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A molecular method for assessing meiofauna diversity in marine sediments

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Title:
A molecular method for assessing meiofauna diversity in marine sediments
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English
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Hamilton, Heather C
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University of South Florida
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Tampa, Fla.
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Subjects / Keywords:
Nematodes
Tampa bay
Benthic
18s rrna gene
Phylogeny
Dissertations, Academic -- Biology -- Masters -- USF   ( lcsh )
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government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

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Summary:
ABSTRACT: A Molecular Approach to Assessing Meiofauna Diversity in Marine Sediments Heather C. Hamilton Abstract The purpose of this study was to determine if a molecular approach could be applied to calculating the diversity of meiofauna in marine sediments from two sites in Tampa Bay, FL, similar to the approach of McCaig et al, 1999 in calculating the diversity of microbes in pastureland soils. The approach includes extracting total DNA directly from the sediment and amplifying the 18S rRNA gene by PCR. Clone libraries from the 18S gene would be created for each site and 300 sequences from each clone library would be obtained. These sequences would then be phylogenetically analyzed and assigned to an OTU, from which diversity indices can be calculated.The phylogenetic analysis of the sequences from the two sites revealed that of the 102 OTUs assigned from the sequences, only 7 OTUs included sequences from both sites, while 93 OTUs contained sequences from one site or from the other. Thus the sites were phylogenetically different from each other. Shannon diversity indices calculated for each site showed a difference between the two sites and paralleled diversity indices for macrofauna data for each site collected by the Hillsborough County Environmental Protection Commission. Sequences from 30 OTUs were completely sequenced and identified by phylogenetic comparison with a metazoan reference alignment. A discrepancy between the sequence data and data collected from preserved samples taken at each site was evident upon analysis: roughly 60% of each preserved sample consisted of nematodes and 10% consisted of copepods, while roughly 30% of the identified OTUs consisted of copepods and 10% consisted of nematodes.
Thesis:
Thesis (MSci)--University of South Florida, 2003.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Heather C. Hamilton.
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Title from PDF of title page.
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Document formatted into pages; contains 69 pages.

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University of South Florida
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oclc - 62789856
usfldc doi - E14-SFE0000624
usfldc handle - e14.624
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A Molecular Approach to Assessing Meiofauna Diversity in Marine Sediments By Heather C. Hamilton A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Biology College of Arts and Sciences University of South Florida Co Major Professor: James R. Garey, Ph.D. Co Major Professor: Susan S. Bell, Ph.D. Stephen A. Karl, Ph.D. Date of Approval July 18, 2003 Keywords: benthic, Tampa Bay, phylogeny, nematodes, 18S rRNA gene Copyright 2003, Heather C. Hamilton

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Acknowledgements I would like to acknowledge Dr. Susan S. Bell and Dr. Magda Vincx for their contribution to the identification of the copepod and nematode species, respectively. I would also like to acknowledge David J. Karlen and the Hillsborough County Environmental Protection Commission for their contribution of macrofauna data to this project.

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i Table of Contents List of Figures iii List of Tables iv Abstract v Introduction 1 Materials and Methods 8 Preliminary Data 8 Nematode and Copepod Sequences 10 Bayshore Boulevard and Courtney Campbell Causeway Sample Collection 10 DNA Extraction 12 Primer Design and Optimization 13 Polymerase Chain Reaction 14 Cloning 14 Sequencing 15 Data Analysis 15 Preserved Samples 17 Results 21 Preliminary Data 21 Courtney Campbell Causeway and Bayshore Boulevard Study 23 Discussion 30

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ii Literature Cited 37 Appendices 43 Appendix 1 Phyloge netic tree of all sequences 44 Appendix 2 Phylogenetic tree of short sequences with reference alignment 52 Appendix 3 Phylogenetic tree of extended sequences with reference alignment 58

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iii List of Figures Figure 1 Primer map for 18S rRNA gene 9 Figure 2 Location of sample collection sites in Tampa Bay 10 Figure 3 Site of 18S11b primer compared to metazoan and non metazoan 18S rDNA 17 Figure 4 Total DNA extracted from Fort DeSoto sediments 2 0 Figure 5 18S rRNA and 16S rRNA genes amplified from DNA extracted from Fort DeSoto sediment 21 Figure 6 PCR optimization of 18S11b primer at 50 o C annealing temperature 23 Figure 7 PCR optimization of 18S11b primer at 55 o C annealing temperature 23 Figure 8 PCR optimization of 18S11b primer at 60 o C annealing temperature 23 Figure 9 18S rDNA PCR from Courtney Campbell Causeway (CC) and Bayshore Boulevard (BB) using the 18S11b/18S2A primer set 24 Figure 10 Rare faction curves for both sites 26

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iv List of Tables Table 1 BLAST search results on mitochondrial 16S rRNA and nuclear 18S rRNA clones 19 Table 2 Sequence groups and the number of sequences from each site per sequence group 21 Table 3 Shannon diversity indices, H max and evenness for macrofauna data collected by the Hillsborough EPC and meiofaunal sequence data from Courtney Campbell Causeway (CC) and Bayshore Boulevard (BB) 27 Table 4 The proport ion of single individual sequence groups/species (singletons) to the total number of sequence groups/species versus the proportion of dominant sequences/species to the total sample 27 Table 5 Percentage of nematodes, copepods, ostracods and other organisms in preserved samples and sequenced samples 28

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v A Molecular Approach to Assessing Meiofauna Diversity in Marine Sediments Heather C. Hamilton Abstract The purpose of this study was to determine if a molecular approach could be a pplied to calculating the diversity of meiofauna in marine sediments from two sites in Tampa Bay, FL, similar to the approach of McCaig et al, 1999, in calculating the diversity of microbes in pastureland soils. The approach includes extracting total DNA directly from the sediment and amplifying the 18S rRNA gene by PCR. Clone libraries from the 18S gene would be created for each site and 300 sequences from each clone library would be obtained. These sequences would then be phylogenetically analyzed and assigned to an OTU, from which diversity indices can be calculated. The phylogenetic analysis of the sequences from the two sites revealed that of the 102 OTUs assigned from the sequences, only 7 OTUs included sequences from both sites, while 93 OTUs cont ained sequences from one site or from the other. Thus the sites were phylogenetically different from each other. Shannon diversity indices calculated for each site showed a difference between the two sites and paralleled diversity indices for macrofauna data for each site collected by the Hillsborough County Environmental Protection Commission. Sequences from 30 OTUs were completely sequenced and identified by phylogenetic comparison with a metazoan reference alignment. A

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vi discrepancy between the sequ ence data and data collected from preserved samples taken at each site was evident upon analysis: roughly 60% of each preserved sample consisted of nematodes and 10% consisted of copepods, while roughly 30% of the identified OTUs consisted of copepods a nd 10% consisted of nematodes. This discrepancy could be explained if the OTUs that were not identified consisted of nematode sequences or if a primer bias were present in the PCR amplification such that the regions flanking the primer site in the nematod e sequences inhibited primer annealing.

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1 Introduction The diversity of organisms in an ecosystem is an important measure of the health of that environment. For example, a variety of microorganisms are involved in soil formation, toxin removal, cycling o f carbon, nitrogen, and phosphorous, and the processing of detritus (Borneman et al. 1996). If the diversity of these organisms is diminished through a factor such as pollution, then the processes the organisms perform in the ecosystem might not be sustai ned and the ecosystem may suffer. Thus, monitoring the health of a polluted environment is important for determining if ecosystem functions have been disrupted. Most programs that monitor polluted marine environments rely on the diversity of macrobenthic fauna (macrofauna) to serve as a representation of the health of that environment (Bilyard, 1987). Macrofauna (those organisms >0.5mm) are important in the food web, serving as prey for fish, birds, crustaceans, and humans (Bilyard, 1987). Humans in tur n consume many of the predators of macrofauna. As such, macrofauna have the potential to transfer toxic substances through the food web to higher trophic levels, thereby initiating pathological responses in predators (Bilyard, 1987). Macrofauna are also important in the recycling of nutrients from the sediments back into the water (Bilyard, 1987). Studies of macrofauna typically collect and identify the fauna to some taxonomic level, which can require a considerable amount of taxonomic

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2 expertise. As Wa rwick (1988) has pointed out, identification of many invertebrate taxa such as polychaetes and amphipods require the skills of specialists and an enormous amount of time can be spent in separating a few of these difficult groups. The requirement of taxonom ic expertise along with the time involved in sampling, processing and identifying the organisms makes using macrofauna diversity in monitoring programs and pollution studies costly. Pollution studies using macrofaunal diversity as an indicator of pollutio n may be complicated by the sensitivity of macrofauna to physical disturbance (Warwick, 1984; McLachlan, 1983; Warwick et al. 1990). Austen et al. (1989) have found that using meiofauna data along with macrofauna data provides greater insight into the pro cesses affecting a polluted area, since meiofauna are not as affected by physical disturbance as macrofauna. The cost of processing and identifying macrofauna is high and incorporating another component of the benthos (i.e. meiofauna) into a study is even more costly, so most studies and monitoring programs focus on the macrofauna or utilize meiofauna instead of macrofauna. Meiofauna, benthic organisms living in the inerstitial spaces of aquatic sediment, range in size from those that fit through a sieve m esh size of 1000 m m and retained on amesh size of 42 m m (Giere, 1993). Because "meiofauna" is a size classification, many different taxa have members represented as meiofauna: of approximately 33 metazoan phyla, 22 have meiofauna representatives (Coull, 1 988). These taxa are not only represented by members that are meiofaunal throughout their life cycle, but by some members that are meiofaunal only during the larval stages of their lifecycle, and are macrofauna as adults (Coull, 1988). They are abundant ( usually around 10 6 organisms per square meter of sediment surface) in both freshwater and marine habitats (Coull, 1988).

