FLORIDA INTERNATIONAL UNIVERSITY
Miami, Florida
ENVIRONMENTAL INFLUENCES ON BACTERIO-PHYTOPLANKTONIC
COUPLING AND BACTERIAL GROWTH EFFICIENCY IN A SUB-TROPICAL
ESTUARY
A thesis submitted in partial fulfillment of the
requirements for the degree of
MASTER OF SCIENCE
in
ENVIRONMENTAL STUDIES
by
Rachel Marie Kotkowski
2014
To: Dean Kenneth G. Furton
College of Arts and Sciences
This thesis, written by Rachel Marie Kotkowski, and entitled Environmental Influences
on Bacterio-phytoplanktonic Coupling and Bacterial Growth Efficiency in a Sub-tropical
Estuary, having been approved in respect to style and intellectual content, is referred to
you for judgment.
We have read this thesis and recommend that it be approved.
_______________________________________
William T. Anderson Jr.
_______________________________________
Christopher R. Kelble
_______________________________________
Leonard J. Scinto, Co-Major Professor
_______________________________________
Joseph N. Boyer, Co-Major Professor
Date of Defense: March 28, 2014
The thesis of Rachel Marie Kotkowski is approved.
_______________________________________
Dean Kenneth G. Furton
College of Arts and Sciences
______________________________________
Dean Lakshmi N. Reddi
University Graduate School
Florida International University, 2014
ii
ACKNOWLEDGMENTS
National Oceanic and Atmospheric Administration (NOAA) Atlantic
Oceanographic and Meteorological Laboratory (AOML) data collection was funded by
NOAA Center for Sponsored Coastal Research (1999-2005), US Army Corps of
Engineers (2006-2010); and most recently by NOAA Deepwater Horizon supplemental
funds. Florida International University (FIU) Southeast Environmental Research Center
(SERC) data collection was funded by NOAA (1991-2008). The Florida Coastal
Everglades (FCE) Long Term Ecological Restoration (LTER) water quality sampling was
funded by the National Science Foundation (NSF). I would like to thank my graduate
committee, Dr. Boyer, Dr. Kelble, Dr. Scinto and Dr. Anderson for imparting their
knowledge and dedicating their time. I would also like to thank my AOML co-workers,
Lindsey Visser, George Berberian, Shaun Dolk and Grant Rawson and FIU staff, Jeff
Absten, Pat Given and Sandro Stumpf for their support of the project.
iii
ABSTRACT OF THE THESIS
ENVIRONMENTAL INFLUENCES ON BACTERIO-PHYTOPLANKTONIC
COUPLING AND BACTERIAL GROWTH EFFICIANCY IN A SUB-TROPICAL
ESTUARY
by
Rachel Marie Kotkowski
Florida International University, 2014
Miami, Florida
Professor Joseph N. Boyer and Professor Leonard J. Scinto, Co-Major Professors
Bacterio-phytoplanktonic coupling and bacterial growth efficiency (BGE)
measurements were used to analyze microbial trophic dynamics and the influence of
environmental factors in Florida Bay, Florida. Phytoplankton gross primary productivity
(GPP) was measured using 24-hour in situ oxygen incubations; bacterial productivity
(BP) was measured using 3H- thymidine incorporation. Weak bacterio-phytoplanktonic
coupling was observed over the sampling period. BP was more influenced by local total
nitrogen concentrations while GPP was more evenly distributed. BGE rates were low but
consistent with marine and estuarine ecosystems worldwide. Results suggest that
bacterioplankton growth in Florida Bay is relatively uncoupled from phytoplankton
production, which may be due in part to the low levels of phytoplankton biomass in the
water column, the large amount of seagrass-derived DOM production in this shallow
lagoon, the loading of nitrogen and organic matter associated with terrestrial runoff,
and/or their combination.
iv
TABLE OF CONTENTS
CHAPTER
PAGE
1.0 INTRODUCTION .........................................................................................................1
1.1 Literature Review...............................................................................................2
1.2 Site Description: Florida Bay...........................................................................16
1.3 Objectives and Hypotheses ..............................................................................20
1.4 Statement of Research......................................................................................22
2.0 METHODS ..................................................................................................................23
2.1 Sampling Design ..............................................................................................23
2.2 Environmental Parameters ...............................................................................25
2.3 Coupling and BGE ...........................................................................................27
2.4 Statistical Analysis ...........................................................................................29
3.0 RESULTS ....................................................................................................................31
3.1 Bacterio-phytoplanktonic Coupling .................................................................31
3.2 Environmental Drivers for Phytoplankton GPP ..............................................35
3.3 Environmental Drivers for BP .........................................................................38
3.4 BGE..................................................................................................................43
4.0 DISCUSSION ..............................................................................................................45
4.1 Florida Bay Bacterio-phytoplanktonic Coupling .............................................46
4.2 Regional Drivers for BP, GPP, and BGE ........................................................50
5.0 CONCLUSIONS..........................................................................................................62
REFERENCES ..................................................................................................................65
v
LIST OF TABLES
TABLE
PAGE
2.1 Station matrix ..............................................................................................................24
3.1 Florida Bay regional summary.....................................................................................34
3.2 Gross primary productivity and environmental correlations ......................................35
3.3 Gross primary productivity regional principal component analysis loadings ............38
3.4 Bacterial productivity regional and seasonal differences ...........................................39
3.5 Bacterial productivity and environmental correlations ...............................................41
3.6 Bacterial productivity regional principal component analysis loadings .....................42
4.1 Worldwide bacterio-phytoplanktonic coupling studies ..............................................47
4.2 Worldwide bacterial growth efficiency values ...........................................................58
vi
LIST OF FIGURES
FIGURE
PAGE
2.1 Combined stations by sub-region ................................................................................24
2.2 Florida Bay mud banks ...............................................................................................26
3.1 Bacterio-phytoplanktonic coupling .............................................................................31
3.2 Bacterial productivity and chlorophyll a .....................................................................32
3.3 Bacterio-phytoplanktonic coupling trends ..................................................................33
3.4 Regional gross primary productivity ..........................................................................36
3.5 Florida Bay gross primary productivity ......................................................................37
3.6 Regional bacterial productivity and bacterial abundance ...........................................40
3.7 Regional total nitrogen ................................................................................................41
3.8 Bacterial growth efficiency .........................................................................................43
3.9 Bacterial growth efficiency vs. total nitrogen .............................................................44
vii
ABBREVIATIONS AND ACRONYMS
AOML
Atlantic Oceanographic and Meteorological Laboratory
BCD
Bacterial Carbon Demand
BGE
Bacterial Growth Efficiency
BP
Bacterial Productivity
CR
Community Respiration
CERP
Comprehensive Everglades Restoration Plan
DO
Dissolved Oxygen
DOC
Dissolved Organic Carbon
DOM
Dissolved Organic Matter
FIU
Florida International University
GOM
Gulf of Mexico
GPP
Gross Primary Productivity
NCFB
North-central Florida Bay sub-region
NEFB
Northeast Florida Bay sub-region
NOAA
National Oceanic and Atmospheric Administration
PP
Primary Productivity
SERC
Southeast Environmental Research Center
SFB
South Florida Bay sub-region
WFB
West Florida Bay sub-region
viii
CHAPTER 1.0 INTRODUCTION
Phytoplankton gross primary productivity (GPP) and bacterial secondary
productivity (BP) dynamics have been examined in a number of freshwater, estuarine,
and marine systems. Bacterial consumption of autotrophic dissolved organic matter
(DOM) is often referred to as the “microbial loop”, which was coined by Azam et al.
(1983), and describes the ability of heterotrophic bacterioplankton to recycle DOM back
into the system as a means of increasing trophic efficiency. When the bacterioplankton
utilize the phytoplanktonic exudates as their primary source of energy, it will manifest in
GPP and BP coupling trends, i.e., when GPP increases, BP increases. A system would be
considered tightly coupled if the bacterioplankton were only using phytoplankton-derived
DOM for sustenance. Examining these coupling trends is one method used to better
understand the dynamics and efficiencies of the lower levels of the food web. In many
studies (Cole et al., 1988; Moran et al., 2002b), bacterio-phytoplanktonic coupling trends
have been determined with the collection and analysis of phytoplankton GPP and BP
data. One would expect to find strong coupling trends in oligotrophic marine
environments, where sources of DOM other than pelagic phytoplankton exudates are
limited, and less coupling in estuaries where terrestrial and coastal DOM is introduced
into the system and serves as a food source for bacterioplankton consumption.
Additionally, bacterial growth efficiency (BGE) has been incorporated into
bottom level trophic assessments to ascertain the quantity of DOM incorporated into
bacterial biomass which indicates an enhanced microbial loop (Anderson and Turley,
2003; Warkentin et al., 2011). BGE is a function of BP and bacterial respiration (BR)
1
and is a tool used to supplement BP and GPP data and enhance understanding of these
intricate biogeochemical coupling processes.
1.1 Literature Review
At the base of freshwater, marine, and estuarine food webs, bacteria and
phytoplankton are critical for supplying higher trophic levels with bioavailable organic
matter (Fouilland and Mostajir, 2010). Phytoplankton primary productivity (PP) and BP
dynamics have been examined throughout the world in varying climates to better
understand environmental influences on different microbial producers (Cole et al., 1988;
Duckow et al., 1999). In open oceans, a phytoplankton exudate (glycolate) as a result of
PP is typically the most bioavailable source of DOM for bacterial consumption
(Rochelle-Newall et al., 2008) as evidenced by bacterio-phytoplanktonic biomass and
production coupling in a number of studies worldwide (Cole et al., 1988; Moran et al.,
2002b; Revilla et al., 2000). In freshwater aquatic systems, the opposite occurs. Nutrient
rich, labile allochthonous sources of DOM enter these systems through natural and
anthropogenic sources, such as river runoff, groundwater discharge, wastewater
treatment, agriculture, irrigation, fertilization, etc. providing additional substrates for BP
(Reitner et al., 1999). However, in estuaries, both allochthonous sources of DOM and
other autochthonous sources from seagrass and macroalgae (Rochelle-Newall et al.,
2008) supply heterotrophic microbes with DOM, which may cause decoupling of
bacterio-phytoplanktonic cycles (Shiah and Ducklow, 1994).
While some studies focus specifically on coupling trends as determined by
positive correlations between PP and BP (Pugnetti et al., 2010; Rochelle-Newall et al.,
2008), others focus on bacterial DOM source analysis (McAllister et al., 2004),
2
community composition (biomass and speciation of phytoplankton and bacterioplankton)
(Passow et al., 2007), spatial and temporal BP and PP trends (Montero et al., 2010), and
the BP:PP ratio as proxies to understand autotrophic and heterotrophic dynamics
(Gonzalez et al., 2010; Almeida et al., 2002). In studies that specifically target
productivity, a high positive correlation between PP and BP measurements suggests the
two are coupled, meaning BP derives its source of DOM directly from autochthonous PP.
A lack of significant correlation between the two indicates BP dependence on other
autochthonous or allochthonous sources of DOM (Pugnetti et al., 2010; Rochelle-Newall
et al., 2008). Isotopic tracers (15N and 13C fractionation) have been used to determine the
sources of DOM used by bacterioplankton (McAllister et al., 2004). The isotopic
signatures of the source DOM are reflected in the bacterioplankton biomass and thus
show coupling; however, if there are significant differences between the DOM and the
bacterioplankton biomass signature, than another source of DOM, allochthonous or other
autochthonous, has been utilized and decoupling is indicated. Additionally, coupling
may be inferred from biomass and community composition response studies (Passow et
al., 2007). Community composition response experiments involve manipulating
phytoplankton community structure or biomass and analyzing subsequent changes in
bacterioplankton community structure or size, or monitoring and correlating changes in
these over time. However, these studies do not specifically target productivity and
therefore may be less credible when compared to other methods. Determining BP
response to phytoplankton PP can also be monitored in situ by evaluating seasonal and
spatial trends over time (Montero et al., 2010); if BP measurements closely mirror
3
phytoplankton PP measurements throughout many sampling events and locations, these
results suggest systemic bacterio-phytoplanktonic coupling.
Another method to evaluate these relationships is to examine the BP:PP ratio
(Gonzalez et al., 2010; Almeida et al., 2002; Anderson and Turley 2003; Jugnia et al.,
2007). By comparing BP (µg C produced L-1 d-1) and phytoplankton PP (µg C produced
L-1 d-1) measurements, one can examine BP:PP consistencies and ascertain the dominance
of the microbial loop in the system. Higher BP:PP ratios represent a larger heterotrophic
component of the microbial productivity of the system, indicating trophic efficiency
through bacterioplankton DOM recycling (Gonzalez et al., 2010; Almeida et al., 2002).
Consistent BP:PP ratios indicate similar productivity trends between microbial
heterotrophs and autotrophs in the system and could represent bacterio-phytoplanktonic
coupling. Microbial loop dominance and drivers are more credibly determined in pelagic
marine systems far from allochthonous and other autochthonous sources of DOM
because phytoplankton derived PP is assumed to be the only existing PP in these
ecosystems (Gonzalez et al., 2010; Almeida et al., 2002). Studies using this approach in
open ocean ecosystems have shown that a BP:PP ratio of ~0.20 is typical, though they
have been measured as high as 0.50 (BP:PP) (Cole et al., 1988).
