USING MOLECULAR TECHNIQUES to INVESTIGATE SOIL INVERTEBRATE COMMUNITIES in TEMPERATE FORESTS a Thesis Submitted to Kent State Un

USING MOLECULAR TECHNIQUES TO INVESTIGATE SOIL INVERTEBRATE

COMMUNITIES IN TEMPERATE FORESTS

A thesis submitted

To Kent State University in partial

Fulfillment of the requirements for the

Degree of Master of Science

Dean James Horton

December, 2015

Thesis written by

Dean James Horton

B.S., Kent State University, 2012

M.S., Kent State University, 2015

Approved by

Christopher B. Blackwood, Associate Professor, Ph.D., Department of Biological Sciences,

Thesis Co-Advisor

Mark W. Kershner, Associate Professor, Ph.D., Department of Biological Sciences, Thesis Co-

Advisor

Laura G. Leff, Chair Professor, Ph.D., Department of Biological Sciences

James L. Blank, Dean, Ph.D., College of Arts and Sciences TABLE OF CONTENTS

I. LIST OF FIGURES …………………………………….…………………… vi

II. LIST OF TABLES ……………………………………..…………………….. x

III. DEDICATION ………………………………………….………………….. xii

IV. ACKNOWLEDGEMENTS ……………………………………………...... xiii

V. CHAPTERS

1. GENERAL INTRODUCTION ………………………...………………... 1

REFERENCES …………………………………………..………………. 4

2. A PRIMER COMPARISON FOR MOLECULAR IDENTIFICATION OF

INVERTEBRATE TAXA FROM SOIL AND LEAF LITTER

ENVIRONMENTAL DNA ………………………………..…………… 11

1. ABSTRACT ………………………………………………………... 11

2. INTRODUCTION ………………………………………………….. 11

3. METHODS …………………………………………………………. 14

3.1. INDIVIDUAL VOUCHER SPECIMEN COLLECTION…….... 14

3.2. PRIMER TESTING ON INVERTEBRATE VOUCHER

SPECIMENS………………………………………………...….. 14

3.3. eDNA IN ENVIRONMENTAL SAMPLES…………………… 16

4. RESULTS…………………………………………………………... 17

4.1. INVERTEBRATE VOUCHER SPECIMENS…………………. 17

4.2. ENVIRONMENTAL SOIL AND LEAF SAMPLES……….…. 18

5. DISCUSSION………………………………………………………. 19

iii 6. REFERENCES……………………………………………………… 21

3. HIGH-THROUGHPUT SEQUENCING REVEALS HIGH SOIL

FAUNAL DIVERSITY AND SMALL-SCALE COMMUNITY

TURNOVER IN TEMPERATE FORESTS…………………………….. 33

1. ABSTRACT………………………………………………………… 33

2. INTRODUCTION………………………………………………...… 34

3. METHODS…………………………………………………………. 38

3.1. STUDY AREA AND SAMPLING DESIGN………………..… 38

3.2. COMMUNITY CHARACTERIZATION……………..…….…. 39

3.3. DATA ANALYSIS…………………………………………..… 41

3.3.1. VARIABILITY AT THE LANDSCAPE SCALE AMONG

SITES…………………………………………………………… 41

3.3.2. WITHIN-SITE SPATIAL ANALYSIS OF ANIMAL

COMMUNITY TURNOVER…………………………………... 43

4. RESULTS…………………………………………………………... 44

4.1. HYPOTHESIS 1. ALPHA-DIVERSITY IN DIFFERENT

HABITAT AND ECOSYSTEM TYPES…………………………… 44

4.2. HYPOTHESIS 2. EFFECTS OF ECOSYSTEM AND HABITAT

TYPE ON COMMUNITY COMPOSITION AT THE FOREST

STAND SCALE…………………………………………………….. 45

4.3. HYPOTHESIS 3. WITHIN-STAND VARIATION IN ANIMAL

COMMUNITY COMPOSITION………………………………….... 46

5. DISCUSSION………………………………………………………. 47

iv 5.1. REGIONAL FAUNAL DIVERSITY PATTERNS…………….. 47

5.2. BETA DIVERSITY AT THE FOREST LANDSCAPE SCALE. 48

5.3. LOCAL (WITHIN-STAND) COMMUNITY TURNOVER…... 50

5.4. CONCLUSIONS……………………………………………….. 51

6. REFERENCES……………………………………………………… 52

v I. LIST OF FIGURES

CHAPTER 2:

Figure 1. Phylum-level community composition of taxa found within environmental samples when targeting 18S and COI genes. “Unclassified” sections of the histograms represent sequences that were not designated to at least phylum level, were assigned to taxonomic groups within alternative biological hierarchies (e.g. Bacteria, Fungi, Plantae), or were not classified by

BLAST. Phylum is the lowest taxonomic level represented in this figure for clarity…………. 28

Figure 2. Animal community composition between leaf litter and soil environments analyzing the 18S dataset. Taxonomic groups shown are the lowest levels to which each 18S sequence was identified, ranging from phylum to genus. Leaf litter analysis represented 101 sequences, while soil analysis represented 10 sequences. These results were obtained through further analysis of the dataset displayed in Fig. 1………………………………………………………………….. 29

CHAPTER 3:

Figure 1. α-diversity Hill metrics for each ecosystem. Histograms represent a. H0, b. H1, c. H2 and d. Hinf. Leaf habitats are represented by dark grey columns, and soil habitats are represented by light grey columns. Ecosystem types are represented by BOWO = Black Oak-White Oak,

SMRO = Sugar Maple-Red Oak, and SMBW = Sugar Maple-Basswood. Error bars represent standard error……………………………………………………………………….…..……… 64

Figure 2. Redundancy analysis ordination of stand-level invertebrate community composition.

Green coloration represents leaf communities, magenta coloration represents soil communities.

Circles represent BOWO communities, squares represent SMRO communities, and triangles represent SMBW communities. Large symbols represent the centroids of each treatment…… 65

Figure 3. Community compositional network of taxonomic groups found in this study. Size of node represents relative abundance of OTUs assigned to that taxonomic group, including those identified to taxonomic groups at lower hierarchical levels. Green nodes represent taxonomic groups with significant correlations from indicator analyses with soil habitats, while purple nodes represent groups with significant correlations with leaf litter habitats. White nodes represent taxa that were not significantly associated with either soil or leaf litter habitats.

Intensity of coloration represents the steepness of the rpb value………………………………... 66

Figure 4. Community compositional network of taxonomic groups found in the leaf litter habitat. Size of node represents relative abundance of OTUs assigned to that particular taxonomic group, including those which identified to taxonomic groups at lower hierarchical levels. Red nodes represent taxonomic groups with significant correlations from indicator analyses. Intensity of coloration represents the steepness of the rpb value. Diamond-shaped nodes represent indicator groups of the BOWO ecosystem, rectangle-shaped nodes represent indicator groups of the SMRO ecosystem, and triangle-shaped nodes represent indicator groups of the SMBW ecosystem. Circular nodes lack significant correlation to a particular ecosystem type……………………………………………………………………………………………… 68

vii

Figure 5. Community compositional network of taxonomic groups found in the soil habitat.

Size of node represents relative abundance of OTUs assigned to that particular taxonomic group, including those which identified to taxonomic groups at lower hierarchical levels. Red nodes represent taxonomic groups with significant correlations from indicator analyses. Intensity of coloration represents the steepness of the rpb value. Diamond-shaped nodes represent indicator groups of the BOWO ecosystem, rectangle-shaped nodes represent indicator groups of the

SMRO ecosystem, and triangle-shaped nodes represent indicator groups of the SMBW ecosystem. Circular nodes lack significant correlation to a particular ecosystem type….…….. 70

Figure 6. Distance-decay plots for soil and leaf litter faunal communities based on geographic distance classes and Hellinger distance. Circles represent soil community points, and triangles represent leaf community points. Red symbols represent Hellinger distance values significantly lower than expected by chance (P < 0.05). a) BOWO, b) SMRO, c) SMBW…………………. 72

Figure 7. Principle Coodinates Analysis ordination based on results from Anderson’s test of multivariate homogeneity of variances on local community dispersion. Green coloration represents leaf communities, magenta coloration represents soil communities. Circles represent

BOWO communities, squares represent SMRO communities, and triangles represent SMBW communities. Large symbols represent the centroids of each treatment. Axis 1 explained 18% of the variance in community composition, while Axis 2 explained 9.4% and Axis 3 explained

6.7%. Ellipses represent 95% confidence intervals around the centroids of each habitat- ecosystem type………………………………………………………………………………..… 73

viii

Supplementary Figure 1. Rarefaction curves illustrating sampling coverage for a. regional faunal community analyses and b. local faunal community analyses. Colored lines represent separate environmental samples. The black line represents the 1:1 slope line for OTU:sequence number…………………………………………………………………………………………...74

ix I. LIST OF TABLES

CHAPTER 2:

Table 1. Comparison of invertebrate specimen identifications determined using traditional

(morphological) and molecular (sequencing of 18S rRNA and mitochondrial COI genes) approaches. Numbers adjacent to Collembola identifications correspond to the number of individuals identified to that taxonomic rank. For all other taxa, there was only a single specimen included in the morphological and molecular identifications. The taxonomic rank for each identification is listed in parentheses following the rank (Codes = P: Phylum; sC: Subclass;

O: Order; sO: Suborder; iO: Infraorder; SF: Superfamily; F: Family; sF: Subfamily; G: Genus; S:

Species). The symbol ‘◊’ indicates taxonomic mismatch between morphological and sequence identifications. “No Amplification” indicates lack of DNA sequencing for that taxon for that particular gene as a result of insufficient PCR amplification. “No Hits” indicates lack of sequence match to sequences within the NCBI database. Assigned taxonomic ranks for molecular identifications were acquired from the NCBI taxonomic database…………………. 30

CHAPTER 3:

Table 1. Distance classes to be generated by transect sampling and used to examine spatial structure. Distances in meters……………………………………………………..……………. 75

x Table 2. Mixed-model ANOVAs testing significance of ecosystem, habitat type, and interaction effects on α-diversity of forest stand communities. Corresponding F-values are reported for each

ANOVA. H0 = OTU richness, H1 = Shannon diversity, H2 = the inverse of Simpson entropy,

Hinf = the Berger-Parker index……………………………………………………….…………. 75

Supplementary Table 1. Indicator species analysis examining indicator species for soil and leaf litter habitats. rpb represents the correlation value. P represents the significance value. Number of indicator OTUs are reported for clarity rather than listing each OTU individually. P-values for OTUs are all < 0.05, while rpb values are variable………………………………...……….. 76

Supplementary Table 2. Indicator species analysis examining indicator species for soil and leaf litter habitats. rpb represents the correlation value. P represents the significance value. Number of indicator OTUs are reported for clarity rather than listing each OTU individually. P-values for OTUs are all < 0.05, while rpb values are variable……………………………..…………… 79

Supplementary Table 3. Indicator species analysis examining indicator species for soil and leaf litter habitats. rpb represents the correlation value. P represents the significance value. Number of indicator OTUs are reported for clarity rather than listing each OTU individually. P-values for OTUs are all < 0.05, while rpb values are variable…………………………………….……. 81

xi II. DEDICATION

I dedicate this work to my mother Connie, my father Dave, my brother Mark, and my grandparents Bull, Maw, and Oma for their love and support of my education in science.

xii III. ACKNOWLEDGEMENTS

There are countless individuals that were integral in my completion of this thesis and my progression as a scientist, and as consequence, have been omitted from this list of acknowledgements to keep this section shorter than the thesis itself. You know of whom you are, so thank you without reserve for your support. However, I would still like to acknowledge those who played particularly monumental roles in my scientific development.

