California State University, Northridge Biodiversity

CALIFORNIA STATE UNIVERSITY, NORTHRIDGE

BIODIVERSITY OF BENTHIC INVERTEBRATE COMMUNITIES IN THE

MOBILE-TENSAW DELTA REGION OF ALABAMA

A thesis submitted in partial fulfillment of the requirements

For the degree of Master of Science in Biology

Christine March

May 2015

ii The thesis of Christine March is approved:

______Dr. David Gray Date

______Dr. Peter Edmunds Date

______Dr. Steve Dudgeon, Chair Date

California State University, Northridge

iii

This is dedicated to Matt, my Mom, Dennis, and Elwood, with love.

Table of Contents

Signature Page iii

Dedication iv

Abstract vii

Chapter 1: Introduction 1

Chapter 2: Materials and Methods 11

Study System and Study Sites 11

Sampling and Laboratory Analysis 14

Data Collection 15

Data Analyses 16

Results 20

Chapter 3: Discussion and Conclusion 30

References 43

Appendix A 53

Appendix B 87

Appendix C 100

Appendix D 127

vi Abstract

BIODIVERSITY OF BENTHIC INVERTEBRATE COMMUNITIES IN THE

MOBILE-TENSAW DELTA REGION OF ALABAMA

Christine March

Master of Science in Biology

The benthic invertebrate macrofauna and larger meiofauna in the Mobile-Tensaw Delta was sampled from December 2010 to June of 2011 along a salinity gradient from the brackish waters of Mobile Bay up into the fresh waters of the Tensaw River. Community composition varied, in both composition and abundance, along the salinity gradient. The brackish regions were dominated by polychaetes and crustaceans. These benthic communities transitioned to oligochaete and chironomid larvae dominance as the salinity decreased. There was high abundance in the number of individuals and the number of higher taxa in the spring and summer months, while the winter had very few benthic invertebrates. The high level of variability at the study sites, over short time scales, suggested that the strength and frequency of biotic interactions is reduced and abiotic factors drive ecosystem dynamics. The realized niche overlap among benthic invertebrates varied through time, from periods of moderate of overlap, to very little overlap. As abiotic factors take precedence in shaping these benthic communities, vii

species distributions and abundances may become relatively independent of one another.

Abiotic effects have implications for how the food web in this tidal freshwater marsh is structured. If what is shaping benthic invertebrates niches in this area, biotic or abiotic variables, could be distinguished, it could indicate that this delta ecosystem may operate more as a mosaic of independent patches, as opposed to an interconnected ecosystem.

Many estuaries are believed to be highly interconnected habitats, not only in terms of food web dynamics and nutrient cycling, but in their ecosystem services to surrounding coastal areas and to humans. It is important to establish the role that abiotic and biotic factors play in shaping the distribution and abundances of the benthic invertebrate communities in this tidal freshwater marsh. Better knowledge about what shapes these freshwater systems can lead to better protection and more informed management choices.

viii

Chapter 1: Introduction

Estuaries and their accompanying coastal zones are characterized by dynamic gradients in chemical, physical and biological features (Ysebaert et al.1993). Estuarine ecosystems consist of complex combinations of several abiotic and biotic factors working together, making the conditions in each estuary unique (Levin and Talley 2000). The environment present at each site in an estuary can vary greatly throughout the day, week, month, and season (Teal 1962; Odum 1988; Levin and Talley 2000). The benthic macroinvertebrate assemblage found in each location is shaped by these changes in the environment. The differences in invertebrate community distribution are so clearly tied to environmental conditions, that the geological record of those communities has been used to show historical boundaries between freshwater and more saline conditions (Odum

1988). Trends in distribution and abundance can emerge when monitored in conjunction with abiotic and biotic factors.

Some trends of invertebrate community distribution that have been observed in regards to salinity are, that the more brackish and freshwater sites in an estuary tend to be dominated by insects and oligochaetes, while the more saline areas are polychaete dominated (Levin and Talley 2000, McLusky et al. 1993, Ysebaert et al. 1993). Due to their geography, estuaries are often composed of a mosaic of different regions, classified by salinity: freshwater and freshwater tidal marsh (≤ 0.5 ppt), oligohaline (0.5-5.0 ppt), mesohaline (5.0-18.0 ppt), polyhaline (18.0-30.0 ppt), the previous 3 classifications constitute “brackish” (0.5-30.0), and euhaline (30.0-40.0 ppt) (Cowardin et al. 1979;

Odum 1988) (Figure 1). These distinctions will be important later in reference to the sites involved in this study, most of which are considered freshwater tidal marshes, while a few were within the range of brackish.

Freshwater tidal marshes can go largely unstudied due to that fact that they lie somewhere between marine biology and limnology (Odum 1988; Ysebaert et al. 1993).

However, this is not to say that the areas are uninhabited or unutilized. They are prone to major urban development, as many people want to live near the water and the resources it provides. A large shipping channel in the Gulf of Mexico spans the area including

Mobile Bay, which requires maintenance dredging, and further increases the environmental impact that humans have in this region (Ellis and Dean 2012). The Gulf region is home to six of the ten most socioeconomically vulnerable coastal counties in the

United States (Ellis and Dean 2012). Wetland areas have been described as:

“significant to public or private water supply, to groundwater supply, to flood control, to storm damage prevention, to prevention of pollution, to protection of land containing shellfish, to protection of fisheries, and to protection of wildlife habitat. (Massachusetts

Wetland Protection Act, Massachusetts General Laws 1987).”

The level of importance that wetlands play in water supply varies greatly (Teal and

Howes 2000), however their multi-faceted values to humans cannot be denied.

These habitats play a critical role as nurseries for many marine species, and therefore in the transfer of energy invested in to coastal ecosystems offshore (Odum et al. 1984).

Tidal freshwater marsh habitat is used by various fish species, marine, estuarine, and freshwater, at some stage in their life history (Odum et al. 1984). The same is true for certain commercially important crustaceans (shrimp) and bivalves (oysters) (Odum et al.

1984). This means these estuarine areas are important to the health of local food webs, local fishing, and as regions of export to Mobile Bay and the Gulf of Mexico, where larger commercial fishing occurs. This makes tidal freshwater estuaries and their estuarine surroundings important study sites in terms of their environmental parameters, biodiversity, species abundances, and distribution.

Benthic infaunal invertebrates assemble into an important community in salt and freshwater tidal marsh habitats, especially because they serve as the base of coastal food webs (discussed below) (Levin and Talley 2000). Some species in the benthic invertebrate community are also important bioindicators, and are commonly used in aquatic health assessments as they are a critical component of any aquatic ecosystem, including rivers, bays, and estuaries (Baustian and Rabalais 2009). The information that can be gained from monitoring benthic macroinvertebrate communities has been widely used to measure environmental and biological trends, to track biological status of ecological conditions in estuaries over time, and often to assess the impacts of anthropogenic influences from industry, farming, and wastewater treatment (Engle and

Summers 1999). The Chironomidae family has multiple stages before adulthood, and represents the single largest family of aquatic insects in virtually every type of aquatic habitat (Coffman and Ferrington 1984; Odum 1988; Diaz 1989; Ysebaert et al. 1993;

Dickman and Rygiel 1996). Chironomid larvae have long been used as indicator species 3

used to gauge pollution or toxicology effects in freshwater and brackish habitats, as they are often found in unfavorable environmental conditions, such as anoxia, wastewater treatment effluent, or areas with heavy metal pollution, to name a few (Diaz 1989;

Dickman and Rygiel 1996; Seys et al. 1999; Chapman 2001). The morphological indicator of exposure to pollution in chironomids is varying levels of deformation in the mouthparts; this is used in conjunction with measured environmental parameters, such as sediment chemistry and/or water chemistry to determine affected areas in aquatic habitats

(Coffman and Ferrington 1984; Dickman and Rygiel 1996; Meregalli et al. 2000).

Oligochaetes have also been used as an indicator species in freshwater and tidal freshwater environments; they have been noted as the dominant species in many polluted areas (Diaz; 1989; Seys et al. 1999; Banks et al. 2013). While they do have species representatives in marine and brackish environments, oligochaetes are much more numerous in freshwater (Seys et al. 1999; Chapman 2001). Due to the burrowing behavior of many oligochaetes, they are also closely tied to sediment chemistry, organic pollutants, and oxygen levels (Lafont 1984; Seys et al. 1999). Aquatic oligochaetes are very useful as bio-indicators considering that they are widely distributed, important in aquatic food webs, well studied, and have a range of sensitivities to toxins and pollutants

(Chapman 2001). In addition to sediment chemistry, water depth and salinity levels shape oligochaete distribution, and should be considered in any assessment of benthic communities (Seys et al. 1999; Chapman 2001).

Oligochaetes and chironomids are not only important ecosystem health indicators, but they serve a vital role at the base of the detrital food chain. Oligochaetes and insects are 4

considered to be primary consumers in freshwater tidal marshes, with autochthonous micro and macroalgae serving as possibly the most important source of energy for these consumers (Odum 1988). Chironomids specifically are a very important food resource for fish, including perch, sunfishes, and killifish, but they can also serve as prey items to amphibians and predatory aquatic insects (Odum et al. 1984). Their large range and dense populations highlight why so many animals rely on chironomids as a food source.

Oligochaetes are an important prey item for some fish, but more so for shorebirds that can sift through the sediments (Odum et al. 1984; Chapman 2001; Shrama 2010).

Since Teal’s (1962) work in Georgia, it has been debated whether or not estuaries export more organic matter than they consume, and how best to categorize production in an estuary. This greater export of organic matter has been shown to be true for certain estuaries, or certain areas of the country, but as a generalization, this did not hold true for many estuaries. The biotic and abiotic variables present in estuarine habitats have strong surges, or “pulses”, of activity as opposed to maintaining a steady-state Odum (2000).

They go back and forth in an ebb and flow, in terms of factors such as species abundance, resource availability, nutrients, the tides, and the import and export of energy (Odum

2000). Due to the potential for frequent environmental changes, the food webs in estuaries can be complex or simple depending on the factors that shape each individual estuary system and each region of an estuary (Diaz and Boesch 1977; Odum et al. 1984).

Benthic invertebrates are often categorized by their diet and assigned a niche or functional group depending on that diet. Most benthic invertebrates are considered detritivores or omnivores, with a small group being carnivorous (Odum et al. 1984). 5

Overall, their diet places them at the base of the detrital food chain, and this position means that benthic invertebrates are essential to understanding food web and trophic energy relationships in their environment (Subrahmanyam et al. 1976). The remainder of the food web relies on benthic invertebrate abundances to supply energy to all other trophic levels (Odum et al.1984).

Benthic invertebrates play a valuable role in food webs; however they also have an important role in the biogeochemical processes of an estuary (Odum 2000). Many benthic invertebrates actively bioturbate the sediment as they feed, allowing for deeper penetration of oxygen within the sediment, which can impact biogeochemical cycling

(Mermillod-Blondin et al. 2005; Baustian and Rabalais 2009). The tube-building nature of many benthic invertebrates also increases the penetration of oxygen and other nutrients into the sediment; but additionally, meiofauna can penetrate the sediments more easily

(Pennings and Callaway 1992; Newell and Porter 2000). The structure added to the sediments by tube-building, along with the deeper penetration of nutrients, can in turn affect the plant community (Pennings and Callaway 1992; Newell and Porter 2000).

Wetland plant communities also impact all of these cycles and this feeds back to influence the composition and distribution of benthic infaunal communities. Plants serve as an important food source for other invertebrates and fishes; functioning in both the detrital and trophic food webs (Moore et al. 2004; Butler et al. 2008). Plants can influence infauna through above and below ground structure, flow modification, sediment accretion, moisture, oxygenation and they set up zonation in accordance with salinity

gradients (Dejong 1978; Pennings and Callaway 1992). While it is clear that salt marsh plant zonation is dependent on a myriad of factors, such as competition and differing tolerances to physical factors, salinity is always an important one (Pennings and

Callaway 1992), and the species present in estuaries are often very different in saline versus freshwater areas (Odum 1988). Therefore, the salinity directly affects the composition of the plant community, which indirectly affects the composition of the benthic invertebrate community. Later, I will go briefly in to discussing general trends for the purpose of highlighting potential indirect effects the plant community may have had on the benthic invertebrate community. However, due to the potentially significant influence plants can have on this community, it is something that should be examined in depth in future studies.

The roles that benthic invertebrates play in estuarine environments are well defined and numerous. Are their niches determined only by a set of abiotic environmental variables (i.e., the fundamental niche, sensu Hutchinson 1957), or do biotic interactions distinguish their realized niche from their fundamental niche? There is so much environmental variability, it seems plausible that their changing distribution and abundance in space and time (their realized niche) corresponds to their respective fundamental niche. Addressing these questions was one of the major goals of this study.

The study sites were located along a salinity gradient, which made examining salinity a focus in this study. Invertebrate species turnover rate was calculated between regions to highlight patterns of benthic invertebrate diversity. Salinity changes depending on input from freshwater and saltwater sources. These changes often occur daily on a small scale, 7

but occur on a larger scale over seasons as well (Odum 1988). I hypothesized that the pattern of invertebrate diversity and community composition would change along the salinity gradient. Past studies have shown that tidal freshwater marshes can have lower diversity than the habitats that lie upstream and downstream from them (Diaz 1977; Diaz and Boesch 1977; Odum et al. 1984). This study examined the tidal freshwater marsh region and the areas downstream. Invertebrate species turnover rates (beta-diversity) were calculated between regions to highlight patterns of benthic invertebrate diversity.

However, salinity is not the only parameter that has daily to seasonal shifts, nor is it the only one shaping these complex communities. Dissolved oxygen (DO), pH, water temperature, and redox potential (ORP) also play potential roles in shaping these communities. Estuarine invertebrates can be sensitive to changes in these variables as well. I also hypothesized that these other variables may be important in shaping the composition of the benthic invertebrate community, and these variables were examined for their contribution as well. Invertebrate species abundances were analyzed in relation to each environmental parameter measured, individually, and as a set of variables in every region.

Loss of dissolved oxygen and hypoxia, often due to salinity stratification, has been a problem in the Mobile Bay area for decades (May 1973; Rabalais et al. 2001; Turner et al. 1987 & 2008; Rabalais 2009; Ellis and Dean 2012). Salinity stratification occurs from denser salt water sinking below a layer of fresher water, and temperature is often a factor as warmer water holds less oxygen. As an example, in Mobile Bay, there has been an event, referred to as the “Jubilee”, that has occurred many times over the years (Loesch 8

1960; May 1973). It is essentially a salinity stratification event that occurs in the summer, where many of the fish and crustacean species present in Mobile Bay temporarily force themselves into the shallows, presumably to escape the hypoxic conditions present in the Bay (Loesch 1960; May 1973). Dissolved oxygen is an extremely important factor in shaping estuarine communities and an indicator of ecological conditions in many environmental monitoring programs (Engle et al. 1999).

There is a connection between lowered DO and increased salinity, though this can be impacted by other processes such as temperature (Engle et al. 1999; Park 2007). Water temperature and pH, are important variables in estuaries, because they can affect survivorship and reproductive success for many invertebrate species (Engle et al. 1999;

Prather et al. 2013). Redox potential is an expression of an environments’ tendency to either gain or lose electrons. It is tied to pH, but is also critical in a number of biogeochemical cycles that occur in aquatic environments (Odum 1988). The sediment redox potential can affect the depth that infauna can penetrate sediment or change the availability of nutrients to the plant community (Mitsch and Gosselink 1986). There are many different environmental variables that can affect the distribution and diversity of benthic invertebrate communities, however the parameters measured in this study were salinity, pH, water temperature, DO, and ORP. It was important in this study to determine of there were any interactions between these variables that could be shaping the communities in this estuary, and see how these variables change on a spatial and temporal scale in this area. Given the multitude of variables measured, it was a goal of

this study to determine the contribution that each abiotic variable had in determining the invertebrate distributions and abundances.

