EFFECTS OF DISSOLVED ORGANIC CARBON AS A BACTERIAL GROWTH
SUBSTRATE AND AS A N ULTRAVIOLET-B RADIATION SUNSCREEN FOR
AQUATIC MICROBIAL FOODWEBS IN MACKENZIE DELTA LAKES,
N O R T m S T TERRITORIES.
Christopher J. Teichreb
B.Sc. Hons. University of Regina 1995
A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
in the Department
of
Biological Sciences
O Christopher J. Teichreb 1999
SIMON FRASER UNIVERSITY
August 1999
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ABSTRACT
The potential effects of dissolved organic carbon @OC) as food supply for
bacteria versus its effects as an attenuator of ultraviolet-B radiation W B ) for aquatic
microbial foodwebs in lakes of the Mackenzie Delta was assessed by conducting a
lirnnocorrai experiment and comparing the results to observations fiom 40 lakes
representing a range of DOC concentrations and W B penetration depths in the delta.
The limnocorrals (bdanced ûiplicated design, 12 iimnocorrals in totai, 2x2 week
durations) received either modest additions of humic DOC to reduce UVB penetration to
50% of surface values at 10 cm depth and modestly increase bactenal food supply
(+DOC, 4.5 r n g - ~ - humic
l
DOC), sufficient DOC to reduce UVB to 1% and substantially
increase bacterial food (*DOC,
12.5 r n g - ~ - lhumic DOC), Mylar-D screening to reduce
UVB to 1% without altering ambient DOC (-WB, 3.6 r n g - ~ -hurnic
l
DOC), or were left
unaltered where W B penetration at 10 cm depth was 64% of surface values (Control, 3.6
r n g - ~ -humic
l
DOC).
Relative to the control, bacterial production increased by 15% in the -UVB
treatment, 25% in +DOC, and 57% in ++DOC. However, highest bacterial biomass
accumulation was in the +DOC (+53%) treatment followed by - b . B(+40%). The
largest additions of DOC (*DOC)
resulted in decreased bacterial biomass relative to the
Control (-15%). Among potential food web effects on the bacterial community, the
decrease in bacterial biomass despite increased bacterial production is best accounted for
by changes in nanoflagellate (bacterial grazers) abundance (+100% in ++DOC). Vims
abundance directly tracked changes in bacterial biomass among the treatrnents and
appeared to be a consequence of host availability rather than a control on bactenal
biomass. Phytoplankton biomass changed modestly among the treatments (+Il % to -8%)
and could not account for the changes in the bacterial community via competition for
...
111
nutrients. Zooplankton biomass changed considerably among the treatments (+350% to
+70%) but appeared to be tracking potential phytoplankton production or nanoflagellate
biomass rather than bacterial production.
Bacterial biomass, viral biomass, and phytoplankton biomass among 40 delta
lakes revealed patterns of change, as a fiinction of DOC concentrations, that were
consistent with the outcome of the limnocorral expriment. However, nanoflagellate
abundance decreased with increasing DOC concentrations and does not appear to account
for the decrease in bactenal biomass with increasing DOC among the set of lakes.
Overall, this snidy indicates bacterial production and the microbial foodweb can
respond strongly to changes in food supply and UVB irradiance as a function of DOC
arnong the lakes of this system even though total DOC concentrations are relatively high
compared to many other lakes.
DEDICATION
To my wife Suzanne,for your continual support and love.
ACKNOWLEDGEMENTS
It's amazing how over just two years, you can a m a s a huge number of people you
wish to thank, even if they may not realize what sort of contribution they made to the
completion of my thesis. If the following is slightly colloquial, it's because the
acknowledgments are one of the few times in your graduate career where you can get
away with this sort of writing style! Without the following people, this thesis would have
never 'gotten off the ground'.
First, my supervisor Lance Lesack. Lance is a great guy, he's got al1 these ideas
and thoughts in his head and offered great perspective on my thesis throughout the
multitude of proposais, experimental design, and revisions. I'rn thankful that he gave me
the chance to work up north, a truly beautifil gem within our own country. I'm also
grateful that he very much encouraged independent thought and self-suficiency. Makes
you a better person, I tell you.
Second, thanks to the staff in the Biology and Geography departments at S.F.U.,
and to the Inuvik Research Centre for their assistance. Especially thanks to Les Kutny
and Steve Halford, technicians at Inuvik and S.F.U. respectively. Without access to their
caches of equipment and supplies, this project would have been either more expensive or
a lot less elaborate. Say, maybe 1 shouldn't thank them then. Just kidding!
Third, thanks to the various people in the scientific community who assisted in
providing advice and data. My cornmittee provided a lot of valuable insight this narrow
mind did not previously see. I'd especially like to thank Richard Robarts at N.H.R.I. in
Saskatoon for answenng so many questions on tntium uptake protocols in a timely and
consistently fiiendly matter. I'm sure he presumed that if he answered just one more of
vi
my e-mails, I'd stop harassing him! To people at the various meetings I've been too,
thanks for your input as well.
Fourth, thanks to my fellow graduate students, you know who you al1 are. The
graduate student community is great for support at those times when you want to whine
about the fact that you spent 10 hours staring into a microscope in a dark room, as well as
being guinea pigs in preparation for your own thesis defense (oh g e e l maybe 1 don? want
this person on my cornmittee, whatsisname is k i n g slaughtered up there).
Finally, thanks to my fiiends and family, wherever you are. 1 know you'll never
read my thesis, and 1 can't blarne you. I'm sick of it by now too, (hey, you do about 8
revisions on a 200 page document and tell me it doesn't get repetitive)! I'd especially Iike
to thank my wife for al1 her support and for maintaining my confidence throughout the
entire process. Despite k i n g poor (nothing new to us) and my being away for the
surnmers, she was always there, from the very start to the finish, from the lows to the
highs, and so 1 dedicate this thesis to het.
This research cost money, and lots of it! So, I'd like to acknowledge the financial
support of the following; a Natural Sciences and Engineering Research Council (NSERC)
research grant and helicopter tirne fiom the Polar Continental Shelf Project to Lance
Lesack, and an NSERC post-graduate scholarship and Northern Sciences Training
Prograrn funding to myself.
Thanks everyone! Now, read on and be enthralled as 1 unravel the mysteries of
arctic microbial foodwebs for you.
vii
TABLE O F CONTENTS
..
APPROVAL PAGE ......................................................................................................... II
ABSTRACT
................................................................................................................... 1...
11
DEDICATION .................................................................................................................
v
ACKNOWLEDGEMENTS ............................................................................................
vi
TABLE OF CONTENTS
.............................................................................................
LIST OF TABLES ................................
...
viii
.
.
..................................................................... xi
LIST OF FIGURES ................... .
.
..............................................................................xiv
CHAPTER 1 : INTRODUCTION ...................................................................................
1 . 1 The Mackenzie Delta ................................................................................................
1 -2 Dissolved organic carbon ..........................................................................................
..........................................................................................
..........................................................................................
1.3.2 Grazers .....................................................................................................
1.3 The microbial food-web
1.3.1 Phytoplankton
1.3.3 Viruses ...................................................................................................
1.3.4 Higher trophic levels ................................................................................
1.4 Interactions within multiple trophic levels
.......................................................
CHAPTER 2: MATERIALS AND METHODS ......................................
.....
1
1
5
II
13
17
19
20
22
......... 30
Studyarea ................................................................................................................
30
Lake site ..................................................................................................................
31
Experimental design ............................................................................................... 34
Limnocorrals ............................
.
....................................................................... 36
2.5 DOC extraction and enrichment ............................................................................. 40
2.6 Sampling .................................................................................................................
42
2.6.1 Water chemistry ....................................................................................... 43
2.6.1.1 pH .............................................................................................. 43
2.6.1.2 N H ~ +and ~ 0 ~ ......................................................................
3 43
2.6.1.3 DOC .......................................................................................... 44
2.6.1.4 Gas chromatography ................................................................
44
2.6.1-5 Suspended sediments .............................
.... ............................ 45
2.6.1.6 Chiorophyll ............................................................................... 45
2.6.2 Bacterial biomass ....................................................................................
46
49
......................................................
2.6.3 Heterotrophic nanoflagellate biomass
2.6.4 Viral biomass ........................................................................................... 50
2.6.5 Zooplankton biomass ............................................................................... 51
2.6.6 Pkjoplankion biomass ............................................................................ 51
2.1
2.2
2.3
2.4
viii
APPENDIX E: Detemination of Bacterial Production Through 3 ~ - T ~ R
Incorporation
........................................................................................ 191
APPENDIX F: Averages, Standard Errors, and Number of Samples Collected for
Expenmental Microbial Biotic Components ....................................... 194
APPENDIX G : Averages, Standard Errors, and Number o f Samples Collected for
Expenmental Abiotic Components ...................................................... 1 95
LIST O F TABLES
I
Predicted changes in microbial biotic components under increased food source
@OC), decreased UV-B radiation, or both. The size of the arrows represents the
relative size of change in that individual component. .......................
.
.
.
....... 28
2
Planned cornparisons for bacterial biomass. A single astensk indicates
significance at an a level of 0.1 O (Bonferroni adjustment to 0.033). A double
asterisk indicated significance at an a level of 0.05 (Bonferroni adjustment to
0.0 17). A triple asterisk, a significance at an a level of 0.0 1 (Bonferroni
adjustment to 0.003). Error mean square value, error degrees of fieedom, and pvalue from the repeated measures ANOVA for the between subjects effect are
also listed. ..........................................................................................................
3
63
Planned cornparisons for heterotrophic nanoflagellate biomass. A single astensk
indicates significance at an a level of 0.10 (Bonferroni adjustment to 0.033). A
double asterisk indicated significance at an a level of 0.05 (Bonferroni adjustment
to 0.017). A triple asterisk, a significance at an a level of 0.01 (Bonferroni
adjustment to 0.003). Error mean square value, error degrees of fieedom, and pvalue from the repeated measures ANOVA for the between subjects effect are
also listed. ..........................................................................................................
65
4
Planned cornparisons for virus biomass. A single astensk indicates significanceat
an a level of 0.10 (Bonferroni adjustment to 0.033). A double asterisk indicated
significance at an a level of 0.05 (Bonferroni adjustment to 0.017). A triple
asterisk, a significance at an a level of 0.01 (Bonferroni adjustment to 0.003).
Error mean square value, error degrees of fieedom, and p-value fiom the repeated
measures ANOVA for the between subjects effect are also listed. .................... 69
5
Planned cornparisons for chlorophyll concentration and phytoplankton biomass.
A single asterisk indicates significance at an a level of O. 1O @onferroni
adjustment to 0.033). A double asterisk indicated significance at an a level of
0.05 (Bonferroni adjustment to 0.017). A triple asterisk, a significance at an a
level of 0.01 (Bonferroni adjustment to 0.003). Error mean square value, error
degrees of fieedom, and p-value fiom the repeated measures ANOVA for the
.
.....................-.. 74
between subjects effect are also listed. ........................... .
6
Planned cornparisons for zooplankton biomass. A single asterisk indicates
significance at an a level of 0.10 (Bonferroni adjustment to 0.033). A double
asterisk indicated significance at an a level of 0.05 (Bonferroni adjustment to
0.017). A triple astensk, a significance at an a level of 0.01 (Bonferroni
adjustment to 0.003). Error mean square value, e m r degrees of fieedom, and pvalue fiom the repeated measures ANOVA for the between subjects effect are
also listed. ..........................................................................................................
xii
82
7
Planned cornparisons for bactetial production. A single asterisk indicates
significance at an a level of 0.10 @onferroni adjustment to 0.033). A double
asterisk indicated significance at an a level of 0.05 (Bonferroni adjustment to
0.017). A triple astensk, a significance at an a IeveI of 0.0 1 (Bonferroni
adjustment to 0.003). Error mean square value, error degrees of freedom, and pvalue fiom the repeated measures ANOVA for the between subjects effect are
also listed.
8
.................
..........
...... . ................. . . ... . . ..-... .
. . .. 87
Regression statistics for components of the Iake survey in the form of y=mx + b.
Squared multiple r value indicates the strength of the relationship between the
cornponents (perfect relationship, r2=1 .O, no relationship, r2=0). . .. .
109
xiii
LIST OF FIGURES
Location of the Mackenzie Delta (upper left box) and the location of South Lake
relative to Inuvik (modified fkom Marsh and Ferguson 1988). ........................... 3
Relationship between the concentration of coloured, W B absorbing humic
fraction of dissolved organic carbon in water and the penetration depth of UVB
radiation at 3 l0nm (based on equations fkom Scully and Lean 1994). ................ 8
Microbial food-web simplified to indicate major interrelationships among taxa 15
Bathymetric map of South Lake where DOC enrichment experiments were
conducted. ...................... .
.
.
...........................................................................
23
General design of experimental enclosures. ......................................................
38
33
Total bacterial biomass per milliliter of lake water for each enclosure plus South
Lake over the course of experiments 1 (a) and 2 (b). ........................................ 61
Total heterotrophic nanoflagellate biomass per milliliter of lake water for each
enclosure plus South Lake over the course of experiments 1 (a) and 2 (b). ...... 67
Total virus biomass per milliliter of lake water for each enclosure plus South Lake
over the course of experiments 1 (a) and 2 (b). ............................. .............. 7 1
Total phytoplankton biomass per cubic meter of lake water for each enclosure
plus South Lake over the course of experiments 2. ......................................... 76
Chlorophyll concentration per liter of lake water for each enclosure plus South
Lake over the course of experiments 1 (a) and 2 (b). ......................................... 78
Total zooplankton biomass per cubic meter of lake water for each enclosure plus
.
.................. 84
South Lake over the course of experiment 2. ............................... .
Total bacterial production rate per liter of lake water for each enclosure plus South
Lake over the course of experiments 1 (a) and 2 (b). ......................................... 89
xiv
Carbon production rate per bacterial ce11 per liter of lake water for each enclosure
plus South Lake over the course of experiments 1 (a) and 2 (b). ................... .... 9 1
Relationship between total dissolved organic carbon concentration and the humic
fraction of dissolved organic carbon concentration for the Inuvik 40-lake survey.
........................ ..........................................................................................
11 1
Relationship between humic dissolved organic carbon concentration and si11
.
elevation for the Inuvik 40 lake survey. ................. ...................................
1 14
Relationship between total dissolved organic carbon concentration and si11
.......
elevation for the Inuvik 40 lake survey. ...................... ...................--...
1 16
*
Ratio of total dissolved organic carbon versus humic organic carbon as a function
of si11 elevation for the Inuvik 40 Iake survey. .................................... ..... . 1 18
Relationship between total suspended sedirnent concentration and si11 elevation
.
.
...............
.
.
.
......... 120
for the Inuvik 40 lake survey. ................... .
Relationship between bacterial biomass and total dissolved organic carbon
concentration for the Inuvik 40 lake survey. ..............................................
122
Relationship between bacterial biomass and humic dissolved organic carbon
concentration for the Inuvik 40 lake survey. ............................................... 124
Relationship between virus biomass and total dissolved organic carbon
concentration for the Inuvik 40 lake survey. ............................................ . 127
Relationship between virus biomass and humic dissolved organic carbon
concentration for the Inuvik 40 lake s w e y . ....................................................
129
Relationship between heterotrophic nanoflagellate biomass and total dissolved
organic carbon concentration for the Inuvik 40 lake survey. ........................... 13 1
Relationship between heterotrophic nanoflagellate biomass and humic dissolved
organic carbon concentration for the Inuvik 40 lake survey. ........................... 1 33
Relationship between bactenal biomass and chlorophyll concentration for the
13 5
Inuvik 40 lake survey. ......................................................................................
Relationship between chlorophyll concentration and total suspended sediment
concentration for the Inuvik 40 lake survey. .................................................... 137
Relationship between chiorophyll concentration and humic dissolved organic
carbon concentration for the Inuvik 40 lake survey. ........................................ 139
Relationship between bacterial biomass and virus biomass for the Inuvik 40 lake
144
survey. .................................... ..........................................................................
Relationship between bacterial biomass and heterotrophic nanoflagellate biomass
147
for the Inuvik 40 lake survey. ..........................................................................
Chernical structure of [3~--]
thymidine @HI TdR). The location of the
label is indicated by an astensk. ..................................... ................................. 182
Pathway by which DNA becomes labeled with 3~ via uptake of exogenously
supplied HI TdR. .......................................................................................... 184
xvi
CHAPTER 1: INTRODUCTION
Global climate change and its effects upon aquatic ecosystems has been addressed
as an important area of study within recent years. Multidisciplinary studies focusing
upon arctic ecosystems have predicted widespread effects such as loss of aquatic habitat,
reduced permafrost, and warmer winters (Rouse et. al. 1997). The validity of these
hypothetical changes rely upon direct testing of hypotheses which address the issue of
global climate change. In the Mackenzie Delta, while some of these issues have been
studied, the potential effect of climate change on the microbial food web has not yet k e n
addressed. The study presented here attempted to quantifi the response of microbial
components to increases in dissolved organic carbon @OC), which has been predicted to
increase with global warming (Rouse et. al. 1997).
With an improved understanding of the dynamics of the aquatic microbial food
web, it was hoped that more accurate predictions about the effects of global w-arrning on
the aquatic food webs of the Mackenzie Delta could be made. The following sections
provide some background on the Mackenzie Delta, the properties of DOC, and the
importance of microbial food webs in aquatic ecosystems. From this knowledge, some
general and specific hypotheses about how climate warming may affect the microbial
components can be made and tested.
1.1 The Mackenzie Delta
The Mackenzie Delta is a system of over 25000 lakes and rivers, making it the
second largest arctic delta in the world (Figure 1). The majority of lakes in the
Mackenzie Delta are s m a l l ( 4 0 ha) and shallow (<4 rn; Mackay 1963). Delta lakes are
unique in that a large proportion are disconnected fiom fiesh riverine inputs for at least a
1
Figure 1. Location of the Mackenzie Delta (upper lefi box) and the location of South
Lake relative to Inuvik (modified from Marsh and Ferguson 1988).
portion of the year. Summer precipitation levels are low, and the majority of fieshwater
cornes from annual spring river flooding events (Marsh and Hey 1989). This flooding
occurs when warmer temperatures in the south melt river ice and surrounding snow. The
resulting melt water flows north until reaching river ice which acts as a dam causing the
river water to flood out ont0 the surrounding landscape, settling in lake basins and
resetiing the ionic and nutrient balance (Lesack er. al. 1998).
Delta lakes may be subjected to a host of changes as a result of increasing
atrnospheric carbon dioxide gas concentrations which is believed to be responsible for
rising global temperatures. General circulation models predict an increase in mean arctic
summer temperatures of 4°C to 9OC in winter under a two times CO2 scenario, higher
than the 6°C increase in winter temperature predicted for southem regions (Rouse et. al.
1997). A warmer arctic climate may lead to reduced spring ice-jarnming and flood levels,
increased terrestriai primary production, and melting of permafrost (Rouse et. al. 1997).
These events rnay affect the carbon concentrations in delta lakes.
Later freeze-over times and earlier spring thaw periods would reduce river ice
thickness. Spnng ice-jamming would be reduced, which is responsible for the major
flooding periods of delta lakes, resulting in lower lake levels throughout the summer.
This may result in loss or alterations in aquatic habitat important not only for aquatic
organisms such as phytoplankton, zooplankton, and fish, but for larger organisms such as
muskrats, waterfowl, moose and humans. Increases in DOC concentration may occur. If
the lake basin is not being flushed out by the river, the DOC produced through the
breakdown of aquatic plants would remain within the basin and increase in concentration
over time.
Melting of permafrost may occur, exposing soil which was previously fiozen.
Groundwater and melt water percolating over this soil will leach out nutrients and
dissolved organic carbon @OC), eventually depositing it into lake basins or river
channels (Rouse et. al. 1997). Permafrost melting may expose banlcs to slurnping
processes which would result in greater sediment, nutrient, and carbon load delivered to
lakes (Rouse et. al. 1997).
Finally, increased terrestrial primary production may result in an increased supply
of DOC to lakes. Warmer arctic conditions would allow the coniferous treeline to move
hirther north (Pienitz and Sm01 1993). A higher turnover rate and higher terrestrial
biomass may result in more humic substances (the coloured, high molecular weight
fraction of DOC) being leached by rain and overland flow into the lakes and rivers.
However, as the terrestrial biomass is rapidly increasing, this may result in decreased
delivery of DOC to lakes. This is likely to occur in the first few years before terrestrial
production reaches a new maximum and starts to turnover, releasing large amounts of
DOC into lakes through leaching processes.
1.2 Dissolved organic carbon
Dissolved organic carbon is operationally defined as that part of the organic
carbon pool smaller than 0.45 Pm. DOC is composed of six fractions; hydrophobic acids,
neutrals, and bases, and hydrophilic acids, neutrals, and bases (Aiken 1988; Glase et. al.
1990). Chemical characterization of DOC has proven problematic due to the dificulty of
isolating homogenous fractions of DOC from the wide variety of dissolved substances in
nature (Aiken 1988; Shuman 1990, Hobbie 1992, Chin et. al. 1994). In addition, the
chemical nature of W C varies with changing environmental conditions (Thurman and
Malcolm 198 1;Francko 1990; DeHaan 1992; Tulonen et. al. 1992).
