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Indirect effects of freshwater discharges on seagrass beds in Southwest Florida:
Mesograzers as mediators of epiphyte growth?
A Thesis
Presented to
The Faculty of the College of Arts and Sciences
Florida Gulf Coast University
In Partial Fulfillment
Of the Requirement for the Degree of
Master of Science
By
Thomas J. Behlmer Jr.
2016
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APPROVAL SHEET
This thesis is submitted in partial fulfillment of the
requirements for the degree of
Master of Science
Thomas J. Behlmer Jr.
Approved: August 26, 2016
James Douglass, Ph. D. Committee Chair/ Advisor
Edwin M. Everham III, Ph. D.
Serge Thomas, Ph. D.
The final copy of this thesis [dissertation] has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. 3
Acknowledgments I owe an immense amount of gratitude to my committee members Dr. James Douglass,
Dr. Edwin Everham, and Dr. Serge Thomas for their help with this research over the past two years. I would especially like to thank my advisor Dr. James Douglass for his continual support and encouragement throughout this time, in addition to all of the time spent on our many discussions. I would also like to thank Dr. Edwin Everham and Dr. Serge Thomas for their guidance and vast knowledge of disturbance and aquatic ecology.
Thank you to the Southern Association of Marine Laboratories, Florida Gulf Coast
University Graduate Studies Program, Coastal Watershed Institute, South Florida Water
Management District, and Sanibel-Captiva Conservation Foundation for their logistical and financial support towards this research.
I would like to thank all of the members of the Douglass Lab including: Mackensea
Larson, Roberto Pozzi, Julian von Kanel, William Mastandrea, Serina Sebilian, Christina
Kennedy, and Lisa Rickards. Your help with species identification, data collection, and data input is greatly appreciated. Finally, I would like to thank my family and friends for their love and support during my time at Florida Gulf Coast University.
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Abstract Seagrass beds in the Caloosahatchee Estuary have declined with increased human development in the watershed, which has altered the timing and volume of freshwater and nutrient inputs. Overgrowth of epiphytic algae may contribute to seagrass declines. Small invertebrate grazers (mesograzers) are thought to aid seagrass through removal of excess epiphytes. The goal of this study is to look at the indirect impacts of freshwater releases on seagrass health in the CRE, as mediated by increased nutrients and reduced mesograzer abundance and diversity.
To do this we recorded seagrass abundance, epiphyte levels, and mesograzer abundance bimonthly for two years at two sites in the Caloosahatchee Estuary. We then compared these responses to seasonal and site variations in salinity related to freshwater discharges. Seagrass was most abundant at the highest salinity site and during the summer months. Epiphyte levels did not exhibit a clear seasonal or salinity-related pattern but showed interesting correlations with mesograzer abundance. Mesograzer species richness was positively correlated with salinity at all sites.
Multivariate data analysis found a clear separation between sites, except during the wet season of 2013, where grazer community structure was heavily impacted at both sites. Because prior studies have demonstrated a link between mesograzer richness and epiphyte grazing efficacy, we propose that reductions in mesograzer diversity by high freshwater discharge events could exacerbate problems of epiphyte overgrowth. Understanding these impacts can aid in improving water management plans for the Caloosahatchee Estuary in order to protect its valuable seagrass beds.
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Table of Contents
Table of Contents Acknowledgments...... 3 Abstract ...... 4 Table of Contents ...... 5 List of Figures ...... 7 List of Tables ...... 9 Chapter 1: Introduction and background ...... 10 Estuarine Ecology ...... 10 Seagrass Ecosystems ...... 10 Seagrasses in peril ...... 14 Current status of the Caloosahatchee River and Estuary ...... 16 Multiple Stressor Effect Model ...... 18 Research Objectives ...... 20 Chapter 2: Methods ...... 24 Study Sites ...... 24 Seagrass Surveys ...... 27 Mesograzer Collection ...... 35 Data Analysis ...... 40 Chapter 3: Site Characterization ...... 42 Abiotic Factors ...... 42 Salinity ...... 42 Flow ...... 45 Biotic Factors ...... 47 Seagrass ...... 47 Macroalgae ...... 53 Epiphytes ...... 55 Mesograzers ...... 61 Chapter 4: Abiotic Influences on Seagrass Community Structure ...... 68 Methods Overview ...... 68 Temporal Relationships ...... 68 Multivariate Analyses of Community Structure ...... 77 Site 6 ...... 81 Site 8 ...... 83 6
Drivers of changes in community composition ...... 85 Site 6 ...... 85 Site 8 ...... 85 Chapter 5: Discussion ...... 87 References ...... 96
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List of Figures
Figure 1.1 Remane Diagram………………………………………………………….…………12
Figure 1.2 Map of Caloosahatchee River and Estuary…………………………………………..17
Figure 1.3 Average seagrass coverage for lower CRE 2004-2013……………………………...21
Figure 1.4 Average seagrass coverage for middle CRE 2004-2013…………………………….22
Figure 1.5 Conceptual model of freshwater discharge within a seagrass community…………..23
Figure 2.1 Submerged aquatic vegetation habitats in CRE map………………………………...25
Figure 2.2 Sanibel-Captiva Conservation Foundation monitoring stations map………………..26
Figure 2.3 Example quadrat……………………………………………………………………..30
Figure 2.4 Quadrat distribution image…………………………………………………………..31
Figure 2.5 Halodule wrightii visual epiphyte estimation key…………………………………...32
Figure 2.6 Thalassia testudinum visual epiphyte estimation key……………………………….33
Figure 2.7 ImageJ blade area example image…………………………………………………...34
Figure 2.8 Virnstein epifaunal sampler………………………………………………………….37
Figure 3.1 SFWMD modeled salinity…………………………………………………………...43
Figure 3.2 SCCF continuously monitored salinity……………………………………………...44
Figure 3.3 30 day mean S-79 flow………………………………………………………………46
Figure 3.4 Submerged aquatic vegetation visually estimated percent cover time series………..50
Figure 3.5 Visually estimated SAV at site 8…………………………………………………….51
Figure 3.6 Mean seagrass percent cover by site and season…………………………………….52
Figure 3.7 Macroalgae percent cover time series……………………………………………….54
Figure 3.8 Visually estimated epiphytes time series……………………………………………57
Figure 3.9 Mean visually estimated epiphytes by site and season……………………………...58 8
Figure 3.10 Chlorophyll a time series at site 6………………………………………………...59
Figure 3.11 Chlorophyll a time series at site 8………………………………………………...60
Figure 3.12 Mesograzers over time……………………………………………………………64
Figure 3.13 Species richness over time………………………………………………………..65
Figure 3.14 Mean species richness by site and season………………………………………...66
Figure 3.15 Shannon diversity index over time………………………………………………..67
Figure 4.1 SFWMD salinity, estimated SAV, and visually estimated epiphytes time series….70
Figure 4.2 Grazer species richness versus 20 day average salinity……………………………71
Figure 4.3 Species richness, Shannon diversity index, and grazer count time series…………72
Figure 4.4 Linear regression of epiphytic Chl a versus species richness……………………...75
Figure 4.5 Grazer community structure cluster analysis of sites 6 and 8……………………...79
Figure 4.6 nMDS of sites 6 and 8……………………………………………………………...80
Figure 4.7 nMDS of site 6……………………………………………………………………..82
Figure 4.8 nMDS of site 8……………………………………………………………………..84
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List of Tables Table 2.1 Visual representation of data collection schedule…………………………………….29
Table 2.2 Species classification for statistical analysis………………………………………….38
Table 3.1 ANOVA results for variance according to site, season, and site x season……………48
Table 3.2 Post-hoc comparisons from ANOVA on site and seasonal differences………………49
Table 3.3 Grazers found at sites 6 and 8…………………………………………………………63
Table 4.1 Linear regression results for factors affecting grazer community structure…………..73
Table 4.2 DistLM results………………………………………………………………………...86
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Chapter 1: Introduction and background
Estuarine Ecology Estuaries are ecotones between freshwater and marine systems (Meire et al. 2005). They are characterized by strong spatial and temporal variation in hydrology (e.g., water flow and salinity). These variable conditions constitute stresses that limit the biological diversity of estuaries (Day et al. 2013). I.e., an estuary usually has fewer species than freshwater systems upstream of tidal mixing or marine systems free of freshwater influence. Despite their relatively low diversity, estuaries often have high biological productivity; the species found in an estuary are frequently very high in numbers and biomass (Meire et al. 2005). One model that depicts the changes in taxonomic diversity across the estuarine gradient is the Remane diagram (Figure 1.1,
Remane 1934, Whitfield 2012). This model proposes that there are high numbers of species in both marine and fresh waters, and fewer brackish water species (Whitfield 2012). As salinity rises from 0 to ~5ppt there is a decline of freshwater species into a region shared with brackish and some marine species. The model then shifts to one dominated by marine species with little amounts of brackish species before transitioning to a completely marine system.
Though salinity constrains species diversity in estuaries, biological productivity is high.
Nutrient input from both freshwater and marine systems create dynamic nutrient transformation zones where benthic ecosystems such as seagrass beds can thrive (Nixon 1986).
Seagrass Ecosystems Seagrasses are true, vascular plants that have secondarily adapted to live fully submerged in marine and estuarine environments (Larkum et al. 2006). The world has approximately 60 species of seagrasses, which occur in shallow, sunlit waters from tropical to cold temperate waters (Short et al. 2007). Seagrass biogeography reflects their evolutionary origins, their 11
environmental tolerances, and their spread by both sexual reproduction and clonal growth
(Spalding et al. 2003). Seagrass beds provide valuable ecosystem services in shallow coastal waters worldwide (Short and Neckles 1999). They provide nursery habitat for many commercially and recreationally important fish species, they stabilize sediments, sequester nutrients and carbon dioxide, improve water quality, and their high primary productivity leads to high secondary productivity (Thayer et al. 1984, Doering et al. 2002, Orth et al. 2006).
Seagrasses root in soft sand and mud substrates, and their blades provide a stable surface for the attachment of epiphytic algae and sessile fauna which would not be found on the bare substratum. These attached organisms contribute to the diversity and productivity of seagrass ecosystems (Borowitzka et al. 2006). Seagrass epiphyte communities are intricate relationships of algae, bacteria, fungi, and protozoans attached to blade surfaces and are major components of seagrass-associated food webs (Neckles et al. 1994). Calcareous red algae are common seagrass epiphytes that add to sediments (Patriquin 1972). Nitrogen fixation occurs through epiphytic cyanobacteria on seagrass leaves, contributing to primary productivity (Pereg-Gerk et al. 2002).
In Florida, Wear et al. (1999) found 50% and 44% of Thalassia testudinum and Halodule wrightii seagrass meadow primary production came from algal epiphytes.