Firstly, I would like to thank my advisors, Dr. Christopher Blackwood and Dr. Mark

Kershner. I have been volunteering, working, or studying within Dr. Blackwood’s lab since

2010, and have learned much of what I know today about cutting edge laboratory techniques, primary scientific research, and ecological theory from these experiences. Dr. Kershner has been one of my greatest teachers and mentors within and outside of the classroom, and I have had the honor of learning natural history and ecological theory through taking his courses, teaching for his labs, and hiking and researching at Kent State’s Jenning’s Woods property.

I would like to thank Dr. Xiaozhen Mou for serving as a committee member for this thesis and providing valuable insight and equipment for the completion of this research. I would also like to thank Dr. Oscar Rocha and Dr. Jennifer Marcinkiewicz for unparalleled mentorship in scientific outreach.

I would like to acknowledge my lab mates and other friends for teaching me lab techniques, assisting with my projects, providing moral support, and keeping the lab a fun environment, including, but not limited to, Devinda Hiripitiyage, Suhana Chattopadhyay, Florence Hsu, Mui

Clark, Matthew Gacura, Eugene Ryee, Larry Feinstein, Oscar Valverde-Barrantes, Amber

xiii Horning, Brendan Morgan, Anna Ormiston, DeShawn Johnson, Mark Horton, Andrew

Thouvenin, Kurtis Thomson, and Karlton Shoemaker.

At last, but certainly not the least, I’d like to thank my grandfather Bill ‘Bull’ McClelland for illuminating the path to science through opening my eyes to my personal passions and goals, and revealing a world of frontier and discovery to me for the rest of my life.

Dean James Horton

November 6th 2015, Kent, Ohio

xiv Chapter 1. General Introduction

Soil invertebrates perform a plethora of important functions within soil food webs, affecting decomposition, biogeochemical cycling, herbivory, and regulation of microbial communities (Decaëns et al., 2008; Lavelle et al., 2006; Sylvain & Wall, 2011). In fact, soil fauna significantly impact litter decomposition rates on a global scale (Cornwell et al., 2008;

García-Palacios et al. 2013), as well as on a local scale, with recalcitrant leaf litter degraded more rapidly in the presence of millipedes, while more labile leaf litter was broken down faster in the presence of earthworms (Hättenschwiler & Gasser 2005). In addition, important microbial decomposer groups, such as fungi, can be regulated by top-down control from various soil faunal trophic guilds (Lenoir et al., 2007; Wardle, 2006). Thus, it is well-documented that biological community functions, and particularly decomposition, are influenced by soil invertebrate communities to a large degree.

Despite the importance of soil invertebrates, investigations into their distribution and diversity are rare, particularly when compared with the abundance of research on above-ground organisms (Decaëns, 2010). Many factors contribute to this lack of attention, including lack of scientific and general public interest, sampling difficulties due to the dense nature of belowground environments, small taxa sizes, high diversity, and declining taxonomic expertise on a global scale (Decaëns, 2010; Sylvain & Wall, 2011). Further, when studies have focused on soil fauna, they have focused on the diversity of a single taxonomic group, such as oribatid mites

(e.g., Caruso et al., 2012; Erdmann et al., 2012; Gergócs et al., 2011; Hansen, 2000; Zaitsev et al., 2013), Collembola (e.g., Cicconardi et al., 2013; Chust et al., 2003; Heiniger et al., 2014), nematodes (e.g., Boag & Yeates, 1998; Imaz et al., 2002), or a combination of a few taxonomic groups (e.g., Birkhofer et al., 2012; Hasegawa, 2001; Nielsen et al., 2010; Vanbergen et al.,

2007). As a consequence of these limited taxonomic approaches, studies are likely missing important food-web interactions among groups of invertebrates, including mutualisms, predation, and competition.

The ability to uncover total soil faunal biodiversity, however, has recently become more possible through molecular methods known to animal ecologists as ‘environmental metabarcoding’ (Shokralla et al., 2012; Taberlet et al., 2012; Yoccoz, 2012). Environmental metabarcoding involves amplification and sequencing of a taxonomic marker gene from a target group of taxa within mixed-species environmental DNA (eDNA). This eDNA can be extracted directly from soil, sediment, or water. Studies have already begun to successfully use eDNA to assess soil fauna diversity at multiple scales (e.g., Deiner & Altermatt, 2014; Wu et al., 2011).

Using cloning and sequencing technology, Wu et al. (2011) surveyed soil fauna on a global scale to uncover relationships between the abiotic environment and total invertebrate community composition. Providing further power to studies directly examining eDNA, Next Generation

Sequencing (NGS) technology is becoming more prevalent in studies of community diversity within natural environments (e.g., Russo et al., 2012; Uroz et al., 2010). NGS currently provides the highest possible resolution of sequence data, and allows for scientists with low or specialized taxonomic expertise to complete studies on highly diverse groups of biota. These techniques have already been used to compare nematode diversity between habitat types and ecosystems, where greater than 90% of the nematode species found were novel (Porazinska et al., 2012).

Based upon studies conducted to date, NGS could likely prove very useful in revealing patterns of animal community distribution and diversity, particularly in forested ecosystems where spatial patterns of the total invertebrate community have yet to be explored.

The goal of this thesis was to explore the use of molecular technology to obtain high- resolution faunal community composition data and examine fauna spatial patterns in a temperate forest landscape. My research focused on 1) refining genetic techniques to allow for massively parallel sequencing of environmental DNA, and 2) applying these tools to understanding spatial patterns of the soil/leaf litter invertebrate community in a temperate deciduous forest landscape.

Currently, two genes are being used in genetic research of metazoans, the mitochondrial cytochrome c oxidase subunit I gene (COI) (Folmer et al., 1994) and the ribosomal 18S gene

(Hamilton et al., 2009). COI is routinely used in DNA barcoding due to its use in identification of animals to the species level, as well as its use in discovery of cryptic species (e.g. Witt et al.,

2006). However, whether this gene would be useful in eDNA research for uncovering community diversity has only been explored in one study (Yang et al., 2013). Animal-specific

18S genes, on the other hand, have been amplified from DNA in aquatic and terrestrial environmental samples (Hamilton et al., 2009; Wu et al., 2011). However, ability to taxonomically categorize sequences of this gene has been generally limited to higher levels of animal taxonomy. In chapter 2, I sought to test primer sets specific to these genes for use in targeting animal DNA in traditional PCR, a step leading up to DNA sequencing technology.

Once a suitable gene was identified for use in targeting metazoan environmental DNA, I used this technology to uncover spatial patterns of invertebrate communities in a temperate deciduous forest landscape (Chapter 3). As previously stated, invertebrates play a large role in regulating ecosystem structure and function, with the community in a given area often mediating

3 ecosystem processes such as decomposition. As such, it is important to understand how these communities are structured through space, which is a difficult task given that spatial patterning and appropriate spatial scales for use in studying invertebrate communities have not been quantified. In order to fully understand spatial patterns of a taxonomic group, one must analyze multiple spatial scales (Chase & Leibold, 2002; Ettema & Wardle, 2002). In fact, in my research, I uncovered community compositional patterns on local to landscape scales in the soil and leaf litter zones, providing unprecedented understanding of invertebrate community relationships and spatial patterns in temperate forests.

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Chapter 2. A primer comparison for molecular identification of invertebrate taxa from soil and

leaf litter environmental DNA

Abstract

The use of environmental DNA (eDNA) for community analysis (i.e., eDNA metabarcoding) is becoming more commonplace within molecular ecology. However, molecular methods for use in soil animal studies require further development before high throughput sequencing can be considered a reliable technique in community ecology. To aid in this effort, I compare two frequently used genetic markers (mitochondrial COI and ribosomal 18S genes) to determine which is most appropriate for use in soil animal eDNA studies. DNA was analyzed from individual invertebrate species to test the efficiency of the primer sets in successfully targeting animal DNA. Primers were also tested for amplification of faunal genes from forest soil and leaf litter eDNA. Targeting the 18S gene resulted in the most successful amplification and correct identification of a wide range of individual invertebrate taxa, and was the most reliable gene for use in eDNA analysis. In contrast, the COI primers were inefficient in identifying a wide range of invertebrates, and amplified mostly bacterial sequences from eDNA.

Introduction

Terrestrial invertebrates, particularly those associated with soil, commonly manifest high taxonomic and functional diversity (Garcia-Palacios, 2013; Wardle, 2006), often playing an

11 important role in structuring local food webs. As a result, it is important to uncover and accurately characterize this biodiversity to fully understand the relative importance of individual taxa within the complex soil community. However, quantifying species richness in soil can be a daunting task, with 1 m2 of temperate forest soil often containing more than 1,000 invertebrate species (Decaëns, 2010). These taxa can display wide phylogenetic diversity, spanning multiple phyla (Giller, 1996) and a broad size range (from small nematodes and mites to earthworms, centipedes and millipedes).