5
Hydrophobie acids comprise the majority of DOC (up to 90%) with the largest
proportion being humic substances (up to 50%; Allard et. al. 1994). Hurnic substances
(hurnic and fulvic acids) contain chromophores which impart a yellowish straw colour to
lake water (Stewart and Wetzel 1982, Morris and Hargreaves 1997). These
chromophores also absorb harmful ultraviolet-B (UVB)radiation, as well as W - A and,
to a lesser degree, photosynthetically active radiation (PAR; Moms and Hargreaves
1997).
DOC in lakes originates fiom both autochthonous (within lake production) and
allochthonous (outside of lake) sources. Decomposition of aquatic macrophytes and
other aquatic organisms provides a large source of DOC. Previous studies have found
that the benthic algae may contribute up to 50% of C inputs into arctic lakes, while 20%
is contributed by phytoplankton (Ramla1 et. al. 1992, 1994). Although macrophyte
production dominates Mackenzie Delta lakes, DOC derived fiom aquatic macrophytes is
often low in the humic fraction due to the low lignin content of aquatic macrophytes as
compared to terrestrial plants (McKnight et. al. 1991, 1994). This DOC may also be
recycled and of low nutritive value for bacteria. The remaining 30% anses from
allochthonous sources and contains a large hurnic portion which is refiactory and often
unavailable for bacterial growth except through production of exogenous enzymes and
UV degradation (Stewart and Wetzel 1981, 1982, Wetzel 1992, Reitner et. al. 1997). The
humic allochthonous source provides both UV protection and, when broken down, a rich
carbon source for bacteria (Stewart and Wetzel 1982, Wetzel 1992, Williamson 1995).
W B (280 to 320 nm)penetration into waters rapidly diminishes as DOC
concentration increases (Figure 2). This relationship depends primarily on the UVB
absorbing hurnic fraction, with DOC concentrations above 3 r n g - ~ -reducing
l
W B
6
Figure 2. Relationship between the concentration of coloured, UVB absorbing humic
fraction of dissolved organic carbon in water and the penetration depth of UVB radiation
at 3 l0nm (based on equations fiom Scully and Lean 1994).
5
10
15
20
1% UV-B penetration depth (ml
penetration to l m or less (Scully and Lean 1994, Moms et. al. 1995, Williamson 1995,
Schindler et. al. 1996). At low concentrations, small changes in DOC rnay lead to large
changes in W B penetration (Williamson et. al. 1996, 1 997, Laurion et. al. 1997). Since
a large number of arctic lakes are shallow, have low humic content, these lakes will be
particularly susceptible to changes in humic DOC concentrations (Satoh et. al. 1992,
Scully and Lean 1997). The importance of UVB radiation in aquatic food webs is
discussed below.
UVB radiation may penetrate up to fifiy meters although the major biological
effects occur in the upper ten meters (Karentz et. al. 1994). Due to the relative
shallowness of North American lakes (zavg=lOm), W B is likely to play a large role in
stnicturing their aquatic ecosystems (Williamson 1995) for the reasons discussed below.
UV radiation can be damaging to aquatic organisms ranging fiom bacteria to fish.
Ambient levels of UVB radiation may inhibit bacterial DNA replication, protein
synthesis, degradative enzyme activities by as much as 40%, disrupt phytoplankton PSI1
systems and electron transport chahs, and halt the developrnent of fish eggs (Herndl et.
ai. 1993, Karentz et. al. 1994, Williamson et. al. 1997). While some species of
phytoplankton and zooplankton have been found to reduce their exposure to UVB
radiation by increased pigmentation or migration, this often decreases fitness through
expenditure of energy or increased visibility to predators (Williamson 1995, Zellmer
1995). Since the majority of organisms are unable to detect W B wavelengths, they may
be 'ambushed' by increased UV radiation and subjected to ce11 damage (Williamson
1995). As well, the majority of organisms dwell within the upper surface waters to obtain
increased light and nutrients (Williamson 1995). However, this is the region of highest
W B exposure.
Of additional concern is that W B radiation results in damaging effects orders of
magnitude greater than those caused by longer wavelengths. For example, at 295 nm,
W radiation is 1000 times more darnaging than 320 nm (Karentz et. al- 1994,
Williamson 1995). However, the presence of even small levels of dissolved organic
carbon may reduce the penetration of W B to just a few decimeters (Scully and Lean
1994).
Upon absorption of UV-radiation, dissolved humic matter (DHM) undergoes a
process of photodegradation and photobleaching. Photodegradation involves breakdown
of high motecular weight DHM (HMW-DHM; generally recalcitrant) to low molecular
weight DHM (LMW-DHM; labile; Francko and Heath 1982, Backlund 1992, Linde11 et.
al. 1995). This breakdown process also results in photobleaching fiom the loss of UVB
absorbing chromophores with a subsequent reduction in water colour (Amador et. al.
1991 , Ssndergaard and Borch 1992, DeHaan 1993, Allard et. al. 1994, Morris and
Hargreaves 1997). Along with the reduction of W B absorption properties, cfeavage of
HMW-DHM may also result in the formation of highly reactive compounds such as
superoxide, CO, singlet oxygen. and hydroxyl radicals (Williamson 1995, Scully et. al.
1996). HMW-DHM bound to metals or pesticides may release these toxic substances
upon breakdown (Stewart and Wetzel 1982).
Breakdown of HMW-DHM by UVB radiation has beneficial effects as weil.
HM W-DHM binds orthophosphate and micronutrients forcing algae and bacteria to
synthesize exogenous enzymes (such as alkaline phosphatase) to obtain these nutrients,
an energy dependent process which can result in lower productivity (Stewart and Wetzel
1982, Kim and Wetzel 1993, Reitner et. al. 1997). At high levels, HM W-DHM may even
bind these exogenous enzymes further reducing production rates and lowering biomass
(Francko and Heath 1982, Koetsier et. al. 1997). Breakdown of these complexes result in
10
the release of this P, micronutrients and enzymes for bacterial and algal use (Stewart and
Wetzel 1982, Jones et. al. 1988, Jones 1992). Bactena are unable to take up HMW-DHM
except through the secretion of exogenous enzymes, but are readily able to utilize the
LMW-DHM produced as a carbon source for their growth and reproduction (Tulonene et.
al. 1992). A fine balance therefore exists between increased levels of harrnfùl W B
radiation and increased mobilization of DHM to the LMW pool for bacterial uptake
(Karentz et. al. 1994, Williamson 1995).
1.3 The microbiai foodweb
The importance of aquatic microbial foodwebs have, until recently, been
overlooked in ecological studies. However, the microbial component is largely
responsible for the decomposition and cycling of carbon as weIl as mineralization of
nutrients within the water column (Cole et. al. 1988, Rublee 1992, Tranvik 1992, Gaedke
et. ai. 1996). While the microbial component used to be thought of as a separate 'food
loop', more recently it has been shown that higher trophic levels are very dependent upon
the microbes for the carbon and nutrients they provide (Pace and Funke 1991, Rublee
1992, Thingstad 1992). Thus, the microbial component has been integrated as an
important part of the îùnctioning of pelagic food webs.
Unfortunately, techniques which permit close observation and manipulation of the
microbial foodwebs have only become available recently (Ducklow 1994). As these
techniques develop, a better understanding of the contribution and connection to the
traditional foodweb is becoming apparent. The following provides a summary of the
microbial foodweb as it relates to bactena. This background information is necessary if a
researcher is to design experiments which test feasible hypotheses.
Bacterial populations c m be divided into two general categories, autotrophic and
heterotrophic. Autotrophic bacteria are capable of synthesizing their own carbon source,
while heterotrophic bacteria rely upon extemal sources of carbon for growth. The
heterotrophic bacteria have been of great interest to aquatic ecologists since the 1970's
when techniques becarne available to study them. It was realized that because of their
ubiquitous nature, rapid reproduction (less than 1 hour per ce11 division), and large
quantities (generally 1.106 . ml-1 or greater), they could be an important source of carbon
recycling (Jost et. al. 1992). It was found that heterotrophic bacteria act as decomposers,
breaking down large organic carbon molecules and assimilating the carbon into their own
cells as a bioavailable form of particulate organic carbon. In addition, bacteria can act as
a tink between dissolved organic carbon and higher trophic levels such as zooplankton
(Riemann 1985, Hessen et. al. 1990). For example, zooplankton are unable to take up
dissolved carbon directly, but may prey upon bacteria which are capable of consuming
DOC (Riemann 1985, Pace 1988).
While their importance is now recognized, it has been difficult to estimate how
much carbon is flowing through the microbial components. Whole lake estimates of
carbon flow are rare due to the difficulty of quantifying al1 the foodweb components over
an entire season as well as the variety of foodwebs present not only in North Arnerican
lakes, but also in lakes throughout the world (Cole et. al. 1982, Cole et. al. 1988, Cole ez.
al. 1989). It has now become more important to focus upon identifying and quantiQing
major biotic components and the way they interact with other trophic levels. With an
understanding of these interactions, more accurate predictions can be made about how
changes in abiotic factors as a result of climate change will affect the foodweb o f a
particular lake (Pace and Cole 1994).
There are several biotic controls on bactenal biomass and production (Figure 3).
The approach presented here looks at each component, and its effects on the microbial
foodweb as deduced fiom the literature. More detailed ecosystem inîeraction effects will
be examined later.
1.3.1 Phytoplankton
Phytoplankton are important resource cornpetitors with heterotrophic bacteria.
Both bacteria and phytoplankton require a source of phosphorus for growth and
maintenance (Bird and Kalff 1984). Ratios of C:P in phytoplankton versus bacterial cells
v q with species, but it is commonly accepted that bacteria have a much higher P content
(Valdstein et. al. 1988, Cole and Caraco 1993). In addition, because of their rapid
generation times, bacteria readily out compete phytoplankton when phosphorus sources
are limited, often accounting for 72 to 98% of phosphorus uptake (Rhee 1972, Cumie and
Kalff 1984a, Vadstein et. al. 1988, Toolan et. al. I991, Cole and Caraco 1993).
However, phytoplankton have been shown to respond to added phosphorus in natural lake
assemblages and often contain a considerable portion of the limnetic phosphorus. Two
possible explmations for this include different P sources used by algae and bacteria, and
carbon limitation of bacteria.
Bacteria have been found to take up primarily orthophosphate while
phytopiankton use organic phosphorus (Currie and Kalff 1984b). In addition,
phytoplankton much more readily hold on to consurned phosphorus, while bactena often
excrete organic P (leaky cells) which is consurned by phytoplankton (Rhee 1972, Curie
and Kalff 1984b). Since the life span of bacteria is relatively short compared to
phytoplankton @ours venus days), phosphorus is unlikely to become bound for long
periods of time in the bacterial comrnunity. As bactena are more efficient at P uptake,
13
Figure 3. Microbial food-web simplified to indicate major interrelationships among taxa.
Width of arrows indicate relative strength of relationship. Horizontal line represents the
break between the microbid foodweb components and higher trophic levels. Key to
relationships:
1 & 3. Uptake of nutrients (and DOC in heterotrophic bactena) for growth and
maintenance.
2,4,6, & 17. Rernineralization of nutrients through organism senescence,
leaky ce11 walls, sloppy feeding, excretion.
5 & 13. Predation by heterotrophic nanoflagellates.
7. Uptake of excreted phosphorus sources from leaky bactenal cells.
8. Uptake of excreted carbon sources from phytoplankton cells.
9, 11 & 12. Infection and lysis from aquatic viruses.
10. Release of nutrients upon lysis of prey or death of viral cell.
14,15 & 16. Grazing by macrozooplankton
HNAN=Hcterotrophic nanoflagellates
DOC=Dissolvtd organic carbon
their turnover and growth rates may Iimit the rate at which phytoplankton are able to use
excreted bacterial P thus keeping algal biomass fiom rapidly increasing (Güde et. al.
1992, Cole and Caraco 1993). When lakes are artificially fertilized with nutrients, the
algae are no longer dependent upon bacteria for P and a massive bloom-bust period of
phytoplankton biomass may follow.
Carbon limitation can be comrnon in lakes containing both low DOC and nutrient
concentrations Waldstein et. al. 1988, Baines and Pace 1991, Heiniinen and Kuparinen
1992). While phytoplankton are able to synthesize their own carbon source via
photosynthetic pathways, heterotrophic bacteria rely upon exogenous sources. Extemal
sources include allochthonous inputs, zooplankton excretion, sloppy feeding, senescence
and lysing of aquatic organisrns, and phytoplankton excretion (Baines and Pace 1991).
Excreted carbon fiom phytoplankton is an ideal carbon source for bacteria, comprising up
to 50% of their required carbon for growth and repair (Cole et. al. 1984, C h e and Kalff
I984a, Baines and Pace 1991, Tranvik 1992). Thus, a feedback loop exists in low
nutrient systems where algal excretions increase bacterial production and growth, which
may lead to depletion of phosphorus sources through stimulated bacterial production. A
decrease in phytopladcton production and associated carbon excretion may occur,
resulting in carbon starvation of bactena. A balance appears to exist where
phytoplankton do not completely out compete bacteria due to their reliance upon bactena
for organic phosphorus and bacteria do not dominate because they rely on excreted
carbon sources fiom phytoplankton (Jordan and Likens 1980, Currie and Kalff 1984a).
Climate warming, which would likely lead to an increase in DOC supply to delta
lakes, is likely to upset this balance in favour of a bacterid dominated system.
Phytoplankton production will decrease because of possible increased PAR absorption by
DOC. Bacteria will respond to the added DOC source and will have less reliance upon
phytoplankton for carbon sources.
The next trophic Ievel consists of bacterial grazers. While larger zooplankion,
such as Daphnia and rotifers, have k e n implicated as potential grazers, the majority of
grazing (90-98%) is done by microzwplanicton less than 64pm in size (Rublee 1992,
Sanders el. al. 1989, Moger and Landry 1992, Sherr and Sherr 1992). This
rnicrozoop1ankton assemblage includes heterotrophic nanoflagellates, phagotrophic
phytoflagellates, ciliated protists, and some smaller species of rotifers, copepods and
cladocerans (Pace 1982, Sherr and Sherr 1992, Sanders et. al. 1989, 1994). However, the
heterotrophic nanoflagellates (HNAN) account for the majority of bacterial predation in
most lakes, consuming upwards of 20.106 bacteria per liter per hour (Porter 1991,
Sanders er. al- 1994).
Organisms which fa11 within the definition of a heterotrophic nanoflagellate are
less than 20pm in size, motile through the use of flagella, and are incapable of
synthesizing their own carbon sources (Shem and Sherr 1994). Since there is a wide
range of species which fa11 under this definition, the HNAN also live within a wide range
of niches within any given lake.
While nutrients, carbon, and phytoplankton cornpetition control bacterial
production from lower or equal trophic levels, grazing plays a major role in balancing the
bacterioplankton population from above, controlling the flow of carbon up through the
foodweb and ensuring that rapidly growing bacterial populations do not dominate lake
assemblages (Figure 3; Sherr and Sherr 1983, 1994). Consumers may regulate microbial
production in three ways (outlined by Pace and Funke (1991) and Sanders et. al.(1992)):
1. Direct predation. Grazing pressure and bacterial production are normally in
equilibriurn. However, this relationship can f d l out of balance if a carbon source is
added. Bacterial production would be stimulated and biomass accumulated more quickly
than grazing pressure can reduce. Eventually, grazers wodd respond with increased
production and biomass. A new baiance would be established at a higher Ievel. The
additional carbon is able to maintain a higher bacterial biomass, and subsequently, a
higher grazer biomass.
2. Indirect effects on microbial resources. These can include stimulation or
inhibition of nutrient cycling. M i l e grazers do consume bacteria, many are nonselective and can consume algae as part of their diet (Sherr and Sherr 1983). The bound
phosphorus of the phytoplankton would then be released by the grazers by excretion or
sloppy feeding, helping to stimulate bactenal growth. Altematively, nanoflagellate
grazing of phytoplankton may result in phosphorus being bound in the nanoflagellates,
potentidly reducing bacterial growth.
Grazers may also inhibit nutrient cycling by the sarne process. Nutrients may be
retained by the grazers for growth and reproduction or transferred to higher biota through
predation processes, thus limiting bacterial and algal growth.
3. Changing microbial habitats. This is unlikely to be important in delta lakes
which rarely stratiQ due to their shallow depths, but is mentioned here for completeness.
Grazers may consume bacteria and phytoplankton in the euphotic zone. However, to
avoid being detected and grazed upon by other zooplankton, many grazers will migrate
18
throughout the day to avoid predation. This migration can lead to movement of nutrient
and excreted carbon from grazing zones higher in the water column to excretion zones
lower down. Bactena could be forced to move to those sources, which have less than
ided conditions for production and accumulation of biomass, such as cooler
temperatures, and less phytoplankton exudates.
Grazers are likely to play an important role in regulating bacterial production and
biomass. With increased DOC concentrations, grazers may respond positively to
increased bacterial biomass as a result of increased food supply for bacteria, Due to the
negative impact of UVB radiation on grazers, the additional DOC which would decrease
UVB penetration would also stimulate grazer biomass.
1.3.3 Viruses
Recent research has suggested that viruses may play a role in controlling bacterial
biomass in aquatic foodwebs. Viruses are even more abundant than bacteria in lakes,
with viral to bacterial ce11 ratios ranging fiom 4.9 to 77.5 with an average near 20 to 25
(Maranger and Bird 1995). Viruses may be responsible for upwards of 68% of bacterial
mortality, although it is more typically around 30 to 40% (Bratbak et. ai. 1994, Suttle
1994). Most of the information on bacterial virus structure and function cornes fiom
marine data, however it has been suggested that similar farnilies of viruses occur in
freshwater as well (Bratbak et. al. 1994). These include the Myorividae, Podoviridae,
and Styloviridae (Suttle 1994).
Some viruses which infect bacterial cells eventually lyse the cells, thereby
releasing DOC and nutrients back into the water (Suttle 1994). It has been suggested that
viruses play more of a role of the disrupter in carbon transfer up the food chah, due to the
19
fact that they are not readily preyed upon by other organisms, and so flow of nutrients
and carbon are diverted from higher trophic levels (Bratbak et. al. 1994)- This reduction
in the transfer of carbon between biotic components is postulated to have two effects on
aquatic food webs. First, by lysing the bactena there are lower concentrations of
particulate organic carbon (POC) available for transfer to the higher biota, such as
bacterial predators, reducing biomass accumulation at those levels (Bratbak et. al. 1994).
If these bacterial predators are being consumed by other organisms, the accumulation of
biomass in these organisms will be affected as well. Second, as carbon is reintroduced
back into the water, it is subjected to environmental effects, such as UVB radiation
photodegradation, eventually decreasing its 'value' to bacteria as a carbon source. More
of this degraded carbon would need to be consurned to obtain the same amount of energy
as DOC which has not become photobleached, resulting in lower biomass produced per
unit of carbon taken up (sensu Cole et. al. 1984, Schindler el. al. 1996).
Since viruses depend on bactenal cells for reproduction, it appears that abiotic and
biotic changes which affect the bacterial population will also affect vinses. Viruses have
been shown to be susceptible to UVB radiation (Karentz et. al. 1994). Therefore, it is
likeIy that decreased UVB radiation through increased DOC concentration will stimulate
viral biomass. The rate at which viruses increase may be limited by the nurnber of
available bacterial hosts.
1.3.4 Higher trophic levels
Organisms from higher trophic levels (such as Daphnia spp., copepods, and other
aquatic crustaceans), because of their feeding appendages, are ofien unable to efficiently
feed upon bacterial cells (Pace and Cole 1994). However, they do indirectly affect
bacterial biomass by consurning bacterial grazerskompetitors, and by binding up
20
nutrients and organic carbon in tissue for long periods, and by transfemng this carbon to
even higher trophic levels (Duckiow 1994). While bacteria do not usually make up a
large portion of their diet, macrozooplankton, when present in large numbers, can
consume as much of the bacterial biomass as when nanoflagellates are the dominant
predator (Riemann 1985, Pace and Cole 1994). This results in a more efficient transfer of
carbon and lower consumption of oxygen per carbon accumulated (Riemann 1985).
However, this situation is usually found only in eutrophic lakes or when fish predation
pressure is released allowing for blooms of Daphnia and other species (Riemann 1985,
Jeppeson et. al. 1992, Pace and Cole 1994).
When macrozooplankton are feeding, several scenarios are possible, al1 of which
will affect bacterial production and biomass. These include:
1. Capture, consumption and complete assimilation of prey organism (only losses
of carbon through zooplankton respiration). This results in an accumulation of
macrozoopiankton biomass and loss of potential sources of nutrients and carbon for
bacterial production.
2. Capture, consumption and partial assimilation (sloppy feeding). The
zooplankton only consumes and assimilates part of the prey. The remainder is retumed
back to the aquatic environment where bacteria will recycle nutrients and carbon (Baines
and Pace 1991). This is likely important when the zooplankton are actively feeding upon
phytoplankton, speeding up the release of nutrients and carbon from the phytoplankton
cells for use by bacteria (Vaqué and Pace 1992).
