BENTHIC COMMUNITY STRUCTURE AND RESPONSES TO ENVIRONMENTAL DRIVERS IN THE FLORIDA KEYS
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 Ashley Lauren Brandt 2016
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APPROVAL SHEET
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science
______Ashley Lauren Brandt
Approved: May 1, 2016
______Michael L. Parsons, Ph.D. Committee Chair/Advisor
______James G. Douglass, Ph.D.
______Tyler B. Smith, Ph.D.
The final copy of this thesis 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. iii
Acknowledgements This project would not have been possible without all of the help I received from
God, my family and friends, and the many faculty and students involved. First and foremost, I would like to thank Jesus Christ for giving me the daily strength and perseverance that enabled me to see this project through to completion. I also want to thank my parents for all their love and support throughout my entire college career and my friends for their support as well.
The most notable credit goes to my primary advisor, Dr. Michael Parsons. I was only able to pursue this research project as a result of his hard work as an active research scientist as well as an engaging professor. Funding for this research was primarily provided through his NOAA ECOHAB funded project, CiguaHAB. I am so thankful that he gave me the opportunity to be a part of the CiguaHAB project and for the resulting field and professional experience it has given me. Dr. Parsons has challenged me to take advantage of multiple professional opportunities that I would not have been able to accomplish without his endless help and encouragement, primarily the work involving this thesis. I would also like to thank my other committee members, Drs. James Douglass and Tyler Smith, for their helpful feedback and multiple edits involved in writing my thesis.
There are many other students at FGCU that contributed immensely to this project. I have to thank Alexander Leynse specifically for all his help in the field and for sharing nutrient data. Amanda Ellsworth and Lacey Rains were helpful in that the monthly trips to the field would not have been possible without them. I would also like to iv thank all the other students involved in CiguaHAB for their help as well: Jessica
Schroeder, Adam Catasus, Jeff Zingre, Rebecca May, Kevin Tyre, and Elena Stanca.
Finally, I want to show my gratitude to Florida Gulf Coast University as a whole for my college career. Additional funds for this research were provided in the form of undergraduate research grants from the Office of Research and Graduate Studies (ORGS) at FGCU. I would like to show my gratitude for the many opportunities I had through receiving financial assistance from FGCU ORGS and the Whitaker Center for STEM
Education. Lastly, I am so grateful for all of the great professors that have contributed to my dynamic through sharing their knowledge and time with me. All of your efforts have been greatly appreciated. v
Abstract
As the frequency and intensity of disturbances threatening coastal ecosystems increase, there is a greater need to determine which factors are actually driving the changes in these environments. This three-year study investigated the possible influences of stressors alongside changes in community ecology of three disparate marine habitats within the Florida Keys. The approach used in this study was purposefully large-scale, involving high-resolution monthly sampling of the benthic community structure and environmental data in order to capture associated relationships between stressors and benthic taxa. The need for this novel approach has been proposed in the scientific literature, as there has been greater recognition towards the influence of multiple stressors and the possibility of synergistic effects. Specifically within the Florida Keys, declining water quality and recent extreme temperature anomalies have caused changes in the dynamics of these habitats. Results of this study showed the most influential factors appear to be waves, nutrients, and temperatures within the three sites examined in this study. Waves appeared to have a regional effect, being influential at all three sites, while nutrients and temperatures showed local effects, only being influential at some sites. High and low extremes of these factors appeared to drive the dynamics seen in the top taxa present at these sites, consisting mostly of macrophytes, with some relationships being supported by correlations. In addition, effects from other stressors appeared to be important once combined with the influential factors at some sites, showing evidence of synergistic effects. While all possible stressors were not addressed within this study, there is evidence that there is further need to use this ecosystem-scale approach in order to determine any and all effects of multiple stressors. This will allow for better vi understanding of integrated effects and provide insight into marine management efforts in terms of anthropogenic and natural stressors or local (manageable) or regional (less controllable) influences.
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Table of Contents Acknowledgements iii Abstract v List of Tables viii List of Figures ix
Chapter 1: Introduction and background information 1
Chapter 2: Methods 16
Chapter 3: Site characterization. 37
Chapter 4: Influence of environmental factors on community structure 67
Chapter 5: Discussion and Conclusions 87
Appendices 104 Appendix A: List of CPCe4.0 codefile subcategories with classifications.
References 110
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List of Tables
Table Page
Chapter 2: 2.1 List of major category and sub-category taxa groupings 28 2.2 Local and regional environmental factor classifications and hypotheses 33 2.3 List of hypotheses tested by each statistical test 36
Chapter 3: 3.1 Community composition of each site 38 3.2 Results of SIMPER analyses of community composition 48 3.3 Environmental characteristics of each site (ranges) 61 3.4 Environmental characteristics of each site (mean and SD) 62 3.5 Results of SIMPER analyses of environmental characteristics 66
Chapter 4: 4.1 Results of DistLM tests 69 4.2 Results of SIMPER analyses of taxon and environmental factors 78 ix
List of Figures
Figure Page
Chapter 1: 1.1 Diagram from Connell et al. (2013) 11
Chapter 2: 2.1 Site locations 17 2.2 Hypothetical diagram of local versus regional influences 19 2.3 Visual aid depicting benthic photoquadrats 22 2.4 Computer screenshot of CPCe 4.0 25 2.5 Graphs of Coefficient of Variance analysis 26
Chapter 3: 3.1 Pie charts displaying top taxa within HGB 39 3.2 Pie charts displaying top taxa within LKH 41 3.3 Pie charts displaying top taxa within TRL 43 3.4 Dendrogram graph showing results of cluster analysis 45 3.5 Results of nMDS analysis of all three sites’ composition 47 3.6 Results of nMDS analyses of HGB 50 3.7 SigmaPlot graph of Thalassia spp. at HGB 52 3.8 Results of nMDS analyses of LKH 54 3.9 SigmaPlot graphs of Laurencia spp. and Halimeda spp. at LKH 55 3.10 Results of nMDS analyses of TRL 57 3.11 SigmaPlot graph of Dictyota spp. at TRL 58 3.12 Results of nMDS analysis of all three sites’ environmental data 64
Chapter 4: 4.1 Time series graph of monthly temperature 72 x
4.2 Time series graph of monthly wave data 74 4.3 Time series graph of monthly nitrate concentrations 76 4.4 Time series graphs of taxa and environmental factors at HGB 79 4.5 Time series graphs of taxa and environmental factors at LKH 82 4.6 Time series graphs of taxa and environmental factors at TRL 85
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Chapter 1: Introduction and background information
Global threats to marine environments
As anthropogenic activities threaten coastal ecosystems, there is a need to ascertain the specific mechanisms influencing these environmental perturbations.
Humans have not only impacted ecosystems directly through the exploitation of goods, but also indirectly by interrupting species competition, recovery, and dispersal mechanisms (O’Neill 2001; Lirman & Biber 2000; Jompa & McCook 2002). Because marine habitats are important to surrounding coastal communities, and to global biodiversity and ecosystem functions, it is vital to understand the stability of these ecosystems in order to effectively protect them and their assets.
The stability of a marine ecosystem is related to its resilience, or its ability to return to its original community composition after a disturbance (Hughes et al. 2007).
This stability is usually achieved via negative feedback loops (i.e., ecological processes such as competition, herbivory, etc.; see Norström et al. 2009) that cause the community proportions within the reef to remain stable relative to original structure, therein practicing resilience despite perturbations (Beisner et al. 2003; Graham et al. 2013;
Hughes et al. 2010). The Intermediate Disturbance Hypothesis (IDH; Connell 1978) has long been accepted to explain how ecosystems appear able to maintain a steady equilibrium of high diversity. The IDH states that an intermediate amount of natural disturbances allow these highly diverse ecosystems, like coral reefs, to maintain relatively stable states by preventing competitive exclusion (i.e., dominance of one or few biota; Connell 1978). However, anthropogenic influences have disrupted ecosystem 2 stability indirectly by affecting how and when species can respond to perturbations, which essentially is caused by an increase in frequency of these disturbances (O’Neill
2001).
Increasing frequency and/or intensity (e.g., storm activity, temperature stress, etc.) as well as an increasing variety of disturbances (e.g., natural and anthropogenic) requires a greater need for effective as well as time-efficient responses from species within the ecosystem (Connell 1978; O’Neill 2001). With such intensification in disturbance, it has been difficult for these systems to recover to their steady states as rates of community recovery are not able to keep up with the rates of environmental change (Connell 1978;
O’Neill 2001). Additionally, more frequent perturbations cause increased stress and, consequentially, decreased ability for organisms to compete with one another (O’Neill
2001; Connell et al. 2011). This offset in competition involving dominant species is what ultimately leads to a regime shift within a community (Beisner et al. 2003).
As a result, marine habitats are changing worldwide (Hughes 1994; Pandolfi et al.
2005; Wilson et al. 2012). In particular, marine ecosystems across tropical and sub- tropical regions have experienced significant alterations in their community structures
(Robblee et al. 1991; Butler et al. 1995; Pandolfi et al. 2005; Collado-Vides et al. 2007;
Maliao et al. 2008). Coral reefs have undergone phase-shifts to algal-dominated systems
(Hughes 1994; Pandolfi et al. 2005; Maliao et al. 2008) due to a combination of stressors.
In addition, there have been declines in seagrass abundance (Robblee et al. 1991;
Collado-Vides et al. 2007) and sponge die-offs (Butler et al. 1995) as a result of changing environmental conditions. Exact causes of these changes remain undetermined, yet 3 anthropogenic influences may be driving these shifts by interrupting vital biological feedback processes that appear to govern ecosystem stability.
As seen in the case of phase shifts on coral reefs, these community shifts can at times result in a new community or an alternative stable state. Alternative stable states are defined as new communities that exhibit different ecosystem processes, functions, and feedback mechanisms from the original community state (Norström et al. 2009).
Typically, this change occurs as a result of the stability of an ecosystem being weakened by multiple disturbances to the point at which it does not recover to its original state, but instead shifts to a newly-defined community (i.e., loss of resilience; Nyström et al. 2000;
Hughes et al. 2007). Some studies have shown evidence of these shifts occurring due to a loss of important feedback mechanisms like herbivory pressures (i.e., decreased grazing;
Hughes 1994; Hughes et al. 2007). Weakened ecosystem stability can result from the combination of increased frequency/duration as well as types of disturbances, as discussed earlier (O’Neill 2001). This lower stability is usually evident when populations are less likely to survive detrimental effects of environmental stressors and/or recover from disturbances, which consequentially, results in a shift in the community structure.
Stressors associated with changes within tropical marine habitats
Bottom-up stressors
Stressors associated with climate change (i.e., rising CO2 concentrations and sea surface temperatures) have been found to impact coral reefs. Elevated temperatures induce stress within corals which can increase susceptibility to disease (Kuta and 4
Richardson 2002) and cause them to expel their endosymbionts, resulting in coral bleaching (Hoegh-Guldberg 1999; Stambler & Dubinsky 2004). Low temperatures (i.e., below 20 °C) can also induce stress and cause mortality of corals (Kemp et al. 2011).
Increased CO2 concentrations cause ocean acidification, which is known to break down coral skeleton as well as decrease calcification rates (Cohen & Holcomb 2009; Kleypas
& Yates 2009).
These stressors have also impacted algae. For example, water temperatures above
30 °C have been shown to cause a decline in algal abundance (Lirman & Biber 2000).
While increased CO2 can stimulate algal growth (Connell et al. 2013), it also inhibits calcareous algae from calcifying through the effects of ocean acidification (i.e., lowering pH; Kleypas & Yates 2009).
Light can also impact corals as it can be a secondary stressor to temperature, causing bleaching due to photoinhibition of coral zooxanthellae (Hoegh-Guldberg 1999;
Stambler & Dubinsky 2004). Similarly to corals, too much light can impact macroalgae.
Increased irradiance levels (high PAR) have been found to cause photoinhibition and therefore decreased photosynthetic rates in macroalgae (Franklin & Forster 1997). A lack of light can also be harmful. Declining water quality due to increased sediment and nutrient loading from coastal watersheds can result in phytoplankton blooms (Diaz &
Rosenberg 2008) which can reduce light availability to benthic primary producers such as corals and seagrasses.
Past studies have also documented harmful effects of increased nutrient levels
(Bruno et al. 2003; Parsons et al. 2008). Increased nutrients have also been found to correlate with stressed corals (Parsons et al. 2008). Nutrients can increase the severity of 5 coral disease, eventually leading to mortality (Bruno et al. 2003). A recent study also found that nutrients may induce disease, in which chronic nutrient exposure was associated with increased disease prevalence (Vega Thurber et al. 2014).
Increased nutrients can also stimulate algal growth (Smith et al. 2001; Littler et al.
2006; Parsons et al. 2008). Nutrient concentrations above the thresholds of 1.0 µM DIN and 0.1 µM SRP have been proposed as thresholds above which macroalgal blooms proliferate on reefs worldwide (Lapointe 1997). Continuous nutrient inputs allow algal blooms on coral reefs to persist (Parsons et al. 2008; Lapointe 1997). Increased nutrients also negatively affect macrophytes (seagrass and algae), in that they increase the nutritional value of seagrass (Tomas et al. 2015) and macroalgae (Jompa & McCook
2002), resulting in increased grazing pressures (Tomas et al. 2015).
Top-down stressors
The large reductions in populations of herbivorous fish due to overfishing have also been implicated in causing regime shifts. A study utilizing cages to replicate reduced herbivory (i.e., herbivore exclusion) corroborated this idea (Hughes et al. 2007). Hughes et al. (2007) found drastic changes in assemblages with herbivore exclusion where fleshy macroalgae became the dominant biota as opposed to corals or algal turfs, following the relative dominance model proposed by Littler and Littler (1984). Coral recruits within the area covered by cages were two-thirds less than the other experimental plots (partial cages and open plots). Because this study was done in the context of how a benthic community will recover from a massive bleaching event (i.e., 1998; Hughes et al. 2007), it may not realistically represent how herbivory will affect these important relationship 6 dynamics in coral-algal competition. However, it does give insight to the influence of herbivory in controlling algal growth.
