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69 results for “Degraded Forest”
Effects of Soil Warming on Bacterial Degradation of Carbohydrates at Harvard Forest 2011
As Earth’s climate warms, soil carbon pools and the microbes that process them may change, altering the way in which carbon is recycled in soil. We used bacterial cultivation to evaluate the hypothesis that experimentally raising soil temperatures by 5°C for 20 years increased the potential for temperate forest soil microbial communities to degrade carbohydrates. A greater proportion of the 295 bacteria from 6 phyla (10 classes, 14 orders, and 34 families) isolated from heated plots in the 20-year experiment were able to depolymerize cellulose and xylan than bacterial isolates from control soils. These findings indicate that the enrichment of bacteria capable of degrading carbohydrates could be important for accelerated carbon cycling in a warmer world. Data for the isolates from the Harvard Forest culturing project is archived at https://osf.io/ahb2v/.
Comparison of polyphenol degrading enzyme activities between forest types and soil horizons from 2003 to 2004
In the southern Appalachians Rhododendron maximum thickets suppress conifer and hardwood regeneration. While there has been research on the effects of R. maximum on physical and chemical environment, the functioning of R. maximum ericoid mycorrhizas has been unexplored. The litter of ericaceous plants tends to be rich in phenolic compounds. These compounds can form recalcitrant complexes with various forms of organic N, and may be responsible for lowering decomposition and N mineralization rates. While polyphenol-organic N complexes are highly recalcitrant, some fungi, particularly ericoid mycorrhizal fungi, have the ability to access this sequestered N. Since the litter of ericaceous plants is rich in phenolic compounds and ericoid mycorrhizal fungi are equipped to degrade phenolic compounds, polyphenol-organic N complexing may represent an N cycling strategy that prevents non-ericaceous plants from accessing sources of organic N. We propose to examine the activities of polyphenol degrading enzymes in the soil of R. maximum thickets and neighboring hardwood forests.
Data from: Forest degradation limits the complementarity and quality of animal seed dispersal
<p><span>Forest degradation changes the structural heterogeneity of forests and species communities, with potential consequences for ecosystem functions including seed dispersal by frugivorous animals. While the quantity of seed dispersal may be robust towards forest degradation, changes in the effectiveness of seed dispersal through qualitative changes are poorly understood. Here, we carried out extensive field sampling on the structure of forest microhabitats, seed deposition sites, and plant recruitment along three characteristics of forest microhabitats (canopy cover, ground vegetation, deadwood) in Europe's last lowland primeval forest (Białowieża, Poland). We then applied niche modelling to study forest degradation effects on multi-dimensional seed deposition by frugivores and recruitment of fleshy-fruited plants. Forest degradation was shown to (1) reduce the niche volume of forest microhabitat characteristics by half, (2) homogenize the spatial seed deposition within and among frugivore species, and (3) limit the regeneration of plants via changes in seed deposition and recruitment. Our study shows that the loss of frugivores in degraded forests is accompanied by a reduction in the complementarity and quality of seed dispersal by remaining frugivores. In contrast, structure-rich habitats, such as old-growth forests, safeguard the diversity of species interactions, forming the basis for high-quality ecosystem functions.</span></p>
Fig. 3 in Javan mongoose (Herpestes javanicus) abundance and spatial ecology in a degraded dry dipterocarp forest
Fig. 3. Map of Sakaerat Biosphere Reserve with radio tracked (December 2019 to January 2021) Javan mongoose (Herpestes javanicus) home ranges and prey grids (PG). 95% utilisation contours (U.C) for male (M6, M1) and female (F1) mongooses are labelled in the legend. 50% U.C are solid line circles within each individual's home range. Prey grids collected ground-dwelling invertebrate mass as well as rodent biomass within the DDF (October to December 2020). Stars indicate where only ground-dwelling invertebrates were collected. Triangles indicate areas where sweep netting for invertebrates occurred in addition to sampling for rodent biomass and ground-dwelling invertebrates.