Regardless of the method, analyzing bacterio-phytoplanktonic coupling trends
gives insights into bottom-level trophic dynamics and serves to determine
bacterioplankton source DOM. Knowing which source of DOM is preferred helps
quantify system efficiency. Furthermore, understanding the balance between use of
autochthonous/allochthonous DOM and the effect of anthropogenic impacts on these
delicate systems can provide information regarding system health to watershed managers.
4
Understanding bacterio-phytoplanktonic coupling trends across varying aquatic systems
can help to assess and understand coupling trends and microbial loop efficiency in
Florida Bay.
Freshwater ecosystem coupling
Freshwater ecosystems are often affected by anthropogenic influences due to
their geographical attributes and locations. Several studies conducted in freshwater
ecosystems have sought to determine the PP and BP coupling dynamics and interactions.
Jugnia et al. (2007) focused their trophic assessment on coupling trends in a recently
flooded Sep Reservoir in France, which was noted for its significant sources of
allochthonous DOM. While the ambient physical and chemical characteristics of the
reservoir did not change appreciably during the study, nutrient input increased (Jugnia et
al., 2007). Jugnia et al. (2007) found phytoplankton PP to remain relatively constant over
the two study years (80.52 ±75.44 g C L-1d-1 and 77.80 ± 65.68 g C L-1d-1 respectively for
1996 and 1997), while BP decreased during the two years (13.60 ± 10.56 g C L-1d-1 to
2.74 ± 1.92 g C L-1d-1). Furthermore, the BP:PP ratio decreased from mean 0.22 to mean
0.05 over the study years, respectively. Lack of similar dynamics displays a decoupling
trend, probably due to dependence of BP on declining subsidies of allochthonous DOM.
In a shallow temperate lake in Austria, Reitner et al. (1999) found similar results
in an assessment of BP at the “reed line”. The study concluded the bacterial carbon
demand (BCD) was an order of magnitude higher than the C sequestered by pelagic
phytoplankton indicating the bacterioplankton were receiving DOM from an
allochthonous source such as the littoral reed belt (Phragmites spp.) which also
5
represents a lack of bacterio-phytoplanktonic coupling. Fouilland and Mostajir (2010)
reviewed existing freshwater bottom-level trophic assessments and concluded that
evidence did not support dependency of microbial heterotrophs on phytoplankton
sequestered C in freshwater aquatic ecosystems. While enclosed freshwater systems
typically vary based on ecological, environmental and geological characteristics,
evidence from existing studies indicates that coupling in freshwater systems is weak if at
all present.
Marine ecosystem coupling
In many cases, marine systems, more specifically open ocean pelagic ecosystems,
have opposite tendencies. Because pelagic phytoplankton sequestered C is the only
organic matter source in many of the marine study locations, bacterioplankton
consumption is limited to this for sustenance. Synthesizing and analyzing findings from
seven open ocean studies throughout the world, Ducklow (1999) found that, on average,
BP comprised about 0.20 of total PP in marine systems and that bacterial variability
closely mirrored phytoplankton dynamics. These dynamics display bacteriophytoplanktonic coupling in open-oceans. Other open ocean assessments have resulted in
similar conclusions. Moran et al. (2002) monitored four locations including one polar
open ocean system and three coastal marine systems. Only in the open ocean location did
they conclude that BP dependence on PP, i.e. bacterio-phytoplanktonic coupling was
significant.
However, there are existing marine studies that present data which do not
display bacterio-phytoplanktonic coupling trends. These highlight the variability in
quantifying ecological trophic dynamics. Passow et al. (2007) demonstrated the
6
resilience of the marine heterotrophic bacterial community by examining the effects of
phytoplankton speciation on the heterotrophic BP dynamics in a manipulated
mesocosm study using seawater from the North Sea, Germany. They concluded that
the bacterioplankton community structure was not dependent on the phytoplankton
community structure. Despite the changes in phytoplankton community structure in
each mesocosm, the bacterial community composition remained relatively similar
(relative Euclidean Distance <20%). Of course availability of DOM to sustain
microbial heterotrophs is necessary, but many other biological, physical, and chemical
factors influence these trends and have been implicated in marine assessments.
In many of these marine studies, top-down grazing pressure has been suggested
as a biological control at sites where PP and BP were not coupled. The existence of
biological controls influencing coupling in the polar Southern Ocean was suggested by
Duarte et al. (2005). They found, in their four mesocosm study, that BP comprised
about 1-10% of total PP and that bacterial populations responded weakly to
phytoplankton dynamics in the polar region. Their explanation for this seemingly
counterintuitive conclusion was that specific top down grazing controlled the
heterotrophic bacterioplankton community. Other studies have indicated top down
controls as a limiter of bacterio-phytoplanktonic coupling. For example, a heterotrophic
bacterial assessment, performed by Fernandes et al. (2008), in the Equatorial Indian
Ocean, showed BP:PP ratios ranging from 1.02 to 1.88. The ratio above 1.00 indicated
that BP was higher than PP and therefor PP was insufficient to support BP.
Additionally, the varying ratios over the course of the study further indicated a lack of
dependence of BP on PP. In this ultra-oligotrophic system, Fernandes et al. (2008)
7
suggested these inordinately high BP:PP ratios were caused by high mesozooplankton
and heterotrophic flagellate grazing rates.
Physical and chemical factors also affect marine coupling. Cho et al. (2001)
related seawater temperature and stability to BP:PP ratios in the Yellow Sea. They found
hydrological controls (well mixed vs stratified water column) affected food-web
dynamics; thus influencing bacterio-phytoplanktonic coupling. Furthermore, they found
a significant negative correlation between the BP:PP ratio and the ratio of PP dependent
on nitrate (NO3- ) versus other forms of N (f-ratio), indicating a N-based nutritional effect
on BP. The overall conclusion is that many ecological factors need to be considered
when examining coupling trends in open ocean trophic dynamics.
Estuarine ecosystem coupling
Estuaries and coastal systems have inherently more complex cycles of organic
matter than the open ocean due to the influx of DOM from both the marine and the
freshwater systems which converge in the estuary. These DOM sources include
autochthonous phytoplankton, benthic algae, and seagrass sources as well as
allochthonous sources from freshwater rivers and runoff (Parker, 2005) and marine
sources from coastal ecosystem. Because of the convergent nature of estuarine
ecosystems, they are also subjected to more environmental factors (tides, geophysical
formations, vegetation, etc.) than other aforementioned systems. Studies have been
conducted that consider many environmental parameters that can influence bacteriophytoplanktonic coupling (Fouilland and Mostajir, 2010). Generalizations of trophic
dynamics in all estuaries are difficult to make due to the inherent variability among
estuarine ecosystems and a need for more scientific knowledge. For instance, while
8
many studies to quantify and assess bacterio-phytoplanktonic coupling in estuaries have
been conducted in temperate climates (Fouilland and Mostajir, 2010; Barrera-Alba et al.,
2008; Rochelle-Newall et al., 2008), few have been conducted in tropical and subtropical regions (Rochelle-Newall et al., 2008). Tropical marine ecosystems are
generally oligotrophic and are characterized by high temperatures, low light attenuation,
low nutrients and low chlorophyll a (chl a) concentrations (Rochelle-Newall et al., 2008).
These tropical and sub-tropical estuarine physiochemical parameters can yield different
results in coupling analyses compared to those typical of more temperate climates.
Nutrients are often limiting factors in marine pelagic PP; however, in estuaries,
nutrients are often added to the ecosystem via freshwater inputs which alleviate or
modify limitation. Nutrient limitation or enrichment can affect PP and BP dynamics as
evidenced by Revilla et al.’s (2000) study in a shallow temperate estuary. Nutrient rich
runoff was associated with inputs of labile DOM from natural freshwater sources as well
as a wastewater treatment plant adjacent to the upper estuary which acted to enhance BP.
Nutrient limitation was also highlighted in a mesocosm study conducted by Borsheim et
al. (2005) during which varying levels of N-fertilization affected phytoplankton biomass
and productivity which was followed by subsequent changes in bacterioplankton biomass
and productivity. The study also included a mesocosm control of direct nutritional effect
on BP. The results indicated BP was carbon limited and therefor suggest BP was
stimulated by the phytoplankton blooms triggered by N-fertilization. However, this study
was conducted under controlled conditions, eliminating allochthonous sources of DOM,
thus these results do not represent entire estuarine ecosystems. Other environmental
factors found to correlate with bacterio-phytoplanktonic coupling include chl a
9
concentrations, temperature and salinity. For example, a multivariate principal
component analysis of 33 studies by Apple et al. (2008) found significant covariance
between BP and chl a. Covariance only occurred while temperature and salinity were
certain values indicating a confounding effect of environmental parameters on microbial
dynamics, which highlights the difficulties of determining direct causation in a dynamic
estuarine ecosystem.
A number of monitoring studies have sought to capture trends in bacteriophytoplanktonic coupling dynamics over time and through seasons. These studies are
particularly informative in temperate areas with drastic seasonal changes. In a temperate
fjord in Chile, Montero et al. (2011) examined the seasonality of phytoplankton PP and
BP and found significant correlation of BP and PP suggesting bacterio-phytoplanktonic
coupling. Moreover, they found that significant seasonal increase in wind-stimulated
phytoplankton blooms resulted in more autochthonous PP in the ecosystem, which in turn
promoted greater bacterio-phytoplanktonic coupling. In months of low productivity, they
suggested that allochthonous DOM accounted for the high levels of BP, indicating that
seasonality and climatological changes such as shifting winds affect estuarine coupling
trends (Montero et al., 2011). In a similar temperate Chilean fjord, Gonzalez et al. (2010)
found concurring information. The results of their study of seasonality in PP dynamics
and carbon budgeting suggest significant changes in food web dynamics from the
productive season (austral winter) to the non-productive (spring) season. During the
productive season PP was two orders of magnitude higher than the non-productive season
and the BP:PP ratio decreased from 0.37 to 0.02, indicating a dominance of BP during the
non-productive season and a shift to a more phytoplankton PP. While the phytoplankton
10
PP in the productive season subsidized BP, during the non-productive season,
allochthonous DOM supported the ecosystem. Gonzalez et al. (2010) also examined
heterotrophic top-down grazing pressure and found seasonal trends in these data as well.
Seasonal and spatial effects on estuarine heterotrophic and autotrophic microbial
communities were also examined in a temperate estuary in Ria De Aveiro, Portugal, by
Almeida et al. (2002). They found a seasonal fluctuation in BP:PP ratios from 0.02-0.35
in the warm season, to 1.00–3.61 in the cold season, indicating a clear seasonal effect on
the microbial community. In the winter, the phytoplankton PP was insufficient to fuel
BP; however, in the warmer weather, BP only accounted for 0.21 of total PP and
bacterioplankton were likely supported by phytoplankton DOM subsidies. Almeida et al.
(2002) also suggested that the phytoplankton community was more heavily influenced by
temperature variations, while the bacterial community appeared to be more influenced by
the salinity gradient in the estuary. These studies highlight dynamic spatio-temporal
effects on bacterio-phytoplanktonic coupling trends
Physical characteristics of study regions can also influence bacteriophytoplanktonic coupling dynamics; for example, protected waters can result in longer
residence times, slopes and shapes of adjacent land features may affect runoff flow, and
basin geomorphology may influence tidal fluxes. Similarly, one estuarine ecosystem
may have different sub-regional physical characteristics. In the sub-tropical estuary of
Canane´ia-Iguape, Brazil, Barerra-Alba et al. (2008) examined coupling trends in a
spatially diverse study site. They noted phytoplankton PP levels between 1.1 and 1.9 µg
-1 -1
-1 -1
C L h and BP levels between 3.4 and 12.8 µg C L h in the northern portion of the
sampling region, indicating BP was largely supported by influxes of allochthonous labile
11
DOM probably from the surrounding mangroves at this particular site. However, in the
southern region of the study ecosystem, a significant correlation was found between BP
-1 -1
-1 -1
(1.5-6.1 µg C L h ) and phytoplankton PP (3.4-6.9 µg C L h ) indicating that
phytoplankton PP made up an appreciable DOM source for BP. Barrera-Alba et al.
(2008) attribute the difference to the lability of the DOM in the northern portion of the
study as indicated by the seasonal mean C:N ratio of 3.2. The appreciable difference in
data collected from the sampling locations indicated a substantial spatial effect on the
ecosystem-wide microbial trophic dynamics.
Physical characteristics of the study area were also found to influence bacteriophytoplanktonic coupling trends in a sub-tropical lagoonal estuary study conducted by
Rochelle-Newall et al. (2008). Using measurements of transparent exopolymer particles
(polysaccharide gel phytoplankton exudates, Passow, 2002; Taylor et al., 2014), bacterial
preference for phytoplankton derived DOC was analyzed in both a semi-enclosed lagoon
and the surrounding coastal zones of New Caledonia. Their results were in contrast to
other estuarine bacterio-phytoplanktonic assessments (Moran et al., 2002).