Current health of marine habitats within the Florida Keys
Within the Florida Keys National Marine Sanctuary (FKNMS), marine habitat health has declined over the past several decades. Incidences of coral bleaching and disease have increased, resulting in a loss of coral cover. Concurrently, other areas have experienced a loss of seagrass and sponge cover. More specifically, studies have documented phase-shifts from coral reef- to algal-dominated systems (Maliao et al. 2008;
Pandolfi et al. 2005), algal intrusion in seagrass beds (Collado-Vides et al. 2007), and mass mortality of sponge communities (Butler et al. 1995). As discussed earlier, the common resultant factor is increasing algal cover.
Waters from Florida Bay and municipal effluent (sewage and septic) from the
Florida Keys are the main sources of nutrient enrichment to these marine ecosystems
(Lapointe 1997; Collado-Vides et al. 2007). Other studies have already found evidence of nutrient availability driving the distribution and abundance of macroalgae across the
Florida Keys National Marine Sanctuary (FKNMS). For example, Collado-Vides et al.
(2007) attributed algal intrusion (i.e., Halimeda spp.) into seagrass beds to be due to higher nutrient concentrations associated with proximity to land. Over the course of their study from 1996 - 2003, these high nutrient areas resulted in higher abundances of calcareous green (e.g., Halimeda spp.) and red (e.g., Laurencia spp.) algae (Collado-
Vides et al. 2007). However, other studies have demonstrated that seagrass die-offs were not always associated with human influences (i.e., nutrients), and instead were 7 hypothesized to be related to extreme temperatures, hypersalinity, and hypoxia (Robblee et al. 1991). These latter factors may also reflect anthropogenic influences (i.e., eutrophication-induced hypoxia; Diaz & Rosenberg 2008), as a result of changes to
Florida Bay via the alteration of freshwater flow from the Everglades.
Similar impacts of declining water quality have also been seen offshore; on reefs within the FKNMS. A long-term study involving water quality and benthic composition data from 1996-2000 revealed that macroalgal cover significantly increased on FKNMS reefs; almost tripling within two years (5.7 to 16.5% from 1996-1998; Maliao et al.
2008). At the same time, hard coral cover decreased from 8.1% to 4.6% during this four year period (Maliao et al. 2008). Maliao et al. (2008) did not attribute these changes to a single variable, but instead discussed the importance of negative effects of both macroalgae and sponges on corals.
Extreme temperatures have also impacted these reefs. The most severe global bleaching event in the last 3000 years was related to a strong El Niño in 1998 and resulted in mass mortality in corals (Jokiel 2004; Hoegh-Guldberg 1999). Impacts to reefs within the Floridian region were first observed on June 25 of that year (Hoegh-
Guldberg 1999). Temperature thresholds for these bleaching events within Florida have been noted around 30 °C (30.11 °C, Brown 1997; 30 °C, Puerto Rico, Jokiel 2004).
Coral cover has yet to recover from the losses due to the warming of 1998 and remains below its 1996 level (i.e., about 12%; Ruzicka et al. 2013). Ruzicka et al. (2013) examined Keys-wide coral reef survey data collected between 1999 and 2009 in order to assess coral recovery. They not only found that coral cover did not recover, but instead continued to decline from 3.6 ± 0.5% (± st er) to 2.8 ± 0.3% (± st er) from 1999 to 2009 8 on shallow fore-reefs (Ruzicka et al. 2013). More recently, there was a massive die-off of corals within the Florida Keys reefs due to two cold fronts during January and February
2010 (Kemp et al. 2011; Lirman et al. 2011). Temperatures dropped below 12 °C and 18
°C, respectively, for about two weeks, which resulted in 100% mortality of several species of coral within reefs of the northern Florida Keys (Kemp et al. 2011). The most severe impacts were seen at inshore habitats in the Middle Keys, which experienced the lowest recorded temperature of 9.5 °C in January 2010, resulting in approximately 40% mean coral mortality (Ruzicka et al. 2013).
These detrimental impacts across the Keys reveal the striking connectivity between varying habitats in this marine environment. For example, because seagrass beds serve as nurseries for many fish (i.e., inhabitants of coral reef and hardbottom habitats;
Robblee et al. 1991), there is a synergy between these communities in which each relies on the health and mere existence of the other. These connections lead to an inter- dependency, in which impacts in one ecosystem can lead to responses in another
(cascading disturbances, Butler et al. 1995).
Many of the mechanisms for these changes in marine ecosystems have been proposed to be most effective through the impacts on interspecies competition (Jompa &
McCook 2002; Connell et al. 2011; Connell et al. 2013). Therefore, it is important to determine the ecological relationships between stressors and marine organisms, as well as the dynamics between multiple stressors. In order to pursue these avenues, conventional research involving short time scales and single, local drivers needs to be replaced with a more transformative approach; one involving a holistic ecosystem perspective addressing integrated regional and local drivers (Russell & Connell 2012). 9
Complexities in stressor interactions
Environmental stressors not only have direct impacts on organisms as discussed above, but can also cause indirect impacts. For example, wave activity is indirectly influential on seagrasses due to the resuspension of sand and silt particles causing scattering of light attenuation (McPherson et al. 2011). Increased nutrients can also promote phytoplankton growth (Diaz & Rosenberg 2008), which can indirectly affect seagrasses and benthic algae in a similar way by increasing turbidity. These scenarios can also be detrimental to corals as shading could potentially block sunlight that is essential for their zooxanthellae (Goreau et al. 1979).
Stressors can also impact organisms indirectly through interspecies competition by affecting interactions among different organisms (see Fig. 1.1; Connell et al. 2013).
As increasing stressors lead to higher mortality of corals, increased bare substrate either allows for new coral recruitment or for algal growth (Kohler & Kohler 1992). As successful colonization of this new space is based on the growth characteristics of each organism as well as their ability to compete, the latter result is unfortunately usually what occurs due to macroalgae being better adapted to the changing environmental conditions, as well as having faster growth rates (Lirman & Biber 2000).
As climate change effects and increased nutrient inputs have created stressful environments for corals and seagrass, these factors have reduced the competitive abilities of these organisms. In contrast, eutrophication (Lapointe 1997; Smith et al. 2001; Jompa
& McCook 2002; Parsons et al. 2008) and increased CO2 concentrations (Connell et al.
2013) have created more beneficial conditions for algal growth (Fig. 1.1). This in turn, 10 supports the ability for algae to compete for space, leading to overcrowding of seagrass beds (Collado-Vides et al. 2007) and smothering of corals (Lirman & Biber 2000 – despite herbivory pressures; Jompa & McCook 2002 – in lessened herbivory pressures).
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Figure 1.1. Diagram from Connell et al. (2013) showing example of direct and indirect stressor effects of increased CO2. Direct effects are seen on mat-forming algae in which growth is stimulated while kelps and corals are inhibited. Indirect effects are then seen on kelps and corals as algae inhibit their growth. Greater impacts are proposed to be seen through indirect effects as they combine with multiple stressors that could increase growth of mat-forming algae.
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Decreased grazing pressures from overfishing can further promote this transition by allowing for macroalgal blooms to persist (Hughes 1994; Wilson et al. 2012). For example, elevated nutrient concentrations in combination with decreased herbivory resulted in the highest macroalgal biomass in a study testing single and synergistic effects of these stressors (Smith et al. 2001). On the other hand, another study found that the interactive effects of CO2 and simulated herbivory on seagrass caused increased seagrass tolerance (positive effect; Tomas et al. 2015). Thus, interactive effects can be more impactful than single driving forces.
In highly impacted ecosystems with multiple stressors, the interactive effects of these stressors on these benthic communities can be compounded, for better or worse.
Integrated stressors can have additive, synergistic, or antagonistic effects (Brown et al.
2014). Additive effects are simply the sum effects of two or more individual factors, while synergistic effects can be seen as greater than the sum of multiple factors (Brown et al. 2014). In contrast, antagonistic effects occur when the addition of one stressor to another cancels out or lessens the effects of the first factor (Brown et al. 2014). These cascading effects are a result of interactive stressors and can cause the offset in competition relationships (or negative feedback processes) that establishes an alternative stable state (Beisner et al. 2003; Russell & Connell 2012). Hence, some of the previously discussed factors have already been recognized as anthropogenic disturbances capable of causing a phase shift in reef benthic composition (Hughes 1994 – grazing/overfishing;
Smith et al. 2001 – grazing, nutrients; Hughes et al. 2007 – grazing/temperatures).
These indirect impacts to competition are the kinds of processes that need to be investigated if we want to understand how changing parameters affect ecosystem 13 resilience (Beisner et al. 2003). However, due to the fact that many environments have already experienced phase shift events, an opposing idea has developed to understand the
“new” resilience dictated by these alternative stable-states (Graham et al. 2013; Hughes et al. 2010) – phase-shift reversal. Recent studies have suggested a new theory of weakening the resilience of these algal-dominated systems in order to induce a phase- shift reversal (Graham et al. 2013, Hughes et al. 2010). Whether by way of natural processes, as seen by a hurricane in Kane’ohe Bay, HI (Stimson & Conklin 2008) and in
Discovery Bay, Jamaica (Idjadi et al. 2006), or deliberate management of species-specific herbivorous fish (Graham et al. 2013), the idea of phase shift reversal seems to be a possible, yet unconventional, pathway towards ecosystem management. Thus, it becomes imperative to identify the influence of manageable factors (e.g., local drivers of salinity and nutrients) versus uncontrollable factors (e.g., regional drivers of temperatures and light) in supporting alternative stable states (Brown et al. 2014).
Approach of this study
As conventional research has previously focused on single stressors, there is a need to study tropical benthic ecosystems in a manner that integrates multiple stressors alongside observed biological changes (Downs et al. 2005; Russell & Connell 2013). The approach utilizing isolated stressors and laboratory-based studies may not account for synergistic effects among multiple stressors within the real environment. Consequently, the actual causes for biological changes remain unknown. By observing biological changes in a larger context of scaling up to a whole ecosystem investigation, the 14 integration of multiple stressors (direct and indirect) can be considered (Pandolfi et al.
2005; Connell et al. 2011; Wilson et al. 2012).
The following study was conducted with such a “holistic ecosystem” perspective in mind. By scaling up to a whole ecosystem investigation, the integration of multiple stressors (Russell & Connell 2013) allowed for observations of stressor impacts alongside biological responses (Downs et al. 2005). Several diverse habitats (seagrass beds, hard bottom substrates, coral reef) were observed. By investigating various environmental factors across a large seascape alongside the observed biological responses, corresponding effects of stressors were documented, providing a “human-environment coupling” perspective (McManus & Polsenberg 2004) in the hopes of identifying the most influential stressors. Although correlation does not necessarily equal causation, it is anticipated that high-resolution and long term monitoring of biological changes alongside multiple environmental factors (natural and anthropogenic) will enable quantification of both change and effect (Downs et al. 2005). This linkage between biological changes and possible causative factors will aid in better understanding the dose-effect relationships required in order to improve management and protection efforts towards these environments (Downs et al. 2005).
Expected results
Due to the deleterious impacts associated with increasing temperatures and nutrients documented in previous studies, it is expected that these stressors will have similar, negative impacts on benthic taxa in this study. The goal of this study, therefore, was to study a diverse array of benthic marine communities in the Florida Keys in order 15 to observe how potential stressors may relate to changes in benthic assemblages in different ecosystem types.
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Chapter 2: Methods
Study Sites: Site descriptions
Three sites within the Florida Keys were studied monthly over a three-year period from September 2011 to August 2014. The study sites represent three distinctive habitats hypothesized to be under varying influences of local and regional drivers (Fig. 2.1): a bay-side seagrass bed (HGB); an ocean-side hardbottom site (LKH); and an ocean-side barrier reef (TRL). These three sites were chosen in the anticipation that results could reflect a gradient from bayside (HGB, Fig. 2.1: #1) to oceanside (LKH and TRL, Fig. 2.1:
#2 and 3), or vice versa. As these sites are all located within the middle keys, it is anticipated that effects associated with decreased water quality will be exhibited (Maliao et al. 2008).
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North
Figure 2.1. Locations of three survey sites off Long Key within the Florida Keys: 1) Heine Grassbed (HGB, bayside); 2) Long Key Hardbottom (LKH, oceanside); 3) Tennessee Reef Lighthouse (TRL, oceanside). 18
Heine Grassbed (Fig. 2.1: #1, HGB) is a near-shore, bayside grassbed site dominated primarily by Thalassia testudinum Banks ex K. D. König with some macroalgal cover. HGB is the shallowest site at about 2-3 meters deep and is located relatively close to shore (within 50 yards) near the entrance of Florida Bay. HGB, therefore, was expected to be most influenced by bay and terrestrially-derived factors
(Fig. 2.2). HGB is also sheltered from weather approaching from southern and eastern directions. Long Key Hardbottom (Fig. 2.1: #2, LKH) is an ocean-side hardbottom site
(4-5 m deep) located next to a channel marker (Channel 5; red #44) and has varying coral, sponge, and macroalgal cover. This site was expected to be a “transition” site between HGB and TRL in terms of environmental influence (Fig. 2.2) as it is located where water from Florida Bay exits and mixes with Atlantic Ocean waters. Tennessee
Reef Lighthouse (Fig. 2.1: #3, TRL; 6-7 m deep), a non-MPA reef located along the reef tract, is characterized by high cover of soft-corals and moderate cover of hard coral, sponge, and macroalgae. It was anticipated TRL would have the least influence from freshwater influences and would be more impacted by oceanic waters (Fig. 2.2). In the same way, it was hypothesized that HGB would show more local effects while TRL would have more regional effects from the different environmental factors surveyed in this study as discussed later.
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Figure 2.2. Hypothetical diagram showing strength of local versus regional influences predicted at each site.
