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150 results for “deforestation”
Commodity-driven deforestation, associated carbon emissions and trade 2001-2022
<p><span>This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.</span></p>
Data for "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments"
<p>The processed data supporting the Global Environmental Change publication "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments".</p> <p>These data can be analyzed and visualized with the code at: <a href="https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare">https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare</a></p> <p>For a description of each file & the variables contained, please look to the README file.</p>
Lista de municípios prioritários no combate ao desmatamento na Amazônia e Cerrado (Deforestation priority list of municipalities in the Amazon and Cerrado)
<h4>Português</h4> <p>A Lista Negra do desmatamento, da Amazônia e do Cerrado, é atualizada pelo Diário Oficial da União (DOU), e determina a identificação de municípios com taxas elevadas de desmatamento. Mais detalhes sobre essa política pública podem ser encontrados em <a href="http://dx.doi.org/10.5380/dma.v42i0.53542" target="_blank" rel="noopener">Bizzo et al. (2017)</a>. </p> <p>Os critérios de inclusão de um município na lista da Amazônia são: área total de floresta desmatada; área total de floresta desmatada nos últimos três anos; e aumento da taxa de desmatamento em pelo menos três, dos últimos cinco anos. As consequências da inclusão na lista são: maior monitoramento e fiscalização; não aprovação de créditos para atividade agropecuária; embargo de atividades relacionadas ao desmatamento; aplicação de multas; divulgação de dados do imóvel rural em que ocorreu a infração, e seu titular. Para a remoção da lista, o município deve: Possuir 80% de seu território, em propriedades rurais, monitorado pelo Cadastro Ambiental Rural (CAR); e manter taxa de desmatamento anual abaixo do limite estabelecido em portaria do Ministério do Meio Ambiente.</p> <p>Os critérios de inclusão para os municípios do Cerrado são: média de desmatamento dos últimos dois anos superior a 25 quilômetros quadrados; desmatamento acima de 20% em áreas de vegetação nativa remanescente no município; ou a exploração da madeira em áreas protegidas. Para o Cerrado, não foram estabelecidos critérios para exclusão da lista.</p> <p>A série temporal contendo os municípios que foram incluídos na lista de prioridade e monitoramento para o controle do desmatamento. A série temporal vai de 2008 a 2022. Os dados foram coletados do Diário Oficial da União (DOU), publicado pelo governo brasileiro.</p> <p>A série temporal completa encontra-se no arquivo "priority_list.csv".</p> <h4>English</h4> <p>Time series containing the municipalities that were included in the priority and monitoring list for deforestation control. The time series ranges from 2008 and 2022. The data were gathered from the Diário Oficial da União (DOU), published by the Brazilian government.</p> <p>The complete time series is found in the "priority_list.csv" file.</p>
Ecological filtering shapes the impacts of agricultural deforestation on biodiversity
<p>This dataset and associated code are for the manuscript titled "Ecological filtering shapes the impacts of agricultural deforestation on biodiversity", which is due to be published in the journal Nature Ecology & Evolution (accepted on September 20, 2023). The abstract of this manuscript is as follows:</p><p> </p><p>The biodiversity impacts of agricultural deforestation vary widely across regions. Previous efforts to explain this variation have focused exclusively on the landscape features and management regimes of agricultural systems, neglecting the potentially critical role of ecological filtering in shaping deforestation tolerance of extant species assemblages at large geographical scales via selection for functional traits. Here we provide a large-scale test of this role using a global database of species abundance ratios between matched agricultural and native forest sites that comprises 71 avian assemblages reported in 44 primary studies, and a companion database of ten functional traits for all 2,647 species involved. Using meta-analytic, phylogenetic, and multivariate