While epiphytes are important in the diversity and productivity of seagrass ecosystems, overgrowth can have negative results. Increased epiphytic cover can reduce light supply to seagrass leaves (Kemp et al. 1983). Gas exchange can also be limited by epiphyte overgrowth
(Kiorbøe 1980), along with competitive nutrient uptake (van Montfrans et al. 1984, Jernakoff
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Figure 1.1: A redrawn version of the original Remane diagram (Remane 1934) as seen in
Whitfield et al. 2012. The areas are depicted as: slanted hashed= freshwater, vertical hashed= brackish species, and white below the curve= marine species. A salinity of 50% seawater is depicted by the vertical dashed line.
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1996). Hydrodynamic drag forces on seagrasses are increased with denser epiphyte cover, which can cause blades to detach or plants to uproot in waves and currents (Jernakoff 1996). Many factors, including light levels, nutrient supply, and seagrass blade turnover rates, can affect epiphyte abundance (Trautman and Borowitzka 1999, Wear et al. 1999, Peterson et al. 2007).
But one of the most significant influences on epiphytes, and on seagrasses themselves, is grazing organisms (Hays 2005).
Historically, organisms such as green turtles, sirenians, and waterfowl most likely had the greatest grazing impact on seagrasses (Valentine and Heck 1999, Domning 2001), but due to overfishing and other human disturbance to coastal environments most are functionally extinct in most oceans (Jackson et al. 2001). In areas where direct grazing by large herbivores has declined the dominant herbivores in seagrass beds are invertebrate grazers, or mesograzers (Valentine and
Duffy 2007). These mesograzers include amphipods, isopod crustaceans, hermit crabs, and gastropods. One of the major functions that mesograzers play in seagrass beds is the transfer of primary production to higher trophic levels such as the finfish and crustaceans targeted by commercial and recreational fisheries (Edgar and Shaw 1995). Another important function of mesograzers is to reduce epiphyte abundance, which can indirectly benefit seagrasses by reducing competition for light (Jernakoff et al. 1996).
Many studies have been conducted worldwide in both mesocosm and field based experiments looking at the impact of mesograzer consumption of epiphytic algae. In Chesapeake
Bay, Virginia, USA, Duffy and Harvilicz (2001) found grazer exclusion in outdoor mesocosms lead to a tenfold increase in epiphyte mass. Similarly, Duffy et al. (2001) found that crustacean mesograzers reduced periphyton mass by around two thirds in outdoor mesocosms (Duffy et al.
2001). Crustacean and gastropod grazers were found to reduce algal mass by 21-100% when 14
placed in single-species monocultures and in six-species polycultures in outdoor mesocosms
(Duffy et al. 2003). Importantly, this study found that epiphyte reduction was greater with grazer polycultures than with single grazer species, on average. Since then, more studies have established that both grazer abundance and grazer species richness have strong effects on grazer, algal, seagrass, and sessile invertebrate biomass. These grazer effects may be comparable in magnitude to the effects of warming temperature or nutrient enrichment (Hughes et al. 2004,
Blake and Duffy 2012, Duffy et al. 2013).
However, grazers are not always able to control epiphyte and macroalgae growth.
Grazing impacts were shown to decrease as nutrient loading increased in a study of macroalgal biomass and growth rates (Hauxwell et al. 1998). The impacts of nutrients and grazers appears to be context-dependent, however. For example, in some seagrass systems, under some circumstances, increasing nutrients actually increases seagrass production. Hays (2005) found that seagrass biomass increased with nutrient addition in the presence of epiphytic grazers, but seagrass growth declined with nutrient addition when grazers were not present. Whalen et al.
(2013) found a similar epiphyte and mesograzer relationship through cage-free experiments during the summer and fall. This study found during the summer that experimental reduction of mesograzers led to a 447% increase in epiphytes and in the fall nutrient additions increased epiphytes only after the seasonal decline of mesograzers showing mesograzers can control epiphytic algae growth.
Seagrasses in peril Globally, seagrass habitat area has declined at an alarming rate of 7% per year since 1990
(Orth et al. 2006, Waycott et al. 2009). Anthropogenic stressors related to human population growth in coastal watersheds appear to be the primary causes of decline. These stressors include 15
eutrophication, sedimentation, physical disturbance, and hydrologic alteration (Kemp et al. 1983,
Short and Wyllie-Echeverria 1996, Peters et al. 1997).
Nutrient enrichment, resulting in eutrophication, is a major anthropogenic impact in freshwater, estuarine, and nearshore marine waters (Cloern 2001). It is thought to be a main reason for worldwide seagrass loss (Orth and Moore 1984, Silberstein et al. 1986, Orth et al.
2006). Increasing nutrient loading can harm seagrass beds by stimulating algal blooms, both in the water column (phytoplankton) and on the benthic environment (macroalgae and epiphytes).
Phytoplankton blooms that occur in deeper water seagrass beds may lead to seagrass declines through shading (Cambridge et al. 1986). Nutrient loading in seagrass beds often leads to epiphyte overgrowth, and in extreme cases seagrass death (Duarte 1995, Hauxwell 2003).
Seagrass beds exposed to eutrophication have been shown to have high epiphytic loading, low shoot densities, low leaf area indices, and low biomass (Tomasko and Lapointe 1991, Delgado et al. 1999). The decline in leaf health may be attributed to the interference of competitive nutrient uptake, limitation of gas exchange, and blocking of light by algal epiphytes (Jernakoff 1996).
In addition to fluctuating levels of light, nutrients, and algal competitors, seagrasses growing in estuarine systems experience a range of salinities over time due to tidal cycling and variable freshwater inputs. Seagrasses have evolved to tolerate moderate variation in salinity, but salinity stress can still occur at both low and high salinity, especially when the stressful conditions persist for a long time. Experiments done on Thalassia testudinum showed spectral differences, indicating possible declines in photosynthetic ability, in specimens kept at 16 ppt for
24 hours compared to those kept at 32 ppt (Thorhaug et al. 2006). Salinity above 20 ppt is considered optimal for growth of Halodule wrightii and T. testudinum with varying lower limits for both species. T. testudinum cannot grow below 17 ppt (Zieman and Zieman 1989) and H. 16
wrightii can show minimal growth at 12ppt (Doering et al. 2002). Low salinity periods in the
Caloosahatchee River and Estuary (CRE), in concert with other stressors such as high nutrient loading and poor optical water quality, have resulted in losses of T. testudinum at the mouth of the estuary in addition to H. wrightii further upstream (Douglass 2014).
Current status of the Caloosahatchee River and Estuary The Caloosahatchee River and Estuary (CRE) are located in southwest Florida, USA. The
CRE today extends from Lake Okeechobee 105 km to San Carlos Bay, where it meet the Gulf of
Mexico. The freshwater region of the river ranges from 50 to 130 m in width and 6 to 9 m in depth, while the estuarine portion ranges from 160 m in width and 6 m in depth in the upper section to 2,500 m and 1.5 m respectively in the lower portion downstream of Beautiful Island
(Barnes 2005).
The watershed of the CRE has been drastically altered through deforestation, urban and agricultural development, river channelization, diversion, damming, and dredging of canals for drainage and regulatory releases from Lake Okeechobee to which it was not naturally connected
(Hopkinson and Vallino 1995). This alteration has resulted in large fluctuations in: freshwater inflow volume, frequency of inflow events, timing of discharges, and water quality in the downstream estuary (Chamberlain and Doering 1998, Barnes 2005). Before alteration, the river was shallow and meandering with headwaters around Lake Hicpochee and tidal influence as far upstream as the town of Labelle (LaRose and McPherson 1980, Antonini et al. 2002, Barnes
2005).The freshwater section was reconfigured into a canal known as C-43 to accommodate navigation, flood-control, and land reclamation (Barnes 2005). 17
Figure 1.2: Map of Caloosahatchee River and Estuary showing distribution of local seagrasses.
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Secondary canals were also built to drain and irrigate agricultural lands, while three lock- and-dam structures (S-77, S-78, and S-79) were constructed to control river flow and stage heights, with S-79 truncating the upper estuary and blocking saltwater intrusion to the river
(Barnes 2005). Large amounts of water are removed from the C-43 canal for irrigating crops and the river is a major source of water for urban and suburban areas in Lee County, including the city of Fort Myers (LaRose and McPherson 1980).
These alterations have had a strong impact on hydrology, resulting in large fluctuations in freshwater inflow and reduced water quality and ecological health in the estuary (Chamberlain and Doering 1998, Douglass 2014). When inflow is greater than 113 m3s-1 it causes the estuary upstream of Shell Point to become freshwater and depresses salinity to less than 15ppt in portions of San Carlos Bay (Chamberlain and Doering 1998). On the other hand, when inflow is low to no flow, at < 2.8 m3s-1, it causes salinity near S-79 to be above 15 ppt, therefore getting rid of any tidal freshwater or oligohaline zone in the estuary (Chamberlain and Doering 1998).
Monthly seagrass and salinity monitoring in the CRE since 2004 reveals moderate declines of
Thalassia testudinum and Halodule wrightii in the lower estuary (Figure 1.3), and serious declines of H. wrightii in the middle estuary (Figure 1.4) during periods of high freshwater inputs and reduced salinity (Douglass 2014).
Multiple Stressor Effect Model When numerous stressors, for example nutrient enrichment and salinity fluctuations, are acting simultaneously, the results can be difficult to predict. One theory classifies the simultaneous impacts of multiple stressors like these as “additive”, meaning that the combined effect is equal to the sum of the separate factors, or “non-additive”, meaning that the combined effect is not equal to the sum of the factors (Folt et al. 1999). Non-additive interactions are where 19
the combined effects are greater (synergism) or less than (antagonism) the sum of the effects by individual stressors (Folt et al. 1999). A second theory predicts that biodiversity can help manage additional stressors through positive species co-tolerance (Vinebrooke et al. 2004). This theory states that ecosystem resistance to a single stressor can occur when more resistant species can compensate for the sensitive species (Vinebrooke et al. 2004). For example, certain mesograzers may have higher tolerance to reduction of salinity than others and may be able to make up for the reduced grazing of the less tolerant species. When looking at the combined effects of increased temperature and freshwater input in seagrass mesocosms, Blake and Duffy (2010) found that more diverse assemblages of mesograzer were better able to control epiphyte growth and reduce the impacts of the environmental stresses on seagrass.
