern (i.e., low levels of disturbance) can have a long-lasting impact on the physiology and survival of wild animals and their next generation.</p>
Disturbances in vegetation detected with BFAST in the Purapel fluvial catchment
<p>This dataset contains the results (69 TIFF files) of seasonal disturbances detected in vegetation in the Purapel catchment (southern Chile) for the period from 2002 to 2019. These disturbances were obtained by applying the Breaks for Additive Season and Trend (BFAST, Verbesselt et al., 2010) algorithm to 745 Landsat 5, 7 and 8 imagery. We used Collection 2 Level 2 surface reflectance products and applied the CFMask algorithm (Foga et al., 2017) for cloud masking before utilizing the BFAST algorithm.</p> <p>The BFAST algorithm detects changes in the NDVI time series of each pixel. To determine which event was considered a disturbance, we used the same intensity thresholds as in Cabezas and Fassnacht (2018). We then filtered the results to keep just the disturbances with areas greater than 1 hectare, eliminating noisy data.</p> <p>Except for 2 big wildfires (2015 and 2017) it was assumed that all of the disturbances were clear cuts, since forestry is the main productive activity in the region. This was confirmed by validating the data with 35 manually drawn polygons that were randomly distributed across the catchment. Then, we performed an accuracy assessment, obtaining a confusion matrix with a balanced accuracy of 0,86 and a F1 score of 0,69.</p> <p>Each TIFF file is a binary grid with “zeros” representing no disturbance and “ones” representing a disturbance in the season that the name of the file indicates.</p> <p>A GIF file is also included, which contains the time series of the disturbances for easier graphical purposes.</p> <p>References</p> <p>J. Cabezas and F. E. Fassnacht. Reconstructing the Vegetation Disturbance History of a Biodiversity Hotspot in Central Chile Using Landsat, Bfast and Landtrendr. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 7636–7639. IEEE, 7 2018. ISBN 978-1-5386-7150-4. doi: 10.1109/IGARSS.2018.8518863.</p> <p>S. Foga, P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. Joseph Hughes, and B. Laue. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194:379–390, 6 2017. ISSN 00344257. doi: 10.1016/j.rse.2017.03.026.</p> <p>J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1):106–115, 1 2010. ISSN 00344257. doi: 10.1016/ j.rse.2009.08.014. URL http://linkinghub.elsevier.com/retrieve/pii/S003442570900265X.</p>
Data archive accompanying "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, doi:10.1002/2017SW001785]
<p>This archive contains the data used to create the plots presented in "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, SWx, doi:10.1002/2017SW001785].</p> <p>Format: MATLAB save file</p> <p>Contents:</p> <p>1. CHAMP and GRACE-A accelerometer-derived densities and ephemeris;</p> <p>2. TIE-GCM GPI model output sampled on both satellites;</p> <p>3. IRIDEA prior and posterior model output sampled on both satellites;</p> <p>4. Short description and units for all variables</p>
Data for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect
<p>Dataset for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect, in final revision for Nature Communications.</p> <p>Includes two files - one behavioral data from uniquely identified worker bumblebees, and the second containing metadata for the colonies from which these data were generated (including experimental treatments, locations, sizes, etc.).</p>
European Forest Disturbance Atlas