3. Capture and rejection of prey. This either results in the prey escaping
unharmed with no net benefit or detriment to bacteria, or partial injury leading to leakage
of organic carbon and nutrients for bacterial uptake.
Since the macrozooplankton affect both bacteria by direct predation or through
predation upon bactend predators/competitors, they are important factors to consider
(Sanders et. al. 1989, Vaqué and Pace 1992). It has been found that they can be the major
consumers of bacteria, but this is generally limited to periods when their biomass is high
and they are not in competition with more efficient bacterial predators like the flagellates.
1.3 Interactions within multiple trophic levels
The above has shown relationships between the bacterial component and
individual factors regulating them. This simple approach does not present the complete
picture as it misses out on other interacting factors. For example, while the HNAN may
increase with an increase in bacterial biomass, larger zooplankton may be preying upon
the HNAN resulting in decreased HNAN biomass. Since there are many trophic levels
made up of many components in the foodweb, it is important to study the entire foodweb
when detennining the effects of abiotic factors such as climate change. Naturally, this
provides logistical problems (seasonality, large quantity of sarnples, unknown or
unmeasurable components), but if the limitations are kept in mind, and a broad range of
sarnples are processed, a better understanding of the ecosystem function will emerge. A
bief overview of some results from studies which looked at these interacting biotic
effects is presented below.
O'Brien et. al. (1992) found that nutrient additions to enclosures in Toolik Lake,
Alaska, led to an increase in phytoplankton biomass followed by a nine fold increase in
22
bactenal biomass over the course of two weeks. However, bacteria dropped back to
reference levels despite high production rates due to increased biomass of
microautotrophs. Fish additions decreased the large-bodied zooplankton biomass and did
not affect bacterial biomass. It was not stated, however, if the authors believed that this
was a result of bacterial predator shift from large zooplankton to rnicrozooplankton, or
whether it was due only to low macrozooplankton densities in both situations.
Bothwell et. al.(1993, 1994) dernonstrated that short term exposure to UV
reduced accumulation of algal biomass in artificial streams, while long-term exposure
actually led to an increased algal biomass. This \vas due to the sensitivity of algal grazers
to UV radiation which reduced their abundance in the W exposed sites. It w a s believed
that the algae, with their fast reproduction times relative to their grazers, were capable of
shifiing to species from predominantly W intolerant to UV tolerant species over time. A
similar situation could occur wïth the bacteria.
Results are ofien difficult to explain when UV effects are studied. A similar
espenment to the one described above was conducted by Kiffney et. ol. (1997) in a
shallow Rocky Mountain strearn. They found a decrease in algal biomass and
invertebrates, but without the eventual increase in benthic algae as seen by Bothwell et.
al-(1993, 1994). While they believed that this may in part be due to the length and set-up
of the expenment, they did emphasize that complex interactions do occur and that
differences may be due to unexamined foodweb effects, not experimental design
di fferences.
Pace et. al. (1998) found that nutrient additions did not result in changes in
bactenal biomass, but did stimulate bacterial production. ï h e y explained this as being
due to the grazer biomass. When no nutnents were added to enclosures, small
23
rnicrozooplankton dorninated the grazer assemblage, but when nutrients were added and
phytoplankton growth was stimulated, large cladoceran biomass increased which grazed
upon both the microzooplankton and bacterial biomass. Under these conditions, bacteria
were probably controlled by the increase in nutrient concentration (increase bacterial
production), increased phytoplankton growth (decrease bacterial production because of
cornpetition), decreased microzooplankton biornass (increase in bacterial biomass), and
increased cladoceran biomass (decrease bacteriai biomass through predation).
The above results emphasize the need to examine the microbial food web and the
interrelationships for each individual lake system, rather than relying upon previously
published data, since the strengths of individual relationships arnongst microbial
components will ultimately determine how each component will respond to abiotic
changes. The primary goals of this study were as follows:
1. Quantify the biomass of microbial components in a lake within the Mackenzie
Delta. This has not been adequately addressed to date.
2. Determine the effect of increased DOC (as a carbon source) on the cornponents
of the microbial foodweb. This was to be done through additions of different levels of
DOC and examining the effect on the microbial food web structure and bactenal
production.
3. Attempt to separate the effects on the microbiaf foodweb that result from food
enhancernent by increased DOC concentration, and the decreased UVB exposure that
accompanies increased DOC concentration. The effects of DOC as a carbon source
versus as a W B screen have rarely been examined, but it is important to determine what
is causing the changes seen in a DOC e ~ c h r n e nexperiment
t
if we are to hlly
comprehend how microbial food webs are stmctured.
4. Assess the degree to which the experirnental outcomes in one lake are
consistent with observations arnong other lakes of the delta.
5. Draw inferences from the outcome of the study systerns about the potential
response of the real system to global climate change.
By having a basic understanding of the microbial food web, and its
interrelationships with other trophic levels, predictions about the response of the aquatic
comrnunity to global climate changes c m be made. From the current knowledge of
microbial foodwebs and how they are affected by changes in DOC, several hypotheses
can be formuiated.
Considering only DOC's properties as a food source for bacteria, additional DOC
should stimulate increases in bacterial production and biomass. Nutrient consumption by
the bacteria should increase, leading to a decrease in phytoplankton biomass. The viruses
should increase since there would be more bacterial hosts to infect. The HNAN should
also increase, but not until the bacteria increase, as they would presumably require the
increase in bacterial biomass to stimulate an inçrease in their own biomass. Depending
on whether the zooplankton are feeding upon bacteria, HNAN's, or phytoplankton, their
biomass should increase or decrease. If they are feeding primarily upon phytoplankton,
then their biomass will decrease, but if they are feeding upon bacteria or HNAN, their
biomass should increase over time.
If only the UVB absorbing property of DOC is considered, an increase in bacterial
production and biomass should be seen, due to the reduction of harmful UVB radiation.
However, removal of W B radiation will prevent the breakdown of humic substances, the
bacteria's food source, so increases in bacterial production and biomass with removal of
W B radiation will likely not be as great as addition of food resources, even taking into
consideration the differences in UV radiation. Removal of W B radiation should
stimulate phytoplankton biomass, leading to greater cornpetition with bacteria for limited
nutrients, and possibly reducing bacterial biomass M e r . However, the increase in
phytoplankton biomass may also stimulate the bacteria slightly by providing increased
algal exudates.
Removal of UVB radiation should have stimulatory effects on the biornass of the
viruses and bacterial predators as well. Vimses should increase in biomass, provided
there are enough bacterial hosts. The HNAN should also increase in biomass because of
decreased W B radiation, but may be limited in this increase due to limited bactenal
biomass. For this reason, 1 would expect that the HNAN increase would be less when
UVB is removed, as compared to when a food source was added. Zooplankton should
increase as a response to increased biomass in the phytoplankton and HNAN. However,
if they prey primarily upon bactena, their biomass may decrease over time. This is
doubtfùl since the majority of zooplankton are non-selective feeders and could likely
switch prey sources in response to declining bacterial populations.
With increased food supply and increased W B protection, a mynad of effects on
the biomass of microbial components is possible. Bactenal production and biomass
should increase as a result of increased food sources and protection from UVB radiation.
A non-linear relationship would be expected. Essentially, bacterial production and
biomass would increase not only to the increased food supply but to the UVB protection.
26
The larger the amount of DOC added, the more food, plus the more UVB protection, both
of which should stimulate bacterial biomass.
Phytoplankton should respond to the removal of UVB radiation, however, they
may be limited in this increase because of high bacterial biornass competing for lirnited
nutrients. The viruses should increase dong with increases in the bacteria due to
increased protection from UVB radiation and greater density of host organisms. The
HNAN biomass should increase greatly in response to the increase in bactenal biomass as
well as W B protection. Since they are being stimulated by both an increase in prey and
UVB protection, their biomass should be highest in this situation. This could lead to
evenrual grazing down of bacterial biomass. However, the increased food supply should
be able to maintain both a higher bactenal biomass as well as a higher HNAN biomass.
The zooplankton should respond positively to both the removal of W B radiation and
increased prey resources. The strength of this increase will depend on which biotic
component (phytoplankton, bacteria or HNAN) are the preferred food source.
The above hypotheses are summarized in Table 1 to allow easier visualization of
how DOC rnay affect microbial food webs in the Mackenzie Delta. The effect of DOC
on microbial food webs was tested through an enrichment experiment where different
levels of humic DOC were added to enclosures in a delta lake. To separate out the effects
of DOC as a food source versus DOC as a UVB attenuator, a nurnber of the enclosures
were shielded fiom al1 UVB radiation.
The following sections provide information on the methods used to quanti@
biomass of the food web components, and outline the experimental design and analysis.
Results of the experiments and lake survey are presented and discussed, focusing upon
each component, and then relating the results of that biotic or abiotic component to the
27
Table 1
Predicted changes in microbid biotic cornponents under increased food source
(DOC), decreased W - B radiation, or both. The size of the arrows represents the relative
size of change in that individual component. Arrows in order of size fiom smallest to
largest are
+, +, and O . Reasoning for the direction of the arrows can be found in the
text.
Phytoplankton
Bactenal
Bacterial
biomass
production
DOC
)tC
P
'
UV-B
P
' or
'l'
'T'
0
't'
*
Both
biomass
HNAN
Virus
biomass
biomass
biomass
rest of the foodweb, giving potentiai explanations for the trends seen. General
conclusions regardhg relationships arnongst the biotic components and their implications
in the context of larger studies are presented last.
C W T E R 2: MATERIALS AND METHODS
2.1 Study area
The Mackenzie Delta is a region rich in lakes (approxirnately 25000) and at
12,000 km2, the Mackenzie Delta is the second largest arctic delta in the world. It has
fomed since the retreat of the Laurentide Ice Sheet around 1 1.5 ka BP. Mean yearly
discharge is 8950 rn3 s-1 representing about 14% of the total fieshwater discharge into
the Arctic Ocean (Marsh and Hey 1989). The delta is within the region of continuous
permafrost, which may be absent under large lakes.
Lakes within the Mackenzie Delta have been broadly divided into three categories
based upon si11 elevation and comection with the Mackenzie River or its tributaries.
These categories are outlined in Marsh and Hey (1989) as:
1. No-closure lakes which are connected to the river channel throughout the
year. These have a si11 elevation of (1 S m average si11 level (a.s.1.) and comprise about
12% of the Iakes within the delta.
2. Low closure lakes which are usually connected to the river channel until
rnidsummer when water levels drop below 1S m a.s.1. The average si11 elevation for these
-
Iakes is 1.5 3.5m a.s.1. The majority of delta lakes (55%) are low-closure.
3. High closure Iakes, which are flooded and connected to the river only in the
springtirne, comprise the fmal33% of lakes. With a si11 elevation of >4m a.s.l., the
magnitude of spring flooding must be suficient to raise water levels by more than 3m.
Approximately 67% of high closure lakes are flooded each year, while the remainder may
go for three or more years before being inundated with fiesh river water.
In general, the higher the si11 elevation, the less amount of time the lake is
connected with the river. As si11 elevation increases, suspended sedirnent concentrations
decrease, DOC concentrations increase, and macrophyte biomass increases (Fee et. al.
1988, Lesack et- al. 1998). Therefore, a lake site with intermediate values for al1 these
properties, especially DOC concentration, was chosen as the site of the experiment so as
to best represent an average delta lake. Results of the experiments could then be more
widely applied to other delta lakes than if the lake chosen were an outlier (example: very
high or low DOC concentrations).
2.2 Lake site
South Lake, a small(0.378 k d ) , shallow (zaVg=2 m), low-closure delta lake was
the chosen location for the DOC enrichment experiments (Figure 4). Since low-closure
lakes make up the majority of Delta lakes, South Lake could be thought of as representing
an average lake.
Flow into the lake is through a channel c o ~ e c t e dat the north-eastern end of the
lake (Figure 4). As river water entes, much of the sediment drops out in the first bay due
to thick macrophyte and Equisetum sp. production, a common phenornenon in delta lakes
(Mackay 1963). The water in the main portion of the lake thus remains clear for much of
the season.
Figure 4. Bathymetrïc map of South Lake where DOC enrichment experiments were
conducted. The location where the enclosures were placed is indicated by the X. The
entrance to South Lake fiom the main river charnel is in the upper right hand corner
(modified from Marsh and Ferguson 1988).
Mackenzie Delta N.W.T.
Waer Level2.3 1 m as1
Contour Inlerval 0.5 m
tOO
O
ZOO
300
Its proximity to the Inuvik Research Centre made it easily accessible throughout
the summer, an important aspect to consider as Iive bacteria were required for monitoring
bacterial production. After tmttling, bacterial ce11 size and production may change within
six hours so choosing a location fùrther away may have been impractical.
Finally, since South Lake has previously been studied, some water chemistry and
biological data are known. As this study extended over the period of only one summer, it
was important to select a relatively well studied lake.
2.3 Experimental design
The main experïments conducted in South Lake were designed to determine the
effects of DOC as a food source versus DOC as a UVB attenuator. The purpose was to
determine how bacterial populations might change with clirnatic warming, specifically
increased carbon sources. However, since bacteria are inextricably linked with other
biotic components of the foodweb, it was important to quanti@ changes in these
components over time as well.
Sampling was done on a fiequent basis (24 or 48 hours) for a number of reasons.
First, bacteria and the rest of the microbial components respond rapidly to changes in
their environment due to their short generation times. Second, the limnocorrals are
subject to 'enclosure effects'. Essentially, a closed body of water has only a limited
supply of nutrients, sediments, and so forth. If the experiment was nin too long, changes
seen in the bacterial community may be due to abiotic factors other than DOC or UVB
radiation, such as nutrient limitation. The large size of the limnocorrals allows a certain
buffering capacity in the short experimental time. Samples were taken after the first 24
hours, as it was believed the change may be the most rapid within this time. Subsequent
samples taken each 48 hours were determined adequate fiom literature results.
To determine the effect of DOC as a food source, three levels of DOC additions
were tested. These were:
1. Control. No DOC was added to these enclosures. Responses seen in the
control bacterial comrnunity should, ideally, be the sarne as those in the rest of the lake
outside the enclosure. However, the control enclosures would account for any enclosure
effects which may affect bactenal biomass.
2. +DOC. The +DOC treatments received organic carbon in a quantity that
reduced UVB penetration to 50% at lOcm depth. The addition of DOC reduced the
reliance of bacteria upon phytoplankton for excreted sources of DOC while also
providing some W B protection. Responses in this treatment were expected to be a
consequence of additional carbon concentration.
3. ++DOC.
DOC was added to reduce UVB penetration to 1% at 1Ocm depth. If
DOC acted only as a food source, and not as a UVB shield, the addition of more DOC in
the ++DOC treatment should have resulted in greater bacterial biomass.
Essentially, fiom the above treatments it was hoped to determine what the
response of the bacteria and the microbial comrnunity was to added DOC. It was thought
that the bacteria should increase in biomass because of an increased food source. Most
DOC enrichment studies fail to look at different concentrations of DOC and its effect on
foodweb interactions. It was hoped that from these treatments, a simple predictive
relationship could be established whereby bacterial biomass may be predicted fiom
35
knowing the concentration of hurnic and fulvic acids and fiom knowing the responses in
other foodweb components (phytoplankton, bacterial predators) as a result of changing
bacterial biomass.
The bacteria in the +DOC treatments may also increase biomass as a consequence
of reduced UVB radiation, and was therefore important to separate out the effects of DOC
as a food source versus DOC as a UVB attenuator. Thus, the fourth treatment, -UVB,
was included in this study. This treatment shielded out UVB radiation with Myiar-D
sheeting (Dupont Canada) to the same extent as the +DOC treatment, without the
addition of any food resources. Ideally, the response of the bacteria in this treatrnent
could be combined with the response in the +DOC treatrnent to determine what the food
effect itself was, not the combined food and UVB shield effect.
2.4 Limnocorrals
Limnocorrals consisted of two integral parts, the large polyethylene bag used to
hoId experimental water, and a wooden frame which supported W-variable sheeting
(polyethylene or Mylar-D; Figure 5). Bags used for the experiment (12 in total) were 3 m
in diameter, 1 m deep and constructed of 4 mil polyethylene sheeting. The total volume
of each bag was approximately 1860 L. To the top of the bags, loops of polyethylene
were attached and foam fitted through allowing for notation above the water surface and
structural support. These foam collars were attached to each side of a square wood
support frarne by hose clamps. When filled with water, the bags retained their cylindncal
shape without significant collapsing of the sides. At the end of the experiments, bags
were checked for holes or tears along the searn to ensure that there had been no leaking.
Figure 5. General design of experimental enclosures
\:l
Overhead
view
I
UV variable
cover
Support
fiame
3m
I
Si de
view
UV variable
- - cover
Support h n e
Floaîs
Enclosure bag
Lake
-,
dace
Frames used to support the bags and UV-variable covers were made of 2"x4"
wood and measured 3m by 3m overall. The h e s were reinforced by adding a length of
wood to each corner. Floats were attached at the corners of each frame to ensure that bag
openings and UV-variable covers remained above the water level. Al1 m e s were tied
together in a line which was attached by rope to trees on opposite shorelines. Enough
slack was left in the rope to allow enclosures to rise and fa11 with changing water levels.
UV-variable sheeting was either polyethylene sheeting or Mylar-D sheeting @th
4 mil thickness). The polyethylene sheeting allows penetration of al1 wavelengths of
radiation, while Mylar-D selectively shields out the majority of UVB radiation, while
remaining wavelengths pass unaltered. For each enclosure, sheeting was stapled to two
3m by 1.Smwooden fiames and placed on top of the wooden support fiame. Each half of
the cover was tied to the wooden frame to ensure that wind could not blow them off.
When sampling, one half of the cover on the limnocorral could simply be loosened and
slid on top of the other half, allowing hl1 access to the bags without fear that the cover
would fa11 into the lake.
Treatment bags were placed in random order along the line of enclosures for each
experiment. Covers for the Iirnnocorrals were not placed upon the fiames until al1 bags
were filled with lake water and DOC added. Start time for the experiments was
considered as being when the covers were f'irst placed upon the frames.
For both experiments, a IOL bucket was filled with lake water and the contents
transferred into each bag until full. This method proved slow and awkward and is
therefore suggested that future researchers consider developing a more suitable method.
Between the first and second experiments, bags were emptied, nnsed with lake water, and
refilled. Bags and covers were checked routinely for damage and any needed repairs
39
completed. The covers did prove to be extremely durable against wind. min, and resting
birds.
Finally, a note should be made to justifi the size of the limnocorrals used for this
expenment. While their size proved to be more awkward to work with than something
smaller would be, it was necessary for two reasons. First, the large size allowed for the
containment of a larger volume of water. This offers greater stability over time with
regards to nutrient chemistry and avoids some of the closed system effects mentioned.
Secondly, the low sun angle at this northem latitude means a lower angle of penetration
through the covers and into the bags of water. At this latitude, the maximum sun angle
(at summer solstice) is 4S0, meaning that with covers 3m2, the maximum penetration
would be 3m. Since lower sun angles did occur, the size of the covers used ensured
adequate penetration depth of W B radiation.
2.5 DOC extraction and enrichment
DOC added to the limnocorrals was obtained fiom tsvo sources; a commercial
hurnic acid extract (Sigma Chemicals) and a South Lake sediment extract. Since the
commercial extract was of an unknown source and chemical make-up, as much sediment
extract as possible was used.
Extraction of humic DOC substances fiom the sediment was based upon the
concentration and extraction procedure for lake water DOC as discussed in T h m a n and
Malcolm (198 1) and Kaplan (1 994). Assuming that sediment particles act similarly to
the XAD-8 resin used in their paper, humic acids were released fiom them via the
addition of 0.1NNaOH. This process was allowed to proceed for two days in a cool,
dark area. Once complete, the liquid concentrate was decanted and pH lowered to
40
background lake levels by adding O. 1N HCl. This concentrated extract was filtered
through a 0.45pm filter and fiozen (-40°C)in dark Nalgene bottles for later use. Ideally,
this extract would have been fieeze-dried, but since this equipment was not available in
Inuvik, a liquid extract was used instead.
A subsample of the lake sediment extract was diluted with distilled deionized
water and DOC concentration calculated using the gas chromatography and
spectrophotornetric methods (see below). The two methods gave very similar values,
indicating that the extract did indeed consist primarily of the UVB absorbing humic
fractions.
The concentration of humic acids needed to decrease UVB penetration in the
enclosures at 1Ocm to either 50% or 1% was determined using formula derived from
Scuily and Lean (1994)and Wetzel and Likens (1991) relating UVB penetration depths
to DOC concentrations. The formula used were as follows:
(In Io - In Id/z = k
where Io
IZ
= irradiance at subsurface (1 00%)
= irradimce at depth (50% or
1%)
z = depth (O. 10m)
k = attenuation coefficient K ~ I B
K~IB
= 0.415 (DOC)
Since attenuation coefficients are based upon the hurnic W B absorbing fiaction
of DOC, the absorbance at 3 lOnm was used to initially estimate total DOC concentration
(according to formula in ScuIly and Lean 1994). The DOC concentration needed to
attenuate light at 1Ocm to 50% or 1% was calculated using the formula above. The
difference between the initial DOC and DOC required was the amount of humic acids (in
41
r n g - ~ - lneeded
)
to reduce W B penetration by the desired amount. While not as accurate
as using a spectroradiometer, it was likely close to real values (Scully and Lean 1994,
Morris et. al. 1995).