Salt marsh and estuarine environments have been studied for many years, but tidal freshwater environments have received less attention, however, they are essential components of many estuaries (Odum 1988, Ysebaert et al. 1993). This has left a gap in the knowledge about what shapes tidal freshwater benthic invertebrate communities and the role that invertebrates play in the environment. The combined species abundance, community composition, and environmental data were used to examine the biodiversity of this tidal freshwater marsh and how it changes among and within communities along a salinity gradient that covaries with other environmental variables. I hypothesized that the benthic invertebrate communities would change in composition and abundance depending on the season as well (spring, summer, and winter). Determining seasonal changes to the community, along with determining the composition and abundances in relation to salinity and other environmental variables (water temperature, pH, dissolved oxygen, and redox potential) were major goals of this study. It was important to understand how various environmental factors and seasonal changes can impact benthic invertebrate communities, diversity, and their distributions, especially in these lesser studied tidal freshwater marsh habitats. Awareness of how these communities are shaped and patterns of distribution could lead to a better understanding of spatial and temporal variability in this delta ecosystem

Chapter 2: Materials and Methods

Study System and Study Sites

The estuary studied lies inland of Mobile Bay in the Mobile-Tensaw delta region near

Mobile, Alabama. Mobile Bay itself is a large triangular shaped estuary system in the

Northern Gulf of Mexico (Park et al. 2007). Other than a shipping channel, it is wide and shallow, having an average depth of about 3 meters (Schroeder et al. 1990). It is bordered by the FORP Morgan Peninsula to the east and Dauphin Island to the west, and receives input from the Mobile and Tensaw Rivers to the north. Mobile Bay is the sixth largest river basin in the United States, and receives the fourth largest river discharge in the continental United States (Sturm et al. 2007; Park 2012). It receives an influx of saltwater from the Gulf of Mexico, along with constant freshwater input from the Mobile and Tensaw Rivers of Alabama. In general, the climate of the Gulf of Mexico coastline is subtropical with fairly high humidity year-round, warm to hot summers, and cooler winters (Ellis and Dean 2012). In terms of precipitation, Alabama receives approximately 1.65 m/year of precipitation along the coast, and approximately 1.35 m/year in the interior, with more falling in the northern regions (Ellis and Dean 2012).

In the study area specifically, there is a peak in rainfall in the spring, which leads to a peak in river discharge events sometime between December and May (Park 2012). In contrast, the lowest rain and river discharge occur from June to November (summer and fall seasons) (Park 2012). Mobile Bay follows a predominantly diurnal tidal cycle, exhibiting a micro-tidal range from <0.1 meters during equatorial tides (diurnal tidal

inequality is at a minimum) and 0.8 meters during tropic tides (diurnal tidal inequality is at a maximum) (Park 2012).

Samples were collected from the area of Mobile Bay from near the mouth of the bay entering the Gulf of Mexico, and up into the Tensaw River (Figures 2-6). These sites were initially chosen as part of project to determine the effects of oil contamination after the Deepwater Horizon oil spill, if hurricane conditions had brought oil into Mobile Bay and the Tensaw River. However, the oil influx did not occur as no hurricanes have occurred, leaving the sites available for general study of the effects of environmental variation. Each site was paired with another site from within the region, based on the prediction that one site from each pair would endure less destruction in the event of a hurricane. It was hypothesized that the pairs were similar in environmental conditions and species distribution. It was one of the goals of this study to estimate how well the pairs were matched to one another (1-3, 2-4, 5-8, 6-7, 9-11, 10-12, 13-14, 15-16).

Sites were also chosen based on previous research sites that were used for long-term plant community cover data collection. However, these previous sites did not cover a broad salinity range to thoroughly represent a gradient and a variety of distances from the

Gulf, so additional sites were sampled (to total 16). A wider gradient was favored to allow for a more thorough representation of invertebrate species distributions across a wider number of salinities to better address salinity’s role in shaping these communities.

A wider gradient also allowed for a larger sample size and a better representation of the

patterns of invertebrate diversity and how they changed from freshwater, to brackish, to saline.

Environmental parameters measured at each site were surface water temperature, dissolved oxygen, salinity, redox potential, and pH. Three seasons were represented with sample collections during three months: December of 2010 (winter), early May of 2011

(spring), and late June of 2011 (summer). Four regions of Mobile Bay were designated for sampling to cover a salinity gradient from saline to freshwater. “Lower” region (L) was near the mouth of Mobile Bay, and the most saline. Inland from there, with decreasing salinity, was “Lower Middle” region (LM), near the top of Mobile Bay,

“Upper Middle” region (UM), near the confluence of the Tensaw River with Mobile Bay, and “Upper” region (U), in the Tensaw River in the middle of the Delta. Within each region, there were four sites: Lower region consisted of sites 13-16 (Figure 3), Lower

Middle region consisted of sites 9-12 (Figure 4), Upper Middle region consisted of sites

5-8 (Figure 5), and Upper region consisted of sites 1-4 (Figure 6). Four samples were taken from plots (1m2 with ≥5 m between plots oriented parallel to the shore) at each site, yielding sixteen samples per region per month. All regions were sampled in all three seasons except Lower region in December 2010. High northerly winds pushed out much of the water from the Bay and made it too shallow for a boat to access the sites. In these exact locations (within Mobile Bay and the Tensaw River), there is not much access to historical data for benthic invertebrate communities. It is my hope that these data can be used as a baseline survey for the area, and that it will continue to be assessed and

monitored. Additionally, it will provide much needed information about freshwater tidal marsh habitat.

Sampling and Laboratory Analysis

At each plot, a sediment sample was taken and a handheld water quality sonde YSI

Model 556 was deployed approximately 25 cm above the substrate, at each collection site to record water temperature (hereafter referred to as temperature), salinity, pH, dissolved oxygen (DO), and redox potential (ORP). A Ponar grab (0.05 m2) was used to take a sediment sample at a minimum depth of 10 cm in clay/silt substrate, or 5 cm in sandy substrates adjacent to each plot. Samples were transported to the laboratory at the

University of South Alabama where they were sieved, fixed, stained, and preserved. A total of 250 mL was kept from each sediment sample. The station identification, water depth, depth of sediment penetration, sediment texture and color, and time of collection, weather conditions, region/site/plot number, and any comments were recorded for future reference.

At the lab, each sample was gently washed through a nested 1 mm and 0.5 mm sieves. The cleaned material retained in each screen was gently washed into separate, labeled sample jars. A relaxant (0.37 M MgCl2 in fresh water) was added to the sample for 15-30 minutes. Then, the relaxant was poured off (using a screened lid, 0.25 mm mesh) prior to being fixed in 10% buffered formalin with 2-3 drops of Rose Bengal solution. Within 72 hours, the samples were rinsed in tap-water and transferred to 50 mL

Falcon tubes with 70% ethanol for storage. All sediment samples were collected and 14

processed according to the San Francisco Estuary Wetlands Regional Monitoring

Program Plan: “Data Collection Protocol Tidal Marsh Benthic Community”, documented by Sarah Lowe at San Francisco Estuary Institute; Richmond, CA (2002).

Data Collection

To process each sample, the volume of sediment (particles > 0.5 mm) leftover from sieving the original 250 mL was recorded. Therefore, while each sample initially started as 250 mL at the time of collection, differences in sediment type and grain size, left each sample with a different final volume after sieving. This was addressed later in statistical analyses with sample standardization. For each sample, all of the contents were sorted through in the 70% ethanol solution; there was no sub-sampling. This sediment was sorted for all benthic invertebrates >0.5 mm in length. Every organism with a discernible

“head-end” present in the sample was accounted for and identified to species level, if possible. Non-head and fragment sections were not counted nor identified. Sorting was done using a Leica S6E dissecting scope (6.3:1 zoom with magnification 6.3X-40X) with a Dolan-Jenner Industries Fiber-Lite MI-150 High Intensity Illuminator light source.

When this was not enough magnification, an AmScope M158C compound scope (40x-

1000x magnification) was used to try and resolve identification issues. Where this was not possible, due to damage, juvenile status, or inability to discern identifying factors, the next highest taxonomic resolution possible was determined. Each sediment sample was returned to its 50 mL Falcon tube in 70% ethanol, and the individuals found were put into a separate tube in 70% ethanol. A voucher specimen for each species, and some higher

taxonomic levels, were permanently archived in a separate tube in 70% ethanol and with a photograph (AmScope M158C with ToupView software), for any future reference or identification. Therefore, the raw data consisted of the numbers of individuals of every taxon present in each sample. The raw data for the environmental variables can be found in Appendix D. The raw data for taxonomic counts will be available in an Excel compatible spreadsheet at a data repository at scholarworks.csun.edu.

Data Analyses

The species counts (number of individuals per m2) were set up in a matrix and standardized to compensate for the different sediment volumes present in all the samples.

These scaled numbers were used for all analyses and results. Abundances based on average number of individuals present by region and month were plotted to compare overall abundances by region and month (Figure 7). Higher taxonomic groups were plotted along with the average salinity by region to examine how community composition changed from region to region in relation to salinity, and to test the hypothesis that benthic invertebrate communities would change along a salinity gradient (Figures 8-10).

Whittaker’s beta diversity (βW) was calculated to determine the species turnover between regions along the salinity gradient, and to test the hypothesis that benthic invertebrate communities would change along the salinity gradient (Figure 11). Plots were made to visualize how well site pairs were matched to one another by individual environmental parameters (salinity, DO, temperature, ORP, and pH). Plots were made for each environmental variable by month, and for every site pair sampled in that month (Figures

12a-16c). This was used to test the hypothesis that the sites within each pair were representative of one another. All of the environmental and species abundance data were analyzed using the Primer-E (version 6.0) statistical software package. Significance values for all tests were set at α=0.05.

The Expectation-Maximization Algorithm (EM Algorithm) was used to estimate the values of missing data in some months based on the other data. After examination of the distributions for each environmental variable, the data for salinity were natural log transformed to improve symmetry and normality. The other variables did not require transformation. However, all variables were normalized prior to a Principal Components

Analysis (PCA) so they could be analyzed using the correlation matrix among the variables. To discern which environmental variables explained the majority of variance among experimental plots, (PCA) was done on the correlation matrix of the entire environmental data set (temperature, salinity, pH, dissolved oxygen, and redox potential)

(Figure 17). This was important to test the hypothesis that salinity played an important role in shaping the benthic invertebrate community, and to determine the role that the other environmental variables played as well.

Next, Principal Coordinates Analysis (PCO), Permutational Multivariate Analysis of

Variance (PERMANOVA), and Permutational Dispersion Analysis (PERMDISP) were performed on the environmental data and the species abundance data. PCO was done to test if there was variability in the species compositions between the regions, and also to determine which taxa were the most influential in shaping the invertebrate communities

present in the different regions (Figure 18). This was necessary to test the hypothesis that the communities would change from region to region along a salinity gradient.

PERMANOVA was used to detect between group versus within group variations, for both community composition and environmental variables, in relation to region, month, and site. This was another test that was important in determining how the communities changed from region to region. PERMANOVA was also necessary to establish that the regions were different statistically different from one another in relation to environmental variables. PERMANOVA was chosen because it was the test best suited to represent the

3-factor experimental design (region, site, and month). It was used to test the interactions among the 3 factors and to see how community structure and environment changed from region to region. PERMDISP was used to test for homogeneity of multivariate dispersion across groups, whether observed differences were due to differences in location or differences in dispersion. PERMDISP was used to test the hypothesis that the dispersion effects within the groups for all 16 sites would be different from one another in terms of invertebrate abundances and environmental variables, and different between seasons as well. Analysis of Similarity (ANOSIM) was done to examine nested comparisons of the

3 different factors and statistically quantify patterns observed in nMDS ordination plots.

However, ANOSIM can only test 2 factors at a time, and therefore it did not fully represent the experimental design. The ANOSIM results can be found in the Appendix.

Euclidean distances were used for the environmental data. Bray-Curtis similarity was used for species abundance data.

Nonmetric Multidimensional Scaling (nMDS) was performed on the environmental data. The nMDS plot of the environmental variables, pooled across months) represents all pairwise Euclidean distances among samples to show which regions and sites differed from one another and which variables were driving that variation. The nMDS was used to see how different the environmental parameters of the four regions were from one another, and to assess whether they changed through time with respect to species data and environmental variables. In other words, they were used to visualize which environmental variables were driving benthic invertebrate community species composition at the different sites (Figure 19). Another nMDS plot was made of dissimilarities of Euclidean distances among environmental variables measured at all 4 regions by month to show seasonal changes by region (Figure 20). A final nMDS was done for environmental variables, based on Euclidean distance, to show seasonal changes in environmental variables (Figure 21). Similarity Percentage (SIMPER) analyses were performed to define the variables that contributed to the clustering groups in comparisons among regions and months. A Biota and Environment Matching Analysis (BEST) was done as a hypothesis test to compare the species and environmental matrices, one for species data (Bray-Curtis similarity) and one for environmental data (Euclidean distances). It estimated the correlation between the distances in the two matrices, where a significant positive result between the two would show an association between changes in the communities with corresponding changes of the best fitting environmental variables responsible for that change, effectively linking the environmental variables to the community structure.

Results

A total of 166 samples were taken in all three seasons, from all 4 regions, which yielded 5,273 individual specimens (from all 3 months, all 4 regions, all 16 sites combined). Every individual was identified to the lowest taxonomic classification possible; yielding 33 separate species, 39 genera, 40 families, and 23 orders, from 5 phyla. A species accumulation plot showed that the sampling effort sufficiently captured the expected numbers of species (Michaelis Menten: Smax = 69.68, max # of spp. expected in Delta, B = 32.03) (Figure 22). The sum of the number of species and other taxonomic groups found indicated that the sampling effort sufficiently captured the diversity for the area.

Overall, there was a lot of variability between the 16 sites. PERMDISP analysis showed that dispersions for invertebrate species abundance at the 16 sites were significantly different from one another (F=13.81, df1=15, df2=147, PPERM=0.001) (Table

1), as well as regions (F=22.267, df1=3, df2=159, PPERM =0.001) (Table 2). The differences among regions (Upper, Upper Middle, Lower Middle, Lower) were more pronounced than the differences between the sites within those regions given their locations along the salinity gradient. Pairwise tests showed significant differences in dispersion of invertebrate abundances across months, with December being less than both

May (PERMDISP, t=9.6373, PPERM =1E-3) and June (t=8.8548, PPERM =1E-3) (Table 3).

However, May and June were not significantly different from one another (PERMDISP, t=0.86088, p=0.52), (F= 43.372, df1=2, df2=160, PPERM = 0.001) (Table 3). Abundance

was highest in May of 2011 (spring), and specifically in the Lower region with 2,219 individuals over all sites 13-16 (Table 4). This was followed by Lower Middle region, sites 9-12 (405 total individuals), Upper region, sites 1-4 (400 total individuals), and

Upper Middle region, sites 5-8 (144 total individuals) (Table 4). May 2011 also had the most individuals present when all four regions (Upper, Upper Middle, Lower Middle,

Lower) were summed, with a total of 3,167 individuals (Table 4). June 2011 abundance was highest in the Lower Middle region with 1,152 individuals (Table 4). The Upper and

Lower regions each had approximately 439 individuals in June (Table 4). December 2010 abundance was extremely low, with only 17 individuals present in Upper, Upper Middle, and Lower Middle regions combined (Table 4). Even though the Lower region was not represented in December, from the loss of abundance in the other 3 regions, it is reasonable to extrapolate that the Lower region would have exhibited this trend as well.

