Open Rojas Dissertation Oct1 2013

Home , Armatimonadetes, Codonosigidae, Endomyxa, Thermus

The Pennsylvania State University

The Graduate School

Department of Ecosystem Science and Management

EFFECT OF VEGETATIVE RECLAMATION ON MICROBIAL DIVERSITY AND

IRON BIOGEOCHEMISTRY IN ACID MINE DRAINAGE PRECIPITATES AT A

50-YR-OLD BARRENS

A Dissertation in

Soil Science and Biogeochemistry

Claudia Macarena Rojas Alvarado

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

December 2013

The dissertation of Claudia Macarena Rojas Alvarado was reviewed and approved* by the following:

Mary Ann Bruns Associate Professor of Soil Microbiology Dissertation Co-Advisor Co-Chair of Committee

Carmen E. Martínez Associate Professor of Environmental Soil Chemistry Dissertation Co-Advisor Co-Chair of Committee

Patrick Drohan Associate Professor of Pedology

Sridhar Komarneni Distinguished Professor of Clay Mineralogy

Rachel A. Brennan Associate Professor of Environmental Engineering

John E. Watson Professor of Soil Physics Graduate Program Head for the Department of Ecosystem Sciences and Management

*Signatures are on file in the Graduate School

iii

ABSTRACT

Acid mine drainage (AMD) barrens result from the death of vegetation resulting from overland flow of acidic metal-rich waters emerging from abandoned underground mines. As acidic waters flow overland, oxidation and hydrolysis reactions result in accumulation of ferric iron (oxy) hydroxide precipitates on soil surfaces. AMD barrens can become a source of further pollution if exposed acidic precipitates are not protected against surface runoff.

In 2006, a restoration experiment was conducted by our research group at a 50-year-old

AMD barrens to determine whether vegetation could be established by altering, rather than removing, surface layers of acidic iron-rich precipitates at the site which is representative of other mining-degraded areas. In that study, three zones were distinguished by thickness and color of precipitate surface layers and by moisture content as influenced by depth to fragipan layers in underlying native soils. Acidic precipitates in experimental plots were amended in place by a one- time incorporation of lime and compost (top 15 cm) and a first-year oats nurse crop to improve growth of a sown reclamation seed mixture. Plant composition in the first, second, and fourth growing season consisted mainly of oats, sown species, and indigenous species, respectively. In all three zones, plots that received compost had greater than 70% vegetative cover at the end of the fourth growing season. The research presented in this dissertation builds on the investigation initiated in 2006 in the zone where subsurface AMD flow was most shallow and focuses on non- reclaimed (control) precipitates covered by mossy biological crusts and reclaimed precipitates sustaining vegetation.

Iron (Fe) biogeochemistry in AMD precipitates was studied to gain an understanding of potential losses of redox-active metals after plant-based reclamation. As mobility of redox-active metals can be increased by enhanced microbial activity in the rooting zones of growing plants, we

iv compared the forms of Fe in the reclaimed and control precipitates five years post-reclamation.

Since Fe is the most abundant metal in many mine drainages, root exudation by growing plants could stimulate Fe(III)-reducing activity in rhizospheres, resulting in losses of soluble Fe(II) from the system. Precipitates were sampled from moist yet unsaturated surface sections (8-cm depth) excised from replicate plots. Four precipitate types, reclaimed root-adhering (RR); reclaimed below-roots (RB); control crust-adhering (CC); and control below-crust (CB) were obtained before selective-extraction analyses and microbial counts were performed. Reclaimed and control precipitates had mean Fe contents of 454 and 690 g kg-1, respectively. Ferrozine tests of extracts indicated that Fe(II) concentrations were three- to five-fold higher in reclaimed precipitates than in control precipitates. Organically bound Fe and amorphous iron oxides, as fractions of total Fe, were also higher in reclaimed than in control precipitates. Estimates of Fe-reducing and Fe- oxidizing bacteria were four- to tenfold higher in root-adherent precipitates than in both types of control precipitates, indicating a potential for increased Fe cycling in plant rhizospheres. Scaling up Fe measurements from experimental plots suggested that potential total Fe losses during the 5- yr period following reclamation were 45 t Fe ha-1 yr-1.

Microbial communities inhabiting AMD-impacted environments have been more extensively studied in aqueous rather than terrestrial systems. Our reclamation study provided the opportunity to gain insights into AMD-derived bacterial and eukaryotic communities in unsaturated, edaphic habitats. Precipitates of the same types as described for the Fe-biochemistry study (RR, RB, CC, and CB) were collected six years post-reclamation. At the time of sampling, all four precipitate types had similar pH levels (2.5-2.7) because reclaimed precipitates had gradually become more acidic following the one-time lime application in 2006. Bacterial and eukaryotic diversity were assessed using 454 pyrosequencing of 16S rRNA (V1-V3/V5 region)

v and the 18S rRNA (V4-V5 region) genes. Contrary to our projections we observed high bacterial and eukaryotic diversity across all samples.

For bacterial libraries, we recovered a total of 3,150 operational taxonomic units (OTUs) at 97% similarity. Approximately 50% of these were exclusively found in reclaimed precipitates

(RR, RB or both), 33% were unique to control precipitates (CC, CB, or both), and 6% were shared among the four precipitate types. Nineteen phyla were identified in the four type of precipitates and 13 of these were found in all samples. Proteobacteria comprised the most abundant representatives in reclaimed precipitates while Acidobacteria were more abundant in root-and crust-adherent precipitates. The latter sample also showed the highest abundance of

Actinobacteria. The bacterial composition of incipient soils developed from AMD did not resemble those typically described for aquatic AMD systems. Among the classical AMD bacteria, only Leptospirillum ferriphilum, Leptospirillum ferrodiazotrophum, and Acidiphilium cryptum were detected in our study but at a very low frequency (≤ 0.1% ).

Eukaryotic diversity was also higher in reclaimed precipitates than in control precipitates, reflecting the positive influence of plant establishment. Of the total 494 OTUs identified at the

95% similarity level, about 62% were found exclusively in reclaimed precipitates (RR, RB or both), 20% were unique to control precipitates (CC, CB, or both), and only 7% were shared among the four precipitate types. Since libraries from control precipitates were dominated by bryophyte sequences, these and other macroeukaryotic sequences were removed before calculating the percentages of microeukaryotic taxa in each precipitate. The main microeukaryotic taxa identified in reclaimed precipitates were Basidiomycota, 48% in RR and

39% in RB. In contrast, Ascomycota were more abundant in control precipitates, 50% in CC and

18% in CB, reflecting a shift in fungal community composition following reclamation. Many taxa

vi reported to be abundant in water-impacted AMD habitats were either very low in abundance or not detected.

These studies demonstrate how acidic precipitates containing redox-active metals respond to plant establishment in hydrologically sensitive environments and increase our knowledge of the microbial biodiversity in AMD impacted terrestrial environments. In addition, these findings help to identify microbial taxa that reflect development of edaphic habitats which could be indicative of reclamation success and restoration of soil ecosystem functions.

vii

TABLE OF CONTENTS

List of Figures ...... ix

List of Tables ...... xi

ACKNOWLEDGEMENTS ...... xiii!

Chapter 1 Introduction ...... 1!

Formation of Acid Mine Drainage ...... 1! Genesis and Chemistry of Acid Mine Drainage Precipitates ...... 2! Microorganisms associated with AMD systems ...... 3! AMD Impacts and Environmental Implications ...... 8! Research site ...... 10! Dissertation organization and objectives ...... 13! References ...... 16!

Chapter 2 Fe biogeochemistry in reclaimed acid mine drainage precipitates—implications for phytoremediation ...... 20!

Introduction ...... 21! Materials and Methods ...... 24! Research Site ...... 24! Sampling and procedures for biogeochemical analyses ...... 26! Most-Probable Number estimates ...... 29! Statistical analysis ...... 30! Results ...... 31! Elemental analyses ...... 31! Biogeochemical characteristics of reclaimed and control precipitates ...... 34! Mineralogy and Fe fractions in reclaimed and control precipitates ...... 35! Discussion ...... 40! Conclusions ...... 43! Acknowledgements ...... 44! References ...... 45!

Chapter 3 Bacterial diversity in vegetated and biological crust-covered soils formed from acid mine drainage precipitates ...... 49!

Introduction ...... 50! Materials and Methods ...... 53! Background and site description ...... 53! Sample collection ...... 57! Soil characterization ...... 57! Soil DNA extraction and pyrosequencing ...... 58! Processing of pyrosequence data ...... 59! Results ...... 61! Soil characteristics ...... 61! Bacterial alpha and beta diversity ...... 62!

viii

Broad-scale taxonomic comparisons ...... 65! Finer-scale comparisons based on taxa making up >0.1% of assigned sequences .. 67! Discussion ...... 72! Conclusions ...... 75! Acknowledgements ...... 76! References ...... 77!

Chapter 4 Microeukaryote diversity in rhizospheres of vascular plants and moss- dominated biological crusts at an acid mine drainage barrens undergoing reclamation ...... 82!

Introduction ...... 84! Materials and Methods ...... 86! Site description ...... 86! Sample collection ...... 87! Soil DNA extraction and pyrosequencing ...... 87! Processing of pyrosequence data ...... 89! Results ...... 90! Discussion ...... 102! Conclusions ...... 107! Acknowledgements ...... 107! References ...... 108!

Chapter 5 General Conclusions ...... 112!

Appendix A Water chemistry for AMD collected at the constructed discharge point (D) and for AMD flowing belowground at the red zone (G)...... 116! Appendix B Vegetation and Soil Development in Compost-Amended Iron Oxide Precipitates at a 50-Year-Old Acid Mine Drainage Barren ...... 117! Research article published in Restoration Ecology ...... 117! Appendix C Plant composition in 2010 of experimental plots receiving lime plus 27 Mg ha-1 compost located in the red zone ...... 127! Appendix D Distribution of well installed at the research site and measurement over a one- year period ...... 128! Appendix E XRD spectra for adherent (a) and non-adherent (b) precipitates obtained from reclaimed and control plots...... 129! Appendix F ...... 131! Estimated total Fe; Fe(II); percentage of Fe(II) on basis of total Fe; percentage of Fe(II) on basis of organically bound Fe; and organic carbon in the upper 8 cm of one hectare of reclaimed precipitates and control precipitates...... 131! Appendix G Protocol utilized to process pyrosequencing data ...... 132! Appendix H Rarefaction curves of total number of bacterial sequences obtained from RR, RB, CC, and CB ...... 133! Appendix I Rarefaction curves per sample at 80, 90, 95, 97, 99% similarity ...... 134! Appendix J Complete eukaryotic taxon distribution ...... 135! Appendix K Rarefaction curves of total number of eukaryotic sequences obtained from RR, RB, CC, and CB ...... 140!

Appendix L Percentage of sequences classified by SILVA in various taxa by sample after equalizing all datasets to 16,118 sequences...... 141!

LIST OF FIGURES

Figure 1-1. 50-yr-old Acid Mine Drainage Barrens (0.5 ha) located in Clearfield County, in central Pennsylvania, U.S.A. (41◦ 01′ 22.00′ N; 78◦ 09′ 08.064′′ W)...... 8!

Figure 1-2. Acid mine drainage sites occurring under different saturation conditions. A. Hughes Borehole located in Cambria County, south-central Pennsylvania. This AMD site (0.6 ha) is covered by up to 2 m of iron oxide deposits which actively precipitate out from overland flow (DeSa et al., 2010). B. The 50-yr-old Acid Mine Drainage Barrens (Sylvan Grove site) showing mound and gullies after AMD sheet flow had converged into a central channel. This site is covered by up to 35 cm of iron oxides precipitates (Lupton et al., 2013)...... 9!

Figure 2-3. Field scheme showing depth of rooting system for successional plants and biological crusts; thickness of iron-rich precipitates and maximum and minimum occurrences of the water table ( ) from June 2010 to August 2011 (data shown in Appendix D). Successional plant roots penetrate mainly up to the upper 5 cm portion of iron-rich precipitates. Precipitates occurred on top of the native buried soil (dark gray) and varied in thickness from 17 to 22 cm. The study area experiences high water table levels, from 9 to 14 cm below surface (upper dotted lines), during spring. Deeper levels, from 24 to 33 cm below surface (lower dotted lines), were observed during summer...... 35!

Figure 2-4. Percentages of amorphous and crystalline Fe oxides in adherent precipitates and non-adherent precipitates for reclaimed plots and control plots. Error bars indicate one standard deviation of the mean (n=3). Different letters (shown for Fe in amorphous iron oxides) indicate significantly different means (p < 0.05)...... 37!

Figure 2-5. Organically bound Fe (A) and Fe(II) (B) in adherent precipitates and non- adherent precipitates for reclaimed plots and control plots. Column charts show mean values with bars representing one standard deviation. Error bars indicate one standard deviation of the mean (n=3). Different letters indicate significantly different means (p < 0.05)...... 38!

Figure 2-6. Correlations observed between Fe(II) concentrations and carbon contents (a) and estimates of culturable iron-reducing bacteria (b). Carbon contents and enumeration of iron-reducing bacteria are able to model Fe(II) contents with high degree of accuracy (R2=0.92, p=0.00016 and R2=0.83, p=0.0017 respectively)...... 39!

Figure 3-1. Venn diagram showing unique and shared OTUs in in reclaimed (RR and RB) and control (CC and CB) precipitates. The values in parenthesis indicate the percentage of OTUs out of the total number (3,150) of OTU’s observed...... 62!

Figure 3-2. Clustering analyses of the four precipitate types using UPGMA algorithm and Bray-curtis distance matrix. Bar represents 0.05 distance among samples...... 65!

Figure 3-3. Relative abundances of all phyla in reclaimed (RR and RB) and control (CC and CB) precipitates. Relative abundance (%) of individual taxa within each community was calculated by dividing the number of sequences assigned to a specific taxon by the number of normalized sequences obtained for that sample (14,666)...... 67!

Figure 4-1. Venn diagrams illustrating the distribution of eukaryotic OTUs (95% similarity) among the four precipitate types. Panel A shows total number of unique OTUs distributed among samples. Panel B shows the distribution of the unclassified OTUs...... 94!

Figure 4-2. Relative distribution (%) of microbial eukaryote taxa across all samples. Dark green color indicates higher percentage while light green color indicates lower percentage, actual values are superimposed over colors. Taxa having at least 0.1% of the micro-eukaryote sequences per sample are shown ...... 95!

xii

LIST OF TABLES

Table 1-1. List of prokaryotes identified in acid mine drainage-impacted systems...... 6!

Table 1-2. List of eukaryotes identified in acid mine drainage-impacted systems...... 7!

Table 2-1. Physicochemical, biological properties and major element composition of adherent and non-adherent precipitates obtained from reclaimed and control plots. Reported valuesa represent arithmetic means unless otherwise indicated and all values are based on soil dry weight. Different letters indicate significantly different means (p < 0.05)...... 32!

Table 2-2. Minor element composition of adherent and non-adherent precipitates obtained from reclaimed and control plots. Reported valuesa represent arithmetic means...... 33!

Table 3-1. Main soil properties collected since 2006 from non-reclaimed and reclaimed precipitates in the red zone...... 56!

Table 3-3. Alpha diversity of reclaimed (RR and RB) and control (CC and CB) precipitates. Sample coverage, Observed OTUs, Ace and Chao richness indices, and Shannon and inverse Simpson diversity indices were calculated from normalized datasets. OTUs were generated at 97% sequence similarity. A total of 3,150 unique OUTs were identified in all samples ...... 63!

Table 4-1. Distribution of observed OTUs (95% similarity) and sequences for individual taxa identified in reclaimed (RR and RB) and control (CC and CB) precipitates...... 92!

Table 4-2. Summary of Taxonomy (GenBank based) for unclassified OTUs by the Silva database and present in at least one of the reclaimed precipitates. Top 20 most abundant OTUs are shown. * Unpublished data retrieved from http://blast.ncbi.nlm.nih.gov/Blast.cgi on May 2013...... 99!

Table 4-4. Taxonomy (GenBank based) for unclassified OTUs by the Silva database and shared among the four precipitate samples.* Unpublished data retrieved from http://blast.ncbi.nlm.nih.gov/Blast.cgi on May 2013...... 101!

xiii

ACKNOWLEDGEMENTS

I would like to acknowledge the Fulbright Program and the Chilean Commission for

Science and Technology (CONICYT) which funded my PhD training through the Fulbright

Graduate Student Equal Opportunity Fellowship (BIO). I would also like to thank to the College of Agriculture at the Pennsylvania State University for support through the Katherine Mabis

McKenna Scholarship, the de la Torre Endowment Award, and the Graduate Competitive Grant programs.

I would like to express my gratitude to my advisor, Dr. Mary Ann Bruns, for having me join her lab and for having shared with me her immense passion and knowledge in environmental microbiology. I would be always grateful for her advises during my initiation in this wonderful discipline. I would also like to thank my co-advisor Dr. Enid Martínez for her guidance and support throughout my research, and my gratitude also goes for the rest of my dissertation committee, Dr. Patrick Drohan, Dr. Shridar Komarneni, and Dr. Rachel Brennan whose suggestions and comments were always beneficial for my research and for the completion of this dissertation.

I could have not accomplished this degree without the support and constant encouragement of my family. Miguel, gracias por haber estado a mi lado desde el comienzo de este proyecto, tu apoyo incondicional fue fundamental para haber finalizado esta carrera con

éxito. Todos mis mejores deseos y energías positivas para que termines tu doctorado con éxito también. Mamá, tú eres la persona que me llena de energías y esperanzas en aquellos momentos de flaqueza, admiro tú fuerza y ganas de vivir la vida de una manera sana y sin complicaciones, me faltará vida para agradecer tus enseñanzas. Papá, por estar junto a mi en todos los momentos especiales y trascendentes de mi vida, no recuerdo ocasión en el que tú no hayas

xiv estado ahí apoyándome, exigiéndome y dándome el ejemplo de perseverar en la vida. A mis queridos hermanos que siempre me han hecho sentir su amor y compañerismo, cada vez que los he necesitado han estado junto a mi. Y por último, pero no menos importante, a mis sobrinos

Anto, Amanda, Omar, y pequeña(o) en camino, por recordarme que la vida es una constante renovación de esperanzas y sueños. A mis tíos y primos por todo el entusiasmo demostrado en los pasos importantes que he dado en mi vida, en especial a mi tía Carmen. Si tú no hubieses sido la primera en creer en mi, hace muchos años atrás, quizás no estaría en esto en este momento.

I would like to thank my former and present lab-mates Morgan, Maina, Mary Kay, Tania,

Rosemary, and Xin for having shared with me their knowledge, enthusiasm, and optimistic thoughts. Also, to my wonderful friends in State College who have became part of my family.

They have been a fundamental source of happiness, support, and motivation during the last five years. And last but not least, to all my friends back home who have contributed not only to my professional development but also to my personal growth.

Chapter 1

Introduction

Formation of Acid Mine Drainage

Acid mine drainage (AMD) originates when pyrite and other sulfide minerals associated with metal-containing or coal ores are exposed to water and oxygen during mining operations

(Eq. 1) (Johnson and Hallberg, 2005). Oxidative dissolution of sulfide minerals generates acidic,

Fe(II)-rich waters which in some cases also contain elevated levels of other metals (Gagliano et al., 2004). This acidity contributes to the formation of AMD, especially where carbonate minerals such as calcite and dolomite are absent or deficient relative to metal sulfide concentrations

(Brady, 1994).

2+ 2- + Eq. 1 FeS2 (pyrite) + 3.5O2 + H2O Fe + 2SO4 + 2H

Abiotic Fe(II) oxidation is initiated spontaneously under circumneutral pH and aerobic conditions; however, as acidification occurs this abiotic oxidation becomes kinetically limited

(Stumm and Morgan, 1996). Under acidic conditions (pH< 3), microorganisms accelerate iron oxidation rates by five orders of magnitude (Singer and Stumm, 1970) and contribute to about

75% of the acid mine drainage generation (Edwards et al., 2000a). Autotrophic iron-oxidizing

(and sulfur-oxidizing) bacteria including Acidithiobacillus ferrooxidans (Schrenk et al., 1998),

Leptospirillum ferrooxidans (Edwards et al 1999; Johnson, 2003), and archaea such as

Ferroplasma acidarmanus (which can grow heterotrophically as well) (Edwards et al., 2000b) are among the prokaryotes known to contribute to Fe(II) oxidation in AMD ecosystems.

2 Genesis and Chemistry of Acid Mine Drainage Precipitates

Emergent acidic water flowing overland is exposed to O2 in air which causes the soluble

Fe(II) to oxidize to the ferric iron form, Fe(III) (Eq. 2) (Moses et al., 1987; Nordstrom, 1982).

Oxidized iron then hydrolyzes to form ferric iron (oxyhydr) oxides that accumulate on soil surfaces as orange or dark brown precipitates (Eq. 3) (Bigham and Nordstrom, 2000; Cornell and

Schwertmann, 2003).

2+ + 3+ Eq. 2 Fe + 0.25O2 + H Fe + 0.5 H2O

3+ + Eq. 3 Fe + 3 H2O Fe(OH)3 + 3H

These AMD precipitates are secondary minerals that spontaneously precipitate out of iron-rich solutions or originate as a result of microbially mediated reactions (Fortin and Langley,

2005). Iron (oxyhydr)oxides usually associated with AMD include ferrihydrite, schwertmannite, jarosite, and goethite (Bigham and Nordstrom, 2000; Bigham et al., 1996; Schwertmann et al.,

1995). The occurrence of these precipitates depends on the rate of oxidation, pH conditions, concentration of metal ions, and complexing ligands, among other environmental conditions

(Cornell and Schwertmann, 2003).

Ferrihydrite (HFe5O8•4H2O) and schwertmannite (Fe8O8(OH)6SO4) are minerals of inherently poor crystallinity that form from the rapid oxidation of ferrous iron, while goethite (α-

FeOOH) and jarosite (KFe3 (OH)6 (SO4)2) are crystalline forms originating from slower oxidation processes (Bigham et al., 1994; Murad et al., 1994). Ferrihydrite precipitates out of acidic waters but is also known to precipitate at near-neutral conditions (pH 5-7) in association with bacterial cells and in the presence of dissolved silica or organic matter. Goethite commonly forms in mine drainage waters with slightly acidic conditions (pH < 6) and sulfate concentrations below 1,000

3 mg/L. As the pH level decreases to values between 2.8 and 4.5 and sulfate increases to concentrations ranging from 1,000 to 3,000 mg/L, schwertmannite is expected to occur in the system. In strongly acidic waters (pH 1.5-3.0) with sulfate concentrations above 3,000 mg/L, jarosite is the most common precipitate to appear (Bigham et al., 1993; Cornell and

Schwertmann, 2003; Schwertmann et al., 1995).

The poorly crystalline iron oxides have a short-range structural order, and thus, a high specific surface area and chemical reactivity with respect to dissolved contaminants, making them effective sorbents of trace metals and oxyanions (Bigham and Nordstrom, 2000). Precipitates containing sorbed metals are of environmental concern because changes in redox conditions or geochemical transformation to more stable minerals promote desorption and release of associated metals into the environment (Bigham and Nordstrom, 2000; Gagliano et al., 2004). Ferrihydrite and schwertmannite are unstable with respect to goethite, the most stable mineral phase associated with acid mine waters (Bigham et al., 1996; Gagliano et al., 2004). Goethite has been identified as the main and probably final product of iron oxide transformation in acid mine drainage systems (Gagliano et al., 2004).

Microorganisms associated with AMD systems

Acid mine drainage systems where microbial communities have been extensively studied include the subterranean Richmond mine at Iron Mountain, CA (Baker and Banfield, 2003) and the Rio Tinto region in southwestern Spain, which comprises the largest pyritic ore belt in the world (Amaral-Zettler et al., 2002; Amaral-Zettler et al., 2003; Amaral-Zettler et al., 2011).

Studies at the subterranean mine in California have strived to characterize the microbial composition of flowing waters and sediments (Schrenk et al 1998; Edwards et al., 1999; Edwards

4 et al, 2000b) and biofilms associated with mine waters (Bond et al., 2000a; Bond et al., 2000b).

Likewise, studies of the mining-polluted Rio Tinto River in Spain have focused on surface waters

(Amaral-Zettler et al., 2011), biofilms and sediments (Amaral-Zettler, 2013). Some studies have focused on microbial populations in recently deposited iron-rich precipitates from newly emerged mine drainage (Brown et al., 2012; Senko et al., 2009).

Microorganisms associated with acidic environments are distributed across the tree of life encompassing the Bacteria, Archaea, and Eukaryote domains (Amaral-Zettler et al., 2011; Baker and Banfield, 2003). Bacteria and Archaea (prokaryotes) typically dominate these microbial communities; however, eukaryotes also contribute to the microbial diversity in these ecosystems

(Amaral-Zettler et al., 2011; Tyson et al., 2004).

Although prokaryotic diversity in acidic environments is lower than that of less extreme environments (Baker and Banfield, 2003), at least two archaeal, and more than ten bacterial phyla, have representatives able to thrive under the extreme conditions typical of AMD (Amaral-

Zettler et al., 2011; Baker and Banfield, 2003) (Table 1-1). The primary bacterial phyla associated with extremely acidic environments include Proteobacteria (α-, β-, and γ-classes), Acidobacteria,

Actinobacteria, Firmicutes, and Nitrospira (Baker et al 2003; Johnson, 2012). The most studied autotrophic iron-oxidizing (and sulfur-oxidizing) bacteria associated with AMD systems is

Acidithiobacillus ferrooxidans, previously in the γ-proteobacteria class and most recently assigned to the new proteobacteria class Acidithiobacillia (Williams and Kelly, 2013).

Additionally, members of the Nitrospira phyla, specifically the autotrophic iron-oxidizing bacteria Leptospirillum, have been recognized as the most abundant and active autotrophs in

AMD systems (Bond et al, 2000a; Johnson, 2003). Leptospirillum spp. isolated from AMD environments are distributed among three phylogenetically distinct groups (I, II, and III)

(Edwards et al, 1999; Bond et al., 2000a; Tyson et al., 2005). Members representing groups I, II,

5 and III are L. ferrooxidans, L. ferriphilum (Baker et al 2003), and L. ferrodiazotrophum (Tyson et al., 2005), respectively. Sulfobacillus species (Firmicutes) capable of using iron or reduced sulfur as energy source have been also detected in AMD systems (Bond et al., 2000b; Johnson and

Bridge, 2002). Heterotrophic iron-oxidizing (and iron-reducing) actinobacteria associated with

AMD systems are Acidimicrobium ferrooxidans and Ferrimicrobium acidophilum (Bond et al.,

2000a; Johnson and Bridge 2002). Other acidophilic heterotrophs identified in acidic environments include the iron-reducing Acidiphilum cryptum (α-Proteobacteria) originally isolated from a iron-rich sediment from a lake associated with a coal mine in eastern Germany

(Küsel et al., 1999); Ferrovum species (β-Proteobacteria) recovered from recently deposited precipitates from newly emerged mine drainage (Brown et al., 2011); and Acidobacterium capsulatum first recovered from an acidic drainage in Japan (Kishimoto et al., 1991).

Archaeal species commonly detected in AMD systems include members of the

Euryarcheota phyla belonging to the Thermoplasmatales order (Baker and Banfield, 2003), specifically to Ferroplasma spp. (Bond et al., 2000b). Among them, Ferroplasma acidarmanus has been identified in sediments and biofilms of subterranean mines at pH values as low as 0

(Edwards et al., 2000b). Crenarchaeota members of the Sulfolobales order, represented by

Metallosphaera prunae, have also been detected in AMD environments (Baker and Banfield,

2003).

6 Table 1-1. List of prokaryotes identified in acid mine drainage-impacted systems. Domain Phyla Examples of environment identified Reference Bacteria α-, β-, γ- and δ-Proteobacteria (α, β) iron-rich sediments / (β) subterranean 1, 2, 3, 6 mine waters / (δ, γ) mine water biofilms Nitrospira bioleaching systems / mine water biofilms / 1, 2 fresh mineral deposits /acidic watercourses Acidobacteria acidic mineral environment / acidic 1, 2, 4, 5 watercourses / underground copper mine Actinobacteria acidic watercourses / underground copper 1, 2, 4, 6 mine/ mine water biofilms Firmicutes subterranean mine waters / bioleaching sites 1, 2, 7 / acidic watercourses Bacteroidetes acidic watercourses 2 Spirochaeta acidic watercourses 2 Planctomyces* acidic watercourses 2 Chlamydiae acidic watercourses 2 Deinococcus-Thermus acidic watercourses 2 Themomicrobia acidic watercourses 2 Caldiserica (OP5)* acidic watercourses 2 OP11* acidic watercourses 2 OD1* acidic watercourses 2 TM6* acidic watercourses 2 TM7* acidic watercourses 2 WS6* acidic watercourses 2 Archaea Crenarchaeota acidic uranium mine / bioleaching reactor / 2, 6 acidic watercourses Euryarchaeota acid-leaching environments / water biofilms 2, 7, 8 / sediments *Uncultured References: 1. Baker et al, 2003; 2. Amaral-Zettler et al 2011; 3. Schrenk et al 1998; 4. Kishimoto et al., 1991; 5. Johnson, 2012; 6. Bond 2000a; 7. Bond et al 2000b; 8. Edwards et al 2000b.

Besides prokaryotes, eukaryotes have been characterized in acidic environments including subterranean mines (Baker et al., 2004; Baker et al., 2009) and the acidic watercourses of the Rio Tinto region (Amaral-Zettler et al., 2002) (Table 1-2). Among the fungi detected in subsurface environments are ascomycetes in the classes Dothideomycetes and Eurotiomycetes, and Basidiomycetes in the class Urediniomycetes. Some of the dothideomycetes isolated from subterranean mines have been related to the acidophilic filamentous Acidomyces richmondensis.

Red algae members of the Rhodophyta lineage and protists from the Vahlkampfiidae family are

7 also confirmed members of subterranean AMD eukaryotic communities (Baker et al., 2004;

Baker et al., 2009). Eukaryotes in acidic watercourses exposed to sunlight and open air also include fungi, red algae, and protists. These systems host photosynthetic eukaryotes such as flagellate protozoa, green algae and diatoms, as well as representatives of other diverse taxa including animals and metazoans (Amaral-Zettler, 2013; Amaral-Zettler et al., 2002).

Table 1-2. List of eukaryotes identified in acid mine drainage-impacted systems. Taxon Environments Reference Fungi Ascomycetes Dothideomycetes subterranean mine waters 1

Eurotiomycetes subterranean mine waters 1

Pezizomycotina biofilms and sediments associated 4 with surface acidic waters Basidiomycetes Urediniomycetes Submerged biofilm on pyrite 2

Red algae Rhodophyta subterranean mine waters 1

Others acidic surface watercourses 3 Amoebae Heterolobosea Vahlkampfiidae subterranean mine waters; 1; 2; 3 submerged biofilm on pyrite; acidic watercourses Stramenopiles Diatomea Bacillariophyta biofilms and sediments associated 4 with surface acidic waters Alveolata ! Ciliophora! Hypotrichia! biofilms and sediments associated 4! with surface acidic waters! Rotifers acidic surface watercourses 3

Cercomonads acidic surface watercourses 3

Euglenozoa Euglenida Euglena biofilms and sediments associated 4 with surface acidic waters Animal-fungal- Clones RT5iin14 and RT5iin16 acidic surface watercourses 3 nucleariid radiation References: 1. Baker et al, 2004; 2. Baker et al., 2009; 3. Amaral-Zettler et al., 2002; 4. Amaral- Zettler 2013

8 AMD Impacts and Environmental Implications

Acid mine drainage (AMD) is one of the most significant environmental problems in mining regions around the world (Lottermoser, 2010; Young, 1997). An estimated 17,000 km of streams in the US Appalachian coal mining region are impaired by AMD (Zink, 2005). In

Pennsylvania alone, an estimated 4,000 km of watercourses and 1,000 km2 of land are affected

(DeSa et al., 2010). Iron (oxyhydr)oxide precipitates with their distinctive dark red-brown colors are recognized as the most prominent feature of AMD pollution arising either from surface mine runoff or from underground mine discharges (Bigham and Nordstrom, 2000; Cornell and

Schwertmann, 2003; Senko et al., 2008).

Figure 1-1. 50-yr-old Acid Mine Drainage Barrens (0.5 ha) located in Clearfield County, in central Pennsylvania, U.S.A. (41◦ 01′ 22.00′ N; 78◦ 09′ 08.064′′ W).

In the case of underground mine discharges, AMD can emerge at the surface for long periods of time to kill vegetation in its flowpath, creating areas devoid of plants known as AMD barrens or “kill zones” (Fig. 1-1). As AMD flows overland, iron-rich precipitates accumulate on soils and prevent recolonization or regrowth of plants (Lupton et al., 2013). AMD barrens are relatively small and isolated areas (0.5-5 ha) varying in landscape features and in chemical and

9 mineralogical composition of precipitates (Brown et al., 2011; DeSa et al., 2010; Lupton et al.,

2012; Senko et al., 2008). They occur throughout coal-mining regions of Appalachia under different AMD flow regimes in former woodlands (Fig. 1-2) (Brown et al., 2011; Lupton et al.,

2013).

A B

In some barrens, the acid mine drainage flowing overland results in continual precipitation of new iron oxides on top of native soils (Fig. 1-2a). In these sites, precipitates occur mostly in poorly crystalline forms under saturated conditions in pools and terraces surrounding woody debris (Brown et al., 2011; DeSa et al., 2010). Over time, AMD can converge into larger channels resulting in areas that become unsaturated and where fresh precipitates are no longer deposited by overland flow. In these areas, precipitates are exposed to drying conditions and temperature fluctuations that promote transformations to more crystalline forms. Over time,

AMD barrens develop into variable landscapes comprised of mounds of unsaturated iron minerals of varying depths intersected by rills and gullies (Fig. 1-2b) (Lupton et al, 2013).

10 AMD barrens can become a source of further pollution if exposed acidic precipitates are not protected against surface runoff (Lupton et al., 2013). As it has been observed in other mine- impacted lands such as tailing disposal sites (Conesa et al., 2007; Mendez and Maier, 2008a), vegetative reclamation of AMD barrens offers a means for in situ stabilization of acidic precipitates (Lupton et al., 2013). The establishment of vegetation in mine-impacted environments ameliorates dispersion of pollutants by reducing the exposure to rainfall and enhancing physical cohesion and biochemical interactions of these materials with roots and associated microbes (Petrisor et al., 2004; Zou et al., 2012). Vegetative-based reclamation of such environments promotes the formation of biologically active soils for restoration of soil ecosystem functions (Mendez and Maier, 2008b; Petrisor et al., 2004). Indeed, the increase in heterotrophic microbes achieved after vegetation of mine tailings has been considered an indicator of soil health

(Mendez el al., 2007). In addition, abundance and composition of soil microbiota are considered to be important criteria for evaluating the restoration of such degraded environments (Mummey et al., 2002).