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3 In most instances, nematodes are most prevalent, making up 50% or more of the total meiofauna. Harpacticoid copepods are usually secon d in abundance (Coull, 1988). In terms of importance within an ecosystem, meiofauna serve as prey items for higher trophic levels; and many published reports have documented the presence of meiofauna in the gut contents of marine fish and invertebrate pre dators (Coull, 1988). In many instances, copepods tend to be the preferred prey item, even when they are not particularly abundant (Coull, 1988). Meiofauna are ideal for the study of pollution because they are generally immobile and live within the sedime nt where toxins accumulate, so that the long term effects of pollution can be studied (Giere, 1993; Warwick et al. 1990). In contrast, many macrofauna live in burrows within the sediment but exchange water and feed from the water column. Meiofauna are di fficult to identify due to the lack of distinguishing morphological characters among many taxa, especially at the species level (Litvaitis et al. 1994). As with macrofauna, the amount of taxonomic expertise needed for identification makes working with meio fauna costly. In many instances, meiofaunal diversity is assessed using only one major taxon, e.g. nematodes or copepods, rather than using community structure to assess diversity (Coull and Chandler, 1992). The cost of sample processing and difficulty i n taxonomic identification of meiofauna make community structure studies in relation to pollution difficult (Coull and Chandler, 1992). Researchers have been making an effort to increase the cost effectiveness of pollution studies by using meiofauna inst ead of macrofauna. Raffaelli and Mason (1981) suggested a simple nematode to copepod ratio to predict levels of pollution. This ratio is based on the abundance of nematodes and copepods, the two major taxa comprising the

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4 majority of meiofauna in most sam ples as well as the most easily recognized taxa, and makes the need for taxonomic expertise unnecessary (Raffaelli and Mason, 1981). This technique assumes that copepods are more sensitive to pollution than nematodes, and that nematodes will be the most a bundant meiofauanal organisms where pollution occurs, which is not necessarily true in all cases (Coull et al. 1981). Warwick (1981) pointed out that sediment granulometry appears to affect the ratio, and suggested that the ratio needs to be refined accor ding to trophic dynamic aspects. Raffaelli (1987) eventually refined the ratio to compare the abundance of copepods to that of nematodes that feed in the same manner as the copepods in the ratio, thus creating the need for some taxonomic expertise. In an other case, Warwick (1988) studied the level of taxonomic discrimination required to detect pollution effects on marine benthic communities. He took data sets from five pollution studies -three macrofauna studies (Pearson, 1975; Pearson, unpublished; Da uvin, 1984) and two meiofauna studies (Gee et al. 1985 [copepods]; Lambshead, 1986 [nematodes]) and subjected the data to multivariate analyses (MDS plots) and univariate analyses (abundance/biomass curves) at the species, family and phylum levels when pos sible. The multivariate analyses showed that pollution effects could still be detected at the phylum level, while the univariate analyses showed that pollution effects were not detectable at higher than family level (Warwick, 1988). While these studies h ave shown that meiofauna can be used in pollution studies without a need for immense taxonomical expertise, sample processing is still a costly component, as samples still need to be processed by sieving and sorted using a microscope. If the cost of sampl e processing could be minimized, pollution studies incorporating meiofauna could be more effective.

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5 Molecular techniques could be beneficial to pollution studies and monitoring programs wanting to incorporate meiofauna data by lowering the cost of sample p rocessing as well as eliminating the need for taxonomic expertise. In one study Litvaitis, et al. (1994) used a fragment of the nuclear 28S rRNA gene to identify meiofaunal turbellarians after extracting the DNA from hand sorted animals. In another study Street and Montagna (1996) used the genetic diversity of copepods to determine the effects of disturbance caused by offshore platforms. However, microbial ecologists have developed a molecular method of determining the diversity of organisms in environme ntal samples by processing the environmental samples directly for molecular analyses that might be much more useful. The method was developed to help microbial ecologists determine the diversity of soil microbes in environmental samples. Traditional cultu re based isolation techniques are not able to measure the vast diversity of environmental microbes because 99% of bacteria from environmental samples can not be cultured (McCaig, et al. 1999). This method uses phylogenetic analysis of a gene that has been amplified, cloned and sequenced from a pool of DNA extracted directly from the environmental sample. The analysis can identify the organisms present in the sample as well as identify novel groups of organisms, and can be used to determine the diversity o f the organisms (McCaig et al. 1999; Purkhold et al. 2000; Bruns et al. 1999, Kuske et al. 1997; Borneman and Triplett, 1997; Borneman et al. 1996; and Stephen et al. 1996). McCaig et al. (1999) published one of the only studies to incorporate the phyloge netic data into diversity indices by determining operational taxonomic units (OTUs) from cloned sequences clustering at a level of sequence similarity of >97% and treating these OTUs as species for the diversity indices. For the purposes of using the data in diversity

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6 indices, each sequence in an OTU would represent an individual of that species (McCaig et al. 1999). One concern with this type of study is the bias introduced by the molecular techniques used to produce sequences for phylogenetic analysis, which may underestimate or overestimate the diversity of the samples (Wintzingerode, et al. 1997). Biases may be introduced during DNA extraction, PCR amplification, and cloning. Lowering the concentration of template DNA in PCR reaction mixtures and poo ling the PCR products from multiple reactions prior to cloning will reduce biases introduced through PCR, such as PCR drift (Wagner, et al. 1994). Combining and mixing individual sediment samples collected at each site prior to molecular analysis can redu ce biases introduced by patchiness of meiofauna in the environment. However, physical mixing of the organisms and the sediment may introduce bias by breaking up softer organisms, which may then be lost during sieving. Samples should be treated identicall y in order to ensure that any biases encountered would occur to the same degree The purpose of this study is to determine if molecular methods similar to those used in McCaig et al. (1999) are useful to assessing meiofauna diversity in marine sediment samp les from two different sites in Tampa Bay, FL. The two sites selected are located in different areas of the Bay and consist of very different assemblages of macrofauna and flora, and so should have different assemblages of meiofauna. The differences in t hese assemblages should be apparent when meiofauna sequences are phylogenetically analyzed and compared between the two sites. Diversity indices calculated from the phylogenetic data collected for both sites should also indicate a difference in the two as semblages. The Hillsborough County Environmental Protection

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7 Commission has monitored these sites using macrofauna diversity, and the data are available for comparison.

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8 Materials and Methods Preliminary data Sediment samples were collected from Eas t Beach at Fort DeSoto Park, St. Petersburg, FL, in 1.5mL microcentrifuge tubes and stored at 80 C. DNA was extracted from the sediment using an SDS based extraction buffer and series of phenol, phenol chloroform, and chloroform extractions, and ethanol precipitated (Hempstead, et al. 1990). The extracted DNA was visualized by gel electrophoresis on a 0.9% agarose gel in 1X Tris Acetate EDTA buffer (1X TAE: 40mM Tris Acetate, 2mM EDTA), pH 8.5. The 18S rRNA and 16S rRNA genes were then amplified using t he polymerase chain reaction (PCR) with primers specific to the 18S rRNA (Winnepenninickx et al. 1995) and 16S rRNA (Garey et al. 1998) genes which had BamH1 (18S) and EcoR1 (16S) restriction sites. The 18S rRNA gene is found among all eukaryotes and is o ne of the most extensively studied genes in metazoan phylogeny because it is a slowly evolving gene, which makes it useful for examining early metazoan evolution (Hillis and Dixon, 1991). The 18S rRNA gene is used to determine interphylum relationships am ong metazoans (Field et al. 1988), but can also be used to infer intraphylum phylogenetic relationships (Blaxter, et al. 1998). As a ribosomal RNA gene, 18S rDNA contains variable regions as well as highly conserved regions (Hillis and Dixon, 1991), making possible the construction of primers specific to metazoans with the ability to screen out other eukaryotic sequences. While the mitochondrial 16S rRNA gene evolves at a faster

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9 rate than the 18S rRNA gene, and therefore could be a better candidate for det ermining species diversity in the environmental samples, the database of 18S rDNA sequences found in GenBank is much more extensive than for 16S rDNA sequences and allows for more specific identification of unknown sequences. Two 18S rDNA primer sets were used to amplify the entire gene. The 18S1A (5 CCG GTCGACGGATCC GTTTTCATTAATCAAGAACG 3) and 18S2A (5 CCG GTCGACGGATCC GATCCTTCCGCAGGTTCACC 3) primers amplified an 800 base pair segment of the gene (figure 1) and contained Bam HI and Sal I restriction sites (underlined in primer sequence). The 18S4 (5 CCG GAATTCAAGCTT GCTTGTCTCAAAGATTAAGCC 3) and 18S5 (5 CCG GAATTCAAGCTT ACCATACTCCCCCCGGAACC 3) primers amplified an 1100 base pair segment (figure 1) and contained Hin dIII and Eco RI restriction sites (underlin ed in primer sequence). The PCR products were also visualized by gel electrophoresis. Libraries of the PCR products were prepared by digesting and ligating the products into the lac z gene of pBluescriptSK (+/ ) plasmids using the appropriate restriction enzymes (Maniatis, et al 1982). 0 1000 2000 18S4 18S7 (371) 18S6 (371) 18S9 (641) 18S9 (641) 18S1A 18S3A (1340) 18S5 18S10 (1670) 18S2A Figure 1: Primer map for 18S rRNA gene. Bold lettering indicates primers used for both PCR and sequencing. Primers not indicated in bold were used for sequencing only.

