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14 results for “Amazon land use”
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
FIGURE 4 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 4 | Relationships between the ecomorphological traits of fish species and environmental variables (land use and local habitat) in the streams evaluated in this study. Traits are represented by labels: relative head length (RHL), relative mouth width (RMW), relative height (RH), relative area of pectoral fin (RAPF) and relative caudal peduncle length (RCPL).
FIGURE 3 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 3 | Relationships between fish functional trophic groups and environmental variables (land use and local habitat) in the streams evaluated in this study. The groups are represented by the labels: Diurnal channel drift feeders (FTG 3), Diurnal backwater drift feeders (FTG 4), Diurnal surface pickers (FTG 7), Diggers (FTG 9) and Ambush and stalking predators (FTG 11). The FTG's with a correlation between 0.2 and -0.2 have been omitted for better visualization of the results.
FIGURE 2 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 2 | Ordination of the types of land use in the catchment areas of the study streams at the Capim River basin, eastern Amazon.
FIGURE 1 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 1 | Location of sampling streams (Middle Capim River basin, Pará State, Brazil). The characteristics of each land use class are described in the Material and Methods section.
Linking land-use and land-cover transitions to their ecological impact in the Amazon
<p>Authors: Cássio Alencar Nunes, Erika Berenguer, Filipe França, Joice Ferreira, Alexander C. Lees, Julio Louzada, Emma J. Sayer, Ricardo Solar, Charlotte C. Smith, Luiz E. O. C. Aragão, Danielle de Lima Braga, Plinio Camargo, Carlos Eduardo Pellegrino Cerri, Raimundo Cosme, Mariana Durigan, Nárgila Moura, Victor Hugo Fonseca Oliveira, Carla Ribas, Fernando Vaz-de-Mello, Ima Vieira, Ronald Zanetti, Jos Barlow</p> <p>Code repository for the paper: Nunes et al. Linking land-use and land-cover transitions to their ecological in the Amazon. Proceedings of the National Academy of Sciences. 2022. In this repository we included codes and data that we used to run the all the analyses presented in the paper.</p>
Data from: Multiple facets of biodiversity are threatened by mining-induced land-use change in the Brazilian Amazon
<p><strong>Aim</strong> </p> <p>Mining is increasingly pressuring areas of critical importance for biodiversity conservation, such as the Brazilian Amazon. Biodiversity data are limited in the tropics, restricting the scope for risks to be appropriately estimated before mineral licencing decisions are made. As the distributions and range sizes of other taxa differ markedly from those of vertebrates – the common proxy for analysis of risk to biodiversity from mining – whether mining threatens lesser-studied taxonomic groups differentially at a regional scale is unclear.</p> <p><strong>Location </strong></p> <p>Brazilian Amazon</p> <p><strong>Methods </strong></p> <p>We assess risks to several facets of biodiversity from industrial mining by comparing mining areas (within 70km of an active mining lease) and areas unaffected by mining, employing species richness, species endemism, phylogenetic diversity, and phylogenetic endemism metrics calculated for angiosperms, arthropods, and vertebrates.</p> <p><strong>Results </strong></p> <p>Mining areas contained higher densities of species occurrence records than the unaffected landscape, and we accounted for this sampling bias in our analyses. None of the four biodiversity metrics differed between mining and non-mining areas for vertebrates. For arthropods, species endemism was greater in mined areas. Mined areas also had greater angiosperm species richness, phylogenetic diversity, and phylogenetic endemism, although lower species endemism than unmined areas.</p> <p><strong>Main Conclusions </strong></p> <p>Unlike for vertebrates, facets of angiosperm and arthropod diversity are relatively higher in areas of mining activity, underscoring the need to consider multiple taxonomic groups and biodiversity facets when assessing risk and evaluating management options for mining threats. Particularly concerning is the proximity of mining to areas supporting deep evolutionary history, which may be impossible to recover or replace. As pressures to expand mining in the Amazon grow, impact assessments with broader taxonomic reach and metric focus will be vital to conserving biodiversity in mining regions.</p>
Amazon land use duration
<p><strong> We access the land use and land cover time series product available for the Amazon for the year 1985-2019 (MapBiomass collection 6). The MapBiomas is based on the classification of Landsat images, map of annual land use and land cover at 30 m spatial resolution. We initially classified each pixel in each plot within site following MapBiomaClass and we reclassified in Forest, Secondary-forest, deforestation and others for each year in a temporal series 1985-2019. The land use duration was calculated as the number of years that the pixel was classified as non-forest cover. </strong></p>