Research site

The AMD site investigated in this dissertation project is a 50-yr-old barrens located within the Appalachian Plateau Province, 5 km north of Kylertown, in central Pennsylvania,

U.S.A. (41◦ 01′ 22.00′ N; 78◦ 09′ 08.064′′ W) (Lupton et al., 2013) (Fig. 1-1 and 1-2b). The barren area was created by overland flow of AMD from a constructed discharge point draining a complex of deep abandoned coal mines which extend for thousands of hectares (Lupton et al.,

2013). Over time, AMD continued to emerge from the discharge point at a rate of at least 3000

L/min, most of it collecting into a central channel which enters a tributary of Brown’s Run

11 flowing into the West Branch of the Susquehanna River. Some discharge also flows belowground in the same direction. Acidic waters emerging from the underground mine complex have pH values ranging from 2.4 to 3.2 and ferrous iron and sulfate contents up to 77 and 861 ppm, respectively (Appendix A). The cessation of overland flow has left behind an undulating landscape, about 0.5 ha in size, with mounds and gullies resulting from flow path alterations overlying the native soils (Fig. 1-2b). Soils underlying the precipitates were developed from mixed colluviums and are classified as Brinkerton (Typic Fragiaqualfs) and Ernest (Aquic

Fragiudults) (Lupton, 2008). Both of these soils have moderate permeability in surface layers, but slow permeability in fragipan layers at depths of 55–70 cm (Soil Survey Staff, 2010).

In 2006, a reclamation experiment was conducted at this site to evaluate whether vegetation could be established by altering, rather than removing, surface layers of acidic iron- rich precipitates (Lupton et al., 2013). Three distinct zones (gray, orange, and red) were identified based on surface color, moisture content, and depth of iron oxide layers. The gray zone was the driest and consisted of exposed subsoil with sparse or no precipitates on the surface (0–2cm). The orange zone was drier with thinner surface layers (2–17cm) of lighter orange precipitates. The red zone was the wettest of the three and had the thickest layers of iron oxides (8 – 35 cm) covered by moss-dominated biological crusts and associated microorganisms (Prasanna et al., 2011). This zone is characterized by a shallow water table as a result of the depth of fragipans in the underlying native soils. Water flowing beneath the red zone has pH values ranging from 2.3 to

2.6, and ferrous iron and sulfate contents up to 95 and 1107 ppm, respectively (Appendix A).

Three reclamation treatments were randomly assigned to duplicate plots (3m x 3m) per zone: (1) lime only (no compost); (2) lime plus 27 Mg ha−1 compost; and (3) lime plus 54 Mg ha−1 compost. The same lime addition was used for all treatments at a rate of 11 Mg ha−1 (5.7 kg

MgCO3 and 4.5 kg Ca(OH)2 per plot), which was calculated (Mehlich 1976) to be sufficient to

12 bring the surface precipitates in the red zone to pH 5, similar to native soils. Each of the treatments were incorporated into the upper 15 cm portion of soil/precipitates by rototilling (Fig.

1-3). Following the lime and compost incorporation, plots were sown with a “mineland reclamation” seed mix (rate of 70 kg ha-1) provided by the PA Department of Environmental

Protection. This mixture consisted of 60% rye (Elymus spp.), 15% fawn tall fescue (Schedonorus phoenix Scop. Holub), 15% Potomac orchard grass (Datylis glomerata L.), 7% Empire birdsfoot trefoil (Lotus corniculatus L.), 2% Alsike clover (Trifolium hybridum L.), and 1% redtop

(Agrostis gigantea Roth). Lastly, each plot was mulched with 9 kg of oat straw containing viable seed for a first-year nurse crop (Lupton et al., 2013).

A B

C D

Figure 1-3. Pictures by Mary Kay Lupton showing vegetative reclamation treatments tested in 2006 in the red zone. Panel A shows the 3x3 plots with treatments. Panel B shows the incorporation of the lime-only treatment into the upper 15 cm of the precipitates/soil layer. Panel C shows the experimental plots covered by oat straw containing viable seed for a first year nurse crop. Lastly, panel D shows the plots in the first growing season (2006) where plant communities were dominated by oats.

13 The outcome of the reclamation experiment was that oats, sown species, and indigenous species

(mainly Betula spp.) dominated plant cover in the first, second, and fourth growing seasons, respectively. After four years of reclamation, compost-amended plots in all three zones supported plant covers greater than 70% and improved soil properties. Despite the similarities in plant cover, aboveground plant biomass in the red zone was significantly higher for the high-rate versus the low-rate compost treatments. Organic matter contents were higher in amended soils than in their respective unamended control areas, reaching up to 11, 5.5, and 2.6% for the red, orange, and gray zones, respectively. Soil pH increases were incrementally higher in treated plots in the red (1.2–1.7), orange (2.0–2.7), and gray (2.7–3.1) zones, respectively when compared to pH values of their unamended counterparts.

Dissertation organization and objectives

The research presented in this dissertation builds on the investigation previously initiated at our site in 2006 (Lupton, 2008; Lupton et al., 2013). For the readers’ reference, Appendix B of this dissertation contains the latter article, “Vegetation and Soil Development in Compost-

Amended Iron Oxide Precipitates at a 50-Year-Old Acid Mine Drainage Barrens,” published in

Restoration Ecology. The following chapters in this dissertation report the results of research conducted on non-reclaimed and reclaimed precipitates from the wettest (red) zone of the barrens, where subsurface flow was most shallow. Chapters II, III, and IV are written in manuscript formats suitable for submission to peer-reviewed journals.

Chapter II describes the effects of a vegetative reclamation approach applied in 2006 on biogeochemistry and potential mobility of iron in areas with shallow subsurface flow. The main research objective in Chapter II was to gain an understanding of the potential losses of redox-

14 active metals following plant-based reclamation. The research approach was based on a comparison of the forms of iron (Fe) in reclaimed (vegetated) and non-reclaimed precipitates

(control) five years post-reclamation. At the time of this dissertation’s submission to the

Pennsylvania State University Graduate School, Chapter II has been peer-reviewed and published in the journal Environmental Pollution.

Chapter III describes how different vegetative cover and management history influenced the bacterial composition of acidic iron-rich precipitates in the red zone of the barrens. The main objective of research reported in this chapter was to compare the bacterial composition of incipient soils (pH 2.7-3.0) formed in vegetatively reclaimed AMD precipitates and “control” precipitates covered by biological crusts. This chapter will be further edited and submitted to the journal Applied and Environmental Microbiology.

Chapter IV of this dissertation extends the microbial community research to the study of eukaryotes inhabiting precipitates covered either by vascular plants or biological crusts. The main research objective for this chapter was to compare eukaryotic communities in reclaimed and non- reclaimed precipitates. This chapter will be further edited and submitted to the journal

Environmental Microbiology. Key findings of this dissertation and main conclusions are summarized in Chapter V.

The findings in this dissertation contribute to our understanding of the effect of vegetative reclamation of surface layers of acidic iron-rich precipitates at a site representative of other mining-degraded areas. The results will shed light on how other redox-active metals besides iron may respond to plant establishment in hydrologically sensitive environments. In addition, bacterial and eukaryotic diversity achieved after vegetation could be used as indicators of soil health and a reference point from which to evaluate the successful restoration of such degraded

15 environments. Lastly, this research expands our knowledge of the biodiversity of acid mine drainage environments and extends our understanding of the ecology of extremely acidic systems.

16 References

Amaral-Zettler, L.A., 2013. Eukaryotic diversity at pH extremes. Frontiers in Microbiology 3:441.

Amaral-Zettler, L.A., Gomez, F., Zettler, E., Keenan, B.G., et al., 2002. Eukaryotic diversity in Spain's river of fire. Nature 417, 137-137.

Amaral-Zettler, L.A., Messerli, M.A., Abby, D.L., Smith, P.J.S., Sogin, M.L., 2003. From genes to genomes: beyond biodiversity in Spain's Rio Tinto. Biological Bulletin 204, 205-209.

Amaral-Zettler, L.A., Zettler, E.R., Theroux, S.M., Palacios, C., Aguilera, A., Amils, R., 2011. Microbial community structure across the tree of life in the extreme Río Tinto. ISME Journal: Multidisciplinary Journal of Microbial Ecology 5, 42-50.

Baker, B.J., Banfield, J.F., 2003. Microbial communities in acid mine drainage. Fems Microbiology Ecology 44, 139-152.

Baker, B.J., Lutz, M.A., Dawson, S.C., Bond, P.L., Banfield, J.F., 2004. Metabolically active eukaryotic communities in extremely acidic mine drainage. Appl Environ Microbiol 70, 6264- 6271.

Baker, B.J., Tyson, G.W., Goosherst, L., Banfield, J.F., 2009. Insights into the diversity of eukaryotes in acid mine drainage biofilm communities. Applied and Environmental Microbiology 75, 2192-2199.

Bigham, J., Schwertmann, U., Carlson, L., 1993. Mineralogy of precipitates formed by the biogeochemical oxidation of Fe (II) in mine drainage. Catena supplement 21, 219-219.

Bigham, J.M., Carlson, L., Murad, E., 1994. Schwertmannite, a new iron oxyhydroxysulphate from Pyhasalmi, Finland, and other localities. Mineralogical Magazine 58, 641–648.

Bigham, J.M., Nordstrom, D.K., 2000. Iron and aluminum hydroxysulfates from acid sulfate waters, in: Alpers, C.N.J., J.L. Nordstrom D.K. (Ed.), Sulfate minerals, crystallography, geochemistry and environmental significance. Reviews in mineralogy and geochemistry. Mineralogical Society of America, Washington, D.C., pp. 351-403.

Bigham, J.M., Schwertmann, U., Traina, S.J., Winland, R.L., Wolf, M., 1996. Schwertmannite and the chemical modeling of iron in acid sulfate waters. Geochimica Et Cosmochimica Acta 60, 2111-2121.

Bolan, N.S., Park, J.H., Robinson, B., Naidu, R., Huh, K.Y., 2011. Chapter four - Phytostabilization: a green approach to contaminant containment, in: Donald, L.S. (Ed.), Advances in Agronomy. Academic Press, pp. 145-204.

Bond, P.L., Smriga, S.P., Banfield, J.F., 2000a. Phylogeny of Microorganisms Populating a thick, subaerial,predominantly lithotrophic biofilm at an extreme acid mine drainage site. Applied and Environmental Microbiology 66, 3842-3849.

17 Bond, P.L., Druschel, G.K., Banfield, J.F., 2000b. Comparison of acid mine drainage microbial communities in physically and geochemically distinct ecosystems. Applied and Environmental Microbiology 66, 4962-4971.

Brady, K.B.C., Perry, E.F., Beam, R.L., Bisko, D.C., Gardner, M.D., Tarantino, J.M., 1994. Evaluation of acid-base accounting to predict the quality of drainage at surface coal mines in Pennsylvania. US Bureau of Mines Special Publication SP 06A, Pittsburgh.

Brown, J.F., Jones, D.S., Mills, D.B., Macalady, J.L., Burgos, W.D., 2011. Application of a depositional facies model to an acid mine drainage site. Applied and Environmental Microbiology 77, 545-554.

Conesa, H.M., Faz, Á., Arnaldos, R., 2007. Initial studies for the phytostabilization of a mine tailing from the Cartagena-La Union Mining District (SE Spain). Chemosphere 66, 38-44.

Cornell, R.M., Schwertmann, U., 2003. The Iron Oxides: Structure, properties, reactions, occurrences and uses. WILEY-VCH Verlag GmbH & Co KGaA Weinheim.

DeSa, T., Brown, J., Burgos, W., 2010. Laboratory and field-scale evaluation of low-pH Fe(II) oxidation at Hughes Borehole, Portage, Pennsylvania. Mine Water and the Environment 29, 239- 247.

Edwards, K.J., Bond, P.L., Banfield, J.F., 2000a. Characteristics of attachment and growth of Thiobacillus caldus on sulphide minerals: a chemotactic response to sulphur minerals? Environmental Microbiology 2, 324-332.

Edwards, K.J., Bond, P.L., Gihring, T.M., Banfield, J.F., 2000b. An archaeal iron-oxidizing extreme acidophile important in acid mine drainage. Science 287, 1796-1799.

Edwards, K.J., Goebel, B.M., Rodgers, T.M., Schrenk, M.O., Gihring, T.M., Cardona, M.M., McGuire, M.M., Hamers, R.J., Pace, N.R., Banfield, J.F., 1999. Geomicrobiology of pyrite (FeS2) dissolution: case study at Iron Mountain, California. Geomicrobiology Journal 16, 155-179.

Edwards, R., Rodriguez-Brito, B., Wegley, L., Haynes, M., Breitbart, M., Peterson, D., Saar, M., Alexander, S., Alexander, E.C., Rohwer, F., 2006. Using pyrosequencing to shed light on deep mine microbial ecology. BMC Genomics 7, 57.

Fortin, D., Langley, S., 2005. Formation and occurrence of biogenic iron-rich minerals. Earth- Science Reviews 72, 1-19.

Gagliano, W.B., Brill, M.R., Bigham, J.M., Jones, F.S., Traina, S.J., 2004. Chemistry and mineralogy of ochreous sediments in a constructed mine drainage wetland. Geochimica Et Cosmochimica Acta 68, 2119-2128.

Johnson, D.B., 2003. Chemical and Microbiological Characteristics of Mineral Spoils and drainage waters at abandoned coal and metal mines. Water, Air and Soil Pollution: Focus 3, 47- 66.

Johnson, D.B., 2012. Geomicrobiology of extremely acidic subsurface environments. Fems Microbiology Ecology 81, 2-12.

18 Johnson, D.B., Hallberg, K.B., 2005. Acid mine drainage remediation options: a review. Science of The Total Environment 338, 3-14.

Kishimoto, N., Kosako, Y., Tano, T., 1991. Acidobacterium capsulatum gen. nov., sp. nov.: An acidophilic chemoorganotrophic bacterium containing menaquinone from acidic mineral environment. Current Microbiology 22, 1-7.

Küsel, K., Dorsch, T., Acker, G., Stackebrandt, E., 1999. Microbial reduction of Fe(III) in acidic sediments: isolation of Acidiphilium cryptum JF-5 capable of coupling the reduction of Fe(III) to the oxidation of glucose. Applied and Environmental Microbiology 65, 3633-3640.

Lottermoser, B.G., 2010. Mine wastes: characterization, treatment and environmental impacts. Springer-Verlag, Berlin, Germany.

Lupton, M.K., 2008. Revegetation of an acid mine drainage – impacted soil using low rates of lime and compost, Department of Crop and Soil Sciences. The Pennsylvania State University, University Park, PA.

Lupton, M.K., Rojas, C., Drohan, P., Bruns, M.A., 2013. Vegetation and soil development in compost-amended iron oxide precipitates at a 50-year-old acid mine drainage barrens. Restoration Ecology 21(3): 320-328.

Mehlich, A., 1976. New buffer pH method for rapid estimation of exchangeable acidity and lime requirement of soils. Communications in Soil Science and Plant Analysis 7:637–652.

Mendez, M.O., Maier, R.M., 2008a. Phytoremediation of mine tailings in temperate and arid environments. Reviews in Environmental Science and Biotechnology 7, 47-59.

Mendez, M.O., Maier, R.M., 2008b. Phytostabilization of mine tailings in arid and semiarid environments-an emerging remediation technology. Environ Health Perspect 116, 278–283.

Mendez, M.O., Neilson, J.W., Maier, R.M., 2008. Characterization of a bacterial community in an abandoned semiarid lead-zinc mine tailing site. Applied and Environmental Microbiology 74, 3899-3907.

Moses, C.O., Kirk Nordstrom, D., Herman, J.S., Mills, A.L., 1987. Aqueous pyrite oxidation by dissolved oxygen and by ferric iron. Geochimica Et Cosmochimica Acta 51, 1561-1571.

Murad, E., Schwertmann, U., Bigham, J.M., Carlson, L., 1994. Mineralogical characteristics of poorly crystallized precipitates formed by oxidation of Fe2+ in acid sulfate waters, in: Alpers, C.N., Blowes, D.W. (Ed.), Environmental Geochemistry of Sulfide Oxidation A.C.S. Symposium Series, pp. 190-200.

Nordstrom, D.K., 1982. Aqueous pyrite oxidation and the consequent formation of secondary iron minerals. Acid Sulfate Weathering sssaspecialpubl, 37-56.

Petrisor, I.G., Dobrota, S., Komnitsas, K., Lazar, I., Kuperberg, J.M., Serban, M., 2004. Artificial inoculation - perspectives in tailings phytostabilization. International Journal of Phytoremediation 6, 1-15.

19 Prasanna, R., Ratha, S., Rojas, C., Bruns, M., 2011. Algal diversity in flowing waters at an acidic mine drainage “barrens” in central Pennsylvania, USA. Folia Microbiologica 56, 491-496.

Schrenk, M.O., Edwards, K.J., Goodman, R.M., Hamers, R.J., Banfield, J.F., 1998. Distribution of Thiobacillus ferrooxidans and Leptospirillum ferrooxidans: implications for generation of acid mine drainage. Science 279, 1519-1522.

Schwertmann, U., Bigham, J.M., Murad, E., 1995. The first occurrence of schwertmannite in a natural stream environment. European Journal of Mineralogy 7, 547-552.

Senko, J.M., Wanjugi, P., Lucas, M., Bruns, M.A., Burgos, W.D., 2008. Characterization of Fe(II) oxidizing bacterial activities and communities at two acidic Appalachian coalmine drainage-impacted sites. Isme Journal 2, 1134-1145.

Singer, P.C., Stumm, W., 1970. Acidic Mine Drainage: The Rate-Determining Step. Science 167, 1121-1123.

Soil Survey Staff, N.R.C.S., United States Department of Agriculture. , 2010. Web Soil Survey.

Stumm, W., Morgan, J.J., 1996. Aquatic chemistry: chemical equilibria and rates in natural waters. Wiley, New York.

Tyson, G.W., Chapman, J., Hugenholtz, P., Allen, E.E., Ram, R.J., Richardson, P.M., Solovyev, V.V., Rubin, E.M., Rokhsar, D.S., Banfield, J.F., 2004. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37-43.

Tyson, G.W., Lo, I., Baker, B.J., Allen, E.E., Hugenholtz, P., Banfield, J.F., 2005. Genome- directed isolation of the key nitrogen fixer Leptospirillum ferrodiazotrophum sp. nov. from an acidophilic microbial community. Applied and Environmental Microbiology 71, 6319-6324.

Williams, K.P., Kelly, D.P., 2013. Proposal for a new class within the proteobacteria, the Acidithiobacillia, with the Acidithiobacillales as the type order. International Journal of Systematic and Evolutionary Microbiology.

Young, P.L., 1997. The longevity of minewater pollution: a basis for decision-making. Science of The Total Environment 194–195, 457-466.

Zink, T., A. Wolfe, and K. Curley, 2005. Restoring the wealth of the mountains: Cleaning up Appalachia's Abandoned Mines. Trout Unlimited, 28.

Zou, T.J., Li, T.X., Zhang, X.Z., Yu, H.Y., Huang, H.G., 2012. Lead accumulation and phytostabilization potential of dominant plant species growing in a lead-zinc mine tailing. Environmental Earth Sciences 65, 621-630.

20 Chapter 2

Fe biogeochemistry in reclaimed acid mine drainage precipitates— implications for phytoremediation

At a 50-year-old coal mine drainage barrens in central Pennsylvania, USA, we evaluated the biogeochemistry of acidic, Fe(III)oxy(hydr)oxide precipitates in reclaimed plots and compared them to untreated precipitates in control areas. Reclaimed plots supported successional vegetation that became established after a one-time compost and lime treatment in 2006, while control plots supported biological crusts. Precipitates were sampled from moist yet unsaturated surface layers in an area with lateral subsurface flow of mine drainage above a fragipan. Fe(II) concentrations were three- to five-fold higher in reclaimed than control precipitates. Organically bound Fe and amorphous iron oxides, as fractions of total Fe, were also higher in reclaimed than control precipitates. Estimates of Fe-reducing and Fe-oxidizing bacteria were four- to tenfold higher in root-adherent than both types of control precipitates. By scaling up measurements from experimental plots, total Fe losses during the 5-yr following reclamation were estimated at 45 T

Fe ha-1 yr-1.

21 Introduction

Phytostabilization is the use of vegetation to reduce erosion and mobility of soil particles and pollutants by physical cohesion and biochemical interactions with roots and associated microbes

(Robinson et al., 2009; Vangronsveld, et al., 2009). Vegetative establishment during reclamation of minelands and mining wastes promotes the formation of biologically active soils for restoration of soil ecosystem functions (Mendez and Maier, 2008; Petrisor, et al., 2004). Mining- impacted lands include strip mines; mine waste areas; and barrens, also known as ‘kill zones’ created by overland flow of acid mine drainage (AMD). Such lands are difficult to revegetate, because they often exhibit low pH, low organic matter, low water-holding capacity, and high concentrations of metals (Hossner, 1988; Mendez et al., 2007).

In a previous study, we used a one-time reclamation treatment to demonstrate that vegetative cover could be established after amendment of acidic, Fe(III)oxy(hydr)oxide precipitates comprising up to 48% Fe by mass (Lupton et al., 2013). The reclamation took place in central Pennsylvania, USA, at a 50-year-old barrens where precipitates had accumulated on soil surfaces through oxidation and hydrolysis reactions during overland flow of AMD from an abandoned underground coal mine. Three zones in the barrens were identified based on pH, thickness of surface precipitates, and moisture content as influenced by depth to fragipans in underlying native soils. In 2006, acidic precipitates in experimental plots were amended in place by a one-time incorporation of compost and lime (15 cm depth), sown with a reclamation seed mixture, and covered with straw containing viable seeds for a first-year oats nurse crop. After five years, successional indigenous plants had replaced sown species in all three zones (70-100% plant cover). This experiment showed that a single, uniform treatment applied to a heterogenenous barrens could achieve plant cover requirements for minelands as put forth in the U.S. Surface

Mining Control and Reclamation Act of 1977. Five years following the treatment, however, the

22 pH of reclaimed precipitates in all three zones had declined by at least one pH unit, and plants showed signs of nutrient deficiency and possible phytotoxicity. Thus, additional lime applications would be required to maintain better plant growth in an actual reclamation program.

In 2010 we took advantage of our initial experiment to gain understanding of how redox- active metals had been affected by vegetative establishment. Since Fe is the most abundant metal in many mine drainages (Bigham et al., 1996; Gagliano et al., 2004), Fe biogeochemistry can shed light on the behavior of other, potentially toxic metals also found in mine drainages but at lower concentrations (Schwertmann, 1991; Senko et al., 2008). Root exudation by growing plants could stimulate Fe(III)-reducing activity in rhizospheres and cause greater losses of soluble Fe(II) from the system. Since greater Fe losses imply greater risk of mobilizing other metals, the areas of greatest vulnerability are where subsurface flow is near root zones, where leached metals can be lost in subsurface drainage (González-Alcaraz and Álvarez-Rogel, 2013). The present study therefore was conducted in the most hydrologically vulnerable zone of the barrens, the red zone

(Fig. 2-1), which had the thickest iron oxide layer, highest moisture content, and shallowest depth to fragipan (i.e., the dense, subsurface layer in native soils). Nearby control plots in this zone supported biological crusts consisting of moss and mixed microbial communities (Prasanna et al.,

2011). The objectives of this study were to compare Fe forms in reclaimed (vegetated) and non- reclaimed (control) precipitates and to evaluate potential Fe losses five years post-reclamation.

Figure 2-1. Photograph of the red zone at the 50-year-old AMD barrens taken in 2011. Biological crusts and successional plants cover the study area of 260 m2. Biological crusts occur naturally in this zone and consist of moss (Pottia spp), fungi, algae, and other bacteria. Successional vegetation resulted from a one-time reclamation treatment and supports a mixed cover of gray birch (Betula populifolia), black birch (Betula lenta), quaking aspen (Populus tremuloides), red maple (Acer rubrum), white pine (Pinus strobes) as well as Spiraea and Crataegus spp., forbs and grasses. Two white monitoring wells installed at the site can be observed.

We hypothesized that reclaimed precipitates would have higher concentrations of Fe(II) and greater proportions of organically bound Fe and amorphous iron oxides than control precipitates. Support for our hypothesis would imply a greater potential for Fe(II) losses from reclaimed than from control precipitates. We also hypothesized that precipitates in the upper layers of each profile would exhibit proportionally greater increases in labile forms of Fe than would deeper precipitates, because the former have more direct contact with plant roots or crust rhizoids. To test these hypotheses, we used selective Fe extraction methods on precipitates from upper and lower layers in an unsaturated area of the red zone. We also enumerated culturable

24 aerobic heterotrophs and Fe-oxidizing and Fe-reducing microorganisms to assess their relationship to the forms of Fe found in each layer.

Materials and Methods

Research Site

The AMD barrens (0.5 ha), located in central Pennsylvania, USA (41◦01’22.00’N; 78◦ 09’

08.064’ W) was created by massive overland flow of discharge from the western end of a network of underground coal mines abandoned since the 1950s (Lupton et al., 2013). Acid mine drainage of pH 2.4-3.2 and 70-81 mg Fe(II) L-1 continues to flow from a constructed discharge point into a central channel with eventual drainage into the West Branch of the Susquehanna

River. Some mine discharge also flows below ground, where depth to saturation is influenced by fragipans of underlying soils (Typic Fragiaqualfs and Aquic Fragiudults) (Soil Survey Staff,

2013). The wettest area of the barrens (red zone) was chosen for the present study because of its susceptibility to Fe(II) losses in surbsurface flow. Biological crusts occur naturally in this area due to wet edaphic conditions and CO2 enrichment from off-gassing from mine drainage

(Atekwana and Fonyuy, 2009). The crust, consisting of mosses, fungi, algae, and other bacteria

(Prasanna et al., 2011) appears as a 2 mm-thick, continuous, green-brown cover which binds together precipitates into a coherent layer about 2 cm thick (Fig. 2-2a).

In 2006, duplicate plots in the red zone had been established with necessarily small areas (9 m2) to accommodate initial treatments within the undulating microtopography. Nearby control plots were established at least 1 m apart from reclaimed plots. The reclamation treatment consisted of incorporating 11 ton ha-1 lime (to pH 4.3) and 27 ton ha-1 compost by rototilling (top

15 cm). Plots were then sown with 70 kg ha-1 of reclamation seed mixture (two legumes and four

25 grasses) and mulched with 9 kg plot-1 oat straw containing viable seeds for a first-year nurse crop.

Oats, sown species, and indigenous species dominated cover in the first, second, and fourth growing seasons, respectively.

A C

2 cm 5 cm B D

Figure 2-2. Image showing the four types of samples collected from our study area. (a) control crust-adherent (CC) corresponding to the upper 2 cm portion of precipitates that stayed attached to biological crusts. (b) control below-crust-adherent (CB) corresponding to the underlying 6-cm layer. (c) reclaimed root-adherent (RR) corresponding to the upper 5 cm portion of precipitates that stayed attached to plant roots once the square was removed. (d) reclaimed below-root- adherent (RB) corresponding to the underlying 3-cm.

In 2010, the reclaimed plots supported a mixed vegetative cover of gray birch, Betula populifolia (about 60% cover), but also black birch (Betula lenta), quaking aspen (Populus tremuloides), red maple (Acer rubrum), and white pine (Pinus strobes) as well as Spiraea and

Crataegus spp. and forbs and grasses (Appendix C). Growth of Betula spp. also was observed outside the peripheries of reclaimed plots. Betula spp. may have been dominant members of these plant communities because of their reported ability to colonize mine waste areas and adapt to acidic edaphic conditions (Marguí et al., 2007; Pratt, 1986). After five growing seasons, plant roots were very dense but localized in the upper 5 cm of precipitates (Fig. 2b), despite prior

26 incorporation of amendments to a 15-cm depth. This shallow rooting zone was likely due to lowered pH (2.7-2.9 in 2010), which would cause Al toxicity as well as nutrient deficiencies

(Kochian et al., 2005).

Spatial variation in precipitate thickness above native soil reflected historical changes in hydrology and overland flow, and previous monitoring had shown that subsurface AMD flow was highest in the spring. To verify that precipitates were unsaturated during the periods before and after sampling, wells (100 cm length, 4-cm diameter PVC) were installed at the edges and in the middle of the experimental area to monitor the water table from June 2010 to August 2011

(Appendix D). Distances from the soil surface to free water in the wells were measured every week for the first four months of the study and every month after that period using a portable water-level meter (Solinst® Model 101).

Sampling and procedures for biogeochemical analyses

In July of 2010, one square section of surface precipitates was excised from each of two reclaimed (vegetated) plots and two control (encrusted) plots. A sampling location was identified by randomly tossing a quadrat (0.25-m2) into each plot and verifying that the location was greater than 0.25 m from the plot edge. Sections were cut and removed with an ethanol-sanitized shovel to a depth of no more than 9 cm, which was the minimal depth to saturation observed during well monitoring. In the field, each section was sliced into an upper and lower layer for separate processing and analyses. Intact layers were placed on ice for transport.

In the lab, precipitates from the topmost layers (5-cm) of reclaimed plots were obtained by grasping the plants and physically shaking the precipitates into a sterile pan until less than 5% of the precipitates remained on the dense root mass. These precipitates therefore represented “bulk

27 soil” and were labeled RR for “reclaimed root-adherent.(Fig. 2b). Precipitates from the underlying 3-cm layers were labeled RB for “reclaimed below-root-adherent.”. For control plots, precipitates from the upper 2-cm layers were labeled CC, for “control crust-adherent,” after being scraped aseptically from the crusts. Precipitates from the underlying 6-cm layers were labeled CB for “control below-crust-adherent” (Fig. 2a). Precipitates from specific plots were designated as

CB1 and CB2 (i.e., from replicate control plots 1 and 2, respectively.) Precipitates from each layer were processed separately by removing roots and other organic debris, mixing, and splitting into three replicate subsamples. Subsamples were further divided into one portion of moist precipitates for gravimetric moisture content and plate counts and a second portion of precipitates that were air-dried for chemical and elemental analyses and bulk density measurements. Standard heterotrophic plate counts were made with one composited sample (per layer) serially diluted in

0.1% peptone buffer for spread plating on R2A medium (Difco, Detroit, MI) and 10 d incubation at 25°C. Colony-forming-unit (CFU) counts were adjusted for gravimetric moisture content and reported on the basis of oven-dry solids.

Bulk densities were determined from weights of known volumes of precipitates and used to calculate masses of measured elements to a depth of 8 cm. Total organic carbon (TOC) was determined with an EA 1110® CHN analyzer (CE instruments, Milan, Italy) on sieved (150-mm) samples dried at 50ºC. All measured carbon was considered organic, since no effervescence was observed in tests with 4 M HCl (Nelson, 1996). Air-dried precipitates were used for other chemical tests, including pH in deionized water at 1:1 w/w ratio (Thomas, 1996) and electrical conductivity in deionized water at 1:5 w/w ratio (Rhoades, 1996). Sulfate content was determined on filtrates obtained from suspensions of 5 g sieved samples (2-mm) in 25 mL 0.5 M

Na2HPO4/NaH2PO4 extractant at pH 6.5 with a Dionex ICS – 3000 Ion Chromatograph

(Kowalenko, 1993).

28 X-ray diffraction (XRD) was performed on one composite sample of air-dried solids from each layer. Samples were sieved (2 mm), ground with acetone and passed through another 75 µm sieve. The randomly oriented, powdered samples were scanned from 10 to 70 deg 2θ in a

PANalytical X’Pert Pro MPD theta-theta system equipped with diffracted-beam graphite monochromator. Scanning employed CuKα radiation and a PIXcel detector in scanning mode with a PSD length of 3.35 deg 2-theta and 255 active channels. Diffraction patterns were analyzed using PANalytical X’Pert software (PANalytical, Almelo, The Netherlands).

Elemental analyses were made on triplicate subsamples of air-dried solids. Total elemental composition was determined by ICP-AES after dissolution of 100 mg sample in 20 mL

6 M HCl with shaking for 48 hours (Gagliano et al., 2004). AMD precipitates, mostly

Fe(III)oxy(hydr)oxides, have been shown to be readily soluble in 6M HCl (Bigham et al., 1990;

Winland et al., 1991). Additional procedures were applied to air-dried precipitates to assess distribution of Fe in different fractions. Ammonium oxalate (AO)-extractable Fe was estimated by preferential dissolution of 50 mg sample (< 150-µm) in 40 mL of 0.2 M ammonium oxalate and a shaking period of 4 hours in the dark (McKeague and Day, 1966). Organically bound Fe was estimated as sodium pyrophosphate (SP)-extractable Fe after reacting 50 mg of ground sample in 25 mL of 0.1 M Na4P2O7 with 16-hr shaking (McKeague, 1967). After centrifugation, all supernatants were filtered (0.2 µm) and analyzed for Fe by inductively coupled plasma atomic emission spectroscopy (ICP-AES). For simultaneous multi-element analysis by ICP-AES, we used calibration standards dissolved in the same solutions used for extractions rather than dissolving standards in 5% HNO3. Such matrix-matched calibration improves the accuracy of concentration estimates (Sadler et al., 1997). The difference between AO-extractable Fe and SP- extractable Fe was used to estimate the content of Fe in amorphous Fe oxide forms. The content of Fe in crystalline forms was obtained by subtracting the amount of Fe in amorphous Fe oxides

29 from total Fe determined after 6M HCl digestions. The percentages of Fe in amorphous and crystalline Fe oxides, as well as in organically bound forms, were estimated by dividing the amount of each fraction by total Fe content in each sample.

Additional samples were obtained in July 2011 for measuring Fe(II) in solution and adsorbed to solid phases. Triplicate samples, 1 g fresh weight, from each plot and depth were weighed in the field and placed immediately in 20-ml glass serum bottles containing 5 ml of 0.5 M HCl.

Parallel subsamples were taken for gravimetric moisture determinations to calculate Fe(II) concentrations based on oven-dry solids. In the field, bottles were sealed with butyl rubber stoppers, shaken for 30 sec, and placed on ice for transport to the laboratory. Samples were kept in an anaerobic chamber in the dark for 24 hours to ensure complete digestion and analyzed for

Fe(II) using the ferrozine method (Lovley and Phillips, 1986). Spectroscopic absorbance of

. treated samples at 562 nm were determined using a FeCl2 8H2O standard curve with the

Shimadzu UV-310-1PC UV-VIS-NIR Scanning Spectrophotometer.

Most-Probable Number estimates

Additional composited samples were obtained from each plot-layer for the enumeration of acidophilic Fe-oxidizing and Fe-reducing bacteria by the Most Probable Number (MPN) technique using tenfold dilutions and five replicates per dilution (Woomer, 1994). A basal growth medium (Fe-TSB, pH 2.7) was prepared with 0.25 g L-1 tryptone soya broth (TSB), 1.25 g

-1 -1 L (NH4)2SO4, and 0.5 g L MgSO4·7H2O (Johnson et al, 1987). MPN medium for Fe(III)- reducing bacteria consisted of Fe-TSB supplemented with 5mM glucose and ferric sulfate (Küsel et al., 1999). A sterile, oxic solution of ferric sulfate (pH 1.9) was added to the autoclaved medium (final concentration 35 mM) to achieve color change of the medium from clear to orange

30 without precipitate formation (final pH 2.3). Reduction of Fe(III) was determined visually by decolorization of the orange medium and analytically by measuring Fe(II) using the ferrozine method. MPN medium for Fe(II)-oxidizing bacteria consisted of Fe-TSB amended with 100 mM filter-sterilized ferrous sulfate added after autoclaving to a final concentration of 10 mM and pH of 2.6 (Johnson et al., 1987; Johnson and McGinness, 1991). Tubes were incubated at 23°C and checked weekly until no further changes were observed (70 d). MPN values were corrected by moisture content and reported as bias-corrected geometric means (Parkin and Robinson, 1993;

Woomer, 1994).

