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 19 Feb 2019, from http://criticalzone.org/national/data/dataset/2628/
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