The highest taxonomic abundance was found in the Lower region in June 2011, with an average of 4.8 species present per site (Figure 10). June 2011 had average salinities of

0.08 ppt in the Upper region, 0.18 ppt in the Upper Middle region, 0.78 ppt in the Lower

Middle region, and 15.44 ppt in the Lower region. This was followed closely by the taxonomic abundance in the Lower Middle region in May 2011 (Figure 9) (4.73), the

Lower Middle region in June (4.6), and the Upper region in June 2011 (4.5). The average salinities in May 2011 were 0.07 ppt in the Upper region, 0.12 ppt in the Upper Middle region, 0.29 ppt in the Lower Middle region, and 16.85 ppt in the Lower region. In May of 2011 (Figure 9), the dominant taxa present in the Upper region were insect larvae (164 individuals) and oligochaetes (138 individuals), with some polychaetes (86 individuals) 21

present as well. In May 2011, the Upper Middle region showed trends similar to the

Upper region, with oligochaetes (46 individuals) and insect larvae (49 individuals) being dominant; in addition, there were a large number of nematodes and crustaceans in the

Upper Middle region. The Lower Middle region still had abundant oligochaetes (130 individuals), but had less insect larvae (93 individuals) and many more crustaceans (143 individuals) in May 2011. The Lower region, the most saline of all the regions, had some polychaetes (168 individuals) and insect larvae (75 individuals), but was dominated by crustaceans (1,888 individuals) in May 2011. In June 2011, Upper and Upper Middle regions maintained comparable trends to May, with insect larvae (196 individuals in the

Upper, 29 in the Upper Middle) and oligochaetes (224 individuals in the Upper, 18 in the

Upper Middle) remaining the dominant taxa (Figure 9). The Lower Middle region shifted in June 2011, with insect larvae (274 individuals), oligochaetes (72 individuals), and polychaetes (82 individuals) still present, but crustaceans were the dominant taxa

(616 individuals). The Lower region, polychaetes were dominant in June (310 individuals), and far less crustaceans were present (34 individuals). As previously mentioned, December 2010 was devoid of most abundance with only 17 individuals present among the 3 sampled regions (Figure 8). In December 2010, the Upper region had 1 bivalve, 1 oligochaete, and1 insect. The Upper Middle region had 1 oligochaete and 5 polychaetes. The Lower Middle region had 4 insects, 2 oligochaetes, and 2 polychaetes. The Lower region was not sampled in December 2010. The average salinities in December 2010 were 0.12 ppt in the Upper region, 0.15 ppt in the Upper

Middle region, and 0.05 ppt in the Lower region. Plots of each month provide a clear

visualization of the taxonomic trends present in each region during the spring, summer, and winter, and highlighted that community diversity was not as reduced in brackish areas.

Whittaker’s beta diversity (βW) showed the highest turnover between regions for

December 2010, however, not all regions were sampled for this month, and abundance were the lowest in winter as well (Figure 11). There was less species turnover in May and June of 2011, when all regions were sampled, and abundance and diversity were relatively high (Figure 11).

The plots for site pairs by salinity (ppt) for December 2010 showed that sites 01-03 and sites 02-04 were not different from their mate, but sites 05-08 and sites 07-06 were different from their mate (Figure 12a). In May 2011, every site within each pair was not different from its mate in terms of salinity (Figure 12b). In June 2011, every site within each pair was not different from its mate in terms of salinity, except 16-15 (Figure 12c).

The plots of site pairs by water temperature (˚C) for December 2010 showed that site pair

01-03 were not different from each other, but that 02-04, 08-05, and 07-06 were different from their mates (Figure 13a). In May 2011, the site pairs that were not different from their mates were 01-03, 02-04, 07-06, and 12-10; however, 08-05, 11-09, and 14-13 were different from their mates in terms of temperature (Figure 13b). In June 2011, site pairs

01-03, 02-04, 08-05, 11-09, and 12-10 were not different from their mates; site pairs 07-

06, 14-13, and 16-15 were different from their mates in terms of water temperature

(Figure 13c). The plot of site pairs by dissolved oxygen (mg/L) for December 2010

showed that site pairs 01-03, 08-05, and 07-06 were not different from their mates, but that 02-04 were different from one another (Figure 14a). In May 2011, sites pairs 01-03,

02-04, and 07-06 were not different from their mates, while 08-05, 11-09, and 12-10 were different from their mates in terms of dissolved oxygen concentration (Figure 14b). In

June 2011, site pairs 01-03, 02-04, 08-05, 07-06, 14-13, and 16-15 were not different from their mates, while 11-9 and 12-10 were different from their mates in terms of dissolved oxygen concentration (Figure 14c). The plot of site pairs by pH for December

2010 showed that 01-03 and 02-04 were not different from their mates, but 08-05 and 07-

06 were different from their mates (Figure 15a). In May 2011, site pairs 01-03, 02-04,

07-06, 12-10, and 14-13 were not different from their mates, while 08-05 and 11-09 site pairs were different from their mates in terms of pH (Figure 15b). In June 2011, site pairs 01-03, 02-04, 07-06, 14-13, and 16-15 were not different from their mates, while

08-05, 11-09, and 12-10 were different from their mates in terms of pH (Figure 15c).

The plot of site pairs by redox potential (mV) for December 2010 showed that 02-04 and

07-06 site pairs were not different from their mates, but 01-03 and 08-05 were different from their mates (Figure 16a). In May 2011, site pairs 01-03 and 07-06 were not different from their mates, while 02-04, 08-05, 11-09, 12-10, and 14-12 were different from their mates in terms of redox potential (Figure 16b). In June 2011, site pairs 02-04,

08-05, and 07-06 were not different from their mates, while 01-03, 11-09, 12-10, 14-13, and 16-15 were different form their mates on terms of redox potential (Figure 16c).

The Principal Components Analysis (PCA) for the environmental data at all 16 sites, across 4 regions for all 3 seasons, showed that 85.2% of the variance between regions 24

could be cumulatively explained by PC1, PC2, and PC3 (Figure 17). Eigenvectors are interpreted in order of descending absolute value. Eigenvalues (Table 5) showed that

PC1 alone explained 37.8% of variation seen between the regions. PC1 was represented pH (Eigenvector=0.566), salinity (Eigenvector=0.562), temperature (Eigenvector=0.502),

DO (Eigenvector=-0.212), and ORP (Eigenvector=0.181) (Table 6). PC2 represented

25.4% of the variance between regions, and was primarily representative of ORP

(Eigenvector=-0.615), DO (Eigenvector=-0.576), temperature (Eigenvector=0.453), pH

(Eigenvector=-0.214), and salinity (Eigenvector=-0.198) (Table 5-6). PC3 was representative of 22% of the variance, and was represented by ORP (Eigenvector=0.514), pH (Eigenvector=-0.470), salinity (Eigenvector=0.252), temperature (Eigenvector=-

0.220), and DO (Eigenvector=-0.0634) (Table 5-6). The final portion of the variation between regions, PC4 and PC5, are not shown on the plot. However, PC4 comprised

10.3% of the variance, represented by temperature (Eigenvector=0.584), salinity

(Eigenvector=-0.574), ORP (Eigenvector=0.565), DO (Eigenvector=0.078), and pH

(Eigenvector=-0.069) (Table 5-6). PC5 represented 4.4% of the variance, and was represented by pH (Eigenvector=0.639), salinity (Eigenvector=-0.467), DO

(Eigenvector=-0.464), temperature (Eigenvector=-0.392), and ORP (Eigenvector=0.073)

(Table 5-6).

The 3-factor PERMANOVA of environmental variables across region (fixed), site

(random), and month (fixed) showed significant variation among the 4 regions (Pseudo-

F(3, 12)=4.98, PPERM =0.003), and that the effect of region was significant (Table 7).

PERMDISP average dispersion among environmental variables, and across regions 25

showed that variation among environmental variables was significantly different among all regions (F=31.481, df1=3, df2=172, PPERM =0.001) (Table 8). PERMANOVA pairwise t-tests for environmental variables across months were significant, and showed that all seasons were different from one another; Dec, May (t=3.6354, PPERM =0.001);

Dec, June (t=7.0122, PPERM =0.001); and May, June (t=2.6604, PPERM =0.003), but that

December was the most different from both May and June (Table 9). The PERMDISP for environmental variables across all 16 sites showed that most sites had significant differences in their environmental variables from one another (F=11.444, df1=15, df2=160, PPERM =0.001) (Table 10). PERMDISP confirmed significant differences in environmental variables between December and both May and June, however May and

June were not significantly different from one another (Table 11).

December (winter 2010) had the coldest temperatures compared to spring or summer, in all regions measured (Table 12). The temperature in the Upper region was the warmest region in December 2010 with an average of 16.6 ˚C, the Upper Middle region average was 15.5 ˚C, and the Lower Middle was the coldest region in December 2010 with an average of 13.9 ˚C (Table 12). May 2011 average temperatures were much warmer than winter with an average of 24.6 ˚C in the Upper region, 26.9 ˚C in the Upper

Middle region, 23.5 ˚C in the Lower Middle region, and 28.3 ˚C in the Lower region

(Table 12). The Lower region was the warmest region in the spring of 2011. While June had the warmest water temperatures, they were comparable to May. For June 2011, average temperatures were 31.0 ˚C in the Upper region, 30.2 ˚C in the Upper Middle region, 28.3 ˚C in the Lower Middle region, and 30.1 ˚C in the Lower region (Table 12). 26

The Upper region was the warmest region in the summer of 2011; however, the other regions were not much cooler.

Salinities in December 2010, for the 3 regions measured, were the Upper region=0.12 ppt, the Upper Middle region was the most saline (0.15 ppt), and the Lower Middle region was the least saline (0.05 ppt) (Table 12). May 2011 salinities were measured in all 4 regions were as follows: the Upper region was the least saline (0.07 ppt), Upper

Middle region (0.12 ppt), Lower Middle region (0.29 ppt), and the Lower region was the most saline (16.85 ppt) region for spring 2011 (Table 12). The Lower region in May

2011 had the highest average salinity for any of the regions in all seasons (16.85 ppt).

June 2011 salinity averages were a bit higher than in May, except in the Lower region; the Upper region was the least saline (0.08 ppt), Upper Middle region (3.16 ppt), Lower

Middle region (0.78 ppt, and the Lower region was the most saline (15.4 ppt) region in summer 2011 (Table 12). Overall, the Lower region exhibited the largest changes in salinity of all the regions.

DO remained fairly constant within each region over all seasons (Table 12). The average DO values for December 2010 were as follows, Upper region (7.08 mg/L),

Upper Middle region (6.96 mg/L), and Lower Middle region (6.98 mg/L), with the Upper region having the highest amount of DO in the winter of 2010 (Table 12). The average

DO values for May 2011 were 7.42 mg/L in the Upper region, 8.14 mg/L in the Upper

Middle region, 7.71 mg/L in the Lower Middle region, and 5.44 mg/L in the Lower region, with the Upper Middle region having the highest level of DO, and the Lower

region having the least in the spring of 2011 (Table 12). The average DO values for June

2011 were lower than the other two months: 5.96 mg/L in the Upper region, 5.57 mg/L in the Upper Middle region, 3.85 mg/L in the Lower Middle region, and 5.53 mg/L in the

Lower region (Table 12). In summer 2011, the Lower Middle region had the lowest average DO (3.85 mg/L) of any region in all three seasons.

The pH in the Upper region remained between 7.6-7.9 across all 3 seasons (Table 12).

The Upper Middle region ranged between 7.04-7.76 across all 3 seasons (Table 12). The

Lower Middle region had the largest range across 3 seasons in this region, from 5.88-8.09

(Table 12). The Lower region ranged from 8.20-8.33, but was only measured in May and

June 2011 (Table 12).

The principal coordinates analysis (PCO) showed that the four most influential taxa in shaping the benthic invertebrate communities by region were Apocorophium louisianum,

Chironomus sp. larvae, and Alitta succinea (Figure 18). The chironomid larvae and the oligochaetes were the most abundant species in the Upper and Upper Middle regions.

They also were present in the Lower Middle region. In the Lower Middle region species dominance began to shift more towards the amphipod, A. louisianum, and the polychaete

A. succinea; both of which were very abundant in the Lower region. The Upper region was the most freshwater of the regions, transitioning down to the brackish Upper Middle and Lower Middle regions, and into the more saline Lower region. These particular species are the most influential in explaining the shifts from freshwater species to more saline tolerant species, and the variation between the regions over the 3 seasons.

The Scatterplot Matrix (SPLOM), along with a correlation matrix, showed that none of the variables have strong correlations with one another, only some weak interactions between pH and temperature (Figure 23). This showed that the variables were not redundant, and each had an independent contribution in shaping the community. BEST

Analysis showed that, for these data, the environmental variables that most correlated with the community structure were salinity and DO (R=0.232, p=0.01) (Table 13).

Chapter 3: Discussion and Conclusion

This study is one of the first to address benthic macroinvertebrate community composition in this particular part of the Mobile-Tensaw Delta, mainly in the tidal freshwater marsh region. Past studies of benthic communities in Gulf of Mexico estuaries have gone in one of two directions: either identifying the environmental factors that influence benthic community structure, or evaluating the health (abundance and diversity) of benthic communities as an indication of environmental disturbances from natural or anthropogenic sources (Engle et al. 1994). While this study addressed environmental variables that shape benthic invertebrate community structure along a salinity gradient, it also addressed the potential effects of the high level of variability that these animals experience. Later, I will discuss some of the potential repercussions of these data in light of niche theory and future research.

Of the parameters measured in this study, salinity, ORP, and temperature were the main environmental variables driving invertebrate community structure. One of the major trends that emerged in light of these variables was that the freshwater sites were dominated by oligochaetes and insect larvae. As the salinity increased, the community composition became dominated by polychaetes and crustaceans. These community trends in relation to salinity have been well established in other estuarine areas as well

(Odum 1984, 1988, 2000).

There were also seasonal trends that occurred along the salinity gradient. May and

June of 2011 showed high levels of species abundance and taxonomic diversity. May 30

2011 in the Lower region showed the highest number of individuals present at any region throughout the study. The spring and summer are common times for there to be a pulse in productivity within estuaries (Odum 1988). The Lower region in May of 2011 was also had the highest salinity of any region during the study. This is an expected result as there have been many studies that suggest that the higher salinity areas of estuaries often boast the highest number of species and diversity when compared to the freshwater tidal marsh areas (Odum 1984, 1988). However, the Upper Middle region in May 2011, had the most diversity. This however was not the case in June of 2011, when the region with the highest number of taxa was the Lower Middle region, with a salinity of less than 1.0 ppt. The Lower and Lower Middle regions both had the highest diversity in June 2011.

This salinity was not drastically different from the May 2011 salinity in Lower Middle region; however the number of individuals almost tripled. This may have been due to a combination of other favorable environmental conditions, aside from salinity, in the region at that time. Insect larvae and crustaceans were the most abundant taxa at the

Lower Middle region in June 2011. The insect larvae showed high abundance in May and June of 2011, but it is important to note that they are ephemeral groups that only live aquatically for a relatively short time (changes depending on species) before maturation

(Coffman and Ferrington 1984). The crustaceans that showed the highest abundance at that time were amphipods. Amphipods are not as ephemeral as insect larvae, but they do have a life cycle that is often completed in a years’ time, with higher abundances in the spring and summer being common (Pardal et al. 2000).

The temperatures at all regions increased in the spring significantly from winter (+10-

15 ˚C); however, the temperature increases from spring to summer were not as drastic

(+2-5 ˚C). The warmer temperatures did not seem to have negative effects on taxonomic diversity or abundance, as the highest values for these were seen in the spring and summer. This may indicate that the taxa present here have reproductive cycles that are timed with the warmer weather in the Delta. The dissolved oxygen and redox potential were influential in shaping the benthic invertebrate communities for these regions. DO levels were not low enough at any site to create hypoxic conditions during this study period (anoxia= 0 mg/L; hypoxia= <2 mg/L), which may have benefitted the local taxa, as higher water temperatures can be associated with anoxia in the Mobile Bay area (May

1973; Park 2012). Additionally, with the majority of the sites being mostly freshwater, up the Tensaw River, salinities were far too low for stratification to occur. For the more saline sites, the lack of stratification could have been due to the water temperature not being high enough to produce salinity stratification, or the winds may have been high enough to produce vertical mixing in the shallow waters that kept DO raised (Schroeder

1990; Park 2012). The river discharge in this delta is the highest from December to May, and this higher flow may have contributed to the normoxic conditions in the tidal freshwater marsh and the more saline Lower region (Park 2007)

This study demonstrated that season has a significant impact on taxonomic abundance and species richness. May and June, which represented the spring and summer in 2011, had high levels of abundance. There were a large number of individuals and a variety of taxa present at all the regions over spring and summer. Winter (December 2010) showed 32

a loss of most taxa in all regions sampled (Upper, Upper Middle, and Lower Middle).

Cooler temperatures in winter are often a potential source of die off in aquatic habitats

(Harper et al. 1981; Gray and Elliott 2009), and temperatures measured in December were much lower than in May and June. Winter months can also follow a time of relative abundance, which leads to higher levels of predation. This, along with cooler temperatures, can lead to a decreased food supply (Harper et al. 1981; Gray and Elliott

2009). In the spring and summer, two of the most numerous taxa present were crustaceans (amphipods) and insect larvae (mostly chironomids). Their short-lived nature also may have contributed to the drop in abundance in the winter. The insect larvae would have matured and left the aquatic habitat, and since most amphipods complete their life cycle in a year or less, they would have had greatly reduced numbers after their bloom in the spring and summer (Coffman and Ferrington 1984; Pardal et al.