Statistical analysis

Statistical analyses were performed with SAS Version 9.3 (SAS, 2011). All data conformed to statistical assumptions of normality and homogeneity of variances as determined by univariate and general linear model procedures. Treatment and depth effects were analyzed by two way

ANOVA using the general linear model procedures. Significant differences between means at the p< 0.05 level were determined by employing the Tukey’s studentized range test (Tukey’s

Honestly Significant Difference, or Tukey’s HSD). Microbiological data, described best by the two-parameter lognormal distribution, were log-transformed for statistical analysis using arithmetic means for plate counts and bias-corrected geometric means for MPNs (Parkin and

Robinson, 1993).

31 Results

Elemental analyses

All precipitate samples were readily soluble in acid, with acid-insoluble residues accounting for less that 1% of solids. Similar acid solubility of precipitates has been reported in other AMD studies (Gagliano et al., 2004; Winland et al., 1991). Reclaimed and control precipitates had mean

Fe contents of 454 and 690 g kg-1, respectively (Table 2-1). While the Fe contents of precipitates from reclaimed plots were within ranges reported for AMD sediments from wetlands and other mine drainage systems, Fe contents of CC (714-751 g kg-1) and CB (641-654 g kg-1) precipitates from control plots were higher than any reported previously (Cravotta, 2005; Essington, 2004;

Gagliano et al., 2004). In reclaimed plots, RR precipitates had 20-25% lower Fe contents than RB precipitates, while in control plots, CC precipitates had 10-12% lower Fe contents than CB precipitates. On the other hand, P, Si, and Al were all higher in reclaimed than in control precipitates, possibly due to the compost incorporated during reclamation. Consistently higher Si and Al also were found in RR and CC precipitates than their non-adherent counterparts (Table 2-

1) suggesting possible translocation of these elements by plant roots and biological crust rhizoids.

In all samples, the total concentrations of Ca, Cr, Cu, K, Mg, Mn, Na, Ni, Pb, and Zn were within the respective concentration ranges reported for unpolluted soils, while As, Cd, and Co contents were in excess (Table 2-2) (Essington, 2004). Concentrations for the latter three elements, however, were in the lower ranges reported for other AMD precipitates (Cravotta, 2005;Winland et al., 1991).

2- b c Depth Moisture pH EC SO4 TOC Bulk CFU Fe Fe Fe P Si Al content density reducers oxidizers (cm) (%) (dS m-1) (mg kg-1) (%) (g cm-3) (log g-1) (x 105 MPN g-1)d ------g kg -1------RR1 5 66 b 3.2 a 0.7 d 236.3 e 2.4 a 0.69 c 7.2 a 12.9 14.7 381.7 d 1.95 a 1.48 a 0.85 a

(3.9; 42.5) (4.5; 48.5) RB1 3 79 a 2.9 ab 0.9 c 272.5 d 2.0 b 0.85 abc 5.1 bc 8.1 5.8 515.0 c 1.91 a 1.15 b 0.65 c (2.5; 26.8) (1.8; 19.1) RR2 5 72 ab 2.7 cb 1.1 c 281.7 d 1.9 b 0.70 c 6.5 ab 4.4 9.2 402.2 d 1.80 b 1.06 c 0.76 b (1.3; 14.4) (2.8; 30.4) RB2 3 69 ab 2.4 c 1.3 b 325.6 bc 1.3 de 0.86 abc 6.0 ab 2.4 4.2 513.1 c 1.75 bc 0.83 d 0.43 d

Reclaimed plots Reclaimed (0.7; 8.0) (1.3; 13.8) CC1 2 70 b 2.5 c 1.7 a 339.6 ab 1.6 c 0.81 bc 4.3 c 1.4 1.4 750.9 a 1.82 ab 0.68 f 0.37 e (0.5; 4.8) (0.5; 4.8)

CB1 6 67 b 2.5 c 1.7 a 353.3 a 1.1 de 1.01 a 3.7 c 2.1 2.1 641.0 b 1.64 c 0.62 g 0.30 f (0.6; 7.0) (0.6; 7.0) CC2 2 68 b 2.6 cb 1.4 b 319.2 c 1.3 d 0.90 ab 4.3 c 1.1 1.3 714.5 a 1.44 d 0.79 de 0.37 e (0.4; 3.7) (0.4; 4.2) CB2 6 65 b 2.5 c 1.4 b 329.4 bc 1.1 e 1.01 a 3.7 c 2.4 0.5 654.3 b 1.42 d 0.75 e 0.37 e

Control plots Control (0.7; 7.8) (0.2; 1.8) a Different letters indicate significantly different means (p < 0.05) within treatments. b TOC, Total Organic Carbon c CFU, Colony Forming Units d Values represent geometric means; lower and upper confidence limits (p = 0.05) are shown in parentheses.

Table 2-2. Minor element composition of adherent and non-adherent precipitates obtained from reclaimed and control plots. Reported valuesa represent arithmetic means.

As Ca Cd Co Cr Cu K Mg Mn Na Ni Pb Zn

(mg kg-1)

RR1 236.2 207.7 52.2 51.9 32.7 30.1 376.0 216.9 43.2 157.2 39.3 87.1 39.2

RB1 247.3 120.9 55.0 50.9 32.6 31.2 355.3 146.6 27.7 152.7 38.8 82.9 36.2

RR2 224.5 140.7 58.0 51.1 34.3 25.1 253.0 157.2 40.5 160.5 39.2 78.8 40.9

Reclaimed plots Reclaimed RB2 230.9 104.5 61.3 50.5 34.3 32.2 224.2 125.9 23.3 147.9 39.1 77.3 38.7

CC1 236.0 135.7 61.0 50.7 32.7 25.7 375.2 144.6 13.0 151.3 38.2 74.7 36.7

CB1 200.5 95.3 59.3 50.7 32.7 28.4 332.4 124.8 10.4 132.6 38.2 73.1 33.0

CC2 215.6 117.1 63.9 50.5 32.8 21.9 266.9 130.5 11.2 131.0 37.7 73.9 38.5 Control plots Control CB2 201.1 100.7 59.6 50.7 32.5 23.8 251.2 123.0 10.8 122.8 37.9 71.7 33.9 aValues based on soil dry weight

Biogeochemical characteristics of reclaimed and control precipitates

Subsurface AMD flow beneath experimental plots was highest in April-May and lowest in

August, when measured water table levels were 9-14 cm and 24-33 cm from the surface, for R1 and R2, respectively (Fig. 2-3). Thus, unsaturated conditions were verified for precipitates sampled to a depth of 8 cm. For all samples, moisture content was consistently high, ranging from 65 to 79% (Table 2-1). Moisture contents of adherent and non-adherent precipitates were similar, except for the significantly wetter RB1 precipitates, which probably reflected the shallower saturated zone beneath this plot (Fig. 2-3). The pH values for adherent and non- adherent precipitates were also similar, and only RR1 precipitates had significantly higher pH than any control precipitates. Electrical conductivity (EC) was generally low overall (< 2 dS m-1) but was slightly lower in reclaimed (0.7 to 1.3 dS m-1) than in control precipitates (1.4 to 1.7 dS m-1). An approximately 20% decrease in sulfate content was observed in reclaimed compared to control precipitates.

The percentage of TOC was on average 50% higher in reclaimed than in control precipitates

(Table 2-1). Significant carbon content differences were observed between adherent and non- adherent precipitates in both reclaimed and control plots. RR and RB precipitates had mean TOC contents of 1.9-2.4 % and 1.3-2.0 %, respectively. Carbon contents in control plots varied less with depth, where CC and CB precipitates had 1.3-1.6 % and 1.1% TOC, respectively. Numbers of culturable heterotrophic microorganisms reflected carbon distribution in precipitates, with

CFUs g-1 being higher in reclaimed than in control plots (Table 2-1). RR1 precipitates had the highest value of log CFUs g-1 (7.2), although this value was not significantly different from those observed for RR2 and RB2 precipitates (6.5 and 6.0, respectively). CC and CB precipitates had similar heterotroph counts of 3.7 to 4.3 log CFU g-1. Estimates of acidophilic iron-reducing and iron-oxidizing bacteria were highest in RR1 precipitates, at 1.3 and 1.5 x 106 MPN g-1 solids,

35 respectively (Table 2-1). Higher mean MPNs for both iron-reducing and iron-oxidizing bacteria were observed in reclaimed (7.0 x 105 and 8.5 x 105 g-1) than in control precipitates (1.8 x 105 and 1.3 x 105 g-1), respectively. Due to the relatively wide 95% confidence intervals associated with tenfold dilutions and five replicates, however, none of the MPN population estimates were significantly different from one another at p < 0.05 (Woomer, 1994).

Mineralogy and Fe fractions in reclaimed and control precipitates

Powder XRD patterns showed broad bands at 2.54 and 2.24 coinciding with d-spacing (Å), characteristic of the poorly crystalline iron oxide ferrihydrite, and sharper peaks at 4.19, 2.69,

36 2.45 d-spacing (Å) typical of the more crystalline iron oxide goethite. Therefore, these are the main Fe oxides making up these precipitates. In addition, Fe oxide bands were superimposed by a sharp peak at 3.33 d-spacing (Å) characteristic of quartz (Appendix E). Since highly similar

XRD patterns were produced by all precipitates, more quantitative information on relative proportions of amorphous and crystalline forms of Fe oxides were obtained from chemical extractions. Crystalline Fe oxides predominated in all precipitates except for RR1 (57% amorphous) (Fig. 2-4), which was consistent with higher carbon content of RR1 (Table 2-1) and the more shallow saturation zone beneath that plot (Fig. 2-3). Significantly lower percentages of amorphous Fe oxides (25-42%) were observed in all other precipitates relative to RR1.

Percentages of amorphous Fe oxides in adherent and non-adherent precipitates were similar for reclaimed plot 2 and control plot 2. In contrast, non-adherent precipitates in control plot 1 had significantly higher amorphous Fe oxides (40%) than adherent precipitates (34%), and this could also be related to closer proximity to subsurface saturation (Fig. 2-3).

Organically bound Fe made up a greater percentage of total Fe in reclaimed precipitates

(1.04-1.85%) than in control precipitates (0.75-0.84%), reflecting the spatial distribution of organic carbon (Fig. 2-5a). About 60% more organically bound Fe was found in RR precipitates when compared to RB precipitates for both plots. In contrast, no significant differences in organically bound Fe were observed between CC (0.75 and 0.83 %) and CB (0.81 and 0.84 %) precipitates (Fig. 2-5a).

Reclaimed precipitates had higher mean concentrations of extractable Fe(II) (183 mg kg-1) than control precipitates (50 mg kg-1) (Fig. 2-5b). Significantly higher mean Fe(II) concentrations also were observed in extracts from RR precipitates (161.9 and 274.3 mg kg-1) than in RB precipitates (85 and 170.8 mg kg-1). Extracts from CC and CB precipitates, on the other hand, had similar Fe(II) concentrations of 44 and 65 mg kg-1 (Fig. 2-5b).

0% 20% 40% 60% 80% 100%

RR1 a

RB1 cb

RR2 b

RB2 b

CC1 cd

CB1 b

CC2 cd

CB2 d

Fe-in Amorphous Fe oxides Fe-in Crystalline Fe oxides

Figure 2-4. Percentages of amorphous and crystalline Fe oxides in adherent precipitates and non- adherent precipitates for reclaimed plots and control plots. Error bars indicate one standard deviation of the mean (n=3). Different letters (shown for Fe in amorphous iron oxides) indicate significantly different means (p < 0.05).

A 2.5 Adherent non-Adherent

2.0 a a

1.5 b c 1.0 d d d d

0.5 % Organically bound Fe

0.0 Rec1 Rec2 Contrl1 Contrl2

350 B Adherent non-Adherent a 300

250 1 b 200 b

mg kg- 150 2+

Fe c 100 d e de e 50

0 Rec1 Rec2 Contrl1 Contrl2

Figure 2-5. Organically bound Fe (A) and Fe(II) (B) in adherent precipitates and non-adherent precipitates for reclaimed plots and control plots. Column charts show mean values with bars representing one standard deviation. Error bars indicate one standard deviation of the mean (n=3). Different letters indicate significantly different means (p < 0.05).

39 Fe(II) concentrations were significantly and positively correlated (R2=0.9, p <0.05) with carbon contents (Fig. 2-6a) and estimates of culturable iron-reducing bacteria (R2=0.8, p<0.05)

(Fig. 2-6b). Thus, increased availability of root-exuded and other organic compounds in

300 y = 169.37x - 158.82 250 R² = 0.92012

200 -1

150 mg kg

2+ 100 Fe 50

0 1.0 1.5 2.0 2.5 % Organic Carbon

300

250 y = 0.0002x + 22.483 R² = 0.82777 200 -1 150

mg kg 100 2+

Fe 50

0 0.0E+00 5.0E+05 1.0E+06 1.5E+06 Fe reducers

Figure 2-6. Correlations observed between Fe(II) concentrations and carbon contents (a) and estimates of culturable iron-reducing bacteria (b). Carbon contents and enumeration of iron- reducing bacteria are able to model Fe(II) contents with high degree of accuracy (R2=0.92, p=0.00016 and R2=0.83, p=0.0017 respectively).

40 reclaimed precipitates could enhance Fe(II) concentrations through chemical and biological

Fe(III)-reduction processes. We evaluated this further by comparing ratios of Fe(II):Fe(III) in organically bound (more bioavailable) forms in reclaimed and control precipitates. We first assumed that all 0.5 N HCl-extractable Fe(II) was derived from organic rather than mineral surfaces and then divided concentrations of 0.5 N HCl-extractable Fe(II) by the difference between pyrophosphate-extractable Fe(II and III) and 0.5N HCl-extractable Fe(II). Reclaimed precipitates had a mean ratio of 2.7:1 Fe(II):Fe(III) in organically bound forms, while control precipitates had threefold lower ratios (0.89:1). Greater Fe(II):Fe(III) ratios thus implied that more Fe reduction activity was occurring in reclaimed than in control precipitates despite maintenance of unsaturated conditions in the rooting zone.

Discussion

Successful establishment of vegetation provides several benefits for restoration of mining- impacted sites with high levels of redox-active metals. Plant cover can reduce heavy and trace metal losses from land surfaces because: 1) organic matter provides increased cation exchange sites for metal sorption (Mendez et al., 2007); 2) roots and their exudates enhance soil aggregation and promote intra-aggregate metal sequestration (Ibrahim and Goh, 2005); 3) roots and microbes secrete siderophores and other dissolved organics that complex metals (Robinson et al., 2009); 4) roots provide surface areas for plaque development, which also sequesters metals

(Neubauer et al., 2007) and 5) roots take up metals into plant biomass and make metals less available to the hydrologic system (Petrisor et al., 2004; Zou et al., 2012). However, vegetation with its associated microorganisms can increase metal solubility and mobility, even in oxic zones, where oxidation of organic matter can be coupled to metal reduction (Fimmen et al., 2008; Küsel et al., 2002; Nowack et al.,, 2010). Organic compounds also can serve as electron shuttles to

41 facilitate electron transfer between microorganisms and redox-active metals (Lovley et al, 1996;

Lovley et al., 1998). Biogeochemical processes that dissolve metal-containing minerals are more likely to introduce reduced metals to shallow subsurface waters.

Iron is the most abundant redox-active metal at the barrens study site. The chemical and microbiological results of this study supported our hypotheses that Fe(II) concentrations would be higher in reclaimed than in control precipitates and also higher in root-adherent than non- adherent precipitates. Although compost had been incorporated (along with the surface crust) into the upper 15 cm of precipitates during reclamation in 2006, an abrupt decline in root densities was observed at 5 cm depths in the reclaimed plots. The dense root systems at the surface would have greatly reduced dispersion and erosion through physical entrainment of precipitates (Oades,

1993), but they also would result in significant increases in organic C relative to underlying precipitates.

Net losses of soluble Fe to subsurface flow following reclamation could be influenced by enhanced formation of Fe complexes with dissolved organic ligands (Huang, 2008). The organic matter introduced into reclaimed precipitates by compost, plants and microbes are sources of dissolved organic compounds that could complex with Fe (Fimmen et al., 2007; Lovley et al.,

1996). Functional groups containing O and N are considered to stabilize Fe(III) preferentially, while functional groups containing S and N stabilize Fe(II) (Essington, 2004). Although analyses of dissolved organic Fe were beyond the scope of this study, further research applying such techniques as Mössbauer spectroscopy or L-edge XANES could shed light on Fe oxidation states in soils formed from these precipitates, in addition to the nature of Fe(II, III)-organic complexes.

Higher organic matter contents also can influence stability of iron oxides by dissolution- precipitation reactions (Huang, 2008). Fe(III) in amorphous oxides, like ferrihydrite is more

42 susceptible to reductive dissolution than Fe in more crystalline oxides (goethite or hematite), which have larger particle size, less surface area, and lower surface reactivity (Schwertmann,

1991). Structural Fe(III) within iron oxide minerals, especially amorphous iron oxides, also can serve as an electron acceptor during oxidation of organic matter making these minerals preferentially altered in soil systems (Gadd, 2008). As a consequence, amorphous forms of iron oxides tend to be associated with greater contents of organic matter and redox dynamics in soils, while more stable forms are usually found in environments where the amount of organic matter is low (Schwertmann, 1991; Schwertmann et al., 1986). In our study, precipitates from one of two reclaimed plots showed an increase in Fe in poorly ordered iron oxides compared to control precipitates. Thus, the net effect of Fe-organic matter interactions in reclaimed plots may have been influenced by microsite variability in root type and distribution, hydrology, and precipitate aging, among other factors.

Higher Fe(II) concentrations in reclaimed precipitates implied that soluble Fe losses to subsurface waters could have occurred during the 5 years following reclamation. To evaluate potential Fe losses more fully, Fe contents in the upper 8-cm of reclaimed precipitates were scaled up to the hectare level using data from experimental plots and bulk density measurements

(Table 2-1). Total Fe contents to a depth of 8 cm in reclaimed precipitates averaged 267 tons Fe ha-1, which was 50% lower than Fe contents in the same volume of control precipitates (518 tons

Fe ha-1) (Appendix F). This comparison yielded a rate loss estimate of 45 t Fe ha-1 yr-1 from the surface 8-cm layers of reclaimed plots since 2006. On a hectare basis, Fe(II) concentrations were two to four times higher in reclaimed than in control precipitates, supporting the hypothesis that biological activity in precipitates associated with roots resulted in net Fe mobilization.

Displacement of Fe by incorporation of 3% compost by volume during reclamation could have accounted for only 15 tons Fe ha-1. Moreover, our estimates of plant Fe uptake accounted for <

43 0.01% of total Fe removed from the same soil volume. These estimates were based on aboveground biomass measurements and analysis of Fe contents in Betula sp. leaf dry matter

(0.075-0.122 g kg-1), which were within the lower range of literature values (0.05-0.25 g Fe kg-1 dry matter) for plant tissue (Roy et al., 2006). In the case of actual reclamation efforts, however, repeated lime applications to offset pH decline could result in greater plant growth and Fe uptake.

The scope of this study did not include determining the exact fate(s) of Fe lost from reclaimed surface layers, which would ultimately depend on precipitation, hydrology, and redox conditions. It is possible that some Fe (and associated ligands) in reclaimed plots were translocated downward and remained for extended periods in deeper precipitates, rather than exiting the system via subsurface flow. Since Fe content, forms and redox states influence the behavior of other metals, many of which are toxic, Fe biogeochemistry is an important factor in evaluating net effects of phytostabilization. Thus, vegetative reclamation approaches in hydrologically vulnerable areas should take into account possible translocation routes and potential losses of redox-active metals and associated toxic metal(loid)s.

Conclusions

To our knowledge, this study is the first to address the effects of phytostabilization on potential Fe mobility, bioavailability and fractionation in iron oxide precipitates in the field. In a previous study we had demonstrated that vegetative restoration of this type of barrens did not require removal of the precipitates, which were transformed at low cost into an incipient soil medium that supported successional plants. After five growing seasons, reclamation resulted in dense but shallow rooting systems and enrichment with organic carbon and microorganisms within the precipitates. Despite successful plant establishment and improvements of soil

44 properties, revegetation of precipitates appeared to induce greater oxidation-reduction activity which increased Fe(II) concentrations. Further research should aim to understand how Fe(II) can become stabilized in reclaimed precipitates, possibly through complexation with desired functional groups known to be enriched in specific amendments.

Acknowledgements

This work was supported by the Fulbright Program and the Chilean Commission for Science and Technology (CONICYT), through a Fulbright Foreign Graduate Student Fellowship. We thank landowner Alan Larson, who permitted site access. We acknowledge the College of

Agricultural Sciences and the Graduate School at the Pennsylvania State University for support of

CR through the McKenna and Graduate Competitive Grant programs

45 References

Atekwana, E.A., and E.W. Fonyuy. 2009. Dissolved inorganic carbon concentrations and stable carbon isotope ratios in streams polluted by variable amounts of acid mine drainage. J. Hydrol. 372:136-148.

Bigham, J.M., Schwertmann, U., Traina, S.J., Winland, R.L., Wolf, M., 1996. Schwertmannite and the chemical modeling of iron in acid sulfate waters. Geochimica Et Cosmochimica Acta 60, 2111-2121.

Cravotta, C.A., III, 2005. Effects of abandoned coal-mine drainage on streamflow and water quality in the Mahanoy Creek Basin, Schuylkill, Columbia, and Northumberland Counties, Pennsylvania, 2001, U.S. Geological Survey Scientific Investigations Report 2004-5291, p. 60 pages plus appendixes.

Essington, M.E., 2004. Soil and water chemistry: an integrative approach. CRC Press LLC, Florida.

Fimmen, R., Richter, D., Vasudevan, D., Williams, M., West, L., 2008. Rhizogenic Fe–C redox cycling: a hypothetical biogeochemical mechanism that drives crustal weathering in upland soils. Biogeochemistry 87, 127-141.

Fimmen, R.L., Cory, R.M., Chin, Y.-P., Trouts, T.D., McKnight, D.M., 2007. Probing the oxidation–reduction properties of terrestrially and microbially derived dissolved organic matter. Geochimica Et Cosmochimica Acta 71, 3003-3015.

Hossner, L.R., 1988. Reclamation of surface-mined lands Volume I. CRC Press, Inc., Boca Raton, Florida, 33431.

Ibrahim, S.M., Goh, T.B., 2005. Changes in macroaggregation and associated characteristics in mine tailings amended with humic substances. Communications in Soil Science and Plant Analysis 35, 1905-1922.

Johnson, D.B., Macvicar, J.H.M., Rolfe, S., 1987. A new solid medium for the isolation and enumeration of Thiobacillus ferrooxidans and acidophilic heterotrophic bacteria. Journal of Microbiological Methods 7, 9-18.

Johnson, D.B., McGinness, S., 1991. A highly effecient and universal solid medium for growing mesophilic and moderately thermophilic, iron-oxidizing, acidophilic bacteria. Journal of Microbiological Methods 13, 113-122.

46 Komárek, M., Vaněk, A., Ettler, V., 2013. Chemical stabilization of metals and arsenic in contaminated soils using oxides – A review. Environmental Pollution 172, 9-22.

Kowalenko, C.G., 1993. Extraction of available sulfur, in: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis. Canadian Society of Soil Science.

Kumpiene, J., Fitts, J.P., Mench, M., 2012. Arsenic fractionation in mine spoils 10 years after aided phytostabilization. Environmental Pollution 166, 82-88.

Küsel, K., Roth, U., Drake, H.L., 2002. Microbial reduction of Fe(III) in the presence of oxygen under low pH conditions. Environmental Microbiology 4, 414-421.

Lovley, D.R., Coates, J.D., Blunt-Harris, E.L., Phillips, E.J.P., Woodward, J.C., 1996. Humic substances as electron acceptors for microbial respiration. Nature 382, 445-448.

Lovley, D.R., Fraga, J.L., Blunt-Harris, E.L., Hayes, L.A., Phillips, E.J.P., Coates, J.D., 1998. Humic substances as a mediator for microbially catalyzed metal reduction. Acta Hydrochimica et Hydrobiologica 26, 152-157.

Lovley, D.R., Phillips, E.J.P., 1986. Organic-matter mineralization with reduction of ferric iron in anaerobic sediments. Applied and Environmental Microbiology 51, 683-689.

McBride, M.B., 1994. Environmental Chemistry of Soils. Oxford University Press, New York.

McKeague, J.A., 1967. An evaluation of 0.1 pyrophosphate and pyrophosphate-dithionite in comparison with oxalate as extractants of the accumulation products in Podzols and some other soils. Canadian Journal of Soil Science 47, 95-99.

McKeague, J.A., Day, J.H., 1966. Dithionite and oxalate extractable Fe and Al as aids in differentiating various classes of soils. Canadian Journal of Soil Science 46, 13-22.

Mendez, M.O., Glenn, E.P., Maier, R.M., 2007. Phytostabilization potential of quailbush for mine tailings. Journal of Environmental Quality 36, 245-253.

Mendez, M.O., Maier, R.M., 2008. Phytostabilization of mine Tailings in arid and semiarid environments-an emerging remediation technology. Environ Health Perspect 116, 278–283.

47 Nelson, D.W., and Sommers, L. E., 1996. Total carbon, organic carbon, and organic matter, in: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3. Chemical Methods. Soil Science Society of America and American Society of Agronomy, Madison, WI 53711, USA.

Neubauer, S.C., Toledo-Durán, G.E., Emerson, D., Megonigal, J.P., 2007. Returning to their roots: iron-oxidizing bacteria enhance short-term plaque formation in the wetland-plant rhizosphere. Geomicrobiology Journal 24, 65-73.

Nowack, B., Schulin, R., Luster, J., 2010. Metal fractionation in a contaminated soil after reforestation: Temporal changes versus spatial variability. Environmental Pollution 158, 3272- 3278.

Oades, J.M., 1993. The role of biology in the formation, stabilization and degradation of soil structure. Geoderma 56, 377-400.

Parkin, T.B., Robinson, J.A., 1993. Statistical evaluation of median estimators for lognormally distributed variables. Soil Sci. Soc. Am. J. 57, 317-323.

Prasanna, R., Ratha, S., Rojas, C., Bruns, M., 2011. Algal diversity in flowing waters at an acidic mine drainage “barrens” in central Pennsylvania, USA. Folia Microbiologica 56, 491-496.

Rhoades, J.D., 1996. Salinity: electrical conductivity and total dissolved solids., in: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3. Chemical Methods. Soil Science Society of America and American Society of Agronomy, Madison, WI 53711, USA.

Robinson, B.H., Bañuelos, G., Conesa, H.M., Evangelou, M.W.H., Schulin, R. 2009. The phytomanagement of trace elements in soil. Critical Reviews in Plant Sciences 28, 240-248.

Roy, R.N., Finck, A., Blair, G.J., Tandon, H.L.S., 2006. Plant nutrition for food security-A guide for integrated management. Food and Agriculture Organization of The United Nations (FAO), Rome, Italy.

Sadler, D.A., Sun, F., Howe, S.E., Littlejohn, D., 1997. Comparison of procedures for correction of matrix interferences in the multi-element analysis of soils by ICP-AES with a CCD detection system. Microchimica Acta 126, 301-311.

SAS, 2011. Data Quality Server Reference. Cary, NC: SAS Institute Inc. SAS Institute Inc, 138.

Schwertmann, U., 1991. Solubility and dissolution of iron oxides. Plant and Soil 130, 1-25.

48 Schwertmann, U., Bigham, J.M., Murad, E., 1995. The first occurrence of schwertmannite in a natural stream environment. European Journal of Mineralogy 7, 547-552.

Schwertmann, U., Kodama, H., Fischer, W.R., 1986. Mutual interactions between organics and iron oxides., in: Huang, P.M., Schnitzer, M. (Ed.), Interactions of soil minerals with natural organics and microbes. Soil Science Society of America, Madison, WI.

Soil Survey Staff, N.R.C.S., United States Department of Agriculture. , 2013. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed [January/15/2013].

Thomas, G.W., 1996. Soil pH and soil acidity, in: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3. Chemical Analysis. Soil Science Society of America and American Society of Agronomy, Madison, WI 53711, USA.

Thompson, A., Chadwick, O.A., Rancourt, D.G., Chorover, J., 2006. Iron-oxide crystallinity increases during soil redox oscillations. Geochimica Et Cosmochimica Acta 70, 1710-1727.

Vangronsveld, J., Herzig, R., Weyens, N., Boulet, J., Adriaensen, K., Ruttens, A., Thewys, T., Vassilev, A., Meers, E., Nehnevajova, E., Lelie, D., Mench, M., 2009. Phytoremediation of contaminated soils and groundwater: lessons from the field. Environmental Science and Pollution Research 16, 765-794.

Winland, R.L., Traina, S.J., Bigham, J.M., 1991. Chemical composition of ochreous precipitates from Ohio coal mine drainage. Journal of Environmental Quality 20, 452-460.

Woomer, P.L., 1994. Most probable number counts, in: Weaver, R.W., Angle, J.S., Bottomley, P.S (Ed.), Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties. Soil Science Society of America Madison, WI, pp. 59-79.

Chapter 3

Bacterial diversity in vegetated and biological crust-covered soils formed from acid mine drainage precipitates

Bacterial richness is reported to be greater in soils with neutral pH than soils at acidic or alkaline pH extremes. Soils formed from acidic parent materials, such as those associated with reclaimed minelands, thus would be expected to have lower bacterial diversity than grassland or agricultural soils. In a previous report, we described how plant cover was established at a 50- year-old acid mine drainage (AMD) barrens in Central Pennsylvania, U.S.A. Vegetation in these barrens had been killed by prolonged overland flow of abandoned mine discharge that caused an accumulation of acidic (pH 2-3) iron oxy(hydr)oxide precipitates on soil surfaces. The objective of the present study was to compare bacterial diversity in revegetated and non-reclaimed precipitates six years after the reclamation treatment in 2006, when precipitates in experimental plots were amended with a one-time incorporation of compost and lime (to pH 4.5). Reclaimed precipitates supported diverse successional vegetation, while non-reclaimed precipitates in control plots supported only naturally occurring, moss-dominated crusts. At the time of sampling, precipitates had similar pH (2.5-2.7) but contrasting inputs of organic carbon. Bacterial diversity was assessed using 454 pyrosequencing of 16S rRNA genes (V1-V3/V5 region) in reclaimed and control precipitates at two depths: reclaimed root-adhering (RR); reclaimed below-roots (RB); control crust-adhering (CC); control below-crust (CB). Contrary to our projections we observed high bacterial diversity across all samples recovering a total of 3,150 operational taxonomic units

OTUs at 97% similarity. Of these, about 50% (1,599 OTUs) were exclusively found in reclaimed precipitates (RR, RB or both), 33% (1,042 OTUs) were unique to control precipitates (CC, CB, or both) and only 6% (185 OTUs) were shared among the four precipitate types. Nineteen phyla

50 were identified in the complete data set, and 13 of these were present in all precipitate types.

Proteobacteria comprised the most abundant representatives in reclaimed precipitates, and

Acidobacteria were more abundant in root-and crust-adherent precipitates, with the latter precipitate type having the highest abundance of Actinobacteria. Bacterial composition of incipient soils developed from AMD did not resemble those typically described for aquatic AMD systems. Among the classical AMD bacteria, only Leptospirillum ferriphilum, Leptospirillum ferrodiazotrophum, and Acidiphilium cryptum were detected in our study but at very low frequency.

Introduction

Soil bacterial communities are recognized as being extremely diverse (Hugenholtz et al.,

1998; Fierer et al., 2012) and strongly influenced by pH (Chu et al., 2010; Lauber et al., 2009;

Nacke et al., 2011). In neutral soils (pH 6-7), bacterial OTU richness based on pyrosequencing of

16S rRNA genes was about twice as high as in soils with acidic (< 4) or alkaline (> 8.0) pH

(Lauber et al., 2009; Rousk et al., 2010). In extremely acidic (pH < 2) systems, such as subterranean mine waters, sediments, and biofilms, bacterial richness is even lower (Schrenk et al

1998; Edwards et al., 1999; Bond et al., 2000a; Bond et al., 2000b). Studies of bacterial communities in these largely aqueous extreme environments have been undertaken precisely because they are less complex and easier to characterize (Denef et al., 2010). In contrast, high diversity in unsaturated soils likely results from myriad introductions of bacteria persisting in varied micro-niches that accumulate during soil development and seasonal cycles of plant growth

(Ettema and Wardle, 2002).

51 Prior studies suggest that soils developed from acidic parent materials would harbor fewer bacterial taxa than soils formed from limestone or other neutral parent materials (Chu et al., 2010;

Lauber et al., 2009; Nacke et al., 2011). Acidic parent materials found on mined lands and areas impacted by acid mine drainage (AMD) are known to have low microbial activity and to provide poor substrates for plant growth (Machulla et al., 2005). In a previous report, we described how acidic iron oxy(hydr)oxide precipitates were vegetated in place at a 50-year-old AMD barrens in

Central Pennsylvania, U.S.A. (Lupton et al., 2013). These barrens had been created by massive overland flow of acidic discharge from a large network of abandoned underground coal mines, and they supported no vegetative growth except for moss-dominated biological crusts in wetter areas. In 2006 experimental plots at the barrens were reclaimed with a one-time incorporation of compost into the upper 15 cm with liming to pH 4.5. Our barrens experiment demonstrated how

AMD precipitates could be converted into a soil that supported > 90% vegetative cover for the next five years, thus meeting SMCRA requirements for mine reclamation (Lupton et al., 2013;

Rojas et al., in press).

The objective of the present study was to compare bacterial diversity in revegetated and non- reclaimed (control) precipitates six years after the reclamation treatment. At the time of sampling in 2011, both types of precipitates had similar pH (2.5-2.7) because pH had declined in reclaimed plots gradually over time. Since reclaimed precipitates supported vascular vegetation, and non- reclaimed precipitates supported naturally occurring, moss-dominated crusts, this study enabled a comparison of bacterial communities in unsaturated incipient soils having similarly low pH but different inputs of organic carbon.

We were also interested in occurrence at the barrens of bacterial taxa recognized for their association with AMD systems (Johnson, 2012). These taxa include the chemolithotrophs

Acidithiobacillus and Leptospirillum spp. (Baker et al., 2003; Tyson et al., 2005) and acidiphilic

52 heterotrophs such as Acidimicrobium spp. and Ferrimicrobium spp. (Bond et al., 2000a; Johnson,

2012). Since the best studied AMD-impacted environments have been more aquatic rather than terrestrial in character, our reclamation study provided the opportunity to gain insights into AMD- derived bacterial communities in unsaturated, edaphic habitats. Moreover, AMD precipitates in the barrens would have been exposed to more drying, sunlight, and temperature fluctuations than fresh sediments in free-flowing or submerged AMD systems described in previous studies (Bond et al., 2000a; Johnson, 2012; Tyson et al., 2005).