The final DOC solution added to the limnocorrals consisted of approximately
80% lake sedirnent extract and 20% commercial hurnic acid extract. The same stock
solution fiom the original sediment extract was used for both experiments to ensure that
initial chemistry and quality of added DOC was identical. DOC was added to ernpty bags
which were then filled with lake water and stirred to ensure an even distribution.
Samples for DOC concentration were taken for each sampling day and more extract
added when necessary (approximately every four days) to maintain the constant target
concentrations.
Molecular size of the DOC in enclosures was determined using the absorbance
ratio of filtered lake water at 250n.mto 365m (De Haan and De Boer 1987, DeHann
1993). If the size class of the DOC was widely different, this may indicate differences in
its availability to bacteria as a substrate; high molecular weight DOC is less available for
bacterial consurnption than is low molecular weight DOC.
2.6 Sampling
Sampling was done on the initial day, 24 hours later, and then every 48 hours over
a total seven sampling dates (including initial day). Water samples were collected over a
0.75m to Om integrated depth using a Van Dom sampling bottle unless othenvise noted.
Al1 water was stored in a cooler at approximately arnbient lake temperatures and in the
dark until brought back to the lab for processing. When sampling fiom enclosures, every
attempt was made to draw water fiom the center. Afier sampling, water was stirred in the
42
enclosure to ensure even distributions of biota and chernicals. Al1 limnocorrals plus the
surrounding lake were included in the sarnpling protocols outlined below. The lake was
included in the sampling protocol to identie significant enclosure effects, so was used for
cornparison to the control bag only.
2.6.1 Water chemistry
Sarnples for DOC, NHq, PO4, pH. conductivity, and temperature were collected
on al1 sampling days. Suspended sediments, chlorophyll concentration, and organic
carbon concentrations were collected on a weekly basis. In the case of conductivity and
temperature only, data were collected fiom Om and 0.75m separately. Conductivity and
temperature were measured in the field using a 3000 T-L-Cmodel field conductivity
probe (Y S.1. Incorporated).
Water pH was measured using an Accurnet pH meter 10 (Fisher-Scientific) model
pH probe in the lab on unfiltered lake water sarnples. Values were corrected for any
temperature differences.
2.6.1.2 N H ~ +and ~ 0 ~ 3 -
Water for amrnonia and phosphate analysis was filtered through a GFIC filter and
refrigerated until analysis (within 12 hours). Nutrient samples were measured
spectrophotometricallyaccording to the methods of Strïckland and Parsons (1 972).
Essentially, filtered water was added to acid rinsed and washed test-tubes, an appropnate
amount of colour reactive reagent added, the reaction allowed to proceed and absorbances
43
measured at 8 8 5 m (PO$)
and 630nm (NHqf) against blanks (OpM) and standards (1
p M ~ 0 ~and
3 1OpM NW+) The absorbance of the sample was then converted to N
and P concentrations based upon absorbances of the known standards.
2.6.1.3 DOC
Dissolved organic carbon concentration was measured using two methods;
spectrophotometrically and using gas chromatography (See Appendix A for more detailed
discussion). For spectrophotometry, water samples were filtered through a 0.4Spm precombusted g l a s fibre filter and absorbances of the water read at 325nm. Filtration is
necessary to remove any particulates which are not part of the dissolved organic
component, but may absorb at this wavelength. Conversions to absorbances at 3 lOm
were based on a previously derived relationship between absorbance at 325nrn and
absorbance at 3 1Onrn as the spectrophotometer available at the Inuvik Research Centre
was not capable of reading into
UVB wavelengths.
The absorbance of filtered water samples was found to be very similar at 32511x11
and 3 10nm with a linear relationship between the two wavelengths holding for
concentrations of humic DOC up to 15 mgl-1, making absorbance at 32511x11
a good
predictor of humic DOC concentrations for these experiments.
2.6.1.4 Gas chromatography
See Appendix A as well for M e r discussion. This method has the advantage
that the total DOC concentration is determined, not just the UV absorbing fraction. The
protocol used was identical to that of McDowell et. of.(1987) with slight modifications
(outlined in Appendix D). This involves first stripping 0.45pm filtered lake water
44
samples of any dissolved inorganic carbon, then adding potassium persulfate which,
when enclosed with the water sarnple and autoclaved, converts organic carbon into CO2.
Evolved CO2 is then stripped fiom the water sample and the concentration analyzed
using an EG&G Chandler Engineering Carle Series 100 AGC mode1 gas chromatography
machine. Blanks of pure water and standards of glucose were also processed according to
this protocol to develop a linear regression upon which sample CO2 concentrations could
be converted to total DOC concentrations.
2.6.1.5 Suspended sediments
On a weekly basis, 1L integrated water samples were collected for suspended
sediments and chlorophyll, and stored in the dark at 4°C until filtration later that
sampling day. Suspended sediments were filtered ont0 pre-weighed GFIC filters, allowed
to dry, and re-weighed. The difference between the weights of the filter before and afier
gave the total suspended sediment concentration per liter of water.
2.6.1.6 Chlorophyll
For determining chlorophyll concentration in algae and cyanobacteria combined,
1L water samples were filtered ont0 non-combusted GF/C filters, wrapped in foil, and
stored in at 40°C in dark containers (black film canisters) until M e r processing. Afier
the end of each experiment, chlorophyll concentration was determined by rnacerating the
filters in 5ml of buffered acetone (100ml distilled deionized water brought up to 1L total
volume with acetone plus two drops NH40H added. Sarnples were then centrifüged to
seale out al1 of the large and fine particles. The liquid extract was then decanted into a
lcm quartz cuvette and absorbances taken at 480nm, 630nm,66411x11, 66511x11,
and 750nm
in a Milton Roy Spectronic 50 1 spectrophotorneter. Absorbances were converted into
45
chlorophyll a,b, c and carotenoid concentrations using the formula provided in Wetzel
and Likens (1991). Samples were then acidified with the addition of 0.2 ml of O.1N HCl,
allowed to sit for 5 minutes, and absorbances taken at 665nm and 750nm to determine
phaeopigment concentrations.
2.6.2 Bacterial biomass
Two samples (20 ml volume each) were collected fiom each enclosure over al1
sampling days and preserved in the field with HPLC grade formaldehyde (37% v/v) to
give a final formalin concentration of 2%. Fixed samples were stored at room
temperature in the dark until termination of the individual experiment (maximum storage
tirne of two weeks) before slide preparation. Bacteria preserved using this method can be
stored at room temperature for up to 10 weeks before any significant ce11 distortion
occurs (Porter and Feig 1980, Fry 1988). Preparation of bacterial slides was done using
the methodology outlined in Porter and Feig (1980).
For slide preparations, d l water used for preparing stock solutions and for rinsing
was
0.22pm filtered and autoclaved. This water is referred to as sterile water (Fry 1988).
Al1 glassware used in preparation of slides (except the slides themselves) was acid
washed and rinsed with sterile water for each sample. The above procedwes were
necessary to minimize extemal contamination of samples by other bacteria.
Filters for slides were 25mrn diameter, 0.22pm pore size polycarbonate
membrane filters. Filters were stained for at least twelve hours using an lrgalan Black
solution (2g-l-l+2Oml acetic acid) to provide a dark background for epifluorescence
analysis. Unlike older filters, new polycarbonate membranes have no hydrophobie areas
and thus stain very evenly (Fry 1988). Filters were thoroughly rinsed with stenle water
before being used for samples to remove any excess Irgalan Black solution.
Bacterial cellular DNA was stained using DAPI (4'6-diamidino-2-phenylindole;
Sigma Chemicals). A stock solution of DAPI (lmg-ml-1) was made with stenle water
and kept in the dark at O°C until needed. At this concentration and temperature, DAPI
remains stable indefinitely (Porter and Feig 1980). However, if thawing or exposure to
light occun on a regular basis for slide preparations, it is a good idea to replace the stock
on a yearly basis. Working solutions of 1 p g - ~ -DAPI
l
were prepared daily with sterile
water. This solution was kept in the dark at 4OC while in use and discarded at the end of
each day of slide preparation.
Stained filters were placed on top of pre-wetted 0.45pm filters and clamped in
place in g l a s filter holders. The backing filter ensured even distribution o f bactena on
the 0.22pm filter. Preserved water sarnples were shaken vigorously and 2ml aliquots
l
placed on filters. DAPI stain was added to a final concentration of 0.0l P g - ~ -and
allowed to incubate for at least 5 minutes. Samples were then gently filtered at 125 mm
Hg pressure. Filtenng pressure was released irnmediately upon completion of filtration
of the sample. A drop of immersion oil (Cargille Type B) was placed on a clear glass
slide and the filter placed on top of the oil. Another drop of oil was placed on the filter
and a 25mm round cover slip placed on top. The slide was stored in a slide box at 4OC
until analysis (20 weeks maximum). Al1 slide preparation was done in a darkened lab and
fumehood due to DAPI's light sensitive properties. Slides prepared using this procedure
are stable for up to 24 weeks at 4°C (Porter and Feig 1980)
Slides were analyzed using a Car1 Zeiss Axioplan epifluorescent microscope fitted
with a HBO 50 mercury larnp and BG38 and KG1 red-free filters. For DAPI
47
fluorescence, a G365 exciter filter, FT395 chromatic barn splitter, and LP 420 barrier
filter were used to allow visualkation of the bluish cellular DNA. Slides were examined
in the dark. For each slide, 10 fields and at least 100 cells were counted at 1000 times
magnification using a 10x eyepiece with built in graticule and a 100/1.30 Plan-
NEOFLUAR oil objective lens. Cells were dassified based upon their shape and size for
later conversion of total nurnbers to biovolume and biomass.
To determine total ce11 numbers per milliliter of sample, the following formula
from Jones (1979) was used:
where Y = mean count per graticule area used
A = effective filtration area of membrane (mm*)
a = graticule area (mm2)
v = volume of sample filtered (ml)
d = dilution factor (if applicable)
Total numbers per milliliter for each ce11 shape were converted to ceIl volumes
and finally to biomass using the conversion factor of 3 16 fg ~ y m - (Fry
3
1988). While
other conversion factors do exist, this appeared to be an average value and since no other
data for the Mackenzie Delta area exists, this seemed appropnate. As well, the results of
each limnocorral treatment is being compared to itself over time and between treatments.
Therefore, any reasonable conversion factor is appropnate as long as it is consistently
applied. The advantages and disadvantages of the above technique for deterrnining
bacterial biomass have been outlined in detail in Appendix B.
2.6.3 Heterotrophic nanoflagellate biomass
Water samples and slides for HNAN biomass were collected and prepared
similarly to bacterial biomass with the following major differences as outlined in Shen
and Shen (1983, 1994), and Cole et. al. (1989):
1. 60ml water samples were collected and preserved with formaldehyde to a final
concentration of 5%.
2. Filters used were 0.8pm polycarbonate membranes stained with the IrgaIan
Black solution.
3. 20ml of gently shaken, preserved water sarnple was filtered ont0 the
membrane. Vigorous shaking will destroy some of the more fragile organisms.
4. Counts were done at 400x magnification. Only 50 individuais were
enumerated due to their sparse distribution relative to bactena and viruses.
5. No backing filter was necessary to ensure even distribution.
Representative ce11 sizes were measured and biovolumes calculated. To obtain
organic carbon weight, a density of 1.O was assumed ( l 0 6 ~ m 3= 1pg) to obtain wet
weight.
Dry weight was assumed to be 20% of wet weight, and organic carbon was
assurned to be 10% of the dry weight.
2.6.4 Viral biomass
Samples for viral biomass were prepared similarly to bactena with the following
exceptions as outlined in Suttle (1 993), Hemes and Suttle (1 999, and Weinbauer and
Suttle (1 997):
1. Lake water was pre-filtered through 0.22pm filters to eliminate the majority of
bactena.
2. Two milliliter sarnples had enough DAPI added to increase the final
concentration to 1p g - ~Sarnples
- l . were incubated for 30 minutes in the dark. This
allows better visuaIization of viral particles when examined under the microscope (Suttle
1993).
3. O.OSpm, unstained Anodisc membrane filters were used instead of stained
polycarbonate membranes.
4. Due to their small size, shape differences could not be determined, only total
nurnber of viral particles.
Viral organic carbon was estimated by determining biovolurnes and assuming a
specific density of 1.O to convert to wet weight. Dry weight was assumed to be 20% of
wet weight, and organic C content 10% of dry weight.
2.6.5 Zooplankton biomass
Samples for macrozooplankrton biomass were collected dwing the second
experiment only and on a weekIy basis (initial, one week, and termination date). Sarnples
Lvere preserved with formaldehyde to a final concentration of 5%. Sampling was done
with a 23crn diameter, 64pm zooplankton net from 0.75to Om depth, a volume of 3 l
liters. Three sample tows were taken and combined in a single sample bottle of 125x111
size. Preserved samples were stored at room temperature until analysis.
When analyzing biomass, zooplankton were concentrated ont0 63pm netting,
rinsed off into a graduated cylinder, and brought up to 50ml total volume with water.
Subsamples of 2ml were removed and total nurnber of zooplankton counted in each
subsample using a dissecting microscope. Individual subsarnples were counted until the
standard error between subsamples was less than 5%. Zooplankton were identified to
-cenus. and, if possible, to species level except in the case of copepods ~vhichwere
identified as harpacticoids, calanoids, or cyclopoids. Representative organisms were
measured for conversion of total numbers to biovolume according to values described in
Rosen (198 1) and Dumont et. al. (1975). Conversion to wet weight was based on the
assumption of a specific density of 1.O. Dry weight was assumed to be 20% of wet
weight, and organic C content 10% of dry weight.
2.6.6 Phytoplankton biomass
Integrated water samples were collected weekly during the second experiment for
phytoplankton biomass. Sarnples were preserved with the addition of enough Lugol's
solution (log KI + 5g 12 dissolved in 250rnl DDW) to give a 'tea' colour to the sample.
Lugol's allows for better visualization of cells as well as rapid settling of phytoplankton
51
cells which take up the solution. Preserved phytoplankton were stored at room
temperature until analysis.
Samples were placed in a settling chamber (25 cm3)for at least 24 hours. This
allows the preserved phytoplankton to sink to the bottom of the slide and was necessary
for thîs lake since phytoplankton biomass wras generally low. Excess water was removed,
and the slide examined under an inverted microscope. Since distribution was spane,
even afier settling, the entire slide was examined and al1 phytoplankton counted (at least
100 cells). Algal cells were identified to the family level or greater when possible. Al1
cells were classified on the basis of ce11 size and shape for later conversion to biovolume
and wet weight (assuming specific density of 1.O). Conversion from wet weight to
organic carbon content was done using the following formula (JStockner, pers. cornrn.):
Cyanophytes
C = B x 0.22
Dinoflagellates
C=Bx0.13
Diatoms
C=BxO.ll
ChIorophytes
C = B x 0.16
Ai1 other species
C=Bx0.11
where C = phytoplankton carbon (pg-l-l C)
B = phytoplankton biomass (pg4-l wet mass)
The total ce11 number per slide was converted to cells per milliliter which was
then converted to algal biovolume. Biovolurnes were converted to biomass on the basis
of family since cellular carbon varies widely among taxa. This biomass was compared to
the algal biomass obtained through organic carbon and ch1 a analyses described before.
2.6.7 Bacterial production
Bacterial production experiments were conducted on each sampling date. The
complete protocol for production work can be found in Appendix E while a bnef
summary follows here. Water was collected fiom al1 enclosures plus the lake and stored
at ambient lake temperatures in the dark until arriva1 at the lab. Ali water used for
experiments was sterile (0.22pm filtered distilled deionized water autoclaved for 60
minutes).
Stenle autoclaved 20ml g l a s containers with screw-on tops had lOml of
unfiltered water samples added and 100pl of 3 ~ - T ~
R final concentration) added
(2OnM
to each. For the controls (one for each Iimnocorral plus the lake), incorporation of 3 ~ TdR was stopped immediately by adding fonnaldehyde (37% v/v) followed by NaOH (10
N) and subsequently placed in the fridge at 4°C. The expenmental sarnples were allowed
to incubate for 20 minutes before addition of formaldehyde and NaOH.
Sarnples were then left on ice until filtration could take place. Samples had 200%
TCA (Trichloroacetic acid) added, were stood on ice, and filtered through pre-soaked
0.22pm cellulose-nitrate filters. The filters and filter apparatus were then rinsed with 5%
TCA, 50% phenol-chloroform, and 80% ice-cold ethanol to extract purified, labeled
DNA. These filters were then placed in scintillation vials and stored at 4°C until analysis.
1 3 ~ - to~ d ~
Standards for each experiment were also prepared by adding 1 0 0 ~of
5ml of sterile water. Two lOOpl aliquots were removed from this and added to 900p1 of
sterile water in two scintillation vials. To each standard, 9ml of scintillation cocktail
(Filter-Count, Packard) was added.
Upon arriva1 at SFU,filters were dissolved with the addition of IOml of FilterCount (Packard) and radioactivity measured in a scintillation counter. From the standard
counts, time incubated, and volume filtered, raw counts were converted ro pmol 3 ~ - T ~ R
incorporated per liter per day. To calculate the arnount of C per pmol of 3 ~ - T ~ R
incorporated, the following formula was used (Wetzel and Likens 1991):
1= C W F
where 1 = g C produced per M 3 ~ - T ~uptake
R
C = cells produced per M 3 ~ - T ~
uptake
R (2.0-1018)
V = average ce11 volume (0.0914~m3for this experiment)
F = carbon conversion factor (3.16- 10-13 g c-pn3for this
experiment)
From the calculated uptake of 3 ~ - T per
~ Rliter per day, and the amount of C
produced per mole of 3 ~ - T ~uptake,
R
the amount of C produced per liter pet day
(espressed as pg c-1-l-day-l) can be calculated. Further, an estimate of the amount of C
produced per bacteria per day can be calculated by dividing the above value by the
bacterial density per liter. A more detailed discussion on the protocol and assumptions
using the above methods for determining bacterial production can be found in Appendix
C.
2.7 Lake survey
At the end of August, a two-day, 40 lake survey was conducted via helicopter
within the Inuvik region. The lakes were chosen based on previously determined
flooding regimes as well as other properties. The lakes chosen also covered a wide range
of DOC concentrations. The purpose of the survey was to put the results of the South
Lake experiment into the context of other delta lakes.
54
Sarnples were collected for DOC concentration (spectrophotometrically and GC),
chlorophyll, bacterial biomass, HNAN biomass, virus biomass, and suspended sediments.
Sarnples were either processed irnrnediately or preserved appropriately for later analysis.
2.8 Statistical analyses
Statistical analyses were conducted in SYSTAT (SPSS Inc.) version 8.0.
Expenments in South Lake were analyzed using repeated measures ANOVA techniques
which accounts not only for the between groups (treatment) effects, but also the within
groups (time) effects. It was important to know if the response seen in bacterial biomass
and other microbial components changed significantly over time and if they were
significantly different from the other treatments. The control enclosures and lake were
cornpared separately to determine if there were any enclosure effects. Unequal sampIing
periods (24 hours for the first sample collection, 48 hours each subsequent sampling
period) were accounted for in al1 statistical analyses. Appendices F and G contain the
averages, standard errors, and number of samples collected for al1 biotic and abiotic
components.
When repeated measures ANOVA analyses were conducted on the data, several
assumptions were made. These include normal distribution within cells, equal covariance
between al1 possible pairs of repeated measures (compound syrnmetry), and equal
variances within cells. Tests of these assurnptions, and corrections to statistical results
were made as necessary.
From the experimental design, there are several planned comparisons which best
address the question of whether DOC as a food source or as a W B shield most
influences bacterial biomass. These include:
1. Control versus treatments. To determine if the treatment was significantly
different from the control over time, ie: whether there \vas a UVB effect and a food effect
of organic carbon additions.
2. +DOC versus ++DOC.To detennine if addition of different arnounts of
organic carbon affected the accumulation of bacterial biomass.
3. *DOC versus - W B . To determine whether the response seen in the bacterial
community as a result of additional DOC was due to the increased food source. W B
shielding, or a combination of both.
Repeated measures ANOVA and the sarne planned cornparisons were conducted
for other components of the microbial food web as well. Since sarnple sizes were equal
in al1 cases, this made planned cornparison and ANOVAfs more powerfbl and robust to
variations in the data. However, the pre-planned comparisons in this case were not found
to be orthogonal. Therefore, values of the type 1 error a were adjusted to a significance
level of 0.037using an experimentwise error rate of 0.10. This adjustment was based
upon the Bonferroni method (Sokal and Rohlf 1995). While some texts suggest that the
DUM-$id& method for adjusting significance levels is slightly less conservative than the
Bonferroni method, there was virtually no difference between the two in this case (Sokal
and Rohlf 1995).
An experimentwise error rate of 0.10 was chosen for two reasons. First, this
increases the power of the experirnent (the probability of avoiding type II errors).