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Regional and local influence
As different ecosystems are hypothesized to be under different levels of influence from environmental drivers, it is anticipated that factors will exhibit local or regional effects. Local influence from factors such as salinity and nutrients (due to freshwater inputs from land) would be expected to have diminishing influences on biological responses along a gradient of distance away from shore. Regional influences are expected from factors that should affect all sites (e.g., from temperature and light) in that they will drive seasonal variations across all sites. However, it is also hypothesized that regional effects will stand out more at oceanside sites in that local influences will be more tempered. Therefore, the hypothetical diagram in figure 2.2 displays the hypothesis that stronger local influences are expected at the sites closest to shore while sites farther from shore will have stronger regional influences (Fig. 2.2). Specific environmental factors and associated hypotheses are discussed later.
Benthic community responses are also expected to differ, or be out-of-phase between sites, accordingly. Comparisons of the biological responses observed across all sites should provide a mechanism to evaluate and compare the influence between local and regional factors. Distinction of the most influential driver(s) should then be possible by examining the timing (synchronization) of the changes to community structure across sites. Thus, the influence of each driver in the benthic community responses can be discerned, thereby providing a mechanism to compare local versus regional influences
(Fig. 2.3).
Benthic Community Surveys 21
To assess benthic community composition, benthic photoquadrats were taken underwater and were later analyzed using Coral Point Count software (CPCe4.0 software,
NCRI; Kohler & Gill, 2005). This method of benthic composition analysis has been shown to result in more precise calculations of percent coverage of taxa than previous methods of in situ identification by individual divers (Preskitt et al. 2004). By taking photoquadrats in the field and using limited persons to later identify taxa, user bias is minimized (Preskitt et al. 2004). This method also allows for permanent records of community composition, providing baseline data for future studies (Preskitt et al. 2004), as well as allowing association with environmental stressors through long-term monitoring (Downs et al. 2005).
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Figure 2.3. Visual aid depicting benthic photoquadrats being taken every meter along three 20 meter transect lines connected by 10 meter transect lines.
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Temporally high resolution surveys were carried out by sampling each site monthly over a three-year period to capture inter-annual, spatial, and seasonal variations in benthic assemblages and associated environmental factors. While SCUBA diving, benthic photoquadrats were taken every meter along both sides of three semi-permanent
20-meter transects at every site (Brown et al. 2004; Fig. 2.3). Following the techniques used in Parsons et al. (2008), a camera with a rod attachment setup was used to ensure quantitative and consistent areal space of each photograph. Although, areal space ranged from 0.35 m2 to 0.58 m2 once measured using Coral Point Count 4.0 software. Proper buoyancy control was used while SCUBA diving in order to take accurate photographs.
The best timing for taking photographs was considered “in between” waves where upright taxa (e.g., sea fans) would not be overshadowing all other bottom taxa. Upon returning from the field, all photos were copied and assessed for clarity. Blurry and/or unusable photos were deleted and then remaining usable photos were labeled and numbered by site, month, and transect line.
Coral Point Count (CPCe4.0) allows for benthic populations to be identified and quantified by randomly overlaying points on each photograph (Fig. 2.4). To ensure proper sampling size, a coefficient of variance (CoV) analysis was done prior to beginning analysis. Using the first set of photoquadrats taken within the first month, all photos were analyzed repetitively with the possible ranges of points. This allowed for the most efficient sampling size and point-analysis to be determined out of the series of photographs (up to 40, Fig. 2.5a) and points (up to 40 per photograph, Fig. 2.5b) possible.
The most efficient number of photographs per transect (n=15) and points (n=20) was then chosen based on these results (Figs. 2.5a & 2.5b) along with the consideration of time and 24 effort required for analysis. Fifteen photographs per transect (n=45 total photographs per site, per month) were chosen as Figure 2.5a shows the CoV value begins to level off at
1.4 to 1.6 at thirteen photos. Twenty points were chosen based off of Figure 2.5b in which the leveling off of the CoV value to 1.4 began at six points, but twenty points were necessary for algal dynamics (a separate CoV was done just on algae and plants but is not shown).
25
Figure 2.4. Computer screenshot of photo analysis using Coral Point Count (CPCe 4.0). Subcategories listing taxa available for classification are shown in the color coded codefile at the bottom of the image. 26
a.
b. Figure 2.5. Graphs created from Coefficient of Variance analysis used to determine proper sampling size of photographs and points used in photo analysis: a) Results of CoV analysis using Tennessee Reef photos for number of photographs; and b) results of CoV analysis performed for number of points using major category data from Long Key Hardbottom (assuming LKH had highest diversity). 27
Photographs were randomly selected for analysis using Microsoft Excel functions
(i.e., RANDBETWEEN) in order to choose fifteen of the remaining photographs from each transect (n=45 per site, per month). The fifteen randomly selected photos were then analyzed using CPCe4.0 and a custom made codefile with the list of possible taxa (Table
2.1). This codefile consisted of species of coral, sponge, algae, and miscellaneous categories expected to be seen at these three sites (Table 2.1). As each photograph was opened, CPCe4.0 randomly overlaid twenty points which were labeled by choosing one of these specific categories through visual analysis (Fig 2.4). Visual analysis was done as objectively as possible by using specific classifications of each category (Appendix A), along with user knowledge of site and taxa characteristics.
28
Table 2.1. List of taxa grouped by major category (as was used in statistical analyses) – and sub-categories from CPCe4.0 codefile. Major Categories Sub-categories Major Categories Sub-categories Major Categories Sub-categories N/A Tunicates (INV-T) live coral Acropora cervicornis (ACP-C) diseased coral Diseased Coral (DIS-C) N/A All Other Invertebrates (INV-O) live coral Acropora palmata (ACP-P) dead coral Dead Coral (DED-C) N/A Fish (FISH) live coral Agaricia agaricites (AGAR-A) dead soft coral Dead Gorgonian (DED-G) N/A Other - Biotic (OBIO) live coral Agaricia tenuifolia (AGAR-T) diseased soft coral Diseased Gorgonian (DIS-G) N/A Other - nonliving (ABIO) live coral Bleached Coral (BLC-C) soft coral Pseudopterogorgia spp (PLU) CCA Encrusting / Coralline (ENC) live coral Colpophylia natans (COLP) soft coral Pterogorgia spp (WHIP) rhodophyte Amphiroa (AMP) live coral Dendrogyra cylindrus (DEND) soft coral Sea Rod (ROD) rhodophyte Acanthophora (ACAN) live coral Dichocoenia stokesii (DICH) soft coral Seafan (FAN) rhodophyte Botryocladia (BOT) live coral Diploria clivosa (DIP-C) soft coral UID Other Gorgonian (UID-G) rhodophyte Galaxaura (GLX) live coral Diploria labyrinthiformis (DIP-L) dead seagrass Dead Seagrass (SGR-D) rhodophyte Gracilaria (GRAC) live coral Diploria strigosa (DIP-S) seagrass Halodule (HALO) rhodophyte Laurencia (LAUR) live coral Favia fragum (FAV) seagrass Syringodium (SYR) rhodophyte UID Other Red Algae (UID-R) live coral Madracis mirabilis (MAD) seagrass Thalassia (THAL) chlorophyte Batophora (BAT) live coral Mancini areolata (MAN) seagrass UID Other Seagrass (UID-O) chlorophyte Caulerpa (CAUL) live coral Meandrina meandrites (MEAN) sponge Anthosigmella varians (VAR-S) chlorophyte Halimeda (HALI) live coral Millepora alcicornis (MIL-A) sponge Aplysina cauliformis (APL-C) chlorophyte Penicillus (PENI) live coral Millepora complanata (MIL-C) sponge Callyspongia vaginalis (CAL-V) chlorophyte Ulva (ULVA) live coral Montastrea annularis (MON-A) dead sponge Dead Sponge (SPO-D) chlorophyte Udotea (UDOT) live coral Montastrea cavernosa (MON-C) sponge Ircinia campana (VAS-S) chlorophyte UID Other Green algae (UID-G) live coral Porites asteroides (POR-A) sponge Ircinia spp. (BRN-S) phaeophyte Dictyota (DICT) live coral Porites porites (POR-P) sponge Spheciospongia vesparium (LOG-S) phaeophyte Padina (PAD) live coral Siderastrea radians (SID-R) sponge Tedania ignis (TED-I) phaeophyte Sargassum (SAR) live coral Siderastrea siderea (SID-S) sponge UID Other Sponge (UID-S) phaeophyte Turbinaria (TURB) live coral Solonastrea hyades (SOL) sponge Xestospongia muta (XES-M) phaeophyte UID Other Brown algae (UID-B) live coral UID Other Stony Coral (UID-SC) hard substrate Hard Substrate (HSUB) UID MAC UID -All Other Algae (UID-A) live coral Zoanthid (ZOAN) rubble Rubble (RUB) turf UID - Turf Algae (UID-T) live coral Montastrea annularis complex (MON-X) sand Sand bottom (SAND) cyanophyte UID filament Lyngbya (LYNG) live coral Cladocora arbuscula (CLAD) N/A Black Hole (BHOLE) cyanophyte UID tuft Schizothrix (SCH) live coral Stephanocoenia mechelinii (STEP) N/A Tape, wand, other (SCHTUFF)
29
After a photograph was analyzed, files were saved by month until all photographs within the transect were analyzed. Once analysis of a transect was completed, data from all fifteen photographs were converted and saved to an Excel spreadsheet using CPCe4.0.
Mean percent abundance (coverage) of each taxon (subcategory) was calculated by
CPCe4.0 for each transect. The mean values for each transect then served as triplicate measurements (three transects) to calculate the mean percent abundance of each taxon per month by site. As monthly data were gathered, a master excel file was created by collating mean percent abundance data (i.e., subcategories) from each transect excel file.
This allowed for easy import of massive data to a Microsoft ACCESS database.
Environmental Parameters
Environmental factors were measured in situ during each sampling trip. Water samples were collected <0.2 m from the bottom along each transect for nutrient analysis,
- ensuring replicates (n=3) per site for each month. Concentrations of nitrite (NO2 ), nitrate
- + (NO3 ), ammonium (NH4 ), and soluble reactive phosphorous (SRP; orthophosphate) were then determined using a Bran + Luebbe Seal AutoAnalyzer 3. Bottom salinity was also determined from these water samples using a refractometer. Temperature (°C) and light (lumen ft-1) data were measured and recorded every 15 minutes using a HOBO UA-
002-64 device (Onset Computer Corporation) that was retrieved, uploaded, and redeployed monthly. This allowed for in situ, high-resolution measurements of temperature and light every 15 minutes for the duration of the month. Light data were converted to PAR (µM m-2 s-1 photons) by multiplying by a factor of 2.44 based on 30 calibration against a Li-Cor LI-250A light meter with an LI-193SA spherical underwater quantum sensor.
Wave data were calculated using modeled wave data collected from Wind Guru
(GFS 27 km; Islamorada; http://windguru.cz/int/), corrected for fetch using wind data retrieved from the National Climatic Data Center (http://www.ncdc.noaa.gov) for the
Marathon Airport (KMTH) using the Daily Summaries dataset. Wind corrections were applied as weights multiplied to the wave data, where winds coming from 10-40 degrees
(NNE) were given weights of 0.5 (oceanside; TRL and LKH) and 0.25 (bayside; HGB);
50-230 degrees (NE – SW) were given weights of 1 (oceanside; TRL and LKH) and 0.1
(bayside; HGB); and 240-360 degrees (SW-N) were given weights of 1 (oceanside; TRL and LKH) and 0.5 (bayside; HGB). These factors down-weighted wave heights (in some cases) to acknowledge shorter fetch caused by the islands (primarily a factor for NE winds oceanside, and all but N-NW winds bayside), as well as the fact that waves are typically smaller bayside versus oceanside. As the wind-weights are hypothetical, the resulting wave heights were not directly comparable between sites, and had to be standardized about site means in order for use in statistical testing across sites. Site specific wave data are therefore in meters while standardized data are unitless.
All environmental data were collated for the month prior to photoquadrats being taken in order to account for environmental conditions over the previous month that could have influenced the benthos as documented in the photoquadrats (e.g., environmental data from August corresponded to September photoquadrats).
Preparation of Data for Analyses 31
Benthic Assemblage data –
All benthic assemblage data were collated in a Microsoft ACCESS database by importing all excel files containing the three transects of data per month for each site. In addition to the subcategory monthly mean percent cover calculations described above, monthly means were also calculated for each major category (Tab. 2.1) by averaging across the three transects for each month. Individual taxon (subcategories) data were used for within-site comparisons while major category data were used for between-site comparisons.
As coral reefs within the FKNMS are considered to be post phase-shift (Maliao et al. 2008), it was anticipated that Tennessee Reef would not have much stony coral cover.
Due to results confirming this with low stony coral coverage, zoanthid abundance was combined with the stony corals to create the “live coral” major category (Table 2.1). This was done for the remainder of analyses (i.e., other than descriptive statistics) in this study under the assumption that zoanthids would respond to environmental variables similarly to other cnidarians (Kemp et al. 2006).
Environmental data –
In order to test for local versus regional influences among the environmental factors, different parameters were calculated and utilized accordingly (Table 2.2). Wave measurements, salinity, and nutrient concentrations were hypothesized to have more local effects (Table 2.2) due to site specific characteristics (e.g., depth, proximity to Florida
Bay, fetch). Monthly mean values of temperature and light were considered to have more regional influences (i.e., both will display similar seasonal trends across sites) in which all sites would be affected by these factors. Because some variables only had one 32 monthly measurement (e.g., salinity, nutrients, waves), these measurements were used as monthly representatives of these factors as opposed to mean values.
33
Table 2.2. Local and regional environmental factor classifications and associated hypotheses for each factor. SRP = soluble reactive phosphate/orthophosphates.
Category Factor Hypotheses - - Nutrients (NO2 , NO3 , Assuming site location and biological processes will + Local NH4 , SRP) be unique among sites. Assuming site depth and location will exhibit local Local Monthly Waves effects. Assuming site location (i.e., proximity to FL Bay, FL Local Salinity current) will exhibit local effects. Assuming a mean monthly value will represent a regional measurement of temperature and Regional Monthly Temperature therefore have regional effects. Assuming a mean monthly value will represent a regional measurement of light and therefore have Regional Monthly Light regional effects.