Fig. 1 in Javan mongoose (Herpestes javanicus) abundance and spatial ecology in a degraded dry dipterocarp forest
Fig. 1. Map and location of Sakaerat Biosphere Reserve with camera trap stations used to estimate Javan mongoose (Herpestes javanicus) abundance in 2017. Prey grid stations were used to calculate yearly averaged rodent biomass from January 2017 to November 2017.
Area Estimates of Forest Degradation and Deforestation in the Country of Georgia by Region
<p>Area estimates of forest degradation and deforestation in the country of Georgia by region from 1987 to 2019. Unit is square kilometers.</p> <p>georgia_forest_def_0512.csv: Area estimates of deforestation</p> <p>georgia_forest_deg_0512.csv: Area estimates of forest degradation</p> <p>Please cite the data as: <a href="https://www.sciencedirect.com/science/article/pii/S0034425721003680">Chen, S., Woodcock, C.E., Bullock, E.L., Arévalo, P., Torchinava, P., Peng, S. and Olofsson, P., 2021. Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis. Remote Sensing of Environment, 265, p.112648.</a></p> <p><a href="https://authors.elsevier.com/a/1devg7qzStnwW">Click here to get 50-day free access without registration</a></p>
Fig. 1 in Assessing the potential for avifauna recovery in degraded forests in Indonesia
Fig. 1. NMDS ordination biplots of bird species (e.g., sp1) that show a significant difference in their densities between less and highly degraded forest with the habitat variables (text) superimposed. Bird species code: (sp1) yellow-bellied bulbul Alophoixus phaeocephalus; (sp2) hairy-backed bulbul Tricholestes criniger; (sp3) green iora Aegithina viridissima; (sp4) scaly-crowned babbler Malacopteron cinereum; (sp5) chestnut-rumped babbler Stachyris maculata; (sp6) rufous-tailed shama Trichixos pyrrhopygus; (sp7) blue-winged leafbird Chloropsis cochincinensis; (sp8) greater racket-tailed drongo Dicrurus paradiseus; (sp9) short-tailed babbler Malacocincla malaccensis; (sp10) black-capped babbler Pellorneum capistratum; (sp11) blue-eared barbet Psilopogon duvaucelii; (sp12) brown barbet Calorhamphus hayii; (sp13) black-headed bulbul Pycnonotus atriceps; (sp14) spectacled bulbul Pycnonotus erythropthalmos; (sp15) olive-winged bulbul Pycnonotus plumosus; (sp16) cream-vented bulbul Pycnonotus simplex; (sp17) sooty-capped babbler Malacopteron affine; (sp18) fluffybacked tit-babbler Macronous ptilosus; (sp19) purple-naped sunbird Hypogramma hypogrammicum.
Active restoration fosters better recovery of tropical rainforest birds than natural regeneration in degraded forest fragments
<ol> <li>Ecological restoration has emerged as a key strategy for conserving tropical forests and habitat specialists, and monitoring faunal recovery using indicator taxa like birds can help assess restoration success. Few studies have examined, however, whether active restoration achieves better recovery of bird communities than natural regeneration, or how bird recovery relates to habitat affiliations of species in the community.</li> <li>In rainforests restored over the past two decades in a fragmented landscape (Western Ghats, India), we examined whether bird species richness and community composition recovery in 23 actively restored (AR) sites was significantly better than recovery in paired naturally regenerating (NR) sites, relative to 23 undisturbed benchmark (BM) rainforests. We measured 8 habitat variables and tested whether bird recovery tracked habitat recovery, whether rainforest and open-country birds showed contrasting patterns, and assessed species-level responses to restoration.</li> <li>We recorded 92 bird species in 460 point-count surveys. Rainforest bird species richness was highest in BM, intermediate in AR, and lowest in NR. Contrastingly, open-country bird species richness was least in BM, intermediate in AR, and highest in NR.</li> <li>Bird community composition varied significantly across treatment types with composition in AR in transition from NR to BM. Bird community dissimilarity between sites was positively related to dissimilarity in habitat structure and floristics, and geographic distance between sites. Variance partitioning indicated that structural and floristic dissimilarity explained 90% of the variation in community composition.</li> <li>Indicator species analysis revealed significant associations of 34 species with one or more treatment types. Species associated with BM and AR treatment types were all rainforest species, while only 38% of species associated with AR and NR treatment types were rainforest species.</li> <li> <em>Synthesis and applications</em>: We show that active restoration of degraded fragments benefits rainforest birds and reduces the infiltration of open-country birds, and highlight the importance of considering rainforest and open-country species separately. In human-modified tropical rainforest landscapes, active restoration of degraded fragments fosters partial recovery and complements protection of mature forests for bird conservation.</li> </ol>