methods, we show that beyond agricultural features, filtering by the extent of natural environmental variability and the severity of historical anthropogenic deforestation shapes the varying deforestation impacts across species assemblages. For assemblages under greater environmental variability – proxied by drier and more seasonal climates under greater disturbance regime – and longer deforestation histories, filtering has attenuated the negative impacts of current deforestation by selecting for functional traits linked to stronger deforestation tolerance. Our study provides a heretofore largely missing piece of knowledge in understanding and managing the biodiversity consequences of deforestation by agricultural deforestation.</p>
Data and script: Catchment scale deforestation increases the uniqueness of subtropical stream communities
<p>These are datasets on benthic diatom and insect communities sampled in 100 streams along a gradient of land use intensification, ranging from streams in pristine forests to agricultural catchments in southeast subtropical Brazil. Data also include information on instream and land-use variables.</p> <p>In addition to the datasets, we also provide the R codes used to investigate whether compositional uniqueness (LCBD) and species contribution to beta diversity (SCBD) of stream diatoms and insects can be predicted by instream and land-use characteristics and by species traits and taxonomic relatedness, respectively. </p>
Dataset from: 'Tropical deforestation accelerates local warming and loss of safe outdoor working hours'
<p>Abstract:</p> <p>'Climate change has increased heat exposure in many parts of the tropics, negatively impacting outdoor worker productivity and health. Although it is known that tropical deforestation causes local warming, the extent to which this warming affects people across the tropics is unknown. Here, we combine worker health guidelines with satellite, reanalysis, and population data to investigate how increases in local temperatures associated with recent deforestation (2003-2018) affects outdoor working conditions across low-latitude countries, and how future global climate change will magnify heat exposure for people in deforested areas. We find that the local warming associated with just 15 years of deforestation has caused losses in safe thermal working conditions for 2.8 million outdoor workers. We also show recent large-scale forest loss caused particularly large impacts on populations in locations such as the Brazilian states of Mato Grosso and Pará. Future global warming and additional forest loss will magnify these impacts.'</p>
Uncovering major types of deforestation frontiers across the world's tropical dry woodlands
<p>These datasets provide maps of deforestation frontier classsification into three themed typologies (severity, spatio temporal patterns and development stage) and archetypes of major frontier patterns. We do this for tropical dry woodland worldwide, for the period of 2000 to 2020, at ~3-km spatial resolution (Coordinate System: WGS_1984_Mollweide, float format). Datasets used for this analysis are publicly available, forest cover and loss data are available at: https://data.globalforestwatch.org/. Deforestation frontiers metrics were calculated and typologies developed in RStudio 1.3.1056. We share the code used to develop frontier metrics, frontier typologies, and archetypes, together with a sample dataset summarized from the originally publicly available dataset.</p> <p>Further details of the datasets can be found in Buchadas et. al. (2022): https://doi.org/10.1038/s41893-022-00886-9 </p> <p>For further questions or issues with the datasets, please contact Ana Buchadas at ana.buchadas@geo.hu-berlin.de.</p>
Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change