Data from: Multiple facets of biodiversity are threatened by mining-induced land-use change in the Brazilian Amazon
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Effect of land use change on Melolonthidae (Coleoptera: Scarabaeoidea) beetle communities in the deforestation arc of the Brazilian Amazon
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Data from: Disentangling the pathways of land use impacts on the functional structure of fish assemblages in Amazon streams
Agricultural land use is a primary driver of environmental impacts on streams. However, the causal processes that shape these impacts operate through multiple pathways and at several spatial scales. This complexity undermines the development of more effective management approaches, and illustrates the need for more in-depth studies to assess the mechanisms that determine changes in stream biodiversity. Here we present results of the most comprehensive multi-scale assessment of the biological condition of streams in the Amazon to date, examining functional responses of fish assemblages to land use. We sampled fish assemblages from two large human-modified regions, and characterized stream conditions by physical habitat attributes and key landscape-change variables, including density of road crossings (i.e. riverscape fragmentation), deforestation, and agricultural intensification. Fish species were functionally characterized using ecomorphological traits describing feeding, locomotion, and habitat preferences, and these traits were used to derive indices that quantitatively describe the functional structure of the assemblages. Using structural equation modeling, we disentangled multiple drivers operating at different spatial scales, identifying causal pathways that significantly affect stream condition and the structure of the fish assemblages. Deforestation at catchment and riparian network scales altered the channel morphology and the stream bottom structure, changing the functional identity of assemblages. Local deforestation reduced the functional evenness of assemblages (i.e. increased dominance of specific trait combinations) mediated by expansion of aquatic vegetation cover. Riverscape fragmentation reduced functional richness, evenness and divergence, suggesting a trend toward functional homogenization and a reduced range of ecological niches within assemblages following the loss of regional connectivity. These results underscore the often-unrecognized importance of different land use changes, each of which can have marked effects on stream biodiversity. We draw on the relationships observed herein to suggest priorities for the improved management of stream systems in the multiple-use landscapes that predominate in human-modified tropical forests.
Data from: Disentangling the pathways of land use impacts on the functional structure of fish assemblages in Amazon streams
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LBA-ECO LC-31 Historical Land-Use in the Amazon: 1940-1995
This data set provides annual spatial patterns of cropland, natural pasture, and planted pasture land uses across Amazonia for the period 1940/1950-1995. Two series of 5-minute grid cell historical maps were generated starting from land use classification products for 1995. Annual data are the fraction of natural pasture, planted pasture, and cropland in each 5-min grid cell. The annual maps are provided in two NetCDF (.nc) format file at 5-minute resolution. The AMZ-C.nc file covers the Brazilian portion of Amazon and Tocantins Rivers basins, and is based on the 1995 land use classification of Cardille et al. (2002), generated through the fusion of remote sensing (AVHRR) and agricultural census data. The second file, AMZ-R.nc, covers the entire Legal Amazon region and adjacent areas and is based on the 1995 land use classification by Ramankutty et al. (2008). The land use classification was generated by the fusion of satellite imagery (MODIS and VEGETATION-SPOT) and data from the agricultural census. A historical land-use reconstruction algorithm was used to generate the annual spatial patterns (based on work from Ramunkutty and Foley, 1999).
LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999
This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest, Non-Forest Vegetation, Urban/Barren, and Water; and a fifth class of Clouds/Shadows. The areas of interest were the four Intensive Study Areas (ISA) of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects: Eastern Intensive Study Area; Northern Intensive Study Area; Southern Intensive Study Area, and Southwestern Intensive Study Area. These areas are in the Northern Ecuadorian Amazon, in the area known as the northern Oriente of Ecuador. The resolution of the data is 30 meters. There are 12 image files (.tif) with this data set.
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