Abstract the Effects of Sewage Effluent On

ABSTRACT

THE EFFECTS OF SEWAGE EFFLUENT ON MACROALGAL AND SEAGRASS ABUNDANCE, DRY WEIGHT AND DIVERSITY WITHIN GRAHAMS HARBOR, SAN SALVADOR, BAHAMAS

By Krista E. Holman

The algal and seagrass abundance, dry weight and diversity were surveyed at a site along the coast of Grahams Harbor in San Salvador, Bahamas in the Caribbean during June 2004. One hundred randomly distributed plots were surveyed via SCUBA to determine the influence of a sewage effluent pipe on algae and seagrass assemblages. The objectives of the study were to determine if there was a distinction among regions of marine algae and seagrasses due to the effluent, and if distance from the effluent source influenced diversity (measured through Shannon’s Diversity Index, H', Simpson’s Diversity Index, D' and Evenness, E'), species richness, percent cover and dry weight. Results showed that the dry weight and percent cover decreased significantly with a decrease in distance from the pipe. Additionally, regions outside the flow of the effluent pipe showed significant increases in species number.

THE EFFECTS OF SEWAGE EFFLUENT ON MACROALGAL AND SEAGRASS ABUNDANCE, DRY WEIGHT AND DIVERSITY WITHIN GRAHAMS HARBOR, SAN SALVADOR, BAHAMAS

A Practicum

Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Environmental Science Institute of Environmental Sciences By Krista Elaine Holman Miami University Oxford, Ohio 2007

Advisor ______Dr. R. Hays Cummins

Reader ______Dr. Mark R. Boardman

Reader ______Dr. Mary C. Henry

TABLE OF CONTENTS LIST OF FIGURES...... IV

LIST OF TABLES ...... V

ACKNOWLEDGMENTS ...... VI I. INTRODUCTION ...... 1 II. EUTROPHICATION ...... 3 DEFINITION AND CHARACTERISTICS ...... 3 Limiting Nutrients in Algal Growth ...... 4 Eutrophic Dominant Algae...... 5

CAUSES OF EUTROPHICATION...... 6 CONSEQUENCES TO THE ECOSYSTEMS ...... 8 Effects on Diversity, Abundance and Evenness ...... 10 Effects on dry weight...... 10 III. STUDY SITE...... 11 HISTORY OF FIELD STATION...... 11 GEOGRAPHICAL DESCRIPTION OF SAN SALVADOR...... 13 PHYSICAL DESCRIPTION OF SAN SALVADOR ...... 13 DESCRIPTION OF STUDY SITE...... 16

IV. METHODS...... 18 SAMPLING SITES...... 20 DATA COLLECTION...... 22 ANALYSIS OF DIVERSITY...... 23 STATISTICAL ANALYSIS...... 25

V. RESULTS...... 30 STATISTICAL ANALYSIS USING THREE MODELS ...... 34 MODEL 3 ANALYSIS...... 36

V. DISCUSSION...... 59 DRY WEIGHT AND PERCENT COVER...... 59 DIVERSITY...... 59 RICHNESS ...... 60

VI. LITERATURE CITED ...... 62

ii VII. APPENDICES ...... 67 APPENDIX A...... 67

DISTANCE FROM THE PIPE...... 67 APPENDIX B: ...... 70

NUMBER OF INDIVIDUALS (IN CLASSES) FROM ALL TRANSECTS...... 70 APPENDIX C: ...... 80

TOTAL DRY WEIGHT ...... 80

APPENDIX D: ...... 81

TOTAL PERCENT COVER AT EACH SAMPLING SITE...... 81

APPENDIX E. DIVERSITY AND SPECIES RICHNESS INDICES...... 82

iii LIST OF FIGURES

FIGURE 1. GENERAL SCHEME OF EUTROPHICATION EFFECTS 9

FIGURE 2. PICTURE OF GERACE RESEARCH CENTER 12

FIGURE 3. LOCATION OF SAN SALVADOR, BAHAMAS 15

FIGURE 4. SAMPLING SITE LOCATION AND CURRENT FLOW 17

FIGURE 5. TRANSECT LINES LOCATED AT SEWAGE PIPE 19

FIGURE 6. SAMPLING SITE TRANSECT 21

FIGURE 7. SAMPLING LOCATION USING MODEL 1 26

FIGURE 8. SAMPLING LOCATION USING MODEL 2 27

FIGURE 9. SAMPLING LOCATION USING MODEL 3 29

FIGURE 10. NUMBER OF INDIVIDUALS OF EACH OF THE TOP FIVE MOST COMMON SPECIES 37

FIGURE 11. MEAN COMPARISON OF DIVERSITY, DRY WEIGHT, AND PERCENT COVER 39

FIGURE 12. DRY WEIGHT SPATIAL ANALYSIS MAP 42

FIGURE 13. PERCENT COVER SPATIAL ANALYSIS MAP 43

FIGURE 14. MARAGALEF’S SPECIES RICHNESS INDEX MEAN VALUES 45

FIGURE 15. MARAGALEF’S SPECIES RICHNESS SPATIAL ANALYSIS MAP 46

FIGURE 16. STANDARD LINEAR REGRESSION OF DRY WEIGHT AND PERCENT COVER 48

FIGURE 17. SIMPSON’S DIVERSITY INDEX SPATIAL ANALYSIS MAP 50

FIGURE 18. SHANNON’S DIVERSITY INDEX SPATIAL ANALYSIS MAP 51

FIGURE 19. SPECIES NUMBER SPATIAL ANALYSIS MAP 52

FIGURE 20. EVENNESS INDEX SPATIAL ANALYSIS MAP 53

FIGURE 21. LOCATION OF DERBESIA SPP. SPATIAL ANAYLSIS MAP 56

FIGURE 22. LOCATION OF BRYOPSIS SPP. SPATIAL ANALYSIS MAP 57

FIGURE 23. LOCATION OF BATAPHORA OERSTEDII SPATIAL ANALYSIS MAP 58

iv LIST OF TABLES

TABLE 1. RANK ABUNDANCE AND FREQUENCY FOR SAMPLING REGION IN GRAHAMS HARBOR 31

TABLE 2. PEARSON CORRELATION MATRIX FOR DIVERSITY INDICES 33

TABLE 3. LINEAR REGRESSION COMPARISON OF DIVERSITY INDICES 35

TABLE 4. FIVE MOST COMMON TAXA IN EACH REGION 38

TABLE 5. LINEAR REGRESSION ANALYSIS OF SPECIES DIVERSITY, DRY WEIGHT AND PERCENT COVER 41

TABLE 6. ONE WAY ANOVA ANALYSIS OF MARAGALEF’S SPECIES RICHNESS 44

TABLE 7. LINEAR REGRESSION ANALYSIS OF SPECIES RICHNESS AND DIVERSITY (D′, H′, E). 48

TABLE 8. BONFERRONI ANALYSIS COMPARING DIVERSITY, DRY WEIGHT AND PERCENT COVER 55

v ACKNOWLEDGMENTS

I would like to take this opportunity to thank the many people who have assisted me throughout the process of completing my research. First and foremost a special thanks to my advisor, Dr. Hays Cummins for all your suggestions and assistance in helping me through this entire process. Thanks to my committee members Dr. Mark Boardman and Dr. Mary Henry for their support, recommendations and extra time. Thanks to Bob Schaefer for, helping me work through my statistical analysis from 2000 miles away. Thank you to all of the undergraduate researchers for helping me gather and analyze data under less than ideal circumstances in the Bahamas.

I would like to thank all the staff of the Institute of Environmental Sciences for all their support during my time at Miami University. Special thanks to Betty Haven and Christine Ingham for helping answer all my numerous emails and working with my difficult schedule.

I would like to thank my family for all their support and putting up with my sporadic and varied adventures and endeavors, particularly my parents for always supporting me no matter what decisions I make. A special thank you to Caleb for being my inspiration and for sitting down with me to work through tedious problems, for showing me how to be positive and encouraging me when I was feeling frustrated with the entire process.

vi I. INTRODUCTION

Marine environments and coastal waters are faced by threats of exploitation and eutrophication on a global scale (Johansson, et al., 1998). Threatened coastal areas are one of the world’s most productive regions on earth (Tibbetts, 2004) and provide a significant amount of ecological diversity (Hillebrand and Sommer, 2000). The productivity of these systems is due in part, to nutrient enrichment from terrestrial runoff into shallow receiving waters. High productivity is also due to the energy received from wind, tidal currents and thermohaline flow (Livingston, 2001). This nutrient input forms the base of marine food webs and ultimately forms the community assemblages of marine systems.

The addition of nutrients from anthropogenic systems (cultural eutrophication) can greatly alter marine ecosystems and the balance of nutrient availability (Karez, et al., 2004). Consequently, nutrient enrichment may result in an increase in macroalgal dry weight. Furthermore, this increase in macroalgal dry weight can cause in increase in “herbivore populations, competition or toxicity towards flora and fauna, alteration of the sediment, recycling of nutrients and pollutants in the ecosystem, and a nuisance for local residents and the reduction of tourism” (Morand and Merceron, 2005). Island communities may contribute to enrichment of these aquatic ecosystems due to insufficient sewage treatment (CEP, 1999). This is often the result of inaccessibility to resources for proper sewage treatment and/or inadequate governmental funding. As a consequence, these islands are unable to treat sewage prior to release into coastal waters and contribute to the depletion of valuable natural resources for local economies. Recent policies have begun to focus on methods for these small island communities to explore adequate methods for sewage disposal to reduce aquatic nutrient inputs (Livingston, 2001; CEP, 1999).

Assembly rules outlined by Diamond (1975) identify that ecological communities are structured based on a set of constraints (rules) on community formation and maintenance (assembly). These assembly rules such as light, depth, distance of parent colonies and nutrient composition, etc, are thought to dictate the structure of marine algal communities. As a result, early stages of eutrophication may alter assembly rules by influencing species abundance, dry

weight, and diversity of macroalgae and thereby altering the future food base and community structure for the entire marine ecosystem (Diamond, 1975).

This study focused on the effects of sewage input along the coast of San Salvador, Bahamas in the Caribbean. The study analyzed a marine algal and seagrass community that has been exposed to periodic surges in sewage effluent over the last four decades. This study took place following one of those surges in eutrophication caused by an increase in people at the Gerace Research Center. The study tested the hypotheses that (i) in the presence of eutrophication, there will be a decrease in diversity: Evenness, E', Shannon’s Diversity Index, H', Simpson’s Diversity Index, D' and species richness (S, R) and (ii) in the presence of eutrophication, there will be an increase in dry weight and percent cover.

II. EUTROPHICATION

DEFINITION AND CHARACTERISTICS

The effects of eutrophication on freshwater and terrestrial ecosystems have extensively been documented (Marcus, 1980; Tilman, 1982, 1997; Pringle, 1990; Carrick et al., 1988; Miller, 1992). However, the effect of eutrophication on benthic marine macroalgae has only recently been analyzed. This is due, in part, to the prior assumption that large quantities of water are exchanged in marine ecosystems resulting in an increased likelihood that nutrients will be diluted and removed from the system (Richardson, K and Jørgensen B., 1996). However, in the last four decades, there has been an increase globally in the biomass of green algae. This increase is characterized by algal blooms known as “green tides” which are attributed to anthropogenic eutrophication (Morand and Merceron, 2005). Nutrient enrichment is thought to be at the initiation of every noted macroalgal bloom (Karez, 2004; Morand and Merceron, 2005).