<p><strong>This repository contains the dataset linked to the following publication:</strong></p> <p><strong>Article title:</strong> Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change</p> <p><strong>Journal title: </strong>Water Research</p> <p><strong>Article Number: </strong>WR_118721</p> <p><strong>doi: </strong>https://doi.org/10.1016/j.watres.2022.118721</p> <p><strong>Abstract: </strong>Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso‑ and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015–2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.<br> </p> <p><strong>Data description</strong><br> by Xiangzhen Kong (xiangzhen.kong@ufz.de; xzkong@niglas.ac.cn)<br> 2022-06-20</p> <p>1. Discharge in the streams from 2010 to 2021 at YRZ site, and from 2010 to 2020 at YHZ_Q site.</p> <ul> <li>File name: dat_discharge_stream_YRZ_YHZ_2010_2021_daily.csv</li> <li>Note: The data is at daily basis but also available at 15-min high frequency basis, which can be requested from the authors.</li> </ul> <p>2. Nitrate concentration in the streams from 2011 to 2019 at both YRZ and YHZ_Q sites.</p> <ul> <li>File name: dat_nitrate_stream_YRZ_YHZ_2011_2019_daily.csv</li> <li>Note: The data is at daily basis but also available at 15-min high frequency basis, which can be requested from the authors.</li> </ul> <p>3. Water quality data in the inflows from 2010 to 2021 at biweekly basis, from YRZ and YHZ_WQ sites.</p> <ul> <li>File name: dat_waterquality_stream_YRZ_YHZ_2010_2021_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at the depth of 0.5m under water surface, from both probe and lab.</li> </ul> <p>4. Water quality data in the predams from 2010 to 2021 at biweekly basis, from YR1 and YH1 sites.</p> <ul> <li>File name: dat_waterquality_predams_YR1_YH1_2010_2021_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at the depth of 0.5m under water surface, from both probe and lab.</li> </ul> <p>5. Water quality data in the predams from 2010 to 2015 at biweekely basis, from YR3 and YH3 sites.</p> <ul> <li>File name: dat_waterquality_predams_YR3_YH3_2010_2015_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at various water depth, only from lab.</li> </ul> <p>6. CTD and BBE probe profile data in the predams from 2010 to 2015 at biweekely basis, from YR3 and YH3 sites.</p> <ul> <li>Note: Data stored in the folder "probe_profiles_predam_YR3_YH3_2010_2015_biweekly". The data is at biweekly basis, measured at various water depths. The measurements include water temperature (Celcius), DO (mg/L), Chl-a (mg/m3), bluegreen, green and diatom (all in Chl-a, mg/m3)</li> </ul>
Benchmark map of deforestation for Amazonia
<p><strong>Title: </strong>Benchmark map of deforestation for Amazonia</p> <p><strong>Contact:</strong> Celso H. L. Silva-Junior (celsohlsj@gmail.com)</p> <p><strong>Data:</strong> Old-growth forest deforestation</p> <p><strong>Coverage:</strong> Amazonia</p> <p><strong>Period:</strong> 1986 to onwards (according to new MapBiomas project collections)</p> <p><strong>Spatial resolution:</strong> 30-meters</p> <p><strong>Temporal resolution:</strong> Annual</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System with Datum WGS-84</p> <p><strong>File format: </strong>The zip file contains nine tiles in compressed TIFF format. The pixel values represent the year of deforestation.</p> <p><strong>Code:</strong> <a href="https://github.com/celsohlsj/amazonia_deforestation">https://github.com/celsohlsj/amazonia_deforestation</a></p> <p><strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please cite the repository and our paper correctly. We also welcome users to invite us for collaboration.</p> <p><strong>Associated publication: </strong>Silveira, M.V. F., Silva-Junior, C.H.L., Anderson, L.O., Aragão, L.E.O.C. Amazon fires in the 21st century: the year of 2020 in evidence. Global Ecology and Biogeography (2022). https://doi.org/10.1111/geb.13577</p> <p><strong>For this dataset, please use the following references:</strong></p> <ul> <li>Silveira, M.V. F., et al. Amazon fires in the 21st century: the year of 2020 in evidence. <em>Global Ecology and Biogeography</em> (2022). doi: 10.1111/geb.13577</li> <li>Silva-Junior, C. H. L. . (2022). Benchmark maps of deforestation for Amazonia [Data set]. <em>Zenodo</em>. https://doi.org/10.5281/zenodo.6808579</li> </ul>
Global mining deforestation footprint data from 2000 to 2019