<p><strong>Description</strong></p> <p>This repository holds maps of annual forest disturbances across 38 European countries derived from Landsat satellite data. The European Forest Disturbance Atlas currently covers the period 1985-2023 and consists of a set of maps:</p> <ul> <li>The <em>year of disturbance</em> layers contain the year of the most recent disturbance event in the time-series, the greatest disturbance in terms of spectral change and stack of annual disturbances indicating undisturbed (0) and disturbed (1).</li> <li>The <em>number of disturbances</em> layer shows the number of disturbance events detected within the time-series.</li> <li>The <em>disturbance severity</em> layer indicates the spectral change in NBR relative to pre-disturbance.</li> <li>The <em>disturbance agent</em> layer summarises the attribution of agents over the full time series. The causal agents assigned are wind/bark beetle complex (1), fire (2), harvest (3) and mixed agents (4, where more than one agent occurred). The stack of disturbance agents provides annual information on causal agent assigned.</li> </ul> <p>The maps are available per country as GeoTIFF. The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). The most current version is 2.1.1. The maps will be updated regularly. <a href="https://albaviana.users.earthengine.app/view/european-forest-disturbance-map">The maps can also be explored online</a>.</p> <p><strong>Version history</strong></p> <ul> <li>2.0.0 - Initial version, covering 1985-2021.</li> <li>2.1.0 - Maps updated until 2023. Added forest land use layer and annual stacks of disturbances (including annual disturbance probabilities and disturbance agents).</li> <li>2.1.1 - Improvements to the disturbance maps introduced and disturbance severity layer added.</li> </ul> <p><strong>Known issues</strong></p> <ul> <li>Some SLC-off artefacts (both version 2.0.0 and 2.1.0).</li> <li>Known errors related to the forest mask of Belarus, Moldova and Ukraine (now corrected in 2.1.0)</li> <li>Known some false disturbances mapped north of 67°N in Norway, Sweeden and Finland.</li> </ul>
Patch metrics and landscape patterns of forest disturbances at the beginning of the 20th Century
<h1>Summary:</h1> <p>The database consists of a compressed .CSV file containing structural information of forest disturbance patches identified between 2002 and 2014 using the Global Forest Change Tree Cover Loss Year dataset version 1.6 (Hansen et al, 2013) available at https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html. Each row in the database represents a patch (249,149,911 in total). The columns (15) represent the structural metrics calculated for each patch, as well as the landscape patterns identified using kmeans cluster analysis. </p> <p>The methods used for building this database are published in the paper: Acil, N., Sadler, J.P., Senf, C. <em>et al.</em> Landscape patterns in stand-replacing disturbances across the world’s forests. <em>Nat Sustain</em> <strong>8</strong>, 86–98 (2025). <a href="https://doi.org/10.1038/s41893-024-01450-3">https://doi.org/10.1038/s41893-024-01450-3</a></p> <p>Aggregated global maps of the patch metrics can be visualised in <a href="https://ee-treemort-disturbances-nacil.projects.earthengine.app/view/patchmetrics2002-2014">Google Earth Engine</a> and accessed in the asset "http://projects/ee-treemort-disturbances-nacil/assets/PatchMetrics_Means_nonLU_2002-2014/". </p> <p>Some of the scripts associated with this project are hosted in <a href="https://github.com/N-Acil/GlobalForestDisturbances_PatchMetrics">GitHub</a> and <a href="https://code.earthengine.google.com/?accept_repo=users/NXA807/%20GlobalForestDisturbances_PatchMetrics">Google Earth Engine</a>.</p> <p>Additional scripts and data will be made available upon request.</p> <p> </p> <p> </p> <h1>Database structure: </h1> <h2>Patch metrics</h2> <h3>Occurrence: </h3> <p>Patch form and year were retrieved from the Global Forest Change tree cover loss year dataset version 1.6 (Hansen et al, 2013).</p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>PID</strong></td> <td>Patch unique identifier in the format Tile_Year_PatchNumber (e.g. 01U_02_00000001).</td> <td> </td> <td>Characters</td> <td> </td> </tr> <tr> <td><strong>X_INT_deg</strong></td> <td>Longitude of the patch's internal centroid</td> <td>Degrees</td> <td>Float</td> <td>[-180-180]</td> </tr> <tr> <td><strong>Y_INT_deg</strong></td> <td>Latitude of the patch's internal centroid</td> <td>Degrees</td> <td>Float</td> <td>[-90-90]</td> </tr> <tr> <td><strong>YEAR_maj</strong></td> <td>Year of patch majority occurrence. </td> <td> </td> <td>Integer</td> <td>[2-14]</td> </tr> <tr> <td><strong>YEAR_n</strong></td> <td>Number of years over which the patch exhibited continuous growth.