ARTICLE

Analysis of bacterial communities associated with the benthicamphipod Diporeia in the Laurentian Great Lakes BasinAndrew D. Winters, Terence L. Marsh, Travis O. Brenden, and Mohamed Faisal

Abstract: Bacterial communities play important roles in the biological functioning of crustaceans, yet little is known about theirdiversity, structure, and dynamics. This study was conducted to investigate the bacterial communities associated with thebenthic amphipod Diporeia, an important component in the Great Lakes foodweb that has been declining over the past 3 decades.In this study, the combination of 16S rRNA gene sequencing and terminal restriction fragment length polymorphism revealeda total of 175 and 138 terminal restriction fragments (T-RFs) in Diporeia samples following treatment with the endonucleases HhaIand MspI, respectively. Relatively abundant and prevalent T-RFs were affiliated with the genera Flavobacterium and Pseudomonasand the class Betaproteobacteria. T-RFs affiliated with the order Rickettsiales were also detected. A significant difference in T-RFpresence and abundance (P = 0.035) was detected among profiles generated for Diporeia collected from 4 sites in Lake Michigan.Comparison of profiles generated for Diporeia samples collected in 2 years from lakes Superior and Michigan showed a significantchange in diversity for Lake Superior Diporeia but not Lake Michigan Diporeia. Profiles from one Lake Michigan site containedmultiple unique T-RFs compared with other Lake Michigan Diporeia profiles, most notably one that represents the genusMethylotenera. This study generated the most extensive list of bacteria associated with Diporeia and sheds useful insights on themicrobiome of Great Lakes Diporeia that may help to reveal potential causes of the decline of Diporeia populations.

Key words: bacteria, Diporeia spp., Laurentian Great Lakes.

Résumé : Les communautés bactériennes jouent un rôle important dans le fonctionnement biologique de crustacés, mais on saitpeu de choses sur leur diversité, la structure et la dynamique. Cette étude a été menée pour étudier les communautés bactéri-ennes associées a la Diporeia amphipode benthique, un élément important dans la chaîne alimentaire des Grands Lacs qui a étéen baisse au cours des trois dernières décennies. Dans cette étude, la combinaison de séquençage du gène de l’ARNr 16S et« terminal restriction fragment length polymorphism » a révélé un total de fragments de 175 et 138 de restriction de la borne(« T-RFs ») dans des échantillons Diporeia après le traitement par les endonucléases HhaI et MspI, respectivement. T-RFs relative-ment abondantes et les plus répandus étaient affiliés a des genres Flavobacterium et Pseudomonas, et la Betaprotéobactéries declasse. T-RFs affiliés a l’ordre des Rickettsiales ont également été détectés. Une différence significative en présence T-RF etl’abondance (P = 0,035) a été détectée chez les profils générés pour Diporeia recueillies auprès de quatre sites dans le lac Michigan.La comparaison des profils générés pour les échantillons prélevés Diporeia en deux ans des lacs Supérieur et Michigan a montréun changement significatif dans la diversité pour le lac Supérieur diporeia mais pas du lac Michigan Diporeia. Profils d’un site dulac Michigan contenaient de multiples T-RFs uniques par rapport aux autres profils lac Michigan Diporeia, notamment celui quireprésente le genre Methylotenera. Cette étude a généré la liste la plus complète des bactéries associées aux Diporeia et apporte desinformations utiles sur le microbiome de Grande Lacs Diporeia qui peuvent aider a révéler les causes possibles du déclin despopulations de Diporeia.

Mots-clés : bactéries, Diporeia spp., des Grands Lacs Laurentiens.

IntroductionDetritivorous amphipods belonging to the genus Diporeia are

important food resources for numerous Great Lakes fish species(Barbiero et al. 2011) and, thus, serve as coupling mechanismsbetween pelagic and benthic zones of the Great Lakes. Histori-cally, Diporeia have been the dominant benthic macroinvertebratein the Great Lakes, averaging over 7000 individuals/m2, reachingmean densities as high as 12 216 /m2, and accounting for approx-imately 70% of the macrobenthic community of the Great Lakes(Nalepa 1987, 1989). However, Diporeia abundances have been de-clining from the majority of its habitats throughout 4 of the5 Great Lakes (Dermott and Kerec 1997; Nalepa et al. 1998, 2005,2007; Dermott 2001; Lozano et al. 2001; Barbiero and Tuchman

2002; Barbiero et al. 2011). As a result, there has been increasedinterest in studying the biology of Diporeia in the Great Lakes. Theaim of the current study is make information on the bacterialcommunities associated with Diporeia available to the scientificcommunity to potentially shed light on the declines.

Considering the ubiquity and large abundances of crustaceansin marine and freshwater environments, tight associations be-tween crustaceans and bacteria can widely affect bacterial behav-ior, growth, and biogeochemical activities (reviewed in Tang et al.2010). Microbial communities within the Great Lakes ecosystemhave been studied for many years using basic microscopy andculturing-based techniques (Inniss and Mayfield 1978; Maki andRemsen 1981; Ishii et al. 2006). However, since a large fraction of

Received 3 July 2014. Revision received 15 October 2014. Accepted 25 October 2014.

A.D. Winters and T.O. Brenden. Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA.T.L. Marsh. Center for Microbial Ecology and Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA.M. Faisal. Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA; Department of Pathobiology and DiagnosticInvestigation, 177K Food Safety and Toxicology Building, Michigan State University, East Lansing, MI 48824, USA.Corresponding author: Mohamed Faisal (e-mail: faisal@cvm.msu.edu).

72

Can. J. Microbiol. 61: 72–81 (2015) dx.doi.org/10.1139/cjm-2014-0434 Published at www.nrcresearchpress.com/cjm on 3 November 2014.

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

microbial life cannot be cultured, a severe dearth regarding thetaxonomic diversity of these microbial communities currentlyexists. Because microbial communities can evolve rapidly in re-sponse to environmental perturbations and could be used as in-dicators of change, it is important to acquire baseline informationagainst which future changes could be identified. In recent years,cultivation-independent surveys employing sequence analysis of16S small-subunit rRNA genes within freshwater sediments haveallowed for the identification of multiple bacterial phylotypesthat appear to be ubiquitous in freshwater ecosystems (Zwartet al. 2002; Briée et al. 2007; Rinta-Kanto et al. 2009; Tang et al.2009; Newton et al. 2011). Unfortunately, knowledge of the struc-ture and function bacterial communities associated with GreatLakes Diporeia is still very limited.

One culture-independent method for investigating bacterialcommunities in samples through fingerprinting is terminal re-striction fragment length polymorphism (T-RFLP). T-RFLP is con-sidered a cost-effective tool for assessing the diversity of complexbacterial communities and for rapidly comparing communitystructure and diversity among different ecosystems that is capa-ble of recovering a resolution of bacterial community structurehighly comparable to that of pyrotag sequencing (Liu et al. 1997;Pilloni et al. 2012). In the present work, the diversity of bacterialcommunities associated with Diporeia collected from multiplesites in lakes Superior, Michigan, and Huron, and the inlandCayuga Lake (New York) were mapped and compared usingT-RFLP of 16S rRNA genes to determine if these communities varyspatially or temporally. In a second step, sequence analysis of16S rDNA clones was applied to a total of 5 Diporeia bacterialcommunities from lakes Superior, Michigan, and Huron, and theinland Cayuga Lake (New York). Results of this study provide im-portant baseline information regarding the structure of bacterialcommunities associated with Diporeia in the Great Lakes ecosystem.

