Microbial Degradation and Ecological Impacts of Biodegradable Plastic Mulch Films in Agricultural Soils

University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange

Doctoral Dissertations Graduate School

12-2019

Sreejata Bandopadhyay University of Tennessee, [email protected]

Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss

Recommended Citation Bandopadhyay, Sreejata, "Microbial Degradation and Ecological Impacts of Biodegradable Plastic Mulch Films in Agricultural Soils. " PhD diss., University of Tennessee, 2019. https://trace.tennessee.edu/utk_graddiss/5682

This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council:

I am submitting herewith a dissertation written by Sreejata Bandopadhyay entitled "Microbial Degradation and Ecological Impacts of Biodegradable Plastic Mulch Films in Agricultural Soils." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Environmental and Soil Sciences.

Jennifer DeBruyn, Major Professor

We have read this dissertation and recommend its acceptance:

Sean Schaeffer, Douglas Hayes, Todd Reynolds

Accepted for the Council:

Dixie L. Thompson

Vice Provost and Dean of the Graduate School

(Original signatures are on file with official studentecor r ds.)

Microbial Degradation and Ecological Impacts of Biodegradable Plastic Mulch Films in Agricultural Soils

A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville

Sreejata Bandopadhyay December 2019

DEDICATION

I dedicate this work to my grandfather, Late Mr. Sudhindra Mohan Banerjee; my parents Mrs. Archita Bandopadhyay and Mr. Satyaban Bandopadhyay; and my sister Amrita. Intellectuals in their own right, my grandfather and my parents have had a profound influence on my educational journey. My grandfather was not only my mentor and science tutor all through my high school, but his influence has shaped me in many ways to be the person I am today. My path to graduate school in the US would not have been successful without the support and encouragement of my loving parents who have taught me that there is no substitute for hard work and those who are true to themselves will always succeed. This is for you.

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ACKNOWLEDGEMENTS

First, I would like to thank my mentor, Dr. Jennifer DeBruyn, for giving me an opportunity to join her lab and pursue my doctoral studies. Her patience, encouragement, and guidance throughout this project has been instrumental in achieving this degree. Being an international student and living so far away from home, I have been fortunate to have had Dr. DeBruyn as an understanding and kind advisor. I would also like to thank my dissertation committee members - Dr. Douglas Hayes, Dr. Sean Schaeffer and Dr. Todd Reynolds - for their suggestions, constructive criticism and critical inputs that led to the successful completion of this project. I am much more confident and curious because of the influence of each one of you.

My sincere gratitude to the Department of Biosystems Engineering and Soil Science at the University of Tennessee - its students, staff and faculty. This department has not only provided me with a platform for mentorship and exchange of scientific knowledge, but I have made life-long connections and friendships with people who have gone out of their way to help me when I needed them.

Next, I would like to thank the Biodegradable Mulch Project Team at the University of Tennessee and Washington State University. I find myself fortunate to have worked with this team as it has given me a unique opportunity of transdisciplinary collaboration. Because of this project, I have worked with scientists and students from diverse disciplines and have learnt to see my own research in the context of and in relation to theirs. I thank my peers in this project, Marie English and Jose Liquet Gonzalez, for always being there for me; their friendship is something I will cherish beyond the realm of graduate school.

My heartfelt gratitude to my friends in Knoxville who helped me through all the highs and lows by being my lifelines here and making Knoxville my second home. Rahul, Divyani, Avik, and Rani – I cannot thank you all enough for the support and love you gave me and for the amazing friendships I share with each one of you. Last, but not the least, I thank my parents and sister for their wisdom, inspiration and faith in me which motivates me to do better. And finally, to Tanmoy, for his unconditional support and unwavering belief in me.

“Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.” -Marie Curie (1867-1934)

ABSTRACT

Agricultural plastic mulch films are used to improve crop yields by reducing weeds, moderating soil temperature and conserving soil moisture. Unfortunately, increased popularity of polyethylene (PE) mulch films have resulted in widespread pollution because they are non-biodegradable and have limited recycling options. Biodegradable plastic mulch films (BDMs) are emerging as a sustainable alternative to PE films. BDMs are meant to be tilled into the soil where they are expected to fully biodegrade. However, the biodegradation process is slow, and fragments can persist in soil for months. Limited research regarding the impacts of BDMs on soil microbial communities, and inadequate information on BDM-degrading microbes and factors that control biodegradation of BDMs have restricted their adoption. The objectives of this study were to 1) evaluate the effects of BDMs and PE on soil microbial community structure and function over two years (Spring 2015 - Spring 2017) in two geographical locations: Knoxville, TN, and Mount Vernon, WA, 2) identify potential BDM-degrading microbes using field and laboratory enrichment studies, and 3) evaluate the impacts of added nitrogen amendments on microbial decomposition of BDMs. Bacterial community structure and function were determined using 16S rRNA amplicon sequencing of soil DNA and extracellular enzyme assays. Microbial community structure and function differed between TN and WA (p < 0.05), and seasons of sampling within each location (p < 0.05); however, mulch treatment differences were not significant (p > 0.05). Microbial communities adhered to field-weathered BDMs (the plastic-ome) demonstrated enrichment of soil microbial taxa. Sequence data from BDMs in lab enrichments and isolates demonstrated that microbial colonization on the BDMs was driven by the composition of the mulch films. BDM decomposition was observed in soil microcosms with and without nitrogen amendments. However, nitrogen amendments to BDMs resulted in reduced mulch decomposition. Nevertheless, addition of mulch had minimal impacts on nitrification processes and enzyme activities in the microcosms, irrespective of nitrogen amendments. Limited effects of BDMs on soil microbial community structure and function suggest that BDMs may be a viable alternative to PE. The initial characterization of the “plastic-ome” lays a strong groundwork for future research on microbes degrading BDMs.

TABLE OF CONTENTS

INTRODUCTION ...... 1 Research questions ...... 2 Organization of the dissertation ...... 2 CHAPTER I LITERATURE REVIEW ...... 3 Plastic film mulching in agriculture ...... 3 Benefits of plastic mulch films ...... 4 Negative impacts of conventional polyethylene (PE) plastic ...... 5 Alternatives to polyethylene (PE) mulches: The shift towards biodegradable mulches ...... 10 Paper based mulches ...... 10 Photodegradable mulches ...... 11 Oxo-degradable mulches ...... 12 Biodegradable plastic mulches (BDMs) ...... 13 Microbial degradation of BDMs ...... 16 Microbial colonization of BDM surface ...... 17 Enzymatic degradation of BDM polymers ...... 17 Microbial utilization of degradation products ...... 31 Knowledge gap ...... 32 Characterizing BDM degraders using metagenomics ...... 33 References ...... 35 CHAPTER II BIODEGRADABLE PLASTIC MULCH FILMS: IMPACTS ON SOIL MICROBIAL COMMUNITIES AND ECOSYSTEM FUNCTIONS ...... 47 Abstract ...... 48 Introduction: Agricultural Plastic Mulch Films ...... 49 Indirect Effects of Plastic Mulches on Soils Via Microclimate Modification .....50 Direct Effects of BDMs Via Incorporation Into Soil ...... 52

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Future Research Opportunities ...... 55 Author Contributions ...... 56 Funding ...... 56 Acknowledgements ...... 56 References ...... 57 CHAPTER III STRUCTURAL AND FUNCTIONAL RESPONSES OF SOIL MICROBIAL COMMUNITIES TO BIODEGRADABLE PLASTIC FILM MULCHING IN TWO AGROECOSYSTEMS ...... 66 Abstract ...... 67 Importance ...... 68 Introduction ...... 68 Materials and methods ...... 70 Plastic Mulch Films ...... 70 Field trial description ...... 70 Soil sampling ...... 74 Soil DNA extraction and quantification ...... 75 Quantitative PCR for bacterial and fungal abundances ...... 75 DNA amplification and sequencing ...... 75 Extracellular enzyme assays ...... 76 Statistical analyses ...... 78 Results ...... 80 Environmental and soil physicochemical data ...... 80 Soil bacterial community diversity and structure ...... 80 Microbial community abundances ...... 86 Microbial community functions ...... 90 Discussion ...... 94 Conclusions ...... 101 Acknowledgements ...... 102

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References ...... 103 Appendix III ...... 111 CHAPTER IV SOIL BACTERIAL AND FUNGAL COMMUNITIES ASSOCIATED WITH BIODEGRADABLE PLASTIC MULCH FILMS ...... 121 Abstract ...... 122 Graphic art ...... 123 Introduction ...... 123 Materials and Methods ...... 125 Plastic mulch films ...... 125 Enrichment Cultures ...... 125 Field study...... 129 Microbial community analysis for lab and field study ...... 130 Statistical analysis ...... 133 Results ...... 134 Biodegradation of BDMs in enrichment cultures ...... 134 Bacterial communities on laboratory enriched BDMs ...... 136 Bacterial and fungal communities associated with bulk soil and field-weathered BDMs ...... 142 Discussion ...... 154 Acknowledgements ...... 162 References ...... 163 Appendix IV ...... 168 CHAPTER V EFFECT OF ORGANIC AND INORGANIC NITROGEN AMENDMENTS ON DEGRADATION OF BIODEGRADABLE PLASTIC MULCH FILMS...... 193 Abstract ...... 194 Introduction ...... 195 Materials and Methods ...... 198 Site description ...... 198

Soil sampling ...... 200 Biodegradable mulch used for incubation study ...... 200 Laboratory incubation ...... 200 Soil analyses before incubation ...... 201 Gas chromatography and microcosm maintenance ...... 202 Soil analyses after incubation ...... 202 Results ...... 207 Soil chemical tests ...... 207 Plastic decomposition in microcosms...... 211 Bacterial and fungal abundances in microcosms ...... 211 Nitrification processes and extracellular enzymatic functions in soil microcosms ...... 218 Discussion ...... 222 Nitrogen application suppressed plastic decomposition in TN and WA soils 222 Responses of soil bacterial, fungal and ammonia-oxidizing bacteria to nitrogen and plastic amendments ...... 225 Responses of soil hydrolytic enzyme activities to nitrogen and plastic amendments ...... 226 Acknowledgements ...... 228 References ...... 229 CONCLUSIONS AND RECOMMENDATIONS ...... 235 References ...... 239 VITA ...... 240

LIST OF TABLES Table 1.1: The percentage of survey respondents using agricultural plastic in different industries (Hurley, 2008)...... 7 Table 1.2: The percentage of survey respondents recycling agricultural plastic in different industries (Hurley, 2008)...... 8 Table 3.1: Manufacturers, major constituents, and physicochemical properties of the mulches used in the study. Bio-based content data was provided by the manufacturers. Data reported from Hayes et al., 2017...... 71 Table 3.2: The seven extracellular soil enzymes assayed for soils collected from Spring 2015- Spring 2017; their respective enzyme functions, substrates used for assays, and the role of each enzyme in biogeochemical cycling. Standards used were MUB (4-methylumbelliferone) and MUC (7-amino-4-methylcoumarin)...... 77 Table 3.3: Results (F values) of PERMANOVA tests for differences in bacterial community composition by location (Knoxville (TN) and Mount Vernon (WA)), season and mulch treatment. Significant differences are in bold; *p < 0.05; **p < 0.01; ***p < 0.001...... 83 Table 3.4: F values of fixed effects and interaction effects obtained from a mixed model analysis of variance of the alpha diversity metrics richness (number of observed OTUs) and diversity index (inverse Simpson) from Spring 2015 to Fall 2016 in Knoxville, TN and Mount Vernon, WA. Significant values are in bold, *p < 0.05; **p < 0.01; ***p < 0.001 ...... 87 Table 3.5: T values from paired t-tests comparing 16S and ITS initial abundances from Spring 2015 to final abundances from Fall 2016 to determine significant changes over the two-year experiment in Knoxville, TN and Mount Vernon, WA. Significant values are in bold, *p < 0.05; **p < 0.01; ***p < 0.001...... 90 Table 3.6: F values of fixed effects and interaction effects obtained from a mixed model analysis of variance of the soil enzyme activities from Spring 2015 to Spring 2017 in Knoxville, TN and Mount Vernon, WA. Significant values are in bold, *p < 0.05; **p < 0.01; ***p < 0.001...... 96 Table A.3.1: Weather data at ETREC, Knoxville, TN and NWREC, Mount Vernon, WA. Adapted from Sintim et al., 2019...... 111 Table A.3.2: PERMANOVA results for comparisons between microbial community composition. Table generated using adonis function (vegan package) in R statistical environment. Df: degrees of freedom, Sums of Sqs: sequential sums of squares, Mean Sqs: mean squares F.model: F statistics, R2: Partial R squared...... 112 Table A.3.3: Mean Richness (number of unique OTUs) and diversity (Inverse Simpson index) estimates for bacterial communities by location, season and treatment...... 114 Table 4.1: Environmental data collected in Fall 2016 when plastic mulches and soil were collected from the field site in TN and WA. Mean values are reported with standard errors. Full soil data is reported in Sintim et al. (2019)...... 130 Table A.4.1: Manufacturers, major constituents, and physicochemical properties of the unweathered mulches used in the study. Biobased content data was provided by the manufacturers. Data reported from Hayes et al., 2017...... 168

Table A.4.2: Esterase enzyme activity in spent culture media after 3 weeks and 6 weeks of incubation. Activities are reported with standard errors of the mean. All technical replicates were assayed in triplicates. Asterix denote significant difference in activities between 3 week and 6week incubation for each treatment (α = 0.05, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)...... 171 Table A.4.3: Results of three-way ANOVAs for bacterial gene copies obtained from lab incubation study (type III tests reported)...... 171 Table A.4.4: Results of three-way ANOVAs for fungal gene copies obtained from lab incubation study (type III tests reported)...... 172 Table A.4.5: Results from three-way ANOVAs for 16S/ITS ratios obtained from lab incubation study (type III tests reported)...... 172 Table A.4.6: Summary statistics for one-way ANOVAs testing differences between 3 weeks and 6 weeks incubation times for 16S, ITS and 16S/ITS ratios (inoculated and uninoculated)...... 173 Table A.4.7: Mean richness (number of observed OTUs) and diversity (inverse Simpson index) for plastic associated bacterial communities in laboratory microcosms. stddev:standard deviation...... 173 Table A.4.8: Results from three-way ANOVAs for richness estimates obtained from lab incubation study (type III tests reported)...... 174 Table A.4.9: Results from three-way ANOVAs for inverse Simpson estimates obtained from lab incubation study (type III tests reported)...... 174 Table A.4.10: Summary statistics from one-way ANOVAs testing differences in richness and diversity metrics in lab enrichments between mulch treatments, inoculated and uninoculated samples and time of incubation...... 175 Table A.4.11: PERMANOVA statistics to test significant differences in microbial community composition a) for plastic associated bacterial communities in consortia, b) for field-weathered plastic and soil associated bacterial communities in TN and WA c) for fungal communities in TN and WA. PERMANOVA computed with Adonis function in R vegan package (***p ≤ 0.001)...... 176 Table A.4.12: Summary statistics for one-way ANOVAs testing differences between inoculated and uninoculated microcosms for 16S, ITS and 16S/ITS ratios (3 weeks and 6 weeks) ...... 177 Table A.4.13: Isolated strains from enrichment cultures...... 178 Table A.4.14: Summary statistics for one-way ANOVAs testing differences between treatments for 16S, ITS and 16S/ITS ratios for 3 weeks and 6 weeks incubations (inoculated and uninoculated)...... 178 Table A.4.15: Tukey post hoc tests (α = 0.05) showing pairwise comparison of 16S and ITS copy numbers between inoculated mulch treatments for only those time points which showed significant differences by one-way ANOVA...... 179 Table A.4.16: Results from a mixed model ANOVA for 16S/ITS ratios obtained from field study (type III tests reported)...... 180 Table A.4.17: Results from a mixed model ANOVA for richness estimates obtained from field study (type III tests reported)...... 180

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Table A.4.18: Mixed model ANOVA for inverse Simpson estimates obtained from field study (type III tests reported)...... 181 Table A.4.19: One-way ANOVA testing differences in 16S/ITS qPCR ratios between locations (for field weathered plastics), treatments of field weathered plastics in TN and WA, and between bulk soil and field weathered plastic...... 181 Table A.4.20: Summary statistics from one-way ANOVAs testing differences in richness and diversity metrics for field enrichment study by mulch treatments (plastic and soil associated bacterial communities), location (plastic and soil associated communities) and soil and plastic communities (TN and WA)...... 182 Table A.4.21: Mean richness (number of observed OTUs) and diversity (inverse Simpson index) for plastic and soil associated bacterial communities in TN and WA. stddev:standard deviation...... 183 Table A.4.22: Mean richness (number of observed OTUs) and diversity (inverse Simpson index) for field weathered plastic associated fungal communities in TN and WA. stddev:standard deviation...... 184 Table A.4.23: Summary statistics from one-way ANOVAs testing differences in richness and diversity metrics for field enrichment study by mulch treatments and location (plastic associated fungal communities)...... 184 Table A.4.24: Percent contribution of individual taxa identified using SIMPER contributing up to 40% variation in microbial community composition between soil and field weathered mulch associated bacterial communities in TN and WA...... 185 Table 5.1: Soil and weather characteristics in Knoxville, TN and Mount Vernon, WA (Sintim et al., 2019, Li et al., 2014)...... 199 Table 5.2: Enzymes assayed for t = 0 and t = 16 weeks samples ...... 206 Table 5.3: F values from a two-way ANOVA showing effects of nitrogen and plastic treatment on cumulative CO2-C evolution after 16 weeks...... 210 Table 5.4: Percent biodegradation of plastic material after 16 weeks...... 215

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LIST OF FIGURES Figure 1.1: Options that would encourage recycling of agricultural plastic by producers (Hurley, 2008)...... 8 Figure 1.2: Some difficulties faced by producers when recycling agricultural plastic (Hurley, 2008)...... 9 Figure 1.3: a) World consumption of biodegradable polymers –2017, b) World consumption of biodegradable polymers by type -2017, c) World consumption of biodegradable polymers by market-2017 (IHS Markit, 2018)...... 14 Figure 1.4: A schematic illustration of enzymatic degradation of a polyester by PHB depolymerase (Hiraishi and Taguchi, 2013). CD: catalytic domain, SBD: substrate binding domain...... 17 Figure 1.5: Scanning electron microscope (SEM) images showing microbial colonization of an experimental PLA/PHA mulch film after 16 weeks of soil incubation in laboratory microcosms at 30ºC. Image on panel b shows a magnified image of panel a. Image taken by Sreejata Bandopadhyay using the Zeiss Auriga Crossbeam SEM, currently located at the University of Tennessee Joint Institute of Advanced Materials...... 18 Figure 1.6: Metabolic pathways in PHA biosynthesis of Pseudomonas and PHB in R. eutropha. In Pseudomonads synthesis and degradation of PHA operate as a continuous cycle with 3 hydroxy fatty acids released from PHA polymers by PhaZ encoded depolymerase and activated to 3-hydroxyacyl CoAs by acyl synthetase with a consumption of one ATP molecule. The activated monomers are either metabolized via fatty acid degradation or reincorporated into PHA. The specific PHA/PHB metabolic pathways are interconnected with the main central pathways that converge in acetyl CoA (Escapa et al., 2012)...... 27 Figure 1.7: PLA breakdown via metabolic intermediates ...... 29 Figure 1.8: Metabolic pathways for PCL and PBS degradation by microbes living in controlled compost (Kunioka et al., 2009)...... 30 Figure 2.1: Indirect (polyethylene and biodegradable mulches (BDMs)) and direct (BDMs only) effects of plastic mulching. All plastic mulches form a barrier on the soil surface which influences soil temperature, moisture and soil-air gas exchange, indirectly altering the microbial communities. BDMs are tilled into the soil at the end of the growing season, introducing physical fragments and a carbon source, along with other components of the plastic films (additives, plasticizers, minerals etc.) which may additionally influence soil communities and their processes ...... 51 Figure 3.1: Bacterial community composition differences between the two field locations, showing communities from all four sampling times. a) Non-metric multidimensional scaling (NMDS) ordination of Bray-Curtis dissimilarities of OTU relative abundances, highlighting differences between location (PERMANOVA p = 0.001). Each point corresponds to the whole microbial community of one plot in the field (4 time points * 3 reps, total 12 points for each treatment). Ellipses denote clustering at 95% confidence. NMDS stress value: 0.14. b) Bar plot showing differences in mean relative abundance of the most abundant classes of bacteria in TN and WA, aggregating all treatments and all four sampling times. Asterices denote significant differences between locations, determined by ANOVA (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001).

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Red stars indicate taxa which cumulatively contributed up to 46% of the variance in microbial communities between TN and WA, determined using SIMPER...... 81 Figure 3.2: NMDS ordination of Bray Curtis dissimilarities of soil bacterial communities a) TN (p = 0.001) and b) WA (p = 0.001). Ellipses denote clustering at 95% confidence. NMDS stress value: 0.17 (TN), 0.16 (WA)...... 84 Figure 3.3: a) Richness (number of unique OTUs) and b) Inverse Simpson estimates over time of soil microbial communities in TN and WA. Error bars indicate SEM of three replicate samples...... 88 Figure 3.4: a) 16S and b) ITS gene copy number changes over two years in TN and WA analyzed using a mixed model analysis of variance. Final time point (Fall 2016) is plotted by subtracting baseline abundances from Spring 2015. Error bars indicate SEM of four replicate samples. Lowercase letters denote significant differences between treatments (p ≤ 0.05, Tukey’s HSD). Asterices indicate treatments which showed significant enrichment using a paired t-test ((*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)...... 91 Figure 3.5: NMDS ordination depicting Bray-Curtis similarity of the functional profile of soil microbial communities (based on 7 soil enzyme activity rates) across a) Location (p < 0.05) and b) Date (p < 0.05)...... 93 Figure 3.6: Changes in soil enzyme activity over time across mulch treatment (p values reported in Table 3.6). Error bars indicate SEM of four replicate samples...... 95 Figure A.3.1: Constrained ordination of soil communities collected in Fall 2016 season, as driven by environmental variables: soilph: soil pH, solsalt: electrical conductivity (dS/m), exk: exchangeable potassium by NH4OAc extraction (mg/ kg), exmg: exchangeable magnesium by NH4OAc extraction (mg/ kg), exca: exchangeable calcium by NH4OAc extraction (mg/ kg), exna: exchangeable sodium by NH4OAc extraction (mg/ kg), no3ppm: Nitrate-N (mg/kg), GSM: Gravimetric soil moisture (g water/ g dry soil), om: Organic matter (%). Data for environmental variables are reported in Sintim et al., (2019)...... 115 Figure A.3.2: Percent relative abundance (averaged across treatments) of the most influential genera cumulatively responsible for about 60% of the seasonal variance between microbial communities. Family and Class level classification is used where classification at genus level was not possible. “Gp” indicates acidobacterial subgroups. The lower and upper hinges of the boxplots correspond to the first and third quartiles (the 25th and 75th percentiles). The upper whisker of the boxplot extends from the hinge to the largest value no further than 1.5 * IQR from the hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles). The lower whisker extends from the hinge to the smallest value at maximum 1.5 * IQR of the hinge. Data beyond the end of the whiskers are outliers and are plotted individually. Letters indicate post-hoc test completed using pairwise Wilcoxon rank sum test using significance at α = 0.05...... 116 Figure A.3.3: Stacked bar plots of depicting the abundant classes of bacteria in TN and WA for all mulch treatments...... 118 Figure A.3.4: NMDS of soil microbial communities for the last season (Fall 2016) in a) TN b) WA. No significant difference between treatments were observed. NMDS stress value: 0.14 (TN), 0.2 (WA)...... 119

Figure A.3.5: NMDS plot of soil extracellular enzyme activities from samples collected in Spring 2017 (final time point for enzyme rate measurements) for a) TN (p > 0.05) and b) WA (p > 0.05)...... 120

Figure 4.1: Cumulative CO2 in headspace after 3- and 6-week incubations. Data represents the mean of n = 3 independent replicate cultures. Significant differences (p ≤ 0.05) between uninoculated and inoculated microcosms indicated by asterices. Error bars represent standard error of the mean...... 135 Figure 4.2: a) 16S rRNA (bacterial) and b) ITS (fungal) gene abundances for 3-week and 6-week incubations. Gene abundances are log transformed. Error bars represent standard error of the mean (n = 3). Asterices denote significant differences between inoculated and uninoculated treatments...... 138 Figure 4.3: a) Diversity (inverse Simpson) and b) richness (number of observed OTUs) for inoculated and uninoculated microcosms. Letters indicate Tukey post-hoc results following a one-way ANOVA. All significances tested at α = 0.05...... 140 Figure 4.4: a) Bacterial taxa distribution (Class level) on BDMs in laboratory enrichment cultures. Relative abundances above a cut-off level of 2% are indicated. “Bacteria unclassified" denote taxa with relative abundance above the cut-off level of 2%, but that could not be classified. b) NMDS ordination of bacterial communities on inoculated BDMs. NMDS stress value: 0.24...... 143 Figure 4.5: 16S/ITS qPCR ratios for field-weathered plasticome and bulk soil DNA. Paired t test results are shown with asterices indicating significant differences in 16S/ITS ratios between soil and plastics. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001) ...... 145 Figure 4.6: a) Eukaryotic taxa distribution (Class level) on field-weathered plastics (Fall 2016) for TN and WA. Relative abundances above a cut-off level of 2% are indicated. “Eukaryota_unclassified" denote taxa with relative abundance above the cut-off level of 2%, but that could not be classified. b) NMDS ordination of fungal communities on field-weathered BDMs in TN and WA (Fall 2016). NMDS stress value: 0.18...... 146 Figure 4.7: a) Bacterial taxa distribution (Class level) on field-weathered plastics and bulk soil (Fall 2016) for TN and WA. Relative abundances above a cut-off level of 2% are indicated. “Bacteria unclassified" denote taxa with relative abundance above the cut-off level of 2%, but that could not be classified. b) NMDS ordination of bacterial communities on field-weathered BDMs and bulk soil in TN and WA (Fall 2016). NMDS stress value: 0.12...... 149 Figure 4.8: a) Diversity (inverse Simpson) and b) richness (number of observed OTUs) of bacterial communities on field-weathered BDMs and bulk soil in TN and WA. Letters indicate Tukey post-hoc results following a one-way ANOVA. All significances tested at α = 0.05. .... 151 Figure 4.9: a) Diversity (inverse Simpson index) and b) richness (number of observed OTUs) of fungal communities on field-weathered plastics in TN and WA (Fall 2016). Letters indicate Tukey post-hoc results following a one-way ANOVA. All significances tested at α = 0.05. .... 152 Figure 4.10: Percent relative abundance of taxa (genera) driving variance in microbial communities between soils and plastics in TN and WA as determined via SIMPER. Differences between plastic treatments were evaluated using Kruskal-Wallis non-parametric test. Significant differences were observed between plastic treatments in TN for Methylobacterium (p = 0.03),

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Arthrobacter (p = 0.03), and Sphingomonas (p = 0.02), and in WA significant differences were seen between plastic treatments for Methylobacterium (p = 0.03) and Sphingomonas (p = 0.03). All significances tested at α = 0.05...... 155 Figure 4.11: Percent relative abundance of taxa (genus level) driving overall differences in fungal communities between field-weathered plastics in TN and WA as obtained via SIMPER. “Taxa_unclassified” denote taxa which could not be classified at the genus level. Differences between plastic treatments were evaluated using Kruskal-Wallis non-parametric test. Significant differences were observed between plastic treatments in TN for Peziza (p = 0.007) and Cladosporium (p = 0.02). In WA significant differences were seen between plastic treatments for Peziza (p = 0.01), Agaricomycetes_unclassified (p = 0.03), Cladosporium (p = 0.03) and Conthreep_unclassified (p = 0.01). All significances tested at α = 0.05...... 157 Figure A.4.1: Thermograms (TGA)(A &C) and differential thermograms (DTG)(B & D) of BioAgri and Organix showing percent weight remaining and rate of weight loss after heating the plastics at a constant rate (10°C/min till 600°C). “Indoor Storage” refers to unweathered off-the- roll mulch films which were kept in a storage cabinet in the dark at room temperature. Peak 1 is attributable to starch in BioAgri, Peak 2 is attributable to PBAT in Organix and BioAgri...... 186 Figure A.4.2: Confocal images of BDMs after 3 weeks enrichment using a Leica™white light laser confocal system. Staining was done using a LIVE/DEAD® BacLight™ Bacterial Viability Kit. Green: live cells, Red: dead cells. Scale bars=25 µm...... 187 Figure A.4.3: Alpha diversity measures calculated for lab enriched plastic-ome communities in a) Inoculated and b) Uninoculated samples. All reads were scaled to even depth. Observed: Observed number of OTUs. se: standard error...... 188 Figure A.4.4: Isolation of BDM degrading microbes on M9 minimal agar plates with plastic as sole carbon source. Inoculum was taken from culture bottles inoculated for 6 weeks. Each picture shows (left to right) negative control (no plastic, no glucose), treatment (with plastic), positive control (media with glucose)...... 189 Figure A.4.5: Alpha diversity measures calculated for field weathered plastic-associated communities in a) TN and b) WA. All reads were scaled to even depth. Observed: Observed number of OTUs. se: standard error...... 190 Figure A.4.6: Alpha diversity measures calculated for field weathered plastic-associated fungal communities in TN and WA. All reads were scaled to even depth. Observed: Observed number of OTUs. se: standard error...... 191 Figure A.4.7: Microbial taxa distribution (genus level) on mulch treatments for the enrichment cultures conducted for 3 weeks and 6 weeks. Relative abundances above a cut-off level of 5% are indicated. “unclassified" denote taxa with relative abundance above the cut-off level of 5%, but that could not be classified...... 192 Figure 5.1: a) pH and b) electrical conductivity (EC) measurements at t = 16 weeks. Lowercase letters indicate significant interaction effects at α ≤ 0.05 for all data except for EC measurements in WA, where letters denote a significant main effect of nitrogen amendment at α ≤ 0.05. Gray solid stars indicate significant main effect of plastic at α ≤ 0.05...... 208

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Figure 5.2: Cumulative CO2-C released over 16 weeks (119 days) in TN and WA soil microcosms with (+250 mg) plastic BDMs or without. (AA=amino acids, AN=ammonium nitrate, No N=no nitrogen added, BDM=biodegradable plastic)...... 212

Figure 5.3: Cumulative CO2-C released over 16 weeks. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters in TN indicate interaction effects at α ≤ 0.05, whereas in WA, it indicates a significant main effect of nitrogen treatment at α ≤ 0.05. Gray solid stars in WA indicates a significant main effect of plastic at α ≤ 0.05...... 213 Figure 5.4: Visualization of plastic pieces after 16 weeks incubation. Only plastics from one replicate jar are shown here. (AA=amino acids, AN=ammonium nitrate, No N=no nitrogen added)...... 214 Figure 5.5: Changes in a) bacterial and b) fungal gene abundances over 16 weeks. All gene abundances were log transformed, then abundances in initial soils subtracted from final (16 week) samples. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters indicate interaction effects at α ≤ 0.05 for bacterial abundance in TN and WA and fungal abundance in TN. Lowercase letters for fungal abundance in WA indicate a significant main effect of nitrogen treatment at α ≤ 0.05. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in gene abundance from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001...... 216 Figure 5.6: Changes in ammonia monooxygenase (amoA) gene abundances over 16 weeks. Gene abundances were log transformed, then abundances in initial soils subtracted from final (16 week) samples. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters indicate interaction effects at α ≤ 0.05 for amoA abundance in TN and a significant main effect of nitrogen treatment at α ≤ 0.05 in WA. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in amoA gene abundance from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001...... 219 Figure 5.7: Changes in a) nitrate and b) ammonium concentration in soils over 16 weeks. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. addition. Lowercase letters indicate a significant main effect of nitrogen treatment at α ≤ 0.05. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in nitrate and ammonium concentrations from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001...... 220 Figure 5.8: Changes in enzyme activities of β- glucosidase (BG), cellubiosidase (CB), and N- acetyl β-D- glucosaminidase (NAG) after 16 weeks. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters indicate a significant main effect of nitrogen treatment at α ≤ 0.05, except for CB activity in TN where lowercase letters indicate interaction effects at α ≤ 0.05. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in enzyme activity from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001...... 223

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INTRODUCTION

Mulching is the practice of using a ground cover on soil such that it maintains a conducive soil environment for plant growth. Plastic mulch films have become popular among growers over the last five decades. These are thin sheets of polymeric films that are used to cover the soil surface such that it moderates the soil temperature, conserves soil moisture and suppresses weeds. These benefits translate into dramatically improved crop yields. However, the most commonly used plastic mulch film is made of polyethylene (PE). Due to absence of hydrolysable bonds in PE, these films are resistant to microbial degradation. This means that once they have been used in the field and crops have been harvested, they must be removed from the field site and disposed of appropriately. This involves labor and associated costs, coupled with the fact that most recycling facilities do not accept used films due to adherence of soil and other chemicals such as fertilizers. PE films can often remain in the field and not be completely removed further causing a possibility of contamination of soil and groundwater.

Biodegradable plastic mulch films (BDMs) are a sustainable and promising alternative to non- biodegradable PE films. BDMs are made of polymers that can be degraded by microbial hydrolysis into carbon dioxide, water and microbial biomass. They do not require disposal as they are meant to be merely tilled into the soil after use, where they are expected to be degraded by resident soil microbes. However, several factors have hindered adoption of BDMs by growers. Some of them include high cost of biodegradable products and insufficient knowledge about BDMs in the farming community. There are two other important barriers to their adoption. First, the impacts of BDMs on soil health remain unclear. Second, the breakdown of these mulches in soil is unpredictable with some mulches degrading very fast in the field and others degrading very slowly. Thus, it is necessary to not only understand how the input of BDMs might affect soil physical, chemical and biological properties but to also understand microbial degradation of these films in the soil environment. The overarching goal of my dissertation work was to understand the impacts of BDMs on soil microbial communities and ecosystem functions, and characterize microbes potentially involved in the degradation process. I also attempted to understand how specific nutrient additions can impact degradation processes in the field by conducting lab scale microcosm studies.

Research questions

There were three specific questions addressed in this dissertation to understand the impacts of BDMs on soil microbiology and characterize microbes involved in BDM degradation:

1) How does the incorporation of BDMs in soil affect the soil microbial community structure, function and microbial counts across diverse locations, sampling seasons, and mulch treatments (including BDMs and PE)? 2) Which specific bacterial groups are predominant on the surface of BDMs exposed to laboratory enrichment cultures and field weathering conditions; do these microbes biodegrade the BDMs being studied? 3) Does the addition of nitrogen amendments such as urea and ammonium nitrate, used as a proxy for fertilizer application in field, impact biodegradation of BDMs in soils from two diverse climates?

Organization of the dissertation

The first chapter focuses on a broad overview of the literature pertaining to the history of mulching practice; disadvantages of PE mulches and the potential for BDMs as alternatives; and known microbial enzymes and metabolic pathways involved in degradation of biodegradable polymers used to make BDMs. The second chapter in the dissertation is a short review on the impacts of biodegradable plastic film mulching on soil microbial communities and ecosystem functions, which was published in Frontiers in Microbiology in 2018 (Bandopadhyay et al. 2018). The third chapter focuses on the first research question. Data from this chapter is currently under review in Applied and Environmental Microbiology. Chapters four and five focus on the second and third research questions, respectively, and have been prepared in manuscript format to be submitted to respective journals. The final chapter includes concluding remarks and recommendations for future research.

CHAPTER I LITERATURE REVIEW

Plastic film mulching in agriculture

Plastic mulch films were first introduced on a marketable scale in 1939 (Byrdson, 1970). During the early 1950s the term “plasticulture” was coined when new developments in plastics gave rise to a novel system of crop production. The plasticulture system, which typically combines plastic mulch film with raised beds and drip irrigation, has helped growers double and triple their yields along with an added benefit of an early harvest (Sanders et al., 1995). Plastic mulch is now used globally in agriculture to prevent weeds, retain soil moisture, moderate soil temperature, reduce fertilizer leaching and cause efficient use of soil nutrients (Kasirajan and Ngouajio, 2012b). More generally plastic mulch films are also used to protect crops from hostile growing conditions such as severe weather, insects, birds and other pests.

Production of plastics has dramatically increased over the years since its introduction in the 1950s. In 2012, the global agricultural plastic film market was estimated to be worth USD 5.87 billion (Grossman, 2015). This market includes films used for mulch, greenhouse covers, and to wrap bales, tubing and pipes. That year’s global demand of agricultural plastic films was more than 9.7 million pounds, with about 40% of this being used in mulching. It is estimated that China is the world’s largest consumer of agricultural plastic films, using about 60% of all such plastic.

Traditionally, the most popular polymers used to make plastic mulch films have been polyethylene (PE), polyvinylchloride and ethylene vinyl acetate. Of these, PE has emerged as the most widely used plastic due to several advantages such as easy processability, exceptional chemical resistance, high durability, flexibility, freedom from odor and toxicity (Clark, 1987, Garnaud, 1974). PE plastic is made from pellets of PE resin which are heated, processed and extruded into flexible sheets of plastic films. Apart from PE, other commonly used polymers include low-density PE, linear low-density PE, and high-density PE (Fleck-Arnold, 2000). Linear low-density PE resins have high puncture resistance and mechanical stretch properties (Kasirajan and Ngouajio, 2012b) hence it is the major PE used in mulches.

