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69 results for “Degraded Forest”
Removing climbers more than doubles tree growth and biomass in degraded tropical forests
<p>Huge areas of tropical forests are degraded, reducing their biodiversity, carbon, and timber value. The recovery of these degraded forests can be significantly inhibited by climbing plants such as lianas. Removal of super-abundant climbers thus represents a restoration action with huge potential for application across the tropics. While experimental studies largely report positive impacts of climber removal on tree growth and biomass accumulation, the efficacy of climber removal varies widely, with high uncertainty as to where and how to apply the technique. Using meta-analytic techniques, we synthesise results from 26 studies to quantify the efficacy of climber removal for promoting tree growth and biomass accumulation. We find that climber removal increases tree growth by 156% and biomass accumulation by 209% compared to untreated forest, and that efficacy remains for at least 19 years. Extrapolating from these results, climber removal could sequester an additional 32 Gigatons of CO<sub>2</sub> over 10 years, at low cost, across regrowth and production forests. Our analysis also revealed that climber removal studies are concentrated in the Neotropics (N=22), relative to Africa (N=2) and Asia (N=2), preventing our study from assessing the influence of region on removal efficacy. While we found some evidence that enhancement of tree growth and AGB accumulation varies across disturbance context and removal method, but not across climate, the number and geographical distribution of studies limits the strength of these conclusions. Climber removal could contribute significantly to reducing global carbon emissions and enhancing the timber and biomass stocks of degraded forests, ultimately protecting them from conversion. However, we urgently need to assess the efficacy of removal outside the Neotropics and consider the potential negative consequences of climber removal under drought conditions and for biodiversity.</p>
Data from: Influence of natural and novel organic carbon sources on denitrification in forest, degraded urban, and restored streams
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Common palm civets (Paradoxurus hermaphroditus) are positively associated with humans and forest degradation with implications for seed dispersal and zoonotic diseases
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Data from: Nurse-based restoration of degraded tropical forests with tussock grasses: experimental support from the Andean cloud forest
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Data from: Forest degradation and invasive species synergistically impact Mimusops andongensis (Sapotaceae) in Lama Forest Reserve, Benin
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Data from: Multiple stages of tree seedling recruitment are altered in tropical forests degraded by selective logging
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Data from: A pantropical analysis of the impacts of forest degradation and conversion on local temperature
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Data from: Evaluating the ability of community‐protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data
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Data from: Degradation of root community traits as indicator for transformation of tropical lowland rain forests into oil palm and rubber plantations
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Data from: A degradation debt? large-scale shifts in community composition and loss of biomass in a tropical forest fragment after 40 years of isolation
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Unexpectedly diverse forest dung beetle communities in degraded rainforest landscapes in Madagascar
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Removing climbers more than doubles tree growth and biomass in degraded tropical forests
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Satellite-Based Estimates Reveal Widespread Forest Degradation in the Amazon
<p>This is the initial release of the repository containing the data used in the Global Change Biology article "Satellite-Based Estimates Reveal Widespread Forest Degradation in the Amazon" By Eric L Bullock, Curtis E. Woodcock, Carlos Souza Jr., and Pontus Olofsson. The github repository address is https://github.com/bullocke/amazon. </p>
The impacts of tropical forest degradation and fragmentation on ant-plant mutualisms, and consequences for plant community dynamics