TPhytoplankton PP in near coastal stations was found to be significantly linked to BP,
while in the offshore locations, coupling trends were less prominent, i.e. phytoplankton
PP rates were insufficient to support the BP. Rochelle-Newall et al. (2008) consider the
bioavailability of DOM as a potentially explanatory variable, mentioning that high levels
of metals (Migon et al., 2007) and the presence of the colonial cyanobacteria
Trichodesmium (Renaud et al., 2005) may negatively influence the bioavailability of
phytoplankton PP to heterotrophic bacterioplankton in regions where coupling trends are
not present.
12
Bacterial Growth Efficiency
Bacterial growth efficiency is an important measurement used to understand the
export of PP from a ecosystem (Ducklow, 2001) via the microbial loop (Anderson and
Turley, 2003; del Giorgio and Cole, 1998). DOM consumed by bacterioplankton is either
incorporated as biomass or respired as CO2. BGE is the percentage of consumed DOM
incorporated as biomass within the cell within the measured time (del Giorgio et al.,
2006; del Giorgio and Cole, 1998). BGE values have been found to be lowest in nutrient
depleted, oligotrophic ecosystems (del Georgio and Cole, 1998; Anderson and Turley,
2003, Eiler et al., 2003) where BR is high due to natural stressors on the lower trophic
levels of the ecosystem (del Giorgio and Cole, 1998). BGE values as low as 2%-8%
were measured in the western Mediterranean Sea, an ultra-oligotrophic marine ecosystem
(Gasol et al., 1998). In ecosystems closer to allochthonous DOM and nutrient subsidies,
such as coasts, estuaries, rivers and lakes, BGE tends to be higher (Findlay et al., 1992;
Benner et al., 1995) with estuarine BGE measured as high as 68% (Apple and del
Giorgio, 2007) and riparian ecosystems measured as high as 46% (Benner et al., 1995).
Bacterial growth efficiency is typically computed as a function of both BP and
BR and different methods are used to calculate each component of BGE. The most
commonly used BP methods are radiolabelled 3H-thymidine or 14C-leucine uptake assays
and proxy incubations of CO2 production or O2 decline for BR measurements (Warkentin
et al., 2011; Apple and del Giorgio, 2007; del Giorgio and Cole, 1998). Another BGE
calculation method involves measuring the ratio of change in DOC to the change in
particulate organic carbon (POC) over several weeks. In this approach, sterilized water
13
samples are reinoculated with a known abundance and community structure of
bacterioplankton and analyzed for organic carbon dynamics (del Giorgio and Cole, 1998;
Bjornsen, 1986). The first of the two methods are generally preferred due to the shorter
incubation periods (usually < 24 hours) (del Giorgio and Cole, 1998).
Bacterial growth efficiency is influenced by a number of environmental
parameters. Light, temperature, nutrient availability, DOM concentration and
bioavailability, can affect both BP and BR which in turn determine BGE. The results of
many studies indicate a nutritional effect on BGE. Keiblinger et al. (2010) manipulated
mineral nutrient content ex situ and measured subsequent bacterial carbon use efficiency
(CUE). CUE is a similar measurement to BGE in that it relates new biomass produced
per unit organic matter consumed. They found bacterial CUE was affected by both N and
P depending on species composition. Total phosphorus (TP) correlations with BGE have
also been found in comprehensive environmental BGE studies. Smith and Prairie (2004)
found a correlation of TP to BGE in a study of temperate lakes in Quebec. They also
found extreme oligotrophy created stress on the bacterioplankton, thus increasing BR and
decreasing BGE rates. In turn, more DOC was respired by the bacterioplankton as CO2
rather than recycled back into the trophic web through biomass incorporation (Smith and
Prairie, 2004).
Although BP and BGE are typically higher in more nutrient rich ecosystems
(Findlay et al., 1992; Benner et al., 1995), many studies have targeted the actual
bioavailability of DOM as having a particularly important influence on BGE (Eiler et al.,
2003; Warkentin et al., 2011), especially in areas where sources of DOM other than
autochthonous phytoplankton PP is available. Apple and del Giorgio (2007) found that
14
lability of DOM was more influential on BP and BGE than the amount of inorganic
nutrients over their two year monitoring program of a temperate estuary in Maryland.
They compared initial DOC concentrations to %DOC consumed over a 24-day
incubation period as a proxy for DOC lability and correlated these results to BGE and
BCD. Additionally, Eiler et al. (2003) investigated BGE among other bacterial dynamics
at varying TOC concentrations using humic lake sediment and artificial lake water. They
found a range of BGE from 0.4%-10.4% during their experiments and that concentrations
of DOC greater than 0.54mM had no effect on BGE. Since most lake ecosystems have
ambient DOC concentrations higher than this, they proposed that DOC concentration is
most likely not a limiting factor for BGE in these ecosystems but that the quality of the
DOC may be more influential. Because the less aged DOC was utilized more readily by
the bacterioplankton, they concluded that the aging process of the humic material used in
the experiments ex situ may have affected the lability of the DOC. They further suggest
that since DOC concentrations in marine environments are lower than in lakes, BGE and
BP are likely limited by DOC in open ocean environments (Eiler et al., 2003; Carlson and
Ducklow, 1996; Church et al., 2000).
Kritzberg et al. (2005) investigated the effects of autochthonous PP on BGE in
lakes which also receive terrestrial DOM input. Their results suggested that the
bacterioplankton had a preference for and a higher BGE rate using the autochthonous
phytoplankton derived C. However, BR values were higher than autochthonous DOC
values, indicating that allochthonous C fueled high respiration rates in this net
heterotrophic ecosystem (Kritzberg et al., 2005). Del Giorgio et al. (2006) suggested that
15
because BGE is a measure of consumed C incorporation into cellular biomass, BGE can
actually control BP in a ecosystem, rather than being an effect of BP and BR.
Another study in a tropical Malaysian estuary and coastal sea found that substrate
quality and temperature affected BR, which in turn influenced BGE values (Lee et al.,
2009). In this study, BGE values ranged from 2%-40% based on temperature and
substrate quality (substrate quality was measured as DOC:DON ratio). The study also
indicated that a BGE of less than 8% rendered the ecosystem net heterotrophic in this
region. They concluded that in order to support the high BR levels, the ecosystem must
be subsidized by organic carbon other than autochthonous PP. Conversely, the results of
a study conducted in the North Sea indicated a net autotrophic ecosystem where
autochthonous PP was sufficient to support BR (Reinthaler and Herndl, 2005). BGE
values were higher in the spring and summer than in the winter indicating a temperature
related effect on BGE as well. Yet still, other studies have considered other factors as
well. Vallino et al. (1996) found thermodynamic energy content of DOM to influence.
Lonborg et al. (2013) examined the effect of photodegradation on DOM and subsequent
bacterial community activities in the NW Iberian Peninsula. They found that UV
exposure was not significantly correlated to BGE although it did affect bacterial
community structure. Because so many factors can affect DOM lability and BGE,
determining these for a given ecosystem can be difficult despite many available methods
(del Giorgio and Cole1998).
1.2 Site Description: Florida Bay
The Florida Everglades, a large marsh in Southern Florida, was designated a
National Park in 1934 and covers approximately 1.5 million acres (0.61 million ha;
16
Loveless, 1959; Davis and Ogden, 1994). Even before the Central and Southern Florida
Project for Flood Control and Other Purposes in 1948, Floridians have attempted to
control, contain and manage the water of the Everglades by creating canals, dykes and
dams as well as filling in marshes (http://www.evergladesplan.org). In 2000, the
Comprehensive Everglades Restoration Project (CERP) authorized $7.8 billion to restore
natural hydrology to the Everglades (http://www.evergladesplan.org). The CERP intends
to “capture, store, and redistribute freshwater previously lost to the tide…” via
management and engineering (http://www.evergladesplan.org).
Although altruistic, restoration may have adverse effects on the nearby coastal
ecosystems, including the Florida Bay estuary to the south, due to the increased transport
of anthropogenic materials including sediments and nutrients (Davis and Ogden 1994).
Florida Bay is a unique lagoonal estuarine ecosystem located at the southern border of
Everglades National Park. The bay occupies about 2200 km2 and the average depth is
less than a meter (Tilmant, 1989). Each of the sub-regions must be considered separately
due to the minimum exchange among these sub-regions (Lee et al. 2006; Lee et al., 2009)
caused by carbonate mud banks that form shallow basins which restrict flow (Bosence,
1989; Boyer et al., 1999) resulting in varying spatial heterogeneity including
geomorphology, surrounding biological and hydrological influences, and residence times
(Boyer et al., 1997).
In addition to land based sources of pollution, the Florida Bay ecosystem faces
challenges of climate change, ocean acidification, and coastal population growth. It is a
high-profile oligotrophic estuary undergoing a massive ecological restoration, with
interesting nutrient-limitation dynamics and spatial heterogeneity and therefore makes an
17
interesting study site for analyzing bottom level trophic dynamics and the coupling of
phytoplankton and bacterial productivity.
The results of many water quality studies in Florida Bay have indicated that
precipitation and evaporation dominate the water budget and thus have the greatest
influence on salinity (Nuttle et al., 2000; Kelble et al., 2007; Chen et al., 2013Although
precipitation accounts for the largest percent of annual freshwater input into Florida Bay,
the northeast (NEFB) is most influenced by freshwater runoff from the Everglades with
five of the six runoff points north of the bay entering this sub-region (Kelble et al., 2007)
resulting in the lowest mean monthly salinity of the sub-regions, particularly in the dry
season. The north-central (NCFB) had the most variability in salinity measurements
most likely due to shallow depths, longer residence times, and relative isolation from
surrounding marine and freshwater influences (Kelble et al., 2007; Lee et al., 2006). The
south (SFB) and west (WFB) have less variable salinities; however; they are still on
average less saline than the surrounding oceanic waters. The WFB is likely moderated by
the freshwater runoff from the southwest Florida shelf to the north, including the Shark
River Slough (Fourqurean et al., 1993; Lee et al., 2006; Kelble et al., 2007). Because
Florida Bay is an oligotrophic estuary, it is sensitive to subtle biogeochemical changes,
and particularly sensitive to anthropogenic inputs (Hunt and Nuttle 2007). The NEFB in
particular, is a potential receiving ground for runoff as a result of increased flows through
Taylor Slough upon completion of the C-111 Spreader Canal Western Project, part of the
greater CERP (http://www.evergladesplan.org).
Armitage and Fourqurean (2009) suggest community structure, productivity, and
trophic dynamics of Florida Bay are affected by this runoff. Hence, the effects of CERP
18
could possibly have implications at all trophic levels. A comprehensive knowledge of the
biogeochemical and trophic dynamics of the ecosystem and environmental influences is
necessary to understand the possible adverse effects of CERP, climate change, and
population growth. The bottom trophic levels, specifically phytoplankton and
bacterioplankton, which partially comprise the base of the entire estuarine food web, are
particularly important. At the base of the aquatic food web, bacteria and phytoplankton
are critical in supplying higher trophic levels with bioavailable organic matter (Fouilland
and Mostajir 2010). One method to assess bottom level trophic dynamics is to analyze
microbial loop efficiency through bacterio-phytoplanktonic coupling trends and BGE.
To effectively assess coupling trends in Florida Bay, physiochemical and
environmental characteristics of the study site must be determined. Each sub-region
(Figure 2.1) is affected differently by nutrient regimes and dissolved organic carbon
(DOC) and DOM sources in addition to freshwater input and salinity patterns. The
Florida Everglades is mainly phosphorous (P)-limited, due to the lack of naturally
occurring P in the terrestrial ecosystem upstream (Childers et al., 2006b; Boyer et al.,
1997). The WFB receives the largest subsidies of P from the Gulf of Mexico (GOM)
thereby rendering it mostly nitrogen (N) limited (Hunt and Nuttle, 2007); as the
ecosystem transitions from west to east, N-limitation gradually shifts to P-limitation as
GOM P subsidies diminish. The NEFB mixes with N-rich freshwater inputs rendering it
mostly P-limited, and the NCFB is isolated due to its geomorphology allowing little
freshwater input and longer residence times as well as build-up of organic N and
ammonium (Hunt and Nuttle; 2007; Boyer et al., 1997).
19
The Everglades and South Florida watershed delivers freshwater to Florida Bay,
introducing allochthonous DOM (and associated nutrients including C, N, P) to the
ecosystem in multiple forms. Leachates from leaf litter of mangroves, spikerush, and
sawgrass export DOC more heavily during the wet season (Childers et al., 2006; Romigh
et al., 2006). Autochthonous sources including phytoplankton, seagrass, algae, and
periphyton also largely contribute to the DOM pool. Many studies have shown that
seagrass, in addition to phytoplankton, provides much of the primary productivity in
Florida Bay (Fourqurean et al., 2012; Chen et al., 2013; Hunt and Nuttle 2007).
Additionally, the NEFB and NCFB are likely influenced by DOM export from the nearby
mangrove fringe to the north (Jaffe et al., 2004; Chen et al., 2013). With the spatial
variations among the sub-regions of Florida Bay, sources of DOC vary from
allochthonous transport and autochthonous productivity. Despite spatial DOC source
variations, in one recent study on the optical properties of DOC in the Florida Bay
ecosystem, seasonal DOC patterns were not found, suggesting DOC sources are not
influenced significantly by seasonal variability such as precipitation (Chen et al., 2013).