Benefits of plastic mulch films

Plastic mulch films provide certain key benefits that help improve crop yields. Apart from conserving soil moisture, moderating soil temperature and reducing weeds, plastic mulch films also prevent contamination of crops with soil (Hayes et al., 2012, Kasirajan and Ngouajio, 2012b). Other associated benefits include an increase in root zone temperatures which promotes faster development of crops and subsequently, earlier harvest; maintenance of a stable moisture content in the root zone by preventing water to evaporate and by channeling excess rainfall away from the root zone. This in turn lessens irrigation demand and prevents water and nutrient related physiological disorders of crops (McCraw and Motes, 1991). Drip irrigation used in conjunction with mulch reduces nitrogen leaching by applying lower amounts of water mixed with fertilizers to the root zone as necessary. This cuts down on the amount of fertilizer required for adequate plant growth. One study showed that compared to no mulching treatment, the treatment of controlled release urea under plastic mulching effectively reduced nitrogen loss by 40.41 % from the underground run off and by 29.30% from the surface run off (International Plant Nutrition Institute, 2015). The total nitrogen lost in runoff from the no mulch treatments were in the sequence of nitrate-N > amide-N > ammonium-N > controlled-release-N. Under plastic mulching, however, total-N lost through run off was observed in the following sequence: ammonium-N > amide-N > nitrate-N > controlled- release N. The minimum N loss was observed in the controlled-release N treatment with plastic mulching, 59.6% less than the ammonium-N treatment. Thus, PE films aid in the retention of nutrients within the root zone thereby allowing more efficient nutrient utilization by the crops (Cannington et al., 1975). Increased soil temperatures due to mulching favors N mineralization and plant N uptake (Liu et al., 2003). Some other benefits of using PE mulch are early ripening, improved fruit quality and a lower incidence of viral diseases (Singh, 1992).

Heating properties of plastic namely reflectivity, absorptivity, transmittance and their interaction with solar radiation are commonly exploited in making plastic mulches. All of these properties have a direct effect on the soil temperature under the plastic mulch (Schales and Sheldrake, 1963). Color plays an important role in determining the optical properties of the plastic such that different types and colors of plastics allow different levels of light radiation to reach the soil, increasing or decreasing the soil temperatures. Most of the early work on the use of plastic mulches for vegetable production was focused on determining the impact of colored mulches on soil and air temperatures,

moisture retention and vegetable yield. Based on this, three main colors (namely, black, clear and white) predominate commercial vegetable production today (Orzolek, 2017). Black and clear mulches show the greatest soil warming potential among other mulch colors (Ham et al., 1993). This increases count of microbes in soil and microbial activity prompting plant germination and growth. Plastic mulches directly impact the microclimate around the plant by modifying the radiation-budget (absorptivity vs reflectivity) of the surface and decreasing soil water loss (Yaghi et al., 2013). However, it is the color of a mulch that largely determines its energy radiating behavior and ultimately influences the microclimate surrounding plants. Color affects the surface temperature of a mulch and the underlying soil temperature. The color of a mulch also has impacts on the vegetables being produced. For example, carrots had significantly increased concentration of phenolics when planted with yellow and black mulches, whereas the use of yellow and white mulches resulted in a higher beta carotene and ascorbic acid content as compared to other colored mulches and bare soil treatment (Antonious and Kasperbauer, 2002). Improvement of plant development, yields, and fruit ripening due to the reflective properties of certain plastics is seen as a debatable benefit and have been challenged by researchers (Decoteau et al., 1988, Lee et al., 1996). Mulches with a reflective coating has been shown to increase soluble solids content, total phenolics (aromatic compounds which give anti-microbial protection), flavanols and antho-cyanin (water-soluble pigments with similar properties as flavonoids) content in Ontario wine grapes (Coventry et al., 2004). Reflective mulch also increases soluble solids in plums (Kim et al., 2008). Other fruits like strawberries ripened over red plastic with high aroma and flavor compounds (Loughrin and Kasperbauer, 2002).

Negative impacts of conventional polyethylene (PE) plastic

PE mulches, despite crop benefits, pose serious problems associated with its use. Soil temperatures under black PE mulches are generally significantly higher than other colored mulches (Rangarajan and Ingall, 2001). PE mulches are known to often greatly warm the soil in spring and summer, potentially reaching levels that are toxic to plants. Even though black PE plastic prevents evaporation of water from the soil, damp growing conditions can lead to infestations and diseases.

The most important concern, however, is the disposal of used plastic mulch films. In 2004, 143,000 tons of plastic mulch were disposed in the U.S. alone. Most of this waste entered the landfill at a

cost to growers of up to $100 acre-1 (Shogren and Hochmuth, 2004). PE is a petroleum-based product that has very little or no biodegradation capability. Disposal of these plastic pieces from soil entail significant labor and costs (Schonbeck, 1995). Although parts of the plastic exposed to light undergo photodegradation, the rest of it usually breaks into pieces during soil preparation for a new crop with fragments buried deep into the soil and the other pieces lingering on the surface. The buried mulch pieces are even slower to decompose since they are usually less exposed to light and high temperatures.

In 2008, a project funded by the Government of California, California Department of Food and Agriculture and U.S Department of Agriculture funded a project to undergo a detailed examination of plastic usage in the agricultural sector in the state of California and tried to also develop a recycling strategy for agricultural plastic in the state (Hurley, 2008). The primary tasks for the project were to: conduct a thorough review of the literature on recycling of agricultural plastics, conduct a focus group with agricultural producers concerning the use and recycling of agricultural plastic, develop and conduct a survey to examine the usage and recycling of agricultural plastic by California producers, and finally use the results from the survey and focus group to develop a plan for recycling agricultural plastic. The study demonstrated that the group of producers recycling some of the agricultural plastic represented 35.94% of the plastic users in the survey. Thus, there was a section of producers interested in recycling their agricultural plastics and who were undertaking the practice. Tables 1.1 and 1.2 show the percentage of survey respondents using agricultural plastic by industry and the participation rate of recycling by industry. Table 1.2 shows that the melon industry had the lowest recycling rate at 13.04% while the nursery industry had the highest at 46.23%.

Figures 1.1 and 1.2 show the options that would likely encourage the producers to recycle agricultural plastic and what were the expected and experienced difficulties with recycling agricultural plastics. The results showed that the greatest incentive for the farmers to recycle is offering them an on-farm pick up for agricultural plastics and that the greatest difficulty with recycling agricultural plastic was that the current recycling facilities were located too far from their operation. These findings suggest that a successful recycling strategy would be helpful in enabling these producers to recycle.

Table 1.1: The percentage of survey respondents using agricultural plastic in different industries (Hurley, 2008). Industry Number of Survey Number Indicating Percentage Using Respondents Plastic Usage Agricultural Plastic (%) Berries other than 36 16 44.44 strawberries Strawberries 64 60 93.75 Grapes 140 33 23.57 Melon 38 24 63.16 Orchard 281 63 22.42 Pepper 52 38 73.08 Tomatoes 102 37 36.27 Vegetables 128 67 52.34 Dairy 65 39 60.00 Hay 168 67 39.88 Greenhouse 94 71 75.53 Nursery 154 107 69.48

Table 1.2: The percentage of survey respondents recycling agricultural plastic in different industries (Hurley, 2008). Industry Number of producers Number of producers Percentage of who reported who do not recycle producers recycling recycling agricultural plastic (%) Berries other than 4 12 25.00 strawberries Strawberries 18 40 31.03 Grapes 14 18 43.75 Melon 3 20 13.04 Orchard 24 39 38.10 Pepper 10 28 26.32 Tomatoes 9 27 25.00 Vegetables 20 46 30.30 Dairy 11 28 28.21 Hay 15 51 22.73 Greenhouse 29 41 41.43 Nursery 49 57 46.23

Figure 1.1: Options that would encourage recycling of agricultural plastic by producers (Hurley, 2008).

Figure 1.2: Some difficulties faced by producers when recycling agricultural plastic (Hurley, 2008).

This research further helped to identify the ‘other vegetable industry’, which include pumpkins, eggplants, squash to name a few, as having the highest amount of disposal and indicated a need for further studies to be conducted to examine the usage and disposal of agricultural plastic by each commodity in this industry. The survey identified that the producers believe that there are way too many restrictions with recycling, however it did not determine which restrictions caused producers difficulties.

From the study above, conducted mainly using PE mulches, one can realize the potential limitations in using PE film in the fields and the concerns that stem from limited recycling options. Thus, a large part of these wastes is left in the field, buried or burned creating serious environmental repercussions. On-site dumping can lead to seepage of water from irrigation or rainfall that has been in contact with buried agricultural plastics, contaminating groundwater with toxic chemicals. Plastics are often disposed of in commercial landfills and their slow biodegradation rates results in massive accumulation.

The more popular method of disposing agricultural plastics is on-site burning as compared to landfilling and dumping on-site. Some farmers prefer burning as it is economically more favorable and does not involve high transportation costs and landfill tipping fees. A survey involving vegetable growers of the state of Pennsylvania showed that 66% of participating growers disposed

of used agricultural plastics by onsite burning, 27% by landfilling, and 25% by burying, dumping, or piling on-site (Garthe, 2004). Persistent organic pollutants (POPs) such as furans and dioxins produced by burning polyvinylchloride plastics are a major environmental contaminant (Jayasekara et al., 2005). Release of POPs mainly occurs due to a low burning temperature of 400– 600°F (200–315°C) of these plastics or incomplete combustion of hydrocarbons in them which releases dioxins into the atmosphere upon burning mulch films contaminated with fertilizers and pesticides. As specified by the World Health Organization (WHO), dioxins mainly accumulate in the fatty tissue of animals and are highly toxic to humans causing reproductive and developmental problems. Other harmful effects of dioxins on human health include damage to the immune system, interference with hormones and cause of cancer. Limited exposure to high levels of dioxins may result in skin lesions and malfunctioning of the liver. Long term exposure can also lead to damage of the nervous system. Exposure to fine particles (diameter < 2.5mm) from open burning areas has also been linked to many health effects, such as an elevated risk of stroke, asthmatic attacks, reduced lung function, occurrence of respiratory diseases, and premature death (Hong et al., 2002). Several organizations and people in general are increasingly becoming aware of the deleterious effects of disposed plastic on the environment specially wildlife and the impact it has on the aesthetic quality of cities and forests.

Alternatives to polyethylene (PE) mulches: The shift towards biodegradable mulches

Paper based mulches

Disposal problems associated with the use of PE mulch make it imperative to develop new alternative materials which do not pose significant pollution problems. Paper based mulches are promising in this regard as they are manufactured from renewable resources and do not need to be removed from the agricultural plot after harvest. They have been used in agriculture since 1914, but they have certain limitations as well. They deteriorate rapidly under field conditions which reduces their efficiency (Li et al., 2014b). Paper mulches are also heavier than PE and there is a significant cost associated with their transportation. Thus, they end up being more expensive than PE. Coolong (2010) evaluated the performance of four easily available papers and compared them with conventional black PE plastic and concluded that in some situations paper mulches could 10

provide a viable alternative to black PE plastic mulch. However, cropping conditions and the overall environment will ultimately influence effectiveness. Most commercial paper mulches use cellulose as it is known to be the most copious biopolymer on earth. WeedGuardPlus® is one such natural weed-controlling commercial vegetable and garden mulch made from cellulose fibers. It offers growers a high-quality, cost-effective replacement for plastic mulches. Numerous bacterial and fungal species possess the enzyme complex necessary to degrade cellulose (Wallenstein and Burns, 2011). This makes cellulose-based paper mulches ideal for the field. A broader understanding of microbial communities degrading cellulose in soil in required to be able to fully investigate the biodegradation capacity of cellulose mulches.

Photodegradable mulches

Photodegradable mulches are degraded in the presence of sunlight by photo-initiated chemical reactions. Photodegradation is mainly considered an abiotic degradation process. Photodegradable mulches are better at preserving moisture, raising soil temperatures, and they also increase yields than the widely used PE films (Kasirajan and Ngouajio, 2012). Most synthetic polymers are known to be susceptible to degradation by UV and visible light.

It has been seen that photodegradation of poly lactic acid (PLA) films entails a bulk erosion mechanism meaning that UV penetrates the films with no significant reduction in its intensity, irrespective of the chemical structure and crystallinity of the polyester (Tsuji et al., 2006). The photodegradability of PLLA chains is greater in the amorphous region than in the crystalline region. Photo-biodegradable PE films containing starch are known to be amenable to degradation after use. The photo-biodegradable PE films buried in soil have been reported to have good degradability (Wang et al., 2004). However, even though the starch component of these films might biodegrade, the PE component will not. Thus, there are certain qualms regarding the ability of photodegradable mulches which are derived from petroleum-based products to degrade completely into water and carbon dioxide (Zhang et al., 2008). Photodegradable mulches are considered unreliable and expensive to use. Degradation is often prevented when the crops grow and cover the mulches to an extent that UV radiation is unable to reach the plastic sheets. Degradation is also dependent on the extent of sunlight available, slowing down in areas which receive less sunlight that those which receive more (Greer and Dole, 2003). Although there is a

need to understand whether/how microbes play a role in degrading photodegradable mulches, current literature lacks comprehensive data on it.

Oxo-degradable mulches

Another category of plastics that behaves similarly to photodegradable mulches is oxo- biodegradable materials. According to CEN (the European Standards Organization), oxo biodegradation is defined as "degradation resulting from oxidative and cell-mediated phenomena, either simultaneously or successively." These are PE films to which a small amount of salt has been added to speed up the oxidative process (Kasirajan and Ngouajio, 2012). The salts added are called pro-oxidants. These are mostly carbonyl groups and metals combined with other ingredients such as cobalt acetylacetonate, nickel or ferrous dithiocarbamate, magnesium stearate, or carboxylate. They accelerate abiotic oxidation and polymer chain breakdown making the product more vulnerable to biodegradation (Kyrikou and Briassoulis, 2007, Reddy et al., 2008). Briassoulis et al. (2015) artificially degraded linear low-density PE mulch films with pro-oxidants to simulate severe fragmentation of these films while being buried in soil for some decades. The artificially degraded film samples with pro-oxidants were transformed into small micro-fragments in soil, in contrast to naturally degraded film that remained undamaged for 8.5 years. These fragments, through a gradual abiotic degradation process under natural soil conditions, were eventually seen to transform into invisible microfragments. Thus, even though addition of pro- oxidants appears to aid in fragmentation of the plastics, the microfragments persist. The toxicity and long-term fate of these microplastics are not extensively documented. Degradation of several PE films with added pro-oxidants have been shown to be impacted by synergistic microbial interactions, with oxidation of carbon backbone producing metabolites used by fungi to partially biodegrade the film (Corti et al., 2010). Thermal oxidization of PE films with pro-oxidants produces low molecular mass products which can increase their bioassimilation by microbes (Jakubowicz, 2003). However, even though partial degradation of oxo-degradable mulches is possible, it is not truly biodegradable due to the presence of the PE component.

Biodegradable plastic mulches (BDMs)

As defined by ASTM International, biodegradable plastics are those “in which the degradation results from the action of naturally occurring microorganisms such as bacteria, fungi and algae” (Mooney, 2009). Biodegradable mulch films (BDMs), at the end of their life, are degraded by microbial transformations into carbon dioxide or methane, water and biomass (Gross and Kalra, 2002, Vert et al., 2002). BDMs do not generate wastes and thus are a viable alternative to PE plastics.

Western Europe is the largest producer and consumer of biodegradable polymers, with North America being the second largest (Figure 1.3a). North America’s consumption of these polymers has grown significantly in recent years. In China, consumption lags behind export which appears to consume a large part of the production. The high cost of biodegradable materials prevents consumption growth. However, strict government regulations and lower polymer production costs is expected to increase Chinese consumption of biodegradable polymers in the future. Figures 1.3b and 1.3c show the world consumption of biodegradable polymers by type and market in the year 2017 (IHS Markit 2018).

BDMs are made from biobased polymers derived from microbes, plants, or fossil-sourced materials and are meant to be tilled into soil after use where resident microorganisms degrade the plastic. The rate of biodegradation depends on the characteristics of the polymer because the polymer is the substrate for the enzymes that microbes produce. One of the main factors that determine the biodegradability of a polymer is the nature of the chemical bonds that are present in it. Biobased products which are derived from renewable resources include starch (storage polymer of plants), cellulose (structural plant polymer), polyhydroxyalkanoates (PHA) which is a microbial polyester and polylactic acid (PLA) which is an aliphatic polyester derived from corn starch, tapioca roots, and sugarcane. All of these products contain hydrolyzable bonds which are ideal for microbial attack during biodegradation. In addition, since nature already has reserves of these polymers microbes usually have enzymes to degrade them. Petroleum based products (or materials which are from nonrenewable sources) typically include polyolefins (such as PE). PE lacks hydrolyzable bonds and are particularly resistant to biodegradation as compared to polyesters

Figure 1.3: a) World consumption of biodegradable polymers –2017, b) World consumption of biodegradable polymers by type -2017, c) World consumption of biodegradable polymers by market-2017 (IHS Markit, 2018).

Figure 1.3: Continued

such as PHA and PLA. The carbon-carbon single bonds in the backbones of polyolefins makes them hydrophobic and more resistant to biodegradation (Kyrikou and Briassoulis, 2007). Recent studies such as one by Touchaleaume et al. (2016) has depicted how biodegradable mulches could be suitable alternatives to PE when taking into account their mulching efficiency and biodegradability. The aromatic content and distribution in fossil fuel derived materials also determine whether it will effectively biodegrade or not. Aromatic content increases hydrophobic character and crystallinity and decreases rates of biodegradation.

However, when using materials which are biodegradable in different applications, such as in BDMs, it is seen that biobased polymers do not always possess the characteristics desired in a plastic. Hence it has become more common for biobased polymers to be mixed with petroleum- based polymers to obtain desired characteristics. For instance, polymers made from starch are not very strong and are usually mixed with petroleum-based plastics such as PE and polyvinyl alcohol to increase their strength (Chai et al., 2009). However, these polymers have their limitations because starch easily biodegrades, but PE and polyvinyl alcohol do not. Hence labelling them as biodegradable can be deceptive. As an alternative to PE, several synthetic biodegradable polymers 15

are now available to be used as polyesters in agricultural mulch applications that can be mixed with biobased polymers to obtain desired characteristics. Several of these are petroleum-based and are still regarded as “biodegradable” including polycaprolactone (PCL), poly(butylene succinate) (PBS), poly(butylene succinate adipate) (PBSA) and aromatic co polyester poly(butylene adipate co-terephthalate) (PBAT). All these polymers are known to be degraded via the action of bacteria and/or fungi (Brodhagen et al., 2015). Polyethylene terephthalate (PET) is an example of a petroleum-based polyester in which each repeating unit of the polymer contains an aromatic ring (from the polycondensation of terephthalic acid). PET is hence considered to be very resistant to microbial attack; however, current research demonstrates that PET can be biodegraded (Shirke et al., 2018). Alternatively, aromaticity can be introduced in short segments such as in PBAT. PBAT is produced by starting with random chain lengths of the aliphatic polyester component formed from 1, 4-butanediol and adipic acid. The aromatic co-polyester then is attached to the aliphatic portion as relatively short segments. Thus, the random segments exist mainly as blocks of aliphatic and aromatic polyesters, yielding enhanced properties while maintaining the ability of the aliphatic segments to hydrolyze (Brodhagen et al., 2015). Increased aromaticity also improves mechanical properties of polyesters but reduces hydrophilicity and rate of biodegradation. Even though previously considered non-biodegradable, an article published in Science has shown that PET is biodegradable by an unique bacterium that uses it as a sole carbon and energy source (Yoshida et al., 2016). This suggests that increased exposure of the natural microbial community to plastic wastes has the potential to generate organisms with evolved capabilities to degrade polymers. Such studies are laboratory based and hence will not reflect field situations, but they do show microbial traits that could be manipulated and used to one’s advantage, such as using them for bioaugmentation in the field. Ultimately, biodegradation of mulch material is subject to the composition of plastic in question and the conditions of biodegradation. For instance, PLA degrades very slowly under ambient conditions but degrades rapidly in compost. Under ambient conditions, a combination of biobased and fossil fuel derived material can be biodegraded more rapidly.

Microbial degradation of BDMs

Three key steps are involved in the biodegradation of BDMs by microbes (Figure 1.4), 1) microbial colonization of BDM surface, 2) enzymatic degradation of BDM polymers and 3) microbial

Figure 1.4: A schematic illustration of enzymatic degradation of a polyester by PHB depolymerase (Hiraishi and Taguchi, 2013). CD: catalytic domain, SBD: substrate binding domain.

utilization of degradation products.

Microbial colonization of BDM surface The first step in microbial degradation of a polymer surface is the microbe’s attachment to it. Microbes secrete extracellular depolymerase enzymes that facilitate the biodegradation process. Both prokaryotic and eukaryotic organisms are known to colonize BDMs in soils (Koitabashi et al., 2012, Sang et al., 2002, Kamiya et al., 2007, Šerá et al., 2016, Scarascia-Mugnozza et al., 2006, Kijchavengkul et al., 2008). Use of SEM imaging has become quite popular to not only visualize microbes colonizing the BDM surface but also look for evidence of enhanced surface erosion of the polymers in the vicinity of microbes (Figure 1.5). Numerous studies have characterized microbial communities on BDMs which suggest enrichment in fungi, and have isolated fungal degraders from BDM surfaces incubated in soils (Koitabashi et al., 2012, Muroi et al., 2016, Kasuya et al., 2009, Sang et al., 2002, Kamiya et al., 2007, Kim et al., 2000, Kim and Rhee, 2003a). Bacterial degraders are also known (Mergaert et al., 1993, Suyama et al., 1998).

Enzymatic degradation of BDM polymers Microbes and Enzymes degrading BDMs The most important property controlling enzymatic hydrolyzability of polymers is crystallinity (Wei and Zimmermann, 2017). Polyesters in BDMs are semicrystalline and thus contain both

Figure 1.5: Scanning electron microscope (SEM) images showing microbial colonization of an experimental PLA/PHA mulch film after 16 weeks of soil incubation in laboratory microcosms at 30ºC. Image on panel b shows a magnified image of panel a. Image taken by Sreejata Bandopadhyay using the Zeiss Auriga Crossbeam SEM, currently located at the University of Tennessee Joint Institute of Advanced Materials.

amorphous and crystalline phases (Kuwabara et al., 2002, Gan et al., 2004). The disordered polyester backbone chains in the amorphous region favors binding and hydrolysis by esterases. Polyesterases first attach to the amorphous region of the polymer increasing the degree of crystallinity of the film as it degrades (Mueller, 2006). Polymers with strong carbon-carbon covalent bonds (such as in PE) and no hydrolyzable groups take much longer to degrade. Enzymatic depolymerization is considered the rate-limiting step in polymer degradation in soils. Microbial utilization of oligo- and monomers is much faster when directly added to soils than of the corresponding polymers in the same soils (Zumstein et al., 2018, Witt et al., 1996).

The class of enzyme degrading hydrolyzable bonds include not only esterases but also several other enzymes such as lipases and proteases. Lipases and esterases differ based on hydrolytic cleavage of acyl glycerols with differing acyl chain length. Lipases hydrolyze acyl esters greater than 10 carbon atoms while esterases break esters with chain length less than 10 carbon atoms (Bornscheuer, 2002, Rhee et al., 2005). Some other hydrolases include pectinases, cellulases, xylanases which degrade pectin, cellulose and xylan respectively. Synthetic biodegradable polymers are mostly degraded by enzymes whose natural substrate’s structures resemble the synthetic polymers. Two such natural polyesters similar to BDM polyesters are cutin and suberin. Thus, many enzymes reported to degrade BDMs are cutinases (Brodhagen et al., 2015). All enzymes catalyzing polyester degradation usually have the same catalytic triad in their active site, typical for serine hydrolases. The difference lies in the form, depth and surrounding of the catalytic center which seem to differ significantly (Shah et al., 2014). The presence of polyesters in BDMs make them prone to microbial attack. This is true for PHA, PLA, cellulose, PBAT, PCL, PBS and PBSA. Enzymes that degrade the above-mentioned polymers are specified below:

Polyhydroxyalkanoates (PHA): PHAs are linear polyesters produced naturally by bacterial fermentation of sugar or lipids and are widely used in manufacturing biodegradable plastics. PHA degradation can be intracellular or extracellular. Intracellular PHB depolymerase has been reported in bacterial species such as Rhodospirillum rubrum, whereas extracellular PHB depolymerase has been seen in Streptomyces ascomycinicus. Plastics degraded by extracellular depolymerases are broken down into smaller oligomers before further degradation inside the cell.

Analysis of the structural genes for extracellular PHB depolymerases show the presence of an N terminal domain (catalytic), C terminal domain (substrate binding), and a threonine rich linker region connecting the two domains (Figure 1.4). Catalytic and binding domains are seen in depolymerization enzymes such as cellulase, xylanase, and chitinase. The catalytic domain contains a lipase box pentapeptide as an active site common for serine hydrolases. The serine at the enzyme’s active site forms a catalytic triad with the amino acids, aspartate and histidine. A transient tetrahedral intermediate of the substrate carbonyl carbon is stabilized by NH groups surrounding the histidine residue. The C terminal domain is also known to act as a substrate binding domain for water insoluble P(3HB) substrate.

Extracellular PHB depolymerases are classified into two types based on the difference in the position of the lipase box in the catalytic domain. Type A enzymes have the lipase box located in the center of the catalytic domain, represented by Alcaligenes faecalis AE122, A. faecalis T1, Pseudomonas lemoignei PhaZ1, P. lemoignei PhaZ2, P. lemoignei PhaZ3, P. lemoignei PhaZ4, P. lemoignei PhaZ5 and Pseudomonas stutzeri, while type B enzymes have the lipase box adjacent to the N-termini and are produced by Comamonas sp., C. acidovorans, C. teststeroni and Streptomyces exfoliatus (Sudesh et al., 2000). The enzymatic hydrolysis of PHA molecules in plastic produces 3HB dimer and small amounts of monomer. The rate of enzymatic hydrolysis of P(3HB) by PHB depolymerase depends on the concentration of the enzyme. The first step is adsorption of the enzyme onto the surface of the P(3HB) material by the substrate binding domain of the enzyme, and the second step is hydrolysis of polymer chains by the active site of the enzyme (Sudesh et al., 2000) (Figure 1.4). PHA degrading fungal strains such as Aspergillus fumigatus and Penicillium funiculosum have also been reported (Shah et al., 2014). Fungal depolymerases possess some important characteristics. Fungal PHB depolymerases usually consist of one single polypeptide chain and possess acidic or neutral pH value; they are mostly glycoproteins and possess disulphide bridges in the active sites which play an essential role in the formation of the tertiary structure of the enzymes (Kim and Rhee, 2003b).

Polylactic acid (PLA): The enzymatic degradation of PLA by proteinase K from Tritirachium album was first reported by Williams (Williams, 1981). Later, the first PLA degrading microbe Amycolatopsis sp. was isolated from soil (Pranamuda and Tokiwa, 1999). A classification of PLA depolymerases into two types has been proposed, one with preference toward poly- L- lactic acid

(PLLA) (type I, protease-type) and another with preference to poly-D-lactic acid (PDLA) (type II, lipase/ cutinase type) (Kawai et al., 2011). Presence of a polyethylene glycol molecule bound to the enzyme’s active site close to the catalytic Ser114 has been demonstrated and it likely mimics the bound PLA substrate (Hajighasemi et al., 2016). The position of the bound ligand in the active site and analysis of the hydrolysis products of PLA suggest that this enzyme can perform endo- as well as exoesterase cleavage of PLA.

Actinomycetes play an important role in PLA degradation. PLA-degrading Actinomycetes belong to the Pseudonocardiaceae family and related genera, such as Amycolatopsis, Lentzea, Streptoalloteichus, Kibdelosporangium and Saccharothrix (Tokiwa and Jarerat 2004). A PLA- degrading enzyme purified from an isolated Amycolatopsis strain-41 has been shown to have substrate specificity on PLA higher than proteinase K (Tokiwa and Jarerat 2004). PLA degrading enzymes from different strains of Amycolatopsis have been identified as proteases, proteins such as ABO1197 and ABO1251 from Alcanivorax borkumensis as esterases (Hajighasemi et al., 2016), and PLA-degrading enzymes from Paenibacillus amylolyticus strain TB-13 as lipases (Akutsu- Shigeno et al., 2003). Fungal enzymes involved in PLA degradation have been reported to be cutinases such as those from Aspergillus oryzae RIB40 (Hajighasemi et al., 2016). Two types of PLA-degrading enzymes, protease (proteinase K) and lipase (cutinase-like enzyme) from Cryptococcus sp. strain S-2 has been identified (Kawai et al., 2011) . Lipases have been purified from an Aspergillus niger sp. showing potential for degradation of PLA and PCL (Nakajima- Kambe et al., 2012).

Extracellular PLA depolymerases have been purified from Pseudomonas sp. strain DS04-T with ability to degrade PLA, PHB and PCL (Wang et al., 2011). PLA depolymerases (PLD) have known activities against casein, fibrin, and high molecular weight PLA. Such a depolymerase has been purified from Amycolatopsis sp. strain K104-1 and has been shown to degrade PLA in emulsion as well as in film form producing lactic acid as the ultimate product. Another study added to this by stating the presence of two more PLAases which were different from each other and also from PLD (Li et al., 2008). Like PLD they shared high homology to serine protease family members from Actinomycetes. Proteases such as Proteinase K are the most commonly known PLA degraders. However, when compared with proteinase K with respect to their innate PLA-degrading and proteolytic activities, the purified enzymes showed greater PLA degrading activity than that

of proteinase K. These results suggest that, when compared with proteinase K, the PLAases may be more capable of catalyzing the hydrolysis of the ester bond between lactate units. The environmental considerations are important to assess biodegradation of PLA. PLA is known to be more readily degraded in compost at high temperatures than at ambient conditions. However, a mesophilic bacterium Bordetella petrii PLA-3 was isolated through enrichment culture and was shown to degrade PLA chains by microbial enzymes (Kim and Park, 2010). A very efficient PLA depolymerizing enzyme is a cutinase-like enzyme isolated from the yeast Cryptococcus. This enzyme was able to degrade high-molecular-weight PLA with 500 times better efficiency than the PLA degrading standard, proteinase K. It was also able to depolymerize PBS and PCL (Masaki et al., 2005, Brodhagen et al., 2015).

Aliphatic-aromatic copolyesters (such as PBAT, PBSTIL): Enzymes degrading aromatic, aliphatic co polyester plastics are suggested to be laccases as they are known to be present in lignin biodegrading fungi where they catalyze the oxidation of aromatic compounds. The bacterium Roseateles depolymerans strain TB-87, isolated from freshwater, has been shown to degrade aliphatic-aromatic copolyesters such as PBAT (Shah et al., 2013b). These depolymerases could degrade PBAT both in emulsion and film form, but the rate of degradation was reported to be slow since the aromatic constituent is more hydrophobic. Leptothrix sp. strain TB-71 can also degrade various aliphatic-aromatic co-polyesters but at a slower rate than strain TB-87 (Nakajima-Kambe et al., 2009, Shah et al., 2013b). Several esterases and cutinases have been shown to degrade aliphatic-aromatic copolyesters. Two different thermoactive esterases from Thermobifida genus (one from T. fusca and another from T. alba) have demonstrated such depolymerizing capabilities (Kleeberg et al., 2005, Hu et al., 2010, Chen et al., 2008). An esterase from an anaerobic Clostridium hathewayi was also seen to hydrolyze PBAT. Characterization of this esterase elucidating the crystal structure revealed that it belonged to the α/β hydrolases family (Perz et al., 2016). A cutinase from Thermobifida fusca has been also known to hydrolyze aromatic polyester poly(trimethylene terephthalate) (PTT) based on detection of water soluble hydrolysis products (Eberl et al., 2008). Even through such knowledge helps in suspecting what enzymes might be useful to degrade plastics with aromatic constituents, these enzymes are known to perform at elevated temperatures and hence application of these in the field sites needs screening of depolymerases which function more in mesophilic conditions than in compost like conditions.

Another common aliphatic aromatic copolyester used in biodegradable plastics is poly(butylene succinate/ terephthalate/ isophthalate)-co-(-lactate) (PBSTIL). Depolymerization of PBSTIL into monomers have been demonstrated by enzymes from Roseateles depolymerans strain TB-87 (Shah et al., 2013a) which could be by a suspected mechanism. The depolymerization of PBSTIL could begin at the point of succinic acid which forms long chain oligomers at first and then degradation of terephthalic acid segments can further occur after shredding the polymer chain. Aliphatic segments break first followed by the aromatic segment which leads to succinic acid being the point of initiation of degradation of the polymer chain. A number of oligomers in the form of pentamers, tetramers, trimers, and dimers were found in the study which shows that the polymer was gradually split up from long to short chain oligomers before final monomerization. Instead of a monomer, lactic acid was detected as a water-soluble oligomer (Shah et al., 2013a, Shah et al., 2014). Terephthalic acid (T)-containing fragments accumulated in higher concentration compared to T- free fragments suggesting that the enzymatic hydrolysis of terephthalate ester might be slower than that of succinate ester.

Polybutylene succinate (PBS) and polybutylene succinate co-adipate (PBSA): The bacterium Roseateles depolymerans strain TB-87 described above can also degrade various aliphatic polyesters such as PBS and PBSA (Shah et al., 2013b). Novel esterases were shown to be present in this strain with wide substrate specificity and high activity against aliphatic copolyesters. Aspergillus oryzae has been known to degrade PBS and PBSA under mesophilic conditions (Maeda et al., 2005). A cutinase like enzyme from Pseudozyma antarctica strain JCM 10317 has been reported to actively degrade emulsified and solid films of PBS, PBSA, PCL and PLA (Shah et al., 2014). Degradation abilities also differ within each strain, for instance, a Leptothrix sp. strain TB-71 has been shown to effectively degrade co-polyester PBSA but could not degrade PBS. An esterase from strain TB-71 played a major role in degrading these polymers, in emulsion as well as in film form, and these polyesters acted as inducers for enzyme production (Shah et al., 2014, Nakajima-Kambe et al., 2009). Plastic films generally have low contact efficacy with enzyme molecules; hence enzymes must be in close proximity to the substrate for efficient activity. Some enzymes, however, have certain properties that enable them to adsorb to plastic materials such as the presence of hydrophobic amino acids close to the catalytic triad (Shinozaki et al., 2013).

Poly(ε-caprolactone) (PCL): PCL and PBS degrading enzymes are confirmed to be lipases or cutinases (Masaki et al., 2005, Maeda et al., 2005) with lipases from Penicillium, Rhizopus and Candida species having demonstrated PCL degrading capabilities (Tokiwa and Suzuki, 1977a, Tokiwa and Suzuki, 1977b). Cutinases have also been isolated from fungal species such as Alternaria brassicicola, A. fumigatus, A. oryzae, Humicola insolens and Fusarium solani which demonstrated PCL hydrolysis at high temperatures and wide pH ranges. It was seen that the enzymes’ overall neutral surface charge and additional disulfide bond formation were the key reasons behind their stability (Baker et al., 2012). Cutinases exhibit lipase as well as esterase characteristics. Engineering such enzymes lead to improved function and stability over a range of temperature and pH conditions. For example, two tandem cutinases were expressed from Thermobifida alba AHK119 in E. coli Roseta-gami (DE3) (Kawai et al., 2013). These recombinant enzymes displayed wide substrate specificity towards aliphatic and aliphatic-aromatic copolyesters, while the activity and thermostability of these enzymes were improved through random and site directed mutagenesis (Kawai et al., 2013).

Cellulose: The breakdown of cellulose gives monomers of glucose, which serves as a major carbon and energy source for all organisms. Talaromyces verruculosus SGMNPf3 is a soil fungus that has been studied for its capability to degrade cellulosic substrates, both natural and commercial (Goyari et al., 2015). The fungus, supposedly acido-mesophilic, grows and produces cellulases most efficiently in culture at a pH of 3.3 and temperature 30°C. Activity of the cellulases secreted by the fungus was seen to be higher on natural substrates than on commercial ones. No end product inhibition was seen as there was a continuous increase in cellulase activity at different time points. Researchers found a higher number of certain individual cellulases, particularly β-glucosidase. The absence of an end product inhibition of the cellulase activity is mainly attributed to the ability of the fungus to produce sufficient β-glucosidase. Two general enzyme-catalyzed mechanisms are predicted for cellulose conversion. Aerobic fungi degrade cellulose catalyzed by the synergistic action of endo-glucanases and exo-cellobiohydrolases that exist extracellularly as free entities; some aerobic bacteria are known to possess a similar system. Other hydrolytic enzymes, such as β-glucosidases, and oxidative enzymes participate in the overall process. In anaerobic bacterial systems, on the contrary, cellulose degradation is brought about by the action of large calcium-

and thiol-dependent multicomponent complexes called cellulosomes located, at least in the early stages of cultivation, on the cell surface (Coughlan, 1991).

Metabolic pathways involved in BDM degradation

Enzymatic breakdown of BDM polymers channels the downstream products into central microbial metabolic pathways such as the tri-carboxylic acid (TCA) pathway (Doi et al., 1992). This generates carbon dioxide and water via the electron transport system. The metabolic pathways involved in the degradation of some common BDM polymers are given below.

Polyhydroxyalkanoates (PHAs): PHA is a storage polyester in microbes, hence, when PHA is present as a component in plastic materials microbes growing on it usually have enzymes for its degradation. For example: Ralstonia eutropha (formerly known as Alkaligenes eutrophus) performs simultaneous synthesis and degradation of polyesters in its cells due to the cyclic nature of PHA metabolism. In this cyclic process, 3 ketothiolase catalyzes the first reaction of biosynthesis and last reaction of degradation. The degradation of poly(3- hydroxybutyrate) (P(3HB)) is initiated by PHA depolymerase to form D(-)-3- hydroxybutyric acid monomers. NAD- specific dehydrogenase oxidizes the acid to acetoacetate, which is then converted to acetoacetyl- CoA (Doi et al., 1992). Thus, acetoacetyl-CoA is an intermediate common to the biosynthesis and degradation of P(3HB). Acetoacetyl CoA is converted to acetyl CoA by a ketothiolase and then can enter the TCA cycle for further degradation into carbon dioxide.