2000). This is a potential cause for the decreased abundance in December. Higher predation in times abundance in the spring and summer, and then the drop in water temperature could have had a negative effect on survivorship of those animals left in the community.

Here, I discuss broad, generalized trends for plant communities in conjunction with trends in the benthic invertebrate communities. Plant community trends can mimic the seasonal trends seen in benthic invertebrate communities, with peak abundance and diversity in spring and summer, and a die-off or lack of abundance in the winter months

(Odum 1984, 1988; Pennings and Callaway 1992; unpublished data). Marsh plant community zonation is most commonly tied to salinity, more so than pother 33

environmental variables (Odum 1984, 1988; Pennings and Callaway 1992). It is a well- established trend in wetland and marsh plant zonation that abundance increases as salinity decreases (Odum 1984, 1988; Pennings and Callaway 1992). This is not to say that other factors are not involved in wetland plant distribution in general, but salinity is often the dominant environmental factor driving their distribution.

I have adapted hypothetical figures for species/taxonomic abundance trends versus salinity from Odum (1988) to show how the benthic invertebrate communities and plant communities have inverse diversity and abundances in relation to salinity (Figure 24).

Plant communities are more diverse and abundant as salinity decreases, while benthic invertebrate abundances often become less diverse and potentially less abundant in brackish regions (Odum 1984, 1988). However, where plants can still have moderate diversity and abundance in brackish regions, many benthic invertebrates seem to have less diversity and abundance in these areas, as opposed to fully fresh or saline areas, due to the potential for environmental changes/stressors that are not physiologically suitable

(Diaz 1977; Odum 1984). However, it has also been theorized that the lack of abundance and diversity of invertebrates in brackish areas may be due to a lack of diverse habitat, often being a muddy, silty bottom (Diaz and Boesch 1977). Diaz and Boesch (1977) noted that it had not yet been shown that any species specialize in this particular habitat.

The inability of most plants to deal with saline conditions showed in their lack of diversity and abundance in the higher saline Lower region. The benthic invertebrates in this study had a surge in diversity and abundance as they moved towards more stable saline conditions, but they maintained moderate abundance and diversity in the tidal 34

freshwater marsh regions as well. It is clear that salinity affects both of these communities, in terms of their distribution and abundance, at least on a spatial scale. The opposing trends in plant and invertebrate distributions, in relation to salinity, for this tidal freshwater marsh, suggest that plants and benthic invertebrates here may have responses to the environment that are independent of one another.

This study sought to address an important question concerning what shapes fundamental niches in this environment, and whether or not the high level of variability in the environment could create a greater independence among species. It was a goal of this to try and distinguish if benthic invertebrate realized niches for this area were determined by a set of abiotic environmental variables, or by their biotic interactions. To build a framework for the discussion of this hypothesis, I will very briefly review some of the major milestones in the evolution of niche theory, and return back to the potential addition that this research may have in this area of research. The evolution of niche theory is a long and debated one that began almost a century ago with Grinnell (1917), and more formally with Hutchinson (1957) (Hubbell 2005). Grinnell (1917) and Elton’s

(1927) description of niche referred to a place in the environment that was capable of supporting a species. They both viewed the niche more as a place, or role, in a community or an environment that could change over time; it could be occupied by different species at different times, or not at all (Colwell and Rangell 2009). Elton (1927) emphasized a species’ resource requirements and impact on the environment (Leibold

1995). Hutchinson (1957) went the direction of emphasizing the role of attributes in species or populations themselves, as opposed to the environment (Pulliam 2000). He 35

also did not agree with the idea of multiple species moving in and out of a niche, or the idea of a niche lying vacant (Colwell and Rangell 2009). Hutchinson’s (1957) niche hypervolume included the introduction of fundamental and realized niches, which were shaped not only by competition, but by physical tolerance ranges and limiting resources as well (Hubbell 2005). It seemed ecologists wanted a niche concept that could sufficiently explain the coexistence of species that had a certain set of traits within various ecological communities (Hubbell 2005).

In between the views of Grinnell and Hutchinson, was Gause’s (1934) tenet of niche theory, or competitive exclusion theory more specifically, where no two species could occupy the same exact niche for an indefinite amount of time; over time, equilibrium would be reached, and there would be a winner for that niche. The principle of competitive exclusion has been shown to have weaknesses in the areas of spatial ecology due to differences in dispersal rates in various communities and species richness (Hubbell

2005), and Hutchinson’s niche (1957) left questions about adaptation, dispersal, coexistence, distribution, and niche evolution. This concept led to debate over changes that may occur in a niche over a temporal and spatial scale. Hurtt and Pacala (1995) introduced the idea that if spatial and temporal scales are always changing, then the process of reaching equilibrium could take an infinite amount of time, thus competitive exclusion may never occur (Hubbell 2005). This idea also included the importance of dispersal, recruitment, and species richness in an environment (Hubbell 2005). Leibold

(1995) tried to reconcile some of the differences between the views of Elton and

Hutchinson. He proposed a theory that included a species’ individual requirements and 36

impacts into its niche, and where competition between species was dependent upon resource availability (Leibold 1995; Kylafis and Loureau 2011). As a way to further incorporate spatial and temporal patterns, Odling-Smee et al. (1996, 2003, 2013) included an organisms modification of its environment to the niche concept (Kylafis and

Loureau 2011). Odling-Smee et al. (1996, 2003, 2013) included the ideas of positive and negative ecosystem engineering into their niche construction theory. Some of the more recent additions involving spatial and temporal patterns, positive and negative interactions, have been geared toward the development of a niche concept that works more effectively with modern technologies and experimental designs (Kylafis and

Loureau 2011). It is important for the examination of niche theory to continue, so that it can be brought into more modern applications and be applied to present-day ecosystems, as many have changed since they were first studied. New ways of thinking about niche theory could alter predictions about population and community ecology, and this is essential to our understanding and conservation of natural communities (Bruno et al.

2003).

Progress in the streamlining and modernization of niche theory has been made by many scientists in the past fifty years. Much of the information added to niche theory has largely focused on competition, and while completion is important, there are other aspects responsible for shaping ecological communities that should not be overlooked

(Hubbell 2005). Neutral theory was proposed by Hubbell in 2001, and it seemed to work from the opposite direction of Hutchinson’s hypervolume, in the sense that it started simply, and added back complexity only as necessary for the given study system and data 37

(Hubbell 2001, 2005). Hubbell (2005) basically asked the question: What is the actual minimum necessary level of dimensionality of the niche that is required to characterize any given ecological community? It assumes that the differences between trophically similar species are “neutral”, or “functionally equivalent”, in light of diversity and abundance (Hubbell 2001, 2005). Bertness and Silliman also viewed trophic relationships as an overlooked aspect of niche theory. However, the aspect of trophic relationships they focused on was facilitation, and how that changed the view of salt marsh plant distribution and trophic cascade. They felt that facilitative relationships and positive species interactions in salt marsh communities had been grossly ignored by previous modifications to niche theory, and that including it could alter the relationship between the fundamental and realized niche, and in turn, predictions about species distribution (Bruno et al. 2003). While competition may determine final species sorting, interspecific interactions actually expand the potential niche a species can occupy, in comparison with the space that a species would be able to occupy alone (Bruno et al.

2003; Van de Koppel et al. 2006). Research by Van de Koppel et al. (2006) showed that even in ecosystems where environmental variability seems to overrule other mechanisms, that scale-dependent interactions between facilitation and competition can strongly affect spatial structure and composition (Van de Koppel et al. 2006). This emphasis on localized consumer control being central to organization and niche structure of salt marsh communities lessens the importance of abiotic control in shaping these communities.

This concept of the niche being an interactive product of biotic factors is important to

consider whenever one is trying to determine the mechanisms responsible for shaping an ecological community.

This review of niche theory evolution returns the discussion to the ecosystem and hypothesis in question: the realized niches of the benthic invertebrate communities of the estuarine region of the Mobile-Tensaw Delta. There are tenets of support for both potential explanations. The concept of abiotic variables shaping the niche of benthic invertebrates is supported by the changing abundances and community compositions between the regions based on environmental variables. The unique combinations of salinity, temperature, dissolved oxygen, pH, and redox potential present in each region did yield community changes. The seasons also produced changes in the benthic invertebrate community abundance and composition. The regions studied met their expected numbers of species, and only showed loss of abundance in winter. However, the spatial and temporal changes seen here could also lend themselves to support the idea that the realized niches of the species here are interdependent on one another. Given that brackish environments have been described as having lower diversity and abundance, perhaps these invertebrates are expanding their realized niches based on a significant biotic dependence on one another.

The dynamic nature of this estuary was demonstrated by the frequent changes seen over a short time scale. The changes in environmental variables and species abundances over that short time scale may suggest that niche overlap was occurring to varying degrees in a short period of time; sometimes there is a lot of overlap, sometimes there is

much less (Figures 7-10). The niches in this wetland ecosystem, and the taxa present, appeared to have a lot of overlap in their occupation of the study sites. They all occupied the food niche at the base of the detrital food chain, except for a few omnivores. This varying overlap could potentially mean these species have a higher level of independence in their interactions with one another. This could also have been due to the fact that food may not have been a limiting factor at the time. When the environment is favorable, all these species can co-exist in abundance due to abundant resources. There is also the possibility for food to have been a limiting factor, but the time scale of investigation was too short to detect the changes to population growth rate or abundance that would have indicated this.

The overlap in short temporal scales could reduce the strength of interspecific interactions with each other. This could mean that abiotic factors, like salinity, play a larger role in this ecosystem than do biotic factors when averaged over the long-term, and could be driving a large portion of what is shaping these communities. However, this overlap could also be viewed from the perspective of Bruno et al. (2003). Perhaps this niche overlap is possible due to the fact that positive interactions between these benthic invertebrate species is extending their distribution, and making their realized niches larger than the spatial scale predicted by their fundamental niche (Bruno et al. 2003).

This is a very important distinction that needs to be made: the short time scale could indicate that interaction strength is weak and variable, but these weak effects can lead to influential indirect interactions that are often overlooked (Berlow 1999). Unfortunately, it does not seem possible to tease apart what (biotic or abiotic) is shaping the fundamental 40

niches of these benthic invertebrates within the scope of this study, but it has the potential to be expanded by future research. To expand on this hypothesis, a study that covers a longer time scale would be necessary. Perhaps measures of sediment chemistry, in conjunction with a longer study, would help reveal whether biotic interactions or abiotic variables are the driving force behind shaping the niches of benthic invertebrates in tidal freshwater marsh and estuarine regions.

If evidence could be provided to show that abiotic variables are more important than biotic interactions in shaping the invertebrate niches in this environment, this could have potential implications for how the entire Delta ecosystem may function. The question could be posed whether or not the many unique habitats within this Delta ecosystem function together as an interconnected whole or as independent patches. Due to the fact that delta regions and estuaries are a combination of freshwater and saltwater habitats, they are often categorized as highly interconnected (Moore et al. 2004). The fact that they are used as nursery areas for a multitude of fishes and other commercially important species, seems to expand on this idea (Odum et al. 1984). If abiotic variables dominate the shaping of these communities, then a patch habitat description may be more appropriate. This could have implications for this specific estuary, or it could be characteristic of tidal freshwater marshes. If this ecosystem has niches where biotic interactions between species are facilitating expanded realized niches for some species, this would have very important implications as well. Silliman (2014) had yet another way to view biotic interactions, and made the argument that production in these systems is driven less by abiotic factors, and more by predation and top-down control. Both of 41

these ideas could mean biotic interactions are more important in shaping the benthic invertebrate niche structures of these communities. An understanding of the scales at which these habitats are integrated, or independent, could provide important knowledge to understanding the mechanisms that are shaping the distribution and abundances of the benthic invertebrate communities in this Delta. Future research would need to reexamine these communities in the Mobile-Tensaw Delta over a longer time scale, including all four seasons, to address if this delta operates as an integrated ecosystem, or a mosaic of independent regions. Ultimately, it needs to be determined if biotic or abiotic variables take the lead role in shaping the benthic invertebrate communities in this Delta to understand the spatial scale of community structure here. Once that is established, this region could be compared to other tidal freshwater marshes and estuaries in the northern

Gulf of Mexico, or other geographically similar marshes. Many estuaries have characteristics based on their general geographic region, so further research could determine if this is a widespread or localized occurrence. This research provided evidence that the factors shaping the benthic invertebrate communities in estuaries and tidal freshwater marshes are more dynamic than previously thought. Tidal freshwater marshes may also have a more complex community structure which needs to be thoroughly studied, and serves as a justification for future research in this area. Overall, this research is an important contribution to the debate of where tidal freshwater systems lie, which is crucial to their protection and management.

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Appendix A

Average Annual Salinity Marsh Type

Non-Tidal Freshwater Limit of Tidal Influence

< 0.5 ppt Tidal Freshwater

< 5.0 ppt Oligohaline

< 18.0 ppt Mesohaline Estuary

< 30.0 ppt Polyhaline

Ocean Euhaline (Marine)

Figure 1. This figure shows the relationship between the type of marsh and the average annual salinity (approximate). Terminology based on Cowardin et al. 1979; figure adapted from Odum et al. 1984.

Figure 2. Overview map of all 16 sites throughout the Mobile-Tensaw Delta region. The regions are grouped into 4 categories: Lower region (L) is marked by red tabs, and represents the most saline of the regions; Lower Middle region (LM) is marked by orange tabs, and are mostly brackish; Upper Middle region (UM) sites are in pink and are mostly freshwater tidal marsh; the Upper region (U) are marked in aqua and are freshwater tidal marsh.

Figure 3. A closer view of the Lower region, sites 13-16.

Figure 4. A closer view of the Lower Middle region, sites 9-12.

Figure 5. A closer view of the Upper Middle region, sites 5-8.

Figure 6. A closer view of the Upper region, sites 1-4.

Figure 7. Average number of individuals present by region and month (December 2010, May 2011, June 2011). The Lower region abundance in May 2011 was the most numerous. U=Upper, UM=Upper Middle, LM=Lower Middle, L=Lower. A logarithmic y-axis was used to allow for better viewing of December 2010 abundance. December columns represent (means ± 1 s.e.), May columns represent (means + 1 s.e.), and June columns represent (means +1 s.e.). Abcissa corresponds to y=1.

Figure 8. Numbers of individuals in each region for the different taxonomic groups shown, and the average salinity in each region sampled in December 2010. Due to the low abundance in December, a logarithmic y-axis scale with a maximum value of 2000 was used so that all seasons could be compared. Columns represent (means ± 1 s.e.). The Lower region was not sampled in December 2010. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region.

Figure 9. Numbers of individuals in each region for the different taxonomic groups shown, and the average salinity in each region sampled in May 2011. Due to the low abundance in December, a logarithmic y-axis scale with a maximum value of 2000 was used so that all seasons could be compared. Columns represent (means ± 1 s.e.). The Lower region was not sampled in December 2010. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region.

Figure 10. Numbers of individuals in each region for the different taxonomic groups shown, and the average salinity in each region sampled in June 2011. Due to the low abundance in December, a logarithmic y-axis scale with a maximum value of 2000 was used so that all seasons could be compared. Columns represent (means ± 1 s.e.). The Lower region was not sampled in December 2010. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region.

Figure 11. Plot of Whittaker’s beta diversity (βW) for rates of invertebrate species turnover among the 4 regions in the Mobile-Tensaw Delta in December 2010, May 2011, and June 2011.

