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214 results for “forest disturbance”
Ungulate-Disturbance Interactions in Hemlock Ecosystems at Harvard Forest 2012-2013
Densities of ungulates are often associated with recent forest disturbances such as fire, logging and insect outbreaks, as increased resources stimulate tree regeneration, leading to abundant available browse. Despite the often significant role that ungulates play in disturbed forests, surprisingly little is known about ungulate-disturbance interactions, and ungulate herbivory is often excluded from examinations of forest response to disturbance. In the Northeastern United States, a large-scale insect outbreak, the hemlock wooly adelgid (HWA), has begun to have important effects on hemlock forests both directly and indirectly (i.e., by preemptive salvage logging). No studies have examined the interactions of both moose and deer activity with these associated canopy disturbances. Our objectives are twofold: to determine (1) the response of ungulates to varying disturbance types and intensities and (2) the influence of ungulate interactions with canopy disturbance on vegetation, other biota, and ecosystem processes. We will examine the relative abundance of moose and deer in four treatments (2 replicates) at the Hemlock Removal Experiment using three indices of ungulate activity: pellet group density, evidence of past browsing on seedlings and saplings, and the occurrence of animals using game cameras. We will also monitor the response of vegetation to ungulate browsing by sampling woody and herbaceous vegetation in fenced exclosures and paired controls in disturbed and undisturbed plots.
Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests
<p><strong>Data description</strong></p> <p>These datasets were generated for the Geostory "Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests" in the context of the Open Earth Monitor Cyberinfrastructure project.</p> <p>We used open source high-resolution Sentinel-1 satellite data to develop a wall-to-wall map of forest disturbances in the four-year period between the start of 2020 and end of 2023 in Estonia. First results are presented. The methodology is based on RADD-alerts developed for the pan-tropics (Reiche et al. 2021). Three years (2017-2019) of imagery was used as a historical period, and detections were generated for ~4 years (2020-2023). Winter images from November through March were not included as frozen conditions can introduce false detections. This will be addressed in the next version. Disclaimer: Disturbance maps have not been validated.</p> <p>Two additional layers are provided for visualization: a forest baseline layer (<em>forestcover</em>), masking out non-forest disturbance detections, was derived from Copernicus 10m 2018 forest cover density and GLAD 30m 2019 tree removal datasets, and a protected areas layer (<em>natura</em>), which displays the extent of Natura 2000 coverage in Estonia.</p> <p>'.SLD' files are provided for visualization (note: the <em>disturbance</em> .SLC file must be adjusted to contain appropriate time reference fields).</p> <p><strong>Naming Convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For instance:</p> <ul> <li>disturbance_radd_c_10m_s_20200101_20200131_eu_epsg.3035_v20240222.tif</li> </ul> <p>with the following fields:</p> <ul> <li>Generic variable name: <strong>disturbance</strong></li> <li>Variable procedure combination i.e. method standard: <strong>radd</strong></li> <li>Position in the probability distribution / variable type: <strong>c</strong></li> <li>Spatial support: <strong>10m</strong></li> <li>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): <strong>s</strong></li> <li>Time reference begin time (YYYYMMDD): <strong>20200101</strong></li> <li>Time reference end time: <strong>20200131</strong></li> <li>Bounding box (2 letters max): <strong>eu </strong></li> <li>EPSG code: <strong>epsg.3035</strong></li> <li>Version code i.e. creation date: <strong>v20240222</strong></li> </ul> <p><strong>Source Data</strong></p> <p>Disturbance maps:</p> <p>Contains modified Copernicus Sentinel data [2017-2023] and Generated using European Union's EEA-10 Copernicus DEM; https://doi.org/10.5270/ESA-c5d3d65</p> <p>Forest baseline:</p> <p>Generated using European Union's Copernicus Land Monitoring Service information; https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791 and GLAD tree removal; https://doi.org/10.1016/j.rse.2023.113797</p> <p>Natura 2000: </p> <p>Generated using European Environmental Agency's Natura 2000 layers; https://sdi.eea.europa.eu/data/dae737fd-7ee1-4b0a-9eb7-1954eec00c65</p>
Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance
Disturbances can change the structural diversity of forests through time, which can be measured from three-dimensional data provided by LiDAR. Discrete-return LiDAR was used to measure a suite of 19 structural diversity metrics that describe the height, cover and openness, vegetation density, and internal and external heterogeneity of forest vegetation at NEON base plots. Discrete-return LiDAR point clouds from the NEON Aerial Observation Platform (DP1.30003.001) were downloaded September of 2020 and used to estimate the metrics within 40 x 40 m base plots. Metrics were estimated from base plots at 15 NEON forested sites from provisional LiDAR data available from 2014 to 2020. The workflow that produced the data was developed in the program R.