Data and code from paper: The carbon sink of secondary and degraded humid tropical forests
<p>This repository contains the data and code produced for the following paper:</p> <p><strong>Title: </strong>The carbon sink of recovering secondary and degraded humid tropical forests</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>Please note:</strong></p> <ul> <li> throughout repository where files include reference to: <...<strong>congo_basin</strong>...> this refers to the <strong>Central Africa </strong>region as it is termed in the main paper.</li> <li>the <strong>code</strong> <strong>has not been amended</strong> for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run. </li> </ul> <p>The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in <strong>R (v3.6) for which the code and output datasets are available here.</strong></p> <p>To access the code used in <strong>Google Earth Engine</strong> that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4 </p> <p>This repository contains the following zipped folders:</p> <ul> <li><strong>data_folder</strong>: this folder contains further folders with all the data produced for this paper.</li> </ul> <ol> <li>Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "<strong>regrowthModel_..RDS</strong>. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.</li> <li>Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "<strong>regrowthModel_..RDS</strong>) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. </li> <li>Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.</li> <li>Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.</li> <li>Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.</li> <li>Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded. </li> </ol> <ul> <li><strong>code_folder.zip</strong>: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. <ul> <li>this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote. </li> </ul> </li> </ul> <p><strong>Data and Code usage:</strong> When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository. </p> <p>Further source data in .xlsx format were also submitted with the main manuscript.</p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Restoration opportunities beyond highly degraded tropical forests: insights from India’s Western Ghats
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Active restoration fosters better recovery of tropical rainforest birds than natural regeneration in degraded forest fragments
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Data from: Forest degradation limits the complementarity and quality of animal seed dispersal
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Data from: Lowland tapirs facilitate seed dispersal in degraded Amazonian forests
The forests of southeastern Amazonia are highly threatened by disturbances such as fragmentation, understory fires and extreme climatic events. Large-bodied frugivores such as the lowland tapir (Tapirus terrestris) have the potential to offset this process, supporting natural forest regeneration by dispersing a variety of seeds over long distances to disturbed forests. However, we know little about their effectiveness as seed dispersers in degraded forest landscapes. Here, we investigate the seed dispersal function of lowland tapirs in Amazonian forests subject to a range of human (fire, fragmentation) and natural (extreme droughts, windstorms) disturbances, using a combination of field observations, camera traps, and Light Detection and Ranging (LiDAR) data. Tapirs travel and defecate more often in degraded forests, dispersing much more seeds in these areas [9,822 seeds per ha/yr (CI95% = 9,106; 11,838)] than in undisturbed forests [2,950 seeds per ha/yr (CI95% = 2,961; 3,771)]. By effectively dispersing seeds across disturbed forests, tapirs may contribute to natural forest regeneration – the cheapest and usually the most feasible way to achieve large-scale restoration of tropical forests. Through the dispersal of large-seeded species that eventually become large trees, such frugivores also contribute indirectly to maintaining forest carbon stocks. These functions may be critical in helping tropical countries to achieve their goals to maintain and restore biodiversity and its ecosystem services. Ultimately, preserving these animals along with their habitats may help in the process of natural recovery of degraded forests throughout the tropics.