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10 Individual colonies were grown in overnight cultures and the recombinant plasmids were prepared by an alkaline lysis procedure (Maniatis, et al. 1982). The inserts were cycle sequenced and analyzed with a 310 Genetic Analyz er (Perkin Elmer, ABI, Foster City, CA). The sequences were assembled using SeqMan II software (DNAstar, Inc., Madison, WI). Sequences were identified by searching GenBank using the BLAST program. Nematode and Copepod Sequences One nematode species, Met achromadora pulvinata and two copepod species, Longipedia helgolandica and a laophontid species, were identified from Courtney Campbell Causeway sediments. A partial 18S rDNA sequence was amplified using the 18S1A/18S2A and 18S4/18S5 primer sets and sequ enced using the primers shown in figure 1. The sequences were assembled using SeqMan II software (DNAstar, Inc., Madison, WI). Bayshore Boulevard and Courtney Campbell Sample Collection Two sites from the Tampa Bay, Tampa, FL (figure 2) were selected for study based on differences in macrofaunal and plant assemblages, as meiofaunal assemblages should also be different between the sites. The first site, just off of Bayshore Boulevard in Tampa, FL (N 27 o 55.428 W 82 o 28.734), consisted of an algal mat co mmunity. The second site, just off of Courtney Campbell Causeway, Tampa, FL (N 27 o 58.292 W 82 o 35.502), consisted of a sandy seagrass community. Sediment samples were collected from the Bayshore Boulevard site one hour after low tide on April 20, 2001 and from the Courtney Capmbell Causeway site during low tide on May 4, 2001.

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11 Figure 2: Location of sample collection sites in Tampa Bay Three core samples taken 0.5m apart in parallel with the shoreline were collected from the Bayshore site using a 60cm 3 corer, and four core samples, also 0.5m apart in parall el with the shoreline, were collected from the Courtney Campbell site. At both sites individual core samples were combined and sieved through 500 m m onto 50 m m mesh sieves. The sediment retained on the 50 m m sieve was gently washed several times with seawat er and thoroughly mixed to ensure uniformity of sampling. Eight subsamples for DNA analysis and four subsamples for analysis of meiofauna composition were sampled from the sediment retained on the 50 m m sieve. The eight subsamples for DNA analysis were co llected in 15mL polypropylene conical tubes, immediately placed on ice for transportation and later frozen at 80 o C until they could be analyzed. The four subsamples for meiofauna composition were collected in 15mL Wheaton bottles and preserved with 95% e thanol. These four subsamples were later stained with Rose Bengal. Samples were designated by location of collection (CC for Courtney Campbell Causeway and BB for Bayshore Boulevard) and subsample number (1 8 for DNA analysis and 1 4 for preserved sample s).

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12 DNA Extraction Three of the eight replicates from each site were randomly selected for DNA extraction and thawed briefly on ice. A modification of Hempsteads protocol for DNA extraction was used to obtain DNA from the samples (Hempstead, et al. 199 0). For each of the six subsamples 8mL of sediment was divided between two conical 15mL polypropylene tubes, and one volume (about 4mL) of homogenization buffer (3.5% SDS in 1M Tris, pH 8.0, and 100mM EDTA) was added to each tube. The two samples from ea ch replicate were then homogenized in the conical tubes using a Teflon tipped pestle previously cleaned with DNA Away (Molecular BioProducts, Inc., San Diego, CA) and rinsed in deionized water. The samples were briefly centrifuged to settle the sediment f rom the supernatant, which was then transferred in 700L amounts to 1.5mL microcentrifuge tubes. An equal amount of phenol (pH 7.9) was added to each of the tubes. The tubes were gently mixed for 5 minutes and centrifuged for 5 minutes in a clinical centr ifuge. The top aqueous layer from the resulting bilayered solution was transferred to a new 1.5mL tube. The previous three steps were repeated one more time using phenol (pH 7.9), twice using a 1:1 solution of phenol (pH 7.9): chloroform isoamyl alcohol (24 parts chloroform to 1 part isoamyl alcohol), and twice with the chloroform isoamyl solution. The DNA in the final aqueous layer that was transferred to a new tube was precipitated overnight at 20 o C with 2 volumes of 100% ethanol and a 0.1 volume of 3 M sodium acetate (pH 6.0). The precipitated DNA was pelleted by centrifugation for 15 minutes, washed with 70% ethanol to remove the sodium acetate salts, and suspended in 100L of deionized water.

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13 If the pellet of precipitated DNA appeared brown or oily an additional clean up step was performed. The aqueous DNA solution and 100L of TE buffer (10mM Tris, 0.1mM EDTA) was added to a Qiagen PCR spin column layered with 0.2g of Chelex resin and 0.3g of polyvinyl propylene, and centrifuged at 10000 X g. An additional ethanol precipitation was performed in the same manner as above. The resulting DNA pellet was suspended in 100L of deionized water and stored at 20 o C. Primer Design and Optimization Sixty nine sequences representative of metazoan phyla acros s the animal kingdom and 11 sequences representing non metazoan and non animal phyla were obtained from the Belgian rRNA server (Wyuts, et al. 2002) in DCSE format (De Rijk and De Wachter, 1993) (figure 3). The sequences were aligned according to rRNA sec ondary structure and searched for an area of sequence in which the non metazoan phyla diverged from metazoan phyla by several base changes, while sequences within the metazoan phyla remained relatively conserved. An 18 nucleotide primer was designed from the 18S rRNA gene (figure 3): 18S11b 5 CCG GTCGACGGATCC GTCAGAGGTTCGAAGGCG 3 (underlined sequence denotes a Sal I Bam HI restriction site). This primer was paired with a universal 18S rRNA primer, 18S2A, and optimized for amplification of metazoan 18S rDN A. The 18S11b/18S2A primer set was tested on genomic DNA from a chicken, a nematode, a fungus, and an alga using PCR at 50 o C, 55 o C, and 60 o C annealing temperatures; reaction mixes and cycling regimes, other than annealing temperature, were held constant a s per PCR protocol below. A universal primer set consisting of the 18S4 and 18S5 primers was used as a control with each of the

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14 genomic DNAs using the same PCR protocols as for the 18S11b/18S2A primer set. The PCR products were visualized using agarose g el electrophoresis (0.9%). Polymerase Chain Reaction For each of the six subsamples, the 18S nuclear rRNA gene was amplified from the extracted DNA using the polymerase chain reaction and the 18S11b/18S2A primer set. All PCR reaction mixes consisted of 1X final concentration of 10X PCR buffer (Enzypol, Denver, CO), 2mM final concentration of magnesium chloride, 0.1M final concentration of each primer, 0.25mM final concentration for each of dATP, dCTP, dTTP, and dGTP, 2L of genomic DNA and 1 unit of Enz yPlus Taq polymerase (Enzypol, Denver, CO) in a final volume of 100L. The PCR reactions were carried out in 0.2mL tubes. PCR was performed on the reaction mixes using the following cycle regime: an initial denature hold at 95 o C for 2 minutes; cycled 45 times through a 95 o C denature step for 45 seconds, a 55 o C annealing step for 1 minute, and a 72 o C extension step for 2 minutes; a final extension hold at 72 o C for 7 minutes; and a final hold at 4 o C. PCR was performed using a Biometra TRIO Thermoblock the rmocycler (Whatman Biometra, Gttingen, Germany). Cloning Amplified 18S rDNA from each of the subsamples was cloned using the TOPO TA cloning kit for sequencing (Invitrogen Corp., San Diego, CA) according to the manufacturers instructions. The transfor med cells were plated on Luria Bertani agar containing 100g/mL ampicillin and 50g/mL X gal ( 5 Bromo 4 chloro 3 indolyl b D

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15 galactoside ) and grown overnight at 37 o C. White colonies were randomly picked and streaked to new gridded plates that were grown o vernight at 37 o C. Plasmid DNA was isolated from the colonies grown on the gridded plates by the alkaline lysis miniprep procedure (Manaitis, et al. 1982). This plasmid DNA was further cleaned using a PEG precipitation procedure (Lis, 1980; and Lis and Sc hleif, 1975). After the PEG precipitation, the plasmid DNA was ethanol precipitated using 2 volumes of 100% ethanol and 0.1 volume 3M sodium acetate, and resuspended in 10 50L of deionized water. The DNA was then quantified using 0.9% agarose gel electr ophoresis. All white colonies that were grown overnight for isolation of plasmid DNA were preserved in 7% DMSO and stored at 80 o C. Clones were designated by subsample (location of collection and subsample number) and grid number from the plates on which the white colonies were streaked. Sequencing Cloned DNA was cycle sequenced using a DYEnamic ET terminator cycle sequencing kit (Amersham Biosciences Corp., Piscataway, NJ). The reaction mix contained 100ng of plasmid template, 2L sequencing reaction mix, 1L 0.8uM sequencing primer, and enough water to bring the total reaction volume to 10uL. All clones for phylogenetic analysis were sequenced using the 18S11b primer. Extended sequences were carried out using the 18S3A and 18S2A primers. All reacti ons were amplified in the Biometra TRIO Thermoblock thermocycler using the following cycling regime: an initial denature at 96 o C for 1 minute; cycled 25 times through a 96 o C denature step for 15 seconds, a 50 o C annealing step for 30 seconds and an exten sion step