34
Data Analyses
To account for missing data due to equipment loss/malfunction (e.g., HOBO device) or poor weather conditions, data extrapolations were used to calculate a monthly value using data from months prior to and after the corresponding month for both taxa data and environmental data. On occasions where these data were also not available, a mean value was calculated from monthly data of the corresponding months from the other two years. These calculations were done using the linear interpolation functions within SPSS 22.0 (IBM).
Simple statistics such as mean, range, and standard deviation were computed to first characterize sites, in both biotic and environmental terms. Overall mean values (i.e., average of three years) of each sub-category and major category were calculated to determine general community composition of each site. Ranges of these data were also used to characterize sites. Environmental site characteristics were determined using means, standard deviation, and ranges.
Seasonal and spatial changes in community composition and environmental data were examined using appropriate univariate (SPSS 22.0) and multivariate tests (PRIMER
– E Ltd 7). SPSS 22.0 (IBM) was used to run Spearman correlations to test for significant relationships between the environmental data and individual taxa (Table 2.3). Non-metric multidimensional scaling (nMDS) was used to compare community compositions both between and within sites in order to visually analyze differences and similarities (Table
2.3; Sathe et al. 2008). Analysis of similarities (ANOSIM) tests were performed to test for significant differences among sites and seasons (Table 2.3; Sathe et al. 2008). In addition to ANOSIM, similarity percentages analysis (SIMPER) was used to characterize 35 and quantify any significant differences and similarities (Sathe et al. 2008; Mumby et al.
2013) between site community and environmental composition (Table 2.3). Relationships between species abundances and environmental factors were assessed using distance based linear models (DistLM) to determine which factors could be driving the resulting assemblages across all sites (regional influences) as well as within sites (local influences;
Table 2.3; Mumby et al. 2013). In using these statistical analyses, conclusions were inferred by identifying the most influential environmental drivers.
Proper data transformations were performed both on taxa percent abundance data and environmental factor data prior to data analyses. Square root transformations were done on major category and sub-category taxa data used in nMDS, SIMPER, ANOSIM
(Sathe et al. 2008; Mumby et al. 2013) and Cluster analyses. All environmental data were normalized before SIMPER and ANOSIM analyses. For DistLM analyses, taxa data were square root transformed but normalization of environmental data was not necessary as
DistLM performs this by default. In order to display real values and scales, original environmental data were used in bubble plots in nMDS diagrams. Original data were also used in all simple statistics (e.g., range, mean, standard deviation, and diversity analyses) and nonparametric analyses of Spearman’s Rho correlation.
36
Table 2.3. List of hypotheses tested by each statistical test.
Statistical Analysis Hypothesis Tested
Benthic assemblages will differentiate sites. Cluster Analysis Benthic assemblages will differentiate sites; benthic assemblages will differentiate sample periods within sites nMDS showing patterns of seasonality. There will be differences in community composition and environmental characteristics among sites and between ANOSIM seasons. Certain sites will be more similar or more different in their community and environmental characteristics; high and low sampling periods of significant environmental variables will correspond to certain taxa that are SIMPER influenced by these variables.
Environmental factors will be influential on changing benthic assemblages over time and among sites. DistLM
Taxa influenced by significant environmental factors will be correlated. Spearman Correlation
37
Chapter 3: Site characterization
Methods Overview
All statistical tests of individual site data were done using subcategory data unless otherwise noted. Between-site comparisons used major category data unless otherwise noted. Seasonal comparisons were done with monthly mean data classified as Spring
(March - May), Summer (June - August), Fall (September - November), and Winter
(December - February). Note: “Stony” coral refers to living hard coral coverage while
“soft” coral refers to octocorals (i.e., seafans, sea plumes, and sea rods). “Live coral” is the major category that includes “stony” coral percent coverages with zoanthid percent coverages that was used in statistical analyses.
Community Descriptions
Heine Grassbed
The community composition at HGB consisted primarily of seagrass (Thalassia testudinum), sand, and dead seagrass (Fig. 3.1a). Live T. testudinum benthic cover ranged from 27.0 to 54.7% over the course of the three year period, whereas dead seagrass ranged from 13.3 to 38.5% (Fig. 3.1b; Table. 3.1). Aside from seagrass, Halimeda spp. was the other prominent taxa at this site (Fig. 3.1b), ranging from 0.9 to 7.3% over the three year study period (Table 3.1). Other chlorophytes were observed at HGB at lesser abundance (e.g., Penicillus spp.). 38
Table 3.1. Community composition of each site. Values shown are ranges of monthly percent coverage means of common taxa over the three year study. Note: data shown were chosen based on relevant taxa at each site in order to be concise (i.e., na = not applicable; does not mean taxa were not present).
HGB LKH TRL
Live seagrass (Thalassia spp.) 27.0 - 54.7% na na Dead seagrass 13.3 - 38.5% na na Halimeda spp. 0.9 - 7.3% na na Chlorophytes 2.3 - 9.5% 8.8 - 27.4% na Soft coral na 9.1 - 20.4% 14.3 - 28.6% Rhodophytes na 0.7 - 33.6% na Laurencia spp. na 0.3 - 30.4% na Turf algae na na 3.6 - 30.2% Phaeophytes (Dictyota spp.) na 1.3 - 13.1% 3.4 - 32.9% Stony coral na na 1.4 - 5.2% Dead stony coral na na 0.0 - 0.4%
39
a.
b. Figure 3.1. Pie charts displaying top taxa within the Heine grassbed (HGB) site as listed by their mean percent cover (overall averages from all three years of data). Taxa shown have an overall average of at least 1%. a) Major category mean percent abundances. b) Sub-category mean percent abundances. 40
Long Key Hardbottom
LKH consisted mostly of sand, soft corals, chlorophytes, and rhodophytes (Fig.
3.2a). More specifically, sea plumes (Pseudopterogorgia spp.), Halimeda spp., Laurencia spp., and Dictyota spp. were the top four biota found at LKH (Fig. 3.2b). Soft corals ranged from 9.1 to 20.4% coverage (Table 3.1) and were primarily sea plumes and sea rods.
The existence of macroalgae at this site appeared to be highly dynamic throughout the study. While chlorophytes were primarily more abundant than rhodophytes (Fig.
3.2a), coverage of rhodophytes was more dynamic than chlorophytes in their range over the three years (0.7 - 33.6% versus 8.8 - 27.4%, respectively; Table 3.1). Chlorophytes at this site included Halimeda spp. and Udotea spp. (Fig. 5b; 11.0% and 1.3%, respectively). Laurencia spp. was the major rhodophyte at this site (Fig. 3.2b), with a range of 0.3 to 30.4% cover over the three years (Table 3.1). This wide range hints to the possible seasonality seen in this algal group (see below). Phaeophytes (i.e., Dictyota spp.,
Fig. 3.2) were also relatively dynamic with a range of 1.3 to 13.1% (Table 3.1).
41
a.
b. Figure 3.2. Pie chart displaying top taxa within the Long Key Hardbottom (LKH) site as listed by their mean percent cover (overall averages from all three years of data). Taxa shown have an overall average of at least 1%. a) Major category mean percent abundances. b) Sub-category mean percent abundances. CCA = coralline crustose algae/encrusting coralline algae; UID = unidentified; MAC = macroalgae. 42
Tennessee Reef Lighthouse
The reef site surveyed in this study consisted mostly of sand, algae, soft corals, and sponges (Fig. 3.3a). Turf algae were the most dominant algal group (Fig. 3.3b) with a range of 3.6 to 30.2% cover over the three years (Table. 3.1). Phaeophytes were also highly dynamic with a range of 3.4 to 32.9% cover (Table. 3.1), and consisted primarily of Dictyota spp. (Fig. 3.3).
Tennessee reef (TRL) had very little live stony coral coverage, ranging from only
1.4 to 5.2% over the three year period (Table. 3.1) with an overall average of only 3.0%
(Fig. 3.3a). However, soft corals were more abundant with an average of 21.4% coverage
(Fig. 3.3a) and a range of 14.3 to 28.6% coverage (Table. 3.1). Zoanthids were almost as common as stony corals, with an average percent cover of 2.0% over the course of the study (Fig. 3.3a). The primary stony coral (i.e., only species with greater than 1% mean coverage) was the fire coral, Millepora alcicornis (Fig. 3.3b). Despite having low coral cover, there were no visible patterns in diseased or dead coral (stony) over the three year period. Dead coral only ranged from 0.0 to 0.4% (Table 3.1). 43
a.
b. Figure 3.3. Pie chart displaying top taxa at the Tennessee Reef (TRL) site as listed by their mean percent cover (overall averages from all three years of data). Taxa shown have an overall average of at least 1%; a) Major category mean percent abundances, b) Sub- category mean percent abundances. CCA = coralline crustose algae/encrusting coralline algae; UID = unidentified; MAC = macroalgae. 44
Comparison of benthic composition among sites
Multivariate Analyses of Community Structure
Cluster analysis results on the comprehensive benthic assemblage data indicated that the three sites were distinct, clustering into different groups (Fig. 3.4). Results of the nMDS analysis on these data were similar, in that there was no overlap among samples from the three sites (Fig. 3.5).
45
Figure 3.4. Dendrogram graph showing results of cluster analysis performed on data from all three sites. H = HGB, T = TRL, and L = LKH. Numbers after site designations refer to sampling month within each project year. 46
ANOSIM analysis on these data demonstrated that the differentiation between sites was significant (R = 0.992, p = 0.001). As expected, Heine grassbed (blue) was extremely different from both the hardbottom and the reef site (Fig. 3.5; R = 1, p =
0.001). LKH and TRL were also significantly different from each other, but less so than from HGB (Fig. 3.5; R = 0.964, p = 0.001). SIMPER analyses of site comparisons were in agreement in that Heine grassbed and Tennessee Reef were the most dissimilar, as their average dissimilarity was 63.23 (Table 3.2). Likewise, the most similar sites were
LKH and TRL with an average dissimilarity of 28.66 (Table 3.2).
SIMPER analysis also revealed that six major categories accounted for more than
75% of the difference between HGB and TRL: seagrass, dead seagrass, soft coral, turf, phaeophytes, and live coral (major category here, including zoanthids; Fig. 3.3a). LKH and TRL dissimilarity was primarily attributed to algae. Phaeophytes and turf algae were more abundant at TRL (Fig. 3.3a), while rhodophytes and chlorophytes were more abundant at LKH (Fig. 3.2a).
47
Figure 3.5. Results of nMDS analysis of all major category taxa data from all three sites. Each point represents the benthic community of one month within the designated site. Distance between three groupings shows site separation by benthic assemblage data.
48
Table 3.2. Results of SIMPER analyses of community composition. Dissimilarity of each site (= 100 - average similarity) shows variability among sampling periods within community assemblages. Dissimilarity comparisons show variability between site community compositions. Bolding indicates results of most dynamic site and most different site comparison. na = not applicable.
HGB LKH TRL Dissimilarity 11.63 16.22 13.72 vs HGB na 57.81 63.23 vs LKH - na 28.66 vs TRL - - na
49
Seasonality within Community Assemblages
The within-site dissimilarity values (Table 3.2) indicate LKH had the highest average dissimilarity and was therefore the most dynamic site over the study period.
Conversely, the benthic community at HGB was the most similar across sampling periods as it had the lowest average dissimilarity (Table 3.2). Site-specific details are provided below.
Heine Grassbed
There were no clear seasonal patterns in the community dynamics at HGB (Fig.
3.6a). In terms of individual taxa, there was also a lack of evidence for extreme seasonal relationships (Fig. 3.6b & c). ANOSIM results confirmed there was little change over the seasons (R = 0.109, p = 0.012). However, the limited change was primarily driven by sand and dead Thalassia spp. coverage, according to the SIMPER results. The nMDS plots in Figure 3.6b and 3.6c show the slight changes over time, although the stress value indicates a poor fit of these data in a multivariate space. Figure 3.7 displays how
Thalassia spp. displays a seasonal cycle when monthly abundances are averaged across project years to show changes over a typical year. Yet, the changes in Thalassia spp. are only on the order of 10% from May to September (Fig. 3.7).
50
a.
b.
c. 51
Figure 3.6. Results of nMDS showing seasonal groupings of benthic assemblages within HGB. a) Seasonal groupings of Fall (F), Winter (W), Spring (S), and Summer (U) groupings. Remaining nMDS figures are bubble plots displaying SIMPER results of taxa differentiating seasonal groupings; b) sand coverage; and c) dead seagrass.
52
HGB Thalassia spp. R = 0.7842 p = 0.0450
Figure 3.7. SigmaPlot graphs of HGB yearly averaged data (i.e., n = 3 for each month) with standard error bars and best-fit peak log normal polynomial for Thalassia spp. abundances (p = 0.0450). 53
Long Key Hardbottom
Similarly to HGB, the community at LKH appeared to lack a seasonal pattern
(Fig. 3.8a). Yet ANOSIM results revealed there were some significant changes over time
(R = 0.282, p = 0.0001). SIMPER analysis results showed Laurencia spp. was the leading taxon contributing to seasonal changes and is therefore shown in the nMDS below (Fig.
3.8b). Seasonal patterns of individual taxa were more evident at LKH than HGB, as two taxa exhibited seasonal patterns as opposed to just one at HGB. As displayed in previous analyses, Laurencia spp. (Figs. 3.8b & 3.9) had the most extreme seasonal signal with the highest peak in abundance during September/October. This could be showing the epiphytic/drift characteristics of Laurencia spp. due to the sudden, extreme increase and then similar decrease. It appears there also could be a succession of algal growth between
Halimeda spp. and Laurencia spp.; Laurencia spp. peaks first around September/October
August, followed by Halimeda spp. with its highest abundance occurring around
December (Fig. 3.9).
54
a.
b. Figure 3.8. Results of nMDS showing seasonal groupings of benthic assemblages within LKH. a) Seasonal groupings of Fall (F), Winter (W), Spring (S), and Summer (U) groupings and b) nMDS figure with bubble plots displaying SIMPER results of Laurencia spp. differentiating seasonal groupings. 55
LKH Laurencia spp. R = 0.9946 p = < 0.0001
a.
LKH Halimeda spp. R = 0.9254 p = 0.0010
b.
