Supplementary Material
Appendix A: Sampling Methods
Tree Occurrence and Biomass
Tree occurrence and biomass were measured using a modified version of the
USFS Forest Inventory and Analysis (FIA) protocol (USDA Forest Service, 2007;
Bechtold and Patterson, 2005). One 0.1 ha plot was established at each well location,
with species and bole diameter measured for each live tree. Trees < 10 cm in diameter
were tallied by mid-point diameter class (0 – 2.5, 2.5 – 5, 5 – 10), while trees greater than
10 cm were sampled for diameter at breast height (DBH). Tree biomass was derived from
diameters and defined as the total aboveground tree biomass per unit area, using the
following biomass regression equation (Jenkins et al., 2003):
bm = Exp (β0 + β1 ln dbh)
(Eq. A.1)
where bm is total overstory biomass (kg dry weight), dbh is diameter-at-breast-height
(cm), and β0 + β1 are species-specific parameters. Plot-level biomass was calculated for
each species by summing individual tree biomass values and estimating values at the sitelevel (Jenkins et al., 2003). Biomass values were then converted to per unit area values
(expressed in Mg/ha).
Groundwater Monitoring
Depth to groundwater was measured in four monitoring wells per site to quantify
temporal and spatial variation in water table and identify differences within and among
sites. Wells were constructed from 3.8-centimeter (cm) diameter, fully-slotted, schedule40 PVC pipe and installed to a maximum of one meter below the soil surface using a
hand auger. From May through August, depth to the water table was manually measured
bi-weekly. The distance from the top of the well casing to the water table was recorded
using a measuring tape and confirmed with an electronic tape. Pressure transducers were
installed in select wells to record hourly water table depth and monitor fine-scale
variation in groundwater variation, using In Situ level troll 100 loggers (In Situ Co., Fort
Collins, CO). Logger data was corrected for barometric pressure using barometric
pressure logger data and adjusting for elevation. Values for depth to water table were
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summarized into annual and growing season values of maximum water table depth,
minimum water table depth, mean depth to water, and number of growing season days
when the water table was within the rooting zone (>-20 cm, Coutts and Philipson 1978,
Wang et al., 2002). The growing season was defined as the 138-day period between May
15th and September 30th (NOAA/NWS Juneau, Alaska). Groundwater pH was measured
in each well using an Orion 3 star pH meter; growing season pH was measured multiple
times during this four-year study, and mean values were used for analysis.
Soil Nitrogen
Extractable soil nitrogen (N) was quantified as the sum of ammonium and nitrate
in 15 cm depth cores (Fellman and D’Amore 2007). Samples were collected three meters
north of each monitoring well, with replicate samples extracted to capture intra-site
variation in N availability. A 10-cm diameter PVC pipe was inserted into the soil surface
to a depth of 15 cm. All plant roots were cut from the core, leaving an intact core of
known depth, diameter, and volume (for detailed methods, see Hart et al., 1994). Cores
were immediately placed in a gallon zip seal plastic bag, kept on ice, and returned to the
lab for analysis. One bulk density sample was also collected per site. Large blocks were
cut from the soil and taken back to the lab. Subsamples of 1.25 mL were extracted from
each block and dried to a constant weight. Bulk density was calculated based on this dry
weight and expressed in Mg/m3. All samples were processed and analyzed within 48
hours of collection.
Samples were extracted following the procedure described in Robertson et al
(1999). Duplicate 15 g portions of each sample were mixed with 100 mL of 1.0M KCl in
120 mL Falcon specimen cups. Samples were shaken for 1 minute on a shaker table, left
to sit overnight, and shaken again for 1 minute the following morning. After settling for
at least 45 minutes, the supernatant in each specimen cup was drawn into a large syringe
through a Whatman GF/D filter. Three laboratory replicates were split among 20 mL
scintillation vials and kept on ice until analyzed. All extracted sub-samples were analyzed
for dissolved NO3-N (nitrate) and NH4-N (ammonium) by flow-through colorimetry at
the University of Georgia analytical laboratory.
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Light
Available light under the forest canopy was measured using hemispherical
photographs. Canopy photographs were taken at each monitoring well using a Nikon
Coolpix 3000 digital camera equipped with a FC-E8 21x fisheye lens. Photographs were
taken over a two day period using the following protocol: under overcast conditions, 1 m
above ground surface, top of lens oriented to true North, and with tripod legs adjusted to
draw lens parallel to canopy. Images were processed using Gap Light Analyzer (GLA)
version 2.0 (Frazer, Canham and Lertzman 1999). GLA settings were adjusted to account
for image orientation, geographic location (latitude and longitude), elevation, and time of
year. Images were first transformed to black-and-white. Image pixels were then separated
into sky versus foliage using the threshold procedure. Each photograph was analyzed
twice to reduce error associated with threshold distinctions. Total light transmission, the
fraction of light reaching the ground relative to the light above the canopy, was derived in
GLA according to Frazer et al (1999).
Appendix B: Statistical Analysis
Preliminary Statistics and Correlation Analysis
Prior to model fitting, we ran principal components analysis (PCA) on all
potential predictors to reduce model parameters to a set of minimally correlated variables
(correlation < 0.65). Exploratory statistical analyses were then implemented to identify
trends in the dataset and to plot relationships between response and potential predictor
variables. To test for spatial autocorrelation in tree species occurrence and biomass, we
used the spdep package to compute Moran’s I and Geary’s C. All analyses were
implemented in R 2.15.2 (R Core Team 2012).
Spatial autocorrelation of tree occurrence and abundance was minimal across the
levels of inference, with Moran’s I falling close to 0 and Geary’s C close to 1 at the well
(I = 0.03, p = 0.07; C = 0.94, p = 0.08) and site (I = -0.12, p = 0.10; C = 1.05, p = 0.10)
levels. Given these results, spatial autocorrelation was not incorporated into the final
models.
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Model Fitting
Model parameters were estimated in R 2.15.2 using Jags (Plummer 2011).
Markov Chain Monte Carlo (MCMC) runs used a Gibbs sampler to generate posterior
prediction means of model parameters. We ran 80,000 iterations with a burn-in of 70,000
on three chains, which allowed autocorrelation in each chain to drop below 0.1 after 10
iterations and the Gelman-Rubin statistic to drop below 0.1. We used posterior estimates
from the MCMC model runs to identify the abiotic and biotic variables driving the
occurrence and biomass of each tree species (P. contorta, P. sitchensis, and T.
heterophylla).
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