Traditionally, soil animal community studies have used identification methods based strictly on organismal morphology (Barrett et al., 2004; Hansen, 2000; Salamon et al., 2006;

Treonis et al., 1999). This approach requires significant taxonomic expertise, often restricting the number of taxa that can be simultaneously surveyed and deterring many scientists from conducting soil invertebrate research. Morphological identification approaches are also exceedingly time-consuming for taxonomists seeking to uncover biodiversity at lower taxonomic levels in large numbers of samples. Given the time requirements and importance of taxonomic expertise, robust molecular techniques that can be used to examine communities through mixed- species environmental DNA (eDNA) analysis may prove useful for unearthing soil biodiversity.

These methods have the potential to identify a diverse community of invertebrates in large numbers of samples (Taberlet et al., 2012a). In particular, high throughput sequencing of eDNA is a promising method for rapidly revealing biodiversity in an increasingly inexpensive manner

(Shokralla et al., 2012; Taberlet et al., 2012b).

However, before high throughput sequencing can become an effective tool for use in soil animal biodiversity studies, PCR primers targeting universal genetic markers must be developed and validated (Taberlet et al., 2012b). Primer selection is a critical step in targeting specific

12 taxonomic groups in eDNA analysis, as primers ultimately determine the accuracy and taxonomic breadth of acquired datasets. An optimal primer set should accurately amplify a targeted marker gene from eDNA, which can then be used to match sequences to specific, desired levels of taxonomic identification. Currently, there are only two genes that can be targeted in invertebrate DNA studies with appropriate background databases for use in sequence identification. The mitochondrial cytochrome oxidase subunit I (COI) has been frequently used in DNA barcoding studies of individual invertebrate specimens (Barrett & Hebert, 2005; Costa et al., 2007; Hajibabaei et al., 2006; Jung et al., 2011), and can be effectively used to determine taxonomic identity at the species-level (Hebert et al., 2003a; Hebert et al., 2003b). However,

Yang et al. (2013) used the COI gene with soil eDNA and found it to be inefficient for identification of eukaryotic taxa. The 18S ribosomal RNA gene has also been used in soil fauna surveys, including several eDNA studies (Hamilton et al., 2009; Robeson et al., 2009; Wu T. et al., 2011; Yang et al., 2013). However, invertebrate 18S ribosomal gene sequences can typically only be identified to higher taxonomic levels (i.e., not to the level of species; Tang et al., 2012;

Yang et al., 2013). Thus, based upon previous studies, it is not clear which gene is most appropriate to target in eDNA studies of animal communities.

In this study, I sought to determine whether 18S rRNA or COI genes would be most suitable for targeting animal taxa in studies of eDNA obtained from soil and leaf litter. One commonly used primer set was chosen for each of these genes. First, I used these primers on genomic DNA extracted from individual voucher specimens of common temperate forest invertebrates to determine primer set success and taxonomic specificity of identification using the resulting genetic sequences. Second, using eDNA extracted from soil and leaf litter samples collected from a temperate forest, I determined which primer pair was most effective at

13 uncovering invertebrate diversity in natural communities. I hypothesized that using the COI gene would result in invertebrate identification at lower taxonomic levels (i.e., species-level).

However, given the success of previous eDNA studies (Hamilton et al., 2009; Wu T. et al.,

2011), and the lack of previous successful metazoan identification of eDNA using the COI gene

(Yang et al., 2013), I also anticipated that 18S ribosomal sequences would allow identification of a wider breadth of invertebrate DNA from eDNA.

Methods

Individual voucher specimen collection

Invertebrate specimens were collected using pitfall samples from Jennings Woods, a temperate forest in northeastern Ohio, USA (site description in Blackwood et al., 2013). To test primer pair breadth and specificity, 94 invertebrate taxa (stored in ethanol) were chosen, spanning multiple phyla. Each specimen was identified to varying levels of taxonomic resolution using microscopy (Table 1). Several replicates of certain collembolan taxa were also chosen to investigate the presence of cryptic diversity.

Primer testing on invertebrate voucher specimens

DNA was extracted from each specimen using the prepGEM™ Insect kit (ZyGEM,

Hamilton, New Zealand), and PCRs were completed with a Stratagene Robocycler thermocycler

(Agilent Technologies, Santa Clara, CA). PCRs targeting the COI gene were carried out using

14 primer set Fol-degen-for (5’ TCNACNAAYCAYAARRAYATYGG 3’) and Fol-degen-rev (5’

TANACYTCNGGRTGNCCRAARAAYCA 3’) (Yu et al., 2012). Reagents for these reactions consisted of 0.03 U taq, 1X BUF, 0.2 mM dNTPS, 1.5-2.5 mM MgCl2, 1 mg/mL BSA, 0.16-0.4

µM PR-f, and 0.16-0.4 µM PR-r. Conditions set for the PCRs consisted of: initial denaturation at 94˚C for 1 min., 35-50 cycles of denaturing at 45 sec. at 94˚C, annealing for 45 sec. at 40˚C, and extension for 45 sec. at 72˚C, followed by a final extension at 72˚C for 10 min. PCRs targeting the 18S rRNA gene were run using primer set 18S11b (5’

GTCAGAGGTTCGAAGGCG 3’) and 18S2a (5’ GATCCTTCCGCAGGTTCACC 3’)

(Hamilton et al., 2009). Concentrations of reagents for these reactions were similar to those described above. Conditions for these PCRs consisted of: initial denaturation at 94˚C for 3 min.,

35-45 cycles of denaturation at 94˚C for 30 sec., annealing at 55˚C for 30 sec., and extension at

72˚C for 90 sec., followed by a final extension at 72˚C for 10 min. Number of cycles for each sample was varied to achieve successful PCR amplification, as determined by gel electrophoresis. PCRs for each specimen sample were replicated and pooled together. 18S

PCRs were purified using the UltraClean® PCR Clean-Up Kit (MoBio, Carlsbad, CA, USA).

COI PCRs were gel-purified using the UltraClean® GelSpin® DNA Extraction Kit (MoBio,

Carlsbad, CA, USA) due to the occurrence of nonspecific amplification of alternate DNA segments. DNA concentration in cleaned-up PCRs was quantified using Quant-iT™ PicoGreen® dsDNA Reagent (Life Technologies, Carlsbad, CA, USA). All samples derived from individual invertebrate specimens were sent to the Advanced Genetic Technologies Center at the University of Kentucky, KY, USA for Sanger sequencing. Sequences were trimmed for quality using

Sequencher (Gene Codes Corporation, Ann Arbor, MI, USA) and compared to the NCBI database using the BLAST algorithm (Altschul et al., 1990). BLAST results were summarized

15 using the last common ancestor algorithm in MEGAN 5 (Huson et. al., 2011) using default parameters.

eDNA in Environmental Samples

Soil and leaf litter samples were collected during August 2011 from Manistee National

Forest, a temperate deciduous forest located in the Northwestern region in the Lower Peninsula of Michigan. In an attempt to target eDNA originating from a wide breadth of terrestrial faunal species, leaf and soil (10 cm depth) samples were collected from three forest types, Black Oak-

White Oak (BOWO), Sugar Maple-Red Oak (SMRO), and Sugar Maple-Basswood (SMBW), varying in dominant tree species, soil properties, and leaf litter quality (Host et al., 1988; Zak et al., 1989). Samples were collected with sterilized equipment and immediately placed on dry ice for transportation back to the laboratory. In the lab, soil samples were passed through a 2mm sieve. Leaves were homogenized by grinding in a ZM100 grinder (Retsch, Haan, Germany; 12 tooth rotor, 0.5 mm mesh ring sieve). Samples were stored at -80˚C until DNA extraction.

eDNA was extracted from soil samples using the PowerSoil® DNA Isolation Kit (MoBio,

Carlsbad, CA, USA). eDNA was extracted from leaf samples using the CTAB method (Wu L. et al. 2011). PCRs were carried out using a Stratagene Robocycler (Agilent Technologies, Santa

Clara, CA) thermocycler, using a similar approach as described above for the individual invertebrate voucher specimens. To obtain successful amplification, annealing temperature needed to be adjusted to 40-47˚C for COI primers to achieve amplification from eDNA.

Successful PCRs were replicated and pooled together for quantification using Quant-iT™

PicoGreen® dsDNA Reagent (Life Technologies, Carlsbad, CA, USA). Using a 1% agarose gel,

DNA cleanup was completed using GelSpin® DNA Extraction Kit (MoBio, Carlsbad, CA, USA) for DNA obtained from both primer sets due to nonspecific amplification.

PCR amplicons from eDNA were cloned using a TA Cloning® Kit and the vector pCR

2.1 (Life Technologies, Carlsbad, CA, USA). Following ligation, plasmids were used to transform One Shot® TOP10 Chemically Competent E. coli cells (Life Technologies). Two- hundred eighty-eight colonies were picked from the six samples and grown in LB broth.

Sequencing of clones was completed with M13 primers at the Advanced Genetic Technologies

Center (University of Kentucky, KY, USA).

Sequences acquired from eDNA samples were analyzed for community composition using Megan 5 with LCA parameters similar to those used for individual invertebrate sample sequences described above. Sequence data was consolidated by habitat type (soil and leaf litter) for further analyses.

Results

Invertebrate voucher specimens

Amplification success occurred across a broad taxonomic range for both targeted genes.

Overall, 85 out of 94 (90.4%) individual invertebrate DNA samples were successfully amplified when targeting the 18S region. However, the COI gene was successfully amplified from only 58 out of 94 (61.7%) invertebrate DNA samples. Collembola (the most tested taxonomic group) amplified successfully using both primer sets, although the 18S primer set outperformed the COI

17 primer set, as it did with most other taxa (Table 1). Only one nematode specimen was amplified by the 18S primer set, while the COI primer set failed to amplify this taxonomic group.

We sequenced 18S amplicons from 58 specimens, and COI amplicons from 36 specimens, which were chosen to represent a wide range of the successfully amplified taxa.

Forty-three of the resulting 18S sequences (74.1%) agreed with the corresponding morphological identifications (Table 1). In contrast, 13 (36.1%) of the COI sequences agreed with morphological identifications (Table 1). 18S sequences could be classified to taxonomic levels between subclass and family, while correctly identified COI sequences ranged from superphylum to species (Table 1).