We also compared bacterial diversity in precipitates at two depths reflecting organic carbon enrichment. Upper depths of reclaimed and control samples corresponded to precipitates directly associated with plant roots and moss rhizoids, respectively. Lower depths corresponded to precipitates immediately beneath the root- or crust-enriched layers, which had more organic carbon that underlying layers. We hypothesized that bacteria in non-reclaimed precipitates would be more similar to taxa previously associated with AMD systems and that bacteria in reclaimed precipitates would be more similar to those found in acidic soils (Hugenholtz et al., 1998; Lauber et al., 2009). To test these hypotheses, we performed 454 barcoded Genome Sequencer FLX pyrosequencing-based analysis of 16S rRNA to assess abundant and rare bacterial taxa. Insights from our study can shed light on acid-tolerant bacteria and on some of the underlying mechanisms of soil development. Such knowledge can help in the development of effective strategies for the ecological restoration of degraded environments.

53 Materials and Methods

Background and site description

The research site is a 50-year-old AMD barrens approximately 5 km north of Kylertown, PA, in Clearfield County, 41◦01’22.00’N; 78◦ 09’ 08.064’ W (Lupton et al., 2013). Due to massive overland flow of AMD from abandoned underground coal mines, surface layers of ferric iron

(oxyhydr)oxide precipitates (up to 35 cm thick) accumulated on native soils, killing native vegetation and precluding its regrowth. The only observable vegetative cover in the barrens were mossy biological crusts that occurred in the wettest areas where mine drainage flowed near the surface (Prasanna et al., 2011). In 2006, experimental plots (3 m x 3 m) were established in moss- covered areas to evaluate whether a one-time reclamation treatment could facilitate growth of sown and successional plants without the removal of acidic precipitates from the site. Precipitates were reclaimed in place by rototilling lime and compost into the upper 15 cm of the plots. A

-1 mixture of MgCO3 and Ca(OH)2 (11 t ha ) was added to achieve a pH similar to that of surrounding forest soils (5.0). Compost (2.8% N) was added at 27 t ha-1 to provide a total N addition of 756 kg N ha-1, most of which was organic N. After rototilling, plots were mulched with oat straw (9 kg per 9 m2 plot) and sown with a reclamation seed mixture (two legumes and four grasses).

In the first reclamation year, pH values of reclaimed and non-reclaimed precipitates were 4.1 and 2.4, with residual acidity at 19 and 30 cmol+ kg-1 soil, respectively (Table 3-1). Bulk density of non-reclaimed precipitates (0.93 g cm-3) was greater than that of reclaimed precipitates (0.76 g cm-3), reflecting the incorporation of compost and straw in experimental plots. Concentrations of macronutrients (K, Ca, Mg, S) were greater in reclaimed than in non-reclaimed precipitates, while extractable P levels were similarly low. Aerobic culturable heterotrophs (10 day incubation at

54 25ºC on R2A medium) were about 70-fold higher in reclaimed (8.9 x 106 cfu g-1) than in non- reclaimed precipitates (1.3 x 105 cfu g-1), with the latter communities dominated by fungi.

-1 -1 Microbial biomass C flush (41-44 µg C g soil) and respiration rates (0.78-1.14 µg CO2-C g soil) were low in both precipitate types (Table 3-1). The reclamation treatment resulted in soil property changes that permitted good germination and growth of the oats nurse crop, which in itself represented an additional source of organic carbon from root exudates and decomposing plant tissue.

Relative to measurements made in the first year of reclamation, reclaimed precipitates exhibited lower pH (3.8) but similar residual acidity (19 cmol+ kg-1 soil) in the second year, while there were no pH or residual acidity changes in non-reclaimed precipitates. Growth of sown plant species achieved >70% areal cover in the second reclamation year, indicating that their germination and establishment were not entirely prohibited by the low pH of reclaimed precipitates. Macronutrient concentrations in reclaimed precipitates were 10-30% lower than they were in the first year, except for Ca which decreased by 50% (Table 3-1). Also in the second year, aerobic culturable heterotrophs were over a thousandfold greater in reclaimed precipitates

(2.6 x 107 cfu g-1 soil) than in non-reclaimed precipitates (4.2 x 103 cfu g-1 soil), with the latter colonies dominated by fungi. Microbial biomass C flush and respiration rates remained low in both precipitate types.

Five years following reclamation, indigenous species consisting of Betula, Populus, Spiraea and Crataegus spp., had replaced the sown plant species in reclaimed plots, which by that year achieved > 90% of areal cover (Rojas et al., in press). During the third to fifth reclamation years, successional plant growth had been maintained in the reclaimed precipitates even though soil pH continued to decline (2.8) to approach that of non-reclaimed precipitates (2.5). Thus, additional lime applications would be needed if the reclamation goal required pH to be maintained at levels

55 similar to those of surrounding soils. Iron-rich AMD precipitates pose a particular challenge in maintaining pH favorable to plant growth because of the secondary acidity generated during

Fe(II) oxidation. As shown in our previous study of this site’s biogeochemistry under unsaturated conditions (Rojas et al., in press), reclaimed precipitates yielded tenfold higher estimates of culturable Fe-oxidizing bacteria, as well as fourfold higher Fe-reducers than non-reclaimed precipitates (Table 3-1). Greater microbial densities in plant rhizospheres due to increased availability of substrates from roots and root exudates can therefore promote Fe oxidation- reduction activity. In the previous study, this was demonstrated by a 50% greater content of organic C and up to fivefold greater extractable Fe(II) in reclaimed precipitates than in non- reclaimed precipitates (Rojas et al., in press).

Table 3-1. Main soil properties collected since 2006 from non-reclaimed and reclaimed precipitates in the red zone. Soil property Non-reclaimed Reclaimed Reference Vegetative cover (2006/2007-2011) Moss / Moss Oats nurse crop / Mixed vascular 1;2 pH (2006 / 2007 / 2010) 2.4 / 2.4 / 2.5 4.1 / 3.8 / 2.8 1;2 Residual acidity (2006 / 2007) 30 / 31 cmol + kg-1 19 / 19 cmol + kg-1 1;2 Bulk density (2010) 0.93 g cm-3 0.76 g cm-3 3 Percentage Moisture (2006 / 2007 / 2010) 68 / 75 / 68 75 / 63 / 72 1;2 Potassium (2006 / 2007) 25 / 18 mg kg-1 64 / 59 mg kg-1 2 Calcium (2006 / 2007) 115 / 179 mg kg-1 2379 / 1105 mg kg-1 2 Magnesium (2006 / 2007) 35 / 56 mg kg-1 484 / 371 mg kg-1 2 Sulfur (2006 / 2007) 1933 / 1957 mg kg-1 2877 / 2063 mg kg-1 2 Phosphorous (2006 / 2007) 1 / 1 mg kg-1 1 / 1 mg kg-1 2 Iron (2010) 690.2 g kg-1 453.0 g kg-1 3 Aluminum (2010) 0.35 g kg-1 0.67 g kg-1 3 EC (2006 / 2007 / 2010) 1.5 / 0.8 / 1.6 dS m-1 3.0 / 1.2 / 1.0 dS m-1 1;2 Aerobic heterotrophs on R2A (2006 / 2007 / 2010) 5.11 / 3.62 / 4.0 log CFU g-1 6.95 / 7.41 / 6.2 log CFU g-1 2;3 Dominant culturable heterotrophs (2006 / 2007) Fungi Bacteria 2 Microbial biomass C flush (2006 / 2007) 44 / 53 µg C g-1 41 / 36 µg C g-1 2 -1 -1 Respiration (2006 / 2007) 0.78 / 0.45 µg CO2-C g 1.14 / 1.01 µg CO2-C g 2 Percentage carbon content 0.8 / -- / 1.3 -- / -- / 1.9 1;3 MPN iron oxidizers (2010) 1.3 x 105 MPN g-1 8.5 x 105 MPN g-1 3 MPN iron reducers (2010) 1.8 x 105 MPN g-1 6.7 x 105 MPN g-1 3 References: 1. Lupton et al., 2013; 2. Lupton 2008; 3. Rojas et al., in press.

Sample collection

Reclaimed and non-reclaimed precipitates were sampled in July, 2011, during the sixth growing season following the reclamation treatment. Plant roots in reclaimed plots were concentrated in the upper 5-cm layer of precipitates, while the thickness of mossy crusts in non- reclaimed areas was 2-3 mm. Two 0.25-m2 sections of precipitates (8-cm thick) were excised from one plot of reclaimed precipitates and an adjacent, non-reclaimed area covered by biological crusts, which we designated as “control precipitates”. In the field, upper and lower layers were separated from the excised sections. For the reclaimed precipitates, the topmost 5-cm layer of precipitates adhering to plant roots was labeled RR for “reclaimed root-adherent.” The underlying

3-cm layer was labeled RB for “reclaimed below-root-adherent” precipitates. For the control precipitates, the topmost 2-cm layer of precipitates adhering to the biological crust was labeled

CC for “control crust-adherent”, after being scraped aseptically from the crust. The underlying 6- cm layer was labeled CB for “control below-crust-adherent”. Samples were placed on ice in a sterile container for transport to the lab, where plants, roots, and large organic debris were removed. Each of the four samples were mixed and split to obtain three replicate subsamples.

Soil characterization

After oven-drying (50°C) and sieving (150 mm) replicate subsamples, total soil organic carbon (C) and nitrogen (N) were determined using an EA 1110® CHN analyzer (CE

Instruments, Milan, Italy). All carbon in the precipitates was considered organic since no effervescence was observed when testing samples with 4 M HCl (Nelson, 1996). The pH and electrical conductivity (EC) of air dried and sieved precipitates (2 mm) were measured in 1:1 ratio and 1:5 ratios of precipitate to deionized water, respectively (Rhoades, 1996; Thomas,

1996). Gravimetric moisture contents were determined by drying the samples at 105°C for 48h.

Statistical analyses were performed with SAS Version 9.3 (SAS, 2011). Treatment and depth effects were analyzed by two way ANOVA using the general linear model procedures.

Significant differences between means at the p< 0.05 level were determined by employing the

Tukey’s studentized range test (Tukey’s Honestly Significant Difference, or Tukey’s HSD).

Soil DNA extraction and pyrosequencing

Procedures used in sample processing for DNA extraction and analyses are depicted in

Appendix E. Microbial community DNA was extracted directly from three replicate subsamples

(0.35 g fresh weight) from each layer (RR, RB, CC, CB) using the MoBio PowerLyzerTM Power

Soil® DNA isolation kit (MoBio Laboratories, Inc., Carlsbad, CA). All manufacturer’s instructions were followed except that use of Solution 3 (inhibitor removal) was omitted to improve yield. The DNA concentrations were determined using a NanoDrop 1000

Spectrophotometer (Thermo Fisher Scientific Inc.). DNAs from the three extracts were pooled and stored at -80 °C prior to pyrosequencing analysis.

Community DNAs were subjected to bar-coded amplicon library preparation (i.e. targeted sequences of DNA) and pyrosequencing using the 454 Genome Sequencer FLX Titanium System

(Roche Diagnostics, Indianapolis, IN, USA) at The Pennsylvania State University Genomics Core

Facility. An amplicon library was prepared for each sample by polymerase chain reaction (PCR) using universal bacterial primers (27F-907R) complementary to the V1, V2, V3 and V5 regions of the 16S rRNA (Lane, 1991). The adapted forward primer (5’-

AGAGTTTGATCMTGGCTCAG-3’) and the reverse primer (5’-

CCCCGTCAATTCMTTTGAGTTT-3’) each contained a 454 Roche-adaptor and a 4-bp key

59 sequence. In addition, a 10-bp nucleotide sequence barcode was included upstream of the universal bacterial primers to identify the sample from which the PCR amplicons were derived.

PCR reactions contained 1 µL (5 µM) of each forward and reverse primer, 0.5 µL of dNTP mix

(10 mM each), 0.25 µL of 5 U FastStart HiFi polymerase, 2.5 µL FastStart buffer (Roche

Laboratories), and 1 µL of DNA extract per 25 µL reaction volume. Reaction conditions consisted of an initial denaturation step at 94° C for 3 minutes followed by 35 cycles, each consisting of denaturation at 94° C for 15 seconds, primer annealing at 55° C for 45 seconds, and extension at 72° C for 1 minute with a final extension at 72° C for 8 minutes. After PCR amplification, products were cleaned using 70% ethanol and AMPure magnetic beads as directed by the manufacturer (Roche Laboratories), and selected for sizes of at least 500 bp in a 1.1% agarose gel. DNA was extracted from size-selected bands using an Agarose Gel Extraction Kit

(Roche Laboratories). Next, samples were subjected to Qubit dsDNA HS assay (Invitrogen,

Carlsbad, CA) and checked on the Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) to verify concentration and purity.

Processing of pyrosequence data

Sequences were processed and analyzed with the mothur software platform, version v.1.24.1

(Schloss et al., 2009) (Appendix G). Sequences were denoised with the mothur implementation of the PyroNoise algorithm (Quince et al., 2009). Sequences were trimmed, assigned by barcode, and quality filtered to remove reads that had more than one mismatch to barcodes and more than two mismatches to the primers, as well as homopolymers longer than 8 bp. After removal of barcode and primer sequences, only those fragments consisting of at least 200 bp in length and showing no ambiguous characters were included in further analyses (Schloss et al., 2011).

Filtered sequences were aligned to a 50,000- column wide SILVA-based reference alignment

60 using the NAST-based aligner implemented in mothur (DeSantis et al., 2006; Schloss, 2009).

Aligned sequences were trimmed to ensure that they started and ended at the same alignment position. Sequences that did not align to the expected region of the 16S rRNA gene were removed. A pre-clustering algorithm was also used to allow up to a 2-bp difference between sequences (Huse et al., 2010; Schloss et al., 2011). Sequences identified as potential chimeras using the database-independent implementation of Uchime also were removed from the dataset

(Quince et al., 2009; Schloss et al., 2011). Curated sequences were classified using the mothur version of the Bayesian classifier and the mothur-compatible greengenes taxonomy database at the 80% confidence threshold (Schloss et al., 2011). Sequences classified as chloroplast, mitochondria, and unknown (referring to sequences that could not be classified at the domain level) were removed from the dataset. After curation and dataset screening, a total of 64,709 sequences were obtained for use in constructing the rarefaction curves (Appendix H and I).

The four datasets were normalized to the lowest number of curated and greengenes-assigned sequences (14,666 sequences for CC) by randomly selecting this number from the CB, RR, and

RB datasets. To identify operational taxonomic units (OTUs) at the 97% sequence similarity level from the normalized datasets, we generated pairwise distance matrices and clustered sequences into OTUs by the furthest neighbor algorithm. Alpha diversity of each dataset was analyzed with rarefaction curves, sample coverage, Ace and Chao richness indices, and Shannon and inverse

Simpson diversity indices. Beta diversity measurements included relative taxa abundance, OTUs distribution among samples and clustering analysis to evaluate the similarity among samples.

Relative abundances at broad- and finer-scales were displayed in heat maps generated in

Excel:Mac 2011 v.14.0. Broad-scale taxonomic distribution included the major phyla in each dataset and was obtained by dividing the number of sequences in each phylum by 14,666. Finer- scale taxonomic distribution included all taxa that comprised at least 0.1% of 14,666 sequences

61 per sample (≥ 15 sequences). The distribution of OTUs among samples was shown in a four-way

Venn diagram. Lastly, a clustering analysis was conducted from a Bray-Curtis distance matrix using an Unweighted Pair Group Means Analysis (UPGMA) and displayed in a dendogram. All calculations were performed using the mothur software platform, version v.1.24.1 (Schloss et al.,

2009) unless otherwise indicated.

Results

Soil characteristics

Chemical properties varied significantly (p<0.05) among RR, RB, CC, and CB with the exception of pH values, which were 2.5 to 2.7 for all samples (Table 3-2). RR had higher C and

N contents (2.7% and 0.18%) than RB (2.4 % and 0.16%), which likely reflected greater localization of plant exudates in upper layers of reclaimed plots. CC also had higher C and N contents (1.8% and 0.01%) than CB (0.7% and 0.06%), likely due to the tight association of surface precipitates with moss rhizoids and exudates. Moisture contents were generally high (61 to 77%) and differed only between RR (77%) and RB (61%). Electrical conductivity (EC) was higher in CC (1.8 dS m-1) than RB and CB (1.1 and 1.2 dS m-1) and lowest in RR (1.0 dS m-1).

Table 3-2. Physical and chemical properties of reclaimed (RR and RB) and control (CC and CB) precipitates. Reported values represent arithmetic means and all values are based on soil dry weight. Reclaimed precipitates Control Precipitates RR RB CC CB Depth (cm) 0-5 5-8 0-2 2-8 Moisture content (%) 77.16a 60.62b 71.81ab 70.42ab pH 2.68a 2.52a 2.50a 2.45a EC (dS m-1) 0.96c 1.12b 1.78a 1.17b C (%) 2.73a 2.43b 1.77c 0.65d N (%) 0.18a 0.16b 0.11c 0.06d Different letters indicate significantly different means (p < 0.05).

62 Bacterial alpha and beta diversity

A total of 64,709 screened pyrosequences with a mean read length of 250 bp were obtained from the four types of precipitates. This sequencing effort resulted in nearly complete sampling coverages of 0.95-0.97 (Table 3-3) and rarefaction curves approaching asymptotes (Appendix H).

The lowest number of sequences, 14,600, was obtained from CB precipitates. Therefore a subset of 14,600 sequences was randomly selected from the datasets of RR, RB, and CC for normalized comparisons. When grouped at 97% similarity, a total of 3,150 OTUs was obtained across all precipitate types. The majority of these OTUs were detected exclusively in reclaimed precipitates

(746 for RR, 546 for RB, with 307 shared). Fewer OTUs were unique to control precipitates (412 for CC, 525 for CB, with 105 shared) (Fig. 3-1). The remaining OTUs were either found in all four precipitate types (185) or shared by at least one reclaimed and one control precipitate (324).

Richness indexb Diversity indexb Sample Number Number of Sampling ACE Chao Shannon Inv-Simpson of total observed coverage sequences OTUsa RR 16,935 1,431 0.95 3,383 (3,196; 3,590) 2,469 (2,274; 2,709) 5.48 (5.45; 5.51) 74 (71; 77) RB 17,898 1,269 0.96 2,994 (2,817; 3,190) 2,248 (2,053; 2,492) 5.28 (5.25; 5.31) 60 (58; 63) CC 15,210 859 0.97 2,341 (2,167; 2,539) 1,526 (1,372; 1,726) 4.65 (4.63; 4.68) 41 (40; 43) CB 14,666 1,059 0.96 2,776 (2,596; 2,977) 1,886 (1,709; 2,111) 4.98 (4.95; 5.01) 50 (48; 52) aTotal number of OTUs identified per sample bValues in parenthesis show 95% confidence intervals as calculated by mothur

Bacterial richness, as estimated by the ACE and Chao indices, was significantly higher

(p<0.05) in RR than in control precipitates (Table 3-3). Bacterial diversity, based on the Shannon index and Simpson’s reciprocal index, was significantly higher (p <0.05) in RR than the other three precipitate types. In addition, the RB diversity indices were higher than those of CC and

CB. In control precipitates, the ACE bacterial richness index and both diversity indices were significantly lower (p <0.05) in CC than in CB precipitates (Table 3-2). This could imply that bacteria in CB precipitates represented older, more established communities maintained under less fluctuating, lower-disturbance conditions. In contrast, bacteria in CC precipitates could have represented communities in earlier successional stages as they responded to growth and decay of biological crusts, more frequent wetting-drying cycles and greater temperature fluctuations at the surface. RR precipitates, which exhibited the most diversity, also had the greatest percentage of

GreenGenes-assignable sequences (78%), followed by CC (72%), RB (64%), and CB (63%).

Clustering analysis of the four types of precipitates separated control from reclaimed precipitates

(Fig. 3-2). Control precipitates were more similar (76%) to each other than reclaimed precipitates

(69%). This clustering pattern reflects the higher numbers of OTUs in both types of reclaimed precipitates.

CCC1S.518F

CBC1B.518F

RR17S.518F

RB17B.518F

0.05 0.05

Figure 3-2. Clustering analyses of the four precipitate types using UPGMA algorithm and Bray- curtis distance matrix. Bar represents 0.05 distance among samples.

Broad-scale taxonomic comparisons

Among classified sequences, a total of 19 phyla were identified. Of these phyla, 13 were shared by all precipitate types (Fig. 3-3). The most abundant phyla in all samples were

Proteobacteria, Acidobacteria and, Actinobacteria (Fig. 3-3), which also represent the most abundant and ubiquitous bacterial phyla in soil environments (Fierer et al. 2012; Janssen, 2006;

Lauber et al., 2009). Proteobacteria comprised a larger percentage of the classifiable sequences in

RR and RB (35 and 31%, respectively) than in CC and CB (16 and 24%, respectively).

Reclaimed precipitates had similar percentages of Acidobacteria (26% and 11% for RR and RB, respectively) as the control precipitates (24% and 12% for CC and CB, respectively.) The fact

66 that Acidobacteria accounted for greater percentages of sequences in RR and CC than in RB and

CB precipitates suggested their association with freshly generated organic C (from either roots or biological crusts). Actinobacteria were more abundant in CC precipitates (21%), compared to 10-

14% in the other three precipitate types, which may reflect a more specific association of actinobacterial taxa with the biological crusts. Other abundant bacterial phyla detected in all four precipitates included Planctomycetes (2.1-8.7%), WPS2 (1.4-3.4%), Chloroflexi (1.0-3.3%),

Firmicutes (0.5-1.2%), TM6 (0.1-0.4%), Nitrospira (0.03-0.3%), Armatimonadetes (0.07-0.2%), and Cyanobacteria (0.02-0.1%), all of which represent phyla typically found in soils (Fierer et al.

2012; Janssen, 2006). No sequences representing the comparatively common soil phylum

Gemmatimonadetes were found in any of the precipitates, which may indicate this group’s intolerance to acid conditions (Lauber et al., 2009). Two other phyla, ZB2 and TM7, were identified in all samples but comprised low percentages (≤0.1%) of the total number of sequences per sample. Phylum OP11 was found only in RR and RB precipitates, which may reflect its introduction from compost or other inputs used to reclaim the barrens. Bacteroidetes and Verrucomicrobia, which also are phyla consistently found in soils (Fierer et al. 2012; Janssen,

2006; Lauber et al., 2009), were only detected as singletons in CB and RR, respectively. Lastly, other three very low frequency phyla-- SC3, Spirochaetes, and GAL15-- were identified only in

CB, RR and CC, respectively.

RR RB CC CB Proteobacteria 34.79 31.45 15.66 23.50 ! ! Acidobacteria 25.88 11.07 23.84 11.94 ! !

Actinobacteria 9.77 13.56 20.65 14.20 ! ! Planctomycetes 2.41 2.12 4.14 8.68 ! ! WPS-2 2.40 1.38 3.40 2.36 ! ! Chloroflexi 1.00 2.17 3.34 1.73 ! !

Firmicutes 0.65 1.20 0.16 0.48 ! ! TM6 0.24 0.35 0.05 0.09 ! ! Nitrospirae 0.14 0.27 0.14 0.03 ! ! 40! Armatimonadetes (OP10) 0.18 0.01 0.31 0.07 35! Cyanobacteria 0.05 0.05 0.02 0.12 ! 30! ZB2 0.04 0.03 0.01 0.01 25! TM7 0.02 0.03 0.02 0.03 20! OP11 0.15 0.04 15! Bacteroidetes 0.00 0.00 0.00 0.01 10!

SC3 0.00 0.00 0.00 0.01 5! %!of!sequences Verrucomicrobia 0.01 0.00 0.00 0.00 1! Spirochaetes 0.01 0.00 0.00 0.00 0.1! GAL15 0.00 0.00 0.01 0.01!

Unclassified 22.25 36.27 28.24 36.75 ! ! ! Figure 3-3. Relative abundances of all phyla in reclaimed (RR and RB) and control (CC and CB) precipitates. Relative abundance (%) of individual taxa within each community was calculated by dividing the number of sequences assigned to a specific taxon by the number of normalized sequences obtained for that sample (14,666).

Finer-scale comparisons based on taxa making up >0.1% of assigned sequences

Of proteobacterial groups, alpha- and gamma- representatives were most abundant in all four precipitate types (6-13% and 3-14%, respectively). Beta- and delta-proteobacteria made up only small proportions (0-1%) of the communities (Fig. 3-4). Rhodospirillales were prominent alpha proteobacteria in all precipitates, ranging from 6-10% of all sequences, while Rhizobiales were present at lesser extent (0.2-4.8%) but more abundant in reclaimed precipitates. The

Rhodospirillales order comprised members of the genera Acidosoma and Gluconobacter, which

68 were recovered from all samples, as well as the genera Acidocella recovered only from RR and

Acidiphilium recovered from RB, CC, and CB. Members of the Rhizobiales were also identified in our samples but at lower abundance (0.2-4.8%), and representatives of the Rhodoplanes genus were only recovered from reclaimed precipitates. Gammaproteobacteria were dominated by members of the Xanthomonadales (3.0-12.4%), with representatives of the Xanthomonadaceae and Sinobacteraceae families recovered mostly from precipitates supporting vegetation. Members of the Chromatiales order were present in our samples as well (2.0-6.1%), with members recovered mostly from RR and RB (Fig. 3-4).

The phylum Actinobacteria was dominated by members of the Actinomycetales order (4.0-

17%). Among members of this group, Mycobacterium was the genus most frequently observed

(1.0-3.0%), particularly in control samples. Members of two other orders were also recovered from our samples but at very low incidence. These were Solirubrobacterales (0.3-0.5%) found in all samples and Acidimicrobiales (0.1 and 0.2%) which were only present in carbon-enriched, adherent precipitates (RR and CC) (Figure 4). Members of the Acidimicrobiales, Acidimicrobium ferrooxidans and Ferrimicrobium acidophilum (Bond et al., 2000b; Johnson and Bridge, 2002) were not detected in any of the precipitates.

The phylum Acidobacteria was dominated by members of the Acidobacteriia taxon (8.4-

22.3%), with the Acidobacteriaceae group exclusively constituting this taxon and at greater abundance in adherent precipitates (RR and CC) than in underlying precipitates.

Acidobacteriaceae representatives belonging to the Edaphobacter genus, assigned to subdivision

1 of Acidobacteria (Koch et al., 2008), were only present in RR. Members of the Solibacteres taxon, belonging to subdivision 3 (Challacombe et al., 2011), made up a small proportion of

Acidobacterial sequences (1.2-1.4%) and were only recovered from reclaimed samples (Figure 3-

4).

69 Firmicutes were exclusively represented by the Clostridiales order and occurred only in non-adherent precipitates (RB and CB). Firmicutes capable of ferric iron and sulfur reduction such as Sulfobacillus spp. (Bond, et al., 2000a; Johnson and Bridge, 2002), were not detected in our samples. Nitrospira members belonging to Leptospirillum Group II (Leptospirillum ferriphilum) were exclusively present in reclaimed precipitates (0.1-0.2%), while members of

Group III (Leptospirillum ferrodiazotrophica) were only present in CC (0.1%).

! ! ! RR RB CC CB

! 34.79 31.45 15.66 23.50 Proteobacteria ! 13.06 7.33 10.05 5.86 _Alphaproteobacteria ! 4.83 1.66 0.14 0.15 _._Rhizobiales ! 1.85 0.70 0.00 0.00 _._._Hyphomicrobiaceae ! 1.85 0.70 0.00 0.00 _._._._Rhodoplanes ! 1.85 0.70 0.00 0.00 _._._._._Unclassified ! 2.99 0.96 0.14 0.15 _._._Unclassified 8.22 5.67 9.91 5.70 _._Rhodospirillales 8.22 5.67 9.91 5.58 _._._Acetobacteraceae 0.00 0.66 1.02 0.87 _._._._Acidiphilium 0.00 0.00 0.00 0.18 _._._._._A. cryptum 0.60 0.85 0.35 0.52 _._._._Acidisoma 0.56 0.00 0.00 0.00 _._._._Acidocella 0.42 0.57 0.89 0.33 _._._._Gluconacetobacter Leyend 0.42 0.57 0.89 0.33 _._._._._G. hanseni Phylum 6.65 3.59 7.65 3.87 _._._._Unclassified _Class 0.00 0.00 0.00 0.14 _._._Unclassified _._Order 0.41 0.00 0.00 0.00 _Betaproteobacteria _._._Family 0.41 0.00 0.00 0.00 _._Unclassified _._._._Genus 0.53 1.01 0.13 0.27 _Deltaproteobacteria _._._._._Specie 0.00 0.00 0.13 0.00 _._MIZ46 40

0.00 0.00 0.13 0.00 _._._Unclassified 35 0.53 1.01 0.00 0.27 _._Syntrophobacterales 30 0.53 1.01 0.00 0.27 _._._Syntrophobacteraceae 25 13.46 12.93 3.16 11.40 _Gammaproteobacteria 20 6.14 2.39 1.77 1.85 _._Chromatiales 15 of sequencesof 6.14 2.39 1.77 1.85 _._._Unclassified 10

% 0.16 0.00 0.00 0.00 _._Legionellales 5 0.16 0.00 0.00 0.00 _._._Legionellaceae 0 0.16 0.00 0.00 0.00 _._._._Unclassified 12.42 5.18 2.49 2.52 _._Xanthomonadales 6.14 2.39 1.77 1.85 _._._Sinobacteraceae

6.14 2.39 1.77 1.85 _._._._Unclassified 6.27 2.79 0.72 0.67 _._._Xanthomonadaceae 5.06 2.79 0.72 0.67 _._._._Dokdonella 5.06 2.79 0.72 0.67 _._._._._D. koreensi 1.21 0.00 0.00 0.00 _._._._Unclassified 0.00 0.65 0.00 1.08 _._Unclassified 2.47 6.65 0.00 2.70 _Unclassified 25.88 11.07 23.84 11.94 Acidobacteria 19.60 8.37 22.30 10.58 _Acidobacteria 19.60 8.37 22.30 10.58 _._Acidobacteriales 19.60 8.37 22.30 10.58 _._._Acidobacteriaceae 0.12 0.00 0.00 0.00 _._._._Edaphobacter 0.12 0.00 0.00 0.00 _._._._._E. modestus 19.48 8.37 22.30 10.58 _._._._Unclassified

Figure 3-4. Relative abundances of dominant taxas in reclaimed (RR and RB) and control (CC and CB) precipitates. Relative abundance (%) of individual taxa within each community was

calculated by dividing the number of sequences assigned to a specific taxon by the number of normalized sequences obtained for that sample (14,666). Taxa having at least 0.1% of the total number of sequences per sample (≥ 15 sequences) are shown.

Continuation figure 3-4. 1.40 1.24 0.00 0.00 _Solibacteres 1.40 1.24 0.00 0.00 _._Solibacterales 1.40 1.24 0.00 0.00 _._._Solibacteraceae 1.40 1.24 0.00 0.00 _._._._"Solibacter" 1.40 1.24 0.00 0.00 _._._._._Unclassified 2.67 0.00 0.00 0.00 _Unclassified 9.77 13.56 20.65 14.20 Actinobacteria 5.68 9.82 18.64 11.40 _Actinobacteria 0.15 0.00 0.11 0.00 _._Acidimicrobiales 0.15 0.00 0.11 0.00 _._._Unclassified 3.98 6.92 17.15 9.84 _._Actinomycetales 0.12 0.00 0.00 0.00 _._._Actinospicaceae 0.12 0.00 0.00 0.00 _._._._Unclassified 0.63 1.27 5.14 2.65 _._._Mycobacteriaceae 0.63 1.27 5.14 2.65 _._._._Mycobacterium 0.39 0.98 5.14 2.65 _._._._._Unclassified 0.24 0.29 0.00 0.00 _._._._._M. celatum 0.00 0.11 0.00 0.00 _._._Propionibacteriaceae 0.00 0.11 0.00 0.00 _._._._Propionibacterium 0.00 0.11 0.00 0.00 _._._._._P. acne 3.23 5.65 11.90 7.19 _._._Unclassified 0.38 0.37 0.30 0.55 _._Solirubrobacterales 0.00 0.26 0.00 0.55 _._._Patulibacteraceae 0.00 0.26 0.00 0.55 _._._._Unclassified 0.38 0.11 0.30 0.00 _._._Unclassified 1.18 2.53 1.08 1.02 _._Unclassified 2.41 2.12 4.14 8.68 Planctomycetes 1.16 1.04 3.31 7.92 _Planctomycea 1.16 1.04 3.31 7.92 _._Gemmatales 0.78 0.68 3.05 6.89 _._._Gemmataceae 0.78 0.68 3.05 6.89 _._._._Unclassified 0.38 0.36 0.26 1.02 _._._Isosphaeraceae 0.38 0.36 0.26 1.02 _._._._Unclassified 2.40 1.38 3.40 2.36 WPS-2 1.99 1.07 3.31 1.93 _Unclassified 1.00 2.17 3.34 1.73 Chloroflexi 0.39 1.55 1.18 1.20 _Ktedonobacteria 0.39 1.55 1.18 1.20 _._Unclassified 0.12 0.24 1.70 0.20 _Unclassified 0.65 1.20 0.16 0.48 Firmicutes 0.00 0.23 0.00 0.18 _Clostridia 0.00 0.23 0.00 0.18 _._Clostridiales 0.00 0.23 0.00 0.18 _._._Sulfobacillaceae 0.00 0.23 0.00 0.18 _._._._YNPFFP6 0.65 0.98 0.16 0.31 _Unclassified 0.14 0.27 0.14 0.03 Nitrospirae 0.12 0.15 0.11 0.00 _Nitrospira 0.12 0.15 0.11 0.00 _._Nitrospirales 0.12 0.15 0.11 0.00 _._._Leptospirillaceae 0.12 0.15 0.11 0.00 _._._._Leptospirillum 0.12 0.15 0.00 0.00 _._._._._L. ferriphilum 0.00 0.00 0.11 0.00 _._._._._L.ferrodiazotrophic 0.18 0.01 0.31 0.07 Armatimonadetes 0.17 0.00 0.22 0.00 _Armatimonadia 0.17 0.00 0.22 0.00 _._Armatimonadales 0.17 0.00 0.22 0.00 _._._WD294 0.17 0.00 0.22 0.00 _._._._Unclassified 22.25 36.27 28.24 36.75 Unclassified

72 Discussion

Acid mine drainage systems are dominated by acidophilic microorganisms that are well adapted to the multiple environmental stresses that these environments impose (Baker and

Banfield, 2003; Johnson, 2012). In this study, despite the extreme acidic conditions reported for our precipitates, many of the taxa associated with aqueous AMD systems were not detected. The well studied autotrophic iron-oxidizing (and sulfur-oxidizing) bacterium, Acidithiobacillus ferrooxidans, previously classified as γ-proteobacteria but recently assigned to the new proteobacteria class Acidithiobacillia (Williams and Kelly, 2013), was not detected in these precipitates. This is consistent with other bacterial community studies of AMD systems where A. ferrooxidans sequences either were not recovered or comprised a minor fraction of the microbial community (Baker, et al., 2003; Edwards, et al., 2000; Bond, et al., 2000b). Instead, iron- oxidizing bacteria belonging to Leptospirillum species have been more commonly detected in

AMD systems (Baker, et al., 2003; Bond et al, 2000b; Johnson, 2003). Although we observed sequences related to Leptospirillum ferriphilum in reclaimed precipitates and for Leptospirillum ferrodiazotrophum in control precipitates, these were detected at very low frequencies. Among heterotrophs characteristic of AMD environments, the iron reducing bacterium Acidiphilium cryptum was the sole representative identified in our study but at very low incidence. The absence of many taxa known to inhabit water bodies affected by AMD suggests their inability to survive or compete in unsaturated or nutrient-enriched environments. Years of exposure to sunlight, seasonal fluctuations in the water table, plant and biological crust exudates, and mineral aging could have resulted in unfavorable conditions for AMD taxa to persist as major members of bacterial communities in these precipitates.

