Landscape-Scale Soil Survey Results for Soil Profile

Data File Identifier: 
Landscape-Scale Soil Survey Results for Soil Profile
Quality Control Level: 
Derived and Interpreted products
Data Set: 
Number of header rows in file(s): 
2

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).

2. Soil Nitrogen
Soil nitrogen and soil C are correlated within each forest type and across the study area, Attempts to find variables that might influence N amount other than forest type and the amount of organic C were unsuccessful.
3. Base cations
Neutral-salt-exchangeable Ca++ is different across forest types (t > p, c; p<.0001) and bedrock types (qd > vc, p<.0001). XRD analysis indicates that some of the soils derived from qd have substantial amounts of feldspar (up to 60%, Zhou 2011) and this probably accounts for the difference related to bedrock type. Mg and K differ only across forest types (t > p, c, p<.0001 in each case) and not in response to any other variable. It is still unclear whether the high exchangeable cation amounts in the tabonuco forest soils are due to the rapid decomposition of organic matter or to the chance correspondence of weatherable feldspar in some of the the tabonuco stands growing on quartz diorite (or perhaps both).
Summary
In the top 80 cm of soils, vegetation type and the C:N ratio of litter (and roots?) are the most important controls on the quantities of C and N and on the amounts of exchangeable base cations. Bedrock influences are minor, and in the data analyzed thus far, confined to the amount of neutral-salt-exchangeable Ca (and the Ca:Mg ratio).

Periodicity of Sample Number: 
1.00
Periodicity of Sample: 
month
From Date: 
Wed, 06/01/2011 - Fri, 07/15/2011
Research Location: 
Core Area(s) and/or Keywords: 

C, N, and exchangeable Ca++, Mg++ and K+

Variables: 
Record ID
Variables: 
Site Name
Variables: 
Catena
Variables: 
Forest Type
Variables: 
Bedrock
Variables: 
Elevation
Variables: 
Precipitation
Variables: 
Profile Carbon
Variables: 
Profile Nitrogen
Variables: 
Profile C:N
Variables: 
Soil Carbon
Variables: 
Soil Nitrogen
Variables: 
Soil C:N
Variables: 
Profile Ca
Variables: 
Profile Mg
Variables: 
Profile K
Variables: 
Soil Calcium
Variables: 
Soil Magnesium
Variables: 
Soil Potasium
Data Set Methods
Sample Medium: 
Soil
Field Collection: 

At each of the 24 sites, we located nine soil pits on three ridge/slope/valley transects located within an area of 3-5 ha. At each soil pit, we collected the organic horizons of the forest floor within a 25 x 25 cm frame fastened to the ground. Coarse woody debris (large branch remnants and palm rhacises) was discarded. The samples were returned to the lab where they were air dried. Next we excavated a 25 x 25 x20 cm quantitative soil pit within the frame. We weighed the soil, roots and rocks separately in the field, and collected a large (>500 g) subsample of the mineral soil for analysis and field weight/oven-dry weight conversions. All of the roots were bagged and retained and the rocks were discarded. Samples for 20-50 cm and 50-80 cm depths were collected within the frame using an AMS Soil Core Sampler (2-1/4" x 12",Ben Meadows Company, Janesville, WI). These core samples were not considered to be reliable for bulk density estimates since often the samples were compressed by the hammer core soil sampler. Accordingly, we excavated 30 additional pits (5 independent replicates in each of the 6 geology/forest type combinations) to obtain BD estimates for the soil from the 20-50, 50-80cm depths. We dug nested holes using a shovel for the 20-50 m depth, a post- hole digger for the 50-80 depth increment. The volumes of the holes were measured using perlite and a 2-l graduated cylinder. Soil samples were kept in plastic bags and shipped to University of Pennsylvania within a week or so where they were air dried, reweighed and sieved through a 2 mm sieve, and stored in plastic bags.

Lab Analysis: 

Forest floor samples were dried at 70oC in an oven for more than 48 hours and ground in a Wiley mill. Approximately 0.25 g of forest floor samples were burned in ceramic crucibles at 480oC for 12 hours and digested with 5ml 5 mol/L HCl solution, then eluted to 50ml with DI water. Air-dried and sieved mineral soil samples were mixed and subsamples were obtained for air dry/oven dry (105oC) weight conversions, and for exchangeable cation analysis. Approximately 2 g samples of finely ground soil were extracted over an 8 hour period with 50 ml of 1mol/L NH4Cl solution and filtered through glass microfiber filters (Whatman, GF/D, Cat# 1823-025). Soil extracts and forest floor digests were analyzed for calcium, magnesium and potassium with Spectro ICP-OES.
Samples from the 0-20 cm depth on ridges were composited, ground and analyzed for their content of primary minerals that contained Ca, Mg and K. Each of the 24 sites was represented by one sample. Mineralogical assessment was performed by X-Ray Wizrds, LLC using a D8 Advance diffractometer (Bruker Corp., Billerica, MA). Patterns obtained were analyzed via reitveld refinement via the TOPAS software, yielding percent crytallinity as well as phase identification. Phase quantities were determined via RIR due to the large amount of secondary silicate and oxide clay minerals; these were not fully characterized as the focus was on the primary mineral content.

Dissemination: 
unrestricted
Publish to National CZO: 
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