<b>Description: </b><p>Myrmecophyte interactions in differing habitats</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/119"><b>The impacts of tropical forest degradation and fragmentation on ant-plant mutualisms, and consequences for plant community dynamics</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>GACR (National, 16-09427S, <a href="NA">NA</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence NA)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3979296">here</a></p><p><b>Files: </b>This consists of 1 file: M.pearsonii_habitat_comparison_OP_Matrix_MH_August.xlsx</p><p><b>M.pearsonii_habitat_comparison_OP_Matrix_MH_August.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>Branch data</b> (described in worksheet Branch_data)</p><p>Description: Branch data</p><p>Number of fields: 86</p><p>Number of data rows: 611</p><p>Fields: </p><ul><li><b>Tree_code</b>: Tree ID (Field type: location)</li><li><b>Branch_code</b>: Branch code (Field type: id)</li><li><b>Coccids</b>: Number of coccids on br0nches (Field type: numeric interaction)</li><li><b>Brood1</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants1</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates1</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen1</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood2</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants2</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates2</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen2</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood4</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants4</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates4</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen4</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood5</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants5</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates5</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen5</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood6</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants6</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates6</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen6</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood7</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants_7</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates7</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen7</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood8</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants8</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates8</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen8</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood9</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants9</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates9</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen9</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood10</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants10</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates10</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen10</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood11</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants11</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates11</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen11</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood12</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants12</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates12</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen12</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood13</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants13</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates13</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen13</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood14</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants14</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates14</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen14</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood15</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants15</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates15</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen15</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood16</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants16</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates16</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen16</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood17</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants17</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates17</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen17</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood18</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants18</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates18</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen18</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood19</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants19</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates19</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen19</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood20</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants20</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates20</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen20</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Brood21</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Ants21</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>Allates21</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>queen21</b>: Number of ants on branch (Field type: numeric interaction)</li><li><b>damaged_queens</b>: Number of damaged ants (Field type: numeric interaction)</li><li><b>wasp</b>: Number of wasps (Field type: numeric interaction)</li><li><b>Notes</b>: Comments (Field type: comments)</li></ul></li><li><p><b>Tree