Second, it has been suggested that statistical tests be slightly more robust for exploratoty
studies such as this one or for those which use new techniques. As has been pointed out
by some authors, the a level of 0.05 has largely been picked out of convenience, not for
any practical reasons (Hurlbert 1984). Statistical results presented in the tables are
reported with analysis at the 0.10 (marginal), 0.05 (standard), and 0.0 1 (high) significance
levels.
Individual regressions were detemined for microbial components and the bacteria
for the lake survey. Regressions were based upon theoretical considerations, raîher than
attempting al1 possible combinations. Transformations were also performed on the data
to determine if a better fit could be made to predict bacterial biomass. These are outlined
in more detail in the results section.
CHAPTER 3: RESULTS AND DISCUSSION
3.1 Limnocorral conditions
When looking at the results of bacterial community response over tirne, an
important consideration is whether these changes are due to treatment effects (UV or
DOC) or due to other factors such as changing temperatures. These generaily
uncontrollable factors result in enclosure effects; changes in the biotic components which
cannot be attributed solely to the treatments. The enclosures resistance to these effects
depends upon their volume and the length of the experiment, the smaller the volume and
longer the experiment is run, the more likely enclosure effects will appear.
For DOC, it was important that consistent levels of humic DOC concentrations
u-ere maintained for both experiments and over the course of the experiment so that the
same UVB shielding effect occurred and the same amount of food was available. The
background levels of total DOC at the begiming of the two experiments were 14.5 mg-l-l
and 16.8 r n g t l , respectively. The estimate of the UVB absorbing humic fraction (or
coloured DOC) via spectrophotometric analysis was 3.6 mgl-1 for both experiments,
corresponding to 64% UVB penetration at 1 Ocm. For 50% UVB penetration at lOcm
depth, a concentration of 4.5 mg-1-1 of coloured DOC was needed. Thus, each
limnocorral needed to be increased by 1.9 mg-1-1 or about 13.49 g per limnocorral (total
volume of 7 100 liters). For 1% W B penetration at 10 cm, the concentration of humic
DOC needed to be increased by 8.9 mgl-1 to a total of 12.5 mg-1.1. This worked out to
70.29 g per 7100 L limnocorral. Coloured DOC levels did not drop significantly between
sampling dates. Overall, total DOC ievels in the lirnnoconals and in the lake did not
change significantly over the course of either experiment (repeated mesures ANOVA
within treatments experiment 1 F=0.809 p=0.680, experiment 2 F=1.138 p=0.355).
58
Nutrients, average temperature, conductivity, and pH did not Vary significantly
over the course of the experiments or between limnocorrals and South Lake. This
indicates that the enclosures were large enough, and the experiments were run for a shon
enough period that there were no apparent enclosure effects which may have influenced
changes in the microbial components.
Total suspended sediment concentration was not significantly different between
enclosures (repeated measures ANOVA between subjects experiment 1 F=0.709 p=0.573,
experiment 2 F=0.843 p=0.508) but did drop significantly over the course of the
experiment from a high value of approximately 1Smg-1-1 on Day 1 to a value of about
0.5rng-l-l by the end of the first week. Observations indicated that this drop may have
occurred by the second sampling day, as indicated by general water clarity. Suspended
sediment concentrations in the control enclosures were significantly lower than the lake
(repeated rneasures ANOVA between subjects experiment 1 F=2940.555 pc0.00 1,
experiment 2 F=1637.068 ~ ~ 0 . 1)0 which
0
maintained suspended sediment concentrations
around 1 Smg-1-1 throughout the experiments.
3.2 Expected versus observed responses in the microbial foodweb
3.2.1 Bacterial biomass
Bacterial biomass prior to DOC additions or W B removal was 0.037 pg C-ml-l
and 0.049 pg C-ml-1 for experiments 1 and 2 respectively (Figure 6). In both
experiments, the largest increase in biomass was seen in the +DOC treatment, resulting in
a final biomass of 0.054 and 0.061 pg C-ml-1 respectively (Figure 6). This was followed
by the -UVB treatments (0.046 and 0.052 pg C ml-1), and finally the *DOC
59
treatment
Figure 6. Total bacterial biomass per milliliter of lake water for each enclosure plus
South Lake over the course o f experiments 1 (a) and 2 (b). Each point represents an
average of three samples.
Lake
x -UV6
A +*O *DOC
O Cmtrd
185
190
M a n day of year
0.02
1
200
1
1
I
205
210
215
Umn ôay of year
I
220
O Cmtrd
which decreased relative to the control and the lake values (final biomass 0.03 1 and 0.025
pg C-ml-l respectively; Figure 6). While there were some variations between the lake
and control enclosure values, bactenal biomass remained relatively constant and did not
Vary significantly between the two (repeated measures ANOVA, expenment 1 F4.434
p=0.297, experiment 2 F=2.396 p=O.lW). Natural variation (standard error) for d l
enclosures and the lake was typically about 10% of the biomass (approximately 0.004 pg
C ml-1). Results of pre-planned multiple comparison tests are summarized in Table 2.
The best approach to take when examining these results in detail is to try
explaining them using the simplest hypothesis, and working upwards to more complex
interactions. The simplest hypothesis would be that there \vas only a food effect (added
DOC) or a UV-B effect, and that there were no food web effects (predators consurning
bacteria, competition with phytoplankton for limited nutrients). Since the *DOC
enclosures had the greatest concentration of humic DOC and UV-B protection, it xvould
be espected that bacterial biomass would be greatest in these treatments. However, from
Figure 6: this does not appear to be the case. \mile bacteria do seem to respond to the
increase in food source and removal of UVB radiation (increased biomass in the +DOC
and -UVB treatments), this does not explain why biomass decreases in the ++DOC
treatment.
It appears Iikely that there are food source, UVB,and foodweb effects controlling
bacterial biomass. Since there were no other apparent abiotic factors to explain the trends
seen (such as enclosure effects), the changes in bactenal biomass which could not be
accounted for by substrate or UVB effects must be due to biotic effects such as predation
and competition. In this situation, there are numerous possible outcornes, which will be
presented in the following sections. However, it is important to note that the results seen
in the bactenal biomass were reproducible and that the treatments followed very similar
62
Planned cornparisons for bacterial biomass. A single asterisk indicates
significance at an a level of 0.10 (Bonferroni adjustment to 0.033). A double asterisk
indicated significance at an a level of 0.05 (Bonferroni adjustment to 0.017). A triple
asterisk, a significance at an a level of 0.01 (Bonferroni adjustment to 0.003). Error
mean square value, error degrees of freedom, and p-value from the repeated measures
ANOVA for the between subjects effect are also listed.
Bacterial Biomass
Experiment I
Experiment 2
p=0.01 O**
p<O.OOl***
Error d.f.
8
8
p-value
0.004
0.00 1
Control versus treatments
MS error
trends in both experiments giving M e r indication that the changes seen in bactenal
biomass were real and that the bactena do respond to changes in their UVB environment
and substrate concentration.
3.2.2 Grazers, cornpetitors, and infectors of bacteria
3.2.2.1 Heterotrophic nanoflagellate biomass
Initial nanoflagellate biomass was 0.0002 pg C-ml-1 for experiments 1 and 2 and
the values were not significantly different between the control and lake (repeated
measures ANOVA, expenment 1 F=O.O6 1 p=O.8 17,experiment 2 F=0.086 p=0.784).
Total numbers of flagellates were similar to the literature values. The largest
accumulation of HNAN biomass occurred in the +DOC treatment, peaking at 0.00035 p
g C-ml-1 for experirnent 1 and 0.00030 pg C-ml-1 for experiment 2 midway through the
esperiments, before steadily declining (Figure 7). Both the +DOC and -UVB treatrnents
responded similarly, increasing IO biomasses of 0.00025 pg C-ml-l and 0.00027 pg C-ml1 for experiment 1 and 2 respectively. These biomasses appear to remain steady afier
reaching these peaks about midway through the experirnents (Figure 7). Results of
multiple cornparison procedures discussed below are summarized in Table 3.
As with the bacteria, the most logical way to attempt to explain trends in HNAN
biomass is by working with simple hypotheses and moving up to more complex ones. A
simple hypothesis would be that HNAN are not consumed by predators themselves, are
not affected by W B radiation, and bacteria ideally responded only to DOC as a food
source and not to changes in W B radiation. Bactenal biomass would be greatest in the
++DOC enclosures since this would have the greatest arnount of substrate available. The
next highest biomass would be in the +DOC enclosures, then the - W B and control
64
Table 3
Planned cornparisons for heterotrophic nanoflagellate biomass. A single asterisk
indicates significance at an a level of 0.10 (Bonferroni adjustment to 0.033). A double
asterisk indicated significance at an a level of 0.05 (Bonferroni adjustment to 0.017). A
triple asterisk, a significance at an a level of 0.01 (Bonferroni adjustment to 0.003).
Error mean square value, error degrees of freedom, and p-value from the repeated
measures ANOVA for the between subjects effect are also listed.
Nanoflagellate Biomass
Expenment 1
Experiment 2
Control versus treatments
p=0.004*
p=O.0OS4
+DOC versus H D O C
p=0.004* *
p=O.O33 *
++DOC versus -UV-B
p=O.OlO**
p=0.024*
MS error
1-10-9
1.10-9
Error d.f.
8
8
p-value
0.001
0.002
Figure 7. Total heterotrophic nanoflagellate biomass per milliliter of lake water for each
enclosure plus South Lake over the course of experiments 1 (a) and 2 (b). Each point
represents an average of three samples.
Mian day
of year
which had no substrate added. The HNAN should follow a similar trend as they would
respond to the increase in bacterial biomass. However, as Figure 7 shows, there is in fact
a significant response in the -UVB treatment relative to the control.
The second hypothesis would be that HNAN are not consumed by predators, but
that there is a direct UVB effect on both the HNAN and bactena, and that the bactena are
also res~ondingto increased food resources. The greatest increase in bactenal biomass
would be in the HDOC enclosure (both added food and increased W B protection),
followed by either the +DOC or -UVB and finally the control. The nanoflagellates would
likely follow a similar trend, with the highest biomass accumulation occumng in the
*DOC
treatment (more bacteria and greater UVB protection), followed by the +DOC
and -UVBtreatments (more bactena and/or greater UVB protection). The control
enclosure should not show a response, since there is no added substrate for the bacteria to
use and no additional protection from W B radiation. The results presented in Figure 7
are consistent with this hypothesis.
3.2.2.2 Virus biomass
Virus biomass appears to fluctuate greatly over the course of the two expenments,
ranging in values frorn 0.01 pg C-mlg1to 0.035 pg C-rnP1 (Figure 8). ïhis translates to a
range of 2-107 to 8.107 organisms per ml. The natural variation for each treatment
averaged about 10% of the mean, or about 0.00 1 pg C-ml-1,which does not explain any
of the trends seen. Some basic trends are evident through both experiments. Both the
+DOC and - W B treatments result in increasing viral biomass over t h e (Figure 8), while
the ++DOC treatment leads to an initial increase, followed by decreasing viral biomass.
The control and lake remain relatively steady, and do not Vary significantly from each
other in either experiment (repeated measures ANOVA; experiment 1 F=2 1-685 p=O.O 10,
68
Table 4
Planned comparisons for virus biomass. A single asterisk indicates significance at
an a level of 0.10 (Bonferroni adjustment to 0.033). A double astensk indicated
significance at an a level of 0.05 (Bonferroni adjustxnent to 0.017). A triple asterisk, a
significance at an a level of 0.01 (Bonferroni adjustment to 0.003). Error mean square
value, error degrees of freedom, and p-value from the repeated measures ANOVA for the
between subjects effect are also listed.
Virus biomass
Experiment 1
Experiment 2
Control versus treatments
p=0.001* *
p=0.017
+DOC versus ++DOC
p=0.003***
p=0.001***
++DOC versus -UV-B
p=0.006*
p=O.OOl***
MS error
3.73-10-6
4.18-10-6
Error d.f.
p-value
Figure 8. Total virus biomass per milliliter of lake water for each enclosure plus South
Lake over the course of experiments 1 (a) and 2 (b). Each point represents an average of
three smples.
Lake
x
-we
a ++-
O +DOC
O
Cmtrd
Lake
x -UV6
A ++O +DOC
O Cmtrd
experiment 2 F=0.13 1 p=0.735). Results of multiple comparison tests are surnmarized in
Table 4.
The simpIest hypothesis would be that the viruses are simply a consequence of
bacterial biomass. Since bacterial and viral replication times are similar, the viruses
could, theoretically, increase their biomass at the same rate as that of the bacteria. If they
were unaffected by abiotic factors (UVB radiation), and used bacteria as their sole hosts,
then their trends of changing biomass should look very similar to that of the bacteria.
From Figure 6, one would expect viral biomass to be highest in the +DOC, followed by
the -UVB treatment, then the control, and finally the ++DOC treatment. While there is a
significant increase in the +DOC and -UVB treatrnents, the ++DOC treatment also
increases at the start of the experiment. In addition, the increase of viral biomass in the
+DOC and -UVB does not seem to be on the same scaIe as that of bacteria. For example,
in esperiment 1, bacterial biomass in the +DOC enclosures increases about 1.5 times
from its starting biomass, while viral biomass increases by a factor of 2.
A second hypothesis to explain the trends seen is that there is a UVB effect on the
viruses and that they are responding to both changes in bacterial biomass and in the UVB
environment. If the bacteria were responding ideally to a combination of increased
substrate and W B protection (highest bacterial biomass in U D O C , followed by +DOC
and - W B and finally control with the lowest biomass), then the viruses should show
sirni1a.r trends. If the bacterial biomass in the +DOC and -UVBtreatment were equal,
virus biomass may possibly be greater in the -UVBbecause of the additional protection
from UVB radiation. As well, the virus biomass would be greatest in the *DOC
where
there are abundant bacteria, and protection from UVB radiation. This does not appear to
be the case. While the t+DOC does show an increase relative to the control, it is smali
compared to the +DOC and -UVBenclosures. As well, the experiments indicate that
72
biomass in the +DOC treatment is as high or higher than the - W B . Since there is an
increase in viral biomass for the *DOC
treatment, even though there was no increase in
the bacterial biomass, this suggests that there may be some UVB effects, but does not
fully explain trends seen in other enclosures.
3.2.2.3 Phytoplankton biomass
Samples for phytoplankton biomass (either total ch1 or direct ce11 counts including
cyanobacteria) were collected on a weekly basis, as their biomass was not expected to
change quickly enough to warrant daily collection. Since chlorophyll can be used as an
estimate of phytoplankton biomass, an idea of what the phytoplankton community was
doing in experiment 1, when phytoplankton were not preserved for counts, can be gained
from this data. ï h e phytoplankton collected in experiment 2 were composed primarily of
diatoms, blue-green algae, and dinoflagellates.
Phytoplankton biomass and chlorophyll concentrations do follow similar trends
for experiment 2. The initial biomass drops in the control enclosures and lake afier the
first sampling day, and then remains constant (Figures 9 and 10). The ++DOC treatment
also decreases, but not as much as the control enclosures (Figures 9 and 10). The +DOC
treatment remains stable over the course of the experiment, and the - W B treatrnent led
to an increase in phytoplankton biomass (Figures 9 and 10). For experiment 1, the
control and lake remain constant over the course of the experiment (Figure 10 (a)), while
increases are greatest in the - W B , followed by the +DOC, and finally by the ++DOC
treatments. The differences between the treatments are similar in both experirnents,
although the trends of biomass accumulation are not, so the experiment was not
completely reproducible. Natural variation in phytoplankton biomass was about 5% of
the mean. Results of multiple cornparison procedures are summarized in Table 5.
73
Table 5
Planned cornparisons for chloro?hylI concentration and phytoplankton biomass.
A single asterisk indicates significance at an a level of 0.10 (Bonferroni adjustment to
0.033). A double asterisk indicated significance at an a level of 0.05 (Bonferroni
adjustment to 0.0 17). A triple asterisk, a significance at an a level of 0.0 1 (Bonferroni
adjustment to 0.003). Error mean square value, error degrees of freedom, and p-value
from the repeated measures ANOVA for the between subjects effect are also listed.
Chlorophyll
Phytoplankton
Experiment 1
Experirnent 2
Control versus treatments
p<O.OO 1* **
p<O.OO 1*
+DOC versus *DOC
p<O.OO 1
+DOC versus -UV-B
MS error
Error d.f.
p-value
*
Experiment 2
p<O.OOf
***
pcO.00 1 * * *
p=0.004**
p<O.OOl***
p<0.007* *
p<O.OO I ** *
0.000363
0.00393
0.0004 1
**
Figure 9. Total phytoplankton biomass per cubic meter of lake water for each enclosure
plus South Lake over the course of experiments 2 determined by ce11 counts. Each point
represents an average o f three samples.
Lake
-WB
++DOC
+DOC
Con trol
205
210
Julian day of year
2 15
Figure 10. Chlorophyll concentration per liter of lake water for each enclosure plus
South Lake over the course of experiments 1 (a) and 2 (b). Each point represents an
average of three samples.
M i n day of year
One hypothesis to explain the trends seen is that DOC acted as a
photosynthetically active radiation shield only, but there was no UVB effect on
phytoplankton. Also, there would be no resource competition with bactena and no
predator effects controlling phytoplankton biomass. In this case, we would expect the
-UVB and control enclosures to have the highest biomass, since they would not be
blocking out PAR. This would be followed by the +DOC and ++DOC. From Figures 9
and 10, this does not appear to be the case. While the -LM3treatrnent is significantly
higher than the +DOC or uDOC treatments, it is also significantly higher than the
control enclosures. Both the +DOC and +DOC treatments are also higher than the
control treatment. Since DOC is not as strong an attenuator of PAR as it is of UVB
radiation, this result is not very surprising.
A second hypothesis is that the DOC acts as both a PAR and W B shield, and that
both PAR and UVB affect accumulation of phyqoplankton biomass. Still presurning there
are no competition effects with bacteria or predator effects controlling phytoplankton
biomass, we would expect that the - W B would have the greatest increase in biomass
since it effectively blocks out most harrnful UVB without blocking out PAR, followed by
the ++DOC which blocks PAR, but also blocks most UVB radiation, then the +DOC
which blocks some PAR and some UVB radiation and finally the control. This is
presuming that DOC is more effective at attenuating W B radiation than PAR, which it
often is. This does not appear to be the case. While biomass is greatest in the -UVB
treatment, the +DOC is significantly greater than the ++DOC treatment (Figures 9 and
1O).
If there were PAR and W B effects, and competition with bacteria for limited
resources, and if it was assumed that bacteria are responding to both increased substrate
79
and decreased UVB radiation, the highest bactenal biomass, and thus the greatest
cornpetition with phytoplankton for limited resources, would be in the *DOC
enclosures, followed by the +DOC and - W B and finally the control. Phytoplankton
biomass would be dependent on whether or not UVB or competition with bacteria for
nutrients was more limiting to their growth. If it was UVB radiation, the ++DOC and -
W B would be the highest, followed by the +DOC and finally the control. If it was
competition which was limiting growth, the highest phytoplankton biomass would be in
the treatment with the lowest predicted bacterial biomass, the control. This would be
followed by the +DOC and -UVB and finally the ++DOC. However, neither of these
predictions are supported by the results.
LVhile competition or W B radiation cannot alone explain the trends in
phytoplankton biomass, it is more likely that a combination of these hvo factors may have
produced the results seen. In this situation, it would be predicted that -UVB would be the
highest (medium competition, high protection from UVB radiation), followed by the
+DOC (medium cornpetition, medium protection from UVB radiation) or the *DOC
(high competition, high protection from UVB radiation) and finally the control (low
competition, low protection from W B radiation). This assumes phytoplankton
accumulation is more likely to be influenced by UVB radiation than by competition,
which is ofien the case. Since this trend is not seen in the results, other factors such as
predation upon algal cells, may be influencing accumulation of phytoplankton. However,
it does appear that the W B and DOC may play some role in stmcturing the
phytoplankton community.
It should be noted that although the differences between treatrnents were similar
for both expenments, the trends which produced these results were not. In the first
experiment, increases in the +DOC, uDOC and -UVB treatments were seen, while in the
80
second experiment, the -UVB increased greatly, and the +DOC only slightly, while the
H D O C actually decreased over time (Figures 9 and 10). This makes it diffxcult to
interpret these results and draw conclusions about how phytoplankton affects bacterial
biomass.
3.2.2.4 Zooplankton biomass
Macrozooplankton were collected only in the second experiment and were
analyzed once per week due to their slower reproduction rates relative to microbial
components. The major changes in zooplankton biomass seen in Figure 11 are due
primarily to shifis in the population size of smaller zooplankton (rotifers, copepod
nauplii, and Bosmina spp.), while large zooplankton biomass (Daphnia spp., and adult
copepods) remained relative1y unchanged.
The biomass of zooplankton increased greatly in the -UVB treatment, from 20 mg
C-m-3 to 90 mg C-rnq by the end of the second week (Figure 1 1). The +DOC also
showed an increase in zooplankton biomass, but only up to 70 mg C-m-3 (Figure 1 1).