</td> <td> </td> <td>Integer</td> <td>>0</td> </tr> </tbody> </table> <h3>Metrics: </h3> <p>These patch and landscape metrics were calculated from the patch delineated.</p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>AREA_G_ha</strong></td> <td>Patch geodesic area</td> <td>Hectares</td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>PERIM_G_m</strong></td> <td>Patch geodesic perimeter</td> <td>Meters</td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>PARA</strong></td> <td>Perimeter-area ratio</td> <td> </td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>SHAPE</strong></td> <td>Shape index</td> <td> </td> <td>Float</td> <td>>=1</td> </tr> <tr> <td><strong>ELONG</strong></td> <td>Elongation index</td> <td> </td> <td>Float</td> <td>[0-1[</td> </tr> <tr> <td><strong>FRAC</strong></td> <td>Fractal dimension index</td> <td> </td> <td>Float</td> <td>[1-2]</td> </tr> <tr> <td><strong>NN5000_T0_n</strong></td> <td>Number of patches assigned the same year within 5 km radius.</td> <td> </td> <td>Integer</td> <td>>0</td> </tr> <tr> <td><strong>NN5000_AREA_T0_perc</strong><strong><br></strong></td> <td>Percent of the total area disturbed over the period 2001-2018 within 5 km radius from the focal patch centroid.</td> <td>%</td> <td>Float</td> <td>[0-100]</td> </tr> </tbody> </table> <h3>Clusters:</h3> <p>Cluster identification was performed using AREA_G_ha, YEAR_n, SHAPE, ELONG, NN5000_T0_n and NN5000_AREA_T0_perc. </p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>CLUSTER_CODE</strong></td> <td>Code assigned to each cluster</td> <td> </td> <td>Integer</td> <td>[1-4]</td> </tr> <tr> <td><strong>CLUSTER_LABEL</strong></td> <td>Name given to the cluster identified. </td> <td> </td> <td>Character</td> <td> <ul> <li>Small-isolated</li> <li>Clustered</li> <li>Complex</li> <li>Large-multiyear</li> </ul> </td> </tr> </tbody> </table> <p> </p>
First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat
<p>Companion dataset to the paper entitled "First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat". The dataset includes the SORTIE CubeSat IVM Level 2 ion density and GPS TEC data used in the study along with the WRF simulation results.</p>
CO2 and CH4 gas fluxes in disturbed and intact northern peatlands
<p>The data were collected in seven Estonian peatlands (5 disturbed and 2 intact) during three to four (2017–2020) years with closed chamber technique. Table 1 (see CO2_CH4_fluxes_README.docx) shows the variables presented in this dataset.</p>
Dataset for "Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra"
<p>Data that support the findings of the study "<strong>Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra</strong>".</p>
Data for "Shell microstructures (disturbance lines) of Arctica islandica (Bivalvia) – A potential proxy for severe oxygen depletion"
<p>All data used in the publication "Shell microstructures (disturbance lines) of <em>Arctica islandica</em> – A potential proxy for severe oxygen depletion" currently under review. This includes in situ environmental data from the Mecklenburg Bight, Baltic Sea (ODIN 2, Leibnitz Institute for Baltic Sea Research, https://odin2.io-warnemuende.de/), and biomineral unit (BMU) morphology measurements in scanning electron microscopy images of shells of <em>Arctica islandica</em>, as well as the BMU classifier used in Ilastik (Berg et al., 2019).</p> <p>Each BMU measurement represents summary statistics of the 15 % largest BMUs within one image (25, 50 and 75 % percentile of each BMU parameter). Environmental data were measured at 20 m water depth, ca. 5 m above the sediment surface.</p>
Tracks of western disturbances (1950-2022) impacting South Asia
