Genetic assessment of meiobenthic community composition and spatial distribution in coastal sediments along northern Gulf of Mexico

Accepted Manuscript Genetic assessment of meiobenthic community composition and spatial distribution in coastal sediments along northern Gulf of Mexico Pamela M. Brannock, Lei Wang, Alice C. Ortmann, Damien S. Waits, Kenneth M. Halanych PII:

S0141-1136(16)30072-1

DOI:

10.1016/j.marenvres.2016.05.011

Reference:

MERE 4172

To appear in:

Marine Environmental Research

Received Date: 22 December 2015 Revised Date:

18 April 2016

Accepted Date: 9 May 2016

Please cite this article as: Brannock, P.M., Wang, L., Ortmann, A.C., Waits, D.S., Halanych, K.M., Genetic assessment of meiobenthic community composition and spatial distribution in coastal sediments along northern Gulf of Mexico, Marine Environmental Research (2016), doi: 10.1016/ j.marenvres.2016.05.011. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Running title: Meiobenthic community composition

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Title: Genetic assessment of meiobenthic community composition and spatial distribution in

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coastal sediments along northern Gulf of Mexico

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Authors: Pamela M. Brannocka, Lei Wangb,c, Alice C. Ortmannb,c,d, Damien S. Waitsa, and

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Kenneth M. Halanycha

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Addresses:

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a

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Auburn, Alabama, 36849, USA

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b

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Alabama, 36688, USA

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Dauphin Island Sea Lab, 101B Bienville Blvd, Dauphin Island, Alabama, 36528, USA

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Department of Fisheries and Ocean Canada, Centre for Offshore Oil, Gas and Energy Research,

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Bedford Institute of Oceanography, Dartmouth, Canada

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Department of Marine Sciences, University of South Alabama, 307 University Blvd, Mobile,

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Department of Biological Sciences, Auburn University, 101 Rouse Life Science Building,

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Corresponding author: P. M. Brannock; Department of Biological Sciences, Auburn

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University, 101 Rouse Life Science Building, Auburn, Alabama, 36849, USA

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Telephone number: +1 334-334-844-3223; FAX number: +1 334-844-1645; email:

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[email protected], [email protected]

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ABSTRACT

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Meiobenthic (meiofauna and micro-eukaryotes) organisms are important contributors to

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ecosystem functioning in aquatic environments through their roles in nutrient transport, sediment

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stability, and food web interactions. Despite their ecological importance, information pertaining

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to variation of these communities at various spatial and temporal scales is not widely known.

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Many studies in the Gulf of Mexico (GOM) have focused either on deep sea or continental shelf

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areas, while little attention has been paid to bays and coastal regions. Herein, we take a holistic

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approach by using high-throughput sequencing approaches to examine spatial variation in

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meiobenthic communities within Alabama bays and the coastal northern GOM region. Sediment

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samples were collected along three transects (MS, FT, and OB) from September 2010 to April

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2012 and community composition was determined by metabarcoding the V9 hypervariable

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region of the nuclear18S rRNA gene. Results showed that Stramenopiles (diatoms), annelids,

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arthropods (copepods), and nematodes were the dominate groups within samples, while there

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was presence of other phyla throughout the dataset. Location played a larger role than time

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sampled in community composition. However, samples were collected over a short temporal

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scale. Samples clustered in reference to transect, with the most eastern transect (Orange Beach:

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OB) having a distinct community composition in comparison to the other two transects

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(Mississippi Sound: MS and Focal Transect: FT). Communities also differed in reference to

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region (Bay versus Shelf). Bulk density and percent inorganic carbon were the only measured

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environmental factors that were correlated with community composition.

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Keywords:

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High-throughput sequencing; Illumina; meiobenthos; metabarcoding; micro-eukaryotes; protists;

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18S rRNA

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INTRODUCTION Meiofauna and benthic micro-eukaryotic organisms (herein referred collectively as meiobenthic community) play a large role in ecosystem functioning in aquatic environments

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through their roles in benthic-pelagic coupling, sediment stability, and food web interactions

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(Mare 1942; Giere 2009). Previous research has noted that meiobenthic abundance varies both

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spatially (Pequegnat et al. 1990; Baguley et al. 2006; Bessière et al. 2007; Landers et al. 2012;

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Brannock et al. 2014; Cowart et al. 2015; Massana et al. 2015) and temporally (De Bovee et al.

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1990; Bessière et al. 2007; Grippo et al. 2011; Massana et al. 2015), and differences in

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meiobenthic community composition have been associated with sediment characteristics (Coull

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1988; Pequegnat et al. 1990; Shimeta et al. 2007; Giere 2009; Lei et al. 2014), water depth (De

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Bovee et al. 1990; Pequegnat et al. 1990; Hausmann et al. 2002; Baguley et al. 2006; Bessière et

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al. 2007; Rohal et al. 2014), temperature, salinity (Coull 1988; Giere 2009; Lei et al. 2014), and

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organic matter (Giere 1993, 2009). In general, most studies have found a negative trend in

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meiobenthic abundance with increasing depth (De Bovee et al. 1990; Pequegnat et al. 1990;

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Hausmann et al. 2002; Baguley et al. 2006; Bessière et al. 2007; Lin et al. 2014; Rohal et al.

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2014), whereas trends in diversity vary by study (Trebukhova et al. 2013; Sevastou et al. 2013;

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Baldrighi et al. 2013; Semprucci et al. 2014).

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Current knowledge of meiobenthic community composition and variation within the Gulf

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of Mexico (GOM) is limited. Researchers generally only focus on either the meiofauna or micro-

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eukaryotic fraction, rather than looking at meiobenthic communities as a whole. Within the

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GOM, previous meiofauna research has focused on offshore (Pequegnat et al. 1990; Baguley et

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al. 2006, 2008; Montagna et al. 2013), continental shelf (Montagna and Harper 1996; Escobar-

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Briones and Soto 1997; Escobar et al. 1997; Landers et al. 2012), or Texas estuaries (Montagna 3

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and Kalke 1992; Montagna et al. 2002) and show meiofauna communities are generally

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dominated by nematodes and copepods (Pequegnat et al. 1990; Montagna et al. 2002; Baguley

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et al. 2006, 2008; Landers et al. 2012). Meiofauna communities differ greatly in abundance

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along a longitudinal scale, with eastern GOM locations having a greater meiofauna abundance

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in comparison to the central and western locations (Pequegnat et al. 1990; Baguley et al. 2006;

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Landers et al. 2012). These differences have been attributed to a combination of sediment

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characteristic differences (Pequegnat et al. 1990) as well as interactions between river outflow

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and oceanographic circulations (Baguley et al. 2006). The coastal GOM is subjected to

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environmental disturbances, some of which disproportionately impact coastal regions compared

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to open ocean, outer continental shelf, or inner bays (e.g. hurricanes and anthropogenic

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disturbances). As such, understanding meiobenthic community structure and variation in GOM

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coastal areas is important for assessing how inner shelf and near shore communities respond to

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disturbance events.