Materials and methods

Sample collectionFor all analyses, Diporeia samples were collected from a total

of 8 locations in the Great Lakes basin and a single location inCayuga Lake, an inland lake in New York, (Fig. 1) at depths rang-

ing from 40 to 186 m. For T-RFLP analysis, replicate samples werecollected at each site in August 2007, and one site in Lake Superior(SU-23B) and one site in Lake Michigan (MI-18M) were resampled inAugust 2008 (Table 1). To identify bacterial taxa present in Diporeiasamples, sequence analysis of cloned 16S rDNA clones was appliedto a single replicate from each of the lakes mentioned above.Additionally, sequence analysis of 16S rDNA clones was applied toa single sample collected from Lake Ontario.

Sediment samples were collected using a Ponar grab (samplingarea: 22.86 × 22.86 mm/8.2 L). Once a sample was returned to thesurface, it was sieved (mesh = 0.25 mm) and Diporeia were identi-fied according to Edmonson (1959). Diporeia were then pooled(5 amphipods/pool), rinsed several times in sterile water to elimi-nate “free-living” bacteria, placed in sterile 80% ethanol in a 1.5 mLtube, and immediately stored at –20 °C until further processing.

DNA isolationGenomic bacterial community DNA was extracted from Diporeia

samples using the PowerSoil DNA Isolation kit (MO BIO Laborato-ries Inc., Carlsbad, California, USA) following the manufacturer’sprotocol. DNA was quantified with a Qubit fluorometer and theQuant-it dsDNA BR Assay kit (Invitrogen Corp., Austin, Texas,USA). The isolated bacterial DNA was then used as a templatefor PCR amplification. The bacterial 16S gene was amplifiedusing the universal eubacterial primer set 27F–1387R (27F: 5=-AGAGTTTGATCMTGGCTCAG-3=) labeled with carboxyfluorescein(6-FAM) and 1387R (5=-GGGCGGWGTGTACAAGGC-3=) (Marchesiet al. 1998). Each 50 �L PCR reaction contained 25 �L of 2× GreenGoTaq Master Mix (Promega Corporation, Madison, Wisconsin,USA), 45 �mol/L each primer, and �100 ng of template DNA. PCRreaction conditions for amplification of partial 16S rRNA geneswere as follows: initial 94 °C for 4 min, followed by 29 cycles of94 °C for 30 s, 56 °C for 30 s, and 72 °C for 1 min, and a finalextension for 7 min at 72 °C. A negative control containing noDNA was included in each set of PCR reactions. Resulting PCRproducts were visualized by agarose gel electrophoresis. Amplifi-cation of the proper gene fragment (�1.36 kb) was confirmed bycomparison with a DNA size ladder. The PCR reaction was carriedout in triplicate for each sample and resulting products were

Fig. 1. Map of the Laurentian Great Lakes and the Finger Lakes showing sampling sites for this study. A circle indicates a sample was analyzedwith terminal restriction fragment length polymorphism (T-RFLP), a triangle indicates a sample was analyzed with 16S rDNA sequencing, anda square indicates a sample was analyzed with both T-RFLP and 16S rDNA sequencing.

Winters et al. 73

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

pooled and purified using a Wizard SV Gel and PCR Clean-UpSystem (Promega) following the manufacturer’s protocol.

T-RFLP analysisTo construct profiles of Diporeia bacterial communities for each

restriction enzyme, 300 ng of purified fluorescently labeled PCRproduct were cut individually with 5 units of HhaI and MspI (NewEngland Biolabs, Beverly, Massachusetts, USA) for 2 h at 37 °C.Digested PCR products were precipitated with 3 volumes of abso-lute ethanol. After 8 h of incubation at –20 °C, the precipitateswere pelleted by centrifugation at 14 000 r/min (16 000g) for15 min. Pellets were resuspended in 6 �L of sterile water (DEPC-treated, DNase, RNase-free). The DNA fragments were separatedon an ABI 3100 Genetic Analyzer automated sequence analyzer(Applied Biosystems Instruments, Foster City, California, USA) inGeneScan mode at Michigan State University’s sequencing facil-ity. The 5= terminal restriction fragments (T-RFs) were detected byexcitation of the 6-FAM molecule attached to the forward primer.The sizes and abundance of the fragments were calculated usingGeneScan 3.7 in relation to the MM1000 internal standard (Bio-Ventures Inc., Murfreesboro, Tennessee, USA). T-RFLP data wasanalyzed with T-REX (http://trex.biohpc.org). T-REX software usesthe methodology described by Abdo et al. (2006) and Smith et al.(2005) to identify and align true peaks, respectively. We used onestandard deviation in peak area as the limit to identify true peaksand defined T-RFs by rounding off fragment sizes to nearest inte-ger. Additionally, only profiles with a cumulative peak height of≥5000 fluorescence units were used in the analysis.

For all analyses, abundance values for both T-RFLP data setswere standardized by expressing the abundance of each OTU as apercentage of total abundance for each sample. Additionally, toaccount for “blind sampling” and large numbers of absences inthe data sets, the data sets were transformed using the Hellingerequation as suggested by Ramette (2007). Species richness anddiversity for the T-RFLP community profiles at each sampl-ing site (for both the HhaI and MspI data sets) were calculatedin R (Pinheiro et al. 2010) with the Vegan package (Oksanen et al.2013). For calculating species richness and diversity, we assumedthat the number of T-RFs present in a profile represented theoperational taxonomic units (OTUs) and that the T-RF peak heightrepresented the relative abundance of each OTU. For characteriz-ing species diversity, we used the Shannon diversity index, whichaccounts for both abundance and evenness of the community ata particular site. For characterizing species evenness, we usedPielou’s Evenness index, which is based on the Shannon index andis constrained between 0.0 and 1.0, where less variation in com-munities between the OTU abundance approaches 1.0.

We tested for significant differences in OTU composition andabundance among sites and years using permutational multi-variate analysis (PERMANOVA). For the PERMANOVA, the Bray–Curtis distance matrix of Hellinger transformed OTU composition

was the response variable and site or year was the independentvariable. The total number of permutations performed was 999.The PERMANOVA analysis was conducted in R using ADONISfunction in the VEGAN library. Additionally, permutation tests(999 permutations) were used to test for significant differences inmultivariate dispersion among sites and years using the BETADISPERfunction in VEGAN, which is a multivariate analogue of Levene’stest for homogeneity of variances. Since the Chao method takesthe fraction of rarely present/low abundant T-RFs into account(Chao et al. 2005), it was used to define � diversity. To determinewhether community structure was significantly correlated withdepth we used Mantel tests (Legendre and Legendre 1998) based onPearson’s product–moment correlation. For Mantel tests, depthvalues were Z-score standardized, and community dissimilaritieswere standardized using Wisconsin standardization (dividing allspecies by their maxima, and then standardizing sites to unittotals). To test for differences in relative abundance of individualT-RFs among samples, MANOVA tests were conducted using thePROC GLIMMIX procedure (SAS Institute Inc., Cary, North Caro-lina, USA).