It has also been seen that PHA depolymerase (encoded by phaZ) degrades PHA and releases 3- hydroxycarboxylic acid monomers which gets activated to hydroxyl acyl CoAs (HA CoAs) and can enter the β-oxidation pathway. Depending on the metabolic state of the cell, the hydroxyacyl- CoAs will either be incorporated into nascent PHA polymer chains by the PHA polymerase or it could be oxidized by the β-oxidation pathway (De Eugenio et al., 2010). In a Ralstonia eutropha strain, 3-hydroxybutyryl CoAs have been seen to be released by the PhaZ depolymerase (Uchino et al., 2007) which is in contrast to the study by de Eugenio et al. (2007) where hydroxyalkanoic acids were released by PhaZ enzyme. As with Ralstonia eutropha (Doi et al., 1992), it has been established by studies that PHA metabolism in a Pseudomonas putida strain is also a cyclic process in which synthesis and degradation of the polyesters are simultaneously active to facilitate the turnover of the polymer (De Eugenio et al., 2010). Another study demonstrated the subcellular

localization of an acyl CoA synthetase on PHA granules both in vitro and in vivo experiments. This localization of an acyl CoA synthetase on PHA granules may play a central role in mobilization of PHA such as the conversion of hydroxycarboxylic acid monomers to hydroxycarboxyl CoA which can be further utilized by the cells (Ruth et al., 2008). In summary, the released monomers from PHA in a plastic could be suspected to be first activated to HA-CoAs by an acyl-CoA synthetase via an ATP dependent reaction (Ruth et al., 2008). These HA-CoAs act as substrates for both PHA synthase and fatty acid β-oxidation cycle and, therefore, will be either incorporated into nascent PHA polymer chains by the PHA synthase, or oxidized by the β- oxidation pathway to generate energy via the TCA cycle (De Eugenio et al., 2010). Alternatively, for some bacteria, a modification of the TCA cycle also exists which is the glyoxylate cycle, which lacks many of the TCA cycle enzyme reactions. In the glyoxylate cycle, oxidation of fatty acids to acetyl-SCoA is carried out by β-oxidation pathway, but pyruvate oxidation is not directly involved. This shunt is known to yield sufficient succinate and malate required for energy production. The glyoxylate cycle also generates other precursor compounds for biosynthesis. It converts oxaloacetate to either pyruvate and CO2 (catalyzed by pyruvate carboxylase) or phosphoenolpyruvate and CO2 (catalyzed by the inosine triphosphate [ITP]-dependent phosphoenolpyruvate carboxylase kinase) (Jurtshuk Jr, 1996). Both triose compounds can then be converted to glucose by reversal of the glycolytic pathway. There is no loss of CO2 in the glyoxylate shunt which could be advantageous for cells in certain situations. Some studies have also suggested that in Ralstonia eutropha, a new mechanism of degradation pathway for PHB is possible which is connected by crotonyl CoA to the β-oxidation cycle. The model suggests that the NADPH dependent synthesis of PHB with (R)-3HB-CoA as intermediate and the PHB degradation yielding (S)-3HB-CoA are separated (Eggers and Steinbüchel, 2013). Metabolic pathways of PHA biosynthesis of Pseudomonas and PHB in R. eutropha are shown in Figure 1.6. Fungal degradation of PHA in soil is mainly caused by extracellular depolymerases (Sang et al., 2006). These break down PHA to low molecular weight products which are then transported into the fungal cell and finally metabolized into carbon dioxide and water.

Polylactic acid (PLA): PLA is widely used in biodegradable plastic materials. PLA degradation is rapid at elevated temperatures suggesting that both biotic and abiotic degradation mechanisms are involved in the process (Itävaara et al., 2002). At similar elevated temperatures, it has been

Figure 1.6: Metabolic pathways in PHA biosynthesis of Pseudomonas and PHB in R. eutropha. In Pseudomonads synthesis and degradation of PHA operate as a continuous cycle with 3 hydroxy fatty acids released from PHA polymers by PhaZ encoded depolymerase and activated to 3-hydroxyacyl CoAs by acyl synthetase with a consumption of one ATP molecule. The activated monomers are either metabolized via fatty acid degradation or reincorporated into PHA. The specific PHA/PHB metabolic pathways are interconnected with the main central pathways that converge in acetyl CoA (Escapa et al., 2012).

observed that the biodegradation of PLA is much faster in anaerobic conditions than in aerobic conditions. The effect of temperature on the biodegradability of PLA indicates that its polymer structure must be hydrolyzed before microbes can utilize it as a nutrient source. It has been seen that, post hydrolysis, the degradation products of poly-L-lactic acid can be metabolized (Chen et al., 2003). PLA degrades to lactic acid via hydrolytic deesterification and lactic acid forms pyruvate via the action of lactate dehydrogenase (Chen et al., 2003). Pyruvate is an important intermediate product in metabolic pathways and can generate glucose in cells via gluconeogenesis.

It can also be metabolized to form CO2 and water by the TCA cycle. The TCA cycle is, thus, the central metabolic pathway involved in the degradation of this polymer; it is completed in the cytoplasm of bacterial cells and in the mitochondria of eukaryotic cells. Since PLA has a high glass transition temperature it is better composted at higher temperatures than biodegraded at ambient conditions. Soil burial tests have shown that degradation of PLA in soil is slow and it takes a long time for degradation to start (Tokiwa and Calabia, 2006). In a composting environment PLA can be hydrolyzed into smaller molecules (oligomers, dimers, monomers) after 45-60 days at 50-60ºC, the smaller molecules are then degraded to CO2 and water by microbes. Since lactate dehydrogenases are present in both bacteria and fungi, the cycles could be suspected to be nearly identical in both. A schematic of PLA degradation is shown in Figure 1.7.

Polybutylene succinate (PBS) and polycaprolactone (PCL): The biodegradability of PBS powders in controlled compost have been studied and pathways for degradation of the same has been proposed. When PBS is biodegraded to organic compounds in a compost, PBS is thought to undergo abiotic and biotic hydrolysis to produce 1,4- butane diol and succinic acid which then enters the TCA cycle to release CO2 and form biomass (Kunioka et al., 2009). This process is known to happen with long incubation times and slow rates of biodegradation. When PCL is degraded in a controlled compost environment, the biodegraded monomer unit is hydroxycaproic acid. This compound is incorporated into the β-oxidation cycle which then actively produces acetyl

CoA. This eventually enters the TCA cycle and releases CO2. PCL degradation in compost occurs with short incubation times and active biodegradation (Kunioka et al., 2009). The microbial degradation pathways of PCL and PBS is depicted in Figure 1.8.

It has also been reported that enzymes from Roseateles depolymerans degrades PBS into succinic acid, and 1,4 butanediol as a result of hydrolytic cleavage of ester bonds (Shah et al., 2013a).

Figure 1.7: PLA breakdown via metabolic intermediates

Figure 1.8: Metabolic pathways for PCL and PBS degradation by microbes living in controlled compost (Kunioka et al., 2009).

Succinic acid is an intermediate in the TCA cycle and can thus be metabolized further depending on cellular needs. PCL degrading filamentous fungi have also been known to hydrolyze PCL to water soluble products.

Polybutylene adipate co-terephthalate (PBAT): A commonly used polymer in mulches is PBAT. This is an aliphatic-aromatic copolyester of 1-4 butanediol, adipic acid, and terepththalic acid and is used in commercial BDMs manufactured by BASF (Ecoflex®) and Mater-Bi® (Novamont). It has been shown that a Bacillus subtilis strain easily degrades aliphatic polyester polybutylene adipate and also dibutyl terephthalate but was incapable of degrading polybutylene terephthalate (Trinh Tan et al., 2008). This suggests that the ester bonds involving adipic acid are degraded much more readily than those with terephthalic acid, further implying that the presence of the aliphatic polyester portion in such plastics are essential to ensure biodegradability of copolyester. Nevertheless, it has been reported that protocatechuic acid can be formed as an intermediate in terephthalate metabolism, indicating potential enzymatic pathways of terephthalate degradation in microbes (Engelhardt et al., 1976). In nature many aerobic microbes have been known to degrade

lignin-derived aromatic molecules through conserved central intermediates such as catechol (in bacteria) and protocatechuate (in most fungi and some bacteria) (Johnson and Beckham, 2015, Fuchs et al., 2011). When these compounds are subjected to aromatic ring cleavage by dioxygenase enzymes, different products are formed that are metabolized via entry into the TCA cycle. Different degradation pathways produce different combinations of succinate, acetyl CoA, and pyruvate. Thus, it can be suspected that microbes will eventually metabolize terephthalate compounds from PBAT plastics by incorporating them into their metabolic systems.

Starch and cellulose: Plastics containing starch or cellulose would be biodegraded by microbes using similar pathways as postulated above, commonly via glycolysis and TCA cycle. However, in addition to glycolysis two other glucose catabolizing pathways are found in bacteria: oxidative pentose phosphate pathway and the Entner Doudoroff pathway which is exclusively found in obligate aerobic bacteria. Most Pseudomonas species, for example, utilize the Entner Doudoroff pathway for glucose catabolism because they lack phosphofructokinase and hence cannot synthesize a key intermediate in the glycolytic pathway.

Microbial utilization of degradation products

The final step in BDM biodegradation is microbial utilization of oligomers and monomers released through enzymatic hydrolysis. These products are either mineralized to CO2 or are used for synthesis of biomolecules. The most commonly used approach to study utilization of mulch polymers is to track the CO2 evolved over time and assess the conversion into microbial biomass. Most degradation products are utilized by bacteria as intermediates as they are formed in their central metabolic pathways. For instance, depending on the energy status of a microbe, PHB degradation can lead to production of compounds such as acetyl CoA which enter the TCA cycle and eventually release carbon as CO2, or the compounds are assimilated into microbial biomass. Pathways that utilize TCA cycle intermediates called cataplerotic reactions serve to synthesize important products and avoid inappropriate build-up of cycle intermediates. Some of these cataplerotic reactions include glucose biosynthesis using oxaloacetate as starting material, lipid biosynthesis requiring acetyl CoA and amino acid biosynthesis using alpha ketoglutarate (which is converted to glutamate by glutamate dehydrogenase). Alpha ketoglutarate and oxaloacetate are also used to synthesize glutamate and aspartate in transamination reactions. Amino acids are first

degraded to TCA cycle intermediates before being converted first to phosphoenol pyruvate, then to pyruvate by pyruvate kinase and finally to acetyl CoA by pyruvate dehydrogenase, leading to complete oxidation of amino acids. The assimilation of degradation products by microbes is important as it provides an indication that the water soluble substances can be mineralized in natural and artificial environments provided suitable microbial populations are present (Tokiwa and Calabia, 2006). Several studies have shown that polymer degrading strains are able to assimilate the degradation products (Pranamuda and Tokiwa, 1999, Jarerat and Tokiwa, 2001).

Knowledge gap

As is evident from this body of literature, the majority of our knowledge pertaining to biodegradation of BDMs stems from our understanding of degradation of pure polyesters. From this, we can infer how microbes might degrade BDMs. However, given that commercially available BDMs are usually a mixture of more than one polymer along with plasticizers and other additives, the mechanisms employed by microbes to degrade them may or may not be similar to degradation of pure polymers (Šerá et al., 2016, Narancic et al., 2018). Furthermore, polyester much films are produced by blown film extrusion which differ from unprocessed polyesters. This processing can further alter physical properties of the individual polyesters and thus mandates the need for more research on commercial BDM films (Li et al., 2015).

The spatiotemporal dynamics of microbial colonization on BDMs remain to be thoroughly investigated (Sander, 2019). Use of SEM imaging needs to be complemented with other techniques such as fluorescence microscopy which allow real time monitoring of microbial colonization. Additionally, techniques need to be developed to further assess the effects of different soil properties on microbial colonization of BDMs, such as nutrient availability. Since BDMs are mostly carbon based, potential microbial colonizers may experience nitrogen limitation in specific soils which can favor fungal growth on BDMs (Güsewell and Gessner, 2009). Furthermore, fungi can outcompete bacteria in film colonization (Sander, 2019).

Lastly, most of the studies on polymer degradation conducted to date focus on isolated strains and their enzymes using culture-based approaches. Limited studies have characterized microbial communities on BDMs using large scale field experiments or culture-independent approaches. Unless the microbial community selectively enriched on BDMs is characterized extensively across

varied soil types, beyond laboratory cultures alone, important microbial degraders and potentially unique degradation pathways might escape characterization.

Characterizing BDM degraders using metagenomics

Characterization of microbes in environmental samples have traditionally focused on easy-to- obtain isolates which readily grow in culture (Tringe and Rubin, 2005). However, these organisms represent only a fraction of living microbes of interest. Many species are difficult to study in isolation because they fail to grow in laboratory cultures or depend on other microbes for survival. One way to circumvent this problem is to extract DNA from the sample and employ DNA sequencing to characterize the microbial community associated with the sample. Use of such DNA sequencing methods have led to the development of a new field referred to as metagenomics. Metagenomics provides a relatively unbiased view of the microbial community structure as well as its functional (metabolic) potential (Hugenholtz and Tyson, 2008).

Theoretically, any environment can be used for metagenomic analyses provided DNA can be extracted from the sample (Hugenholtz and Tyson, 2008). New and improved protocols and kits are now available which incorporate inhibitor removal technology such as the DNeasy PowerLyzer PowerSoil DNA extraction kit (Qiagen) which can not only be used for soil DNA extractions but can be optimized for any other sample of interest. Use of such advanced kits have enabled successful extraction and amplification of pure microbial genomic DNA from compost, sediment, manure and other environmental samples.

About four decades ago, the most widely used sequencing was Sanger sequencing, which was based on the selective incorporation of chain terminating dideoxynucleotides by DNA polymerase during in vitro DNA replication. It produces longer DNA sequence reads of greater than 500 nucleotides. However, the more commonly used sequencing methods are now termed as “next generation sequencing”, with the major difference between the two being the sequencing volume. While the Sanger method sequences a single DNA fragment at a time, next generation sequencing is massively parallel, and can sequence millions of fragments simultaneously in a single run. This high throughput process leads to sequencing of thousands of genes at one time and offers the power to detect novel or rare variants with deep sequencing. The Illumina MiSeq next generation sequencer is widely used to classify microbes from a metagenomic sample.

One important question addressed using metagenomics is: “Who is there?” Characterization of prokaryotic and eukaryotic microbes in environmental samples have typically relied on the 16S and 18S rRNA genes, which contain both conserved and variable regions facilitating sequencing and phylogenetic classification. The conserved regions provide primer binding sites (Baker et al., 2003, Munson et al., 2004), whereas the hypervariable regions provide species-specific signature sequences used for taxonomical classification (Van de Peer et al., 1996). These hypervariable regions (V1-V9) further contain varying degree of conservation (Chakravorty et al., 2007), with the more conserved regions used for determining the higher-ranking taxa and whereas the quickly evolving ones helps identify genus or species. Since the 16S rRNA hypervariable regions exhibit different degrees of sequence diversity, no single region can differentiate among all bacteria, thus, studies that compare the relative advantages of each region for specific goals help provide some insight. The most widely sequenced region of the 16S rRNA gene is the V4 region. Its short length (~250 base pairs) allows for fully overlapping forward and reverse reads using MiSeq 250 paired end reads and results in lowest error rates (de la Cuesta-Zuluaga and Escobar, 2016). With the 18S rRNA gene, it has been seen that V2, V4 and V9 regions were best suited for biodiversity assessments (Ki, 2012, Hadziavdic et al., 2014).

The power of metagenomics lies in its potential for serendipitous discovery (Hugenholtz and Tyson, 2008). For instance, proteorhodopsin proteins were first identified from environmental DNA from bacterioplankton. They are now found to be widely distributed and highly expressed in microbial groups in marine habitats (Hugenholtz and Tyson, 2008). The more recent discovery of ammonia monooxygenase gene adjacent to an archaeal marker gene revealed that archaea was the main source of ammonia oxidation in many terrestrial and marine ecosystems (Hugenholtz and Tyson, 2008). In the context of the present study, metagenomics has the potential to reveal novel microbes involved in BDM degradation by making use of high-throughput sequencing technology.

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CHAPTER II BIODEGRADABLE PLASTIC MULCH FILMS: IMPACTS ON SOIL MICROBIAL COMMUNITIES AND ECOSYSTEM FUNCTIONS

A version of this chapter was originally published by Sreejata Bandopadhyay, Lluis Martin- Closas, Ana M. Pelacho and Jennifer M. DeBruyn.

Sreejata Bandopadhyay, Lluis Martin-Closas, Ana M. Pelacho and Jennifer M. DeBruyn. “Biodegradable Plastic Mulch Films: Impacts on Soil Microbial Communities and Ecosystem Functions.” Frontiers in Microbiology (2018).

This review article was revised for inclusion in the present dissertation. The author contributions are as follows: SB and JD conceived of the review topic and were responsible for final editing. SB, LM-C, AP, and JD all wrote portions of this review.

Abstract

Agricultural plastic mulch films are widely used in specialty crop production systems because of their agronomic benefits. Biodegradable plastic mulches (BDMs) offer an environmentally sustainable alternative to conventional polyethylene (PE) mulch. Unlike PE films, which need to be removed after use, BDMs are tilled into soil where they are expected to biodegrade. However, there remains considerable uncertainty about long term impacts of BDM incorporation on soil ecosystems. BDMs potentially influence soil microbial communities in two ways: first, as a surface barrier prior to soil incorporation, indirectly affecting soil microclimate and atmosphere (similar to PE films) and second, after soil incorporation, as a direct input of physical fragments, which add carbon, microorganisms, additives, and adherent chemicals. This review summarizes the current literature on impacts of plastic mulches on soil biological and biogeochemical processes, with a special emphasis on BDMs. The combined findings indicated that when used as a surface barrier, plastic mulches altered soil microbial community composition and functioning via microclimate modification, though the nature of these alterations varied between studies. In addition, BDM incorporation into soil can result in enhanced microbial activity and enrichment of fungal taxa. This suggests that despite the fact that total carbon input from BDMs is minuscule, a stimulatory effect on microbial activity may ultimately affect soil organic matter dynamics. To address the current knowledge gaps, long term studies and a better understanding of impacts of BDMs on nutrient biogeochemistry are needed. These are critical to evaluating BDMs as they relate to soil health and agroecosystem sustainability.

Introduction: Agricultural Plastic Mulch Films

Agricultural plastic mulch films are used in production of specialty crops to modify soil temperatures, conserve soil moisture (Kader et al., 2017) and reduce weed pressure (Martín-Closas et al., 2017), ultimately improving crop productivity. Low-density polyethylene (PE) is the most commonly used plastic mulch because it is inexpensive, easily processed, highly durable and flexible (Kasirajan and Ngouajio, 2012). However, widespread use of PE, which is not biodegradable, has resulted in serious environmental contamination (Teuten et al., 2009; Liu E.K. et al., 2014; He et al., 2015; Steinmetz et al., 2016).

A growing concern is that plastic mulches are never completely removed from a field, leaving remnants which remain in soil for decades (Feuilloley et al., 2005; Kyrikou and Briassoulis, 2007; Briassoulis et al., 2015; Ramos et al., 2015). In China, long term use of plastic film mulches has resulted in an estimated accumulation of 50–260 kg hm-2 of residual plastics in topsoil (0–20 cm), which can inhibit plant growth (Liu E.K. et al., 2014). While PE is considered to be chemically inert, accumulated PE fragments can affect soil physically and may enter the food chain (Barnes et al., 2009; Teuten et al., 2009; Sivan, 2011; Rillig, 2012; Duis and Coors, 2016; Huerta Lwanga et al., 2016). Plastic mulches also introduce various additives such as plasticizing agents which may pollute soil (Van Wezel et al., 2000; Fu and Du, 2011; Kong et al., 2012; Magdouli et al., 2013; Wang et al., 2013, 2015).

Biodegradable plastic mulches (BDMs) have been developed as substitutes to PE mulch films and are designed to be tilled into soil after use where resident microorganisms degrade the plastic. BDMs can be prepared from biobased polymers derived from microbes or plants, or fossil-sourced materials (Marechal, 2003). Common biobased polymers used in BDMs include polylactic acid (PLA), starch, cellulose, and polyhydroxyalkanoates (PHA). Fossil-sourced polyesters used in BDMs include poly(butylene succinate) (PBS), poly(butylene succinate-co-adipate) (PBSA), and poly(butylene-adipate-co-terephthalate) (PBAT) (Kasirajan and Ngouajio, 2012). Polymers used in BDMs contain ester bonds or are polysaccharides, which are amenable to microbial hydrolysis (Brodhagen et al., 2015). In theory, BDMs should be completely catabolized by soil microorganisms, converted to microbial biomass, CO2 and water (Malinconico et al., 2002; Feuilloley et al., 2005; Imam et al., 2005; Dintcheva and La Mantia, 2007; Kyrikou and

Briassoulis, 2007; Kijchavengkul et al., 2008; Lucas et al., 2008). In practice, complete breakdown in a reasonable amount of time is not always observed (Li et al., 2014b). Regulators and growers cite concerns about unpredictable or incomplete breakdown and the ultimate fate of BDM constituents and their effect on soil ecosystems (Goldberger et al., 2015; Miles et al., 2017). Due to increased demand for eco-friendly substitutes to PE, the global market for BDMs is expected to continue to grow. Soil health is a key component of agroecosystem sustainability, thus there is a need to understand the effects of BDMs on both crop productivity and soils. To date, the majority of soil studies related to plastic mulching have focused on PE. The objective of this review is to highlight research concerning impacts of plastic mulches on soil microbial communities and their processes with an emphasis on BDMs. Gaps in our current understanding of how plastics affect soil ecosystems are highlighted.

Indirect Effects of Plastic Mulches on Soils Via Microclimate Modification

One way that plastic mulches (both BDMs and PE) may indirectly affect soil ecosystems and microbial community functioning is via modification of soil microclimate and atmosphere. As a barrier on the soil surface, plastic mulches reduce evaporation and gas exchange, increase temperature and reduce light transmissivity (Figure 2.1; Kasirajan and Ngouajio, 2012). The extent of these modifications depends on their physicochemical properties; for example, PE mulches result in greater warming compared to BDMs (Moreno and Moreno, 2008; Kader et al., 2017) and are less vapor-permeable (Touchaleaume et al., 2016) resulting in accumulation of soil CO2 (Zhang et al., 2015; Yu et al., 2016). By serving as a barrier to evaporation, plastic mulches can result in increased soil moisture levels (Qin et al., 2015) which can ultimately alter soil physical structure; for example, by increasing the proportion of water stable aggregates (Siwek et al., 2015). Favorable moisture and temperature conditions under plastic mulches also affect plant roots, typically stimulating root development and increasing root exudation (Li et al., 2004b; Subrahmaniyan et al., 2006; Wang et al., 2016). This results in greater nutrient availability for rhizosphere microorganisms (Subrahmaniyan et al., 2006; Lin et al., 2008; Maul et al., 2014; Liu et al., 2015). Since levels of soil moisture, temperature, vapor diffusivity and presence of roots modulate microbial activity, it follows that modifications to soil microclimate under plastic mulches affect soil microbial communities. Plastic mulching can also decrease populations of soil invertebrates 50

Figure 2.1: Indirect (polyethylene and biodegradable mulches (BDMs)) and direct (BDMs only) effects of plastic mulching. All plastic mulches form a barrier on the soil surface which influences soil temperature, moisture and soil-air gas exchange, indirectly altering the microbial communities. BDMs are tilled into the soil at the end of the growing season, introducing physical fragments and a carbon source, along with other components of the plastic films (additives, plasticizers, minerals etc.) which may additionally influence soil communities and their processes

(Schonbeck and Evanylo, 1998; Miñarro andDapena, 2003), which may reduce top-down grazing pressures on soil microbes. A 28-year study in Shenyang, China, demonstrated that plastic film mulching increased relative abundances of Proteobacteria and Actinobacteria (Farmer et al., 2017). Other studies reported improved control of Phytophthora capsici (Núñez-Zofío et al., 2011) or increased mycotoxigenic fungi under plastic mulches (Munoz et al., 2015). From PE studies, we can infer that BDMs may have similar indirect effects and alter microbial community structure and diversity. In addition to changes in microbial community structure, plastic film mulches affect microbial functioning. Some studies report increased microbial activity under mulches (Mu et al., 2014, 2016; Zhang et al., 2015; Chen H. et al., 2017), while others report decreased activity (Moreno and Moreno, 2008). The response is most likely dependent on the amount of warming under the mulches: where ambient temperatures are cool, mulches bring soil temperature closer to microbial optima and increase activity, whereas in warmer seasons, the mulches may push temperatures above optima, limiting soil microbial activity (Moreno and Moreno, 2008). The changes in microbial activity ultimately influence nutrient cycling and storage. The effect of plastic on soil organic carbon (SOC) is the result of the balance between increased root growth and exudate secretion, and microbial decomposition and loss to CO2 (Wien et al., 1993; Nan et al., 2016). Thus,

it is not surprising that studies examining SOC under plastic mulches have yielded mixed results, with some reporting increased microbial biomass carbon (Li et al., 2004a; An et al., 2015) and SOC (Munoz et al., 2017) and others no change (Wang et al., 2016) or decreased SOC (Cuello et al., 2015). It should be noted that changes in SOC take place over longer time intervals, so the short term (one or two seasons) nature of most mulching experiments do not capture these longer term dynamics. Plastic mulching also affects cycling and losses of nitrogen in soils (Qin et al., 2015; Nan et al., 2016). Because plastic mulching improves water use efficiency (WUE), nitrate leaching is reduced (Romic et al., 2003). Indeed, Qin et al. (2015) estimated up to 60% increase in nitrogen use efficiency (NUE) under PE mulching compared to no-mulch controls. With respect to N2O gas release, results are mixed, with some studies reporting decreases (Berger et al., 2013; Li et al., 2014d; Liu J.L. et al., 2014) and others, increases (Okuda et al., 2007; Arriaga et al., 2011; Nishimura et al., 2012; Cuello et al., 2015; Chen H. et al., 2017). Together, these studies show that plastic mulching, independent of composition, has significant effects on soil microbes and their processes via environmental modification. In several cases, improved crop productivity with mulch was accompanied by a loss of soil organic matter and increased release of greenhouse gasses (Steinmetz et al., 2016). It is important to note that PE films often result in higher soil temperatures and are more effective in suppressing weeds compared to BDMs (Bonanomi et al., 2008). As a physical barrier, BDMs are expected to have similar, though not identical, indirect effects on soil microbes via microclimate modification; the outstanding question is how these effects play out when direct incorporation and biodegradation of BDMs are taken into consideration.

Direct Effects of BDMs Via Incorporation Into Soil

While BDMs may have comparable effects as PE mulches when used as a surface barrier, they are distinctly different when considering their ultimate fate. After the growing season, PE films should be removed from the soil surface, while BDMs are meant to be tilled in and biodegraded by microorganisms. BDM fragments are both a physical and a biogeochemical input (Figure 2.1). This aspect is unique to BDMs and may have effects on soil ecology and functioning that cannot be predicted from studies of non-biodegradable plastics such as PE. Biodegradable plastic mulch fragments may physically modify soil before they are fully biodegraded. For example, PE plastic fragments reduce soil infiltration and water absorption; their accumulation may affect soil 52

ecosystems and ultimately plant germination and growth (Liu E.K. et al., 2014). Therefore, it is conceivable that under conditions restricting soil microbiological activity (e.g., water scarcity), BDM fragments may accumulate in soil with similar effects on soil and plants.

From a toxicology standpoint, the fragments of BDMs incorporated into the soil are generally considered to be safe. For example, tests of the starch-copolyester blend Mater-Bi ® (Novamont, Novara, Italy) have shown no ecotoxic effects (Sforzini et al., 2016), nor adverse effect on nitrification potential (ISO 14238:2012) (Ardisson et al., 2014), Enchytraeus albidus reproduction (ISO/CD 16387), or Vibrio fischeri growth (ISO 11348 flash test) (Kapanen et al., 2008). Similarly, soil samples containing Ecoflex® (BASF), PHB, and PLA show no demonstrated visual phytotoxicity (ISO 11269-2) (Rychter et al., 2006, 2010). It should be noted that these studies focus on acute responses; possible effects of longer exposure are untested.

Plastic mulches are composed not only of the main polymers but also of small amounts of organic (e.g., additives, plasticizers, etc.) and inorganic (e.g., Cu, Ni, etc.) components, whose effects are largely unknown. Traditional plant tests for toxicity have not been adapted to identify effects of compounds released from BDMs. First, different compounds are released at different times during the biodegradation process. Second, frequently used tests fail to reckon the changing needs and responses throughout plant development by only focusing on germination. Finally, the diversity of plant responses in the ecosystem is narrowly represented by tests that analyze early growth in a few, mostly vigorous, plant species. Despite these constraints, some effects have emerged. A phytotoxicity test of several chemicals used in bioplastics found that some exhibited a concentration-dependent inhibition of plant growth (Martin-Closas et al., 2014). Acrylate polymers used to maintain soil humidity damaged maize root and shoot development (Chen et al., 2016). Organic compounds released from mulch polymers have been found to be absorbed by crop plants (Du et al., 2009; Li et al., 2014c; Chen N. et al., 2017). Given some of the demonstrated effects on plants, these additives may also impact soil microbes and their functions, though these effects are largely unexplored.

Tilled into soil, BDMs are an input of carbon, albeit a very small one when taking into account the volume of soil into which they are incorporated. However, the growth of soil microbes in agricultural soil is usually carbon-limited and several studies have demonstrated responses by soil microbes to these small inputs. BDMs have caused increases in microbial biomass and enzyme

activities (Li et al., 2014a; Yamamoto- Tamura et al., 2015) and changes in soil microbial community structures (Koitabashi et al., 2012; Li et al., 2014b; Muroi et al., 2016). There is evidence that BDMs enrich for certain taxa, for example, PBSA films preferentially selected for fungal genera Aspergillus and Penicillium, and protists such as Acanthamoeba (Koitabashi et al., 2012) and PBAT film surfaces were enriched in Ascomycota (Apodus, Saccharicola, Setophoma), and Proteobacteria (Hyphomicrobium, Caenimonas) (Muroi et al., 2016). Several studies have also noted increased fungal abundances in soil as a result of BDM incorporation (Rychter et al., 2006; Li et al., 2014b; Ma et al., 2016; Muroi et al., 2016). The majority of these studies examine only one soil type or location; one of the few studies to examine responses in multiple locations showed an enrichment of fungi in one location and Gram-positive bacteria in another (Li et al., 2014b) indicating that microbial responses to BDMs may be affected by environment, soil type and/or management legacies.

In order to tease apart whether observed changes in microbial communities are a result of microclimate effects (i.e., changes that would be expected regardless of the plastic material used) or are specific to BDMs tilled into soil, results from studies that directly compare microbial communities under PE and BDMs in the same experiment are required. The few studies available reported increased microbial abundances, respiration, and enzyme activities under BDMs compared to PE treatments (Moreno and Moreno, 2008; Li et al., 2014a; Yamamoto-Tamura et al., 2015; Barragán et al., 2016; Hajighasemi et al., 2016; Ma et al., 2016) suggesting that incorporation of BDMs does have some effect on microbial activity. Evidence of enhanced degradative activities by soil microbes suggests that BDMs may ultimately change carbon cycling and storage in soil. The total amount of carbon in BDMs is small, and much of it is expected to be respired as CO2. However, repeated tilling of BDMs into soil may have an effect over time. In one study, use of BDMs resulted in increased microbial biomass carbon compared to PE mulches (Moreno and Moreno, 2008), suggesting an impact on soil carbon dynamics that may accumulate over time. It should also be considered whether enhanced BDM decomposition would impact cycling of other nutrients. Studies on nutrient transformation related to BDM use are limited; two studies reported that BDMs, like PE films, had no measurable impact on nitrification potential of soils (Kapanen et al., 2008; Ardisson et al., 2014); effects on other nutrients remain unknown.

Taken together, the changes in microbial community structures, stimulated microbial decomposition, and increased microbial biomass suggest enhanced nutrient and carbon cycling under BDMs, which may result in long term effects on soil organic matter dynamics. However, with limited research on long term studies, it remains unknown if BDMs may impact soil functions differently than PE and what implications this has for sustainability of this technology for crop production.

Future Research Opportunities

Biodegradable plastic mulches are a promising alternative to PE plastic film mulches. However, there are considerable gaps in our understanding of how long-term use of BDMs affects soil ecosystems that are critical to crop productivity. Effects of conventional PE mulches on soil microclimate, microbial communities and biogeochemistry provide insight into how BDMs may be indirectly influencing soil. As a surface barrier, plastic mulches can alter soil microbial community composition and functioning in terms of carbon and nitrogen cycling via microclimate modification, though the nature of these alterations has varied between studies. Additionally, there is a lack of knowledge regarding the ecological consequences of BDM degradation products (Lambert and Wagner, 2017). Repeated tilling of BDM fragments into soil may alter the soil physical environment and act as a new source of carbon for microbes. In this regard, effects of BDMs on soils are unique compared to other plastics. The dearth of research directly comparing BDMs to PE renders it difficult to tease apart whether BDMs have an impact on soil microbes and their activities above and beyond what would be expected from a PE plastic film. The few available comparative studies show that microbial activity is enhanced under BDMs. This suggests that despite the fact that total carbon input from BDMs is minuscule, a stimulatory effect on microbial activity may contribute to soil microbial biomass and ultimately soil organic matter.

Several key gaps remain in our understanding of BDMs and their impacts on soil ecosystems. First, studies to date have focused on short term effects, generally one or two growing seasons, or acute toxicity, so long term effects are unknown. Second, the relationship between plastic composition and microbial responses needs exploration: different types of biodegradable plastics will likely differentially affect soil microbes, based on both the parent polymer composition and breakdown products. Third, additives have been demonstrated to leach out of plastic and affect plants; but

their effects on soil microbes are unknown. Fourth, several studies have indicated that BDMs may stimulate decomposition; however, effects on nutrient biogeochemistry are largely unexplored. To address these knowledge gaps, long term studies are needed to assess soil health and sustainability impacts, particularly with respect to soil carbon and/or chronic toxicity effects. In addition, studies should include a direct comparison of PE to BDMs to determine whether BDMs affect soils differently than conventional plastic mulches. Addressing these knowledge gaps will provide much needed information to growers and regulators on the safety and sustainability of BDMs for agroecosystems.

Author Contributions

SB and JD conceived of the review topic and were responsible for final editing. SB, LM-C, AP, and JD all wrote portions of this review.

Funding

This work was supported by the United States Department of Agriculture (Award 2014-51181- 22382 to JD) and the Spain Ministry of Education and Science (Ref. AGL2008-03733 to LM-C and AP).

Acknowledgements

We are grateful to D. Hayes, S. Guerrini, D. Martens, and L. Tymon for providing valuable critical feedback on a draft of this manuscript.

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Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

CHAPTER III STRUCTURAL AND FUNCTIONAL RESPONSES OF SOIL MICROBIAL COMMUNITIES TO BIODEGRADABLE PLASTIC FILM MULCHING IN TWO AGROECOSYSTEMS

A version of this chapter prepared by Sreejata Bandopadhyay, Henry Y. Sintim and Jennifer M. DeBruyn was submitted to Applied and Environmental Microbiology:

Sreejata Bandopadhyay, Henry Y. Sintim, Jennifer M. DeBruyn. “Structural and Functional Responses of Soil Microbial Communities to Biodegradable Plastic Film Mulching in Two Agroecosystems”.

This article was revised after submission to the journal and for inclusion in the present dissertation. Conceived and designed experiments: SB and JMD. Sample collection: SB, HYS and JMD. Performed the experiments: SB. Analyzed the data: SB. Wrote the paper: SB and JMD. Contributed to critical feedback on manuscript: HYS.

Abstract

Polyethylene (PE) plastic mulch films are used globally in crop production but incur considerable disposal and environmental pollution issues. Biodegradable plastic mulch films (BDMs), an alternative to PE-based films, are designed to be tilled into the soil where they are expected to be mineralized to carbon dioxide, water and microbial biomass. However inadequate research regarding the impacts of repeated soil incorporation of BDMs on soil microbial communities has partly contributed to limited adoption of BDMs. In this study, we evaluated the effects of BDM incorporation on soil microbial community structure and function over two years in two geographical locations: Knoxville, TN, and in Mount Vernon, WA, USA. Treatments included four plastic BDMs, a completely biodegradable cellulose mulch, a non-biodegradable PE mulch and a no mulch plot. Bacterial community structure determined using 16S rRNA amplicon sequencing revealed significant differences by location and season. Differences in bacterial communities by mulch treatment were not significant for any season in either location, except for Fall 2015 in WA where differences were observed between BDMs and no-mulch plots. Extracellular enzyme rate assays were used to characterize communities functionally, revealing significant differences by location and sampling season in both TN and WA but minimal differences between BDMs and PE treatments. Limited effects of BDM incorporation on soil bacterial community structure and soil enzyme activities when compared to PE suggest that BDMs have comparable influences on soil microbial communities, and therefore could be considered an alternative to PE. 67

Importance

Plastic film mulches increase crop yields and improve fruit quality. Most plastic mulches are made of polyethylene (PE), which is poorly degradable, resulting in undesirable end-of-life outcomes. Biodegradable mulches (BDMs) may be a sustainable alternative to PE. BDMs are made of polymers which can be degraded by soil microbial enzymes and are meant to be tilled into soil after use. However, uncertainty about impacts of tilled-in BDMs on soil health has restricted adoption of BDMs. Our previous research showed BDMs did not have a major effect on a wide range of soil quality indicators (Sintim et al., 2019); here we focus on soil microbial communities, showing that BDMs do not have detectable effects on soil microbial communities and their functions, at least over the short term. The results presented in this study will inform growers and regulators about the comparable impacts of BDMs to PE in crop production systems, paving a way for an agricultural practice that reduces environmental plastic pollution.