Both the control and uDOC showed increases in zooplankton biomass relative to the
lake, increasing to final biomasses of 43 and 32 mg c
- ~respectively
J
(Figure 11).
Natural variation was typically about 5% of the mean or around 1 mg C-m-3. When a
repeated mesures ANOVA was conducted to examine the difference in accumulation
rates between the lake and control, significant enclosure effects were found (F=15.67 1
p=0.004). These enclosure effects were expected and are explained in the discussion
section. Results of multiple cornparison test procedures are summarized in Table 6.
If it is assumed that bactena were the major food source for macrozooplankton,
which they can be, then under increased food source, and no effect of UVB radiation on
81
Table 6
Planned cornparisons for zooplankton biomass. A single asterisk indicates
significance at an a level of 0.10 (Bonferroni adjustment to 0.033). A double astensk
indicated significance at an a level of 0.05 (Bonferroni adjustment to 0.017). A triple
asterisk, a significance at an a level of 0.01 (Bonferroni adjustment to 0.003). Error
mean square value, error degrees of freedorn, and p-value from the repeated measures
ANOVA for the between subjects effect are also listed.
Zooplankton Biomass
Experiment 2
Control versus treatments
p<0.001***
+DOC versus *DOC
p<O.OOl***
*DOC versus - W - B
p<o.oo 1*
MS error
10.045
Error d.f.
8
p-value
<o.ooo 1
Figure 11. Total zooplankton biomass per cubic meter of lake water for each enclosure
plus South Lake over the course of expenment 2. Each point represents an average of
three sarnples.
Lake
-UV%
++DOC
+DOC
Con t rol
205
210
Julian day of year
215
either bacteria or zooplankton, zooplankton biomass should be greatest in the *DOC
(highest bacterial biornass due to greater amounts of substrate), followed by the +DOC
(additional substrate producing some additional bactenal biomass), and finally by the
control and - W B , which should be equal since no DOC was added. However, the
greatest biomass is in the - W B , followed by the +DOC treatment (Figure 11). The
*DOC
treatment is actually lower than the control (Figure 1 1).
If UVB effects were included in the above hypothesis, then zooplankton biomass
shouId be greatest in the ++DOC, followed by +DOC and/or -UVB and finally the
control. This does not appear to be what is occumng, so it is unlikely that bactena are the
only food source for zooplankton. However, the large increase in biomass in the -UVB
enclosures indicates that the zooplankton may be partially responding to the increased
protection from UVB radiation.
Another hypothesis is that the response seen in the zooplankton is a result of
changes in the phytoplankton community. Zooplankton biomass follows a similar pattern
to phq-toplankton biomass in experiment 2 (Figures 9 and 1 1). It may be that the small
increase in phytoplankton biomass \\.as as a result of zooplankton grazing.
Zooplankton biomass appears to be controlled by a combination of UVB radiation
and biomass of bacteria, HNAN and phytopldton, with UVB and phytoplankton best
explaining the trends seen. Since zooplankton are opportunistic feeders, and since the
zooplankton assemblage of a lake is so diverse, it is not surprising that they may be
feeding on a number of trophic levels, possibly including themselves (Jeppeson et. a[1992).
3.2.3 Bacterial production
Since the results have already indicated that bacteria are controlled by food web
effects other than UVB radiation and food sources, it is important to look at their
production rates to get an idea of how the bactena responded to abiotic changes.
Bacterial biomass accumulation may be suppressed as a result of predation, cornpetition,
or lytic pressures, but production rates will show responses to the abiotic treatrnents
(addition of DOC,removal of UVB), if there are indeed any.
Total bacterial production rates are s h o w in Figure 12. These rates are based
upon mean bacterial ce11 weight and volume, and thus do not take into account
differences in total bactenal biomass. Essentially, there may be higher production rates
simply because there are more bacterial cells per milliliter of lake water. Since bacterial
density has already been shoun to Vary between treatments in this esperirnent the
production rates on a per ce11 basis were ca!culated and presented in Figure 13.
From Figure 13, we see that bacterial production is greatest in the ++DOC
treatment, increasing from 1.7-10-8 pg C-1-1 -day-l-cellolto 3 -010-8 pg c.1-1 .day-ll - 3.9.1
l
0-8 pg C-1-1 -day-1cell-l in experiment 1 and fiom 1S.10-8 pg ~ - l - l - d a ~ - l - c e lto
cell-l in experiment 2. The increasing production rate leveled off, after the third
sampling date. Bactenal production rates also increase in the +DOC and - W B
treatment, but this increase is much more gradua1 than the *DOC (Figure 13). Finally,
the control enclosures and lake followed the same trend, ~ 4 t only
h
slight variations in
production rates. Natural variation in al1 cases was approximately 5% of the mean or
around 1- 10-9 pg C-1-1-day0I-tell-1 . Results of multiple cornparison procedures are
summarized in Table 7.
Table 7
Planned cornparisons for bacterial production. A single asterisk indicates
significance at an a level of 0.10 (Bonferroni adjustment to 0.033). A double asterisk
indicated significance at an a leve1 of 0.05 (Bonferroni adjustment to 0.01 7). A triple
asterisk, a significance at an a level of 0.01 (Bonferroni adjustment to 0.003). Error
mean square value, error degrees of freedom, and p-value fiom the repeated rneasures
ANOVA for the between subjects effect are also listed.
Total bacterial production
Production rate per bacteria
Experiment I Experiment 2 Expenment 1 Experiment 2
Control versus treatments
p=0.007**
p=O.O 12**
p=0.002***
p=0.043
+DOC versus ++DOC
p=0.003***
p=0.002***
p=0.033*
p=0.02 1
UDOC versus -UV-B
p=O.O11**
p=0.006**
p=0.023*
p=0.03 1*
MS error
8.215
8.782
O. 1384
0.1412
Error d.f.
8
8
8
8
p-value
<O.OOO 1
<O.OOO 1
0.0049
0.00025
Figure 12. Total bacterial production rate per liter of lake water for each enclosure plus
South Lake over the course of expenments 1 (a) and 2 (b). Each point represents an
average of three samples.
Lake
x -UV8
a ++O +DOC
O Contrd
Lake
x -UVB
a +*=
O +DOC
O Contrai
Figure 13. Carbon production rate per bactenal ce11 per liter of lake water for each
enclosure plus South Lake over the course of experiments 1 (a) and 2 (b). Each point
represents an average of three samples.
M i n day of year
Men
chy of
year
Hypotheses to explain the trends seen in bactenal production rates are identical to
those presented to explain trends in bacterial biomass. Therefore, the simplest hypothesis
is that there is no foodweb effect and no W B effect, only a food source effect. In this
case, production would be greatest in the *DOC,
followed by the +DOC and finally the
- W B and control, which should be equal. While production rates are significantly
higher in the H D O C compared to al1 other treatments, the - W B is also significantly
higher than the control (Figure 13).
If there were no foodweb effects, but there were DOC effects as a food source and
UVB screen, the *DOC
treatment would show the highest production rate, followed by
the +DOC and -UVBand finally, the control. This hypothesis appears to be substantiated
by the results shown in Figure 13. The +DOC and -UVB are both significantly higher
than the control enclosures in both experiments, and the ++DOC has the highest
production rate. As was noted, production rates are generally unaffected by foodweb
relationships, since the remaining bacterial population is still able to respond to the
abiotic changes. However, production rates c m be enhanced by foodweb effects, by
stimulating rapid turnover of bacterial resources, or from bacterial response to grazing
pressure.
The results of the production work indicate that bacteria do indeed respond to
both food source effects and UVB screening effects of added dissolved organic carbon.
The high production rate in the ++DOC treatment indicates that a combination of both
food and W B protection enhances bactenal production. Changes in bacterial biomass
did not follow the trends seen in production rate, so are explainable only by changes in
the rest of the foodweb. As a final note, it is important to consider both bacterial
production rates and biomass when exarnining results of other components of the
microbial food web, otherwise very different conclusions may be drawn.
92
3.3 Potential explanation for experimental outcome
3.3.1 Changes in unmanipulated abiotic factors
From the experimentaI results, it appears that abiotic changes c m be ruled out as
the major cause of changes in bacterial biomass since none of these factors changed
significantly over time. One exception was suspended sediments which did decrease
significantly in the limnocorral relative to the lake; however, this drop was consistent
across al1 limnocorrals and could not account for the differences between treatments in
bacterial biomass. Changes in suspended sediment concentrations could have led to
changes in the bacterial biomass in control enclosures relative to the Iake. This, however,
does not appear to be the case. M i l e dissolved oxygen content was not measured, this
was unlikely to have changed either since the enclosures were shallow and exposed to
enough wind rnixing to ensure adequate levels of dissolved oxygen.
Bacteria are often associated with suspended sediments as sedirnents have
nutrients and organic carbon associated with them (Kirchman et. al. 1982, O'Brien et. al.
1992, Lind et. al. 1997). It rnight be expected that if the suspended sediment
concentration in the control enclosures dropped significantly, lower bacterial biomass
would result. Consequently, finding minimal differences in the bacterial biomass of
control and lake was somewhat surprising. However, South Lake is shaped such that the
majority of sediments drop out in the first basin and by the time the water reached the
limnocorrals and where lake samples were taken (see Figure 4), suspended sedirnent
levels were relatively low. Given that suspended sediment concentration was relatively
low and total dissolved organic concentration was high, the fraction of DOC associated
with suspended sediments was likely low in the lake as well as in the enclosures.
93
3.3.2 Increased food supply.
The increase of food supply had an effect on bacteria biomass, but not necessarily
as expected. Since the design of the experiment was to add DOC as a substrate and for
UVB protection, it was fûlly expected that changes would occur in the DOC addition
treatments. As expected, the biomass increased substantially when a small amount of
DOC was added (53% relative to control). Surprisingly, larger additions of humic DOC
resulted in reduced bacterial accumulation (1 5% decrease relative to control).
Previous experiments have generally found an increase in bacterial biomass with
an increase in humic DOC concentration (Jones 1992, Koetsier et. al. 1997). Bacterial
biomass starts to decrease in extremely high humic DOC systems (>20rng.~-l)due to
binding of nutrients and possibly enzymes produced by bacteria to obtain limited
nutrients (Stewart and Wetzel 1982, Kim and Wetzel 1993). Production rates in these
high humic systems are also very low (Stewart and Wetzel 1982). However, the bacteria
in these experiments did increase production rates with additions of DOC, and it appears
that nutrient levels were sufficient throughout the experiments. The drop in bacterial
biomass with levels of DOC is likely due to biotic effects.
3.3.3 Increased protection from UVB radiation.
Removal of UVB radiation by covering enclosures with Mylar-D sheeting
enhanced bacterial biomass in both experiments (Figure 6), but not to the degree that
addition of small amounts of DOC (+DOC treatments) did. Two plausible hypotheses
whïch may explain the difference in bacterial biomass between the +DOC and - W B
treatments are changes in food supply, and UVB tolerance effects. As will be explained
94
in more detail below, the +DOC enclosures had greater substrate availability relative to
the - W B enclosures which may lead to increased bactenal biomass. However, the - W B
enclosures offered greater protection fiom UVB radiation than did the +DOC ones, and
so offers an advantage to bacteria or their predators.
The +DOC treatment added a sunscreen effect and increased the food supply.
Relative to the control, the +DOC increased substrate by 25% and UVB protection by
22% (decreased UVB penetration from 64% at l Ocm in the control enclosures to 50% at
1Ocm in the +DOC enclosures). Humic DOC is being considered as the prirnary food
source because of its relatively high nutritional value for bacteria compared to noncoloured, photobleached DOC which may offer linle or no nutrition for bacteria (Reitner
es. al. 1997). It may have been that the bacteria benefited from the further decrease in
UVB in the -UVBtreatment (near 100% reduction), but did not have a food source to
allow their biomass to grow to their full potential. While the +DOC did not offer as
much W B protection, it was likely that they were able to cope with the UVB levels well
enough that they could take advantage of the additional available substrate as suggested
b y other experimental results (Rae and Vincent 1997).
Higher levels of UVB radiation in the +DOC treatment relative to the -UVB
treatment may have aided in the breakdown of the DOC for bacterial consumption.
Slight increases in W B radiation to facilitate this process have been shown to stimulate
bacterial biomass (Linde11 et. al. 1995, Williamson 1995). Therefore, in the - W B
treatment, bacterial growth may have been limited by UVB radiation, since it would not
be breaking down high molecular weight humic substance into smaller, more suitable
sub-units for bacterial growth. However, the fact that they did increase indicated
beneficial effects of shielding W B radiation for reasons other than carbon availability.
As well, an analysis of the molecular size of the carbon (DeHaan and DeBoer 1987)
95
through comparison of the absorbance of filtered lake water at 2 5 0 m to that at 3 6 5 m
indicated no differences in size which would be expected if DOC was not being broken
down by W B radiation.
Bactena and other small organisms, while ofien the first organisms to be killed by
increased levels of UVB radiation due to their small size and simple structures, have fast
reproduction times which may prove to be an advantage (Mostajir et. al. 1999). Since
bacterial populations reproduce so rapidly, a shift to more UVB tolerant strains can occur.
Changes of smaller organisms to more tolerant species can occur more rapidly than
similar shifis in larger organisms, has been previously observed in UV expenments
(Bothwell et. al. 1994, Mostaj ir et. al. 1999).
The most logical comparison to make is the - W B and the ++DOC treatment,
since both are shielding out UVB to more or less an equal amount. The only differences
seen in this case should be effects due to DOC as a food source. The +DOC biomass
decreased over time, even though production rates were much greater than that of the UVB treatment. Production rates per ce11 increased by about 57% in the *DOC
treatment and by 15% for the -UVB. Biomass in the WDOC decreased by about 15%
relative to control, while the -UVB treatment increased by 40%. There is apparently
something about the DOC as a food source, not the UVB radiation protection it is
providing, which is causing this decline in biomass.
Overall conclusions about UVB are that removal of UVB radiation by itself does
indeed stimulate increases in bactenal biomass. The removal of UVB radiation appears
to have a greater effect on bacterial biomass than additions of UVB absorbing DOC since
small increases in production, presumably from the removal of UVB radiation, results in
large increases in bacterial biomass. When small amounts of UVB absorbing substrate
96
are added, bacterial production is stimulated, but the conversion of this production into
bacterial biomass is not as efficient as in the -UVB enclosures. \%en larger
concentrations of UVB absorbing substrate was added, removing al1 W B radiation,
bacterial production is at its highest, but accumulation of this production as bactenal
biomass was at its lowest. It appears that the bacteria are responding to the increased
substrate and increased protection fiom W B radiation, but that the substrate is having a
negative effect, either directly or through secondary effects.
3.3.4 Changes in predator populations.
3.3.4.1 Heterotrophic nanoflagellates
Since abiotic factors such as DOC and UVB can only partially explain the trends
seen in the bacterial biomass, biotic factors likely account for the other changes- As the
nanoflagellates ofien make up the majonty of bacterial predators and may play the largest
role in influencing bacterial populations, these will be examined first,
The largest increase in HNAN biornass was in the ++DOC treatment (55%
increase relative to start). As HNAN's are the major predators of bacteria (Sherr and
Shen 1992, 1994), this offers a potential explanation of why bactenal biomass remained
at control levels in the uDOC enclosures, despite high production rates. The eventual
decline of the HNAN biomass in the ++DOC treatment indicates that in fact they did
become resource limited and follows closely what has occurred in other experimental
results (O'Brien et. al. 1992). Generally, an increase in bacterial biomass is followed by
an increase in HNAN biomass (Pace 1988, O'Brien et. al. 1992). At this point, bacteria is
either grazed down, followed by a downwards trend in HNAN biomass, or is able to
maintain itself at a higher level along with higher predator biomass (Pace and Funke
1991).
The rapid increase in HNAN biornass at the beginning of the expenments is likely
due to W B radiation tolerance effects. It is possible that there was sufficient bacterial
biomass in the control enclosures to allow increases in HNAN biomass, however, the
HNAhTmay have been unable to respond because of the levels of W B radiation. Higher
IeveIs of UVB radiation have been found to be damaging to HNAN, ofien reducing their
feeding rates by up to 70% and biomass by upwaards of 60% over a penod of a few days
(Sornrnoruga et. 02. 1996, Ochs 1997, Mostajir et. al. 1999). HNAN have been found to
respond very rapidly to decreases in UVB radiation, even if there is not an accompanying
increase in bacterial biomass (Mostajir ei. al. 1999). This is contrary to the results of
O'Brien et. al. (1992) and Pace (1 988) who suggest that the change in bacterial biomass
stimulated changes in HNAN populations. However, these were oligotrophic systems
where bacteria biomass, not UVB radiation, may have been the Iimiting factor. In this
system, HNAN growth \vas controlled prirnarily by UVB, not by prey availability, but
once stimulated, it does have a significant effect on accumulation of bacterial biomass.
What cannot be explained is why HNAN biomass increased in the ++DOC by
55%, but only by 25% in the -UVB enclosures, since they both offer equivalent UVB
protection and if it is assumed that they had sufficient bacterial resources previously. To
my knowledge, HNANs have not been found to use extemal sources of DOC relying
instead upon bacterially produced carbon, nor do they respond solely to increases in
bacterial production rates which would explain the disparity. Although the HNAN had
sufficient resources to rapidly increase biomass at the start of the experiment, the higher
bacterial production rate in the H D O C treatment could have sustained a higher HNAN
biomass over a longer period of time. However, they can also make up a large portion of
98
the diet of macrozooplankton (Sanders et. al. 1989, Pace et. al. 1998). If
macrozooplankton increased in the -UVBbut not the *DOC treatment, this could have
an effect on HNAN biomass. This will be discussed in the following section.
Overall conclusions from the heterotrophic nanoflagellate data suggests that they
can indeed play a strong role in influencing bacterial biomass, as does substrate and W B
radiation resources. The rapid increase in HNAN biomass without an eadier increase in
bacterial biomass suggests that increases in their biomass may have been limited by W B
radiation, and that rernoval of this in the enclosures was the main reason a rapid increase
in HNAN biornass was seen.
3.3.4.2 Macrozoopiankton
The changes in zooplankton biomass were not due to changes in the large
zooplankton, but rather the small ones such as rotifers, copepod nauplii, and Bosmina.
Since large zooplankton can take more than a month to reproduce, it is not surprising that
changes were not seen in their numbers. Increases in small zooplankton biomass, even in
the controls (40% relative to lake), cannot be accounted for by the removal of fish
predation, since fish would not feed on them naturally. Rather, it seems likely that
changes in invertebrate predator biomass in the enclosures versus the lake would account
for difference in the biomass of smaller zooplankton.
On one sampling day (August 4, DOY=216), specimens of Leprodoru kindfii were
found in preserved zooplankton samples collected from South Lake. These large
cladocerans are known zooplanklivores. nie size of their appendages and mouthparts
restricts them to feeding primarily upon smaller zooplankton (Pemak 1989). Previous
experience has shown that these cladocerans hide near the sediments during the day to
99
avoid fish predation (C. Teichreb, unpublished data). Since water was taken near the
surface to fil1 limnoconals, it is likely that they were not transferred into the enclosures.
Also, persona1 experience has shown that Leprodora are extremely fiagile and easily
expire when handled compared to the other zooplankton found in the limnocorrals. Since
they were not found in any of the limnocorrals, it seems that part of the response seen in
the zooplankton biomass can be attributed to their absence. As they were missing from
al1 enclosures, it is likely that this effect was consistent across al1 treatrnent bags.
The trends in zooplankton biornass are quite different from that of the bacteria.
The greatest response this time is seen in the -UVB treatment (a 350% increase),
foIlowed by the +DOC (a 250% increase; Figure 1l), indicating that the zooplankton are
benefiting, either directly or indirectly, from the increased protection from UVB
radiation. A positive response to removal of UVB radiation has been well documented in
rotifers, copepods, and Daphnia and so it \vas not surprising to observe this result here.
However, they do not increase in the ++DOC enclosure, so their biomass \vas likely
controlled by biotic factors as well as UVB radiation.
The macrozooplankton have been found to be capable of feeding at a number of
trophic levels, including bacteria, HNAN, and phytoplankton. In fishless, eutrophic
lakes, they can be the major predators of bacteria (Riemann 1985, Pace and Cole 1994).
However, the trends in zooplankton biomass do not seem to explain those observed for
bacterial biomass. Typically, macrozooplankton have an indirect effect upon bacteria
through feeding upon bacterial predators and competitors, which is likely what is
occumng in these experiments.
Consumption of nanoflagellates by macrozooplankton is well documented
(Riemann 1985, Porter 1991, Arndt 1993, Gilbert and Jack 1993, Sanders et. al. 1994,
1O0
Pace er. al. 1998). Zooplankton are capable of grazing ciliates and nanoflagellates down
to levels which no longer have significant grazing mortalities on bacterial biomass (Pace
and Cole 1994). HNAN biomass was hypothesized to be primarily under the control of
W B effects, but HNAN biomass was much higher in the H D O C treatments as
compared to the -UVB treatments. M i l e it is likely that this difference was due
primarily to differences in bacterial production and biomass, since macrozoopIankton
biomass increased greatly in the -UVBtreatment, while remaining relatively constant in
the ++DOC treatment, this may have also influenced HNAN biomass accumulation.