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Both the Mississippi and Mobile River basins influence the northern GOM (nGOM) (Schroeder 1979; Walker et al. 2005). In addition to meiofaunal abundance responding to

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riverine inputs, Baguley et al. (2006) also asserted that higher meiofauna abundance in the

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eastern nGOM region results from the interaction of the Loop Current and the Mississippi River

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outflow. However, research concerning the influence of the Mobile Bay River basin on

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meiobenthic communities in this geographic region is lacking. Mobile Bay is highly influenced

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by fresh water river drainages (Schroeder 1979), with maximum discharge during late winter to

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early spring and minimum discharge in late summer to early autumn (Stumpf et al. 1993). Thus,

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Mobile Bay plays a large role in transport of fresh, nutrient rich waters to Mississippi Sound and

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nGOM through the Pass-aux-Herons and the Main Pass, respectfully (Fig. 1). With up to 85% of

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the Mobile Bay water exchange occurring through the Main Pass (Schroeder 1979; Kim and Park

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2012), the nGOM region in the general vicinity of this area is greatly influenced by the discharge

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through this pass Most previous studies examining meiobenthic communities used traditional

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morphological based-approaches that involve elutriation, sieving, and identifying individuals via light microscopy. These protocols are time consuming and require expertise for taxonomically

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identifying organisms; therefore, researchers typically focus on only a few taxonomic groups for

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a given study. This approach greatly limits the ability to understand composition and variation of

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the whole community. More recently, high-throughput sequencing approaches along with

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bioinformatic pipelines have allowed examination of community composition with a holistic

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perspective on a faster timeframe than traditional methods. As these technologies continue to

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develop, more studies are taking a holistic view of meiobenthic community diversity (Fonseca et

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al. 2010, 2014; Bik et al. 2012a; b; c; Creer and Sinniger 2012; Brannock et al. 2014; Carugati et

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al. 2015; Cowart et al. 2015; Fontaneto et al. 2015; Lallias et al. 2015). However,

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implementation of these approaches in eukaryotic systems is still in its infancy in comparison to

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prokaryotic systems. Therefore, care needs to be taken when analyzing and interpreting data

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from these approaches (e.g. Creer et al. 2010; Brannock and Halanych 2015; Carugati et al.

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2015).

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Herein, we used high-throughput sequence approaches to examine spatial variation of

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meiobenthic communities along three transects in the nGOM spanning a 20 month period. Goals

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of this study were to begin to understand spatial and temporal variation of meiobenthic

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communities within the nGOM, from a holistic perspective, and gather needed baseline

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information about these communities so that ecosystem responses to natural events and

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anthropogenic disturbances can be understood.

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MATERIALS AND METHODS

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Sample collection

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Subtidal sediment samples were collected along three transects in the nGOM near the Alabama and Mississippi coasts (Table S1; Fig.1). The most western transect, referred to as the

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Mississippi Sound transect (MS), included 5 sampling locations. Three stations (St.5-7) were

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within Mississippi Sound and two stations (St.3-4) extended from Horn Island Pass into the

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nGOM. The middle transect was a part of the Dauphin Island Sea Laboratory’s (DISL) Fisheries

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Oceanography of Coastal Alabama (FOCAL) research program that has been utilized in previous

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studies (e.g. Graham et al. 2010; Ortmann et al. 2011; Ortmann & Ortell 2014; Carassou et al.

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2014; Brannock et al. in review). The FOCAL transect (FT) included one sample location (DI)

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within Mobile Bay and two sample locations (T10 and T20) extending southwest into the

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nGOM. The third, and most eastern, transect was the Orange Beach transect (OB) which

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included one sample location within the Bayou Saint John near Ono Island (OI) and four sample

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locations (PPN, PPS, WJ1, and WJ2) extending south from Perdido Pass in the nGOM (Table

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S1; Fig. 1).

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Sediment samples were collected between September 2010 and April 2012. The MS

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transect was sampled in September 2010 after the Deepwater Horizon (DWH) oil spill (April-

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June 2010). All three transects were sampled at the end of June/beginning of July 2011 and were

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followed by approximately tri-monthly sampling (September 2011, January 2012, and April

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2012) depending on weather conditions. All samples were collected using the DISL R/V E.O.

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Wilson. A small 0.05 m2 box core was used for the MS September 2010 sample from which the 6

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top 3 cm was retained. Samples collected from 2011-2012 were collected by isolating the top 3

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cm of a sediment core using the MC-400 Hedrick/Marrs Multi-Corer (Ocean Instruments, San

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Diego, CA). The June/July 2011 sample date included one to two cores from each location that

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were divided up into approximately thirds. One third was placed in a Whirl-Pak® and frozen

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immediately on dry ice to be used for DNA isolation and subsequent community composition

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analysis. The remaining two thirds were preserved in either DMSO EDTA Salt Solution (DESS)

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(Yoder et al. 2006) or 10% buffered formalin solution to be used for additional studies. The

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remaining sampling dates in 2011 (September) and 2012 (January and April) had one to two

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cores from each location collected. For each of these, half was immediately frozen and the other

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half preserved in DESS. Upon returning to Auburn University, frozen sediment samples were

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stored at -80 ˚C until they were processed.

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DNA extraction and sequencing

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Direct extraction of DNA from sediment was employed to allow comparison to an

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independent study on the microbial fraction from these samples (Ortmann, unpubl. data). Total

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genomic DNA was extracted from one core per sample location per time point using the MoBio

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Laboratories (Carlsbad, CA) PowerMax® Soil DNA Isolation Kit (Cat. #12988-10) using

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manufacturer’s supplied protocol. The core used for analysis was haphazardly chosen if multiple

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cores were collected. Sediment was allowed to only thaw slightly on ice until a rubber mallet

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could be used to gently break up the sediment and retrieve ~10.5 g of sediment. DNA integrity

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was visualized by gel electrophoresis. DNA concentration was determined on a Qubit 2.0

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Fluorometer (Invitrogen). Samples were either concentrated using the manufacturer’s protocol

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(MoBio Laboratories) or diluted to a desired concentration (10-25 ng/µl). DNA was stored at -20

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˚C until amplified and sequenced.