Even though conditions in acidic precipitates sustaining successional plants and biological crusts did not favor typical AMD taxa, they offered microbial habitats for OTU richness

73 comparable to those found in less acidic soils. In a pyrosequencing study of bacterial diversity in soils under different vegetation and management, forest soils (pH 3.3-6.4) had 2,200 to 4,100

OTUs while grassland soils (pH 5.1-7.2) had 1,700 to 3,800 OTUs at 97% similarity cutoff

(Nacke et al., 2011). (These OTU numbers were calculated from a sampling size similar to that used in our study, which was 14,666 sequences per sample.) In another study of soils with a wide range in pH, it was found that soil pH was the main factor influencing bacterial community composition and abundance. At 97% similarity with a sampling effort of up to ~1,650 sequences per sample, soils with near-neutral pH had 950 bacterial OTUs, while soils with pH of 4.5-5.0 had

580-780 OTUs (Lauber et al,. 2009). In our study, control precipitates had OTUs (859-1059) comparable to the near-neutral soils in the Lauber et al (2009) study, while reclaimed precipitates had considerably higher numbers (1269-1431) but lower than OTUs reported for forest and grassland soils by Nacke et al (2011). Furthermore, our OTU numbers greatly exceeded those reported for systems that closely resemble AMD precipitates such as mine tailings (Hur, et al

2001). In that study, OTU richness at 97% similarity (and 1,300 sequences) in rhizosphere samples of acidic tailings (pH 2.8-4.6) ranged from 218 to 421. We acknowledge that different sequence analysis methods, sampling effort or primer affinities could have accounted for varied findings. However, it is also possible that edaphic conditions in the unsaturated reclaimed and control precipitates provided many microniches for development of more diverse bacterial communities. Further investigations in adjacent forest soils and seasonal exploration of AMD precipitates sustaining vegetation and biological crusts are necessary to elucidate the high diversity identified in the present study.

Bacterial taxonomic composition in newly vegetated acid mine drainage precipitates differed from that of control precipitates covered by biological crusts despite their similar acidic conditions (pH 2.5-2.7). The relative abundance of Proteobacteria representatives was greater in

74 reclaimed precipitates than in control precipitates. High abundance of Proteobacteria has been associated with high availability of soil carbon (Fierer et al., 2007). However, their presence in acidic soils has been shown to decrease relative to their abundances at near-neutral pH (Lauber et al., 2009; Rousk et al 2010). In our study, the relatively greater abundance of Proteobacteria in reclaimed precipitates could have been driven by the type and amount of available soil carbon rather than soil pH. The increases in carbon contents due to compost amendment and root exudates might have provided new conditions for proliferation, especially of the α-proteobacteria order, Rhizobiales. This suggests that bacterial communities in reclaimed precipitates have the potential to resemble communities found in less extreme environments such as near-neutral soils where Rhizobiales are abundant (Lauber et al., 2009). In addition, γ-proteobacteria of the

Chromatiales, Xanthomonadales, and Legionellales orders could be candidate indicators of root colonization of acidic materials as they were more abundant or solely present in reclaimed precipitates.

Acidobacteria were more abundant in RR and CC precipitates having direct association with plant roots and crust rhizoids, suggesting their preference for freshly generated organic carbon. There have been 26 reported Acidobacteria subdivisions based on 16S rRNA gene phylogeny (Barns et al., 2007) of which subdivisions 1, 3, 4, and 6 are most abundant in soils

(Jones et al., 2009). Soil acidobacterial communities have been shown to be predictable across a wide range of soil and ecosystem types using soil pH as a single predictor (Jones et al., 2009).

Members of the subdibision 1 and 3 tend to be more abundant in slightly acidic soils while members of the subdivision 4 and 6 are more abundant in near-neutral conditions (Jones et al.,

2009). Previously reported distributions of Acidobacteria based on soil pH could explain why among classified sequences, only members of subdivision 1 and 3 were detected in our study. In addition, distribution of soil acidobacteria varies according to carbon type and availability

75 (Eichorst et al., 2011). Acidobacteria members of subdivision 1 have been reported to grow from fresh (readily oxidizable) carbon as the sole carbon and energy source while subdivision 3 members have been observed to grow better from more recalcitrant plant polymers (Eichorst et al., 2011). Although these findings are based on cultured bacteria, they are consistent with the distribution of acidobacterial groups in precipitates with different organic matter inputs, since reclaimed precipitates contained both fresh exudates and stable compost while control precipitates contained only fresh deposits from crusts rhizoids.

Actinobacteria have been identified as the most abundant heterotrophs and one of the main components of bacterial diversity in biological crusts of arid lands (Gundlapally and Garcia-

Pichel, 2006). However, they were not detected in temperate soil crusts covering sand dunes with low pH (Smith et al., 2004). In our study Actinobacteria, dominated by the Actinomycetales order, appeared to have a close association with biological crusts under extremely acidic conditions. Therefore, it is possible that novel Actinobacteria taxa inhabit AMD precipitates covered by moss-dominated biological crusts.

Conclusions

Bacterial communities in acid mine drainage precipitates supporting successional plants and biological crusts do not resemble those typically associated with aqueous AMD-impacted systems. Vegetative reclamation of iron-rich precipitates derived from AMD resulted in increased bacterial diversity, especially in upper portions of reclaimed precipitates, which more closely resembled soil environments. Indeed, bacterial diversity reported in our study was comparable to bacterial diversities reported for less extreme soils. Proteobacteria were most abundant in reclaimed precipitates, possibly due to their increased carbon contents. Acidobacteria, in contrast,

76 seemed to respond positively to fresh carbon deposits in root- and rhizoid-associated precipitates.

Lastly, Actinobacteria were most abundant in precipitates associated with moss-dominated biological crusts. We have demonstrated that incipient soils formed from AMD precipitates harbor high bacterial diversity; however, the specific factors promoting such high diversity require further investigation.

Acknowledgements

We thank landowner Alan Larson, who permitted site access; Jason Kaye, for financial support. We acknowledge the Penn State College of Agricultural Sciences for support through de la Torre Graduate Competitive Grant program and the Penn State Center for Environmental geoChemistry and Genomics for support through Bioinformatics Workshop Award.

77 References

Baker, B.J., Banfield, J.F., 2003. Microbial communities in acid mine drainage. Fems Microbiology Ecology 44, 139-152.

Belova, S.E., Pankratov, T.A., Detkova, E.N., Kaparullina, E.N., Dedysh, S.N., 2009. Acidisoma tundrae gen. nov., sp. nov. and Acidisoma sibiricum sp. nov., two acidophilic, psychrotolerant members of the alphaproteobacteria from acidic northern wetlands. International Journal of Systematic and Evolutionary Microbiology 59, 2283-2290.

Bond, P.L., Druschel, G.K., Banfield, J.F., 2000a. Comparison of acid mine drainage microbial communities in physically and geochemically distinct ecosystems. Applied and Environmental Microbiology 66, 4962-4971.

Bond, P.L., Smriga, S.P., Banfield, J.F., 2000b. Phylogeny of microorganisms populating a thick, subaerial, predominantly lithotrophic biofilm at an extreme acid mine drainage site. Applied and Environmental Microbiology 66, 3842-3849.

Borneman, J., Skroch, P.W., O'Sullivan, K.M., Palus, J.A., Rumjanek, N.G., Jansen, J.L., Nienhuis, J., Triplett, E.W., 1996. Molecular microbial diversity of an agricultural soil in Wisconsin. Applied and Environmental Microbiology 62, 1935-1943.

Challacombe, J.F., Eichorst, S.A., Hauser, L., Land, M., Xie, G., Kuske, C.R., 2011. Biological consequences of ancient gene acquisition and duplication in the large genome of candidatus Solibacter usitatus Ellin6076. PLoS ONE 6, e24882.

Chu, H., Fierer, N., Lauber, C.L., Caporaso, J.G., Knight, R., Grogan, P., 2010. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environmental Microbiology 12, 2998-3006.

Davis, T.A., Volesky, B., Mucci, A., 2003. A review of the biochemistry of heavy metal biosorption by brown algae. Water Research 37, 4311-4330.

Denef, V.J., Mueller, R.S., Banfield, J.F., 2010. AMD biofilms: using model communities to study microbial evolution and ecological complexity in nature. Isme Journal 4.

DeSantis, T.Z., Hugenholtz, P., Keller, K., Brodie, E.L., Larsen, N., Piceno, Y.M., Phan, R., Andersen, G.L., 2006. NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Research 34, W394-W399.

Ebringerová, A., Heinze, T., 2000. Xylan and xylan derivatives – biopolymers with valuable properties, 1. Naturally occurring xylans structures, isolation procedures and properties. Macromolecular Rapid Communications 21, 542-556.

78 Edwards, K.J., Bond, P.L., Gihring, T.M., Banfield, J.F., 2000. An archaeal iron-oxidizing extreme acidophile important in acid mine drainage. Science 287, 1796-1799.

Ettema, C.H., Wardle D.A. 2002. Spatial soil ecology. Trends Ecol. Evol. 17:177-183.

Fierer N, Bradford MA, Jackson RB. 2007. Toward an ecological classification of soil bacteria. Ecology 88: 1354–1364.

Fierer, N., Leff, J.W., Adams, B.J., Nielsen, U.N., Bates, S.T., Lauber, C.L., Owens, S., Gilbert, J.A., Wall, D.H., Caporaso, J.G., 2012. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proceedings of the National Academy of Sciences 109, 21390-21395.

Gundlapally, S., Garcia-Pichel, F., 2006. The Community and phylogenetic diversity of biological soil crusts in the Colorado Plateau studied by molecular fingerprinting and Intensive cultivation. Microbial Ecology 52, 345-357.

Huang, L.-N., Tang, F.-Z., Song, Y.-S., Wan, C.-Y., Wang, S.-L., Liu, W.-Q., Shu, W.-S., 2011. Biodiversity, abundance, and activity of nitrogen-fixing bacteria during primary succession on a copper mine tailings. Fems Microbiology Ecology 78, 439-450.

Hugenholtz, P., Goebel, B.M., Pace, N.R., 1998. Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity. Journal of Bacteriology 180, 4765-4774.

Huse, S.M., Welch, D.M., Morrison, H.G., Sogin, M.L., 2010. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environmental Microbiology 12, 1889-1898.

Janssen, P.H., 2006. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA Genes. Applied and Environmental Microbiology 72, 1719-1728.

Johnson, D.B., 2003. Chemical and microbiological characteristics of mineral spoils and drainage waters at abandoned aoal and aetal aines. Water, Air and Soil Pollution: Focus 3, 47-66.

Johnson, D.B., 2012. Geomicrobiology of extremely acidic subsurface environments. Fems Microbiology Ecology 81, 2-12.

Johnson, D.B., Bridge, T.A.M., 2002. Reduction of ferric iron by acidophilic heterotrophic bacteria: evidence for constitutive and inducible enzyme systems in Acidiphilium spp. Journal of Applied Microbiology 92, 315-321.

Jones, R.T., Robeson, M.S., Lauber, C.L., Hamady, M., Knight, R., Fierer, N., 2009. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. Isme Journal.

79 Kimoto, K.-i., Aizawa, T., Urai, M., Bao Ve, N., Suzuki, K.-i., Nakajima, M., Sunairi, M., 2010. Acidocella aluminiidurans sp. nov., an aluminium-tolerant bacterium isolated from Panicum repens grown in a highly acidic swamp in actual acid sulfate soil area of Vietnam. International Journal of Systematic and Evolutionary Microbiology 60, 764-768.

Kishimoto, N., Kosako, Y., Tano, T., 1993. Acidiphilium aminolytica sp. nov.: an acidophilic chemoorganotrophic bacterium isolated from acidic mineral environment. Current Microbiology 27, 131-136.

Kishimoto, N., Kosako, Y., Wakao, N., Tano, T., Hiraishi, A., 1995. Transfer of Acidiphilium facilis and Acidiphilium aminolytica to the genus Acidocella gen. nov., and emendation of the genus Acidiphilium. Systematic and Applied Microbiology 18, 85-91.

Koch, I.H., Gich, F., Dunfield, P.F., Overmann, J., 2008. Edaphobacter modestus gen. nov., sp. nov., and Edaphobacter aggregans sp. nov., acidobacteria isolated from alpine and forest soils. International Journal of Systematic and Evolutionary Microbiology 58, 1114-1122.

Lane, D.J., 1991. 16S/23S rRNA sequencing. Nucleic acid techniques in bacterial systematics.

Lauber, C.L., Hamady, M., Knight, R., Fierer, N., 2009. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Applied and Environmental Microbiology 75, 5111-5120.

Machulla, G., M.A. Bruns, and K.M. Scow. 2005. Microbial properties of mine spoil materials in the intial stages of soil development. Soil Sci. Soc. Am. J. 69:1069-1077.

Mendez, M.O., Neilson, J.W., Maier, R.M., 2008. Characterization of a bacterial community in an abandoned semiarid lead-zinc mine tailing site. Applied and Environmental Microbiology 74, 3899-3907.

Moynahan, O.S., Zabinski, C.A., Gannon, J.E., 2002. Microbial community structure and carbon- Utilization diversity in a mine tailings revegetation study. Restoration Ecology 10, 77-87.

Nacke, H., Thürmer, A., Wollherr, A., Will, C., Hodac, L., Herold, N., Schöning, I., Schrumpf, M., Daniel, R., 2011. Pyrosequencing-based assessment of bacterial community structure along different management types in german forest and grassland soils. PLoS ONE 6, e17000.

Nelson, D.W., and Sommers, L. E., 1996. Total carbon, organic carbon, and organic matter, in: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3. Chemical Methods. Soil Science Society of America and American Society of Agronomy, Madison, WI 53711, USA.

Prasanna, R., Ratha, S., Rojas, C., Bruns, M., 2011. Algal diversity in flowing waters at an acidic mine drainage “barrens” in central Pennsylvania, USA. Folia Microbiologica 56, 491-496.

80 Quince, C., Lanzen, A., Curtis, T.P., Davenport, R.J., Hall, N., Head, I.M., Read, L.F., Sloan, W.T., 2009. Accurate determination of microbial diversity from 454 pyrosequencing data. Nature methods 6, 639-641.

Rhoades, J.D., 1996. Salinity: Electrical conductivity and total dissolved solids., in: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3. Chemical Methods. Soil Science Society of America and American Society of Agronomy, Madison, WI 53711, USA.

Rojas, C., Martínez, C.E., Bruns, M.A., in press. Fe biogeochemistry in reclaimed acid mine drainage precipitates: implications for phytoremediation. Submitted to Environmental Pollution Journal.

Rousk, J., Baath, E., Brookes, P.C., Lauber, C.L., Lozupone, C., Caporaso, J.G., Knight, R., Fierer, N., 2010. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME Journal 4.

SAS, 2011. Data Quality Server Reference. Cary, NC: SAS Institute Inc. SAS Institute Inc, 138. Schippers, A., Jozsa, P.-G., Sand, W., Kovacs, Z.M., Jelea, M., 2000. Microbiological pyrite oxidation in a mine tailings heap and its relevance to the death of vegetation. Geomicrobiology Journal 17, 151-162.

Schloss, P.D., 2009. A high-throughput DNA sequence aligner for microbial ecology studies. PLoS ONE 4, e8230.

Schloss, P.D., Gevers, D., Westcott, S.L., 2011. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS ONE 6, e27310. Pipeline and taxonomy database retrieved from http://www.mothur.org on April, 22012.

Schloss, P.D., Handelsman, J., 2006. Toward a census of bacteria in soil. PLoS Comput Biol 2, e92.

Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537-7541.

Smith, SM, Abed, RMM, Garcia-Pichel, F. 2004. Biological soil crusts of sand dunes in Cape Cod National Seashore, Massachusetts, USA. Microbial Ecol 28: 200–208.

81 Thomas, G.W., 1996. Soil pH and soil acidity, in: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3. Chemical Analysis. Soil Science Society of America and American Society of Agronomy, Madison, WI 53711, USA.

Wichlacz, P.L., Unz, R.F., Langworthy, T.A., 1986. Acidiphilium angustum sp. nov., Acidiphilium facilis sp. nov., and Acidiphilium rubrum sp. nov.: acidophilic heterotrophic bacteria isolated from acidic coal mine drainage. International Journal of Systematic Bacteriology 36, 197-201.

Williams, K.P., Kelly, D.P., 2013. Proposal for a new class within the proteobacteria, the acidithiobacillia, with the acidithiobacillales as the type order. International Journal of Systematic and Evolutionary Microbiology.

Chapter 4

Microeukaryote diversity in rhizospheres of vascular plants and moss- dominated biological crusts at an acid mine drainage barrens undergoing reclamation

Eukaryotic communities in acid mine drainage (AMD)-impacted environments have been investigated mainly in aqueous systems such as subterranean mine waters, mining-polluted rivers, and submerged sediments. Mining-impacted systems that are more terrestrial in character include

AMD barrens, where vegetation has been killed by overland flow of mine discharge and regrowth prevented by the accumulation of acidic (pH 2-3) iron oxy(hydr)oxide precipitates on soil surfaces. Our previous research at a 50-year-old AMD barrens in Central Pennsylvania, U.S.A., showed that such precipitates can be revegetated in place using a one-time incorporation of compost and lime (to pH 4.5). The objective of the present study was to evaluate microeukaryotic taxa in root-associated and underlying precipitates six years following the reclamation treatment and to compare these with taxa in non-reclaimed precipitates supporting moss-dominated biological crusts. Eukaryotic diversity was assessed using 18S rRNA gene libraries from 454 pyrosequencing of the V4-V5 region from four types of precipitates: reclaimed root-adhering

(RR); reclaimed below-roots (RB); control crust-adhering (CC); and control below-crust (CB) were constructed using 454 pyrosequencing of the V4-V5 region of 18S rRNA genes. At the time of sampling, all four precipitate types had a similar pH (2.5-2.7) because reclaimed precipitates had gradually become more acidic following the one-time lime application in 2006. After quality screening and normalization to obtain 16,118 pyrosequences for each precipitate type, 494 operational taxonomic units OTUs were identified at the 95% similarity level. Of these, about

62% (307) were found exclusively in reclaimed precipitates (RR, RB or both), 20% (98 OTUs)

83 were unique to control precipitates (CC, CB, or both) and 7% (34 OTUs) were shared among the four precipitate types. Since libraries from control precipitates were dominated by bryophyte sequences, these and other macroeukaryotic sequences were removed before calculating percentages of microeukaryotic taxa in each precipitate. The main microeukaryotic taxa identified in reclaimed precipitates were Basidiomycota (48% and 39% in RR and RB, respectively). In contrast, Ascomycota were more abundant in control precipitates (50% and 18% in CC and CB, respectively), reflecting a shift in fungal community composition following reclamation. The occurrence of other fungal taxa also differed, with only reclaimed precipitates containing

Glomeromycota and the LKM15 group. Differences in percentages of green algae, protists, and metazoans also were observed among precipitate types. Many taxa reported to be abundant in subterranean/submerged AMD habitats were either very low in abundance or not detected.

Findings from this study expand our understanding of acid-tolerant microeukaryotes in terrestrial environments and help identify taxa that reflect development of edaphic habitats indicative of reclamation success and restoration of soil ecosystem functions.

Introduction

Microbial communities in acid mine drainage (AMD)-impacted environments are dominated by prokaryotes but also harbor diverse microscopic eukaryotes (Amaral-Zettler et al.,

2011; Tyson et al., 2004; Johnson, 2012). Fungi, red algae, and protists have been identified by nucleic acid-based and cultural methods in highly acidic subterranean mine waters of the

Richmond Mine at Iron Mountain, CA. (Baker et al., 2004; Baker et al., 2009). Eukaryotes also comprise important members of microbial communities in the Rio Tinto mining region in southwestern Spain, where open-air habitats support phototrophy and therefore greater eukaryotic diversity than subsurface mine waters (Amaral-Zettler et al., 2011; Baker et al., 2009). These eukaryotes are thought to play important roles in carbon cycling and regulation of microbial populations by predation and grazing (Baker et al., 2004).

To date, eukaryotic diversity in AMD-impacted environments has been described for subterranean mine waters, mining-polluted rivers, and submerged sediments, all of which represent aqueous rather than terrestrial habitats. Edaphic habitats created by AMD include barrens or “kill zones” resulting from prolonged overland flow of acidic mine discharge or mine runoff (Lupton et al., 2008; Brown et al., 2011). When vegetation in AMD flowpaths is killed, regrowth is prevented by the accumulation of acidic (pH 2-3) iron oxy(hydr) oxide precipitates on soil surfaces (Rojas et al., in press). In previous reports, we demonstrated that precipitates (pH

2.7-3.3) at a 50-year-old AMD barrens in Central Pennsylvania, U.S.A., could be revegetated with a one-time application of lime and compost (Lupton, 2008; Lupton et al., 2013). Through reclamation, these precipitates were converted into incipient soils supporting diverse successional vegetation, in contrast to non-reclaimed precipitates that were covered by moss-dominated

85 biological crusts.

In the present study, our objective was to compare eukaryotic communities in reclaimed and non-reclaimed precipitates six years following the reclamation treatment. We assessed eukaryotic diversity in precipitates at two depths reflecting organic carbon enrichment. Upper depths of reclaimed and control samples corresponded to precipitates directly associated with plant roots and moss rhizoids, respectively. Lower depths corresponded to precipitates immediately beneath the root- or crust-enriched layers, which had more organic carbon that underlying layers. At the time of sampling, all four precipitate types had similar pH (2.5-2.7) because reclaimed precipitates had gradually become more acidic following the one-time lime application in 2006.

We expected that eukaryotic taxa in the AMD barrens would differ from taxa reported for aqueous and submerged AMD systems, because edaphic niches in unsaturated precipitates would favor attached rather than planktonic microbial lifestyles. We also hypothesized that eukaryotes at both depths in reclaimed precipitates would exhibit greater diversity than those in control areas.

In addition, we anticipated that eukaryotes in reclaimed precipitates would reflect a transition to soil-like environments where mycorrhizal fungi associated with vascular plant roots would be more abundant than saprophytic fungi which derive their nutrients from decaying organic matter.

To test our hypotheses, we performed 454 barcoded pyrosequencing-based analysis of 18S rRNA genes to identify abundant and rare taxa. The findings of this study should expand understanding of eukaryotes in acidic environments and help identify eukaryotic signatures of edaphic conditions.

86 Materials and Methods

Site description

Our study was conducted at a 50-year-old AMD barrens located approximately 5 km north of

Kylertown, PA, in Clearfield County, 41◦01’22.00’N; 78◦ 09’ 08.064’ W (Lupton et al., 2013;

Rojas et al., in press.). In 2006, experimental plots were established at the barrens in three zones distinguished by thickness and color of precipitated surface layers and moisture content as influenced by depth to fragipan layers in underlying native soils. Acidic precipitates in experimental plots were amended in place by a one-time incorporation of lime and compost (top

15 cm) and a first-year oats nurse crop to improve growth of a sown reclamation seed mixture

(Lupton et al., 2013). Plant composition in the first, second, and fourth growing season consisted mainly of oats, sown species, and indigenous species, respectively. In all three zones, plots that received compost had >70% vegetative cover at the end of the fourth growing season.

The present study was undertaken to evaluate eukaryotic communities in reclaimed and non- reclaimed (control) precipitates in the area where subsurface AMD flow was most shallow (9-14 cm below the surface) and where surfaces were covered by mossy biological crusts (Prasanna et al., 2011; Rojas et al., in press). In 2010-2011 the reclaimed precipitates in this zone had > 90% plant coverage and supported a mixed plant community consisting mainly of birch, Betula populifolia (about 60% cover), the shrubs Spiraea and Crataegus spp., and minor coverage by red clover, birdsfoot trefoil, and orchard grass. After five growing seasons, plant roots were localized in the upper 5 cm of the reclaimed precipitates enhancing microbial densities and organic carbon contents in these materials. The reclaimed precipitates had 100-fold higher culturable heterotroph densities than control precipitates (Rojas et al., in press).

87 Sample collection

A complete description of sampling procedure and chemical characterization of precipitates is reported elsewhere (Rojas et al., in prep.). In brief, samples were collected on July 2011 from two

0.25-m2 sections, one from a plot of reclaimed precipitates and another from an adjacent control area covered by biological crusts, at two different depths. In the field, upper and lower layers were separated from the two excised sections. Reclaimed precipitates from the topmost 5-cm layer adhering to plant roots and the underlying 3-cm layer were labeled as RR and RB for

“reclaimed root-adherent” and “reclaimed below-root-adherent” precipitates, respectively.

Control precipitates from the topmost 2-cm layer of precipitates adhering to the biological crust

(rhizoid-associated) and the underlying 6-cm layer were labeled as CC and CB for “control crust- adherent” and “control below-crust-adherent” precipitates, respectively. Samples were placed on ice in a sterile container for transport to the lab, where plants, roots, and large organic debris were removed by hand. Each of the four samples were mixed and split to obtain three replicate subsamples (approximately 250 to 350 g fresh weight) per subsample. The precipitates had similar acidic conditions with CC, CB, and RB having pH of 2.5 and RR having pH of 2.7.

Reclaimed precipitates had carbon contents of 2.7% in RR and 2.4% in RB while control precipitates had lower contents of 1.8% in CC and 0.7% in CB (Rojas et al., in prep).

Soil DNA extraction and pyrosequencing

In this study, microbial community DNA extracts amplified in the previous bacterial community study (Rojas et al., in prep) were amplified with eukaryotic primers. DNA had been extracted using the MoBio PowerLyzerTM Power Soil® DNA isolation kit (MoBio Laboratories,

Inc., Carlsbad, CA) following the manufacturer’s instructions except that use of Solution 3

88 (inhibitor removal) was omitted to improve yield. DNA was obtained from each layer (RR, RB,

CA, CB) in triplicate subsamples (0.35 g fresh weight). The DNA quality was determined using a

NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific Inc.). DNA from each of the three aliquot were pooled together and stored at -80 °C until the pyrosequencing analysis.

Community DNA was subjected to bar-coded amplicon library preparation (i.e. targeted sequences of DNA) and pyrosequencing using the 454 Genome Sequencer FLX Titanium System

(Roche Diagnostics, Indianapolis, IN, USA) at The Pennsylvania State University Genomics Core

Facility. An amplicon library was prepared for each sample by polymerase chain reaction (PCR) using eukaryotic primers (518F-1193R) complementary to V4 and V5 regions of the 18S rRNA

(Baker et al., 2004). The adapted forward primer

(5’-GAGGRCMAGTCTGGTGC-3’) and the reverse primer (5’-GGGCATMACDGACCTGTT-

3’) each contained a 454 Roche-adaptor and a 4-bp key sequence. In addition, a 10-bp nucleotide sequence barcode was included upstream to identify the sample from which the PCR amplicons were derived. PCR reactions contained 1 µL (5 µM) of each forward and reverse primer, 0.5 µL of dNTP mix (10 mM each), 0.25 µL of 5 U FastStart HiFi polymerase, 2.5 µL FastStart buffer

(Roche Laboratories), and 1 µL of DNA extract per 25 µL reaction volume. Reaction conditions consisted of an initial denaturation step at 94° C for 3 minutes followed by 35 cycles, each consisting of denaturation at 94° C for 15 seconds, primer annealing at 55° C for 45 seconds, and extension at 72° C for 1 minute with a final extension at 72° C for 8 minutes. After PCR amplification, products were cleaned using 70% ethanol and AMPure magnetic beads as directed by the manufacturer (Roche Laboratories) and selected for sizes of at least 500 bp amplicon band in 1.1% agarose gel. DNA was extracted from size-selected bands using Agarose Gel Extraction

Kit (Roche Laboratories) and checked with a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) to confirm concentration and purity.

89 Processing of pyrosequence data

Sequences were processed and analyzed with the mothur software platform, version v.1.24.1 (Schloss et al., 2009). Denoising was performed with the mothur implementation of the

PyroNoise algorithm (Quince et al., 2009). Sequences were trimmed, assigned by barcode, and quality filtered to remove reads that had more than one mismatch to barcodes, more than two mismatches to primers, and homopolymers longer than 8 bp. After removal of barcode and primer sequences, only those fragments longer than 200 bp and showing no ambiguous characters were included in further analyses (Schloss et al., 2011). Filtered sequences were aligned using the

NAST-based algorithm with the Eukarya alignment database implemented in mothur (DeSantis et al., 2006; Schloss, 2009). Aligned sequences were trimmed to ensure that they started and ended at the same alignment position. Sequences that did not align to the expected region of the 18S rRNA gene were removed. A pre-clustering algorithm was also used to allow up to a 2-bp difference between sequences (Huse et al., 2010; Schloss et al., 2011). Additionally, sequences identified as potential chimeras using the database-independent implementation of Uchime were removed from the dataset (Quince et al., 2009; Schloss et al., 2011). Curated sequences were pre- classified using the mothur version of the Bayesian classifier and the mothur-compatible Silva taxonomy database (Schloss and Westcott, 2007) to identify unknown sequences. Sequences that could not be classified at the domain level were removed from the dataset. In order to build operational taxonomic units (OTUs) defined at 95% sequence similarity level, pairwise distance matrices were generated to cluster sequences into OTUs by the furthest neighbor algorithm. For the OTU analysis, we standardized the number of sequences per sample to the smallest number

(16,118) by randomly sub-sampling each of the four sample datasets. OTUs were assigned to taxa using the Silva-ARB online database (Ref 111) at an 80% confidence threshold (Pruesse et al.,

2007). The relative abundances (%) of microeukaryotic taxa within each community were

90 determined after subtracting macro-eukaryote sequences (primarily bryophytes) which were particularly abundant in CC and CB. Percent abundances were calculated by dividing the number of sequences assigned to a specific taxon by the number of micro-eukaryotic sequences identified in each sample. The relative distribution of taxa having at least 0.1% of the micro-eukaryote sequences recovered from each sample are shown in Fig. 4-2. For complete taxon distribution, see Appendix I. Venn diagrams were generated by mothur (v.1.24.1) to illustrate the distribution of all OTUs among the four precipitate samples, as well as of OTUs which could not be assigned to a phylum below the eukaryote domain (Fig. 4-1). Representative sequences for each of the unclassified eukaryotic OTUs were subjected to a BLAST search to sequences in GenBank to identify their closest relatives and the environmental occurrence of similar members. A summary of these assays are given in the following tables: Table 4-2 indicates the 20 most abundant OTUs occurring in one or both of the reclaimed precipitates; Table 4-3 the 10 most abundant OTUs occurring in one or both of the control precipitates; and Table 4-4 the shared OTUs among the four samples studied.

Results

A total of 85,781 screened pyrosequences (mean length 256), ranging from 16,118 to

28,297 sequences per sample, were obtained from all four precipitate types. This sequencing effort resulted in rarefaction curves approaching asymptotes and provided evidence for greater eukaryotic richness in reclaimed than in control precipitates (Appendix J). The numbers of sequences per sample were normalized to 16,118 to generate a subset of 64,472 sequences and a total of 494 OTUs grouped at 95% similarity across all samples (Table 4-1). The majority of these OTUs were detected exclusively in reclaimed precipitates (127 for RR, 118 for RB, and 62 shared between them). Fewer OTUs were unique to control precipitates (39 for CC, 36 for CB,

91 and 23 shared between them) (Fig. 4-1a). The remaining OTUs were either found in all four precipitate types (34) or shared by at least one reclaimed and one control precipitate (55) (Fig.

1a). Although reclaimed precipitates had more OTUs than control precipitates, they comprised lower percentages of sequences assignable to the phylum level (83.7% for RB and 62.0% for RR) than control precipitates (93.5% for CC and 86% for CB).

Table 4-1. Distribution of observed OTUs (95% similarity) and sequences for individual taxa identified in reclaimed (RR and RB) and control (CC and CB) precipitates. OTUs|Sequences Silva (Ref 111) Assignment RR RB CC CB 267|16118 246|16118 117|16118 136|16118 Total Eukaryotes

98|6139 992633 34|1055 41|2237 Unclassified 9|329 5|322 8|12925 9|7512 Total Macro-eukaryotes 1|108 1|262 6|12875 3|7232 Chloroplastida|Charophyta|Phragmoplastohyta|Streptophyta|Embryophyta|Bryophyta 1|10 0|0 0|0 2|6 Chloroplastida|Charophyta|Phragmoplastohyta|Streptophyta|Embryophyta| 2|55 0|0 1|49 1|250 Opisthokonta|Metazoa|Arthropoda|Chelicerata|Arachnida 4|42 1|4 1|1 1|1 Opisthokonta|Metazoa|Arthropoda|Hexapoda|Collembola eukaryotes - 0|0 0|0 0|0 1|4 Opisthokonta|Metazoa|Arthropoda|Hexapoda|Insecta 1|114 2|52 0|0 0|0 Opisthokonta|Metazoa|Gastrotricha|Gastrotricha|Chaetonotidae

Macro 0|0 0|0 0|0 1|19 Opisthokonta|Metazoa|Platyhelminthes|Tubellaria|Catenulida 0|0 1|4 0|0 0|0 Opisthokonta|Metazoa|Craniata|Mammalia 160|9650 142|13163 75|2137 86|6369 Total Micro-eukaryotes 1|1 1|12 1|46 2|500 Chloroplastida|Charophyta|Phragmoplastohyta|Zygnematales 0|0 0|0 1|1 0|0 Chloroplastida|Chlorophyta|Chlorophyceae|Chlamydomonas 5|208 6|165 2|15 3|89 Chloroplastida|Chlorophyta|Trebouxiophyceae 1|5 2|46 1|31 1|1 SAR 10|76 6|24 8|236 7|279 SAR|Stramenopiles

9|258 16|501 9|102 11|1608 SAR|Alveolata 23|101 12|144 10|68 11|125 SAR|Rhizaria 0|0 1|1 0|0 0|0 Amoebozoa|Lobosa|Tubulinea|Arcellinida|Arcellina|Arcella 1|2 1|1 3|3 1|2 Amb-18S-6341 eukaryotes

- 1|2 1|1 0|0 0|0 Opisthokonta 1|4 0|0 0|0 0|0 Opisthokonta|Metazoa 6|203 4|306 3|41 2|22 Opisthokonta|Metazoa|Nematoda|Chromadorea|Aphelenchida Micro 2|452 1|120 0|0 0|0 Opisthokonta|Metazoa|Porifera|Porifera 4|274! 9|1500! 3|138! 4|803! Opisthokonta|Metazoa|Porifera|Rotifera! 4|39 2|15 3|14 4|80 Opisthokonta|RT5iin14 16|1012 12|1004 12|320 11|766 Opisthokonta|Fungi|Basal Fungi 21|1360 17|2557 11|1080 9|1146 Opisthokonta|Fungi|Ascomycota 17|4636 195100 4|7 12|613 Opisthokonta|Fungi|Basidiomycota 371007 30|1599 3|33 6|331 Opisthokonta|Fungi|Others

When sequences were assigned to either macro-and micro-eukaryote phyla (Table 4-1), the majority of macroeukaryote sequences in control precipitates were classified as Bryophyta

(80% in CC and 45% in CB), reflecting these precipitates’ physical association with biological crusts (Appendix K). Fewer sequences were classified as Arthropoda (Arachnida, Collembola and

Insecta); Gastrotricha; Turbellaria; and Mammalia (Table 4-1). When only microeukaryote sequences were considered (Figure 4-2), the most abundant taxa in RR and RB precipitates were

Basidiomycota, while Ascomycota and Alveolata were most abundant in CC and CB precipitates, respectively. The LKM11 group of fungi were found in all precipitate types, while the LKM15 group was found only in reclaimed precipitates. Rotifera sequences were found in all precipitate groups but were more abundant in RB and CB (11.4% and 12.6%, respectively) than in RR and

CC (2.8% and 6.4% respectively). Porifera sequences occurred in reclaimed but not control precipitates (Fig. 4-2).