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16 at 60 o C for one minute; a 60 o C extension step for 7 minutes; and a final hold at 4 o C. The cycle sequencing products were purified as per manufacturers instructions and analyzed using an ABI 310 genetic analyzer (Perkin Elmer, Foster City, CA ). Data Analysis Sequences were checked and corrected for ambiguous bases (Ns) called by the sequencing software. Data sets for all sequences, for sequences from only the Courtney Campbell replicates, and for sequences from only the Bayshore Boulevard replicates were compiled and aligned using ClustalX (Thompson, et al. 1997). Phylogenetic analysis of these alignments was performed using MEGA version 2.1 (Kumar, et al. 2001) to produce neighbor joining trees showing the number of differences. Sequence s from the tree containing all 573 sequences were assigned to operational taxonomic units (OTUs) according to the number of differences and the topology of the tree. OTUs were designated as containing sequences which differ from each other by less than 5 differences and which group together as a clade when analyzed phylogenetically. The alignment of each OTU containing two or more sequences was visually inspected using a text editor and misalignments were corrected. Molecular diversity was calculated as nucleotide diversity for each site using Kimura 2 parameter distance method as calculated by Arlequin 2.001 (Schneider, et al. 2000) and MEGA version 2.1 (Kumar, et al. 2001). Extended sequences were assembled using Seqman II software (DNAstar, Inc., Madi son, WI), and corrected for ambiguous or incorrect bases. These sequences were added to a data set containing metazoan and non metazoan reference sequences and

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17 aligned using ClustalX. The neighbor joining method was used to construct a phylogenetic tree based on the Kimura 2 parameter distance method. Species abundance curves (Odum, 1971), also known as rarefaction curves, were plotted for each site. The order of individual sequences from each site was randomized on an Excel spreadsheet and plotted to p roduce the unresampled individual rarefaction curves. Ecosim7 (Gotelli and Entsminger, 2003) was used to create individual rarefaction curves using 50 replicates of resampled data. Preserved Samples Samples preserved in 95% ethanol and stained with Rose Bengal were sorted under a dissecting scope. The numbers of nematodes, copepods and ostracods were counted, as well as other unidentified organisms stained with Rose Bengal. The proportion of nematodes, copepods, ostracods, and other stained organisms w as calculated for each site and compared with the proportion of putative nematode, copepod, ostracod and other organism sequences from each site.

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18 2010 2020 2030 2040 2050 2060 2070 2080 ....|....|....| ....|.. ..|....|....|... .|....|....|....|....|....|....|....|....| CAA GA ACGA AA GT TGTGG GCG CG AAG GCG AT CA GATAC C GCC C TAGTCA CA AC CAT AAAC 1 Branchiostoma floridae M97571 CAA GA GCGA AA GT CAGAG GAT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CGT AAAC 2 Scutopus ventrolineatus X91977 CAA GA GCGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC C TAGTTC TG AC CAT AAAC 3 Molgula bleizi L12418 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 4 Mytilus californianus L33449 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CGC AAAC 5 Tridacna squamosa D84190 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 6 Elliptio complanata AF117738 CAA GA ACGA AA GT CAGAG GTT CG AA G ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 7 Solemya reidi AF117737 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 8 Littorina littorea X91970 CAA GA ACGA AA GT CAGAG GCG CG AAG ACG AT CA GATAC C GTC G TAGTTC TG A C CAT AAAC 9 Aplysia sp. X94268 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 10 Glottidia pyramidata T12647 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 11 Phoronis architecta T362 71 CAA GA ACGA AA GT TAGAG GCT CG AAG ACG AT CA GATAC C GTC C TAGTTC TA AC CAT AAAC 12 Palythoa variabilis AF052892 CAA GA ACGA AA GT CGCGG GAT CG AAC GGG AT TA GATAC C CCG G TAGTCG CG AC CGT AAAC 13 Sagitta crassa D14363 CAA GA ACGA AA GT CAGAG GTT CG A AG ACG AT TA GATAC C GTC C TAGTTC TG AC CAT AAAC 14 Symbion pandora Y14811 CAA GA ACGA AA GT TGGAG GCT CG AAG ACG AT CA GATAC C GTC C TAGTTC CA AC CAT AAAC 15 Beroe cucumis D15068 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC C TAGTTC TG AC CA T AAAC 16 Barentsia benedeni T36272 CAA GA ACGA AA GT CGGAG GTT CG AAG ACG AT CA GATAC C GTC C TAGTTC CG AC CGT AAAC 17 Harrimania sp.CC 03 2000 AF236799 CAA GA ACGA AA GT CGGAG GCG AG AAC ACG AT CA GATAC C GTG G TAGTTC CG AC CAT AAAC 18 Ochetostoma ery throgrammon X79875 CAA GA ACGA AA GT CGGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC CG AC CAT AAAC 19 Pycnophyes kielensis T67997 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 20 Chaetonotus sp. AJ001735 CAA GA ACGA A A GT CGGAG GTT CG AAG GGG AT CA GATAC C CCC C TAGTTT CG AC CAT AAAC 21 Gnathostomula paradoxa Z81325 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 22 Alcyonidium gelatinosum X91403 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 23 Milnesium tardigradum T49909 CAA GA ACGA AA GT TGGAG GTT CG AAG ACG AT TA GATAC C GTC C TAGTTC CA AC CAT AAAC 24 Brachionus plicatilis T29235 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GACAC C GTC C TAGTTC T G AC CAT AAAC 25 Stenostomum leucops AJ012519 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 26 Limulus polyphemus L81949 CAA GA ACGA AA GT CAGAG GAT CG AAG GCG AT TA GATAC C GCC C TAGTTC TG AC CGT AAAT 27 Aduncospiculu m halicti T61759 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG RT CA GATAC C GCC C TAGTTC TG AC CGT AAAC 28 Brumptaemilius justini AF036589 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TG AC CGT AAAC 29 Brugia malayi AF036588 CAA GA ACGA A A GT CAGAG GTT CG AAG GCG AT TA GATAC C GCC C TAGTTC TG AC CGT AAAC 30 Bursaphelenchus sp. AF037369 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG AT TA GATAC C GCC C TAGTTC TG AC CGT AAAC 31 Caenorhabditis elegans X03680 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 32 Chromadoropsis vivipara AF047891 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TG AC CGT AAAC 33 Dentostomella sp. AF036590 CAA -A ACGA AA GT AATGG GTT CG AAG GCG AT CA GATAC C GCC C TAGTCA T T AC CGT AAAC 34 Diplolaimelloides meyli AF036644 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 35 Enoplus brevis T88336 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 36 Longidorus el ongatusAF036594 CAA GG ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 37 Mermis nigrescens AF036641 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 38 Metachromadora sp. AF036595 Figure 3, cont inued

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19 CAA GG ACGA TA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 39 Mylonchulus arenicolus AF036596 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TG AC CGT AAAC 40 Plectus sp. T61761 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TG AC CGT AAAC 41 Toxocara canis AF036608 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC GGT AAAC 42 Trichinella spiralis T60231 CAA GA GCGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC C TAG TTC TG AC CAT AAAC 43 Lineus sp. X79878 CAA GA ACGA AA GT CAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TG AC CTT AAAC 44 Plumatella repens T12649 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 45 Siboglinum fiordicum X79876 CAA GA ACGA AA GT CAGAG GTT CG AAG GTG AT CA GATAC C GCC C TAGTTC TG AC CAT AAAC 46 Priapulus caudatus AF025927 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 47 Achelia echinata AF005438 CAA GA ACGA AA GT TAGAG TTT CG AAG ACG AT TA GATAC C GTC G TAGTTC TA AC CGT AAAC 48 Cephalodiscus gracilis AF236798 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 49 Tubifex sp. T67145 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 50 Tenebrio molitor X07801 CAA GA ACGA AA GT CAGAG GAT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 51 Phascolosoma granulatum X79874 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 52 Acant hopleura japonica X70210 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC C TAGTTC TG AC CAT AAAC 53 Glycera americana T19519 CAA GA ACGA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC C TAGTTC TG AC CAT AAAC 54 Nephtys hom bergii T50970 CAA GA AC GA AA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC C TAGTTC TG AC CAT AAAC 55 Nereis virens Z83754 CAA GA GCGA CA GT CAGAG GTT CG AAG ACG AT CA GATAC C GTC G TAGTTC TG AC CAT AAAC 56 Polydora ciliata T50971 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 57 Ophiomyxa brevirima Z80953 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 58 Cassidulus mitis Z37148 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 59 B risingaster robillardi AF088802 CAA GA ACGA AA GT TGAGG GTT CG AAG GCG AT CA GATAC C GCC C TAGTCT TA AC CAT AAAC 60 Polycheira rufescens X90531 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CGT AAAC 61 Balanus eburneus L26510 C AA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 62 Homarus americanus AF235971 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 63 Artemia salinaX01723 CAA GA ACGA AA GT TAAAG GTT CG AAG GCG AT TA GATAC C GCC C TAGTTT TA AC CAT AAAC 64 Calanus pacificus L81939 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 65 Eucyclops serrulatus L81940 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 66 Cancrincola plumipes L81938 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 67 Euphilomedes cacharodonta L81941 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 68 Ceriodaphnia dubia AF144208 CAA GA ACGA AA GT TAGAG GTT CG AAG GCG AT CA GATAC C GCC C TAGTTC TA AC CAT AAAC 69 Gonodactylus sp. L81947 CAA GA ACGA AA GT TGGGG GAT CG AAG ATG AT TA GATAC C ATC C TAGTCT CA AC CAT AAAC 70 Chlorophyta A.acetabulum Z334 CAA GA ACGA A A GT TAGGG GAT CA AAG ACG AT CA GATAC C GTC G TAGTCT TA AC TAT AAAC 71 Ciliophora P. caudatum AF217655 GAA GA GCGA AG GT TGGGG GAA CA AAG AGG AT CA GATAC C CTC G TAGTCC TATTT ACATCAAA 72 Foram Peneroplis sp. AJ132368 CAA GA ACGA AA GT TAGGG GAT CG AAG ACG AT CA GATAC C GTC C TAGTCT TA AC CAT AAAC 73 Dinophyceae Symbiodinium AB016595 CAA GA ACGA AA GT TAGGG GAT CG AAG ACG AT CA GATAC C GTC G TAGTCT TA AC CAT AAAC 74 Choanoflagellate A. unguicul CAA GA ACGA AA GT TAGGG GAT CG AAG ATG AT CA GATAC C GTC G TA GTCT TA AC CAT AAAC 75 Fungi S. cerevisiae Z75578 CAA GA ACGA AA GT TAGGG GAT CG AAG ATG AT TA GATAC C ATC G TAGTCT TA AC CAT AAAC 76 Diatom D. brightwelli X85386 Figure 3 continued

