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195 results for “Amazon forest”
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<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 "Fig1a_f_plot.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. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</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_plot.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 are 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_plot.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 code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </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_plot.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-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (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 as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Fig. 1 in Functional trophic composition of the ichthyofauna of forest streams in eastern Brazilian Amazon
Fig. 1. Location of the 18 sampled streams reaches (enlarged detail) in the northeastern region of Pará. The road network shown on the map relates only to the main roads.
Figure 3 in Diversity of cellulolytic and xylanolytic fungi associated with the digestive tract of aquatic insect larvae in streams of the Amazon Forest and Cerrado in Brazil
Figure 3. Percentage of fungal isolates from the DT of Stenochironomus (Diptera: Chironomidae) from trunks in Amazon Forest (A), trunks in Cerrado (B) and leaves in Cerrado (C) producers and non-producers of xylanase (Xyl) and cellulase (CMCase).
Figure 2 in Diversity of cellulolytic and xylanolytic fungi associated with the digestive tract of aquatic insect larvae in streams of the Amazon Forest and Cerrado in Brazil
Figure 2. Principal component analysis (PCA) of the physicochemical parameters of the streams sampled in the Amazon Forest - Amazonas (A) and Cerrado - Tocantins (T) in the north of Brazil.
Figure 1 in Diversity of cellulolytic and xylanolytic fungi associated with the digestive tract of aquatic insect larvae in streams of the Amazon Forest and Cerrado in Brazil
Figure 1. Map of the sampling sites of Stenochironomus (Diptera:Chironomidae) in low-order streams in the Adolpho Ducke Forest Reserve in the state of Amazonas (Amazon Forest Biome) and at the Lajeado State Park (LSP) in the state of Tocantins, (Cerrado Biome) Brazil.
Figure 2 in Diversity and enzymatic capabilities of fungi associated with the digestive tract of larval stages of a shredder insect in Cerrado and Amazon Forest, Brazil
Figure 2. Percentage of fungal isolates from the DT of Triplectides (Trichoptera: Leptoceridae) in Cerrado (A) and Amazon Forest (B) biomes producers and non-producers of xylanase (Xyl) and cellulase (CMCase).
Figures 20–21 in A new species of the sharpshooter genus Soosiulus (Insecta: Hemiptera: Cicadellidae: Cicadellini) from the Brazilian Amazon Forest, with notes on a putative complex of species
Figures 20–21. (20) map showing the known distribution of the genus Soosiulus (dark yellow) and the location of the type locality of S. belterrensis sp. nov. (star); (21) type locality: border of a forest at Fazenda Treviso [3°9'10.86"S, 54°50'32.04"W], Belterra, state of Pará, northern Brazil (photo: Pedro Souza-Dias). BOL: Bolivia, BRA: Brazil, COL: Colombia, CRI: Costa Rica, PAN: Panama, PER: Peru. Brazilian states: AC: Acre, AM: Amazonas, MA: Maranhão, MT: Mato Grosso, PA: Pará, RO: Rondônia, RR: Roraima, TO: Tocantins.
Figure 2 in Comparison of dung beetle communities (Coleoptera: Scarabaeidae: Scarabaeinae) in oil palm plantations and native forest in the eastern Amazon, Brazil
Figure 2 Extrapolation and rarefaction of species richness in forest and oil palm plantation dung beetle communities. Shaded area represents 95% confidence limits. This figure is in color in the electronic version.
Figure 1 in Comparison of dung beetle communities (Coleoptera: Scarabaeidae: Scarabaeinae) in oil palm plantations and native forest in the eastern Amazon, Brazil
Figure 1 Location of the study area in the Brazilian Amazon, in the state of Pará. The right map represents the study area and the spatial distribution of 10 transects (red lines) in forest and oil palm habitats. Green and orange areas indicate primary forest and oil palm plantations, respectively (modified from Mendes-Oliveira et al., 2017). This figure is in color in the electronic version.
Fig. 1 in Effects of small-scale anthropogenic alterations in Euglossini bees at eastern Amazon forest
Fig. 1. Sampling transects located in the Caxiuanà National Forest, northeastern of ParÁ State, Brazil.
Fig. 2 in Effects of small-scale anthropogenic alterations in Euglossini bees at eastern Amazon forest
Fig. 2. Abundances of individuals (A), species richness (B) and species diversity (C) of the Euglossine bees between areas (altered and preserved) in the Caxiuanà National Forest, ParÁ, Brazil.
Fig. 3 in Effects of small-scale anthropogenic alterations in Euglossini bees at eastern Amazon forest
Fig. 3. PCoA ordination showing differences in species composition of Euglossini bees sampled in transects of the preserved area (triangles) and transects of the altered area (circles) at Caixuanà National Forest, ParÁ, Brazil.