The harmful impacts of nutrient enrichment and salinity reduction on tropical seagrasses have been examined separately (Heck et al. 2000, Lirman and Cropper 2003), but understanding of their combined effects is limited (Wernberg et al. 2012). Freshwater stresses and nutrient inputs are important to look at since they are likely to co-occur during heavy rainfall events or managed releases from freshwater reservoirs. Such events lower salinity, but also convey large fluxes of nutrients from terrestrial/freshwater environments to the estuary. Freshwater stress constitutes a direct physiological harm to seagrasses, and nutrient inputs harm seagrass indirectly by causing eutrophic overgrowth of algae. There may be additional indirect threats of these stressors, however, mediated by the small invertebrate grazers (mesograzers) that consume epiphytes (Hays 2005, Valentine and Duffy 2007, Whalen et al. 2013). If mesograzer abundance and/or diversity is reduced by freshwater stress, the remaining mesograzer community may not be sufficient to provide epiphyte control (Douglass et al. 2010), especially if epiphyte growth is being simultaneously stimulated by increased nutrient inputs. 20
Research Objectives The goal of this study is to look at the indirect impacts of freshwater releases on seagrass health in the CRE, as mediated by increased nutrients and reduced mesograzer abundance and diversity. We expect to observe reduced grazer abundance and diversity, and increased epiphytic growth on seagrass beds, during periods of low salinity (Figure 1.5). In addition to the possible conservation and management implications for submerged aquatic vegetation and invertebrate grazer species in southwest Florida, the goal is to look at the impact of freshwater inflow on this ecosystem. If this study finds that grazer diversity reduces algal epiphytes or that freshwater inflow reduces grazer diversity it can add to the growing number of studies on this subject. In the case that both of these instances are found, this information can be used by researchers around the world in the management of seagrasses.
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FIGURE 1.3: Average percent cover of H. wrightii and T. testudinum in the lower CRE (San
Carlos Bay) between 2004 and 2013 as seen in Douglass (2014). Values are averages from two
SAV monitoring sites. Extreme low salinities occurred in late 2005 due to hurricanes.
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Figure 1.4: Percent cover of H. wrightii in the middle CRE between 2004 and 2013 as seen in
Douglass (2014). Values are averages from two SAV monitoring sites. Salinities were low from
2004 – 2006.
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Figure 1.5: Conceptual model showing known and unknown relationships as related to freshwater discharge within a seagrass community. The positive and negative signs reflect the positive or negative impact on the relationships.
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Chapter 2: Methods
Study Sites The Caloosahatchee River and Estuary (CRE) is located in Southwest Florida, USA
(26.63°N, 81.85°W). We studied two seagrass beds in the CRE over an approximately two-year period from January 2013 to November 2014. The beds are referred to as “Site 6” and “Site 8.”
Their numbering reflects their original designation as sites within the South Florida Water
Management District’s patch-scale seagrass monitoring program, established in the late 20th century and continued today. Each site is an approximately 0.7 hectare polygon specified in the
SFWMD’s geographic information system (GIS) database. We chose to focus on these two sites because they represented distinctly different levels of freshwater influence and correspondingly different salinity regimes and seagrass communities (Figures 2.1, 2.2). Site 8 is a relatively high salinity site close to the mouth of the estuary near Kitchel Key (26.48°N 82.01°W). It is a mixed seagrass bed consisting of Halodule wrightii and Thalassia testudinum. Site 6 is a monospecific bed of Halodule wrightii located upstream of the Shell Point bottleneck in the lower salinity waters of Iona Cove (26.5°N 81.98°W). Macroalgae is often present at both sites. Both sites are shallow, ~1 m deep at mid-tide, and relatively low-energy with a muddy sand substrate. 25
Figure 2.1: Location of submerged aquatic vegetation habitats and study sites within the
Caloosahatchee Estuary: 6) Iona Cove, 8) Kitchel Key (Gulf side).
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6
8
Figure 2.2: Sanibel-Captiva Conservation Foundation continuous monitoring stations locations
(Shell Point= near Site 6, Tarpon Bay= near Site 8).
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Seagrass Surveys Seagrass surveys were conducted bimonthly at each site to gain an understanding of how
H. wrightii and T. testudinum communities (including epiphytes) respond to natural and anthropogenic environmental fluctuations within the Caloosahatchee Estuary. Seagrass community characteristics of the surveys included: seagrass blade morphometrics, estimated seagrass percent cover, canopy height, macroalgal cover, epiphyte level, grazing intensity, and epifaunal abundance and diversity. Seagrass and macroalgae characteristics were assessed using
30 1 m2 quadrats distributed evenly within each site (Figure 2.3), whereas epifaunal and epiphyte samples were taken from 10 haphazardly selected points within the site, sometimes on a different day than the seagrass quadrat survey (Figure 2.4).
Epiphyte level was assessed by both a visual ordinal scale applied at each quadrat
(Figures 2.5 and 2.6) and through epiphytic chlorophyll a analysis of ten haphazardly selected shoots as in Douglass et al. (2010). Epiphytes were gently scraped off both sides of each seagrass blade into a shallow container using a glass microscope slide and then washed into a filter tower onto a 47 mm 0.7 µm pore GFF filter (Millipore Corporation) using matched-salinity water.
Water was vacuumed out of the tower, and the sides of the tower were sprayed to wash any remaining epiphytes onto the GFF filter. The filter was folded and placed into a labeled 15 ml plastic centrifuge tube and placed in a dark cooler until all samples were completed before being placed into a -20°C freezer. A photograph of the scraped blades lying flat in the tray, with a ruler for scale and a tag identifying the sample number, was then taken for blade area calculation
(Figure 2.7). Shoot surface area was determined using ImageJ software (Rasband 1997).
Extraction of chlorophyll a from the filter was done by adding 20 ml of 90% acetone to each centrifuge tube, then placing the tube into a -20°C freezer for 24 hours. Next, the samples 28
were removed from the freezer and kept in a dark cooler while each test tube was vortexed for 5 seconds and then placed into the centrifuge for five minutes at 3000 rpm to separate the acetone- chlorophyll supernatant and GFF filter. After centrifuging, spectrophotometric absorbance of the supernatant was measured at 480, 510, 630, 647, 664, and 750 nm using a Shimadzu UV-2450 spectrophotometer (Shimadzu Scientific Instruments Inc., Columbia, MD). “Blank” measurements of 90% acetone were taken every 10 measurements to ensure instrument accuracy.
Chl a was estimated using the trichromatic equation (Lorenzen 1967) followed by the calculation of chlorophyll a mass and normalization to blade area as a substitution for epiphyte density.
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Seagrass surveys Epiphytic Chl a analysis Virnstein Date S6 S8 S6 S8 S6H S6M S8H S8T S8M 13-Feb X X X X X X X 13-Mar X X X X X 13-Apr X X 13-May X X 13-Jun X X X X X 13-Jul X X 13-Aug X X X X X 13-Sep X X X X X X 13-Oct 13-Nov 13-Dec X X X X 14-Jan X 14-Feb X X X X X X 14-Mar X X 14-Apr X X X X X 14-May X X X X X X X 14-Jun 14-Jul X X X X X X X 14-Aug X X 14-Sep X X X X X X X 14-Oct 14-Nov X X X X X
Table 2.1: Visual representation showing the months when seagrass, epiphyte, and mesograzer data was collected. S6= Site 6, S8= Site 8, H= H. wrightii, M= Macroalgae, and T= T. testudinum, Virnstein= mesograzer collection.
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Figure 2.3: Picture showing a 1 m2 quadrat subdivided into 25 equal quadrats.
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A
B
Figure 2.4: Examples of quadrat distribution within site 6 (A) and site 8 (B). 32
0
1
2
3
Figure 2.5: Key showing ordinal epiphyte visual estimate on Halodule wrightii in ascending
order 0-3: 0= no epiphytes, 1=light “dusting” of epiphytes, some but can see blade, 2= moderate,
blade mostly covered, and 3= heavy, completely covered. 33
0
1
2
3
Figure 2.6: Key showing ordinal epiphyte visual estimate on Thalassia testudinum in ascending
order 0-4: 0= no epiphytes, 1=light “dusting” of epiphytes, some but can see blade, 2= moderate,
blade mostly covered, and 3= heavy, completely covered.
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Figure 2.7: Picture of scraped seagrass blades used to calculate blade area using ImageJ software (Rasband 1997).
35
Mesograzer Collection Grab samples of seagrass and epifauna were collected using a sampler based on the design by Virnstein and Howard 1987, Figure 2.8). The sampler collects seagrass blades and associated epibenthos from a 20 x 20 cm2 square of the bottom, but does not penetrate deeply into the sediment and therefore minimizes “bycatch” of infauna (Douglass et al. 2010). The sampling device is fitted with a 500 m mesh bag, into which the collected materials are funneled. During each sampling event, at each site, 10 grab samples were taken. For some dates more than 10 samples were taken from Site 8, i.e., a separate set of samples was taken for each macrophyte type present- T. testudinum, H. wrightii, and macroalgae. Collected samples were kept in a dark, ice-filled cooler during transport back to the laboratory. In the laboratory the samples were transferred from the mesh collection bags into plastic bags for storage. A quick blast of tap water was used to remove all epifauna from the collection bags. Epifaunal grab samples were frozen at -20°C until sorting. During sorting, the macrophytes and sessile epifauna from each grab sample were separated by taxon and macrophytes parsed into aluminum tins for drying and weighing. All sessile epifauna and seagrass species were identified to the lowest taxonomic level possible, except macroalgae which were grouped as “macroalgae”. Seagrass blade length was measured for the first ten blades pulled from the sample. These blades were also checked for signs of grazing- a tally of bite marks was made on each of the ten blades.
Macrophyte and sessile epifauna samples were dried at 60°C and weighed. Mobile epifauna remained in the sorting tray after macrophytes and sessile epifauna were removed. These fauna were sorted by size class by pouring them through a series of sieves (5.6, 4.0, 2.8, 2.0, 1.4, 1.0,
0.71, and 0.50 mm), then they were identified to the lowest taxonomic category possible, and counted. Counts of individuals in each class size were multiplied by empirically derived coefficients to determine ash-free dry mass (AFDM) as in Edgar (1990). This mass for each size 36
class was then pooled to find AFDM for each epifaunal taxon within a collected sample
(Douglass et al. 2008, 2010).
With a large volume of epifaunal samples to sort the help of undergraduate interns was used. These interns were taught how to identify mesograzers and were under graduate student supervision. In order to control for possible misidentification, species that we were unsure of their accuracy were changed to a higher order of classification, usually to genus or family (Table
2.2). Species that were later classified as grazers were done so based on work by Lefcheck et al.
2015.
37
Figure 2.8: Virnstein epifaunal sampler with attached mesh collection bag.
38
Table 2.2: Table showing reclassification of species to higher levels of classification for statistical analysis.