Figure 3.9. SigmaPlot graphs of LKH yearly averaged data (i.e., n = 3 for each month) with standard error bars and best-fit peak polynomial for a) Laurencia spp. abundances (p < 0.0001); and best-fit sine wave polynomial for b) Halimeda spp. abundances (p = 0.0010). 56
Tennessee Reef Lighthouse
The nMDS plots of Tennessee Reef assemblages show that all sampling periods were pretty well dispersed, regardless of season (Fig. 3.10a). ANOSIM results for TRL were in agreement in that there were no significant differences between any of the seasons (R = -0.025, p = 0.765). Therefore, it appears there were no seasonal patterns in the benthic community of Tennessee Reef. However, Dictyota spp. seemed to be a leading taxa in seasonal transitions according to SIMPER analysis (data not shown).
Figure 3.10b shows how Dictyota spp. might change over time, but maybe not in a seasonal pattern. However, over a typical year, individual taxa at TRL show some evidence of seasonality (Fig. 3.11), but not as strongly as taxon from LKH. Dictyota spp. appeared to be more abundant at TRL in the spring (Fig. 3.11). The trends are evident here because the data are treated continuously versus as categories (Seasons) for the
ANOSIM.
57
a.
b. Figure 3.10. Results of nMDS showing seasonal groupings of benthic assemblages within TRL. Seasonal groupings of Fall (F), Winter (W), Spring (S), and Summer (U) grouping and b) nMDS figure with bubble plots displaying SIMPER results of Dictyota spp. differentiating seasonal groupings. 58
TRL Dictyota spp. R = 0.8061 p = 0.0314
Figure 3.11. SigmaPlot graphs of TRL yearly averaged data (i.e., n = 3 for each month) with standard error bars and best-fit sine wave polynomials for Dictyota spp. abundances (p = 0.0314). 59
Although there was not much change within community structure at the sites, environmental characteristics could relate similarly to where there is a lack of seasonality. Since communities did not change substantially, this lack of seasonality could be foretelling that anticipated effects of environmental factors were more attenuated than expected. On the other hand, if a few environmental factors exhibit stronger trends, such results could reveal that single factors may be more influential on fewer benthic categories as opposed to multiple factors.
Environmental Characteristics
Methods Overview
All statistical tests conducted were similar to those done on the community composition data. Seasonal comparisons were done with monthly mean data classified as
Spring (March - May), Summer (June - August), Fall (September - November), and
Winter (December - February).
Results of the descriptive statistical analyses of environmental factors for each site revealed apparent trends according to site characteristics. For example, due to it being the shallowest of the sites, HGB had the most variable ranges in mean one month temperatures (Table 3.3) as well as mean values for one month light (Table 3.4).
Similarly, salinity and standardized waves were the most dynamic at HGB (Table 3.3).
- + Interestingly, it appeared TRL had the most variability in its range of NO3 and NH4 ,
- while HGB had the most variable range of NO2 and orthophosphate (Table 3.3). This 60 variability seen at HGB was also reflected in the SIMPER results (discussed below). In terms of overall monthly mean values of environmental characteristics, all three sites were relatively similar (Table 3.4). This is later confirmed by ANOSIM results showing there was some, but not much, difference in the environmental composition between sites.
61
Table 3.3. Environmental characteristics of each site. Values shown are ranges of monthly means of environmental measurements. Note: Mwave is site-specific monthly wave data acquired from modeled wave data and STANDwave is standardized wave data, for comparisons across sites. 1mT = monthly temperature; 1mL = monthly light, BottomSal = bottom salinity.
HGB LKH TRL
STANDwave -0.81 - 1.12 -0.71 - 0.83 -0.71 - 0.81 Mwave (m) 0.04 - 0.50 0.05 - 0.33 0.05 - 00.33 1mT (C) 22.1 - 32.5 22.6 - 32.0 23.6 - 30.1 1mL (PAR) 59.8 - 215.9 40.1 - 169.3 48.9 - 156.0 BottomSal (ppt) 26.6 - 41.5 26.8 - 38.0 26.0 - 38.0 - NO3 (µM) 0.017 - 2.188 0.022 - 2.070 0.013 - 5.845 OrthoP (µM) 0.017 - 0.394 0.021 - 0.313 0.020 - 0.202 - NO2 (µM) 0.003 - 0.247 0.003 - 0.072 0.003 - 0.090 + NH4 (µM) 0.499 - 3.588 0.486 - 3.694 0.158 - 5.248
62
Table 3.4. Environmental characteristics of each site. Values shown are overall (3 year data) mean ± standard deviation of all environmental parameters used in analyses. Note: Mwave is site-specific monthly wave data acquired from modeled wave data and STANDwave is standardized wave data, for comparisons across sites. 1mT = monthly temperature; 1mL = monthly light, BottomSal = bottom salinity.
HGB LKH TRL
STANDwave -1.67E-16 ± 0.48 3.76E-16 ± 0.38 4.04E-16 ± 0.38 Mwave (m) 0.23 ± 0.11 0.18 ± 0.07 0.18 ± 0.07 1mT (C) 27.1 ± 3.3 27.1 ± 2.9 27.2 ± 2.2 1mL (PAR) 117.7 ± 44.4 101.5 ± 30.0 93.0 ± 29.4 BottomSal (ppt) 35.6 ± 2.9 35.9 ± 1.8 35.9 ± 1.9 - NO3 (µM) 0.271 ± 0.508 0.232 ± 0.365 0.538 ± 1.100 OrthoP (µM) 0.084 ± 0.070 0.085 ± 0.099 0.077 ± 0.044 - NO2 (µM) 0.020 ± 0.048 0.013 ± 0.037 0.019 ± 0.052 + NH4 (µM) 1.863 ± 0.913 1.295 ± 0.724 1.449 ± 1.113
63
Multivariate Analyses of Environmental Characteristics
Results of the 2-way nested ANOSIM analysis of all environmental data indicate there was significant, but little, difference between the sites (R = 0.039, p = 0.034), although seasonal differences were evident (R = 0.698, p = 0.001). Correspondingly, the nMDS results indicate that the environmental characteristics of these three sites were similar (Fig. 3.12a). Additionally, HGB sampling periods were slightly dispersed, whereas LKH and TRL had more compact groupings of sampling periods with some outliers (Fig. 3.12a). These dispersion patterns indicate that environmental conditions were more dynamic at HGB, and more stable at the oceanside sites. The seasonal variation in the environmental factors is depicted in the semi-circular pattern moving from fall to winter to spring to summer shown in the nMDS plot (Fig. 3.12b). The specific seasonal patterns of influential environmental factors will be discussed in chapter
4.
64
a.
b. Figure 3.12. Results of nMDS analysis of all environmental data a) across all three sites; and b) of seasonal groupings across all three sites
65
The SIMPER analyses corroborated with the nMDS interpretations; environmental conditions were the most consistent at LKH over time out of all three sites. LKH had the lowest average square distance of the three sites (Table 3.5). In contrast, HGB had the highest average square distance (Table 3.5), suggesting the environment at HGB was the most variable across time compared to the other two sites.
66
Table 3.5. Results of SIMPER analyses of environmental characteristics. Average square distance of each site shows variability of environmental data among sampling periods. Site comparisons were not included as ANOSIM testing did not result in significant site differences. Bolding indicates results of most dynamic site. na = not applicable.
HGB LKH TRL Ave Square 9.31 6.50 8.07 Distance vs HGB na na na vs LKH na na na vs TRL na na na
67
Chapter 4: Influence of environmental factors on community structure
Methods Overview
Relationships between species abundances and environmental factors were first assessed using distance based linear models (DistLM) to determine which factors could be driving the resulting assemblages across all sites (regional factors) as well as within sites (local factors) (Table 2.3). SIMPER analyses were then performed on the benthic data in order to see which taxa responded most to the influential environmental factors at each site. Sampling periods corresponding to the highest and lowest quartile (i.e., 9 months each out of the total of 36 months) for each environmental factor (e.g., months where temperatures were highest and lowest for the one month temperature parameter) were identified. These quartiles were then used as factor groupings within the SIMPER test, and corresponding benthic data were grouped accordingly for each environmental factor within each site. The top three taxa exhibiting the most change between the two quartiles were chosen based on ecological significance (i.e., sand might have changed a lot between top and bottom quartiles of nitrate, but would not be listed due to ecological significance considerations). Additionally, Spearman Rho correlation tests were also done on influential variables and major category data at each site in order to test for continuous relationships between the variables.
Regional and Local Drivers 68
Distance based linear models (DistLM) testing was done using the variables in
Table 2.2 and the selection procedure and selection criterion of “Best” and AIC in
PRIMER 7. This specific testing allows all possible combinations of variables to be assessed and then gives the Best combination solution in regards to the AIC criterion.
These results for each site’s DistLM are displayed in Table 4.1.
69
Table 4.1. Results of DistLM tests showing significant individual factors and results of interactive factors of Best combinations with corresponding AIC and r2 values showing amounts of variation. Individual factor values are proportions of benthic cover variation explained by the variables at a significance level of p ≤ 0.05 for each site.
Site Individual factors Best combination (interactive factors) Monthly waves, monthly temperatures, Monthly waves (0.065) nitrate, orthophosphate, ammonium HGB Nitrate (0.060) (176.93, 0.263) Monthly waves, monthly temperatures, Monthly waves (0.067) monthly light, bottom salinity (197.7, LKH Monthly temperatures (0.124) 0.243) Monthly waves and orthophosphate TRL Monthly waves (0.048) (201.93, 0.089)
70
Heine Grassbed
DistLM with AIC and Best analysis revealed monthly waves (p = 0.019) and nitrate (p = 0.036) were the only individual variables that were significantly related to variations within assemblages at HGB (Table 4.1). Both of these variables contributed to approximately 6% of the variation in community change at this site (Table 4.1). In terms of interactive effects, the five factors of monthly waves, monthly temperatures, nitrate, orthophosphate, and ammonium were the overall best solution of combined variables.
These five factors were responsible for 26.3% of the variation within HGB (AIC =
176.93, Table 4.1).
Long Key Hardbottom
At LKH, monthly waves were significantly influential (p = 0.016), in addition to one month temperature (p = 0.001; Table 4.1). Temperatures were more influential than waves at LKH in that monthly temperatures influenced 12.4% of the variability at LKH compared to 6.7% from monthly waves (Table 4.1). The best combination at this site with the largest influence of 24.3% was monthly waves, monthly temperatures, monthly light, and bottom salinity (AIC = 197.7, Table 4.1).
Tennessee Reef Lighthouse
The only significant individual environmental factor at TRL was monthly waves, which influenced 4.8% of the variability at this site (p = 0.035; Table 4.1). However,
TRL had interactive effects when orthophosphate (SRP) concentrations were combined with monthly waves. The overall best solution was the combination of these two 71 variables which explained 8.9% of the variation at Tennessee Reef Lighthouse (AIC =
201.93, Table 4.1).
Seasonality of Influential Environmental Factors
All individual site statistical analyses were done using site-specific wave data unless otherwise noted.
As expected, temperature showed a very prominent pattern in seasonality at all three sites (Fig. 4.1). Mean monthly temperatures at Heine Grassbed (HGB) were the most variable at this site and consistently had the highest maximum and lowest minimum temperatures recorded among the three sites (Fig. 4.1). This result was anticipated, as
HGB is the shallowest site. LKH had more variable temperatures on a monthly basis than
TRL (Fig. 4.1), which was also expected as it is next to a channel which would mean it is in a high transition zone (i.e., where waters from Florida Bay would be intermixing with
Atlantic oceanic waters). Also as anticipated, Tennessee Reef had the least variable temperatures of the sites (Fig. 4.1). Despite these general observations, the regional factor of monthly temperature was only significant as an individual factor at LKH (Table 4.1).
72
Figure 4.1. Mean monthly values of temperature data of all three sites across the three year study period. Note: vertical scale starts at 15.0 degrees C.
73
Site-specific monthly wave data were used in the DistLM tests described above and are shown in the time series in Fig. 4.2. As wave data were not standardized, this graph should not be used for site comparisons, but only for within-site observations.
Monthly waves were significant in influencing the communities at each site (Table 4.1), although there were not strong seasonal signals at all three sites (Fig. 4.2). For example,
HGB appears to have a more stable pattern of larger waves during winter months and smaller waves during summer months (Fig.4.2). In periods where HGB had large waves, it can be expected that winds were coming mostly from the northwest, where fetch can build up across Florida Bay. In contrast, when winds come from the southeast, protection from land probably causes the period of the smallest waves found at HGB. The oceanside sites, on the other hand, appear to have more variable highs and lows within seasons (Fig.
4.2) probably due to less protection from land. As LKH and TRL were both oceanside
(and similarly computed), it is fitting that their wave data corresponded well, except for smaller wave heights at LKH during November, 2013 (due to LKH-specific fetch effect;
Fig. 4.2).
74
Figure 4.2. Mean values of monthly wave data (not standardized) of all three sites across the three-year study period. Note: Because this is site specific data (not standardized), this graph should not be used to compare waves between sites, but instead is useful to observe any patterns in within-site seasonality.
75
Nitrate was both individually and interactively influential at HGB; however, it did not have any influence at the other two sites (Table 4.1). Concentrations of nitrate at
HGB appeared to be relatively stable with random peaks in September, October, and
January (Fig. 4.3). However, the nitrate concentrations at TRL displayed a strong pattern of seasonality with peaks every fall (i.e., September, October, Fig. 4.3). Concentrations at
LKH were the most variable with lower and more frequent peaks than the other sites (Fig.
4.3).
76
- Figure 4.3. Mean values of monthly nitrate (NO3 ) concentrations (µM) of all three sites across the three-year study period. 77
Influence on community composition
According to the results of the SIMPER analysis which compared the top and bottom quartiles of influential environmental factors, monthly waves at HGB appeared to mostly affect seagrass and sand coverage (Table 4.2). During periods of decreased monthly waves, there were higher abundances of Thalassia spp. (Table 4.2; Fig. 4.4a).