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Methods employed hereafter generally follow that of Brannock et al. (2014) and Brannock and Halanych (2015). DNA was sent to Genomics Services Laboratory (GSL) at

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HudsonAlpha Institute of Biotechnology (Huntsville, AL) for metabarcode sequencing of the

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hypervariable V9 region of the 18S small subunit ribosomal RNA (SSU rRNA) gene with each

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sample amplified twice. The V9 gene region was chosen due to sequencing limitations of the

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Illumina platform at the time the study was conducted. Duplicate reactions served as technical

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replicates (Brannock et al. 2014; Brannock and Halanych 2015). All samples from April 2012,

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St.6 from September 2010, and DI from June 2011 had one technical replicate run as 150-bp

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paired end (PE) on an Illumina MiSeq and one run as 100-bp PE on an Illumina HiSeq 2500. All

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other samples had both technical replicates run as 100-bp PE on the Illumina HiSeq 2500.

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Previous studies show that samples run on HiSeq and MiSeq platforms produce consistent results

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(Caporaso et al. 2012; Brannock et al. 2014). Demultiplexed raw reads were provided by the

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GSL and have been deposited in the National Center for Biotechnology Information (NCBI)

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Sequence Read Archive (SRA) database (Project number PRJNA291850 with BioSample

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accession numbers SAMN03956304-SAMN03956358).

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Bioinformatics

Raw forward and reverse reads were overlapped using PandaSeq v2.5 (Masella et al.

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2012) following protocol and parameters stated in Brannock et al. (2014). However, prior to

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overlapping, MiSeq raw reads were trimmed using the fastx_trimmer command in the FastX

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Toolkit (Hannon Laboratory 2009) to remove low quality base calls at the end of reads. Both

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forward and reverse reads were trimmed to a length of 95 bp. This length removed a majority of

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the low quality bases while allowing for reads to be overlapped with the majority of sequences

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falling in the 130-140 bp range. All overlapped sequences were quality filtered using UPARSE

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(Edgar 2013) with the maximum expected error (-fastq_maxee) set to 1 (Brannock and Halanych

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2015). After sequence quality filtering, chimera filtering through operational taxonomic unit

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(OTU) table filtering followed Brannock et al. (2014) with the SILVA (Quast et al. 2013)

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database (v119) employed as a reference set for both OTU clustering and taxonomic assignment

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commands in QIIME v1.8 (Caporaso et al. 2010b). Hereafter, the OTU table that excluded those

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sequences that failed to align with PYNAST (Caporaso et al. 2010a) or were classified as

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Bacteria or Archaea, will be referred to as the final OTU (fOTU) table.

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A hierarchical clustering analysis based on the proportional fOTU table for the 110

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samples (55 samples with two PCR replicates each) was performed. Technical replicates were

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more similar in OTU composition in reference to each other than to any other sample (as in

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Brannock et al. 2014; Brannock and Halanych 2015). Therefore, sequences for technical

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replicates were combined and treated as one sample for the remainder of analyses, referred to

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herein as the combined OTU (cOTU) table.

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A rarefaction analysis was conducted on the cOTU table in order to determine sufficient

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subsampling level for the samples. Briefly, the cOTU table was subsampled from 10 to 233,810

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sequences in steps of 23,380 sequences. Ten subsample replicates were conducted for each of the

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ten subsampling levels. Average alpha-diversity (Chao1, phylogenetic distance: PD, Shannon

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diversity, and number of OTUs) measurements were plotted against subsampled sequence depth

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in QIIME.

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In order to directly compare diversity measures between different samples, sequence

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depth was normalized to 53,900 sequences per sample to produce a normalized OTU (nOTU)

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table. This conservative number was chosen as it represents the number of sequences in our

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smallest sample (in cOTU table) with greater than 50,000 sequences. Normalization to 53,900

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sequences per sample was repeated to yield 100 replicates. For each replicate nOTU table, four

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alpha-diversity measurements (Chao1, phylogenetic distance: PD, Shannon diversity, and

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observed number of OTUs) and three beta-diversity measurements (weighted Unifrac distance:

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Lozupone and Knight 2005, Bray Curtis dissimilarity, and binary Jaccard dissimilarity metrics)

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were calculated in QIIME. For all alpha-diversity metrics, the 100 estimates were averaged to

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generate one estimate per sample. For all beta-diversity metrics, the averaged distance matrix

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generated by the jackknifed_beta_diversity.py workflow in QIIME was used. A two-way

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analysis of variance (ANOVA) was performed in R version 3.0 (R Development Core Team

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2013) to compare alpha-diversities between transects (MS, FT, and OB) over the different time

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points as well as region (Bay and Shelf) over the study time frame. Only time points (July 2011

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to April 2012) where all three transects were collected were tested. T20 and St. 5 from April

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2012 were not included in diversity (alpha or beta) analyses as each of these samples contained

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less than 53,900 sequences in the cOTU table (Table S2). A Tukey’s Honest Significant Test

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(HSD) was conducted in R to examine all possible pairwise comparisons when significant effects

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were detected. Additionally, an analysis of similarity (ANOSIM) was performed using all three

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beta diversity matrices in Primer version 7 (Primer-E Ltd, UK) to compare community

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composition between transects (MS, FT, and OB), region of the sample (Bay versus Shelf), and

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month the sample was collected (Table S1). For month sampled, only July 2011 to April 2012

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time points where all three transects were collected were tested. The MS transect was sampled in

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September 2010 and 2011, allowing ANOSIM to test for community composition differences

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between years. An examination of sediment characteristics (see below) that best explains (or

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correlates) community composition differences observed was performed using BIO_ENV

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analysis in Primer version 7. Environmental variables were normalized in Primer prior to running

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the BIO_ENV analysis. Community composition was examined following Brannock et al. (in

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review) protocol.

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Sediment analysis

There were obvious visual differences in the sediment collected. Samples collected

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within MS and FT transects were composed mainly of muddy, silty sediment, except for MS St.4

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which was almost exclusively sand. In contrast, OB samples were predominately sand with

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locations south of Perdido Pass containing a mixture of sand and mud. Sediment dry bulk

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density, % porosity, % total nitrogen (N) and carbon (C), % inorganic N and carbon C, and %

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organic N and C were measured at DISL for each core. Dry bulk density was measured by taking

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5 ml sediment subsample using a modified 10-ml syringe. Samples were dried at 60 °C and dry

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weights were recorded (Brady 1984). Porosity was calculated from bulk density using the

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equation: porosity= 1 - (bulk density/particle density), assuming the particle density is 2.65

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g/cm3 (Brady and Weil 1996).