HNAN biomass does not explain why macrozooplankton biomass remained low
in the +DOC treatment despite high HNAN biomass and protection from UVB
radiation. However, trends in phytoplankton biomass are similar to those in the
macrozooplankton biomass. Although the zooplankton follow a similar pattern, their
biomass appears to change much too rapidly (up to 350% increase) to be accounted for by
the relatively rninor changes in phytoplankton biomass (mauimum 11% increase).
Phytoplankton production and biomass, in the absence of predators, \vil1 ofien
respond very strongly to the removal of UVB radiation by up to 70% (Moeller 1994,
Mostajir et. al. 1999). Additions of zooplankton have been s h o w to be strongly related
to chlorophyll concentrations in similar enclosure experiments (O'Brien et. al. 1992) and
were likely influencing phytoplankton biomass in this experiment. Without knouing
phytoplankton production rates, it is difficult to draw any strong conclusions about why
zooplankton and phytoplankton biomass was low in the uDOC treatment.
Zooplankton biomass accumulation w a s controlled by changes in their UVB
environrnent and phytoplankton biomass. These shifts appear to have influenced HNAN
biomass through grazing effects. Zooplankton play an important role in Mackenzie Delta
101
foodwebs, but are not the most important regulators of bacterial biomass. Rather, they
are important regulators of the bacterial cornmunity through indirect processes (predation
upon bacterial predators or competitors). This is consistent with results of other studies
which have found zooplankton to be major bacterial predators primarity in fishless,
eutrophic lakes (Riemann 1985, Jeppeson et. ai. 1992, Pace and Cole 1994).
3.3.5 Changes in infector populations
Since virus trends do not simply track total bacterial biomass, other abiotic factors
must be considered. Exposure to natural levels of UVB radiation has been found to
reduce viral lytic activity and total viral numbers (Bratbak et. ai. 1994). The increase in
viral biomass in the *DOC,
despite low bacterial biomass, suggests that they benefited
from removal of the UVB radiation. Total viral biomass per milliliter of lake water was
similar to that of the bacteria, so viruses may have been restricted in their capability to
increase in biomass if they were dependent upon bacteria as their sole hosts. It was
difficult to determine if virus biomass from this esperiment was similar to published
results, since viral biomass is rarely, if ever, reported. However, the total viral numbers
in this experiment were similar to values reported in the iiterature (Maranger and Bird
1995). Given that up to 40% of bacteria have been found to be infected with viruses and
up to 80% of the bacterial population may be lysed in a single day, this host-limitation
hypothesis does merit some attention.
Compared to the viruses, HNAN's have a much lower biomass per milliliter of
lake water (ranging from 0.0002 to 0.00035 pg C-ml-1; Figure 7) and are presumably
able to undergo larger fluctuations in their biomass as a response to changes in bactenal
production and biomass. It appears then that the viruses are responding to changes in
bacterial biomass and W B radiation. Viral biomass may not have controlled shifts in
102
bacterid density, but instead may have slowed the rate of accumulation of increased
bacterial production as bacterial biomass, diverting it into the open water.
The relatively large fluctuations in total virai biomass seen in the experiments is
likely due to a number of sources. First, natural variation. Since viruses c m respond
rapidly to increased availability of hosts, and since one infected host by one virus can
produce up to 100 viral particles upon lysis, rapid changes in the bacterial population
could lead to large fluctuations in the viral community. Second, it is impossible to tell
which viral particles are inactive versus active or which are specific for bactena using
DAPI. While some dyes do allow distinguishing between active and inactive viruses
(such as Yo-Pro-1), they still cannot distinguish between viruses specific for bacteria or
for other organisms (Hennes and Suttle 1995).
A lot remains to be learned about the role of viruses in structuring aquatic
ecosystems. It appears that viral biomass wvill change as a result of changes in the UVB
environrnent or changes in bacterial biomass. Due to their high numbers, they may be
playing an important role as disrupters of carbon flow from bacteria to higher trophic
levels. Carbon which was destined for HNAN or other predators could be diverted by
viruses through lytic processes (Bratbak et. ai. 1994). Since viruses are carbon rich, c m
contain a large portion of the phosphorus pool (up to 9% in marine systems), and are not
usually preyed upon by other organisms, this dismption of the flow of carbon could
potentially have effects at al1 trophic levels.
3.3.6 Changes in cornpetitor populations.
Phytoplan'on are capable of responding rapidly to changes in their W B
environrnent. Villafaiie et. al. (1995) found a 40% increase in photosynthetic rates within
1O3
one day in enclosure systems within an Antarctic lake. When W B radiation \vas
enhanced, Mostajir et. al.(1 999) found an increase of 56% in diatom biomass over seven
days, which seemed attributable to increases in the microzooplankton community.
Relative to these, and other studies, the increase in phytoplankton biomass and
chlorophyll with UVB removal was quite low (1 1% increase in biomass relative to start).
As mentioned, zooplankton biomass accumulation may have potentially suppressed
increases in phytoplankton biomass. While PAR absorption by humic DOC is known to
occur, it is generally not a significant factor until hurnic DOC levels are above 14mg.~-l
(Williamson et. aL 1996) and wouId not explain why the phytoplankton did not respond
positively in the *DOC
treatrnent, ~vherethe removal of UVB radiation would have
Iikely had a greater affect than the removal of PAR.
Phytoplankton biomass increases may have been limited by available nutrients,
especially if bacterial production was high, competing for Iimited nutrients. Looking at
total bacterial production rates on the !ast day of the esperiment, production w s highest
in the +DOC followed by the -UVB, the *DOC
and finally the control (Figure 12).
Phytoplankton biomass should have been highest in the control (least amount of
competition with bacteria for nutrients), followed by the ++DOC,-UV8 and then the
+DOC (highest bacterial production, greatest competition with phytoplankton for
available nutrients). This does not seem to be the case, indicating that phytoplankton
cornpetition with bacteria either did not occur because of sufficient nutrient resources, or
preferences for different nutrient sources. Cornpetition with bactena may be occming,
but over two weeks, phytoplankton could possibly rely upon intemal phosphoms stores.
None of the above hypotheses is able to fûlly explain the disparity in
phytoplankton biomass between the -UVB and *DOC
enclosures, where UVB
protection was identical. It may be a combination of UVB, predator, and competitor
1 O4
effects. Whatever the cause, it appears that the algal biomass influences
macrozooplankton biomass more strongly than it does bacterial biomass. ïhis is
consistent with results which have found that abiotic changes (nutrient additions, removal
of W B radiation, zooplankton manipulations) will essentially break the dependency that
phytoplankton and bacteria may have had on each other for carbon andlor nutrients
(O'Brien et. al. 1992, Pace et. al. 1998, Mostajir et. al. 1999).
Finally, During the second experiment, an increase in phytoplankton biomass was
seen in the -UVBtreatment only. Other treatments either maintained steady
phytoplankton biomass (+DOC), or resulted in decreased phytoplankton biomass over
time (++DOC, Control, and lake). This trend appears to be different from the first
esperirnent where the lake and control maintained a constant biomass, but al1 other
treatments increased in chlorophyll concentration over time (Figure 10). Therefore,
dra~vingstrong conclusions from the results of phytoplankton for these expenments,
should be done with caution.
Phytoplankton seem to be affected by abiotic factors, much like the bacteria,
responding to changes in the UVB and possibly the PAR environment. They do not
appear to be strongly influenced by bactenal cornpetition for nutrients, but instead their
biomass seems to be more influenced by or is influencing zooplankton biomass, resulting
in indirect effects (preying of zooplankton upon HNAN) which may ultimately affect
bacteria biomass accumulation.
3.4 Most plausible controls on bacteria in the experimental system
Bacterial biomass in these experiments appeared to be controlled by a number of
factors. Addition of an external source of organic carbon stimulated production rates, but
1os
did not necessarily lead to an increase in bacterial biomass. Shielding fiom W B
radiation led to an increase in production, but not as great as when substrate concentration
waas increased. Apparently, the substrate was having a secondary, negative effect on
accumulation of bacterial production as biomass.
The phytoplankton increased when UVB radiation was decreased. However, their
biomass is likely under control of multiple components such as bacterial production,
UVB penetration, light availability, and predation control. Future experiments which
quanti@ phytoplankton production rates are needed to c h i @ how they responded to the
abiotic treatments in this experiment.
The nanoflagellates appear to increase in biomass when the bacterial production is
stimulated either through addition of carbon and/or removal of W B radiation. The high
HNAN biomass in the *DOC
treatment suggests that they grazed the bacteria in this
treatment down to reference levels and below. The initial increase in HNAN biomass
\vas likely due to sufficient bacterial biomass being present under natural lake conditions
to allow a bloom of m A N , but hannfiil UVB radiation limiting their increase in
biomass. Removal of this UVB radiation in the +DOC, ++DOC and -UVBtreatments
may have allowed the HNAN to potentially increase greatly in biomass without the need
for a bacterial bloom which would trigger this increase. Sustained increases in HNAN
biomass were likely a result of differences in production of bacterial carbon. Similar
changes in HNAN biomass without a corresponding change in bacterial biomass have
been observed in other experiments (Mostajir et. al. 1999)
While zooplankton seemed to follow phytoplankton biomass quite closely, and
likely had their greatest effects on the phytoplankton, it is well known that they are nonselective feeders and likely consumed bactena and predators as well as phytoplankton
1O6
because ultimately they are after nutrients and carbon, not chlorophyll. However, in these
experirnents they do not appear to play a strong role in determining bacterial biomass,
except possibly through their indirect effects upon bacterial predators (consumption of
HNAN in the -UVB treatment). Zooplankton biomass was enhanced by UVB removal
and likely by increases pnmarily in the phytoplankton biomass.
Rather than exerting a direct control on bacterial biomass, viruses seemed to be
tracking the bactena comrnunity as well as responding positively to removal of UVB
radiation. Estimates suggest that lysis may result in death of anywhere from 20 to 80% of
the bacterial community per day. Since they can contain a large portion of the available
phosphorus and carbon, and are not readily consumed, their presence may slow the flow
of carbon to the upper trophic Ievels. It appears that because the biomass of viruses is
close to that of bacteria, that they are unable to respond to their full potential with shifis
in UVB radiation. However, there may be problems with carbon conversion factors used
to estimate viral biomass and this should be carefùlly considered. Viruses increase in the
++DOC, but may have become limited by the availability of bactenal hosts. HNAN are
better able to take advantage of shifis in bacterial populations and shifis in their abiotic
environment, since they have a lower biomass and would thus be better supported by
production of bacterial carbon. Better techniques for analyzing and classiQing viruses
will allow a more thorough examination of their impacts on microbial foodwebs. As they
have a relatively high biomass, this may help provide a more complete carbon budget for
iakes when taken into consideration, which they rarely have been to date.
Bacteria in these experiments were stimulated from the bottom-up (DOC and
W B ) , but are controlled from the top down (HNAN, viruses, and possibly zooplankton).
There are likely a number of feedback loops operating which potentially determining the
structure of the bacterial comrnunity (example: increased bacterial biomass results in
1O7
increased HNAN biomass which decreases bacterial biomass, and subsequently, HNAN
biomass). Changes in DOC concentration and changes in the UVB environment resulted
in changes in the strengths of individual loops. This will ultimately result in changes in
the bacterial comrnunity and the flow of carbon to higher trophic levels.
3.5 Expected versus observed biomasses among lakes of the delta
The lake survey was considered an essential complement to the main experiments.
The experiments were conducted in a single lake and it was not known whether or not
South Lake \vas representative of other delta lakes.
Samples from the Inuvik 40-lake set were collected for bacterial biomass, virus
biomass, HNAN biomass, chlorophyll, suspended sediments, and DOC (total, humic and
non-hurnic fractions and molecular size). In addition, information on average si11
elevation was available for use. Relationships between these components, and simple
regression statistics of those components considered imponant in determining bacterial
biomass are presented in Figures 14 to 27 and Table 8.
An interesting feature of this system is the relationship between the concentration
of humic DOC (coloured, UVB absorbing fraction) and total DOC concentration (Figure
14). At low concentrations of humic DOC (approximately 6 mgl-l), there is about 14
mg-1-1 of non-humidnon-coloured DOC. When humic DOC increases by 1.5 times to 9
mg-l-l, total DOC increases by approximately 2 times to 40 mg-1-1, and when hurnic
DOC is doubled to 12 mg-1-1, total DOC increases 3 fold. This disparity is evident in the
regression between humic and total DOC, with a dope of greater than one and the r2
being only 0.449, much lower than literature values which predict total DOC from the
UVB absorbing humic fraction (Scully and Lean 1994, Moms et. al. 1995).
1O8
Table 8
Regression statistics for components of the lake survey in the form of y=mx + b.
Squared multiple r value indicates e strength of the relati nship between the
components (perfect relationship, ?=l .O, no relationship, rS=O).
Key:
bactena = bacterial biomass (pg C d - 1)
Total DOC = total dissolved organic carbon (mg-1-1)
HNAN = heterotrophic nanoflag llate biomass (pg C-mlo1)
-1
Virus = virus biomass (pg C-ml )
Humic DOC = humic fraction of dissolved organic carbon ( r n g ~ ' ~ )
TSS = total suspended sedirnents (rngl-l)
DOC size = relative size of the humic fraction of DOC (caiculated as absorbance
at 250x1111 / absorbance at 365nm)
si11 elevation = average si11 elevation (m)
chlorophyll o = chlorophyll a concentration (rngl-l).
Y
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
HNAN
HNAN
Virus
Virus
Chlorophyll a
Chlorophyll a
Chlorophyll a
Total DOC
Total DOC
Humic DOC
HumicDOC
TSS
X
Total DOC
HNAN
Virus
Humic DOC
TSS
DOC size
Si11 elevôtion
Chlorophyll a
Total DOC
Humic DOC
Total DOC
Hurnic DOC
Total DOC
Hurnic DOC
TSS
Humic DOC
Si11 elevation
Si11 elevation
TSS
Si11 elevation
Multiple r'
0.296
0.780
0.352
0.239
0.023
<o.oo 1
O. 199
€0 .O01
O. 1%
0.146
0.204
O. 137
<o.oo 1
<o.oo 1
O. 159
0.449
O. 140
O. 194
0.034
0.268
3
Figure 14. Relationship between total dissolved organic carbon concentration and the
humic fraction of dissolved organic carbon concentration for the Inuvik 40-lake survey.
The concentration of humic DOC in the control and *DOC
enclosures of the
experiments is indicated by the vertical lines on the lefi and right side respectively. The
two horizontal lines represent the lowest and highest concentrations of total DOC
measured in the experimental enclosures.
Total DOC = 4.141 ' Humic DOC + 2.170
5
10
Hurnic DOC concentration (mg
Ï
)
Si11 elevation can be used as an indicator of lake closure, the higher the si11
elevation, the more likely the lake is to be isolated fiom riverine inputs. Both the humic
DOC and total DOC increase as si11 elevation increases (Figure 15 and 16) although
increases in total DOC are relatively greater (2.5 times) than increases in humic DOC (2
tirnes) as shown by the ratio of total DOC to humic DOC in Figure 17. This change in
humic DOC represents a change in UVB penetration at 1Ocm depth fiom approximately
50% to 3.7%, essentially covenng that range seen in the experiments. Total suspended
sediments decrease from about 6 mg-1-1 to 0.1 mgl-1 as si11 elevation increases, likely a
result of decreased riverine inputs and flow within the lake basin (Figure 18). Total
suspended sedirnents for the experiments were in the range of 0.5 to 1 mg-1-1,rvithin the
range of lakes shown in Figure 18.
Concentrations of humic DOC in the main experiment ranged from 3.6 mgl-l to
12.5 mgl-1. Bacterial biomass in the lake survey at different DOC concentrations
appears to be similar to that of esperimental results. Bacterial biomass in the +DOC
treatment (1.5 mg-1-1) reached a maximum of approsimately 0.057 pg C-ml-* and in the
*DOC
(12.5 mg-1-1) treatrnent, a biomass of about 0.025 pg C-ml-1 which is very close
to the values seen at these concentrations in the lake survey (Figures 19 and 20).
However, background levels of DOC in South Lake (3.6 mg4-1) collected during the
experiments were lower than the lowest value obtained from the lake survey. This may
have been due to the time of year sarnples were collected and not because South Lake is
not representative of other delta lakes. River flow rates are lowest at the end of August,
and the greatest potential for accumulation of humic DOC through breakdown of plant
material without being flushed out of the lakes would occur during this period.
Similar to the bacteria, HNAN and virus biomasses decrease as humic DOC
concentrations increase. For the viruses, survey results are consistent with experimental
112
Figure 15. Relationship between humic dissolved organic carbon concentration and sill
elevation for the Inuvik 40 lake survey.
O
1
2
3
Sill eleva tion (ml
4
Figure 16. Relationship between total dissolved organic carbon concentration and si11
elevation for the Inuvik 40 lake survey.
2
3
Sill elevation (m)
4
Figure 17. Ratio of total dissolved organic carbon venus humic organic carbon as a
fiinction of si11 elevation for the Inuvik 40 lake survey.
Figure 18. Relationship between total suspended sediment concentration and si11
elevation for the Inuvik 40 lake survey.
2
3
Si11 elevation (m)
4
Figure 19. ReIationship between bacteriai biomass and totai dissolved organic carbon
concentration for the Inuvik 40 Iake s w e y .
1O
20
30
40
50
Total DOC concentration (mg
60
Ï '1
70
Figure 20. Relationship between bacterial biomass and humic dissolved organic carbon
concentration for the Inuvik 40 lake survey.
4
6
8
10
Humic DOC concentration (mg
12
1
-1
14
results where the +DOC increased to about 0.025 pg c d - 1 and ++DOC to about 0.0 10
pg C-ml-1 (Figures 21 and 22). However, experimental results for the HNAN are not
consistent with that of the lake survey. The HNAN in the +DOC treatment of the
experiments increases to a biomass of approximately 0.00025 pg C-ml-1, considerably
lower than the s w e y value of about 0.00035 pg C-ml-1 (Figures 23 and 24). Even more
disconcerting is the fact that rather than increase biomass as humic DOC concentrations
increase, the nanoflagellates show a downwards trend (Figure 24). In the experiment, the
*DOC
treatment resulted in biomass increasing to about 0.00027 pg C-ml-1 while the
corresponding biomass fiom the lake survey is down to 0.0001 pg C-ml-l.
Chlorophyll concentration does not appear to be strongly related to bacterial
density although it does appear that higher bacterial biomass results in slightly lower
phytoplankton biomass (Figure 25). The poor relationship between chlorophyll and total
suspended sediments (Figure 26 and Table 8) is somewhat unusual, since suspended
sediments ofien control PAR in delta lakes, and presumably, chlorophyll concentrations.
Chlorophyll concentration in the experiment is sornewhat different from that of the lake
survey. In the +DOC treatment, chlorophyll concentration is about 1.7 pg-l-l while in the
lake survey, it is about 3 pg-l-l. In the *DOC
treatrnent, chlorophyll concentration was
around 1.4 pg-l-l and in the lake survey is around 1.5 pg-l-l (Figure 27).
Overall, the lakes sarnpled in this survey showed a wide range of values in abiotic
and biotic parameters, with an interesting observation that humic DOC concentrations do
not increase linearly with total DOC. M i l e most of the biotic parameters were within
the range of those found in the expenment, chlorophyll concentration and HNAN
biomass were not. Chlorophyll concentrations did show the downward trend as humic
DOC increased, but at a much greater rate in the lake survey as compared to the
experiments. The nanoflagellates showed an opposite trend, decreasing in biomass as
125
Figure 2 1. Relationship between virus biomass and total dissolved organic carbon
concentration for the Inuvik 40 lake survey.
20
30
40
50
Total DOC concentration (mg
60
Ï1)
Figure 22. Relationship between virus biomass and humic dissolved organic carbon
concentration for the Inuvik 40 l&e swvey.
Hurnic DOC concentration (ma
Ï1)
Figure 23. Relationship between heterotrophic nanoflagellate biomass and total
dissolved organic carbon concentration for the Inuvik 40 lake survey.
0.0000
10
20
30
40
50
Total DOC concentration (mg
60
1-
1
70
Figure 24. Relationship between heterotrophic nanoflagellate biomass and humic
dissolved organic carbon concentration for the Inuvik 40 lake s w e y .
4
6
8
10
12
-1
Humic DOC concentration (mg I 1
14
Figure 25. Relationship between bacterial biomass and chlorophyll concentration for the
Inuvik 40 lake survey.
O
2
4
6
Chlorophyll concentration (pg
8
I
-1
10
1
Figure 26. Relationship between chlorophyll concentration and total suspended sediment
concentration for the Inuvik 40 lake survey.
O
2
4
6
Total suspended sediments (mg
8
10
-1
I
Figure 27. Relationship between chlorophyll concentration and humic dissolved organic
carbon concentration for the Inuvik 40 lake survey.
4
6
8
10
iiurnic DOC concentration (ma
12
Ï1
)
14
DOC concentrations increased, as opposed to increasing in total biomass like in the
expenments.