DNA sequence analysisThe PCR products generated for a total of 5 samples from lakes

Superior, Michigan, Huron, and Ontario, and Cayuga Lake (SU-20B,MI-27M, HU-54M, ON-41, and Cayuga), were amplified with theunlabeled primer set (27F–1387R) and cloned using a TOPO TACloning kit (Invitrogen, Grand Island, Nebraska, USA) followingthe manufacturer’s protocol. A total of 399 clones (between93 and 95 for the Great Lakes and 23 for Cayuga Lake) werecultured on Luria–Bertani agar plates (Fisher Scientific Inc.,Waltham, Massachusetts, USA) containing 50 �g/mL kanamycin,as directed by the manufacturer’s protocol, and screened for posi-tive transformation with PCR using the primer set M13F (5=-GTTTTCCCAGTCACGAC-3=) and M13R (5=-CAGGAAACAGCTATG-ACC-3=). Transformants were then purified using a QIAprepSpin Miniprep kit (Qiagen Inc., Valencia, California, USA), andthe resulting purified plasmid DNA was partially sequencedfrom the 5= end using either the M13F or M13R primer. Allsequences generated in this study were screened for chimeraswith Pintail (Ashelford et al. 2005), and deposited in GenBank(accession Nos. JQ772645–JQ773027 and KC436139–KC436264).Phylogenetic assignments were determined using the RibosomalDatabase Project (RDP) (Cole et al. 2014) “Classifier” using a 95%confidence threshold and “Seqmatch” (Wang et al. 2007).

Comparison of Diporeia T-RFLP profiles with profiles generatedfor individual 16S rDNA clones was conducted in an attempt toidentify and quantify the presence of particular bacterial groups,including potential pathogens, in Diporeia populations. T-RFLPanalysis was performed on a total of 10 clones, which showed highsimilarity (≥98%) to bacterial sequences contained in GenBank.The purified cloned DNA was digested and analyzed using T-RFLP

Table 1. Name, location, and depth of sampling sites, year of collection, and number of consensusterminal restriction fragment length polymorphism (T-RFLP) profiles generated for Diporeia for eachrestriction endonuclease for this study.

Waterbody Site Latitude Longitude Depth (m)

HhaI MspI

2007 2008 2007 2008

Lake Michigan MI-18M 42.733 –87.000 161 3 4 4 4MI-27M 43.600 –86.917 112 3 — 3 —MI-40 44.760 –86.967 160 3 — 3 —MI-47 45.178 –86.375 186 3 — 2 —

Lake Huron HU-54M 45.517 –83.417 91 5 — 5 —Lake Superior SU-20B 46.883 –90.283 116 3 — 3 —

SU-23B 46.598 –84.807 62 3 4 4 4Cayuga Lake Cayuga 42.538 –76.553 40 3 — 3 —

Note: —, indicates a sample was not taken.

74 Can. J. Microbiol. Vol. 61, 2015

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

as described above to both ensure that complete digestion of PCRproducts was achieved and to help determine the placement ofeach represented bacterial group in the Diporeia community pro-files. Additionally, to obtain corresponding T-RFLP fragments, anin silico digest of clones was carried out in MEGA 5.0 (Tamura et al.2011).

Estimation of bacterial community coverageAnalyzing the community structure of bacteria in Diporeia sam-

ples included estimating coverage, which is defined as an estima-tion of the percentage of bacterial groups or OTUs identified in asample out of the total number of groups or OTUs. Calculatingdistances between known and unknown 16S rRNA sequences isimportant in comparing such sequences. In previous studies, dis-tance values of 0.03 have been used to differentiate bacteria at thespecies level, 0.05 at the genus level, 0.10 at the family and classlevel, and 0.20 at the phylum level (Stackebrandt and Goebel 1994;Hugenholtz et al. 1998; Hughes et al. 2001; Sait et al. 2002). Withthis in mind, bacterial community coverage was calculated basedon a distance value of 0.03 using the formula

C � [1 � (n/N)] × 100

where n is the number of unique clones, and N is the total numberof clones analyzed. To gain a better understanding of the bacterialdiversity associated with Great Lakes Diporeia, an overall estimateof coverage was obtained by removing the Cayuga Lake sequencesand analyzing a single composite library constructed from se-quences from the 4 Great Lakes Diporeia clone libraries (SU-20B,MI-27M, HU-54M, and ON-41).

Sequence alignment phylogenetic affiliationFor phylogenetic analyses of cloned Diporeia sequences,

Methanocaldococcus jannaschii (accession No. L77117) was used as theoutgroup taxon. A total of 353 16S rRNA gene sequences and thesingle outgroup sequence were aligned with the Jukes–Cantorcorrection (Jukes and Cantor 1969) using RDP II. The mean lengthof sequences in the final alignment file was 719 bp. The alignmentfile was visually checked for alignment gaps and missing data innucleotide positions using MEGA 5.0 (Tamura et al. 2011). Thephylogenetic tree containing all 16S rRNA sequences from thisstudy was generated using the neighbor-joining (Saitou and Nei1987) method included in MEGA 5.0 using Maximum CompositeLikelihood as a measure of genetic distance.

To test for significant clustering of taxa among the 5 clonelibraries based on phylogenetic relationships, the UniFrac lineage-specific analysis was used to break the tree up into the lineagesat a specified distance from the root (Lozupone et al. 2006). Todetermine taxonomic assignments and phylogenetic relation-ships, additional trees were generated for cloned sequences andsequences identified as closely related reference sequences con-tained in RDP. Sequences were aligned and trees were generatedas described above. Trees were then annotated to indicate theenvironment of origin for each sequence.

Results

Bacterial communities of DiporeiaAnalysis of T-RFLP data revealed the presence of diverse bacte-

rial communities in Diporeia samples. For the HhaI data set, a totalof 175 T-RFs ranging from 50 to 983 bp in size were identifiedacross all samples; for the MspI data set, a total of 138 T-RFs rang-ing from 55 to 983 bp in size were identified across all samples. Forthe HhaI data set, the mean (±SE) number of T-RFs identified at thesampling sites was 23.91 (±2.00) and ranged from 5 to 53 across allsites. For the MspI data set, the mean (±SE) number of T-RFs iden-tified at the sampling sites was 21.46 (±1.25) and ranged from 7 to35 across all sites. Species richness values and diversity indices

(Shannon–Weiner diversity index and Pielou’s Evenness index)showed that diversity of profiles for Diporeia communities variedamong each sampling event (Fig. 2). For both the HhaI and MspIdata sets, the MI-18M samples from 2008 had the highest diversityand relatively high species evenness compared with the othersamples analyzed. Additionally, for both the HhaI and MspI datasets the MI-18M samples from 2008 had the highest number ofOTUs and the lowest species evenness.