Introduction

Plastic mulch films are widely used in crop production systems to improve soil microclimate and suppress weeds, translating into increased crop yields and/or improved fruit quality. Some of the agronomic benefits of using plastic mulch films include reduction of weed pressure (Martín-Closas et al., 2017), conservation of soil moisture (Kader et al., 2017, Shahi et al., 2017), and moderation of soil temperature, among others. Low density polyethylene (PE) mulch has traditionally been favored by growers due to its many attractive properties such as easy processability, high durability, flexibility etc. (Kasirajan and Ngouajio, 2012a, Bandopadhyay et al., 2018). However, PE does not readily biodegrade, and thus must be disposed at the end of the growing season, contributing to our global plastic waste problem (Brodhagen et al., 2015, Liu et al., 2014). Even when removed from a field, fragments of film are left behind in the soil, which can affect soil function and soil biota (Barnes et al., 2009, Teuten et al., 2009, Sivan, 2011, Rillig, 2012, Huerta Lwanga et al., 2016, de Souza Machado et al., 2018b) or leach out into water systems and pollute aquatic ecosystems (Van Wezel et al., 2000, Fu and Du, 2011, Kong et al., 2012, Magdouli et al., 2013, Wang et al., 2013, Wang et al., 2015). As these plastics break down in soil, they form microplastics (de Souza Machado et al., 2018a), contributing to terrestrial microplastic pollution (de Souza Machado et al., 2018a, de Souza Machado et al., 2018b). 68

Plastic mulch use is expected to increase to meet increasing global food demands; therefore, it is imperative to find alternatives that will reduce the environmental footprint. Biodegradable mulch films (BDMs) are a potential alternative: BDMs are made of polymers that can be degraded by microbial action (Riggi et al., 2011, Kyrikou and Briassoulis, 2007, Hayes et al., 2012, Kasirajan and Ngouajio, 2012b). In the field, BDMs perform like other plastic films by altering the soil microclimate and improving crop yields (DeVetter et al., 2017). However, unlike PE plastics, which require removal and disposal, BDMs are designed to be tilled into the soil where resident soil microbes are expected to degrade them over time. Under ideal circumstances, they should eventually be mineralized into carbon dioxide and water. Despite being a promising sustainable alternative, adoption of BDMs has been limited (Goldberger et al., 2015). Currently available BDMs are not certified for use in organic crop production in North America as they are not 100% bio-based (Miles et al., 2017). Regulators are hesitant to allow use of BDMs in soils until there is convincing evidence that they are safe for soil ecosystems. Thus, evaluating the impacts of incorporation of BDMs into soil on soil health is a critical part of adoption and policy development surrounding BDMs (Brodhagen et al., 2017). BDMs can impact soil health in two ways: indirectly, in a manner similar to PE films, by acting as a surface barrier to soil and modifying the soil microclimate, and directly, by addition of physical fragments and mulch carbon into soil after tillage (Bandopadhyay et al., 2018). The body of research on the impacts of polyethylene films on soil microbial communities and functions can help us predict the indirect effect of BDMs on soil health. However, research on the direct effects of BDMs on soil microbial community structure and function remains poorly answered due to a dearth of research that directly compares BDMs and PE in the same study. Unless there is a direct comparison of BDMs and PE, it is difficult to tease apart whether the observed changes are above and beyond what you would expect from the application of PE mulch to the soil surface (Bandopadhyay et al., 2018). These answers are critical if widespread use of BDMs is to be advocated. Previous studies have analyzed impacts of BDMs on soil microbial communities using phospholipid fatty acid (PLFA) profiling (Li et al., 2014b) and pyrosequencing (Moore-Kucera et al., 2014) methods. However, these studies did not use PE as a negative control so effects of BDM tilling on soil microbial community structure and function remain uncertain. In this study, we evaluated the impacts of BDM use on soil microbial communities by directly comparing to PE mulch in a two-year vegetable crop field trial in two diverse climates (Knoxville,

TN, in the southeastern USA and Mount Vernon, WA, in the northwestern USA). During this field trial, measurement of a suite of soil physiochemical properties and calculation of soil health indices revealed that the overall effect of mulching on soil health was minimal and that BDMs performed comparably to PE (Sintim et al., 2019). The study by Sintim et al. (2019) showed that the effect of location, time and their interactions were greater compared to the effects of mulch treatments. To build on this finding, we focused on biological soil health, evaluating the impacts of BDMs on 1) soil microbial community structure, characterized using 16S rRNA gene amplicon sequencing 2) soil microbial abundances, estimated using qPCR and 3) soil microbial community function, estimated by a suite of soil extracellular enzyme rates over the two-year field trial experiment. We tested the hypothesis that plastic mulches would significantly alter soil microbial community structure and function, but that there would be no significant differences between PE and BDM mulches.

Materials and methods

Plastic Mulch Films Three commercially available biodegradable mulch films (BioAgri®, Naturecycle, Organix A.G. Film™,) and one experimental film comprised of a blend of polylactic acid (PLA) and polyhydroxyalkanoates (PHA) were tested alongside a polyethylene (PE) mulch (negative control), and cellulose mulch (WeedGuard Plus®, positive control). Physicochemical properties of mulches are reported in Table 3.1.

Field trial description The mulches were tested in the field over two years (2015 to 2016) under pie pumpkin (Cucurbita pepo) as a test crop, with full experimental details described in Sintim et al. (2019) and Ghimire et al. (2018). Field experimental stations were set up in two locations: East Tennessee Research and Education Center (ETREC), University of Tennessee, Knoxville, TN and the Northwestern Washington Research & Extension Center (NWREC), Washington State University, Mount Vernon, WA. The soil at Knoxville is a sandy loam (59.9% sand, 23.5% silt, and 16.6% clay), classified as a fine kaolinitic thermic Typic Paleudults. The soil at Mount Vernon is a silt loam (14.2% sand, 69.8% silt, and 16% clay), classified as a fine-silty mixed nonacid mesic Typic

Table 3.1: Manufacturers, major constituents, and physicochemical properties of the mulches used in the study. Bio-based content data was provided by the manufacturers. Data reported from Hayes et al., 2017. Mulches Manufacturer Major constituentsa Weight Thickness Elongationb Contact Total Biobased

(g m-2) (µm) (%) anglec (º) carbon content

(%) (%)

BioAgri® BioBag Mater-Bi® grade 18.0 26 260 87.6 57.6 20-25

Americas, Inc., EF04P (blend of

Dunedin, FL starch and PBAT)

Naturecycle Custom Blend of starch and 25.4 48 213 69.2 54.8 ˜ 20

Bioplastics, polyesters

Burlington, WA

Organix A.G. Organix BASF®ecovio® 17.8 20 273 86.2 51.4 10-20

Film™ Solutions, grade M2351(blend

Maple Grove, of PLA and PBAT)

Table 3.1: Continued

Mulches Manufacturer Major Weight Thickness Elongationb Contact Total Biobased

constituentsa (g m-2) (µm) (%) anglec (º) carbon content

(%) (%)

Experimental Metabolix Inc., 88.4% MD05-1501 25.0 33 247 67.8 47.5 86

PLA/PHA Cambridge, MA (56% Ingeo PLA,

24% Mirel™

amorphous PHA,

15%

CaCO3 and 5%

plasticizer and

processing

additives), 10.0%

Techmer

PLA M91432 (20%

carbon black

in PLA 3052) and

1.6% PLA

Table 3.1: Continued

Mulches Manufacturer Major Weight Thickness Elongationb Contact Total Biobased

constituentsa (g m-2) (µm) (%) anglec (º) carbon content

(%) (%)

WeedGuardPlus® Sunshine Paper Cellulose 240 479 6.4 <10 46.0 100

Co., Aurora,

Polyethylene Filmtech, Linear low- 25.4 47 578 79.3 82.9 < 1

Allentown, PA density

polyethylene aPBAT: Polybutylene co-adipate co-terephthalate; PLA: Polylactic acid; PHA: Poly(hydroxyalkanoate); bMeasured in machine direction; cMeasured at 22°C.

Fluvaquents. Henceforth in the paper, Knoxville will be referred to as TN and Mount Vernon will be referred to as WA.

Each field site was arranged as a randomized complete block design with four replications of seven main plot treatments (six mulch treatments and one no mulch control). Before mulch application began in TN and WA, the plots were under winter wheat (Triticum aestivum) cover crop in TN and clover (Trifolium spp.) at WA. Mulches were machine-laid on raised beds. Pumpkins (Cucurbita pepo) were grown during the growing season. The PE mulch was removed after pumpkin harvest, while the BDMs were tilled into the soil with a rototiller.

Soil water content and temperature were monitored as described in Sintim et al. (2019). Briefly, sensors (5TM, Decagon Devices Inc., Pullman, WA) installed in the center of each mulch treatment at 10-cm and 20-cm soil depths for one field block were connected to data loggers (EM50G, Decagon Devices Inc., Pullman, WA) that recorded the soil water and temperature data hourly. Soil water content and temperature data is reported in Sintim et al. (2019). Air temperature, precipitation, relative humidity, wind, and solar radiation were collected from a meteorological station located at the field site at TN (Decagon Devices Inc. Weather Station, Pullman, WA), and about 100 m away from the field site at WA (WSU AgWeatherNet Station, Mount Vernon, WA). Weather data for the two locations for 2015-2017 are reported in Table A.3.1.

Soil physical, chemical, and biological properties were assessed over the two-year study for this site, in order to assess changes in soil health. Detailed protocols for these measurements and raw data is provided in Sintim et al. (2019).

Soil sampling Soil samples were collected from each of the 28 plots (7 treatments, replicated 4 times) at both locations in the Spring (May) and Fall (September) of 2015 and 2016. Soil was collected from the top 10 cm, using a 2 cm diameter stainless steel auger. Thirty 10-cm soil cores were taken and composited for each of the plots. All sampling equipment was cleaned with 70% ethanol before and in between plots to limit cross contamination. Roots and pebbles were removed by hand, and soils homogenized and stored in plastic bags for transport back to the lab. Soils were stored at -80°C until DNA extraction and extracellular enzyme assays.

Soil DNA extraction and quantification Extraction of DNA from soil samples was completed using the MoBio™ PowerLyzer™ Power Soil DNA isolation kit (now branded under Qiagen™) with inhibitor removal technology, as per manufacturer’s instructions. 0.25 grams of soil were used for the extractions, and the DNA obtained after the final elution step was stored at -20 ⁰C until further analyses.

Quantification of the DNA extracted from soil was completed using the Quant-It™ PicoGreen™ dsDNA Quantification Kit (ThermoFisher Scientific) per manufacturer’s instructions. Standard curves generated had R squared values of 1. Mean DNA concentration of the soil samples was 13 ng µl-1 DNA.

Quantitative PCR for bacterial and fungal abundances As a proxy for bacterial and fungal abundances, 16S rRNA (bacteria) and ITS (fungi) gene copy abundances were quantified from soil DNA samples using Femto™ Bacterial DNA quantification kit (Zymo Research) and Femto™ Fungal DNA quantification kit (Zymo Research) following the manufacturer’s protocol. DNA extracts were diluted 1:10 prior to quantification and 1 µl of the diluted samples was used for each qPCR reaction. All samples were analyzed in triplicate. No template negative controls were included in each run. Bacterial and fungal DNA standards were provided in the kit and the ng DNA standard per well was converted to copy numbers which were used for final calculations. qPCR reactions were performed in a CFX Connect Real-Time PCR Detection System (BioRad). qPCR efficiencies averaged around 85% and 90% for bacterial and fungal assays, respectively. Standard curves had R squared values ranging from 0.98 to 1.

DNA amplification and sequencing 16S rRNA amplicon sequencing of DNA extracts was conducted by the Genomic Services Laboratory (GSL) at Hudson Alpha, Huntsville, AL, following their standard operating procedures. Extracted DNA samples were shipped frozen in 96 well plates. The V4 region of the 16S rRNA gene was amplified using primers 515F (GTGCCAAGCAGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT) (Caporaso et al., 2012). The first PCR was run with V4 amplicon primers, Kapa HiFi master mix, and 20 cycles of PCR. All aliquots and dilutions of the samples were completed using the Biomek liquid handler. PCR products were purified and were stored at -20⁰C until further processing was completed. The PCR indexing was later completed for

the 16S (V4) amplicon batch. Products were indexed using GSL3.7/PE1 primers, Kapa HiFi master mix, and 12 cycles of PCR. Products were purified using magnetic beads using the Biomek liquid handler. Final libraries were quantified using Pico Green. V4 amplicon size obtained was 425 bp for the soil samples. The amplified 16S rRNA genes were sequenced using 250 paired-end reads on an Illumina MiSeq platform.

Raw sequence data was processed using mothur v.1.39.5 following the MiSeq SOP (Schloss et al., 2009). Before aligning to the reference database (SILVA release 102), unique sequences were identified, and a count table generated. After alignment to SILVA database, sequences were filtered to remove overhangs at both ends, and sequences de-noised by pre-clustering sequences with up to two nucleotide differences. Chimeras were removed using the VSEARCH algorithm.

All sequences including 18S rRNA gene fragments and 16S rRNA from Archaea, chloroplasts, and mitochondria were classified using the Bayesian classifier (Wang et al., 2007) against the mothur-formatted version of the RDP PDS training set (v.9) with a bootstrap value of > 80% (Wang et al., 2007). Following this step, untargeted (i.e. non-bacterial) sequences classified as Eukaryota and Arachaeota were removed. Sequences were finally binned into phylotypes according to their taxonomic classification at the genus level. A consensus taxonomy for each OTU was generated by comparison to the RDP training set. The resulting OTU count table and taxonomy assignments were imported into R (v. 3.4.0) (R, Core Team 2018) for further downstream statistical analyses. Mothur code, R code and associated input files are available at: https://github.com/jdebruyn/BDM-Microbiology.

Extracellular enzyme assays Fluorescence microplate enzyme assays were conducted using fluorescently labelled substrates to assess enzyme activities in soil (Bell et al., 2013). Seven enzymes were assayed using their respective fluorescent substrates and standards (Table 3.2).

Soil slurries were prepared in a sodium acetate trihydrate buffer whose pH was matched closely with the soil pH. 800 µl of soil slurry was pipetted into deep well 96 well plates. Separate plates were prepared for MUB and MUC standard curves for each sample. 200 µl of appropriate standards and substrates were added to the soil slurries. The plates were sealed and inverted to mix the contents. Incubation was done for 3 hours at room temperature, after which the substrate and

Table 3.2: The seven extracellular soil enzymes assayed for soils collected from Spring 2015-Spring 2017; their respective enzyme functions, substrates used for assays, and the role of each enzyme in biogeochemical cycling. Standards used were MUB (4- methylumbelliferone) and MUC (7-amino-4-methylcoumarin). Indicator of Enzyme microbial Abbreviation Enzyme name function Substrate used activity

hemicellulose 4-MUB-β-D- XYL β-xylosidase degradation xylopyranoside Carbon cycling sugar 4-MUB-β-D- BG β-glucosidase degradation glucopyranoside Carbon cycling sugar 4-MUB-α-D- AG α- glucosidase degradation glucopyranoside Carbon cycling Carbon and N-acetyl β chitin Nitrogen NAG glucosaminidase degradation 4-MUB-N-acetyl- cycling β-D-glucosaminide β-D cellulose CB cellubiosidase degradation 4-MUB-β-D-cellobioside Carbon cycling phosphorus Phosphorus PHOS Phosphatase mineralization 4-MUB phosphate cycling L- leucine-7-amido-4- Leucine amino protein methylcoumarin Nitrogen LAP peptidase degradation hydrochloride mineralization

standard plates were centrifuged at 1500 rpm (~327 x g) for 3 min. The supernatants were pipetted into black 96 well plates and fluorescence measured at 365 nm excitation wavelength and 450 nm emission wavelength in a BioTek® Synergy plate reader.

Statistical analyses Beta diversity was computed using Bray-Curtis distances of microbial community composition using the vegan package (v 2.4-3) in R version 3.4.0 (R, Core Team 2018) based on OTU tables, and were then visualized using non-metric multidimensional scaling (NMDS) using phyloseq package v.1.21.0 in R (McMurdie and Holmes, 2013). To determine whether significant differences existed in bacterial community composition between bacterial communities across different locations, seasons, and mulch treatments, a permutational multivariate analysis of variance (PERMANOVA) was performed using the ADONIS function implemented in R, based on the Bray-Curtis dissimilarity matrix. All libraries were scaled to even depth (minimum sample read count, i.e. smallest library size, of 34,266) before analysis was performed. Similarity percentage analyses (SIMPER) was completed in R to reveal the most influential OTUs driving differences between soil bacterial communities in different locations, and across different seasons. Difference in relative abundances of taxa between locations and seasons were determined using Kruskal-Wallis rank sum non-parametric test (kruskal.test in R). A post-hoc test was completed using pairwise Wilcoxon rank sum test (pairwise.wilcox.test in R) if significant differences were reported using Kruskal-Wallis test. P values were adjusted using the method of Benjamini & Hochberg (1995) to control the false discovery rates (p < 0.05). Canonical analysis of principal coordinates (CAP) was done to relate environmental variables reported in Sintim et al. (2019) to changes in bacterial community composition. The ordination axes were constrained to linear combinations of environmental variables, then the environmental scores were plotted onto the ordination. A PERMANOVA was performed on the CAP axes. These analyses were completed in R following the online tutorial by Berry M (Berry, 2016).

Alpha diversity was computed by subsampling the libraries to the minimum number of reads (34,336). This was done with replacement to estimate species abundance of the real population by normalizing sampling effort. The subsampling was repeated 100 times and the diversity estimates from each trial were averaged. The estimate_richness function was used in R phyloseq package to calculate observed richness and inverse Simpson indices (for diversity). A mixed model analysis

of variance was completed using the generalized linear mixed model (GLIMMIX) procedure in SAS V. 9.3 to assess changes in richness and inverse Simpson over time. The fixed effects were location (TN and WA), mulch treatments (7 treatments total) and date/season of soil sampling (4 time points), while random effect was block (total 3 blocks to serve as replicates). Repeated measures were incorporated in the model as sampling was done over time, twice a year in Spring and Fall seasons in 2015 and 2016. The model was a completely randomized design (CRD) split- split-plot with repeated measures in the sub-sub plot. Normality of data was checked using Shapiro-Wilk test (W > 0.9) and equal variance using Levene’s test (α = 0.05). All data were normal and hence no transformations were performed. Raw experimental values and standard errors are reported in the figures.

To visualize differences in the functional profile of the communities; i.e. all seven enzyme rates), NMDS ordination of Bray-Curtis similarities was done in Primer 7 v. 7.0.13 (PRIMER-E). A mixed model analysis of variance with repeated measures was completed using the generalized linear mixed model (GLIMMIX) procedure in SAS V. 9.3 to assess changes in enzyme activities over time. Fixed and random effects were same as specified above. However, location as a class was not included in this model as PERMANOVA results from PRIMER-E were used to report differences between locations. Boxplots for equal variance and outliers, reported in SAS, were used to remove outliers in the dataset. Normality was checked using Shapiro-Wilk test (W > 0.9) and probability plots for residuals, and equal variance using Levene’s test (α = 0.05). Data were log transformed as necessary when these conditions were not met. Raw experimental values and standard errors are reported in the figures. All graphics were plotted using R. v. 3.4.0. Type III tests of fixed effects and interaction effects are reported.

To assess for potential enrichment of bacteria and fungi, a paired t-test was conducted using initial and final 16S and ITS gene copy abundances (determined by qPCR) from Spring 2015 and Fall 2016 to see if there was a significant change. Initial 16S and ITS gene copy abundances from Spring 2015 were also subtracted from final abundances in Fall 2016 to get change in abundance over time. To determine if the enrichment or depletion of bacterial and fungal abundances was significantly different between treatments, a mixed model analysis of variance in SAS v. 9.3 using the GLIMMIX procedure was conducted on the differences. Significance level of all analyses were assessed at α = 0.05. All data were checked for normality using Shapiro-Wilk test (W > 0.9).

Results

Environmental and soil physicochemical data Environmental data collected during the experiment is reported in Sintim et al. (2019) and in Table A.3.1. The mean daily air temperature in Knoxville, TN during experimental years of 2015 to 2016 was about 4 ºC higher than in Mount Vernon, WA (Table A.3.1). The total annual precipitation during the experimental years was higher in Knoxville, TN than in Mount Vernon, WA.

Soil temperature, moisture and physicochemical properties were measured and reported previously by Sintim et al. (2019). In summary, significantly increased soil temperature was observed in the early growing seasons in the plastic mulch plots compared to the cellulose and no-mulch plots. On average, the monthly soil temperature was greater in TN than in WA. The soil water content varied more among the mulch treatments, with PE mulch having the highest soil water content for the greatest time; PE generally has a lower gas permeability as well (Martín-Closas et al., 2007, Domagała-Świątkiewicz and Siwek, 2013, Steinmetz et al., 2016). Overall, mulched plots had higher water content than the no mulch plots. The soil health analysis revealed some effects of mulching on certain properties (namely aggregate stability, infiltration, soil pH, electrical conductivity, nitrate, and exchangeable potassium), but these were not consistent among BDMs, nor across sampling times and locations.

Soil bacterial community diversity and structure The NMDS ordination revealed a clear difference in community structure between TN and WA when combining data from all four sampling seasons (Spring 2015 to Fall 2016) (Figure 3.1a). Permutational ANOVA (PERMANOVA) tests confirmed significant differences between TN and WA soil microbial communities (Table 3.3, Table A.3.2). The mean relative abundances of the most abundant classes of bacteria are shown in Figure 3.1b. Similarity percentage tests (SIMPER) revealed the most influential OTUs contributing to the variation seen between location (Figure 3.1b). The most influential OTUs belonged to several classes of microbes such as Acidobacteria_Gp7, Acidobacteria_Gp16, Acidobacteria_Gp4, Planctomycetacia and Spartobacteria. CAP analysis revealed that the differences in soil communities between TN and WA were most related to pH, soil moisture and organic matter content: the communities in TN

Figure 3.1: Bacterial community composition differences between the two field locations, showing communities from all four sampling times. a) Non-metric multidimensional scaling (NMDS) ordination of Bray-Curtis dissimilarities of OTU relative abundances, highlighting differences between location (PERMANOVA p = 0.001). Each point corresponds to the whole microbial community of one plot in the field (4 time points * 3 reps, total 12 points for each treatment). Ellipses denote clustering at 95% confidence. NMDS stress value: 0.14. b) Bar plot showing differences in mean relative abundance of the most abundant classes of bacteria in TN and WA, aggregating all treatments and all four sampling times. Asterices denote significant differences between locations, determined by ANOVA (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001). Red stars indicate taxa which cumulatively contributed up to 46% of the variance in microbial communities between TN and WA, determined using SIMPER.

Figure 3.1: Continued

Table 3.3: Results (F values) of PERMANOVA tests for differences in bacterial community composition by location (Knoxville (TN) and Mount Vernon (WA)), season and mulch treatment. Significant differences are in bold; *p < 0.05; **p < 0.01; ***p < 0.001. Factor/treatment Levels TN (F) WA (F) Location TN, WA 117.34*** Season Spring 2015, Fall 2015, Spring 17.83*** 32.84*** 2016, Fall 2016 Mulch treatments 7 treatments: 0.61 0.81 (Spring 2015) 5 BDMs (BioAgri, Organix, Mulch treatments PLA/PHA, Naturecycle, 0.87 1.96** (Fall 2015) Weedguard), PE, no mulch control Mulch treatments 0.84 0.81 (Spring 2016) Mulch treatments 1.15 1.26 (Fall 2016) BDMs = biodegradable mulches; PE = polyethylene

were related to increased pH, whereas moisture and organic matter were positively related to communities in WA (Figure A.3.1).

In addition to locational differences, bacterial communities also differed significantly between the different seasons (Table 3.3, Table A.3.2). For both locations, Spring communities were more similar to each other than Fall communities (Figure 3.2a, b). SIMPER tests revealed that several genera of Acidobacteria. Planctomycetaceae, Spartobacteria and Actinobacteria (such as Steptomyces sp.) were cumulatively responsible for 60% of the seasonal variance in bacterial communities (Figure A.3.2 and A.3.3). Interestingly, Streptomyces spp. increased in percent relative abundance over time from Spring 2015 to Fall 2016 in both TN and WA (Figure A.3.2).

Unlike location and season, the mulch treatments did not have a significant effect on bacterial community structure. Because of the locational and seasonal differences, we additionally analyzed each time-location set separately, and did not detect any significant effects of treatment on community structure (Figure A.3.4, Table 3.3, Table A.3.2).

Alpha diversity of the soil bacterial communities was estimated using observed species richness and inverse Simpson index of diversity (Table A.3.3). The observed species richness estimator

Figure 3.2: NMDS ordination of Bray Curtis dissimilarities of soil bacterial communities a) TN (p = 0.001) and b) WA (p = 0.001). Ellipses denote clustering at 95% confidence. NMDS stress value: 0.17 (TN), 0.16 (WA).

Figure 3.2: Continued

measures count of unique OTUs in each sample. There were significant differences between TN and WA (p < 0.05) in richness estimates (Table 3.4, Figure 3.3a). TN had greater richness than WA throughout the experiment, ranging from 260 to 300 unique OTUs. WA richness estimates ranged from 250 to 280 OTUs over the two years. The locational differences in richness were due to a lower richness in Fall 2015, Spring 2016 and Fall 2016 in WA (Figure 3.3). The Inverse Simpson diversity index ranges were similar between TN and WA, ranging from 7 to 11.

For both TN and WA, there was a significant difference between seasons in both richness and inverse Simpson index (Table 3.4). The richness estimates in TN significantly differed between 2015 and 2016 (Figure 3.3a). In WA, Fall 2015 differed in richness from the other time points. In TN, Fall 2016 diversity was significantly higher than other seasons. Diversity estimates were significantly lower in Spring than in the Fall seasons for WA (Figure 3.3b).

In TN, PE had the lowest richness and BioAgri had the highest. However, treatment differences in richness estimates were not significant (Table 3.4) when analyzing data using a mixed model. Inverse Simpson diversity indices were also not significantly different between treatments (Table 3.4). Looking at the final time point in TN, diversity estimates were highest for Weedguard, and lowest for PE, and in WA, the estimates were highest for Weedguard, followed by PE with BDMs having lower diversity than PE or Weedguard, however these differences were not significant (Figure 3.3b). It is important to note, however, that Weedguard completely disintegrated in TN after the field season which could explain some of the treatment differences observed in the alpha diversity metrics.

Microbial community abundances As a proxy for bacterial and fungal abundances, bacterial (16S) and fungal (ITS) rRNA gene copies were quantified using qPCR assays for soil samples from all seasons. In order to assess if gene abundances had significantly changed over the course of the experiment (Spring 2015 to Fall 2016) for each mulch treatment, a paired t-test was used to identify changes that are significantly different from zero (Table 3.5). There was a significant increase in bacterial gene copies under BDM and Weedguard treatments in WA, but no significant change for no mulch and PE treatments (Table 3.5). There was also a significant enrichment in fungal gene copies over time for two of the BDMs (PLA/PHA and Naturecycle) in WA. In TN, significant enrichment in bacterial gene copies was seen under Organix, PLA/PHA and PE treatments (Table 3.5) but no enrichment was seen in fungal 86

Table 3.4: F values of fixed effects and interaction effects obtained from a mixed model analysis of variance of the alpha diversity metrics richness (number of observed OTUs) and diversity index (inverse Simpson) from Spring 2015 to Fall 2016 in Knoxville, TN and Mount Vernon, WA. Significant values are in bold, *p < 0.05; **p < 0.01; ***p < 0.001 Factor/treatment Levels Richness F Diversity value F value Location TN, WA 24.42*** 2.98

Treatment 7 treatments: 1.93 1.20 5 BDMs (BioAgri, Organix, PLA/PHA, Naturecycle, Weedguard), PE, no mulch control Location*Treatment 1.22 1.58 Time Spring 2015, Fall 2015, 19.28*** 122.23*** Spring 2016, Fall 2016 Location*Time 6.06*** 3.84** Treatment*Time 2.4** 1.63 Location*Treatment*Time 0.55 1.09 BDMs = Biodegradable mulches; PE = polyethylene

Figure 3.3: a) Richness (number of unique OTUs) and b) Inverse Simpson estimates over time of soil microbial communities in TN and WA. Error bars indicate SEM of three replicate samples.

Figure 3.3: Continued

Table 3.5: T values from paired t-tests comparing 16S and ITS initial abundances from Spring 2015 to final abundances from Fall 2016 to determine significant changes over the two-year experiment in Knoxville, TN and Mount Vernon, WA. Significant values are in bold, *p < 0.05; **p < 0.01; ***p < 0.001.

TN WA Mulch Treatments and Controls

16S ITS 16S ITS

BioAgri 0.83 0.76 -4.30* -2.27 Treatments Naturecycle -1.30 -1.45 -4.02 -6.87* Organix -3.9* -0.52 -4.30* -3.05 Experimental PLA/PHA -3.51* -0.20 -8.34** -5.64** Weedguard -0.80 -0.89 -3.52* -1.50 Polyethylene -4.06* -0.23 -2.53 -0.02 Controls No mulch -0.65 -1.21 -1.74 -0.38

gene copies. In order to determine if these changes were significantly different between treatments, the differences between the final (Fall 2016) and the initial (Spring 2015) abundances were analyzed using a mixed model analysis of variance in SAS v 9.3 and Tukey post hoc tests. In both locations, mulch treatments did not have a significant effect on the changes in either 16S or ITS gene copies over the course of the experiment (Figure 3.4 a, b).

Microbial community functions To assess potential functional responses of the soil microbial communities, extracellular enzyme potential rate assays were conducted for common carbon, nitrogen, and phosphorus cycling enzymes in soil (Table 3.2). The data were combined over the two years to visualize Bray Curtis similarities of the enzyme rate profiles (Figure 3.5). Locational differences in the enzyme profile were significant (p < 0.05), as were seasonal differences in both TN (p < 0.05) and WA (p < 0.05) evaluated using PERMANOVA (Figure 3.5). However, mulch treatment did not have a significant effect on the enzyme profile for any of the seasons at either location (p < 0.05). NMDS ordination for the final sampling time point Spring 2017 is shown in Figure A.3.5, showing no clear treatment differences.

Figure 3.4: a) 16S and b) ITS gene copy number changes over two years in TN and WA analyzed using a mixed model analysis of variance. Final time point (Fall 2016) is plotted by subtracting baseline abundances from Spring 2015. Error bars indicate SEM of four replicate samples. Lowercase letters denote significant differences between treatments (p ≤ 0.05, Tukey’s HSD). Asterices indicate treatments which showed significant enrichment using a paired t-test ((*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001).

Figure 3.4: Continued

Figure 3.5: NMDS ordination depicting Bray-Curtis similarity of the functional profile of soil microbial communities (based on 7 soil enzyme activity rates) across a) Location (p < 0.05) and b) Date (p < 0.05).

In general, the enzyme activity rates oscillated between higher activities in the Spring and lower activities in the Fall. When analyzed separately for each enzyme, the data over the two years revealed a significant effect of sampling time in TN for all seven enzymes assayed. In WA, enzyme activities of β-xylosidase, β-glucosidase, α-glucosidase, N-acetyl β-glucosaminidase and phosphatase were significantly different between sampling times (Figure 3.6). In WA, cellobiosidase and leucine amino peptidase activities remained unchanged across the seasons (10- 22 nmol activity g-1 dry soil h-1 for cellobiosidase and 200-375 nmol activity g-1 dry soil h-1 for leucine amino peptidase) (Figure 3.6).

When averaged across seasons, mulch treatment differences were not significant for any soil enzymes in WA (Table 3.6). However, in TN, an effect of mulch treatment was observed for N- acetyl β-glucosaminidase activities (Table 3.6). N-acetyl β-glucosaminidase activity was reduced under BDMs and PE compared to no mulch plots. Interaction effects of mulch treatment and time of sampling were not detectable for any of the enzymes assayed in TN or WA (Table 3.6).

Discussion

Characterizing the soil microbial communities under the different biodegradable mulches and non- biodegradable PE mulch revealed no significant effect of mulch type on bacterial community structure. This is in contrast to other studies that have reported altered bacterial communities in soils under BDMs (Koitabashi et al., 2012, Muroi et al., 2016, Li et al., 2014b), and under non- biodegradable plastic mulches (Farmer et al., 2017, Munoz et al., 2015). Such opposite findings could be due to differences in methodology: for example, the studies by Koitabashi et al. (Koitabashi et al., 2012) and Muroi et al. (Muroi et al., 2016) were shorter laboratory incubation studies in controlled conditions (28 to 30°C), used pure polymer feedstock (rather than commercial film formulations which include plasticizers and other additives) and relied on detection methods such as polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Laboratory studies under controlled conditions often result in more rapid microbial responses to treatments as opposed to field studies where variable environments introduces more noise. Our lack of observed difference may also be because of a realistic, but low, plastic to soil ratio: for example, Muroi et al. (2016) used soil burial studies with an artificially high 1.8 g PBAT films in 300 g soil with added 30 ml of basal medium. Finally, our aim was to characterize responses in

Figure 3.6: Changes in soil enzyme activity over time across mulch treatment (p values reported in Table 3.6). Error bars indicate SEM of four replicate samples.

Table 3.6: F values of fixed effects and interaction effects obtained from a mixed model analysis of variance of the soil enzyme activities from Spring 2015 to Spring 2017 in Knoxville, TN and Mount Vernon, WA. Significant values are in bold, *p < 0.05; **p < 0.01; ***p < 0.001. Leucine β- β- α- N-acetyl β β-D amino Location Factor xylosidase glucosidase glucosidase glucosaminidase cellubiosidase Phosphatase peptidase TN Treatment (F) 2.21 1.49 2.62 2.53* 1.03 1.37 1.71 Time (F) 46.48*** 29.56*** 40.16*** 34.60*** 32.82*** 68.23*** 28.83*** Treatment*Time (F) 1.55 0.92 1.52 1.26 0.88 1.04 0.96 WA Treatment (F) 0.89 0.84 1.12 0.64 0.75 1.13 0.34 Time (F) 5.12*** 3.44* 13.31*** 6.06*** 0.27 4.10** 0.65 Treatment*Time (F) 0.88 0.91 0.65 0.78 0.72 0.96 0.77

bulk soil communities to understand the overall system level response to plastic films, so we likely missed changes happening on smaller spatial scales. For example, Li et al. (2014b) reported changes in microbial communities in soils that were sampled in close proximity to buried mulch films, indicating that microbial communities in the immediate vicinity of the films may be affected. Here we show that any local effects of mulch films are not detectable at a plot/field scale, at least over a 2-year period.

We did note significant differences in soil bacterial composition by location and season, which has been observed in other studies (Li et al., 2014b, Moore-Kucera et al., 2014), confirming that mulch effects are minimal compared to other drivers of community structure variation. It is well accepted that local soil conditions such as temperature, moisture and pH play a pivotal role in shaping microbial communities (Moore-Kucera et al., 2014, Rousk et al., 2010, Fierer and Jackson, 2006). In this study, the location differences in communities were attributed to higher relative abundances of Acidobacteria, Actinobacteria and Planctomycetes in TN and higher abundances of β- and γ- Proteobacteria in WA. This corresponds with higher pH and saturated K in TN and higher soil organic matter and soil moisture in WA. Both pH and water content are major edaphic factors that influence temporal and spatial variation in soil microbial communities (Docherty et al., 2015, Rousk et al., 2010). Changes in soil physicochemical properties and different climates and soil types between TN and WA could explain such locational differences. Seasonal differences in communities were driven by significantly increased percent relative abundance of Acidobacter Gp6, Gp4 and Gp7 in Spring in TN as compared to Fall. Additionally, significantly greater abundances of Planctomycetaceae and Streptomyces were seen in Fall compared to Spring in TN. In WA, Acidobacteria_Gp6 and Spartobacteria showed significantly greater percent abundances in Spring compared to Fall whereas Streptomyces sp. showed significantly higher percent abundance in Fall compared to Spring (Figure A.3.2). Seasonal tillage operations often reset many of the soil properties which can explain why the abundances of some taxa oscillated between Spring and Fall. Streptomyces sp. belongs to class Actinobacteria and have demonstrated polymer degrading capabilities (Pathak and Navneet, 2017). However, because we did not observe differences in the relative abundance of this taxa between BDMs, PE or no mulch control, this increase is likely attributable to the agronomic management of the plots (e.g. plant species, irrigation or fertilizer regimes etc.).

Richness estimates showed significant differences across locations and seasons in TN and WA. Diversity estimates were only significantly different between seasons, but not location. Mulch materials did not have a consistent impact on bacterial richness or diversity. A previous study evaluating microbial diversity using PCR-DGGE showed no difference in ammonia oxidizer diversity under biodegradable and non-biodegradable mulching materials one year after tilling plastics into soil (Kapanen et al., 2008).The higher richness estimates under BDMs compared to PE treatments, which was significant in Fall 2015 in WA, suggested that tilled BDMs may help promote richness in the soil environment. Increased warming potential under PE mulch could also contribute to suppression of microbial activity which may also have impacts on community richness or diversity.

Gene copy abundances in soils were used as a proxy for bacterial and fungal abundances. In WA we observed an enrichment of both bacteria and fungi under BDM and Weedguard treatments over the course of the two-year experiment. In TN, we observed bacterial, but no fungal, enrichment in two of the four BDM plots and PE plot. A mixed model analysis of changes over the course of the experiment was not able to detect significant differences between treatments. It should be noted that enrichment was observed under BDM but not the PE plots in WA, suggesting that this is response to the incorporation of BDMs into the soil (as opposed to an indirect effect of microclimate modification, such as soil warming). Previous studies have also demonstrated increased fungal abundances in soil because of BDM incorporation (Li et al., 2014b, Ma et al., 2016, Muroi et al., 2016, Rychter et al., 2006). Fungi have been observed to be important colonizers and degraders of BDMs (Moore-Kucera et al., 2014, Koitabashi et al., 2012, Muroi et al., 2016). Tilled into soil, BDMs are a very small input of carbon when taking into account the volume of soil into which they are incorporated (Bandopadhyay et al., 2018). For comparison, the input of mulch carbon added to the soil in this study was a significantly smaller amount (6-25 g C m-2) (Hayes et al., 2017) compared to the amount added from cover crop residues (142 g C m-2) (Al-Kaisi and Lal, 2017). However, the growth of soil microbes in agricultural soil is usually carbon-limited and several studies have demonstrated responses by soil microbes to these small inputs (Bandopadhyay et al., 2018).