A RR RB 127 118 (26%) (24%) 62 7 7 (13%) (1.4%) (1.4%) CC CB 39 36 (8%) 4 17 (7.3%) (0.8%) (3.4%)

34 1 (7%) 10 (0.2%) (2.5%) 3 6 (0.6%) (1.2%)

23 (5%)

B RR RB 49 56

24 2 4 CC CB 13 16 2 6

9 0 2 0 2

Reclaimed precipitates were dominated by Basidiomycota, which comprised 48% and

39% of microeukaryotic sequences in RR and RB precipitates, compared to 0.3% and 9.7% in CC and CB, respectively (Fig. 4-1). For all precipitate types, the great majority of Basidiomycota sequences (95% to 100%) belonged to Agaricomycetes (Appendix I). This class also was identified in acidic biofilm-associated sediments collected at the Davis Mine in Rowe, MA

(Amaral-Zettler, 2013). Occurrence of other basidiomycete taxa was variable and generally low

(< 0.1% of microeukaryote sequences in each sample) and included members of subphylum

Pucciniomycotina (classes Agaricostilbomycetes, Cystobasidiomycetes, and

Microbotryomycetes), subphylum Ustilagomycotina (Exobasidiomycetes), and Wallemiomycetes

(phylum incertae sedis). Sequences classified as Exobasidiomycetes (Malassezia sp.), were slightly more abundant in reclaimed (0.5% and 1.1% in RR and RB) than in control precipitates

(0% and 0.2% in CC and CB) (Appendix J).

RR RB CC CB 0.01 0.09 2.15 7.85 Archaeplastida |Chloroplastida|Charophyta|Phragmoplastophyta|Zygnematales 2.16 1.25 0.70 1.40 Archaeplastida|Chloroplastida|Chlorophyta|Trebouxiophyceae 0.79 0.18 11.04 4.38 SAR|Stramenopiles 2.67 3.81 4.77 25.25 SAR|Alveolata 1.05 1.09 3.18 1.96 SAR|Rhizaria 2.10 2.32 1.92 0.35 Opisthokonta|Metazoa Nematoda 4.68 0.91 0.00 0.00 Opisthokonta|Metazoa Porifera 2.84 11.40 6.45 12.61 Opisthokonta|Metazoa Rotifera 0.40 0.11 0.65 1.26 Opisthokonta|RT5iin14 14.09 19.43 50.51 17.99 Opisthokonta|Fungi Ascomycota 48.04 38.74 0.33 9.62 Opisthokonta|Fungi Basidiomycota 4.68 0.77 2.01 0.64 Opisthokonta|Fungi Basal fungi Chytridiomycota 3.63 1.89 Opisthokonta|Fungi Basal fungi Glomeromycota 2.18 4.97 13.00 11.38 Opisthokonta|Fungi Basal fungi Mucoromycotina 5.83 9.12 1.17 5.10 Opisthokonta|Fungi LKM11 2.33 1.40 Opisthokonta|Fungi LKM15 ! ! 1 Figure! 4-2. Relative distribution (%) of microbial eukaryote taxa across all samples. Dark green color! indicates higher percentage while light green color indicates lower percentage, actual values ! are superimposed over colors. Taxa having at least 0.1% of the micro-eukaryote sequences per sample are shown

In contrast, Ascomycota was the dominant fungal phylum in control precipitates, which comprised 51% and 18% of all microeukaryotic sequences CC and CB, compared to 14% and

19% in RR and RB, respectively (Fig. 4-1). Four of seven Ascomycota classes detected in our precipitates also were detected in acidic mine waters (Amaral-Zettler, 2013; Baker et al., 2009), namely Dothideomycetes, Leotiomycetes, Eurotiomycetes, and Sordariomycetes (Appendix J).

The former two classes were identified only in RR whereas the latter two were present in all four precipitate types. The other three Ascomycota classes identified in our study were

Leconoromycetes, Saccharomycetes, and Pezizomycetes (Appendix J). The first two were found only in reclaimed precipitates and the latter recovered from all but RB.

Basal fungal phyla including Chrytridiomycota, Glomeromycota, and Mucoromycota were less abundant than dikaryotic fungal taxa, and their occurrence varied among samples (Fig.

4-2). Chrytridiomycota were identified in all samples but were more frequent in RR (4.7%) and

1 The relative abundances (%) of microeukaryotic taxa within each community were determined after subtracting macro-eukaryote sequences which were particularly abundant in CC and CB. Percent abundances were calculated by dividing the number of sequences assigned to a specific taxon by the number of micro-eukaryotic sequences identified in each sample.

96 CC (2.0%), compared to RB (0.8%) and CB (0.6%). Mucoromycota were also found in all samples but were more common in CC (13.0%) and CB (11.4%), compared to RR (2.2%) and RB

(5.0%). The Glomeromycota, in contrast, were only found in RR (3.6%) and RB (1.9%). An additional fungal group, LKM15, was recovered only from reclaimed precipitates (2.3% and

5.0% for RR and RB, respectively), while group LKM11 was detected in all precipitates, ranging from 1.2 to 9.1% abundance (Fig. 4-2.).

The SAR lineage, whose name is derived from the acronym Stramenopiles, Alveolata, and Rhizaria groups (Adl et al., 2012), was present in all samples but was clearly more abundant in control precipitates. The Stramenopiles were mainly found in CC (11%) and CB (4.0%) while reclaimed precipitates had less that 1% of the microeukaryotic sequences assigned to this group.

Alveolata consisted mainly of representatives assigned to the Haptoria group and were mostly found in CB (25%). See Appendix H for complete Silva taxonomy assignment. Rhizaria representatives were higher in CC (3.2%) followed by CB (2.0%) whereas reclaimed precipitates had 1% of microeukaryotic sequences assigned to this group (Fig. 4-2).

The occurrence of metazoa members differed among precipitate types. Nematoda sequences were present in all samples but were slightly more abundant in RR (2.1%) and RB

(2.3%) than in CC (1.9%) and CB (0.4%). Rotifera were less abundant in RR (2.8%) than in the three other precipitate types (6.5-12.6%). In contrast, Porifera were found only in reclaimed precipitates. Green algae represented by Zygnematales and Trebouxiophyceae were present in all samples, but Zygnematales were more common in control precipitates, especially in CB (7.8%), than in reclaimed precipitates (0.01-0.1%). On the other hand, abundances of Trebouxiophyceae sequences varyied less across the four precipitate types (0.7-2.2%) (Fig. 4-2). Lastly, sequences related to RT5iin14, a relatively new eukaryotic lineage branching at the base of the animal- fungal-nucleariid radiation and first identified in the Rio Tinto system (Amaral-Zettler et al.,

97 2002; Amaral-Zettler et al., 2003), was also recovered from all precipitates, with the highest occurrence in CB (1.3%).

Of the 494 total OTUs observed across samples, 189 could not be classified below the eukaryotic domain (Table 4-1 and Fig 4-1b). The majority of unclassified OTUs, (129, or 68%), were found only in reclaimed precipitates (Fig. 4-1b). This suggests that the environmental characteristics of these precipitates provided niches for diverse uncharacterized eukaryotes. Of these OTUs, 24 were shared between RR and RB, while 49 others were found exclusively in RR and 56 in RB. Fewer unclassified OTUs (33, or 17%) were identified only in control precipitates.

About half of these were detected only in CC (13) or CB (16), with a small number (4) shared between these two precipitate types. The rest of the unclassified OTUs (27, or 15%) were shared by at least one reclaimed and one control precipitate, which suggests the capacity of these taxa to adapt to the acidic conditions of precipitates regardless of vegetative cover. To further understand the taxonomy and occurrence of these unclassified OTUs in other systems, we used Blast to identify closest relatives. OTUs exclusively found in one or both reclaimed precipitates had relatives (≥ 77% similarity) identified in a wide variety of engineered systems such as water treatment plants and natural environments including marine and fresh water ecosystems, arid soils, and rhizosphere habitats (Table 4-2). These OTUs were related to uncultured eukaryotes as well as to the Fungi, Alveolata, and Metazoa lineages (Table 4-2). For example, the most abundant OTU (013) identified in RR and RB was related (≥ 77% similarity) to an uncultured eukaryote detected in chlorinated water of a Parisian treatment plant (Poitelon et al., 2009).

Likewise, the second most abundant OTU (023) in RR and RB was related (≥ 81% similarity) to an uncultured eukaryote found in barren fumarole soils surrounding a high altitude volcano near the Chilean-Argentine border in the Puna de Atacama (Andean plateau) ecoregion (Costello et al.,

2009). Among other abundant OTUs in reclaimed precipitates, one (071) was found to have ≥

98 84% similarity to sequences assigned to the Alveolata linage recovered from a study of rhizosphere microbial responses to elevated concentrations of atmospheric CO2 (Table 4-2)

(Lesaulnier et al., 2008).

013 632|443 FJ577832 Chlorinated finished drinking water 97 77 Uncultured eukaryote

023 562|363 FJ592330 Cold-fumarole soil at the Puna de Atacama 100 81 Uncultured eukaryote

020 401|168 JN207883 High Arctic microbial mat 93 95 Uncultured eukaryote Naturally acidic (pH 3.9) mountain stream 008 143|316 AY689723* 81 96 Uncultured eukaryote sediment 059 32|22 FJ810604 Coal tar waste-contaminated groundwater 95 84 Uncultured eukaryote

080 8|16 AB275085 Sediment at a deep-sea methane cold seep 28 99 Uncultured eukaryote

056 34|6 AY082969 Rio Tinto 96 77 Uncultured eukaryote

090 0|12 JX457420 Temperate marine lagoon, Magdalen Islands 36 84 Uncultured eukaryote

OTUs exclusively found in one or both control precipitates had low similarities to sequences in GenBank. Only four of the top 10 OTUs found in these precipitates had at least 77% similarity to sequences submitted from other environments (Table 4-3), two of which (490 and

458) are related to novel eukaryotic lineages identified in the Rio Tinto system (Amaral-Zettler et al., 2002; Amaral-Zettler et al., 2003). OTU 490 was present only in CB and had high similarity

(99%) to the clone RT5iin44, which is a relative of the filose amoeba Filamoeba nolandi. In contrast, OTU 458 was identified only in CC and was related (82% similarity) to the clone

RT5iin3, assigned to the fungi (Amaral-Zettler et al., 2002; Amaral-Zettler et al., 2003). The two other OTUs with BLAST hits were related to sequences recovered from groundwater systems

(Table 4-3). The OTU 435, recovered from both CC and CB, was assigned to an uncultured eukaryote from a limestone aquifer, and the OTU 461, only found in CC, was assigned to an uncultured cryptomycota from a groundwater aquifer in Iowa (Table 4-3).

Table 4-3. Summary of Taxonomy (GenBank based) for unclassified OTUs by the Silva database and present in at least one of the control precipitates. Top 10 most abundant OTUs are shown.* Unpublished data retrieved from http://blast.ncbi.nlm.nih.gov/Blast.cgi on May 2013. GeneBank metadata OTU Num. Sequences Acc. Isolation source Query Max. Taxonomy RR|RB Number cover Identity (%) (%) 432 104|58 ------435 25|2 KC306511* Groundwater from limestone aquifer 99 79 Uncultured eukaryote 440 17|5 ------446 10|15 ------490 0|13 AY082989 Rio Tinto 100 99 Uncultured eukaryote clone RT5iin44 494 0|6 ------452 4|0 ------491 0|3 ------458 2|0 AY082996 Rio Tinto 98 82 Uncultured eukaryote clone RT5iin3 461 2|0 JN612980* Groundwater aquifer 100 82 Uncultured Cryptomycota Table 4-4. Taxonomy (GenBank based) for unclassified OTUs by the Silva database and shared among the four precipitate samples.* Unpublished data retrieved from http://blast.ncbi.nlm.nih.gov/Blast.cgi on May 2013. GeneBank metadata OTU Num. Sequences Acc. Isolation source Query Max. Taxonomy RR|RB|CC|CB Number cover Identity (%) (%) 032 1563|79|671|1506 EF586117 Opanuku stream biofilm 100 79 Uncultured eukaryote 028 1293|137|107|292 AB695462 Aquatic moss pillars 100 78 Uncultured eukaryote 051 609|32|44|68 GQ995267 Unvegetated soils at high-elevation sites 100 80 Fungi|Chytridiomycota|Uncultured Chytridiomycota 137 90|3|15|40 KC708426 Horwort thallus 100 79 Fungi|Fungi incertae sedis|Early diverging fungal linages| Mucoromycotuna|Unclassified Mucoromycotina 127 11|3|82|22 JN825685 Microbialites from Alchichica alkaline lake 100 80 Choanoflagellida|Uncultured choanoflagellate maintained in aquarium (glass wall biofilm) 079 31|25|4|53 KC631758 Drinking water 90 85 Uncultured eukaryote 089 1|10|1|18 ------124 2|5|5|15 ------192 1|1|1|4 AF084231* --- 100 80 Choanoflagellida|Codonosigidae|Desmarella

Nine OTUs that were not assignable to phyla were shared by all four precipitate types and two of these had less than 77% similarity to sequences in GenBank (Table 4-4). The most abundant of these OTUs (032) was related (≥ 79% similarity) to an uncultured eukaryote sequence recovered from stream biofilms in Auckland, New Zealand (Dopheide et al., 2008). Two other OTUs had ≥

79% similarity to eukaryotes associated with non-vascular plants. One of these (028) was related to an uncultured eukaryote sequence recovered from aquatic moss pillars of the Leptobryum species in a freshwater lake in East Antarctica (Nakai et al., 2012). The other OTU (137) was related to a sequence for an unclassified Mucoromycotina fungi recovered from the thallus of hornworts (Desirò et al., 2013). One other interesting OTU (051) had 80% similarity to chytrids found to dominate the fungal community in high-elevation unvegetated soils (Freeman et al.,

2009).

Discussion

Microbial communities in acidic environments are recognized as being less diverse than those in environments with more neutral pH. Moreover, eukaryotic richness has been estimated to be an order of magnitude lower than prokaryotic richness in acidic environments (Amaral-Zettler et al.,

2011). Previous studies of eukaryotic diversity in 1-g samples of biofilms and sediments associated with acidic waters (pH 2.0-2.75) in the Rio Tinto mining area revealed a comparatively low number (32) of observed OTUs at the 94% similarity level (Amaral-Zettler, 2013). In contrast, numbers of eukaryotic OTUs were four to eight times higher in community DNA extracted from 0.3-g samples of precipitates analyzed in our study (at 95% similarity). It is possible that the greater complexity of eukaryotic communities observed in AMD precipitates as compared with aquatic environments is a response to development of edaphic conditions and increased nutrient availability from root and rhizoid exudates.

103 Although we expected richness to be higher in reclaimed than in control precipitates, we did not expect these vales to be comparable to those reported for less extreme environments, such as unpolluted soils supporting vascular vegetation (Lesaulnier et al., 2008). Indeed, vascular vegetation growing in reclaimed plots might explain why the eukaryotic richness in these precipitates was greater than in precipitates supporting a biological crust despite their similar acidic conditions. This suggests that surface biota and new nutritional conditions resulting from reclamation provided niches for new taxa. However, it is also possible that populations in reclaimed precipitates had been introduced with compost, lime, straw, and seeds utilized during reclamation.

In regards to eukaryotic taxonomic diversity in AMD-impacted systems, much of what is currently known originates from intensive studies of the subterranean Richmond mine at Iron

Mountain, CA (Baker and Banfield, 2003) and the Rio Tinto River in southwestern Spain

(Amaral-Zettler et al., 2002; Amaral-Zettler et al., 2003; Amaral-Zettler et al., 2011). One of the main eukaryotic taxa identified in the biofilms associated with acidic waters in the subterranean

Iron Mountain AMD were Ascomycetes in the classes Dothideomycetes (Acidomyces richmondensis) and Eurotiomycetes, as well as Basidiomycetes in the class Urediniomycetes

(Baker et al., 2004; Baker et al., 2009). Sequences classified as Eurotiomycetes were the most abundant ascomycete taxa found in all precipitates (ranging from 1.7-12%), while

Dothidiomycetes were found only in RR precipitates at low abundance. Other eukaryotic taxa in

Iron Mountain biofilms include red algae in the Rhodophyta lineage and protists in the

Vahlkampfiidae family (Baker et al., 2004; Baker et al., 2009). Neither of these lineages were detected in barrens precipitates of this study.

Lineages of heterotrophic fungi, red algae, and Vahlkampfiid amoebae also have been detected in the open-air AMD systems of the Rio Tinto. These systems also supported

104 photosynthetic eukaryotes including Euglenozoa (euglenids), Viridiplantae (closely related to

Chlorella spp. and Zygnema spp.) and the Bacillariophyta (diatoms) lineages. More recently, diatoms in the genus Pinnularia and members of the Euglena mutabilis complex have been reported to dominate eukaryotic communities of Rio Tinto’s acid waters (Amaral-Zettler, 2013).

Non-photosynthetic representatives in the acidic river comprise phagotrophic lineages, such as

Alveolata (Ciliates), Cercomonads, and Stramenopiles. The Rio Tinto system is also known to host novel eukaryotic lineages that branch at the base of the animal-fungal-nucleariid radiation

(Amaral-Zettler et al., 2002; Amaral-Zettler et al., 2003). Sequences belonging to this lineage were found in all four precipitate types, with slightly higher abundances in CC and CB (0.6% and

1.3%), compared to RR and RB (0.4% and 0.1% respectively).

Cosmopolitan taxa identified in the Rio Tinto system, as well as in our terrestrial environment, included RT5iin44 and RT5iin3 (Amaral-Zettler et al., 2002). Likewise, Spumella

(Stramenopiles), Hypotrichia (Alveolata), Leotiomycetes and Sordariomycetes (both

Ascomycota) and Agaromycetes (Basidiomycota) were common for three acidic environments investigated by Amaral-Zettler (2013) (including the Rio Tinto) and our precipitates. Organisms detected only in the AMD barrens system included green algae of the Zygnematales and

Trebouxiophyceae groups; Nematoda; Porifera; and fungi including Chytridiomycota,

Glomeromycota, and Mucoromycotina.

In our control precipitates, fungi consisted mainly of Ascomycota fungi, followed by Basal fungi (Mucoromycotina). Ascomycota are commonly associated with other types of biological crusts including those dominated by lichens and cyanobacteria (Bates and Garcia-Pichel, 2009;

Bates et al., 2010). Lichen-associated microeukaryotes comprise mycobionts of the class

Lecanoromycetes as well as several non-biont ascomycetes in the classes Dothideomycetes,

Leotiomycetes, Orbiliomycetes, and Eurotiomycetes (Bates et al., 2011). It is interesting to note

105 that CC precipitates had the greatest abundance of Eurotiomycete sequences since these precipitates were most closely associated with the bryophyte-dominated biological crust.

Similarly, Mucoromycotina fungi have been found to form symbioses with some groups of non- vascular plants of the hornworts lineage which are closely related to mosses (Desirò et al., 2013;

Duff et al., 2007). Sequences in this taxon also were more abundant in crust-covered control precipitates than in reclaimed precipitates. Other eukaryotic taxa that were found more frequently in control precipitates, and previously identified in association with biological crust at our site

(Prasanna et al., 2011) were green algae of the Zygnematales group. These green algae have also been found to form biological crusts together with cyanobacteria and lichens (Büdel, 2005), and some species belonging to this group are known to develop in acidic habitats with low nutrient contents (Hoppert et al., 2004).

Fungi, in general, dominated the eukaryote communities of reclaimed precipitates.

Accordingly, Basidiomycota fungi were the most abundant eukaryotes identified in precipitates sustaining vegetation. Their increased abundance suggests that vegetated precipitates might offer conditions resembling those of forest soils where Basidiomycota are found to form ectomycorrhizal associations with woody vascular plants (Lauber et al., 2008; Paul, 2007).

Another ecological role recognized for Basidiomycota, which can also explain the occurrence of these fungi in vegetated precipitates, is their ability to decompose leaf- and wood- debris (Lundell et al., 2010). This might explain the relative abundance of members of the Basidiomycota belonging to the Agaromycetes class which have been found to dominate eukaryotic communities in forest soils (Buée et al., 2009). Other eukaryotes more frequently observed in reclaimed precipitates and thus potential indicators of soil development were Nematodes, which have been used as indicators of soil health (Neher, 2001), Trebouxiophyceae green algae, and

Chytridiomycota. Three other eukaryotic taxa, Glomeromycota, LKM15, and Porifera, were

106 detected only in reclaimed precipitates, with niches for the former likely provided by herbaceous species in successional plant cover of reclaimed precipitates.

Improvements in microbial diversity of materials similar to AMD precipitates, such as mine tailings, have been used as indicators of vegetative reclamation success (Mummey et al., 2002) since biologically active soils promote restoration of soil ecosystem functions and plant growth

(Mendez and Maier, 2008b; Petrisor et al., 2004). In our previous study on bacterial communities in AMD precipitates, we observed greater bacterial diversity in reclaimed precipitates than in control precipitates as well as taxa typically associated with plant roots (Rojas et al., in prep.). In the present study, a comparison of the abundance and composition of microeukaryotes between control and reclaimed precipitates is one means to evaluate the impact of vegetative reclamation on incipient soil development and ecosystem function. As an example, the greater abundance of

Basidiomycota and the sole presence of Glomeromycota in reclaimed precipitates could reflect a transition to soil-like environments where mycorrhizal fungi associated with plant roots enhanced plant establishment at this AMD-impacted site. Indeed, natural colonization of uranium tailings by trees including Betula, Populus, and Pinus species, all of which were identified in our study, has been attributed to ectomycorrihizal associations (Kalin and Stokes, 1981). Additionally, arbuscular mycorrhizal fungi (AMF) belonging to the phylum Glomeromycota have been found in the root system of dominant indigenous plant species growing in heavy metal polluted areas adjacent to open pit mines (Zarei et al., 2010). Therefore, our study not only allowed the exploration of terrestrial environments affected by acid mine drainage but also the evaluation of how vegetative reclamation of an AMD barrens performed over time. Further investigations are needed to enhance our understanding of the efficacy of vegetative reclamation of mine-degraded lands, particularly the soil factors that lead to increased microbial diversity to levels similar to those of less extreme environments.

107 Conclusions

Acid mine drainage precipitates deposited on soil surfaces by overland flow of mine discharge can be reclaimed to support vascular plants. Reclaimed as well as control precipitates in this study were found to contain greater diversity of microeukaryotes than has been reported previously for subterranean mine waters and biofilms and sediments associated with mining- polluted rivers. Although greater diversity was expected in reclaimed than in control precipitates, microbial eukaryotic richness approached that of less extreme environments, which was an unanticipated finding. Further investigations concerning soil factors promoting increased diversity would help to elucidate associations between specific taxa and edaphic niches. Many taxa identified in reclaimed precipitates were related to taxa detected in other more neutral soil environments, while taxa in control precipitates also have been found in association with other types of biological soil crusts. Our study contributes to understanding of eukaryotic diversity in

AMD-impaired environments and the use of eukaryotic taxa as indicators of the success of vegetative reclamation of acidic precipitates.

Acknowledgements

We thank landowner Alan Larson, who permitted site access; Dr. Jason Kaye, who provided research support through the NSF-Spain exchange program; Erin Scully for data analysis guidance; and Miguel Ramos and Juan Pablo Fernandez for field assistance.

108 References

Adl, S.M., Simpson, A.G.B., Lane, C.E., Lukeš, J., Bass, D., Bowser, S.S., Brown, M.W., Burki, F., Dunthorn, M., Hampl, V., Heiss, A., Hoppenrath, M., Lara, E., le Gall, L., Lynn, D.H., McManus, H., Mitchell, E.A.D., Mozley-Stanridge, S.E., Parfrey, L.W., Pawlowski, J., Rueckert, S., Shadwick, L., Schoch, C.L., Smirnov, A., Spiegel, F.W., 2012. The revised classification of eukaryotes. Journal of Eukaryotic Microbiology 59, 429-514.

Amaral-Zettler, L.A., 2013. Eukaryotic diversity at pH extremes. Frontiers in Microbiology 3:441.

Amaral-Zettler, L.A., Gomez, F., Zettler, E., Keenan, B.G., et al., 2002. Eukaryotic diversity in Spain's River of Fire. Nature 417, 137-137.

Amaral-Zettler, L.A., Messerli, M.A., Abby, D.L., Smith, P.J.S., Sogin, M.L., 2003. From genes to genomes: beyond biodiversity in Spain's Rio Tinto. Biological Bulletin 204, 205-209.

Baker, B.J., Banfield, J.F., 2003. Microbial communities in acid mine drainage. Fems Microbiology Ecology 44, 139-152.

Baker, B.J., Lutz, M.A., Dawson, S.C., Bond, P.L., Banfield, J.F., 2004. Metabolically active eukaryotic communities in extremely acidic mine drainage. Appl Environ Microbiol 70, 6264- 6271.

Bates, S.T., Berg-Lyons, D., Lauber, C.L., Walters, W.A., Knight, R., Fierer, N., 2011. A preliminary survey of lichen associated eukaryotes using pyrosequencing. Lichenologist 44, 137.

Bates, S.T., Garcia-Pichel, F., 2009. A culture-independent study of free-living fungi in biological soil crusts of the Colorado Plateau: their diversity and relative contribution to microbial biomass. Environmental Microbiology 11, 56-67.

Bates, S.T., Nash Iii, T.H., Sweat, K.G., Garcia-Pichel, F., 2010. Fungal communities of lichen- dominated biological soil crusts: Diversity, relative microbial biomass, and their relationship to disturbance and crust cover. Journal of Arid Environments 74, 1192-1199.

Büdel, B., 2005. Microorganisms of biological crusts on soil surfaces, in: Buscot, F., Varma, A. (Eds.), Soil biology. Springer, Berlin pp. 307– 323.

109 Buée, M., Reich, M., Murat, C., Morin, E., Nilsson, R.H., Uroz, S., Martin, F., 2009. 454 pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. New Phytologist 184, 449-456.

Costello, E.K., Halloy, S.R.P., Reed, S.C., Sowell, P., Schmidt, S.K., 2009. Fumarole-supported islands of biodiversity within a hyperarid, high-elevation landscape on socompa volcano, Puna de Atacama, Andes. Applied and Environmental Microbiology 75, 735-747.

Desirò, A., Duckett, J.G., Pressel, S., Villarreal, J.C., Bidartondo, M.I., 2013. Fungal symbioses in hornworts: a chequered history. Proceedings of the Royal Society B: Biological Sciences 280.

Dopheide, A., Lear, G., Stott, R., Lewis, G., 2008. Molecular characterization of ciliate diversity in stream biofilms. Applied and Environmental Microbiology 74, 1740-1747.

Duff, R.J., Villarreal, J.C., Cargill, D.C., Renzaglia, K.S., 2007. Progress and challenges toward developing a phylogeny and classification of the hornworts. The Bryologist 110, 214-243.

Freeman, K.R., Martin, A.P., Karki, D., Lynch, R.C., Mitter, M.S., Meyer, A.F., Longcore, J.E., Simmons, D.R., Schmidt, S.K., 2009. Evidence that chytrids dominate fungal communities in high-elevation soils. . Proc Natl Acad Sci USA 106, 18315–18320.

Hoppert, M., Reimer, R., Kemmling, A., Schröder, A., Günzl, B., Heinken, T., 2004. Structure and reactivity of a biological soil crust from a xeric sandy soil in Central Europe. Geomicrobiology Journal 21, 183-191.

Huse, S.M., Welch, D.M., Morrison, H.G., Sogin, M.L., 2010. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environmental Microbiology 12, 1889-1898.

Johnson, D.B., 2012. Geomicrobiology of extremely acidic subsurface environments. Fems Microbiology Ecology 81, 2-12.

Lauber, C.L., Strickland, M.S., Bradford, M.A., Fierer, N., 2008. The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biology and Biochemistry 40, 2407-2415.

Kalin, M., Stokes, P.M., 1981. Macrofungi on uranium mill tailings — associations and metal content. Science of The Total Environment 19, 83-94.

Lesaulnier, C., Papamichail, D., McCorkle, S., Ollivier, B., Skiena, S., Taghavi, S., Zak, D., Van Der Lelie, D., 2008. Elevated atmospheric CO2 affects soil microbial diversity associated with trembling aspen. Environmental Microbiology 10, 926-941.

Lundell, T.K., Mäkelä, M.R., Hildén, K., 2010. Lignin-modifying enzymes in filamentous basidiomycetes – ecological, functional and phylogenetic review. Journal of Basic Microbiology 50, 5-20.

110 Lupton, M.K., Rojas, C., Drohan, P., Bruns, M.A., 2012. Vegetation and soil development in compost-amended iron oxide precipitates at a 50-year-old acid mine drainage barrens. Restoration Ecology 21, 320-328.

Mendez, M.O., Maier, R.M., 2008b. Phytostabilization of mine tailings in arid and semiarid environments-an emerging remediation technology. Environ Health Perspect 116, 278–283.

Mummey, D.L., Stahl, P.D., Buyer, J.S., 2002. Microbial biomarkers as an indicator of ecosystem recovery following surface mine reclamation. Applied Soil Ecology 21, 251-259.

Nakai, R., Abe, T., Baba, T., Imura, S., Kagoshima, H., Kanda, H., Kohara, Y., Koi, A., Niki, H., Yanagihara, K., Naganuma, T., 2012. Eukaryotic phylotypes in aquatic moss pillars inhabiting a freshwater lake in East Antarctica, based on 18S rRNA gene analysis. Polar Biology 35, 1495- 1504.

Neher, D.A., 2001. Role of Nematodes in Soil Health and Their Use as Indicators. Nematology 33, 161-168.

Paul, E.A., 2007. Soil Microbiology, Ecology, And Biochemistry. Elsevier Academic Press.

Poitelon, J.B., Joyeux, M., Welté, B., Duguet, J.P., Peplies, J., DuBow, M.S., 2009. Identification and phylogeny of eukaryotic 18S rDNA phylotypes detected in chlorinated finished drinking water samples from three Parisian surface water treatment plants. Letters in Applied Microbiology 49, 589-595.

Prasanna, R., Ratha, S., Rojas, C., Bruns, M., 2011. Algal diversity in flowing waters at an acidic mine drainage “barrens” in central Pennsylvania, USA. Folia Microbiologica 56, 491-496.

Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W., Peplies, J., Glöckner, F.O., 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Research 35, 7188-7196.

Quince, C., Lanzen, A., Curtis, T.P., Davenport, R.J., Hall, N., Head, I.M., Read, L.F., Sloan, W.T., 2009. Accurate determination of microbial diversity from 454 pyrosequencing data. Nature methods 6, 639-641.

Rojas, C., Gutierrez, R., Ghosh, D., Bruns, M.A., in prep. Bacterial community diversity in vegetated and biological crust-covered soils formed from acid mine drainage precipitates.

Rojas, C., Martínez, C.E., Bruns, M.A., in rev. Fe biogeochemistry in reclaimed acid mine drainage precipitates: implications for phytoremediation. Submitted to Environmental Pollution Journal.

Schloss, P.D., 2009. A High-throughput DNA sequence aligner for microbial ecology studies. PLoS ONE 4, e8230.

111 Schloss, P.D., Gevers, D., Westcott, S.L., 2011. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS ONE 6, e27310. Pipeline and taxonomy database retrieved from http://www.mothur.org on April, 22012.

Schloss, P.D., Westcott, S.L., 2007. Greengenes reference files. Retrieved from http://www.mothur.org/wiki/Taxonomy_outline on April, 2012.

Zarei, M., Hempel, S., Wubet, T., Schäfer, T., Savaghebi, G., Jouzani, G.S., Nekouei, M.K., Buscot, F., 2010. Molecular diversity of arbuscular mycorrhizal fungi in relation to soil chemical properties and heavy metal contamination. Environmental Pollution 158, 2757-2765.

Chapter 5

General Conclusions

Acid mine drainage barrens can contribute to downstream pollution for decades if exposed acidic precipitates are not protected against surface runoff. Vegetative reclamation of

AMD barrens offers an alternative for in situ stabilization of acidic precipitates where dispersion can be ameliorated by reducing exposure to rainfall and by enhancing physical cohesion and biochemical interaction with roots and associated microbes. In a previous study, we demonstrated that vegetative reclamation of a 50-year-old AMD barrens in Central Pennsylvania did not require removal of highly acidic (pH 2-3) iron oxy(hydr)oxide precipitates. Rather, improvements in the edaphic conditions of these materials after a one-time incorporation of compost into the upper 15 cm permitted growth of an oats nurse crop in the first growing season, succeeded by sown and indigenous species in the second and fourth growing seasons, respectively.

After five growing seasons, indigenous species consisting mainly of members of the

Betula, Populus, Spiraea, and Crataegus genera achieved greater than 90% areal coverage.

However, plant roots were localized in the upper 5 cm of precipitates despite prior incorporation of lime and compost amendments to a 15-cm depth. Plant growth over the last five years provided conditions for the formation of biologically active soils in rooting zones of reclaimed precipitates.

Enrichment of organic carbon and microorganisms in precipitates associated with plant roots induced greater oxidation-reduction activity which increased Fe(II) concentrations.

Vegetative reclamation approaches for metal-rich environments, therefore, should consider the main redox-active metals in the system in order to anticipate their potential mobility and toxicity once organic matter is incorporated and plants are established. In the case of Fe, the presence of organic matter and biological activity, even in unsaturated conditions as demonstrated

113 in this study, can facilitate the reduction of Fe(III) resulting in increased Fe(II) mobility. This could also apply to manganese which is often found in combination with iron in soil environments: Mn(IV/III) could be reduced to Mn(II), increasing its potential leaching throughout the system. Particular attention would be required in the vegetative reclamation of arsenic-rich materials. Though arsenate(V) is harmless, the reduced form arsenite(III) is toxic. Therefore reducing conditions in organic and microbial enriched materials would be of particular concern especially if sulfide species are not present to aid arsenite precipitation. Reducing conditions in rooting zones of reclaimed chromium-rich materials could enhance retention of Cr(III) as this is present in soils as cationic species, and thus would be less susceptible to leaching. However, the contrary would occur with Cr(VI) in anionic species that are toxic and mobile. The findings of our Fe biogeochemistry study encourage further research to understand how Fe can become stabilized in reclaimed precipitates through, for example, complexing processes by favorable functional groups containing O, N, and S. Such studies would shed light on how other redox- active metals could be retained in rooting zones of reclaimed mine-impacted environments.