Though determining seasonal DOC sources with certainty is difficult in dynamic
estuarine ecosystems, examining bacterio-phytoplanktonic coupling trends can isolate
bacterioplankton preference for phytoplankton derived DOC. Despite multiple sources of
DOM, Childers et al., (2006) and Williams and Jochem (2006) suggest that the BP in
Florida Bay is limited by DOC. DOC limitation indicates that bacterioplankton may be
very sensitive to ecosystem changes in DOC sources and availability, which could
potentially result in close bacterio-phytoplanktonic coupling, due to bacterioplankton
utilizing DOC exudates from the phytoplankton (Ducklow, 1999).
20
1.3 Objectives and Hypotheses
In this study, spatio-temporal microbial loop efficiency and bottom-level trophic
dynamics in Florida Bay were evaluated and potential environmental drivers for
microbial heterotrophic and autotrophic productivity were investigated. The first
objective was to comprehensively analyze environmental drivers for phytoplankton
primary productivity and secondary bacterioplankton productivity and their coupling and
decoupling characteristics on spatio-temporal scales by combining historic water quality
databases from AOML and SERC. The second objective was to supplement this existing
phytoplankton and bacterioplankton productivity database with an additional study
examining bacterial respiration to determine bacterial growth efficiency. The scientific
hypotheses to be addressed by this thesis are:
H1 – The sub-region isolated from allochthonous nutrient and DOM input in
Florida Bay (WFB), will show strong bacterio-phytoplanktonic coupling. The
sub-region in proximity to allochthonous nutrient and DOM inputs (NEFB) will
show relatively less bacterio-phytoplanktonic coupling.
H2 - BGE will increase in the more productive region (WFB). Nutrients will
correlate to BGE indicating a nutritional effect on enhanced microbial loop
efficiency.
Variations of phytoplankton PP, BP, and BGE were examined for both spatial and
temporal trends. I hypothesized that stronger bacterio-phytoplanktonic coupling trends
would manifest in the WFB which is less influenced by runoff than the NEFB. In the
NEFB, I hypothesized that runoff from C-111 canal, Trout Creek, and Taylor Slough
would cause some decoupling. Moreover, decoupling was expected to intensify in the
NEFB in the wet season (May-November) with increased runoff. From west to east, in
Florida Bay, a gradient occurs from N-limitation to P-limitation (Boyer et al., 1997). The
21
natural nutrient gradient, which influences PP, was suspected to also affect the bacteriophytoplanktonic coupling trends i.e. increased phytoplankton PP results in increased BP
(Childers et al., 2006). Other physiochemical factors such as temperature, salinity, DO
and turbidity (Cole et al., 1988, Fouilland and Mostajir, 2010) were also analyzed with
BP and PP for significant correlations. I hypothesized that if BP and PP data displayed
coupling trends in zones significantly influenced by allochthonous DOM inputs then the
bacteria and phytoplankton would be responding similarly to outside factors.
1.4 Statement of Research
The study aimed to enhance understanding of the bottom trophic levels in a subtropical estuary, Florida Bay, Florida, USA, bordered by South Florida Everglades to the
north and the Florida Keys to the south, via analysis of bacterio-phytoplanktonic coupling
and BGE trends and their environmental drivers. The dynamics of these interactions
were examined both spatially, for variation across physiochemical gradients, and
temporally for seasonality and inter-annual variability, throughout the entire study site.
Using BP and GPP measurements, an analysis of environmental influences on bacteriophytoplanktonic coupling and BGE was completed to further scientific understanding of
microbial dynamics in Florida Bay and to develop a potentially useful tool for safe and
efficient ecological management of the ecosystem.
22
CHAPTER 2.0 METHODS
2.1 Sampling Design
To examine spatial distributions, Florida Bay was delineated into four subregions NEFB, SFB, WFB, and NCFB (Fig. 2.1) consistent with prior delineations based
upon salinity patterns (Nuttle et al., 2000; Kelble et al., 2007) and water quality (Briceno
et al., 2013). The National Oceanic and Atmospheric Administration Atlantic
Oceanographic and Meteorological Laboratory (NOAA/AOML) conducted bimonthly
water quality surface sampling aboard the R/V Virginia K in Florida Bay at 40 sites from
1998-2012. Additionally, the Florida International University, Southeast Environmental
Research Center (FIU/SERC) conducted monthly water quality sampling at 28 sites in
Florida Bay from 1991-2008 and microbiological data from 2003-2007. FIU/SERC has
continued monthly sampling at three of the Florida Bay sites, (9, 21, and 27, Fig. 2.1)
renamed TS/PH-9-11 respectively, as part of the National Science Foundation’s Florida
Coastal Everglades Long Term Ecological Research (FCE-LTER) Network.
Historical data from both AOML and SERC monitoring stations were combined
first according to basins geomorphologically separated by mud banks (Fig. 2.2). Only
stations where either GPP or BP was measured, or in the same basin in which GPP or BP
were measured, were used for this analysis. These monitoring stations were combined to
analyze the maximum number of measured environmental variables for each basin where
GPP and BP were measured. Where measurements were redundant, SERC
measurements were used. Where monthly measurements were missing, values were
interpolated from the prior and subsequent monthly values. The stations were then
23
grouped according to the sub-regions (Fig. 2.1; Table 2.1) to increase the number of
samples (n) and enhance the significance of the analysis.
Figure 2.1. Combined stations by sub-region. FIU (SERC) and AOML stations are plotted, grouped into
combined stations and displayed for each sub-region.
Table 2.1. Station Matrix. Matrix of AOML and SERC stations and sub-regional combinations.
AOML Station
SERC Station
Combined Station
1
9
C1
6
13
C17
7
14
32
15
C16
26
C12
27
C11
19
C9
34
15
27
28
29
36
21
24
Sub-region
NEFB
NCFB
WFB
SFB
Over the six year study period, phytoplankton GPP was measured at six of the
AOML stations: 1,6,7,14,15, and 36 combined with other AOML and SERC stations
geographically and renamed C1, C17, C16, C12, C11 respectively (36 was not able to be
grouped with any SERC station due to geographical isolation) (Fig. 2.1, Table 2.1).
From 2008-2012, BP was measured at three of the SERC sites: 9, 21, and 27 and
regrouped into the combined stations for the purposes of this study. SERC stations 9 and
27 were combined into stations C1 and C11 respectively. SERC station 21 was unable to
be combined due to geographic isolation (Fig. 2.1). Because of the limited number of
overlapping samples, coupling trends were only able to be analyzed for the WFB and the
NEFB for 2011 to 2012. However, when BP and GPP were considered separately, more
data were available for analysis providing valuable information regarding seasonal and
regional environmental drivers for BP and GPP separately. In 2012, BR measurements
were added to the observations to determine BGE for the sub-regions. BGE
measurements are only available for 2012.
2.2 Environmental parameters
Measurements from the SERC database were used for temperature, salinity,
dissolved oxygen (DO), turbidity, ammonium (NH4+), nitrite (NO2-), nitrate (NO3-), total
nitrogen (TN), total phosphorus (TP), soluble reactive phosphorus (SRP), total organic
carbon (TOC), and chl a. A salinity-conductivity-temperature probe (Orion model 140)
was used to measure surface salinity and temperature. Surface DO was measured using
an (Orion model 840) oxygen electrode. TOC, TN, TP and turbidity were measured
using unfiltered duplicate water samples collected in sample rinsed high density
polyethylene (HDPE) bottles kept in the dark at ambient temperature for transport. For
25
TOC measurements, the samples were first acidified to a pH<2 and then analyzed in a
Shimadzu TOC-5000 by direct injection onto a hot platinum catalyst (Boyer 2006). An
ANTEK 7000N Nitrogen Analyzer was used to measure TN (Boyer 2006; Boyer et al.,
1997; Frankovich and Jones, 1998). A dry ashing acid hydrolysis technique was used to
determine TP (Solorzano and Sharp 1980). An HF Scientific model DRT-15C
turbidimeter was used to determine turbidity reported in NTU (Boyer 2006; Boyer et al.,
1997).
Figure 2.2. Florida Bay mud banks. Aerial view of combined stations plotted in each region separated
geomorphologically by mud banks (satellite image of Florida Bay).
Dissolved nutrients were analyzed using water samples collected into samplerinsed 150 ml syringes through a 25mm Watman GF/F filter into 60 ml HDPE bottles.
26
These samples were transported in the dark on ice back to the laboratory. The filters
were stored in 1.5 ml plastic centrifuge tubes and preserved in 90% acetone (Boyer 2006;
Boyer et al., 1997; Strickland and Parsons 1972). After being stored for at least 2 days at
-20°C, the filters were then used to measure chl a concentrations with a Gilford Fluoro
IV Spectrofluorometer. Alpkem model RFA300 four channel autoanalyzer was used to
analyze filtrate for SRP, NO2, NO3 and NH4 (Boyer 2006; Boyer et al., 1997).
From the AOML database, silica (Si) and totally suspended solids (TSS)
measurements were used. For silica (Si) determination, measured as silicate (SiO2),
samples were filtered into a sample-rinsed 60ml HPDE bottle using a Cole-Parmer 25mm
0.45µm polytetrafluoroethylene (PTFE) syringe filter and transferred in the dark on ice
back to the laboratory. Nutrients were analyzed using gas-segmented continuous flow in
a Seal Analytical Auto-analyzer (Hydes et al., 2010). TSS samples were collected in 1L
HDPE bottles and transferred on ice to the lab for filtration and analysis. The samples
were filtered using 47mm 0.4 µm polycarbonate filters. TSS is measured as the weight of
solids collected on the filter per unit volume (AOML-NL-SOP-009).
2.3 Coupling and BGE
Primary Productivity
Phytoplankton GPP was quantified by measuring the change in DO concentration
over 24 hour incubations in situ. Surface samples were collected in 2 L light and dark
Niskin bottles as well as a control sample and moored at the site for approximately 24
hours. Triplicate samples were transferred from each Niskin bottle to sample rinsed 125
ml Pyrex vials via silicone tubing and fixed with manganous chloride and alkaline
sodium iodide for transfer back to the laboratory. DO content was measured using a
27
modified Winkler titration method in an Amperometric Oxygen Titrator (Wambeke et al.,
2008; Culberson and Huang, 1987). Change in oxygen concentration proxies were then
converted from µg O2 L-1 h-1 to µg C L-1 h-1 using typical O2 to C conversions: mg O2 L-1
*0.698 = ml O2 L-1; ml O2 L-1*0.536 = mg C L-1.
Bacterial Productivity
Bacterial abundance, biomass, and production were measured monthly in the
Microbial Ecology Laboratory at SERC. Bacterial abundance samples were collected in
sample-rinsed polypropylene bottles, filtered onto 25mm 0.2 µm polycarbonate
membrane filters, preserved with 2% formaldehyde and transferred in the dark on ice
back to the laboratory where they were stained, mounted onto slides, and counted under a
Leica Epifluorescence Microscope (1250x) (Noble and Fuhrman 1998; Weinbauer et al.,
1998; Barrera-Alba et al., 2008). The counts are performed for free and particle attached
bacteria using a fluorescent DNA stain, DAPI (Porter and Feig 1980). BP samples were
collected in sample-rinsed polypropylene bottles and transported to the laboratory on ice
in the dark. BP was determined for each sample via the 3H-thymidine incorporation
method (Fuhrman and Azam 1982). Specific activity was determined analyzing
triplicates of the samples along with a 2% formalin blank. Using an automatic liquid
scintillation counter, disintegrations per minute (dpm) of bacterial activity from livekilled (dpm) versus the actual activity of the 3H thymidine (dpm) were compared to
determined picomoles of thymidine incorporated. Moles of thymidine L-1 h-1 were
converted to µg C L-1 h-l using the equation µg C L-1 h-l = (moles thymidine L-1 h-1)*(cells
mole-1)*(carbon cell-1), where the thymidine conversion factor of 2*1018 cells mole-1 was
28
multiplied by the moles of thymidine. The carbon conversion rate of 10 fg C cell-1 was
used (Bell, 1993).
Bacterial Respiration
To better characterize the efficiency of bacterio-phytoplanktonic coupling,
samples were analyzed for bacterial growth efficiency (BGE). BGE was measured as a
function of BP and BR using the equation BGE= [BP/(BP+BR)]x100 (Warkentin et al.,
2011; del Giorgio et al., 2006; Pringault et al., 2009). For BR samples, surface water was
collected with a peristaltic pump through sample rinsed silicone tubing. Because BR
measurements were only available for January, 2012-June, 2012 in the WFB and NEFB
sub-regions, BGE calculations could only be computed for these regions and dates.
Triplicate sample-rinsed Pyrex 125ml vials were filled directly for controls and fixed
with manganese chloride and alkaline sodium iodide for transport back to the laboratory.
An inline 47mm 1µm Watman GF/B filter was used to filter water into dark 1L HPDE
bottles for 24 hour in situ incubation (del Giorgio et al., 2006) at the three existing
phytoplankton GPP locations (C1,C11, and AOML station 36). DO was then sampled
and measured using the method mentioned above.