Two mechanisms seem to initiate macroalgal blooms. These mechanisms are either the bottom-up effects of nutrient loading (i.e. cultural eutrophication) or grazer-related top-down process (i.e. gazer reduction). Decreases in the number of grazing fishes by overfishing or disease can result in macroalgal blooms (Valiela et al, 1997). Although green algal blooms tend to be less toxic than other types of algal blooms, green blooms have been on the rise over the last four decades (Morand and Merceron, 2005; Valiela, et al., 1997). Macroalgal proliferation, a bottom up effect, provides an increase in nutrient supply to other organisms. As a result, organisms that feed on macroalgae may have an altered community structure given the changes in the food base.

Eutrophication has a variety of definitions and sources but is defined as an “increase in the rate of supply of organic carbon to an ecosystem” (Nixon, 1995). This organic nutrient enrichment may be due to “natural” or anthropogenic sources. Eutrophication as described by Gray (1992) is a “process of enrichment of water with nitrogen and phosphorus, leading sometimes to phytoplankton blooms.” In addition, eutrophication can be described by various

3 abiotic measures such as changes in plant nutrients or oxygen concentrations (Gray, 1992). Eutrophication tends to significantly affect regions that are semi-enclosed such as lagoons (Kitsiou and Karydis, 2000). This is in part due to the shallow depths of many lagoons and the decreased current velocities which typically cycle nutrients.

Limiting Nutrients in Algal Growth

There has been considerable research identifying nutrients that limit the growth and distribution of marine macroalgae (Lobban, 1985; Gault, 1998; Richardson and Jørgensen 1996; Gray, 1992). Nitrogen in particular, is considered the main limiting nutrient in marine algal and seagrass growth. According to Lobban, nitrogen, phosphorus and iron occur in seaweeds at higher concentrations compared to seawater (1985). However, the ratio of N/P necessary for one algal species may differ compared to another algal species (Tilman et al., 1982; Tilman, 1997). Consequently, increasing the availability of nutrients is known to stimulate the growth of most macroalgae in coastal waters (Valiela et al., 1996; Karez et al., 2004). However, due to differences in nutrient requirements of algae, there is often an increase in a few dominant species (decrease in evenness) in eutrophic ecosystems. Increase in nutrients may not only alter phytoplankton and macroalgae community assemblages but may also alter the structure of the entire marine food web (Richardson and Jørgensen, 1996; Karez et al., 2004). The growth of non-dominant species may be inhibited by sewage effluent given that effluent can influence the levels of salinity and therefore alter the chemical requirements for a given algal species (Guist et al., 1976).

Limiting nutrients are also dependent on the time of year. For example, nitrogen may limit growth only during the peak seasons for primary production. Furthermore, the type of sediment substrate can determine the limiting nutrient. Phosphate will adsorb to carbonates and therefore, may limit growth in tropical carbonate rich environments. In nutrient poor or tropical waters, macroalgal blooms are often more diverse compared to those in temperate waters.

Eutrophic Dominant Algae

Increased nutrients in coastal systems due to point source eutrophication may influence distribution, dry weight, and diversity of macroalgae species, providing a competitive edge for some species (Johansson, 1998). As a result, eutrophic environments are often characterized by certain taxa of algae. Many macroalgae are fast growing and have high requirements for nitrogen and phosphorus (Karez et al., 2004). These algae tend to be toxic and opportunistic in nature (i.e. Caulepra) (Morand and Merceron, 2005). In addition, these algae tend to be fast growing, filamentous annuals. As a result, declines in perennial algae are thought to occur in enriched coastal waters due to an increase in growth of annual species (Johansson et al., 1998). Most eutrophic environments are characterized by proliferating algae such as Ulva, Chaetomorpha, Enteromorpha, Gracilaria (Virnstein and Carbonara, 1985) and Cladophora (Thybo-Christesen et al., 1993). In particular, growth of the genera Ulva, Enteromorpha, and Cladophora are thought to be stimulated specifically by sewage effluent (Guist et al., 1976; Karez et al., 2004). This is mainly due to the fact that acetate, a byproduct of bacterial decomposition of sewage, is easily consumed by algae such as Ulva (Guist et al., 1976).

CAUSES OF EUTROPHICATION

Nutrient enrichment is most commonly the result of agricultural runoff, sewage discharge, industrial pollutants and aquaculture byproducts (Morand and Merceron, 2005); although the main source of anthropogenic eutrophication is thought to be associated with agricultural runoff. Lagoons in particular can serve to trap nutrients produced from human instigated runoff. This is due in part to the shallow topography, significant photosynthetic activity, weaker rates of water renewal and rapid water warming (Morand and Merceron 2005) that occurs in lagoons.

Traditional methods for treating waste typically do not reduce the amount of nitrogen put into an ecosystem (Ho, 1998). This addition of nitrogen can cause significant impacts on marine resources and human health. Studies indicate an overall increase globally in nitrogen over the last century (Valiela, 1997; Morand and Merceron, 2005). The most common point source of nutrients includes centralized wastewater treatment plants and industrial discharges.

Some forms of tertiary sewage treatment appear to decrease substantial nitrogen quantities (the main contributor being human waste) (Kasten, 1998; Savage and Elmgren, 2003). Wastewater and sewage effluent appear to be the largest sources of nitrogen (48%) followed by atmospheric deposition (30%) and fertilizer use (15%) (Gault, 1998). In Florida, nitrogen from wastewater has been linked to decreases in seagrass and marine sponge die offs, mangrove declines, and phytoplankton and algal blooms. Seventy Percent of nutrient loading is attributed to domestic wastewater discharges from land-based resources (Gault, 1998).

According to Rutger Tosenberg of the Institute of Marine Research, “it seems probable that nutrient inputs, principally of Nitrogen from terrestrial drainage, atmospheric deposition and urban discharges have increased progressively throughout the world this century. Shallow coastal areas and shelf seas in many parts of the world can thus be regarded as potentially eutrophic risk areas. Indeed eutrophication of inshore marine areas may not be a potential but a present threat” (Rosenberg, 1985). In particular, areas that are semi-enclosed seas restrict

6 water exchange. Lagoons, bays and harbors have been shown to suffer the most substantial effects of cultural eutrophication (Nixon, 1990).

CONSEQUENCES TO THE ECOSYSTEMS

Significant increases in the proliferation of macro and microalgae may have numerous and varied effects on the entire aquatic ecosystem (Morand and Merceron, 2005). Although the purification of aquatic systems through the absorption of toxins and excess nutrients from an increase in algae may be beneficial to a certain extent, increases in algal abundance overall have a detrimental effect on the surrounding ecosystems (Sfriso and Marcomini, 1994; Marlea and Haritondidis, 2000). In particular, since primary production in coastal waters is limited by the amount of nitrogen deposited into the system, a eutrophic event (an increase in nitrogen) can cause an increase in primary production. In addition, the increase in production can deplete the amount of dissolved oxygen in the water. Decreases in oxygen can alter fish and mollusk communities (Gault, 1998; 1998; Johnson and Welsh, 1985; Caddy, 2000). Recovery from eutrophic events is thought to require years in order for the marine substrate to replenish nontoxic nitrogen and phosphorus levels (Morand and Merceron, 2005).

Increases in marine algae dry weight can block the amount of light reaching the submerged aquatic vegetation such as seagrasses and sedentary macroalgae. As a result, many fast growing, epiphytic algae and free-floating macroalgae will outcompete seagrasses and lead to seagrass and/or coral death (Kinney and Roman, 1998). In all cases of algal blooms, seagrasses are displaced as well as corals, brown and red algae. The loss of these functional groups, such as brown algae, will influence the entire ecosystem (Johansson et al., 1998). It is hypothesized that the change in assemblages (decline in sea grasses) will alter food webs in marine estuaries (Deegan et al., 2002). Seagrasses are essential components of marine ecosystems in maintaining high rates of productivity, stabilizing food webs, serving as a source of habitat and stabilizing sediments (Figueira de Paula et al., 2003).

Eutrophication to most marine systems will stimulate an increase in the plant biomass. This increase in food availability often results in an increase in fish biomass. According to Gray, this is referred to as the enrichment phase (1992). The enrichment phase can have a cascading influence and many secondary effects (Figure 1).

Enrichment Increase in Increase in Increase in Increase in Fish phase macro algae Phytoplankton Benthos Biomass Biomass Biomass Biomass

Initial effect Change in species Composition

Secondary Shading depth Hypoxia Toxic/ Behavioral effects effect reduction nuisance blooms

Mass growth Extreme effect Ulva, Toxic effects Mortality of species Cladophora

Ultimate effect Anoxia/Mass Mortalities

FIGURE 1. GENERAL SCHEME OF EUTROPHICATION EFFECTS (RICHARDSON AND JORGENSEN 1996, MODIFIED FROM GRAY 1992).

Furthermore, increase in some dominant algal species may lead to toxic conditions. As algae decompose and organic matter is oxidized, noxious conditions may result (Morand and Merceron, 2005). These conditions have been known to lead to death of benthic animals (Johnson and Welsh, 1985). Accumulation of dead algae can also alter the coastal sediment from sand substrate to muddy substrate (Atkins et al., 1993 in Morand and Merceron, 2005).

Effects on Diversity, Abundance and Evenness

Eutrophication has shown to decrease overall ecological diversity, but few studies have been done to determine whether there will be a reduction in the diversity of marine macroalgae due to an eutrophication event (Hillebrand and Sommer, 2000). In addition to the effects of eutrophication, algal diversity can be influenced by a wide variety of features such as periodic disturbance, habitat patchiness, maturity of source species and evolutionary histories (Carrick et al., 1988). Artificial eutrophic studies have led to a decrease of diversity and the increase in the dominance of a few species (i.e. reduced evenness) (Hillebrand and Sommer, 2000).

Effects on dry weight

Biomass (dry weight) is the preferred method of measuring species abundance, richness and evenness (Brathen and Hagberg, 2004; Guo and Rundel, 1997; Chiaruccie et al., 1999; Mason et al., 2002). Dry weight diversity has been shown to increase stability of food webs and as a result, increased diversity in aquatic systems (Aoki and Mizushima, 2001). According to Aoki and Mizushima, if there is an increase in the number of pathways for flow of energy in a food web, there will be greater ecosystem diversity and less ways to destroy that pathway (2001). Although Aoki and Mizushima add that this stability, due to biomass diversity, may not be applicable to other ecological systems (2001). Increases in biomass may also result in an increase in fish and other faunal herbivory. Overtime however, alterations in algae biomass may lead to toxic sediment conditions for the marine flora and fauna (Morand and Merceron, 2005).

III. STUDY SITE

Land drainage, sewage effluent and agricultural fertilizer runoff are known to cause the eutrophication of marine systems (Lobban et al., 1985). As a result, San Salvador, Bahamas is an ideal location to study the influence of eutrophication on algal and seagrass species. This is due in part to the relatively small number of land uses, the limited urban development and the presence of a point source for nutrient input located at the Gerace Research Station. The natural gradient that exists along these coastal waters makes the location an ideal habitat for studying spatial patterns of species abundance and diversity (Middelboe et al., 2004).

