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111 results for “secondary forests”
LBA-ECO CD-17 Secondary Forest Survey, Para and Rondonia, Brazil: 2002-2003
This data set provides measurements for diameter at breast height (DBH), tree height, distance from tree stems to the furthest canopy element, and a species survey of secondary forests in Para and Rondonia, Brazil, from 2002-2003. The forest areas were defined as Type A and Type B stands. Measurements were made in the overstory, understory, and midstory of each stand. Type A stands were sampled intensively, with the goal of providing high-fidelity spatial information about the 3-dimensional structure of the stand. These stands were 60 x 60-m (0.36-ha) areas divided into 10 x 10-m grids of uniform clearing and abandonment history and were identifiable from Landsat images. Type B stands were sampled extensively, with the goal of providing unbiased estimates of biomass, along with some information about the vertical structure of the stand and of spatial variability. These stands were polygons of uniform clearing and afforestation history based on multitemporal Landsat imagery, and varied in size and shape. The Landsat files provide classified land cover for each scene and can be used as a time series to evaluate land cover change over time. Each file is a geolocated land cover map based on 30-m Landsat data. NOTE: There were additional files which could not be archived due to file problems. Data Quality Statement: The Data Center has determined that this data set has missing or incomplete data, metadata, or other documentation resulting in diminished usability of this product. Known Problems: Some unresolved issues remain where data values are inconsistent with the variable descriptions provided with the data set. The site identification and plot identification values are not consistently used in all three data files. The variables are not adequately described.
LBA-ECO ND-02 Secondary Forest Small Stem, Non-Woody Biomass, Para, Brazil: 1999-2005
This data set reports biomass from small stems and non-woody vegetation measured from 1999 to 2005 in plots of a secondary-growth forest fertilization experiment. The study location was Fazenda Vitoria, 6.5-km northwest of the town of Paragominas, Para, Brazil, in a 6-year old secondary-growth forest. Vegetation life-forms with diameters less than or equal to 2 cm (grasses, herbs, vines and dead material) were destructively sampled in November 1999, June 2000, June 2001, July 2003, July 2004, and July 2005. All data are provided in a single comma-separated file. The site was divided into three blocks with four treatment plots (each 20m x 20m) located in each block (3 reps x 4 treatments = 12 plots). Three of the twelve plots were fertilized with nitrogen (100 kg N/ha as urea), three were fertilized with phosphorus (50 kg P/ha as superphosphate), three were fertilized with both nitrogen and phosphorus. The remaining three plots were not fertilized and served as the experimental control.
LBA-ECO CD-37 Secondary Forest Biomass and Age Class, Rondonia, Brazil
This data set provides tree age, forest formation, and land cover classification maps, and estimates of landscape-level above-ground live woody biomass (AGLB) for secondary forests in Rondonia, Brazil. The Threshold Age Mapping Algorithm (TAMA) was applied to a densely spaced time series of Landsat images (1975 to 2003) to derive forest type and age classification maps. The AGLB of the secondary forest was estimated by combining the forest classification map with coincident biomass estimates from the Geoscience Laser Altimeter System (GLAS). There are five raster images and three comma-delimited data files with this data set.
LBA-ECO ND-04 Secondary Forest Recovery, Structure, and LAI, Central Amazonia, Brazil
This data set reports measurements of the canopy and structure of secondary forests regenerating from abandoned pastures. These secondary forests are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53), and Fazenda Dimona (km 72). Ten secondary forest study sites were selected within the three fazendas where post-pasture forest recovery ranged from 0 to 14 years since abandonment.From 2000-2001 estimates of leaf area index (LAI) and canopy cover were derived from hemispherical canopy digital photographs, and estimates of aboveground biomass and basal area were derived utilizing allometric equations from diameter at breast height (DBH) measurements. Estimates were classified by growth-form and diameter class. See Feldpausch et al. (2005) for more information. There are four comma-delimited data files with this data set and one companion file with information regarding the allometric equations relating diameter at breast height (for dbh > 5 cm) to dry weight for biomass calculations.
LBA-ECO ND-04 Secondary Forest Carbon and Nutrient Stocks, Central Amazonia, Brazil
This data set reports the carbon and nutrient stocks of above-ground vegetation and soil pools at three locations where post-pasture secondary forest recovery ranged from 0 to 14 years since abandonment. These sites are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53) and Fazenda Dimona (km 72). From September 2000 to July 2001, measurements were obtained for aboveground biomass (cite ND-04 Sec For Recovery), foliage and wood samples were collected and analyzed for total nutrient (C, N, P, K, Ca and Mg) concentrations, and soil samples from 0 to 45 cm depth were collected and analyzed for total nutrient (C, N, P, K, Ca and Mg) concentrations. Total carbon (C) and nutrient stocks were calculated for various vegetation and soil pools to gain an understanding of the dynamics of nutrient and C buildup in regenerating secondary forests in central Amazonia (Feldpausch et al., 2004). There are 2 comma-delimited ASCII data files with this data set.
The mechanisms underlying soil fungal α and β diversity patterns during forest secondary succession
<p>This repository contains key data related to the study.</p>
FIG. 1 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
FIG. 1. — Localities of 168 islands in the Thousand Island Lake, China.
Fostering Optimal Regulation of Emotion for Prevention of Secondary Trauma (FOREST)
ClinicalTrials.gov study NCT05942469. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1_analysis_all_variables_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. </li> <li>Zipped folder:<strong> Fig1_confidence_intervals.zip</strong> - all the files need to produce the confidence intervals seen in Figure 1a-e of the main paper: units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_variable_importance.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files include the sample data used to build the random forest model at each iteration - as a .csv file and the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_analysis_byAllRegions_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The folder also contains another set of files "<strong><REGION>_whole_class.csv" </strong>these files do not distinguish disturbance and can be used to model the regrowth for the whole region (this is not shown in any of the Figures). The code takes data in AGB and converts to AGC.</li> <li>Zipped folder:<strong> Fig3_confidence_intervals.zip</strong> - all the files needed to produce the confidence intervals seen in Figure 3a-d of the main paper. These filenames are in the format <region>_number of the region_<number referring to the disturbance combination>_confidence_interval_asAGC.csv. Where the number of the region: 1 - SW; 2 - SE; 3 - NW; 4 - NE. Where the disturbance combination: 1 - No disturbance; 2 - Only fire disturbance; 3 - Only deforestation disturbance; 4 - Both disturbances. so the file NE_4_1_confidence_interval_as_AGC.csv, contains the confidence intervals of the regrowth model in the NE sector of the Amazon under No disturbance. " Units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_analysis.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-amazonia-regrowth">heinrichTrees/secondary-forest-amazonia-regrowth: This repository contains the code used to produce data shown in Heinrich et al. (github.com)</a> </p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Tables of direct, indirect and networks estimates and Forest plots of pairwise comparisons for all Secondary Outcomes
Open the record for dataset details and reuse information.
Microbiome resilience of Amazonian forests: agroforest divergence to bacteria and secondary forest succession convergence to fungi
<p>This data contains the information on the sites collected for the paper: <strong><em>Microbiome resilience of Amazonian forests: agroforest divergence to bacteria and secondary forest succession convergence to fungi</em></strong></p>
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