Data from: UV radiation doubles microbial degradation of standing litter in a subtropical forest
<p><span>UV radiation has been recognized as a direct driver of litter decomposition by photodegrading organic matter in dryland ecosystems. </span><span>However, the importance and mechanism of UV radiation on litter decomposition, especially on standing litter, in humid forest ecosystems remain unclear. </span></p> <p><span>We conducted a factorial experiment in a humid subtropical forest gap, manipulating the effects of UV radiation on the decomposition of standing litter under different microbial conditions. </span></p> <p><span>After 366 days of standing incubation, under normal conditions (UV pass with microorganisms), up to 40.63% of the litter mass was lost. However, under a UV pass without microorganisms, litter mass loss was only 16.30%. Under a UV block, the mass loss of litter with microorganisms was 27.68% and that of litter without microorganisms was 15.54%. Without microorganisms, UV radiation had no significant effect on the mass loss of litter carbon. However, UV radiation increased the DOC concentration of litter. And in the presence of microorganisms, UV radiation contributed to an increased mass loss of lignin by 16.72% and of cellulose by 14.75%. No negative effects of UV radiation on microorganisms were observed. These results suggest that UV radiation increased the net mass loss of litter by 106.67%,</span> <span>and this doubling promotion was achieved through microbial degradation. </span></p> <p><strong><em><span>Synthesis</span></em></strong><span>. The increase in microbial degradation under UV radiation may be linked to the increased degradability of lignin and cellulose caused by photodegradation. Our study indicates that direct photodegradation by UV radiation could be weak in subtropical forests, but UV photofacilitation generates rapid turnover of carbon in this system.</span></p>
Occurrence of blood feeding terrestrial leeches in a degraded forest ecosystem
<b>Description: </b><p>This dataset includes the abundance of two species of terrestrial leech collected at multiple sites at the SAFE project in Sabah, Malaysia. Leech collections took place over two seasons, one in the dry season of 2015 and one in the wet season of 2016. For each of the sites, four repeated visits took place and 20 minute searches were conducted within the boundaries of 25 m2 vegetation plots. As these sites have been subjected to differennt degrees of current and historic degradation, the vegetation structure data is also included for each site. For a subset of the leech sites there is corresponding mammal detection data from camera traps across the landscape, which is also included in this dataset.</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/10"><b>The effects of rainforest fragmentation on mammal community assemblages using leech blood-meal analysis</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant , NE/K016148/1)</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>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3476542">here</a></p><p><b>Files: </b>This consists of 1 file: Drinkwater2019_leech_occurrence.v2.xlsx</p><p><b>Drinkwater2019_leech_occurrence.v2.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Leech abundance and survey-covariates 2015</b> (described in worksheet abundance2015)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2015 between February and June. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Leech abundance and survey-covariates 2016</b> (described in worksheet abundance2016)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2016 between September and December. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Site specific covariates</b> (described in worksheet covariates)</p><p>Description: Vegetation structure data associated with each site for which leech surveys were conducted. The metrics include canopy height, moran's I and plant-area-index. These data were extracted from LiDAR data with a 50 m2 buffer around the centroid for each site.</p><p>Number of fields: 6</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point code (Field type: location)</li><li><b>tch</b>: Top of canopy height per site (Field type: numeric)</li><li><b>canopy_height_moran</b>: Habitat heterogeneity - Morans I - per site (Field type: numeric)</li><li><b>canopy_height_sd</b>: Standard deviation of canopy height (Field type: numeric)</li><li><b>pai_mean</b>: Mean plant area index at site (Field type: numeric)</li><li><b>pai_sd</b>: Plant area index standard deviation (Field type: numeric)</li></ul></li><li><p><b>Mammal detections </b> (described in worksheet mammals)</p><p>Description: This dataset contains the mammal detections recorded from camera traps at a subset of the leech survey locations. Sampling effort is also included as a measure of survey effort. </p><p>Number of fields: 27</p><p>Number of data rows: 83</p><p>Fields: </p><ul><li><b>Camera</b>: Name of camera (Field type: location)</li><li><b>CTNs</b>: Measure of trapping effort - number of nights the cameras were operational (Field type: numeric)</li><li><b>Asian Elephant</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banded Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banteng</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bearded Pig</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bornean Yellow Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Common Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Greater Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Leopard Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Lesser Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Marbled Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Masked Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Moonrat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Mousedeer sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Muntjac sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Orangutan</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Pig-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Red Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sambar Deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sun Bear</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sunda Pangolin</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Thick-spined Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-02-01 to 2016-12-31</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> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  Annelida <br> -  -  -  Clitellata <br> -  -  -  -  Arhynchobdellida <br> -  -  -  -  -  Haemadipsidae <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  -  <i>Haemadipsa picta</i> <br></div><p></p>
Insect RTUs from the degraded forest fragments in the Attappady and Anaikatti landscapes.