Data_Amazon_forest_regeneration
<p><strong>Data and maps produced for the study "Simple Ecological Indicators Benchmark Regeneration Success of Amazonian Forests"</strong></p> <ul> <li> <p><strong>data_plot.csv</strong> → Data on forest attributes and predictor variables used to analyze the drivers of regeneration.</p> </li> <li> <p><strong>data.figure2.csv</strong> → GLMM model results and effect sizes used to generate Figure 2.</p> </li> <li> <p><strong>raster.bdod_1km_mask.tif</strong> and <strong>raster.clay_1km_mask.tif</strong> → Raster data of soil bulk density and soil clay at 1 km resolution, used to generate predicted regeneration curves for the entire Amazon (Figure 3).</p> </li> <li> <p><strong>agb1mask_20_250m.tif</strong> → Map of Aboveground Biomass values along the optimal successional trajectory across the Amazon, predicted using GLMM models.</p> </li> <li> <p><strong>ba_250m_year_20.tif</strong> → Map of Basal Area values along the optimal successional trajectory across the Amazon, predicted using GLMM models.</p> </li> <li> <p><strong>div_hill_250m_year_20.tif</strong> → Map of Species Diversity values along the optimal successional trajectory across the Amazon, predicted using GLMM models.</p> </li> <li> <p><strong>max_dbh_250m_year_20.tif</strong> → Map of Maximum DBH values along the optimal successional trajectory across the Amazon, predicted using GLMM models.</p> </li> <li> <p><strong>sh_250m_year_20.tif</strong> → Map of Structural Heterogeneity (SH) values along the optimal successional trajectory across the Amazon, predicted using GLMM models.</p> </li> <li> <p><strong>spc_rich_250m_year_20.tif</strong> → Map of Species Richness values along the optimal successional trajectory across the Amazon, predicted using GLMM models.</p> </li> </ul> <ul> <li><strong>Drivers_forest_Regeneration_Fig2.R</strong> → R script for GLMM model analysis, which loads the dataset <strong>Regenera_all_plot_data_2022_05_16d.csv</strong> and generates Figure 2 using <strong>data.figure2.csv</strong>.</li> <li> <p><strong>Predict_Amazon_Curves_Figure_3.R</strong> → R script to generate predicted regeneration curves across the Amazon based on GLMM results, loading <strong>raster.clay_1km_mask.tif</strong> and <strong>raster.bdod_1km_mask.tif</strong>.<br><br><strong>GEE_code_Predict_Maps.txt</strong> → Google Earth Engine code to generate predicted maps of ecological integrity at 250 m resolution for the entire Amazon.</p> </li> </ul>
FIG. 1. — Ptiloneura castroi n in The genus Ptiloneura Enderlein, 1901 (Psocodea, 'Psocoptera', Ptiloneuridae) in the Brazilian Amazon Forest and Atlantic Forest: new species, variations in forewings and a key to the species
FIG. 1. — Ptiloneura castroi n. sp., holotype: A, front view of head; B, right forewing; C, right hindwing; D, lacinia; E, hypandrium; F, phallosome; G, left paraproct and epiproct. Abbreviations: aes, anterior endophallic sclerites; ep, external parameres; ms, mesal sclerite; msp, mesal sclerite processes; ples, postero-lateral endophallic sclerites. Scale bars: A, D-G, 0.1 mm; B, C, 1 mm.
FIG. 3 in The genus Ptiloneura Enderlein, 1901 (Psocodea, 'Psocoptera', Ptiloneuridae) in the Brazilian Amazon Forest and Atlantic Forest: new species, variations in forewings and a key to the species
FIG. 3. — Variation of forewing venation in Ptiloneura baiana (Silva-Neto, García Aldrete & Rafael, 2018): A, right forewing with zoom on crossvein between A2 and wing margin of female holotype (Silva-Neto et al. 2018a); B, right forewing of male specimen B of Silva-Neto et al. 2021; C, left forewing of male specimen B of Silva-Neto et al. 2021; D, right forewing of male with zoom at the spur vein; E, left forewing of male. Scale bars: 1 mm.
FIG. 2. — Ptiloneura leonardoi n in The genus Ptiloneura Enderlein, 1901 (Psocodea, 'Psocoptera', Ptiloneuridae) in the Brazilian Amazon Forest and Atlantic Forest: new species, variations in forewings and a key to the species
FIG. 2. — Ptiloneura leonardoi n. sp. holotype: A, front view of head; B, right forewing; C, right hindwing; D, left forewing; E, left hindwing; F, phallosome; G, hypandrium; H, left paraproct and epiproct. Abbreviations: aes, anterior endophallic sclerites; ep, external parameres; les, lateral endophallic sclerites; ms, mesal sclerite; msp, mesal sclerite processes; pes, posterior endophallic sclerites; st, side struts. Scale bars: A, F-H, 0.1 mm; B-E, 1 mm.
Conversion from forest to agriculture in the Brazilian Amazon from 1985 to 2021
<p>This file collection contains data with conversion length from forests to agriculture in the Brazilian Amazon, from 1985 to 2021. Calculations were based in the MapBiomas thematic maps. More information can be found in the repository website (<a href="https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0">https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0</a>).</p> <p>The collection is composed of six sets of data:</p> <p><strong>c_raster_mosaic</strong>: raster files of the Amazon biome with values of conversions;</p> <p><strong>c_raster_tiles</strong>: raster files of smaller raster tiles, with values of conversions;</p> <p><strong>c_tabular_dataset</strong>: a group of tables that contains data about conversions;</p> <p><strong>figures</strong>: data to create figures of the manuscript;</p> <p><strong>raw_raster_tiles</strong>: raster files of smaller raster tiles with land use and land cover classification data from MapBiomas;</p> <p><strong>validation</strong>: data used to performed validation of the conversion results.</p>
Amazon and Congo forest moisture recycling 1979-2014
<p>Evaporation and precipitation recycling of the rainforests in South America and Africa 1979-2014. Do not use the spin-up years 1979 and 2014. </p>
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