Figure 12a. Salinity (ppt) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season on a logarithmic scale. Columns represent (means ± 1 s.e.). The Lower Middle and Lower regions were not sampled in December 2010. Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 12b. Salinity (ppt) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Sites 15 and 16 were not sampled in May 2011. Each pair is plotted together within region, and by season on a logarithmic scale. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 12c. Salinity (ppt) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14, 15-16). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season on a logarithmic scale. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 13a. Water temperature (˚C) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). The Lower Middle and Lower regions were not sampled in December 2010. Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 13b. Water temperature (˚C) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Sites 15 and 16 were not sampled in May 2011. Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 13c. Water temperature (˚C) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14, 15-16). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 14a. Dissolved oxygen (mg/L) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). The Lower Middle and Lower regions were not sampled in December 2010. Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 14b. Dissolved oxygen (mg/L) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Sites 15 and 16 were not sampled in May 2011. Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 14c. Dissolved oxygen (mg/L) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14, 15-16). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 15a. pH for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). The Lower Middle and Lower rergions were not sampled in December 2010. Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 15b. pH for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Sites 15 and 16 were not sampled in May 2011. Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 15c. pH for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14, 15-16). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 16a. Redox potential (mV) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel The Lower Middle and Lower rergions were not sampled in December 2010. Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 16b. Redox potential (mV) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Sites 15 and 16 were not sampled in May 2011. Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 16c. Redox potential (mV) for sites paired within each region (U=Upper region: 1-3, 2-4; UM=Upper Middle region: 5-8, 6-7; LM=Lower Middle region: 9-11, 10-12; L=Lower region:13-14, 15-16). It was hypothesized that they would have similar environmental parameters, and that 1 site within each pair was more likely to suffer from destruction (exposed) in the event of a hurricane, as opposed to the other (sheltered). Each pair is plotted together within region, and by season. Columns represent (means ± 1 s.e.). Panel a) represents December 2010, b) represents May 2011, and c) represents June 2011.

Figure 17. Principal Components Analysis (PCA) for the environmental variable data for 16 sites in 4 regions measured from December 2010 through June 2011. Environmental variables include temperature (˚C), salinity (ppt), pH, dissolved oxygen (DO, mg/L), and redox potential (ORP, mV). U=Upper (green▲), UM=Upper Middle (blue ▼), LM=Lower Middle (purple ■), L=Lower (red ♦).

Figure 18. Principal coordinates analysis (PCO) plot of invertebrate abundances based on the most abundant species in each region based on Bray-Curtis similarity. Apocorophium louisianum, Chironomus sp. larvae, and Alitta succinea were the most influential species in the LM region. U=Upper (green ▲), UM=Upper Middle (blue ▼), LM=Lower middle (purple ■), LO=lower (red ♦).

Figure 19. Nonmetric Multidimensional Scaling (nMDS) plot for all invertebrate abundances in all 4 regions, and all 16 sites, based on Bray-Curtis similarity. U=Upper (green ▲), UM=Upper Middle (blue ▼), LM=Lower Middle (purple ■), L=Lower (red ♦).

Figure 20. Nonmetric Multidimensional Scaling (nMDS) plot for all environmental variables in all 4 regions based on Bray-Curtis similarity. U=Upper (green ▲), UM=Upper Middle (blue ▼), LM=Lower Middle (purple ■), L=Lower (red ♦).

Figure 21. Nonmetric Multidimensional Scaling (nMDS) of dissimilarities of Euclidean distances among environmental variables measured at all 4 regions by month (season). December 2010=winter (blue *); May 2011=spring (green ○); June 2011=summer (red □).

Figure 22. Species accumulation plot estimating cumulative species over samples, the number of species that should be present in the Mobile Delta given sampling effort (Michaelis Menten: Smax = 69.68, max # of spp expected in Delta, B = 32.03).

0.25

3566 -

0.24351

0.3150

4 0.45 0.51 0.19

0.32 0.03 0.08

- 4453 5059

Figure 23.0.14288 Scatterplot matrix (SPLOM) was used to determine if there was a linear correlation between environmental variables (salinity, dissolved oxygen, pH, and redox potential) for all 4 regions and 16 sites. The numbers are the output from an environmental correlation matrix created from environmental variable data from 16 sites in 4 regions measured from December 2010 through June 2011.

Figure 24. Representation of hypothetical trends in total species numbers of vascular plants and invertebrates versus mean annual salinities along a salinity gradient (figure adapted from Odum et al. 1984.

Appendix B

Table 1. PERMDISP average dispersion among invertebrate species abundance data across all 16 sites based on Bray-Curtis similarity (F=13.81, df1=15, df2=147, PPERM=0.001). Region Site Size Average S.E.

Upper 1 9 25.204 2.551

Upper 2 11 25.363 2.1621

Upper 3 11 14.853 2.1303

Upper 4 12 6.8311 1.9246

Upper Middle 5 11 13.535 2.8414

Upper Middle 6 11 5.2437 1.2681

Upper Middle 7 9 8.4651 2.0382

Upper Middle 8 12 1.5684 0.5852

Lower Middle 9 10 33.86 5.7771

Lower Middle 10 12 17.865 2.9766

Lower Middle 11 12 16.083 2.6239

Lower Middle 12 12 22.711 5.1423

Lower 13 8 32.558 2.943

Lower 14 8 21.952 1.8583

Lower 15 8 43.724 3.1761

Lower 16 7 10.477 2.8418

Table 2. PERMDISP pairwise comparisons for invertebrate species abundance across region based on Bray-Curtis similarity (F=22.267, df1=3, df2=159, PPERM =0.001). U=Upper region, UM=Upper Middle region, LM=Lower Middle region, LO=Lower region. Groups T P(perm) Average (± s.e). Average (± s.e).

(U,UM) 6.7908 1E-3 U =20.346 (1.508) UM=6.991 (1.263)

(U,LM) 0.75847 0.568 U =20.346 (1.508) LM=22.643 (2.565)

(U,LO) 3.8307 2E-3 U =20.346 (1.508) LO=32.706 (3.179)

(UM,LM) 5.3585 1E-3 UM=6.991 (1.263) LM=22.643 (2.565)

(UM,LO) 8.359 1E-3 UM=6.991 (1.263) LO=32.706 (3.179)

(LM,LO) 2.4723 6.8E-2 LM=22.643 (2.565) LO=32.706 (3.179)

Table 3. PERMDISP average dispersion among invertebrate abundance across months (December, May, June) based on Bray-Curtis similarity (F= 43.372, df1=2, df2=160, PPERM = 0.001). Groups T P(perm) N Average (± s.e.) Average (± s.e.)

(Dec, Jun) 8.8548 1E-3 Dec=39 Dec=0.766 (0.235 Jun=23.665 (2.026)

(Dec, May) 9.6373 1E-3 Jun=63 Dec=0.766 (0.235 May=26.175 (2.099)

(Jun, May) 0.8609 0.52 May=61 Jun=23.665 (2.026) May=26.175 (2.099)

Table 4. This table shows the higher taxonomic classifications found at the 4 regions in all 3 seasons. The numbers in the cells are the numbers of organisms summed across all plots at all sites for each region and month. The Lower region was not sampled in December 2010 due to weather. December=Winter 2010, May=Spring 2011, June=Summer 2011,Mo=month, M=May 2011, J=June 2011, D=December 2010, R=region, U=Upper, UM=Upper Middle, LM=Lower Middle, L=Lower, Cl=class, Phy=phylum, Subcl=subclass, Poly=Polychaeta, Oligo=Oligochaeta, Nema=Nematoda, Hiru=Hirudinea, Echino=Echinodermata, Biv=Bivalvia, Crust=Crustacea, Gastro=Gastropoda. Mo R Cl. Cl. Cl. Phy. Subcl. Phy. Cl. Subphy. Cl. Total Insecta Poly Oligo Nema Hiru Echino Biv Crust Gastro M U 164 86 138 5 1 0 0 6 0 400

M UM 49 22 46 8 0 0 4 8 6 143

M LM 93 11 130 22 0 0 0 143 7 406

M L 75 168 79 8 0 0 0 1888 1 2219

J U 196 5 224 12 0 0 1 1 0 439 J UM 29 3 18 5 0 0 0 2 1 58

J LM 274 82 72 97 6 0 0 616 6 1153

J L 45 310 32 9 1 6 2 34 1 440

D U 1 0 1 0 0 0 1 0 0 3

D UM 0 5 1 0 0 0 0 0 0 6

D LM 4 2 2 0 0 0 0 0 0 8

D L N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Table 5. Eigenvalues for PCA of environmental variable data for 16 sites in 4 regions measured from December 2010 through June 2011. PC1, PC2, and PC3 explain 85.2% of the variance. PC1 is representative of salinity, pH, and temperature. PC2 is primarily representative of ORP. PC3 is representative of DO, pH, and ORP. PC Eigenvalues % Variation Cum. % Variation

1 1.89 37.8 37.8

2 1.27 25.4 63.2

3 1.1 22.0 85.2

4 0.517 10.3 95.6

5 0.222 4.4 100.0

Table 6. Eigenvectors for PCA of environmental variable data for 16 sites in 4 regions measured from December 2010 through June 2011. These are the coefficients in the linear combinations of variables making up PC's. PC1 is representative of salinity, pH, and temperature. PC2 is primarily representative of redox potential (ORP). PC3 is representative of dissolved oxygen (DO), pH, and ORP. Variable PC1 PC2 PC3 PC4 PC5

Temp 0.502 0.453 -0.220 0.584 -0.392

Salinity 0.562 -0.198 0.252 -0.574 -0.467

DO -0.212 -0.576 -.0634 0.078 -0.464

pH 0.566 -0.214 -0.470 -0.069 0.639

ORP 0.181 -0.615 0.514 0.565 0.073

Table 7. 3-factor PERMANOVA of environmental variables by site, month, and region based on Euclidean distance. Differences in environmental variable data across regions, sites, and months. Re=region Si=site Mo=month. A=all; upper, upper middle, lower middle (U, UM, LM), Lo=Lower, L=Lower Middle, U=Upper and Upper Middle (U, UM). Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 225.79 75.262 4.9839 0.003 999

LoVS_A 1 162.05 162.05 12.56 0.001 775

LoVS_L 1 70.009 70.009 3.6436 0.001 70

LoVS_U 1 186.66 186.66 36.205 0.001 418

Mo 2 213.29 106.64 17.302 0.001 998

Si(Re) 12 186.8 15.567 82.088 0.001 997

Si(LoVS_A) 14 250.54 17.896 94.37 0.001 999

Si(LoVS_L) 6 143.64 23.94 77.78 0.001 999

Si(LoVS_U) 10 69.287 6.9287 76.545 0.001 998

RexMo** 5 66.405 13.281 2.1548 0.026 998

LoVS_AxMo** 1 21.909 21.909 3.149 0.043 999

LoVS_LxMo** 1 34.643 34.643 3.4327 0.043 999

LoVS_UxMo** 1 10.809 10.809 2.3932 0.112 999

Si(Re)xMo -21 129.43 6.1635 32.502 0.001 997

Si(LoVS_A)xMo 25 173.93 6.9572 36.687 0.001 999

Si(LoVS_L)xMo 9 90.829 10.092 32.788 0.001 999

Si(LoVS_U)xMo 17 76.785 4.5168 49.899 0.001 998

Res 132 25.032 0.1896

Total 175 875

Table 8. PERMDISP average dispersion among environmental variables by regions (U=Upper, UM=Upper middle, LM=Lower middle, L=Lower) by Euclidean distances (F=31.481, df1=3, df2=172, PPERM =0.001). U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region. Groups T P(perm) N Average (± s.e.) Average (± s.e.)

(U,UM) 3.837 2E-3 U=48 U= 1.297 (6.148E-2) UM=1.685 (8.017E-2)

(U,LM) 6.972 1E-3 UM=48 U= 1.297 (6.148E-2) LM=2.428 (0.150)

(U,LO) 1.262 0.283 LM=48 U= 1.297 (6.148E-2) LO=1.194 (4.048E-2)

(UM,LM) 4.367 1E-3 L=32 UM=1.685 (8.017E-2) LM=2.428 (0.150)

(UM,LO) 4.737 1E-3 UM=1.685 (8.017E-2) LO=1.194 (4.048E-2)

(LM,LO) 6.599 1E-3 LM=2.428 (0.150) LO=1.194 (4.048E-2)

Table 9. PERMANOVA pairwise t-test for all environmental variables across months based on Euclidean distance. Dec=December 2010, May=May 2011, Jun=June 2011. Groups t P(perm) Unique perms

Dec, May 3.6354 0.001 999

Dec, Jun 7.0122 0.001 998

May, Jun 2.6604 0.003 997

Table 10. PERMDISP average dispersion of environmental variables across all 16 sites based on Euclidean distances (F=11.444, df1=15, df2=160, PPERM =:0.001). Region Site Size Average SE

Upper 1 12 1.4385 8.8672E-2

Upper 2 12 1.3359 4.6304E-2

Upper 3 12 1.1574 9.6905E-2

Upper 4 12 0.92861 0.1204

Upper Middle 5 12 1.2867 6.0722E-2

Upper Middle 6 12 1.5012 0.12816

Upper Middle 7 12 1.5822 0.12805

Upper Middle 8 12 1.687 0.17133

Upper Middle 9 12 1.4287 9.0085E-2

Lower Middle 10 12 2.7805 0.31061

Lower Middle 11 12 1.7132 0.12692

Lower Middle 12 12 1.6589 0.18257

Lower Middle 13 8 0.88538 1.6115E-2

Lower Middle 14 8 0.76246 8.2153E-2

Lower 15 8 1.159 2.4199E-3

Lower 16 8 1.078 1.5281E-2

Table 11. PERMDISP average dispersion among environmental variables across months (December, May, June) based on based on Euclidean distance (F= 12.881, df1=2, df2=173, PPERM = 0.001). Groups T P(perm) N Average (± s.e.) Average (± s.e.)

(Dec, May) 3.4821 2E-3 Dec=48 1.3356 (9.789E-2) 1.842 ( 0.10227)

(Dec, Jun) 5.6524 1E-3 May=64 1.3356 (9.789E-2) 1.9757 (6.5069E-2)

(May, Jun) 1.1033 0.322 June=64 1.842 ( 0.10227) 1.9757 (6.5069E-2)

Table 12. This table shows the average values for environmental variables measured at 16 sites in 4 regions over 3 seasons. December 2010=winter, May 2011=spring, and June 2011=summer, U=upper, UM=upper middle, LM=lower middle, L=lower. Month/Year Region Temp (˚C) Salinity DO (mg/L) pH ORP (mV) (ppt) Dec 2010 U 16.6 0.12 7.08 7.57 66.2 Dec 2010 UM 15.5 0.15 6.96 7.04 94.2 Dec 2010 LM 13.9 0.05 6.98 5.88 186.6 Dec 2010 L N/A N/A N/A N/A N/A May 2011 U 24.6 0.07 7.42 7.72 45.4 May 2011 UM 26.9 0.12 8.14 7.76 8.3 May 2011 LM 23.5 0.29 7.71 8.10 131.9 May 2011 L 28.3 16.85 5.45 8.20 141.2 June 2011 U 31.0 0.08 5.96 7.90 73.1 June 2011 UM 30.2 3.16 5.57 7.59 17.6 June 2011 LM 28.3 0.78 3.85 7.72 45.9 June 2011 L 30.1 15.44 5.53 8.33 150.2

Table 13. BEST Analysis showed that, for these data, the environmental variables that most correlated with the invertebrate community structure were salinity and DO (R=0.232, p=0.01). Variables represented this table and used for correlations 1=temperature, 2=log(salinity), 3=dissolved oxygen (DO), 4=pH, and 5=redox potential (ORP).

No. Vars Corr. Selections 2 0.232 2, 3 3 0.192 2, 3, 5 1 0.186 3 3 0.171 2-4 4 0.164 2-5 1 0.151 2 2 0.135 3, 5 2 0.128 2, 4 3 0.121 2, 4, 5 3 0.116 3-5

Appendix C

Table 1. PERMANOVA multiple comparison average of distance between/within groups for all environmental variables based on Euclidean distance. Dec May Jun

Dec 1.8311

May 2.9972 2.558

Jun 3.6461 2.9141 2.6638

100

Table 2. Results from multivariate permutational analysis (PERMANOVA) of differences in invertebrate species abundance across regions, sites, and months. Re=region (fixed), Si=site (random), Mo=month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM). Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 7670.2 2556.7 1.5607 0.081 998

LoVS_A 1 4018.2 4018.2 2.8161 0.03 999

LoVS_L 1 2307.8 2307.8 0.9756 0.407 653

LoVS_U 1 4706.5 4706.5 3.0153 0.011 998

Mo 2 5280.9 2640.4 2.2008 0.033 999

Si(Re) 12 20494 1707.8 4.4753 0.001 998

Si(LoVS_A) 14 24028 1716.3 4.4975 0.001 998

Si(LoVS_L) 6 17088 2848.1 4.8913 0.001 996

Si(LoVS_U) 10 17988 1798.8 6.0524 0.001 998

RexMo** 5 7593.5 1518.7 1.2353 0.258 998

LoVS_AxMo** 1 3871.1 3871.1 3.0361 0.023 998

LoVS_LxMo** 1 4287.8 4287.8 1.9518 0.14 998

LoVS_UxMo** 1 2944.3 2944.3 2.3247 0.078 997

Si(Re)xMo 21 26488 1261.4 3.3053 0.001 995

Si(LoVS_A)xMo 25 30207 1208.3 3.1663 0.001 996

Si(LoVS_L)xMo 9 20014 2223.8 3.8192 0.001 999

Si(LoVS_U)xMo 17 19841 1167.1 3.927 0.001 999

Res 119 45412 381.61

Total 162 1.1607E5

101

Table 3. Pairwise tests for PERMANOVA of invertebrate species abundances across months based on Bray-Curtis similarity.