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After measuring bulk density, dried samples were ground using a mortar and pestle and

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total C and N was measured using a ECS 4010 CHNSO analyzer (Costech Analytical

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Technologies, Inc., Valencia, CA) following Pennock and Cowan’s (2001) protocol.

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Approximately 15 µg of dried ground sediment was loaded for MS and FT samples, while about

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50 µg of dried ground sediment was used for OB samples due to lower C and N contents. To

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measure total organic C and N, samples were treated with 96% sulfuric acid and approximately

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15 µg (MS and FT) and 50 µg (OB) of the treated dried sample was measured on the ECS 4010

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CHNSO analyzer (Costech Analytical Technologies, Inc., Valencia, CA) (Nelson and Sommers

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1996). To obtain the % inorganic C and N, % organic C and N measurement were subtracted

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from the % total C and N. To compare between the Bay and Shelf samples as well as among the MS, FT, and OB

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transects, differences in environmental parameters were tested in JMP 9.0 (SAS Institute, NC)

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using the Wilcoxon/Kruskal-Wallis test. A Bonferroni correction for multiple comparisons was

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applied adjusting the alpha to p < 0.01. In addition, PERMANOVA was run in Primer. For the

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PERMANOVA the environmental data was first normalized and then used to calculate the

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Euclidian distance. Two samples, WJ2 April 2012 and St.4 January 2012, had values for %

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organic N that were below the level of detection of the machine; therefore, a value of zero was

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used for all analysis for these two samples. Porosity is linearly related to bulk density and thus

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not tested separately.

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RESULTS

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Sequencing and clustering

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From the 110 technical replicates sequenced (55 samples each with 2 replicates),

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18,757,974 demultiplexed reads in each direction were obtained with technical replicates

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averaging 170,527 reads in each direction (Table S2). After read overlapping, quality filtering,

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and chimera filtering (Table S2), very few sequences (0.001% or 140 sequences) were discarded

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due to the sequence length being less than 75 bp. A total of 13,156,522 sequences went into OTU

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clustering resulting in 36,480 OTUs. After filtering OTUs that failed to align with PYNAST

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(4.1% or 1,498 OTU’s) and/or were classified as Bacteria or Archaea (3.3% or 1,164 OTUs),

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33,818 OTUs remained in the fOTU table.

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Of the 33,818 OTUs, a majority were classified as Non-Metazoa (75.6%, 25,570 OTU), 18.6% Metazoa (6,297 OTUs), 3.1% as Fungi (1,031 OTUs), and 2.7% No Hit (920 OTUs).

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Non-Metazoa OTUs were mainly classified as Stramenopiles (6,721 OTUs, 26.3%), followed by

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Cercozoa (4,696 OTUs, 18.4%), Myzozoa (3,441 OTUs, 13.5%), and Ciliophora (2,802 OTUs,

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11%). The remaining groups each represented <10% of the Non-Metazoa OTUs. Within

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Metazoa, a majority of the OTUs were classified as Nematoda (1,589 OTUs, 25.2%), followed

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by Annelida (1,134 OTUs, 18%), Arthropoda (1,019 OTUs, 16.2%), and Platyhelminthes (740

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OTUs, 11.8%). All other phyla found within the Metazoa category each represented <6% of the

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OTUs. A majority (75.5%, 695 OTUs) of the OTUs classified as No Hit did not match any

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sequence in NCBI’s GenBank Database (see (Brannock et al. 2014).

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Alpha- and beta-diversities

There were no significant interactions of factors in any of the 2-way ANOVAs performed

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(F ≤ 0.687, p ≥ 0.661). All alpha-diversity metrics showed significant differences between

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transects (F ≥ 3.56, p ≤ 0.037). The Tukey’s HSD showed that OB had significantly higher

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values for the number of OTUs and Chao1 in comparison to both MS and FT (p ≤ 0.031), while

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OB had a significantly greater Shannon diversity and PD in comparison to FT only (p ≤ 0.038).

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April 2012 had a significantly higher PD than September 2011 (p=0.04). All other dates for PD

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and the rest of the alpha-diversity metrics were not significantly different (p ≥ 0.06). There was

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no significant difference in any of the alpha-diversity metrics in reference to region (F ≤ 2.343, p

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≥ 0.133).

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There was significant clustering of samples in reference to locality (Table 1; Fig. 2). FT and MS transect samples clustered together and had a significantly different community

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composition than the OB transect sites (Table 1; Fig. 2A). However, MS sample St.4 was a

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distinct outlier and appeared to cluster more closely with OB sites (Fig. 2A). When St.4 was

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removed from the analysis, clustering of FT and MS samples away from OB became more

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significant (Table 1) for all three beta-diversity metrics. A significant difference in community

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composition between the Bay and Shelf regions (Table 1; Fig. 2B) was also observed. However,

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the significant difference was observed only when examining the presence (Jaccard metric) and

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abundance (Bray Curtis) of taxa, but not when including phylogenetic relationship with

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abundance (weighted Unifrac) (Table 1). There was no significant difference in community

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composition between collection date for any of the beta-diversity metrics (Table 1). Likewise,

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there were no significant differences in community composition between MS September 2010

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and 2011 for all three beta-diversity metrics (0.068 ≤ R ≥ -0.080, 0.738 ≤ p ≥ 0.167). Thus,

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samples clustered more by locality rather than time collected (Table 1; Fig. 2C).

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Taxonomic representation

The most abundant (proportionally) taxa throughout the data set were Stramenopiles (average abundance of 30.2%), annelids (19.4%), arthropods (13.7%), and nematodes (5.9%)

316

(Fig. 3). All remaining taxa had an average abundance of <5% each. There was variation in

317

proportions of taxa present within a sample location throughout the year, with some locations

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showing more variation than others (Fig. 3). After examining the four most abundant taxa

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(Stramenopiles, annelids, arthropods, and nematodes) with greater taxonomic resolution, some

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differences between locations became apparent. In reference to transect, Thalassiosiraceae

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(Stramenopiles), Capitellidae (Annelida), Centropagidae (Arthropoda), Limnocytheridae

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(Arthropoda), Loxoconchidae (Arthropoda), Paradoxostromatidae (Arthropoda), and

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Comesomatidae (Nematoda) had higher abundance in MS and FT transects compared to OB

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(Table 2; Fig. S1). On the other hand, Bacillariaceae (Stramenopiles), Sellaphoraceae

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(Stramenopiles), Nephtyidae (Annelida), Syllidae (Annelida), Temoridae (Arthropoda),

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Miraciidae (Arthropoda), and Onocholaimidae (Nematoda) showed the opposite pattern (Table

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2). When examining differences in taxonomic proportions in reference to region (Bay and Shelf),