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20 CAA GA ACGA AA GT TAGGG GAT CG AAG ATG AT TA GATAC C ATC G TAGTCT TA AC CAT AAAC 77 Chrysophyceae P. butcheri AF CAA GA ACGA AA GT TAGGG GAT CG AAG ATG AT TA GATAC C ATC G TAGTCT TA AC CAT AAAC 78 Phaeophyceae L.japonica AB022817 CAA GA ACGA AA GT TGGGG GCT CG AAG ACG AT CA GATAC C GTC C TAGTCT CA AC CAT AAAC 79 Seagra ss T. testudinum AF168878 CAA GA ACGA AA GT AAGGG GAT CG AAG ACG AT CA GATAC C GTC G TAGTCT TT AC TAT AAAC 80 Rhodophyta A. japonica AB0176 ------23' -------------27 -----------------------------------27' --------------81 Helix numbering eukaryote CCG GTCGACGGATCC GT CAGAG GTT CG AAG GCG ----------------------------------------18S 11b primer Figure 3: Site of 18S11b primer compared to metazoan and non metazoan 18S rDNA. Underlined primer sequence denotes the SalI BamHI restriction site (respectively) beyond a CCG tail. Sequence corresponding to primer sequence is in bold.

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21 Results Preliminary Data Preliminary data was collected from a Fort DeSoto, FL sediment sample in order to determine which of two genes, the nucl ear 18S rRNA gene or the mitochondrial 16S rRNA gene, would be more useful in identifying meiofauna sequences from DNA DNA 23.1kb 9.9kb 6.6kb 4kb 2.3kb 2.0kb 0.5kb Figure 4: Total DNA extracted from Fort DeSoto sediments Lane 1 contains 0.5ug of Hin dIII cut lambda marker. Lane 2 contains the environmental DNA. 1 2

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22 extracted from an environmental sample. DNA was extracted and amplified from a Fort DeSoto, FL sediment sample and is shown in figures 4 and 5, respectively. Libraries were made from PCR products using both the 18S rDNA primer set and the 16S rDNA primer set. Six of the 18S clones and two of the 16S clones from the libraries were sequenced. BLAST searches to GenBank revealed that th e closest matches to the 18S sequences represented nematodes or copepods (table 1). Close matches were not found for the 16S sequences. 23.1kb 9.9k b 6.6kb 4kb 2.3kb 2.0kb 0.5 kb Figure 5: 18S rRNA and 16S rRNA genes amplified from DNA extracted from Fort DeSoto sediment. Lane 1 of each gel contains 0.5ug of Hin dIII cut lambda standard marker and lane 2 of each gel contains the PCR products. 23.1kb 9.9kb 6.6kb 4kb 2.3kb 2.0kb 0.5 kb 18S PCR products 16S PCR products 1 2 1 2

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23 Table 1: BLAST search results on mitochondrial 16S rRNA and nuclear 18S rRNA clones. The two closest match es for each clone and the score assigned by BLAST for each match are listed. The higher the score assigned to the match, the more likely the match is correct. Courtney Campbell Causeway and Bayshore Boulevard Study DNA was successfully extracted from the Courtney Campbell Causeway and Bayshore Boulevard samples and amplified after further purification us ing a Chelex purification protocol. Initially the 18S1A and 18S2A primer set was used to amplify and clone the environmental DNA. However, upon screening the sequences from several clones from the DNA amplified with this primer set, it was evident that n on metazoan DNA, mainly from diatoms, was amplified along with the metazoan DNA. The 18S11b primer was designed which specifically amplified metazoan DNA when paired with the 18S2A primer (figure 3). The 18S11b and 18S2A primer set was tested at 50 o C, 55 o C and 60 o C annealing temperatures using chicken, nematode, fungi, and algae genomic DNA. The results from the optimization of this primer set show that DNA from all four organisms was amplified at the 50 o C annealing temperature, but that DNA from only t he Clone Two closest matches Score Taxon Kasendria kansiensis 78 Insecta Hemiptera 16S LP4 Graminella nigrifons 78 Insecta Hemiptera Kasendria kansiensis 78 Insecta Hemiptera 16S SP4 Reventazonia sp. 78 Insecta Hemiptera Cancrinola plumipes 617 Copepoda Harpacticoida 18S clone #1 Eucyclops serrulatus 607 Copepoda Cyclopoida Pontonema vulgar e 1082 Nematoda 18S clone #2 Adoncholaimus sp. 1043 Nematoda Desmodora ovigera 866 Nematoda 18S clone #3 Xyzzors sp. 846 Nematoda Pontonema vulgare 851 Nematoda 18S clone #4 Adoncholaimus sp. 831 Nematoda Eucyclops serrulatus 1128 Co pepoda Cyclopoida 18S clone #5 18S clone #5 Calanus pacificus 1035 Copepoda

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24 Figure 7: PCR optimization of 18S11b primer at 55 o C annealing temperature. M = Hin dIII cut lambda marker (0.5ug); C = chicken DNA; N = nematode DNA; F = fungus DNA; A = alga DNA. M 18S11b/18S2A C N F A 18S4/18S5 C N F A M C N F A C N F A 18S11b/18S2A 18S4/18S5 Figure 6: PCR optimization of 18S11b primer at 50 o C annealing temperature. M = Hin dIII cut lambda marker (0.5ug); C = chicken DNA; N = nematode DNA; F = fungus DNA; A = alga DNA. M C N F A C N F A 18S11b/ 18S2A 18S4/18S5 Figure 8: PCR optimization of 18S11b primer at 60 o C annealing temperature. M = Hin dIII cut lambda marker (0.5ug); C = chicken DNA; N = nematode DNA; F = fungus DNA; A = alga

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25 chicken and nematode was amplified at the 55 o C and 60 o C annealing temperatures (figures 6, 7, and 8). All subsequent PCR amplifications were performed with the 55 o C annealing temperature (figure 9). T he Courtney Campbell Causeway clon e library yielded 298 metazoan sequences that were used in subsequent analyses, and the Bayshore Boulevard library yielded 275 metazoan sequences used in the analyses. One hundred and two OTUs were assigned from the neighbor joining tree (using the number of differences as the basis of the tree) constructed using the full data set of 573 sequences (Appendix 1). One sequence from each of the 102 OTUs was chosen randomly and added to a data set of reference metazoan and non metazoan sequences for phylogenetic analysis (Appendix 2). CC BB 1 23.1 kb 9.1kb 6.5kb 4.3kb 2.3kb 2.0kb 0.5kb Figure 9: 18S rDNA PCR from Courtney Campbell Causeway (CC) and Bayshore Boulevard (BB) using the 18S11b/18S2A primer set. Lane 1 contains 0.5ug of Hin dIII cut lambda standard marker.