2.4 Statistical Analysis
The data were analyzed for bacterio-phytoplanktonic coupling trends using
different methods. Phytoplankton GPP and BP were tested (Pugnetti et al., 2010;
Rochelle-Newall et al., 2008) using the non-parametric Spearman correlation and the
BP:GPP ratio was analyzed for spatio-temporal trends (Gonzalez et al., 2010;Almeida et
al., 2002) with R statistical software (The R Foundation for Statistical Computing version
2.14.1). GPP and BP values were plotted by month to display temporal trends for each
29
sub-region. Significance of linear regressions between productivity values and time were
indicated. Additionally, BP was regressed with chl a values as another proxy for
coupling (Apple et al., 2008). The non-parametric Wilcoxon Rank-Sum test statistic
(Bradley, 1968) was used to determine statistical differences (p < 0.05) among mean
ranks of GPP and BP values for each region and between each season using R.
To determine environmental influences, R was used to run non-parametric
Spearman correlations of GPP, BP, and BGE as the dependent variables and each
measured environmental parameter as independent variables for each sub-region.
Principal Component Analysis (PCA) was used for ordination of environmental variables
including BP and GPP at each sub-region. The “principal” function was used to perform
a PCA for each sub-region for both BP and phytoplankton GPP. The data were first
standardized and then divided into principal components to collectively explain the most
variance in the data in each sub-region. The data were rotated using VARIMAX rotation
to better fit the model (Apple et al., 2008; Boyer et al., 1997).
30
CHAPTER 3.0. RESULTS
3.1 Bacterio-phytoplanktonic coupling
Bacterio-phytoplanktonic coupling was not significant in either sub-region of
Florida Bay (Fig. 3.1; Fig. 3.2). No significant relationship was found between BP and
GPP in either the NEFB or the WFB (Fig. 3.1). BP and chl a figures did indicate a
significant regression of very low value for both sub-regions as well (Fig. 3.2),
representing a minimal relationship between the two.
Figure 3.1. Bacterio-phytoplanktonic coupling. Bacterial productivity (BP) plotted against gross
primary productivity (GPP) for each sub-region.
Coupling: West Florida Bay
In WFB, the highest BP:GPP ratio (0.38) occurred during the dry season in
February, 2012. The lowest BP:GPP ratio (0.03) occurred during the wet season in June,
2012. Because of the lack of temporal consistency, the data suggest that bacterioplankton
are not using the phytoplankton generated GPP as their main source of energy in WFB,
which furthermore indicates a lack of bacterio-phytoplanktonic coupling. However,
when BP and GPP in WFB by month are examined separately (Fig. 3.3), there appears to
be some similarities for the April-June 2012 transition from wet season to dry season.
Relatively low values were observed in April (0.67 µg C L-1 h-1 and 0.06 µg C L-1 h-1 for
31
GPP and BP respectively), higher values were seen in May (9.41 µg C L-1 h-1 and 0.55 µg
C L-1 h-1 for GPP and BP respectively), and moderate values occurred in June (7.02 µg C
L-1 h-1 and 0.24 µg C L-1 h-1 for GPP and BP respectively). For both BP and GPP, the
lowest and highest values for the entire study occurred during the same months (April
and May, 2012). Both BP and GPP increased an order of magnitude between April and
May and then decreased again in June. Although fluctuations for the wet season in WFB
display similarities, statistical significance could not be determined due to a low number
of samples. Related environmental drivers for both BP and GPP during the wet season
could also explain these apparently similar fluctuations.
Figure 3.2. Bacterial Productivity and chl a. Bacterial productivity (BP) plotted against chl a for each
sub-region. Significant regressions are indicated (p<0.05).
Coupling: Northeast Florida Bay
The BP and GPP data, collected from May, 2011-April, 2012 in the NFFB, were
not significantly correlated using the non-parametric Spearman correlation. Additionally,
the BP:GPP ratio was examined for consistency as a proxy for coupling (Gonzalez et al.,
2010; Almeida et al., 2002) (Fig. 3.3). The highest ratio (0.76) occurred during the end
of the dry season in April, 2012. The lowest value of 0.01 occurred during the wet
32
season in September, 2011. The ratio of BP:GPP remained below 0.13 for the entire
study in NEFB except for April, 2012 when it spiked to 0.76, because GPP plummeted to
0.25 µg C L-1 h-1, an order of magnitude below all previous samples, while the BP value
of 0.20 µg C L-1 h-1 remained within the range of previous samples (0.07 µg C L-1 h-1and
0.31 µg C L-1 h-1).
Figure 3.3. Bacterio-phytoplanktonic coupling trends. Gross Primary Productivity (GPP), Bacterial
Productivity (BP) and the BP:GPP ratio for both Northeast Florida Bay (NEFB) and West Florida Bay
(WFB) displayed per month.
33
The BP:GPP ratio was also examined with the April, 2012 sample removed to
determine coupling trends prior to the GPP drop. Even with the April, 2012 sample
removed, BP and GPP were not significantly correlated. The inconsistent BP:GPP ratio
and the insignificant correlation results indicate bacterio-phytoplanktonic coupling did
not occur during the study in the NEFB. The BP and GPP values were also examined
separately for trends during the coupling study (Fig. 3.3). The data do not appear to have
similar trends; the highest GPP value of 15.81µg C L-1 h-1occurred in September, 2011
and the lowest GPP value occurred in April, 2012. Conversely, the highest BP value of
0.32 µg C L-1 h-1 occurred in January, 2012 and the lowest value of 0.07 µg C L-1 h-1
occurred in February, 2012. The lack of similar BP and GPP trends in the NEFB further
indicate a lack of bacterio-phytoplanktonic coupling.
Because bacterio-phytoplanktonic coupling was not detected in either region, BP
and GPP were analyzed separately to determine potential environmental drivers for each
(Table 3.1). Community respiration (CR) was measured as the total of bacterioplankton
and phytoplankton respiration. When reviewed separately, a much larger number of
observations, in additional regions, over a longer period of time, were available for
consideration.
Table 3.1. Florida Bay regional summary. Summary samples from each region expressed as mean ±
SD.
Florida Bay Regional Summary Statistics
GPP (µg C L-1 h-1)
CR (µg C L-1 h-1)
BP(µg C L-1 h-1)
BR(µg C L-1 h-1)
BGE
NEFB
WFB
NCFB
SFB
9.0 ± 10.6
22.6 ± 27.8
26.8 ± 30.9
24.4 ± 28.9
5.8 ± 5.5
5.4 ± 5.5
7.1 ± 8.7
5.8 ± 6.3
0.2 ± 0.3
0.4 ± 0.4
ND
0.6 ± 0.8
1.7 ± 1.3
6.1 ± 3.8
ND
ND
23% ± 19%
5% ± 4%
ND
ND
Florida Bay
20.8 ±26.3
6.1 ± 6.4
0.4 ± 0.6
4.2 ± 3.6
13% ± 15%
34
3.2 Environmental drivers for phytoplankton GPP
GPP was not found to be statistically different among regions (NEFB, WFB, SFB
and NCFB) or between seasons (wet and dry) using the non-parametric Wilcoxon ranksum test statistic. However, when the GPP values for each region were examined over
the entire monitoring period, a regression of GPP values over time indicated a significant
decline occurred in the WFB, SFB, and NCFB from September, 2006 to June, 2012
(Fig. 3.4). Additionally, the data pooled from all of the regions indicated a significant
bay-wide decline in GPP over the course of the monitoring period (Fig. 3.5).
Table 3.2. Gross primary productivity and environmental correlations. Statistically significant
(p<0.05) gross primary productivity and environmental parameter Spearman correlation coefficients (ρ) for
each region of Florida Bay.
NEFB
Temp
Salinity
DO
Turb
NH4
NO2
NO3
TSS
TN
TP
SRP
TOC
Chla
Si
0.25
0.21
-0.13
0.12
0.22
0.13
-0.15
-0.29
0.04
0.07
-0.45
0.23
-0.1
0.24
WFB
0.29
0.37
-0.06
0.22
-0.6*
0.35
0.11
0.19
-0.3
0.41
-0.17
-0.02
0.25
-0.23
SFB
NCFB
Total FB
-0.31
-0.24
-0.21
0.79*
-0.23
-0.19
-0.35
0.59*
0.79*
0.02
-0.73**
0.46
0.2
0.16
-0.04
-0.2
0.03
0.94**
-0.37
0.14
-0.07
0.77**
0.49
0.17
-0.24
0.37
0.84***
0.35
0.04
0.18
-0.05
0.36*
-0.31*
-0.07
-0.22
0.46***
0.04
0.2
-0.27
0.2
0.36**
0.2
*=p<0.05 **=p<0.01 ***p=<0.001
35
GPP and environmental correlations
When pooled for the entire Bay, GPP was significantly correlated to turbidity,
NH4+, chl a, and TSS. Because chl a is used as a proxy of phytoplankton biomass, it is
considered a useful measurement rather than a driver. The data were then separated and
tested against environmental parameters within each region to better understand
environmental influences at a sub-regional level. In the WFB, GPP was negatively
correlated to NH4+. In the SFB, GPP was positively correlated to turbidity, TSS and TN,
and negatively correlated to SRP (Table 3.2). In the NCFB, GPP was strongly correlated
to TSS, turbidity and chl a (Table 3.2).
Figure 3.4 Regional gross primary productivity. Gross Primary Productivity (GPP) values for region
per month over the entire AOML GPP monitoring period (September, 2006-June, 2012). Trend lines and
formulae indicated significant trends (p<0.05).
36
Figure 3.5. Florida Bay gross primary productivity. GPP values for the entire Florida Bay for
September, 2006-June, 2012. Trend line and formulas indicates significant trend (p<0.001).
GPP Principal Component Analysis
A PCA was completed for each of the four sub-regions of Florida Bay. The
environmental factor loadings of the first two PCs, the most influential modes of
variation, of each of the sub-regions are displayed in Table 3.3. For the NEFB, PC1 is
highly positively loaded with TP, chl a and TOC and highly negatively loaded with NO3-.
NEFB PC2 is highly positively loaded with turbidity, NH4+, NO2-, NO3- and GPP and
highly negatively loaded with TSS. In the WFB, PC1 is highly positively loaded with the
inorganic nutrients NO2-, NO3-, and SRP. WFB PC2 is highly positively loaded with Si,
TP and chl a. In NCFB, PC1 is highly positively loaded with Si, TSS, chl a, and GPP
and highly negatively loaded with NH4+ and temperature. NCFB PC2 is highly positively
loaded with NO2-, TP and SRP. In the SFB, PC1 is highly positively loaded with the
nutrients SRP, TP and NO3-; and PC2 is highly positively loaded with Si and chl a.
37
Table 3.3. Gross primary productivity regional principal component analysis loadings. Principal
component factor loadings for each region computed separately after VARIMAX rotation. The proportion
of variance explained by each component is displayed at the bottom of each column. For each region, the
cumulative proportion of variance explained by the first two rotated PC’s is displayed at the bottom of each
region’s column. Factors loaded more than 0.50 were highlighted.
PCA Loadings: GPP with Environmental Parameters
NEFB PCA
WFB PCA
NCFB PCA
Loadings
Loadings
Loadings
Temp
Salinity
DO
Turb
NH4
NO2
NO3
TN
Si
TP
SRP
TOC
Chl a
TSS
GPP
CR
Proportion var
Cumulative var
*
SFB PCA
Loadings
PC1
PC2
PC1
PC2
PC1
PC2
PC1
PC2
0.11
0.02
0.11
0.05
-0.76
0.1
0.29
0.32
-0.45
0.00
0.00
-0.03
0.01
0.07
0.43
-0.12
0.45
-0.10
ND
ND
ND
ND
ND
ND
0.04
0.66
ND
ND
ND
ND
ND
ND
-0.04
0.64
0.30
-0.15
-0.76
0.44
-0.03
-0.06
-0.32
0.88
0.97
-0.10
-0.17
0.82
0.12
-0.05
-0.55
0.57
0.94
-0.10
-0.1
-0.04
0.88
-0.08
-0.09
0.10
ND
ND
ND
ND
ND
ND
0.14
-0.17
0.07
0.77
0.66
0.31
0.02
0.95
0.94
0.11
-0.33
0.84
0.26
0.84
0.8
0.42
-0.24
0.10
0.70
0.60
-0.12
0.96
0.96
-0.04
0.86
-0.14
ND
ND
ND
ND
ND
ND
0.69
-0.44
0.01
0.90
0.8
0.18
0.05
0.93
-0.37
-0.56
0.37
-0.01
0.94
0.07
-0.08
0.02
0.00
0.74
-0.13
0.01
0.82
-0.1
-0.1
0.44
0.20
0.05
-0.01
0.11
0.05
-0.15
-0.22
0.22
0.2
0.19
0.39
0.22
0.21
0.32
0.43
0.54
0.22
0.22
0.19
0.41
*Cumulative proportion of variance explained by the first two PC’s.