Environmental soil and leaf samples

Overall, 79.3% of environmental 18S sequences were identified as Metazoa across soil and leaf litter samples (Fig. 1). These sequences were then assigned to phyla, including (but not limited to) Arthropoda, Tardigrada, Gastrotricha, and Nematoda, which demonstrates the wide scope of taxa that were successfully amplified and identified from eDNA (Fig. 1, 2). Low numbers of soil sequences obtained was a result of unsuccessful cloning of amplicons originating from the soil samples. Remaining sequences were either misamplified or misidentified as plants, assigned to taxonomic levels higher than Metazoa, or did not identify with sequences within the

NCBI database. Taxonomic specificity of 18S sequence classifications ranged from phylum down to genus, depending on the taxa (data not shown).

In contrast, only 45.6% of environmental COI sequences were identified as belonging to

Metazoa across leaf litter and soil samples (Fig. 1). Instead, most COI sequences were bacterial

(52.1%), assigned to taxonomic levels greater/other than Metazoa, or unable to match with sequences within the NCBI database. Of the eight metazoan sequences uncovered, only six of these sequences had their best matches to sequences from animals that would be likely to be present in a temperate forest ecosystem.

Discussion

Environmental sequencing of invertebrate communities depends on use of efficient and accurate PCR primers that target an informative gene shared by this broad group of organisms.

My results demonstrate clearly superior performance using an assay targeting the 18S gene, as compared to an assay targeting the COI gene. The 18S primers amplified a broader taxonomic range of individual invertebrates during PCR when compared with the COI primers. Further, using 18S sequences resulted in increased accuracy of taxonomic identifications for individual invertebrate specimens relative to using COI sequences. This is inconsistent with my prediction based upon widespread use of the COI marker in species-level identifications and DNA barcoding studies. However, the COI marker had not previously been rigorously tested for use in community-level eDNA studies, despite being proposed as a favorable alternative to the 18S marker (Tang et al., 2012).

COI sequences acquired from mixed-species eDNA were largely of bacterial origin (Fig. 2), a result consistent with Yang et al. (2013). The presence of bacterial DNA was not surprising as eDNA contains a diverse mixture of DNA originating from organisms spanning the three domains. Given this, a proper primer set for eDNA studies must be able to discriminate among all DNA sequences of non-target organisms present in a sample (in this case, bacteria, fungi,

19 plants, etc.) (van der Heijden et al., 2008). These results suggest that the flanking regions commonly used as targets for PCR amplification of metazoan COI DNA are similar to regions of

DNA within genomes of other taxonomic groups, including the bacteria amplified from eDNA in this study. The degeneracy of the COI primer set may partly explain why it amplified primarily bacterial sequences (Yu et al., 2012), although it is unlikely that this attribute alone accounted for all issues. More generally, the COI marker has been most effectively used when identifying

DNA of individual specimens or mixtures of invertebrates that have been extracted from an environmental sample and then homogenized into a single sample (Yu et al., 2012), which would allow increased exclusion of DNA from alternate taxonomic groups found within the soil (e.g., bacteria, fungi, plants, etc.). COI was also difficult to amplify from voucher specimens, and it has been previously observed that the “universal” primer set may not be as universal as once presumed (Geller et al., 2013, Lobo et al., 2013). A final problem noted with the COI gene was that some COI sequences of metazoan descent matched database sequences of organisms unlikely to be present in my terrestrial samples, including marine, freshwater, or non-local taxa.

However, of the few invertebrate sequences that were obtained from specimens for the COI gene, several of them were identified to the level of genus and/or species (Table 1).

The 18S primer set appears to have significant advantages for use in eDNA studies due to its ability to specifically and consistently amplify animal DNA, despite its proposed limitations in classifying sequences to lower taxonomic levels (Yang et al., 2013). This problem may not be as extensive as previously indicated, however, because I found that sequences could be identified confidently to taxonomic levels as low as family or genus in several taxa (Table 1).

While targeting environmental DNA has been proven to be useful for biodiversity studies, caution must be taken when interpreting obtained sequence data. For example,

20 degradation and persistence of eDNA depends on specific local environmental conditions

(Andersen et al., 2012) and eDNA could last for many years within an environment, which could in turn originate from both living or dead organisms (Thomsen & Willerslev, 2015).

Additionally, gene copy numbers vary between species (Hamilton et al., 2009), and PCR biases can preferentially amplify the DNA of organisms dependent upon annealing affinity of the primer set (Thomsen & Willerslev, 2015). Taxonomic identification through eDNA sequencing is also dependent upon the reference database which the sequences are compared to (Taberlet et al., 2012b), which are reliant on previous sequence identification of morphologically identified taxa. As such, data obtained from eDNA sequencing must be carefully interpreted.

Based upon this study, targeting the ribosomal 18S gene is favorable to using the COI gene in studies targeting community faunal DNA from environmental samples. While the COI marker has been found useful in identification of metazoans to the species level in specimen-level barcoding studies, it is clear that the current universal primer sets available for the COI gene are not effective at separating animal DNA from bulk environmental DNA for taxonomic community-level studies. In contrast, the accuracy and consistency of results generated by the

18S primer provide a reliable assay for eDNA studies centered on identification of metazoan taxa.

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BLAST. Phylum is the lowest taxonomic level represented in this figure for clarity.

100%

90%

80%

70%

60% Unclassified Tardigrada 50% Nematoda Gastrotricha 40% Arthropoda

30%

20%

10%

0% COI 18S

Table 1. Comparison of invertebrate specimen identifications determined using traditional

O: Order; sO: Suborder; iO: Infraorder; SF: Superfamily; F: Family; sF: Subfamily; G: Genus; S:

Morphological ID 18S DNA ID COI DNA ID Collembola - Bourlettiellidae (2; F) Collembola (1; O) ◊ - Hypogastrura (1; G) Symphypleona (1; sO) No Amplification (1)

Collembola - Dicyrtomidae (7; F) Collembola (1; O) Arthropoda (1; P) Symphypleona (6; sO) No Hits (6)

Collembola - Entomobryidae (10; F) Entomobryidae (8; F) Lepidocyrtus paradoxus (1; S) Entomobryoidea (2; SF) Entomobrya (1; G) No Hits (1) No Amplification (7)

Collembola - Hypogastruridae (1; F) No Amplification ◊ - Entomobryidae (1; F)

Collembola - Isotomidae (11; F) ◊ - Entomobryidae (4; F) ◊ - Paronellidae (1; G) Isotoma (2; G) No Hits (4) Isotomidae (1; F) No Amplification (6) ◊ - Orchesellinae (4; sF)

Collembola - Katiannidae (2; F) Symphypleona (2; sO) No Amplification (2)

Collembola - Tomoceridae (6; F) Tomoceridae (6; F) Collembola (1; O) No Hits (1) No Amplification (4)

Coleoptera (O) Nitidulidae (F) No Hits

Coleoptera - Pterostichus melanarius (S) No Amplification ◊ - Pterostichus coracinus (S)

Coleoptera -Curculionidae (F) ◊ - Psocidae (F) No Amplification

Coleoptera -Staphylinidae (F) ◊ - Psocidae (F) No Amplification

Diptera (O) Muscomorpha (iO) Brachycera (sO)

Hemiptera (O) Heteroptera (sO) Drymus (2; G)

Hemiptera - Cicadellidae (F) Hemiptera (O) No Amplification

Hymenoptera - Camponotus No Hits Camponotus (G) pennsylvanicus (S)

Hymenoptera - Diapriidae (F) Hymenoptera (O) No Amplification

Lepidoptera (O) Neolepidoptera (iO) Renia adspergillus (S)

Orthoptera - Ceuthophilus maculatus (S) Rhaphidophoridae (F) No Hits

Psocodea (O) No Amplification Neoptera (sC)

Arachnida - Acari Morph 1 (sC) Anystis (G) No Amplification

Arachnida - Acari Morph 2 (sC) Monogynaspida (sO) No Amplification

Arachnida - Acari Morph 3 (sC) No Amplification No Hits

Arachnida - Acari Morph 4 (sC) No Amplification No Hits

Arachnida - Wadotes hybridus (S) ◊ - Psocidae (F) No Amplification

Arachnida - Opiliones (O) Palpatores (sO) Leiobunum vittatum (S)

Arachnida - Pseudoscorpionida (O) No Hits Chthonius tetrachelatus (S)

Chilopoda - Lithobiidae (F) No Amplification No Hits

Diplopoda - Julidae (F) Helminthomorpha (sC) No Amplification

Diplopoda - Oxidus gracilis (S) Polydesmida (O) No Amplification

Isopoda - Oniscus asellus (S) ◊ - Psocidae (F) Arthropoda (P)

Gastropoda - Arion subfuscus(S) No Amplification No Hits

Clitellata - Lumbricidae (F) Megascolecidae (F) No Hits

Nematoda (P) ◊ - Psocidae (F) No Amplification

Chapter 3. High-throughput sequencing reveals high soil faunal diversity and small-scale

community turnover in temperate forests

Abstract

Invertebrate communities are important in regulating processes and services in terrestrial ecosystems. Spatial patterns, therefore, are important in understanding the extent of these communities across a landscape, as community composition shifts over space through environmental heterogeneity and species interactions. However, current research is grounded in examining specific groups of taxa (e.g. Collembola, mites, nematodes, etc.), and a comprehensive understanding of factors structuring total invertebrate communities on multiple spatial scales has yet to be accomplished. While this would be a daunting task using traditional microscopy, recent advances in DNA sequencing technology has allowed for efficient alternative methods to uncover the composition of local invertebrate biodiversity. In this study, I uncovered spatial patterns of the full invertebrate community within soil and leaf litter from local to landscape scales in a temperate forest system. I accomplished this through high-throughput

DNA sequencing of environmental samples along a spatially-nested design within multiple forested ecosystem types. Habitat and ecosystem type were found to be important in structuring invertebrate communities on all levels of scale. Soil environments harbored higher α-diversity and β-diversity with respect to leaf litter. Increased habitat heterogeneity likely explains elevated

33 levels of diversity within forest soil. Vegetative community composition is likely driving faunal compositional differences between ecosystems through variances in plant species attributes and litter composition, as well as soil physical properties. Several taxonomic groups were preferentially associated to particular habitats and ecosystems, potentially as consequence of food web structure. Spatial structuring was weak from local to regional scales, however, significant autocorrelation of species turnover was found at fine distances of 1-5 m across the forested landscape. Small-scale habitat heterogeneity established by zones of plant influence could influence species distributions at these short scales. Exploring smaller spatial scales may uncover increased levels of spatial structuring, and the role of tree zones of influence should be taken into account for future research. This study has exemplified the usefulness of environmental DNA sequencing in uncovering terrestrial fauna community patterns in a nontransparent terrestrial environment.