Shrimp populations variability in numbers and sizes in response to disturbance and seasons on 20 pools along the reach of Quebrada Prieta, Luquillo Experimental Forest
Shrimp populations were monitored at approximately 3 week intervals to determine the variability in numbers and sizes of each species in response to disturbance and seasons. 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.
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>
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>
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.
Disturbance legacies and resilience simulation using an individual-based forest landscape model on the Andrews Experimental Forest
Disturbances are key drivers of forest ecosystem dynamics, and forests are well adapted to their natural disturbance regimes. However, as a result of climate change, disturbance frequency is expected to increase in the future in many regions. It is not yet clear how such changes might affect forest ecosystems, and which mechanisms contribute to (current and future) disturbance resilience. We here studied the 6364-ha HJ Andrews Experimental Forest landscape to investigate how patches of remnant old-growth trees (as one important class of biological legacies) affect the resilience of forest ecosystems to disturbance. Using the spatially explicit, individual-based forest landscape model iLand we analyzed the effect of three different levels of remnant patches (0%, 12%, and 24% of the landscape) on 500-year recovery trajectories after a large, high severity wildfire. In addition, we evaluated how three different levels of fire frequency (no fire, a historic fire return interval of 262 years, and a reduced fire return interval of 131 years) modulate the effects of initial legacies. The study investigated effects of legacies on the resilience of forest ecosystem structure (represented by canopy complexity as described by the rumple index), composition (proportion of late-seral species), and functioning (total ecosystem carbon storage). For each scenario of initial legacy and fire return interval 25 replicates were simulated. More information on the simulation methodology as well as the code and executable used for this study can be obtained at http://iLand.boku.ac.at. The dataset is completed and no further analyses are planned at this point. The results are published in Ecological Applications http://dx.doi.org/10.1890/14-0255.1.
Figure 1 in Deadwood and saproxylic beetle diversity in naturally disturbed and managed spruce forests in Nova Scotia
Figure 1. Non-metric multidimensional scaling ordination diagrams of forest sites in two-dimensional space defined by (A) beetle assemblage and (B) habitat structures. Symbols signify disturbance history class of forests: 1= CLEARCUT, D= THINNED, ·= FIRE, and Ɨ= WIND. Numbers identify the specific forest. Forest number 25 was excluded as an outlier in (B) due to much higher deadwood volumes than all other sites.
Fig. 5 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 5. Human detections along five, two-kilometre transects, one each in five habitats/subtypes (young dipterocarp forest, flooded dipterocarp forest, mixed deciduous forest, mature dipterocarp forest, and wetlands) per month in Chatthin Wildlife Sanctuary. Effort = number of habitat subtypes * total months sampled per year * 2 km, X-axis= year, Y-axis= human disturbance index (number of human detections per one unit of effort).
Fig. 1 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 1. Map of Chatthin Wildlife Sanctuary with land-cover changes; flooded dipterocarp forest (FL), mixed deciduous forest (MD) and young dipterocarp forest (YI) in Chatthin Wildlife Sanctuary between 1999 and 2020 and locations of bird survey points.
Fig 4 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig 4. Seasonal (y-axis) and long-term trends of abundances of six avian guilds in Chatthin Wildlife Sanctuary from 1999 to 2020. Redder = higher abundances, white/paler = lower abundances, and grey = no data.