Original Final Final classification Original classification classification classification Microprotopus Alpheus normanni Alpheus sp. Microprotopus raneyi sp. Ameroculodes Ameroculodes sp. Monocorophium sp. Corophiidae edwardsi Amphilochus sp. Amphilochidae Nassarius acutus Nassarius sp. Apocorophium sp. Corophiidae Nassarius albus Nassarius sp. Apolochus casahoya Amphilochidae Nassarius vibex Nassarius sp. Architectonica nobilis Architectonica sp. Odostomia acutidens Odostomia sp. Architectonica Boonea Architectonica sp. Odostomia impressa peracuta impressa Balcis sp. Unk. Gastropod Odostomia laevigata Odostomia sp. Palaemonetes Batea catharinensis Batea sp. Palaemonetes intermedius sp. Palaemonetes Bemlos mackinneyi Bemlos sp. Palaemonetes paludosus sp. Palaemonetes Blue crab Callinectes sapidus Palaemonetes pugio sp. Palaemonetes Caecum floridanum Caecum sp. Palaemonetes vulgaris sp. Caprella equilibra Caprella sp. Paramicrodeutopus sp. unk. Amphipod Caprella penantis Caprella sp. Penaeus brasiliensis Penaeus sp. Cerapus benthophilus Cerapus sp. Penaeus duorarum Penaeus sp. Cerapus tubularis Cerapus sp. Penaeus semisulcatus Penaeus sp. Cittarium pica Unk. Gastropod Penaeus setiferus Penaeus sp. Periclimenaeus Crepidula fornicata Crepidula sp. Periclimenaeus ascidiarum sp. Periclimenes Elasmopus laevis Elasmopus sp. Periclimenes harringtoni sp. Erichthonius Erichthonius Periclimenes Periclimenes magnus brasiliensis punctatus sp. Periclimenes Eurypanopeus sp. unk. Mud Crab Periclimenes pandionis sp. Farfantepenaeus Penaeus sp. Phasianella umbilicata unk. Gastropod aztecus Gemophos tinctus Unk. Gastropod Pollia fumosa unk. Gastropod
39
Gitana sp. Amphilochidae Polycheria sp. unk. Amphipod Haminoea solitaria Haminoea sp. Prunum apicinum Prunum sp. Haminoea succinea Haminoea sp. Prunum succinea Prunum sp. Hippolyte Pyramidella Hippolyte sp. Pyramidella candida curacaoensis sp. Rudilemboides Hippolyte nicholsoni Hippolyte sp. Rudilemboides naglei sp. Hippolyte Hippolyte sp. Scylla sp. unk. Mud Crab pleuracanthus Hippolyte zostericola Hippolyte sp. Stenothoe sp. Stenothoidae Hourstonius laguna Hourstonius sp. Stenothoe valida Stenothoidae Idotea sp. Unk. Isopod Syngnathus scovelli Syngnathus sp. Libinia dubia Libinia sp. Turbonilla levis Turbonilla sp. Litopenaeus setiferus Penaeus sp. Urocaris longicaudata Urocaris sp. Litopenaeus vannamei Penaeus sp. Xanthidae unk. Mud Crab Longchaeus suturalis unk. Gastropod Zebina browniana Zebina sp. Lysmata wurdemanni Lysmata sp.
40
Data Analysis Seagrass community response variables from this observational study were: seagrass and macroalgal percent cover and canopy height by species, visually estimated epiphyte density on an ordinal scale, epiphyte density as Chl a µg*cm-2, epifaunal abundance and biomass (total and by species and functional groups, e.g., grazers), and grazer species richness and the Shannon
Diversity Index. Epifaunal abundance measures were calculated both “raw” (per-sample) and normalized to plant biomass from the sample they came from (e.g., mg epifaunal AFDM per g plant DM). The Shannon Diversity Index was calculated both with raw abundance data and with plant-normalized data. Plant adjusted abundance was calculated by dividing either total count or total biomass by the mass of flora (seagrass and macroalgae) collected with the sample.
Simple statistics like mean, standard deviation, and standard error of the mean were computed for each response variable for each individual site and sample date, and time series were constructed for each response variable. Overall mean values (2 year mean) of each response variable were computed for generalization of each site. This was done for all response variables.
T-test and ANOVA were used to test the significance of differences among sites, dates, and macrophyte species.
We assessed a suite of abiotic and biotic characteristics of Sites 6 and 8, evaluated differences between the sites, and tracked how the characteristics of each site varied over time.
Seasonal comparisons were done as Wet season (June-October) and Dry season (November-
May) in addition to Spring (March-May), Summer (June-August), Fall (September-November), and Winter (December-February).
Seagrass community response variables were compared using linear regression with abiotic environmental data collected by United States Geological Survey (USGS), the South 41
Florida Water Management District (SFWMD), and the Sanibel-Captiva Conservation
Foundation (SCCF). Freshwater flow through the S-79 lock and dam was used from USGS.
Modeled salinity data as described in Wan et al. (2013) was used from the SFWMD. SCCF data that was used included continuous (hourly) monitored salinity.
In addition to assessing individual response variables and their interrelationships, we assessed seagrass epifaunal communities with a multivariate framework in the PRIMER-7 software package (Primer-E Ltd 7). Seasonal and spatial differences in multivariate community composition were assessed with cluster analysis and non-metric multidimensional scaling
(nMDS), and the statistical significance of these differences was tested with the ANOSIM procedure (Rees et al 2005) and PERMANOVA procedure (Anderson and Walsh 2013).
Individual community components responsible for the differences were teased apart with the similarity percentage analysis (SIMPER) procedure, and environmental drivers of community composition changes were examined by incorporating abiotic environmental data in distance based linear models (DistLM procedure) used to look at what whether salinity and chlorophyll a concentration were driving any differences found.
Proper data transformations were performed on taxa abundance data prior to analyses.
Square root transformations were done on taxonomic data used in Cluster, nMDS, ANOSIM, and
SIMPER (Anderson et al. 2005). Environmental data was normalized before ANOSIM and
SIMPER analyses. Taxonomic data was square root transformed for DistLM analyses, but normalization of environmental data was not performed as DistLM performs this. Plant- normalized data were used in all simple statistics (mean, standard deviation, standard error, T- test, ANOVA, and diversity analyses).
42
Chapter 3: Site Characterization
Abiotic Factors Salinity Estimated daily mean salinities for each site followed a wet/dry seasonal trend at both sites (Figure 3.1). During the first year of study (2013), Site 6 salinity remained above 10 psu until June, and Site 8 salinity remained above 20 psu until July (Figure 3.1). These salinities are the minimum thresholds for growth of Halodule wrightii and Thalassia testudinum, respectively
(Zieman and Zieman 1989, Doering et al. 2002). The salinity at site 6 was below the 10 psu threshold from June until November 2013, while below 20 psu conditions at site 8 lasted until near the end of September 2013 (Figure 3.1). Site 8 remained above the 20 psu threshold throughout 2014, but Site 6 dropped below the 10 psu threshold for H. wrightii briefly in October
2014 (Figure 3.1).
Continuously monitored salinity data from the Sanibel Captiva Conservation Foundation
(SCCF) showed the same trends as the modeled salinity data for 2013, however, there were minor differences (Figure 3.2). The SCCF data shows lower salinities at the Shell Point monitoring station than were estimated for nearby site 6, however, this might be explained by the slight difference in locations for the two collection points as the SCCF station is at Shell Point while the modeled salinity is for just upstream Iona Cove. Shell Point often reaches zero psu in
July through September 2013 (Figure 3.2). In September 2014 the Shell Point sensor indicates conditions below the 10 psu threshold for H. wrightii growth occurring longer and more frequently than the modeled data for Site 6 depicts (Figures 3.1 and 3.2). The differences between the modeled and the continuously monitored data are not unexpected as the continuously monitored data include short-term, tide-related excursions above and below the daily mean, which are not captured in the modeled salinity data, which are daily means only. 43
Modeled daily mean salinity at Sites 6 and 8 Site 6 Site 8
40
35
30
25
20
Salinity (psu) Salinity 15
10
5
0
Jul-13 Jul-14
Jan-13 Jan-14
Sep-13 Sep-14
Mar-13 Mar-14
Nov-13 Nov-14 May-14 May-13 Figure 3.1 Simulated daily mean salinity at Sites 6 and 8, produced by the model described in Wan et al. (2013). Solid gray line depicts the lowest salinity threshold for Thalassia testudinum (20psu) growth and solid black line depicts the lowest salinity threshold for Halodule wrightii growth (10psu).
44
SCCF continuously monitored salinity Sensor 13-Shell Point (near Site 6) Sensor 11- Tarpon Bay (near Site 8) Series3 Series4 40
35
30
25
20
15 Salinity (psu) Salinity
10
5
0
Jul-13 Jul-14
Jan-13 Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 3.2 Continuously monitored (hourly) salinity data from Sanibel-Captiva Conservation Foundation. Solid gray line depicts the lowest salinity threshold for Thalassia testudinum (20psu) and solid black line depicts the lowest salinity threshold for Halodule wrightii (10psu).
45
Flow The SFWMD 30-day mean flow data from the S-79 lock and dam (Figure 3.3) shows an approximate mirror image of the salinity data (Figures 3.1 and 3.2). A wet/dry seasonal trend is shown in the flow data collected at S-79 during the two years of study. 2013 shows this trend more dramatically than 2014, with flows of over 79.29 cubic meters per second (m3s-1) from
June up to October, while there are small bursts of freshwater inflow reaching over 85 m3s-1 occurring in September 2014 (Figure 3.3). Doering et al. (2002) recommended a 30-day mean flow between 12.75 and 79.29 m3s-1 for estuarine health, however, flows frequently reach above the upper limit of this envelope during these two time periods.
46
30 day mean S-79 Flow 30 day mean flow 12.75 cms 79.29 cms 350
300
250
)
1
- s
3 200
150 Flow Flow (m 100
50
0
Jul-13 Jul-14
Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 3.3 Time series of 30 day mean flow in cubic meters per second (m3s-1) recorded by the United States Army Corps of Engineers at S-79 showing the recommended flow envelope recommended by Doering et al. 2002 between 12.75 (gray line) and 79.29 m3s-1 (black line).
47
Biotic Factors Seagrass Over the two year period bimonthly benthic surveys found strong site differences and seasonal trends in seagrass cover and species composition (Figures 3.4 and 3.5). Site 6 had just
Halodule wrightii, whereas Site 8 had both Halodule wrightii and Thalassia testudinum. Both sites 6 and 8 showed an increase in seagrass beginning in spring before a summer peak (Figures
3.4 and 3.5). This was followed by a fall die-off toward low percent cover during the winter
(Figures 3.4 and 3.5).