HISTORY OF FIELD STATION The Gerace Research Center (formally the Bahamian Field Station) was founded in 1971. The research center encompasses 8 acres and 15 buildings. The research station was originally constructed in 1951 by the United States Navy in part as a down range missile-tracking station, a Coast Guard station and a submarine tracking station. The US military constructed a variety of buildings (Figure 2), an electrical power station and a paved airstrip (Gerace, 1999). The island was opened to tourism in the late 1960’s but many of the facilities and basic infrastructure remained unchanged even after the military left in the early 1960’s. As a result, the field station still utilizes some remaining water storage tanks, military housing, and sewage treatment and removal facilities. Over the years, the research station has served as government buildings, a teachers training college, a high school and finally the Gerace Research Center (Gerace, 1999).

Currently, the research station houses approximately 200 students and faculty and is supported by funds from classes that conduct research at the station. The Gerace Research Center (GRC) employs a cafeteria crew, several maintenance workers, a housekeeping staff, and an administrative staff; most of whom are residents of San Salvador. Some structural improvements have taken place over the last four decades including the installation of new water tank storage but overall the basic infrastructure is still in use and functional some forty years later.

FIGURE 2. AERIAL VIEW OF THE GERACE RESEARCH STATION. (CARLETON GEOLOGY DEPARTMENT, 2002).

GEOGRAPHICAL DESCRIPTION OF SAN SALVADOR

San Salvador today has a population of approximately 800-1000 residents. Many of the inhabitants live in small establishments surrounding the exterior of the island. Much of the island relies on the infrastructure of the capital, Cockburntown. The capital houses the government facilities, the police station, telecommunication facilities and the electrical utilities company. Much of the island, particularly the northeast side of the island, does not have access to electricity and telephone utilities (Gerace et al., 1999). The island has remained relatively unchanged compared to other neighboring Bahamian Islands. The exception to this stasis was the reopening of Club Med during the summer of 2003. The resort employs some local residents and comprises approximately 89 acres of the island. Since the opening of the resort, the island has experienced some economic and population growth. However, most individuals still rely on the services provided by the weekly mail boat for commodities and food. Subsistence farming was once conducted on the interior portion of the island but has subsided for the last several decades.

PHYSICAL DESCRIPTION OF SAN SALVADOR

San Salvador is among the 700 islands which comprise the Bahamian Archipelago, located off the coast of Florida at 24°3′N latitude and 74°30′ W longitude (Figure 3). The pod-shaped island is approximately 11.2 km east to west and 19.25 km north to south and is one of the outermost (Atlantic-wards) islands in the archipelago (Gerace Research Center, 2002 and Carleton Geology Department, 2002). Fringing reef systems surround the perimeter of the island (Gerace et al., 1999). Many of these reefs form protected embayments including the reef system with in Grahams Harbor (Figure 3).

Rugged offshore cays circle the island and serve to house one of the largest breeding colonies of tropical and sub tropical birds in the Bahamas. San Salvador serves as a nesting site for fourteen sea bird species of the seventeen that breed within the Bahamian region. These species include: Audubon's Shearwater, White-tailed Tropicbird, Magnificent Frigate, two species of Booby, Double-crested Cormorant, Laughing Gull, and seven species of tern.

These bird colonies are supported by offshore cays, bluff systems and hypersaline interior lakes (American Birding Association, 2005).

The topography of the island consist of living and dead coral-algae reefs, active carbonate sand shoals, a muddy tidal estuary, and shallow, wave-swept inshore areas that host a variety of bottom-dwelling, carbonate-secreting organisms. Additionally, the island contains a network of ponds and underground caves that vary in salinity from hypersaline to freshwater ponds. These ponds host a variety of carbonate secreting species (Carleton Geology Department, 2002).

The climate on San Salvador tends to be moderate due to the warm ocean currents produced by the Antilles Current. Temperatures range from 22-32˚C in the summer and 17-23˚C in the winter months. Average rainfall for the island is approximately 101 cm. The island is often exposed to environmental stochasticity such as tropical depressions, tropical storms and hurricanes. Soils tend to be shallow and have low water retention. As a consequence, heavy rains easily erode the topsoil (Gerace Research Center, 2002).

FIGURE 3. SAN SALVADOR, BAHAMAS. LAND USE AND LOCATION MAP (ROBINSON AND DAVIS, 1999).

DESCRIPTION OF STUDY SITE

The study site was located west of the Gerace Research Center within Grahams Harbor. Due to the position of the outlying coral reefs, the lagoon is protected from strong trade winds and currents. However, ocean currents typically allow for moderate water flow from east to west (Figure 4) (Gerace et al., 1999). Graham Harbor is located on the north side of the island. While considered to be a high-energy lagoon, trade wind impacts are moderated by North Point on the eastern side of Grahams Harbor (Figure 4). This shallow lagoon is approximately six meters deep at low tide and is bounded on three sides by San Salvador Island to the south, North Point and Cut Cay to the east, and Catto Cay and a series of barrier reefs to the north. Much of Grahams Harbor supports sea grass beds and algae communities (Gerace et al., 1999). Fringing reefs serve to protect Grahams Harbor against environmental stochasticity but also serve to support coral and algal communities. These communities of sea grasses, coral reefs and algae are intricately connected and are subjected to nutrient fluxes from terrestrial sources (Thrash, 2002).

A potential source of nutrient input is from untreated wastewater generated at the Gerace Research Center and that is released into Grahams Harbor. Wastewater and sewage from the research center is discharged directly to Grahams Harbor. This primary level discharge method is through gravitational drainage. Wastewater from the field station is released through a six inch steel pipe and released at approximately eight meters into the bay. Flow

. from the pipe is therefore pushed westward. Throughout the past forty years, this site has received sporadic nutrient overflow due to high fluctuations of inhabitants at the field station. During the summer of 2004, the sewage drainage system experienced excess flow due to the high volume of occupants. As a result, the system discharged excess nutrients into the surrounding bay waters. An apparent algal bloom resulted from this overflow of nutrients and human waste.

Fringing Reef

Atlantic Ocean

Cut Cay

Grahams Harbor

GRC

FIGURE 4. SAMPLING SITE LOCATION AND CURRENT FLOW IN GRAHAMS HARBOR, NORTHERN SAN SALVADOR, BAHAMAS (GERACE, 1999).

IV. METHODS

The study was carried out along the Northern Coast of San Salvador Island within Grahams Harbor. One hundred 0.25m2 quadrants were sampled in close proximity to the sewage outfall pipe from the Gerace Research Center in San Salvador, Bahamas during June of 2004 (Figure 5). The study site ranged in ocean depths of 0.5 meters to approximately 4 meters. The maximum distance from shore was 20 meters. Dry weight values, percent cover, species frequency and diversity, including Evenness, E´ and Shannon, H´ and Simpson’s Diversity Indices, D´ were quantified at each sampling site. Additionally, relative abundance was determined by the overall number of a given species compared to the total number of all species over the entire sampling region. Frequency of occurrence was determined for each species by comparing the number of sites occupied by a species compared to the total number of sites.

▪Relative abundance = # of individuals of a species / total # of species

▪Frequency of occurrence = # of sites occupied by a species / total # of sites

FIGURE 6. TRANSECT LINE ALONG THE NORTH SIDE OF THE ISLAND, GRAHAMS HARBOR, SAN SALVADOR, BAHAMAS

FIGURE 5. TRANSECT LINE ALONG THE NORTH SIDE OF THE ISLAND, GRAHAMS HARBOR, SAN SALVADOR, BAHAMAS.

SAMPLING SITES

Transect lines were established at every 12.5 meter interval perpendicular (transect lines K-O) to the coastline for a total of five transect lines. Additional transect lines were established and every 5 meter interval parallel to the shore for a total of five transect lines (transect lines A-J). Twenty-five meter long parallel (to shore) transect lines ran on either side of the pipe. Perpendicular (to shore) transect lines ran for a total of 20 meters from the pipe (Figure 5). Ten sampling locations were determined at random and along each transect line. The distance from the pipe for each sampling location was measured and given X, Y coordinates (Appendix A) for spatial analysis. Sampling quads of 0.25 m2 were placed at each site. Each quadrant was divided into 25-100 cm2 squares. These squares were numbered A-E horizontally and 1-5 vertically (Figure 6). The quadrant was placed with the center of the transect line bisecting the quadrant into two equal halves. Dry weight quadrants were determined at random for each of the 100 sites. Above ground/photosynthetic dry weight data was collected from one of the 100 cm2 quadrant from each 0.25 m2 quadrant.

20 FIGURE 6. SAMPLING SITE TRANSECT TAKEN AT SAN SALVADOR BAHAMANS, JUNE, 2004.

21 DATA COLLECTION

At each sampling site, the following data was collected: species present, number of individuals (relative abundance) (N), percent cover. Samples for dry weight were also collected. Percent cover was estimated between three researchers. Comparisons of percent cover values were performed before the sampling began to eliminate subjectivity. Cover analysis served as a secondary method of dry weight diversity analysis (Brathen and Hagberg, 2004). Algal species and number of individuals within each species was tabulated. Additionally, total number of individuals (among all species) was calculated and placed into a numeric class. These seven classes included: Class 0: 0 individuals, Class 1: 1-10 individuals, Class 2: 10-20, Class 3: 20-30 individuals, Class 4: 30-40 individuals, Class 5: 40 – 50 individuals, Class 6: 50-60 individuals, Class 7: 60-70 individuals. No sampling sites had more than 70 individuals. For statistical calculations, the higher end number was designated for the total number of individuals at each site. From this data, the relative frequency of each species was determined. Species were recorded on underwater slates. Species were identified to genus or species if possible. Specimens that could not be identified in the field were taken back to the laboratory for identification.

Aboveground dry weight (photosynthetic) samples were collected from one section (100 cm2 quadrant) of the 0.25 m2 transect. Sections for each quadrant were predetermined through random number generation. Above ground tissue was removed from the sandy substrate including the stipe and blade of each individual. The holdfast (below ground biomass) of each alga was removed and discarded. Specimens were bagged and labeled with the appropriate quadrant number. Specimens were then drained and placed on drying racks. Each rack was dried for approximately 12 - 24 hours at 43°C. Once specimens were dried, total dry weight for each site was measured. Specimens were then separated by species and the dry weight quantities were measured for each species. Dry weight values did not account for calcium carbonate composition of certain species (i.e. Halimeda, Penicillus, etc).

22 ANALYSIS OF DIVERSITY

Species diversity can be divided into two main components: species richness (number of species, S) and relative abundance (number of individuals, N) (Magurran, 1988). Species richness is a count of the total number of species in a given habitat and is largely determined by the available species pool (Therriault and Kolasa, 1999). Relative abundance of species is calculated independently of species number and is a total count of all individuals (Middelboe et al., 2004). One of the most important considerations with species richness is that the value depends on the sample size. A value of species richness is therefore meaningless without an indication of the sample size for which species richness was calculated. Consequently, species richness was further analyzed by determining a Species Diversity Index (R) for each sampling region. This index was determined by utilizing the number of species and the total number of individuals. Maragalef’s Species Richness Index (R) was used to account for a difference in sampling size. This value yielded a Species Richness Density (d) (World Agroforestry Center, 2003).