<p>WDs are identified using the feature-tracking algorithm described in Hunt et al (2018). Relative vorticity is averaged across the 450-300 hPa layer, and then spectrally truncated to T42 to remove high-frequency noise that hinders tracking. For each region of positive vorticity, the centroid is located and labelled as a candidate WD. These centroids are connected between timesteps using a nearest-neighbour algorithm, biased to take into account the steering winds of the subtropical jet. Systems that do not on average travel eastward, last fewer than 48 hours, or do not pass through the box [20-42.5°N, 60-80°E] are rejected.<br> Applied to ERA5, this gives over seventy years of track data (1950-2022). The method followed here is identical to Nischal et al (2022), except the northern edge of the catching box is extended from 36.5°N to 42.5°N, to ensure that all WDs that potentially impact North India are included.<br> <br> Column titles are:<br> <strong>timestep</strong>: a counter indicating the number of 3-hourly timesteps that have passed since 1950-01-01 00:00<br> <strong>track_id</strong>: a unique identifier linking points into tracks<br> <strong>time</strong>: string describing the date and time<br> <strong>lon</strong>: longitude<br> <strong>lat</strong>: latitude<br> <strong>vort</strong>: vorticity measured at the centre of the WD averaged over the 450-300 hPa layer. Can be used for intensity filtering.<br> <strong>eccentricity</strong>: eccentricity of the region of positive vorticity. Can be used to understand local dynamics.<br> <br> <br> <br> Hunt, K. M. R., Turner, A. G., & Shaffrey, L. C. (2018). The evolution, seasonality and impacts of western disturbances. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>144</em>(710), 278-290.<br> <br> Nischal, Attada, R., & Hunt, K. M. (2022). Evaluating winter precipitation over the western Himalayas in a high-resolution Indian regional reanalysis using multisource climate datasets. <em>Journal of Applied Meteorology and Climatology</em>, <em>61</em>(11), 1613-1633.</p>
Diversity loss from multiple interacting disturbances is regime-dependent
<p>Data and R code for 'Diversity loss from multiple interacting disturbances is regime-dependent'.</p> <p>Information about the files can be found in the ._README.txt file.</p>
Dataset for Disturbance: a double-edged sword for restoration in a changing climate: Western Oregon and Washington upland prairies 2019-2020
Data associated with the paper submitted to Restoration Ecology in November 2021: Disturbance: a double-edged sword for restoration in a changing climate Alejandro Brambila1, Paul B. Reed1, Scott D. Bridgham1, Bitty A. Roy1, Bart R. Johnson2, Laurel Pfeifer-Meister1 and Lauren M. Hallett1 1. Institute of Ecology and Evolution, University of Oregon 2. Department of Landscape Architecture, University of Oregon In this project, we used this data to test how fire disturbance, designed to enhance restoration seeding success, combines with climate and initial vegetation conditions to shift perennial versus annual grass dominance and overall community diversity in Pacific Northwest grasslands. We seeded both native and introduced perennial grasses and native forbs in paired, replicated burned-unburned plots in three sites along a latitudinal climate gradient from southern Oregon to Washington. Past restoration and climate manipulations at each site had increased the variation of starting conditions between plots. This data is to be used with the script, full_disturbance_script.R, which can be accessed at https://github.com/HallettLab/hops. Includes the tables: plotkey.csv spkey.csv mixkey.csv vegplot.csv vegplot2020.csv
Disturbance effects on soil processes in the Andrews Experimental Forest (1995 Stand Age Study)
This study was designed to determine how edges generated by clear-cutting old-growth forests influence patterns of soil carbon and nitrogen cycling and the distribution of ectomycorrhizal mats and to determine how these patterns change with time after harvest. A secondary objective was to measure how clear-cutting within different climatic regimes influenced soil nitrogen and carbon cycling.
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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.