<p>The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/<br><br>This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.</p> <p>The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li><em>area<double></em> area of the polygon in squared kilometres</li> <li><em>geom<polygon></em> the geometry of the features in geographical coordinates WGS84</li> </ul> <p>The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from <em>Ecoregions 2017 <sup>© Resolve </sup></em>(Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).</p> <p>This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>id_hcluster<string></em> unique feature identifier</li> <li><em>list_of_commodities<string></em> a comma-separated list of commodities</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li>ecoregion<em><string></em> ecoregion name</li> <li><em>biome<string></em> biome name</li> <li><em>year<numeric></em> the year</li> <li>area_forest_loss_XXX_YYY<double> the area of forest cover loss within a polygon per year in squared kilometres.</li> </ul> <p>The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).</p> <ul> <li>XXX can take one of: <ul> <li>000: total tree cover loss independently from the initial tree cover</li> <li>025: tree cover loss on pixels with initial tree cover between 0 and 25%</li> <li>050: tree cover loss on pixels with initial tree cover between 25 and 50%</li> <li>075: tree cover loss on pixels with initial tree cover between 50 and 75%</li> <li>100: tree cover loss on pixels with initial tree cover between 75 and 100%</li> </ul> </li> <li>YYY can take one of: <ul> <li>la: tree cover loss within strict nature reserve</li> <li>Ib: tree cover loss within wilderness area</li> <li>II: tree cover loss within national park</li> <li>III: tree cover loss within natural monument or feature</li> <li>IV: tree cover loss within habitat/species management area</li> <li>V: tree cover loss within protected landscape/seascape</li> <li>VI: tree cover loss within PA with sustainable use of natural resources</li> <li>p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported</li> <li>none: when YYY is omitted, total tree cover loss within the polygon</li> </ul> </li> </ul> <p>For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The <em>id</em> can be used to link polygons to forest loss data.</p>
Crop Prices and Deforestation in the Tropics
<p>We provide the stata files that allow to reproduce the results presented in the paper <br> "Crop Prices and Deforestation in the Tropics" by Nicolas Berman, Mathieu Couttenier, Antoine Leblois and Raphaël Soubeyran.</p> <p>The replication folder contains different files:<br> 1- *.dta file: database<br> 2- *.do file: do-file containing the codes to replicate the results (figures and tables)<br> 3- * Ancillary data: <br> .csv file: data needed to produce a map of the initial forest cover (in 2000). <br> .dta additional files to run sensitivity analysis</p> <p>Simply change the path to files (on line 25 of the replication_code.do file) to re-run the analysis:<br> ** Change pathway to load and save the data<br> global dir ".../Replication_files_BCLS_2022"</p> <p><br> Stata 17 was used for this work.</p>
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Data and code for the publication: "Deforestation as an anthropogenic driver of mercury pollution"
<p>A. Feinberg, Sep 2023<br> arifeinberg@gmail.com</p> <p>Essential data and code for the publication: Feinberg et al. : Deforestation as an anthropogenic driver of mercury pollution</p> <p>The directories include:<br> 1) analysis_scripts/ - all analysis scripts used to produce input data and figures for paper<br> 2) Erosion_data/ - Erosion model (GloSEM) output<br> 3) GC_code/ - Archived GEOS-Chem code used to simulate the runs in this paper<br> 4) GC_data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:<br> HIST - run0311<br> BAU - run0312<br> GOV- run0313<br> SAV - run0315<br> RFR - run0314<br> Deforesting different regions for EF calculations:<br> DFR_Afrotropic - run0321<br> DFR_Indomalayan - run0322<br> DFR_China - run0323<br> DFR_Neotropic - run0324<br> DFR_Palearctic - run0325<br> DFR_Australasia - run0326<br> DFR_Nearctic - run0327<br> DFR_Amazon = SAV - run0315</p> <p>5) input_data/ - input data used to run GEOS-Chem</p> <p>Please refer to other README.md files within sub-directories and contact me for any questions</p>
High-resolution maps of rubber and rubber-related deforestation for Southeast Asia
<p>This dataset contains maps of rubber plantations in 2021, and maps of rubber-related deforestation between 1993-2016 for Southeast Asia. The rubber maps have a 10 m pixel size, and the deforestation maps have a 30 m pixel size. The dataset and the methods for generating it are described in Wang et al. 2023. High-resolution maps show that rubber causes substantial deforestation. <em>Nature</em>. <strong>Please note that an update of this dataset will follow in September 2025.</strong> </p>