▪Maragalef’s Species Richness Index, R R = (S-1) / log N

▪Species Richness Density, d

Density = d / Area

However, richness and abundance only illustrate one aspect of diversity within a community. Spatial diversity can be further measured by species Evenness. Species Evenness, E´, a measure of the distribution of species over a given area is derived from abundance values and location of species. Evenness is expressed as “the degree of equality in species abundance” (Kitsiou and Karydis, 2000). It is well known that abiotic factors influence these diversity measures (Therriault and Kolasa, 1999). Diversity or ecological indices are useful response variables for measuring the effects of environment stress such as a short-term eutrophic event

23 (Hillebrand and Sommer, 2000; Kitsiou and Karydis, 2000). Diversity was determined by utilizing seven diversity indicators including number of species/species richness, (S); the total number of individuals/relative abundance (N) (Middelboe, 2004; Gray, 2000; Hillebrand and Sommer, 2000); Species Richness Index, (R); Species Density, (d); Shannon Diversity Index, (H´); the Simpson’s Diversity Index, (D´); and Evenness, (E´).

Shannon’s diversity index was determined by both the number of species and the even distribution of individuals among those species (Evenness). The index shows the degree of uncertainty of predicting the species of a given individual picked at random from the community. The larger the value for this index, the greater the diversity.

▪Shannon Diversity Index, H´

▪Evenness, E´

H′ E = /log N

The complement of the Simpson’s Diversity Index was used in order to compare the diversity of the assemblages. The original value of the Simpson’s diversity index is a measure of the probability that two randomly selected individuals in a sample will represent the same species (Hillebrand and Sommer, 2000). Using dry weight data, the Simpson’s diversity index will account for the distribution pattern of dry weight as well as the richness of each sampling site (Aoki, 2001). The Simpson’s index can be expressed based on the total dry weight values or in terms of the contribution to the total number of organisms (Hillebrand and Sommer, 2000). For the purpose of this study, the Simpson’s Diversity Index was used as a measure of the total number of organisms.

▪Simpson’s Diversity Index, D´

24 STATISTICAL ANALYSIS

Regions were established within the sampling site to generate three different models for statistical analysis. These models were designed to account for the potential difference in current flow, wind velocity, pipe discharge velocity and discharge volume. Each model represents three possible scenarios for ocean current and discharge pipe velocities at the sewage outfall pipe in Grahams Harbor.

Model 1 divided the sampling location into two regions with region 1 “up current” from the pipe and region 2 “down current” from the pipe (Figure 7). Model 1 accounted for a high velocity of discharge from the pipe, allowing for the discharge to reach across the entire sampling region downstream. This model assumed that the effect of the pipe was the same for every point in a given region downstream from the pipe. Down current sites were identified starting with (and including) transect line M and all sites westward of this line. All sites east of transect line M (0, 25) were considered up current to the effluent pipe and assumed to not be under the influence by the pipe.

Model 2 divided the site into five regions. In Model 2, the five sites were defined by taking into account approximately half the ocean current velocity and pipe discharge flow as Model 1 (Figure 8). The assumption of this model is that Regions 1 and 2 would be under the greatest influence of the pipe and given weaker velocity of the effluent, Regions 3 and 4 would be less influenced by the pipe outfall. Model 2 incorporated the transect line directly in front of the pipe, transect M (0, 25), into region 5 which was assumed to not be under the influence of the pipe. As a result this model accounted for a slow discharge of effluent. As a consequence, transect M would not be under the influence of the effluent pipe.

J I

FIGURE 7. SAMPLING LOCATION FROM GRAHAMS HARBOR, BAHAMAS USING MODEL 1.

I J

FIGURE 8. SAMPLING LOCATION FROM GRAHAMS HARBOR, BAHAMAS USING MODEL 2.

The regions in Model 3 were divided in the same method as Model 2 with the exception of the transect line M. Transect line M was divided into regions assumed to be under the influence of the pipe (regions 1, 2, and 3) (Figure 9). Region 4 was assumed to be under less influence of the effluent compared to Regions 1 and 2. Region 5 encompassed an area from 24 meters (A0 through A24) by 20 meters east of the pipe for a total area of 500 meters2. Regions 1 through 4 encompassed an area of 250 m2 each.

Models and diversity parameters (H´, D´, E, S, R, d) were evaluated based on their explained variance (r2) and the significant variables in the model. Furthermore, for linear analysis and One way ANOVA tests, the level of significance was set at α = 0.05. A Bonferroni (Dunn) t- test was performed for diversity indices, dry weight and percent cover values. This test uses critical values from the t distribution for multiple comparisons. The test controls for Type 1 experimentwise error rate but it generally has a higher Type II error rate (consultation with statistician, Robert Shaeffer).

Spatial analysis using ArcGIS Version 9.1 (ESRI) was used to visually display the following parameters: Evenness, E' Shannon’s Diversity Index, H', Simpson’s Diversity Index, D', Species Richness, S, Maragalef’s Species Richness Index, R, dry weight values and percent cover for each sampling site. This analysis served to visually validate the flow of the outfall pipe and to identify patterns established by the presence of the effluent. Five intervals, based on natural breaks, were represented by proportional symbols.

J I

FIGURE 9. SAMPLING LOCATION FROM GRAHAM’S HARBOR, BAHAMAS USING MODEL 3.

29 V. RESULTS A total of 23 macroalgal genera were identified at the site in Graham Harbor. Rank abundance and relative frequency of species per transect were determined for the overall sampling region (Table 1). Seagrasses (i.e. Syringodium filiforme and Thalassia testudinum) had the greatest relative abundance, with the latter having the highest frequency over the entire sampling site (Table 1).

TABLE 1. RANK ABUNDANCE (BASED ON NUMBERS OF INDIVIDUALS), RELATIVE ABUNDANCE, AND FREQUENCY OF OCCURANCE OF SPECIES IN EACH SAMPLING SITE AS SAMPLED IN GRAHAMS HARBOR, BAHAMAS. Algal Taxa Number of Relative Frequency of individuals abundance (%) occurrence Thalassia testudinum 244 22.0416 0.8384 Syringodium filiforme 230 20.7769 0.06364 Dictyota spp. 146 13.1888 0.5555 Halimeda incrassata 69 6.2331 0.6162 Padina spp. 59 5.3297 0.4444 Derbesia spp. 58 5.2394 0.1414 Penicillus capitatus 52 4.6974 0.5354 Acetabularia crenulata 50 4.5167 0.4646 Bataphora oerstedii 39 3.523 0.5455 Bryopsis spp. 35 3.1617 0.1111 Penicillus pyroformis 26 2.3487 0.2727 Rhipocephalus phoenix 22 1.9874 0.2121 Halodule spp. 22 1.9874 0.0909 Udotea cyanthiformis 19 1.7164 0.1818 Udotea flabellum 10 0.9033 0.1010 Chaetomorpha spp. 8 0.7227 0.0364 Caulepra curessoides 5 0.4517 0.0404 Dictyosphearia cavernosa 4 0.3613 0.0303 Udotea occidentalis 4 0.3613 0.0404 Cymopolia barbata 3 0.271 0.0303 Stypopodium zonale 1 0.0903 0.0101 Avrainvilla longicaulis 1 0.0903 0.0101

31 The correlation between all variables/diversity indices was determined in an attempt to decrease the bias due to statistical properties of the data (Therriault and Kolasa, 1999). Based on analysis of raw data, distance from the pipe was negatively correlated with dry weight, percent cover, and Simpson’s diversity index D′. Conversely, Evenness E′, Shannon’s diversity index H′, and number of species N, were positively correlated with distance from the pipe. As expected, dry weight and percent cover were fairly correlated (0.481). Additionally, number of species and Evenness were also highly correlated (0.849) (Table 2).

TABLE 2. PEARSON CORRELATION MATRIX FOR DIVERSITY (D′, H′, E), DRY WEIGHT AND PERCENT COVER.

Distance Dry % Cover S D′ H′ E′ from pipe weight

Distance from pipe 1

Dry weight -0.16907 1

% Cover -0.29238 0.480918 1

S 0.357192 -0.01299 0.096947 1

D′ -0.08546 0.022065 0.092669 -0.37875 1

H′ 0.246281 0.009757 0.098583 0.091564 -0.44721 1

E′ 0.185624 -0.00077 0.075562 0.849205 -0.42665 0.975279 1

33 STATISTICAL ANALYSIS USING THREE MODELS

During data collection, measurements of effluent discharge rates or ocean currents velocities were not collected. As a result, the data was fit into three different models to account for the additional variables of ocean currents and pipe discharge velocity. Linear regression analysis was conducted on the six different diversity variables under the assumptions of Model 1, Model 2 and Model 3. These variables included above-ground, photosynthetic dry weight, percent cover, and Number of Species N, Simpson Diversity Index D', Shannon Diversity Index H', and Evenness E'. An ANOVA on Model 2 did not yield significantly different results between two regions. Model 1 yielded more statistically significant results relative to Model 1 (Table 3). Model 3, which divided the study site into five regions explained the largest variation of the diversity indices. Dry weight and percent cover data from Model 3 yielded highly significant values (p<0.0001). With a p-value of 0.0000039 and 0.000012, there is a 95% confidence that there is a difference in one of the five regions for dry weight and percent cover and Species Number. Region 5 (in Model 3) showed the greatest difference. All other measures of diversity were not significant (Table 3). Model 3 was used for all further statistical analyses.

TABLE 3. LINEAR REGRESSION COMPARISON OF DIVERSITY (D′, H′, E), DRY WEIGHT, AND PERCENT COVER P-VALUES FOR THE THREE DIFFERENT MODELS.A

E′ S H′ D′ Dry weight Percent cover

Model 1 0.3388 0.042398* 0.046265* 0.614249 0.01636* 0.000154*

Model 2 0.07665 0.063009 0.098079 0.9966 0.0000146* 0.005229*

Model 3 0.074424 0.023609* 0.079794 0.077446 0.0000039* 0.000012*

aThe table provides the regions of the sampling site and the results of the regression: p-level of the regression model for species richness S, diversity indices H′ and D′ and evenness E′. *Significant at p<0.05.

35 ANOVA analysis of diversity indices, as well as dry weight and percent cover, yielded significant results. The resultant p-value for Shannon’s diversity index, dry weight and percent cover indicated that a significant difference existed between the regions using Model 3 (p-values 0.023609, 0.0000039, and 0.000012 respectively).

MODEL 3 ANALYSIS

Based on the Regions established in Model 3, the most common genera were identified for each region. Region 5 contained the largest number of individuals as well as the greatest number of species (Figure 10, Table 4). However, Thalassia testudinum and Syringodium filiforme were common species within each region regardless of model. Region 1 showed the presence of Derbesia spp. Region 5 contained Bataphora oerstedii.

The top ten most common species were identified for the entire study site. Thalassia testudinum was the most common species for the entire sampling site followed by Derbesia spp., Dictyota spp., and Padina spp. Region 5 had most individuals of the top three species. As a result, sea grasses and brown algae were among the most common species found in both the overall study site as well as in Region 5 (Figure 10, Table 4).

Mean values for all diversity indices, dry weight and percent cover were compared. Region 5 and 3 yielded the highest values for Species Richness, Evenness, and Shannon’s diversity (Figure 11). However, these differences were not found to be significant. Region 1 appeared to have the highest values for dry weight and percent cover. These values were found to be highly significant (p<0.0001). Regions 1 and 4 had the greatest value for Simpson’s Diversity

36 Thalassia testudinum

Syringodium filiforme

Dictyota spp

Halimeda incrassata

Region 5 Bataphora oerstedii Region 4 Region 1 Acetabularia crenulata Region 2 Region 3

Derbesia spp

Penicillus pyriformis

Padina spp

Bryopsis pennata

0 20406080100120 Number of Individuals

FIGURE 10. NUMBER OF INDIVIDUALS OF EACH OF THE TOP TEN MOST COMMON SPECIES FOUND IN EACH REGION USING MODEL 3.