Groups t P(perm) Unique perms

Dec, Jun 2.2979 0.008 997

Dec, May 2.5117 0.012 998

Jun, May 0.93558 0.48 997

102

Table 4. Average Similarity between/within groups by month for PERMANOVA of invertebrate species abundances based on Bray-Curtis similarity. Dec June May

Dec 98.67

Jun 77.442 65.965

May 74.635 63.841 62.458

103

Table 5. PERMANOVA of all 5 diversity indices (Hill’s Diversity Index, Margalef Diversity Index, Pielou Diversity Index, Shannon Diversity Index, and Simpson’s Evenness Measure) contrasting regions and months based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM).

Unique Source df SS MS Pseudo-F P(perm) perms

Re 3 174.72 58.241 5.4636 0.002 998

Lo_vs_A 1 30.155 30.155 1.9844 0.158 999

LoL 1 135.1 135.1 12 0.001 999

Lo_vs_U 1 70.798 70.798 5.4414 0.011 999

Mo 2 26.124 13.062 1.2253 0.265 998

RexMo** 3 24.385 8.1282 0.76251 0.647 998

Lo_vs_AxMo** 1 3.8296 3.8296 0.25201 0.759 999

LoLxMo** 1 7.2464 7.2464 0.64364 0.565 999

Lo_vs_UxMo** 1 2.6354 2.6354 0.20255 0.859 998

Res 23 245.18 10.66

Total 31 499.86

104

Table 6. PERMANOVA estimates of the components of variation based on 5 diversity indices (Hill’s Diversity Index, Margalef Diversity Index, Pielou Diversity Index, Shannon Diversity Index, and Simpson’s Evenness Measure) based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), S(TR)=sum of squared treatment effects (divided by degrees of freedom), V(Res)=residual variation. Source Original Estimate Adjusted Estimate % of Total Adjusted Estimate

S(Re) 6.1764 6.1764 18.97%

S(Lo_vs_A) 1.4024 1.4024 4.31%

S(LoL) 8.0166 8.0166 24.62%

S(Lo_vs_U) 6.0194 6.0194 18.48%

S(Mo) 0.28777 0.28777 0.88%

S(RexMo) -0.65724 0 0.00%

S(Lo_vs_AxMo) -2.1312 0 0.00%

S(LoLxMo) -0.51941 0 0.00%

S(Lo_vs_UxMo) -2.1616 0 0.00%

V(Res) 10.66 10.66 32.74%

105

Table 7. PERMANOVA Shannon’s Diversity Index for invertebrate species abundances across region, site, and month based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM). Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 0.4608 0.1536 0.5682 0.627 999

Lo_vs_A 1 0.1355 0.1355 0.5447 0.461 997

LoL 1 0.1366 0.1366 0.4869 0.463 996

Lo_vs_U 1 2.3153E-2 2.3153E-2 0.1250 0.715 996

Mo 2 13.6 6.7998 25.156 0.001 997

RexMo** 5 1.4379 0.2876 1.0639 0.404 998

Lo_vs_AxMo** 1 1.0607 1.0607 4.2624 0.049 997

LoLxMo 2 8.6461E-2 4.3231E-2 0.1541 0.856 999

Lo_vs_UxMo** 1 0.7227 0.7227 3.9023 0.045 998

Res 34 9.1902 0.2703

Total 44 25.231

106

Table 8. PERMANOVA estimate of the components of variation for Shannon’s Diversity Index based on Euclidean distance. Re=Region (fixed), S=Site (random), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), S(TR)=sum of squared treatment effects (divided by degrees of freedom), V(Res)=residual variation. Source Original Adjusted % of Total Adj. Estimate Estimate Estimate

S(Re) -1.073E-2 0 0%

S(Lo_vs_A) -9.3514E-3 0 0%

S(LoL) -6.8871E-3 0 0%

S(Lo_vs_U) -1.5193E-2 0 0%

S(Mo) 0.45736 0.45736 47.31%

S(RexMo) 4.2287E-3 4.2287E-3 0.44%

S(Lo_vs_AxMo) 0.13401 0.13401 13.86%

S(LoLxMo) -3.3257E-2 0 0%

S(Lo_vs_UxMo) 0.10079 0.10079 10.43%

V(Res) 0.2703 0.2703 27.96%

Total Adjusted Estimate 0.966687

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Table 9. This was the data used to produce the relative abundance plots. It represents the averages for abundance and species counts for each region and month. It also includes the standard deviations (Stdev) and standard errors (S.E.) for abundances and species counts. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region.

Month Region Abundance Abun. Stdev S.E. Species Spp. Stdev S.E. May U 25.269 20.912 4.16008 3.125 1.8929694 1.07083 May UM 9.575 14.003 4.52535 1.66667 1.4960265 1.15882 May LM 26.984 34.196 6.58297 4.73333 3.1952345 1.46865 May L 147.977 277.037 22.7741 3.6 2.5856748 1.36277 June U 22.8958 25.0288 5.23072 4.5 2.4765567 1.16746 June UM 3.70208 4.07077 2.11570 1.625 1.3102163 1.02782 June LM 77.50228 136.671 15.5245 4.6 2.2614787 1.05442 June L 30.00159 38.2630 6.98564 4.8 3.1892677 1.45569 Dec U 0.27273 0.46710 0.89443 0.27272 0.4670994 0.89443 Dec UM 0.5 1.16775 1.65145 0.33333 0.6513389 1.12815 Dec LM 0.5 0.89443 1.26491 0.375 0.6191392 1.01105 Dec L N/A N/A NA N/A N/A N/A

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Table 10. This table is a list of all the species found from all 4 regions and 16 sites. Genus Species Heteromastus filiformis (Claparède, 1864) Streblospio benedicti Webster, 1879 Hobsonia florida (Hartman, 1951) Namalycastis abiuma (Grube, 1872) Actinobdella pediculata (Hemingway, 1908) Limnodrilus hoffmeisteri Claparède, 1862 Eclipidrilus palustris (Smith, 1900) Myzobdella lugubris (Leidy, 1851) Alitta succinea (Leuckart, 1847) Tubificoides annulus Erséus, 1986 Pectinaria regalis Verrill, 1901 Hypereteone lactea (Claparède, 1868) Lumbriculus variegatus (Müller, 1774) Haemonais waldvogeli Bretscher, 1900 Laeonereis culveri (Webster, 1879) Chaetogaster diastrophus (Gruithuisen, 1828) Chaetogaster limnaei Baer, 1827 Helobdella fusca (Castle, 1900) Sphaeroma terebrans Bate, 1866 Melita nitida Smith, 1873 Gammarus tigrinis Sexton, 1939 Gammarus mucronatus Say, 1818 Leptochelia rapax Harger, 1879 Grandidierella bonnieroides Stephensen, 1947 Pachygrapsus transversus (Gibbes, 1850) Apocorophium louisianum (Shoemaker, 1934) Callinectes sapidus Rathbun, 1896 Uromunna reynoldsi (Frankenberg & Menzies, 1966) Clibanarius vittatus (Bosc, 1802) Polymesoda floridana (Conrad, 1846) Neritina usnea (Röding, 1798) Rangia cuneata (G. B. Sowerby I, 1832) Trichuris trichura (Linnaeus, 1771)

109

Table 11. This table represents the full taxonomy for every individual identified from all 4 regions and all 16 sites. Every taxa listed has at least 1 individual identified. Phylum Class Order Family Genus Species

Annelida Clitellata/ Rhynchobdellida Glossiphoniidae Actinobdella pediculata Subclass Hirudinea Annelida Clitellata Haplotaxida Tubificidae Limnodrilus hoffmeisteri Annelida Clitellata Enchytraeida Enchytraeidae Annelida Clitellata Annelida Clitellata Lumbriculida Lumbriculidae Eclipidrilus Palustris

Annelida Clitellata/ Rhynchobdellida Piscicolidae Myzobdella Lugubris Subclass Hirudinea Annelida Clitellata/ Rhynchobdellida Piscicolidae Levinsenia sp. Subclass Hirudinea Annelida Clitellata Haplotaxida Tubificidae Tubificoides Annulus

Annelida Clitellata Lumbriculida Lumbriculidae Lumbriculus variegatus Annelida Clitellata Naidinae Annelida Clitellata Tubificida Annelida Clitellata Haplotaxida Tubificidae Annelida Clitellata Haplotaxida Tubificidae Chaetogaster diastrophus Annelida Clitellata Haplotaxida Tubificidae Chaetogaster Limnaei Annelida Clitellata/ Rhynchobdellida Glossiphoniidae Helobdella Fusca Subclass Hirudinea Annelida Polychaeta Capitellidae Heteromastus Filiformis Annelida Polychaeta Spionida Spionidae Streblospio Benedicti Annelida Polychaeta Terebellida Ampharetidae Hobsonia Florida

Annelida Polychaeta Phyllodocida Nereididae Namalycastis Abiuma Annelida Polychaeta Phyllodocida Pilargidae Annelida Polychaeta Eunicida Lumbrineridae Annelida Polychaeta Phyllodocida Glyceridae Annelida Polychaeta Annelida Polychaeta Phyllodocida Nereididae Alitta Succinea Annelida Polychaeta Terebellida Cirratulidae Aphelochaeta sp. Annelida Polychaeta Eunicida Eunicidae Marphysa sp. Annelida Polychaeta Terebellida Pectinariidae Pectinaria Regalis Annelida Polychaeta Annelida Polychaeta Phyllodocida Phyllodocidae Hypereteone Lactea Annelida Polychaeta Phyllodocida Nereididae Laeonereis Culveri

110

Phylum Class Order Family Genus Species Annelida Polychaeta Phyllodocida Nereididae Annelida Annelida Haplotaxida Naidinae Haemonais waldvogeli Annelida Arthropoda Insecta Diptera Chironomidae Chironomus sp. Arthropoda Insecta Trichoptera Leptoceridae Nectopsyche sp. Arthropoda Insecta Ephemeroptera Arthropoda Insecta Diptera Ceratopogonidae Bezzia sp. Arthropoda Insecta Odonata/ Suborder Anisoptera Arthropoda Insecta Ephemeroptera Ephemerellidae Ephemerella sp. Arthropoda Insecta Odonata Gomphidae Stylurus sp. Arthropoda Insecta Diptera Arthropoda Insecta Diptera Tabanidae Arthropoda Insecta Diptera Culicidae Arthropoda Insecta Ephemeroptera Caenidae Arthropoda Insecta Coleoptera Arthropoda Insecta Arthropoda Malacostraca Isopoda Sphaeromatidae Sphaeroma terebrans Arthropoda Malacostraca Amphipoda Melitidae Melita nitida Arthropoda Malacostraca Amphipoda Arthropoda Malacostraca Amphipoda Gammaridae Gammarus tigrinis Arthropoda Malacostraca Amphipoda Gammaridae Gammarus mucronatus Arthropoda Malacostraca Tanaidacea Leptochellidae Leptochelia rapax Arthropoda Malacostraca Amphipoda Aoridae Arthropoda Malacostraca Amphipoda Aoridae Grandidierella bonnieroides Arthropoda Malacostraca Decapoda Grapsidae Pachygrapsus transversus Arthropoda Malacostraca Amphipoda Corophiidae Apocorophium louisianum Arthropoda Malacostraca Amphipoda Gammaridae Gammarus Arthropoda Malacostraca Tanaidacea Arthropoda Malacostraca Decapoda Portunidae Callinectes sapidus Arthropoda Malacostraca Cumacea Arthropoda Malacostraca Isopoda Munnidae Uromunna reynoldsi Arthropoda Malacostraca Decapoda Diogenidae Clibanarius vittatus Arthropoda Maxillopoda Arthropoda Ostracoda Arthropoda

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Phylum Class Order Family Genus Species Echinodermata Ophiuroidea Ophiuroida Ophiuridae Echinodermata Ophiuroidea Echinodermata Asteroidea

Mollusca Adenophorea Trichocephalida Trichuridae Trichuris Trichura Mollusca Bivalvia Veneroida Cyrenidae Polymesoda floridana Mollusca Bivalvia Mollusca Bivalvia Veneroida Mactridae Rangia Cuneata Mollusca Gastropoda Cycloneritimorpha Neritidae Neritina Usnea Mollusca Gastropoda Nematoda Gastropoda Pyramidellidae Nematoda

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Table 12. Key to factors in 3-factor PERMANOVA of environmental variables by site, month, and region. The same design was used for the invertebrate species abundances data set. Name Abbrev. Type Levels Region Re Fixed 4 Site Si Random 16 Month Mo Fixed 3

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Table 13. PERMANOVA Hill Measure of Diversity for invertebrate species abundances across region, site, and month based on Euclidean distance. Re=Region (fixed), Si=Site (random), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), V=variance. Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 4.6796 1.5599 2.0213 0.198 999

Lo_ 1 1.259E-2 1.259E-2 1.5078E-2 0.898 997

L/ 1 0.8046 0.8046 0.9562 0.322 998

LoV 1 0.3883 0.3883 0.4746 0.517 990

Mo 2 13.767 6.8834 23.725 0.001 999

Si(Re) 12 9.6556 0.8046 4.5133 0.001 997

Si(Lo_) 14 14.235 1.0168 5.7033 0.001 997

Si(L/) 14 13.011 0.9294 5.213 0.001 999

Si(LoV) 10 9.3936 0.9394 5.3503 0.001 999

RexMo** 5 2.9631 0.5926 2.0145 0.121 998

Lo_xMo** 1 0.1579 0.1579 0.4290 0.508 999

L/xMo 2 1.3529 0.6765 2.0489 0.136 999

LoVxMo** 1 2.5769E-2 2.5769E-2 0.1178 0.767 994

Si(Re)xMo 21 6.2696 0.2986 1.6746 0.035 998

Si(Lo_)xMo 25 9.2716 0.3709 2.0802 0.003 998

24 7.9718 0.3322 1.8631 0.017 999 Si(L/)xMo** 17 5.6059 0.3298 1.8782 0.035 999 Si(LoV)xMo Res 119 21.216 0.1783

Total 162 62.892

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Table 14. PERMANOVA estimate of the components of variation for Hill Measure of Diversity based on Euclidean distance. Re=Region (fixed), Si=Site (random), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), S(TR)=sum of squared treatment effects (divided by degrees of freedom), V(Bl)=variation due to blocks, V(TR*BL)=variation in treatment effects among blocks, V(Res)=residual variation. Source Original Estimate Adjusted estimate % of Total Adj. Estimate

S(Re) 2.1565E-2 2.16E-2 2.51%

S(Lo_) -1.7847E-2 0 0%

S(L/) -5.4633E-4 0 0%

S(LoV) -1.0428E-2 0 0%

S(Mo) 0.13785 0.13785 16.05% V(Si(Re)) 6.495E-2 6.50E-2 7.56%

V(Si(Lo_)) 8.5505E-2 8.55E-2 9.96%

V(Si(L/)) 7.8578E-2 7.86E-2 9.15%

V(Si(LoV)) 8.3134E-2 8.31E-2 9.68%

S(RexMo) 2.152E-2 2.15E-2 2.51% S(Lo_xMo) -9.7579E-3 0 0%

S(L/xMo) 1.4347E-2 1.43E-2 1.67%

S(LoVxMo) -1.5459E-2 0 0%

V(Si(Re)xMo) 3.343E-2 3.34E-2 3.89%

V(Si(Lo_)xMo) 5.3243E-2 5.32E-2 6.20%

V(Si(L/)xMo) 4.2591E-2 4.26E-2 4.96% V(Si(LoV)xMo) 4.3699E-2 4.37E-2 5.09%

V(Res) 0.17828 0.17828 20.76%

Total Adjusted Estimate 8.59E-01

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Table 15. PERMANOVA Margalef Diversity Index for invertebrate species abundances across region, site, and month based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM). Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 10.793 3.5978 0.92251 0.511 999