For both the HhaI and MspI data sets, a significant difference in� diversity was observed among all Diporeia sampling events forall indices of similarity (HhaI MANOVA: df = 9, F = 3.583, P = 0.001;MspI MANOVA: df = 9, F = 5.6686, P = 0.001). For the HhaI data set,a significant difference in multivariate dispersions among allDiporeia sampling events was observed (df = 9, F = 4.406, P = 0.015).T-RF’s 489 HhaI, 496 HhaI, and 484 MspI occurred in 23.53%, 20.59%,and 60.00% of samples, respectively, with mean relative abundancesof 7.77%, 1.67%, and 1.96%, respectively. Although T-RF 846 HhaI ac-counted for 3.20% of the mean relative abundance for the T-RFLPprofiles, it only occurred in the 3 Cayuga Lake samples, with meanrelative abundances ranging from 24.44% to 29.95%. For both theentire HhaI and MspI data sets, a significant positive correlation (Man-tel test) between diversity and depth was observed for Diporeia (HhaI:P = 0.002; MspI: P = 0.001).

A number of interesting findings were observed among profilesgenerated for Diporeia samples collected from multiple locationsin Lake Michigan (MI-18M, MI-27M, MI-40, and MI-47). First, for theMspI data set, a significant difference in � diversity was observedonly among Lake Michigan Diporeia profiles (df = 3, F = 2.50,P = 0.035). Second, results of multivariate analysis of variance(ANOVA) showed that site MI-18M was unique in that it had higherabundances of certain T-RFs compared with the other sites. Third,for the HhaI and MspI data sets, 20 and 27 T-RFs were shown to beunique to MI-18M profiles, respectively. For the T-RFs that wereunique to MI-18M in the HhaI data set, with the exception of T-RF366 HhaI, which had an mean relative abundance of 21.15% ±4.68%, the overall mean relative abundance was 2.03% ± 1.09%.Similarly, for the T-RFs that were unique to MI-18M in the MspIdata set, the overall mean relative abundance was 1.18% ± 1.09%.No significant association was found between depth and the di-versity of Lake Michigan Diporeia profiles (HhaI Mantel: P = 0.898;MspI Mantel: P = 0.892). Additionally, for both the HhaI and MspIdata sets, no significant difference in variance was detectedamong Lake Michigan Diporeia samples.

Temporal shifts in bacterial diversityFor the Lake Superior site that was sampled in both years, the

� diversity in bacterial community structure in 2008 was found tobe significantly greater than in 2007 for the HhaI data set but notthe MspI data set (HhaI MANOVA: df = 1, F = 3.682, P = 0.021; MspIMANOVA: df = 1, F = 1.859, P = 0.101). However, for Lake Michiganno significant differences in � diversity were found for the revis-ited site (HhaI MANOVA: df = 1, F = 0.171, P = 0.456; MspI MANOVA:df = 1, F = 1.766, P = 0.146). Significant differences in the abundanceof particular T-RFs (MANOVA) were observed between 2007 and2008 profiles for both SU-23B and MI-18M (Table 2). For the HhaIdata set, of the 45 total T-RFs present for MI-18M, 27 were detectedin samples from both 2007 and 2008, 12 were unique to 2007, and6 were unique to 2008. For the MspI data set, of the 57 total T-RFspresent for MI-18M, 37 were detected in samples from both 2007and 2008, 11 were unique to 2007, and 9 were unique to 2008. Forthe HhaI data set, of the 141 total T-RFs present in SU-23B, 41 weredetected in samples from both 2007 and 2008, 20 were unique to2007, and 80 were unique to 2008. For the MspI data set, of the75 total T-RFs present in SU-23B, 34 were detected in samplesfrom both 2007 and 2008, 18 were unique to 2007, and 23 wereunique to 2008.

Winters et al. 75

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

Fig. 2. Mean diversity index values (±SE) for 2 terminal restriction fragment length polymorphism (T-RFLP) data sets (amplified 16S rDNAdigested with HhaI and MspI) generated for replicate Diporeia samples collected from lakes Michigan (MI), Superior (SU), and Huron (HU), andCayuga Lake (New York) between 2007 and 2008. Richness is the number of operational taxonomic units, diversity is the Shannon–WeinerDiversity index, and Evenness is Pielou’s Evenness.

Table 2. Significant differences (P < 0.05) in relative peak heights (MANOVA) forboth 16S rRNA terminal restriction fragment length polymorphism (T-RFLP) datasets (HhaI and MspI) generated for Diporeia samples collected from lakes Michiganand Superior between 2007 and 2008.

Lake Michigansites (2007)

MI-18M(2007–2008)

SU-23B(2007–2008)

T-RFLPdata set T-RF P value T-RF P value T-RF P value

HhaI 60 <0.0001 67 0.039 61 0.03483 0.004 231 0.045 205 0.004

330 0.001 367 0.034366 0.002 375 0.004

MspI 127 0.012 312 0.038 127 0.045146 0.001 128 0.026167 0.049 146 0.024428 0.039 496 0.034534 0.015563 0.009

Table 3. Ribosomal Database Project (RDP) matches (≥98%) and terminal restriction fragment (T-RF)lengths based on virtual restriction digestion and terminal restriction fragment length polymor-phism (T-RFLP) analysis of 16S rRNA gene clones from Great Lakes Diporeia.

T-RF size (bp) with:

HhaI MspI

CloneAcc. No.(clone) Phylogenetic group (RDP match) Virtual T-RFLP Virtual T-RFLP

1 JQ772925 Bacillus sp. (Firmicutes) 574 576 162 1572 JQ772936 Comamonadaceae (Betaproteobacteria) 188 188 491 4943 JQ772844 Flavobacterium sp. (Bacteroidetes) 87 86 82 804 JQ772993 Methylotenera sp. (Betaproteobacteria) 366 365 488 4915 JQ772911 Microbacteriaceae (Actinobacteria) 369 372 279 2776 JQ772796 Pseudomonas sp. (Gammaproteobacteria) 205 205 488 4897 JQ772985 Rhodobacteraceae (Alphaproteobacteria) 569 568 440 4408 JQ772737 Rickettsiaceae (Alphaproteobacteria) 526 528 450 4499 JQ772726 Sphingobacteriaceae (Bacteroidetes) 93 90 539 54210 JQ772740 Oxalobacteraceae (Betaproteobacteria) 565 566 487 494

76 Can. J. Microbiol. Vol. 61, 2015

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

Virtual digestion and phylogenetic analysisRDP’s “classifier” revealed the presence of Alpha-, Beta-, Gamma-,

and Deltaproteobacteria, Actinobacteria, Bacteroidetes, Chloroflexi,Firmicutes, Planctomycetes, and Verrucomicrobia in Diporeia 16S rRNAclone libraries. The in silico digestion of the representativeclones provided for the putative identification of several of T-RFs.Despite evidence of “T-RF drift” (T-RF length differing from truelength; Kaplan and Kitts 2003), patterns were observed in that anumber of T-RFs can be matched to cloned sequences (Table 3). Forexample, T-RFs at 86 HhaI/80 MspI are likely to match Flavobacterium;T-RFs at 205 HhaI/489 MspI are likely to match Pseudomonas; andT-RFs at 366 HhaI/493 MspI are likely to match Methylotenera.