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Thalassiosiraceae (Stramenopiles), Capitellidae (Annelida), Ampharetida (Annelida),

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Echtinosomatidae (Arthropoda), Limnocytheridae (Arthropoda), Paradoxostomatidae

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(Arthropoda), Chromadoridae (Nematoda), and Xyalidae (Nematoda) were higher in the Bay

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compared to the Shelf (Table 2; Fig. S1). Stephanopyxidaceae (Stramenopiles), Naviculaceae

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(Stramenopiles), Opheliidae (Annelida), Spionidae (Annelida), Cirratulidae (Annelida),

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Miraciidae (Arthropoda), Misophiriidae (Arthropoda), and Oncholaimidae (Nematoda) had a

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lower abundance in the Bay in comparison to the Shelf (Table 2; Fig. S1). In addition, Ostracoda

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and Maxillopoda within Arthropoda had opposite trends in reference to the Bay and Shelf

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locations. Ostracoda (mainly Podocopida) were found in higher abundances in the Bay (49.2%)

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in comparison to the Shelf (24.5%), while Maxillopoda (mainly Calanoida and Harpacticoda

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copepods) had lower abundances in the Bay (36.7%) in reference to the Shelf (65.9%).

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There were also cases where a given taxon was abundant in a single sample but not generally abundant throughout the dataset (Fig. 3). For example, there was a high abundance of

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Celphalochordata (Branchiostoma) in PPS during the June 2011 sample. There was also a greater

342

abundance of Echinodermata during the same time point for OI and PPN (Fig. 3). OI was

343

dominated by Astropectinidea, mainly Astropectin, while PPN was mainly comprised of

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Mellitidae represented by Encope. Kinorycha showed higher abundances at St.5, St. 6, and T10

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in January 2012 (Fig. 3), all dominated mainly by Echinoderes, Echinoderidae.

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346 347 348

Correlation with environmental parameters The BIO-ENV analysis revealed community composition was correlated with sediment bulk density. There were significant differences in the presence (Rho=0.485, p=0.001) and

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abundance (Rho=0.352, p=0.001) of taxa with bulk density. However, there was no significant

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difference with the relatedness of taxa and environmental conditions (Rho=0.151, p=0.047).

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When removing St.4 from the analyses, community composition was correlated with both

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sediment bulk density and % inorganic C for all three beta-diversity metrics (0.256 ≤ Rho ≥

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0.562, p≤0.009)

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When examining environmental parameters, bulk density (p=0.0001), % inorganic N (p= 0.0004), and % inorganic C (p=0.0042) were all significantly lower in Bay samples in

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comparison to the Shelf samples (Table 3). When comparing environmental variables among

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transects, these same environmental parameters showed significant differences between transects

359

along with % total C and % organic C (Table 3). PERMANOVA analysis showed significant

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Shelf-Bay differences (p perm=0.001) and transect differences (p perm=0.001). Pairwise

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differences show larger differences in environmental parameters between OB and the other

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transects (both comparisons p=0.001) than between MS and FT (p=0.039).

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DISCUSSION

Spatial differences appear more important than temporal variation for driving community

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composition of nGOM meiobenthos (Fig. 2). Previous studies have reported that location, rather

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than season, has a larger impact on intertidal meiofauna community composition (Brannock et al.

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2014) and pelagic micro-eukaryote (Brannock et al. in review) communities within the same

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geographic region, while pelagic bacterial communities in that region showed significant spatial

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and season (especially in the Bay) patterns (Ortmann and Ortell 2014) . In the current study, we

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were able to resolve a distinct community structure for each sampling location (with limited

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overlap; Table 1; Fig.2A). Samples revealed no such clustering by time of collection (Fig. 2C).

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However, samples were collected over the course of one year. In order to better understand and

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grasp influence of temporal component samples should be collected over multiple years.

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Interestingly, samples from the south and west of Mobile Bay (transects FT and MS) are clearly

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distinct from the eastern transect (OB). FT and MB transects have a lower sediment bulk density

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(1.00 g/cm3 and 0.63 g/cm3, respectively) in comparison to OB (1.34 g/cm3), most likely due to

378

high impact of the Mobile River on FT and MB locations. Organic matter and sediment carried

379

by the river can be deposited in nearshore areas, resulting in sediment characteristics distinct

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from regions with low river impacts. Notably, different species from the same general taxa

381

account for OTU differences between locations, explaining observed significant correlations of

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both the Jaccard (Rho=0.485, p=0.001) and Bray Curtis (Rho=0.352, p=0.001) metrics with

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environmental characteristics, but no correlation with the weighted Unifrac (Rho=0.151,

384

p=0.047) diversity metric. Bacterial assemblages from these sample sites show similar patterns in

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terms of the importance of spatial versus temporal variation accounting for community structure

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(Ortmann, unpubl. data).

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The most abundant phyla observed within the current study were Stramenopiles,

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annelids, arthropods, and nematodes. Polychaetes, copepods, and nematodes are reported as

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dominating meiofaunal communities in the nGOM (Escobar et al. 1997; Baguley et al. 2006;

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Landers et al. 2012) and diatoms make up a large proportion of the microalgae found within

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nGOM coastal sediment (Grippo et al. 2010, 2011). More specific examination of OTUs within

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different transects revealed that Thalassiosiraceae, Capitellidae, Centropagidae, Loxoconchidae,

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and Comesomatidae are twice as common in MS and FT transects than in OB (Table 2; Fig. S1).

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In contrast, OB has at least 2x more Bacillariaceae, Syllidae, Onocholamidae, and Trichodoridae

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compared to MS and FT. Factors such as physical disturbance, food and nutrient availability,

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sediment characteristics (see below), and interactions with other taxa most likely are playing a

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role in the distribution patterns observed. For example, Comesomatidae nematodes were reported

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to be higher in frequencies and abundances in enriched estuary sediments that may also be low in

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oxygen (Moens et al. 2013). MS and FT transects are most likely enriched in nutrients due to

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influences of the Mobile Bay river basin through the exchange of water through the Pass-aux-

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Herons and the Main Pass, respectfully. Capitellidae are opportunistic polychaetes that are often

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found in organically enriched sediments.

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BIO-ENV analysis revealed community composition differences were correlated with sediment bulk density. Sediment type, size, and structure all play a large role in meiobenthic

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community composition (Coull 1988; Pequegnat et al. 1990; Shimeta et al. 2007; Giere 2009).