This could be expected for seagrass, although this was not a consistent relationship as there was no correlation between waves and live Thalassia spp. (Table 4.2). However, there was a significant negative correlation between waves and sand (r = -0.380) as well as a significant positive correlation between waves and dead seagrass (r = 0.446; Table
4.2). This is evident in that more sand coverage was seen during periods of smaller waves while higher abundances of dead seagrass were during periods of larger waves (Table
4.2).
Increased abundances of dead seagrass were also seen during periods of lower concentrations of nitrate; which was supported by the negative correlation with nitrate concentrations (r = -0.459; Table 4.2; Fig. 4.4b). In terms of algae, higher abundances of
Halimeda spp. were observed during times of larger waves as well as during months with increased nitrate concentrations (Table 4.2; Fig. 4.4c). In addition, Penicillus spp. abundances were higher during periods of decreased waves, but higher concentrations of nitrate (Table 4.2). However, the only other significant correlations associated with nitrate concentrations were negative relationships with populations of Laurencia spp. (r =
-0.358) and Batophora spp. (r = -0.397; Table 4.2); which were less important algal groups at this site.
78
Table 4.2. Results of SIMPER analyses comparing sampling periods of taxon data corresponding to top and bottom quartiles of significant environmental parameters. Note: Abundance values are square root transformations of percent coverages with bold text designating higher abundances. Asterisks (*) designate a significant correlation (p ≤ 0.05) between taxa and environmental variable based on Spearman’s Rho correlation (i.e., + or - designates positive or negative relationship). Mwaves = monthly waves; and 1mT = monthly temperatures.
Average Average abundance abundance Average Contributing Site Parameter Taxon (bottom Q) (top Q) dissimilarity % HGB Mwaves *Sand (-) 5.41 4.95 1.87 11.43 Thalassia spp. 6.40 6.37 1.41 8.61 Penicillus spp. 1.39 1.19 1.13 6.91 *Dead seagrass (+) 4.67 5.12 1.37 8.41 UID macroalgae 0.29 0.56 0.94 5.79 Halimeda spp. 1.49 1.74 0.87 5.34 - NO 3 *Dead seagrass (-) 5.14 4.48 1.68 10.82 *Laurencia spp. (-) 0.63 0.22 1.07 6.87 *Batophora spp. (-) 0.49 0.23 0.91 5.87 Penicillus spp. 1.05 1.41 1.19 7.71 Halimeda spp. 1.64 1.67 0.83 5.35 UID other Gorgonian 0.45 0.64 0.54 3.49 LKH Mwaves *Laurencia spp. (-) 3.36 1.99 2.21 9.49 *Dictyota spp. (-) 2.51 2.05 1.04 4.47 Turf 1.50 1.27 0.91 3.88 CCA/ENC 1.55 1.66 1.39 5.98 *Dead seagrass (+) 0.42 1.31 1.37 5.88 Halimeda spp. 3.23 3.43 1.10 4.71 1mT CCA/ENC 1.79 1.24 1.48 6.27 *UID other sponge (-) 1.84 1.36 0.92 3.92 Dead coral 1.09 0.72 0.82 3.49 *Laurencia spp. (+) 1.49 3.62 2.77 11.78 *Penicillus spp. (+) 0.31 1.15 1.14 4.85 Dictyota spp. 2.09 2.63 0.99 4.23 TRL Mwaves Turf 4.32 3.70 1.38 5.97 CCA/ENC 1.68 1.37 1.17 5.05 UID other sponge 2.28 1.94 1.01 4.35 Dictyota spp. 3.52 3.58 1.24 5.35 *Sand (+) 4.51 5.21 1.18 5.12 Diploria strigosa 0.46 0.91 0.69 2.97
79
a.
b.
c. 80
Figure 4.4. Time series graphs of most prominent taxa and most influential environmental factors at HGB: a) Thalassia spp. and monthly waves; b) dead Thalassia spp. and monthly waves* (+) and nitrate* (-); and c) Halimeda spp. and monthly waves and nitrate. Asterisks (*) designate a significant correlation (p ≤ 0.05) between taxa and environmental variable based on Spearman’s Rho correlation (i.e., + or - designates positive or negative relationship). 81
Larger monthly waves were also associated with higher abundances of dead seagrass (r = 0.334) and Halimeda spp. at LKH (Table. 4.2). However, the taxa most influenced by environmental variables at LKH appeared to be populations of Laurencia spp. and Dictyota spp., in that both monthly waves and monthly temperature were influential on their abundances (Table 4.2; Fig. 4.5). Laurencia spp. and Dictyota spp. both had higher abundances during periods of decreased monthly waves (Table 4.2; Figs.
4.5a & 4.5b). These relationships were supported by the negative correlations (Table 4.2) found between monthly waves and Laurencia spp. (r = -0.430; Fig. 4.5a) and Dictyota spp. (r = -0.363; Fig. 4.5b). In contrast, both of these algal groups had higher abundances in periods of higher monthly temperatures (Table 4.2; Figs. 4.5c & 4.5d). However, only
Laurencia spp. had a significant positive correlation with monthly temperatures (r =
0.697; Table 4.2) supporting the consistent relationship seen between this algal group and temperatures (Fig. 4.5c). Conversely, coralline crustose algae (CCA) had higher abundances in lower temperatures and larger waves (Table 4.2). Although there was not a substantial abundance of dead coral (Fig. 3.4), monthly temperatures corresponded to dead coral in that there was more dead coral in the lower quartiles of both of these factors
(Table 4.2).
82
a.
b. 83
c.
d.
Figure 4.5. Time series graphs of the most prominent taxa and most influential environmental factors at LKH: a) Laurencia spp. and *monthly waves (-); b) Dictyota spp. and *monthly waves (-);c) Laurencia spp. and *monthly temperatures (+); and d) Dictyota spp. and monthly temperatures. Asterisks (*) designate a significant correlation (p ≤ 0.05) between taxa and environmental variable based on Spearman’s Rho correlation (i.e., + or - designates positive or negative relationship). 84
Being the most dominant algal group at TRL (Fig. 3.5), turf algae was the top taxon affected by monthly waves (Table 4.2). Higher abundances of turf occurred during periods of smaller waves (Table 4.2, Fig. 4.6a). Another prominent algal group, Dictyota spp. (Fig. 3.5), was also influenced by monthly waves. In contrast to turf algae, Dictyota spp. had higher abundances during times of larger waves (Table 4.2, Fig. 4.6b). Waves also had a different influence on coralline crustose algae (CCA) at TRL than at LKH.
Higher coverage of CCA was found during the bottom quartile of monthly waves at TRL
(Table 4.2). Additionally, the top quartile of monthly waves at TRL had a higher coverage of sand which was different from the influences of monthly waves at HGB
(Table 4.2).
The positive correlation seen between sand and monthly waves (r = 0.390; Table
4.2) was the only significant correlation involving the individual factor of waves at TRL.
However, there were several significant correlations between taxa and the only other influential factor at TRL, orthophosphate (SRP) concentrations. As Dictyota spp. was a top algal group (Fig. 3.3b) and was the only taxon to exhibit change at TRL (Figs. 3.10b,
3.11), the positive relationship between Dictyota spp. and orthophosphate (r = 0.340;
Table 4.2) was chosen to be displayed in the time series in Figure 4.6c.
85
a.
b.
c. 86
Figure 4.6. Time series graphs of most prominent taxa and most influential environmental factors at TRL: a) Turf and monthly waves; b) Dictyota spp. and monthly waves; and c) Dictyota spp. and orthophosphate*concentrations. Asterisks (*) designate a significant correlation (p ≤ 0.05) between taxa and environmental variable based on Spearman’s Rho correlation (i.e., + or - designates positive or negative relationship).
87
Chapter 5: Conclusions and Discussion
Changes in community structure and environmental characteristics
Results of the site comparisons turned out as anticipated in that all three sites had significantly different communities (Fig. 3.4 & 3.5). The results characterized TRL and
HGB as being the most different (Table 3.2), while TRL and LKH were more similar
(Fig. 3.5), were anticipated in that LKH could serve as a mid-point/middle ground for the three sites (Fig. 2.2). Interestingly, environmental characteristics at these sites did not differ very much aside from seasonal changes (Figs. 3.12a & b).
Overall, characteristics of the community composition from each site were as expected. HGB was a seagrass bed dominated by the most common seagrass in south
Florida, Thalassia testudinum (Fourqurean et al. 2001) with some macroalgae (Fig. 3.1).
LKH was a hardbottom site with a diverse number of taxa including soft corals, algae, sponge, and some stony corals (Fig. 3.2). The types of algal coverage at these sites were consistent with the study done by Collado-Vides et al. (2007); the algal community was dominated by chlorophytes at HGB, and by chlorophytes and rhodophytes at LKH.
The Tennessee reef community was predominantly made up of soft corals, turf algae, and macroalgae (Fig. 3.3). As expected, TRL had low average stony coral cover.
TRL only had minimal stony coral with a mean range of 1.4 to 5.2% (Table 3.1), with an overall mean of 3.0% coverage over the three year period (Fig. 3.3a). The most predominant coral at TRL, Millepora alcicornis, only had an average of 1.5% cover over the course of the study (Fig. 3.3b). These results were consistent with averages of stony 88 coral cover found by other Florida Keys reef assessments; 2.8 ± 0.3% (± st er) in 2009
(Ruzicka et al. 2013) and 2.1% ± 3.4% (± st dev) in 2004 (Sathe et al. 2008). The combination of findings of 4.6% mean coral coverage in 2000 (Maliao et al. 2008) with the above percentages in the following decade confirms that stony coral have yet to recover to their 1996 levels (about 12%; Ruzicka et al. 2013) from the 1998 bleaching event.
In contrast, soft coral abundances at TRL were high compared to recent studies.
The overall average abundance of 21.4% (Fig. 3.3a) was higher than the 13.8 ± 1.7% (± st er) found on shallow forereefs within the Keys by Ruzicka et al. (2013) in 2009. As the same study found an average of 5.8 ± 1.0% (± st er) in 1999, it appears soft coral are still increasing in abundance on these reefs (Ruzicka et al. 2013). Likewise, TRL macroalgal cover (i.e., phaeophytes, 13.2% and chlorophytes, 4.9%; Fig. 3.3a) exceeded the macroalgal coverage of 16.5% found on reefs within the FKNMS (Maliao et al. 2008).
The three groups of turf, Dictyota spp. and Halimeda spp. coverage together (16.3%,
13.2%, and 4.6%, respectively; Fig. 3.3b) were within the range of algal percent cover of
25.8 to 56.7% in Northern Keys reefs in 1998 (Lirman & Biber 2000). However, other studies have shown considerable variation in the abundance of macroalgae on reefs in the
Florida Keys over the past two decades (see Ruzicka et al. 2013, Vroom et al. 2005).
Based on the results found in the current study, it can be confirmed that Tennessee Reef has undergone a phase shift to a soft coral- (Ruzicka et al. 2013) and algal-dominated community (Maliao et al. 2008).
Interestingly, distinct patterns in seasonality within community structure were not found as anticipated as very few taxa show little, if any, changes over time. There were 89 substantial patterns in changes in environmental factors, however, and DistLM results revealed those that had the biggest influence within each site. Sand coverage and dead seagrass were the only categories that appeared to change at HGB (Figs. 3.6b & c). These results, combined with the lack of changes in Thalassia spp. abundance, may be revealing side effects of analyzing a 3-d area in a 2-d way in which changes in sand coverage or dead seagrass (i.e., laying down) will be more evident than live seagrass (i.e., upright). A more effective way to assess seagrass abundance and seasonality could be to use a density measurement as used in Fourqurean et al. (2001). Therefore, these results might still be reflecting changes in Thalassia spp. (i.e., indirectly through changes in sand and dead seagrass cover), even though SIMPER analysis and nMDS did not pick up these changes. That being said, Thalassia spp. was still the only taxa at HGB that appeared to exhibit seasonal change (i.e., at about 10% in a typical year model; Fig. 3.7).
LKH had a slight seasonal pattern (Fig. 3.8a); however, this was most likely driven by Laurencia spp. growth dynamics (Figs. 3.8b & 3.9a) as it would cover and replace other leading taxa characterizing the benthos of this site (e.g., sand, CCA).
Laurencia spp. (Figs. 3.8b & 3.9a) and Halimeda spp. (Fig. 3.9b) significantly changed over a typical year at LKH. Likewise, the algal group of Dictyota spp. (Figs. 3.10b &
3.11), was the only taxon to exhibit change at TRL. The apparent seasonal trend of
Dictyota spp. over a typical year (Fig. 3.11) was different from the findings of a study done on northern keys reefs where Dictyota spp. was more abundant in the summer
(Lirman & Biber 2000). However, there was some evidence of an inverse relationship between Dictyota spp. and turf algae which was also seen by Lirman & Biber (2000). 90
Despite algal dynamics, there was a lack of an overall seasonal pattern within benthic composition at TRL (Fig. 3.10a).
Influential factors and associated taxa
In order to be concise, focus was placed on relationships with significant taxa and environmental factors at each site.
Waves, nutrients, and temperature parameters appear to be the strongest driving forces influencing benthic assemblages within these three sites. Monthly waves were significant in affecting benthic assemblages at each of the three sites (Table 4.1).
Interestingly, nutrient parameters were influential at HGB, but not at TRL (except for interactive effects) or LKH (Table 4.1). Similarly, temperatures were only influential at
LKH and did not show up at HGB (except for interactive effects) or TRL (Table 4.1).
Although waves are not an anthropogenic stressor, this factor has been found to have correlations with benthic composition. For example, a negative correlation between stony coral and wave activity was found in non-impacted reef sites in the south Pacific
(Williams et al. 2013).
HGB
As this site is dominated by Thalassia spp. (Fig. 3.1, about 40%), the seasonal variability (Fig. 3.6a) is probably a reflection of the seasonality of seagrass. Thalassia spp. has been found to have highest abundances during summer months and then decrease to about 50% of summer abundances during winter in Biscayne Bay, Florida
(Zieman 1975). Although results of this current study only found at most a 10% decrease 91 in abundance, seagrass did appear to reflect this seasonal cycle (Fig. 3.7). Robblee et al.