There is also precedent for the differential responses in microbial enrichment we observed between the two locations, with both fungal and bacterial enrichment in WA, but only bacterial enrichment

in TN. In a similar study comparing BDM effects in three locations, it was found that BDMs resulted in soil fungal enrichment in Lubbock, TX, and bacterial enrichment in Knoxville, TN (Li et al., 2014b). In one study, soil pH was shown to be the best predictor of bacterial community composition across different land use types, while fungal communities were shown to be most closely associated with changes in soil nutrient status such as extractable P concentrations and C:N ratios (Lauber et al., 2008). Both TN and WA soils had comparable fungal gene abundances initially in the Spring of 2015. However, since the microbial communities in WA were seen to be controlled by the presence of organic matter (Figure A.3.1) and WA soils had higher C:N ratios than TN soils this could have contributed to a fungal enrichment in WA but not in TN.

Enzyme assays were conducted to assess potential activity rates for common carbon, nitrogen and phosphorus cycling enzymes in soil. As with bacterial community structure, enzyme activity profiles showed the greatest differences by location and season (Figure 3.5, Table 3.6). The seasonal oscillation in enzyme activities seen for almost all the enzymes could be attributed to seasonal tillage operations which tend to offset many of the soil biological functions (Alam et al., 2014, Busari et al., 2015, Zuber et al., 2015) (Figure 3.6). This was also observed for many of the soil physicochemical properties (Sintim et al., 2019). Mulch treatments had significant effects on N- acetyl-β -glucosaminidase (NAG) in TN. NAG was decreased under mulches compared to no mulch treatments, with the greatest decrease observed under PE. NAG catalyzes the hydrolysis of chitin oligomers to form amino sugars which are major sources of mineralizable nitrogen in soils and thus is important in carbon and nitrogen cycling in soils. Xylosidase activity was also reduced under mulch treatments compared to no mulch plots in TN though not significant. Because we saw decreases under all mulch treatments for NAG in TN, this is likely an indirect effect of the mulches via microclimate modification, rather than a direct effect of mulch fragments tilled into the soil. All mulches warm the soil, with PE often having a greater soil warming potential compared to BDMs (Moreno and Moreno, 2008, Kader et al., 2017). Mulches also increase soil moisture levels (Qin et al., 2015). Consequently, changes in soil temperature and moisture will affect enzyme pool sizes (Steinweg et al., 2013). The reduction in activity under plastic mulches may be because TN has a warmer climate where plastic mulches can push temperatures above optima limiting soil microbial activity (Moreno and Moreno, 2008). Mean soil temperatures in summer under mulched plots were 24.7 ºC at 10 cm depth in TN, whereas in WA it was 18.7 ºC. Un-mulched plots had mean summer soil temperatures of 23.8 ºC for TN and 17.0 ºC for WA (Sintim et al., 2019). In the

month of June in both years, soil temperatures exceeded 30 ºC under mulched plots in TN, but were less than 30 ºC for no mulch plots. It has been reported that fungal and bacterial growth rates have optimal temperatures around 25 to 30 °C in agricultural and forest humus soils, while at higher temperatures lower growth rates are found (Pietikainen et al., 2005). This decrease in growth rate was shown to be more drastic for fungi than for bacteria, resulting in an increase in the ratio of bacterial to fungal growth rate at higher temperatures. Thus, the high temperatures under mulches in the summer in TN were above optimum growth conditions for soil microbes and may have reduced soil enzyme activities. Cold-adapted microorganisms, which are expected to be more prevalent at the WA site, tend to respond more efficiently to increased temperature than warm-adapted microbes (Brzostek and Finzi, 2011). The greatest relative temperature sensitivity of decomposition processes has been observed at low temperatures (Kirschbaum, 1995). Warming experiments have revealed reduced xylosidase activity in soils (5-15 cm deep) under medium- warmed plots compared to unwarmed plots (Steinweg et al., 2013). It has also been reported that warming induces decreases in the temperature sensitivity of β-xylosidase activity in the H horizon (Souza et al., 2017). One study reported greater increase of the relative temperature sensitivity of XYL and NAG (important for C cycling) at lower temperatures, compared to amino peptidase enzymes suggesting that temperature plays a pivotal role in regulating the use of substrates. Thus, the turnover of easily degradable C substrates (like glucose) is more sensitive to temperature than higher molecular compounds, at least for cold soils (Koch et al., 2007).

Looking specifically at studies which assessed soil enzyme activities after treatment with biodegradable plastic film, one field study reported that soil microbial biomass and β-glucosidase activity were most responsive to mulch incorporation; however that study did not have PE as a control, so it is unclear if this response was specific to BDMs or just related to plastic mulching generally (Li et al., 2014a). The cited study also focused on soils in close proximity to plastic, rather than bulk soil responses. Laboratory studies have shown increased esterase activity in soils during the degradation of poly(butylene succinate-co-adipate) (PBSA) (Yamamoto-Tamura et al., 2015), and increased microbial activity as per a fluorescein diacetate hydrolysis test during the degradation of a variety of biodegradable polymers (Barragán et al., 2016). These studies provide insight into the potential of these enzymes in the degradation process of BDMs. Other studies that have looked at more general activity responses by microbes under plastic mulches (i.e. respiration) have reported mixed results: some have observed increases in activity under plastic mulches (Mu

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et al., 2016, Mu et al., 2014, Chen et al., 2017, Zhang et al., 2015), while others report decreased activities (Moreno and Moreno, 2008).

In our recent paper from the same field sites as mentioned in the present study we have shown that biodegradable mulches do not have a significant impact soil health in terms of a suite of soil quality parameters tested over two years in TN and WA (Sintim et al., 2019). Our findings corroborate the results from Sintim et al. (2019) where it has also been shown that locational and seasonal variations are more important drivers of change in overall soil health under BDM tillage operations as compared to mulch treatment itself.

Conclusions

Two years of biodegradable and PE mulch treatments in a vegetable agroecosystem in two locations revealed some minor effects on soil microbial communities and their functions. While we were not able to detect any significant effect of plastic mulches on bacterial community structure, richness or diversity, we did observe other impacts on the communities that were location dependent. In particular, we noted that in WA, biodegradable mulch incorporation into soil caused a significant enrichment in both soil bacterial and fungal abundances, suggesting a direct response to BDM incorporation into soils; in contrast only bacterial enrichment was apparent in TN, and only for three of the five plastics tested. We additionally observed decreases in specific enzyme activities (NAG) under mulch treatments in TN but not WA, which may be attributable to increased temperatures under the plastics (i.e. microclimate modification) rather than mulch fragment incorporation into soil. Together, this shows that plastic mulches have minor impacts on soil microbial communities and their functions, and that BDMs may have effects different from PE plastic mulches. As microbes are the drivers of soil carbon and nutrient cycling, changes in bacterial and fungal abundances and/or activity can have repercussions for soil organic matter dynamics and nutrient availabilities. Longer term studies of repeated BDM incorporation are needed to determine if these microbial responses will significantly affect soil functioning and health. In addition, the fact that we saw different responses by the communities in two locations under identical management may mean that the ultimate impact of plastic mulching on soil functioning may be dependent on local climate and soil conditions.

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Acknowledgements

This work was supported by the United States Department of Agriculture Specialty Crops Research Initiative, CAP (Award 2014-51181-22382 to JMD). Field experiments were designed and managed by A. Wszelaki, C. Miles, D. Inglis and D. Hayes, with help from staff at the East Tennessee Research and Education Center (Knoxville, TN) and Northwestern Washington Research and Extension Center (Mount Vernon, WA). We are grateful to BioBag Americas, Inc. (Palm Harbor, FL, USA), Organix Solutions (Maple Grove, MN, USA), Custom Bioplastics (Burlington, WA, USA), (Metabolix Inc., (Cambridge, MA, USA), and Sunshine Paper Co. (Aurora, CO, USA) for the donation of mulches for the research experiments, and Techmer PM (Clinton, TN, USA) for preparation of the carbon black dye masterbatch and compounding of the PLA/PHA formulation used to prepare the PLA/PHA mulch film. Arnold Saxton provided statistical advice. We thank M English, J Moore, S Schexnayder, M Valendia, S Schaeffer, M Anunciado, K Henderson, S Keenan, LS Taylor, J Liquet, Shuresh Ghimire, Andy Bary, M Starrett, D Cowan-Banker and other members of the JMD and MF labs for help with soil sample collection and processing. M Flury and D Hayes provided critical feedback on the manuscript.

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Appendix III Table A.3.1: Weather data at ETREC, Knoxville, TN and NWREC, Mount Vernon, WA. Adapted from Sintim et al., 2019. Year Average relative Average wind Air temperature Total

-1 humidity (%) speed (ms ) Tmin (ºC) Tmean (ºC) Tmax (ºC) precipitation

(mm)

Knoxville (January to December)

2015 NA 1.24 10.4 15.8 21.2 1313

2016 74.1 1.55 10.0 15.8 22.6 866

2017 76.9 1.56 10.3 15.7 22.0 1374

Mount Vernon (January to December)

2015 82.7 1.65 6.97 11.6 16.6 885

2016 79.7 1.81 7.32 11.5 16.1 945

2017 80.1 1.70 5.95 10.4 15.3 987

In Knoxville, 2015 data was taken from National Oceanic and Atmosphere Administration (NOAA) station at Knoxville McGhee Tyson Airport, 12 km from the field site, and the 2016 and 2017 data were taken from a weather station installed at the research site. In Mount Vernon, the 2015, 2016, and 2017 data were taken from Washington State University AgWeatherNet Station located 100 m from the field site. Tmin, Tmean, Tmax: minimum, mean, and maximum air temperature, respectively. NA = not available.

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Table A.3.2: PERMANOVA results for comparisons between microbial community composition. Table generated using adonis function (vegan package) in R statistical environment. Df: degrees of freedom, Sums of Sqs: sequential sums of squares, Mean Sqs: mean squares F.model: F statistics, R2: Partial R squared.

Df Sums of Sqs Mean Sqs F.Model R2 P value Location Location 1 1.228 1.228 117.34 0.414 0.001 *** Residuals 166 1.737 0.01 0.586 Total 167 2.966 1 Seasons in TN Season_Year 3 0.436 0.145 17.831 0.401 0.001 *** Residuals 80 0.651 0.008 0.599 Total 83 1.087 1 Treatments (TN) Spring 2015 Treatment 6 0.027 0.004 0.61 0.207 0.893 Residuals 14 0.103 0.007 0.793 Total 20 0.13 1 Fall 2015 Treatment 6 0.046 0.008 0.865 0.27 0.598 Residuals 14 0.125 0.009 0.73 Total 20 0.171 1 Spring 2016 Treatment 6 0.034 0.006 0.843 0.265 0.607 Residuals 14 0.094 0.007 0.735 Total 20 0.128 1 Fall 2016 Treatment 6 0.073 0.012 1.146 0.329 0.313 Residuals 14 0.149 0.011 0.671 Total 20 0.223 1 Seasons in WA Season_Year 3 0.359 0.12 32.835 0.552 0.001 *** Residuals 80 0.292 0.004 0.448 Total 83 0.65 1

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Table A.3.2: Continued

Treatments (WA) Spring 2015 Treatment 6 0.013 0.002 0.806 0.257 0.86 Residuals 14 0.037 0.003 0.743 Total 20 0.05 1 Fall 2015 Treatment 6 0.039 0.006 1.964 0.457 0.004 ** Residuals 14 0.046 0.003 0.543 Total 20 0.085 1 Spring 2016 Treatment 6 0.011 0.002 0.805 0.257 0.887 Residuals 14 0.033 0.002 0.743 Total 20 0.044 1 Fall 2016 Treatment 6 0.04 0.007 1.259 0.35 0.167 Residuals 14 0.073 0.005 0.65 Total 20 0.113 1

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Table A.3.3: Mean Richness (number of unique OTUs) and diversity (Inverse Simpson index) estimates for bacterial communities by location, season and treatment.

TN WA Time Treatment Number of Inverse Number of Inverse unique OTUs Simpson index unique OTUs Simpson index (mean±SEM) (mean±SEM) (mean±SEM) (mean±SEM) Spring BioAgri 282 ± 5 8.000 ± 0.405 272 ± 4 8.244 ± 0.214 2015 Naturecycle 269 ± 5 7.723 ± 0.36 273 ± 3 7.938 ± 0.108 Organix 277 ± 5 7.409 ± 0.281 270 ± 2 8.153 ±0.114 PLA/PHA 266 ± 4 8.2 ± 0.89 273 ± 6 8.345 ± 0.166 Weedguard 273 ± 3 8.012 ± 0.234 275 ± 7 8.098 ± 0.381 Polyethylene 283 ± 8 7.679 ± 0.525 278 ± 2 8.391 ± 0.457 No mulch 268 ± 7 7.634 ± 0.131 274 ± 3 8.402 ± 0.55 Fall 2015 BioAgri 280 ± 14 7.918 ± 0.259 261 ± 5 9.095 ± 0.369 Naturecycle 281 ± 10 8.031 ± 0.406 269 ± 2 9.031 ± 0.281 Organix 271 ± 6 7.685 ± 0.232 259 ± 4 8.804 ± 0.143 PLA/PHA 268 ± 7 8.255 ± 0.444 250 ± 4 8.053 ± 0.085 Weedguard 272 ± 4 8.199 ± 0.114 264 ± 11 8.43 ± 0.318 Polyethylene 254 ± 1 7.599 ± 0.61 252 ± 3 8.825 ± 0.217 No mulch 293 ± 7 7.792 ± 0.248 280 ± 2 9.926 ± 0.796 Spring BioAgri 303 ± 6 8.195 ± 0.22 270 ± 9 8.254 ± 0.305 2016 Naturecycle 284 ± 7 8.216 ± 0.379 271 ± 8 8.027 ± 0.118 Organix 292 ± 8 7.875 ± 0.411 272 ± 3 8.028 ± 0.143 PLA/PHA 286 ± 2 8.341 ± 0.173 269 ± 9 8.196 ± 0.215 Weedguard 284 ± 6 8.579 ± 0.206 270 ± 6 8.01 ± 0.227 Polyethylene 281 ± 5 7.333 ± 0.381 270 ± 4 8.394 ± 0.169 No mulch 287 ± 10 7.713 ± 0.22 278 ± 5 8.302 ± 0.255 Fall 2016 BioAgri 299 ± 8 10.18 ± 0.463 272 ± 3 10.32 ± 0.397 Naturecycle 294 ± 3 10.53 ± 0.302 278 ± 4 9.525 ± 0.615 Organix 288 ± 4 9.234 ± 0.27 275 ± 6 9.479 ± 0.867 PLA/PHA 284 ± 2 10.32 ± 0.77 277 ± 5 10.31 ± 0.76 Weedguard 288 ± 10 11 ± 0.961 277 ± 2 11.45 ± 0.822 Polyethylene 271 ± 6 8.777 ± 0.436 275 ± 4 10.65 ± 0.677 No mulch 290 ± 7 9.967 ± 0.312 277 ± 7 10.12 ± 0.382

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Figure A.3.1: Constrained ordination of soil communities collected in Fall 2016 season, as driven by environmental variables: soilph: soil pH, solsalt: electrical conductivity (dS/m), exk: exchangeable potassium by NH4OAc extraction (mg/ kg), exmg: exchangeable magnesium by NH4OAc extraction (mg/ kg), exca: exchangeable calcium by NH4OAc extraction (mg/ kg), exna: exchangeable sodium by NH4OAc extraction (mg/ kg), no3ppm: Nitrate-N (mg/kg), GSM: Gravimetric soil moisture (g water/ g dry soil), om: Organic matter (%). Data for environmental variables are reported in Sintim et al., (2019).

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Figure A.3.2: Percent relative abundance (averaged across treatments) of the most influential genera cumulatively responsible for about 60% of the seasonal variance between microbial communities. Family and Class level classification is used where classification at genus level was not possible. “Gp” indicates acidobacterial subgroups. The lower and upper hinges of the boxplots correspond to the first and third quartiles (the 25th and 75th percentiles). The upper whisker of the boxplot extends from the hinge to the largest value no further than 1.5 * IQR from the hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles). The lower whisker extends from the hinge to the smallest value at maximum 1.5 * IQR of the hinge. Data beyond the end of the whiskers are outliers and are plotted individually. Letters indicate post-hoc test completed using pairwise Wilcoxon rank sum test using significance at α = 0.05.

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Figure A.3.3: Stacked bar plots of depicting the abundant classes of bacteria in TN and WA for all mulch treatments.

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Figure A.3.4: NMDS of soil microbial communities for the last season (Fall 2016) in a) TN b) WA. No significant difference between treatments were observed. NMDS stress value: 0.14 (TN), 0.2 (WA).

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a) 15-17 all enzyme data Non-metric MDS Standardise Samples by Total Transform: Square root Resemblance: S17 Bray-Curtis similarity 2D Stress: 0.04 Treatment BioAgri Naturecycle No Mulch Organix PHOS PLA/PHA Polyethylene Weedguard LAP BG NAG

XYLAG

15-17 all enzyme data Non-metric MDS b) Standardise Samples by Total Transform: Square root Resemblance: S17 Bray-Curtis similarity 2D Stress: 0.09 Treatment BioAgri Naturecycle No Mulch Organix PLA/PHA

PHOSBG Polyethylene Weedguard

LAP XYL

NAG AG

Figure A.3.5: NMDS plot of soil extracellular enzyme activities from samples collected in Spring 2017 (final time point for enzyme rate measurements) for a) TN (p > 0.05) and b) WA (p > 0.05).

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CHAPTER IV SOIL BACTERIAL AND FUNGAL COMMUNITIES ASSOCIATED WITH BIODEGRADABLE PLASTIC MULCH FILMS

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A version of this chapter is being prepared for publication in:

Sreejata Bandopadhyay, Kelsey B. Henderson, Marife B. Anunciado, Jose Liquet y Gonzalez, Douglas G. Hayes, Jennifer M. DeBruyn. “Bacterial and Fungal Communities Associated with Biodegradable Plastic Mulch Films” in Environmental Science and Technology.

The author contributions are as follows: Conceived and designed the experiments: SB and JMD. Performed the experiments: SB, KBH, MBA and JLG. Analyzed the data: SB. Wrote the paper: SB and JMD.

Abstract

Agricultural plastic mulch films provide a favorable soil microclimate for plant growth, improving crop yields. Biodegradable plastic mulch films (BDMs) have emerged as a sustainable alternative to widely used non-biodegradable polyethylene (PE) films. BDMs are tilled into the soil after use and are expected to biodegrade in field conditions. However, little is known about the microbes involved in biodegradation and the relationships between microbes and plastics in soils. In order to capture the consortium of soil microbes associated with (and thus likely degrading) BDMs, field-weathered plastics from two locations were studied alongside laboratory plastic enrichment experiments to assess differences in the microbial community structure associated with BDMs and PE. We observed an enrichment of fungi on field-weathered plastics compared to bulk soil. Amplicon sequencing revealed that field-weathered BDM-associated bacterial communities differed relative to bulk soil, with Methylobacterium, Arthrobacter and Sphingomonas enriched on BDMs compared to PE. Microbes enriched on BDMs in laboratory culture were able to degrade the plastics, and the composition of these microbial communities was influenced by mulch film composition. Our initial characterization of the biodegradable “plastic-ome” lays the groundwork for understanding biodegradation dynamics of BDMs in the environment.

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Graphic art

Introduction

Plastic mulch films provide several benefits to fruit and vegetable production, such as reduced weed pressure (Martín-Closas et al., 2017), improved moisture conservation (Kader et al., 2017), and increased soil temperatures. Most plastic films are comprised of polyethylene (PE) (Kasirajan and Ngouajio, 2012), which is poorly biodegradable. With limited recycling options, PE waste generally ends up in landfills or is burned (Ingman et al., 2015, Shogren and Hochmuth, 2004) which can lead to soil, groundwater and atmospheric pollution (He et al., 2015, Steinmetz et al., 2016) . Biodegradable plastic mulch films (BDMs) can be a sustainable alternative to PE films. BDMs are made of polymers derived from or mimicking those present in bacteria or plants; thus, microbes have the metabolic capacity to degrade them. Microbes acting on BDMs secrete extracellular depolymerase enzymes, which break down the complex polymers into simpler oligomeric and monomeric units. These simpler components are then assimilated back into the microbial biomass and a part of it is released into the atmosphere as carbon dioxide (Kyrikou and Briassoulis, 2007). Some of the most commonly used biobased polymers used to manufacture

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BDMs include polylactic acid (PLA), starch and cellulose; some experimental films have also been manufactured using polyhydroxyalkanoates (PHAs) and polylactic acid (PLA). Other synthetic biodegradable polymers used in BDMs include poly(butylene succinate adipate) (PBSA) and copolyesters such as poly(butylene-adipate-co-terephthalate) (PBAT).

BDMs are designed to be tilled into the soil after use, alleviating the disposal issues associated with PE. However, biodegradation of BDMs in the field can be unpredictable. While we accept that microbial biodegradation is the ideal fate of BDMs, there is a considerable knowledge gap regarding the mechanisms of biodegradation, including the specific microbial taxa involved (Bandopadhyay et al., 2018). Among the few studies that have been done, the majority have focused on culturing single strains of bacteria or fungi (Bailes et al., 2013, Moore-Kucera et al., 2014) which may miss degrading organisms that are not easily cultured in the lab. In addition, research in this field has focused on degradation of pure polymers under laboratory conditions (Chen et al., 2003, De Eugenio et al., 2010, Kunioka et al., 2009, Shah et al., 2014). However, BDMs represent a mixture of polymers and other additives such as fillers, plasticizers and dyes, and the degradation of these mixtures have not been well documented. Thus, the single organism and single polymer studies done to date may not always explain BDM biodegradation dynamics in the field.

Microbial communities in proximity to BDMs have so far only been examined in bulk soil samples taken from the fields where BDMs have been used, or in laboratory soil incubation studies with BDMs (Li et al., 2014b, Koitabashi et al., 2012, Muroi et al., 2016). Additionally, limited studies have compared BDMs and PE in the same experiment. The few studies that have done this comparison have reported increased microbial abundances, respiration rates, and potential extracellular enzyme activity rates in soil under BDMs compared to PE treatments (Barragán et al., 2016, Li et al., 2014a, Ma et al., 2016, Moreno and Moreno, 2008) implying that BDMs enhance microbial activity in soils. Further examination of microbial communities directly associated with field-deployed plastic mulch films will help further our knowledge of microbes degrading BDMs. A direct comparison of the plastic-associated communities to the bulk soil communities would confirm taxa that are potentially enriched on and degrading BDMs.

In this research, our goal was to investigate microbial communities associated with plastic mulch films, i.e. the BDM “plastic-ome”. The objectives of this study were to characterize 1) the

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microbial communities on field-weathered plastic mulches and the underlying bulk soil collected from two locations with diverse climates, and 2) the microbial communities associated with BDMs in enrichment cultures. We hypothesized that Proteobacteria, Actinobacteria, Firmicutes and Ascomycota would be enriched on BDMs compared to PE; all of these phyla include candidates with demonstrated high molecular weight polymer degrading potential. Since most of the BDMs tested in the study contained PBAT, we hypothesized that the microbial communities enriched on BDMs would be determined by the differences in the composition of the films, with PLA- containing plastics such as Organix and PLA/PHA harboring more similar taxa compared to those containing starch as a component. With our results we hope to convey important information regarding taxa which could potentially degrade BDMs and lay a foundation for future work on BDM degradation mechanisms.

Materials and Methods

Plastic mulch films For the laboratory and field enrichment studies, four unweathered BDMs were used. Three of these were commercially available: BioAgri®, Naturecycle and Organix™; the fourth was an experimental PLA/PHA film. For the field study, a fully biodegradable cellulose mulch (WeedGuardPlus®, positive control) was used along with non-biodegradable PE film (negative control), in addition to the four BDMs. The major constituents and properties of these mulch films are in Table A.4.1 adapted from Hayes et al. 2017. Off-the-roll unweathered BDMs were also kept in a storage cabinet in the dark at room temperature and these were used as controls for thermogravimetric analysis (described below).

Enrichment Cultures Enrichment culture experimental design Soils used for the enrichment study were collected in May 2016 from the East Tennessee Research and Education Center (ETREC) located in Knoxville, TN. Biodegradable and PE mulches were being tested at ETREC over two years (2015 to 2016) under pie pumpkin (Cucurbita pepo) as a test crop, with full experimental details described in Sintim et al. (2019) and Ghimire et al. (2018). Since soils were collected in May 2016, the soils were exposed to only one complete field season

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in 2015. The soil at Knoxville is a sandy loam (59.9% sand, 23.5% silt, and 16.6% clay), classified as a fine kaolinitic thermic Typic Paleudults. Each mulch treatment had four replications in the field. Soil used in the study was collected from one of the four plots covered with biodegradable mulch, BioAgri, which was tilled into the soil in the field experiment conducted in 2015. About 30 subsamples of soil collected at 10 cm depth was composited. After all measurements were done, soils were stored at -20ºC until enrichment culture set up.

Enrichment cultures were set up in June 2016 in Wheaton™ media bottles with butyl septa to enable headspace CO2 sampling for respiration measurements. 30 g soil was mixed with 270 ml phosphate buffered saline (PBS) resulting in a 10:1 dilution. A 1X PBS solution was used containing 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4 and 1.8 mM KH2PO4 adjusted to a final pH of 7.4. This soil extract was used for inoculation. For each BDM used, bottles were either inoculated with 5 ml of soil extract or left un-inoculated. 5 ml of soil extract was added to 45 ml of M9 minimal media in inoculated bottles resulting in a final dilution of 10-2. Uninoculated bottles received BDMs and media but no soil extract. A negative control containing only media (no soil inoculum or BDMs) was also used. All treatments and controls were done in triplicate.

Plastic samples were obtained from the rolls received from the manufacturers (i.e. were unweathered), cut into 4 x 4 cm2 pieces and sterilized by UV irradiation in a biosafety cabinet for one hour on each side using a modified protocol of Bailes et al. (2013). Sterilized films were kept in sterile containers inside the hood until they were added to culture bottles. Three pieces of each BDM were put into each bottle. Two separate enrichment incubations were set up: one set was incubated for 3 weeks and the other set for 6 weeks. BDM pieces were collected after 3 and 6 weeks of incubation: one BDM piece was used to conduct confocal microscopy to visualize live/dead microbes adhering to BDMs; a second piece was used to extract DNA for sequencing of adherent soil microbes; and the third piece was used to assess material properties of the plastic. The plastics used for imaging and sequencing were not cleaned. The plastic used for testing material properties was cleaned gently; more detail is provided in the method section describing thermogravimetric analysis.

BDM biodegradation assessment

CO2 evolution data was used to assess biodegradation of BDMs. A LICOR 820 infrared gas analyzer was used to measure CO2 headspace from culture bottles. A syringe was used to collect

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0.5 ml gas sample from the headspace of the culture bottles which had a butyl septa cap for easy gas sampling. Gas standards (1,000 ppm, 10,000 ppm and 50,000 ppm) were used to create standard curves and the ppm CO2 value recorded from observed peaks. Sample CO2 ppm values were then derived from standard curves and final values reported as cumulative CO2 (ppm) over time.

Confocal microscopy Plastics sampled after 3- and 6-week incubations were stained using a LIVE/DEAD® BacLight™ Bacterial Viability Kit (Invitrogen™ Molecular Probes™, Eugene, OR, USA). The protocol was modified and adjusted for optimal staining of plastic pieces: 10 ul of Component A (SYTO 9 dye, 1.67 mM / Propidium iodide, 1.67 mM, 300 μL solution in DMSO) was mixed with 10 ul of Component B (SYTO 9 dye, 1.67 mM / propidium iodide, 18.3 mM, 300 μl solution in DMSO). Components were centrifuged in a microcentrifuge tube. 3 μl of the dye mixture was mixed with 1 ml of M9 minimal media. The reagent mixture contributed roughly 0.3% DMSO to the staining solution. The plastic piece to be visualized was mounted on a glass slide and the mixture was poured onto it and covered with an aluminium foil. Slides were incubated at room temperature in the dark for 15 minutes. After incubation, the plastic was rinsed off gently with M9 minimal media to avoid background plastic being stained. A drop of mounting oil was placed on the plastic piece and covered with a cover slip. Imaging was completed using a Leica™ Scanning Laser Confocal System at the University of Tennessee, Science and Engineering Research Facility. Five to ten fields of vision were scanned. Scale bars were added to images using ImageJ® software (Schneider et al., 2012).

Isolates from plastic degrading consortia Isolation of colonies on BDM-agar plates was conducted after 6 weeks of incubation. Inoculum was taken from the spent media in culture bottles and spread on minimal media agar with sterilized BDM films as the sole carbon source, following the method of Bailes et al. (2013). Colonies were selected from near the films and re-streaked several times to obtain pure cultures. DNA was extracted from these isolates using a Microbial DNA isolation kit (MoBio™) per manufacturer’s instructions. 16S rRNA genes were PCR-amplified and sequenced using Sanger sequencing at the University of Tennessee Genomics Lab for identification.

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Thermogravimetric Analysis of BDMs BioAgri and Organix plastics were sampled from enrichment culture bottles after 6 weeks and gently rinsed with deionized water to remove adherent microbes and dried at room temperature for thermogravimetric analysis (TGA). Mulch films of approximately 2 mg were cut (~ 5 mm square) and placed onto platinum TGA pans. Samples were then heated at a constant rate of 10°C per minute from room temperature (25°C) to 600°C using TGA 550 thermogravimetric analyzer (TA Instruments). Onset temperatures of degradation processes, weight remaining (%) and rate of weight loss were determined. Results were compared to indoor-stored off-the-roll BDMs.

Esterase assay An absorbance-based esterase assay was conducted using a modified version of the lipase-esterase assay described by Margesin et al. (2002). The protocol, which was initially developed for test tube scale soil assays, was modified and standardized for a 1.5 ml, 96 deep well plate assays. Saturation curves were conducted to determine optimal volume of the para-nitrophenyl butyrate substrate needed to add to the soil-buffer system: 200 µl and 250 µl substrate was deemed optimal for the 3- and 6-week samples. 100 µl of spent media extract from each enrichment bottle was added to triplicate wells in a 96 deep well plate. In addition, standard curves (0-100 µg ml-1 para- nitro phenol (pNP)) were separately run using each of the spent media extracts in the same plate to account for any background signal. The same protocol was followed for the standard and test samples. Phosphate buffer (adjusted to total volume of 500 µl reaction mixture) was added to the spent media and the plate was pre-warmed in water bath at 30ºC for 10 minutes. The previously determined optimal volume of para-nitrophenyl butyrate substrate was then added to each well. For standard curves, the appropriate volume of pNP was added. After addition of substrates and standards, well contents were mixed, and plates incubated at 30ºC for exactly 10 minutes. The reaction was stopped by cooling on ice for 10 minutes. Finally, the plate was centrifuged at 2000 xg at 4ºC for 5 min. Supernatants in the plate was pipetted into a new 96-well clear plate and absorbance of the released pNP in samples were read at 400 nm on a microplate reader (Biotek® Synergy H1).

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Field study

Field sites Two experimental stations were used for the field study. In addition to the field site at ETREC in Knoxville, TN, an identical experimental station was set up in Northwestern Research and Extension center (NWREC) at Mount Vernon, WA. The soil at Mount Vernon is a silt loam (14.2% sand, 69.8% silt, and 16% clay), classified as a fine-silty mixed nonacid mesic Typic Fluvaquents. Both field sites were set up as randomized complete block design experiments with four replications of seven main plot treatments totaling to 28 plots at each location. These treatments included four BDM treatments, a PE mulch, a cellulose mulch and a no-mulch (bare ground) treatment. Full experimental design and details can be found in Ghimire et al. (2018) and soil physiochemical properties are reported in Sintim et al. (2019). Summary of weather data for 2016 is reported in Table 4.1 with data over two years reported in Sintim et al. (2019). The present study reports data on soil and plastic mulch samples collected in Fall of 2016 after a complete growing season using pie pumpkin, Cucurbita pepo cv. Cinnamon girl, as a test crop in both TN and WA.

Soil and mulch sampling

Bulk soil

Soil samples were collected from all 28 plots in ETREC and NWREC in September 2016 (after harvest) as described in Sintim et al. (2019). Briefly, 30 subsamples of 10-cm deep soil were obtained from the middle of five rows of each plot using sterilized soil augers and composited. Soils were transferred to the laboratory in Ziplock® bags, then 20 g of each soil sample was aliquoted into sterile Whirlpak® bags and stored at -80°C for DNA extraction.

Field-weathered mulches

Field-weathered mulches were collected from both ETREC and NWREC field sites at the end of the growing season in 2016 (October) from all mulched plots (six mulch treatments with four replicate plots for each, for a total of 24 plots). Six pieces of mulches approximately 8 cm2 were cut from the middle row of each plot using scissors sterilized with 70% ethanol. Scissors were sterilized before sampling from each plot to prevent cross contamination. The six pieces were

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Table 4.1: Environmental data collected in Fall 2016 when plastic mulches and soil were collected from the field site in TN and WA. Mean values are reported with standard errors. Full soil data is reported in Sintim et al. (2019).

Mean annual Bulk soil Mean annual density Soil temperature precipitation Location (g cm-3) texture Soil type pH (°C) (mm) ETREC, Knoxville, 1.22 ± Silt Typic TN 0.01 loam hapludult 5.8 ± 0.08 16.8 ± 0.18 46.77 NWREC, Mount 1.13 ± Silt Typic 5.54 ± Vernon, WA 0.01 loam fluvaquents 0.03 12.54 ± 0.12 49.55

placed in a sterile Whirlpak® bag and immediately placed on ice. Mulch samples were stored at -20ºC for DNA extractions.

Microbial community analysis for lab and field study

DNA extraction and quantification DNA extraction from soil samples was completed using the Qiagen™ (formerly MoBio™) PowerLyzer Power Soil DNA isolation kit with inhibitor removal technology, as per manufacturer’s instructions.

Two pieces of plastic (0.7*0.7 cm2) were cut from each of the six pieces of field-weathered mulches sampled from the field resulting in 12 pieces total. Four pieces went into one bead beating tube provided in the PowerLyzer Power Soil DNA kit (Qiagen™), and DNA extractions performed according to manufacuter’s protocol. Triplicate DNA extractions (i.e. 4 pieces of plastic per tube) were performed for each plot and pooled prior to DNA quantification, qPCR and sequencing. BDMs retrieved from enrichment culture bottles were similarly cut into small square pieces (0.7*0.7 cm2 each) using sterile scissors and forceps inside a biosafety cabinet. DNA extraction was done as described for the field-weathered plastics.

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Quantification of extracted DNA was completed using a Quant-It PicoGreen dsDNA Quantification Kit (Invitrogen™) per manufacturer’s instructions. For soil DNA quantification, standard curves generated had R squared values of 1. Mean DNA concentration of the soil samples were 12 ng ul-1 DNA. For quantification of DNA extracted from BDMs, standard curves had an R squared value of 0.99 to 1 for all runs performed. Mean DNA concentration of the samples were 3.3 ng ul-1.

Quantitative PCR 16S rRNA (bacteria) and ITS (fungi) gene copy numbers were quantified from DNA samples using Femto™ Bacterial DNA quantification kit and Femto™ Fungal DNA quantification kit. This was done for both plastic and soil DNA samples. Known amounts (in ng) of bacterial and fungal DNA were provided in the kit as standards. These were converted to copy numbers and used to generate a standard curve to calculate copy numbers in our samples. Mean qPCR efficiencies were approximately 85% and > 90% for the bacterial and fungal assays, respectively. All standard curves had R squared values ranging from 0.98 to 1. Bacterial to fungal copy number ratios were calculated to look for relative enrichment of bacteria versus fungi in soil and plastic samples.

16S rRNA amplification and sequencing 16S rRNA amplicon sequencing of extracted DNA samples was conducted by the Genomic Services Laboratory (GSL) at Hudson Alpha, Huntsville, AL. DNA samples were shipped in 96 well plates with dry ice, and GSL performed all library preparation and sequencing. V4 region was amplified using primers 515F (GTGCCAAGCAGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT) (Caporaso et al., 2011). The first PCR was run with V4 amplicon primers, Kapa HiFi master mix, and 20 cycles of PCR. All aliquots and dilutions of the samples were completed using the Biomek liquid handler. PCR products were purified and were stored at -20ºC until further processing was completed. The PCR indexing was later completed for the 16S (V4) amplicon batch. Products were indexed using GSL3.7/PE1 primers, Kapa HiFi master mix, and 12 cycles of PCR. Products were purified using magnetic beads using the Biomek liquid handler. Final libraries were quantified using PicoGreen. A subset of libraries was profiled on the Agilent DNA1000. The amplified 16SrRNA genes were sequenced using 250 PE (paired- end) reads on an Illumina MiSeq platform. Data was transferred from GSL to University of Tennessee via BaseSpace.

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Sequence data was processed using Mothur v.1.39.5 (Schloss et al., 2009) following the standard operating protocol for MiSeq libraries. First, primers were trimmed from the sequences. This was followed by making contigs of the paired end reads, and removing sequences with ambiguous bases and long homopolymers. SILVA v102 was used as a reference alignment by customizing it to our region of interest which was the 16S rRNA gene. First, an E. coli 16S rRNA gene sequence was obtained and trimmed to start and end with our primer pair. The resulting fasta file was aligned to the SILVA reference file. The summary of the alignment provided the start and end positions (or the coordinates) to use in pcr.seqs command to trim the SILVA alignment file to our region of interest, i.e. the V4 region. After alignment to SILVA reference database, sequences were filtered to remove overhangs at both ends, and sequences de-noised by pre clustering them up to 2 differences between sequences. Chimeras were removed using VSEARCH algorithm. Unwanted sequences classified as Eukaryota and Arachaeota were removed. Sequences were binned into phylotypes according to their taxonomic classification and cut off set at the genus level. A consensus taxonomy for each OTU was generated using the RDP reference database version 9. The resulting OTU table and taxonomy file were imported into R for further analyses.