Lo_vs_A 1 0.63819 0.63819 0.14776 0.71 999

LoL 1 1.4697 1.4697 0.36511 0.709 998

Lo_vs_U 1 1.1876 1.1876 0.21964 0.717 999

Mo 2 16.288 8.1438 2.0881 0.09 999

RexMo** 3 16.827 5.6091 1.4382 0.222 999

Lo_vs_AxMo** 1 6.3629E-2 6.3629E-2 1.4732E-2 0.887 998

LoLxMo** 1 6.8982 6.8982 1.7137 0.201 997

Lo_vs_UxMo** 1 0.65973 0.65973 0.12202 0.81 997

Res 23 89.701 3.9

Total 31 133.77

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Table 16. PERMANOVA estimate of the components of variation for Margalef Diversity Index based on Euclidean distance. Re=Region (fixed), Si=Site (random), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), S(TR)=sum of squared treatment effects (divided by degrees of freedom), V(Res)=residual variation. Source Original Estimate Adjusted Estimate % of Total Adj. Estimate

S(Re) -3.923E-2 0 0%

S(Lo_vs_A) -0.3451 0 0%

S(LoL) -0.16544 0 0%

S(Lo_vs_U) -0.43952 0 0%

S(Mo) 0.50844 0.50844 9.73%

S(RexMo) 0.44369 0.44369 8.49%

S(Lo_vs_AxMo) -0.79793 0 0%

S(LoLxMo) 0.37193 0.37193 7.12%

S(Lo_vs_UxMo) -0.989 0 0%

V(Res) 3.9 3.9 74.65%

Total Adjusted Estimate 5.22406

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Table 17. PERMANOVA Pielou Diversity Index for invertebrate species abundances across region, site, and month based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM). Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 0.1420 4.7327E-2 1.6975 0.209 999

Lo_vs_A 1 0.113 0.113 4.0863 0.064 995

LoL 1 3.0612E-2 3.0612E-2 0.8925 0.364 998

Lo_vs_U 1 0.1040 0.1040 3.3216 0.084 996

Mo 2 2.787E-2 1.3935E-2 0.4998 0.616 999

RexMo** 3 0.1986 6.6198E-2 2.3744 0.082 998

Lo_vs_AxMo** 1 0.1222 0.1222 4.4198 0.061 994

LoLxMo** 1 1.1597E-3 1.1597E-3 3.3811E-2 0.847 998

Lo_vs_UxMo** 1 6.2366E-2 6.2366E-2 1.9911 0.195 998

Res 23 0.6412 2.788E-2

Total 31 0.9904

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Table 18. PERMANOVA estimate of the components of variation for Pielou Diversity Index based on Euclidean distance. Re=Region (fixed), Si=Site (random), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), S(TR)=sum of squared treatment effects (divided by degrees of freedom), V(Res)=residual variation. Source Original Estimate Adjusted Estimate % of Total Adj. Estimate S(Re) 2.5244E-3 2.5244E-3 1.49% S(Lo_vs_A) 8.0009E-3 8.0009E-3 4.72% S(LoL) -2.387E-4 0 0% S(Lo_vs_U) 7.5745E-3 7.5745E-3 4.46% S(Mo) -1.6707E-3 0 0% S(RexMo) 9.9479E-3 9.9479E-3 58.64% S(Lo_vs_AxMo) 1.7731E-2 1.7731E-2 10.45% S(LoLxMo) -4.2904E-3 0 0% S(Lo_vs_UxMo) 6.4674E-3 6.4674E-3 3.81% V(Res) 2.788E-2 2.788E-2 16.43% Total Adjusted Estimate 1.70E-01

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Table 19. PERMANOVA Simpson’s Evenness Measure Index for invertebrate species abundances across region, site, and month based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed). Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM). Source df SS MS Pseudo-F P(perm) Unique perms

Re 3 1.3131 0.4377 1.5729 0.178 999

Lo_vs_A 1 0.3376 0.3376 1.1201 0.236 996

LoL 1 0.4409 0.4409 1.5435 0.254 999

Lo_vs_U 1 0.4767 0.4767 1.2338 0.299 998

Mo 2 0.6175 0.3088 1.1096 0.252 999

RexMo** 3 0.7792 0.2597 0.9335 0.516 998

Lo_vs_AxMo** 1 1.5995E-2 1.5995E-2 5.3062E-2 0.837 998

LoLxMo** 1 0.3011 0.3011 1.0541 0.398 998

Lo_vs_UxMo** 1 1.2327E-2 1.2327E-2 3.1908E-2 0.893 998

Res 23 6.4001 0.2783

Total 31 9.2895

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Table 20. PERMANOVA estimate of the components of variation for Simpson’s Evenness Measure based on Euclidean distance. Invertebrate species abundances across region, site, and month based on Euclidean distance. Re=Region (fixed), Mo=Month (fixed), Si=Site. Lo=Lower, L=Lower & Lower Middle (LM), U=Upper & Upper Middle (UM), A=All (U, UM, LM), S(TR)=sum of squared treatment effects (divided by degrees of freedom), V(Res)=residual variation. Source Original Adjusted Estimate % of Total Adj. Estimate Estimate

S(Re) 2.0695E-2 2.0695E-2 6.32%

S(Lo_vs_A) 3.3941E-3 3.3941E-3 1.04%

S(LoL) 1.005E-2 1.005E-2 3.07%

S(Lo_vs_U) 9.41E-3 9.41E-3 2.87%

S(Mo) 3.6532E-3 3.6532E-3 1.12%

S(RexMo) -4.8077E-3 0 0%

S(Lo_vs_AxMo) -5.352E-2 0 0%

S(LoLxMo) 2.001E-3 2.001E-3 0.61%

S(Lo_vs_UxMo) -7.7919E-2 0 0%

V(Res) 0.27826 0.27826 84.97%

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Table 21. This table shows the location coordinates (longitude and latitude), for all 16 sites in all 4 regions. U=Upper region (sites 1-4), UM=Upper Middle region (sites 5-8), LM=Lower Middle region (sites 9-12), L=Lower region (sites 13-16).

Site Longitude, Latitude U01 N30.94570, W087.88271 U02 N30.94511, W087.89286 U03 N30.93078, W087.90753 U04 N30.93138, W087.91631 UM05 N30.79412, W087.93464 UM06 N30.785133, W087.937472 UM07 N30.79337, W087.91095 UM08 N30.79456, W087.89868 LM09 N30.68901, W087.98759 LM10 N30.68322, W087.96171 LM11 N30.70458, W087.89417 LM12 N30.70984, W087.88594 L13 N30.349814, W088.407117 L14 N30.416878, W088.40255 L15 N30.249686, W087.955517 L16 N30.414581, W087.831961

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Table 22. ANOSIM two-way crossed analysis tests for differences in environmental variables between region groups across all months (R=0.551, p=0.001) based on Euclidean distance. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region.

Possible Actual Number >= Groups R Statistic Signif. Level % Perms Perms Observed U, UM 0.292 0.1 Very large 999 0 U, LM 0.436 0.1 Very large 999 0 U, LO 0.959 0.1 Very large 999 0 UM, LM 0.39 0.1 Very large 999 0 UM, LO 0.991 0.1 Very large 999 0 LM, LO 0.55 0.1 Very large 999 0

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Table 23. ANOSIM two-way crossed analysis tests for differences in environmental variables between month groups across all region groups (R=0.555, p=0.001) based on Euclidean distance.

Groups R Statistic Signif. Level % Possible Actual Perms Number >= Observed Perms Dec, May 0.574 0.1 Very large 999 0 Dec, Jun 0.789 0.1 Very large 999 0 May, Jun 0.376 0.1 Very large 999 0

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24. ANOSIM two-way nested analysis pairwise tests for differences in environmental variables between site groups across all regions (R=0.218, p=0.001), and differences between region groups using site groups as samples (R=0.569, p=0.001) based on Euclidean distance. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region. Groups R Statistic Signif. Level % Possible Actual Perms Number Perms >=Observed U, UM 0.104 14.3 35 35 5 U, LM 0.26 5.7 35 35 2 U, LO 1 2.9 35 35 1 UM, LM 11.4 35 35 4 0.333 UM, LO 2.9 35 35 1 0.969 LM, LO 2.9 35 35 1 0.448

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Table 25. ANOSIM two-way nested analysis pairwise tests for differences in invertebrate abundances between site groups across all region groups (R=0.091, p=0.001) and for differences between region groups using sites as samples (R=0.069, p=0.019) based on Bray-Curtis similarity. U=Upper region, UM=Upper Middle region, LM=Lower Middle region, L=Lower region. Groups R Signif. Level % Possible Actual Perms Number >= Statistic Perms Observed U, UM 11.4 35 35 4 0.083 U, LM 14.3 35 35 5 0.073 U, LO 8.6 35 35 3 0.104 UM, LM 28.6 35 35 10 0.021 UM, LO 11.4 35 35 4 0.073 LM, LO 5.7 35 35 2 0.083

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Appendix D

Table 1. This table represents the raw environmental variable data collected at each regions, site, and plot. Measurements were taken for water temperature (˚C), salinity (ppt), dissolved oxygen (DO) (mg/L), pH, and redox potential (ORP) (mV). Measurements were taken in December 2010, May 2011, and June 2011. There were 4 regions, U=Upper region, UM=Upper Middle region, LM=Lower Middle region, and L=Lower region, sites 1-16, and plots 1-4 at each site. Upper region=sites 1-4, Upper Middle region=sites 5-8, Lower Middle region=sites 9-12, and Lower region=sites 13-16. June has two sets of measurements, A and B. A (.) symbol marks that a measurement was not taken for that plot. Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Dec-10 1 1 01 01 Dec 2010 16 0.11 6.6 7.53 101.2 Dec-10 1 2 01 02 Dec 2010 15.9 0.11 6.63 7.52 104 Dec-10 1 3 01 03 Dec 2010 16 0.11 6.74 7.49 104.4 Dec-10 1 4 01 04 Dec 2010 16 0.11 6.8 7.46 123 Dec-10 2 1 02 01 Dec 2010 15.9 0.11 6.85 7.59 53.3 Dec-10 2 2 02 02 Dec 2010 15.8 0.11 6.89 7.59 52.2 Dec-10 2 3 02 03 Dec 2010 15.8 0.11 6.87 7.59 52 Dec-10 2 4 02 04 Dec 2010 15.8 0.11 6.89 7.61 56.8 Dec-10 3 1 03 01 Dec 2010 16 0.11 7.02 7.56 49.9 Dec-10 3 2 03 02 Dec 2010 16.1 0.11 6.99 7.55 48.8 Dec-10 3 3 03 03 Dec 2010 16.2 0.11 6.91 7.52 62 Dec-10 3 4 03 04 Dec 2010 16.3 0.11 6.97 7.47 74.1 Dec-10 4 1 04 01 Dec 2010 18.7 0.14 7.58 7.67 45 Dec-10 4 2 04 02 Dec 2010 18.7 0.14 7.78 7.7 47.6 Dec-10 4 3 04 03 Dec 2010 18.5 0.14 7.73 7.7 41.2 Dec-10 4 4 04 04 Dec 2010 18.5 0.14 8.02 7.58 43.8 Dec-10 5 1 05 01 Dec 2010 16.4 0.28 7.31 7.78 57.2 Dec-10 5 2 05 02 Dec 2010 16.3 0.28 7.48 7.81 62.1 Dec-10 5 3 05 03 Dec 2010 16.4 0.28 7.68 7.82 33.8 Dec-10 5 4 05 04 Dec 2010 16.3 0.25 7.8 7.84 37.2 Dec-10 6 1 06 01 Dec 2010 17.4 0.21 6.4 7.34 116.4 Dec-10 6 2 06 02 Dec 2010 17.4 0.19 6.1 7.4 121.9 Dec-10 6 3 06 03 Dec 2010 17.2 0.16 6.95 7.55 81.1 Dec-10 6 4 06 04 Dec 2010 17.1 0.16 7.2 7.61 57 Dec-10 7 1 07 01 Dec 2010 14.3 0.09 6.4 6.93 52.7 Dec-10 7 2 07 02 Dec 2010 14.2 0.09 6.43 6.93 72.3 Dec-10 7 3 07 03 Dec 2010 14.2 0.09 6.32 6.9 78.9

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Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Dec-10 7 4 07 04 Dec 2010 14.2 0.09 6.3 6.89 91.2 Dec-10 8 1 08 01 Dec 2010 14.2 0.04 7.86 5.96 160.8 Dec-10 8 2 08 02 Dec 2010 14.2 0.04 7.33 5.95 156.7 Dec-10 8 3 08 03 Dec 2010 13.9 0.04 6.88 5.98 163.9 Dec-10 8 4 08 04 Dec 2010 13.9 0.04 6.99 5.96 164.4 Dec-10 9 1 09 01 Dec 2010 . . . . . Dec-10 9 2 09 02 Dec 2010 . . . . . Dec-10 9 3 09 03 Dec 2010 . . . . . Dec-10 9 4 09 04 Dec 2010 . . . . . Dec-10 10 1 10 01 Dec 2010 . . . . . Dec-10 10 2 10 02 Dec 2010 . . . . . Dec-10 10 3 10 03 Dec 2010 . . . . . Dec-10 10 4 10 04 Dec 2010 . . . . . Dec-10 11 1 11 01 Dec 2010 15.7 . 7.68 6.3 . Dec-10 11 2 11 02 Dec 2010 15.6 0.19 6.6 6.2 166.9 Dec-10 11 3 11 03 Dec 2010 15.6 0.02 6.73 6.16 162.2 Dec-10 11 4 11 04 Dec 2010 15.6 0.02 6.68 6.15 162.3 Dec-10 12 1 12 01 Dec 2010 11.5 0.01 7.84 5.33 219.6 Dec-10 12 2 12 02 Dec 2010 13.9 0.06 6.39 5.72 185.9 Dec-10 12 3 12 03 Dec 2010 11.8 0.02 7.13 5.62 193.1 Dec-10 12 4 12 04 Dec 2010 11.6 0.02 6.8 5.57 216 May-11 1 1 01 01 May 2011 24.2 0.07 7.03 7.6 -15 May-11 1 2 01 02 May 2011 24.6 0.07 6.73 7.7 23 May-11 1 3 01 03 May 2011 24.5 0.07 7.34 7.78 40 May-11 1 4 01 04 May 2011 24.3 0.07 6.99 7.76 39 May-11 2 1 02 01 May 2011 24.8 0.07 7.63 7.79 154 May-11 2 2 02 02 May 2011 24.8 0.07 7.74 7.79 155 May-11 2 3 02 03 May 2011 24.9 0.07 8.13 7.81 157 May-11 2 4 02 04 May 2011 25.1 0.07 8.07 7.82 158 May-11 3 1 03 01 May 2011 24.8 0.07 7.72 7.59 -26 May-11 3 2 03 02 May 2011 24.8 0.07 7.2 7.68 -6 May-11 3 3 03 03 May 2011 24.4 0.07 7.69 7.73 1 May-11 3 4 03 04 May 2011 24.3 0.07 7.55 7.74 -2 May-11 4 1 04 01 May 2011 24.1 0.07 7.41 7.7 35 May-11 4 2 04 02 May 2011 24.5 0.07 6.92 7.64 -1 May-11 4 3 04 03 May 2011 24.5 0.07 7.45 7.69 -3 May-11 4 4 04 04 May 2011 24.4 0.07 7.1 7.72 17 May-11 5 1 05 01 May 2011 25.6 0.17 8.52 8.16 65 128