ND=Not Determined
3.3 Environmental drivers for BP
BP was measured at three stations between 2008 and 2012 representing three subregions of Florida Bay, NEFB, WFB and SFB. The data were analyzed to determine
spatio-temporal differences in environmental influences on BP by the non-parametric
Wilcoxon-Rank Sum test (Table 3.4). NEFB BP measurements were significantly lower
38
than both the WFB and the SFB sub-regions (Table 3.4). All of the regions experienced
and spike in BP for the January, 2010 sample (Fig. 3.5). In the NEFB, the spike reached
1.52 µg C L-1 h-1, despite a NEFB mean BP of 0.20 µg C L-1 h-1 and standard deviation of
0.25 µg C L-1 h-1 over the entire observation period (Table 3.1). In the WFB, the January,
2010 sample had a value of 2.56 µg C L-1 h-1, with a sub-regional mean BP of 0.40 µg C
L-1 h-1 and a standard deviation of 0.43 µg/C/L/h (Table 3.1). In the SFB, the January,
2010 sample had a value of 2.83 µg C L-1 h-1, with a sub-regional mean BP of 0.56 µg C
L-1 h-1 and standard deviation of 0.79 µg C L-1 h-1. Although all three sub-regions display
a high value for BP in January, 2010, only the WFB displays a spike in bacterial
abundance (BA) of 1.2 x 107 cells/ml (Fig. 3.5). The mean BA for WFB over monitoring
program was 3.2 x 106 cells/ml and the standard deviation was 1.9 x 106 cells/ml.
Table 3.4. Bacterial productivity regional and seasonal differences. Significant bacterial productivity
(BP) seasonal and regional differences using Wilcoxon Rank-Sum test.
Regional Differences
BP Wilcoxon Rank-Sum Comparisons
NEFB WFB(higher)
Significance p<0.001
NEFB SFB (higher)
Significance p<0.05
BP and environmental correlations
Because BP varied spatially among sub-regions, environmental influences were
analyzed within each sub-region (Table 3.5). BP was significantly positively correlated
with TN in all three sub-regions (Table 3.5). Furthermore, the January, 2010 spike in BP
at all three sub-regions was followed by a drop in TN in all three sub-regions (Fig. 3.7).
In the NEFB, BP was positively correlated with temperature and TOC using Spearman’s
test statistic. Moreover, in NEFB, BP was negatively correlated with DO. BP was only
39
correlated to BA in the NEFB, indicating the number of cells per unit volume did not
necessarily influence the amount of productivity per unit volume. In the SFB, BP was
also correlated witch chl a.
Figure 3.6. Regional bacterial productivity and bacterial abundance. BP and BA values for region per
month over the entire SERC microbial monitoring period (November 2008-June, 2012).
40
Figure 3.7. Regional total nitrogen. Total Nitrogen (TN) values from July, 2009- June, 2010,
surrounding the January, 2010 spike in BP.
Table 3.5. Bacterial productivity and environmental correlations. Gross primary productivity (GPP)
and environmental parameter Spearman correlation coefficients (ρ) for each observed region of Florida
Bay.
NEFB BP
Temp
Salinity
Turb
DO
NH4
NO2
NO3
TSS
TN
TP
SRP
TOC
Chla
Si
0.42**
-0.13
-0.08
-0.34*
0.22
0.18
0.24
0.20
0.34*
-0.05
-0.08
0.34*
0.28
0.14
WFB BP
SFB BP
0.1
-0.02
0.08
-0.11
0.26
0.00
-0.01
0.12
0.34*
-0.07
-0.29
0.26
0.09
0.12
0.26
0.14
0.1
-0.12
-0.19
-0.05
0.19
ND
0.39**
-0.10
0.25
0.19
0.39**
ND
*=p<0.05 **=p<0.01 ***p=<0.001
ND= Not Determined
41
BP Principal Component Analysis
The environmental factor loadings on the first two components (PCs), which
explained the largest cumulative variation of the six components of each region, are
displayed (Table 3.6). PC1 in the NEFB had positive high factor loadings of turbidity,
NH4+, NO2-, and NO3- and a high negative loading of salinity. NEFB PC2 was highly
Table 3.6. Bacterial productivity regional principal component analysis loadings. Principal
component factor loadings for each region computed separately after VARIMAX rotation. The proportion
of variance explained by each component is displayed at the bottom of each column. For each region, the
cumulative proportion of variance explained by the first two rotated PC’s is displayed at the bottom of each
region’s column. Factor loadings higher than 0.50 are highlighted.
Temp
Salinity
DO
Turb
NH4
NO2
NO3
TN
Si
TP
SRP
TOC
Chla
TSS
BP
BA
PCA Loadings: BP with Environmental Parameters
NEFB PCA
SFB PCA
WFB PCA
Loadings
Loadings
Loadings
PC1
PC2
PC1
PC2
PC1
PC2
-0.23
0.71
0.87
0.00
-0.81
-0.24
-0.55
0.38
0.75
0.13
-0.85
0.05
0.10
-0.89
-0.90
-0.09
0.70
0.34
0.73
-0.04
-0.32
0.02
0.30
0.80
0.66
-0.06
0.16
-0.03
-0.13
0.03
0.76
-0.11
-0.09
0.09
-0.08
0.03
0.65
-0.06
-0.01
0.52
0.49
-0.09
0.09
0.10
0.24
0.13
-0.19
-0.06
-0.05
0.30 ND
ND
0.04
0.16
-0.15
0.01
0.21
0.14
-0.15
0.80
0.11
-0.64
0.05
0.05
0.19
0.15
0.09
-0.02
-0.06
0.79
0.59
-0.10
0.20
0.57
0.29
0.22
0.13
0.78
0.30
0.02 ND
ND
-0.10
0.19
0.15
0.09
0.35
0.59
0.20
0.02
-0.22
0.07
0.13
0.72
-0.13
0.18
Proportion var
Cumulative var *
0.16
0.14
0.30
0.19
0.14
0.33
*Cumulative proportion of variance explained by the first 2 PC’s
ND=Not Determined
42
0.17
0.14
0.31
positively loaded with temperature and chl a and was negatively loaded with DO and
SRP. In the WFB, PC1 was highly positively loaded with DO and TOC and negatively
loaded with temperature and salinity. WFB PC2 was positively loaded with turbidity, TP
and chl a. In the SFB, PC1 was positively loaded with temperature and salinity and
highly negatively loaded with DO. PC2 in SFB was positively loaded with NO3-, TOC,
BP and BA. DO, temperature, and salinity were the only environmental parameters to
heavily load either PC1 or PC2 in all three sub-regions; however, these factor loadings
were not consistently positive or negative throughout indicating a spatial difference in
ecosystem dynamics for each sub-region.
3.4 BGE
In the WFB, the highest BGE of 20% was achieved in May (Fig. 3.8). The lowest
BGE value of 1% occurred in April during the end of the dry season (Fig. 3.8). In the
NEFB, the highest BGE value of 44% occurred during the end of the dry season in April
and the lowest BGE value of 9% occurred during the dry season in March. The highest
BGE values for each sub-region occurred during the two different seasons; conversely,
the lowest BGE values for each sub-region occurred during the end of the dry season.
Using the non-parametric Wilcoxon-Rank Sum statistic to test for sample
Figure 3.8. Bacterial Growth Efficiency. BGE values for each region displayed per month.
ND= Not Determined
43
seasonal and spatial variation, no significant spatio-temporal trends were found, possibly
due to a low number of samples (n). The BGE measurements were then pooled to test for
significant correlations with the environmental parameters over the entire Florida Bay.
The mean BGE of the pooled data was 0.13 and the standard deviation of the pooled data
was 0.13. Spearman correlation tests indicated that the only environmental parameter
BGE was significantly correlated to TN (Fig. 3.9).
Figure 3.9. Bacterial growth efficiency vs. total nitrogen. BGE is plotted against TN displaying
correlation (p < 0.01).
44
CHAPTER 4.0 DISCUSSION
I hypothesized that the WFB sub-region, isolated from allochthonous nutrient and
DOM input, would show strong bacterio-phytoplanktonic coupling and the NEFB subregion, closer to allochthonous DOM inputs, would show less significant bacteriophytoplanktonic coupling. The results of this study showed that bacterio-phytoplanktonic
coupling was not significantly present in either sub-region of Florida Bay despite the
spatial gradient. The lack of coupling throughout the entire bay indicated that the
bacterioplankton are using DOM other than phytoplankton exudates for their primary
sustenance. Additionally, I hypothesized that BGE would increase in the more
productive region and that nutrients would correlate to BGE levels enhancing microbial
loop efficiency. The BGE levels were relatively higher in the less productive region
(NEFB); however, a significant correlation of BGE with TN was found throughout the
Bay. Because bacterio-phytoplanktonic coupling was not present in the Bay, the data
suggests other bioavailable sources of DOM were utilized by the bacterioplankton.
Knowing sources and concentrations of DOM can help understand the preferred
sustenance for the bacterioplankton. In Florida Bay, sources and lability of DOM vary
spatially and have been investigated in a number of studies using varying methods
(Childers et al., 2006, Williams and Joechem 2006, Cho et al., 2013, Sutula et al., 2003.)
Cho et al. (2013) examined fluorescence qualities of DOM to determine sources and
lability. Maie et al. (2012) used excitation emission matrix fluorescence with parallel
factor analysis to determine drivers of DOM composition and sources. Furthermore,
Florida Bay DOM quality has been determined to be influenced by Everglades DOM
inputs (Maie et al., 2012, Maie et al., 2006b, Jaffe et al., 2008). Sutula et al., (2003)
45
suggested that up to 85% of refractory DOM in Florida Bay is supplied by the freshwater
input from the Everglades, a substantial freshwater vector into the bay. Still Borsheim et
al., (2005) suggests grazing of herbivores as another source of DOC due to an estimated
egestion rate of 20-40%; part of the egested particulate detritus can be converted quickly
to DOC via hydrolysis.
Bacterio-phytoplanktonic coupling trend analysis aids in pinpointing source DOM
used for heterotrophic recycling via the microbial loop. The results of this study indicate
BP is at least partially subsidized by sources of labile DOM other than from
phytoplankton due to the lack of coupling, both temporally and spatially. Another
autochthonous primary producer in Florida Bay is seagrass (Fourqurean et al., 2012)
which could be providing a more bioavailable source for the bacterioplankton.
4.1 Florida Bay bacterio-phytoplanktonic coupling
Examining coupling trends spatially can better determine ecosystem dynamics.
Many studies have determined coupling characteristics varying over spatial gradients
(Table 4.1) (Rochelle-Newall et al., 2008, Berra-Alba et al., 2008, Moran et al., 2002). A
known salinity and nutrient gradient has been determined for Florida Bay (Maie et al.,
2006; Boyer et al., 2006) resulting in varying environmental correlations with
productivity measurements. Although significant coupling trends were not found in
either the WFB or NEFB sub-regions, data were still examined for temporal trends in
each region.
Coupling fluxes did occur in Florida Bay (Fig. 3.3); however, findings were not
statistically significant. Although coupling was not significant, examination of the
BP:GPP ratio suggests a relatively similar trend in the WFB. The low Florida Bay mean
46
ratio of 0.15 is similar to that expected of an oligotrophic oceanic ecosystem (Anderson
and Turley 2003; Cole 1998; Anderson and Ducklow 2001 ). Cho et al. (2001)
investigated the BP:GPP ratio in the Yellow Sea and found a ratio even as low as 0.03 in
one instance. Although the Florida Bay mean ratio of BP:GPP is similar to that of an
oceanic ecosystem, the lack of coupling is more similar to freshwater ecosystems or
estuaries (Table 4.1) indicating that allochthonous or other autochthonous sources of
DOM are subsidizing BP.
Table 4.1. Worldwide bacterio-phytoplanktonic coupling studies. Coupling values across numerous
worldwide aquatic ecosystems.