data</b> (described in worksheet Tree_data)</p><p>Description: Tree data, including soil nutrient profiles</p><p>Number of fields: 23</p><p>Number of data rows: 84</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Tree</b>: Tree ID (Field type: id)</li><li><b>Tree_code</b>: Tree ID code (Field type: location)</li><li><b>Habitat</b>: Habitat type (Field type: categorical)</li><li><b>Number_ants_first_5_leaves</b>: Number of ants on first 5 leaves (Field type: numeric trait)</li><li><b>DBH</b>: Diametre at breast height (Field type: numeric trait)</li><li><b>Height</b>: Tree height (Field type: numeric trait)</li><li><b>N</b>: Canopy cover (Field type: numeric trait)</li><li><b>S</b>: Canopy cover (Field type: numeric trait)</li><li><b>E</b>: Canopy cover (Field type: numeric trait)</li><li><b>W</b>: Canopy cover (Field type: numeric trait)</li><li><b>Canopy_cover</b>: Canopy cover (Field type: numeric trait)</li><li><b>Leaf_biomass</b>: Leaf biomass (Field type: numeric trait)</li><li><b>Total_branches</b>: Total number of branches (Field type: numeric trait)</li><li><b>Phosphate</b>: Leaf phosphates (Field type: numeric trait)</li><li><b>Nitrate</b>: Leaf nitrates (Field type: numeric trait)</li><li><b>Total_wet_weight</b>: Total soil wet weight (Field type: numeric trait)</li><li><b>Wet_weight_sample</b>: Soil wet weight (sample) (Field type: numeric trait)</li><li><b>Dry_weight_sample</b>: Soil dry weight (sample) (Field type: numeric trait)</li><li><b>pH</b>: Leaf pH (Field type: numeric trait)</li><li><b>Dry_Wet_ratio</b>: Soil wet: dry weight ratio (Field type: numeric trait)</li><li><b>Total_dry_weight</b>: Total soildry weight (Field type: numeric trait)</li><li><b>Density</b>: Soil density (Field type: numeric trait)</li></ul></li><li><p><b>All M.Pearsonii-Herbivory data</b> (described in worksheet All_Pearsonii-Herbivory_data)</p><p>Description: Summarised version of data used for analysis</p><p>Number of fields: 17</p><p>Number of data rows: 86</p><p>Fields: </p><ul><li><b>Date</b>: Date the measurements were taken (Field type: date)</li><li><b>Tree</b>: Tree tag (Field type: id)</li><li><b>Tree_Code</b>: Tree code (Field type: id)</li><li><b>Habitat</b>: Habitat type (Field type: categorical)</li><li><b>Tree_Height_Rank</b>: Tree height rank (Field type: categorical)</li><li><b>Corrected_Leaf_Biomass</b>: Corrected leaf biomass (Field type: numeric trait)</li><li><b>Herbivory</b>: Leaf herbivory (Field type: numeric trait)</li><li><b>Coccids</b>: Coccid abundance (Field type: numeric interaction)</li><li><b>Ant_abundance</b>: Ant abundance (Field type: numeric interaction)</li><li><b>Ant_ranked_abundance</b>: Ant coverage ranked (Field type: categorical)</li><li><b>Brood</b>: Ant abundance (Field type: numeric interaction)</li><li><b>Biomass_height_ratio</b>: Tree biomass to height ratio (Field type: numeric trait)</li><li><b>Coccid_ant_ratio</b>: Coccid to ant ratio (Field type: numeric)</li><li><b>Brood_ant_ratio</b>: Brood to ant ratio (Field type: numeric trait)</li><li><b>Attendence_ratio</b>: Attendence ratio (Field type: numeric)</li><li><b>DomTaxa</b>: Taxa record for the dominant species (Field type: taxa)</li><li><b>Dominant_species</b>: Is there a dominant species? (Field type: categorical interaction)</li></ul></li></ol><p><b>Date range: </b>2016-11-19 to 2017-11-24</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</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> -  -  -  -  Malpighiales <br> -  -  -  -  -  Euphorbiaceae <br> -  -  -  -  -  -  <i>Macaranga</i> <br> -  -  -  -  -  -  -  <i>Macaranga pearsonii</i> <br> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  -  sp.1 <br> -  -  -  -  -  -  sp.2 <br> -  -  -  -  -  -  sp.4 <br> -  -  -  -  -  -  sp.5 <br> -  -  -  -  -  -  sp.6 <br> -  -  -  -  -  -  sp.7 <br> -  -  -  -  -  -  sp.8 <br> -  -  -  -  -  -  sp.9 <br> -  -  -  -  -  -  sp.10 <br> -  -  -  -  -  -  sp.11 <br> -  -  -  -  -  -  sp.12 <br> -  -  -  -  -  -  sp.13 <br> -  -  -  -  -  -  sp.14 <br> -  -  -  -  -  -  sp.15 <br> -  -  -  -  -  -  sp.16 <br> -  -  -  -  -  -  sp.17 <br> -  -  -  -  -  -  sp.18 <br> -  -  -  -  -  -  sp.19 <br> -  -  -  -  -  -  sp.20 <br> -  -  -  -  -  -  sp.21 <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coccidae <br></div><p></p>
Riparian forests can mitigate warming and ecological degradation of agricultural headwater streams
<p>1. Riparian forests are commonly advocated as a key management option to mitigate the effects of agriculture on headwater stream biodiversity and ecosystem functions. However, the benefits of riparian forests might be reduced by uninterrupted catchment-scale pollution.</p> <p>2.We studied the effects of riparian land use on multiple ecological endpoints in headwater streams in an agricultural landscape. We studied stream habitat characteristics, water temperature and algal accrual, and macrophyte, benthic macroinvertebrate and fish communities in 11 paired forested and open agricultural headwater stream reaches that differed in their extent of riparian forest cover but had similar water quality.</p> <p>3. Hydromorphological habitat quality was higher in forested reaches than in open reaches. Riparian forest had a strong effect on the summer water temperature regime, with maximum and mean water temperatures and temperature variation in forested reaches substantially lower than in open reaches.