Dataset Listing

Northeastern Puerto Rico - Stream Ecology, Vegetation - Luquillo LTER (1975-2018)

Variables:  Many

Standard Variables:  Luquillo LTER

Date Range:  (1975-2018. Ongoing)

Dataset Creators/Authors:  Luquillo Long Term Ecological Research (LTER)

Contact:  Miguel Leon,

Field Area:   Northeastern Puerto Rico and the Luquillo Mountains

Keywords & XML
  • Description

    The Luquillo Long-Term Ecological Research (LUQ) program takes place in the Luquillo Mountains of Puerto Rico (Figure 1). This tropical setting has steep environmental gradients, a varied natural disturbance regime, and a history of human land use. Of the mountain area 11,330 hectares are included in the Luquillo Experimental Forest (LEF), which is congruent with the El Yunque National Forest, part of the U.S. National Forest system. The mountains rise to over 1075 m. Prevailing winds coming off the ocean from the east drop rain as they rise over the mountains; thus rainfall increases with elevation, ranging from about 3530 mm year-1 at low elevations to 4850 mm year-1 higher up. February through April are the drier months, but monthly rainfall is variable. Mean monthly temperatures at lowest elevations range from about 23.5ºC in January to 27ºC in September, and at the highest elevations from 17ºC to 20ºC (see article Climate and Hydrology in this web page).
  • Keywords

    Ecology, Disturbance inorganic nutrients, organic matter, populations, primary production,

    XML Metadata

    XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.

  • Citation for This Dataset

    Please cite individual dataset.

    Citation for This Webpage

    Luquillo Long Term Ecological Research (LTER) (2018). "CZO Dataset: Northeastern Puerto Rico - Stream Ecology, Vegetation (1975-2018) - Luquillo LTER." Retrieved 23 Feb 2020, from


Northeastern Puerto Rico - LTER- website

(data)   Data Level 4

Northeastern Puerto Rico - Soil Organic Carbon, Total Nitrogen, for CENTURY model

(a110)   Data Level 2

Northeastern Puerto Rico - Canopy Trimming Experiment (CTE)

(-cte)   Data Level 3

Data Use Policy
Data Sharing Policy
  • Data Use Policy

    DRAFT v.0.4.0

    1. Use our data freely. All CZO Data Products* except those labelled Private** are released to the public and may be freely copied, distributed, edited, remixed, and built upon under the condition that you give acknowledgement as described below. Non-CZO data products — like those produced by USGS or NOAA — have their own use policies, which should be followed.

    2. Give proper citation and acknowledgement. Publications, models and data products that make use of these datasets must include proper citation and acknowledgement. Most importantly, provide a citation in a similar way as a journal article (i.e. author, title, year of publication, name of CZO “publisher”, edition or version, and URL or DOI access information. See Also include at least a brief acknowledgement such as: “Data were provided by the NSF-supported Southern Sierra Critical Zone Observatory” (replace with the appropriate observatory name).

    3. Let us know how you will use the data. The dataset creators would appreciate hearing of any plans to use the dataset. Consider consultation or collaboration with dataset creators.

    *CZO Data Products.  Defined as a data collected with any monetary or logistical support from a CZO.

    **Private. Most private data will be released to the public within 1-2 years, with some exceptionally challenging datasets up to 4 years. To inquire about potential earlier use, please contact us.

  • Data Sharing Policy

    DRAFT v.0.2.5

    All CZO investigators and collaborators who receive material or logistical support from a CZO agree to:

    1. Share data privately within 1 year. CZO investigators and collaborators agree to provide CZO Data Products* — including data files and metadata for raw, quality controlled and/or derived data — to CZO data managers within one year of collection of samples, in situ or experimental data. By default, data values will be held in a Private CZO Repository**, but metadata will be made public and will provide full attribution to the Dataset Creators†.

    2. Release data to public within 2 years. CZO Dataset Creators will be encouraged after one year to release data for public access. Dataset Creators may chose to publish or release data sooner.

    3. Request, in writing, data privacy up to 4 years. CZO PIs will review short written applications to extend data privacy beyond 2 years and up to 4 years from time of collection. Extensions beyond 3 years should not be the norm, and will be granted only for compelling cases.

    4. Consult with creators of private CZO datasets prior to use. In order to enable the collaborative vision of the CZO program, data in private CZO repositories will be available to other investigators and collaborators within that CZO. Releasing or publishing any derivative of such private data without explicit consent from the dataset creators will be considered a serious scientific ethics violation.

    * CZO Data Products. Defined as data collected with any monetary or logistical support from a CZO. Logistical support includes the use of any CZO sensors, sampling infrastructure, equipment, vehicles, or labor from a supported investigator, student or staff person. CZO Data Products can acknowledge multiple additional sources of support.

    ** Private CZO Repository. Defined as a password-protected directory on each CZO’s data server. Files will be accessible by all investigators and collaborators within the given CZO and logins will be maintained by that local CZO’s data managers. Although data values will not be accessible by the public or ingested into any central data system (i.e. CUAHSI HIS), metadata will be fully discoverable by the public. This provides the dual benefit of giving attribution and credit to dataset creators and the CZO in general, while maintaining protection of intellectual property while publications are pending.

    † Dataset Creators. Defined as the people who are responsible for designing, collecting, analyzing and providing quality assurance for a dataset. The creators of a dataset are analogous to the authors of a publication, and datasets should be cited in an analogous manner following the emerging international guidelines described at

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