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26 OTU CC total BB total OTU CC total BB total OTU CC total BB total 1 28 0 35 2 88 69 0 1 2 2 0 36 1 0 70 1 0 3 1 0 37 1 0 71 1 2 4 3 0 38 4 0 72 0 7 5 24 0 39 4 0 73 2 0 6 1 0 40 14 0 74 1 0 7 1 0 41 7 0 75 0 1 8 4 0 42 14 0 76 1 0 9 0 36 43 31 0 77 2 0 10 0 1 44 1 0 78 0 7 11 0 1 45 1 0 79 0 1 12 1 0 46 3 0 80 0 1 13 1 0 47 0 2 81 0 3 14 1 0 48 0 5 82 1 0 15 1 0 49 0 1 83 0 1 16 2 0 50 1 0 84 0 6 17 0 1 51 1 0 85 2 0 18 1 0 52 0 1 86 5 0 19 0 2 53 0 2 87 2 0 20 0 1 54 0 4 88 2 1 21 0 13 55 9 0 89 2 0 22 0 6 56 1 6 90 8 0 23 0 1 57 1 0 91 4 6 24 0 1 58 1 0 92 0 2 25 1 0 59 2 0 93 3 0 26 39 4 60 1 0 94 0 5 27 3 2 61 1 0 95 1 0 28 7 0 62 0 1 96 4 0 29 0 1 63 0 1 97 1 0 30 1 0 64 0 1 98 1 0 31 5 0 65 11 0 99 0 5 32 0 13 66 1 0 100 1 0 33 0 1 67 1 0 101 0 1 34 0 5 68 0 1 102 12 23 When gaps were excluded from this alignment, a total length of about 230 nucleotides resulted. Fifteen percent of the cloned sequences could be assigned to a taxon Table 2: Sequence grou ps and the number of sequences from each site per sequence group. These data were used to calculate Shannon diversity, maximum diversity and evenness.

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27 represen ted by the metazoan reference sequences, based on 60% or greater bootstrap support. In an effort to increase identification the entire 18S11b/18S2A PCR product was sequenced and analyzed phylogenetically. The resulting alignment length, excluding gaps, i ncreased to about 403 nucleotides. When these extended sequences were added to the metazoan reference data set (Appendix 3) the percent of cloned sequences that could be assigned to a metazoan taxon with a 60% or greater bootstrap support increased to 70 percent. Rarefaction curves were calculated for the meiofauna sequences from each site to determine if the sample size from each site was large enough that the species/OTUs reached a saturation point (figure 10). Both individual and subsampled rarefact ion curves were generated. Figure 10: Rarefaction curves for both sites Individual rarefaction curve (on left) showing the number of distinct sequences per number of sequences. The sequences for each site were randomized and sampled without replacement to create the curves. Subsampled rarefaction curve (on right) showing the distinct number of OTUs per subsample. Each subsample was randomized and sampled 50 times. 0 10 20 30 40 50 60 70 0 50 100 150 200 250 300 Number of Sequences 0 10 20 30 40 50 60 70 0 50 100 150 200 250 300 Number of Sequences

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28 Table 3: Shannon diversity indices, H max and evenness for macrofauna data collected by the Hillsborough EPC and meiofaunal sequence data from Courtney Campbe ll Causeway (CC) and Bayshore Boulevard (BB). Diversity indices were calculated using an Excel spreadsheet for the meiofaunal sequences (data from table 2) as well as the macrofauna data collected by the Hillsborough EPC for the two Tampa Bay sites (table 3). Proportions of species or Sample Site # of individuals # of species Shannon diversity index H max Evenness CC 555 52 2.53 3.95 0.64 Macrofauna BB 239 29 1.82 3.37 0.54 CC 298 65 3.43 4.17 0.82 Meiofauna BB 275 44 2.75 3.78 0.73 Sample Site Proportion of sing letons to total number of OTUs/species Proportion of dominant sequences/species to total sample CC 0.33 0.41 (bivalve Mysella ) Macrofauna BB 0.52 0.56 (bivalve Mysella ) CC 0.46 0.13 (OTU #26 -copepod) Meiofauna BB 0.45 0.32 (OTU #35 -copepod) OTUs having only one individual (singletons) and proportions of dominant species or sequences groups were calculated for each site (table 4). Table 4: The proportion of single individual sequence groups/species (singletons)to the total number of sequence groups/species versus the proportion of dominant sequences/species to the total sample.

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29 The proportions of nematodes, copepods, ostracods and other organisms stained with Rose Bengal were calculated fr om the numbers of each of these groups sorted and counted from the preserved samples from each site. The proportions from each of these groups were compared with the proportions of putative nematode, copepod, ostracod and other organismal sequences sequen ced from each site (table 5). Sample Site Nematodes Copepods Ostracods Other Preserved CC 60% 9% 28% 3% BB 66% 7% 9% 17% Sequenced CC 9% 36% 0% 55% BB 11% 41% 7% 41% Molecular diversity was calculated as nucleotide diversity using Arlequin 2. 001 (Schneider, et al 2000) and MEGA version 2.1 (Kumar, 2001). Nucleotide diversity for Bayshore Boulevard sequences was calculated as 0.183127+/ 0.087251 by Arlequin 2.001 using the Kimura 2 parameter distance method, and 0.1358 +/ 0.0088 by MEGA 2.1 as Mean distance between groups using pairwise deletions with p distance. Nucleotide diversity for Courtney Cambell Causeway sequences was calculated as 0.176242 +/ 0.083991 by Arlequin 2.001 and 0.1327 +/ 0.0089 by MEGA 2.1 using the same parameters as for the Bayshore Boulevard sequences. Table 5: Percentage of nematodes, copepods, ostracods and other organisms in preserved samples and sequenced samples. Percentage of organisms from the sequenced samples were calculated from the number of sequences in the sequence groups putatively identified f rom the phylogenetic tree in Appendix 3.

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30 Discussion The goal of this study was to determine the usefulness of the 18S rDNA sequence analysis in characterizing the diversity of meiofauna at two ecologically different sites in Tampa Bay. A total of 573 sequences from both sites were collected resulting in 102 groups of sequences when analyzed by the neighbor joining method using the number of differences as the basis for the tree (Appendix 1). Seven of these OTUs contained the majority of sequences (28 7 sequences) while fifty OTUs contained only a single sequence. This method was able to discriminate between the two sites using phylogenetic analysis: only seven OTUs contained sequences from both Courtney Campbell Causeway and Bayshore Boulevard, while 93 OTUs consisted of sequences from one site or from the other site. Identification of the OTUs using a reference data set of metazoan and non metazoan sequences was most successful when the number of nucleotides analyzed was increased (Appendices 2 and 3) The identification rate increased from 15% to 70% when the number of nucleotides was increased from 230 to 403. Inclusion of the nematode and two harpacticoid copepod sequences from the Courtney Campbell Causeway site in the phylogenetic analysis of th e environmental meiofauna sequences indicated that such specifically identified sequences could be recognized in the meiofauna data, as indicated by the extended CC6 125 sequence from OTU 102 grouping with the nematode ( Metachromadora pulvinata ) with 100% bootstrap support (Appendix

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31 3). Individual rarefaction curves that were not resampled, plotted for the Courtney Campbell Causeway and Bayshore Boulevard sites, show that the sample size of the Courtney Campbell Causeway sample was not large enough for t he number of distinct OTUs to reach a saturation point even after all 298 sequences were plotted. In contrast, the Bayshore Boulevard sample reached a saturation point at 40 distinct OTUs, corresponding to 160 of the 275 sequences for that sample, indicat ing that the sample size was adequate to encompass the number of distinct sequences in the sample (figure 10 left panel). However, the subsampled individual rarefaction curves calculated using Ecosim (Gotelli and Entsminger, 2003) for both sites showed le ss saturation (figure 10, right panel). The saturation observed in the un resampled individual rarefaction curve for the Bayshore Boulevard site could be an artifact due to the large number of sequences in group 35. The diversity of each site was assesse d using the Shannon diversity index (H) (Shannon and Weaver, 1949), which takes into account both the richness and evenness of a sample. H for both sites is high, indicating both samples are highly diverse (table 3). In comparison with the macrofauna d ata from the Hillsborough EPC, the meiofauna diversity is much higher at both sites than the macrofauna diversity. There were more different meiofaunal OTUs at both sites than macrofauna species even though the sample size of the macrofauna was larger. C omparing the two sites, the diversity of both the meiofauna and macrofauna at C ourtney C ampbell C auseway was higher than at Bayshore Boulevard. The Courtney Campbell Causeway samples also displayed more evenness of OTUs than the Bayshore Boulevard sample s. When combined with the higher number of species at Courtney Campbell Causeway, the higher measure of evenness indicates that

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32 the Courtney Campbell Causeway site is more diverse than the Bayshore Boulevard site. However, since the individual rarefactio n curve for the Courtney Campbell Causeway site shows no saturation point, indicating that not all species were detected in the sample, the measures of diversity, maximum diversity and evenness may not accurately reflect the actual diversity of the site. Many more sequences would have to be obtained from the Courtney Campbell Causeway clone library for the collectors curve to reach a plateau. Preserved samples were collected from each site at the same time as samples for DNA analysis. The major groups o f organisms, nematodes, copepods and ostracods as well as any other metazoan organisms (designated as other), were sorted and counted from the preserved samples from each site and presented as percentages of the total number of metazoans counted from eac h site (table 5). The percentage of nematode, copepod, ostracod and other sequences were calculated from the putatively identified sequence groups from the phylogenetic tree in Appendix 3 for comparison with the same groups sorted from the preserved sampl es (table 5). In the preserved samples, nematodes were the dominant taxon in both samples, comprising 60% or more of the total meiofauna counted, followed by ostracods and copepods, respectively. In contrast, sequences designated as other (not being pu tatively identified as nematodes, copepods or ostracods) were the dominant group for the Courtney Campbell Causeway sequences, comprising 55% of the total number of sequences, while other sequences and putative copepod sequences for the Bayshore Boulevar d sequences each comprised 41% of the total number of sequences. A portion of the other category of sequences is comprised of polychaete (2.5% of the Bayshore Boulevard sample), bryozoan (17% of the Courtney Campbell Causeway sample) and cirriped sequen ces (1.5% of the Bayshore Boulevard