Microbial communities inhabiting AMD-impacted environments have been more extensively studied in aqueous rather than terrestrial systems. Our reclamation study provided the opportunity to gain insights into AMD-derived bacterial and eukaryotic communities in unsaturated, edaphic habitats. Our study confirmed that bacterial and eukaryotic communities in acid mine drainage precipitates supporting successional plants and biological crusts are more diverse than those reported for aqueous AMD-impacted systems and do not resemble the microbial composition typically associated with these environments. The absence of many taxa known to inhabit water bodies affected by AMD suggests their inability to survive or compete in unsaturated or nutrient- enriched environments. Years of exposure to sunlight, seasonal fluctuations in the water table, plant and biological crust exudates, and mineral aging could have resulted in unfavorable

114 conditions for AMD taxa to persist as major members of bacterial communities in these precipitates.

Even though conditions in acidic precipitates sustaining successional plants and biological crusts did not favor typical AMD bacterial and eukaryotic taxa, they did offer microbial habitats supporting levels of bacterial diversity and eukaryotic richness similar to those in less extreme terrestrial environments. We acknowledge that alternative sequence analysis, sampling effort, or primer affinities could have accounted for difference between our findings and other studies of microbial diversity in less extreme soils. However, it is also possible that edaphic conditions in our reclaimed and control precipitates provided suitable conditions for development of more diverse microbial communities. Among the four precipitate types, the highest microbial richness was found in precipitates in direct association with plant roots followed by precipitates immediately beneath them. This suggests that surface biota and new nutritional conditions resulting from reclamation provided niches for new microbial taxa. However, it is also possible that populations in reclaimed precipitates had been introduced with compost, lime, straw, and seeds utilized during reclamation. Contrary to expectation, control precipitates in direct contact with biological crusts were less diverse than precipitates not in direct contact with biological crusts. This could imply that bacteria and eukaryotes in the latter precipitates represented older, more established communities maintained under lower-disturbance conditions. In contrast, bacteria and eukaryotes in precipitates associated with biological crusts could have represented communities in earlier successional stages as they responded to growth and decay of biological crusts, more frequent wetting-drying cycles, and greater temperature fluctuations at the surface.

Bacterial and eukaryotic taxonomic composition in newly vegetated acid mine drainage precipitates differed from that of control precipitates covered by biological crusts despite their similar acidic conditions (pH 2.5-2.7). Microbial communities in reclaimed precipitates appeared

115 to resemble communities found in soils since many microbial taxa identified in reclaimed precipitates were related to taxa detected in soil environments. The increase in carbon content due to compost amendment and root exudates might have provided new conditions for the proliferation of typical soil bacteria and eukaryotic representatives as it was the case for the α- proteobacteria order, Rhizobiales, and the Basidiomycota and Glomeromycota fungi.

In this study we have demonstrated that vegetative establishment during reclamation of acid mine drainage barrens promoted the formation of biologically active soils for restoration of soil ecosystem functions. However, further investigations concerning soil factors promoting increased diversity would help to elucidate associations between specific taxa and edaphic niches. Future work focusing on bacterial and eukaryotic species identified in reclaimed precipitates would enable the use of these representatives as indicators of vegetative reclamation success and possibly as inoculants for the improvement of vegetation establishment during reclamation of other mine-impacted environments.

Appendix A Water chemistry for AMD collected at the constructed discharge point (D) and for AMD flowing belowground at the red zone (G).

2- pH ORP EC T Fe Fe(II) SO4 Al Ca Co K Mg Mn Na Ni P Si Zn (mV) dS/m (°C) ppm D 2.90 489 1.51 10.4 81.67 77.19 861.2 29.2 25.0 BDL 0.4 25.8 3.2 1.0 0.25 0.8 7.8 BDL ±0.02 ±2.12 ±0.01 ±0.36 ± 3.7 ± 5.8 ± 38 ±0.13 ±0.06 ±0.003 ±0.09 ±0.01 ±0.01 ±0.01 ±0.03 ±0.09

G 2.44 400 2.22 22.3 95.4 94.6 1106.7 37.6 38.4 0.09 4.0 34.5 5.6 3.3 0.34 0.4 13.2 0.08 ±0.11 ±8.49 ±0.18 ±1.63 ± 6.7 ±1.6 ± 23.8 ±0.91 ±0.46 ±0.02 ±0.27 ±0.58 ±0.24 ±0.16 ±0.002 ±0.02 ±0.19 ±0.01

! As, Cd, Cr, Cu below detection limit (BDL)

Water samples were collected in triplicate and filtered (0.2 mm) in the field both from the constructed discharge point and from ground water obtained from the monitoring well located in the middle of the four plots. Samples obtained for sulfate analysis were not preserved. Samples for analysis of ferrous iron were preserved with 0.5 M HCl, and samples for analysis of dissolved metals were preserved with 0.5 M HNO3.

Appendix B Vegetation and Soil Development in Compost-Amended Iron Oxide Precipitates at a 50- Year-Old Acid Mine Drainage Barren Research article published in Restoration Ecology

118

RESEARCH ARTICLE Vegetation and Soil Development in Compost-Amended Iron Oxide Precipitates at a 50-Year-Old Acid Mine Drainage Barrens

Mary Kay Lupton,1 Claudia Rojas,1 Patrick Drohan,1 and Mary Ann Bruns1,2

Abstract growing seasons. The method consisted of applying 11 t/ha Acid mine drainage (AMD) barrens result from destruc- lime and 27 or 54 t/ha compost before rototilling (top tion of vegetation within AMD flow paths. When exposed to 15 cm) and mulching with oat straw containing viable seeds air, soluble iron in AMD undergoes oxidation and hydrol- for a nurse crop. Lime-only plots were included for comparison, and all amended plots were sown with a mine ysis to form ferric iron (oxyhydr)oxides which accumulate reclamation seed mix. Oats, sown species, and indigenous on soil surfaces. A restoration experiment was conducted species dominated cover in the first, second, and fourth at a 50-year-old AMD barrens created by discharge from growing seasons, respectively. In the fourth year follow- an abandoned underground coal mine. The objective was ing reclamation, compost-amended plots had >70% cover to determine whether vegetation could be established by and improved soil properties in all three zones, providing altering rather than removing surface layers of acidic pre- evidence to reject our hypothesis. Vegetative restoration of cipitates at a site representative of other mining-degraded AMD barrens did not require removal of highly acidic pre- areas. Three zones in the barrens were identified based cipitates, since they could be transformed at low-cost into on moisture content, pH (2.7–3.3), and thickness of pre- a medium that supports indigenous plants. cipitates (0–35 cm). Our hypothesis was that application of the same reclamation method to all zones would fail Key words: coal mining, organic amendment, plant cover, to sustain >70% vegetative cover in each zone after four soil acidity, soil restoration.

Introduction extensive minelands left from open pit and strip mining Acid mine drainage (AMD) is a persistent environmental (Zink et al. 2005). The surfaces of AMD barrens consist problem in mining regions around the world (Younger 1997; of fine-textured ochreous precipitates that overlie relatively Lottermoser 2010). AMD is generated when previously buried intact native soil profiles (Lupton 2008). The extremely acidic sulfide minerals in ores are exposed to water and oxygen nature of these precipitates (pH < 3) prevents growth of during mining operations (Kaufmann et al. 1992). In the vascular vegetation, and it has been assumed that precipitate Appalachian region of the United States, one little studied layers must be removed before vascular plants can become legacy from two centuries of coal mining is the existence re-established. During open pit and strip mining, on the of AMD barrens created by massive overland flow of acidic other hand, native soil profiles are destroyed and soil is discharges from underground abandoned mines (Demchak removed as “overburden” to gain access to ore bodies. et al. 2004). When metal-rich waters emerge aboveground, While mineland surfaces consist of extremely heterogeneous ferrous iron oxidizes to ferric iron (oxyhydr)oxides that mixtures of subsoils, rocks, and overburden materials, they accumulate as orange (ochreous) precipitates on soil surfaces can be revegetated readily (Pensa et al. 2004; Halofsky & as AMD flows overland toward natural streams (Bigham & McCormick 2005a; Stehouwer et al. 2006). Indeed, any Nordstrom 2000; Cornell & Schwertmann 2003). mining operations ceasing after 1978 must meet plant cover The numerous but relatively smaller and isolated requirements as put forth in the U.S. Surface Mining Control areas of AMD barrens (1–5 ha) differ markedly from and Reclamation Act (SMCRA) of 1977 (Zink et al. 2005). In contrast, most AMD barrens are associated with orphan mine discharges not subject to reclamation requirements. Since 1Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA U.S.A. removal, transport, and disposal of acidic precipitates would 2Address correspondence to M. A. Bruns, email [email protected] be extremely costly, efforts are not made to remediate these barrens, and they persist as eyesores for decades. The purpose of this study was to determine whether indige-  2012 Society for Ecological Restoration doi: 10.1111/j.1526-100X.2012.00902.x nous vegetation could be established by altering rather than

320 Restoration Ecology Vol. 21, No. 3, pp. 320–328 MAY 2013

119

Vegetating Acid Mine Drainage Barrens removing acidic precipitates at a 50-year-old AMD barrens in central Pennsylvania (Lupton 2008). In contrast to mineland Iron oxide precipitates reclamation employing biosolids, sludges, or manures (Sopper 1993; Halofsky & McCormick 2005a; Madajon´ et al. 2010), Red Zone Grey Zone Exposed soil we avoided use of nutrient-rich residuals because high nutri- Fragipan ent concentrations can inhibit growth of indigenous plants Orange Zone (Halofsky & McCormick 2005b). High rates of manures and biosolids applied to minelands in the absence of plants to take up nutrients also can result in substantial losses of N and P to ground and surface waters (Stehouwer et al. 2006). Finally, Figure 1. (Left) A 50-year-old acid mine drainage barrens, where manures and biosolids are not feasible for use by community mounded deposits of iron oxide precipitates of varying depths are groups interested in low-cost reclamation. interspersed with gulleys and areas where eroded native soils are exposed after sheet flow has ceased. (Right) View of central channel Our revegetation approach employed moderate amounts of flowing through the barrens, showing exposure of subsoil and fragipan compost and lime and a first-year annual nurse crop, with horizons on the side of the channel. the goal of facilitating establishment of indigenous plants. Because overland AMD flow patterns had been erratic over day at a rate of 3000 L/min. Most of the discharge now collects the previous 50 years, the barrens comprised an undulating in a central channel entering a tributary of Brown’s Run landscape of mounds, gullies, and variable thicknesses of flowing into the West Branch of the Susquehanna River, but ochreous precipitates. To assess the efficacy of a single recla- some discharge also flows belowground. Currently mounds mation approach across this variable landscape, we established and gullies resulting from flow path alterations overlie native plots in three distinct zones representing experimental blocks. soils (Fig. 1). Soils, developed from mixed colluviums, are These zones, designated red, orange, and gray, differed in classified as Brinkerton (Typic Fragiaqualfs) and Ernest (Aquic thickness of precipitate layers, pH, and moisture content. Fragiudults), with moderate permeability in surface layers but Because the gray zone consisted mainly of native subsoils slow permeability in fragipan layers at depths of 55–70 cm exposed by erosion, it had no or very thin layers of ochreous (Soil Survey Staff 2010). precipitates and was least acidic. We therefore expected the gray zone to support the most plant growth and considered it as a reference for assessing whether AMD precipitates need to Initial Properties of Parent Materials in Three Zones be removed before plants can become sustainably established. In 2005 spatial heterogeneity of the barrens was mapped Our hypothesis was that the same one-time treatment applied by measuring the depths of ochreous precipitates every 3 m to all three zones would fail to meet SMCRA requirements (Fig. 2). Three zones were identified as blocks for plot for mine reclamation in each zone (i.e. would not sustain establishment. The red zone was wettest, with thickest layers >70% vegetative cover after five growing seasons). Rejecting of iron oxides (8–35 cm) covered by a crust of algae, the hypothesis would be a favorable outcome for reclamation cyanobacteria, and mosses due to wet conditions (Prasanna efforts, because it would mean that one low-cost treatment et al. 2011). The orange zone was drier with thinner surface could be applied across an entire barrens. layers (2–17 cm) of light orange precipitates. The gray zone was driest and consisted of exposed subsoil, with sparse or no precipitates on the surface (0–2 cm). Methods Soil samples for initial characterization of parent materials from each zone were collected in July 2006. Fifty randomly Study Area located soil cores (1.9 cm wide, 15 cm depth) were composited The AMD barrens (0.5 ha) is located within the Appalachian per zone and air-dried and ground to <2 mm. Subsamples Plateau Province, 5 km north of Kylertown, Pennsylvania, were dissolved in 6 M HCl for ICP-OES analysis of total U.S.A. (41◦ 01" 22.00" N; 78◦ 09" 08.064"" W). The barren Al, Fe, and S (Winland et al. 1991). A 1:1 suspension of area was created by overland flow of AMD from a constructed air-dried soil:deionized water was used to measure pH. Total discharge point draining a complex of deep coal mines soil carbon was measured with a Thermo EA1110 CHNSO covering thousands of hectares. The Clearfield Bituminous Elemental Analyzer (Nelson & Sommers 1996). Ca, Mg, and K Coal Corporation and other companies mined the Lower were determined by ICP after extraction with Mehlich 3 (Wolf Kitanning coal seam from 1882 to 1945 in this area (Feldmeier & Beegle 2011). Acidity was determined using the Modified 2005). After several mine headings became flooded, the Mehlich buffer test procedure (Wolf et al. 2008). Fractions discharge was installed in the 1920s to direct drainage away of acidity above 15 cmol+/kg were considered to represent from the footslope of the westernmost ridge of the mine nonexchangeable acidity for Pennsylvania soils (A. Wolf 2012, network. In 1950, the external pipeline broke, resulting in Pennsylvania State University, University Park, PA, personal uncontrolled flow of AMD across a private homestead (A. communication). Cation exchange capacity (CECsum,Ross& Larson 2010, Kylertown, PA, personal communication). Ketterings 2011) was estimated from summation of Ca, Mg Land on the former homestead became an AMD barrens, and K and exchangeable acidity ( 15 cmol+/kg). For initial with acidic discharge (pH 2.4–3.2) continuing to the present soil color and mineralogical data,≤ three 15-cm surface cores

MAY 2013 Restoration Ecology 321

Vegetating Acid Mine Drainage Barrens

Following rototilling, plots were hand seeded at 70 kg/ha with a wildlife seed mix of 60% rye (Elymus sp.), 15% fawn tall fescue (Schedonorus phoenix Scop. Holub), 15% Potomac orchard grass (Datylis glomerata L.), 7% Empire birdsfoot trefoil (Lotus corniculatus L.), 2% Alsike clover (Trifolium hybridum L.), and 1% redtop (Agrostis gigantea Roth). Each plot was mulched by broadcasting oat straw at a rate of 10 Mg 1 ha− . Oat seed was included in the straw. All plots were separated by at least 0.6 m-wide perimeters of untreated areas.

Soil and Plant Analyses Following Reclamation In October 2006 and October 2007, soil from plots and control areas (top 5 cm) were sampled randomly and composited (50 cores per composite). Soil samples were split, with one portion kept moist and the other portion air-dried and ground for analyses by the Penn State Ag Analytical Lab: soil pH (1:1 water); available P, K, Ca, Mg, Zn, Cu, and S with Mehlich 3 extractant and ICP. Summation was used for determination of cation exchange capacity (CEC). Organic matter was obtained in 2007 by loss on ignition. Electrical conductivity was measured using a 1:2 ratio of soil:water. Results of microbiological analyses performed on moist soils are reported elsewhere (Lupton 2008). Seedling germination rate in each plot was determined by counting seedlings in ten 100-cm2 quadrats per treatment and Figure 2. Site map of barrens showing sampling points in 2005 (3 m averaged per area for comparison. Percent plant cover was apart) and depths of surface ochreous precipitates (ranged from 0 to 35 cm). Emergence point from mine network is at lower right. Main determined by using a 10-pin frame in 2007; 100 pin readings stream channel is shown as dark central line. Rectangles show areas were were taken per plot (Bureau of Land Management 1999). vegetation plots were established in 2006 in red (thickest precipitates Aboveground plant biomass was measured by harvesting layers), orange (intermediate), and gray zones (thin or no precipitates). four quadrats (0.25 m2) per treatment by cutting with a hand trimmer 2.5 cm from the soil surface. Botanical composition of plants was determined after separating by species and from unamended areas in each zone were composited in 2010 drying at 60◦Cfor48h.In2009theframequadratmethod for Munsell color evaluation (air dry) and powdered X-ray was used to measure plant coverage and composition. We diffraction analysis (PANalytical, Almelo, The Netherlands). grouped cover measurements into six categories (<1%, 1–5%, 6–25%, 26–50%, 51–75%, and >76% cover) to compare data from all years. Reclamation Treatments All data were checked in SAS for conformation to statisti- Six experimental plots (9 m2) were established in each zone cal assumptions of normality and homogeneity of variances as in June, 2006. Three treatments were randomly assigned to determined by Proc Univariate. Treatment effects were deter- duplicate plots per zone: (1) lime only (no compost); (2) mined by two-way analysis of variance (ANOVA) with loca- 1 1 lime plus 27 Mg ha− compost; and (3) lime plus 54 Mg ha− tion as a blocking variable. Tukey’s multiple range test was compost. The same lime addition was used for all treatments at used to determine differences in treatment effects. A p value of 1 arateof11Mgha− (5.7 kg MgCO3 and 4.5 kg Ca(OH)2 per <0.05 was used for ANOVA and Tukey’s effect comparisons. plot), which was calculated (Mehlich 1976) to be sufﬁcient to bring the surface precipitates in the red zone to pH 5, similar to native soils. Lime and compost were incorporated Results by rototilling (15 cm depth). Compost from the Penn State Organic Materials Processing and Education Center (food Initial Properties of Parent Materials waste, manure, and landscaping debris) had pH 7.5, 43.6% Red zone precipitates had lowest pH (2.7) and highest mean moisture content, and C:N ratio of 10.8 (58.5% organic matter moisture content (69%) compared to orange and gray zone and 2.8% total N, dry weight basis). Other nutrients consisted precipitates (Table 1). The red zone was signiﬁcantly wetter of 1.03% P (as P2O5), 1.38% K (as K2O), and 2.5 mg/g throughout the year, with water contents ranging from 63 NH4+ –N. Compost N was mainly in organic form (NO3− –N to 84% by weight, consistent with the red zone’s shallower was below detection), so that the two compost rates provided fragipan layer (Lupton 2008). Orange zone precipitates had 1 67 and 134 kg NH4+ –Nha− , respectively. somewhat higher pH (3.1), lower moisture content (23%), and

322 Restoration Ecology MAY 2013

121

Vegetating Acid Mine Drainage Barrens

Table 1. Chemical and mineralogical properties of ochreous precipitates in red, orange, and gray zones.

Thickness Precipitate Munsell Color Score Mean Moisture Soil Mean Acidity Fe S Al Layer (cm) Mineral Composition (hue/value/chroma) Content (%) Carbon (%) pH (cmol+/kg) (g/kg) (mg/kg) (mg/kg) Red zone 8–35 Ferrihydrite/goethite Yellowish red 5YR 69 0.8 2.7 26 480 1306 400 Quartz 4/6 Orange zone 2–17 Quartz Yellowish brown 23 1.1 3.1 16 187 757 1200 Ferrihydrite/goethite 10YR 5/8 Gray zone 0–2 Quartz Muscovite Light gray 2.5Y 16 1.1 3.3 12 49 311 3000 Kaolinite, gypsum 7/2

approximately half the Fe and S found in red zone precipitates. plant biomass were observed for low- and high-rate compost Despite the lack of visible precipitates, gray surface materials treatments in the orange and gray zones. Plant cover was lower still had a low pH of 3.3, reflecting alteration by past overland in plots amended with lime only, being least in the red zone AMD flow. Compared to red and orange zones, the gray zone (6–25%), higher in the orange zone (26–50%), and highest in was also driest, with mean moisture content of 16%, and the gray zone (51–75%). Thus, plant cover responses in lime- lowest Fe and S contents. Total carbon contents ranged from only plots in 2007 appeared to be related to differences in pH. 0.8 to 1.1%. Electrical conductivity was comparatively low, At the end of the fourth growing season (2009), all compost- ranging from 2 to 3 dS/m across all zones. amended plots had >76% plant cover except for high-rate These chemical measurements were consistent with color compost plots in the orange zone, which had 26–50% cover. and x-ray diffraction data obtained for samples from una- Lime-only plots of the red zone achieved >76% cover, while mended areas in 2010. Red zone precipitates were yellowish lime-only plots in the orange and gray zones had 6–25% and red (5YR 4/6) and consisted of 57% FeO(OH) and 43% 51–75% plant cover, respectively. Thus, moisture availability, quartz (Table 1). Orange zone precipitates were yellowish particularly in the lime-only plots, appeared to influence plant brown (10YR 5/8) and contained only 3% FeO(OH) with cover in 2009. 97% quartz. Gray zone surface materials were light gray (2.5 Y 7/2) and contained no detectable Fe, consisting of 48% Plant Composition quartz, 43% muscovite, 7% kaolinite, and 3% other minerals Plant composition in 2009 varied among the three zones such as gypsum. (Fig. 3), with greatest diversity observed in the red zone, where sown grasses (Elymus sp., Arundinaria gigantea)andforbs Plant Growth (Lotus corniculatus, Trifolium hybridum L.) accounted for no Germination of oats during the first growing season (2006) was more than 50% of plant cover. Indigenous tree seedlings, sun- similar in all treatments and zones (seedling density range of dew/mosses (Drosera intermediafolia and Polytrichum sp.), 2 74–100 seedlings 100 cm− , p 0.81), indicating that higher forbs, and sedges (Carex scoparia) contributed 25, 14, 8, and acidity in orange and red zones= after amendment did not 4% of total cover, respectively, in red zone plots. Gray birch impede nurse crop germination. Due to rapid growth of oats (Betula populifolia) was the predominant tree in these plots, (93% plant cover in all plots), sown species accounted for only but black birch (Betula lenta), hawthorn (Crataegus sp.), red a minor proportion of cover (4% rye and lower amounts of tall maple (Acer rubrum), and white pine (Pinus strobus)were fescue and orchard grass). Average plant biomass in lime-only also present. Sown species accounted for greater proportions of treatments was significantly lower than in compost-amended plant cover in the orange and gray zones. Plant cover in orange plots, but biomass did not differ significantly between low- zone plots was dominated by sown grasses (71%), particularly and high-rate compost treatments in the three zones (Table 2). orchard grass, followed by 17% forbs, 10% mosses, and 3% In 2007, sown plant species became established in the sedges. No tree seedlings were observed in orange zone plots, winter-killed oats residue. Sown and indigenous plant species and they accounted for less than 1% of plant cover in the gray accounted for 94 and 6% of plant cover, respectively, in all zone (mainly gray birch). In gray zone plots, sown legumes plots. Orchard grass was the predominant grass (53%), even and indigenous forbs (50%) dominated plant cover, followed though the sown seed mix had contained a greater proportion by 28% grasses (mainly orchard grass), mosses (21%), and of rye. In all three zones, plots that received compost had less than 1% sedges. >76% vegetative cover at the end of the second growing Indigenous forbs included steeplebush (Spiraea tomen- season. No differences in plant cover in 2007 were observed in tosa), wrinkle-leaf solidago (Solidago rugosa), flat-top golden- plots amended with the two different compost rates, and deer rod (Euthamia gramanifolia), hay-scented fern (Dennstaedtia browsing was evident in all compost-amended plots. Despite punctuloba), dew berry (Rubus flagellaris), verbena (Verbena the similarities in plant cover, aboveground plant biomass in urticifolia L.), white meadowsweet (Spiraea alba), and calico the red zone was significantly higher in high-rate than in low- aster (Symphyotrichium lateriflorum). Plant richness appeared rate compost treatments (Table 3). No significant differences in to be similar in the two compost-based treatments. After the

MAY 2013 Restoration Ecology 323

122

Vegetating Acid Mine Drainage Barrens S (mg/kg) Cu (mg/kg) Zn (mg/kg) 0.05). No differences were observed in < p Ca -tests ( (mg/kg) t Mg (mg/kg) ne-tailed, paired K (mg/kg) P (mg/kg) c /kg) + Acidity (cmol /kg) sum + CEC (cmol b 7568 3.984 4.1 3332 3.7 3128 5.0 29 2231 5.6 17 1929 5.2 1 18 2231 6.2 1 19 731 19 6.1 1 13 4 64 6.3 593 1.5 13 8 50 484 15 1953 2 2 70 372 2379 2 3 0.2 70 248 2082 2 2 0.2 0.3 87 1565 5.5 293 0.2 0.7 76 3036 6.5 244 0.2 2123 79 0.6 2877 264 1844 95 0.3 0.6 236 2820 1735 0.5 1207 233 0.7 1746 0.6 0.8 2151 1108 0.7 1.3 1138 0.8 1.5 235 4.0 219 309 (%) pH Moisture a A A AB D ABC BCD ABC CD BCD 0.05). < (kg/ha) Average p Dry Biomass /kg are considered exchangeable and nonexchangeable, respectively. + UnUnUnUnUnUnUnUn 75Un 75 2.6 75 2.4 18 29 2.5 16 27 3.0 18 30 27 2.9 16 30 24 3.0 1 16 29 25 3.4 1 16 19 26 18 3.4 1 17 22 25 3.2 1.5 90 16 19 23 1 35 16 15 44 426 2 52 18 115 39 35 0.4 1 21 543 48 0.6 2 29 187 0.5 45 0.6 2 35 0.7 2155 120 0.5 46 81 0.7 1933 206 41 0.5 0.9 58 2142 239 0.4 46 1.1 847 217 1.1 1.1 135 992 1.0 2.0 885 1.0 1.3 398 1.0 344 438 Am Low-compostAm High-compost 1651 2580 Am Low-compostAm High-compost 1159 1844 Am Low-compostAm High-compost 2144 2740 Chemical properties of amended and unamended soils in October 2006 following first-year growth of oats. Mean moisture content of all amended soils in orange and gray zones were slightly but significantly higher than they were in unamended control areas in o Different letters indicate significantly different means ( Fractions of acidity below and above 15 cmol moisture contents between amended and unamended soils in the red zone. Table 2. Red zone Am Lime-only 442 a b c Orange zone Am Lime-onlyGray zone 945 Am Lime-only 928

324 Restoration Ecology MAY 2013

123

Vegetating Acid Mine Drainage Barrens S (mg/kg) Cu (mg/kg) Zn ned from composite samples (mg/kg) 0.05). No differences were observed in < p Ca (mg/kg) -tests ( t Mg (mg/kg) K ne-tailed, paired (mg/kg) P (mg/kg) d /kg) + Acidity (cmol /kg) sum + CEC (cmol (%) pH c (%) Moisture b 11 80 4.0 26 18 1 59 455 1364 0.2 1.0 2285 9.19.0 79 634.8 4.35.3 3.8 27 295.5 24 261.4 17 5.3 262.0 19 5.2 16 25 12.6 5.2 17 28 1 6.9 39 6 16 28 7.2 59 8 11 573 1.5 7.1 8 11 1366 371 118 2 0 14 1105 0.1 2 310 0 117 0.4 0.2 2 1384 0 133 312 0.3 2568 7.5 0.2 1124 249 74 2063 17 106 0.9 1260 0.1 287 229 151 1019 0.1 1617 0.7 1686 189 0.9 1.2 912 9.2 2450 892 4.1 2.3 7.2 130 9.0 88 147 Matter Organic LOI a 0.05). B A B B B D BC CD D < p (kg/ha) Average Dry Biomass /kg are considered exchangeable and nonexchangeable, respectively. + Un 10 79 2.5 16 29 1 13 45 139 2.3 1.0 1929 UnUnUnUnUnUn 9.7Un 9.2Un 77 75 4.8 2.5 3.1 2.4 16 26 3.3 16 24 1.4 30 3.0 24 1.6 31 3.1 16 22 1 1.7 3.0 16 23 1 3.5 17 16 16 23 3.4 17 18 14 44 3.0 3.3 17 14 56 3.1 164 56 12 15 179 3 3.1 55 14 34 1.5 2.4 0.3 15 45 51 165 2.5 33 1.1 1885 201 0.6 32 1.5 37 1957 74 0.7 146 1.1 38 52 250 1.5 0.8 951 43 165 3.2 801 0.9 140 3.8 4.5 831 2.0 7.1 390 3.3 337 369 Am Low-compostAm High-compost 934 1440 Am Low-compostAm High-compost 651 1001 Am Low-compostAm High-compost 915 865 Chemical properties of amended and unamended soils in October 2007 following second-year of plant growth in amended plots. Values are for means obtai 2). = Organic matter was determined by the loss on ignition method. Fractions of acidity below and above 15 cmol Different letters indicate signiﬁcantly different means ( Mean moisture content of all amended soils in orange and gray zones were slightly but signiﬁcantly higher than they were in unamended control areas in o n moisture contents between amended and unamended soils in the red zone. ( Orange zone Am Lime-only 314 Table 3. Red zone Am Lime-only 23 a b c d Gray zone Am Lime-only 241

MAY 2013 Restoration Ecology 325

124

Vegetating Acid Mine Drainage Barrens

Figure 3. Pie charts showing plant cover percentages of different plant groups in 2009 in the red, orange, and gray zones for all three treatments combined.

Pyrolysis-MS data showed that after four growing seasons, soil C contents were higher in amended soils than in their respective unamended control areas, reaching up to 2.6, 2.0, and 2.7% for the red, orange, and gray zones, respectively. Reclamation with either compost rate yielded similar increases in soil carbon in the red and orange zones, but not in the gray zone, where a 20% higher carbon content was observed in the high-rate compost treatment. Despite its lower soil carbon content after four growing seasons, the low-rate treatment in Treated Red Zone Plot the gray zone supported plant biomass (Table 3) and plant cover comparable to that of the high-rate compost treatment.