Introduction

Soil fauna serve important roles in nutrient cycling, soil structure, plant productivity, food web structure, and other direct and indirect ecosystem services (Decaëns et al., 2008). The effects of soil invertebrates can vary depending on taxonomic composition, with different taxa often filling different ecological niches (e.g. Hättenschwiler & Gasser, 2005; Lavelle et al., 2006;

Lenoir et al., 2007; Wardle et al., 2004). Change in community composition of organisms over space (i.e., beta-diversity) can occur in both homogenous and heterogeneous environments as a result of population dynamics, environmental filtering (Ettema & Wardle, 2002), or neutral processes (Hubbell, 2001). However, in comparison with above-ground organisms, the diversity

34 and distribution of belowground organisms have rarely been studied (Decaëns, 2010). As a result, there is a deficit in current knowledge of faunal diversity and spatial patterning in soil and leaf litter habitats, despite the importance of fauna in terrestrial ecosystems.

Differences in taxonomic diversity within these habitats can be driven by many potential factors, including habitat heterogeneity (Ettema & Wardle, 2002) and energy availability (Chase

& Leibold, 2012; Giller, 1996, Wright, 1983). Physical structure of soil (e.g., aggregation and decreased pore size) can result in increased habitat heterogeneity and isolation of habitat patches by restricting faunal mobility and water and chemical mixing (von Luetzow et al., 2006). As such, α-diversity should increase with soil physical structure. Finer soil texture also leads to increased soil moisture, which has been associated with higher plant productivity and overstory biomass (Host et al., 1988), increasing energy available to invertebrates through leaf litter and root exudation and turnover. α-diversity has been shown to increase with productivity at regional scales (Chase & Leibold, 2002), so I would expect to see higher α-diversity in ecosystems with higher amounts of energy transferred to the invertebrate food web through increased primary production. However, plant litter chemistry should also play an influential role in faunal diversity within leaf litter, as different plant species provide differing and variable amounts of available resources (Kögel-Knabner, 2002) which faunal species utilize differently.

Because soil physical structure is not influential in leaf litter, leaf type and abundance should have the largest effect on community diversity in that habitat.

Mechanisms generating β-diversity, such as habitat heterogeneity and dispersal limitation, can be inferred through investigating community spatial structure and distance-decay relationships at multiple scales (Ettema & Wardle, 2002; Nekola and White, 1999). At regional

(i.e., ecosystem-level) scales, forest stand type can influence variability in faunal community

35 composition (Aubert et al., 2005; Sylvain & Buddle, 2010). This variability can be driven by differences in dominant tree species identity (Eissfeller et al., 2013; Negrete-Yankelevich, 2008) and soil properties (Nielsen at al., 2010, Vanbergen et al., 2007). Further, microbial community composition is affected by resource availability, soil properties, and vegetation types (Hackl et al., 2005; Lauber et al., 2008; Myers et al., 2001), and these differences in microbial communities could influence metazoan community composition through trophic interactions and feeding preferences (Ferris & Matute, 2003; Hedlund et al., 2004). Despite these trends, many previous studies examining soil invertebrate spatial structure have not involved ecosystem-level replication, so spatial patterns at a landscape scale cannot be confidently extrapolated. Prior research also generally focuses on small subsets of faunal taxa, and therefore does not account for landscape patterns of the entire community.

Community spatial patterns at local scales (within ecosystems) are likely driven by additional environmental factors, such as soil pore size and rhizosphere zones of individual trees

(Bonkowski et al., 2009). In addition, neutral processes, such as priority effects (i.e., effects of the order of colonization by different species) and dispersal limitation, could further structure faunal communities at local scales. Previous studies have examined small-scale community structure typically only for specific subsets of the faunal community, and in soil or leaf litter without comparing the two habitats. For example, spatial patterns of soil mite communities at local scales are likely influenced by both environmental filtering and dispersal mechanisms in grassland and forest ecosystems (Caruso et al., 2012; Nielsen et al., 2012). Further, nematode genera exhibit aggregated distance-decay patterns in forest soils on short scales (30 cm - 4 m), which suggests nematode communities can be structured by small-scale habitat heterogeneity attributed to soil properties and resource heterogeneity (Ettema & Yeates, 2003). As

36 consequence of the limited taxonomic scope of previous research, there is limited generalizable information in the literature regarding spatial structure of entire faunal communities within soil and leaf zones across entire landscapes.

While comprehensively examining soil faunal communities is a daunting task when using traditional collection and microscopic identification methods, such a feat is facilitated through use of molecular methods to examine environmental DNA (eDNA). These methods have revealed patterns in a broad range of invertebrate taxa on a global scale (Wu T. et al., 2011).

High-throughput sequencing technology has revolutionized the world of microbial ecology, allowing deep sequencing of environmental samples to provide high resolution community composition data sets (Shokralla et al., 2012), and unveiling patterns in microbial communities at taxonomic levels that were masked by low-resolution techniques (Hartmann et al., 2014). We suggest that these same techniques can be applied to eDNA found in soil and leaf samples to uncover a broad range of animal taxa present in these habitats.

My study investigated diversity and distribution of invertebrate communities associated with soil and leaf litter in temperate deciduous forests. This is the first attempt to unmask total invertebrate community composition within a forested landscape using environmental DNA and high-throughput sequencing techniques. I compared three ecosystem types within that landscape to discover patterns of species turnover in soil and leaf litter environments from local to regional scales. More specifically, I tested the following hypotheses: Hypothesis 1a: I anticipate α- diversity to be higher in soil versus leaf litter as a result of increased levels of isolation of habitat patches and reduced dispersal ranges in soil. Hypothesis 1b: I further expect higher α-diversity in ecosystems with higher amounts of labile organic matter because this should correlate with the rate of energy supply to the saprotrophic food web (Chase & Leibold, 2002; Evans et al., 2005;

Wright, 1983). Hypothesis 2: Differences in habitat type (soil vs. leaf litter) are expected to have a stronger effect on faunal β-diversity (i.e., community composition) than ecosystem type. Leaf litter and soil are fundamentally different habitats both physically and chemically, thus, I anticipate these habitat differences to be stronger than differences in ecosystem type in structuring faunal communities. Hypothesis 3: Finally, due to the density and lack of faunal mobility in soil environments relative to aboveground environments, I expect community distance-decay to occur over shorter distances (i.e., displaying shorter-range autocorrelation) within the soil matrix compared with leaf litter.

Methods

Study area and sampling design

Samples were collected from Manistee National Forest, located in the northwest region of

Lower Michigan. Three ecosystems were explored in detail, including black oak-white oak forests (BOWO), sugar maple-red oak forests (SMRO), and sugar maple-basswood forests

(SMBW). These ecosystem types vary in multiple environmental and geological attributes that have been delineated in previous studies (see Host et al., 1988; Zak et al., 1989). BOWO forest stands occur on glacial outwash plain landforms, with typic udipsamment soils that are extremely well-drained. SMRO and SMBW stands occur on glacial interlobate moraines and well-drained typic haplorthods. The soils of all sites primarily consist of sand (> 90%). However, silt, clay, and fine sand content was higher in stands located on moraines, whereas coarse and medium sands are higher in BOWO. These ecosystem types also differ in accumulated plant biomass per

38 year, understory vegetation community composition (Host et al., 1988), microbial community composition (Blackwood et al., 2007), and N-cycling rates (Zak et al., 1989).

I collected samples from three replicate forest stands of each ecosystem type. Within each site, a 1 km transect was established. Pairs of samples were taken at 16 locations along each transect using a spatially nested design. Samples within a pair were separated by 1 m in a direction perpendicular to the transect, essentially creating parallel transects. Distances among sampling points along the transects varied from 5 m to 1 km in order to capture spatial variation on a wide variety of spatial scales (Table 1). Thirty leaves were collected and pooled together at each sampling point. Soil (10 cm depth) was also collected at the same points using sterile equipment. Samples were stored and transported to the lab on dry ice. Leaves were homogenized by grinding in a ZM100 grinder (Retsch, Haan, Germany; 12 tooth rotor, 0.5 mm mesh ring sieve). Soil was passed through an ethanol-rinsed 2 mm-mesh sieve. Resulting samples were stored at -80˚C.

Community characterization

Environmental DNA was extracted from soil using the PowerSoil® DNA Isolation Kit

(MoBio, Carlsbad, CA, USA). DNA was extracted from leaf litter using a CTAB protocol (Wu

L. et al. 2011). To test my first and second hypotheses, extracted DNA from samples within each forest stand was pooled in equal volumes. This resulted in 18 pooled samples (9 leaf and 9 soil). Analysis of these pooled samples allowed me to compare invertebrate community composition on the regional scale (5-50 km). My third hypothesis was tested by analyzing each

39 individual sample along the parallel transects in one forest stand per ecosystem type, allowing me to examine community composition at a broad range of within-stand scales (1-1000 m; Table

1).

The 18S ribosomal gene was targeted during PCR using forward primer 18S11b (5’-

GTCAGAGGTTCGAAGGCG-3’) and reverse primer 18S2A (5’-

GATCCTTCCGCAGGTTCACC-3’) (Hamilton et al., 2009), which were modified with pyrosequencing barcodes for use in downstream analysis. This universal primer pair was chosen due to its ability to efficiently target metazoan taxa in eDNA (Yang et al., 2013; Chapter 2 of this thesis). PCR reagent concentrations were as follows: 0.03-0.05 U Taq polymerase (B-Bridge

International, Santa Clara, CA, USA), 1X PCR buffer, 0.2-0.24 mM dNTPS (New England

Biolabs, Ipswich, MA, USA), 1.5-2.5 mM MgCl2, 1 mg/mL bovine serum albumin, 0.16-0.36 uM of each primer (Integrated DNA Technologies, Coralville, IA, USA). PCR conditions consisted of an initial denaturation at 94˚C for 3 min, 29-42 cycles (until successful amplification of the targeted amplicon) of denaturation at 94˚C for 30 s, annealing at 55˚C for 30 s, and extension at 72˚C for 90 s, followed by a final extension at 72˚C for 10 min using a Stratagene

Robocycler platform (Agilent Technologies, Santa Clara, CA). PCR amplicons were electrophoresed on 1.5% agarose gels to check for nonspecific amplification and contamination.