</p> <p>4. Macrophyte communities differed between forested and open reaches. The mean abundance of bryophytes was higher in forested reaches but the difference to open reaches was only marginally significant, whereas graminoids were significantly more abundant in open reaches. Within-stream dissimilarity of benthic macroinvertebrate community structure was significantly related to the difference in riparian land use between reach pairs. The relative DNA sequence abundance of pollution-sensitive EPT (Ephemeroptera, Plecoptera, Trichoptera) species tended to be higher in forested reaches than in open reaches. Finally, fish densities were not significantly different between forested and open reaches, although densities were higher in forested reaches.</p> <p>5. This unequivocal evidence for the ecological benefits of forested riparian reaches in agricultural headwater streams suggests that riparian forest can partly mitigate the adverse impacts of agricultural diffuse pollution on biota. The strong effect of forests on stream water temperature suggest that riparian forest could also mitigate harmful effects on headwater stream biodiversity and ecosystem functions of the predicted more frequent high summer temperatures. </p>
Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index
India has one of the largest assemblages of tropical biodiversity, with its unique floristic composition of endemic species. However, current forest cover assessment is performed via satellite-based forest surveys, which have many limitations. The present study, which was performed in the Eastern Ghats, analysed the satellite-based inventory provided by forest surveys and inferred from the results that this process no longer provides adequate information for quantifying forest degradation in an empirical manner. The study analysed 21 soil properties and generated a forest soil quality index of the Eastern Ghats, using principal component analysis. Using matrix modules and geospatial technology, we compared the forest degradation status calculated from satellite-based forest surveys with the degradation status calculated from the forest soil quality index. The Forest Survey of India classified about 1.8% of the Eastern Ghats' total area as degraded forests and the remainder (98.2%) as open, dense, and very dense forests, whereas the soil quality index results found that about 42.4% of the total area is degraded, with the remainder (57.6%) being non-degraded. Our ground truth verification analyses indicate that the forest soil quality index along with the forest cover density data from the Forest Survey of India are ideal tools for evaluating forest degradation.
Data from: Effects of forest degradation on Amazonian ferns in a land-bridge island system as revealed by non-specialist inventories
<p>Background: Tropical deforestation and degradation worldwide have rapidly outpaced biodiversity field sampling. No study to date has assessed the effects of insular habitats induced by hydroelectric dams on Amazonian understorey plants. Fern community responses to anthropogenic effects on tropical forest islands can be efficiently revealed through simple and cheap, yet informative protocols that can be applied by non-specialists. </p> <p>Aims: This study seeks to both understand the drivers of fern and lycophyte assemblages on forest islands and investigate the relative costs and effectiveness of a simplified sampling protocol that can be implemented by non-specialists and has potential to be used to crowdsource ecological field data acquisition.</p> <p>Methods: Fern and lycophytes species were sampled by a non-specialist in 17 quarter-hectare plots on 10 forest islands at the lake of Balbina Hydroelectric Dam, central Amazonia. Sampling was carried out opportunistically during a field expedition planned to conduct tree inventory on permanent plots. We used a set of locally measured or GIS-derived predictors for each of the surveyed sites. We used Principal Coordinates Analysis and Generalized Linear Mixed Models (GLMMs) to further assess the influence of predictors on patterns of fern species richness and composition.</p> <p>Results: A total of 286 photographed individual ferns or lycophytes represented 23 taxa. The average number of taxa per plot was 6.1 on islands and 14.3 in the mainland. The insular species pool was a subset of the mainland pool of fern species. Richness was positively related to island size and negatively related to isolation and fire severity. Area, isolation and fire severity significantly explained variation in community composition. The relative cost of the non-specialist picture-based fern protocol was very modest (in our case, only 4% of the total expedition budget), even compared to the typically low cost of alternative orthodox field campaigns.</p> <p>Conclusion: Fern community structure in this forest archipelago was primarily driven by island size, isolation and fire disturbance. We show that a simple sampling protocol carried out by a non-specialist can lead to inexpensive and highly reliable ecological data. This opens an avenue for crowdsourcing ecological fern data collections using a citizen science approach.</p>
Survival and growth data for tree species planted to reforest degraded tropical peat swamp forests and functional trait data for peat swamp forest species across Southeast Asia