37 TABLE 4. FIVE MOST COMMON TAXA IN EACH REGION DESIGNATED BY MODEL 3.

Region Species Thalassia testudinum Syringodium filiforme Region 1 Dictyota spp. Derbesia spp. Padina spp.

Syringodium filiforme Bryopsis pennata Region 2 Dictyota spp. Thalassia testudinum Padina spp.

Syringodium filiforme Thalassia testudinum Region 3 Dictyota spp. Halimeda incrassata Penicillus capitatus

Thalassia testudinum Syringodium filiforme Region 4 Dictyota spp. Padina Halimeda incrassata, Penicillus capitatus

Thalassia testudinum Syringodium filiforme Region 5 Dictyota spp. Halimeda incrassata Bataphora oerstedii

2 0.4

1.5 0.3

1 0.2 Index (H') 0.5 Index (D') 0.1

0 0 Mean Shannon's Diversity 12345 Mean Simpson's Diversity 12345 Regions in Model 3 Regions in Model 3

10 0.5 8 0.4 6 0.3 4 0.2 2 0.1

0 Mean Evenness (E') 0 Mean species richness Mean species 12345 12345 Regions in Model 3 Regions in Model 3

20 100 15 80

10 60 40 5 20

Mean percent cover 0

Mean Biomass (grams) Mean Biomass 0 12345 12345 Regions in Model 3 Regions in Model 3

FIGURE 11. STANDARD ERROR AND MEAN COMPARISON OF DIVERSITY (D′, H′, E), DRY WEIGHT, AND PERCENT COVER OVER FIVE SAMPLING REGIONS.

39 Linear regression analysis of the mean values for each region illustrated that overall dry weight and percent cover were significantly different among the five regions (Table 5). Region 5 and Region 1 (Model 3) showed significant differences between one another, with Region 1 having significantly higher values for dry weight. Regions 5, 1, and 4 showed significant differences when compared, with Regions 1 having significantly larger values of percent cover compared to Regions 4 and 5. Dry weight results show significant differences among regions (p<0.0046, r2 =0.764). As a result an increase supply of nutrients from the effluent pipe yielded larger dry weight quantities (Table 5, Figure 12).

Using GIS spatial analysis techniques, Regions 1 and 2 appeared to have the largest biomass values. This visual representation illustrated that larger dry weight values tend to follow the path of the effluent downstream (Figure 12). The largest two ranges of dry weight values (20.61 - 39.51 grams and 11.76 - 20.61grams) appeared to be in the regions most influenced by the effluent flow including the region directly in front of the pipe (transect M). A similar pattern appeared with percent cover (Figure 13). However, for percent cover index appeared to have larger values (90-100 and 60-89) in Region 1 and the region directly in front of the outfall pipe (Transect M).

TABLE 5. LINEAR REGRESSION ANALYSIS OF DRY WEIGHT AND PERCENT COVER.

Term Coefficient SE p 95% CI of Coefficient

Intercept 3.2591 0.6098 <0.0001 2.0479 to 4.4703

Dry weight* -0.0484 0.0166 0.0046 -0.0814 to -0.0153

Percent cover* -0.0174 0.0053 0.0015 -0.0280 to -0.0069

Source of variation SSq DF MSq F p

Due to regression 83.343 6 13.891 7.94 <0.0001

About regression 160.980 92 1.750

Total 244.323 98 bThe table provides results of the linear regression: p-level of the regression model for dry weight, percent cover. *Significant at p<0.05.

FIGURE 12. DRY WEIGHT SPATIAL ANALYSIS MAP OF SAMPLING SITES FROM GRAHAM’S HARBOR

42 FIGURE 13. PERCENT COVER SPATIAL ANALYSIS MAP OF SAMPLING SITES FROM GRAHAM’S HARBOR.

43 Linear regression analysis of Maragalef’s Species Richness Index (R) did not yield significant differences between each region at p<0.06 (Figure 14) (Table 6). Spatial analysis using Arch GIS spatial analysis validated regions established in Model 3. Based on this analysis, Maragalef’s Species Richness Index values appeared to be larger in Region 4 and 5 although this was not supported with statistical significance (Figure 15). Both Region 4 and 5 were considered to be outside the zone of effluent discharge.

TABLE 6. ONE WAY ANOVA ANALYSIS OF MARAGALEF’S SPECIES RICHNESS, R.

Source of Variation df SS MS F

Regression 1 7.84E-07 7.84E-07 2.41674E-05

Residual 3 0.097321084 0.032440361

Total 4 0.097321868

Coefficients Standard Error t Stat P-value

Intercept 0.55304 0.188903143 2.927637894 0.061117249

X Variable 1 0.00028 0.056956441 0.004916038 0.996386215

δThe table provides results of a One-way ANOVA of Maragalef’s Species Richness. *Significant at p<0.05.

7.5 7 6.5 6

5.5 5 4.5 4 3.5 3 2.5 2 1.5 1

Margalef's Species Richness Index Richness Species Margalef's 0.5

012345

Sampling Regions in Model 3

FIGURE 14. MARAGALEF’S SPECIES RICHNESS INDEX (R) MEAN VALUES FOR EACH OF THE FIVE SAMPLING REGIONS.

FIGURE 15. MARAGALEF’S SPECIES RICHNESS INDEX SPATIAL ANALYSIS MAP OF SAMPLING SITES FROM GRAHAMS HARBOR.

46 An increase in nutrients from the effluent pipe did not appear to greatly affect the diversity parameters H′ (p=0.4405, r2 =0.2862) D’ (p=0.1781and E′ (p=0.4576. r2 = 0.3257) (Table 7). Region 5 showed the greatest Species Richness value of 72.8, where species richness was defined as a total count of species. Region 5 also has the highest value for Simpson’s Diversity Index, Shannon’s diversity Index and Evenness although these values were not significantly different (Figure 16). Spatial patterns were not apparent for Evenness, Shannon’s Diversity Index, Simpson’s Diversity Index or Number of Species (Figure 17, 18, 19, and 20).

8 7

3 Species Richness Species

0 012345 Regions in Model 3

2 1.8 1.6 1.4 1.2 E' H' D' 1 0.8 0.6 0.4 0.2 0 012345 Regions in Model 3

FIGURE 16. STANDARD ERROR OF SPECIES RICHNESS (UPPER PANEL) DEFINED AS A TOTAL COUNT OF SPECIES AND DIVERSITY INDICES: SHANNON’S DIVERSITY INDEX H', SIMPSON’S DIVERSITY INDEX, D' AND EVENNESS E' (LOWER PANEL).

48 TABLE 7. LINEAR REGRESSION ANALYSIS OF SPECIES RICHNESS AND DIVERSITY (D′, H′, E).

Term Coefficient SE p 95% CI of Coefficient

Intercept 3.2591 0.6098 <0.0001 2.0479 to 4.4703

D′ 1.3184 0.9715 0.1781 -0.6112 to 3.2479

H′ -1.4130 1.8241 0.4405 -5.0358 to 2.2097

E 4.9806 6.6775 0.4576 -8.2814 to 18.2426

Species Richness, S 0.3132 0.1725 0.0727 -0.0294 to 0.6557

Source of variation SSq DF MSq F p

Due to regression 83.343 6 13.891 7.94 <0.0001

About regression 160.980 92 1.750

Total 244.323 98 bThe table provides results of the linear regression: p-level of the regression model for species richness S, diversity indices H′ and D′ and E′. *Significant at p<0.05.

49 FIGURE 17. SIMPSON’S DIVERSITY INDEX SPATIAL ANALYSIS MAP OF SAMPLING SITES FROM GRAHAMS HARBOR.

FIGURE 18. SHANNON'S DIVERSITY INDEX SPATIAL ANALYSIS MAP OF SAMPLING SITES FROM GRAHAMS HARBOR.

FIGURE 19. NUMBER OF SPECIES SPATIAL ANALYSIS MAP OF SAMPLING SITES FROM GRAHAMS HARBOR.

52 FIGURE 20. EVENNESS SPATIAL ANALYSIS MAP FROM SAMPLING SITES FROM GRAHAMS HARBOR.

Additionally, sites were compared using Bonferroni analysis to examine how closely each region was to the other (Table 8). Dry weight and percent cover yielded significant results. For dry weight, Region 1 was significantly different compared to Region 5 (p<0.0001). Region 1 was also significantly different compared to Region 4 (p<0.0001). Region 1 appeared to receive the largest amount of flow from the effluent pipe. Comparatively, Region 5 was not in direct contact with effluent discharge. Region 4 appeared to be at too great a distance to be influenced substantially by the effluent.

Within Regions 1 and 2, there was an increase in certain species such as Derbesia spp. and Bryopsis spp. which are considered to be a fast growing and opportunistic algae under eutrophic conditions. As illustrated in the spatial analysis, these species tended to congregate around the outfall of the pipe (Figure 19, 20) within Region 1 and 2. Bryopsis in particular, appeared in only Regions 1 and 2. This species was not present in Region 4 or 5. Additionally, Bataphora oerstedii, which is considered a rare, ecological sentinel species that is particularly sensitive to contaminants, tended to be more abundant in Region 5 which was located outside the outfall of the pipe (MacRae et al., 2001).

TABLE 8. BONFERRONI (DUNN) T – TEST ANALYSIS COMPARING MEAN VALUES FOR EACH DIVERSITY INDEX (D′, H′, E), DRY WEIGHT AND PERCENT COVER WITHIN THE FIVE REGIONS OF MODEL 3C. Region 1 Region 2 Region 3 Region 4 Region 5

D’ 0.285 0.178 0.251 0.294 0.239

H’ 1.363 1.289 1.607 1.353 1.574

E 0.289 0.277 0.330 0.284 0.334

Dry weight 16.02* 8.42 5.06 2.51** 4.14**

Percent cover 72.94* 65.87 65.83 51.77 41.86**

S 5.10 4.60 6.667 5.471 6.136 cThis table provides the means of diversity indices, dry weight and percent cover and the results from ANOVA: Bonferroni method of comparison: *region 1 differs significantly from region 4 and 5 for dry weight (p < 0.0001). **Region 1 differs significantly from region 5 for Percent cove r (p <0.05).

Derbesia

FIGURE 21. LOCATION OF DERBESIA SPP. USING SPATIAL ANALYSIS OF SAMPLING SITES FROM GRAHAMS HARBOR.

Bryopsis

FIGURE 22. LOCATION OF BRYOPSIS SPP. USING SPATIAL ANALYSIS OF SAMPLING SITES FROM GRAHAMS HARBOR.

Bataphora

FIGURE 23. LOCATION OF BATAPHORA SPP. USING SPATIAL ANALYSIS OF SAMPLING SITES FROM GRAHAMS HARBOR.

58 V. DISCUSSION

Based on the findings in this research, there is evidence to suggest that eutrophic conditions are altering community assemblages within the macroalgae and seagrass community in Grahams Harbor San Salvador, Bahamas following a decades long eutrophic event. Patterns of species diversity, dry weight and percent cover changed along the nutrient gradient produced by the effluent pipe. Under long-term eutrophic events, it is apparent that this continual change in species composition might further escalate the alteration of community composition, evenness and diversity. Contrary to expectations, nutrient concentrations did not appear to significantly influence all diversity metrics (i.e. Shannon’s Diversity Index and Evenness). This may indicate that the study site is in the early stages of eutrophication where dry weight quantities are increasing but community composition has yet to be altered (Gray, 1992). The long periods of time where effluent levels are decreased may allow the community enough time to rebound and maintain community composition.