Study Site
Urdaibai Estuary
Ecosystem
Estuary
Author
Revilla et al., 2000
Method
ratio examination
Hopavagen
Lagoon of Venice
New Caledonia
Chilean fjord
Ria De Aveiro, Portugal
Coastal Bay
Estuary
Estuary
Estuary
Estuary
Borsheim et al., 2005
Pugnetti et al., 2010
Rochelle Newall et al., 2008
Gonzalez et al., 2010
Almeida et al., 2002
mesocosm fertilization study
DPP and BCD analysis
DPP and BP analysis
ratio examination
ratio examination
Chilean fjord
Canane´ia-Iguape, Brazil
Estuary
Estuary
Montero et al., 2010
Barerra-alba et al., 2008
trends over time
correlations/spatial analysis
Hudson River
Fresh
Findlay et al., 1996
BP source DOM analysis
Lake Neusiedl
Fresh
Reitner et al., 1999
BCD, BP and PP analysis
Sep Reservior
Literature Review
Literature Review
Fresh
Fresh
Marine
Jugnia et al., 2007
Fouilland and Mostjir, 2010
Ducklow, 1999
ratio examination
literature review
literature review
Yellow sea
Offhore Antarctic Ocean
Marine
Marine
Cho et al., 2001
Moran et al., 2002
ratio examination
correlation
Equatorial Indian Ocean
Marine
Fernandes et al., 2008
ratio examination
Southern Ocean
Marine
Duarte et al., 2005
mesocosm study
Coupling
no coupling (ratio 0.17-0.39)
yes coupling (bacterial response
to algal bloom die off)
no coupling (BCD>DPP)
spatial coupling
Seasonal coupling
Seasonal coupling
Seasonal coupling
coupling during windy season
Spatial coupling
No coupling - more allochthonus
sources
No coupling (BP more dependent
on macrophyte production)
No coupling (inconsistent
ratio 0.20-0.05)
No coupling
Yes coupling (average ratio 0.20)
No coupling (ratio 0.03-0.40)
**hydrological controls
Yes coupling (average ratio 0.30)
No coupling (ratio 1.02-1.88)
**top down grazing pressure
Moderate coupling
(ratio 0.01-0.10)
**top down grazing pressure
The NEFB data did not display any temporal coupling trends. However, when
analyzed separately, the standard deviation of the NEFB sub-region was much greater
indicating a larger variability in the data which could be explained by environmental
47
fluctuations in the sub-region. The highest and lowest ratios recorded were in the NEFB
sub-region indicating even less coupling than the WFB further suggesting that
bacterioplankton are in part subsidized by allochthonous sources of DOM or other
autochthonous sources of DOM in this region. In April 2012, near the end of the dry
season, the highest recorded BP:GPP ratio indicated heterotrophic productivity was
almost as high as autotrophic productivity in the NEFB sub-region. Data from the
National Data Buoy Center indicated a decline in salinity during the last week of April,
2012 likely due to precipitation from the onset of the wet season
(http://www.ndbc.noaa.gov/). The coupling measurements were sampled the week
before the salinity drop when salinity had reached the highest levels recorded (31-32 ppt)
to that point for 2012 (http://www.ndbc.noaa.gov/). Although freshwater input from the
Taylor Slough and C-111 canal have been determined to be present NEFB, it only
consists of 10% of the total freshwater inputs into the region on average per year (Kelble
et al., 2007). Furthermore, Kelble et al. (2007) determined a four month lag time in
runoff following the onset of the wet precipitation season. A study of the fluorescence
properties of DOM indicated that much of the DOM in the eastern region of the bay
(subdivision indicated by Boyer et al., 1997 which partially coincides with the NEFB
region used in this study) comes largely from a marine component indicating
autochthonous DOM sources such as seagrass and other phytoplankton; although a
terrestrial signature was noted when salinity was below 24 ppt. During our April 2012
sample salinity was still higher, thus autochthonous sources other than phytoplankton
GPP were likely supporting the consistent BP measurements. Studies have found that up
to 33% of mangrove litter results in DOM leachates (Aiken et al., 2011, Davis et al.,
48
2003) and 64-94% of the leachates were incorporated into bacterial biomass (Aiken et al.,
2011, Benner et al., 1986). Mangrove fringe line and mangrove keys throughout the bay
are expected to subsidize the DOM pool but determination of the extent is difficult (Maie
et al., 2012). It is likely that bacterioplankton are utilizing DOM derived from sources
other than phytoplankton in the NEFB sub-region; perhaps mangroves, seagrass, grazeregested dissolved detritus. Top down controls could be in play here as well, such as
varying grazing pressures on bacteria versus phytoplankton.
No significant correlation between BP and phytoplankton PP was found in the
WFB sub-region. However, when examined temporally, similar trends in BP and GPP
measurements in the 2012 transition from dry to wet season in WFB were observed (Fig.
3.3). Both BP and GPP spike in May 2012 after the beginning of the wet season and the
occurrence of systemic drops in salinity (http://www.ndbc.noaa.gov/) which could be due
to a dominance of seagrass production (Zieman et al. 1989) in the dry season
transitioning to phytoplankton GPP dominance in the wet season. Additionally, DOM
associated with the mangrove rivers zone of the Florida Coastal Everglades was found in
the western portion of Florida Bay indicating an allochthonous vector of DOM in the
region (Maie et al., 2006). When considering the four month lag time (Kelble et al.,
2007), the similar trends beginning in April 2012 could be in part due to the end of the
runoff from the 2011 wet season carrying mangrove derived DOM. In Feb. 2012, a high
ratio of BP to GPP was due to relatively low phytoplankton GPP levels coupled with
relatively higher BP levels, though phytoplankton GPP still dominated the region. Apple
et al. (2008) found that warmer temperatures lead to a stronger bacterio-phytoplanktonic
coupling (BP: chl a relationship) during these times. Similarly, in WFB, though the
49
results were not conclusive, the trends indicate a more closely coupled relationship (Fig.
3.3) in the transition from dry to wet season as in situ water temperature increases with
summer approaching. These results could indicate coupling in WFB for the transitional
period; however, the number of samples it too low to determine statistical significance.
Another explanation for the similar trends could be that similar environmental drivers
influenced GPP and BP measurements during this time.
4.2 Regional Drivers for BP, GPP and BGE
Bacterial productivity, phytoplankton PP and BGE were examined separately for
environmental drivers in each region as well to attempt to explain the lack of coupling.
Also, the number of samples for each region was much higher when examined separately.
Although the results did not indicate significant coupling, when BP and GPP were
examined separately, some similar environmental drivers were found (Table 3.2; Table
3.5). It is important to consider these processes separately as similar environmental
drivers can affect BP and PP differently. As such, Apple et al. (2008) found an
overwhelming effect of temperature driving bacteria processes more so than
phytoplankton processes.
Phytoplankton Primary Productivity
When considered separately, significant regional and spatial differences in GPP
measurements across the four sub-regions of Florida Bay were not found. However, a
significant decline in phytoplankton GPP was detected from 2006-2012 (Fig. 3.5).
Briceno and Boyer (2010) highlight a decline in chl a (used as a proxy for phytoplankton
biomass) from 1991 through 2007 in the Bay in their research as well. Though a decline
50
could indicate that the pelagic ecosystem in Florida Bay is becoming more oligotrophic,
the significant declining trend could be a spurious finding as a result of a bloom in the
Bay in 2007. Across the bay, a phytoplankton bloom, evidenced by increased chl a
concentrations, in February-May 2007, triggered a spike in productivity at every site (Fig.
3.4). The Bay-wide bloom could have been caused by wind driven resuspension of
sediments with it containing P. Increased TSS and turbidity could suggest resuspension
of nutrient and DOM rich sediment influencing phytoplankton GPP levels.
However, because of the systemic spatial heterogeneity, data were analyzed for
environmental influences on phytoplankton GPP for each sub-region.
The NEFB sub-region had the lowest phytoplankton GPP values of the four subregions investigated by half (Table 3.1). It is well documented that the Florida Coastal
Everglades are P-limited and that limitation would be maximized in the NEFB sub-region
furthest from P subsidies from the GOM (Boyer et al., 1997; Childers et al., 2006). The
sub-regional analysis of environmental parameters indicated an inverse relationship of
CR and SRP in the NEFB which could represent a systemic microbial community shift
from heterotrophy to autotrophy in the presence of the bioavailable inorganic P form;
therefore, this result could be indicative of the known P-limitation in the NEFB subregion. Additionally, a positive organic matter vector (Table 3.3) within the NEFB subregion, suggested the importance of labile organic matter in temporal environmental
fluctuations in this sub-region. There is a positive inorganic N vector explaining
variability in the NEFB (NEFB PC2, Table 3.3) as well. A corresponding rise in TN and
turbidity occurred in the NEFB in May, 2007 during a phytoplankton bloom which
indicated that TN corresponded to phytoplankton biomass and increased levels of TN
51
could have resulted in increased phytoplankton biomass. During this time, P species
remained consistently at least an order of magnitude lower than N species. The results
could suggest a secondary N-limitation on phytoplankton GPP in the NEFB sub-region or
a potential N and P co-limitation.
In the WFB, mean GPP was more than double that of the NEFB, despite mean CR
being lower than the NEFB region. A significant negative relationship between NH4+
and phytoplankton GPP was found in the WFB. The WFB has been suggested to be Nlimited (Boyer et al., 1997; Childers et al., 2006); therefore, the negative relationship
could represent the end-member in this situation, where the N is completely utilized and
results in an apparent negative relationship although actually representing N-limitation.
Conversely, an elevated GPP rate in May 2007 corresponded to elevated levels of TP,
SRP, and TOC in the WFB which could suggest that GPP may be dependent on P in this
region as well. At such low productivity levels, subtle changes in nutritional lability
clearly have confounding effects on microbial dynamics as evidenced: phytoplankton
appear to be limited by both N and P in this sub-region.
In the WFB sub-region, a strong influence of inorganic nutrients NO2-, NO3-, and
SRP was present (Table 3.3) which highlights the importance of nutrient dynamics and
the subsequent effects on microbial productivity and also supports the idea of N and P colimitation in the WFB. Additionally, silicate (SiO2), which represents the allochthonous
freshwater vector that has been measured in the WFB in previous studies (Jurado et al.,
2007) and has been determined to be present in the WFB (Fourqurean et al., 1993; Lee et
al., 2006; Kelble et al., 2007) was indicated as a source of environmental variability in
52
the sub-region. The influence of allochthonous DOC could in part account for the lack of
bacterio-phytoplanktonic coupling in the WFB as well.
In the SFB sub-region, productivity levels were similar to those in the WFB,
though the SFB was subjected to convergence of the N-limited GOM and Shark River
Slough influenced WFB and the P-limited Taylor Slough and C-111 canal influenced
NEFB. The convergence was highlighted by significant correlations between
phytoplankton GPP and turbidity, TSS, and TN and a significant negative correlation of
phytoplankton GPP with SRP. The positive correlation of TN to GPP could also indicate
N-limitation in the SFB sub-region. Additionally, an N and P co-limitation could have
occurred in the SFB as well. The negative correlation to the labile inorganic SRP could
represent an end member suggesting P-limitation, i.e. the phytoplankton have used all of
the bioavailable SRP and thus has resulted in lower levels of SRP correlated to higher
levels of GPP in the SFB which could have resulted in a secondary limitation of TN after
the SRP was depleted.
In the SFB, the largest proportion of variation was explained by an inorganic
nutrient vector containing SRP and NO3- (Table 3.3). The second node of variation
contained chl a and SiO2. The SiO2 again represented an influence of a freshwater runoff
vector in the SFB sub-region. In July, 2007, the phytoplankton GPP spike corresponded
with elevated levels of TN, TP, and SiO2 as well as chl a and TSS further indicating the
importance of nutrient limitation and co-limitation and potential resuspension of
sediments in the SFB sub-region.
The NCFB is isolated by geomorphological mud banks and is subject to longer
residence times. Significant positive correlations between phytoplankton GPP and TSS,
53
chl a, and turbidity were found in this region which could indicate wind-driven
resuspension of sediments rich in nutrients and labile organic matter positively influenced
phytoplankton GPP in the NCFB sub-region. During the February, 2007 phytoplankton
GPP spike, corresponding relative elevations in TN were indicated as well which could
represent a possible N-limitation in the NCFB sub-region. In the NCFB the largest
proportion of variation was explained by a vector largely positively influenced by SiO2,
chl a, TSS and GPP, and negatively influenced by NH4+ and temperature which
represented a phytoplankton vector and a potential freshwater vector, as indicated by the
SiO2 (Jurado et al., 2007), in this sub-region. The correlation of GPP to turbidity and
TSS could also indicate the GPP in the NCFB is influenced by wind-driven suspension of
sediments (Sutula et al., 2003). Additionally, the importance of inorganic nutrients and P
species in the region were highlighted by a node of variation containing NO2-, TP and
SRP.
Bacterial Productivity
Ducklow (2001) describes the net effect of BP as moving organic matter from one
pool to another and is an important tool in quantifying ecosystem efficiency and creating
C budgets (Cole and Pace 1995). Baines and Pace (1991) estimated that 10% of primary
production is re-oxidized by autotrophic respiration and another 13% is lost to the C pool
(Kritzberg et al., 2005). Since BP is dependent on labile DOM, if sources other than
phytoplankton are providing more bioavailable DOM, the bacterioplankton will use labile
sources instead of phytoplankton PP. Other factors influence BP in addition to source
DOM, such as nutrients, temperature, salinity and top down grazing pressures. Ogebebo
54
and Ochs (2008) even found that ultraviolet radiation, when added with N and P, resulted
in a confounding effect on BP.
In Florida Bay, significant regional differentiation in BP was determined.
Additionally, although phytoplankton GPP declined over the study period, BP displayed
no such trend. Dissimilar trends could indicate separate environmental drivers for
phytoplankton GPP and BP, which cannot be substantiated due to the lack of BP data for
2006-2007. A spike in BP in January, 2010 corresponded to an order of magnitude drop
in TN (Fig. 3.7) at all three sites and TN was significantly correlated to BP at all three
sites as well. The positive correlation of BP and TN, which was carried throughout the
entire bay, could indicate an N-limitation of BP in the bay. Nitrogen has been
determined to influence BP in other coastal ecosystems (Cuevas et al., 2011).
In the NEFB, the analysis of BP and environmental variables conveyed that
inorganic N species and turbidity explain much of the variance in the sub-region, further
indicating an influence of N in the sub-region which could affect BP (Table 3.6). The
second node of variation contained temperature which has been proven to positively
influence BP rates (Lee et al., 2009). In the WFB sub-region the mean BP was also
higher than in the NEFB sub-region (Table 3.1). The TN and BP positive correlation was
also found in the WFB, indicating the same potential N-limitation of BP. Additionally,
the January, 2010 spike in BP values corresponded to elevated levels of chl a, turbidity
and TOC in addition to the drop in TN. Although the drop in TN with corresponding
elevations in BP is apparently unexpected due to the correlation of TN and BP, the drop
could also represent the end member in which all of the TN was used by the BP thus
further supporting the idea of N-limitation in the WFB sub-region.