18S rRNA amplification and sequencing Fungal sequencing of the field-weathered plastic samples was completed by amplifying the 18S region using primers 574F (CGGTAATTCCAGCTCYV) and 1132R (CCGTCAATTHCTTYAART). The 18S amplicon PCR products were used at an input of 10 µl of gDNA. Kapa HiFi master mix and 20 cycles of PCR was used. PCR products were purified using magnetic beads on the Biomek. The indexing PCR reaction was then set up using GSL3.7_I7 primers and the truncated PE1 primer. PCR products were then again purified using magnetic beads on the Biomek. 18S libraries were quantified using PicoGreen as described above. A subset of final libraries was analyzed on an Agilent DNA1000 chip. Kapa qPCR was completed for samples and a MiSeq 300 bp PE run was completed.

The fastq files from the run were converted to a ‘.files’ format which was then used to trim off primers, make contigs, remove sequences with ambiguous bases and long homopolymers using Mothur v.1.39.5 (Schloss et al., 2009) as described for the 16S libraries. SILVA v132 was used as reference alignment by customizing it to 18S rRNA following the same workflow as for the 16S rRNA gene. Instead of using E. coli, Saccharomyces cerevisiae sequence was used to trim to begin

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and end with the primer pair used for 18S sequencing. The S. cerevisiae sequence was saved as a fasta file and aligned to the SILVA reference file to get the start and end coordinates to use to trim the SILVA reference to the V2 amplicon region.

Statistical analysis

T-tests were used to assess the differences in CO2 evolved between inoculated and un-inoculated BDM samples in the enrichment experiment. Three-way ANOVAs were conducted to assess differences in 16S rRNA, ITS gene abundances as well as 16S/ITS ratios between times of incubation (3-week and 6-week incubations), inoculated and uninoculated samples, and between plastic treatments. In case of significant interaction effects, one-way ANOVAs were done to report simple main effects. Tukey post hoc tests were conducted following one-way ANOVAs with significant differences. For the field study, a mixed model ANOVA was used to analyze differences in 16S/ITS ratios between locations, soil and plastic communities, and plastic treatments; block was used as a random effect with all other parameters being fixed effects. Simple main effects were also evaluated for the field 16S/ITS ratios for all the parameters (location, soil/plastic, treatment) using one-way ANOVAs. T-tests were also conducted to assess differences in 16S/ITS ratios between field-weathered BDMs and bulk soil for Fall 2016. All statistics were done in R v. 3.4.0 (R Core Team 2018).

Bray-Curtis distances of microbial community composition were calculated using the vegan package (v 2.4-3) in R platform. The distances were then visualized using non-metric multidimensional scaling (NMDS) plots using phyloseq package in R. To determine whether significant differences existed between treatments in bacterial and fungal community compositions, a permutational multivariate analysis of variance (PERMANOVA) was performed using the ADONIS function implemented in the R statistical platform (version 3.4.0), based on the Bray-Curtis dissimilarity matrix. Similarity percentage analyses (SIMPER) was computed in R, revealing the most influential OTUs driving differences in bacterial and fungal community composition between soil and field-weathered plastic, locations, and between mulch treatments. Difference in relative abundances of bacterial and eukaryotic taxa between treatments were determined using Kruskal-Wallis rank sum non-parametric test (kruskal.test in R).

For alpha diversity measures, number of observed OTUs (richness) and inverse Simpson indices (diversity) of bacterial and fungal communities were calculated in R for BDMs exposed to lab 133

enrichment, field-weathered BDMs and bulk soil. The libraries were subsampled with replacement (subsampling was done to the minimum number of reads) and repeated 100 times to estimate the species abundance while standardizing sampling effort; the estimates were then averaged from each trial. Three-way ANOVAs were conducted to test for interaction effects for the lab study. In case of significant interaction effects, a one-way ANOVA was used in R to test for simple main effects; simple main effects for lab study were tested using time of incubation, inoculated/uninoculated treatments, and mulch treatments as factors. Tukey post-hoc tests were conducted following one-way ANOVAs on treatments. For the field study, a mixed model ANOVA was done to account for fixed effects and random effects similar to the analysis for 16S/ITS ratios. Simple main effects were also evaluated using one-way ANOVAs for the alpha diversity estimates obtained from the field study using location, soil/plastic treatments, and mulch treatments as factors, followed by a Tukey post-hoc test. All sequence reads were scaled to minimum library size before analysis was performed. Rarefaction curves were obtained using Chao1 (Chao, 1984) estimates and observed number of OTUs in R. The richness estimator, Chao1, is based on distribution of singletons and doubletons and attempts to account for unobserved OTUs by extrapolating from the observed abundances.

Results

Biodegradation of BDMs in enrichment cultures

Biodegradation of BDMs was observed in the enrichment cultures. All inoculated BDM treatments showed significantly higher CO2 evolution than the negative control (no BDM) for both 3- and 6- week cultures (p ≤ 0.05). Inoculated bottles had significantly higher CO2 (p ≤ 0.05) than uninoculated samples for Naturecycle and Organix treatments (Figure 4.1) for the 3-week incubation. For the 6-week incubation, cumulative CO2 evolution for all treatments was greater than the 3-week incubation (Figure 4.1); however, no significant differences between inoculated and un-inoculated samples were observed for any plastic. Thermogravimetric analyses showed changes in material properties of BioAgri (PBAT and starch) and Organix (PBAT and PLA) after being in enrichment culture for 6 weeks (Figure A.4.1). The peak attributable to starch shows slight evidence of depolymerization; both inoculated and un-inoculated BDMs slightly shifted the

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differential thermogram (DTG) curves to lower temperatures. Gel permeation chromatography (GPC) data (not shown) also indicated a slight decrease of molecular weight for PBAT in the inoculated BioAgri treatment. A larger change is seen for the peak attributable to PBAT in Organix. The inoculated treatment led to a more profound shift of the PBAT DTG peak (centered at 400°C) compared to the uninoculated treatment. Fourier Transform Infrared Spectroscopy (FTIR) data for Naturecycle indicated a decrease of OH stretching region (3800-3200 cm-1) suggesting that starch maybe the preferred carbon source for Naturecycle in the microcosms (data not shown). However, the stretching region directly attributable to the polyesters (1800-1200 cm- 1) hardly changed at all from the initial off-the-roll mulch samples (stored indoor) used as controls in the run. Due to proprietary information of the composition of Naturecycle, TGA was not done on these samples. FTIR results of PLA/PHA (not shown) also did not reveal any peak changes indicative of biodegradation. GPC (not shown) also indicated that, for PLA/PHA, there was no significant change of molecular weight for either the inoculated or uninoculated treatments. Evidence for microbial colonization in inoculated and un-inoculated BDMs in enrichment was seen in confocal images using a LIVE/DEAD® Bacterial Viability kit (Figure A.4.2). Live cells stained green, while dead cells and fungal hyphae stained red. Both live and dead cells were seen on BDMs in inoculated and un-inoculated enrichments. In general, inoculated BDMs showed qualitatively denser microbial colonization than un-inoculated BDMs. Propidium iodide used as part of the staining kit also stains fungal hyphae, as demonstrated by red colored hyphae on Naturecycle-inoculated treatment (Figure A.4.2). The potential activity of extracellular esterase was assayed in the inoculated media following the 3- and 6-week incubation. Esterase activity significantly increased over time for PLA/PHA and BioAgri (Table A.4.2). For 3-week incubated samples, highest activities were seen for PLA/PHA and Organix with esterase activities of 2.14 ± 0.06 and 1.8 ± 0.46 µg pNP released ml-1 min-1 (Table A.4.2) respectively; whereas for 6-week incubated samples, the highest activity was observed for Naturecycle and PLA/PHA with activities of 2.63 ± 0.21 and 2.73 ± 0.13 µg pNP released ml-1 min-1.

Bacterial communities on laboratory enriched BDMs

Comparison between times of incubation Due to the presence of interaction effects between inoculation and treatment factors (Tables A.4.4, A.4.5), main effects were evaluated using one-way ANOVAs for bacterial and fungal gene 136

abundances. When all treatments were analyzed together, there was no significant difference in bacterial or fungal abundances between the 3- and 6-week incubations (Table A.4.3, A.4.4). However, there were slight differences between 3- and 6- week incubations for individual treatments demonstrated via one-way ANOVAs. For example, inoculated Organix had significantly greater bacterial abundance at 3 weeks (Figure 4.2, Table A.4.6, p ≤ 0.05), but uninoculated Organix had significantly greater ITS gene abundance at 6 weeks (Figure 4.2, Table A.4.6, p < 0.001). Inoculated PLA/PHA treatment showed significantly greater 16S/ITS gene abundance ratio at 6-week compared to 3-week incubation (p ≤ 0.05, Table A.4.6). Richness (number of observed OTUs) and diversity (inverse Simpson indices) estimates are reported in Table A.4.7 and Figure 4.3. Rarefaction curves are included in Figure A.4.3. Interaction effects between inoculation and treatment factors were significant for diversity estimates but not for richness estimates (Tables A.4.8, A.4.9). Thus, one-way ANOVA results were used (Table A.4.10). Neither richness or diversity was significantly different between the 3- and 6-week incubations. Similarly, no differences were seen in bacterial community composition between 3- and 6- week incubation times for each treatment (in inoculated and un-inoculated microcosms) (Table A.4.11a).

Comparison between inoculated and uninoculated samples Significant differences were seen in microbial gene abundances between inoculated and uninoculated samples. Naturecycle had significantly greater 16S rRNA (p ≤ 0.01, Figure 4.2a, Table A.4.12) and ITS (p ≤ 0.05) (Table A.4.12, Figure 4.2b) gene abundance on inoculated compared to uninoculated treatments for 3-week and 6-week incubation. Naturecycle showed significantly greater 16S/ITS gene abundance ratio on uninoculated treatments compared to inoculated for 3-week and 6-week incubation (p ≤ 0.05). PLA/PHA had significantly greater bacterial abundance (p ≤ 0.05) on inoculated treatments for 6-week incubation and had greater 16S/ITS ratio on inoculated treatments (p ≤ 0.01, Table A.4.12) for 6-week incubations. Organix showed significantly greater ITS gene abundances on inoculated treatments compared to uninoculated ones (p ≤ 0.05) for 3-week incubation. No significant change was observed between any other inoculated and uninoculated treatments. Inoculated treatments had significantly higher richness and diversity estimates compared to uninoculated samples when averaged by treatments for both 3 week and 6-week incubations (Figure 4.3). Communities associated with BDMs in enrichment culture showed significant differences in composition between inoculated and un-

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Figure 4.2: a) 16S rRNA (bacterial) and b) ITS (fungal) gene abundances for 3-week and 6- week incubations. Gene abundances are log transformed. Error bars represent standard error of the mean (n = 3). Asterices denote significant differences between inoculated and uninoculated treatments.

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Figure 4.2: Continued

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a b

a a

a a a a a a a a a a a a

b) a a a a a a a a a b

a ab a

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Figure 4.3: a) Diversity (inverse Simpson) and b) richness (number of observed OTUs) for inoculated and uninoculated microcosms. Letters indicate Tukey post-hoc results following a one-way ANOVA. All significances tested at α = 0.05.

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inoculated samples for both 3 week (p = 0.001) and 6-week incubations (p = 0.001) as determined by PERMANOVA (all BDM treatments were analyzed together, Table A.4.11a). Actinobacteria and Bacilli were higher in relative abundance in all un-inoculated treatments (Figure 4.4a). When looking at total abundances of Actinobacteria, the abundances across 3-week and 6-week samples ranged from 4070 to 39763 for inoculated treatments, compared to 7239 to 65051 for un-inoculated treatments. When looking at total abundances of Bacilli, the abundances across 3-week and 6- week samples ranged from 57 to 401 for inoculated treatments, compared to 87 to 72713 for uninoculated treatments. Sphingobacteria, Gammaproteobacteria, Alphaproteobacteria and Betaproteobacteria dominated BDMs that were inoculated. After the incubation period, strains were isolated from media in inoculated culture bottles using agar plates with sterilized BioAgri and Naturecycle film as the sole carbon source (Figure A.4.4). Isolates were identified based on 16S rRNA gene sequences as Streptomyces sp., Arthrobacter sp., Pseudomonas sp., Variovorax sp. and Microbacterium sp. (Table A.4.13).

Comparison between treatments Significant differences were seen in 16S gene copy abundances between inoculated BDM treatments for both 3-week (p ≤ 0.05) and 6-week (p ≤ 0.05) incubations (Table A.4.14). For the 3-week incubation, there was a quantitative enrichment of bacteria on Naturecycle and PLA/PHA which had significantly higher gene copies than Organix. For the 6-week incubation, Naturecycle and PLA/PHA had significantly higher bacterial gene copies than both BioAgri and Organix (Table A.4.15, Figure 4.2). For inoculated microcosms, no significant differences in richness estimates were observed between treatments for 3-week and 6-week incubations. However, inverse Simpson index was significantly higher for PLA/PHA inoculated treatments for 3-week incubation (Table A.4.10, Figure 4.3). For uninoculated treatments, there were no significant differences in inverse Simpson indices between treatments for 3- week or 6- week incubations. Richness was also unchanged between treatments, except for an increased richness in uninoculated Organix at 6-weeks. For each incubation time period, significant differences were seen in bacterial community composition between the inoculated plastic treatments (p = 0.001). Gammaproteobacteria, Alphaproteobacteria, Sphingobacteria, Flavobacteria, Betaproteobacteria were higher in relative abundance in starch containing BDMs whereas Gammaproteobacteria, Alphaproteobacteria, and Betaproteobacteria were more enriched on 141

Organix and PLA/PHA experimental films containing PLA (Figure 4.4a). Microbial community differences from inoculated BDM samples are shown using an NMDS of Bray Curtis dissimilarities (Figure 4.4b). BDM-associated communities (inoculated) cluster together by treatment.

Bacterial and fungal communities associated with bulk soil and field-weathered BDMs Mixed model ANOVAs showed significant interaction effects between treatment, soil/plastic type and location for gene abundance ratios, bacterial richness and diversity estimates (Tables A.4.16, A.4.17, A.4.18). Due to significant interaction effects, simple main effects were evaluated using one-way ANOVAs (Tables A.4.19, A.4.20).

Locational differences were observed for gene abundance ratios, and alpha and beta diverity estimates. One-way ANOVAs showed significantly greater 16S/ITS ratios on TN field-weathered plastics compared to WA (Figure 4.5, Table A.4.19). When averaged across treatments, bacterial richness estimates were significantly higher in TN soils than WA soils, whereas diversity was significantly higher in WA plastic than TN plastic (Table A.4.20, figure not shown). Mean richness and diversity estimates for plastic and soil bacterial communities are given in Table A.4.21. Rarefaction curves are shown in Figure A.4.5. Fungal richness and diversity was significantly different between locations with WA plastics having significantly higher number of observed OTUs (richness) and inverse Simpson index (diversity) than TN plastics (Table A.4.22, A.4.23). Fungal rarefaction curves are provided in Figure A.4.6.

Bacterial community composition on field-weathered plastics differed between TN and WA as determined by PERMANOVA (p = 0.001, Table A.4.11b). Bacterial taxa that contributed up to 40% of the differences in plastic-associated communities between TN and WA include Methylobacterium, Deinococcus, Hymenobacter, Bacilli, Sphingomonas, and unclassified Comamonadaceae. PERMANOVA tests on plastic fungal communtiy composition showed significant differences between locations (p = 0.001) (Table A.4.11c, Figure 4.6). SIMPER revealed predominant fungal taxa driving differences between TN and WA (at the genus level) as Cladosporium, Vishniacozyma, unclassified Cladosproriaceae, unclassified Agaricomycetes, Filobasidium, Peziza, and unclassified Conthreep, cumulatively contributing to about 55% of the variation.

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Figure 4.4: a) Bacterial taxa distribution (Class level) on BDMs in laboratory enrichment cultures. Relative abundances above a cut-off level of 2% are indicated. “Bacteria unclassified" denote taxa with relative abundance above the cut-off level of 2%, but that could not be classified. b) NMDS ordination of bacterial communities on inoculated BDMs. NMDS stress value: 0.24.

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Figure 4.4: Continued

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** ** ** ** ** *** *** *** *** *** ***

Figure 4.5: 16S/ITS qPCR ratios for field-weathered plasticome and bulk soil DNA. Paired t test results are shown with asterices indicating significant differences in 16S/ITS ratios between soil and plastics. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)

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Figure 4.6: a) Eukaryotic taxa distribution (Class level) on field-weathered plastics (Fall 2016) for TN and WA. Relative abundances above a cut-off level of 2% are indicated. “Eukaryota_unclassified" denote taxa with relative abundance above the cut-off level of 2%, but that could not be classified. b) NMDS ordination of fungal communities on field- weathered BDMs in TN and WA (Fall 2016). NMDS stress value: 0.18.

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Figure 4.6: Continued

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Soil and plastic-associated communities also differed in gene abundance ratios, and alpha and beta diversity estimates. The 16S/ITS ratio was significantly lower on field-weathered plastics compared to bulk soil in both locations (p < 0.001) (Table A.4.19). Mean 16S/ITS ratios for soil were 1.13 ± 0.01 and 1.10 ± 0.01 for TN and WA, respecitively, while field-weathered BDMs had mean 16S/ITS ratios of 1.01 ± 0.01 and 0.94 ± 0.01 for TN and WA, respectively (Figure 4.5). Paired t-tests also revealed significantly lower 16S/ITS ratios on field-weathered plastics for all treatments except Naturecycle in TN; Naturecycle tended to also rapidly disintegrate in the field compared to the other BDMs. Plastic also had significantly lower richness (number of OTUs) compared to soil in TN and WA. Interestingly, in WA only, there was a significantly higher diversity on field-weathered plastics compared to soil (Table A.4.20). The plastic-associated bacterial community composition was significantly different from the soil bacterial community composition in both locations (p = 0.001, Table A.4.11b). Several bacterial taxa were enriched on field-weathered plastics (Figure 4.7), including Actinobacteria, Alphaproteobacteria, and Deinococci. In TN, the differences were driven by bacteria in the genera Methylobacterium, Bacillus, Sphingomonas, Arthrobacter, and Deinococcus all of which were enriched on plastics compared to the bulk soil communities (Table A.4.24). In WA, plastic communities were enriched in bacteria belonging to genera Deinococcus, Hymenobacter, Sphingomonas, Methylobacterium, Arthrobacter and unclassified Comamonadaceae.

Bacterial communities associated with field-weathered BDMs showed significant differences in gene abundance ratios, alpha and beta diversity metrics. Significant differences in 16S/ITS ratios were seen between field-weathered mulch treatments in WA (p < 0.01) with PLA/PHA having significantly higher 16S/ITS ratio than Organix and Weedguard (post-hoc results not shown). However, treatment differences in gene abundance ratios were not observed in TN (Table A.4.19, Figure 4.5). Inverse Simpson indices did not significantly differ between treatments for soil and plastic bacterial communities in TN and WA. Interestingly, richness (bacterial OTUs) was significanty different between field-weathered mulch treatments in TN as well as WA, with PE having significantly greater richness than BioAgri, Naturecycle, and Weedguard treatments (Table A.4.20, Figure 4.8). No differences were seen between mulch treatments in fungal richness or diversity in TN, but significant differences were seen in richness and diversity indices between treatments in WA, with Weedguard having significantly lower richness and diversity than the other BDMs and PE (Table A.4.23, Figure 4.9). Despite the fact that soil bacterial community

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Figure 4.7: a) Bacterial taxa distribution (Class level) on field-weathered plastics and bulk soil (Fall 2016) for TN and WA. Relative abundances above a cut-off level of 2% are indicated. “Bacteria unclassified" denote taxa with relative abundance above the cut-off level of 2%, but that could not be classified. b) NMDS ordination of bacterial communities on field-weathered BDMs and bulk soil in TN and WA (Fall 2016). NMDS stress value: 0.12.

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Figure 4.7: Continued

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a a

a a a a a a a a a a a a a a a a a a a a a a

a a a a a a a a b a a a a b ab

ab a ab ab a a a a a

Figure 4.8: a) Diversity (inverse Simpson) and b) richness (number of observed OTUs) of bacterial communities on field-weathered BDMs and bulk soil in TN and WA. Letters indicate Tukey post-hoc results following a one-way ANOVA. All significances tested at α = 0.05. 151

a) a a a a

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Figure 4.9: a) Diversity (inverse Simpson index) and b) richness (number of observed OTUs) of fungal communities on field-weathered plastics in TN and WA (Fall 2016). Letters indicate Tukey post-hoc results following a one-way ANOVA. All significances tested at α = 0.05.

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Figure 4.9: Continued

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composition did not differ significantly between mulch treatments for TN or WA (p > 0.05), bacterial communities on field-weathered mulches were significantly different between mulch treatments (p = 0.001, Table A.4.11b). At both locations, Methylobacterium, Arthrobacter, Sphingomonas were all higher in relative abundance on BDMs compared to PE. WA mulches (BDMs and PE) were additionally enriched in Hymenobacter and unclassified Comamonadaceae; however, they did not show preferential enrichment on BDMs compared to PE (Figure 4.10).

Fungal community composition significantly differed between field-weathered mulch treatments (p = 0.001) in TN and WA with the differences mostly being driven by Peziza, Agaricomycetes, Cladosporium, Rhodotorula, Verticillium, and Ascomycota (SIMPER, Figure 4.11). Cellulosic Weedguard mulch had greater relative abundances of Peziza and Agaricomycetes in both TN and WA. Cladosporium was significantly higher on BDMs and PE compared to Weedguard mulch. PE mulch also had a significantly greater relative abundance of the ciliate group Conthreep compared to other mulch treatments.

Discussion

Carbon dioxide evolution data, esterase activity assays and thermogravimetric analyses confirmed that biodegradation of BDMs was taking place in the enrichment cultures. Higher CO2 evolution from 6-week incubated BDM samples compared to 3-week incubated samples demonstrate that microbes respired more mulch carbon over time. Evidence of depolymerization of BDMs was provided by the differential thermograms obtained by thermogravimetric analyses. The peak attributable to starch showed shifts to lower temperatures for both inoculated and un-inoculated BioAgri treatment compared to indoor-stored BDMs indicating slight decrease in thermal stability for starch (Figure A.4.1). However, the bigger shift in peak attributable to PBAT for inoculated Organix treatment likely indicates a significant loss of molecular weight, and a decrease of crystallinity suggesting depolymerization of PBAT. Esterase activity in spent media of enriched cultures suggests biodegradation of polyester containing BDMs. Both PLA/PHA and Organix contain high concentration of esters such as PLA (for PLA/PHA) and PBAT (for Organix) justifying the increased esterase activities reported for these two BDMs at 3-week incubation. Significantly higher esterase activity was seen at 6 weeks for PLA/PHA and BioAgri (inoculated microcosms) compared to 3-week incubations suggesting active microbes after a month of

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Figure 4.10: Percent relative abundance of taxa (genera) driving variance in microbial communities between soils and plastics in TN and WA as determined via SIMPER. Differences between plastic treatments were evaluated using Kruskal-Wallis non- parametric test. Significant differences were observed between plastic treatments in TN for Methylobacterium (p = 0.03), Arthrobacter (p = 0.03), and Sphingomonas (p = 0.02), and in WA significant differences were seen between plastic treatments for Methylobacterium (p = 0.03) and Sphingomonas (p = 0.03). All significances tested at α = 0.05.

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Figure 4.11: Percent relative abundance of taxa (genus level) driving overall differences in fungal communities between field-weathered plastics in TN and WA as obtained via SIMPER. “Taxa_unclassified” denote taxa which could not be classified at the genus level. Differences between plastic treatments were evaluated using Kruskal-Wallis non- parametric test. Significant differences were observed between plastic treatments in TN for Peziza (p = 0.007) and Cladosporium (p = 0.02). In WA significant differences were seen between plastic treatments for Peziza (p = 0.01), Agaricomycetes_unclassified (p = 0.03), Cladosporium (p = 0.03) and Conthreep_unclassified (p = 0.01). All significances tested at α = 0.05.

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incubation. Degradation of PLA oligomers has been shown to be accelerated by several esterase type enzymes (Fukuzaki et al., 1989). Some esterase enzymes can break down polyester type polyurethanes releasing diethylene glycol and adipic acid, the latter of which is released upon degradation of PBAT (Nakajima-Kambe et al., 1995). Although such previous studies are based on pure polymers, we suggest similar mechanisms of ester degradation for commercial polymers blends in mulch films. Taken together, these results demonstrate that biodegradation of mulch films was taking place.

Sterilization of plastics posed a challenge for the enrichment culture set up. UV is the most commonly used method for surface sterilization and has been used to sterilize polymer surfaces (Bailes et al, 2013). However, surface sterilization by UV might not be the best decontamination method when using BDMs for laboratory culture work as microbes could remain in between polymer layers where UV might not reach. A dominance of Actinobacteria was seen in uninoculated treatments which results due to contamination from the plastics. Actinobacteria are morphologically diverse ranging from coccoid, fragmenting hyphal forms to those with a highly differentiated branched mycelium. Many of these bacteria also produce external spores that are resistant to UV (ultraviolet) light and dehydration, which could explain why despite the long UV exposure of the BDMs prior to the experiment start, the microbes still survived (Dittmann et al., 2015, Dineen et al., 2010). Bacilli also appear in high relative abundance in un-inoculated samples. Various species of Bacillus, including B. subtilis, are known to produce dormant spores which are 5 to 50 times more resistant to UV radiation than growing cells (Setlow, 2001). In the present experiment, the shaking of the culture bottles at 30°C might have released the microbes trapped between polymer layers and caused them to thrive. Since high temperatures in autoclaves would alter structural properties of the polymers, other nonthermal sterilization options should be explored in future studies such as gamma irradiation and ethylene oxide gas sterilization.

Differences in CO2 respiration between inoculated and un-inoculated samples as seen for 3-week incubation (Organix and Naturecycle) cease to exist over 6-week time which might be due to growth of contaminant organisms in the plastics. However, since the dominant microbial taxa captured on inoculated BDMs differed in composition than those on un-inoculated BDMs (Figure 4.4a) for both 3 week and 6 week incubation, it suggests that soil microbes were respiring mulch carbon in inoculated microcosms, and that it was not solely due to contaminant microbes.

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Furthermore, isolates from 6-week inoculated culture bottles included taxa that appeared in the metagenomic profile of inoculated bottles, such as Pseudomonas sp, belonging to Gammaproteobacteria (Table A.4.13) suggesting that the strains were possibly derived from the soil inoculum and that soil microbes can outcompete contaminant microbes if present on BDMs. However, a 6-week time period is probably not sufficient to cause any major shifts in microbial community composition on the BDMs (Figure 4.4a).

Confocal microscopic images show both live and dead bacterial cells on inoculated BDMs suggesting possible biofilm formation on BDM surface by soil microbes (Bayles, 2007). Dense colony formation on PBAT and starch containing Naturecycle plastic was seen on agar plates as well as in confocal images (Figure A.4.2, A.4.4) suggesting that Naturecycle could be more amenable to degradation compared to other BDMs exposed to similar controlled conditions. Lower diversity estimates for Naturecycle coupled with the high CO2 evolved supports this argument. Overall, for the enrichment cultures, treatment differences largely depended on whether samples received soil inoculum. Treatment differences in bacterial gene abundances for both inoculated incubations suggest that the composition of BDMs might play a role in enriching for microbes on the BDM surface. Both richness and diversity indices were significantly higher for inoculated compared to uninoculated microcosms for 3- and 6- week incubations when treatments were analyzed together. Taken together, these results suggest that when soil inoculum is added, there appears to be a successful colonization of microbes on BDMs which select for specific taxa.

Higher relative abundances of Sphingobacteria, Gammaproteobacteria, Alphaproteobacteria and Betaproteobacteria on inoculated BDMs compared to un-inoculated samples indicate a successful enrichment of these taxa. Furthermore, starch containing BioAgri and Naturecycle had similar enriched taxa, whereas PLA containing Organix and PLA/PHA experimental films had identical colonizers (Figure 4.4a). This would be expected as microbes utilizing starch would employ different mechanisms than those that can utilize PLA. The major component shared among the mulches BioAgri, Naturecycle and Organix is PBAT. PBAT film surfaces have previously been reported to be enriched in Proteobacteria such as Hyphomicrobium, Caenimonas (Muroi et al., 2016). Flavobacteria and Sphingobacteria had higher relative abundance on starch containing films Naturecycle and BioAgri compared to PLA containing films, Organix and PLA/PHA (Figure 4.4, Figure A.4.7). Previous studies have revealed β-glucuronidase activity and starch hydrolyzing

159

capabilities of Flavobacteria (Petzel and Hartman, 1986). Furthermore, Flavobacteria appears to display highly specialized abilities in utilizing oligo- and polysaccharides at very low concentrations (van der Kooij and Hijnen, 1983). The enrichment of fungi on field-weathered plastics compared to bulk soil is supported by previous literature (Moore-Kucera et al. 2014), where fungal isolates were obtained from buried mulch pieces in soil and further used to assess mulch degradation in pure culture. Most of the cultured fungal isolates were within the family Trichocomaceae (Moore-Kucera et al., 2014). For the present field study, even though we found Trichocomaceae in our fungal libraries, they were not important in contributing to the overall differences between treatments and locations, and neither were they a dominant taxa in terms of their relative abundances. However, previous studies have assessed degraders in pure cultures (Moore-Kucera et al., 2014) where only a few isolates can achieve detectable mulch degradation. This further justifies the need to analyze fungal libraries employing next generation sequencing technology to broaden the captured community. Furthermore, with bulk soil, mixed results have been obtained with regard to fungal enrichment after BDM incorporation in soil, with some showing fungal enrichment in soil (Li et al., 2014b, Ma et al., 2016, Muroi et al., 2016, Rychter et al., 2006) and some other bacterial enrichment (Li et al., 2014b). The interaction effects seen for 16S/ITS ratios (Table A.4.16) between soil and plastic communities, location (TN and WA), and treatment is expected as location and soil/plastic environment would harbor significantly different microbial communities and hence would have an effect of the treatments tested. This would also confound the effects of treatment on richness and diversity index which were thus all evaluated as simple main effects.

Differences in bacterial communities between field-weathered BDMs and bulk soil for TN and WA suggests preferential enrichment of specific taxa on field-weathered BDMs (Figure 4.7 a, b). Influence of different climates and soil parameters could have resulted in the selection of different communities in soil and, further, on field-weathered plastics in different locations (Bandopadhyay et al., under review). The differences in soil bacterial communities in TN were correlated to pH, percent saturated elements such as K, Ca, and H whereas, in WA, the differences in soil communities were correlated with gravimetric soil moisture and organic matter content (Bandopadhyay et al., under review). The bacterial taxa that contributed up to 40% of the differences in soil bacterial communities between TN and WA included Acidobacteria_Gp6,

160

Planctomycetaceae (unclassified at genus level), Acidobacteria_Gp4, Spartobacteria, Acidobacteria_Gp7, and Xanthomonadaceae (unclassified at genus level). The bacterial taxa enriched on field-weathered BDMs compared to PE included Methylobacterium, Arthrobacter and Sphingomonas. This suggests that microbes belonging to these genera could include potential BDM degraders, which should be further explored for mulch degrading potential. Some future work with isolates belonging to these genera could include quantification of genes expressing depolymerase enzymes, purification and characterization of enzymes involved in degradation of mulch constituents etc. Such information would further our knowledge pertaining to metabolic pathways of BDM degradation which might be used to improve BDM formulation. Bacterial richness estimates were significantly higher on PE compared to BDMs for both TN and WA. This would be expected as the total number of species would ideally decrease on field-weathered BDMs as they get colonized by specific microbial degraders which outcompete others in the field over time. This would mean positive selection of microbial taxa which are utilizing mulch carbon. On the other hand, PE being non-biodegradable would host a wide range of microbes with no apparent selection, explaining the increased richness estimates.

Higher fungal richness estimates are seen in WA compared to TN which is supported by the qPCR results showing significantly decreased bacterial to fungal ratios in WA compared to TN. Fungal communities captured on field-weathered BDMs reveal significantly different communities on Weedguard (cellulose) mulch compared to BDMs and PE. Agaricomycetes belonging to division Basidiomycota was found to be predominant in both TN and WA on Weedguard mulch. Basidiomycetous fungi are some of the most potent degraders of cellulose growing on dead wood or litter. For the degradation of cellulose, Basidiomycetes utilize a set of hydrolytic enzymes typically composed of endoglucanase, cellobiohydrolase and β-glucosidase. Pezizomycetes belonging to class Ascomycota was also significantly higher on Weedguard compared to other mulches. Cellobiose dehydrogenase is an extracellular enzyme produced by both Ascomycetes and Basidiomycetes which efficiently oxidizes cellobiose and degrades not only cellulose but also xylan and lignin in the presence of H2O2 and chelated Fe ions (Baldrian and Valášková, 2008, Henriksson et al., 1995). On the BDMs there was a dominance of Dothideomycetes and Sordariomycetes in TN both belonging to division Ascomycota and Dothidiomycetes and Tremellomycetes (division Basidiomycota) in WA. The distinct fungal community seen on Weedguard mulch compared to other mulches, in both TN and WA, would be expected given the

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wide array of fungi that preferentially degrade cellulose compared to starch and polyester compounds. Interestingly, studies have reported the presence of the ciliate group Conthreep on polyethylene terephthalate plastics (Oberbeckmann et al., 2016) as well as polyethylene microplastics (Kettner et al., 2019), indicating the need to explore possible depolymerization mechanisms in organisms of the Eukaryota domain.

Taken together, the results provide the identification of dominant soil microbial taxa that colonize biodegradable plastic mulch films in lab and field conditions. An enriched bacterial community was observed on BDMs compared to PE films in the field study. The enrichment cultures demonstrate that microbes colonize these plastics based on their composition, revealing potential BDM degraders. Future research should focus on in-situ buried mulch pieces for improved characterization of microbial communities on BDMs in the field over the long term.

Acknowledgements

Fang Liu from the Department of Entomology at the University of Tennessee helped with troubleshooting in R. Dr. Arnold Saxton helped with statistical advice. This work was supported by USDA award 2014-51181-22382 to JMD.

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Appendix IV

Table A.4.1: Manufacturers, major constituents, and physicochemical properties of the unweathered mulches used in the study. Biobased content data was provided by the manufacturers. Data reported from Hayes et al., 2017. Mulches Manufacturer Major constituents Weight Thickness Elongation Contact Total Biobased

(g m-2) (µm) (%) angle (º) carbon content

(%) (%)

BioAgri® BioBag Mater-Bi® grade 18.0 26 260 87.6 57.6 20-25

Americas, Inc., EF04P (blend of

Dunedin, FL starch and PBAT)

Naturecycle Custom Blend of starch and 25.4 48 213 69.2 54.8 ˜ 20

Bioplastics, polyesters

Burlington, WA

Organix A.G. Organix BASF®ecovio® 17.8 20 273 86.2 51.4 10-20

Film™ Solutions, Maple grade M2351(blend

Grove, MN of PLA and PBAT)

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Table A.4.1: Continued

Mulches Manufacturer Major constituents Weight Thickness Elongation Contact Total Biobased

(g m-2) (µm) (%) angle (º) carbon content

(%) (%)

Experimental Metabolix Inc., 88.4% MD05-1501 25.0 33 247 67.8 47.5 86

PLA/PHA Cambridge, MA (56% Ingeo PLA,

24% Mirel™

amorphous PHA,

15%

CaCO3 and 5%

plasticizer and

processing

additives), 10.0%

Techmer

PLA M91432 (20%

carbon black

in PLA 3052) and

1.6% PLA

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Table A.4.1: Continued

Mulches Manufacturer Major constituents Weight Thickness Elongation Contact Total Biobased

(g m-2) (µm) (%) angle (º) carbon content

(%) (%)

WeedGuardPlus® Sunshine Paper Cellulose 240 479 6.4 <10 46.0 100

Co., Aurora, CO

Polyethylene Filmtech, Linear low-density 25.4 47 578 79.3 82.9 < 1

Allentown, PA polyethylene

PBAT: Polybutylene co-adipate co-terephthalate; PLA: Polylactic acid; PHA: Poly(hydroxyalkanoate)

170

Esterase enzyme activity in spent T stat media of enrichment culture (activity expressed in µg p- nitrophenol released µl -1 minute -1) BioAgri 3 week 1.06 ± 0.23 -4.26* BioAgri 6 week 3.05 ± 0.31 PLA+PHA 3 week 2.14 ± 0.06 -4.50* PLA+PHA 6 week 2.73 ± 0.13 Organix 3 week 1.80 ± 0.46 0.25 Organix 6 week 1.46 ± 0.87 Naturecycle 3 week 1.59 ± 0.35 -2.13 Naturecycle 6 week 2.63 ± 0.21

Table A.4.3: Results of three-way ANOVAs for bacterial gene copies obtained from lab incubation study (type III tests reported).

Factor F value Time (3 weeks and 6 weeks) 0.603 Inoculation (inoculated, uninoculated) 1.291 Treatment (BioAgri, Naturecycle, 1.995 PLA/PHA, Organix) Time:Inoculation 1.241 Time:Treatment 0.204 Inoculation:Treatment 1.507 Time:Inoculation:Treatment 1.010

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Table A.4.4: Results of three-way ANOVAs for fungal gene copies obtained from lab incubation study (type III tests reported).

Factor F value Time (3 weeks and 6 weeks) 0.141 Inoculation (inoculated, uninoculated) 3.03 Treatment (BioAgri, Naturecycle, 3.548* PLA/PHA, Organix) Time:Inoculation 0.006 Time:Treatment 1.902 Inoculation:Treatment 5.305** Time:Inoculation:Treatment 1.421

Table A.4.5: Results from three-way ANOVAs for 16S/ITS ratios obtained from lab incubation study (type III tests reported).