(1991) proposed quicker declines in seagrass during fall and spring as a result of the massive die-offs seen by in their study. However, this outcome was not attributed to anthropogenic stressors, but instead was proposed to be due to salinity and/or temperature changes (Robblee et al. 1991). Similarly, Zieman (1975) investigated the seasonality of
Thalassia spp. in Biscayne Bay, Florida, and found that seasonal patterns were driven by changes in temperature and salinity. Although we did not see evidence of temperature or salinity driving seasonality here, there are other possibilities for the slight changes in dead seagrass.
Influential environmental factors at HGB (i.e., monthly waves and nitrate) appeared to mostly affect seagrass, which is expected as seagrass comprised the majority of HGB benthic composition. Abundances of dead seagrass were positively correlated with monthly waves (Fig. 4.4b), while higher abundances of Thalassia spp. occurred during periods of lower waves (Table 4.2; Fig. 4.4a). This could be expected as higher waves might cause a decrease in seagrass. For example, a study done in Puget Sound found that wave energy limited seagrass growth and proposed this to be due to high turbidity (reduced light), alteration of site characteristics, or erosion of rhizomes (Stevens
& Lacy 2012).
In terms of nutrients, dead seagrass was negatively correlated with concentrations of nitrate (Table 4.2; Fig. 4.4b). This finding may indicate an indirect effect of nitrate on
Thalassia spp. populations, where increased nitrate does not directly cause a decrease in seagrass, but instead could be correlative with wave activity (as waves were positively correlated to dead seagrass, Table 4.2). High nutrient concentrations could also impact 92
Thalassia spp. indirectly through stimulating growth of macroalgae (Connell et al. 2011).
Although the correlations were not significant, Penicillus spp. and Halimeda spp. both generally had higher abundances when nitrate concentrations were elevated (Table 4.2).
High nutrient concentrations, however, have also been found to directly affect seagrasses.
For example, La Nafie et al. (2012) found that Zostera noltii had reduced strength in its leaves when exposed to elevated nutrient levels. High wave activity was also found to impact Z. noltii by decreasing growth rates and affecting other morphological factors (La
Nafie et al. 2012). Therefore, high nutrient concentrations could be directly affecting
Thalassia spp. abundances through interactions with wave activity.
If high waves and high nitrate concentrations are associated with higher abundances of dead seagrass, this finding could be reflecting a die-off of seagrass during winter months, when wave activity is higher and nutrient inputs from Florida Bay can be expected to be higher due to wet season run-off. While the nMDS bubble plots did not show evidence of this (Fig. 3.6c), the SigmaPlot graph of Thalassia spp. did show a decline in seagrass during winter months (Fig. 3.7). Alternatively, another possibility is that higher nitrate concentrations increased the nutritional value of Thalassia spp. and thus, increased grazing pressures (Tomas et al. 2015). Although these associations with
Thalassia spp. were not supported by ANOSIM and SIMPER results, the limitations of the methods used to determine percent cover of Thalassia spp. (discussed earlier) might be influencing this. Hence, it is suggested that further stressor analyses on seagrass use a density parameter of Thalassia spp. (Fourqurean et al. 2001) as opposed to a 2-d analysis of photoquadrats.
LKH 93
According to the results for Long Key Hardbottom, two of the top three dominant algal types (Fig. 3.2b) responded similarly to the three influential environmental factors.
Decreased wave activity and higher temperatures were both associated with increased abundances of Laurencia spp. and Dictyota spp. (Table 4.2). This result is most likely a reflection of time periods of where warmer, calmer waters typically seen during summer months would allow for increased algal growth. This supposition is supported by the negative correlation between Laurencia spp. and Dictyota spp. with monthly waves
(Table 4.2; Figs. 4.5a & 4.5b). Previous studies have reported that increased wave energy can create open gaps in temperate ecosystems by way of dislodgment of canopy algae
(Thomson et al. 2012). Therefore, such negative relationships between algae and waves can be expected.
Interestingly, the possible role of temperature as a driver at LKH was only seen between Laurencia spp. and monthly temperatures (Table 4.2; Fig. 4.5c). This relationship may be explained by conclusions drawn by Herren et al. (2013), who conducted a study in the vicinity (Long Key, Florida), and found that high fragmentation
(i.e., reproduction/dispersal) rates of Laurencia poiteaui occurred during summer months when temperatures are warmest in Florida Bay. As this conclusion was the function of a positive relationship between Laurencia spp. and temperatures (Fig. 4.5c), temperatures could be driving the strong seasonal pattern in Laurencia spp. abundance at LKH in this study (Fig. 3.9a). If fragmentation rates are highest during warm summer months and the initiation of wet season-associated nutrient inputs within Florida Bay (Herren et al. 2013), these fragments could be dispersing to oceanside sites (i.e., LKH) and growing into large biomasses seen during this study in later summer and fall months (Fig. 3.9a). While 94
Herren et al. (2013) assessed attachment of L. poiteaui fragments to other macroalgae and sand, the majority of fragments recruited to LKH appear to have attached to soft corals, as large bundles of Laurencia spp. would frequently be seen attached to Gorgonians spp.
(pers. obs.).
TRL
Dictyota spp. and turf algae were most influenced by monthly waves at TRL
(Table 4.2; Fig. 4.6). Higher wave activity appeared to allow for increased abundances of
Dictyota spp. (Fig. 4.6b), while decreased waves allowed for higher abundances of turf
(Fig. 4.6a). Although this correlation was not significant (Table 4.2), these results are consistent with higher wave activity being associated with higher macroalgae cover in
Micronesia (Mumby et al. 2013). Similarly to Laurencia spp., Dictyota spp. can reproduce asexually through vegetative fragmentation (Herren et al. 2006). As fragmentation starts with a disturbance breaking up Dictyota spp. into fragments (Herren et al. 2006), waves generated from storms may allow for rapid growth of this algae
(Vroom et al. 2005). Such a scenario could therefore explain why higher abundances of
Dictyota spp. were seen during months with larger waves, due to reproduction via fragmentation being promoted by the disturbance of high wave activity (Herren et al.
2006). However, the lack of significant correlation seen here is probably due to a time lapse from the disturbance causing fragmentation to actual population recovery following attachment (Herren et al. 2006).
The significant correlation between orthophosphate and Dictyota spp. (Fig. 4.6c) was included in these results because monthly waves did not fully explain the seasonality in Dictyota spp. at TRL (Fig. 3.11) and orthophosphate was the only other candidate as 95 an influential factor in this study (Table 4.1). While monthly waves and orthophosphate concentrations were both influential on Dictyota spp. abundances, the only significant correlation was between Dictyota spp. and orthophosphate (Table 4.2; Fig. 4.6c). This result is reasonable, as the mean concentration of orthophosphate at TRL (Table 3.4) generally exceeded the 0.1 µM threshold for sustaining algae on reefs (Lapointe 1997).
Additionally, the maximum concentrations of 0.2 µM at this site (Table 3.3) were double this threshold concentration. Therefore, the concentrations of orthophosphate were enough to sustain algal growth on TRL, at least in terms of a positive correlation with
Dictyota spp. (Fig. 4.6c). These nutrient concentrations could allow Dictyota spp. to have a competitive advantage in the competition for space, and provide a prime example of the indirect effects of nutrients on corals as similarly seen in Parsons et al. (2008). In addition, Dictyota spp. have been known to rapidly colonize space on Floridian reefs, as they have quick growth rates in both microcosm- and field-based studies, which would give this genus of algae both of the requirements necessary to outcompete corals (Lirman
& Biber 2000).
In comparing turf abundances and Dictyota spp. abundances over time (Figs. 4.6a
& b), an inverse relationship is evident between the two algal groups. Over the course of this three-year study, when Dictyota spp. decreased (Fig. 4.6b), turf increased (Fig. 4.6a).
This observation is supported in part by the results of the SIMPER analyses comparing effects of monthly waves on these algal groups (Table 4.2). Similarly, as orthophosphate was an interactive factor, nutrients may be playing a role as well. This idea is consistent with the Relative Dominance Model from Littler and Littler (1984) which suggests that frondose algae (Dictyota spp.) will dominate reefs with higher nutrient concentrations. 96
Littler et al. (2006) found that interactive effects between elevated nutrient concentrations and reduced grazing rates allowed for the highest abundances of Dictyota spp. Therefore, the inverse relationship seen between Dictyota spp. and turf at TRL in this study, may be evidence that this reef is experiencing decreased grazing rates through over-fishing. This hypothesis may also provide evidence of environmental factors being able to indirectly influence abundances of coral by directly impacting algal competition (i.e., influence of nutrients impacting spatial competition between Dictyota spp. and turf).
Environmental factors as local or regional driving forces of community composition
The results of this study did not fully support all the hypotheses of environmental factors having regional versus local effects. The hypotheses of monthly light and monthly temperature having primarily regional effects (Fig. 2.2; Table 2.2) were not supported as light was not found to be influential at any of the sites, while temperatures were only influential at one (LKH). While temperature and light did not exhibit regional influences
(Table 2.2), they did exhibit local effects either individually (monthly temperatures) or interactively (monthly light) at LKH (Table 4.1). The lack of a strong regional effect of light could be due to site-specific characteristics such as turbidity, biotic effects (i.e., plankton), or changes in weather resulting in exhibiting different, more local effects in light between sites. Such a localized effect may have influenced the benthos at LKH through shading or particle scattering of light (Diaz & Rosenberg 2008; McPherson et al.
2011) as this site frequently experienced high turbidity due to its transitional location
(pers. obs; Fig. 2.1). Similarly, salinity was not a significant factor on an individual basis at any of the three sites, but it did have interactive effects at LKH (Table 4.1). Therefore, 97 salinity may still be a local factor, although this study was unable to capture its role in this regard in that there was not much site differentiation in environmental factors (i.e., salinity was not significantly different between the sites, Fig. 3.12a).
Monthly temperatures appeared to be significant only at LKH and HGB (Table
4.1). Therefore, this result does not support the hypothesis that monthly temperatures are a regional factor (i.e., affecting all sites; Table 2.2). Rather, this result supports the idea that temperature exhibited site specific, local effects. While temperatures were not influential as an individual factor at HGB, they were one of the interactive factors at this site (Table 4.1). Temperatures may be serving as a complementary secondary stressor at
HGB, in which it compounds the effects of other influential factors (e.g., monthly waves or nutrients; Table 4.2; Brown et al. 2014). However, as temperature significantly influenced approximately 12% of the variation at LKH (Table 4.1), one can conclude that temperature is a strong local factor at this site.
Despite having varying influences among the three sites, temperature followed a strong regional seasonal pattern in that all site patterns were correlated on a time series basis (Fig. 4.1). These patterns also confirmed the idea that temperature variation would be more attenuated at LKH and TRL (due to depth), as opposed to HGB (shallow). As shown in the monthly temperature time series graph (Fig. 4.1), TRL had the least- variable temperatures, as it was the deepest site and the farthest from freshwater influences of Florida Bay (Fig. 2.1). The volume of water at TRL is greater than at other sites so temperature changes will be tempered. Additionally, thermoclines have been observed at TRL (pers. obs). The slight differences in temperature may allow for the 98 alleviations in low and high temperatures seen at this site while the temperatures at the other sites were not alleviated (e.g., Jan/Feb 2012, Aug 2014; Fig. 4.1).
While monthly waves were not hypothesized to have regional effects, they were influential at every site. Therefore, this study therefore supports the idea that waves can have regional effects in that they will influence each site in some way, but maybe not the same way. Monthly wave activity appeared to reflect a strong seasonal trend at HGB
(Fig. 4.2) and was the strongest environmental driver at this site (i.e., 6.5% of influence;
Table 4.1). On the other hand, while LKH and TRL lacked a strong seasonal pattern in wave activity (Fig. 4.2), waves still influenced prominent taxa at these sites (Table 4.2).
Nutrients were not consistently influential at the sites. Both HGB and TRL had nutrients as interactive factors, but only HGB had nutrients as being individually significant (Table 4.1). Thus, we can conclude that nutrients did have site-specific, local effects as anticipated. While nutrient factors were influential at HGB and TRL, there were differences in terms of which nutrient was influential. Results suggested that nitrate was most important at HGB while orthophosphate was the only influential nutrient at
TRL (Table 4.1). Interestingly though, mean nutrient concentrations did not match these results. The maximum concentrations of nitrate at HGB were less than at TRL (2.188 and
4.845 µM, respectively) while the concentrations of orthophosphate at TRL were less than at HGB (0.2015 and 0.3940 µM, respectively; Table 3.3). The results of nitrate being most influential at HGB may suggest that this system is nitrate limited; receiving plenty of phosphate from runoff, whereas TRL was only impacted by orthophosphate and therefore, may be phosphate limited. This hypothesis should be tested in further studies; however, the opposite maximum concentrations of nitrate and orthophosphate between 99 the two sites suggests that each site appeared to have an abundance of the other, non- influential nutrient.
Synergistic effects and other factors
The lack of seasonal patterns seen within this study could be due to these ecosystems being resilient in the particular “stable state” they are currently exhibiting.
HGB can still be considered stable as a seagrass bed since it is still dominated by
Thalassia spp. with usually less than 10% algae (Table 3.1; Fig. 3.1). However, there is evidence for the possibility that this site may be experiencing macroalgal invasion by way of increased nutrients (Collado-Vides et al. 2007; Connell et al. 2011). In contrast,
LKH and TRL are currently stable as soft coral- and algal-dominated reefs. Large mounds of dead coral covered in algae - sometimes overturned - provide evidence that
LKH was most likely a hardbottom site on its way to being a patch reef at one time (pers. obs). However, it is hard to understand why LKH has not been able to retain stony coral cover while TRL has, when these sites experience similar environmental conditions (Fig.
3.12a).