Significantly more seagrass was found at site 8 during both the wet and dry season than at site 6 during the dry season (Figure 3.6, Tables 3.1 and 3.2). However, there was no significant difference in the amount of seagrass at site 6 during the wet season than at other time periods at either site (Figure 3.6, Tables 3.1 and 3.2). In the two way ANOVA, site accounted for 32% of the variation seen in seagrass coverage while season accounted for 11%. The interaction of the two had no effect.
48
Table 3.1 ANOVA results for variance in H. wrightii and T. testudinum community responses according to study site (S), season (T), and their interaction (SxT). Df = 1 for each main effect and interaction effect, error dfseagrass community=18, total error dfseagrass community=21, error dfgrazer=20, 2 and total dfgrazer=23. Omega squared (ω ) estimates the proportion of variation explained by each 2 -1 2 factor or interaction. ω = [SSeffect-(dfeffect)(MSerror)] x (MSerror + SStotal) . Bold ω values show significant effects (p < 0.05). Seagrass % cover= estimated seagrass percent cover, log epiphytic chl a= logarithmic transformation of epiphytic chlorophyll a, sq rt ordinal epiphytes= square root transformation of ordinal estimation of epiphytes, log macroalgae % cover= logarithmic transformation of estimated macroalgae percent cover, Grazer SR= grazer species richness, and Grazer H’= Grazer Shannon Diversity Index.
Effect Site (S) Season (T) S x T Response R2 (adj) MS Error MS ω2 MS ω2 MS ω2
Seagrass % cover 0.432296 122.874 1659.96 0.31822 646.866 0.11225 777.837 0
Log epiphytic chl a 0.16917 0.7594 3.22569 0.06943 1.36666 0.03043 1.84179 0.0087
Sq rt ordinal epiphytes 0.527341 0.06664 0.847522 0.22082 0.458333 0.12993 0.584978 0.12686
Log macroalgae % cover 0.128327 1.65162 0.95102 0 6.23428 0.13817 3.11053 0.02232
Grazer SR 0.197581 2.73794 13.1646 0.12069 10.5075 0.09566 7.90656 0
Grazer H' 0.06547 0.10326 0.138946 0.00838 0.330759 0.08602 0.158721 0
Grazer count 0.1165 1226.79 1464.34 0.01053 1699.54 0.0163 1078.47 0
49
Table 3.2 Post-hoc comparisons from ANOVA on site and seasonal differences in biotic characteristics of Caloosahatchee Estuary seagrass beds. Letters indicate significantly different groups. Tukey Response Variable Site Season Total Mean p-value HSD Dry 7.142 B 6 Wet 20.243 AB Seagrass 0.004 Dry 26.525 A 8 Wet 35.203 A Dry 4.783 A 6 Wet 1.880 A Macroalgae 0.176 Dry 10.161 A 8 Wet 0.787 A Dry 0.848 B 6 Wet 2.067 A Ordinal epiphytes 0.001 Dry 2.255 A 8 Wet 2.260 A Dry 0.361 A 6 Epiphytic Chlorophyll Wet 0.758 A 0.099 a Dry 0.884 A 8 Wet 0.836 A Dry 4.943 AB 6 Wet 3.691 B Species Richness 0.061 Dry 6.499 A 8 Wet 5.067 AB Dry 0.995 A 6 Shannon Diversity Wet 0.790 A 0.236 Index Dry 1.175 A 8 Wet 0.904 A Dry 25.093 A 6 Wet 38.660 A Grazer count 0.811 Dry 37.797 A 8 Wet 58.368 A
50
Visually estimated SAV % cover at Sites 6 and 8 Site 8 Site 6 100 90 80 70 60 50 40 30 20
10 Estimated Mean % Cover of SAV (visual) of Cover SAV Mean Estimated %
0
Jul-13 Jul-14
Jan-13 Jan-14
Jun-13 Jun-14
Oct-13
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 May-14 May-13 Figure 3.4 Time series of average submerged aquatic vegetation cover for both sites over a two year time period. Error bars depict standard error of the mean (SEM).
51
Visually estimated SAV at Site 8 Site 8 H Site 8 T 90
80
70
60
50
40
Percent Cover Percent 30
20
10
0
Jul-13 Jul-14
Jan-13 Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-14 Feb-13 Sep-13 Sep-14
Apr-13 Apr-14
Dec-12 Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14
May-13 May-14
Figure 3.5 Time series depicting percent cover of SAV by species at site 8. H= H. wrightii, T= T. testudinum. Error bars depict standard error of the mean (SEM).
52
Mean seagrass percent cover 50 A 40 A 30 AB 20 B
10 Seagrass cover percent Seagrass 0 6 dry 6 wet 8 dry 8 wet Site and Season
Figure 3.6 Mean seagrass percent cover by site and season (Two-way ANOVA, p= 0.004). Based on Tukey HSD, Significantly more seagrass occurred at site 8 during both the wet and dry season than at site 6 during the dry season. There was no difference in the amount of seagrass at site 6 during the wet season than at other time periods at either site. Letters indicate significantly different groups. Error bars depict standard error of the mean (SEM).
53
Macroalgae Macroalgae percent cover was highly variable but did not show a consistent seasonal pattern (Tables 3.1 and 3.2, Figure 3.7). Cover of macroalgae remained below ten percent at site
6 for most of the period, except during April and May 2013 where it peaked around 15 percent, before dropping to near zero for a majority of the remainder of the study (Figure 3.7). Site 8 also remained below 10 percent cover for a majority of the time period, however, a spike of macroalgal cover occurred between October 2013 and February 2014 reaching around 35 percent cover (Figure 3.7). In the two way ANOVA, site accounted for 0% of the variation seen in macroalgae coverage while season accounted for 14%. The interaction of the two accounted for
2% of variation.
54
Visually estimated % macroalgae cover at Sites 6 and 8 Site 6 Site 8 50 40 30 20
10 Percent cover Percent
0
Jul-14 Jul-13
Jan-13 Jan-14
Sep-13 Sep-14
Mar-13 Mar-14
Nov-13 May-14 May-13 Figure 3.7 Time series showing percent cover of macroalgae at sites 6 and 8 over the two year study period. Error bars depict standard error of the mean (SEM).
55
Epiphytes Visually estimated epiphytes were highly variable from month to month at site 6, making it difficult to discern a seasonal pattern. However, site 6 did tend to have less epiphyte cover during the winter months than during the summer (Figure 3.8). Site 8 had higher epiphyte cover overall, but showed a more defined seasonal trend of lower epiphytic cover during the winter months and higher during the summer (Figure 3.8). Lower levels of visually estimated epiphytes were found at site 6 during the dry season than at other sites in either season (Tables 3.1, 3.2 and
Figure 3.9). This could be due to scouring away of epiphytes by wave action and sand movement at site 6 during periods of low winter seagrass coverage. In the two way ANOVA, site accounted for 22% of the variation in visually estimated epiphytes, while season and the interactive effect each accounted for 13% (Table 3.1). Caution should be taken in interpreting the results for visually estimated epiphytes, as not all assumptions for running the ANOVA were met. Visually estimated epiphyte data is normally distributed but the variance for site requirements are met for
O’Brien [.5] and season meets requirements for O’Brien [.5] and Brown-Forsythe. Other data transformations did not lead to meeting equal variance requirements.
Chlorophyll a (Chl a) analysis of H. wrightii epiphytes at site 6 show a similar pattern as visually estimated epiphytes with higher levels during the summer months and lower during the winter months, but showed more increase in epiphytes during February 2014 (Figures 3.10 and
3.11). Analysis of H. wrightii at site 8 shows the same increase in epiphytic cover during the summer months (Figure 3.11). Chl a analysis of T. testudinum at site 8 shows a peak of epiphytic cover during April and December 2013, but minimal amounts of Chl a from April 2014 to the end of the study, in contrast with the visual estimations of high epiphyte load at site 8(Figure
3.11). Chlorophyll a analysis did not show a significant difference at sites 6 and 8 between the wet and dry seasons (Tables 3.1 and 3.2). In the two way ANOVA, site accounted for 7% of the 56
variation seen in Chlorophyll a, season accounted for 30%, while the interaction accounted for less than 1% of the variation.
57
Visually estimated epiphytes at Sites 6 and 8 Site 8 Site 6 3.5
3
2.5
2
1.5
1 Epiphyte (visual) cover Epiphyte
0.5
0
Jul-13 Jul-14
Jan-13 Jan-14
Jun-13 Jun-14
Oct-13
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 May-14 May-13 Figure 3.8 Time series showing visually estimated epiphytes at both sites over two years. Error bars depict standard error of the mean (SEM).
58
Mean visually estimated epiphytes 3 2.5 A A A 2 1.5 B 1 0.5
Average epiphytes ordinal Average 0 6 dry 6 wet 8 dry 8 wet Site and Season
Figure 3.9 Mean visually estimated epiphytes by site and season (Two-way ANOVA square root transform, p=0.001). Based on the Tukey HSD, lower levels of epiphytes were found at site 6 during the dry season than at either site during the wet or dry season. Letters indicate significantly different groups. Error bars depict standard error of the mean (SEM).
59
Site 6 Chlorophyll a
2 2)
- 1.5
1
(µg/cm a
0.5 Chl
0
Jul-13 Jul-14
Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 3.10 Chlorophyll a (Chl a) analysis for site 6 H. wrightii between February 2013 and November 2014. Error bars depict standard error of the mean (SEM).
60
Site 8 Chlorophyll a S8H S8T
5 2)
- 4 3 2
1 Chl a a (µg/cm Chl
0
Jul-13 Jul-14
Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 3.11 Chlorophyll a (Chl a) analysis for site 8 H. wrightii (S8H) and T. testudinum (S8T) between February 2013 and November 2014. Error bars depict standard error of the mean (SEM).
61
Mesograzers There was considerable overlap in mesograzer species composition between Site 6 and
Site 8. No mesograzer species were found exclusively at Site 6. However, three species,
Costoanachis sp., Dulichiella appendiculata, and Microprotopus sp., were found only at site 8
(Table 3.3). These three mesograzers may be less tolerant to salinity change than the other mesograzers found at both sites. Mesograzer density (the number of individuals per g of plant) was higher at site 8 compared to site 6 for most of the period of record, with the exception of
September 2013 and November 2014 (Figure 3.12). The highest densities of mesograzers were found at site 6 during the fall months and at site 8 during the winter of 2013 and fall 2014
(Figure 3.12). The lowest were found at site 6 during late summer 2013 and spring 2014, and at site 8 during summer 2013 and winter 2014 (Figure 3.12). In two-way ANOVA, no interaction was found between site and season (Table 3.2).
