Appendix S1. Supplemental Text: Procedures for root analysis, identification and statistical analysis.
Site description: Jennings Woods is a 30 ha hardwood forest with three delineated ecosystems based
primarily on steep topographic gradients (Boerner 2006). The eastern side of the woods contains 600 m
of the West Branch of the Mahoning River, a fourth-order stream. The area within 80 m of the river is a
periodically flooded meandering riverbed, and was classified as “riparian forest”. Soils in the riparian
area are officially classified as Holly silt loam (a fine-loamy, mixed, active, nonacid, mesic Fluvaquentic
Endoaquept), but actually vary from flood-deposited sand dunes to poorly drained oxbows found at the
base of steep slopes that form the topographic edges of the ecosystem. There is a plateau area 15-20 m
higher in elevation than the riparian forest that was classified as “upland forest”. This area is primarily
comprised of well-drained soils in the Chili loam series (a fine-loamy, mixed, active, mesic Typic
Hapludalf). There is also a lower elevation plateau which is very poorly drained, and much of which is
saturated with water throughout the year. We classified this area as “bottomland forest”. Soils in this
area are officially classified as Holly silt loam or in the Geeburg-Glenford silt loam complex.
Very fine root biomass (VFRB, roots <1mm in diameter) ranged from 290 to 526 g m2 between
bottomland and upland areas, with no significant differences among sites. The uppermost 15 cm
accumulated 45 to 61% of the total VFRB with a shallower distribution of root biomass in upland areas.
Despite differences in root distribution among sites, we did not find evidence of niche segregation by
depths for the tree species studies (sampling up to 60 cm deep, n=44, details in Valverde-Barrantes et
al., in prep).
Analysis of soil samples: Soil was dried at 60°C to determine gravimetrically the percent moisture. Soil
pH was measured in a 1:1 mixture with distilled water. Total carbon (C) and nitrogen (N) were
determined on an elemental analyzer (Costech Analytical, Ventura, CA USA). To measure particulate
organic matter (POM) C and N, the sand fraction and associated organic matter was isolated following
Cambardella and Elliott (1992), and C and N content were determined using the method described
above. Readily available soil inorganic phosphorous (P) and easily mineralizable organic P were
extracted from pulverized oven dried soil by adding 0.5 M NaHCO3 (pH 8.5) and shaking at 100 rpm on
an orbital shaker (Lab-Line, Melrose Park, IL USA) for 30 min (Olsen et al., 1954). Inorganic P was
determined colorimetrically using the modified ascorbic acid method (Kuo, 1996) directly on the
NaHCO3 extracts, while organic P was determined by the increase in P detected after NaHCO3 extract
digestion with 1.8 N H2SO4 and (NH4)2S2O2.
Soil texture was measured after oxidation of organic material using concentrated H2O2 heated to
90°C, followed by dispersion overnight in sodium metaphosphate. The size spectra of mineral particles
was measured by laser diffraction using a MasterSizer 2000 (Malvern Instruments, Worcestershire,
U.K.), resulting in measurements of relative mass in each of 100 categories ranging from 0.02 to 2000
µm.
Root analysis. A root sample typically consisted of 1-2 orders of non-lignified roots attached to a woody
central segment ~ 10 cm in length and < 1 mm in diameter, similar to the root system module (RSM)
defined by (Xia et al. 2010). Each RSM was divided into two samples: a small sample (~0.05 g), consisting
of 1st and 2nd order roots was frozen at -80 °C for posterior DNA extraction; the rest of the sample was
scanned and then weighed after oven-drying. Scanned images were analyzed with WinRhizo (version
2007d, Regent Instrument; following (Bouma et al 2000)) for fractal dimension, average diameter,
number of tips, total root area, average link length, and total root length estimates. Roots were scanned
in a flatbed scanner (EPSON Perfection V700 Photo) with 800 dpi resolution. Scanning parameters from
WhinRhizo (Pro version, 2007, Regent Instruments, Quebec, Canada) included standard precision for
diameter interpolation, exclusion of isolated links, remove of objects > 4 in ratio length/wide, and pixel
classification based on gray levels and selecting manually the threshold. In this way we were able to
correct for potential merging or broken links among roots.
Procedure for root identification. Total genomic DNA was isolated from air-dried frozen (-20 ° C) root
tips. Following extraction, the plastid region of the plant chloroplast DNA was specifically amplified by
the primers of the trnL gene (5’-CGAATTCGGTAGACGCTACG-3’) and the trnH-psbA spacer (5’ACTGCCTTGATCCACTTGGC-3’). A typical PCR amplification reaction consisted of the following
components; 2 l template DNA, 16.25±2 l sterile distilled water, 2±0.1 l deoxyribonucleotides, 3 l
NH4 buffer,1.5±0.1 l MgCl, 0.25 l BSA, 0.25±0.001 l of each primer, and 0.125±0.001 L Taq DNA
polymerase (BioLab). In general, DNA preparations were used in PCR reactions either undiluted or
diluted by one hundred. Samples were amplified using a Trademark Thermal Cycler. A seven minute hot
start was followed by PCR cycling as follows: one minute 95 ° followed by 35 cycles of denaturation at 94
° for 45 s, annealing at 48 ° for 56 seconds, ramping to 72 ° for 55 s with a one second extension after
each cycle, and extension at 72 ° for 2 min and 10 s. A final extension step was added for seven min at
72 °, and then the temperature was held at 4 °. PCR products were visualized on 1.5% agarose gels
stained with ethidium bromide.
