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47 results for “selective logging”
Clearcutting and selective logging have inconsistent effects on liana diversity and abundance but not on liana–tree interaction networks
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Agent‐based modeling of the effects of forest dynamics, selective logging, and fragment size on epiphyte communities
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Impacts of selective logging on the oxidative status of tropical understory birds
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LBA-ECO LC-21 Selective Logging Activity in the Brazilian Amazon: 1999-2002
This data set provides the results of analyses of Landsat Enhanced Thematic Mapper Plus (ETM+) images for selective logging activity in the Brazilian states of Para, Mato Gross, Rondonia, Roraima, and Acre over the years 1999 through 2001. Images were analyzed using the Carnegie Landsat Analysis System (CLAS) to detect and to quantify the amount of damage due to selective logging in the major timber-production states of the Brazilian Amazon. This approach provided automated image analysis using atmospheric modeling for detection of forest canopy openings, surface debris, and bare soil exposed by forest disturbances; and pattern-recognition techniques. CLAS provides detailed measurements of forest-canopy damage at a spatial resolution of 30 x 30m. Fifteen GeoTiff format files are included -- one for each of the three years from 1999-2001 for each of the five states. Each GeoTiff is a single band image where each pixel represents if logging activity was or was not detected. A zero (0) value indicates that no logging was detected, while a value of one (1) indicates that damage from logging was detected. The 15 GeoTiff (*.tif) files have been compressed into one *.zip file.
Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests
<p>Species mean information for the six leaf water-related traits used in the paper titled: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests.</p>
Selected data sets for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'
<p>Data sets used in the for the manuscript <strong>Marsh<em> </em>et<em> </em>al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'</strong>. The DOIs that link to all other data sets used in the publication are available in Tables S2-5 of the supplementary information. The z-score standardised data, and outputs of RMarkdown documents outline all the steps in the processing and analysis of the data are available at https://zenodo.org/uploads/13161799.</p> <p> </p> <p>This repository contains data used for:</p> <h3><strong><em>Mean canopy height</em></strong></h3> <p>Canopy height and vertical profiles of forest structure were compiled using airborne remote sensing with LiDAR collected by NERC’s Airborne Research Facility (ARF) in November 2014, using a Leica ALS50-II LiDAR. A Beer-Lambert approximation was used to convert point clouds to plant area density (PAD) distributions, a similar measure to leaf-area index, but where methods do not distinguish between leaves and branches or trunks. LiDAR measurements for the carbon plots were converted to rasters with 0.5 × 0.5 m cell size. Plots were rotated to a North-South axis if necessary</p> <h3><br><em><strong>Spectral diversity</strong></em></h3> <p>Spectral measurements were made on five leaves attached to tree branches used to measure leaf chemical traits. Leaves were randomly selected but we avoided damaged and young plant material to avoid potential confounding factors. Reflectance spectra (350–2500 nm) were acquired using a FieldSpec 4, produced by Analytical Spectral Devices (ASD, Boulder, Colorado, USA). The spectroradiometer's contact probe was mounted on a clamp and firmly pushed down onto the sample against a black background so that no extraneous light was included in the measurement. Spectral measurements were taken halfway between the petiole and leaf tip, and between the main vein and the leaf edge, with the abaxial surface pointing towards the probe. The readings were calibrated against a Spectralon white reference panel every five samples. Leaf reflectance measured at 430 nm, 660 nm, 1450, 1980 nm and 2350 nm align closely with absorption features for pigments, water content, proteins and cellulose. Spectral diversity calculated from these absorption features can provide an integrated measure of the functional trait variability within plant communities and may be used as a proxy for functional diversity.