406

Both bulk density and porosity relate to these sediment characteristics and are inversely related

407

to one another (Table S1). Pequegnat et al. (1990) examined meiofauna offshore in the nGOM

408

and reported that the GOM can be divided into two major sediment regions that is delimited by

409

the DeSoto Cayon. They reported locations to the east of the DeSoto Cayon and south along the

410

Florida coast are characterized as carbonate sediment, whereas sediments to the west are

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described as terrigenous (Pequegnat et al. 1990). They found that as the percent of sand

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increased numbers of nematodes and harpacticoids (arthropods) decreased, while the amount of

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clay increased so did the numbers of these two groups (Pequegnat et al. 1990); however,

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correlations they observed were weak. Samples in the current study showed visible differences in

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sediment characteristics based on the location they were collected. MS and FT transects largely

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consisted of muddy sediment (except for MS St.4 which was all sand), while OB contained a

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higher visible sand composition (Brannock, pers. observ.). Sediment composition could

418

potentially explain why St. 4 clusters in community composition with OB transect. Samples

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collected within MS (excluding St. 4) and FT transects had an average bulk density of 0.63 g/cm3

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and 1.00 g/cm3 or 76.1% and 62.1% porosity, respectively, while samples within OB had a

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greater average bulk density (1.34 g/cm3) and lower porosity (49.4%). Looking at the average

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bulk density (1.35 g/cm3) and porosity (49.1%) for St.4, it is more similar to samples within OB

423

transect than either MS of FT transects. Courser sediment (sand and gravel) generally tends to

424

have higher bulk density and a lower porosity than finer sediment (silt and clay). This

425

characteristic complements visual observations of OB transect and St.4 containing a higher sand

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component, while the MS and FT were described to be comprised of more muddy silty particles.

427

The current study does not corroborate the Pequegnat et al. (1990) finding that nematodes and

428

harpacticoids decrease with increasing amount of sand. Nematodes in OB and St.4 were slightly

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higher in abundance (average 6.7%) in comparison to MS and FT locations (average 4.9%)

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where sand was low relative to silt and clays.

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Furthermore, community structure was related to whether samples were collected from Bay or Shelf localities (Fig. 2B). Recent studies (Brannock et al. in review; Ortmann and Ortell

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2014) examining both bacterial and micro-eukartyotic fractions of surface water along the

434

FOCAL transect have found a significant community composition difference between the Bay

435

and Shelf sample sites. Thus, findings herein mirror recent results for the overlying water

436

column. Meiobenthic communities differ in reference to region (Bay or Shelf) the sample was

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obtained from. Both different taxa and abundance of those taxa appear to account for much of

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the observed variation; however, OTUs are represented by closely related species, and hence the

439

nonsignificant weighted Unifrac result (Table 1, 2; Fig. S1). In reference to diatoms, our results

440

show a mixture of phytoplankton and benthic microalgae, with Bay habitats having a higher

441

presence of phytoplankton (e.g. Thalassiosiraceae, a centric diatom), and Shelf locations having

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higher presence of benthic forms (e.g. Naviculaceae, a pennate diatom). Diatoms found in

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sediments within the nGOM region have previously been shown to differ in reference to

444

sediment type. Sandy sediments are usually dominated by benthic microalgae forms, while

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muddy sediments contain higher contributions from settled phytoplankton (Grippo et al. 2010).

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A majority of the Bay locations are comprised of muddy substrate with OI being the only

447

predominately sandy location (Brannock, pers. observ.). Comparing sediment characteristics

448

between the two regions, the Bay had a lower bulk density (0.80 g/cm3) and higher porosity

449

(69.9%) in comparison to the Shelf (1.2 g/cm3 and 54.7%), indicating that the Shelf region

450

contains more coarser (sandy) sediment and the Bay had finer (silty) sediment. These results

451

resemble those of Grippo et al. (2010) in that sediment with higher presence of sand contains

452

more of the benthic microalgae and sediments with a higher mud content contain higher

453

phytoplankton types. Higher levels of settled phytoplankton in Bay locations also corresponds

454

with Brannock et al.’s (in review) finding of pelagic diatoms in greater abundance in the Bay in

455

comparison to the Shelf. They attributed higher abundance of pelagic diatoms in the Bay to be

456

influenced by fresh nutrient-rich water brought to Mobile Bay from the Mobile River (Brannock

457

et al. in review). All the Bay locations within the current study are influenced by this river

458

system (Schroeder 1979) except OI. In addition, previous research has shown that in the nGOM

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the presence of pelagic diatoms decreases the further away from the shore (Lambert et al. 1999;

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Qian et al. 2003; Chakraborty and Lohrenz 2015).

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461

Previous studies have shown that both temperature and salinity (Coull 1988; Giere 2009; Lei et al. 2014) (not measured herein) can also influence meiobenthic community composition.

463

As mentioned, the Mobile River basin greatly influences Mobile Bay which in turn transports

464

fresh, nutrient rich water to both Mississippi Sound and the nGOM through the Pass-aux-Heron

465

and the Main Pass, respectively (Schroeder 1979). Once leaving through Main Pass, currents

466

generally flow westward (Chuang et al. 1982; Schroeder et al. 1985, 1987). Therefore, greatly

467

influencing sample locations within the MS and FT transects. Differences seen between the Bay

468

and Shelf locations could be the result of salinity as previous research has shown that Mobile

469

Bay has significantly lower and more variable salinity compared to adjacent GOM sample

470

locations (Ortmann and Ortell 2014). Temperature varied seasonally, but not significantly

471

between the two regions (Ortmann and Ortell 2014). As shown, the community composition

472

between the Bay and Shelf locations are significantly different (Table 1). Removal of OB and

473

MS4 locations from the analyses (removing the sites that had previous differences), produced

474

larger dissimilarity between the Bay and the Shelf regions. Further exploration of how salinity

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impact subtidal meiobenthic communities is warranted.

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Previous work has shown meiofauna abundances in the nGOM change with depth

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(Pequegnat et al. 1990; Baguley et al. 2006); however, these studies examined larger depth

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ranges (200-3800 m) than our current study (0-20 m). A shallower depth (50-200 m) and narrow

479

depth range study showed no difference in meiofauna abundance between depths (Landers et al.

480

2012), but these studies used traditional morphological approaches and may not detect

481

differences in morphologically similar species. Because ranges of depths sampled in this study

482

were narrow (0-20 m, Table S1), we cannot easily tease apart influences of depth versus the Bay

483

or Shelf location on community structure herein. Further exploration of both region (Bay verses

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484

Shelf) as well as depth needs to be explored to better understand the influence on meiobenthic

485

communities in this geographic area.

