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Title: Madidi Project Full Dataset

This item contains version 5.0 of the Madidi Project's full dataset. The zip file contains (1) raw data, which was downloaded from Tropicos (www.tropicos.org) on August 18, 2020; (2) R scripts used to modify, correct, and clean the raw data; (3) clean data that are the output of the R scripts, and which are the point of departure for most uses of the Madidi Dataset; (4) post-cleaning scripts that obtain additional but non-essential information from the clean data (e.g. by extracting environmental data from rasters); and (5) a miscellaneous collection of additional non-essential information and figures. This item also includes the Data Use Policy for this dataset.

The core dataset of the Madidi Project consists of a network of ~500 forest plots distributed in and around the Madidi National Park in Bolivia. This network contains 50 permanently marked large plots (1-ha), as well as >450 temporary small plots (0.1-ha). Within the large plots, all woody individuals with a dbh ≥10 cm have been mapped, tagged, measured, and identified. Some of these plots have also been re-visited and information on mortality, recruitment, and growth exists. Within the small plots, all woody individuals with a dbh ≥2.5 cm have been measured and identified. Each plot has some edaphic and topographic information, and some large plots have information on various plant functional traits.

The Madidi Project is a collaborative research effort to document and study plant biodiversity in the Amazonia and Tropical Andes of northwestern Bolivia. The project is currently lead by the Missouri Botanical Garden (MBG), in collaboration with the Herbario Nacional de Bolivia. The management of the project is at MBG, where J. Sebastian Tello (sebastian.tello@mobot.org) is the scientific director. The director oversees the activities of a research team based in Bolivia. MBG works in collaboration with other data contributors (currently: Manuel J. Macía [manuel.macia@uam.es], Gabriel Arellano [gabriel.arellano.torres@gmail.com] and Beatriz Nieto [sonneratia@gmail.com]), with a representative from the Herbario Nacional de Bolivia (LPB; Carla Maldonado [carla.maldonado1@gmail.com]), as well as with other close associated researchers from various institutions. For more information regarding the organization and objectives of the Madidi Project, you can visit the project’s website (www.madidiproject.weebly.com).

The Madidi project has been supported by generous grants from the National Science Foundation (DEB 0101775, DEB 0743457, DEB 1836353), and the National Geographic Society (NGS 7754-04 and NGS 8047-06). Additional financial support for the Madidi Project has been provided by the Missouri Botanical Garden, the Comunidad de Madrid (Spain), the Universidad Autónima de Madrid, and the Taylor and Davidson families. 
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Award ID(s):
1836353
NSF-PAR ID:
10377920
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Zenodo
Date Published:
Edition / Version:
5.0
Subject(s) / Keyword(s):
["biodiversity","forest","elevational gradient","tree","woody plant","Madidi National Park","forest plot","Andes","Amazonia"]
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    File descriptions:

    Datasets are available in three spatial reference systems:

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    Source data:

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    • Bizkaia province: https://web.bizkaia.eus/es/inspirebizkaia
    • Gipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/
    • Navarra region: https://inspire.navarra.es/services/BU/wfs
    • Other regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xml
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    Technical notes:

    Gridded data

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    ./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tif

    Regions:

    • all: complete territory of Spain
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    Variables: evolution

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    • resbia: residential building indoor area

    Variables: physical

    • bia: building indoor area
    • bufa: building footprint area
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    • dwel: number of dwellings

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    Variable: landuse

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    • Country: 34
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    • Province (PROV_CODE): 04
    • LAU code: 04001 (province + municipality code)
     
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    After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. 

    The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). 

    Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.   

    The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.  

    Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. 

    Filenames: 

    monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N.

    seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N.

    annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.

    Please contact igifford@earth.miami.edu for any queries. {"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\u202fJournal of the Meteorological Society of Japan. Ser. II,\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\u202fJournal of physical oceanography,\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\u202fJournal of Physical Oceanography,\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\u202fJournal of Climate,\u202f2(12), pp.1507-1516."]} 
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  5. Data Description:

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    ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]
    ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]
    ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]
    ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]
    ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]
    ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]
    ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]
    ├── depthWT_30arc_us.tif                                                        [depth to water table]
    ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]
    ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]
    ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]
    ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]
    ├── pondingFre_30arc_us.tif                                                     [ponding frequency]
    ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]
    ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]
    ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]
    ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]
    ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]
    └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]

    SOC TIF files:

    ├──30cm SOC mean.tif                             [30 cm depth soil SOC]
    ├──100cm SOC mean.tif                           [100 cm depth soil SOC]
    ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]
    └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]

    site observations csv files:

    ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

    ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data


    Data format:

    Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.

    Geospatial projection

    GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]]

     

     
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