These observations could simply be due to interactive disturbances. As described in Nyström et al. (2000), coral reef organisms are currently dealing with anthropogenic stressors alongside the natural, physical factors (e.g., depth, location, etc.) they have adapted to acclimate to for years. There are several physical differences between LKH and TRL that could be serving as primary, although “natural”, stressors. The location of
LKH indicates it might experience more extreme temperature changes than TRL due to mixing of Florida Bay waters. Depth could also be a contributing factor on the ability for 100 temperature to have an effect. The tops of large boulder corals (now dead) at LKH would have been residing within 2.5 meters of water while corals at TRL are at a depth of at least 5.5 meters (pers. obs.). As discussed earlier, this depth at TRL has allowed for thermoclines (pers. obs) where effects of sudden temperature changes (i.e., hot or cold waters) could be avoided by the benthos through the separation of water bodies. This is less likely to occur at LKH, which is presumably the reason temperature parameters were influential at LKH. Therefore, maybe certain taxa that are temperature sensitive at LKH are “better suited” at TRL due to the possible “protection” of a thermocline at TRL, whereas benthos at LKH would experience effects right away. While these influences were not seen in terms of corals in this current study, the lack of living stony corals at
LKH may be evidence that this has already occurred.
On the other hand, temperature influences between these two sites could be simply due to site location. A recent study on the most recent cold-water anomaly in 2010 found coral mortality was strongly related to an inshore to offshore gradient of water temperatures (Lirman et al. 2010). Lirman et al. (2010) found that coral mortality was most severe at inshore, shallow environments which corresponded with the most severe cold temperatures seen during 2010. These coral mortality and temperature patterns both decreased on an inshore to offshore gradient, with coral mortality also decreasing with depth (Lirman et al. 2010). As LKH could be considered an inshore site in the Middle
Keys, it is important to note that the most severe duration of cold temperatures (140 hours below 16 °C) and the highest levels of coral mortality (39.1% mean recent mortality) were experienced by the inshore sites in the Middle Keys (Lirman et al. 2010). 101
Whether the impacts seen in the current study are due to freshwater influences or simply a site depth gradient (i.e., both inshore to offshore gradients), there is clear evidence that temperatures can have strong local influences. Thus, the hypothesis of site depth being able to influence temperature effects on the benthos could be applied to future studies to assess synergistic combinations of natural disturbances (e.g., differences in site morphology) and anthropogenic stressors (e.g., climate change effects; Nyström et al. 2000).
Wave energy within marine environments can clearly distinguish the abundance and distribution of seagrass (Stevens & Lacy 2012) and algae (Thomson et al. 2012) as seen in this study. While wave activity was negatively correlated with Dictyota spp. at
LKH (Fig. 4.5b), there was evidence for the opposite relationship at TRL (Fig. 4.6b).
Although the positive relationship between waves and Dictyota spp. at TRL was not significant (Table 4.2), this finding may suggest that wave activity is a secondary factor to Dictyota spp. abundances at this reef site. Such a scenario could be due to other factors serving as primary drivers, such as nutrients (e.g., SRP) or the influence of herbivory pressures (Mumby et al. 2013). As La Nafie et al. (2012) found in terms of the interactive effects of waves and nutrients on seagrass, a similar scenario could occur within other marine habitats due to other factors affected by waves (e.g., light, nutrients, etc.,
Thomson et al. 2012). Thomson et al. (2012) noted that the direct effects of waves through dislodgement of canopy algae could also serve as indirect effects on corals and other space competitors through creation of open gaps.
Similarly, herbivory pressures may control algal abundances on reefs either by way of consumer effects (Connell et al. 2011) or top-down control (Littler et al. 2006). 102
Because coral mortality and decreased coral recruitment has been associated with algal growth (Wilson et al. 2012), overfishing may impact corals indirectly through unchecked algal growth as a result of decreasing grazing pressures (Smith et al. 2001; Jompa &
McCook 2002; Hughes et al. 2007). While herbivory was not assessed in the current study, the stressor of overfishing should be considered as it has been shown to have interactive effects with other stressors (Smith et al. 2001; Littler et al. 2006). However, the influences of grazing pressures are still undetermined (see Lirman & Biber 2000 – grazing pressures are not enough to compensate for algal growth; Jompa & McCook
2002 – grazing pressures can compensate for nutrient effect if high enough; Mumby et al.
2013 – acanthurid abundance could keep algal abundance down).
There was evidence of synergistic effects of drivers on the community structures of these sites. TRL displayed a slight interactive effect in which the combination of waves and SRP were responsible for 8.9% of the variation, while waves were individually influential at 4.8% (Table 4.1). Monthly waves and temperatures influenced about 19% of the variation at LKH individually (6.7% and 12.4%), yet, 24.3% of the variation was explained by the addition of light and salinity (Table 4.1). HGB experienced the largest difference in synergistic effects as 26.3% of the variation seen was due to a combination of nutrients, waves, and temperatures (Table 4.1). This suggests synergistic effects were strongest at this site because the influence of the combination was more than double the influence of individual stressors (i.e., both individual stressors were responsible for about 6% each; Table 4.1). The combination of nutrients and temperatures (i.e., climate change effect) may indicate the interactive effects addressed in Brown et al. (2014) in which they assessed the effect of reducing a 103 local stressor (nutrients) in the presence of a global stressor (temperature). Brown et al.
(2014) discovered that nutrient reduction allowed for some reduction in synergistic effects in high climate change stress areas with the greatest gain in regions with low climate change risk. Therefore, if HGB is about to experience a regime shift to an algal- dominated state, nutrient reductions would be extremely beneficial; most likely reducing stressor effects to be solely associated with waves (i.e., temperature and nutrient effects could be improved).
Conclusions
Fortunately, the study of synergistic effects is becoming more popular in research surrounding marine ecosystems. As research involving multiple stable states (Beisner et al. 2003) continues, the resilience of these habitats through multiple disturbances is being addressed in order to learn how to reverse certain “undesirable” stable states (e.g., algal dominated systems; Hughes et al. 2010; Graham et al. 2013), as well as how to maintain desirable stable states (e.g., seagrass beds, coral reefs; Connell et al. 2011). This pathway involves ecosystem-scale assessments that will include local and global influences
(Russell & Connell 2012; Brown et al. 2014), which will allow for better understanding of the integrated effects of natural disturbances - which can maintain biodiversity - combined with human-induced disturbances. The increased frequency and duration of anthropogenic stressors must be considered in determining this concept of resiliency as these aspects determine the context to which individual organisms deal with stress
(Russell & Connell 2012), and thus, affect the recovery times of communities (Nyström et al. 2000). 104
Appendices
Appendix A. List of CPCe4.0 codefile subcategories with classification notes and assumptions describing how visual analysis was done. CPC SUBCATEGORIES CLASSIFICATION ASSUMPTIONS NOTES All Other Invertebrates (INV-O) Shells, snails, possible egg sacks, Christmas tree worms Abiotic, other nonliving Any artificial substrate
Black hole When point lands on black area, cannot identify anything Hard Substrate (HSUB) Bare substrate, when HSUB only used when grey/light purple and no bare - not covered by boring holes sand. Light purple with boring holes is ENC- reef cement not HSUB. Rubble (RUB) Larger rocks/dead coral pieces/dead calcareous algae laying down Sand bottom (SAND) Sandy bottom
UID - Turf Algae (UID-T) When completely covered “Hairy” appearance is beyond ID or point is directly turf algae with on turf, 3-D appearance, based periphyton coverage, on morphology “matte” appearance is just periphyton coverage UID -All Other Algae (UID-A) When morphology is macro, but color is unidentifiable Batophora (BAT) Green fuzzy appearance; usually covered and cylindrical Caulerpa (CAUL) Green algae connected by stolons; palm tree-like, bubble shaped, or pennate structures Halimeda (HALI) Calcareous green branching algae Penicillus (PENI) "Shaving brush" appearance 105
UID Other Green algae (UID-G) Unidentifiable but definite green coloring and macro morphology Udotea (UDOT) Rigid leaf structure, sometimes has layering appearance at top Ulva (ULVA) Flakey, leaf-like structure
Acanthophora (ACAN) Branching structure with Assuming longer pointy knobs branches (less branching) of red is ACAN, also sometimes darker than LAUR Botryocladia (BOT) Red algae with bubbles, similar looking to Caulerpa racemosa structure Encrusting / Coralline (ENC) Usually reef cement - purplish Usually mostly covered covering with boring in sand; if holes are holes(not uniform, usually uniform, probably an large and small holes) encrusting sponge instead Galaxaura (GLX) Calcareous, red to pinkish coloring, cylindrical shape with holes at ends Gracilaria (GRAC) Softer, hairy like structure, red
Laurencia (LAUR) Red to yellow color, knobby branching structures Neogoniolithon spp. (NEO) Hard, calcareous white/pink structure Amphiroa (AMP) Similar to Galaxaura but more white, not as cylindrical, instead comes to more of a point at ends (cone shape) UID Other Red Algae (UID-R) Unidentifiable but definite red coloring and macro morphology Dictyota (DICT) Flat, branching structure; usually gold in color, can be blue Padina (PAD) Scrawled, pencil shaving structure, 106
Sargassum (SAR) Tall, with bubbles and leaves in structure Turbinaria (TURB) Brown "mermaid cup" structure UID Other Brown algae (UID-B) Unidentifiable but brown coloring and macro morphology Filament Lyngbya (FIL) Brown to brownish-red coloring, whispy stringy/filamentous, usually growing on something else, connective Tuft Schiz/Spiro (TUFT) Tan tuft structure with darker coloring on top, fuzzy Acropora cervicornis (ACP-C) Branching structure with pointy knobbed polyps, tan coloring with white on ends Acropora palmata (ACP-P) Larger branching structure with pointy knobbed polyps, tan coloring with white on ends Agaricia agaricites (AGAR-A) Thicker leaf-like coral, light tan in color Agaricia tenuifolia (AGAR-T) Thin leaf-like coral, light tan in color Bleached Coral (BLC-C) Coral with white areas of bleaching, if point lands on bleached area Colpophylia natans (COLP) Large, zipper brain coral, with ridges and valleys, darker in color Dead Coral (DED-C) When still standing & Laying down/coral recognizable, slightly covered rubble = rubble. with turf (Millepora spp); Completely covered in when whole colony is white, turf = turf. or little turf coverage (Scleractinians) Dendrogyra cylindrus (DEND) Cylinder shaped pillar coral, fuzzy look with polyps extracted Dichocoenia stokesii (DICH) Golf ball coral with individual, rough starlet 107
polyps
Diploria clivosa (DIP-C) Brain coral with singular ridges and valleys, but grows with knob like structure Diploria labyrinthiformis (DIP-L) Brain coral with double ridges formed in labyrinth formations Diploria strigosa (DIP-S) Brain coral with singular ridges and valleys Diseased Coral (DIS-C) Black band with fuzzy turf ridge growth Favia fragum (FAV) Golf ball coral with inverted smoother polyps Madracis mirabilis (MAD) Fuzzy pencil, pillar coral
Mancini areolata (MAN) Single, "rose" like structure
Meandrina meandrites (MEAN) Pillar zipper coral
Millepora alcicornis (MIL-A) Fire coral, growing in pennate structure, sometimes overgrowing things Millepora complanata (MIL-C) Fire coral, plate like flat structure Montastrea annularis (MON-A) Large boulder coral, usually tan in color Montastrea cavernosa (MON-C) Large boulder coral, usually large individual polyps Montastrea annularis complex Unidentified Montastrea (MON-X) coral, either M. annularis, M. faveolata, M. franksi, or M. complanata Cladocora arbuscula (CLAD) Thin tube-like structure
Stephanocoenia michelini (STEP) Blushing star coral, starlet shaped polyps, lighter outside but darker coloring within polyps Porites asteroides (POR-A) Mustard hill coral, yellow in color, and lumpy structure 108
Porites porites (POR-P) Branching finger coral
Siderastrea radians (SID-R) Usually smaller than S. siderea but lighter in color, darker around polyps Siderastrea siderea (SID-S) Large boulder coral with uniform tan coloring Solonastrea hyades (SOL) Knobby growth with red- brownish coloring UID Other Stony Coral (UID-SC) Unidentified scleractinian coral Zoanthid (ZOAN) Similar to corals, but usually encrusting; white in coloring with larger polyps Dead Gorgonian (DED-G) Seafan or plume structure lying flat; detached from substrate Diseased Gorgonian (DIS-G) Aspergillosis
Pseudopterogorgia spp (PLU) Gorgonians with bipennate structure Pterogorgia spp (WHIP) Gorgonians with flat or triangular structure Sea Rod (ROD) Gorgonians with random lateral growth, single rods Seafan (FAN) Gorgonians with flat, fan-like structure UID Other Gorgonian (UID-G) Cannot determine type of Gorgonian due to photo cut- off Dead Seagrass (SGR-D) Detached; translucent brown color Halodule spp. (HALO) Seagrass with thin flat blades
Syringodium spp. (SYR) Thin, cylindrical seagrass
Thalassia spp. (THAL) Thicker, flat blades; usually green and brown at tip UID Other Seagrass (UID-O) Unidentifiable or other species of seagrass 109
Anthosigmella varians (VAR-S) Variable sponge, amorphous or incrusting Aplysina cauliformis (APL-C) Purple color, protruding pores instead of internal spores Callyspongia vaginalis (CAL-V) Tube-like structure, usually purple Dead Sponge (SPO-D) Any detached sponge
Ircinia campana (VAS-S) Vase-structure, usually light brown/tan Ircinia spp. (BRN-S) assorted colors, typically brown, with dot-like surface Spheciospongia vesparium (LOG- Dark brown/black color, many S) spores located at top, closer together Tedania ignis (TED-I) Fire sponge; orange to red coloring UID Other Sponge (UID-S) Unidentified sponge species, When covered in sand encrusting - usually dark and sponge underneath brown, or light orange color, is unidentifiable = UID uniform randomized pores. sponge, not sand Used frequently as visual identification of sponges is difficult due to sand coverage and color variations. Xestospongia muta (XES-M) Large vase-structure, medium brown coloring, "lumpy" sides Other - nonliving (ABIO) Abiotic items, trash, permanent blocks or pins for transects Tape, wand, other (SCHTUFF) Camera stick, transect line or reel Other - Biotic (OBIO) Any other biotic taxa not listed Fish (FISH)
Tunicates (INV-T) Invertebrate with siphon
110
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