486

Intertidal meiofauna communities with the same geographic region were shown to have a large fungal presence in September of 2010, suggested as a response to oil contamination from

488

the Deepwater Horizon (DWH) spill (Bik et al. 2012a). No such fungal signature was found

489

within the subtidal samples studied herein (Fig. 3). This discrepancy could be caused by

490

differences in sample processing and region of the 18S rRNA amplified between the studies as

491

well as the potential differences in oil exposure. However, due to the lack of quantitative

492

hydrocarbon data, interpretation relative to the DWH oil spill is limited. Therefore, this work

493

differs from Bik et al. (2012a) and is focused on an examination of community composition

494

along spatial and limited temporal scales. However, differences between fungal presence

495

between intertidal and subtidal locations within the same geographic region warrants further

496

exploration.

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As several meiofaunal taxa have been shown to be sensitive to environmental changes (Vincx and Heip 1991; Kennedy and Jacoby 1999; Schratzberger et al. 2000), these organisms

499

are often used as bio-indicators to assess natural or anthropogenic impacts and explore the

500

quality of environmental conditions on communities (e.g. Vincx and Heip 1991; Kennedy and

501

Jacoby 1999; Danovaro 2000; Danovaro et al. 2009; Baguley et al. 2015). High-throughput

502

sequencing, mainly metabarcoding, approaches do allow for a more holistic examination of

503

meiofaunal community composition and can be conducted in a faster time frame than traditional

504

morphological approaches. Implementation of these approaches to eukaryotic systems is still

505

relatively new in comparison to prokaryotic systems. Nonetheless, these approaches show

506

usefulness in exploring spatial and temporal trends (Bik et al. 2012a; Brannock et al. 2014;

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Cowart et al. 2015; Lallias et al. 2015) as well as community impacts at higher taxonomic levels

508

(Bik et al. 2012a). We caution, however, that limited representation of meiobenthic taxa that

509

have been properly validated in publically available genetic databases currently restricts the

510

ability to assign lower taxonomic levels (e.g. genus and species) to sequences representing

511

meiobenthic organisms. Furthermore, differences in methods from sample collection through

512

data analysis have been shown to greatly impact results (Brannock and Halanych 2015; Carugati

513

et al. 2015), limiting comparisons. For metabarcoding approaches to be more useful, especially

514

in terms of determining faunal representation, standardization of methods and efforts to increase

515

molecular references of morphologically identified individuals is required.

516

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ACKNOWLEDGEMENTS

We thank Dr. Ron Kiene for the use of the multi-corer as well as all the DISL captains of

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the R/V E. O. Wilson, first mate Willie, Will Harper, Kyle Weis, and William Montgomery for

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their assistance with the boat and multi-corer operations during the sampling cruises. We also

521

appreciate the assistance Dr. Holly M. Bik provided in the field and Dr. Scott R. Santos for his

522

bioinformatics support. This research was funded by Year 1 Gulf of Mexico Research Initiative

523

(GOMRI) awarded by Marine Environmental Science Consortium (MESC) awarded to K.M.H.

524

as well as the BP Rapid Response MESC grant and BP-Northern Gulf Institute Year 1 and 2

525

grants awarded to A.C.O. This is Molette Biology Laboratory contribution ## and Auburn

526

University Marine Biology Program contribution ###.

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DNA of nematodes. Nematology 8: 367–376.

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FIGURE LEGENDS

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Fig. 1. Geographic distribution of sample locations within Mobile Bay, Alabama and the

750

northern Gulf of Mexico (nGOM) off the coast of Alabama and Mississippi. Triangles (▲) are

751

locations within the Mississippi Sound transect (MS). Squares (■) are locations within the

752

FOCAL transect (FT). Circles (●) are locations within the Orange Beach transect (OB). Site

753

abbreviations are defined in Table S1.

754

Fig. 2. Non-metric multidimensional scaling (nMDS) plot based on the Binary Jaccard beta-

755

diversity matrix and categorizing samples by A. transect, B. region, and C. month sampled

756

(Table S1). Panel A also shows the groupings of all samples from the same sample sites. In Panel

757

B and C the location of the samples remain consistent.

758

Fig. 3. Community composition of sediment samples collected within Mobile Bay, Alabama and

759

along the shelf of the northern Gulf of Mexico (nGOM). Classifications of sample locations are

760

found within Table S1 and a geographic representation of the collection sites are found within

761

Figure 1. Proportion of taxa illustrated based on unrarefied cOTU table.

762

Fig. S1. Relative abundance of families for the four most abundant phyla within the dataset.

763

Family and overall phyla information is provided for all three transects (MS, FT, and OB) and

764

region (Bay and Shelf).

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34

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Table 1. Analysis of Similarity (ANOSIM) results of spatial and temporal community composition comparisons. Numbers represent ANOSIM R-statistic*. Bold indicates significant values (R ≥ 0.20, p ≤ 0.05). Numbers in parentheses represents the ANOSIM R-values when MS St4 was removed from the analysis. Beta Diversity Metric Binary Jaccard Bray-Curtis Weighted Unifrac 0.469 (0.722) 0.387 (0.626) 0.275 (0.394) 0.018 (0.209) 0.049 (0.213) 0.083 (0.176) 0.609 (0.962) 0.504 (0.864) 0.341 (0.531) 0.926 (0.926) 0.690 (0.690) 0.403 (0.403) 0.105 0.368 0.272 0.102 0.052 0.012

Comparison

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Test

SC

Global MS, FT Transect MS, OB FT, OB Location Bay, Shelf Month Global

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*An R-statistic of 0 represents complete random grouping, while a value of 1 indicates dissimilarity between groups.

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Table 2. The percent average abundance for the top 5 families in the 4 most abundant phyla. Data are shown for across the whole dataset (average) as well as divided into transect and location. Transect

25.3% 10.4% 7.9% 7.1% 5.2% 25.7% 16.5% 8.3% 7.2% 7.2% 12.3% 9.2% 8.9% 8.5% 8.2% 24.2% 22.8% 15.8% 10.4% 3.2%

AC C

EP

TE D

Thalassiosiraceae Troceratiacea Stramenopiles Bacillariaceae Naviculaceae Stephanopyxidaceae Capitellidae Spionidae Annelida Syllidae Ampharetidae Other/Unknown Centropagidae Loxoconchidae Arthropoda Limnocytheridae Ectinosomatidae Misophriidae Chromadoridae Xyalidae Nematoda Comesomatidae Oncholaimidae Trichodoridae