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33 sample). The sequences that identify closely as Polydora ciliata correspond to the Bayshore Boulevard site, which consisted of a silty sand at the time of sediment collection. The larvae of Polydora ciliata are mud dwe lling and so might be found at this site (Rupert and Barnes, 1994). The Bayshore Boulevard site was located very close to a seawall, where oysters may be found. Because Polydora ciliata bores into oyster and clam shells (Rupert and Barnes, 1994), it is n ot unlikely that Polydora ciliata sequences would be found at the Bayshore site. The cirriped sequences were also found in the Bayshore sample, but barnacles prefer to settle on hard substrate (Ruppert and Barnes, 1994) and would not be likely to be found in a sediment sample, but a larva could possibly go astray and be collected before being able to settle. The bryozoan sequences found at the Courtney Campbell Causeway site are probably from an epiphytic colony (Rupert and Barnes, 1994) that became detac hed from the seagrass and settled onto the sediment where it was collected. Small patches of seagrass, upon which bryozoans might be found, characterize the Courtney Campbell site. The remaining sequences in the other category (38% of the Courtney Cam pbell Causeway sample and 37% of the Bayshore Boulevard sample) that were unable to be identified from the phylogenetic tree presented in Appendix 3 are possibly fast evolving nematode or arthropod sequences with no close matches in Genbank or the phylogen etic tree. Either more complete sequences of the environmental samples or more reference sequences would be needed to identify sequences in the other category. Among the sequences putatively identified as nematode, copepod or ostracod, the copepod seque nces were dominant at both sites, followed by nematodes and ostracods, respectively, which is opposite of the preserved sample data. The discrepancy between the proportion of

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34 nematodes and copepods in the hand sorted samples compared with the molecular sa mples could be explained in part if the large number of unidentified sequences (55% of Courtney Campbell Causeway samples and 41% of Bayshore Boulevard samples) were from nematodes. Another possibility is that the mechanical processing of the sediment sam ples possibly caused the delicate copepods to break so that copepod DNA was present for molecular analysis, but leaving them unrecognizable as copepods in the preserved samples. Clearly, a more uniform and gentle method for processing samples would be use ful and advantageous. The discrepancy between the numbers of nematodes counted from the preserved samples and from the sequences could be explained if the sequences that were not included in the tree in Appendix 3 turn out to be nematode sequences. To en sure that primer mismatch to the rDNA sequence was not the cause of this discrepancy, the primer sequence was compared to the primer site in 100 nematode sequences downloaded from the Ribosomal Database Project (Cole et al. 2003). Twenty one percent of th e nematode sequences differed from the primer by one base, and one percent of the nematode sequences differed from the primer by two bases. The remaining nematode sequences did not differ at all from the primer sequence. Another explanation for this discr epancy may stem from a bias encountered in the PCR amplification. A bias may occur in rDNA when the regions flanking the primer site within the rDNA inhibit the initial PCR steps, possibly due to secondary structure, thus causing the rDNA from different o rganisms to amplify disproportionally to the amount of DNA present in the PCR reaction (Hansen et al. 1998). Performing the amplification with two different rDNA primer sets could reduce this bias (Hansen et al. 1998).

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35 Nucleotide diversity was calcula ted from the sequence data for each site using Arlequin 2.001 and MEGA 2.1. The diversity values calculated using Arlequin 2.001 are slightly larger than those calculated using MEGA 2.1, most likely because Arlequin counted gaps as characters while MEGA d id not. The values for the two sites are very similar, which is not surprising because each site is a community of different organisms, which would act to homogenize the nucleotide diversity. To observe a difference in nucleotide diversity between the tw o sites, the sites would have to differ in sequence content by an extreme measure, such as one site being dominated by a few extremely different sequences and the other site being dominated by many similar sequences. This study is one of the first to use phylogenetic methods to assess the diversity of metazoans in marine sediments. Several studies have used phylogenetic methods to assess the diversity of environmental microbial communities (McCaig, et al. 1999; Purkhold, et al. 2000; Bruns, et al. 1999, K uske, et al. 1997; Borneman and Triplett, 1997; Borneman, et al. 1996; and Stephen, et al. 1996). The majority of these microbial diversity studies have sought to compare environmental microbial diversity and community structure determined from sequence d iversity to environmental diversity determined through laboratory culture, and have used 130 sequences or less in their analyses. Most of these studies have not used the sequence data to calculate diversity indices. Only McCaig, et al. (1999) increased t he number of sequences analyzed to more than 200 (275 in all), and used the Shannon diversity index, dominance and evenness to assess diversity of the microbial communities they were studying. Recently a few studies have used phylogenetic methods to asses s the diversity of eukaryotes, such as Lopez Garcia (2001), who analyzed the diversity of deep sea Antarctic plankton using 101

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36 sequences, and Lopez Garcia et al. (2003), who analyzed the diversity of eukaryotes in deep sea hydrothermal vent sediments, usi ng 291 sequences. Neither of these two studies used the phylogenetic data to calculate diversity indices. In conclusion, phylogenetic methods used to assess the diversity of meiofauna were successful in discriminating between two different sites within T ampa Bay. The use of existing macrofauna data to which the meiofauna data could be compared showed that diversity of meiofauna assessed using phylogenetic methods reflected the macrofauna diversity for each site. The amount of sequence data that would ne ed to be collected for phylogenetic assessment of diversity differs for each site studied so that in some instances the amount of sequence data needed to accurately assess the diversity of a site may become costly. With careful consideration to sampling m ethods, site selection and bias reduction, phylogenetic assessment of diversity of environmental samples can be a useful tool for environmental monitoring, particularly as sequencing costs decrease and high throughput sample handling facilities become more common.

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37 Literature Cited Austen M, Warwick R, Rosado C (1989) Meiobenthic and macrobenthic community structure along a putative pollution gradient in southern Portugal. Marine Pollution Bulletin 20:398 405 Bilyard G (1987) The value of benthic infauna in marine pollution monitoring studies. Marine Pollution Bulletin 18:581 585 Borneman J, Skroch P, O'Sullivan K, Palus J, Rumjanek N, Jansen J, Nienhuis J, Triplett E (1996) Molecular microbial diversity of an agricultural soil in Wisconsin. Applied and En vironmental Microbiology 62:1935 1943 Borneman J, Triplett E (1997) Molecular microbial diversity in soils from eastern Amazonia: evidence for unusual microorganisms and microbial population shifts associated with deforestation. Applied and Environmental Microbiology 63:2647 2653 Bruns M, Stephen J, Kowalchuk G, Prosser J, Paul E (1999) Comparative diverstiy of ammonia oxidizer 16SrRNA gene sequences in native, tilled and successional soils. Applied and Environmental Microbiology 65:2994 3000 Cole JR, Chai B, Marsh TL, Farris RJ, Wang Q, Kulam SA, Chandra S, McGarrell DM, Schmidt TM, Garrity GM, Tiedje JM (2003) The Ribosomal Database Project (RDP II): previewing a new autoaligner that allows regular updates and the new prokaryotic taxonomy. Nucleic Acid s Research 31(1):442

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38 Coull B, Chandler G (1992) Pollution and meiofauna: Field, laboratory, and mesocosm studies. Oceanography and Marine Biology Annual Review 30:191 271 Coull B, Hicks G, Wells J (1981) Nematode/copepod ratios for monitoring pollution: A rebuttal. Marine Pollution Bulletin 12:378 381 Coull BC (1988) Chapter 3: Ecology of the Marine Meiofauna. In: Higgins R, Thiel H (eds) Introduction to the study of meiofauna. Smithsonian Institution Press, Washington, DC, p 18 38 Dauvin J (1984) Dyna mique d'ecosystemes macrobenthiques des fondes sedimentaires de la Baie de Morlaix et leur perturbation par les hydrocarbures de l'Amoco Cadiz. Pierre et Marie Curie University, Paris De Rijk P, De Wachter R (1993) DCSE v2.54, an interactive tool for seque nce alignment and secondary structure research. Comput Applic Biosci 9:735 740 Field KG, Olsen GJ, Lane DJ, Giovannoni SJ, Ghiselin MT, Raff EC, Pace NR, Raff RA (1988) Molecular phylogeny of the animal kingdom. Science 239:748 53 Friedrich M, Tautz D (200 1) Arthropod rDNA phylogeny revisited: a consistency analysis using Monte Carlo simulation. Ann Soc Entomol Fr (N.S.) 37:21 40 Garey JR (2001) Ecdysozoa: the relationship between Cycloneuralia and Panarthropoda. Zoologischer Anzeiger 240:321 330 Gee J, W arwick R, Schanning M, Berge J, Ambrose J, WG (1985) Effects of organic enrichment on meiofaunal abundance and community structure in sublittoral soft sediments. Journal of Experimental Marine Biology and Ecology 91:247 262 Giere O (1993) Meiobenthology: the microscopic fauna in aquatic sediments. Springer Verlag, Berlin