</p> <p> </p> <h3><em><strong>Liana abundance</strong></em></h3> <p>Percentage liana cover for large canopy and emergent trees. The four quadrants of the canopy were scored as 0 (no lianas), 1 (1-20%), 2 (20-40%), 3 (40-60%), 4 (60-80%) and 5 (80-100%).</p> <p> </p> <h3><em><strong>Leaf-area index<br></strong></em></h3> <p>Leaf area index (LAI) for carbon plots was derived from hemispherical photos (Sigma 8mm SRL fish eye lens and Canon EOS 600D digital camera, mounted on a tripod at 1 m height). Between 5-27 photos were taken over time in each subplot. Images were processed with Hemisfer® software (www.wsl.ch/dienstleistungen/produkte/software/hemisfer/index_EN). LAI was calculated with the method by Thimonier et <em>al</em>. (2010) <em>European Journal of Forest Research</em> 129, 543–562 (2010), with a canopy clumping correction applied from Chen & Cihlar (1995) <em>IEEE Transactions on Geoscience and Remote Sensing</em> 33, 777–787.</p> <p> </p> <h2>Funding</h2> <p>Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) using the following funding:</p> <ul> <li>NERC's Human Modified Tropical Forests research programme (grant number NE/K016377/1 awarded to the BALI consortium)</li> <li>MHN was supported by a PhD scholarship from the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant No. 201516/2014-4) from Brazil</li> </ul>
Selective logging and plantation impacts on dung beetle abundances in Malaysian Borneo
<p>If you wish to use these data, please contact grcerullo AT gmail DOT com. </p> <p>This dataset represents a geolocated and standardised dung beetle sampling effort spanning a logging and plantation gradient in Sabah, Malaysian Borneo. Habitat types sampled in Jun-Aug 2022 include:</p> <ul> <li><strong>Primary forest </strong>(12 traps, 1 site)</li> <li><strong>Restored forest </strong>(once-logged forest subjected to liana cutting and enrichment planting) (24 traps, 2 sites)</li> <li><strong>Once-logged forest</strong> left naturally regenerating (24 traps, 2 sites)</li> <li><strong>Twice-logged forest </strong>(12 traps, 1 site)</li> <li><strong><em>Albizia falcataria</em></strong> plantations, grown on 12-yr rotation (36 traps, 3 sites)</li> <li><strong><em>Eucalyptus pellita</em> </strong>plantations, grown on 6-yr rotations. (36 traps, 3 sites)</li> </ul> <p>Efforts were made to sample across a chronosequence of harvesting and plantation ages. These data supplement historic dung beetle sampling efforts throughout the Ulu-Segama area, which will also be made openly available by Gianluca Cerullo (grcerullo AT gmail.com) on Zenodo prior to manuscript publication. </p> <p>Traps were located 200 m apart with sites > 1km apart, where possible. Pitfall traps were baited with human dung and left for 96 hrs, with beetles collected every 24hs and with traps rebaited after 48hrs. </p> <p>Dung beetles were identified by Gianluca Cerullo to species-level following the GBIF backbone. Reference collections are stored in the Entomology laboratory of UMS, in Kota Kinabalu, Sabah. All beetles collected have been stored in ethanol and are being shipped to NTU in Singapore.</p> <p><em>SHAPEFILES ARE ALSO PROVIDED FOR: </em></p> <p><strong>SabahSoftwoodCompartments</strong>: the area, size and location of different plantation compartments within the Sabah Softwood tree plantation area. This represents the largest timber plantation in Sabah with Eucalyptus pellita and Albizia falcataria the main plantation species. </p> <p><strong>SabahSoftwoodsPlantingYrs2022</strong>: The species planted in the plantation, plus the year of planting. These data were correct as of July 2022. </p> <p><strong>DanumValley_PrimaryForest: </strong>Shows the boundaries of primary forest in Danum Valley. </p> <p><strong>INFAPRO: </strong>Shows the location of areas logged and and then subjected to vine-cutting and strip-planting of native dipterocarp seedlings. </p> <p><strong>COUPES:</strong> Shows the location and age of different logging coupes in the Yayasan Sabah logging concession. </p> <p>Further information on sampling protocol and the study area can be found in Chapter 3 (and the appropriate Appendices) of GC's thesis: https://www.repository.cam.ac.uk/items/617f455b-2eb5-4b14-903a-9e6bbd968886 (this section will be updated upon manuscript publication). </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)
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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.