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Supplementary Methods
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Land cover classification and catchment delineation
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West Virginia GIS Technical Center (http://wvgis.wvu.edu/) provided spatial data downloaded
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into ArcMap 10.0. We delineated catchments for samplings location from 30 meter DEM
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(digital elevation dataset) (US Geological Survey National Elevation Dataset) with ArcMap 10.0
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spatial analyst hydrology tools. Land cover classification of mines and reclaimed mines were
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identified using 1-meter color orthophotos from the USDA’s National Agriculture Imagery
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Program.
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Biofilm community 16S rRNA gene analysis
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We homogenized biofilm from 2 of the veneers and subsampled 0.25g wet mass for DNA
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extraction. Community DNA was extracted from the biofilm using PowerBiofilmTM DNA
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isolation kit (MO BIO Carlsbad, CA, USA) and stored at -20°C. Bacteria community DNA was
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amplified at the 27 to 338 region of the 16S (small-subunit ribosomal) RNA gene (regions V1
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and V2 using the Escherichia coli genome numbering system). Forward 27F primer had a Roche
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Titanium Fusion Primer Adapter A, followed by a 4 nucleotide key, then a 454-specific 10
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nucleotide MID barcode (Supplementary Table S2) for each sample site and finally, the 16S
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template-specific sequence (5’CCA TCT CAT CCC TGC GTG TCT CCG AC TCAG NNN
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NNN NNN N AG AGT TTG ATC CTG GCT CAG 3’). Reverse primer 338R (5’ GCT GCC
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TCC CGT AGG AGT 3’) had no barcode, thus sequencing was unidirectional using Roche 454
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Lib-L kit. The polymerase chain reaction recipe was 2.5μL (10mM) of each forward and reverse
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primer, 1μL of template DNA, 4μL of dNTPs (1mM each), 2.5μL BSA (10mg/mL), 0.75μL
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MgCl2 (50mM), 2.5μL 10x buffer, 1μL Platinum Taq polymerase, 9.1μL ddH2O. MgCl2, 10x
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buffer, and Taq polymerase were all from Invitrogen Platinum kit.
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The PCR program was 5 min initial denaturation at 95°C followed by 25 cycles of
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amplification: 95 °C for 60 s, 52 °C for 60 s, and 72 °C for 105 s. After the last cycle
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amplification was extended at 72 °C for 7 min. Samples, including negative controls (no
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template added), were amplified in triplicate. All PCR work was completed in a laminar flow
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hood. Replicate PCR samples were pooled and purified with QIAquick PCR Purification Kit
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(Qiagen, Valencia, CA). Purified PCR products were normalized with SequalPrep™
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Normalization Plate Kit (Applied Biosystems®, Life Technologies Grand Island, NY, USA).
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Equimolar purified PCR amplicons were combined in 3 microcentrifuge tubes each with a set of
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barcodes with MID1-10. Samples were sent to Genome Sequencing and Analysis Core Resource
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at Duke University (Durham, NC, USA) for pyrosequencing with a Roche 454 Life Sciences
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Genome Sequencer Flex Titanium instrument (Branford, CT, USA).
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Bacterial community analyses
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started at random configurations with the nmds function in ecodist (Goslee & Urban, 2007). The
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final solution for both the Bray-Curtis distance and GUniFrac distance NMDS ordinations were
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created using a stepdown procedure. GUniFrac distances were based on a UPGMA phylogenetic
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tree (Chen et al., 2012). The final solution used three axes to relieve stress from the
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configuration, but the third axis did not increase explanatory power of the ordination. We rotated
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the ordination result to achieve highest variance on Axis 1. We partitioned variation in our
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community distance matrices (both Bray-Curtis and GUniFrac) among our a priori and post hoc
NMDS ordinations using counts relativized to 1543 sequences were created using 200 runs
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groupings with permutational multivariate analysis of variance (perMANOVA , (Anderson,
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2001). For the a priori grouping, we decomposed total variation among three orthogonal
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contrasts: mined vs. unmined, reclaimed vs. active, and mainstem vs. valley fill. For the post hoc
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grouping we decomposed total variation among two orthogonal contrasts: mined vs. unmined,
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and group A vs. group B. We tested the significance of each contrast using a pseudo F-statistic
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generated from random permutation of the site categories This test was carried out using 999
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permutations in the adonis function of the vegan package (Oksanen et al., 2011) in R version
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2.14.1 (R Development Core Team 2011).
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We characterized taxa driving the multivariate patterns using indicator species analysis
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(Dufrêne & Legendre 1997; De Cáceres et al. 2010; De Cáceres & Legendre 2009) with PC-
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ORD software. This technique analyzed the association between relative abundance and
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frequency of taxa and their designated sample site groups and identified which species had the
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greatest indicator value (percent of perfect indication, where 100% signifies perfect indication)
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in a designated site group. The index is greatest for an OTU when it is found within all of the
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sites comprising one group. This analysis was done using PC-ORD software (McCune &
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Mefford, V6). Monte Carlo randomizations were used to test for statistical significance of the
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OTU indicator using 4999 permutations. OTUs selected as good indicators were those with
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indicator values >0.3 and p< 0.05 as recommended by Dufrene & Legendre (1997).
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Bacteria taxa and environmental analysis
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Environmental variables used in the PCA were those that differed significantly (p ≤ 0.05)
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between mined and unmined sites or between post hoc A and B sites (described below) using a
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two-tailed student’s t-test. They included average water chemistry that differed between mined
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and unmined sites (Table 1) as well as biofilm Ca, Cd, Mg, Mn, Ni, Sr, Th and Zn. Chemistry
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variables, biofilm biomass, and biofilm C content that failed the Shapiro-Wilk normality test
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were transformed using log, square root, or inverse functions as needed.
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Bacterial diversity along the mining gradient
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Chao1 richness estimator (Chao, 1984) is a useful index for phylotype richness of uncultured
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microbial communities that include many rare taxa because it uses a non-parametric estimation
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that accounts for datasets skewed towards low-abundance classes, which is typical for microbial
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community datasets. Shannon diversity index is a commonly used estimate of macroorganism
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diversity that accounts for group abundance and evenness, but focuses on the diversity of
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common taxa (Hill, 1973).
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References
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Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance.
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Chao, A. (1984). Nonparametric estimation of the number of classes in a population.
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Chen, J., Bittinger, K., Charlson, E. S., Hoffmann, C., Lewis, J., Wu, G. D., … Li, H. (2012).
Associating microbiome composition with environmental covariates using generalized
UniFrac distances. Bioinformatics (Oxford, England), 1–8.
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De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites:
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De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by
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Dufrene, M., & Legendre, P. (1997). Species assemblages and indicator species: the need for a
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McCune, B., & Mefford, M. J. (n.d.). PC-ORD. Multivariate analysis of ecological data.
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Oksanen, A. J., Blanchet, F. G., Kindt, R., Legen-, P., Minchin, P. R., Hara, R. B. O., …
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