As displayed in Fig. 3, phylogenetic analysis of 16S rRNA se-quences using UniFrac revealed significant clustering amongDiporeia clone libraries, indicating a unique bacterial structure forDiporeia from each lake. Further analysis of the sequences in com-parison with similar reference sequences derived from RDP re-vealed the presence of diverse bacterial groups present in Diporeiaclone libraries. The phylum Actinobacteria, the class Betaproteobacteria,the order Pseudomonadales (Gammaproteobacteria), and the genusFlavobacterium (Bacteroidetes) were present in all 5 libraries. For thecomposite Diporeia clone library (353 16S rRNA sequences), a cov-erage level of 81.1%, 89.1%, 96.1%, and 100% was obtained for agenetic distances of 0.02, 0.05, 0.10, and 0.20, respectively. At agenetic distance of 0.02, a total of 126 OTUs were observed.

For the phylum Bacteroidetes (Fig. 4), 2 significant clusters wereobserved. One large cluster contained 121 sequences from all5 clone libraries, where the vast majority of sequences were fromthe Lake Ontario library. In the second smaller Bacteroidetes clustercontaining 11 sequences, the majority of sequences were from theLake Michigan library. Phylogenetic analysis of these sequencesshowed that, while a greater genetic diversity was observed forthe Lake Ontario Diporeia sequences, both clusters were repre-sented by Flavobacterium spp. Additionally, although not signifi-cant (P > 0.05) based on the specified distance from the root(11.3333), a third Flavobacterium cluster characterized by an abun-dance of sequences from the Lake Superior library was observed.For Alphaproteobacteria (Fig. 5a), sequences belonging to the orderRhodobacteriales were from both the Lake Michigan and CayugaLake libraries. Sequences belonging to the order Sphingomonadaleswere only detected in the Lake Ontario library. Interestingly, abacterium belonging to the order Rickettsiales was only detected in

Fig. 3. Distance tree of 353 16S rRNA gene sequences detected inDiporeia spp. collected from 5 waterbodies. The tree was generatedwith the neighbor-joining algorithm and the Jukes–Cantorcorrection. Lineage-specific significance (*) was determined usingUniFrac (blue, Lake Superior; red, Lake Michigan; light blue, LakeHuron; green, Lake Ontario; yellow, Cayuga Lake). “Others” refers toActinobacteria, Chloroflexi, Deltaproteobacteria, Firmicutes, Planctomycetes,and Verrucomicrobia. Note: to readers of the print issue, please seethe Web site at http://www.nrcresearchpress.com/doi/abs/10.1139/cjm-2014-0434 to view a coloured version of the figure.

Fig. 4. Neighbor-joining consensus phylogeny of Bacteroidetes16S rRNA gene sequences of clones from Diporeia and closely relatedreference sequences. Bootstrap values (1000 replicates) greater than70% are indicated at the nodes. Taxa observed in Diporeia samplescollected from lakes Superior, Michigan, Superior, and Ontario(Great Lakes), and Cayuga Lake (inland lake) are in bold. Triangleindicates a collapsed branch containing multiple taxa. Value to theright of triangle indicates the number of taxa in the collapsed branch.

Winters et al. 77

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

the Lake Ontario library. However, the corresponding T-RF (450 MspI)was only detected in Diporeia samples collected from lakes Mich-igan and Superior, with a prevalence of 0.03% and an meanrelative abundance of 1.73% ± 0.56%. For the class Betaproteobacteria(Fig. 5b), a significant clustering of sequences similar to Rhodoferaxspp. and Albiferax spp. was observed, with the higher proportionof sequences from the Lake Michigan Diporeia library having thehighest relative contribution to the overall differences betweenenvironments. For the class Gammaproteobacteria (Fig. 6a), a signif-icant clustering of sequences similar to Perlucidibaca piscinae wasobserved, with the higher proportion of sequences from the LakeOntario Diporeia library having the highest relative contribu-tion to the overall differences between environments. For Gram-positive bacteria belonging to the phylum Firmicutes (Fig. 6b), withthe exception of the Lakes Huron library, bacteria belonging tothe orders Actinomycetales and Acidimicrobiales (Actinobacteria) weredetected in all libraries. A bacterium belonging to the orderBacilliales (Firmicutes) was only detected in the Lake Michiganlibrary.

DiscussionThis study investigated the bacterial communities associated

with Diporeia, an ecologically important organism in the GreatLakes, which has declined considerably in abundance acrossmuch of the lakes (Barbiero et al. 2011). Results of this study showthat the diversity of the bacterial communities associated withDiporeia is relatively high compared with what has been previ-

ously reported for both freshwater and marine crustacean species(Møller et al. 2007; Peter and Sommaruga 2008; Grossart et al.2009; Tang et al. 2009). Recent studies using culture-independentmethods have identified as many as 6 major bacterial groupsassociated with freshwater crustacean species (Actinobacteria,Firmicutes, Bacteroidetes, Alpha-, Beta-, and Gammaproteobacteria). In thecurrent study, we identified 10 major bacterial groups associatedwith Great Lakes Diporeia (Actinobacteria, Bacteroidetes, Chloroflexi,Firmicutes, Planctomycetes, Verrucomicrobia, Alpha-, Beta-, Gamma-,and Deltaproteobacteria).

It is unknown how the composition of the bacterial communityin the surrounding sediment influences that of Diporeia. However,comparison of the 16S rDNA genes associated with Diporeia ob-served in the current study with those reported to be associatedwith the surrounding Great Lakes sediment in a recent study(Winters et al. 2014) show that, while the sediment is largelydominated by Actinobacteria followed by Acidobacteria, Beta-, andGammaproteobacteria, Diporeia is largely dominated by Bacteroidetesfollowed by Beta- and Gammaproteobacteria. This finding suggeststhat a number of the observed bacteria are intimately associatedwith Diporeia. While we cannot be sure which bacteria came fromthe surface or gut of Diporeia or how the fullness of individual gutsaffected the overall bacterial community structure, results of16S rRNA sequence and T-RFLP analysis demonstrate that the bac-terial communities of Diporeia were dominated by Flavobacteriumspp. (Bacteroidetes) and Pseudomonas spp. (Gammaproteobacteria).

Fig. 5. Neighbor-joining consensus phylogeny of Alphaproteobacteria (a) and Betaproteobacteria (b) 16S rRNA gene sequences of clones fromDiporeia and closely related reference sequences. Bootstrap values (1000 replicates) greater than 70% are indicated at the nodes. Taxa observedin Diporeia samples collected from lakes Superior, Michigan, Superior, and Ontario (Great Lakes), and Cayuga Lake (inland lake) are in bold.Triangle indicates a collapsed branch containing multiple taxa. Value to the right of triangle indicates the number of taxa in the collapsedbranch.