Species richness (SR) at site 6 did not show a consistent seasonal trend but was lowest in late summer 2013 and highest in winter 2013-2014 (Figure 3.13). SR at site 8 also did not show a seasonal trend, but declined into summer 2013, followed by an increase into the winter, decline and rise through spring 2014, and decline through the summer with a slight rise during the fall
(Figure 3.13). SR was significantly higher during the dry season at site 8 compared to the wet season at site 6 (Table 3.1 and Figure 3.14). In the two way ANOVA, site accounted for 12% of the variation seen in SR while season accounted for 10%. The interaction of the two had no effect (Tables 3.1, 3.2 and Figure 3.14).
While Site 8 generally had higher SR than site 6, the two sites were more similar in terms of their Shannon Diversity Index (H’) values for mesograzer communities. There was even a brief period in early summer of 2013, and a longer period in summer 2014, when Shannon 62
Diversity at Site 6 was greater than at site 8 (Figure 3.15). In the two way ANOVA, site accounted for 1% of the variation seen in Shannon Diversity while season accounted for 9%. The interaction of the two had no effect. Site, season, and site x season did not have a significant effect on Shannon Diversity (Table 3.1 and Table 3.2).
Caution should be taken when looking at Shannon Diversity and grazer count in the
ANOVA results because not all statistical assumptions could not be met through data transformation. Shannon Diversity Index data is normally distributed and meets the requirements of equal variance for O’Brien [.5] and Brown-Forsythe but does not for Levene, Bartlett or F
Test 2 Sided when looking at site. All equal variance requirements were met when looking at season. Data transformations did not improve unequal variances. Grazer count data is normally distributed and meets the requirements of equal variance for site differences, however only meets the requirements of equal variances for seasonal differences for O’Brien [.5], Brown-Forsythe, and Levene but not Bartlett or F Test 2-sided.
63
Table 3.3 Tabular representation of grazers found at Sites 6 and 8 over the course of the study period. Numbers depict grazer count per gram of plant collected at each site.
Grazer Site 6 Site 8 Ampithoe sp. 8.70 0.31 Astyris lunata 22.10 2.01 Batea sp. 0.12 0.07 Bittiolum varium 8.06 6.77 Bulla occidentalis 2.18 0.69 Caprella sp. 0.01 0.01 Costoanachis sp. 0.00 0.08 Cymadusa compta 14.06 2.51 Dulichiella appendiculata 0.00 0.00 Elasmopus sp. 0.03 0.02 Erichsonella attenuata 7.34 0.27 Gammarus mucronatus 1.11 0.06 Haminoea sp. 0.17 0.14 Microprotopus sp. 0.00 0.00 Paracaprella tenuis 0.09 0.04
64
Mesograzers over time Site 6 Site 8
250
200
150
100
Number Number individuals of 50
0
Jul-13 Jul-14
Jan-13 Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13 Dec-14
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 Nov-14
May-13 May-14
Figure 3.12 Time series of average plant adjusted grazer count per site. Individual species totals per Virnstein sample were divided by associated plant mass collected for standardization. Error bars depict standard error of the mean (SEM). 65
Species richness over time 9 Site 6 SR Site 8 SR 8
7
6
5
4
Species Richness Species 3
2
1
0
Jul-13 Jul-14
Jan-13 Jan-14
Jun-13 Jun-14
Oct-13 Oct-14
Feb-13 Sep-13 Feb-14 Sep-14
Apr-13 Apr-14
Dec-13
Mar-13 Mar-14
Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 3.13 Time series depicting grazer plant adjusted Species Richness (SR) over time per site. Individual species totals per Virnstein sample were divided by associated plant mass collected for standardization. Error bars depict standard error of the mean (SEM).
66
Mean species richness 8 A 7 AB 6 AB B 5 4
3 Mean Mean SR 2 1 0 6 dry 6 wet 8 dry 8 wet Site and Season
Figure 3.14 Mean Species Richness (SR) by site and season (Two-Way ANOVA, p=0.061). Based on the Tukey HSD, SR was greater at site 8 during the dry season than at site 6 during the wet season. There was no difference in SR at site 6 during the dry season and site 8 during the wet season compared to other sites and seasons. Letters indicate significantly different groups. Error bars depict standard error of the mean (SEM). 67
Shannon diversity over time
Site 6 H' Site 8 H'
1.6
1.4
1.2
1
H' 0.8
0.6
0.4
0.2
0
Jul-13 Jul-14
Jan-13 Jan-14
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Figure 3.15 Grazer plant adjusted Shannon Diversity Index (H’) over time per site. Individual species totals per Virnstein sample were divided by associated plant mass collected for standardization. Error bars depict standard error of the mean (SEM).
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Chapter 4: Abiotic Influences on Seagrass Community Structure
Methods Overview Having quantified the site differences and seasonal trends in individual abiotic factors and biotic features of Caloosahatchee Estuary seagrass beds, our next step was to interpret the interrelationships among these multiple aspects of the ecosystem, and to interpret how they affected each other over time. Statistical tests of individual site data were done comparing among sites and seasons. Seasonal comparisons were done as Wet season (June-October) and Dry season (November-May). We also used multivariate analyses to examine overall community differences among sites and seasons and in response to salinity changes.
Temporal Relationships Looking at many data types plotted along a common timeline allowed for the qualitative assessment of possible interrelationships among those data. Linear regression was also used to quantify relationships among aspects of grazer community structure (abundance, species richness, and Shannon diversity) and likely influencing factors (salinity, epiphyte abundance, and seagrass cover, Table 4.1).
In the time series plots from this study, there appears to be a relationship between extended periods of decreased salinity and reduced grazer species richness (Figure 4.1). This is most clearly seen at site 6 but can also be seen at site 8. Salinity drops to 0 psu at site 6 in June
2013 and fluctuates between 0 and 15 psu through September 2013. This corresponds with the major decline in species richness at site 6 seen in July 2013. The salinity vs. species richness pattern continues as salinity increases in the fall months of 2013 to over 20 psu in December
2013, where a correspondingly higher species richness is seen. Site 8 has a lower impact, but during the time in July 2013 where salinity drops to ~15-20 psu species richness also declines 69
where fewer species are found at site 8 during this summer. Site 8 species richness increases again in December 2013 as salinity rises.
Species richness at site 6 is mainly stable through 2014 and this may reflect that the decline in salinity in the summer of 2014 is much less dramatic than in 2013, only reaching ~5 psu for around a week in September 2014. The result of 2014’s higher salinity is the stability found in grazer species richness. Salinity at site 8 remains ~25 psu or higher throughout 2014.
Species richness at Site 8 also remains higher in 2014 than it did during 2013, however, fluctuation throughout 2014 suggests that more is happening at this site than just salinity effects.
A simple linear regression to predict grazer species richness based on the 20 day average of salinity at each site had an R2 of 0.52 (F [1, 22] = 23.82, p < 0.0001, Table 4.1, Figure 4.2).
Species richness increased 0.22 for each psu of salinity.
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A Site 6 Site 8 40 30 20
10 Salinity (psu) Salinity
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Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 4.1 A) Simulated daily mean salinity at Sites 6 and 8, produced by the model described in Wan et al. (2013). Solid gray line depicts the lowest salinity threshold for Thalassia testudinum (20psu) growth and solid black line depicts the lowest salinity threshold for Halodule wrightii growth (10psu), B) Estimated percent cover of SAV where gray line=20ppt threshold for T. testudinum growth and black line=10ppt threshold for H. wrightii growth, and C) Visually estimated epiphytes
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Grazer SR vs. 20 day salinity 10
8
6 y = 0.2203x - 0.78 R² = 0.5199 4
2 Grazer species species richness Grazer 0 0 10 20 30 40 20 day average salinity
Figure 4.2 Linear regression of 20 day average salinity at both sites versus grazer species richness (Table 4.1).
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A 10 Site 6 Site 8 8 6 4
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Aug-13 Nov-13 Aug-14 Nov-14 May-14 May-13 Figure 4.3 A) Species Richness time series, B) Shannon Diversity Index time series (H’), and C) Grazer count time series.
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Table 4.1 Table showing the results of linear regression for factors affecting grazer community structure. Numbers in bold show p values depicting a significant relationship.
Independent Dependent df F p value R2 variable variable SR 1,22 23.8238 <0.0001 0.5199 20 day salinity H' 1, 21 5.1636 0.0337 0.1974 Grazers per g plant 1, 22 1.5240 0.2300 0.0648 SR 1,20 0.4308 0.5191 0.0211 Chl a H' 1,20 0.7278 0.4037 0.0351 Grazers per g plant 1, 20 0.0300 0.8642 0.0015 SR 1, 20 1.6140 0.2185 0.0747 % seagrass cover H' 1, 19 1.0365 0.3214 0.0517 Grazers per g plant 1, 20 2.3899 0.1378 0.1067
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When looking at the relationship of epiphytic chlorophyll a (Chl a) and grazer community structure there was not a clear pattern (Figure 3.5, Figure 4.3), and the relationship was not significant in linear regression results (Table 4.1). Site 6 grazer community H’ appeared to follow epiphytic chlorophyll a up to June 2013 but then showed no clear pattern for the remainder of the study. This may be a result of an extended grazer recovery period after sharp decline at this site in the summer of 2013. Overall Site 8 grazer community species richness and
H’ show similar patterns as that of Chl a for Thalassia testudinum Virnstein samples at site 8 throughout the two years of study. This is the dominant species of seagrass in this site and shows an apparent greater influence on grazer community structure at this downstream site.
A simple linear regression was calculated to predict grazer species richness based on epiphytic Chl a at each site (Figure 4.4), but the relationship was non-significant (F[1,20] =
0.4308, p = 0.5191), with an R2 of 0.0211.
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Grazer SR vs. Chl a 10
8
6
4
2 y = 0.4197x + 5.0908
Grazer species species richness Grazer R² = 0.0211 0 0 0.5 1 1.5 2 2.5 Chl a µg/cm2
Figure 4.4 Linear regression of epiphytic chlorophyll a (Chl a) versus species richness (Table
4.1).
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Collection of epiphyte data through visual estimation may lead to different results than through chlorophyll a analysis since the material seen on seagrass blades may not be entirely living photosynthetic material. The relationship between visually estimated epiphytes and epiphytic Chl a shows a different pattern between sites as well as between Halodule wrightii found at both sites (Figure 4.1c, Figure 3.10, Figure 3.11), and this may be because of a mixture of photosynthetic algae combined with detritus and sediment. At site 6 we found similar trends in both epiphyte measurements with peaks occurring in June 2013, February and March 2014, and
September 2014. However, there are points of increased visually-estimated epiphytes but lower levels of Chl a in March 2013 and July 2014. This could possibly be attributed to different people collecting data throughout the study. Site 8 is more difficult to interpret as it shows fairly stable visually-estimated epiphytes but greater fluctuation in Chl a. This could indicate that while the total amount of epiphytic material on Thalassia testudinum blades is relatively stable, the proportion of that material that which is live algal cells varies more widely.
