We sampled soils from 216 profiles representing 24 sites in the El Yunque National Forest to determine amounts C, N and neutral-salt-extractable Ca++, Mg++ and K+. Following the classic paradigm, we assessed the influence of climate (modeled precipitation, modeled temperature and/or elevation as a surrogate variable for both), forest type (tabonuco, colorado, palm), parent material (quartz diorite, volcaniclastics), and topography (catena positions ridge, slope, valley and % slope) on the distribution of these nutrients. To separate the effects of vegetation from those of climate, half of the sites were located between 500 and 700 m in the three forest types where rainfall and temperature were not significantly different. Using a combination of ANOVA (or Kruskal-Wallis) and univariate regression trees we determined that the amount of carbon in the top 80 cm of soil was influenced primarily by forest type (c > p > t) probably driven by differences in litter and/or root C:N ratios. Topographic position was significantly correlated with C amount (v > s, r), with the higher C amounts in the valleys probably driven by low O2 levels. Bedrock type was significantly correlated with C amount in c and p stands, but not in the tabonuco type. N was strongly correlated with C as expected. Exchangeable Ca was different across forest types (t > c, p) and bedrock type (qd > vc). Mg and K were differed by forest type, but not by bedrock type (t > c, p) or any other variables.
The next phases of this project are (1) to determine levels of these nutrients below the root zone (80-140 cm) and the factors controlling their distribution; and (2) establish field experiments to test the results of the regression trees which indicate that the C:N ratio of litter and/or root inputs is the most important variable influencing C distribution. The latter represents a first step in exploring the usefulness of regression trees as a way of sorting out the relative importance of each of the state factors (climate, topography, organisms, parent material and time) in the classic paradigm relating environmental variables to soil properties.
Soil C differs markedly across forest types (c> p> t, p<.0001), and across catena positions (v> s, r, p<.001), but not across bedrock types in spite of the higher clay content of soils derived from vc. C:N ratio (in any horizon, or in the whole profile) is the best predictor of soil C amount. The differences in soil C correspond to the differences in litter C:N. Tabonuco stands have the least soil C but the highest litter input rates (c= 9.1, p= 7.2, c= 7.2 Mg ha-1yr-1, Weaver and Murphy, 1990, Frizano 1999, Lugo 1992) and Sullivan et al. (1999) measured substantially faster decomposition of tabonuco litter over 100d. Those findings support the idea that soil C amount is driven by differences in decomposition rate related at least in part to C:N ratios. Similar results were obtained in the 500-700m elevation band where only vegetation differs (soil C: c= 21.5+ 3, p= 19.3+ 2, t= 14.7+ 1). Univariate regression trees identify soil C:N ratio as the most important variable explaining soil C in all combinations of candidate predictor variables. For the past several decades, determining the influence of individual state factors on soil properties has been difficult due to the fact that some of the state factors are correlated with each other, and all 5 of the environmental variables can influence one soil property. Such problems are inherent in areas like the EYNF where vegetation changes along climate gradients. We plan to test the regression-tree result indicating that the C:N ratio of litter (and perhaps roots) is a more important control on decomposition rate than temperature and rainfall with field and laboratory incubations.
Valley soils have more C than ridge or slope soils. Depth profiles of soil C show equal C in the 0-20 cm layer across the catena, but greater amounts of C in the 20-80 cm layer in the valley soils (data not shown). This suggests a minimal role for down-slope movement of litter, and that the greater C content of valley soils is driven more by slower decomposition related to the lower O2 levels measured in soil air in the valleys (Silver et al. xxxx).
Carbon, Nitrogen, Exchangeable Ca++, Exchangeable Mg++, Exchangeable K+
XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.
Citation for This Dataset
Johnson A.J., Xing Hao. Landscape-Scale Soil Survey Results for Soil Profile. 2013. https://www.sas.upenn.edu/lczodata/content/landscape-scale-soil-survey-results-soil-profile
Citation for This Webpage
Johnson A.J.; Xing Hao (2012). "CZO Dataset: Northeastern Puerto Rico and the Luquillo Mountain - Soil Survey (2011-2012)." Retrieved 23 Mar 2019, from http://criticalzone.org/luquillo/data/dataset/2628/
Data Use Policy
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 http://www.datacite.org/whycitedata). 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
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 http://www.datacite.org/whycitedata.