DRY WEIGHT AND PERCENT COVER

Results showed significant differences between the regions outside the eutrophic zone (Region 5 and 4) in terms of dry weight and percent cover. Additionally, there was an overall increase in dry weight and percent cover as the concentration of nutrients increased. Analysis of the dry weight in the five regions indicated that Region 1 yielded the largest mean dry weight values (Figure 9). This region was in the closest proximity to the effluent pipe. Furthermore, Region 2, with the second largest value for dry weight, was also in close proximity to high effluent vectors. Furthermore, spatial analysis of dry weight, percent cover and Species Number reveal an obvious trend within the community assemblages. It is apparent that the effluent outfall is serving to dictate the community structure in terms of these three indices.

DIVERSITY Region 5 and 3 showed the overall greatest values for number of species, Evenness and diversity, measured through Simpson’s diversity index. However, Evenness and Shannon’s

59 index were not significantly different when comparing Regions 1, 2, and 4. Conversely, Region 2, which was in direct line of the effluent flow, showed the lowest values for number of species, Shannon’s diversity, Evenness. Shannon’s diversity did not appear to show significant differences in the various regions (Region 5) assumed to not be under the influence of the effluent pipe. Furthermore, results show as the distance from the pipe increased, there was an overall increase in Shannon’s Diversity index, Evenness (although not significant), and Species Number. Spatial analysis maps of the diversity indices (E', H', and D') support the statistical findings that the pipe is not causing an apparent change in community structure in terms of diversity.

RICHNESS

Region 3 and 5 appeared to have the greatest number of species. Region 4 appeared to a have a low number of species, this could be due to strong effluent velocity. However, it would appear to not be due to the effluent given that large number of species in Region 3. Another possibility for the low numbers of species found in region 4 is the depth of the site and the distance from source of colonists (Middelboe and Sand-Jensen, 2004). Spatial analysis maps support the findings that there appears to be a greater species richness in Regions outside of the effluent flow, particularly Region 5.

Seagrasses (Syringodium filiforme and Thalassia testudinum) appeared to be among the top five species regardless of region. However, in Regions 1 and 2, there was a presence of Derbesia and Bryopsis, which are fast growing, filamentous and problematic species. In addition, Bryopsis has the potential to be opportunistic and is known to be highly opportunistic in eutrophic conditions (University of Hawaii, 2005). These algae grew in the regions closest to the effluent pipe (Region 1 and 2). Interestingly, Bryopsis was not present in Region 5 although it had a fairly dominant presence in regions surrounding the outfall pipe. The presence of these opportunistic species may, over the long term, serve to be the main catalyst of community structure change. If eutrophic conditions continue, these species may continue to dominate, altering seafloor Cover and aquatic chemical composition.

60 Bataphora oerstedii was on of the most common species in Region 5. Bataphora oerstedii is considered a rare, ecological sentinel species that is particularly sensitive to contaminants (MacRae et al., 2001). Decreased numbers of Bataphora were found in regions in the direct flow of the effluent pipe (Region 1, and 2).

Numerous factors, other than eutrophic conditions, may contribute to the spatial arrangement of macroalgae and seagrass communities including depth of sampling transects, differential ocean current flow, sunlight intensity, seasonal fluctuations of current, water chemistry, and length of eutrophic event (Morand and Merceron, 2005). These factors were not considered in the analysis of community structure. Furthermore, the emergence of macroalgae vegetation and the observable discharge of effluent were the only means of measuring the presence of eutrophication. In addition, the temporal span of the eutrophic event may have not been long enough to alter community structure. Alternatively, effluent could be transported downstream of the sampling regions, although there was no apparent, observable increase in algal growth in those regions.

Although it was predicted that diversity values would be negatively correlated with high nutrient concentration, it appears that diversity was not significantly affected by the initial stages of eutrophication. Although many studies have shown that long-term eutrophication decreases diversity over time, further studies are necessary to determine the long-term effects on diversity following a short-term eutrophic event. Given enough time for eutrophic events to take place and the presence of opportunistic species, these diversity indices may begin to decline with continual increases in nutrient concentrations.

The study served to establish a baseline of diversity following a short-term eutrophic event for future work. Studies on a longer temporal scale would determine whether this short-term event lead to a permanent decrease in diversity. Additionally, further studies should account for the many and varied abiotic factors involved in structuring this community such as current dynamics and water chemistry. Additional studies should also be conducted whether diversity values will be altered following the termination of nutrient enrichment.

61 VI. LITERATURE CITED American Birding Associations. 2005. Accessed on October 9, 2006 from http://www.americanbirding.org/ifo/workshops/2006caribbean.html.

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66 VII. APPENDICES APPENDIX A. Distance from the pipe, estimated depth and X, Y coordinates of each sampling site at Grahams Harbor, San Salvador, Bahamas as sampled on June 2004. X coordinate Y coordinate Z Estimate with pipe at with pipe at Distance from Sampling Depth (meters) 0,0 0,0 pipe (meters) A3 0.5 3 0 3 A4 0.5 4 0 4 A7 0.5 7 0 7 A16 0.5 16 0 16 A25 0.5 25 0 25 B3 0.5 -3 0 3 B4 0.5 -4 0 4 B13 0.5 -13 0 13 B19 0.5 -19 0 19 B21 0.5 -21 0 21 C1 1.375 1 5 5.0990195 C7 1.375 7 5 8.6023253 C11 1.375 11 5 12.083046 C13 1.375 13 5 13.928388 C19 1.375 19 5 19.646883 D9 1.375 -9 5 10.29563 D13 1.375 -13 5 13.928388 D16 1.375 -16 5 16.763055 D21 1.375 -21 5 21.587033 D25 1.375 -25 5 25.495098 E1 2.25 1 10 10.049876 E9 2.25 9 10 13.453624 E17 2.25 17 10 19.723083 E22 2.25 22 10 24.166092 E25 2.25 25 10 26.925824 F8 2.25 -8 10 12.806248 F10 2.25 -10 10 14.142136 F13 2.25 -13 10 16.401219 F22 2.25 -22 10 24.166092 F23 2.25 -23 10 25.079872 G4 3.125 4 15 15.524175 G9 3.125 9 15 17.492856 G13 3.125 13 15 19.849433 G23 3.125 23 15 27.45906 G25 3.125 25 15 29.154759 H3 3.125 -3 15 15.297059 H7 3.125 -7 15 16.552945 H8 3.125 -17 15 22.671568 H14 3.125 -14 15 20.518285

67 X coordinate Y coordinate Z Estimate with pipe at with pipe at Distance from Sampling Depth (meters) 0,0 0,0 pipe (meters) I1 4 1 20 20.024984 I3 4 3 20 20.223748 I14 4 14 20 24.413111 I18 4 18 20 26.907248 I19 4 19 20 27.586228 J4 4 -4 20 20.396078 J6 4 -6 20 20.880613 J16 4 -16 20 25.612497 J18 4 -18 20 26.907248 J23 4 -23 20 30.479501 K2 0.85 25 2 25.079872 K3 1.025 25 3 25.179357 K4 1.2 25 4 25.317978 K5 1.375 25 5 25.495098 K10 2.25 25 10 26.925824 K13 2.775 25 13 28.178006 K14 2.95 25 14 28.653098 K15 3.125 25 15 29.154759 K16 3.3 25 16 29.681644 K20 4 25 20 32.015621 L2 0.85 12.5 2 12.658989 L3 1.025 12.5 3 12.85496 L4 1.2 12.5 4 13.124405 L9 2.075 12.5 9 15.402922 L11 2.425 12.5 11 16.650826 L12 2.6 12.5 12 17.327723 L14 2.95 12.5 14 18.768324 L18 3.65 12.5 18 21.914607 L20 4 12.5 20 23.584953 M1 0.5 0 1 1 M2 0.85 0 2 2 M3 1.025 0 3 3 M4 1.2 0 4 4 M5 1.375 0 5 5 M9 2.075 0 9 9 M12 2.6 0 12 12 M13 2.775 0 13 13 M18 3.65 0 18 18 M19 3.825 0 19 19 N2 0.85 -12.5 2 12.658989 N3 1.025 -12.5 3 12.85496 N5 1.375 -12.5 5 13.462912 N7 1.725 -12.5 7 14.326549

68 X coordinate Y coordinate Z Estimate with pipe at with pipe at Distance from Sampling Depth (meters) 0,0 0,0 pipe (meters) N9 2.075 -12.5 9 15.402922 N16 3.3 -12.5 16 20.303941 N17 3.475 -12.5 17 21.100948 N18 3.65 -12.5 18 21.914607 N19 3.825 -12.5 19 22.743131 N20 4 -12.5 20 23.584953 O1 0.5 -25 1 25.019992 O2 0.85 -25 2 25.079872 O3 1.025 -25 3 25.179357 O4 1.2 -25 4 25.317978 O7 1.725 -25 7 25.96151 O9 2.075 -25 9 26.570661 O11 2.425 -25 11 27.313001 O12 2.6 -25 12 27.730849 O17 3.475 -25 17 30.232433 O20 4 -25 20 32.015621

APPENDIX B: Number of Individuals (in classes) from all transects at Grahams Harbor, San Salvador, Bahamas as sampled on June 2004. ALGAE TAXA TRANSECT LINE A TRANSECT LINE B SITE A3 A4 A7 A16 A25 B3 B4 B13 B19 B21 Thalassia testudinum 4 4 1 0 0 4 0 0 0 0 Caulepra curessoides 1 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 1 0 0 0 0 0 0 5 0 0 Derbesia 5 5 0 0 0 5 5 0 5 0 Penicillus capitatus 0 1 1 0 0 1 1 0 0 0 Dictyota 0 0 4 4 0 0 0 0 0 4 Padina 0 0 1 0 0 0 1 1 3 1 Udotea cyanthiformis 0 0 1 0 0 0 0 0 0 0 Acetabularia crenulata 0 0 0 0 0 0 0 0 0 0 Bataphora oerstedii 0 0 0 6 1 0 0 0 0 0 Chaetomorpha 0 0 0 4 0 0 0 0 0 0 Udotea flabellum 0 0 0 0 0 0 0 0 0 0 Halimeda incrassata 0 0 0 0 0 0 0 1 0 0 Rhipocephalus phoenix 0 0 0 0 0 0 0 0 0 0 Penicillus pyroformis 0 0 0 0 0 0 0 0 0 0 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filliforme 0 0 0 0 0 0 0 0 0 0 Halodule 0 0 0 0 0 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 1 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTALS SPECIES / SITE 4 3 6 3 1 3 5 3 2 2 INDIVIDUALS/ SITE 78 72 39 120 1 71 43 44 60 35

