Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
47
datasets available to search
ShareScore release 0.7.1
Dataset results
47 results for “selective logging”
Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations
The impact of logging and subsequent recovery after logging is predicted to vary depending on specific life history traits of the logged species. The Eco-gene simulation model was used to evaluate the long-term impacts of selective logging over 300 years on two contrasting Brazilian Amazon tree species, Dipteryx odorata and Jacaranda copaia. D. odorata (Leguminosae), a slow growing climax tree, occurs at very low densities, whereas J. copaia (Bignoniaceae) is a fast growing pioneer tree that occurs at high densities. Microsatellite multilocus genotypes of the pre-logging populations were used as data inputs for the Eco-gene model and post-logging genetic data was used to verify the output from the simulations. Overall, under current Brazilian forest management regulations, there were neither short nor long-term impacts on J. copaia. By contrast, D. odorata cannot be sustainably logged under current regulations, a sustainable scenario was achieved by increasing the minimum cutting diameter at breast height from 50 to 100 cm over 30-year logging cycles. Genetic parameters were only slightly affected by selective logging, with reductions in the numbers of alleles and single genotypes. In the short term, the loss of alleles seen in J. copaia simulations was the same as in real data, whereas fewer alleles were lost in D. odorata simulations than in the field. The different impacts and periods of recovery for each species support the idea that ecological and genetic information are essential at species, ecological guild or reproductive group levels to help derive sustainable management scenarios for tropical forests.
Drivers of soil microbial community assembly during recovery from selective logging and clear cutting
<p>Despite important progress in understanding the impacts of forest clearing and logging on aboveground communities, how these disturbances affect soil microbial β-diversity and the ecological processes driving microbial assemblages are poorly understood. Further, whether and how the microbial shifts affect vegetation composition and diversity during recovery of post-logged forests remain elusive. 2. Using a spatial grid experiment design in a primary tropical forest intermixed with post-logged patches naturally recovered for half century in Hainan Island, China, we characterized and explained the distance-decay relationships of soil microbial similarities in primary, selectively logged and clear cut forests. 3. Selectively logged sites showed a lower spatial turnover rate of bacterial assemblages based on phylogenetic and taxonomic β-diversity, but a higher spatial turnover rate of fungal assemblages based on phylogenetic β-diversity, suggesting a higher level of phylogenetic variability in fungal composition. Clear cut sites showed lower spatial turnover for both bacterial and fungal assemblages based on the two β-diversity, indicating community homogenization. Main drivers of microbial assemblages shifted from soil properties in primary forest to tree composition in selectively logged sites, whereas microbial-tree associations declined in clear cut sites, leading to stochastically organized microbial assemblages. 4. Synthesis and applications. The increased fungal phylogenetic turnover with tree turnover following selective logging promotes unassisted recovery of plant diversity. In contrast, the decoupling of tree and microbial turnover following clear-cutting suggests restoration approaches based on tree planting, and tree species that have strong associations with bulk soil microbial community should be considered. Our findings advance the understanding of spatial patterns, processes, and drivers of soil microbial assemblages in parallel with tree community recovery during regeneration of post-logged tropical forests, and highlight the importance of coupling assemblage patterns between tree and soil fungal communities for conserving tropical forest biodiversity.12-Jul-2021 --</p>
Data from: Responses of interspecific associations in mixed-species bird flocks to selective logging
Open the record for dataset details and reuse information.
Data from: Impacts of selective logging on inbreeding and gene flow in two Amazonian timber species with contrasting ecological and reproductive characteristics
Open the record for dataset details and reuse information.
Data from: Inter-annual dynamics and persistence of small mammal communities in a selectively logged tropical forest in Borneo
Open the record for dataset details and reuse information.
Data from: Incorporating intraspecific trait variation into functional diversity: impacts of selective logging on birds in Borneo
Open the record for dataset details and reuse information.
Data from: Seed and pollen dispersal distances in two African legume timber trees and their reproductive potential under selective logging
Open the record for dataset details and reuse information.
Drivers of soil microbial community assembly during recovery from selective logging and clear cutting
Open the record for dataset details and reuse information.
Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations
Open the record for dataset details and reuse information.
Data from: Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species
Open the record for dataset details and reuse information.
Data from: Selective logging intensity in an East African rain forest predicts reductions in ant diversity
Open the record for dataset details and reuse information.
Data from: Effects of reduced-impact selective logging on palm regeneration in Belize
Open the record for dataset details and reuse information.