<p>Datasets were collected as part of the project titled "EVALUATING THE EFFICEINCY OF RESTORATION EFFORTS IN REVIVING TROPICAL FORESTS USING GROUND INSECTS AS INDICATORS."</p>
Data for: How do harvesting methods applied in continuous-cover forestry and rotation forest management impact soil carbon storage and degradability in boreal Scots pine forests?
<p>Forest management affects soil carbon (C) storage through forest composition, microclimate and litter inputs. How two major forest management systems, continuous-cover forestry (CCF) and clear-cut-based rotation forest management (RFM), differ in their impact on soil C in boreal forests is still poorly understood, however. We compared their effects on soil organic carbon (SOC) storage and quality in boreal Scots pine <span>(<em>Pinus sylvestris</em></span> L.) dominated forests in eastern Finland. We tested the hypotheses that (1) colder microclimates and continuous litter inputs will lead to higher SOC stocks in CCF plots than in clear-cuts and (2) the more labile litter in clear-cuts with varying ground vegetation will enhance SOC decomposition rates. We sampled uncut mature forests, clear-cuts, retention-cuts and gap-cuts, in which we analysed SOC concentrations and calculated the stocks. We measured stand characteristics such as diameter-at-breast height, basal area, dominant tree height, and understorey species coverage of the various treatments and modelled the above- and belowground litter inputs based on these parameters. We used laboratory incubation and sequential fractionation of SOC to assess its degradability under standardized conditions. To estimate the decomposition rate in the various environments we incubated cellulose bags in situ. We assessed the impact of microclimate on SOC decomposition, using data from soil-temperature and soil-moisture field measurements. We quantified the microbial biomass C pool, using chloroform fumigation extraction to gain insight on the impact of forest management practice on soil microbes. The SOC concentrations and SOC stocks did not differ significantly between the treatments, despite the presence of a warmer microclimate and lower litter inputs in the clear-cut plots. However, we found differences in the quality of the SOC. Soils in clear-cut sites showed lower proportions of labile SOC compounds than did the other treatments. As hypothesized, the decomposition rates were elevated in clear-cuts, but were equally as high within the canopy gaps on gap-cut stands. Our work highlights that forest management affects the quality, degradability, long-term accumulation and storage of SOC. We conclude that the accumulation of labile compounds in uncut forests and retention-cuts, combined with the decreased decomposition rates, indicate a higher potential for future C accumulation in the soil than in clear-cuts.</p>
Composition of non-volant small mammals inhabiting a degradation gradient in a lowland tropical forest in Uganda
<p><span>A study aimed at assessing the structure of rodent and shrew assemblages inhabiting a degradation gradient while considering rainfall patterns was conducted in one of the few remaining lowland tropical forests in Eastern Africa. We collected a unique dataset of 1411 rodents and shrews, representing 24 species (19 rodents, 5 shrews). The most abundant species alternated in dominance as species abundance significantly fluctuated across the study period following a degradation gradient, </span><span>While only generalist species were observed near the degraded forest edge, habitat specialists such as <em>Deomys ferrugineus, Malacomys longipes</em> and <em>Scutisorex congicus</em>, were observed in the primary forest interior suggesting<span class="gnkrckgcgsb"><span> a significant</span></span></span><span class="gnkrckgcgsb"><span> association between species and their associated habitats and habitat attributes</span></span><span>. There was also an observed correlation between rainfall patterns and species abundance. Capturing more species in adjacent fallows and along the degraded forest edge suggests that many species are able to live in degraded habitats that offer a variety of food resources. The continued pressure on forest resources, however, may lead to changes in habitat structure. This, coupled with the dependence of forest ecological functions on rainfall, which is typically not the case, may ultimately cause the local extinction of highly specialized but less adaptable species.</span></p>
Data from: Fire, fragmentation, and windstorms: a recipe for tropical forest degradation
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Data from: Using soil amendments and plant functional traits to select native tropical dry forest species for the restoration of degraded Vertisols
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