78 Can. J. Microbiol. Vol. 61, 2015

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

Analysis of the phylogenetic tree using UniFrac revealed thata number of bacterial species were common among differentDiporeia populations, indicating the presence of a core micro-biome. Additionally, analysis of the phylogenetic tree usingUniFrac revealed that a number of significant clusters were presentwithin the composite clone library, indicating the bacterial com-munities associated with the surrounding sediment influencedthe structure the Diporeia bacterial communities. The extent ofhow sediment bacterial communities influence Diporeia bacterialcommunities is unknown. Additionally, it is possible that amountof gut contents contained within individual Diporeia within eachsample had varying effects on the observed bacterial communitystructure.

Interestingly, in a survey of trends in Diporeia abundancesthroughout the Great Lakes between 1997 and 2009, Barbiero et al.(2011) showed that, although Diporeia abundances had signifi-cantly declined throughout Lake Michigan, significant declineswere not observed at the 2 most southerly deep sites (MI-11 andMI-18M), which exhibited a negative (–0.60) albeit nonsignificant(P = 0.06) trend. Results of the current study showed that MI-18Mprofiles contained 20 (HhaI data set) and 27 (MspI data set) uniqueT-RFs compared with other Lake Michigan samples. Additionally,of the significantly different relative T-RF abundances for the LakeMichigan Diporeia profiles, all were enriched in samples collectedfrom site MI-18M. Furthermore, of all the samples analyzed in thisstudy, on average, samples from MI-18M had relatively high spe-cies diversity. Collectively, the findings of this study together withthose of Barbiero et al. (2011) point to the possibility that theprofiles of MI-18M Diporeia represent a healthy bacterial community.

A number of differences were observed among the profiles forDiporeia samples collected from multiple sites in Lake Michigan in2007 (MI-18M, MI-27M, MI-40, and MI-47). The finding that theChao similarity index revealed a significant difference in � diver-sity among Lake Michigan profiles (MspI data set) suggests that theobserved difference is due to low relative abundances of multiplebacterial species. This is further supported by the findings thatMI-18M had higher species richness compared with other LakeMichigan profiles and that a high number of T-RFs with lowmean relative abundances were unique to the MI-18M profiles. Al-together, these findings suggest the bacterial communities asso-ciated with Diporeia at site MI-18M are distinctly different fromothers in Lake Michigan.

Temporal shifts were observed for Diporeia samples collectedfrom Lake Superior (SU-23B). Whether the observed temporalshifts are due to the relatively shallow depth of this site, it’sunique location in Whitefish Bay near the Saint Mary’s Riverthrough which Lake Superior drains into Lake Huron, or someunknown limnological feature remains to be determined. Thecause(s) and potential biological significance of the shifts are un-known and warrant further investigation.

A highly significant correlation between depth and the diver-sity of Diporeia bacterial communities was observed for the com-posite data set, suggesting depth may play a role in regulating thebacterial communities associated with Diporeia. However, thesame correlation was not observed when only Lake Michigan datawere considered. It is possible that a significant correlation be-tween depth and diversity would have been observed if a largerrange of depths were sampled for Lake Michigan. Further research

Fig. 6. Neighbor-joining consensus phylogeny of Gammaproteobacteria (a) and Firmicutes (b) 16S rRNA gene sequences of clones from Diporeiaand closely related reference sequences. Bootstrap values (1000 replicates) greater than 70% are indicated at the nodes. Taxa observed inDiporeia samples collected from lakes Superior, Michigan, Superior, and Ontario (Great Lakes), and Cayuga Lake (inland lake) are in bold.Triangle indicates a collapsed branch containing multiple taxa. Value to the right of triangle indicates the number of taxa in the collapsedbranch.

Winters et al. 79

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

is required to determine the relationship between depth and thediversity of Diporeia bacterial communities.

While the exact cause for the decline in Diporeia populationsremains unexplained, bacterial pathogens could be a contribut-ing factor. In the current study, we detected some bacterial groupsthat contain members known to be pathogens of aquatic organ-isms. In terms of obligate pathogens, a bacterium belonging toRickettsiales, an order of Bacteria containing known pathogens forDiporeia and other freshwater amphipods (Federici et al. 1974;Larsson 1982; Graf 1984; Messick et al. 2004), was detected in theLake Ontario Diporeia clone library. One T-RF representing theorder Rickettsiales (449 M) had a prevalence of 0.03% and an meanrelative abundance of 1.73% ± 0.56%. This would be considered arelatively high abundance for a parasite in a free-ranging organ-ism like Diporeia and can contribute, either alone or combinedwith other opportunistic bacteria, to Diporeia declines (Andersonand May 1981). Additionally, Flavobacterium spp. and Pseudomonasspp. genera that are known to contain pathogens of aquatic ani-mals were detected in Diporeia samples.

Despite the constraints that can be associated with T-RFLP anal-ysis of complex bacterial communities, such as multiple taxa shar-ing similar T-RFs and the possibility of pseudo T-RF formation(Egert and Friedrich 2003), the combination of analysis of multi-ple restriction enzyme data sets (HhaI and MspI) with T-RF patternconfirmation by in silico digestion of cloned 16S rRNA gene se-quences allowed for considerable elucidation of Diporeia bacterialcommunities. Furthermore, the reasonably high estimate of cov-erage obtained for the composite Great Lakes Diporeia 16S rRNAlibrary suggests a large portion of the bacteria associated withDiporeia in the Great Lakes was observed. Even though we did notsample to saturation (100% coverage), after 16S rRNA gene cloneswere analyzed with T-RFLP, a few corresponding T-RFs were notfound in the community profiles. Similar findings were observedin an interlaboratory comparison for the microbial communitiesof seafloor basalts (Orcutt et al. 2009). With this in mind, it is likelythese detected bacteria represent a rather small fraction of bacte-rial communities associated with the Diporeia.

In conclusion, the combination of T-RFLP analysis and 16S rRNAgene sequencing proved to be a powerful tool for investigating thebacterial diversity of Diporeia. Since a similar study has never beenconducted on Diporeia, this study represents the most extensivelist of bacteria associated with this amphipod in relation to itssurrounding environment and provides useful insights on themicrobiota of Diporeia. Our hope is that the knowledge gained inthis study will shed light on the potential causes of the decline ofDiporeia populations and will foster the development of effica-cious management strategies for the restoration and conservationof both Diporeia and other Great Lakes organisms that rely on Diporeia.

AcknowledgementsWe are thankful to the crew and staff of the R/V Lake Guardian

for helping in sample collection. This study was partially fundedby the United States Environmental Protection Agency – GreatLakes National Protection Office (grant No. GL00E36101) and theGreat Lakes Fisheries Trust (grant No. 2009.1058).

ReferencesAbdo, Z., Schütte, U., Bent, S., Williams, C., Forney, J., and Joyce, P. 2006. Statis-

tical methods for characterizing diversity in microbial communities by anal-ysis of terminal restriction fragment length polymorphism of 16S rRNA.Environ. Microbiol. 8: 929–938. doi:10.1111/j.1462-2920.2005.00959.x. PMID:16623749.