Data from: Multiple stages of tree seedling recruitment are altered in tropical forests degraded by selective logging
Open the record for dataset details and reuse information.
Data from: Carbon recovery dynamics following disturbance by selective logging in Amazonian forests
Open the record for dataset details and reuse information.
Data from: Prolific fruit output by the invader Bellucia pentamera (Melastomataceae) is enhanced by selective logging disturbance
Open the record for dataset details and reuse information.
Impacts of selective logging on the oxidative status of tropical understory birds
<p>1. Selective logging is the dominant form of human disturbance in tropical forests, driving changes in the abundance of vertebrate and invertebrate populations relative to undisturbed old-growth forests.<br> 2. A key unresolved question is understanding which physiological mechanisms underlie different responses of species and functional groups to selective logging. Regulation of oxidative status is thought to be one major physiological mechanism underlying the capability of species to cope with environmental changes.<br> 3. Using a correlational cross-sectional approach, we compared a number of oxidative status markers among 15 understory bird species in unlogged and selectively logged forest in Borneo in relation to their feeding guild. We then tested how variation of markers between forest types was associated with that in population abundance.<br> 4. Birds living in logged forests had a higher activity of the antioxidant enzyme superoxide dismutase and a different regulation of the glutathione cycle compared to conspecific birds in unlogged forest. However, neither oxidative damage nor oxidized glutathione differed between forest types. We also found that omnivores and insectivores differed significantly in all markers related to the key cellular antioxidant glutathione irrespective of forest type. Species with higher levels of certain antioxidant markers in a given type of forest were less abundant in that forest type compared to the other.<br> 5. Our results suggest that there was no long-term effect of logging (last logging rotation occurred ~15 years prior to the study) on the oxidative status of understory bird species. However, it is unclear if this was owing to plasticity or evolutionary change. Our correlative results also point to a potential negative association between some antioxidants and population abundance irrespective of forest type.</p>
Clearcutting and selective logging have inconsistent effects on liana diversity and abundance but not on liana–tree interaction networks
<p class="MsoNoSpacing">Understanding the effects of forest management on lianas and their interaction with trees is an important step towards effective forest management. Our study therefore aimed at quantifying the patterns of liana diversity and abundance, and liana-tree interaction network structure in response to logging disturbance in a moist semi-deciduous forest in Ghana. We sampled lianas (diameter at 1.3 m ≥ 1 cm) and their host trees (diameter at breast height ≥ 5 cm) in 90 20 × 20 m plots among three forest management regimes: clearcut-logged, selectively-logged and old-growth forests. Liana species diversity and abundance in the selectively-logged forest was similar to that of the old-growth forest, while that in clearcut-logged forest was significantly lower than both above-mentioned forest types. Liana-tree interaction networks showed anti-nested structure, which is a form of nonrandom community organization. There were significant modularity and degree of specialization, but no significant connectance in the network structure. Largely, most of the species were peripherals, while a few species acted as structurally important species (i.e. module hubs, network hubs and connectors) in the three networks. A different set of species acted as structurally important species in the different forest management regimes. Our findings call for a re-examination of clearcutting logging in forest management in view of its negative effects on lianas, and we recommend prioritizing important modules in liana-tree network for future conservation.</p>
Agent‐based modeling of the effects of forest dynamics, selective logging, and fragment size on epiphyte communities
<p>Forest canopies play a crucial role in structuring communities of vascular epiphytes by providing substrate for colonization, by locally varying microclimate, and by causing epiphyte mortality due to branch or tree fall. However, as field studies in the three-dimensional habitat of epiphytes are generally challenging, our understanding of how forest structure and dynamics influence the structure and dynamics of epiphyte communities is scarce. Mechanistic models can improve our understanding of epiphyte community dynamics. We present such a model that couples dispersal, growth, and mortality of individual epiphytes with substrate dynamics, obtained from a three-dimensional functional-structural forest model, allowing the study of forest-epiphyte interactions. After validating the epiphyte model with independent field data, we performed several theoretical simulation experiments to assess how (1) differences in natural forest dynamics, (2) selective logging, and (3) forest fragmentation could influence the long-term dynamics of epiphyte communities. The proportion of arboreal substrate occupied by epiphytes (i.e. saturation level) was tightly linked with forest dynamics and increased with decreasing forest turnover rates. While species richness was, in general, negatively correlated with forest turnover rates, low species numbers in forests with very low turnover rates were due to competitive exclusion when epiphyte communities became saturated. Logging had a negative impact on epiphyte communities, potentially leading to a near-complete extirpation of epiphytes when the simulated target diameters fell below a threshold. Fragment size had no effect on epiphyte abundance and saturation level but correlated positively with species numbers. Synthesis: The presented model is a first step towards studying the dynamic forest-epiphyte interactions in an agent-based modelling framework. Our study suggests forest dynamics as key factor in controlling epiphyte communities. Thus, both natural and human-induced changes in forest dynamics, e.g. increased mortality rates or the loss of large trees, pose challenges for epiphyte conservation.</p>
Figure 1 from: Amosu A, Mahmood H (2018) PyLogFinder: A Python Program for Graphical Geophysical Log Selection. Research Ideas and Outcomes 4: e23676. https://doi.org/10.3897/rio.4.e23676
Figure 1 Graphical representation of logs present in several LAS files. The green color represents logs present and the white color represent logs which are not present.