Supporting material for 'Eighty-six EU policy options for reducing imported deforestation'
<p>This dataset constitutes supporting material to the publication 'Eighty-six EU policy options for reducing imported deforestation', forthcoming in the journal One Earth. Contained in this dataset are the 1,141 original policy proposals that are identified in grey literature and a European Commission (EC) public consultation, as well as the assessment of the political feasibility of each of the 86 summary policy options derived from the original proposals. Political feasibility is assessed across three determinants: (1) ‘advocacy’, which measure the support for a policy across different actors; (2) ‘institutional setting’, which captures the institutional complexity of defining and adopting a given policy; and (3) ‘costs’, which express the magnitude and distribution of societal costs resulting from policy implementation.</p>
Research data: Counterfactual assessment of protected area avoided deforestation in Cambodia version 4
<p>This dataset includes the data, the R scripts used for analysis and results that are the basis of the journal article: Black, B., Anthony, B. In review. Counterfactual assessment of protected area avoided deforestation in Cambodia: Trends in effectiveness, spillover effects and the influence of establishment date. Global Ecology and Conservation.</p> <p>Each folder includes a specific readme file in .txt format which includes metadata and instructions for reproducing the research.</p> <p> </p>
Data from: Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago
<p>This archive contains data produced in a study of the vegetation trajectories of Ogasawara Islands in 77 years related to following article:</p> <p>Ohashi, H., Kato, H., Murao, M., Kato, H., Kawakami, K., Kurokawa, H., Oguro, M., Kimura, F., Niiyama, K., Matsui, T., and Shibata, M. (2024) Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago. <em>Applied Vegetation Science</em>, 27 (1), e12767. <a href="https://doi.org/10.1111/avsc.12767">https://doi.org/10.1111/avsc.12767</a></p> <p> </p> <p><strong>Archive contents</strong><br>The archive contents are organized into five parts, each stored as a .zip compressed file.</p> <p><strong>X1_tif_original_vegmap_scan_georeference</strong></p> <p>Scanned and georeferenced original vegetation maps in GeoTiff format, which was drawn in 1935, scanned at 300 dpi. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>kitanoshima_isl_WGS84.tif<br>mukojima_isl_WGS84.tif<br>yomejima_isl_WGS84.tif<br>ototojima_isl_WGS84.tif<br>anijima_isl_WGS84.tif<br>nishijima_isl_WGS84.tif<br>chichijima_isl_WGS84.tif<br>hahajima_isl_WGS84.tif<br>mukohjima_isl_WGS84.tif<br>kitaiwoto_isl_WGS84.tif<br>iwoto_isl_WGS84.tif</em></p> <p> </p> <p><strong>X2_shp_vegmap</strong></p> <p>Shapefile of the geospatial polygon data of vegetation map of Ogasawara Islands surveyed in 1935, and stored as a .zip compressed file. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.dbf<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.prj<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shp<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shx<br>attribute_ForSect_code_en.csv<br>attribute_Veg_name_en.csv<br>metadata_vegmap_shp_ogasawara1935_en.csv</em></p> <p>Following files includes Japanese character (which may corrupt in non-Japanese environment):</p> <p><em>attribute_ForSect_jp.csv<br>attribute_Veg_name_jp.csv<br>metadata_vegmap_shp_ogasawara1935_jp.csv</em></p> <p> </p> <p><strong>X3_tif_vegmap_converted_from_shp</strong></p> <p>Rasterized data of polygon data of vegetation map for analysis. Coordinate reference system was set at JGD2000 / Japan Plane Rectangular CS XIV (EPSG: 2456)</p> <p>This directory includes:</p> <p><em>vegmap_1935.zip (compressed “vegmap_1935.tif (0.7GB)”)<br>vegnap_1979.zip (compressed “vegmap_1979.tif (1.5GB)”)<br>vegmap_2011.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>islcode_raster.