<p>Degraded tropical peat swamp forests are harsh environments so difficult to restore. Evidence from past restoration projects can inform selection of species for planting. As part of a systematic review, we collated and synthesised survival and growth monitoring data on trees planted in degraded tropical peat swamp forests across Southeast Asia. A key aim of the systematic review and meta-analysis was to determine which tree species survive best when planted to restore tropical peat swamp forests. We also investigated the impact of seedling and site treatments and climatic conditions (El Niño-Southern Oscillation) on tree seedling survival and growth and the potential to use plant functional traits to predict survival and growth. </p> <p>Full methodological details of the systematic review, including: search strategy, article screening and inclusion criteria, critical appraisal of screened articles, data processing and data analysis can be found in the published article and supporting information stated below.</p> <p>Smith SW, Rahman NEB, Harrison ME, Shiodera S, Giesen W, Lampela M, Wardle DA, Chong KY, Randi A, Wijedasa LS, Teo PY, Fatimah, YA, Teng NT, Joanne YKQ, Alam MJ, Brugues Sintes P, Darusman T, Graham LLB, Katoppo DR, Kojima K, Kusin K, Lestari DP, Metali F, Morrogh-Bernard HC, Nahor MB, Napitupulu RRP, Nasir D, Nath TK, Nilus R, Norisada M, Rachmanadi D, Rachmat HH, Ripoll Capilla B, Salahuddin, Santosa PB, Sukri RS, Tay B, Tuah W, Wedeux, BMM, Yamanoshita T, Yokoyama EY, Yuwati TW, Lee JSH. Tree species that ‘live slow, die older’ enhance tropical peat swamp restoration: evidence from a systematic review. <em>Journal of Applied Ecology. </em>DOI:<a href="https://doi.org/10.1111/1365-2664.14232">10.1111/1365-2664.14232</a></p> <p>In this data repository, we have uploaded the following data used in the meta-analysis to generate the findings presented in the systematic review, specifically:</p> <ul> <li>Screening sheets of eligible articles across languages (English, Indonesian, Japanese and German) read in detailed by multiple authors on the review</li> <li>Survival monitoring data, including predicted half-life (duration until 50% mortality) derived from functional line-fitting</li> <li>Height monitoring data, including standardized relative growth rates (cm × cm<sup>-1 </sup>month<sup>-1</sup>) derived from functional line-fitting</li> <li>Plant functional traits, selected leaf nutrient contents and wood densities for those species used in the functional trait analyses</li> </ul> <p>Each data file has an associated meta-data file explaining the column headers and variables. Please note, data contributors from some studies wished to retain control over access to their monitoring data, but are willing to share this data on request. The relevant study-site code those studies used in the analyses in our systematic review can be found in the meta-data sheets. Details given include study-site code (used in the systematic review), site name and location, author name(s), author contact email(s). All these details have been provided with permission from relevant data contributor co-author(s). </p>
Changes in leaf litter decomposition of primary Korean pine forests after degradation succession into secondary broad-leaved forests
<p>Forest degradation succession often leads to changes in forest ecosystem functioning. Exactly how the decomposition of leaf litter is affected in a disturbed forest remains unknown. Therefore, in our study, we selected a primary Korean pine forest (PK) and a secondary broad-leaved forest (SF) affected by clear-cutting degradation, both in Northeast China. The aim was to explore the response to changes in the leaf litter decomposition converting PK to SF. The mixed litters of PK and SF were decomposed in situ (one year). The proportion of remaining litter mass, main chemistry, and soil biotic and abiotic factors were assessed during decomposition and then we made an in-depth analysis of the changes in the leaf litter decomposition. According to our results, leaf litter decomposition rate was significantly higher in the PK than that in the SF. Overall, the remaining percent mass of leaf litter's main chemical quality in SF was higher than in PK, indicating that leaf litter chemical turnover in PK was relatively faster. PK had a significantly higher amount of total phospholipid fatty acids (PLFAs) than SF during decomposition. Based on multivariate regression trees, the forest type influenced the soil habitat factors related to leaf litter decomposition more than decomposition time. Structural equation modeling revealed that litter N was strongly and positively affecting litter decomposition, and the changes in actinomycetes PLFA biomass played a more important role among all the functional groups. Selected soil abiotic factors were indirectly driving litter decomposition through coupling with actinomycetes. This study provides evidence for the complex interactions between leaf litter substrate and soil physical-chemical properties in affecting litter decomposition via soil microorganisms.</p>
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
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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)
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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.