Factor F value Time (3 weeks and 6 weeks) 0.381 Inoculation (inoculated, uninoculated) 2.352 Treatment (BioAgri, Naturecycle, 1.605 PLA/PHA, Organix) Time:Inoculation 0.125 Time:Treatment 1.92 Inoculation:Treatment 4.011* Time:Inoculation:Treatment 1.805

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Table A.4.6: Summary statistics for one-way ANOVAs testing differences between 3 weeks and 6 weeks incubation times for 16S, ITS and 16S/ITS ratios (inoculated and uninoculated).

Treatment 16S (F value) ITS (F value) 16S/ITS ratio (F value) Inoculated BioAgri 4.279 0.761 2.24 PLA/PHA 4.805 7.162 7.541* Naturecycle 0 0.038 0.16 Organix 7.561* 2.585 2.847 Uninoculated BioAgri 0.431 0.129 0.027 PLA/PHA 0.141 0.82 2.171 Naturecycle 1.621 0.127 0.028 Organix 1.509 257.6*** 4.565

Table A.4.7: Mean richness (number of observed OTUs) and diversity (inverse Simpson index) for plastic associated bacterial communities in laboratory microcosms. stddev:standard deviation.

mean Mean inverse Simpson Treatment Time richness±std dev ±std dev BioAgri-Inoculated 3 weeks 58 ± 4 6.355 ± 0.523 BioAgri-Inoculated 6 weeks 66 ± 5 7.385 ± 2.22 BioAgri-Uninoculated 3 weeks 34 ± 19 3.336 ± 0.659 BioAgri-Uninoculated 6 weeks 28 ± 8 3.982 ± 0.782 Naturecycle-Inoculated 3 weeks 59 ± 2 6.553 ± 2.923 Naturecycle-Inoculated 6 weeks 64 ± 2 8.907 ± 2.246 Naturecycle-Uninoculated 3 weeks 18 ± 4 2.137 ± 0.873 Naturecycle-Uninoculated 6 weeks 21 ± 5 3.061 ± 1.083 Organix-Inoculated 3 weeks 56 ± 8 7.447 ± 1.221 Organix-Inoculated 6 weeks 58 ± 6 10.15 ± 1.318 Organix-Uninoculated 3 weeks 30 ± 16 2.563 ± 1.142 Organix-Uninoculated 6 weeks 49 ± 2 5.279 ± 1.221 PLA+PHA-Inoculated 3 weeks 60 ± 4 12.75 ± 1.131 PLA+PHA-Inoculated 6 weeks 59 ± 1 12.15 ± 2.19 PLA+PHA-Uninoculated 3 weeks 23 ± 5 3.025 ± 1.023 PLA+PHA-Uninoculated 6 weeks 36 ± 7 3.271 ± 0.649

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Table A.4.8: Results from three-way ANOVAs for richness estimates obtained from lab incubation study (type III tests reported).

Factor F value Time (3 weeks and 6 weeks) 1.574 Inoculation (inoculated, uninoculated) 14.021*** Treatment (BioAgri, Naturecycle, 0.133 PLA/PHA, Organix) Time:Inoculation 2.615 Time:Treatment 0.365 Inoculation:Treatment 1.589 Time:Inoculation:Treatment 2.646

Table A.4.9: Results from three-way ANOVAs for inverse Simpson estimates obtained from lab incubation study (type III tests reported).

Factor F value Time (3 weeks and 6 weeks) 0.721 Inoculation (inoculated, uninoculated) 6.193* Treatment (BioAgri, Naturecycle, 12.372*** PLA/PHA, Organix) Time:Inoculation 0.05 Time:Treatment 1.528 Inoculation:Treatment 5.778** Time: Inoculation:Treatment 0.304

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Table A.4.10: Summary statistics from one-way ANOVAs testing differences in richness and diversity metrics in lab enrichments between mulch treatments, inoculated and uninoculated samples and time of incubation.

Treatment 3 weeks (F value) 6 weeks (F value) inoculated uninoculated inoculated uninoculated richness 0.306 0.908 2.551 11.78** inverse Simpson index 9.433** 0.934 2.946 3.257 Inoculated and uninoculated samples 3 weeks (F value) 6 weeks (F value) richness 77.34*** 20.21** inverse Simpson index 12.94* 26.14** Time (3 week and 6 weeks) inoculated uninoculated richness 2.807 0.977 inverse Simpson index 0.572 4.017

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Table A.4.11: PERMANOVA statistics to test significant differences in microbial community composition a) for plastic associated bacterial communities in consortia, b) for field-weathered plastic and soil associated bacterial communities in TN and WA c) for fungal communities in TN and WA. PERMANOVA computed with Adonis function in R vegan package (***p ≤ 0.001). a)

Time (between 3 weeks and 6 weeks F value incubation) PLA/PHA uninoculated 0.82 PLA/PHA inoculated 0.563 Organix uninoculated 1.774 Organix inoculated 2.207 Naturecycle uninoculated 0.993 Naturecycle inoculated 0.827 BioAgri uninoculated 1.153 BioAgri inoculated 0.912 Treatment (inoculated only) 3 weeks 12.488*** 6 weeks 10.119*** Inoculated and uninoculated samples 3 weeks 7.815*** 6 weeks 7.316*** b)

Soil and plastic community type F value TN 59.551*** WA 107.77*** Location (TN and WA) Soil 45.458*** Plastic 9.856*** Treatment Plastic (TN) 5.172*** Plastic (WA) 2.833*** Soil (TN) 1.035 Soil (WA) 1.133

176

Table A.4.11: Continued

Location F value

Field weathered plastic 16.666*** Treatment

TN field weathered plastic 6.782*** WA field weathered plastic 10.419***

Table A.4.12: Summary statistics for one-way ANOVAs testing differences between inoculated and uninoculated microcosms for 16S, ITS and 16S/ITS ratios (3 weeks and 6 weeks)

Treatment 16S (F value) ITS (F value) 16S/ITS ratio (F value) 3 weeks BioAgri 3.096 3.731 4.721 PLA/PHA 4.078 0.871 0.996 Naturecycle 17.32** 22.53** 12.7* Organix 2.305 9.901* 0.994 6 weeks BioAgri 0.16 2.831 6.024 PLA/PHA 8.781* 11.52* 39.39** Naturecycle 20.69** 8.559* 11.6* Organix 1.139 1.218 0.398

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Table A.4.13: Isolated strains from enrichment cultures.

BDM Possible identity (Alignment Sequence identity with NCBI using 8F and 1492R primers) Database Streptomyces sp. 1 97% Naturecycle Streptomyces sp. 2 98% Arthrobacter sp. 95% Streptomyces sp. 3 99%

Microbacterium sp. 98%

Streptomyces sp. 4 97% Variovorax sp. 99% Arthrobacter sp. 97% BioAgri Pseudomonas sp. 96% Streptomyces sp. 98% Organix Devosia sp. 93%

Table A.4.14: Summary statistics for one-way ANOVAs testing differences between treatments for 16S, ITS and 16S/ITS ratios for 3 weeks and 6 weeks incubations (inoculated and uninoculated)

16S (F value) ITS (F value) 16S/ITS ratio (F value) Inoculated 3 weeks 9.35 ** 4.504 2.47 6 weeks 22.19*** 1.846 3.475 Uninoculated 3 weeks 1.368 4.218* 2.55 6 weeks 0.466 8.619** 20.82***

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Table A.4.15: Tukey post hoc tests (α = 0.05) showing pairwise comparison of 16S and ITS copy numbers between inoculated mulch treatments for only those time points which showed significant differences by one-way ANOVA.

16S or ITS abundance Pairwise comparison (Tukey post hoc) P value 16S copy numbers/3 Naturecycle -BioAgri 0.056 week incubation Organix -BioAgri 0.45 (inoculated) PLA+PHA -BioAgri 0.15 Organix -Naturecycle 0.007 PLA+PHA -Naturecycle 0.89 PLA+PHA -Organix 0.02 16S copy numbers/6 Naturecycle -BioAgri 0.004 week incubation Organix -BioAgri 0.19 (inoculated) PLA+PHA -BioAgri 0.04 Organix -Naturecycle 0.000 PLA+PHA -Naturecycle 0.33 PLA+PHA -Organix 0.002 ITS copy numbers/3 Naturecycle -BioAgri 0.19 week incubation Organix -BioAgri 0.87 (inoculated) PLA+PHA -BioAgri 0.23 Organix -Naturecycle 0.07 PLA+PHA -Naturecycle 0.999 PLA+PHA -Organix 0.08

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Table A.4.16: Results from a mixed model ANOVA for 16S/ITS ratios obtained from field study (type III tests reported).

Factor F value Treatment (BioAgri, Naturecycle, 1.59 PLA/PHA, Organix, Weedguard, Polyethylene) Type (Soil and Plastic) 2704.66*** Location (TN and WA) 57.84*** Treatment:Type 9.26*** Treatment:Location 0.72 Type:Location 44.98*** Treatment:Type:Location 2.75*

Table A.4.17: Results from a mixed model ANOVA for richness estimates obtained from field study (type III tests reported).

Factor F value Treatment (BioAgri, Naturecycle, 7.31*** PLA/PHA, Organix, Weedguard, Polyethylene) Type (Soil and Plastic) 355.95*** Location (TN and WA) 0.03 Treatment:Type 14.63*** Treatment:Location 0.96 Type:Location 21.13*** Treatment:Type:Location 0.35

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Table A.4.18: Mixed model ANOVA for inverse Simpson estimates obtained from field study (type III tests reported).

Factor F value Treatment (BioAgri, Naturecycle, 0.999 PLA/PHA, Organix, Weedguard, Polyethylene) Type (Soil and Plastic) 33.019*** Location (TN and WA) 21.970** Treatment:Type 1.673 Treatment:Location 1.908 Type:Location 24.316*** Treatment:Type:Location 1.062

Table A.4.19: One-way ANOVA testing differences in 16S/ITS qPCR ratios between locations (for field weathered plastics), treatments of field weathered plastics in TN and WA, and between bulk soil and field weathered plastic.

Factor F value Location (field weathered plastics) 57.46*** TN WA Type (soil, plastic) 174.2*** 377.9*** six field weathered plastic 1.402 treatments 4.564** (BioAgri, Naturecycle, PLA/PHA Organix, Polyethylene, Weedguard)

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Table A.4.20: Summary statistics from one-way ANOVAs testing differences in richness and diversity metrics for field enrichment study by mulch treatments (plastic and soil associated bacterial communities), location (plastic and soil associated communities) and soil and plastic communities (TN and WA).

Treatment Plastic (F value) Soil (F value) TN WA TN WA richness 7.374** 5.567** 2.204 0.226 inverse Simpson 0.239 2.538 1.755 1.133 Location Plastic (F value) Soil (F value) richness 1.105 10.84** inverse Simpson 17.41** 0.017 Soil and plastic TN (F value) WA (F value) richness 50.36 *** 16.52** inverse Simpson 0.976 21.2***

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Table A.4.21: Mean richness (number of observed OTUs) and diversity (inverse Simpson index) for plastic and soil associated bacterial communities in TN and WA. stddev:standard deviation.

Mean richness±std Mean inverse Location Treatment Type dev Simpson ±std dev TN BioAgri Plastic 209 ± 14 10.028 ± 3.322 WA BioAgri Plastic 215 ± 32 13.443 ± 4.481 TN Naturecycle Plastic 210 ± 22 12.397 ± 3.833 WA Naturecycle Plastic 222 ± 20 14.055 ± 2.117 TN Organix Plastic 223 ± 17 11.555 ± 2.441 WA Organix Plastic 251 ± 28 21.138 ± 3.904 TN PLA/PHA Plastic 235 ± 17 9.641 ± 1.499 WA PLA/PHA Plastic 249 ± 6 14.625 ± 4.889 TN Polyethylene Plastic 265 ± 13 9.48 ± 5.066 WA Polyethylene Plastic 286 ± 14 22.348 ± 5.921 TN Weedguard Plastic 194 ± 8 10.041 ± 6.677 WA Weedguard Plastic 211 ± 15 19.354 ± 3.185 TN BioAgri Soil 314 ± 15 10.046 ± 0.849 WA BioAgri Soil 283 ± 4 10.01 ± 0.679 TN Naturecycle Soil 305 ± 9 10.466 ± 0.681 WA Naturecycle Soil 289 ± 5 9.246 ± 1.002 TN Organix Soil 303 ± 7 9.167 ± 0.477 WA Organix Soil 285 ± 11 9.293 ± 1.389 TN PLA/PHA Soil 298 ± 3 10.206 ± 1.287 WA PLA/PHA Soil 289 ± 8 10.001 ± 1.229 TN Polyethylene Soil 282 ± 15 8.875 ± 0.804 WA Polyethylene Soil 285 ± 11 10.348 ± 1.104 TN Weedguard Soil 302 ± 19 10.955 ± 1.639 WA Weedguard Soil 287 ± 5 11.154 ± 1.374

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Table A.4.22: Mean richness (number of observed OTUs) and diversity (inverse Simpson index) for field weathered plastic associated fungal communities in TN and WA. stddev:standard deviation.

Location Treatment Mean richness±std dev Mean inverse Simpson ±std dev TN BioAgri 55 ± 4 5.413 ± 1.596 WA BioAgri 87 ± 6 12.97 ± 3.891 TN Naturecycle 49 ± 3 2.951 ± 0.962 WA Naturecycle 81 ± 5 12.29 ± 1.036 TN Organix 55 ± 7 6.058 ± 1.401 WA Organix 84 ± 10 9.886 ± 1.657 TN PLA/PHA 52 ± 14 6.158 ± 3.467 WA PLA/PHA 85 ± 6 12.9 ± 1.219 TN Polyethylene 45 ± 8 8.173 ± 1.303 WA Polyethylene 81 ± 8 11.19 ± 2.349 TN Weedguard 44 ± 5 6.533 ± 1.562 WA Weedguard 60 ± 10 5.516 ± 2.158

Table A.4.23: Summary statistics from one-way ANOVAs testing differences in richness and diversity metrics for field enrichment study by mulch treatments and location (plastic associated fungal communities).

Treatment TN (F value) WA (F value) richness 0.755 6.132** inverse Simpson 1.236 6.31** Location F value richness 45.16*** inverse Simpson 13.2**

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Table A.4.24: Percent contribution of individual taxa identified using SIMPER contributing up to 40% variation in microbial community composition between soil and field weathered mulch associated bacterial communities in TN and WA.

Location OTU Genus % number contribution

TN 28 Methylobacterium 14

1 Bacteria_unclassified 11 93 Bacillus 5 20 Sphingomonas 6 71 Arthrobacter 4

38 Deinococcus 3

WA 38 Deinococcus 8 1 Bacteria_unclassified 8 29 Hymenobacter 7 20 Sphingomonas 7

28 Methylobacterium 5

71 Arthrobacter 4 35 Comamonadaceae_unclassified 4

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Figure A.4.1: Thermograms (TGA)(A &C) and differential thermograms (DTG)(B & D) of BioAgri and Organix showing percent weight remaining and rate of weight loss after heating the plastics at a constant rate (10°C/min till 600°C). “Indoor Storage” refers to unweathered off-the-roll mulch films which were kept in a storage cabinet in the dark at room temperature. Peak 1 is attributable to starch in BioAgri, Peak 2 is attributable to PBAT in Organix and BioAgri.

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BioAgri– Inoculated BioAgri– Un-inoculated

Naturecycle– Inoculated Naturecycle- Un- inoculated

Figure A.4.2: Confocal images of BDMs after 3 weeks enrichment using a Leica™white light laser confocal system. Staining was done using a LIVE/DEAD® BacLight™ Bacterial Viability Kit. Green: live cells, Red: dead cells. Scale bars=25 µm.

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Figure A.4.3: Alpha diversity measures calculated for lab enriched plastic-ome communities in a) Inoculated and b) Uninoculated samples. All reads were scaled to even depth. Observed: Observed number of OTUs. se: standard error.

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Figure A.4.4: Isolation of BDM degrading microbes on M9 minimal agar plates with plastic as sole carbon source. Inoculum was taken from culture bottles inoculated for 6 weeks. Each picture shows (left to right) negative control (no plastic, no glucose), treatment (with plastic), positive control (media with glucose).

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Figure A.4.5: Alpha diversity measures calculated for field weathered plastic-associated communities in a) TN and b) WA. All reads were scaled to even depth. Observed: Observed number of OTUs. se: standard error.

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Figure A.4.6: Alpha diversity measures calculated for field weathered plastic-associated fungal communities in TN and WA. All reads were scaled to even depth. Observed: Observed number of OTUs. se: standard error.

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Figure A.4.7: Microbial taxa distribution (genus level) on mulch treatments for the enrichment cultures conducted for 3 weeks and 6 weeks. Relative abundances above a cut- off level of 5% are indicated. “unclassified" denote taxa with relative abundance above the cut-off level of 5%, but that could not be classified.

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CHAPTER V EFFECT OF ORGANIC AND INORGANIC NITROGEN AMENDMENTS ON DEGRADATION OF BIODEGRADABLE PLASTIC MULCH FILMS

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A version of this chapter is being prepared for publication:

Sreejata Bandopadhyay, Marie English, Marife B. Anunciado, Mallari Starrett, Jialin Hu, Sean Schaeffer, Douglas Hayes, Jennifer DeBruyn. “Effect of organic and inorganic nitrogen amendments on decomposition of biodegradable plastic mulch films”.

The author contributions are as follows: Conceived and designed the experiments: SB JMD DGH SMS. Performed the experiments: SB, ME, MBA, MS and JH. Analyzed the data: SB. Wrote the paper: SB and JMD.

Abstract

Biodegradable mulch films (BDMs) are a sustainable and promising alternative to non- biodegradable polyethylene mulches used in crop production systems. Nitrogen amendments in the form of fertilizers are used by growers to enhance soil and plant-available nutrients, however, there is limited research on how these additions impact biodegradation of BDMs tilled into soils. A four-month soil microcosm study was used to investigate the effects of nitrogen application on biodegradable mulch decomposition; soil bacterial, fungal and ammonia oxidizing microbial abundance; soil nitrogen pools and enzyme activities. Soils from two diverse climates of Knoxville, TN and Mount Vernon, WA were used in the study with BioAgri mulch film made of Mater-Bi®; a bioplastic raw material containing starch and poly(butylene adipate-co- terephthalate) (PBAT). Results show that both organic and inorganic nitrogen amendments inhibited mulch decomposition, soil bacterial abundances and enzyme activities. The greatest inhibition of biodegradation was observed with urea amendment in TN and ammonium nitrate amendment in WA. Increased amoA gene abundances were observed for all nitrogen treatments and controls in TN, but they were reduced for all treatments and controls in WA. However, a significantly higher nitrate and lower ammonium concentration was seen for all nitrogen treatments compared to controls in both TN and WA. This study suggests that addition of nitrogen, particularly inorganic amendments, could have a negative effect on mulch decomposition but that mulch decomposition does not negatively affect soil nitrification activity.

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Introduction

Plastic mulch films provide several key benefits to crop production systems. In addition to conserving soil moisture and moderating soil temperatures, mulches suppress weed growth and limit contamination of crops by soil (Hayes et al., 2012, Kasirajan and Ngouajio, 2012). The development of such a conducive microclimate enhances crop yields (Miles et al., 2012, Moreno and Moreno, 2008). Multiple crops are grown using mulch films such as wheat (Chakraborty et al., 2008, Li et al., 2005), cotton (Chen et al., 2014) and vegetables such as okra, squash and bell peppers (Mahadeen, 2014, Filipović et al., 2016).

Most of the mulch films used in agriculture are made of polyethylene (PE). These films are poorly biodegradable and hence require disposal after the growing season, typically ending up in landfills (Hayes et al., 2012). Fragments of PE mulches may also remain in the soil and cause potential harm to terrestrial ecosystems by accumulating over long periods of time. This can pose additional problems when transported into aquatic systems (Bandopadhyay et al., 2018). Biodegradable mulch films (BDMs) provide a sustainable alternative to PE films because they are made of biodegradable polymers such as starch, polyesters and lignin. BDMs are meant to be tilled into soils where microbial activity break them down in the field over time into carbon dioxide, water and microbial biomass. Additionally, if using BDMs, growers could save on labor costs involved with PE mulch retrieval from fields.

One concern that growers have is the unpredictable breakdown of BDMs in the field, with some mulches having faster degradation rates than others. Both above-soil and in-soil degradation rates differ among different BDMs. Even though there is no standard currently available pertaining to BDM degradation in field conditions, there are several standards that exist which have been used to certify biodegradability of BDMs. ASTM International standards pertaining to biodegradable plastics include ASTM D6400 (2012) with International Organization for Standardization (ISO) having equivalent standards (Dentzman, 2019). The ASTM D6400 tests biodegradation under industrial composting conditions and is the most commonly cited standard in reference to BDMs. The European committee for Standardization (CEN) released the European Standard EN 17033 in January 2018, which was the first standard to certify plastic mulch films as “biodegradable” in ambient soil (EN 17033:2018). Both ASTM D6400 and EN 17033 address inherent

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biodegradability and ecotoxicity. For EN 17033, a standardized laboratory test mimicking soil conditions is to be used, such as ASTM D5988, whereas ASTM D6400 employs the standardized test method ASTM D5338 which utilizes a laboratory test simulating industrial composting conditions. While the EN 17033 standard requires 90% biodegradation in two years using ASTM D5988 test, the ASTM D6400 standard requires 90% biodegradation within 6 months by the ASTM D5338 test. The purpose of the ASTM D6400 and EN 17033 standards, however, is not to guarantee 90% biodegradability in the field, but to validate claims of biodegradability for commercial products. None of these standards are specific to BDMs used in field conditions where active farming is in progress. Different farms have highly variable crop, soil and management conditions, and these factors collectively determine the extent of biodegradation taking place (Hayes, 2018). Thus, while BDMs may achieve 90% biodegradability in two years and be certified as biodegradable using standardized testing procedures in the laboratory, in the field, complete biodegradation may not always be observed.

Consequently, understanding the biodegradation rate of BDMs in the field is critical to support and propagate use of BDMs within the farming community. The possible accumulation of BDM fragments in soil over time could impact soil infiltration rates further affecting water uptake by plants and microbes. Even though BDMs are a small carbon input compared to cover crop residues (~1-6% of crop residues) (English et al., 2019), BDM fragments in soil can affect ecosystem functions over the long term. Short term studies of two years demonstrate that BDMs have comparable effects to PE films and do not alter soil quality and soil biological functions significantly in that time period (Sintim et al., 2019). However, effective ways to facilitate mulch decomposition in the soil is warranted in a way that would not adversely affect soil health.

Combining plastic mulching with fertilizer application have been known to increase crop yield almost two fold (Li et al., 2003, Lamont, 2005). Fertilizers have long been used in the field by growers to improve soil functioning and crop yields by providing essential nutrients to plants. Fertilizers are typically added to soils in the form of nitrogen, phosphorus, and potassium in different proportions. Common inorganic nitrogen fertilizers include ammonium nitrate, urea, ammonium sulphate and diammonium phosphate and organic fertilizers commonly include amendments such as manure and straw. Even though addition of organic fertilizers can increase bacterial to fungal ratio in soil (Marschner et al., 2003) and rapidly increase CO2 evolution (Ajwa

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and Tabatabai, 1994), their nutrients are slowly released and hence often not immediately available to plants. Inorganic fertilizers act rapidly, but can accumulate toxic concentration of salts in soils and vegetables if over-applied (Santamaria, 2006). When fertilizers are used in soils, these nutrient pulses could shift soil C:N ratios, causing enhanced or reduced decomposition of organic material in the soil (Marschner et al., 2003).

It has long been recognized that soil mineral elements, particularly nitrogen, plays a critical role in assessing the rate of organic matter decomposition. Increased organic matter decomposition due to nitrogen deposition is well documented in the literature (Boxman et al., 1995, Vitousek et al., 1997). Several studies have reported significantly faster decomposition rates in response to increased external nitrogen availability (Hunt et al., 1988, Hobbie, 2000, Hobbie and Vitousek, 2000, Knorr et al., 2005). Nitrogen additions are also known to accelerate decomposition of light soil carbon fractions (Neff et al., 2002). Even though, most studies point towards a positive effect of nitrogen addition on decomposition of litter and organic matter, a few studies also point towards a negative effect of nitrogen on microbial activity and organic matter decomposition (Fog et al., 1988, Fang et al., 2007, Janssens et al., 2010). Thus, mixed results are encountered when looking at the effects of nitrogen on stability and turnover of soil carbon pools.

Some environmental factors that affect biodegradation rates of BDMs are considered to be 1) exposure conditions such as temperature, moisture and pH, and 2) physicochemical properties of the polymeric materials (Kijchavengkul et al., 2008). Other environmental factors affecting biodegradation rates include aerobic vs. anaerobic conditions. Under aerobic conditions, microbes will use oxygen and consume carbon from the BDM as a food source, releasing carbon dioxide and water. Under anaerobic conditions, when oxygen supply is depleted, aerobic microbes change their metabolism rate eventually reducing biodegradation rates. On the other hand, anaerobic microbes consume carbon from the BDM and release methane and carbon dioxide. Microbes can consume carbon from polymers at different rates, thus biodegradation of BDMs in aerobic and anaerobic conditions may vastly differ (Kale et al., 2007). However, information regarding the impacts of soil-specific factors such as nitrogen content on BDM degradation processes is sparse, though it is widely believed that nitrogen-limited microorganisms may not produce esterases that degrade BDMs given that their secretion and hydrolysis of polyesters does not increase nitrogen availability (Sander, 2019). One study showed better correlation of the degree of degradation of

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biodegradable plastics with the total nitrogen content of soil than with the total carbon content (Hoshino et al., 2001). Availability of carbon substrates is known to regulate enzyme production inhibiting or enhancing polymer degradation rates. For instance, production of cutinase (an important class of enzymes degrading BDMs) in Fusarium solani was shown to be suppressed by glucose but induced by the products of cutin hydrolysis (Lin and Kolattukudy, 1978, Murphy et al., 1996).

The objectives of the study are to assess if 1) addition of nitrogen amendments in the form of organic and inorganic treatments stimulates the decomposition of biodegradable mulch films and 2) addition/degradation of BDMs has an impact on soil nitrification activity. Since the polymers used in BDMs are composed of C, O, and H, microbes need to acquire nitrogen from the surrounding soil for growth. Therefore, microbes may experience nitrogen limitation in the soil or on the surfaces of rapidly depolymerizing polymers that supply excess carbon relative to nitrogen to microbes. Thus, we hypothesized that nitrogen amendments would enhance mulch decomposition and nitrification by serving as a nutrient pulse in the system compared to treatments with no added nitrogen. For the second objective, we hypothesize that biodegradation of mulch material will have minimal effect on soil nitrification processes based on prior observations in the literature which tested mulches with similar composition as in the present study.

Materials and Methods

Site description Soils for this experiment were collected from two locations: East Tennessee Research and Education Center (ETREC), University of Tennessee, Knoxville, TN and the Northwestern Washington Research & Extension Center (NWREC), Washington State University, Mount Vernon, WA. The soil at Knoxville is a sandy loam (59.9% sand, 23.5% silt, and 16.6% clay), classified as a fine kaolinitic thermic Typic Paleudults. The soil at Mount Vernon is a silt loam (14.2% sand, 69.8% silt, and 16% clay), classified as a fine-silty mixed nonacid mesic Typic Fluvaquents. Soil and weather characteristics are included in Table 5.1.

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Table 5.1: Soil and weather characteristics in Knoxville, TN and Mount Vernon, WA (Sintim et al., 2019, Li et al., 2014).

Knoxville, TN Mt. Vernon, WA

Soil type Typic Hapludult Typic Fluvaquent

Texture 59.9% sand 14.2% sand

23.5% silt 69.8% silt

16.6% clay 16% clay

pH 6.03 6.24

-1 CEC (cmolc kg ) 7.23 9.19

Nitrate-N (mg kg-1) 20.9 4.49

P (mg kg-1) 72.6 77.4

Organic matter (%) 1.43 2.36

Mean annual precipitation (mm) 1355 831

Mean daily annual temperature (℃) 14.1 10.5

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Soil sampling Soil samples were obtained from control (unmulched) plots from a vegetable cropping field experiment comparing different mulch treatments (experimental details can be found in Ghimire et al.) Thirty soil cores were collected using soil augers (0 to 10 cm depth) from the four replicated unmulched plots in TN and WA in July 2018. Soils were collected into pre-sterilized buckets and any roots or debris were picked out. Soils from one plot were composited by homogenizing by hand and then put into Ziplock® bags. These were stored in a cooler with ice packs and transported to the lab. After being brought into the laboratory, the field-moist soil was passed through a 2–mm sieve to remove any remaining coarse rock and plant material. Soils from the four no-mulch plots at each location were then mixed together to form a homogenized soil sample. These soils were kept at room temperature for 7 days at 25ºC.

Biodegradable mulch used for incubation study A starch-PBAT (poly (butylene adipate-co-terepthalate)) blend BDM, BioAgri, made by BioBag® USA was used in this study. Field-weathered plastic mulch samples were collected at the end of the 2017 growing season at the TN field site. Mulch pieces were cleaned using a dry, clean brush to dust off soil by gentle dabbing prior to use. Plastic pieces were cut into 2 cm * 2 cm pieces and 250 mg plastic weighed in aluminum tins to be added to respective microcosms. About 25 to 30 mulch pieces were added per jar in the study. Henceforth, mulch would be referred to as plastic in the text because their intended use for this experiment was not as ground cover, but merely a source of weathered polymer material.

Laboratory incubation After incubation at room temperature, 100 g of field moist soil was weighed into 473 ml glass canning jars and adjusted to 50% water holding capacity (WHC) by adding deionized water. After adjusting WHC, jars containing soils were pre-incubated in the dark at 25ºC for an additional 5 days. The experiments consisted of two treatment factors, plastic addition (0 mg, 250 mg), and nitrogen amendments in the form of urea (CH4N2O, from Fisher Chemical, purity 99.7%), ammonium nitrate (NH4NO3, from Fisher Chemical, purity 99.9%), amino acids (as a complete supplemental medium (CSM) powder from Sunshine Science Products), and a no nitrogen treatment. 790 mg of the CSM powder contained the following amounts of each amino acids: adenine hemisulfate (10 mg), L-arginine (50 mg/L), L-aspartic acid (80 mg), L-histidine 200

hydrochloride monohydrate (20 mg), L-isoleucine (50 mg), L-leucine (100 mg), L-lysine hydrochloride (50 mg), L-methionine (20 mg), L-phenylalanine (50 mg), L-threonine (100 mg), L-tryptophan (50 mg), L-tyrosine (50 mg), L-valine (140 mg) and uracil (20 mg). On the day of start of incubation, jars containing soil were taken out of incubator and nitrogen amendments were added at a rate of 50 mg N kg-1 soil on an N equivalent basis, similar to field application rates in TN. Soils were taken out of pre-incubated jars and put in a large weigh boat where the soil was mixed with the respective amendments using a spatula. 1 ml of 0.2 M solution of urea and ammonium nitrate, and 1 ml of 31.6 g L-1 CSM, each dissolved in deionized (DI) water, was added to the respective soils and 1 ml of DI water was added to soils which received no nitrogen treatment. All treatments were done in triplicate. After mixing of amendments, soil and field- weathered plastic pieces (pre-cut into 2 cm * 2 cm squares) were arranged alternately in the jars in a way such that two layers of plastic remained buried within the soils, ensuring that all the plastic was covered by soil. Incubation was carried out in the dark at 30ºC for 16 weeks after amendment application. A total of 54 experimental units were set up.

Soil analyses before incubation For all t = 0 measurements, 10 additional jars were set up with soils adjusted to 50% WHC (five from TN, five from WA). These were also pre-incubated along with all the experimental units. On the day of start of the experiment (t = 0, when N amendments were added), soils from three of the five jars were used for pH measurement, gravimetric soil moisture and potassium sulphate (K2SO4) extractions. 1 ml of DI water was added to the fourth jar to account for change in moisture after adding nitrogen amendment and soils from that jar were stored in the -80ºC freezer for DNA extractions and enzyme assays to be done later; gravimetric moisture content was also determined for these soils. The fifth jar was not used for analyses.

K2SO4 extractions were done on t = 0 samples. For this, 10 g soils (pre-incubated at 50% WHC) were weighed out from three of the five replicate jars (total six, from TN and WA) and 40 ml of

0.5M K2SO4 was added to the soils. One blank (control) was set up with only K2SO4. A total of seven extractions were completed from unamended samples. K2SO4 extractions were also completed with samples with added nitrogen amendments to account for any differences in carbon and nitrogen pools which might result due to addition of these at the start of the experiment. For this, 5 g of soils from three of the five replicate jars were weighed out and urea, ammonium nitrate

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and amino acids (CSM) were added at a rate equivalent to the amount added to 100 g soil in microcosms. 20 ml of 0.5M K2SO4 was added to these jars and extractions were completed. All samples were shaken in an incubator shaker at 160 rpm for four hours, after which these were filtered through a vacuum manifold using 1 µm glass microfiber filters and stored at -20ºC. These extracts were used for nitrate and ammonium assays.

Gas chromatography and microcosm maintenance Headspace gas samples were collected in 12 ml gas vials starting from the day of start of experiment. Gas sampling was done every two to three days for the first two weeks after which it was reduced to once a week sampling until day 119 (~16 weeks). For the first day of gas sampling, after addition of N amendments, jars were vented and then first gas samples were taken using a 30 ml syringe. 15 ml of gas was used to over pressurize 12 ml vials (pre-evacuated to 200 mTorr). Needles were cleaned out between samples to avoid cross contamination. For subsequent gas sampling days, jars were vented for a minute after gas sample collection to avoid excessive gas build-up in jars. Preliminary results from a trial run indicated the need to dilute gas samples. Thus, before reading gas samples, all vials were diluted in highly pure N2-gas about 13-fold. Diluted gas samples were then read in a gas chromatograph (Shimadzu GC-2014, Shimadzu, Japan). Majority of the dilutions were prepared by one person to avoid human variation introduced with different techniques of dilution.

Microcosms were opened every sampling day to vent and during that time jars were weighed and kept at a constant moisture. Jars weights taken at t = 0 were used as reference and the weights were kept constant by adding DI water as needed. Any unburied plastic pieces were buried back into the soil. Gas samples were also taken after venting of jars was complete to make sure that the venting method was yielding similar gas concentrations in all jars. GC data confirmed that the venting was consistently done throughout the experiment.

Soil analyses after incubation Soil and plastic sampling strategy At the end of the experiment, soil samples from each microcosm were sieved through a 2 mm sieve to separate out plastics and soil samples. Soils from each jar was weighed out for pH, electrical conductivity (EC), gravimetric soil moisture, K2SO4 extractions, nitrate assays, ammonium assays,

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DNA extractions and enzyme assays. Plastic samples were picked out by forceps and collected for material analyses. Plastics collected from one of the replicates were laid out on a white paper (12 cm*12 cm) and imaged using a 12 MP camera, which was put on a level to stabilize.

Soil physical and chemical tests Gravimetric soil moisture was measured on the soils collected at t=16 weeks by weighing ~3 g of soil from the jars and then drying them in an oven (Heratherm OGS100, Thermo Scientific) for 48 h at 105ºC. The dry weight was noted after 48 h and then the gravimetric soil moisture was calculated using Equation 1.

푀푎푠푠 − 푀푎푠푠 % 푔푟푎푣푖푚푡푒푟푖푐 푠표푖푙 푚표푖푠푡푢푟푒 = ( 푤푒푡 soil 푑푟푦 푠표𝑖푙) ∗ 100 (Equation 1) 푀푎푠푠푑푟푦 푠표𝑖푙 where:

Masswet soil = Mass of wet soil (g)

Massdry soil = Mass of dry soil (g) pH and electrical conductivity (EC) were measured from soils collected at t=16 weeks by preparing soil slurries (5 g soil: 10 mL deionized water). pH was measured using a benchtop pH meter (SevenCompact™ Benchtop pH meter, Mettler Toledo) whereas EC was measured using a handheld multiparameter meter (Orion Star A329, Thermo Scientific) with each probe being calibrated prior to use. EC was measured immediately after vortexing the slurry, and pH was measured after approximately 15 min, allowing the fine particles to settle.

Percent biodegradation calculation The starting amount of plastic carbon in the soil microcosms was determined to be 1322.5 µg -1 -1 plastic carbon g dry soil. Using the CO2 evolution data (ug-C released g dry soil), the amount of plastic carbon released over 16 weeks was calculated. This along with the starting plastic carbon value was used in Equation 2 to determine percent biodegradation for each treatment after 16 weeks.

푀퐶푂2−퐶,푚푢푙푐ℎ − 푀퐶푂2−퐶,푛표−푚푢푙푐ℎ %푏푖표푡ℎ푒표 = ( ) ∗ 100 (Equation 2) 푀퐶,푚푢푙푐ℎ where:

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%biotheo = Theoretical % biodegradation

-1 MCO2-C, mulch = CO2-C evolved in mulch treatments (μg C g dry soil)

-1 MCO2-C, no-mulch = CO2-C evolved in treatments without mulch (μg C g dry soil)

-1 MC,mulch = Total amount of C added in the form of mulch C (μg C g dry soil)

Soil DNA extraction Extraction of DNA from soil samples was completed using the MoBio™ PowerLyzer™ Power Soil DNA isolation kit (now branded under Qiagen™) per manufacturer’s instructions. 0.25 grams of soil were used for the extractions, and the DNA obtained after the final elution step (60 µl) was stored at -20⁰C until further analyses. The extracted DNA was used to quantify bacterial, fungal and nitrifier gene abundances in the soil collected both at t=0 and t=16 weeks.