Multiple stages of tree seedling recruitment are altered in tropical forests degraded by selective logging
<b>Description: </b><p>Tree locations, tree size measurements, seed trap data, seed germination and seedling survival data</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/132"><b>The impact of logging on density-dependent predation and recruitment of dipterocarp seeds during a mast-fruiting year</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=221">here</a></p><p><b>Files: </b>This consists of 1 file: Pillay_R_et_al_Dryobalanops_lanceolata_AllData.xlsx</p><p><b>Pillay_R_et_al_Dryobalanops_lanceolata_AllData.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>TreeSize</b> (described in worksheet TreeSize)</p><p>Description: Size measurements of experimental trees</p><p>Number of fields: 8</p><p>Number of data rows: 13</p><p>Fields: </p><ul><li><b>ftype</b>: Forest Type (Field type: Categorical)</li><li><b>tree.id</b>: Unique ID of experimental trees (Field type: Location)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>dbh_cm</b>: Tree DBH (Field type: Numeric)</li><li><b>measured_height_m</b>: Measured tree height (Field type: Numeric)</li><li><b>researcher_height_m</b>: Researcher height (Field type: Numeric)</li><li><b>height_m</b>: Tree height (measured height + researcher height) (Field type: Numeric)</li><li><b>crown_diameter_m</b>: Tree crown diameter (Field type: Numeric)</li></ul></li><li><p><b>SeedfallTrapByDist</b> (described in worksheet SeedfallTrapByDist)</p><p>Description: Seed trap data</p><p>Number of fields: 9</p><p>Number of data rows: 312</p><p>Fields: </p><ul><li><b>ftype</b>: Forest Type (Field type: Categorical)</li><li><b>tree.id</b>: Unique ID of experimental trees (Field type: Location)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>transect</b>: Unique ID of transects around each experimental tree (Field type: ID)</li><li><b>bearing</b>: Transect compass bearing (Field type: Numeric)</li><li><b>trap</b>: Unique ID of seed traps along transects (Field type: ID)</li><li><b>distance</b>: Distance of seed trap along transect (Field type: Numeric)</li><li><b>PC1</b>: Principal Components Analysis variable used as a surrogate for tree size (Field type: Numeric)</li><li><b>seeds</b>: Total number of seeds that fell into each seed trap over the study period (Field type: Numeric)</li></ul></li><li><p><b>NaturalPlotsSingleRec-Seed</b> (described in worksheet NaturalPlotsSingleRec-Seed)</p><p>Description: Seed germination and seedling survival data in natural plots</p><p>Number of fields: 23</p><p>Number of data rows: 2069</p><p>Fields: </p><ul><li><b>FTYPE</b>: Forest Type (Field type: Categorical)</li><li><b>TREE.ID</b>: Unique ID of experimental trees (Field type: Location)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>TRANSECT</b>: Unique ID of transects around each experimental tree (Field type: ID)</li><li><b>BEARING</b>: Transect compass bearing (Field type: Numeric)</li><li><b>PLOT</b>: Unique ID of natural plots along transects (Field type: ID)</li><li><b>DISTANCE</b>: Distance of natural plot along transect (Field type: Numeric)</li><li><b>SEED.ID.FIELD</b>: Unique identification number assigned to each seed in the field (Field type: ID)</li><li><b>SEED.ID.ANALYSES</b>: For statistical analyses, a continuous numbering scheme was followed with respect to the unique identification number assiged to each seed. This facilitated calculating summary statistics and overall analyses. (Field type: ID)</li><li><b>START</b>: Date at which a seed entered the study (Field type: Date)</li><li><b>STOP</b>: Date at which a seed exited the study (i.e. died) (Field type: Date)</li><li><b>STAGE</b>: Survival stage a seed reached during the study (Field type: Categorical)</li><li><b>GERM</b>: Germination status coded as 0: germinated (right-censored) or 1: failed to germinate (i.e. died) (Field type: Numeric)</li><li><b>SURV</b>: Survival status coded as 0: survived beyond end of study (right-censored) or 1: died (Field type: Numeric)</li><li><b>AGE</b>: Age of a seed from the date it entered the study until the date it died (Field type: Numeric)</li><li><b>CANOPY.COV</b>: Proportion canopy cover available to each seed in a given natural plot (Field type: Numeric)</li><li><b>PC1</b>: Principal Components Analysis variable used as a surrogate for tree size (Field type: Numeric)</li><li><b>TOTAL.SEEDTRAP</b>: The total number of seeds around each focal tree as a measure of medium-scale seed density around focal trees. Obtained from seed trap data (Field type: Numeric)</li><li><b>TOTALDENS.SEEDTRAP</b>: TOTAL.SEEDTRAP divided by the number of 1 sq.m. traps (24) to obtain average seed density around each focal tree (Field type: Numeric)</li><li><b>CONSP.ALL</b>: The total number of conspecific seeds surrounding a given seed in a natural plot (Field type: Numeric)</li><li><b>CONSP.ALIVE</b>: The number of conspecific seeds that were alive at a census and surrounding a given seed in a natural plot. This variable was used as a measure of local-scale (1 sq. m.) conspecific seed/seedling density in survival analyses (Field type: Numeric)</li><li><b>AGENT.CATEGORY.MORTALITY</b>: Mortality agents. Seedlings that survived beyond the end of the study were coded as NA in this column (Field type: Categorical)</li><li><b>PREDATOR</b>: Description of the mortality agents of each seed/seedling. NA (for seedlings that survived) (Field type: Categorical)</li></ul></li><li><p><b>ExclosureSingleRec-Seed</b> (described in worksheet ExclosureSingleRec-Seed)</p><p>Description: Seedling survival data in experimental (exclosure) and control plots</p><p>Number of fields: 17</p><p>Number of data rows: 1540</p><p>Fields: </p><ul><li><b>FTYPE</b>: Forest Type (Field type: Categorical)</li><li><b>TREE.ID</b>: Unique ID of experimental trees (Field type: Location)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>TRANSECT</b>: Unique ID of transects around each experimental tree (Field type: ID)</li><li><b>BEARING</b>: Transect compass bearing (Field type: Numeric)</li><li><b>PLOT</b>: Unique ID of natural plots along transects (Field type: ID)</li><li><b>DISTANCE</b>: Distance of natural plot along transect (Field type: Numeric)</li><li><b>SEED.ID</b>: Unique ID assigned to each seed in the field at the time of seed addition (Field type: ID)</li><li><b>DENS.TRT</b>: Density of seeds added to each plot (Field type: Numeric)</li><li><b>TRT</b>: Treatment coded as primary (unlogged) excl, primary-ctrl, logged excl and logged ctrl (Field type: Categorical)</li><li><b>START</b>: Date at which a was added or entered the study (Field type: Date)</li><li><b>STOP</b>: Date at which a seed exited the study (i.e. died) (Field type: Date)</li><li><b>STAGE</b>: Survival stage a seed reached during the study (Field type: Categorical)</li><li><b>SURV</b>: Survival status coded as 0: survived beyond end of study (right-censored) or 1: died (Field type: Numeric)</li><li><b>AGE</b>: Age of a seed from the date it entered the study until the date it died (Field type: Numeric)</li><li><b>MORTALITY</b>: Mortality agents. Seedlings that survived beyond the end of the study were coded as NA in this column (Field type: Categorical)</li><li><b>PREDATOR</b>: Description of the mortality agents of each seed/seedling. NA (for seedlings that survived) (Field type: Categorical)</li></ul></li></ol><p><b>Date range: </b>2014-08-12 to 2014-11-04</p><p><b>Latitudinal extent: </b>4.6896 to 4.7505</p><p><b>Longitudinal extent: </b>116.9643 to 117.5824</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Plantae<br> - Tracheophyta<br> -  - Magnoliopsida<br> -  -  - Malvales<br> -  -  -  - Dipterocarpaceae<br> -  -  -  -  - <i>Dryobalanops</i><br> -  -  -  -  -  - <i>Dryobalanops lanceolata</i><br></div><p></p>
ScienceDex guides
Understand access before you commit
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.