Looking at the relationship between the time series of salinity and estimated percent cover of submerged aquatic vegetation shows that while there is seasonality in seagrasses in this system, there is also a lagged effect of low salinity on seagrass coverage leading to decline when salinity decreases below the salinity tolerance threshold for each species (Figures 4.1a and b).
Another abiotic factor that may affect seagrasses in SW Florida is colored dissolved organic matter (CDOM) which diminishes light available to seagrasses. Time of year also can have an impact on light reaching seagrass in south Florida with reduced a photoperiod in the winter months. Lower temperatures during the winter may reduce seagrass growth as well. Seagrasses in South Florida grow in a seasonal pattern that start with a growth phase in the spring and into the summer, which is followed by a decline in the fall leading to a winter die off. The seasonality 77
trend of Florida seagrasses is more defined at site 8 where the influence of freshwater inflow is lessened due to its location at the mouth of the estuary. This is more difficult to see at site 6 as the prolonged time period of very low salinity resulted in single digit percent coverage of H. wrightii through the fall and winter of 2013. After salinity remained below the threshold of survival for each species of seagrass there is a lag period of around a month before seagrass decline was observed.
There are a couple of patterns seen in epiphytic cover related to percent cover of seagrass
(Figure 3.10, Figure 3.11, Figure 4.1). Visual estimation of epiphytes follows the same trajectory as seagrass percent cover, especially at site 8, with both showing seasonal trends of increase during the late spring and summer months, with both possibly responding to the increased photoperiod during the summer. Chl a follows the same seasonal pattern as seagrass at site 6 during 2013 but does not show as clear of a pattern during 2014 with an increase in February
2014. Chl a at site 8 shows a similar summer increase from winter into spring during 2013 but remains low for both H. wrightii and T. testudinum in 2014. The synchronous seasonal patterns of SAV and epiphytes are likely due to similar physiological responses to increased temperature and photoperiod in the spring and summer months.
Multivariate Analyses of Community Structure Cluster analysis results on all sites by plant type and date indicated six significantly different groups. The groups tended to aggregate samples from the same sites and from similar times of year (Figure 4.5). The group most different from the others included samples from site 6 taken in the fall and winter months after the high freshwater inflow of summer 2013 (September and December 2013, and February 2014). Another grouping of interest is that of June 2013 sites
6 and 8 with August 2013 H. wrightii, as this is the time of lowest salinity at both sites. Results 78
of the Non-metric Multidimensional Scaling (nMDS) analysis on these data were similar to the results of the cluster analysis, showing separation between sites as well as seasonality trends within sites (Figure 4.6). PERMANOVA confirmed a difference in both site (p=0.001) and wet/dry season (p=0.002) but the site-season interactive effect was not significant (p=0.163), indicating that the effect of seasonal changes was similar at the two sites. Higher abundances of
Cymadusa compta, Erichsonella attenuata, Ampithoe sp., and Astyris lunata at site 6, and lower abundances of Bittiolum varium at site 6, were the main differences between sites 6 and 8 according to SIMPER.
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Figure 4.5 Dendrogram showing the results of cluster analysis performed from both sites 6 and 8. 6H= Site 6 H. wrightii, 8M= Site 8 macroalgae, 8T= Site 8 T. testudinum, and 8H= Site 8 H. wrightii.
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Figure 4.6 Results of nMDS analysis of grazer community assemblage from sites 6 and 8. Spacing between data points show separation between sites. 6H= Site 6 H. wrightii, 8M= Site 8 macroalgae, 8T= Site 8 T. testudinum, and 8H= Site 8 H. wrightii.
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Site 6 nMDS showed clear seasonal pattern in grazer community dynamics at site 6 with groupings of similar time periods (Figure 4.7). Distinct groupings occurred at high freshwater inflow (June and August 2013) as well as the recovery months that followed (September and
December 2013, February 2014). Final groups were found consisting of the months that preceded and followed these two periods. ANOSIM results confirmed a difference between wet and dry season months (R = 0.263, p = 0.044). Species that had leading influence on the changes between seasons were Cymadusa compta, Erichsonella attenuata, and Bittiolum varium which had lower abundances during the summer of 2013 than any other time according to SIMPER.
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Figure 4.7 Results of nMDS for site 6 showing groupings based on similar times of the two years of study.
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Site 8 A seasonal pattern was found in grazer community dynamics at site 8 when looking at wet and dry season (Figure 4.8). Seasonal clustering occurs with the wet season months clustered in the center of the nMDS with the dry season months around the edges of this cluster. However,
ANOSIM results showed that this seasonal difference was not significant at Site 8 (R = 0.174, p
= 0.09), maybe due to variability in dry season species composition. ANOSIM comparisons of grazer species composition among different plant species at site 8 (Thalassia testudinum,
Halodule wrightii, and macroalgae) showed no significant differentiation among the grazer communities associated with the different plants (R = 0.005, p = 0.431). Though there was no overall difference by plant species at Site 8, there was a large difference between H. wrightii beds in April 2014 and all other time periods due to higher abundances of C. compta, Astyris. lunata, and Ampithoe sp. compared to any other time and higher abundances of B. varium in
2013 but lower abundances in 2014 according to SIMPER.
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Figure 4.8 Results of nMDS for site 8 shown by plant type.
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Drivers of changes in community composition In order to try to determine the relationships between environmental factors (salinity and epiphyte abundance) and the patterns seen in grazer community structure, DistLM analysis compared epiphytic chlorophyll a (Chl a) and 20 day average salinity to grazer community composition. Data for Chl a were not collected in August 2013, so August 2013 could not be included in that analysis. The DistLM results of individual factors and the combination within and between both sites are displayed in Table 4.2.
Site 6 DistLM at site 6 showed that neither 20 day salinity nor Chl a were significantly related to variations within assemblages at that site (Table 2). Average salinity accounted for about 16% of the variation seen in grazer community structure at site 6. Epiphytic Chl a contributed about
7% of the variation. The interactive effect of these two variables were responsible for 23% of the variation seen in grazer community structure at this site. Though these results were not significant, they do show a trend of salinity effects on grazer community composition, which is consistent with our univariate analyses.
Site 8 At site 8 DistLM analyses of 20 day average salinity (p = 0.614) and Chl a (p = 0.29) were not significant (Table 4.2). Both factors had small influences on grazer community structure, at 4% and 6.6% respectively. The interactive effect of these two variable were responsible for 10.6% of variation seen at site 8.
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Table 4.2 Results of DistLM tests showing individual factor p values and whole model R2 values for each analysis.
Factors p value R2 20 day average salinity Combined Sites (0.149) 0.003 0.179 Epiphytic Chl a (0.034) 0.372 20 day average salinity Site 6 (0.162) 0.164 0.232 Epiphytic Chl a (0.069) 0.607 20 day average salinity Site 8 (0.040) 0.614 0.106 Epiphytic Chl a (0.066) 0.29
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Chapter 5: Discussion Site and seasonal comparisons found significant differences in abiotic conditions among sites, seasons, and years. Salinity in both the middle estuary and lower estuary was greatly reduced during the summer of 2013, a period of high freshwater discharge, (Figures 3.1, 3.2,
3.3). However, in periods of less extreme discharges like the summer of 2014 an impact was seen at site 6 but not as strong of an impact at site 8.
Most aspects of the biotic community that we assessed varied among sites, seasons, and years, as well. Seagrass coverage varied dramatically (Table 3.1). One factor that may be driving seagrass coverage is salinity, as both sites experienced a decline in seagrass coverage after both sites dropped below the minimum salinity thresholds for the dominant seagrass species found at each site in 2013 (Figures 3.1 and 3.4). Another factor that may be driving the coverage of seagrass in the CRE is seasonal variation in temperature and insolation, as there are declines of seagrass in the region during the colder winter months (Figure 3.4). This would be similar to seagrass beds found in the Indian River Lagoon, Florida region where seagrasses were seasonally driven by a combination of insolation, temperature and salinity (Zieman 1979).
Our study found a relationship between salinity and grazer community structure (i.e. species composition, richness, and Shannon diversity) while the relationship between epiphytes and grazer community structure is unclear at this point in the CRE. However, there are some limitations to this study. The project was constrained to a two year period by limited funding and by students’ academic timetables for graduation. In such a short period there is an increased likelihood that idiosyncratic fluctuation in conditions will affect conclusions, and there is a reduced ability to determine underlying mean conditions. While an “average year” does not exist, this data set does show data from two distinctly different years as 2013 was a much wetter year than average. This difference between years may have had a strong effect on our wet vs. dry 88
seasons comparisons, i.e., the interannual variation we saw may have obscured the normal seasonal pattern.
Though this study measured several seagrass ecosystem characteristics, there were a vast number of unaccounted abiotic factors that we did not measure, such as nutrients, turbidity, and
CDOM, which could have affected results. It would always be beneficial to collect as much abiotic data as possible, especially nutrient data to look at the effects on both seagrass and epiphyte growth, but unfortunately these data were not collected or available. Unmeasured biotic factors could also have importance in this system. The number of mesograzers found within a system can also be controlled by something other than food availability (Valentine and Duffy
2007). Predation on mesograzers can occur, which would impact grazer community structure, especially if predation varies among sites and seasons or in response to abiotic factors. While epiphytic grazers are important in determining the community structure of primary producers
(Shurin et al. 2002), they are also important in transferring energy to higher trophic levels in shallow marine ecosystems (Edgar and Shaw 1995). However, predator-free mesocosm experiments have been found to lead to destruction seagrasses by ampithoids and idoteid isopods
(Short et al. 1995). Blue crabs (Callinectes sapidus) may be an important predator found within our region, field caging experiments found that juvenile blue crabs strongly decreased mesograzer populations which cascaded down to increase epiphyte biomass (Douglass et al.
2007). Possible juvenile blue crab predation within the CRE may have played a role in mesograzer populations as juvenile blue crabs were collected in some Virnstein samples. Further adding to the complexity of these systems, the addition of adult blue crabs, which are cannibalistic, removed predation pressure from mesograzers (Harris et al. unpublished data as cited in Valentine and Duffy 2007). Predation by small fishes could also be important. Omnivory 89
of some fish species can add further complexity to these systems (Heck et al. 2000). In our system, both pinfish Lagodon rhomboides and mullet Mugil spp. are known to feed on epiphytic algae, among other plant and animal food sources (Hansen 1969, Odum 1970).