Anderson, R.M., and May, R.M. 1981. The population dynamics of microparasitesand their invertebrate hosts. Phil. Trans. R. Soc. Lond. B Biol. Sci. 291: 451–424. doi:10.1098/rstb.1981.0005.

Ashelford, K.E., Chuzhanova, N.A., Fry, J.C., Jones, A.J., and Weightman, A.J.2005. At least 1 in 20 16S rRNA sequence records currently held in publicrepositories is estimated to contain substantial anomalies. Appl. Environ.Microbiol. 71: 7724–7736. doi:10.1128/AEM.71.12.7724-7736.2005. PMID:16332745.

Barbiero, R.P., and Tuchman, M.L. 2002. Results from GLNPOS’s biological openwater surveillance program of the Laurentian Great Lakes. U.S. Environmen-tal Protection Agency, Great Lakes National Program Office, 2002.

Barbiero, R.P., Schmude, K., Lesht, B.M., Riseng, C.M., Warren, G.J., andTuchman, M.L. 2011. Trends in Diporeia populations across the LaurentianGreat Lakes, 1997-2009. J. Great Lakes Res. 37: 9–17. doi:10.1016/j.jglr.2010.11.009.

Briée, C., Moreira, D., and López-García, P. 2007. Archaeal and bacterial commu-nity composition of sediment and plankton from a suboxic freshwater pond.Res. Microbiol. 158: 213–227. doi:10.1016/j.resmic.2006.12.012. PMID:17346937.

Chao, A., Chazdon, R.L., Colwell, R.K., and Shen, T.J. 2005. A new statisticalapproach for assessing similarity of species composition with incidence andabundance data. Ecol. Lett. 8: 148–159. doi:10.1111/j.1461-0248.2004.00707.x.

Cole, J.R., Wang, Q., Fish, J.A., Chai, B., McGarrell, D.M., Sun, Y., et al. 2014.Ribosomal Database Project: data and tools for high throughput rRNA anal-ysis. Nucleic Acids Res. 41: D633–D642. doi:10.1093/nar/gkt1244. PMID:24288368.

Dermott, R. 2001. Sudden disappearance of the amphipod Diporeia from easternLake Ontario, 1993-1995. J. Great Lakes Res. 27: 423–433. doi:10.1016/S0380-1330(01)70657-0.

Dermott, R., and Kerec, D. 1997. Changes to the deepwater benthos of easternLake Erie since the invasion of Dreissena: 1979–1993. Can. J. Fish. Aquat. Sci.54(4): 922–930. doi:10.1139/f96-332.

Edmonson, W.T. 1959. Fresh-water Biology. 2nd ed. John Wiley and Sons, NewYork, USA.

Egert, M., and Friedrich, M.W. 2003. Formation of pseudo-terminal restrictionfragments, a PCR-related bias affecting terminal restriction fragment lengthpolymorphism analysis of microbial community structure. Appl. Environ.Microbiol. 69: 2555–2562. doi:10.1128/AEM.69.5.2555-2562.2003. PMID:12732521.

Federici, B.A., Hazard, E.I., and Anthony, D.W. 1974. Rickettsia-like organismcausing disease in a crangonid amphipod from Florida. Appl. Microbiol. 28:885–886. PMID:4441067.

Graf, F. 1984. Présence de bactéries dans les caecums postérieurs de l’intestinmoyen du Crustacé hypogé Niphargus virei (Gammaridae : Amphipoda). Can. J.Zool. 62: 829–833. doi:10.1139/z84-266.

Grossart, H.-P., Dziallas, C., and Tang, K.W. 2009. Bacterial diversity associatedwith freshwater zooplankton. Environ. Microbiol. Rep. 1(1): 50–55. doi:10.1111/j.1758-2229.2008.00003.x.

Hugenholtz, P., Goebel, B.M., and Pace, N.R. 1998. Impact of culture-independentstudies on the emerging phylogenetic view of bacterial diversity. J. Bacteriol.180: 4765–4774. PMID:9733676.

Hughes, J.B., Hellmann, J.J., Ricketts, T.H., and Bohannan, B.J.M. 2001. Countingthe uncountable: statistical approaches to estimating microbial diversity.Appl. Environ. Microbiol. 67: 4399–4406. doi:10.1128/AEM.67.10.4399-4406.2001. PMID:11571135.

Inniss, W.E., and Mayfield, C.I. 1978. Psychrotrophic bacteria in sediments fromthe Great Lakes. Water Res. 12: 237–241. doi:10.1016/0043-1354(78)90092-1.

Ishii, S., Yan, T., Shively, D.A., Byappanahalli, M.N., Whitman, R.L., andSadowsky, M.J. 2006. Cladophora (Chlorophyta) spp. harbor human bacterialpathogens in nearshore water of Lake Michigan. Appl. Environ. Microbiol.72: 4545–4553. doi:10.1128/AEM.00131-06. PMID:16820442.

Jukes, T.H., and Cantor, C.R. 1969. Evolution of protein molecules. In Mamma-lian protein metabolism. Vol. 3. Edited by H.N. Munro. Academic Press,Massachesetts, USA. pp. 21–123.

Kaplan, C., and Kitts, C.L. 2003. Variation between observed and true terminalrestriction fragment length is dependent on true TRF length and purinecontent. J. Microbiol. Methods, 54: 121–125. doi:10.1016/S0167-7012(03)00003-4.PMID:12732430.

Larsson, R. 1982. A rickettsial pathogen of the amphipod Rivulogammarus pulex.J. Invertebr. Pathol. 40: 28–35. doi:10.1016/0022-2011(82)90033-7.

Legendre, P., and Legendre, L. 1998. Numerical ecology. 2nd English ed. Elsevier,Amsterdam, Netherlands.

Liu, W., Marsh, T.L., Cheng, H., and Forney, L.J. 1997. Characterization of micro-bial diversity by determining terminal restriction fragment length polymor-phisms of genes encoding 16S RNA. Appl. Environ. Microbiol. 63: 4516–4522.PMID:9361437.

Lozano, S.J., Scharold, J.V., and Nalepa, T.F. 2001. Recent declines in benthicmacroinvertebrate densities in Lake Ontario. Can. J. Fish. Aquat. Sci. 58(3):518–529. doi:10.1139/f01-002.

Lozupone, C., Hamady, M., and Knight, R. 2006. UniFrac — an online tool forcomparing microbial community diversity in a phylogenetic context. BMCBioinformatics, 7: 371. doi:10.1186/1471-2105-7-371. PMID:16893466.

Maki, J.S., and Remsen, C.C. 1981. Comparison of two direct-count methods fordetermining metabolizing bacteria in freshwater. Appl. Environ. Microbiol.41: 1132–1138. PMID:16345767.

Marchesi, J.R., Sato, T., Weightman, A.J., Martin, T.A., Fry, J.C., Hiom, S.J., et al.1998. Design and evaluation of useful bacterium-specific PCR primers thatamplify genes coding for bacterial 16S rRNA. Appl. Environ. Microbiol. 64:795–799. PMID:9464425.

Messick, G.A., Overstreet, R.M., Nalepa, T.F., and Tyler, S. 2004. Prevalence ofparasites in amphipod Diporeia spp. from Lakes Michigan and Huron, U.S.A.Dis. Aquat. Organ. 59: 159–170. doi:10.3354/dao059159. PMID:15212283.