Replicate PCR products for each sample were pooled together and run on a 1% low- melting point agarose gel for purification. Bands were excised and purified using the

UltraClean® GelSpin® DNA Extraction Kit (MoBio, Carlsbad, CA, USA). PCR amplicons were then further purified using Agencourt AMPure beads (Beckman Coulter, Inc., Pasadena, CA).

Purified amplicons were then run on FlashGel™ cassettes (Lonza Group, Basel, Switzerland) to ensure purifications had removed nonspecific amplification and primer dimers. DNA

40 concentration was measured using Quant-iT™ PicoGreen® dsDNA Reagent (Life Technologies,

Carlsbad, CA). PCR amplicons were then pooled into sets of 30 samples for pyrosequencing.

All pyrosequencing and sequencing preparation steps were conducted using 454 high- throughput DNA sequencing technology (Hoffman-La Roche, Basel, Switzerland). Emulsion

PCR was performed using the Titanium emPCR Lib-L kit. Pyrosequencing was accomplished using a 454 GS Junior System following the manufacturer’s instructions. Individual samples that did not result in a sufficient amount of sequences were sent to the Advanced Genetic

Technologies Center at the University of Kentucky (Lexington, KY) for further purification, emulsion, and sequencing on the 454 Genome Sequencer FLX System.

Sequence data was analyzed using QIIME v1.8.0 (Caporaso et al., 2010). Sequence libraries were demultiplexed, followed by clustering into OTUs based on 97% sequence similarity using Uclust default parameters. Nearest neighbors of representative sequences for each OTU were searched for in the NCBI sequence database using BLAST (Altschul et al.,

1990), with database environmental sequences excluded from the search. Taxonomic assignments for each OTU were made (to genus if possible) by the “last common ancestor” algorithm applied to BLAST results using MEGAN 5 (Huson et. al., 2011), followed by manual assignment of sequences not classified below the phylum level. Singletons and doubletons were filtered from the sequence dataset and were removed from further analyses.

Data Analysis

Variability at the landscape scale among sites. Data analysis was performed using

RStudio (Version 0.98.932) and the Vegan (Oksanen et al., 2007) and nlme (Pinheiro et al.,

2007) packages. To calculate Hill number α-diversity metrics (Hill, 1973), rarefaction was performed by randomly sampling sequences to achieve evenly sampled communities.

Sequencing depth for each community was subsampled to 1,175, which was the amount of obtained sequences for the sample with the lowest sequencing depth. Average Hill numbers were used from 100 rarefactions per sample. The Hill numbers used represent a series of indices that place increasing weight on OTU evenness and decreasing weight on OTU richness. The indices include H0, H1, H2, and Hinf, representing total OTU richness, Shannon diversity, the inverse of Simpson entropy, and the Berger-Parker index, respectively (Hill, 1973; Jost, 2006).

Mixed-model ANOVAs were used to test Hypothesis 1a and 1b and detect effects of ecosystem, habitat type (soil vs. leaf litter), and interactions on α-diversity of forest stand communities.

To test Hypothesis 2 and calculate β-diversity between forest stands, rarefaction was performed to simulate evenly sampled animal communities, followed by Hellinger transformation to create a distance matrix based on quantitative data and excluding joint- absences. Redundancy analysis (RDA; Legendre & Legendre 1998) was then performed to determine the effects of ecosystem type, habitat type, and interactions on community composition of forest stands. P-values were obtained by comparing the empirical pseudo-F statistic to the distribution obtained from 500 random permutations of sample identity under the null hypothesis of no effects of habitat or ecosystem type.

Anderson’s test of multivariate homogeneity of variances (Anderson et al., 2006) was implemented to determine whether β-diversity measured within habitat and ecosystem types was significantly different among habitat or ecosystem types. This analysis was based on Hellinger distance, and significance of differences in β-diversity were assessed using post-hoc Tukey tests.

Indicator species analysis (De Cáceres et al., 2010) was performed using the point- biserial correlation index on all levels of taxonomic hierarchy (phylum, class, order, family, genus, and OTU) to detect groups preferentially associated with either a specific habitat type or ecosystem type. Significance values for correlations were calculated by permutation (De

Cáceres et al., 2010).

Cytoscape v3.2.1 (Shannon et al., 2003) was used to represent taxonomic hierarchies of uncovered faunal communities similar to Hartmann et al. (2014). Results of indicator species analysis were mapped onto taxonomic nodes within each map.

Within-site spatial analysis of animal community turnover. To test Hypothesis 3, β- diversity (Hellinger distance) within each ecosystem and habitat was compared to geographic distance. To avoid exclusion of a large amount of samples while obtaining substantial sequencing depth, sampling points were only used in this analysis if more than 400 sequences were obtained, and rarefaction was performed to equalize sampling depth. Rarefaction and the analyses were repeated to determine the robustness of results. Mantel tests (Mantel, 1967) were performed on each dataset to test for a linear relationship between community Hellinger distance and geographic distance values. Rate of community turnover was also analyzed for non-linear patterns of autocorrelation by aggregating community distances by spatial distance class, using spatial distance classes that had been established before sampling. Significant autocorrelation was tested for by comparing the empirical average Hellinger distance in a spatial distance class to the value obtained during random permutation of geographic coordinates under the null hypothesis of no spatial autocorrelation. Anderson’s test was implemented to assess differences in community dispersion of sampling points between habitat and ecosystem types.

Results

A total of 425,046 environmental faunal sequences were uncovered in the soil and leaf litter habitats for analysis after removal of low quality sequences, as well as singletons and doubletons. Obtained sequence totals for local community samples ranged from 41 - 14,584 sequences. 38 samples were removed from analyses as consequence of low sequencing depth (<

400 sequences). Obtained sequence totals for regional community samples ranged from 1,175 –

6,873 sequences. Sequence coverage was sufficient based on rarefaction curves (Supplementary figure 1) and consistent significant results with multiple iterations of rarefied communities.

Hypothesis 1. Alpha-diversity in different habitat and ecosystem types

In agreement with Hypothesis 1a, habitat type (soil vs. leaf litter) significantly influenced faunal community diversity for all α-diversity Hill number metrics (Table 2). Soil communities had greater α-diversity than leaf litter, both in richness- and evenness-weighted Hill numbers

(Fig. 1). There were also significant interactions between habitat and ecosystem type for OTU richness (H0), and evenness-weighted Hill numbers (H2, Hinf) (Table 2). The strongest difference in the H0 index between soil and leaf litter environments was in the BOWO ecosystem (Fig. 1a).

In contrast, differences between soil and leaf litter for H2 and Hinf indices were much more pronounced in SMBW sites than BOWO and SMRO sites (Fig. 1c,d).

Within habitat types, differences in H0 among ecosystems were significant in leaves (P <

0.05) but not soil. In agreement with Hypothesis 1b, BOWO leaf litter communities had lower

OTU richness (H0) than SMBW and SMRO communities (Fig. 1). Differences in H2 and Hinf among ecosystems were significant in soil (P < 0.05) but not leaves. For these evenness-

44 weighted indices, and again in agreement with Hypothesis 1b, SMBW soil communities had much higher diversity values than the other two ecosystem types (Fig. 1).

Hypothesis 2. Effects of ecosystem and habitat type on community composition at the forest stand scale

Habitat, ecosystem type, and interactions between ecosystem and habitat explained significant variation in community composition according to RDA (P < 0.01). Habitat effects on variation in community structure were extensive (adj. R2 = 0.420), directly confirming

Hypothesis 2. Ecosystem (adj. R2 = 0.056) and interaction effects (adj. R2 = 0.046) on community composition were also both significant, but explained less variation than habitat type.

In the RDA ordination, habitats were strongly separated along the first axis. Separation along the second axis was based principally on ecosystem type (Fig. 2). Anderson’s Dispersion test showed lack of significant differences in dispersion (P < 0.05), suggesting that there were similar levels of variation among forest stands for different ecosystems and habitat types.

Indicator species analysis based on habitat type detected a significant association for one phylum, Nematoda, which was associated with the leaf litter habitat (rpb = 0.335). Two classes were also significantly associated with leaf litter habitat (Insecta, rpb = 0.332, and crustacean

Maxillopoda, rpb = 0.172). Two classes were associated with soil habitat (nematode class

Enoplea, rpb = 0.713, and Ellipura (including Collembola), rpb = 0.463). Further associations from order to OTU level can be seen in Fig. 3 and Supplementary Table 1. Indicator species associated with each ecosystem type were also searched for within leaf and soil habitats separately. Nematoda was significantly associated with SMRO sites in leaf litter (rpb = 0.29), and with SMBW sites in soil (rpb = 0.463). Three classes were linked to ecosystems within the

45 leaf litter habitat, including Ellipura and Insecta associated with SMBW (rpb = 0.32 and 0.24, respectively), and Enoplea with SMRO (rpb = 0.361). Several lower-level taxonomic groups were also indicative of specific ecosystem types within habitats (Figs. 4 and 5; Supplementary

Tables 2 and 3).

Hypothesis 3. Within-stand variation in animal community composition

Animal community distance decay was not linearly correlated with spatial distance over the entire range of transects for any habitat or ecosystem type (Mantel analysis, P > 0.05).

However, upon analysis of distance classes, autocorrelation was found over ~1-5 m distances

(i.e., variance was lower than expected at short distances, P < 0.05) for most ecosystems and habitat types (Fig. 6). This suggests that spatial patterns in metazoan community turnover may be apparent over finer local distances (< 5 m) rather than extended, more regional-scale distances ranging from 5 m-1 km.

Similarity in ranges of spatial autocorrelation in leaves and soil contrasts with Hypothesis

3, that community turnover would occur over shorter spatial ranges in soil. However, there was general support for the idea that β-diversity would be higher in soil than leaves across all scales within forest stands. β-diversity measures were consistently higher in soil samples than leaf samples in all ecosystem types and almost all distance classes (Fig. 6). Anderson’s multivariate dispersion test indicated that β-diversity was significantly higher in soil samples than leaf samples within each forest stand (i.e., higher dispersion of soil samples around the ecosystem centroid than in leaf samples) (P < 0.0001). In the PCoA ordination of within-stand community variation, there is a much larger dispersion of community composition in soil samples than leaf samples based on the first and third axes of ordination (Fig. 7).