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) May-11 5 2 05 02 May 2011 25.6 0.17 8.85 8.15 62 May-11 5 3 05 03 May 2011 25.5 0.17 8.82 8.15 48 May-11 5 4 05 04 May 2011 25.5 0.18 9.08 8.2 48 May-11 6 1 06 01 May 2011 26.1 0.2 8.23 7.76 -21 May-11 6 2 06 02 May 2011 25.9 0.2 8.36 7.77 -22 May-11 6 3 06 03 May 2011 26 0.2 8.23 7.78 -25 May-11 6 4 06 04 May 2011 25.6 0.2 8.27 7.73 -34 May-11 7 1 07 01 May 2011 27.3 0.07 8.72 7.84 0 May-11 7 2 07 02 May 2011 27.7 0.07 9.27 7.99 -24 May-11 7 3 07 03 May 2011 26.8 0.07 8.94 8.03 13 May-11 7 4 07 04 May 2011 27.1 0.07 9.19 8.11 24 May-11 8 1 08 01 May 2011 28.6 0.05 6.36 7.08 5 May-11 8 2 08 02 May 2011 28.8 0.05 6.64 7.12 2 May-11 8 3 08 03 May 2011 29.1 0.05 6.26 7.11 -7 May-11 8 4 08 04 May 2011 29.2 0.05 6.52 7.18 -1 May-11 9 1 09 01 May 2011 21.3 0.78 6.46 8.26 124.8 May-11 9 2 09 02 May 2011 21.8 0.92 8.74 8.7 103.7 May-11 9 3 09 03 May 2011 21.8 0.92 7.98 8.91 103.9 May-11 9 4 09 04 May 2011 21.8 0.93 8.14 8.9 102.9 May-11 10 1 10 01 May 2011 24.6 0.15 13.37 9.87 96.7 May-11 10 2 10 02 May 2011 24.1 0.15 13.1 9.83 92.7 May-11 10 3 10 03 May 2011 23.6 0.16 13.5 9.9 111 May-11 10 4 10 04 May 2011 22.6 0.16 8.4 8.74 67.9 May-11 11 1 11 01 May 2011 23.9 0.06 5.2 7.07 176.8 May-11 11 2 11 02 May 2011 24.1 0.06 5.45 7.14 181.2 May-11 11 3 11 03 May 2011 24.1 0.06 5.07 7.19 183 May-11 11 4 11 04 May 2011 24.2 0.06 5.07 7.19 179.1 May-11 12 1 12 01 May 2011 24.3 0.05 5.64 7.03 164.5 May-11 12 2 12 02 May 2011 24.3 0.05 5.57 6.88 148.9 May-11 12 3 12 03 May 2011 24.5 0.05 5.64 6.96 130 May-11 12 4 12 04 May 2011 24.6 0.05 5.96 6.98 143.8 May-11 13 1 13 01 May 2011 27.4 17.39 5 8.44 94.2 May-11 13 2 13 02 May 2011 27.4 17.38 5.12 8.45 94.2 May-11 13 3 13 03 May 2011 27.4 17.39 5.15 8.44 93.2 May-11 13 4 13 04 May 2011 27.4 17.39 5.2 8.44 92.9 May-11 14 1 14 01 May 2011 29 15.86 5.93 7.98 225 May-11 14 2 14 02 May 2011 29 15.87 6.2 7.96 206 May-11 14 3 14 03 May 2011 29 15.86 6.18 7.98 206 129

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) May-11 14 4 14 04 May 2011 29.9 17.62 4.79 7.88 118 May-11 15 1 15 01 May 2011 . . . . . May-11 15 2 15 02 May 2011 . . . . . May-11 15 3 15 03 May 2011 . . . . . May-11 15 4 15 04 May 2011 . . . . . May-11 16 1 16 01 May 2011 . . . . . May-11 16 2 16 02 May 2011 . . . . . May-11 16 3 16 03 May 2011 . . . . . May-11 16 4 16 04 May 2011 . . . . . Jun 01 01 Jun A2011 1 1 A2011 30.8 0.08 5.45 7.87 149.9 Jun 01 02 Jun A2011 1 2 A2011 30.9 0.08 6.21 7.96 186.7 Jun 01 03 Jun A2011 1 3 A2011 31 0.08 5.95 8 191.3 Jun 01 04 Jun A2011 1 4 A2011 31.3 0.08 7.03 8.08 195 Jun 02 01 Jun A2011 2 1 A2011 30.4 0.08 6.82 7.98 47.6 Jun 02 02 Jun A2011 2 2 A2011 31.1 0.08 6.87 7.92 42.1 Jun 02 03 Jun A2011 2 3 A2011 31.2 0.08 6.87 7.9 28.9 Jun 02 04 Jun A2011 2 4 A2011 31 0.08 6.77 7.9 27.4 Jun 03 01 Jun A2011 3 1 A2011 30.6 0.08 6.5 7.84 46.7 Jun 03 02 Jun A2011 3 2 A2011 30.1 0.08 6.58 8 71.2 Jun 03 03 Jun A2011 3 3 A2011 30.8 0.08 5.59 7.93 62 Jun 03 04 Jun A2011 3 4 A2011 31.1 0.08 6.17 7.9 13.8 Jun 04 01 Jun A2011 4 1 A2011 31.1 0.08 6.98 8.1 25.3 Jun 04 02 Jun A2011 4 2 A2011 31 0.08 7.06 7.97 -28 Jun 04 03 Jun A2011 4 3 A2011 31.4 0.08 6.75 8.04 21.5 Jun 04 04 Jun A2011 4 4 A2011 31.2 0.08 7.76 8.21 45 Jun 05 01 Jun A2011 5 1 A2011 29.6 0.27 5.88 7.78 -25 Jun 05 02 Jun A2011 5 2 A2011 29.8 0.27 6.1 7.81 -14.3 Jun 05 03 Jun A2011 5 3 A2011 29.7 0.29 5.99 7.81 -5.3 130

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Jun 05 04 Jun A2011 5 4 A2011 30 0.33 6.06 7.8 6.2 Jun 06 01 Jun A2011 6 1 A2011 31.7 0.29 5.88 7.69 -59.4 Jun 06 02 Jun A2011 6 2 A2011 31.7 0.28 5.79 7.66 -83.4 Jun 06 03 Jun A2011 6 3 A2011 31.7 0.28 5.49 7.58 -63.7 Jun 06 04 Jun A2011 6 4 A2011 31.6 0.28 5.99 7.74 -40.6 Jun 07 01 Jun A2011 7 1 A2011 29.9 0.1 6.54 7.74 -33 Jun 07 02 Jun A2011 7 2 A2011 30.2 0.1 7.42 7.78 -34.7 Jun 07 03 Jun A2011 7 3 A2011 30.3 0.11 7.43 7.84 -36.7 Jun 07 04 Jun A2011 7 4 A2011 29.8 0.1 7.56 7.75 -16.2 Jun 08 01 Jun A2011 8 1 A2011 27.6 0.05 5.31 7.02 -0.1 Jun 08 02 Jun A2011 8 2 A2011 27.5 0.05 5.68 6.85 -2.6 Jun 08 03 Jun A2011 8 3 A2011 27.8 0.05 5.32 6.95 -5.8 Jun 08 04 Jun A2011 8 4 A2011 27.9 0.05 5.56 6.97 -8.8 Jun 09 01 Jun A2011 9 1 A2011 29.5 1.54 5.55 9.05 137.9 Jun 09 02 Jun A2011 9 2 A2011 29.5 1.54 5.36 8.99 138.1 Jun 09 03 Jun A2011 9 3 A2011 29.5 1.54 5.12 8.95 138.5 Jun 09 04 Jun A2011 9 4 A2011 29.5 1.54 5.05 8.87 139.2 Jun 10 01 Jun A2011 10 1 A2011 26.9 0.66 2.59 8.94 105.1 Jun 10 02 Jun A2011 10 2 A2011 27.2 0.66 3.76 9.03 126.4 Jun 10 03 Jun A2011 10 3 A2011 27 0.67 2.73 8.97 142.3 Jun 10 04 Jun A2011 10 4 A2011 26.8 0.67 2.07 8.76 163 Jun 11 01 Jun A2011 11 1 A2011 28.2 0.12 3.41 6.88 5.1 Jun 11 02 Jun A2011 11 2 A2011 28.2 0.12 3.49 6.86 16 Jun 11 03 Jun A2011 11 3 A2011 28.2 0.12 3.51 6.84 21.9 Jun 11 04 Jun A2011 11 4 A2011 28.3 0.12 3.2 6.83 12.2 131

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Jun 12 01 Jun A2011 12 1 A2011 25.4 0.03 4.32 6.76 72.8 Jun 12 02 Jun A2011 12 2 A2011 28.2 0.05 4.16 6.75 56.7 Jun 12 03 Jun A2011 12 3 A2011 24.9 0.02 4.29 6.81 77.2 Jun 12 04 Jun A2011 12 4 A2011 26.1 0.03 4.06 6.78 53.3 Jun 13 01 Jun A2011 13 1 A2011 28.4 18.31 4.57 8.38 222.5 Jun 13 02 Jun A2011 13 2 A2011 28.4 18.28 4.37 8.34 217.4 Jun 13 03 Jun A2011 13 3 A2011 28.4 18.26 4.45 8.3 213.5 Jun 13 04 Jun A2011 13 4 A2011 28.4 18.26 4.45 8.29 209.5 Jun 14 01 Jun A2011 14 1 A2011 29.9 17.6 4.82 7.85 96.3 Jun 14 02 Jun A2011 14 2 A2011 29.9 17.62 5.22 7.89 116.3 Jun 14 03 Jun A2011 14 3 A2011 30 17.62 4.68 7.88 116.6 Jun 14 04 Jun A2011 14 4 A2011 29.9 17.62 4.79 7.88 117.8 Jun 15 01 Jun A2011 15 1 A2011 30.5 17.05 6.73 8.54 198.6 Jun 15 02 Jun A2011 15 2 A2011 30.6 17.04 6.5 8.52 200.1 Jun 15 03 Jun A2011 15 3 A2011 30.6 17.02 6.49 8.52 200.6 Jun 15 04 Jun A2011 15 4 A2011 30.4 17.08 6.71 8.54 200.7 Jun 16 01 Jun A2011 16 1 A2011 31.9 5.04 7.47 8.77 138.6 Jun 16 02 Jun A2011 16 2 A2011 32 5.08 7.55 8.85 137 Jun 16 03 Jun A2011 16 3 A2011 32 5.02 7.52 8.87 137.3 Jun 16 04 Jun A2011 16 4 A2011 32.1 5.01 7.69 8.88 137.1 Jun 01 01 Jun B2011 1 1 B2011 30.4 0.08 4.46 7.67 94.9 Jun 01 02 Jun B2011 1 2 B2011 30.4 0.08 4.42 7.67 94 Jun 01 03 Jun B2011 1 3 B2011 30.4 0.08 4.6 7.67 96.3 Jun 01 04 Jun B2011 1 4 B2011 30.4 0.08 4.64 7.68 94.9 Jun 02 01 Jun B2011 2 1 B2011 30.7 0.08 5.25 7.8 89.2 132

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Jun 02 02 Jun B2011 2 2 B2011 30.8 0.08 5.49 7.82 86.2 Jun 02 03 Jun B2011 2 3 B2011 30.9 0.08 5.32 7.84 83.1 Jun 02 04 Jun B2011 2 4 B2011 30.9 0.08 5.66 7.85 82.7 Jun 03 01 Jun B2011 3 1 B2011 31.4 0.09 5.55 7.85 69.8 Jun 03 02 Jun B2011 3 2 B2011 31.8 0.09 5.7 7.87 60.3 Jun 03 03 Jun B2011 3 3 B2011 31.6 0.09 5.92 7.87 69.1 Jun 03 04 Jun B2011 3 4 B2011 31.8 0.09 6.06 7.88 66.3 Jun 04 01 Jun B2011 4 1 B2011 31.7 0.09 6.25 7.96 53.3 Jun 04 02 Jun B2011 4 2 B2011 31.2 0.09 5.43 7.88 64.5 Jun 04 03 Jun B2011 4 3 B2011 31.2 0.09 5.09 7.9 57.4 Jun 04 04 Jun B2011 4 4 B2011 31.2 0.09 5.39 8.01 51.4 Jun 05 01 Jun B2011 5 1 B2011 30.8 0.3 6.74 7.52 185 Jun 05 02 Jun B2011 5 2 B2011 30.8 0.3 4.35 7.64 189 Jun 05 03 Jun B2011 5 3 B2011 . . . . . Jun 05 04 Jun B2011 5 4 B2011 . . . . . Jun 06 01 Jun B2011 6 1 B2011 31.2 0.39 5.23 7.75 47 Jun 06 02 Jun B2011 6 2 B2011 . . . . . Jun 06 03 Jun B2011 6 3 B2011 . . . . . Jun 06 04 Jun B2011 6 4 B2011 . . . . . Jun 07 01 Jun B2011 7 1 B2011 30.5 0.09 4.74 7.71 54 Jun 07 02 Jun B2011 7 2 B2011 30.5 0.09 4.7 7.62 -14 Jun 07 03 Jun B2011 7 3 B2011 30.5 0.09 4.54 7.6 -23 Jun 07 04 Jun B2011 7 4 B2011 30.5 0.09 4.77 7.65 -7 Jun 08 01 Jun B2011 8 1 B2011 . . . . . Jun 08 02 Jun B2011 8 2 B2011 . . . . . 133

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Jun 08 03 Jun B2011 8 3 B2011 . . . . . Jun 08 04 Jun B2011 8 4 B2011 . . . . . Jun 09 01 Jun B2011 9 1 B2011 29.8 1.87 5.04 8.22 60 Jun 09 02 Jun B2011 9 2 B2011 29.9 1.94 6.35 8.77 67 Jun 09 03 Jun B2011 9 3 B2011 29.9 1.94 6.15 8.92 77 Jun 09 04 Jun B2011 9 4 B2011 29.9 1.95 6.6 8.99 83 Jun 10 01 Jun B2011 10 1 B2011 27.3 1.51 1.02 7.65 -71 Jun 10 02 Jun B2011 10 2 B2011 27.4 1.51 0.89 7.8 -76 Jun 10 03 Jun B2011 10 3 B2011 27.3 1.5 0.87 7.98 -69 Jun 10 04 Jun B2011 10 4 B2011 27.8 1.48 1.67 7.48 -148 Jun 11 01 Jun B2011 11 1 B2011 29 0.22 3.84 7.17 44 Jun 11 02 Jun B2011 11 2 B2011 29.4 0.29 4.09 7.03 60 Jun 11 03 Jun B2011 11 3 B2011 29.3 0.41 3.83 6.94 -41 Jun 11 04 Jun B2011 11 4 B2011 29.4 0.49 4.59 7.3 36 Jun 12 01 Jun B2011 12 1 B2011 28.3 0.1 3.71 6.59 14 Jun 12 02 Jun B2011 12 2 B2011 29.2 0.11 4.12 6.71 6 Jun 12 03 Jun B2011 12 3 B2011 29.1 0.11 3.97 6.74 2 Jun 12 04 Jun B2011 12 4 B2011 29.2 0.1 3.73 6.76 20 Jun 13 01 Jun B2011 13 1 B2011 29.2 19.33 5.7 8.45 176 Jun 13 02 Jun B2011 13 2 B2011 29.2 19.33 5.65 8.45 178 Jun 13 03 Jun B2011 13 3 B2011 29.3 19.29 5.57 8.46 179 Jun 13 04 Jun B2011 13 4 B2011 29.2 19.29 5.5 8.44 165 Jun 14 01 Jun B2011 14 1 B2011 30.8 18.1 3.66 7.86 89 Jun 14 02 Jun B2011 14 2 B2011 30.9 18.13 3.76 7.9 95 Jun 14 03 Jun B2011 14 3 B2011 31 18.08 3.91 7.9 101 134

Date Site Plot PlotID Temp(˚C) Salinity (ppt) DO(mg/L) pH ORP (mV) Jun 14 04 Jun B2011 14 4 B2011 30.8 18.09 3.81 7.92 111 Jun 15 01 Jun B2011 15 1 B2011 29.9 23.17 5.08 8.37 161 Jun 15 02 Jun B2011 15 2 B2011 30 23.18 5.16 8.4 158 Jun 15 03 Jun B2011 15 3 B2011 30 23.15 5.12 8.45 157 Jun 15 04 Jun B2011 15 4 B2011 30.1 23.14 5.16 8.47 156 Jun 16 01 Jun B2011 16 1 B2011 30.2 4.96 6.21 8.23 107 Jun 16 02 Jun B2011 16 2 B2011 30.1 4.88 6.1 8.26 105 Jun 16 03 Jun B2011 16 3 B2011 30.2 4.96 6.21 8.27 105 Jun 16 04 Jun B2011 16 4 B2011 30.2 4.97 6.24 8.28 105

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