MS 35.2% 8.5% 5.1% 7.4% 2.2% 31.9% 17.5% 4.9% 13.8% 12.2% 14.4% 10.1% 16.4% 11.1% 7.8% 27.8% 25.5% 15.8% 5.7% 1.2%

FT 29.5% 19.5% 4.6% 3.0% 7.6% 42.0% 20.7% 0.1% 2.4% 3.9% 24.2% 15.5% 4.3% 6.1% 5.1% 16.5% 24.5% 26.9% 5.0% 1.6%

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Average OB 9.9% 7.6% 13.6% 8.9% 7.9% 7.9% 12.7% 17.5% 1.3% 2.5% 2.6% 4.3% 1.7% 6.3% 10.6% 23.8% 18.4% 9.5% 16.9% 6.7%

SC

Family

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Phylum

Location Bay 39.1% 8.9% 6.4% 2.7% 2.3% 40.0% 8.7% 6.5% 16.5% 11.5% 12.8% 7.8% 19.4% 10.9% 4.8% 29.9% 29.7% 15.1% 6.1% 3.1%

Shelf 16.1% 11.3% 8.9% 10.0% 7.2% 16.1% 21.7% 9.5% 1.3% 4.5% 12.0% 10.1% 1.9% 6.8% 10.5% 20.3% 18.3% 16.3% 13.3% 3.2%

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Sample Size (N) Variable

Range

Bulk density (g cm-3)

0.29 - 1.36

Porosity (%)

Bay 22 Mean (Standard Range Deviation)

Range

1.2 (0.27)

0.29 -1.44

48.72 - 89.09 72.48 (14.94)

45.85 - 90.00

54.64 (10.36)

46.77 -89.09

% Total Nitrogen

0.00 - 0.22

0.04 (0.06)

0.00 - 1.57

0.19 (0.37)

% Total Carbon

0.06 - 2.03

0.50 (0.62)

0.07 - 2.56

0.98 (0.75)

0.00 - 0.15

0.02 (0.03)

0.00 - 0.21

0.02 (0.05)

0.05 - 1.67

0.23 (0.35)

0.01 - 1.95

0.24 (0.45)

% Inorganic Nitrogen

0.00 - 0.02

0.02 (0.05)

0.00 - 1.56

0.16 (0.37)

% Inorganic Carbon

0.00 - 1.89

0.27 (0.49)

0.00 - 1.97

0.78 (0.40) A

Range

Transect FT 11 Mean (Standard Deviation)

Range

OB 19 Mean (Standard Deviation)

0.26 - 1.30

0.99 (0.37) A

1.23 - 1.42

1.34 (0.05) B

70.73 (14.99)

51.02 - 90.00

62.79 (13.97)

46.37 - 53.40

49.41 (1.84)

0.00 - 0.17

0.04 (0.06)

0.01 - 0.24

0.06 (0.09)

0.00 - 1.57

0.29 (0.46)

0.06 - 1.95

0.46 (0.59) A

0.27 - 2.56

0.87 (0.81) B

0.07 - 2.04

1.16 (0.69) B

0.00- 0.15

0.02 (0.03)

0.00 - 0.21

0.05 (0.08)

0.00 - 0.02

0.01 (0.01)

0.02 - 1.67

0.22 (0.35) A

0.08 - 1.86

0.56 (0.69) B

0.02 - 0.24

0.08 (0.05) A

0.00 - 0.08

0.02 (0.05) A

0.00 - 0.03

0.01 (0.01) A

0.00 - 1.56

0.28 (0.46) B

0.00 - 1.49

0.24 (0.43) A

0.04 - 0.61

0.32 (0.17) B

0.00 - 1.89

1.08 (0.68) C

TE D

EP

AC C 0.73 (0.65)

MS 25 Mean (Standard Deviation)

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0.26 - 1.44

% Organic Nitrogen % Organic Carbon

0.73 (0.40)

Shelf 33 Mean (Standard Deviation)

SC

Location

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Table 3. Environmental variable’s range, mean, and standard deviation when grouped by location (Bay and Shelf) or transect (MS, FT, and OB). Bold indicates significant differences from the Wilcoxon/Krustall-Walis test after a Bonferroni correction for multiple comparisons was applied (adjusted alpha p>0.01). Letter in the Mean column in the transect factor indicates where the difference was found within each significant variable. Porosity was not tested due to the linear relationship with bulk density.

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RI PT Florida

30˚48'

30˚18'

Mississippi Sound

St.5

30˚12' 30˚06'

St.6

St.7

St.4

Horn Island Pass

30˚00' −88˚36'

St.3 −88˚24'

Pass-aux-Herons

DI

T10 T20 −88˚12'

SC

M AN U

30˚24'

Main Pass

Gulf of Mexico

−88˚00'

−87˚48'

0

−87˚36'

PPN PPS

WJ2

TE D

Mobile Bay

Perdido Pass

WJ1

EP

30˚30'

Alabama

AC C

30˚36'

Mississippi

30˚42'

OI

10

20

−87˚24'

Gulf of Mexico

2D Stress: 0.11

T20

St. 3 ACCEPTED MANUSCRIPT WJ2

WJ1

T10

St. 6 St. 5

PPS

PPN

St. 4

Trasect FT MS OB

A

2D Stress: 0.11

Region

AC C

Shelf Bay

EP

TE D

M AN U

SC

OI

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DI

St. 7

PPS

Month April July September January

B 2D Stress: 0.11

C

100% 90%

September 2010

80% 70% 60% 50% 40% 30% 20% 10% 0% 100%

ACCEPTED MANUSCRIPTNo

90%

Other Eukaryotes

70%

Other Rhizaria

60%

Stramenopiles

50%

Myzozoa

40%

Cryptophyta

30%

RI PT

June/July 2011

80%

20%

Ciliophora

10%

Chlorophyta

SC

Cercozoa

90%

Amoebozoa

80%

M AN U

Other Metazoa

70%

Platyhelminthes

60%

Nemertea

50% 40%

Nematoda

30%

TE D

September 2011

Proportion of Taxa

0% 100%

20% 10%

EP

0% 100%

90%

70% 60% 50% 40%

Myxozoa Mollusca Kinorhyncha Gastrotricha Echinodermata

AC C

January 2012

80%

Cnidaria Cephalochordata Brachiopoda

30%

Arthropoda

20%

Annelida

10%

Fungi

0% 100% 90% 80% 70% 60% 50% 40% 30% 20%

Mississippi Sound

FOCAL

OI PPN PPS WJ1 WJ2

0%

DI T10 T20

10%

ST7 ST6 ST5 ST4 ST3

April 2012

Blast Hit

Orange Beach

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Highlights

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Holistic approach to diversity and variation in meiobenthos through metabarcoding Mobile River Basin influence accounts for similarity in community composition Diatoms, annelids, copepods, and nematodes dominate northern GOM meiobenthos Within major taxonomic groups, dominant families varied with location Bulk density and percent inorganic carbon were correlated with community composition

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• • • • •