The PCoA plot shown in Fig. 7 also confirmed Hypothesis 2, displaying the importance of leaf type and ecosystem type compared to within-stand variation. 95% confidence intervals around each sample type centroid indicated that communities varied significantly between habitat types (Fig. 7). BOWO leaf community composition was also significantly different from

SMBW and SMRO leaf communities on the second PCoA axis.

Discussion

Regional faunal diversity patterns

My results were consistent with the hypothesis that faunal α-diversity patterns would be primarily driven by habitat type, as α-diversity (both richness and evenness) in soil was much higher than in leaf litter. Similarly, Porazinska et al. (2012) found that nematode diversity was higher in soil versus leaf litter within a temperate rainforest ecosystem. Soil environments are characterized by a complex arrangement of roots, pores, and aggregates with varying spatial scales and connectivity, resulting in stable physical structure that restricts the mobility of most organisms (Ettema & Wardle, 2002). Together, these factors result in decreased habitat patch size, and increased isolation in soil (von Luetzow et al., 2006), allowing for increased diversity through higher environmental heterogeneity. This is in direct contrast with leaf litter, where organisms are more vagile and there are fewer mobility barriers.

My study also found significant differences in α-diversity among ecosystem types within each habitat. Lower faunal OTU richness in BOWO leaf litter could be related to the more chemically recalcitrant carbon and nutrient availability in BOWO compared with the leaf litter in

SMRO and SMBW stands. Leaf chemical composition can be limiting during decomposition

(Kögel-Knabner, 2002; von Luetzow et al., 2006) because few organisms can break down lignin and other recalcitrant compounds (Gessner et al., 2010). Species richness decreases with lower available energy (Wright et al., 1983; Evans et al, 2005), and it is likely that we see lower OTU richness in BOWO stands as a consequence.

α-diversity in the SMBW soil was higher than in SMRO and BOWO soil. This increase in diversity could also be linked to increased bioavailability of nutrients and labile organic matter in that ecosystem. However, the species-energy relationship is primarily focused on changes in richness with available energy, not evenness. SMBW soil had significantly higher evenness- weighted diversity indices (H2 and Hinf), but not OTU richness (H0), when compared with SMRO or BOWO ecosystems. Thus, higher resource availability alone may be inadequate to explain higher evenness in SMBW stands. Rather, this pattern is more likely explained by increased isolation of habitats as a result of decreased pore size in SMBW stands, preventing dominance of successfully competitive species. In contrast, SMRO and BOWO have increased coarse and medium sand content (large soil particles), potentially resulting in less habitat isolation.

Increased soil aggregation in SMBW stands could be driving this relationship as well, however, while there is evidence for a relationship between soil texture and porosity, predictability of soil aggregation based on soil texture alone is weak and more related to soil chemical properties

(Regelink et al., 2015), which were not explored in this study.

Beta diversity at the forest landscape scale

My results support Hypothesis 2, which states that habitat type is the primary factor that drives community composition, followed by ecosystem type. This is not surprising, as soil and leaf litter habitats are fundamentally different in physical and chemical attributes. As such, these

48 two habitats provide very different niches, possessing fauna with highly different physiologies, resource preferences, and life history strategies. Indeed, soil properties have a particularly important influence on community composition for oribatid and mesostigmatid mites (Nielsen et al., 2010), and mites and springtails have been found to be associated with forested ecosystems compared to grassland systems based on their preference for leaf litter (Birkhofer et al., 2012).

Faunal community composition was markedly different among ecosystem types in both soil and leaf litter habitats, with BOWO communities differing greatly from SMBW and SMRO communities. As these ecosystems differed in tree identity and leaf chemical properties, community composition was likely affected by faunal feeding preferences for specific resources provided by each leaf type. Indeed, terrestrial fauna span a broad range of specialized feeding behaviors (Giller, 1996; Nielsen et al, 2012; Porazinska et al., 2012), with many fungivorous or bacterivorous faunal taxa. Different leaf chemistries have pronounced effects on community composition of bacteria and fungi, as well as their relative biomass (Wu L. et al., 2011). As a consequence, faunal communities could be structured based on leaf chemistries (Hedlund et al.,

2004). In our landscape in particular, higher fungal to bacterial biomass ratios occur in BOWO leaf litter compared to SMBW litter (Gallo et al., 2004; Myers et al., 2001), as fungi are known to be more active in decomposing recalcitrant organic compounds such as in the BOWO ecosystem type (Neely et al., 1991; Blackwood et al., 2007). Therefore, I expect to find more fungivorous faunal taxa associated with BOWO stands compared to SMBW stands. In my results, I found that several hundred taxonomic groups and OTUs showed preference for a particular habitat or ecosystem. Similarly, Negrete-Yankelevich et al. (2008) and Eissfeller at al.

(2013) also found different dominant faunal taxa and communities among tree species and litter types.

Local (within-stand) community turnover

Community beta diversity was significantly lower than expected at the finest scales examined (1-5 m), suggesting that environmental heterogeneity or dispersal limitation is important in determining community composition at these small scales. Surprisingly little spatial structuring was found at larger within-stand scales (~5-1000 m), likely as a result of environmental stochasticity at larger scales and strong priority effects once a community has been established in smaller habitat patches. Thus, forested ecosystem types may appear to invertebrates to be spatially unstructured at larger scales, but could harbor small, discrete habitat patches at finer scales within soil and leaf litter. Habitat heterogeneity at the scale of 1-5 m could reflect the zones of influence of individual trees (Ettema & Wardle, 2002). The importance of tree identity in structuring community composition has been shown for several animal groups, including earthworms (Schwarz et al., 2015), oribatid mites (Eissfeller et al.,

2013), and nematodes (Cesarz et al., 2013). Individual plant species provide a unique combination of organic compounds, nutrients, and environmental niches, which in turn attract a specific community of microbes and fauna that specialize on those resources.

However, while our results suggest small-scale spatial structuring, it is likely that spatial patterns differ for alternative landscapes. Indeed, in contrast to my data, nematodes were found to be spatially structured at distances of up to 40 m in a broadleaf/podocarp forest by Ettema &

Yeates (2003). While our study did not find spatial structuring at distances greater than 5 m, I recognize that each environment may be idiosyncratic in physical, chemical, and biological structure, making it difficult to extrapolate patterns found here to other environments. In addition, we examined the entire community of fauna, encompassing a wide range of faunal

50 body sizes from the micro- to the macrofauna. Body size of organisms is an important factor in understanding the distribution of different taxonomic groups in soil (Decaëns, 2010; Ettema &

Wardle, 2002), primarily as consequence of mobility restrictions. Thus, although the comprehensive taxonomic approach we used provides for a high level of generalizability, it may mask patterns within more specific groups that could be examined in future research.

Interestingly, differences in dispersion among communities (β-diversity) depended upon the scale at which community composition was analyzed. Dispersion of faunal communities at the regional scale (between forest stands) was not significantly different between habitat or ecosystem types. However, soil communities were more variable than leaf litter communities at local scales within forest stands, suggesting that local community dissimilarity was higher in soil than leaf litter in my study. Increased magnitude of β-diversity among soil habitat types on the local scale could be due to higher local habitat heterogeneity within soil compared to leaf litter.

Microhabitats (such as small soil pores and aggregates) are likely present at scales smaller than I studied, which could increase isolation of habitat patches within soil. Isolation of habitat patches could increase diversity through decreasing species interactions and in turn decrease competition among taxa.

Conclusions

Historically, understanding spatial patterning of soil animal communities has been difficult due to the complexities of identifying small, cryptic, and highly diverse organisms in a nontransparent environment. My study utilized an innovative approach using environmental

DNA to uncover faunal community diversity and spatial patterning from local to landscape levels in a forested landscape. I showed that diversity of entire faunal communities is much

51 higher in soil than leaf litter, and higher in ecosystems with greater soil structure and available resources. Further, I also demonstrated clear associations for multiple taxonomic groups and

OTUs for each ecosystem and habitat type, which could indicate structuring of communities based on litter chemistry and soil properties. Intensive studies of individual regions will be important to understand global patterns in faunal communities. For example, in a previous study examining global patterns, low mean annual temperature in forested regions was associated with dominance of arthropods over nematodes (Wu T. et al., 2011). However, my forest sites were located in an area with low mean annual temperature (~ 7˚C), where I found relatively even numbers of arthropod and nematode OTUs (911 and 949, respectively), suggesting that previous conclusions may have been an artefact of very few samples. I suggest that total faunal communities within terrestrial environments should be surveyed at fine scales as well as across multiple ecosystem types within a landscape in order to truly extrapolate patterns of animal community composition on a global scale. Through the use of contemporary scientific techniques, ecologists will gain greater insight into the ecology of soil and litter animal communities at increasingly fine scales of resolution, providing better understanding of the importance of animal communities in driving processes, such as decomposition, in terrestrial ecosystems.

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Figure 1. α-diversity Hill metrics for each ecosystem. Histograms represent a) H0, b) H1, c) H2 and d) Hinf. Leaf habitats are represented by dark grey columns, and soil habitats are represented by light grey columns. Ecosystem types are represented by BOWO = Black Oak-White Oak,

SMRO = Sugar Maple-Red Oak, and SMBW = Sugar Maple-Basswood. Error bars represent standard error.

a. 200 b. 80 180 70 160 60 140 120 50 100 40 80 30

OTU OTU Richness 60

ShannonDiversity 20 40 20 10 0 0 BOWO SMRO SMBW BOWO SMRO SMBW

c. 40 d. 14 35 12 30 10 25 8

20 ParkerIndex

- 6 15 4

1 / 1 Simpson Index 10 Berger 5 2 0 0 BOWO SMRO SMBW BOWO SMRO SMBW

Figure 2. Redundancy analysis ordination of stand-level invertebrate community composition.

Green coloration represents leaf communities, magenta coloration represents soil communities.

Circles represent BOWO communities, squares represent SMRO communities, and triangles represent SMBW communities. Large symbols represent the centroids of each treatment.