Quantitative PCR Bacterial and fungal qPCR

As a proxy for bacterial and fungal abundances, 16S rRNA (bacteria) and ITS (fungi) gene copy numbers were quantified from soil DNA samples using Femto™ Bacterial DNA quantification kit (Zymo Research) and Femto™ Fungal DNA quantification kit (Zymo Research) following the manufacturer’s protocol. DNA extracts were diluted 1:10 prior to quantification. All samples were analyzed in triplicate. No template negative controls were included in each run. Bacterial and fungal DNA standards were provided in the kit and used to calculate to copy numbers. qPCR reactions were performed in a CFX Connect Real-Time PCR Detection System (BioRad). qPCR efficiencies averaged around 79% for bacterial and fungal assays. Standard curves had R squared values ranging from 0.98 to 0.99.

Ammonia monooxygenase subunit A (amoA) qPCR

Quantitative PCR of ammonia oxidizing bacteria (AOB) amoA genes was performed using the SYBR Green Master Mix and a CFX Connect Real-Time PCR Detection System (Bio-Rad laboratories, Hercules, CA, USA). Primers amoA1F and amoA2R (Rotthauwe et al., 1997) were used to quantify AOB amoA gene abundance. Standard curves were constructed with plasmids containing cloned amoA products from environmental DNA. The molecular cloning was conducted using the TOPO TA Cloning Kit for Sequencing (Invitrogen, Life Technologies) with

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the One Shot TOP10 Competent Cells (Invitrogen, Life Technologies). The plasmids containing amoA gene were extracted from the bacteria solution using the PureLink Quick Plasmid Miniprep Kits (Invitrogen, Life technologies) after an overnight incubation in the shaker at 200 rpm at 37 ℃. qPCR efficiencies averaged around 86%. Standard curves had R squared values ranging from 0.98 to 0.99.

Soil enzyme assays Fluorescence microplate enzyme assays were conducted to determine soil enzyme activity rates before and after the incubation study. Assays were completed using fluorescently labelled substrates targeted for three common carbon and nitrogen cycling enzymes that are known to degrade sugar, chitin and cellulose. (Bell et al., 2013). The synthetic fluorescent indicators used in the study were 4-methylumbelliferone (MUB) and 7-amino-4-methylcoumarin (MUC). The enzyme activity was measured as the fluorescent dye was released from the substrate by an enzyme-catalyzed reaction, with higher fluorescence indicating more substrate degradation compared to lower fluorescence. The targeted enzymes, their respective fluorescently labelled substrates are given in Table 5.2.

Soil slurries were prepared in a sodium acetate trihydrate buffer whose pH was matched closely with the soil pH. 800 µl of soil slurry was pipetted into deep well 96 well plates. Separate plates were prepared for 4-methylumbelliferone (MUB) and 7-amino-4-methylcoumarin (MUC) standard curves for each sample. 200 µl of appropriate standards and substrates were added to the soil slurries. The plates were sealed and inverted to mix the contents. Incubation was done for 3 hours at room temperature, after which the substrate and standard plates were centrifuged at 1500 rpm (~327 x g) for 3 min. The supernatants were pipetted into black 96 well plates and fluorescence measured at Excitation Wavelength = 365 nm and Emission Wavelength = 450 nm in a plate reader (BioTek® Synergy H1).

Nitrate and ammonium assays Nitrate and ammonium concentrations in soil samples before and after 16 weeks incubation were determined using colorimetric assays. K2SO4 extractions were completed after 16 weeks on all 48 soil samples collected from the experimental units. 10 g of soil was mixed with 40 ml of 0.5M

K2SO4 and blanks were also extracted using only K2SO4. All samples were shaken in an incubator

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Table 5.2: Enzymes assayed for t = 0 and t = 16 weeks samples Target substrate Indicator of in natural Substrate used microbial Abbreviation Enzyme name environment in experiment activity 4-MUB-β-D- BG β-glucosidase sugar glucopyranoside Carbon cycling N-acetyl β- Carbon and NAG glucosaminidase chitin 4-MUB-N-acetyl- nitrogen cycling β-D- glucosaminide

β-D 4-MUB-β-D- CB cellubiosidase cellulose cellobioside Carbon cycling MUB = 4-methylumbelliferone; MUC = 7-amino-4-methylcoumarin

shaker at 160 rpm for four hours, after which these were filtered through a vacuum manifold using 1 µm glass microfiber filters and stored at -20ºC. Nitrate and ammonium were measured on these extracts. Ammonium in soil extracts was measured according to Rhine et al. (1998). Briefly, 70 µl of sample extract and ammonium standards (ammonium sulfate [(NH4)2SO2]) were pipetted into respective wells of a 96-well plate; all samples were analyzed in triplicate. Reagents (50 µl citrate reagent, 50 µl 2-phenylphenol-nitroprusside, 25 µl buffered hypochlorite reagent, 50 µl milli-Q water) were allowed to react with the sample by shaking for 30s on a table shaker. After incubation at room temperature for 2 hours, absorbance (660 nm) was measured on a Biotek® Synergy H1 plate reader. Some samples were run again using a 1:1 dilution so that they could be read using the standard curve; samples were diluted in 0.5M K2SO4. Nitrate in soil extracts was measured according to Doane and Horwáth (2003). 30 µl of sample and nitrate standards (potassium nitrate,

KNO3) were added to respective wells in 96-well plates with all samples being analyzed in triplicate. The reagent, vanadium (III) chloride solution (300 µl), was then added to each well and the plate incubated for 5 hours at room temperature. Absorbance (540 nm) was measured on the plate reader. All samples were diluted 1:10 in 0.5M K2SO4.

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Statistical analysis A Student’s t- test was conducted to look at differences in bacterial, fungal and amoA gene abundances; nitrate and ammonium concentrations and enzyme activity rates at t = 16 weeks from t = 0. Additionally, this study used a two-way ANOVA with plastic and nitrogen amendment as factors to isolate the effects of nitrogen treatment on all response metrics except enzyme assays. Analysis was conducted using R version 3.4.0. When the ANOVA indicated significant effects (p < 0.05), a post hoc analysis with Tukey’s HSD (Honest significance test) was applied to compare the means. The experiment design included three replications of each treatment. Data were checked for normality using the Shapiro-Wilk test (W > 0.9). Outliers were removed and if normality was not achieved the data was log transformed as appropriate. For microbial abundances, nitrate and ammonium concentrations and enzyme activity rates, the starting value of the soil was subtracted from the measured value after 16 weeks and this change score was used for analyses.

Results

Soil chemical tests When comparing treatments with and without plastic, it was observed that ammonium nitrate significantly reduced EC with plastic compared to ammonium nitrate treatment without plastic for TN soils, whereas for WA soils all treatments had significantly reduced EC with plastic as compared to without plastic. Urea application resulted in significantly greater pH with plastic for both TN and WA soils. When comparing across treatments without added plastic, all nitrogen amendments showed significantly greater EC compared to no nitrogen treatment for both TN and WA soils. On the other hand, all nitrogen amendments showed significantly reduced pH compared to no nitrogen without plastic for both TN and WA soils. For treatments with added plastic, all nitrogen amendments resulted in significantly elevated EC compared to the treatment without nitrogen in both TN and WA soils, whereas all nitrogen amendments showed significantly reduced pH compared to the treatment without nitrogen in TN and WA soils, except for urea treatment with plastic in WA soil, where there was no difference with no nitrogen treatment with plastic. Significant interaction effects were seen between nitrogen and plastic for all pH and EC data in TN and WA soils, except for EC measurements in WA soil where main effects for nitrogen and plastic treatments are reported (Figure 5.1, Table 5.3).

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Figure 5.1: a) pH and b) electrical conductivity (EC) measurements at t = 16 weeks. Lowercase letters indicate significant interaction effects at α ≤ 0.05 for all data except for EC measurements in WA, where letters denote a significant main effect of nitrogen amendment at α ≤ 0.05. Gray solid stars indicate significant main effect of plastic at α ≤ 0.05.

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Figure 5.1: Continued

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Table 5.3: F values from a two-way ANOVA showing effects of nitrogen and plastic treatment on cumulative CO2-C evolution after 16 weeks. Log Log Log (amoA BG NAG (bacterial (fungal gene (nmol (nmol CO2-C gene gene copies NO3 (µg NH4 (µg activity CB (nmol activity -1 -1 -1 -1 -1 -1 -1 -1 -1 (µg C g copies g copies g g dry NO3 g NH4 g g dry activity g g dry Location Factor pH EC dry soil) dry soil) dry soil) soil) dry soil ) dry soil) soil hr-1) dry soil hr-1) soil hr-1)

Nitrogen 30.15*** 557.51*** 30.02*** 30.86*** 10.01*** 5.22* 174.86*** 62702*** 4.62* 157.95*** 20.15*** TN Plastic 0.51 6.1* 304.18*** 0.43 0.15 1.67 5.37* 0.08 1.31 13.52** 14.16** Nitrogen*Plastic 6.64** 4.31* 12.68*** 16.29*** 6.33** 7.45** 0.51 0.11 0.13 5.05* 2.87 Nitrogen 57.38*** 242.94*** 11.76*** 1.01 5.78** 4.67* 27.53*** 71573*** 2.25 3.39* 5.33** WA Plastic 8.34* 7.86* 158.58*** 10.46** 5.10* 9.10** 0.31 1.25 0.43 0.21 0.09 Nitrogen*Plastic 12.08*** 1.08 1.49 3.44* 0.74 1.21 0.03 2.25 1.77 1.29 0.92 EC: electrical conductivity, BG: β-glucosidase, CB: β-D-cellubiosidase, NAG: N-acetyl β-glucosaminidase

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Plastic decomposition in microcosms

-1 Nitrogen addition resulted in similar responses in CO2 (µg C g dry soil) evolution in both TN and

WA soils (Figure 5.2). When analyzing the cumulative CO2-C evolved after 16 weeks, significant interaction effects were seen with nitrogen and plastic factors in TN, hence, interaction effects are reported (Table 5.3, Figure 5.3). In WA, interaction effects were not significant, hence, main effects of nitrogen amendment and plastic addition are reported. When plastic was added, cumulative CO2-C respiration was significantly greater for the no nitrogen treatment compared to amino acid, ammonium nitrate and urea in TN, and ammonium nitrate and urea in WA (TN no nitrogen treatment: 230.4 µg C g-1 dry soil, WA no -nitrogen treatment: 155.06 µg C g-1 dry soil,

Figure 5.3). The amino acid treatment had the least reduction in CO2-C evolution over time in both locations, whereas the inorganic amendments urea and ammonium nitrate had the greatest reduction in CO2-C evolution over 16 weeks. Plastic treatments showed significantly greater CO2-

C evolution compared to treatments without plastic in both TN and WA. When only nitrogen amendments were added without plastic, CO2-C evolved was significantly higher for amino acid amendment compared to ammonium nitrate in TN whereas in WA, CO2-C was significantly greater for no nitrogen and amino acid compared to ammonium nitrate and urea. The increased plastic degradation observed with no nitrogen and amino acid treatments in TN and WA was corroborated visually by images of remaining plastic material in microcosms taken after 16 weeks (Figure 5.4) and reflected in maximum percent biodegradation estimates (Table 5.4).

Bacterial and fungal abundances in microcosms For bacterial abundances, significant interaction effects were observed between nitrogen and plastic factors in TN and WA, hence, interaction effects are reported (Figure 5.5a, Table 5.3). For fungal abundances, interaction effects were observed with TN but not with WA soils (Figure 5.5b). When comparing plastic treatments, it was observed that plastic addition tempered the bacterial reduction effect from ammonium nitrate with treatments without plastic showing significantly reduced bacterial abundance compared to treatments with plastic in TN. Similarly, in WA, the only significant difference was seen with the no-nitrogen treatment with plastic addition tempering the bacterial reduction effect. There were no significant differences in fungal abundance between treatments with and without plastic in TN for any of the nitrogen treatments. However, for WA

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212

213

No nitrogen Amino acid Ammonium nitrate Urea

Figure 5.4: Visualization of plastic pieces after 16 weeks incubation. Only plastics from one replicate jar are shown here. (AA=amino acids, AN=ammonium nitrate, No N=no nitrogen added).

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Table 5.4: Percent biodegradation of plastic material after 16 weeks. Treatment-Plastic added (mg) Location Theoretical plastic Theoretical %

CO2-C released biodegradation Urea-250 TN 49 4% Amino acid-250 TN 83 6% Ammonium nitrate-250 TN 74 6% No Nitrogen-250 TN 132 10% Urea-250 WA 47 4% Amino acid-250 WA 46 4% Ammonium nitrate-250 WA 41 3% No Nitrogen-250 WA 63 5% Total amount of carbon added in the form of plastic at t=0 was 1323 µg C g-1 dry soil

soils, plastic addition caused a significantly greater fungal abundance compared to treatments without plastic for all nitrogen amendments.

Effects of nitrogen amendments on bacterial and fungal abundances were evaluated for treatments without added plastic (Figure 5.5). It was seen that without plastic, ammonium nitrate resulted in significantly reduced bacterial abundance in TN compared to other nitrogen treatments. However, no differences were observed between nitrogen treatments without plastic in WA. Ammonium nitrate without plastic also resulted in significantly reduced fungal abundances compared to amino acid and urea without plastic in TN, whereas the no nitrogen treatment had significantly reduced fungal abundance compared to amino acid treatment. In WA, amino acid treatment without plastic showed significantly greater fungal abundances compared to other treatments without plastic (Figure 5.5). For treatments with plastic, nitrogen amendments did not cause a significant change in bacterial abundances for TN and WA. Nitrogen treatments with plastic also did not cause a significant change in fungal abundances for TN; however, in WA, amino acid treatment resulted in a significantly greater fungal abundance compared to other treatments (Figure 5.5). Overall, for both TN and WA, a reduction in bacterial abundance was observed after 16 weeks. However, there was a significant increase in fungal abundance for both locations over 16 weeks.

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Figure 5.5: Changes in a) bacterial and b) fungal gene abundances over 16 weeks. All gene abundances were log transformed, then abundances in initial soils subtracted from final (16 week) samples. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters indicate interaction effects at α ≤ 0.05 for bacterial abundance in TN and WA and fungal abundance in TN. Lowercase letters for fungal abundance in WA indicate a significant main effect of nitrogen treatment at α ≤ 0.05. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in gene abundance from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.

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Figure 5.5: Continued

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Nitrification processes and extracellular enzymatic functions in soil microcosms For ammonia monooxygenase (amoA) gene abundances, significant interaction effects were observed between nitrogen and plastic in TN while main effects are reported for WA (Table 5.3). When comparing between plastic treatments, the no nitrogen treatment with plastic was shown to cause a significantly higher amoA gene abundance compared to no nitrogen treatment without plastic in TN (Figure 5.6). In WA, treatments without plastic showed a significantly greater reduction in amoA gene abundances when compared to plastic treatments for all nitrogen amendments. Urea without plastic treatments in TN showed significantly greater amoA abundance compared to ammonium nitrate and no nitrogen treatments without plastic, whereas, in WA, amino acid and urea without plastic showed significantly reduced amoA abundances compared to no nitrogen without plastic. No significant difference in amoA gene abundance was observed for nitrogen treatments with plastic in TN. In WA, amino acid and urea with plastic showed significantly reduced amoA abundances compared to no nitrogen with plastic. Overall, trends differed between TN and WA, with increased amoA abundance in TN but a decreased abundance in WA after 16 weeks.

Nitrification was increased post nitrogen amendment addition as demonstrated via changes in nitrate and ammonium concentrations after 16 weeks (Figure 5.7). All added nitrogen treatments resulted in significantly increased nitrate and decreased ammonium concentrations compared to no nitrogen treatments in both TN and WA soils after 16 weeks (Figure 5.7). Plastic treatments had significantly lower nitrate concentration compared to treatments without plastic in TN, but no difference was seen in WA soils. The results were supported by amoA gene abundances in TN which were significantly increased from t = 0 for urea and amino acid treatments with no plastic added, and no nitrogen and ammonium nitrate with added plastic.

Most of the enzyme activities measured decreased after 16 weeks except for no nitrogen treatment (with and without plastic) and ammonium nitrate (without plastic) in TN which showed increased CB activity (Figure 5.8). BG and CB activities increased for ammonium nitrate treatment (with added plastic) in WA after 16 weeks but was not significant.

There was no difference in enzyme activities when comparing between plastic treatments, with a few exceptions. Addition of plastic caused a greater reduction in NAG activity for all nitrogen treatments and a reduction in CB activity for ammonium nitrate treatment in TN after 16 weeks.

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Figure 5.6: Changes in ammonia monooxygenase (amoA) gene abundances over 16 weeks. Gene abundances were log transformed, then abundances in initial soils subtracted from final (16 week) samples. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters indicate interaction effects at α ≤ 0.05 for amoA abundance in TN and a significant main effect of nitrogen treatment at α ≤ 0.05 in WA. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in amoA gene abundance from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.

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Figure 5.7: Changes in a) nitrate and b) ammonium concentration in soils over 16 weeks. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. addition. Lowercase letters indicate a significant main effect of nitrogen treatment at α ≤ 0.05. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in nitrate and ammonium concentrations from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.

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Figure 5.7: Continued

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When comparing nitrogen treatments without plastic addition, it was seen that ammonium nitrate had a significantly greater reduction in BG activity compared to no nitrogen and amino acid treatment in TN. For CB activity in TN, amino acid and urea had a significant reduction in activity compared to no nitrogen and ammonium nitrate which caused increases in CB activity after 16 weeks. In WA, urea caused a significantly greater reduction in CB activity compared to ammonium nitrate. Nitrogen amendments without plastic caused a significantly greater reduction in NAG activity in TN compared to no nitrogen treatment, whereas in WA, urea and no nitrogen caused a greater reduction in NAG activity compared to ammonium nitrate.

When comparing nitrogen treatments with added plastic, it was observed that ammonium nitrate had significantly reduced BG activity compared to no nitrogen and amino acid treatments in TN after 16 weeks. Amino acid and urea caused significantly greater reductions in CB activity compared to ammonium nitrate in TN, whereas only the no nitrogen treatment had an increase in CB activity after 16 weeks. In WA, no nitrogen and amino acid treatments caused reduced CB activity after 16 weeks, with only ammonium nitrate having a significant increase in CB activity compared to the reduction observed for urea treatment. All nitrogen amendments with plastic had reduced NAG activity compared to no nitrogen in TN, whereas in WA, no nitrogen and urea caused significant reductions compared to an increase in NAG activity for ammonium nitrate.

Discussion

Nitrogen application suppressed plastic decomposition in TN and WA soils The plastics added in the microcosms underwent biodegradation, as evidenced by the increased

CO2-C released in microcosms with added plastic, with about 10% and 5% biodegradation in no nitrogen treatments in TN and WA, respectively. The greater plastic decomposition in TN suggests that the soils may have had a different C: N ratio than WA. Contrary to our hypothesis that nitrogen addition would stimulate plastic decomposition, we saw a suppression of plastic decomposition with added nitrogen amendments. Addition of nitrogen resulted in reduction in plastic biodegradation by 6% over 16 weeks for urea treatment in TN; in WA the greatest reduction was seen with ammonium nitrate, causing a 2% reduction in biodegradation. The CO2-C results were validated with visual inspection of the plastics, showing decreased macroscopic plastic degradation in nitrogen-amendment microcosms. Even though TN and WA soils had different starting soil properties, the trends for repression in CO2-C was the same between treatments in both locations, 222

Figure 5.8: Changes in enzyme activities of β- glucosidase (BG), cellubiosidase (CB), and N- acetyl β-D- glucosaminidase (NAG) after 16 weeks. Each bar represents a mean of 3 replicate microcosms and error bars are standard error. Lowercase letters indicate a significant main effect of nitrogen treatment at α ≤ 0.05, except for CB activity in TN where lowercase letters indicate interaction effects at α ≤ 0.05. Gray solid stars indicate a significant main effect of plastic at α ≤ 0.05. Black asterices indicate significant increase or decrease in enzyme activity from t = 0. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.

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indicating that both soils were probably not nitrogen limited to begin with. Studies with forest soils have reported similar findings where nitrogen deposition was shown to prevent organic matter decomposition where nitrogen was not limiting microbial growth (Janssens et al., 2010). However, in such a situation it would be expected that the responses of nitrogen addition would be comparable to no nitrogen treatments. But the fact that there was a suppression in microbial activity with nitrogen amendments in the present study suggest a possible negative effect. Excessive nitrogen deposition in an ecosystem marks a point of nitrogen saturation which could have severe environmental impacts on soil chemistry and water quality (Aber, 1992). There could also be a decrease in productivity in terrestrial ecosystems through the loss of base cations and decreased phosphorus availability (Janssens et al., 2010, Jin-yan and Jing, 2003).

The form of nitrogen added dictated the magnitude of the degradation repression. Addition of amino acids in the form of a complete supplemental media showed greater plastic decomposition in TN compared to inorganic amendments suggesting a possibility that carbon and nitrogen in the amino acids spiked microbial activity in the presence of added plastic inputs. When only nitrogen amendments were added without plastic, microbial activity was highest for amino acid amendment in both TN and WA. It has been reported that nitrogen addition to decomposing organic matter often has no effect or a negative effect on microbial activity, at least over the long term (Fog, 1988). However, with complex organic nitrogen sources, there seems to be a positive effect, where it could be a vitamin effect or an effect of the carbon source, rather than a nitrogen induced effect (Fog, 1988). This situation appears to be the case when we look at the effect of amino acid on plastic decomposition in TN in the present study. Negative trends are reported to be more apparent in long term experiments lasting months which would explain the results from this study which was carried over a period of four months. A detailed review of various studies reported by Fog (1988) points towards a few reasons as to why such as negative effect could be seen: 1) nitrogen addition can affect the competition between potent and less potent decomposers, 2) through ‘ammonia metabolite repression’ where nitrogen blocks production of certain enzymes, especially in fungi, and 3) production of toxic or inhibitory compounds formed by the reaction of amino compounds with polyphenols and other decomposition products. Although it is generally known that addition of nitrogen does not always promote decomposition of organic matter it was not until the research conducted by Keyser et al. (1978) that it was clearly demonstrated how nitrogen can

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have a negative effect on production of certain enzyme involved in organic matter decomposition in fungi, particularly in basidiomycetes.

Responses of soil bacterial, fungal and ammonia-oxidizing bacteria to nitrogen and plastic amendments An overall decrease in bacterial gene abundance was observed for all treatments after 16 weeks. However, a significantly enriched fungal gene abundance was observed over 16 weeks for all nitrogen treatments with and without plastic in TN and WA. Increased fungal abundances in all nitrogen treatments with added plastic, coupled with reduced plastic decomposition observed in only nitrogen added treatments points towards a possibility of ammonia metabolite repression in the nitrogen added treatments. Such a repression has been demonstrated with basidiomycetes where enzymes degrading organic matter, such as lignin, are produced by the fungus only when the fungus has switched from primary to secondary metabolism. This switch is induced only when the supplies of available nitrogen, such as ammonium, have been exhausted (Fog, 1988). Several studies point towards the important role of fungi in the degradation of biodegradable mulch with evidence of enrichment of Aspergillus and Penicillium (Koitabashi et al., 2012) and several genera of Ascomycota (Muroi et al., 2016) under biodegradable mulch application. Cellulose and polyester degrading basidiomycetes are also common (de Souza, 2013, Watanabe et al., 2014). A repression of enzymatic function due to added nitrogen amendment could explain the reduced mulch degradation observed.

The presence of nitrifying bacteria in the soil microcosms was confirmed by increased amoA gene abundances in TN soils after 16 weeks and changes in soil nitrogen pools in both TN and WA soil microcosms. Increased bacterial amoA gene abundances were observed for a few treatments in TN soils along with increased nitrate concentration and reduced ammonium concentrations for both TN and WA soils after 16 weeks for all nitrogen treatments. As ammonia monooxygenases from ammonia oxidizing bacteria convert ammonium to nitrite, ammonium concentrations would be predicted to decrease. This reduction in ammonium was not seen for no nitrogen treatments which also had significantly reduced nitrate concentrations. These observations point towards active nitrification processes taking place in the soil microcosms with added nitrogen. Furthermore, reduced pH observed with nitrogen amended soils could explain formation of nitric acid from urea and ammonium salts supporting potential nitrification processes in the microcosms with added

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nitrogen (Yan et al., 1996). Previous studies have reported that urea-N additions can lead to significant increases in ammonia oxidizing bacteria (AOB) (Chen et al., 2013). In this study, the highest nitrate concentrations were also observed with urea amendment in both TN and WA soils.

There was minimal effect of plastic addition on the nitrifying ability of the soil microbial community. This suggests that the plastic material used in the study does not inhibit nitrification processes of the system to a great extent. This observation is supported by previous literature where use of Mater-Bi® demonstrated no ecotoxic effect on the soil (Ardisson et al., 2014). The presence of plastic influenced nitrate concentrations only in TN soils where plastic addition reduced nitrate concentrations for all treatments. In WA soils, nitrate concentrations did not differ between plastic and no plastic treatments. Similarly, in both TN and WA soils, ammonium concentrations did not differ between plastic and no plastic treatments. The majority of the studies to date focus on effects of surface film mulching on soil nitrification processes, but limited studies have been done to study this effect with BDMs. One study using plastic film as a surface mulch along with nitrogen fertilizers showed increased soil nitrate-N content compared to no mulching after a three-year field experiment (Gao et al., 2009). Studies focusing on denitrification processes have shown decreases in N2O emissions under film mulching (Berger et al., 2013) whereas others have showed no differences of N2O emissions between no-mulching and plastic film mulching treatments (Liu et al., 2014). However, observations from the present study indicate that BDMs buried in soil either have comparable effects to no plastic treatments on nitrification processes or a suppressed effect as demonstrated by reduced nitrate concentrations (with plastic) in TN soils. However, the reduction of nitrate concentration with plastic in TN is irrespective of nitrogen addition. Even though active nitrification was observed in the microcosms, as demonstrated by reduced ammonium concentration after 16 weeks, the residual nitrogen in the system could have resulted in inhibited microbial decomposition of plastic material.

Responses of soil hydrolytic enzyme activities to nitrogen and plastic amendments A general trend of reduced soil enzyme activities was observed for all treatments after 16 weeks. Decrease in pH after addition of nitrogen amendments indicate acidification of soils in the microcosms. This decrease can lead to reduced enzyme activities as a result of effects on microbial physiology and base cation limitation (Shi et al., 2018). Reduced CB activity was observed for most nitrogen amendments after 16 weeks whereas a significantly increased activity was seen for

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no- nitrogen treatment in TN. A reduced BG, NAG and LAP activity was also observed in the present study (LAP data not shown); however, the reduction in the activities of these enzymes over 16 weeks were seen for both nitrogen and no nitrogen treatments. Reduced NAG activity has been reported in the literature post urea addition to soils at a rate of 392 kg N ha−1 yr−1 over a seven- year period (Zhang et al., 2016). A few other studies have reported no change in BG activity after nitrogen application in temperate grasslands (Wang et al., 2014) whereas another study found that nitrogen addition over just a two-year period led to significant decreases in urease activity (Zhou et al., 2012). Decrease in peptidase activity has been documented by several other studies, where some report depressed activity after ten years of ammonium nitrate addition (Stursova et al., 2006), and others reporting dramatically reduced activities after nitrogen addition (Saiya-Cork et al., 2002). Thus, these trends seem to point towards a general decline in enzyme activity after nitrogen addition in both short and long-term experiments. A negative relationship between NAG activities + and soil NH4 -N content is also reported by Zhang et al. (2016) , indicating that ammonia could have an inhibitory effect on N-hydrolases. However, significantly increased CB activity for both with and without plastic treatments for no nitrogen amendment suggests that the increased activity was not due to addition of plastic. The only effect of plastic that was observed was for NAG activity in TN, where addition of plastic seemed to cause a greater reduction in NAG activity after 16 weeks compared to treatments without plastic. With field studies, it has been observed that addition of BDM in soil has very little effect on the activities of common carbon and nitrogen cycling enzymes when compared to treatments without mulch, at least over a two-year period (Sintim et al., 2019).

In summary, we found that plastic decomposition was significantly lowered under inorganic nitrogen amendments, with the highest plastic decomposition seen for the no nitrogen treatment, followed by amino acid treatments. Identical CO2-C respiration trends were observed for both soils from TN and WA although percent biodegradation of plastic varied between locations (10% degradation in TN versus 5% in WA). Increased soil nitrification and amoA gene abundances were observed under nitrogen treatments compared to no nitrogen with reduced ammonium concentrations and higher nitrate concentrations supporting active nitrification in the microcosms. Reduced soil enzyme activity rates after 16 weeks suggest potential inhibitory effect on hydrolases. The effect of plastic addition had minimal effects on nitrification processes and enzyme activities after 16 weeks with only nitrate concentration being higher in treatments with no plastic in TN,

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and a lesser reduction in NAG activity for treatments without plastic in TN. The results indicate that addition of nitrogen fertilizers in cropping systems with tilled-in BDM fragments may inhibit decomposition of mulch material without compromising nitrification processes.

Acknowledgements

We thank Dr. Sindhu Jagadamma and Shikha Singh for the training and help provided to operate the Shimadzu Gas Chromatograph, Xiangru Xu and Tori Beard for help with soil processing and Dr. Arnold Saxton for statistical advice.

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CONCLUSIONS AND RECOMMENDATIONS Agricultural plastic mulching has clear benefits of improving crop yields; however, limited studies have confirmed whether the input of BDMs into soil pose concerns regarding soil health. In the first research chapter, we showed that the input of BDMs into soil over a two-year period did not affect soil microbial community structure and function when compared to PE treatment for two diverse geographical locations, suggesting BDMs have comparable effects on soil communities as PE. Effect of location and sampling season were more pronounced compared to the effect of mulch treatment. Other measurements conducted as part of this project aimed at assessing changes in soil quality over two years; results from these studies also showed that the effect of location and season was more pronounced than treatment effects, at least over the short term (Sintim et al., 2019). In many crop production systems, mulches are typically used for a single growing season, whereas in other cases, mulches are applied year after year (Miles et al., 2017). Repeated tilling of BDMs into soil can lead to accumulation of plastic residuals. Miles et al. (2017) reported that in such a scenario, after 8 years of annual mulch application, mulch residuals in the soil would exceed twice the amount of mulch applied each year. Thus, it is important to consider that even if a BDM meets the 90% biodegradation threshold (specified by ASTM and other organizations) in the field within 2 years, mulch residuals can accumulate in the soil due to repeated BDM inputs. However, field studies to date have indicated that BDMs may not meet the 90% biodegradation criteria in two years (Miles et al., 2017). It is to be noted that the standards used to certify mulches as biodegradable are not necessarily designed to be applied to biodegradation in field situations and such an accumulation in soil over time could cause an impact to soil health parameters (physical, chemical and biological). Thus, long term studies, aimed at evaluating these metrics over a period of 5-10 years are needed to be able to assess impacts on the soil ecosystem.

To our knowledge, the study described in the second research chapter, which characterizes the BDM plasticome, is the first of its kind to demonstrate enrichment of soil microbes on BDMs alongside PE in two diverse climates. This study demonstrated the presence of microbial taxa which were predominant in both lab enrichment and field studies. Studies using single strains and laboratory cultures have advanced our understanding of enzymatic degradation processes of the polymers that compose BDMs to a great extent. However, when using culture-based methods to characterize BDM degraders, inherent biases are introduced, resulting from controlled environment studies. Furthermore, such an approach limits our information to only those microbes 235

which are growing on a petri-plate with no clear mechanism of selection of important degraders. In a complex soil environment, BDMs will potentially be degraded using intricate pathways and utilization of products from a diverse suite of microbes. Such a dynamic community will influence each other’s activities in serving collective functions. Metagenomic approaches provide a platform to not only provide quantitative estimation of a community’s individual entities, but also ways to select and further study the taxa which could be the most important drivers of change between the treatments in question. This dissertation provides a first step to that, which is to assess what microbes are present in the soil and plastic community across different mulch treatments tested in the field and lab. Three important questions are to be answered to show the complete story of BDM degradation.

1) Who is there? 2) What are they doing? 3) Who is doing what? While we do address the first question completely using 16S and 18S rRNA sequencing, we were only able to understand what the the function of these microbes could be using associated measurements that served as proxies for biodegradation such as monitoring CO2 evolution, conducting enzymatic assays and microscopy to visualize microbial colonization. However, to know the precise functions of such a community requires functional metagenomic approaches focusing on gene-centric analysis. Thus, functions within an environmental sample would be revealed by treating a community as an aggregate, and then communities can be compared to each other in terms of functional profiles (Tringe et al., 2005). However, a more expansive question remains to be answered. “Who is doing what?”. Gene centric analyses ignore the context of individual species; thus, even though functions of genes can be predicted, it is not always possible to place them in the context of specific metabolic pathways (Chistoserdova, 2009). Several pathways share genes and enzymes in common, for instance, the TCA cycle (whose main role is in energy generation) and the methylcitric acid cycle (whose main role is in propionate utilization). The accurate differentiation between the two pathways can only be achieved when we possess the information on an entire or almost entire gene complement (Chistoserdova, 2009). This will establish a connection between the organism and its specific function. Even though such an assembly is not easy to execute, several examples exist where researchers have successfully shown metabolic reconstruction of the genomes of the dominant species of a low complexity community.

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The acid mine drainage community remains a classic example of such a reconstruction (Tyson et al., 2004). Nearly complete genomes and reconstruction of the metabolism of a Leptospirillum group II bacterium and a Ferroplasma group II archaeon provided important insights into their ecological roles and the functions of the community. Both the Leptospirillum and the Ferroplasma species were shown to possess multiple pathways for carbon fixation, while the Ferroplasma species were also equipped for a heterotrophic lifestyle.

In order to obtain insights into the genomes of the minor members of a BDM degrading community, enrichment strategies could be further explored. Enrichment cultures have the potential to selectively grow a mixed population such that organisms not available in pure culture could reach a relatively high proportion large enough to warrant high coverage for a genome when using whole genome sequencing approaches. Several studies have utilized this technique successfully. For instance, researchers have enriched Kuenenia stuttgartiensisin in a laboratory reactor in which its population comprised approximately 73% of total cell counts (Strous et al., 2006). A massive sequencing effort resulted in only five contigs with more than 98% of the genome captured. Metabolic reconstruction revealed unexpectedly high metabolic versatility of the organism and high degree of functional redundancy (Chistoserdova, 2009). Over 200 genes identified were involved in catabolism and respiration. Similar approaches to metagenome assembly of enriched communities on BDMs have the potential to reveal important pathways carried out by specific organisms.

In order to understand the factors that control biodegradation of BDMs in the field, we hypothesized that BDM incorporation in soil could lead to nitrogen limitation for microbes on the polymer surface which supplies excess carbon relative to nitrogen. Thus, addition of nitrogen would enhance decomposition. However, our observations indicated that adding nitrogen had a negative effect on mulch decomposition with the greatest degradation observed for BDM treatments which did not receive nitrogen. The greatest reduction in BDM degradation was observed for inorganic nutrients such as urea and ammonium nitrate whereas organic amendments had the least effect. The field site from which the soils were collected for this study were under pie pumpkin cultivation in 2015 and 2016 in both TN and WA, whereas and peppers were grown in TN and corn in WA from 2017 to 2018 (Sintim et al., 2019, Ghimire et al., 2018). Fertilizers were applied at both location from 2015 to 2018. Even prior to this field study which started in

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2015, the same plots were under winter strawberry cultivation and received nitrogen fertilizers. Thus, the nitrogen content of the soils could have been high enough through repeated fertilization in both locations suggesting that nitrogen may not have been limited in the system, and more nitrogen may even be toxic to the microbial degraders. Thus, in order to make assumptions about the effect of an added nutrient on BDM decomposition in a particular soil, the management history of the plots from which the soil is collected is important to know. As such, the negative effect of nitrogen application on BDM decomposition in both TN and WA soils suggests a need for a careful assessment of fertilizer application regimes, particularly inorganic fertilizers. However, this would come with a trade-off of increased yields due to fertilization or having a sustainable cropping system which favors degradation of BDMs. In such a situation, use of organic fertilizers might prove to be better due to limited impact of these on BDM decomposition. A possible way to confirm this result would be to leach out nitrogen from the soils and monitor BDM degradation in the soils before and after nitrate leaching. Given the results from the present study, such an experiment could show enhanced BDM decomposition after removal of nitrogen.

In conclusion, results from this body of research shows no significant effect of BDMs on the soil microbiome and associated ecosystem functions compared to PE after two years of BDM tillage in soil. This suggests BDMs are a safe alternative, at least, over the short term. However, long- term studies should be continued in these plots to assess the continued impacts of BDMs on soil health. This study identified important bacterial and fungal taxa that were enriched on BDM surface after exposure to field conditions over a complete growing season in two diverse climates. Lab enrichment cultures corroborated the findings from the field study. The next step should be to undertake a functional metagenomics approach to better understand BDM degradation pathways in complex soil and plastic communities. Finally, this study examined the impact of nitrogen addition on BDM decomposition in soils and found that the management history of soils from a location is important in making predictions about BDM decomposition. The study suggests that inorganic fertilizers should not be applied to a great extent in the BDM plots used in this study if mulch decomposition is to be favored; whereas organic fertilizers might have limited negative effects. Collectively, these results demonstrate that BDMs are a promising alternative to PE, with continued investigation on its long-term impacts ultimately deciding its potential in sustainable agriculture.

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VITA Sreejata Bandopadhyay was born and raised in Kolkata, the capital city of the Indian state of West Bengal. She received her Bachelor of Science and Master of Science degree in Microbiology from the University of Calcutta in Kolkata. Before pursuing her graduate studies in the US, she worked for a year in Dr. Parthib Basu’s ecology lab at the University of Calcutta where she developed a deeper interest in studying the ecology of microbes in perturbed ecosystems. Sreejata joined Dr. Jennifer DeBruyn’s lab in the Department of Biosystems Engineering and Soil Science at the University of Tennessee in 2015 where she focused her research on soil microbial ecology. She has presented her work at several national and international conferences including the Soil Science Society of America and American Society for Microbiology annual conferences. Sreejata defended her dissertation on August 5, 2019.

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