Møller, E.F., Riemann, L., and Søndergaard, M. 2007. Bacteria associated with

80 Can. J. Microbiol. Vol. 61, 2015

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.

copepods: abundance, activity and community composition. Aquat. Microb.Ecol. 47(1): 99–106. doi:10.3354/ame047099.

Nalepa, T.F. 1987. Long-term changes in the macrobenthos of southern LakeMichigan. Can. J. Fish. Aquat. Sci. 44(3): 515–524. doi:10.1139/f87-064.

Nalepa, T.F. 1989. Estimates of macroinvertebrate biomass in Lake Michigan.J. Great Lakes Res. 15(3): 437–443. doi:10.1016/S0380-1330(89)71499-4.

Nalepa, T.F., Hartson, D.J., Fanslow, D.L., Lang, G.A., and Lozano, S.J. 1998. De-clines in benthic macroinvertebrate populations in southern Lake Michigan,1980–1993. Can. J. Fish. Aquat. Sci. 55(11): 2402–2413. doi:10.1139/f98-112.

Nalepa, T.F., Fanslow, D.L., and Messick, G. 2005. Characteristics and potentialcauses of declining Diporeia spp. populations in southern Lake Michigan andSaginaw Bay, Lake Huron. Great Lakes Fishery Commission Technical Report 66.

Nalepa, T.F., Fanslow, D.L., Pothoven, S.A., Foley, A.J., and Lang, G.A. 2007. Long-term trends in benthic macroinvertebrate populations in Lake Huron overthe past four decades. J. Great Lakes Res. 33(2): 421–436. doi:10.3394/0380-1330(2007)33[421:LTIBMP]2.0.CO;2.

Newton, R.J., Jones, S.E., Eiler, A., McMahon, K.D., and Bertilsson, S. 2011. A guideto the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev.75: 14–49. doi:10.1128/MMBR.00028-10.

Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O’Hara, R.B.,et al. 2013. Package ‘vegan’. Community ecology package. Version 2.0-0.Available from http://CRAN.R-project.org/package=vegan.

Orcutt, B., Bailey, B., Staudigel, H., Tebo, B.M., and Edwards, K.J. 2009. An inter-laboratory comparison of 16S rRNA gene-based terminal restriction fragmentlength polymorphism and sequencing methods for assessing microbial di-versity of seafloor basalts. Environ. Microbiol. 11: 1728–1735. doi:10.1111/j.1462-2920.2009.01899.x. PMID:19508561.

Peter, H., and Sommaruga, R. 2008. An evaluation of methods to study the gutbacterial community composition of freshwater zooplankton. J. PlanktonRes. 30(9): 997–1006. doi:10.1093/plankt/fbn061.

Pilloni, G., Granitsiotis, M.S., Engel, M., and Lueders, T. 2012. Testing the limits of454 pyrotag sequencing: reproducibility, quantitative assessment and com-parison to T-RFLP fingerprinting of aquifer microbes. PLoS One, 7: e40467.doi:10.1371/journal.pone.0040467. PMID:22808168.

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., and R Development Core Team.2010. nlme: linear and nonlinear mixed effects models. R package version3.1-97. R Foundation for Statistical Computing, Vienna, Austria.

Ramette, A. 2007. Multivariate analyses in microbial ecology. FEMS Microbiol.Ecol. 62: 142–160. doi:10.1111/j.1574-6941.2007.00375.x. PMID:17892477.

Rinta-Kanto, J.M., Konopko, E.A., DeBruyn, J.M., Bourbonniere, R.A., Boyer, G.L.,and Wilhelm, S.W. 2009. Lake Erie Microcystis: relationship between micro-cystin production, dynamics of genotypes and environmental parameters ina large lake. Harmful Algae, 8: 665–673. doi:10.1016/j.hal.2008.12.004.

Sait, M., Hugenholtz, P., and Janssen, P.H. 2002. Cultivation of globally distrib-uted soil bacteria from phylogenetic lineages previously only detected incultivation-independent surveys. Environ. Microbiol. 4: 654–666. doi:10.1046/j.1462-2920.2002.00352.x. PMID:12460273.

Saitou, N., and Nei, M. 1987. The neighbor-joining method: a new method forreconstructing phylogenetic trees. Mol. Biol. Evol. 4: 406–425. PMID:3447015.

Smith, C.J., Danilowicz, B.S., Clear, A.K., Costello, F.J., and Wilson, B. 2005.T-Align, a web-based tool for comparison of multiple terminal restrictionfragment length polymorphism profiles. FEMS Microbiol. Ecol. 54: 375–380.doi:10.1016/j.femsec.2005.05.002. PMID:16332335.

Stackebrandt, E., and Goebel, B.M. 1994. A place for DNA–DNA reassociation and16S rRNA sequence-analysis in the present species definition in bacteriology.Int. J. Syst. Bacteriol. 44: 846–849. doi:10.1099/00207713-44-4-846.

Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M., and Kumar, S. 2011.MEGA5: molecular evolutionary genetics analysis using maximum likeli-hood, evolutionary distance, and maximum parsimony methods. Mol. Biol.Evol. 28(10): 2731–2739. doi:10.1093/molbev/msr121. PMID:21546353.

Tang, K.W., Turk, V., and Grossart, H.P. 2010. Linkage between crustacean zoo-plankton and aquatic bacteria. Aquat. Microb. Ecol. 61: 261–277. doi:10.3354/ame01424.

Tang, X., Gao, G., Qin, B., Zhu, L., Chao, J., Wang, J., et al. 2009. Characterizationof bacterial communities associated with organic aggregates in a large, shal-low, eutrophic freshwater lake (Lake Taihu, China). Microb. Ecol. 58: 307–322. doi:10.1007/s00248-008-9482-8. PMID:19169740.

Wang, Q., Garrity, G.M., Tiedje, J.M., and Cole, J.R. 2007. Naïve Bayesian classifierfor rapid assignment of rRNA sequences into the new bacterial taxonomy.Appl. Environ. Microbiol. 73: 5261–5267. doi:10.1128/AEM.00062-07. PMID:17586664.

Winters, A.D., Marsh, T.L., Brenden, T.O., and Faisal, M. 2014. Molecular charac-terization of bacterial communities associated with sediments in the Lauren-tian Great Lakes. J. Great Lakes Res. 40: 640–645. doi:10.1016/j.jglr.2014.04.008.

Zwart, G., Crump, B.C., Kamst-van Agterveld, M.P., Hagen, F., and Han, S.K. 2002.Typical freshwater bacteria: an analysis of available 16S rRNA gene sequencesfrom plankton of lakes and rivers. Aquat. Microb. Ecol. 28: 141–155. doi:10.3354/ame028141.

Winters et al. 81

Published by NRC Research Press

Can

. J. M

icro

biol

. Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

MIC

HIG

AN

ST

AT

E U

NIV

ER

SIT

Y o

n 04

/12/

18Fo

r pe

rson

al u

se o

nly.