Sample identification was performed using restriction fragment length polymorphism (RFLP)
procedure using the Hinf I restriction enzyme. Digests were performed in a total volume of 30 mL,
consisting of 27 mL of PCR product, and 3 mL of enzyme, then resolved on a 2.5% NuSieve Agarose gel
(FMC Bio Products) by electrophoresing at 200 V for 2.5 h. Gels were stained for 45 min in Gel Start,
destained in distilled water for 20 min, and photographed on an uv-transilluminator. RFLP band sizes
were estimated by comparison to a standard 100 base pair (bp) molecular weight and  ladder. Samples
of the same morphotype that were run on different gels were recorded as generating identical RFLP
patterns when no variation was discernible upon manual comparison with the 100 bp molecular
standard. Banding patterns were compared to RFLPs previously generated from leaves collected in
Jennings Woods from at least two separated individuals.
Statitstical analysis. All statistical analyses were performed with R software (version 2.12.0), using the
packages bipartite 1.17 (Dormann et al 2008) and vegan 1.17.0 (Oksanen 2010). Root traits were
transformed when necessary to fulfill homoschedasticity and normal residual distribution assumptions.
Redundancy Analysis (RDA) procedure. To analyze trait variation for a given species, we created a root
trait matrix that included all morphological trait values for a species for each core where the species was
present. To remove the effects of differing scales, each trait was individually standardized to have a
mean equal to zero and standard deviation equal to one. This multivariate trait matrix served as the
response variable in RDA. For each species, two predictor variable matrices were created. One predictor
variable matrix was a species presence-absence matrix for the other 13 most common species identified
in the soil cores, which we refer to as the “root community matrix”. A soil condition matrix was created
using soil factors standardized to have zero mean and standard deviation of one. Parsimonious subsets
of competitor species and soil factors as explanatory factors were then selected following the procedure
of Blanchet et al. (2008). First, a global analysis was performed testing the importance of each factor
matrix separately (ter Braak 1986). If the global analysis was significant, a stepwise RDA was performed,
adding variables in order of declining explanatory power and using both the significance of the added
variable and adjusted R-squared as stopping criteria (Blanchet et al. 2008). Variance partitioning among
soil abiotic factors and root neighbors was performed as described in Peres-Neto et al. (2006).
Community-aggregated traits were analyzed using the same procedure as described above, except that
the response variable matrix was comprised of community-aggregated traits instead of individual
species traits. All species were included in the root community matrix.
Testing of filtering and competitive trait displacement effects. Filtering and competitive trait
displacement effects were tested sequentially (Kraft et al 2008, Stubbs and Wilson 2004). First, we
tested the filtering predictions by estimating the observed variance and range values in each core. To
create a null distribution for each soil core under the assumption of no soil environment effect on root
trait assembly, we randomly selected RSMs from among all observations in our study, holding the
number of RSMs and tree species constant for each core. Root trait variance and range was calculated
for each randomly generated root trait assemblage, and this process was repeated 999 times for each
soil core. We then estimated the P-value for each core as the proportion of times that the simulated
variance or range was lower than the empirical value (Stubbs and Wilson 2004). Finally, to use the
cumulative evidence across the entire forest stand to test the null hypothesis of no difference between
the observed and randomly assembled plots, we converted P-values from all cores studied into
corresponding Z-values and combined them according to Stouffer’s method to generate a combined Pvalue (Whitlock 2005).
Competitive trait displacement was tested by estimating kurtosis and NNSD values for soil cores
with two or more species (84 samples). NNSD was calculated for a soil core by sorting species trait
values within the core and considering ‘neighbor distance’ to be the difference between two adjacent
members in the sorted list. NNSD for a soil core is the standard deviation of these neighbor distances. To
determine whether the observed kurtosis and NNSD for a given core was smaller than expected under
the null distribution of random spacing, we created 999 randomly assembled plots holding species
richness constant. The P-value for each core was calculated as the number of times the simulated
assemblage kurtosis or NNSD values were lower than the observed. To exclude any bias in this
procedure from potential habitat filtering, species selection for a given core was limited to those species
whose PCA_fertility ranges encompassed the value found in the given core (“potential community
members” sensu Cornwell and Ackerly 2009). A significance value for assembly of root traits across the
entire site was then estimated following Stouffer’s method as above (Whitlock 2005).
Checkerboard patterns test procedure. Using the presence/absence matrix for all sampled sites, we
compared our observed species distribution patterns against 99 randomly assembled communities
following the null model randomization procedure recommended by Gotelli (2000). Simulated matrices
were assembled maintaining row and column sums (SIM9 model, Gotelli (2000); see swap modification
(Miklós and Podani 2004) and matrices were compared using both the C-score (Stone and Roberts 1990)
and the more conservative CHECKER index (Gotelli 2000, Diamond 1975). P-values were estimated as
the proportional number of times that the simulated checkerboard index was higher than the observed
index.
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