The salinity and grazer community relationship found in this study showed that as salinity decreased there was a decline in grazer community richness and diversity. This relationship of a declining grazer community when placed in a declining salinity environment adds to the growing number of studies showing that some mesograzer species are impacted greater than others during times of reduced salinity. Blake and Duffy (2010) found in a mesocosm study that Elasmopus levis was less tolerant of freshwater shock when compared to
Gammarus mucronatus and Erichsonella attenuata. It is interesting to note we did not see this tolerance by E. attenuata, possibly due to the much longer period of low salinity at site 6 during the summer of 2013 compared to the 15-20 minutes of freshwater pulse in the mesocosm study of Blake and Duffy (2010). G. mucronatus was also absent during the summer of 2013 at site 6 possibly for the same reason, while the E. levis population acted the same as in Blake and Duffy
(2010).
The theory of positive species co-tolerance states that some species will be better able to withstand an abiotic change better than others, and that the tolerant species may be able to increase their population to fill the niche left behind by the more impacted species (Vinebrooke et al. 2004). A relationship within our region was found between grazers and salinity through linear regression, however, it was not as clear when looking at DistLM analysis. Further study should be done to better understand this complex relationship. Though the relationship is unclear, some invertebrate grazer species have been found to be functionally redundant in their grazing abilities (Blake and Duffy 2010). This could mean that while some species will be 90
impacted by a change in salinity more than others, the system may remain stable due to the amount of species grazing on algal epiphytes.
The relationship between epiphytic algae and mesograzers has been studied in both mesocosm and field experiments, usually finding that mesograzers reduce epiphytes. However, our observational study did not find any clear relationship between the abundance or diversity of mesograzers and the abundance of epiphytes (Table 4.1, Figure 4.4). Both linear regression and
DistLM analysis found that epiphytes were not a significant driving force for grazer community structure. One possibility for this muddled result is that we were collecting epiphyte samples for chlorophyll a analysis and visually estimating epiphyte coverage once every two months and that the mesograzers are most likely moving around the region following their food source. This would mean that we could possibly be finding seagrass blades that have little to no epiphyte biomass with little to no mesograzers as well. The opposite could also be true, meaning we would collect high amounts of mesograzers in areas with high concentrations of epiphytic algae because this is where the food source is. Gore et al. (1981) completed a study that indicated this trend of higher level of epiphyte cover leading to greater number and diversity of mesograzers.
In order to further study this relationship, greater numbers of sampling periods with less time between collections may be beneficial. It might also be helpful to do more spatially explicit analyses, looking at mesograzer numbers and epiphyte levels on a shoot by shoot basis rather than at the scale of entire seagrass beds.
An alternative explanation for the unusual patterns in epiphyte abundance, particularly the patterns at site 6, could be that epiphytes were reaching a critical mass. This would mean that as the epiphytes continued to grow out from the seagrass blade they reach a size where an abiotic force such as hydrodynamic drag may become strong enough to separate the epiphytic algae 91
from the seagrass blade (Thomas 2000). Yet another possible explanation for the epiphyte pattern could be related to the reproductive strategy of red and brown epiphytic algae, as the propagules of these epiphytes are non-motile and reliant on hydrodynamics (Borowitzka et al.
2007). These species may not be present during certain times of the year up the estuary, such as summer, when high levels of freshwater are being discharged into the Gulf of Mexico and carrying the propagules with it. Light is extremely important for epiphytic growth, as well, with light intensity affecting the rate of growth (Lewis et al. 2002). Epiphyte biomass has been found to be significantly higher in Amphibolis beds of 50% canopy cover compared to 100% possibly due to higher light availability in less dense beds (Carruthers 1994). This could be one explanation of the high amounts of epiphytes found in low levels of seagrass cover seen in
Figure 4.1b.
Nutrients are an important factor in the growth of epiphytes found in seagrass beds (Orth and Moore 1983, Tomasko and Lapointe 1991). While nutrient data was not collected for this study, it is an important aspect of this region. Doering (2006) found that the annual average discharge of freshwater from the S-79 Lock and Dam was 1.27 million acre-ft per year, with an annual loading of total nitrogen (TN) average of 2412 metric tons/year and total phosphorus of
220 metric tons/year. This study also found that the major driving force of nutrient loading was the rate of freshwater discharge through S-79. Upper regions of the CRE exceeded the nutrient standard of chlorophyll a for this system 60% of the time in the upper and mid regions of the estuary, however the lower estuary and San Carlos Bay were below the criterion most of the time
(Doering 2006). It can be suggested that a higher rate of freshwater discharge can allow higher amounts of nutrients to reach the lower estuary due to increased residence time, thereby preventing nutrients from settling in the upper estuary and reaching the mouth of the estuary, 92
This may be a driving force of epiphyte growth in both study sites where an increase is seen during the wet summer months (Figure 3.8).
The relationship in grazers and epiphytes could have been impacted by species-specific differences in diet of grazer species in conjunction with variation in grazer community composition (Zimmerman et al. 1979, Kitting 1984, Duffy and Hay 2000, Duffy and Harvilicz
2001, Duffy et al. 2001). We found 74 epifaunal taxa over the two year period of the study, however only 13 of these species had previously been identified as algal grazers (Lefchek et al.
2015), and little is known about the specific grazing rates and preferences of these species. Many of the epifaunal species found were known predators and detritivores, and some of the species’ feeding habits have not been classified yet. Some of these unclassified species may be algal grazers. Incorporating them in our “total grazers” category could potentially change our conclusions. Future studies need to be done to determine the diets of these organisms.
In other studies some mesograzer species (e.g., Idotea resecata) have been found to consume both seagrasses and epiphytes depending on abiotic conditions. I.e., under different abiotic conditions this species may have a positive or negative effect on seagrasses (Williams and Ruckelshaus 1993). Few Idotea sp. were found in this study, but, one species found at both sites in higher levels of abundance than most other species collected was Cymadusa compta.
While primarily an epiphyte and macroalgal grazer and detritivore (Luczkovich et al. 2002),
Kelly et al. (1990) found that it appears to feed sparingly on live seagrass, and Nelson (1979) found some vascular plant material in gut content analysis. Perhaps a species such as C. compta may experience a shift in diet under certain environmental conditions? Other groups found within this region that may be feeding on both epiphytes and seagrass are ampithoids and idoteid isopods which when in predator-free mesocosms have been found to be destructive of seagrasses 93
(Kirkman 1978, Williams and Ruckelshaus 1993, Duffy et al. 2001, 2003). We need more understanding of the diet of the particular grazer species when manipulated (Hughes et al. 2004).
Another reason for the declines in epiphytes at different times of the year could be the direct grazing of epiphytic algae by fish grazers. Experimental reduction of fleshy chlorophyte epiphytic algae was seen with pinfish Lagodon rhomboides and black mullet Mugil cephalus
(Gacia et al. 1999). This same result was seen by Heck et al. (2000) when looking at L. rhomboides, but without identifying the species of epiphytes. This species of fish is common in the CRE and may be controlling epiphytes at different times of the year when epiphytes consist of fleshy chlorophytes. Since we did not identify the composition of epiphytic assemblage, further study would need to be done to confirm this theory.
When specifically looking at the summer months of 2013 which experienced a strong decline in salinity, there was a sharp decline in the grazer diversity and richness found at both sites, but especially at site 6 (Figures 4.3A and B). Grazer species recovered after this impact the following summer, however, Douglass et al. (2010) found limited amounts of the isopod
Paracerceis caudata in Chesapeake Bay, Virginia which was common prior to intense freshwater inflow from Hurricane Agnes in 1972 (Marsh 1973). This is of major concern for our region because of the high amounts of freshwater discharge occurring every summer. With continued high levels of freshwater input there is the risk of this same situation occurring with a species found within the CRE.
Cluster Analysis and nMDS showed that seasonal changes in biotic and abiotic factors within the CRE have an impact on grazer community structure as both site 6 and 8 remained separated from each other for a majority of the two year study period (Figure 4.6). The one time that this was not true was during the high freshwater input period of the summer of 2013 which 94
resulted in low salinity. This may most likely be attributed to salinity stress as both sites dropped by ~20 psu. When looking at site 6 there is a pattern that appears to show a deviation from normal community structure before cycling back (Figure 4.7). This pattern of a normal community is affected by a change leading to an impacted community which moves into a period of recovery before shifting back to a community similar to before the impact. This system during this study period appears to be a dynamic system consisting of seasonal change, but it may be argued that if the system is impacted at a higher level that the recovery to the initial community may not occur.
As seagrasses and the associated invertebrates are also harmed by high discharge events, we suggest a new management practice for freshwater release. One way to decrease the rapid decline in salinity caused by releasing large volumes of water during a few day period is to release that same volume of water over a longer period of time through a series of pulses. By releasing this volume of water over a longer time period, such as over a month compared to a few days, salinity within the estuary will be able to remain at a higher level when smaller amounts of freshwater mix with the estuarine water, then flow out of the system before another volume of freshwater is released. Additionally, there is an urgent need to find an alternate system for controlling Lake Okeechobee water levels, other than purging the lake into the
Caloosahatchee and St. Lucie Estuaries. We suggest that buying land in the Everglades
Agricultural Area (EAA) south of Lake Okeechobee would be the most efficient way to cope with excess lake water, clean it, and send it on its way southward into the Everglades where it is urgently needed.
Moving forward, this study has opened up many questions for this region. The first question regards the relationship between nutrients, epiphytes, and grazers (Harrington and 95
Douglass 2016 in progress). This full factorial field experiment is looking at the effect of exclusion of amphipod grazers from seagrass plots and the addition of nutrients. A second major question of this study is to investigate the influence mesograzer predators have on the relationship between mesograzers and epiphytes. Possible next steps may be to run mesocosm studies which incorporate mesograzer predators into a factorial experiment of grazer and nutrient addition. A second way to proceed with this question may be to create predator exclusion cages to look at the effect of removing grazer predation from experimental plots compared to cage-free plots. A final question from this study would be to continue looking at the capacity of seagrass ecosystems to survive the impacts of eutrophication. This is especially important in Southwest
Florida where the population is ever increasing and water systems are highly managed, for better or worse. Studies have shown that systems with balanced mesograzer populations can handle increased nutrient loading (Hays 2005, Myers 2013, Whalen et al. 2013). However, nutrient loading is a serious concern in this region where annual freshwater discharges have the ability to decrease the mesograzer diversity (this study) while simultaneously elevating nutrients (Doering
2006).
In summary, this study set out to look at the indirect impacts of freshwater releases on seagrass health in the CRE, as mediated by increased nutrients and reduced mesograzer abundance and diversity (Figure 1.5). We found a negative relationship between species richness and salinity with lesser relationships between Shannon Diversity Index or the number of grazers per gram of plant collected and salinity. The relationship between grazers and epiphytes remains unclear at this point as epiphytes were highly variable. In order to better understand this complex system further study needs to be done in the CRE looking at the effects that nutrients and predators have on mesograzers before conclusions can be drawn. 96
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