55
In the SFB sub-region, BP was significantly positively correlated with both TN
and chl a. The relationship between chl a and BP has been studied as a proxy for
bacterio-phytoplanktonic coupling (Apple et al., 2008) and could represent a slight
dependence of BP on phytoplankton exudates in the SFB sub-region (Fig. 3.2). Although
the SFB was not analyzed for coupling in this study, further investigation into coupling at
this site could provide information about BP dependence on phytoplankton in the subregion. In the SFB, a vector comprised of temperature and salinity, explaining the largest
proportion of variation, highlighted the importance of seasonal variation in the SFB. A
second node of variation indicated the relationship of BP and BA with organic carbon
and inorganic N. Furthermore, the January 2010 bay-wide spike in BP also corresponded
to drops in TN and TON in SFB and spikes in NH4 and DIN which could indicate the
preference of BP for organic N species followed by an inorganic N-limitation.
Bacterial Growth Efficiency
BGE is a measure of the percentage of organic carbon incorporated in the bacterial
biomass; the rest lost as CO2 via respiration (Apple and del Georgio, 2007). Similarly, it
is generally understood that transitioning along a gradient of oligotrophic marine
environment to a productive coastal environment, BGE would tend to increase (Apple
and del Georgio, 2007) due to a higher BCD (Apple and del Georgio, 2007). Del Giorgio
and Cole (1998) suggest the increase in BGE over this environmental gradient is due to
decreased respiration rates as the ecosystem transitions to increased productivity. In
addition to coupling, BGE can indicate DOM lability and help understand microbial loop
dynamics.
56
Overall, BGE rates in Florida Bay (1-44%) fell within the literature of marine and
estuarine ecosystems worldwide (Table 4.2); however, when each region was considered
separately they behaved differently. Understanding the role of regional and seasonal
environmental drivers which influenced these dynamics is critical. Although the results
do not indicate significant spatial and temporal heterogeneity between the BGE values,
the figures do display varying BGE values in each sub-region when examined separately
(Fig. 3.6). BGE was also correlated to TN when the samples were pooled. The seasonal
discrepancy in each sub-region could be caused by the seasonal bioavailability of TN in
each region.
Historically, the regions represent a gradient of nutrient limitations and varying
allochthonous inputs, providing contrasting environmental drivers on microbial
productivity dynamics. Table 4.2 displays a summary of BGE measurements over
varying ecosystems. The data indicated that mean BGE levels bay-wide (Table 3.1) are
similar to relatively low BGE levels of marine and oligotrophic estuarine ecosystems
which indicates that Florida Bay is more like an oceanic ecosystem with regards to
secondary productivity and incorporating productivity into biomass than a typical
estuary.
The NEFB had a BGE range of 9-44% which is higher and more indicative of a
coastal productive ecosystem (del Giorgio and Cole, 1998) than the WFB sub-region.
The highest BGE value in the NEFB sub-region was measured during the dry season in
April 2012 just before the transition to the wet season. The wet season provides more
allochthonous nutrients and DOM in the NEFB region via runoff in addition to higher
temperatures, which could account for the high BGE values during this time. Although
57
the BGE values are similar to that of an estuary in this region (Table 4.2), productivity
levels are relatively low in this region indicating that different drivers influence
productivity versus BGE. For instance, N species may affect BGE more intensely than P
in this region as demonstrated in the positive correlation of TN and BGE (Fig. 3.7).
Table 4.2. Worldwide bacterial growth efficiency values. BGE values across numerous worldwide
aquatic ecosystems.
Study Site
System Type
BGE
Reference
Oregon Coast
Temperate
Ocean
13% +/- 1% spikes to 20% in productive
areas
and dips in oligotrophic near gyres
del Giorgio et
al., 2011
Norwegian Sea
Arctic Ocean
In situ average: 20%
34-95% with addition of N,P,C
lab experiments
Sargasso Sea
Ocean
4-30%
LA Shelf
Subtropical
Ocean
18-55%
Hansell et al.,
1995;
Carleson and
Ducklow 1996
Biddanda et al.,
1994
Upstream
Monie
Bay, MD
Temperate salt
marsh estuary
30-34% average 6-68% limits
Apple and del
Giorgio 2007
Temperate
Estuary
18-61%
Findlay et al.,
1992
Brackish estuary
25-60% mean ~40%
Kroer 1993
Anabanilja
Reservoir
subtropical Lake
14-30%
Amazon River
Tropical River
3-46%
Hudson River
Santa Rosa
Sound
Cuevas et al.,
2011
Romanenko et
al., 1976;
del Giorgio &
Cole 1998
Benner et al.,
1995
Although this study did not incorporate DOC and DOM lability measurements,
DOM sources and sinks in Florida Bay have been studied using many different methods
to understand their implications for the ecosystem as a whole (Maie et al., 2012, Chen et
al., 2013, Troxler et al., 2013). In fact, Chen et al. (2013) characterized two of the
Florida Bay sites used in this BGE study to investigate the optical properties of DOM.
58
They found that during the dry season, WFB had the lowest values of DOC recorded in
the Greater Everglades. These low values of DOC could contribute to the low bay-wide
BGE values measured which is consistent with a review of over 328 BGE studies
spanning over 30 years across four classes of aquatic ecosystems that concluded BGE
increases in more productive coastal ecosystems and decreases in open ocean
oligotrophic ecosystems (del Giorgio and Cole, 1998). Despite many studies finding BP
and BGE to be significantly correlated (del Giorgio et al., 2006; Apple and del Giorgio,
2007), the Florida Bay study did not result in similar findings. In Florida Bay, BGE was
relatively lower in the WFB sub-region which had higher BP and relatively higher
phytoplankton GPP values. Due to the relatively low BGE values, the WFB was more
indicative of a marine oceanic ecosystem (Table 4.2) which appears to be a testament to
the ecosystem’s oligotrophic nature. The finding suggests that different drivers may
affect BP and GPP versus BGE in certain circumstances. The WFB had lower BGE
levels than the NEFB sub-region. These lower levels are at least partly due to much
higher levels of BR in the sub-region (Table 4.2). Lee et al. (2009) found respiration had
apparent different drivers from BP, such as substrate quality and temperature. Higher BR
levels can also be caused by increased stressors on bacterioplankton in oligotrophic
conditions such as nutrient and DOM limitations. Environmental influences on BR in the
WFB sub-region is worth further investigation due to the interesting sub-regional
dichotomy of the data. Furthermore, because of the significant positive correlation of TN
to BGE (Fig. 3.7), the N-limitation in the WFB could possibly limit the BGE in the WFB
as well. Perhaps the DOC and DOM fueling relatively higher BP and phytoplankton
GPP rates in the WFB sub-region are also fueling higher BR rates leading to low BGE
59
values. It is important to note that when the bay is considered as a whole ecosystem, the
levels are consistent with estuaries worldwide (Table 4.2). Further investigation into
BGE levels across the bay could help elucidate the results of this analysis.
As with many ecological assessments, the need for more knowledge regarding
aquatic bottom-level trophic dynamics is pressing in order to have a better understanding
of the ecosystems. Models continue to become more comprehensive as scientific
knowledge develops with each study. Furthermore, studies incorporating many microbial
trophic processes attempt to elucidate more encompassing dynamics in the ecosystem.
For example, Legendre and Rivkin, in their review in 2008, synthesize phytoplankton
GPP and BP dynamics in pelagic marine and freshwater ecosystems through a
compilation of microbial loop studies and propose a “Microbial Hub” approach. The new
model assessing trophic dynamics offers a new approach to studying the microbial food
web, simultaneously analyzing microbial interactions with higher trophic levels. In the
Florida Bay study, top down controls were not examined but are worth future
examination.
Furthermore, when studying biogeochemical processes and bottom-level trophic
dynamics, it is difficult to perform experiments which answer all questions, account for
all biases, and represent entire ecosystems. Often studies will mention in the discussion
the need for more data (Pringault et al., 2009). Similarly, authors who develop and run
models usually state the limitations of the model or excluded parameters (Passow et al.,
2007) since methods used do not and cannot always account for potential limitations. For
instance, in 2009, Pringault et al., tested the common ecological experimental assumption
that CR in the dark is the same as CR in the light when analyzing water column
60
productivity and respiration levels and found that CRlight can be up to 8 times the rate of
CRdark which clearly underscores a methodological weakness of a commonly used
experiment in ecological assessments and should be noted in data analyses. Similarly,
ecological models will often fail to include all potential factors into their development.
For example, Faure et al. (2010) created a model for the oligotrophic SW Lagoon in New
Caledonia which incorporated bacterioplankton and DOM to better capture the recycling
dynamics of the microbial loop; however, an insufficient scientific understanding of
microbial community dynamics and the inability to incorporate the quantitative lability of
DOM into the model limit its accuracy. Carlson et al. (1999) attempted to remove error
associated with proxies and separate sample jars by measuring mass carbon cycles
directly ex situ from sealed samples from the Ross Sea. Inherent methodological
limitations are unavoidable and our research in Florida Bay is no different.
When evaluating our bacterio-phytoplanktonic coupling data, it should be
remembered that the phytoplankton GPP and BP were measured separately using
different methods most often on different days. While different methodology is
necessary for varying measurements, limitations of extrapolating conclusions must be
considered when discussing results. For instance, bacterioplankton species composition
has been found to have an effect on BGE; however, our study did not include a species
composition analysis. A species composition analysis in the study site could enhance the
results found and explain further the BGE and TN correlation.
61
CHAPTER 5.0 CONCLUSIONS
Understanding the bacterio-phytoplanktonic relationships is essential to
comprehending the ecosystem’s trophic functions at a microscopic level. Determining
how hydrogeology, i.e. runoff, affects DOM and nutrient flows (which furthermore
influence the adjacent estuarine ecosystems) is essential to facilitate this understanding.
Analyzing the heterotrophic and autotrophic link helps determine the carbon
bioavailability and microscopic ecosystem dynamics. Many studies have been conducted
in temperate climates (Fouilland and Mostajir 2010, Barrera-Alba et al., 2008, RochelleNewall et al., 2008) to quantify and assess bacterio-phytoplanktonic coupling in estuaries
over spatial and temporal scales which is critical to analyzing the sources and sinks of
organic carbon and its biogeochemical dynamics in the estuarine bottom trophic level.
Despite the number of studies, few have been conducted in tropical climates (RochelleNewall et al., 2008). Tropical aquatic ecosystems are generally oligotrophic and are
characterized by high temperatures, high light attenuation, low nutrients and low chl a
concentrations (Rochelle-Newall et al., 2008). Florida Bay exhibits these oligotrophic
characteristics and thus is sensitive to subtle environmental changes.
Bacterio-phytoplanktonic coupling trends are affected by a number of
environmental influences present in the ecosystem in which they are examined. There
exist no concrete standards regarding the exact conditions which dispel or create these
specific coupling trends, only environmental factors which enhance or hinder the
apparent trends in the data. In freshwater ecosystems, allochthonous sources of DOM
appear to result in weak PP and BP coupling dynamics. In the open ocean studies,
biological grazing factors, temperature, and water column stability appear to influence
62
coupling. In estuarine ecosystems, many other factors have been correlated to bacteriophytoplanktonic coupling trends including runoff, physical characteristics, climate, land
structure, chemical composition, and biological controls. In Florida Bay, a lack of spatiotemporal bacterio-phytoplanktonic coupling was determined due to the dynamic and
convergent nature of the ecosystem, though some similar sub-regional and spatial trends
were discussed. Furthermore, other sources of autochthonous and allochthonous DOM
are more bioavailable to these bacterioplankton than the DOC and DOM provided by the
phytoplankton community.
Because of the lack of bacterio-phytoplanktonic coupling in Florida Bay,
potential increase in phytoplankton blooms associated with increased runoff from the
proposed C-111 Spreader Canal Western Project, part of the greater CERP, should not
theoretically affect BP or the trophic efficiency of microbial loop recycling in Florida
Bay. However, changes in these environmental influxes may influence BP
independently of the phytoplankton community. For instance, the results of the study
indicate BP and BGE are significantly correlated to TN throughout the ecosystem.
Potential increases in N inputs could therefore trigger higher rates of BP and BGE in
Florida Bay which could potentially increase the amount of C stored in the bacterial
community and decrease the amount lost as inorganic C through respiration. The
bacterial role in C cycling should be considered when investigating C budgets in
Florida Bay.
Florida Bay is an environmentally significant ecosystem providing home not only
for mangroves and seagrasses, but also for economically relevant juvenile fish species
including spotted sea trout, grey snapper, red drum, among others (Tilment 1989). In
63
order to protect these habitats and their species diversity, we must fully understand how
the base of the food web is affected by changing environmental conditions. Studying the
bacterio-phytoplanktonic relationships in this ecosystem has provided information critical
to comprehending the ecosystems trophic functions at a microscopic level. Measuring
bacterio-phytoplanktonic coupling has provided one critical tool for analyzing how
hydrogeology, i.e. runoff, associated with DOM and nutrient flows, (which furthermore
influence the adjacent estuarine ecosystems) affects heterotrophic and autotrophic
interactions which is essential to understanding future implications of the CERP, and, on
a larger scale, the possible impacts from global Climate Change and population growth.
64
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