Figure 3. Community compositional network of taxonomic groups found in this study. Size of node represents relative abundance of

OTUs assigned to that taxonomic group, including those identified to taxonomic groups at lower hierarchical levels. Green nodes represent taxonomic groups with significant correlations from indicator analyses with soil habitats, while purple nodes represent groups with significant correlations with leaf litter habitats. White nodes represent taxa that were not significantly associated with either soil or leaf litter habitats. Intensity of coloration represents the steepness of the rpb value.

Figure 5. Community compositional network of taxonomic groups found in the soil habitat. Size of node represents relative abundance of OTUs assigned to that particular taxonomic group, including those which identified to taxonomic groups at lower hierarchical levels. Red nodes represent taxonomic groups with significant correlations from indicator analyses. Intensity of coloration represents the steepness of the rpb value. Diamond-shaped nodes represent indicator groups of the BOWO ecosystem, rectangle-shaped nodes represent indicator groups of the SMRO ecosystem, and triangle-shaped nodes represent indicator groups of the SMBW ecosystem. Circular nodes lack significant correlation to a particular ecosystem type.

Figure 7. Principle coodinates analysis ordination based on results from Anderson’s test of multivariate homogeneity of variances on local community dispersion. Green coloration represents leaf communities, magenta coloration represents soil communities. Circles represent

6.7%. Ellipses represent 95% confidence intervals around the centroids of each habitat- ecosystem type.

a. b.

Table 1. Distance classes generated by transect sampling and used to examine spatial structure.

Distances in meters.

Min dist Max dist Ave dist # pairs

0 1 1 16 1 10 5 20 10 50 47 18 50 100 85 22 100 150 121 26 150 200 183 56 200 250 217 50 250 300 282 24 300 400 368 64 400 500 429 56 500 600 565 46 600 700 637 38 700 800 771 28 800 1000 881 32

Table 2. Mixed-model ANOVAs testing significance of ecosystem, habitat type, and interaction effects on α-diversity of forest stand communities. Corresponding F-values are reported for each

ANOVA. H0 = OTU richness, H1 = Shannon diversity, H2 = the inverse of Simpson entropy,

Hinf = the Berger-Parker index.

H0 H1 H2 Hinf Habitat Type 9.683* 36.52*** 51.073*** 48.114*** Ecosystem Type NS NS NS NS Interaction 7.022* NS 7.884* 10.858** P = * < 0 .05, ** < 0.01, *** < 0 .001

Taxonomic group Habitat Level rpb P Nematoda Leaf Phylum 0.335 0.005

Insecta Leaf Class 0.332 0.005 Maxillopoda 0.172 0.03

Enoplea Soil 0.713 0.005 Ellipura 0.463 0.005

Monhysterida Leaf Order 0.61 0.005 Tylenchida 0.443 0.005 Desmodorida 0.403 0.005 Rhabdocoela 0.31 0.005 Lepidoptera 0.285 0.005 Coleoptera 0.261 0.005 Paucitubulatina 0.248 0.005 Thysanoptera 0.208 0.005 Psocoptera 0.201 0.005 Hymenoptera 0.179 0.005 Hemiptera 0.146 0.01

Triplonchida Soil 0.605 0.005 Rhabditida 0.581 0.005 Endeostigmata 0.467 0.005 Collembola 0.463 0.005 Dorylaimida 0.43 0.005 Oribatida 0.428 0.005 Enoplida 0.35 0.005 Prostigmata 0.316 0.005 Mermithida 0.264 0.005 Mesostigmata 0.231 0.005 Lecithoepitheliata 0.177 0.005 Mononchida 0.176 0.005

Monhysteridae Leaf Family 0.621 0.005

Plectidae 0.468 0.005 Aphelenchoididae 0.426 0.005 Microlaimidae 0.403 0.005 Typhloplanidae 0.323 0.005 Anguinidae 0.321 0.005 Macrobiotidae 0.32 0.005 Dorylaimidae 0.277 0.005 Haliplectidae 0.256 0.005 Chaetonotidae 0.199 0.01 Euzetidae 0.171 0.005 Salticidae 0.153 0.015 Thripidae 0.153 0.005 Dictynidae 0.117 0.05

Prismatolaimidae Soil 0.58 0.005 Teratocephalidae 0.57 0.005 Longidoridae 0.381 0.005 Rhagidiidae 0.274 0.005 Mermithidae 0.264 0.005 Cephalobidae 0.247 0.005 Tullbergiidae 0.244 0.005 Eupodidae 0.238 0.005 Cylindrolaimidae 0.235 0.005 Diphtherophoridae 0.232 0.005 Brachychthoniidae 0.213 0.01 Qudsianematidae 0.213 0.005 Gehypochthoniidae 0.206 0.005 Rhabdolaimidae 0.195 0.015 Aporcelaimidae 0.19 0.005 Ctenacaridae 0.181 0.005 Neanuridae 0.177 0.01 Prorhynchidae 0.177 0.005 Ironidae 0.17 0.005 Mononchidae 0.168 0.025 Veigaiidae 0.154 0.005 Alycidae 0.152 0.005 Tylencholaimidae 0.151 0.005 Xyalidae 0.147 0.01 Isotomidae 0.134 0.035 Mylonchulidae 0.131 0.005 Zerconidae 0.126 0.005 Hypochthoniidae 0.085 0.025

Geomonhystera Leaf Genus 0.622 0.005 Tylocephalus 0.461 0.005 Aphelenchoides 0.404 0.005 Diphascon 0.403 0.005 Prodesmodora 0.403 0.005 Macrobiotus 0.352 0.005 Laimaphelenchus 0.326 0.005 Hypsibius 0.289 0.005 Haliplectus 0.256 0.005 Eumonhystera 0.228 0.005 Chaetonotus 0.226 0.005 Subanguina 0.19 0.01 Euzetes 0.171 0.005 Mesodorylaimus 0.163 0.03

Prismatolaimus Soil 0.58 0.005 Metateratocephalus 0.48 0.005 Alicorhagia 0.384 0.005 Xiphinema 0.373 0.005 Teratocephalus 0.367 0.005 Stigmalychus 0.295 0.005 Rhagidia 0.275 0.005 unclassifiedBrachychthoniidae 0.27 0.005 unclassifiedEupodidae 0.247 0.005 Tullbergia 0.244 0.005 Cylindrolaimus 0.235 0.005 Tylolaimophorus 0.232 0.005 Gehypochthonius 0.206 0.005 Rhabdolaimus 0.195 0.005 Ctenacarus 0.181 0.005 Clarkus 0.179 0.005 Ironus 0.17 0.005 Bimichaelia 0.156 0.005 Veigaia 0.154 0.005 Tylencholaimus 0.151 0.005 Aporcelaimellus 0.148 0.02 Theristus 0.147 0.02 Domorganus 0.139 0.05 Mylonchulus 0.131 0.005 Geocentrophora 0.129 0.035 Hypochthonius 0.085 0.02

157 OTUs Leaf OTU < 0.05

168 OTUs Soil < 0.05

Supplementary Table 2. Indicator species analysis examining indicator species for soil and leaf litter habitats. rpb represents the correlation value. P represents the significance value.

Number of indicator OTUs are reported for clarity rather than listing each OTU individually. P- values for OTUs are all < 0.05, while rpb values are variable.

Taxonomic group Ecosystem Level rpb P Nematoda SMRO Phylum 0.29 0.005

Ellipura SMBW Class 0.316 0.005 Insecta 0.24 0.035 Enoplea SMRO 0.361 0.005

Tylenchida BOWO Order 0.321 0.01 Parachela 0.285 0.015 Coleoptera SMBW 0.349 0.005 Collembola 0.316 0.005 Oribatida 0.307 0.015 Thysanoptera 0.275 0.025 Monhysterida SMRO 0.488 0.005 Triplonchida 0.388 0.005 Paucitubulatina 0.288 0.01 Enoplida 0.276 0.02 Dorylaimida 0.26 0.015

Aphelenchoididae BOWO Family 0.353 0.005 Salticidae 0.311 0.005 Hypsibiidae 0.294 0.01 Anguinidae 0.284 0.01 Haliplectidae 0.256 0.02 Brachychthoniidae SMBW 0.371 0.005 Euzetidae 0.324 0.005 Thripidae 0.289 0.005 Anystidae 0.26 0.01 Tomoceridae 0.227 0.005

Monhysteridae SMRO 0.488 0.005 Prismatolaimidae 0.377 0.005 Dorylaimidae 0.347 0.005 Plectidae 0.325 0.005 Longidoridae 0.324 0.005 Tylencholaimidae 0.258 0.045 Chaetonotidae 0.258 0.015 Coccinellidae 0.175 0.04 Enchytraeidae 0.159 0.025

Subanguina BOWO Genus 0.375 0.005 Aphelenchoides 0.355 0.005 Haliplectus 0.256 0.03 Euzetes SMBW 0.324 0.005 Liochthonius 0.324 0.005 Anystinae 0.26 0.015 unclassifiedBrachychthoniidae 0.256 0.005 Tomocerus 0.227 0.005 Geomonhystera SMRO 0.486 0.005 Prismatolaimus 0.377 0.005 Hypsibius 0.347 0.005 Xiphinema 0.324 0.01 Tylocephalus 0.316 0.01 Tylencholaimus 0.258 0.02 Chaetonotus 0.236 0.035

39 OTUs BOWO OTU < 0.05 31 OTUs SMBW < 0.05 51 OTUs SMRO < 0.05

Taxonomic group Ecosystem Level rpb P Nematoda SMBW Phylum 0.463 0.005

Oribatida BOWO Order 0.496 0.005 Dorylaimida SMBW 0.274 0.045

Gehypochthoniidae BOWO Family 0.412 0.005 Linyphiidae 0.227 0.005 Laelapidae 0.215 0.015 Leptonchidae SMBW 0.302 0.01 Tylencholaimidae 0.288 0.025 Ironidae 0.243 0.03 Longidoridae SMRO 0.314 0.035

Gehypochthonius BOWO Genus 0.412 0.005 Leptonchus SMBW 0.302 0.01 Tylencholaimus 0.288 0.01 Ironus 0.243 0.015 Xiphinema SMRO 0.302 0.01

23 OTUs BOWO OTU < 0.05 23 OTUs SMBW < 0.05 5 OTUs SMRO < 0.05