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>attribute_integratedveg_ecoltype.csv<br>attribute_vegid_1935.csv<br>attribute_vegid_1979.csv<br>attribute_vegid_2011.csv</em></p> <p> </p> <p><strong>X4_scanned_image_vegdata</strong></p> <p>Scanned images of original vegetation data in 1935.</p> <p>The directory includes:<br><em>vegetation_survey_sheet_1.pdf<br>vegetation_survey_sheet_2.pdf</em><br><em>vegetation_survey_sheet_3.pdf</em></p> <p> </p> <p><strong>X5_digitized_vegdata</strong></p> <p>Digitized vegetation data.</p> <p>The directory includes:<br><em>plot_species_abundance_matrix_v0.csv<br>plotinfo_v0.csv<br>attribute_Species_en_v0.csv</em></p> <p>Following file includes Japanese character (which may corrupt in non-Japanese environment)<br><em>attribute_Species_jp_v0.csv</em><br> </p> <p><strong>X6_code_for_analysis</strong></p> <p>Tentative.</p> <p> </p> <p>このアーカイブには、小笠原諸島の77年間の植生の変遷(1935年、1979年、2012年)に関するデータが含まれています。</p> <p> </p>
Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land
<p>Data and model source code for the manuscript: "Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land"</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Christopher P.O. Reyer1, Felicitas Beier1,2, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> September 2022</p> <p>See https://github.com/abhimishr/magpie/releases and https://github.com/magpiemodel/magpie/releases/tag/v4.5.0 for further details</p>
Higher temperature variability in deforested mountain regions impacts the competitive advantage of nocturnal species
<p>Deforestation is a major contributor to biodiversity loss, yet the impact of deforestation on daily microclimate variability and its implications for species with different daily activity patterns remain poorly understood. Using a recently developed microclimate model, we investigated the effects of deforestation on the daily temperature range (DTR) in low-elevation tropical areas and high-elevation temperate areas. Our results show that shade loss due to deforestation substantially increases DTR in these areas, suggesting a potential impact on species interactions. To test this hypothesis, we studied the competitive interactions between nocturnal burying beetles and all-day active blowfly maggots in forested and deforested habitats in Taiwan. We show that deforestation leads to increased DTR at higher elevations, which enhances the competitiveness of blowfly maggots during the day and leads to a higher failure rate of carcass burial by the beetles at night. Thus, deforestation-induced temperature variability not only modulates exploitative competition between species with different daily activity patterns but also likely exacerbates the negative impacts of climate change on nocturnal organisms. Our study highlights the need to protect forests, especially in areas where deforestation can greatly alter temperature variability, in order to prevent potential adverse effects on species interactions and their ecological functions.</p>
Dataset to: Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils
<p>Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils<br> T. Peplau, C. Poeplau, E. Gregorich, J. Schroeder</p> <p>This repository contains a dataset of soil temperature, soil parameters, farm management and additional site informations.</p> <p>Soil_temperature_data_Yukon.zip: Temperature data from different farms across the Yukon.<br> Each .xlsx file contains data from one temperature logger that logged soil temperature every 2 hours. The individual sheets are named in the following scheme:<br> Farm_landuse_depth.xlsx<br> Farm contains two letters corresponding to the identifier in the soil data set<br> landuse contains either F ("Forest"), CM ("Cropland / Market Garden") or G ("Grassland")<br> Depth is either 10 cm or 50 cm</p> <p>teabags.csv contains raw data about the initial weight of the teabags buried, their location and their weight after two years in the soil</p> <p>tea_decomposition contains the mean decomposition (n=3) of the tabags from each plot and corresponding temperature statistics, based on the logger data</p> <p>Soil_I_IV.csv contains soil parameters from soil samples at 0-10 cm and 40-60 cm</p> <p>site_data_R.csv contains geographical information and soil data that has only been measured once per site</p>
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Allen Brain Atlas
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