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39 Gotelli NJ, Entsminger GL (2003) EcoSim: Null models software for ecology. Version 7. Acquired Intelligence Inc. & Kesey Bear. Burlington, VT http://homepages.together.net/~gentsmin/ecosim .htm Hansen MC, Tolker Nielsen T, Givskov M, Molin S (1998) Biased 16S rDNA PCR amplification caused by interference from DNA flanking the template region. FEMS Microbiology Ecology 26: 141 149 Hillis D, Dixon M (1991) Ribosomal DNA: Molecular evolution a nd phylogenetic inference. The Quarterly Review of Biology 66:411 426 Kumar S, Tamura K, Jakobsen I, Nei M (2001) MEGA2: Molecular Evolutionary Genetics Analysis software. Bioinformatics Vol. 17 12:1244 1245 Kuske C, Barns S, Busch J (1997) Diverse unculti vated bacterial groups from soils of the arid southwestern United States that are present in many geographic regions. Applied and Environmental Microbiology 63:3614 3621 Lambshead, P (1986) Sub catastrophic sewage and industrial waste contamination as reve aled by marine nematode faunal analysis. Marine Ecology Progress Series 29:247 260 Lis J (1980) Fractionation of DNA fragments by polyethylene glycol induced fractionation. Methods Enzymol 65:347 353 Lis J, Schleif R (1975) Size fractionation of double str anded DNA by precipitation with polyethylene glycol. Nucleic Acids Research 2:383 389 Litvaitis M, Nunn G, Thomas WK, Kocher T (1994) A molecular approach for the identification of meiofaunal turbellarians (Platyhelminthes, Turbellaria). Marine Biology 120 :437 442

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40 Lopez Garcia P, Philippe H, Gail F, Moreira D (2003) Autochthonous eukaryotic diversity in hydrothermal sediment and experimental microcolonizers at the Mid Atlantic Ridge. Proceedings of the National Academy of Sciences 100:697 702 Lopez Garcia P Rodriguez Valera F, Pedros Allo C, Moreira D (2001) Unexpected diversity of small eukaryotes in deep sea Antarctic plankton. Nature 409:603 607 Maniatis T, Fritsch E, Sambrook J (1982) Molecular cloning: a laboratory manual. Cold Spring Harbor Press, p 545 McCaig AE, Glover LA, Prosser JI (1999) Molecular analysis of bacterial community structure and diversity in unimproved and improved upland grass pastures. Appl Environ Microbiol 65:1721 30 McLachlan A (1983) Sandy beach ecology a review. In: McLachl an A, Erasmus T, Junk W (eds) Sandy beaches as ecosystems, The Hague, p 321 380 Odum, E (1971) Principles and concepts pertaining to organization at the community level. In: Fundamentals of ecology, Saunders College Publishing, Philadelphia, PA, p 140 1 61 Pearson T (1975) The benthic ecology of Loch Linnhe and Loch Eil, a sea loch system on the west coast of Scotland. IV. Changes in the benthic fauna attributable to organic enrichment. Journal of Experimental Marine Biology and Ecology 20:1 41 Prosser J (2002) Molecular and functional diversity in soil micro organisms. Plant and Soil 244:9 17 Purkhold U, Pommerening Roser A, Juretschko S, Schmid M, Koops H, Wagner M (2000) Phylogeny of all recognized species of ammonia oxidizers based on

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41 comparative 16S r RNA and amoA sequence analysis: implications for molecular diversity surveys. Applied and Environmental Microbiology 66:5368 5382 Raffaelli D (1987) The behaviour of the nematode/copepod ratio in organic pollution studies. Marine Environmental Research 23 :135 152 Raffaelli D, Mason C (1981) Pollution monitoring with meiofauna, using the ratio of nematodes to copepods. Marine Pollution Bulletin 12:158 163 Ruppert EE, Barnes RD (1994) Invertebrate Zoology, 6 th ed. Saunders College Publishing, Orlando, FL S chneider S, Roessli D, Excoffier, L (2000) Arlequin ver. 2.000: A software for population genetics data analysis. Genetics and Biometry Laboratory, University of Geneva, Switzerland Stephen J, McCaig A, Smith Z, Prosser J, Embley T (1996) Molecular divers ity of soil and marine 16S rRNA gene sequences related to B subgroup ammonia oxidizing bacteria. Applied and Environmental Microbiology 62:4147 4154 Street G, Montagna P (1996) Loss of genetic variability in harpacticoid copepods associated with offshore p latforms. Marine Biology 126:271 282 Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG (1997) The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 25:4876 82 Wagn er A, Blackstone N, Cartwright P, Dick M, Misof B, Snow P, Wagner C, Bartels J, Murtha M, Pendleton J (1994) Surveys of gene families using polymerase chain reaction: PCR selection and PCR drift. Systematic Biology 43:250 261

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42 Warwick R (1981) The nematode/ copepod ratio and its use in pollution ecology. Marine Pollution Bulletin 12:329 333 Warwick R (1984) Species size distributions in marine benthic communities. Oecologia (Berlin) 61:32 41 Warwick R (1988) The level of taxonomic discrimination required to detect pollution effects on marine benthic communities. Marine Pollution Bulletin 19:259 268 Warwick R, Platt H, Clarke K, Agard J, Gobin J (1990) Analysis of macrobenthic and meiobenthic community structure in relation to pollution and disturbance in Hami lton Harbor, Bermuda. Journal of Experimental Marine Biology and Ecology 138:119 142 Winnepenninckx B, Backeljau T, Mackey LY, Brooks JM, De Wachter R, Kumar S, Garey JR (1995) 18S rRNA data indicate that Aschelminthes are polyphyletic in origin and consis t of at least three distinct clades. Mol Biol Evol 12:1132 7 Wintzeringode F, Goebel U, Stackebrandt E (1997) Determination of microbial diversity in environmental samples: pitfalls of PCR based rRNA analysis. FEMS Microbiology Review 21:213 229 Wyuts J, V an de Peer Y, Winkelmans T, De Wachter R (2002) The European database on small subunit ribosomal RNA. Nucleic Acids research 30:183 18

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43 Appendices

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44 Appendix 1 Phylogenetic tree of all sequences Appendix 1 contains the phylogenetic tree of all 573 seque nces created using MEGA 2.1 (Kumar, 2001). The phylogenetic tree was created using the neighbor joining method based on the number of differences between sequences and complete deletion of gaps. Sequence groups are noted to the right of each group, and c onsist of sequences having no more than 5 differences between them. The scale bar on the last page of the tree indicates the number of differences per length of the bar.

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45 Appendix 1 (Continued)

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46 Appendix 1 (Continued)

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47 Appendix 1 (Con tinued)

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48 Appendix 1 (Continued)

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49 Appendix 1 (Continued)

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50 Appendix 1 (Continued)

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51 Appendix 1 (Continued)

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52 Appendix 2 Phylogenetic tree of short sequences with reference alignment Appendix 2 contains the ClustalX alignment and phylogenetic tree o f the sequences from each of the 102 OTUs with the reference data set. Organisms and their GenBank accession numbers are given in the table below. The phylogenetic tree was created in MEGA 2.1(Kumar, 2001) using the neighbor joining method based on the K imura 2 parameter distance method and complete deletion of gaps. The sequence group to which a sequence is assigned is noted after each sequence name.

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53 Appendix 2 (Continued)

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54 Appendix 2 (Continued)

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55 Appendix 2 (Continued)

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56 Appendix 2 (Continued)

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57 Appendix 2 (Continued)

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58 Appendix 3 Phylogenetic tree of extended sequences with reference alignment Appendix 3 contains the ClustalX alignment and phylogenetic tree of the extended meiofana sequences with the reference data set of metazoan and non me tazoan sequences. The phylogenetic tree was created in MEGA 2.1 (Kumar, 2001) using the neighbor joining method based on the Kimura 2 parameter distance method and complete deletion of gaps.

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59 Appendix 3 (Continued)

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60 Appendix 3 (Continued)

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61 Appendix 3 (C ontinued)


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ABSTRACT: A Molecular Approach to Assessing Meiofauna Diversity in Marine Sediments Heather C. Hamilton Abstract The purpose of this study was to determine if a molecular approach could be applied to calculating the diversity of meiofauna in marine sediments from two sites in Tampa Bay, FL, similar to the approach of McCaig et al, 1999 in calculating the diversity of microbes in pastureland soils. The approach includes extracting total DNA directly from the sediment and amplifying the 18S rRNA gene by PCR. Clone libraries from the 18S gene would be created for each site and 300 sequences from each clone library would be obtained. These sequences would then be phylogenetically analyzed and assigned to an OTU, from which diversity indices can be calculated.The phylogenetic analysis of the sequences from the two sites revealed that of the 102 OTUs assigned from the sequences, only 7 OTUs included sequences from both sites, while 93 OTUs contained sequences from one site or from the other. Thus the sites were phylogenetically different from each other. Shannon diversity indices calculated for each site showed a difference between the two sites and paralleled diversity indices for macrofauna data for each site collected by the Hillsborough County Environmental Protection Commission. Sequences from 30 OTUs were completely sequenced and identified by phylogenetic comparison with a metazoan reference alignment. A discrepancy between the sequence data and data collected from preserved samples taken at each site was evident upon analysis: roughly 60% of each preserved sample consisted of nematodes and 10% consisted of copepods, while roughly 30% of the identified OTUs consisted of copepods and 10% consisted of nematodes.
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