70 ALGAE TAXA TRANSECT LINE C TRANSECT LINE D C1 C7 C11 C13 C19 D9 D13 D16 D21 D25 SITE Thalassia testudinum 4 3 4 3 4 2 4 2 4 3 Caulepra curessoides 1 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 0 0 0 0 0 0 0 0 0 Penicillus capitatus 1 0 1 1 0 1 1 1 1 1 Dictyota 1 0 3 1 0 0 0 3 5 0 Padina 0 1 0 1 0 1 2 3 3 0 Udotea cyanthiformis 0 0 1 0 0 1 0 0 1 1 Acetabularia crenulata 0 0 0 0 0 0 0 0 0 2 Bataphora oerstedii 0 0 0 1 0 0 0 0 1 1 Chaetomorpha 0 0 0 4 0 0 0 0 0 0 Udotea flabellum 1 0 0 1 0 0 0 0 0 0 Halimeda incrassata 1 0 1 0 0 0 1 1 1 1 Rhipocephalus phoenix 0 0 1 0 1 0 0 1 0 0 Penicillus pyroformis 0 0 0 0 0 0 0 1 0 0 Udotea occidentalis 0 0 1 0 0 0 0 0 0 Syringodium filliforme 0 0 0 1 3 3 5 3 3 4 Halodule 0 0 0 0 0 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 1 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTALS SPECIES/SITE 5 7 7 9 3 5 5 8 8 7 INDIVIDUALS /SITE 45 22 62 69 51 46 91 95 117 75

71 ALGAE TAXA TRANSECT E TRANSECT F SITE E1 E9 E17 E22 E25 F8 F9 F10 F22 F23 Thalassia testudinum 0 1 3 3 3 0 0 3 3 2 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 0 0 0 0 0 0 0 0 0 Penicillus capitatus 0 1 1 1 1 0 0 0 1 0 Dictyota 0 2 0 0 0 0 0 2 1 0 Padina 0 0 0 0 0 0 0 0 1 0 Udotea cyanthiformis 0 0 0 0 1 0 0 0 0 0 Acetabularia crenulata 0 0 1 1 2 0 0 0 1 0 Bataphora oerstedii 0 0 0 1 1 0 0 1 0 0 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 0 0 0 0 0 0 0 0 0 Halimeda incrassata 0 0 0 1 1 0 0 0 1 0 Rhipocephalus phoenix 0 0 0 0 0 0 0 0 0 0 Penicillus pyroformis 0 1 0 0 0 0 0 0 0 1 Udotea occidentalis 0 0 0 1 0 0 0 0 0 0 Syringodium filliforme 0 0 4 1 3 0 0 0 3 4 Halodule 0 0 0 3 0 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 4 0 6 TOTALS SPECIES/SITE 0 4 4 8 7 0 0 4 7 4 INDIVIDUALS /SITE 0 23 55 56 63 0 0 65 67 93

72 ALGAE TAXA TRANSECT LINE G TRANSECT LINE H

SITE G4 G9 G13 G23 G25 H3 H7 H8 H14 H22 Thalassia testudinum 4 3 5 6 5 0 0 1 4 4 Caulepra curessoides 0 2 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 4 0 0 0 0 0 0 0 0 Penicillus capitatus 0 0 1 0 1 0 0 0 1 1 Dictyota 0 0 4 4 4 5 0 0 1 1 Padina 0 0 0 0 0 5 0 0 0 0 Udotea cyanthiformis 1 0 0 0 0 0 0 0 0 0 Acetabularia crenulata 0 1 2 1 1 1 0 0 1 1 Bataphora oerstedii 4 1 1 0 0 0 0 0 0 0 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 1 0 0 0 1 0 0 0 0 Halimeda incrassata 0 1 0 0 1 0 0 0 1 1 Rhipocephalus phoenix 0 1 0 0 0 1 0 0 0 0 Penicillus pyroformis 1 0 1 1 0 0 0 0 1 1 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filliforme 5 4 4 4 4 2 0 0 4 4 Halodule 0 0 0 0 4 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 1 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTALS SPECIES/SITES 5 9 7 5 7 6 0 2 7 7 INDIVIDUALS /SITE 105 106 114 115 144 87 0 13 80 78

73 ALGAE TAXA TRANSECT I TRANSECT J SITE I1 I3 I4 I8 I9 J4 J6 J16 J18 J23 Thalassia testudinum 4 3 3 3 3 4 4 4 4 4 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 4 5 0 0 0 0 0 0 0 Penicillus capitatus 0 0 1 1 0 0 0 0 0 0 Dictyota 0 0 0 0 0 1 1 1 1 0 Padina 0 0 0 0 0 1 0 1 0 0 Udotea cyanthiformis 0 0 0 0 0 0 0 1 0 0 Acetabularia crenulata 1 1 1 1 1 1 1 2 1 1 Bataphora oerstedii 0 2 1 0 1 1 1 1 0 0 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 1 0 0 0 0 1 0 0 0 Halimeda incrassata 1 1 1 1 1 1 1 1 1 1 Rhipocephalus phoenix 0 0 1 0 0 0 0 0 0 0 Penicillus pyroformis 1 0 1 1 1 0 1 0 1 1 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filliforme 4 4 3 3 3 4 4 4 4 4 Halodule 0 0 3 0 1 0 0 0 0 4 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTAL SPECIES / SITE 5 7 10 6 7 7 8 8 6 6

INDIVIDUALS /SITE 67 102 117 50 55 76 77 80 72 112

74 ALGAE TAXA TRANSECT K

SITE K2 K3 K4 K5 K10 K13 K14 K15 K16 K20 Thalassia testudinum 3 4 3 3 3 3 3 3 3 3 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 0 0 10 0 0 0 0 0 0 Penicillus capitatus 0 0 1 0 1 0 1 2 1 1 Dictyota 3 4 4 3 3 0 3 3 4 4 Padina 1 1 0 1 0 0 0 0 0 0 Udotea cyanthiformis 0 0 0 0 1 0 1 0 0 0 Acetabularia crenulata 0 0 0 1 1 1 1 1 0 1 Bataphora oerstedii 0 0 1 0 1 0 0 0 0 0 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 0 0 0 0 0 0 0 0 0 Halimeda incrassata 0 1 0 4 1 1 1 3 1 1 Rhipocephalus phoenix 0 1 0 0 0 1 1 0 1 0 Penicillus pyroformis 0 0 0 0 0 0 1 0 0 0 Udotea occidentalis 0 0 0 0 1 0 0 0 0 0 Syringodium filliforme 0 3 3 3 3 3 4 4 3 3 Halodule 0 0 0 0 0 0 2 0 0 3 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTALS

SPECIES/SITE 2 6 5 7 9 5 10 6 6 7 INDIVIDUALS /SITE 42 98 76 109 80 51 95 104 80 96

ALGAE TAXA TRANSECT L SITE L2 L3 L4 L8 L9 L11 L12 L14 L18 L20 Thalassia testudinum 4 3 2 2 3 3 3 4 4 4 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 1 1 0 0 0 0 0 0 0 0 Derbesia 0 0 2 0 0 0 0 0 0 0 Penicillus capitatus 1 0 0 0 0 1 0 0 0 1 Dictyota 3 2 4 4 0 3 1 0 3 3 Padina 0 1 0 0 0 0 0 0 0 0 Udotea cyanthiformis 0 1 0 1 0 1 1 1 0 1 Acetabularia crenulata 0 0 0 0 0 2 3 1 1 1 Bataphora oerstedii 0 0 0 0 1 1 1 1 0 0 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 1 0 1 0 0 0 0 0 0 0 Halimeda incrassata 1 1 1 1 1 0 1 1 2 1 Rhipocephalus phoenix 1 1 0 1 1 0 1 0 0 1 Penicillus pyroformis 1 1 0 1 0 1 0 0 1 0 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filiforme 0 0 0 4 2 4 3 4 4 4 Halodule 0 0 0 0 0 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTALS SPECIES/SITE 8 8 5 7 4 8 8 6 6 8 INDIVIDUALS /SITE 68 43 55 80 40 93 78 75 102 92

76 ALGAE TAXA TRANSECT M

SITE M1 M2 M3 M4 M5 M8 M12 M13 M18 M19 Thalassia testudinum 0 2 0 0 1 0 0 0 0 4 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 6 6 0 0 0 0 0 0 0 0 Penicillus capitatus 0 4 0 1 0 1 0 0 1 0 Dictyota 4 4 4 4 4 4 4 4 4 4 Padina 4 0 2 1 4 0 4 0 1 1 Udotea cyanthiformis 0 0 0 0 0 0 0 0 1 0 Acetabularia crenulata 0 0 0 0 0 0 0 0 0 0 Bataphora oerstedii 0 0 0 1 0 0 0 0 0 0 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 0 0 0 0 1 0 0 0 0 Halimeda incrassata 0 0 0 1 1 1 0 0 0 1 Rhipocephalus phoenix 0 0 0 0 0 1 0 0 1 2 Penicillus pyroformis 0 0 0 0 0 0 0 0 0 1 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filliforme 0 0 0 0 0 0 0 0 0 0 Halodule 0 0 0 0 0 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 4 4 4 3 2 1 0 0 0 0 TOTALS SPECIES/SITE 4 5 3 6 5 6 2 1 5 6 INDIVIDUALS /SITE 140 140 70 67 91 45 60 30 41 80

77 ALGAE TAXA TRANSECT N SITE N2 N5 N7 N9 N12 N15 N17 N18 N19 N20

Thalassia testudinum 2 4 2 3 3 3 2 3 3 2 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0

Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 0 0 0 0 0 0 0 0 0 Penicillus capitatus 1 1 1 1 1 1 1 1 1 1 Dictyota 2 0 0 0 0 0 1 0 0 0 Padina 3 2 1 1 1 1 1 1 0 0 Udotea cyanthiformis 0 0 0 0 0 0 1 0 2 0 Acetabularia crenulata 0 0 0 1 1 2 1 1 0 2 Bataphora oerstedii 0 0 0 0 2 1 0 1 1 1 Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 0 0 0 0 0 0 0 0 0 Halimeda incrassata 0 1 1 2 1 1 1 1 1 1 Rhipocephalus phoenix 0 0 0 0 0 0 0 0 0 0 Penicillus pyroformis 0 0 0 1 0 0 0 0 1 0 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filiforme 2 3 6 2 4 3 3 3 3 2 Halodule 0 0 0 0 0 0 0 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0 Stypopodium zonale 0 0 0 0 0 0 0 0 0 0 Cymopolia barbata 0 0 0 0 1 0 0 0 0 1 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 0 0 0 0 0 0 0 0 0 TOTALS SPECIES/SITE 5 5 5 7 8 7 8 7 7 7 INDIVIDUALS /SITE 62 69 75 62 79 62 41 61 66 57

ALGAE TAXA TRANSECT O SITE O1 O2 O3 O4 O7 O9 O11 O12 O17 O20

Thalassia testudinum 4 0 2 4 2 3 2 2 2 1 Caulepra curessoides 0 0 0 0 0 0 0 0 0 0 Dictyosphearia cavernosa 0 0 0 0 0 0 0 0 0 0 Derbesia 0 0 0 0 0 0 0 1 0 0 Penicillus capitatus 0 0 1 0 1 1 1 1 1 1 Dictyota 0 0 0 3 4 2 1 0 4 4 Padina 0 1 1 1 0 0 0 0 0 0 Udotea cyanthiformis 0 0 0 0 0 0 0 0 0 0 Acetabularia crenulata 0 0 1 1 0 1 1 1 0 1

Bataphora oerstedii 3 0 0 0 0 0 0 0 0 0

Chaetomorpha 0 0 0 0 0 0 0 0 0 0 Udotea flabellum 0 0 0 0 0 0 0 0 0 0 Halimeda incrassata 0 0 1 1 1 1 1 1 1 1 Rhipocephalus phoenix 1 0 1 0 0 0 0 0 1 1 Penicillus pyroformis 0 0 0 0 0 0 0 0 0 0 Udotea occidentalis 0 0 0 0 0 0 0 0 0 0 Syringodium filliforme 4 0 3 3 0 4 4 4 4 4 Halodule 0 0 0 1 4 0 2 0 0 0 Dictyopteris jolyana 0 0 0 0 0 0 0 0 0 0