Soil pH and Chemical Measurements Compared to pH values for unamended control areas in 2006, Untreated Red Zone soil pH increases were incrementally higher in treated plots in the red (1.2–1.7), orange (2.0–2.7), and gray (2.7–3.1) zones, respectively (Table 2). Because all plots received the Figure 4. Photograph of vegetation in treated plot (low-rate compost) same amount of lime, contrasting pH increases in the three plot in red zone in 2011, showing dominance of gray birch seedlings and zones appeared to be due to differences in acidity and mineral steeplebush plants. Foreground shows unamended red zone supporting composition of precipitates. This pattern also was observed biological soil crust. in 2007, when soil pH increases in treated plots ranged from 1.4 to 1.8 in the red zone, 2.1 to 2.3 in the orange zone, and fourth growing season, areal coverage by indigenous plants 3.4 to 3.8 in the gray zone (Table 3). In 2006 and 2007, no increased to the greatest extent in red zone plots (Fig. 4). significant differences in soil pH were observed between low- and high-rate compost treatments in all zones, and increased soil pH due to reclamation was sustained through the fourth Soil Moisture and Organic Matter growing season. Mean moisture contents of amended soils in the orange and Treated plots in all three zones showed consistently higher gray zones were slightly but significantly higher than they were extractable levels of K, Mg, and Ca than unamended areas in their respective unamended control areas in both 2006 and in 2006 and 2007 (Tables 2 and 3), apparently because of 2007 (Tables 2 and 3). Higher soil moisture contents reflected lower acidities following treatment. Extractable P levels in water-holding capacity of organic matter additions, which red and orange zone plots in 2007, however, remained low would have provided greater benefit to the drier zones than to even after treatment. Only compost-amended gray zone plots the red zone. Although unamended precipitates from all three showed clear increases in extractable P, which appeared to be zones prior to reclamation in 2006 had similar carbon contents due to absence of residual acidity in 2007 (Table 3). Although of 0.8–1.1% (Table 1), organic matter contents of unamended treated plots in the gray zone showed higher extractable P and precipitates, as determined by loss on ignition in 2007, showed Ca than similarly treated plots in the orange and red zones, clear differences among zones, with means of 9.6, 3.7, and extractable K and Mg in gray and orange zones were similar. 1.6%, for red, orange, and gray zones, respectively (Table 3). Red zone plots continued to have highest acidity through 2007. The loss-on-ignition method may be a poor indicator of organic In the red zone, CEC increased in treated plots relative to matter differences in iron-rich materials, however, because their respective unamended areas, but CEC did not change measurements using pyrolysis-MS in 2009 were similar to the significantly as a result of reclamation treatment in the orange 2006 C data, with 1.5, 1.1, and 1.0% carbon for unamended or gray zones. With the exception of compost-treated plots in precipitates in red, orange, and gray zones, respectively. the gray zone, Zn and Cu levels were lower in all amended

326 Restoration Ecology MAY 2013

125

Vegetating Acid Mine Drainage Barrens than in unamended plots. Sulfur levels, on the other hand, Mining Control and Reclamation Act of 1977 (Sopper 1993). increased in treated plots in the red and orange zones relative Our organic amendments included compost, straw, nurse crop to unamended areas, but decreased in treated plots in the gray root exudates, and decayed nurse crop residues, all of which zone. These differences in chemical composition did not result provided slower release of mineral nutrients than manures, in marked differences in plant cover in the three zones. biosolids, or mineral N fertilizers. In the United States, 1 biosolids application rates of up to 135 Mg ha− have been used on mine lands (Sopper 1993) and have resulted in first- year available N levels of >1000 kg N ha 1,withsignificant Discussion − N losses to ground and surface waters (Stehouwer et al. 2006). A theoretical framework for converting AMD barrens into soils Additional reasons for avoiding high available N during plant supporting diverse plant communities must take into account establishment are that such conditions reduce plants’ allocation acidity, toxic metals, electrical conductivity, organic matter of photosynthate to roots and soils (Kuzyakov and Domanski inputs, and short- and long-term nutrient availability (Wong 2000) and suppress establishment of beneficial associations 2003). In the present study, concentrations of Zn and Cu between plants and microorganisms (Brockwell et al. 1995; were within normal ranges for Pennsylvania soils. However, Egerton-Warburton et al. 2007). We propose that exudates drainages from other geological formations, particularly those and secretions from nurse crop roots deliver high-quality mined for metals, can pose greater risks of heavy metal tox- nutrients that enhance soil biological activity and organic icity (Arocena et al. 2010; Lottermoser 2010). As our surface matter accretion in developing soils. precipitates also had comparatively low electrical conductiv- High nutrient availability during initial restoration stages ities, we conclude that the main factor preventing vegetative can also influence plant community composition, favoring fast- establishment in these AMD barrens was high aciditiy. growing introduced species that outcompete native plants. In a The rapid methods used to measure CECsum and acidity, study by Halofsky and McCormick (2005b), biosolids addition while appropriate for agronomic soils (Wolf & Beegle 2011), during mineland reclamation led to long-term dominance likely underestimated both of these properties in AMD pre- by early successional species, most notably grasses, and cipitates. Underestimation of acidity was evident in the pH consequently a low establishment of woody and indigenous range achieved for amended red zone precipitates (3.7–4.3), species. They also observed that many grass species commonly because the one-time lime application had been calculated planted in reclamation mixes have aggressive growth habits to bring the pH to 5. Prior to amendment, red zone precip- that favor dominance. Provision of slower-release nutrients itates had highest nonexchangeable acidity, likely due to their should foster natural plant colonization, a reported key factor high Fe contents. Red zone precipitates also exhibited larger in ecosystem recovery at mine sites (Arocena et al. 2010; increases in CECsum after liming than precipitates from other Rufaut & Craw 2010). Natural revegetation on the peripheries zones. Such pH-dependent CEC appeared to be associated with of amended plots also occurred, similar to observations by exchange sites on iron oxides rather than organic matter, since McClanahan (1986) that numerous small islands, rather than liming alone achieved increases in CECsum that were similar a few large ones, favor seed dispersal and plant colonization to those with lime plus compost. Future in situ restoration of at mine sites. Provision of large amounts of available mineral AMD precipitates should begin with development of specific nutrients during initial stages of restoration can therefore work methods for fuller characterization of exchangeable, nonex- against plant diversity in the longer term. changeable, and potential acidity from Al(III), Fe(II), Fe(III), and reduced S, all of which are typical for mine-related wastes (Bigham & Nordstrom 2000). Implications for Practice Once acidity has been remediated, iron oxides in AMD Acidic surface layers of mining-impacted barrens can be • precipitates can serve to accelerate organic matter accretion revegetated in place. in these incipient soils. Several studies have demonstrated The same treatment of lime and slow-release nutrients • correlations between soil organic carbon and iron oxide achieved sufficient plant cover in zones with different contents (Boudot 1982). In the presence of organic matter, acidity, mineralogy, and hydrology. hydrated iron oxides such as amorphous ferrihydrite provide Site-specific methods for characterizing acidity of mine • reactive surfaces that facilitate organo-mineral associations wastes are recommended. (Tipping 1981), influencing nutrient exchange and availability Avoidance of high nutrient availability during initial • Guzman et al. (1994). Iron oxides have also been reported stages of restoration may promote greater diversity of to coprecipitate with microbial proteins to stabilize carbon native plants. and play a role in microaggregate formation (Oades & Waters 1991). Therefore, iron oxide interactions with root and microbial exudates should enhance nutrient retention and Acknowledgments reduce erosion and loss of sediment and metals. We thank landowner Alan Larson, who permitted site access; We used comparatively low rates of compost (27 and Jeff Feldmeier, who provided historical information; Dr. Curtis 1 54 Mg ha− )toachieve>70% plant cover which was sustained Dell for laboratory and equipment access; Doug Sayler, PA for five years, meeting requirements of the U.S. Surface Department of Environmental Protection for seed mix; and

MAY 2013 Restoration Ecology 327

126

Vegetating Acid Mine Drainage Barrens

Christy Rollinson and Nick Hebrock for field assistance. We the growth of a native Australian grass on sulphidic gold mine tailings. acknowledge the Penn State College of Agricultural Sciences Restoration Ecology 18:175–183. for support through McKenna and Graduate Competitive Grant McClanahan, T. R. 1986. Seed dispersal from vegetation islands. Ecological Modeling 32:301–309. programs. Mehlich, A. 1976. New buffer pH method for rapid estimation of exchangeable acidity and lime requirement of soils. Communications in Soil Science LITERATURE CITED and Plant Analysis 7:637–652. Nelson, D. W., and L. E. Sommers. 1996. Total carbon, organic carbon, Arocena, J. M., J. M. vanMourik, M. L. Schilder, and A. F. Cano. 2010. and organic matter. Pages 961–1010 in Methods of soil analysis, Initial soil development under pioneer plant species in metal mine waste part 3—chemical methods. Soil Science Society of America, Madison, deposits. Restoration Ecology 18:244–252. Wisconsin. Bigham, J. M., and D. K. Nordstrom. 2000. Iron and aluminum hydroxysulfates Oades, J. M., and A. G. Waters. 1991. Aggregate hierarchy in soils. Australian from acid sulfate waters. Pages 351–403 in C. N. Alpers, J. L. Journal of Soil Research 29:815–828. Jambor, and D. K. Nordstrom, editors. Sulfate minerals, crystallography, Pensa, M., A. Sellin, A. Luud, and I. Valgma. 2004. An analysis of vegetation geochemistry and environmental significance: reviews in mineralogy and restoration on opencast soil shale mines in Estonia. Restoration Ecology geochemistry. Mineralogical Society of America, Washington, DC. 12:200–206. Boudot, J. P.. 1982. Influence of iron oxides on the surface area of soil. Prasanna, R., S. K. Ratha, C. R. Rojas, and M. A. Bruns. 2011. Algal European Journal of Soil Science 33:443–449. diversity in flowing waters at an acidic mine drainage “barrens” in central Brockwell, J., P. J. Bottomley, and J. E. Thies. 1995. Manipulation of rhizobia Pennsylvania, USA. Folia Microbiologica 56:491–496. microflora for improving legume productivity and soil fertility: a critical Ross, D., and Q. Ketterings. 2011. Recommended soil tests for determining assessment. Plant Soil 174:143–180. soil cation exchange capacity. Pages 75–86 . Recommended soil testing Bureau of Land Management, National Applied Resource Sciences Center. procedures for the Northeastern United States. Northeastern Regional 1999. Sampling vegetation attributes. Interagency Technical Reference, Publication No. 493. 3rd edition. University of Delaware Agric. Exp. Cooperative Extension Service. U.S. Department of Agriculture, NRCS Stat., Newark. Grazing Land Technology Institute. Rufaut, C. G., and D. Craw. 2010. Geoecology of ecosystem recovery at an Cornell, R. M., and U. Schwertmann. 2003. The iron oxides: structure, inactive coal mine site, New Zealand. Environmental Earth Sciences properties, reactions, occurrences and uses. Wiley-VCH, Weinheim, 60:1425–1437. Germany. Soil Survey Staff, Natural Resources Conservation Service, United States Demchak, J., J. Skousen, and L. M. McDonald. 2004. Longevity of acid Department of Agriculture. Web Soil Survey, 2010. (available from discharges from underground mines located above the regional water http://websoilsurvey.nrcs.usda.gov/) [accessed November 10, 2010]. table. Journal of Environmental Quality 33:656–668. Sopper, W. E. 1993. Municipal sludge use in land reclamation. Lewis Egerton-Warburton, L. M., N. C. Johnson, and E. B. Allen. 2007. Mycorrhizal Publishing, Boca Raton, Florida. community dynamics following nitrogen fertilization: a cross-site test in Stehouwer, R., R. L. Day, and K. E. Macneal. 2006. Nutrient and trace five grasslands. Ecological Monographs 77:527–544. element leaching following mine reclamation with biosolids. Journal of Feldmeier, J. L. 2005. An overview of the underground coal-mining era Environmental Quality 35:1118–1126. at Grassflat and environs. Bulletin of the Clearfield County Histor- Tipping, E. 1981. The adsorption of aquatic humic substances by iron oxides. ical Society, Clearfield, Pennsylvania (available from http://grassflat. Geochimica et Cosmochimica Acta 45:1991–1999. bravehost.com/mining.html) [accessed January 14, 2012]. Winland, R. L., S. J. Traina, and J. M. Bigham. 1991. Chemical composition Guzman, G., E. Alcantara, V. Barron, and J. Torrent. 1994. Phytoavailability of ochreous precipitates from Ohio coal mine drainage. Journal of of phosphate adsorbed on ferrihydrite, goethite, and hematite. Plant Soil Environmental Quality 20:452–460. 159:219–225. Wolf, A. M., D. B. Beegle, and B. Hoskins. 2008. Comparison of Shoemaker- Halofsky, J. E., and L. H. McCormick. 2005a. Establishment and growth of McLean-Pratt and modified Mehlich buffer tests for lime requirement experimental grass species mixtures on coal mine sites reclaimed with on Pennsylvania soils. Communications Soil Science Plant Analysis municipal biosolids. Environmental Management 35:569–578. 39:1848–1857. Halofsky, J. E., and L. H. McCormick. 2005b. Effects of unseeded areas Wolf, A. M., and D. B. Beegle. 2011. Recommended soil tests for macro and on species richness of coal mines reclaimed with municipal biosolids. micronutrients. Pages 39–48. Recommended soil testing procedures for Restoration Ecology 13:630–638. the Northeastern United States. Northeastern Regional Publication No. Kaufmann, P. R., A. T. Herlihy, and L. A. Baker. 1992. Sources of acidity 493. 3rd edition. University Delaware Agric. Exp. Stat, Newark. in lakes and streams of the United States. Environmental Pollution Wong, M. H. 2003. Ecological restoration of mine degraded soils, with 77:115–122. emphasis on metal contaminated soils. Chemosphere 50:775–780. Kuzyakov, Y., and G. Domanski. 2000. Carbon input by plants into soil—a Younger, P. L. 1997. The longevity of minewater pollution: a basis for decision- review. Journal of Plant Nutrition and Soil Science 163:421–431. making. Science of Total Environment 195:457–466. Lottermoser, B. G. 2010. Mine wastes: characterization, treatment and envi- Zink, T., A. Wolfe, and K. Curley. 2005. Restoring the wealth of the ronmental impacts. Springer-Verlag, Berlin, Germany. mountains: Cleaning up Appalachia’s abandoned mines. Trout Unlimited, Lupton, M. K. 2008. Revegetaton of an acid mine drainage-impacted soil using Arlington, Virginia (available from http://www.tu.org/atf/cf/) [accessed low rates of lime and compost. Thesis. The Pennsylvania State University, June 1, 2010]. University Park. Madajon,´ E., A. I. Doronila, J. T. Sanchez-Palacios, P. Madejon,´ and A. J. M. Baker 2010. Arbuscular mycorrhizal fungi (AMF) and biosolids enhance

328 Restoration Ecology MAY 2013

127 Appendix C Plant composition in 2010 of experimental plots receiving lime plus 27 Mg ha-1 compost located in the red zone

Growth Scientific Name Common Name Experiment Experimental Habits al plot 1 plot 2 Tree Acer rubrum Red maple - * Betula lenta Sweet birch ** ** Betula populifolia Gray birch *** *** Pinus strobus Eastern white pine * - Populus grandidentata Bigtooth aspen - ** Populus tremuloides Quaking aspen * * Shrub Crataegus L. Hawthorn * * Spiraea alba White meadowsweet * - Spiraea tomentosa Steeplebush * ** Subshrub Rubus flagellaris Northern dewberry - * Forb/herb Dennstaedtia Eastern hayscented fern * - Trifolium aureum Golden clover ** - Symphyotrichum Calico aster - * Lotus corniculatus Bird’s-foot trefoil* ** * Solidago rugosa Wrinkleleaf goldenrod * * Drosera intermedia Spoonleaf sundew ** * Euthamia graminifolia Flat-top goldentop * ** Verbena urticifolia White vervain - * Graminoid Elymus repens Quackgrass * - Scirpus L. Bulrush * - Carex scoparia Broom sedge * - Agrostis gigantea Redtop* * * Bromus L. Brome * ** Dactylis glomerata L. Potomac orchar grass* * * *Species sown in 2006.

128 Appendix D Distribution of well installed at the research site and measurement over a one-year period

w6 w3 w8 13 m 13 Control Reclaimed Control plot w1 w4 plot 1 plot 2 2 w2 Reclaimed plot 1

w7 w5 w9 20m

------2010------2011------Jun.1" 2"Jul. 3"Aug.4" Sep.5" 6"Oct.7" Nov.8" Dec.9" 10" Jan.11" Feb.12" 13" Mar.14" Apr.15" 16"May.17" Jun.18" Jul.19" 20"Aug 0 10 20 30 40 50 60 70 80 Depth of AMD subsurface flow (cm)AMD subsurface flow of Depth W1" W2" W3" W4" W5" W6" W7" W8" W9"

Calculation (only the shallowest values from wells close to plots were used for calculations): W1 and W4 (average) was use to calculate depth of subsurface AMD flow beneath Rec. plot 1. The shallowest point was reported at 10.8 cm in May 2011 and the deepest at 24.2 cm in August 2011. W4 only was used to calculate depth of subsurface AMD flow beneath Rec. plot 2 and Con. plot 1. The shallowest point was reported at 14 cm in May 2011 and the deepest at 33.5 cm in August 2010. W2 only was used to calculate depth of subsurface AMD flow beneath Con. plot 2. The shallowest point was reported at 9 cm in April 2011 and the deepest at 30.6 cm in September 2010.

Appendix E XRD spectra for adherent (a) and non-adherent (b) precipitates obtained from reclaimed and control plots.

130 B

Appendix F Estimated total Fe; Fe(II); percentage of Fe(II) on basis of total Fe; percentage of Fe(II) on basis of organically bound Fe; and organic carbon in the upper 8 cm of one hectare of reclaimed precipitates and control precipitates.

Total Fe Fe (II) Fe(II) expressed as Fe(II) expressed as % Organic-C T ha-1 T ha-1 % total Fe of org. bound Fe T ha-1

Reclaimed 1 261.3 0.14 0.053 3.62 13.3

Reclaimed 2 272. 6 0.08 0.029 1.94 10.0

Control 1 508.7 0.03 0.007 0.82 9.2

Control 2 526.8 0.04 0.007 0.85 9.0

132 Appendix G Protocol utilized to process pyrosequencing data

DNA!extrac:on!amplicon !and!sequencing!!

DNA%extrac0on% Barcoded%amplicon%prepara0on!! MoBio!PowerLyzerTM!Power! 16S!rRNA!!targeted!V1,!V2,!V3!and!V5! Soil®!DNA!isola:on!kit!! Priemers!27F?907R+454!adaptor+10!base!pares!adaptor! 0.35%g!! (?Solu:on!3?inhibitor!removal)! fresh!sample!

Pyrosequencing%! %%!! PCR%ampliﬁca0on%

/blog/tag/ Amplicon%cleaning%and%size%selec0on%% 35!Cycles!! 70%!ethanol!!and!magne:c!beads!! 94°!C,!3!min.! 500!bp!in!agarose!gel!! !94°!C,!15!sec.! /! Qubit!DNA!assay! !55°!C,!45!sec.!! %%!! 72°!C,!1!min.! sco^.sherrillmix.com 72°!C,!8!min.!%

h^p:// pyrosequencing %%!!

Processing!of!pyrosequence!data!(Mothur!soZware!v.1.24.1)!! ! Reducing%sequencing%error%(quality%ﬁltering)% SILVAHbased%reference%alignment%% PyroNoise!(trimming,!barcode!assignment,! Screened!≥!200bp!sequences! NAST?based!aligner!(sequences!overlap!in! mismatches!and!homopolymers!removed)!! the!same!alignment!space,!Pre?clustering)!

Sequence%classiﬁca0on%% Chimera%removal% Greengenes!taxonomy!database!(80%!threshold)!! Uchime% (chloroplast,!mitochondria,!and!seq.!not!classiﬁed! at!the!domain!level!removed)%

Bacterial!community!analysis! ! 64,709!curated! Rarefac0on%curves% OTU%building%%% sequences!classiﬁed!! Pairwise!distance!matrices!(dist.seqs)!!! at!the!domain!level!! 97%!similarity! (rarefac0on.single)!! Furthest!Neighbor!Algorithm!(97%!similarity)%(cluster)% % Alpha%diversity%%% Rarefac:on!curves!(80,!90,!95,!97,!99%!similarity)!(rarefac0on.single)!! Sample!coverage,!richness!indices!(Ace!and!Chao),!diversity!indices!(Shannon! and!Simpson ?1)!(summary.single)% Normaliza0on%% 14,666!seq.!randomly! Beta%diversity%%% selected!for!each! Rela:ve!taxa!abundance?Heatmaps! sample!!! OUT!distribu:on?Venn!diagram!(venn)%! Sample!similarity?Bray?Cur:s!distance!matrix,!UPGMA!,!dendogram!(tree.shared%)%% Commands%in%mothur% !

133 Appendix H Rarefaction curves of total number of bacterial sequences obtained from RR, RB, CC, and CB .

The rarefaction curves were calculated by Mothur at 97% similarity

1800 RR RB CC CB

1600

1400

1200

1000

800

Number of OTUs Number of 600

400

200

0 0 6000 12000 18000 Number of Sequences

Appendix I Rarefaction curves per sample at 80, 90, 95, 97, 99% similarity

RR RB 3000 3000 0.01 0.03 0.01 0.03 0.05 0.1 2500 0.2 2500 0.05 0.1 0.2 2000 2000

1500 1500

1000 1000 Number of OTUs Number of 500 500 Number of OTUs Number of

0 0 0 6000 12000 18000 0 6000 12000 18000 CB CC 3000 3000 0.01 0.03 0.01 0.03 0.05 0.1 0.05 0.1 2500 2500 0.2 0.2

2000 2000

1500 1500

1000 1000 Number of OTUs Number of Number of OTUs Number of 500 500

0 0 0 6000 12000 18000 0 6000 12000 18000 Number of Sequences Number of Sequences

Appendix J Complete eukaryotic taxon distribution

Relative distribution2 (%) of microbial eukaryote taxa across all samples. Dark green color indicates higher percentage while light green color indicates lower percentage, actual values are superimposed over colors.

2 The relative abundances (%) of microeukaryotic taxa within each community were determined after subtracting macro-eukaryote sequences which were particularly abundant in CC and CB. Percent abundances were calculated by dividing the number of sequences assigned to a specific taxon by the number of micro-eukaryotic sequences identified in each sample.

136

0.00 0.22 0.00 0.00 Otu061 Opisthokonta|Fungi 0.00 0.17 0.00 0.00 Otu063 Opisthokonta|Fungi 0.12 0.17 0.00 0.00 Otu065 Opisthokonta|Fungi 0.12 0.15 0.00 0.00 Otu072 Opisthokonta|Fungi 0.47 0.22 0.00 0.00 Otu076 Opisthokonta|Fungi 0.06 0.11 0.00 0.00 Otu085 Opisthokonta|Fungi 0.00 0.10 0.00 0.00 Otu086 Opisthokonta|Fungi 0.03 0.08 0.00 0.00 Otu091 Opisthokonta|Fungi 0.00 0.01 0.00 0.00 Otu251 Opisthokonta|Fungi 0.00 0.01 0.00 0.00 Otu258 Opisthokonta|Fungi 137 0.32 0.00 0.00 0.00 Otu262 Opisthokonta|Fungi 0.29 0.00 0.33 0.00 Otu263 Opisthokonta|Fungi 0.22 0.00 0.00 0.00 Otu264 Opisthokonta|Fungi 0.04 0.00 0.00 0.00 Otu314 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu346 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu359 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu368 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu378 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu379 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu404 Opisthokonta|Fungi 0.01 0.00 0.00 0.00 Otu422 Opisthokonta|Fungi 0.00 0.00 0.05 0.00 Otu527 Opisthokonta|Fungi 0.00 0.07 0.00 0.00 Otu092 Opisthokonta|Fungi|Nucletmycea| Fonticula 0.00 0.00 0.09 0.02 Otu472 Opisthokonta| Fungi| Ascomycota 0.08 0.00 0.00 0.00 Otu275 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Dothideomycetes| Dothideomycetidae| Capnodiales| Mycosphaerellaceae 0.01 0.00 0.00 0.00 Otu328 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Dothideomycetes| 0.01 0.00 0.00 0.00 Otu315 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Eurotiomycetes| Chaetothyriomycetidae| Chaetothyriales 1.72 7.28 11.97 7.33 Otu005 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Eurotiomycetes| Eurotiomycetidae| Eurotiales 0.00 0.02 0.00 0.00 Otu134 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Lecanoromycetes 0.01 0.00 0.00 0.00 Otu358 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Leotiomycetes 0.02 0.00 0.00 0.00 Otu372 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Leotiomycetes 0.00 0.00 0.05 0.00 Otu533 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Pezizomycetidae| Pezizales| Pezizaceae| Iodowynnea 0.01 0.00 0.00 0.00 Otu396 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Pezizomycetidae| Pezizales| Pezizaceae|Pachyphloeus 0.00 0.00 0.19 0.14 Otu449 Opisthokonta 0.00 0.00 0.05 0.00 Otu524 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Pezizomycetidae| Pezizales| Pezizaceae| 0.00 0.00 0.00 0.02 Otu469 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Pezizomycetidae| Pezizales 1.70 2.59 7.34 1.63 Otu007 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Sordariomycetes 0.54 1.27 9.54 3.58 Otu015 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Sordariomycetes 0.84 0.33 0.00 0.00 Otu044 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Sordariomycetes 0.09 0.00 0.00 0.00 Otu295 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Sordariomycetes 0.01 0.00 0.00 0.00 Otu409 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Sordariomycetes 0.00 0.00 0.05 0.02 Otu463 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| Sordariomycetes 4.67 4.04 21.00 5.20 Otu019 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.00 0.01 0.00 0.00 Otu232 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.00 0.01 0.00 0.00 Otu255 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.16 0.00 0.00 0.00 Otu269 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.08 0.00 0.05 0.06 Otu302 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.01 0.00 0.00 0.00 Otu397 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.00 0.00 0.19 0.00 Otu496 Opisthokonta| Fungi| Ascomycota| Pezizomycotina| 0.00 0.14 0.00 0.00 Otu074 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| Saccharomycetales| Anamorphic Saccharomycetales| Candida 3.01 2.98 0.00 0.00 Otu022 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 1.07 0.71 0.00 0.00 Otu030 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu144 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu186 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.02 0.01 0.00 0.00 Otu204 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.02 0.02 0.00 0.00 Otu223 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu228 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu235 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.01 0.00 0.00 0.00 Otu430 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 36.72 17.65 0.00 0.22 Otu001 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 7.23 13.58 0.00 0.08 Otu002 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 2.30 1.39 0.00 0.00 Otu014 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.19 1.03 0.14 1.19 Otu018 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.02 0.23 0.00 4.79 Otu068 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.03 0.05 0.00 0.00 Otu087 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.06 0.10 0.05 0.00 Otu088 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.06 0.00 0.00 Otu110 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.03 0.00 0.00 Otu133 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.02 0.00 0.08 Otu164 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.01 0.00 0.00 Otu219 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.05 0.00 0.00 0.02 Otu292 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.06 0.00 0.00 0.00 Otu293 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.01 0.00 0.00 0.00 Otu354 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.01 0.00 0.05 0.00 Otu383 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.01 0.00 0.00 0.00 Otu390 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.00 0.00 0.03 Otu459 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.27 1.86 0.09 3.00 Otu012 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.00 0.60 0.00 0.00 Otu035 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.00 0.27 0.00 0.00 Otu066 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.00 0.08 0.00 0.00 Otu093 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.53 0.62 0.00 0.00 Otu037 Opisthokonta| Basidiomycota| Agaricomycotina| 0.00 0.00 0.00 0.02 Otu467 Opisthokonta| Basidiomycota|| Basidiomycota| Wallemiomycetes| Wallemiales| Wallemia 0.01 0.00 0.00 0.00 Otu377 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Agaricostilbomycetes 0.00 0.02 0.00 0.00 Otu143 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Cystobasidiomycetes 0.00 0.00 0.00 0.02 Otu477 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Cystobasidiomycetes 0.00 0.02 0.00 0.00 Otu153 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Microbotryomycetes 0.53 1.15 0.00 0.17 Otu017 Opisthokonta| Fungi| Basidiomycota| Ustilaginomycotina| Exobasidiomycetes| Malassezia 0.01 0.00 0.00 0.02 Otu370 Opisthokonta| Fungi| Basidiomycota| Ustilaginomycotina| Exobasidiomycetes| Malassezia 4.37 0.41 0.00 0.05 Otu039 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.02 0.30 0.19 0.02 Otu062 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.04 0.00 0.00 Otu114 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.02 0.00 0.00 Otu158 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.16 0.00 0.00 0.00 Otu282 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.05 0.00 0.00 0.00 Otu299 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.03 0.00 0.00 0.00 Otu311 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.03 0.00 0.00 0.00 Otu323 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.01 0.00 0.00 0.00 Otu326 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.01 0.00 0.00 0.00 Otu428 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.98 0.42 Otu442 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.42 0.16 Otu444 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.09 0.00 Otu492 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.19 0.00 Otu493 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.05 0.00 Otu509 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.05 0.00 Otu511 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota

0.00 0.01 0.00 0.00 Otu144 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu186 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.02 0.01 0.00 0.00 Otu204 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.02 0.02 0.00 0.00 Otu223 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu228 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.00 0.01 0.00 0.00 Otu235 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 0.01 0.00 0.00 0.00 Otu430 Opisthokonta| Fungi| Ascomycota| Saccharomycotina| Saccharomycetes| 36.72 17.65 0.00 0.22 Otu001 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 7.23 13.58 0.00 0.08 Otu002 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 2.30 1.39 0.00 0.00 Otu014 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.19 1.03 0.14 1.19 Otu018 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.02 0.23 0.00 4.79 Otu068 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.03 0.05 0.00 0.00 Otu087 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.06 0.10 0.05 0.00 Otu088 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.06 0.00 0.00 Otu110 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.03 0.00 0.00 Otu133 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.02 0.00 0.08 Otu164 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.01 0.00 0.00 Otu219 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.05 0.00 0.00 0.02 Otu292 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.06 0.00 0.00 0.00 Otu293 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.01 0.00 0.00 0.00 Otu354 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.01 0.00 0.05 0.00 Otu383 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.01 0.00 0.00 0.00 Otu390 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.00 0.00 0.00 0.03 Otu459 Opisthokonta| Basidiomycota| Agaricomycotina| Agaricomycetes 0.27 1.86 0.09 3.00 Otu012 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.00 0.60 0.00 0.00 Otu035 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.00 0.27 0.00 0.00 Otu066 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.00 0.08 0.00 0.00 Otu093 Opisthokonta| Basidiomycota| Agaricomycotina| Tremellomycetes 0.53 0.62 0.00 0.00 Otu037 Opisthokonta| Basidiomycota| Agaricomycotina| 0.00 0.00 0.00 0.02 Otu467 Opisthokonta| Basidiomycota|| Basidiomycota| Wallemiomycetes| Wallemiales| Wallemia 0.01 0.00 0.00 0.00 Otu377 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Agaricostilbomycetes 0.00 0.02 0.00 0.00 Otu143 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Cystobasidiomycetes 0.00 0.00 0.00 0.02 Otu477 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Cystobasidiomycetes 138 0.00 0.02 0.00 0.00 Otu153 Opisthokonta| Fungi| Basidiomycota| Pucciniomycotina| Microbotryomycetes 0.53 1.15 0.00 0.17 Otu017 Opisthokonta| Fungi| Basidiomycota| Ustilaginomycotina| Exobasidiomycetes| Malassezia 0.01 0.00 0.00 0.02 Otu370 Opisthokonta| Fungi| Basidiomycota| Ustilaginomycotina| Exobasidiomycetes| Malassezia 4.37 0.41 0.00 0.05 Otu039 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.02 0.30 0.19 0.02 Otu062 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.04 0.00 0.00 Otu114 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.02 0.00 0.00 Otu158 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.16 0.00 0.00 0.00 Otu282 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.05 0.00 0.00 0.00 Otu299 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.03 0.00 0.00 0.00 Otu311 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.03 0.00 0.00 0.00 Otu323 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.01 0.00 0.00 0.00 Otu326 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.01 0.00 0.00 0.00 Otu428 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.98 0.42 Otu442 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.42 0.16 Otu444 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.09 0.00 Otu492 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.19 0.00 Otu493 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.05 0.00 Otu509 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.05 0.00 Otu511 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 0.00 0.00 0.05 0.00 Otu539 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Chytridiomycota 3.62 1.89 0.00 0.00 Otu016 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Glomeromycota 0.01 0.00 0.00 0.00 Otu410 Opisthokonta Fungi| Fungi| Basal fungi| Basal fungi| Glomeromycota 1.88 4.63 11.51 11.07 Otu006 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.00 0.14 0.00 0.00 Otu077 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.00 0.10 0.00 0.00 Otu084 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.09 0.04 0.51 0.08 Otu120 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.00 0.03 0.89 0.02 Otu129 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.00 0.02 0.00 0.00 Otu140 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.04 0.01 0.09 0.14 Otu181 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.10 0.00 0.00 0.05 Otu283 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.05 0.00 0.00 0.00 Otu296 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.01 0.00 0.00 0.00 Otu356 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.00 0.00 0.00 0.02 Otu468 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 0.00 0.00 0.00 0.02 Otu478 Opisthokonta| Fungi| Fungi| Basal fungi| Basal fungi| Mucoromycotina 3.59 8.13 0.00 0.00 Otu003 Opisthokonta| Fungi| LKM11 0.66 0.69 1.17 4.99 Otu041 Opisthokonta| Fungi|LKM11 1.35 0.15 0.00 0.00 Otu070 Opisthokonta| Fungi|LKM11 0.00 0.05 0.00 0.00 Otu102 Opisthokonta| Fungi|LKM11 0.01 0.04 0.00 0.00 Otu167 Opisthokonta| Fungi|LKM11 0.00 0.01 0.00 0.02 Otu195 Opisthokonta| Fungi|LKM11 0.00 0.01 0.00 0.00 Otu203 Opisthokonta| Fungi|LKM11 0.00 0.01 0.00 0.00 Otu214 Opisthokonta| Fungi|LKM11 0.00 0.01 0.00 0.00 Otu225 Opisthokonta| Fungi|LKM11 0.04 0.01 0.00 0.00 Otu234 Opisthokonta| Fungi|LKM11 0.00 0.01 0.00 0.00 Otu245 Opisthokonta| Fungi|LKM11 0.03 0.01 0.00 0.03 Otu246 Opisthokonta| Fungi|LKM11 0.00 0.01 0.00 0.00 Otu250 Opisthokonta| Fungi|LKM11 0.13 0.00 0.00 0.00 Otu272 Opisthokonta| Fungi|LKM11 0.01 0.00 0.00 0.02 Otu360 Opisthokonta| Fungi|LKM11 0.01 0.00 0.00 0.00 Otu382 Opisthokonta| Fungi|LKM11 0.00 0.00 0.00 0.05 Otu456 Opisthokonta| Fungi|LKM11 0.52 0.73 0.00 0.00 Otu026 Opisthokonta| Fungi|LKM15 1.23 0.45 0.00 0.00 Otu046 Opisthokonta| Fungi|LKM15 0.45 0.17 0.00 0.00 Otu058 Opisthokonta| Fungi|LKM15 0.03 0.03 0.00 0.00 Otu155 Opisthokonta| Fungi|LKM15 0.00 0.02 0.00 0.00 Otu239 Opisthokonta| Fungi|LKM15 0.02 0.00 0.00 0.00 Otu324 Opisthokonta| Fungi|LKM15 0.02 0.00 0.00 0.00 Otu333 Opisthokonta| Fungi|LKM15 0.01 0.00 0.00 0.00 Otu353 Opisthokonta| Fungi|LKM15 0.02 0.00 0.00 0.00 Otu373 Opisthokonta| Fungi|LKM15 0.01 0.00 0.00 0.00 Otu393 Opisthokonta| Fungi|LKM15 0.01 0.00 0.00 0.00 Otu402 Opisthokonta| Fungi|LKM15 0.01 0.00 0.00 0.00 Otu408 Opisthokonta| Fungi|LKM15 0.19 0.15 0.00 0.00 Otu067 Opisthokonta|Metazoa|Nematoda|Chromadorea|Aphelenchida 0.00 0.01 0.00 0.00 Otu170 Opisthokonta|Metazoa|Nematoda|Chromadorea|Aphelenchida 0.08 0.00 0.00 0.00 Otu291 Opisthokonta|Metazoa|Nematoda|Chromadorea|Aphelenchida 0.00 0.00 0.05 0.00 Otu540 Opisthokonta|Metazoa|Nematoda|Chromadorea|Aphelenchida 0.00 0.00 0.05 0.00 Otu541 Opisthokonta|Metazoa|Nematoda|Chromadorea|Aphelenchida 0.08 0.05 1.82 0.25 Otu113 Opisthokonta|Metazoa|Nematoda|Chromadorea|Cephalobidae 0.02 0.00 0.00 0.00 Otu335 Opisthokonta|Metazoa|Nematoda|Chromadorea 0.31 0.00 0.00 0.00 Otu266 Opisthokonta|Metazoa|Nematoda|Enoplea|Prismatolaimidae

139

140 Appendix K Rarefaction curves of total number of eukaryotic sequences obtained from RR, RB, CC, and CB .

The rarefaction curves were calculated by Mothur at 95% similarity.

350 RR" RB" CC" CB" 300

250

200

150

Number of OTUs Number of 100

0 0 5000 10000 15000 20000 25000 30000

Number of Sequences

141 Appendix L Percentage of sequences classified by SILVA in various taxa by sample after equalizing all datasets to 16,118 sequences.

Taxon RR RB CC CB Chloroplastida Bryophyta 0.67 1.63 79.8 44.9 Opisthokonta Fungi 49.7 63.7 8.9 17.7 Opisthokonta Metazoa 5.8 11.9 1.1 5.1 Stramenopiles-Alveolata-Rhizaria 2.7 4.2 2.5 12.4 Unclassified 38.0 16.3 6.5 13.8

142 VITA

Claudia M. Rojas Alvarado

Education 2013 Ph.D. Soil Science & Biogeochemistry, The Pennsylvania State University 2007 Professional Degree. Agricultural Engineering, Universidad de Chile 2002 B.S. Agronomy, Universidad de Chile

Research experience 8/2008-present Research Assistant, Department of Ecosystem Science and Management, Penn State University 1/2006-12/2007 Research Assistant, Center for Agricultural and Environmental Research, Universidad de Chile 10/2003-12/2005 Undergraduate Research Assistant, Department of Engineering and Soils, Universidad de Chile 7/2003-9/2003 Internship, Chilean Department of Agriculture, Division for the Protection of Natural Resources

Teaching experience Fall 2011 Lab Instructor, Soil Ecology (SOILS 412W), Penn State University Spring 2010 and 11 Teaching Assistant, Introduction to Soil Science (SOILS 101), Penn State University Fall 2005, 06, and 07 Teaching Assistant, Biotransformation and environmental management of organic wastes, Universidad de Chile

Honors and Awards 2008-2012 Fulbright Graduate Student Equal Opportunity Fellowship ($105,000). Fulbright-CONICYT 2009-2012 Katherine Mabis McKenna Award ($7,900). College of Agriculture, Pennsylvania State University 2010-2011 Graduate Student Grant ($2,000). College of Agriculture, Pennsylvania State University

Publications Rojas, C. Martínez, C.E., Bruns, M.A. Fe biogeochemistry in reclaimed acid mine drainage precipitates: implications for phytoremediation. in press Environmental Pollution Journal. Lupton, M.K., Rojas, C., Drohan, P., Bruns, M. 2013. Vegetation and soil development in compost-amended iron oxide precipitates at a 50-year-old acid mine drainage barrens. Restoration Ecology 21, 320-328. Prasanna, R., Ratha, S., Rojas, C., Bruns, M.A, 2011. Algal diversity in flowing waters at an acidic mine drainage “barrens” in central Pennsylvania, USA. Folia Microbiologica 56, 491-496.