Feral pig (Sus scrofa) disturbance facilitates establishment of resource-acquisitive species in Hawaiian forest understories
<p>In this study, we quantify the effects of leaf traits and dispersal attributes on species responses to pig soil disturbance at two spatial scales – 0.5 m<sup>2</sup> patches embedded along 20 m transects within sites – across a gradient of pig density in a Hawaiian montane wet forest using Bayesian mixed models. </p> <p>Native and non-native species demonstrated divergent responses, with increasing presence and abundance of non-native species in the understory as soil disturbance within patches and sites increased. Dominant patterns in measured traits tracked the leaf economic spectrum (LES), with non-native species tending toward resource-acquisitive traits. Species with resource-acquisitive traits, regardless of identity, were favored with disturbance and responded positively to light availability in disturbed sites. Models showed species primarily dispersed by wind were more prevalent in disturbed patches and sites than those dispersed by endozoochory, while seed mass had no effect.</p>
Digital repository for: Large-scale forest disturbance and associated management shape bird communities in Central European spruce forests
<p>Repository containing R-script and data to reproduce analysis and main figures on the effect of large-scale forest disturbance and associated pre- and post-disturbance management on bird communities in the Harz Mountains, Germany.</p> <p>R-script includes:</p> <ul> <li>indicator species analysis (R package indicspecies; Cáceres & Legendre, 2009)</li> <li>non-metric multidimensional scaling (R package vegan; Oksanen et al., 2016)</li> <li>rarefaction- and extrapolation of Hill numbers (R package iNEXT; Hsieh et al., 2019)</li> <li>multi-species community distance sampling (R package sp Abundance; Doser et al., 2023)</li> </ul> <p>Attached files:</p> <ul> <li><strong>bird_data_Graser_et_al.csv </strong>(row data of bird species point counts per distance category)</li> <li><strong>bird_data_abundance_100_Graser_et_al.csv </strong>(abundance of species per sampling site, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>siteCovs_Graser_et_al.csv</strong> (environmental variables for each sampling point)</li> <li><strong>A_species_matrix_100_new_Graser_et_al.csv</strong> (species-site matrix of <strong>bark-beetle disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>B_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>windthrow disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>C_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle/windthrow disturbance, underplanted, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>D_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, salvage-unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>E_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, underplanted, salvage-unlogged </strong>sites for rarefaction and extrapolation, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong> F_species_matrix_100_new_Graser_et_al.cs</strong>v (species-site matrix of <strong>mature spruce plantation </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>msHDS_bird_data_management_model_Graser_et_al.rds</strong> (R-data set for multi-species community distance sampling of the effect of different pre- and post-disturbance management groups)</li> <li><strong>msHDS_bird_data_stand_age_model_Graser_et_al.rds </strong>(R-data set for multi-species community distance sampling of the effect of post-disturbance forest succession)</li> </ul> <p>A more detailed description of the data can be found in the README.txt document.</p> <p><span>References:</span></p> <p><span>Cáceres, M. D., & Legendre, P. (2009). </span><span>Associations between species and groups of sites: Indices and statistical inference. <em>Ecology</em>, <em>90</em>(12), 3566–3574. https://doi.org/10.1890/08-1823.1</span></p> <p><span>Doser, J. W., Finley, A. O., Kéry, M., & Zipkin, E. F. (2023). spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models. <em>Methods in Ecology and Evolution</em>, <em>15</em>(6), 1024–1033. https://doi.org/10.1111/2041-210X.14332</span></p> <p><span>Hsieh, T. C., Ma, K. H., & Chao, A. (2019). <em>iNEXT-package: Interpolation and extrapolation for species diversity</em>. https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.html</span></p> <p><span>Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., Minchin, P. R., Gavin, L., & Henry, H. (2016). Vegan: Community ecology package. R package version 1.17-4. <em>Http://CRAN. R-Project. </em></span><em><span>Org/Package=vegan</span></em><span>.</span></p> <p></p> <p></p>
FIG. 3 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 3. — Co-correspondence analysis biplot illustrating the ordination of most frequent bryophyte species colonizing ground in relation to the statistically significant environmental variables (n=75). Abbreviations: Age, forest age; DBH.max, maximal trees diameter at breast height; DW.vol, dead wood volume; L, Ellenberg indicator value (EIV) for light; T, EIV for temperature; F, EIV for moisture; R, EIV for pH; N, EIV for nitrogen; the first four letters of species indicate genus name, next four indicate species names and next four letters subspecies names.
0.0 0.5 1.0 1.5 RDA 1 FIG. 4 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
0.0 0.5 1.0 1.5 RDA 1 FIG. 4. — Redundancy analysis biplot illustrating the ordination of most frequent bryophyte species colonizing tree trunks in relation to the statistically significant environmental variables for the plots with the presence of tree host species (n=41). Abbreviations as in Fig. 3.
FIG. 6 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 6. — Redundancy analysis biplot illustrating the ordination of most frequent bryophyte species colonizing dead wood in relation to the statistically significant environmental variables for the plots with the presence of decaying logs (n=80). Abbreviations as in Fig. 3
FIG. 5 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 5. — Redundancy analysis biplot illustrating the ordination of most frequent bryophyte species colonizing tree bases in relation to the statistically significant environmental variables for the plots with the presence of tree host species (n=96). Abbreviations as in Fig. 3.
FIG. 2 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 2. — Frequency of occurrence of bryophytes generally and in the four types of substrates considered.
ScienceDex guides
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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.