Stypopodium zonale 0 0 0 0 0 0 0 0 0 0

Cymopolia barbata 0 0 0 0 0 0 0 0 0 0 Avrainvilla longicaulis 0 0 0 0 0 0 0 0 0 0 Bryopsis pennata 0 4 4 4 0 0 0 0 0 0 TOTALS SPECIES/SITE 4 2 8 8 5 6 7 6 6 7 INDIVIDUALS /SITE 83 35 70 109 75 71 65 53 82 83

79 APPENDIX C: Total dry weight at each sampling site, Grahams Harbor, San Salvador Bahamas as sampled in June 2004 Sample site number Total dry weight (g) Sample site number Total dry weight (g) Sample site number Total dry weight (g) A3 14.39 G23 0 L14 6.15 A4 33.56 G25 2.94 L18 4.26 A7 0 H3 2.71 L20 0 A16 10.24 H7 0.25 M1 17.29 A25 0 H8 0.2 M2 2.89 B3 3.96 H14 7.69 M3 18.95 B4 9.51 H22 5.19 M4 9.96 B13 39.51 I1 1.67 M5 4.14 B19 20.94 I3 2.15 M9 2.26 B21 2.53 I14 1.35 M12 0 C1 0 I18 1.25 M13 2.23 C7 13.64 I19 0.54 M18 3.72 C11 0 J4 7.73 M19 0 C13 0 J6 5.44 N2 38.06 C19 0 J16 7.72 N3 3.59 D9 8.63 J18 7.81 N5 19.94 D13 3.99 J23 1.22 N7 0 D16 25.52 K2 1.18 N9 0 D21 37.58 K3 9.53 N16 0 D25 15.19 K4 1.27 N17 0.71 E1 0.001 K5 0.94 N18 1.03 E9 0.32 K10 0.93 N19 0.71 E17 6.4 K13 1.37 N20 1.5 E22 1.14 K14 4.15 O1 0 E25 3.92 K15 13.6 O2 9.8 F8 0 K16 0 O3 25.42 F10 0.22 K20 5.42 O4 11.43 F13 3.03 L2 9.27 O7 2.02 F22 25.08 L3 1.63 O9 0.77 F23 29.87 L4 7.29 O11 3.19 G4 0 L9 0.27 O12 1.03 G9 0 L11 1.51 O17 11.78 G13 2.97 L12 13.04 O20 1.5

APPENDIX D: Total percent cover at each sampling site, Grahams Harbor, San Salvador, Bahamas as sampled in June 2004. TRANSECT A TRANSECT B SITE A3 A4 A7 A16 A25 B3 B4 B13 B19 B21 PERCENT COVER (%) 92 98 0 10 0 90 80 100 95 90

TRANSECT C TRANSECT D SITE C1 C7 C11 C13 C19 D9 D13 D16 D21 D25 PERCENT COVER (%) 0 80 0 0 10 100 98 90 100 90

TRANSECT E TRANSECT F9 SITE E1 E9 E17 E22 E25 F8 F10 F13 F22 F23 PERCENT COVER (%) 60 15 60 50 50 0 0 10 90 20

TRANSECT G TRANSECT H SITE G4 G9 G13 G23 G25 H3 H7 H8 H14 H22 PERCENT COVER (%) 0 0 40 50 40 90 0 5 60 85

TRANSECT I TRANSECT J SITE I1 I3 I14 I18 I19 J4 J6 J16 J18 J23 PERCENT COVER (%) 60 60 40 30 20 50 50 70 75 70

TRANSECT K SITE K2 K3 K4 K5 K10 K13 K14 K15 K16 K20 PERCENT COVER (%) 20 20 20 30 11 40 40 50 0 50

TRANSECT L SITE L2 L3 L4 L8 L9 L11 L12 L14 L18 L20 PERCENT COVER (%) 60 20 40 70 50 50 40 40 50 50

TRANSECT M SITE M1 M2 M3 M4 M5 M8 M12 M13 M18 M19 PERCENT COVER (%) 100 100 100 60 90 90 80 60 30 30 TRANSECT N SITE N2 N5 N7 N9 N12 N16 N17 N18 N19 N20 PERCENT COVER (%) 90 95 70 60 75 60 60 50 40 60

TRANSECT O SITE O1 O2 O3 O4 O5 O9 O11 O12 O17 O20 PERCENT COVER (%) 95 95 80 90 60 60 70 60 70 50

81 Appendix E. Diversity and species richness indices SAMPLING SIMPSON DIV SHANNON DIV EVENNESS SPECIES SITE (D) (H) (E) RICHNESS INDEX (S) A3 0.2727273 1.3421132 0.2855268 4.982561036 A4 0.40625 0.9743148 0.2223434 4.191993293 A7 0.2653061 1.4750763 0.3471997 6.425083526 A16 0.3609467 1.0579054 0.2173391 4.191993293 A25 1 0 0 3.322259136 B3 0.42 0.9433484 0.2048455 4.191993293 B4 0.3057851 1.3667111 0.2907599 5.722460658 B13 0.5510204 0.7963116 0.1874338 4.191993293 B19 0.53125 0.6615632 0.150972 4.191993293 B21 0.68 0.5004024 0.127914 3.322259136 C1 0.2592593 1.5810938 0.351369 5.722460658 C7 0.15625 1.9061547 0.4349939 7.099768311 C11 0.2066116 1.7677615 0.376081 7.099768311 C13 0.1805556 1.907284 0.398389 8.383986586 C19 0.4285714 0.9556999 0.2249502 4.191993293 D9 0.25 1.4941751 0.3409781 5.722460658 D13 0.3155556 1.3378607 0.2670042 5.722460658 D16 0.1555556 1.9564632 0.3904621 7.751079615 D21 0.205 1.8032983 0.340353 7.751079615 D25 0.2066116 1.7677615 0.376081 7.099768311 E1 0 0 0 0 E9 0.25 1.3862944 0.3758036 4.982561036 E17 0.34375 1.2130076 0.2768143 4.191993293 E22 0.1570248 1.972247 0.4195841 7.751079615 E25 0.1805556 1.820076 0.3801732 7.751079615 F8 0 0 0 0

82 SAMPLING SIMPSON DIV SHANNON DIV EVENNESS SPECIES SITE (D) (H) (E) RICHNESS INDEX (S) F10 0 0 0 0 F13 0.09375 1.2131868 0.2768552 4.982561036 F22 0.18 1.834372 0.3983288 7.099768311 F23 0.125 1.1270882 0.2354235 4.982561036 G4 0.2871972 1.3795824 0.2686208 5.722460658 G9 0.1640625 1.9927981 0.3926561 8.383986586 G13 0.203125 1.732868 0.3414401 7.751079615 G23 0.2622222 1.4322139 0.2858348 6.425083526 G25 0.1855956 1.7770332 0.3386745 7.099768311 H3 0.3148148 1.3768861 0.2651449 6.425083526 H7 0 0 0 0 H8 0.5 0.6931472 0.2313782 3.322259136 H14 0.2853186 1.5106104 0.2878985 7.099768311 H22 0.2853186 1.5106104 0.2878985 7.099768311 I1 0.349481 1.2306991 0.2396315 5.722460658 I3 0.1952663 1.7782333 0.3653253 7.099768311 I14 0.1855956 2.0086361 0.3828144 9 I18 0.1604938 1.8891592 0.4198309 7.751079615 I19 0.1604938 1.8891592 0.4198309 7.099768311 J4 0.2853186 1.5106104 0.2878985 7.099768311 J6 0.26 1.6335952 0.3083234 7.751079615 J16 0.26 1.6335952 0.3083234 7.751079615 J18 0.3148148 1.3768861 0.2651449 5.722460658 J23 0.2416 1.5843113 0.286937 7.099768311 K2 0.3877551 1.0042425 0.236376 4.191993293 K3 0.2244898 1.6114722 0.3261005 6.425083526 K4 0.25 1.4735024 0.3077817 5.722460658

83 SAMPLING SIMPSON DIV SHANNON DIV EVENNESS SPECIES SITE (D) (H) (E) RICHNESS INDEX (S) K5 0.1796875 1.8080457 0.356253 7.099768311 K10 0.125 2.1383331 0.44665 8.383986586 K13 0.2244898 1.549826 0.3647941 5.722460658 K14 0.1418685 2.1192809 0.4126488 9 K15 0.1952663 1.6977336 0.3487872 6.425083526 K16 0.2189349 1.6313454 0.3351482 7.099768311 K20 0.1796875 1.8080457 0.356253 7.099768311 L2 0.1570248 1.972247 0.4195841 7.099768311 L3 0.16 1.9730014 0.4284318 7.751079615 L4 0.2653061 1.4750763 0.3471997 5.722460658 L9 0.28 1.332179 0.3405346 4.982561036 L11 0.1527778 1.9792045 0.4134116 7.751079615 L12 0.1597633 1.9512595 0.4008723 7.751079615 L14 0.22 1.6434177 0.3568636 6.425083526 L18 0.2088889 1.6565511 0.330607 6.425083526 L20 0.1597633 1.9512595 0.4008723 7.751079615 M1 0.2148438 1.4825399 0.2921161 5.722460658 M2 0.2148438 1.4825399 0.2921161 5.722460658 M3 0.171875 1.2554969 0.2865106 4.982561036 M4 0.1604938 1.676788 0.3726353 6.425083526 M5 0.2469136 1.464452 0.3254475 5.722460658 M9 0.1728395 1.8307653 0.4068539 7.099768311 M12 0.5 0.6931472 0.1692938 3.322259136 M13 1 0 0 0 M18 0.2653061 1.4750763 0.3471997 5.722460658 M19 0.2066116 1.6726254 0.3558414 6.425083526 N2 0.21875 1.5595812 0.3559041 5.722460658

84 SAMPLING SIMPSON DIV SHANNON DIV EVENNESS SPECIES SITE (D) (H) (E) RICHNESS INDEX (S) N3 0.1734694 1.9085353 0.3862148 4.191993293 N5 0.3888889 1.2342679 0.257811 5.722460658 N7 0.3553719 1.2945452 0.275407 5.722460658 N9 0.1735537 1.8462202 0.3927727 7.099768311 N16 0.1900826 1.7986522 0.3826528 7.099768311 N17 0.1358025 2.0431919 0.4540618 7.751079615 N18 0.1900826 1.7986522 0.3826528 7.099768311 N19 0.1805556 1.820076 0.3801732 7.099768311 N20 0.16 1.8866968 0.409691 7.099768311 O1 0.2839506 1.3107837 0.2912976 4.982561036 O2 0.04 0.500899 0.1280409 3.322259136 O3 0.0694444 1.9072649 0.398385 7.751079615 O4 0.1015625 1.9273308 0.3797566 7.751079615 O7 0.2892562 1.3896812 0.2956466 5.722460658 O9 0.2231405 1.6417347 0.3492696 6.425083526 O11 0.2066116 1.7677615 0.376081 7.099768311 O12 0.2592593 1.5810938 0.351369 6.425083526 O17 0.25 1.5607104 0.3259975 6.425083526 O20 0.2189349 1.7118451 0.3516863 7.099768311