3.5.1 DOC and suspended sediment gradient amongst lakes
Humic DOC concentrations do not increase as rapidly as total DOC
concentrations dong si11 elevation (Figures 14 to 16). A possible explanation for this
trend is the different sources of ongin of DOC in these lakes.
In no-closure lakes (those lakes with the lowest si11 elevation), total DOC
concentrations are low because DOC is brought in mainly by riverine inputs. These lakes
generally have low phytoplankton and macrophyte biomass since suspended sediment
concentrations are so high, limiting PAR penetration depths. However, the relative
proportion of coloured DOC to non-coloured DOC in these lakes is high compared to low
and high closure lakes. This is because the DOC in rivers is derived primarily from
terrestrial sources, such as grasses, shnibs, trees, and so forth. These plants have a high
lignin content, and have been shomn to contain relatively high concentrations of humic
DOC (McKnight et. al. 1991, 1994).
As lake sill elevation increases, suspended sediment concentrations decrease as
riverine inputs decrease (Figure 18). This allows greater increase in phytoplankton and
macrophyte biomass which have lower Iignin content and thus lower humic DOC
content. High closure lakes may have no riverine inputs and are ofien dominated by
macrophyte production (Mackay 1963). While total DOC levels may be high as a result
of infrequent flushing by river inputs and decomposition of previous years macrophfle
biomass, the hurnic concentration is ver-low. The non-humic fraction in Mackenzie
Delta lakes should be given special consideration, since it is not photobleached DOC,but
140
&ses from macrophyte decomposition and may potentially play a more important role in
the microbial foodweb.
Humic DOC concentration in the Iakes sarnpled was within the range used in the
experiments. The lowest concentration of humic DOC in these lakes was around 4 mg-1-1
while that of South Lake during the experiments was 3.6 mgl-1. This is likely due to the
fact that river flow rates were ôt a minimum during this period, and thus water residence
tirne of Iakes would be at their maximum, allowing for potential concentration of DOC
through decomposition of organic material or possibly evaporative concentration.
Suspended sediment concentrations decrease as si11 elevation increases (Figure
18) since silty riverine inputs in high si11 elevation lakes is lower. Lower flow rates into
and out of the lake lead to a drop in suspended sediment concentration. Suspended
sediment concentrations in South Lake (approximately 1 mg-1-1) was lower compared to
the concentrations found in the lake suxvey. This is due to the shape of South Lake.
Looking at Figure 4, water enters the lake into the first basin (upper right corner) where
the majority of sediments settle out. The main basin, where the enclosures were situated.
was thus relatively fiee of suspended sediments. If there were any effects of suspended
sediment binding to DOC on bactenal biomass, it is unlikely that it would be seen in the
lirnnocorral expenments, since suspended sediment concentration was low to begin with.
However, the extent to which suspended sedirnents bind to humic DOC should be
examined more thoroughiy in hiture surveys since delta lakes have a uide range of
suspended sediment concentrations. These suspended sediments do influence
phytoplankton and macrophytic biomass (Margaret Squires, pers. comm.), and may have
partially detemined bacterial biomass even though the relationship between these two
components was poor (Table 8).
14 1
3.5.2 Bacterial biornass
Bacterial biomass is controlled primarily by the coloured UVB absorbing humic
fraction of DOC (either through food supply or UVB protection; Reitner et. ai. 1997).
Therefore, survey results of bacterial biomass will be discussed based upon the humic
DOC concentration. From the enclosure experiments, it would be expected that at levels
of humic DOC concentration similar to the +DOC treatment (about 4.5 mgl-l), bacterial
biomass observed among the lakes would be at a maximum. Bacterial biomass at
concentrations similar to the ++DOC treatment (about 12.5 mg-1-l), should have a much
lower total biomass.
This is indeed the pattern seen across the lakes (Figure 20). The biomass of the
bacteria at 4.5 mg-1-1 and 12.5 mg-1-1 is very similar for both the experiment and the lake
sumey. This indicates that similar to experimental results, an increase in carbon source
ultimately has a negative effect upon bactenal biomass across l a k s of the Mackenzie
Delta.
3.5.3 Viruses
The viruses seem to follow bacterial biomass closely in the survey as they did in
the experiment (Figure 28) with biomasses among the lakes and in the enclosures being
v e v similar at the different concentrations of hurnic DOC (Figure 22). The lower
squared r-value between bacteria and viruses (r2=0.352) compared to bacteria and HNAN
suggest that viral biomass is not completely controlled by bacterial biomass. In the
experiments, viral biomass was increasing when UVB radiation was removed which may
be occurring here. However, if viruses are dependent upon bacteria as one of their sole
142
Figure 28. Relationship between bacterial biomass and virus biomass for the Inuvik 40
lake survey.
0.0 1
Virus biomass (pg
0.02
C
- 11
ml
hosts, they will closely track whatever the bacterial biomass is doing. They are likely
important as regulators of carbon flow, but do not explain the trend in bacterial biomass
among the lakes.
3.5.4 Heterotrophic nanoflagellates
From the enclosure expenment, it wras expected that HNAN biomass would
increase as humic DOC concentration increases among the study lakes because of
increasing production of bacterial carbon and protection from UVB shielding. Wowever,
in the lake survey, HNAN biomass decreased as humic DOC concentration increased
(Figure 24). If the bactena presurnably had a high production rate in the high DOC lakes,
they should have had a much higher biomass, because the HNAN biomass was relatively
Iow in high DOC lakes. The strong relationship between HNAN biomass and bacterial
biomass (r*=0.780; Figure 29) suggests there is a c o ~ e c t i o nbetween the t ~ \ ~but
o , does
not explain why the HNAN responded negatively to protection frorn UVB shielding and
potentially an increased food supply.
3.5.5 Phytoplankton
Phytoplankton biomass, as indicated by chlorophyll concentrations, tends to
increase as humic DOC concentrations increase (Figure 27). Phytoplankton biomass also
increases as suspended sediment concentration increases among the lakes (Figure 26).
This was expected since suspended sediments are the main attenuators of PAR in the
Mackenzie Delta. However, the relationships between phytoplankton and either of these
components are relatively weak, and as such, strong inferences about their control of algal
biomass cannot be drawn.
Figure 29. Relationship beîween bacteriai biomass and heterotrophic nanoflagellate
biomass for the Inuvik 40 lake survey.
-.
m
-
-
0.00
0.0000
I
1
1
0.0001
0.0002
0.0003
Nanoflagellate biomaçs (pg
C
mi-')
0.0004
The pattern of chlorophyll concentration in the expenment was different from that
among the lakes surveyed with a wider range of values in the lakes surveyed. This wider
range may have been a result of differences in suspended sediment concentration (higher
concentrations shielding out PAR leading to decreases in algal biomass), and possibly
nutrient concentrations (higher concentrations of nutrients leading to increased algal
biornass).
3.5.6 Potential explanations for outcome
The lake survey supported many of the experimental findings. Bacteria,
phytoplankton, and viruses al1 followed similar trends in response to different levels of
DOC in both the experiments and lakes, and with very similar biomasses. However, the
HNAN did not increase in abundance as humic DOC levels increased as was the case in
the enclosures. This increased HNAN biomass explained why bacterial biomass
decreased in the enclosure experiment. Based on the results of the lakes sun7ey,there
does not seem to be any reason why the HNAN decreased. The HNAN had abundant
protection from UVB radiation, and bacterial production was likely high in high DOC
lakes because of the available carbon source, which would have given the HNAN a
carbon source to feed upon.
Since HNAN biomass decreases as DOC concentration and W B protection
increases, this then fails to explain why the bacteria decreased in the lake survey and were
apparently not controlled by HNAN predation like they were in the experiments. As with
the HNAN, there does not appear to be a logical reason for the bacteria to decrease when
food sources and UVB protection were optimal. Further work suggested by this outcome
would be to determine if bacterial production was high in high DOC lakes. This would
indicate whether bactena were responding to the increased substrate concentrations, or
148
whether some biotic factor, other than HNAN, was responsible for their decreasing
biomass.
High macrophyte biomass with associated epiphytic growth may have potentially
reduced open-water nutrient concentration in high si11 elevation laices. The bacteria in
these lakes may therefore have been nutrient limited, rather than predator limited. This
would not have been seen in the expenment, given that macrophytes were excluded. As
weII, special consideration should be given to the non-humic fraction in delta lakes wrhich
is a result of low-humic macrophyte decomposition, and may have different chemical and
biological effects compared to photobleached non-humic DOC sources.
The lake survey provided valuable data which supported the experiment, but
raised fUrther questions. To get a more complete answer, bacterial production should be
measured, and potentially other biotic components such as zooplankton should be
collected. If predictions are to be made about bacterial biomass based upon DOC
concentration, it is important to consider whether this is refemng to the humic DOC or
total DOC concentrations, since the proportion of humic to total DOC shifts across the
Iakes surveyed.
3.6 General implications of the research
The microbial foodweb in the Mackenzie Delta plays an important role in carbon
cycling and transfer within the system. It is not, as some research suggests, a separate
entity from the traditional foodweb, but plays a direct role in influencing the higher
trophic levels. Responses at the bacterial level are not simple additive effects, but
indicate complex interactions occurring between the bacteria and other components of the
foodweb. As has been emphasized, it is important to take this whole-system approach
149
when looking at climate studies or any studies in general (Pace and Cole 1994). If
isolated samples of bacteria were incubated in the lab with different levels of carbon, then
the results of this expenment suggest that indeed, bacterial biomass would continue to
increase as long as carbon increased and no other factors (space, nutrients) were limiting.
While this one organism approach yielded the building blocks that hypotheses for
this and other experirnents are built upon, the results fiorn those expenments often do not
hold true in field settings. Another exarnple is the relationship between bacteria and
phytopldton. Lab settings which have examined cornpetition effects between algae and
bacteria for nutrients have found these two components to be tightly linked (Rhee 1972,
Currie and Kalff 1984). However, this often is not the case in field settings (O'Brien et.
al. 1992, Pace et. al. 1998), including this expenment. While it may be that there was
sufficient nutrients, phytoplankton were also being grazed upon by z o o p l ~ - t o n .These
multiple trophic interactions are important to examine to understand the functioning of
the entire ecosystem (Pace and Cole 1994).
Climate warming is likely to have an impact on the microbial food web and
carbon cycling. Increasing temperatures that lead to an increase in organic carbon \ d l
stimulate bacterial production and, under ideal conditions, bacterial biomass. Large
increases in organic carbon, while stimulating production, result in accumulation of
biomass in the predators and viruses, instead of the bactena. Overall, this means the
carbon will spend more time in the microbial food-loop. Carbon that is transferred to
higher trophic levels will likely have undergone greater recycling than it does currently.
Quality will be lower requiring more grazing to get the same amount of energy per unit
carbon (sensu Riemann 1985). This would result in lower reproduction rates and lower
biomass accumulations. This could have repercussions throughout the foodweb, likely
leading to lower biomass throughout the food web. While this is speculative in the case
150
of the Mackenzie Delta, it has k e n shown that conversion of DOC into usable carbon by
microbes can in fact support fish biomass as well as other higher trophic levels (Cole et.
al. 1989, Fee et. al. 1988).
The increase in non-photosynthesizing organisms in this experiment codd
possibly lead to greater CO2 production and global warming, M e r aggravating the
problems already found. If this led to a greater increase in carbon, the system would
likely crash at some point when DOC concentrations are so high that they start to
effectively bind nutrients and decrease bacterial production and biomass (Francko and
Heath 1982, Stewart and Wetzel 1982, DeHaan 1993). This wouId lead to eventual
infilling of the lakes, since organic products would not be broken d o m and since
terrestrial production under climate warming would likely increase delivering more
organic carbon to the lakes. Some researchers might argue that this is a natural cycle for
lakes, and while 1 would agree, it should be pointed out that global climate warming only
contnbutes to this problem by speeding up the process. Since small changes in carbon
concentration brought about large changes in the microbial components, it is very likely
that the overall structure of foodwebs in these lakes will also be affected under climate
w m i n g scenarios which would increase DOC concentrations in lakes. It should be
pointed out that this is a hypothetical situation and under increased carbon concentrations,
it is diffïcult to predict the response of arctic foodwebs (Kling et. al. 1991).
It is surprising that although coloured DOC is widely recognized as an important
regulator of aquatic ecosystems (see Williamson et. al. 1999 for a review), there does not
appear to be any published attempt to separate out the effects of DOC as a food source
versus as a W B attenuator. The majority of UVB literature focuses upon depleting
atmospheric ozone concentrations as one of the primary controllers of UVB penetration
into lakes (Karentz et. al. 1994, Williamson el. al. 1996). However, it has been
15 1
recognized that at low concentrations, shifts in hurnic DOC concentration through climate
warming, acidification, and other processes will have a greater influence on the UVB
environment in lakes than ozone depletion would (Williamson et. al. 1996).
Perhaps the problem of extracting consistently homogenous fractions of DOC
from water a d o r sediment has been the main deterrent to conducting DOC enrichment
experiments, or the fact that DOC can absorb in other wavelengths other than UVB,
making it difficult to separate out the UVB effects. However, studying only the UVB
aspect of humic DOC will only provide half of the story. Since bactenal biomass has
been found to affect al1 trophic levels, it is important to look at the effects of DOC as a
food source in addition to UVB effects. This study provides, to my knowledge, the first
attempt to look at the effects of DOC on a large portion of the aquatic foodweb.
The experimental results and lake sunfeyindicated that even at high DOC levels
found arnongst the delta lakes, small changes in humic DOC concentration can lead to
significant changes within the microbial foodweb. This is contrary to the current
literature which suggests the majority of changes occur when humic DOC concentrations
are in the 1 to 5 m g - ~ -range
l
(Cole et. al. 1989, Mostajir et. al. 1999, Williamson et. al.
1999). Although some delta lakes fa11 within this range, a large proportion have humic
concentrations greater than 5 r n g ~ - l .Wis suggests that high DOC lake systerns should
not be ignored when looking at the potential effects of organic carbon and W B radiation.
CHAPTER 4: CONCLUSIONS AND RECOMMENDATIONS
The aquatic microbial foodweb of delta lakes is under the control of both abiotic
and biotic factors. Bacteria in the experiment responded positively to increases in food
supply and/or decreases in harmfùl UVB radiation similar to other experimental findings.
Addition of DOC as a food source and UVB shield greatly stimulated bacteria!
production, but did not necessarily result in accumulation of bacterial biomass due to
predation effects. The removal of UVB radiation also stimulated increases in the biomass
of mAN, viruses, phytoplankton, and zooplankton.
While al1 of the above biotic components may potentially have an effect on the
accumulation of bacterial biomass, it appears that HNAN had the strongest influence, as
\vas originally predicted. As bacterial production, and presumably biomass increases,
HNAN biomass also increases. In the experiments, the rapid increase in HNAN biomass
\vithout a prior bloom of bacteria strongly suggests that the HNAN were responding to
removal of UVB radiation, and that there may have been suffrcient background levels of
bacteria to allow this bloom to occur. The increase in HNAN biomass and decrease in
bacterial biomass in the t+DOC, despite high bactenal production rates, suggests that the
HNAN were effectively grazing bactena as their own biomass was increasing.
The viruses seem to follow the bacterial biomass trends fairly closely. While they
did respond to removal of UVB radiation in the ++DOC treatment, their relative increase
in biomass is small, compared to that of the HNAN. Since bacterial biomass was
decreasing in this treatrnent and since virus biomass \vas similar in magnitude as the
bacteria, it may be that the viruses were limited in their increases as a result of removing
UVB by the limited number of bacterial hosts. The large viral numbers found, combined
153
with the fact that they can contain a large fraction of the limnetic phosphonis pool within
their population and are not readily grazed upon, suggests that they may play an
important role in dismpting the flow of carbon to higher trophic levels.
Contrary to the literature, phytoplankton did not appear to be influenced by shifis
in bacterial populations, likely a result of sufficient nutrient resources or differing
preferred sources, dampening the cornpetition effect between the bactena and algae in
this system. It must also be remembered that previous expenments which showed a
dependence of bactena biomass on algal exudates were generally conducted within
isolated cultures, not natural lake assemblages and so neglected to address other possible
food effects such as zooplankton grazing (Rhee 1972). The phytoplankton do show a
positive response to the removal of UVB radiation, and their higher biomass in the -UVB
treatment may have potentially stimulated zooplankton biomass.
Zooplankton did not appear to play a strong influencing role on bacterial biomass.
ZoopIankton biomass increased substantially m e r the course of the esperirnent,
especially in the -UVB treatment, suggesting that they were being suppressed by UVB
radiation either through direct effects or indirect effects (e-g.: suppression of algal
biomass). They do show similar trends in changing biomass as does the phytoplankton,
indicating they were grazing primarily at this level. In addition, the patterns indicate that
they may be indirectly influencing bacterial biomass through predation upon the
nano flagellates.
The trends of biotic components in the experiment were largely similar to the
trends in the lake s w e y . As expected from the expenments, bactenal, viral, and
phytoplankton biomass decreased as hurnic DOC concentrations increased. However,
what was not expected was the decrease in HNAN biomass as humic DOC concentrations
t 54
increased. The HNAN should have responded positively to the increased W B protection
and, based on experimental results, increased bactenal production rates. The differences
between the lake survey and experimental results may be due to differences in suspended
sedirnent concentration, concentration of non-humic DOC,or other factors which were
not examined.
Several recornmendations for future experiments involving the microbial foodweb
in the Mackenzie Delta can be made. These include:
1. Detemine where additional DOC no longer becomes beneficial for bacterial
biomass, but does for predator biomass through reduced UVB radiation or increased
bacterial production. This can be accomplished by a series of DOC enrichment
experiments run over a short period with sarnples for bacterial and HNAN biomass
analyzed. An upwards trend in bacterial biomass should be seen at low additions of UVB
absorbing humic DOC,but then should begin to decline as HNAN respond to the
increasing bacterial production and protection frorn UVB radiation.
2. Determine the extent to which UVB plays a role in controlling the microbial
food web. While UVB does have effects, the results of this experiment do not indicate
how much of the response seen in the bacteria is due to UV protection versus carbon
sources. If possible, experiments with different levels of UVB shading should be run and
microbial food components sampled to determine what the relationship is with W B
radiation. The problem is obtaining Mylar-D sheeting which is sufficiently thin to shield
out partial UVB and can still stand up to field conditions.
3. More sampling of the Mackenzie Delta should be done, both in terms of lake
number, and components samples (such as bacterial production and zooplankton).
155
Ideally, al1 the components sampled in the experiment should have been sampled in the
survey. However, due to logistical constraints, this was not possible. If a person had
more time, this could be done in the future.
4. Determine the effect of higher trophic levels on the microbial food web. In the
case of this experiment, this would mean including fish in enclosures. Longer experiment
times would be required, since fish biomass would take longer to change. Studies like
this have been conducted in the past (see O'Brien el. al. 1992), and are therefore possible.
Also, macrophytes should be included in the experiment, and their biomass determined
for lake surveys to determine their interaction with the rnicrobial foodweb.
5. Better carbon conversion factors need to be used to estimate biomass of the
various components. The high viral carbon biomass and low HNAN seem inconsistent
with the belief that HNAN play the major role in bacterial predation, since they did not
show large changes in their own biomass, but did in total numbers. Inaccurate carbon
conversion factors are a drawback constantly referred to in the literature and need to be
recti fied.
6. If a similar experiment was run, other microbial predators (ciliates,
phytoflagellates) should be identified for completeness. As well, since the non-humic
DOC in these lakes is not photobleached, but is simply low humic DOC arising from
macrophyte decomposition, it might be worthwhile changing the concentration of nonhumic DOC as well as the DOC fraction to allow the expenment to represent a more
accurate picture of what is occumng arnongst delta lakes.
Overall, this experiment and survey proves that the microbial food web is an
important component of Mackenzie Delta lakes. Climate warming is likely to have an
156
effect on this system. While attempts are being made to curb production of greenhouse
gases, they are still on the rise and likely to keep nsing well into the 2 1 st century before
they level off. The results of this expenment should contribute to our understanding of
how the Mackenzie Delta may respond to the stresses of global changes.
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Zellmer, I.D. 1995. WB-tolerance of alpine and arctic Daphnia. Hydrobiol. 307: 153159.
Appendix A
Extraction and Analysis of Dissoked Organic Carbon
Determining DOC concentrations may be done either by combustion or using a
spectrophotometer. Both have their advantages and disadvantages as discussed below.
The combustion technique involves filtering water samples through a 0.45 pm
filter and either adding strong oxidizen and acid, or high heat to release COZ This
released CO2 is then analyzed using gas chromatography or a total organic carbon
analyzer. Drawbacks include expense of rnethods and the relatively long preparation time
for multiple samples. However, this does account for al1 fractions of the DOC making it
the more accurate of the two rnethods.
Colourmetric techniques are based upon the knowledge that one of DOCts
properties is the absorption of W B radiation (Scully and Lean 1994, Brandsetter et. al.
1996). By irradiating a sample with UVB wavelength of light and measurîng the
absorption, this may be related back to the humic component concentration. Drawbacks
include having to develop a calibration curve of known DOC concentration versus
absorbance and that the non-absorbing DOC fractions are overlooked (Brandsetter et. al1996). The non-absorbing fiaction can be included if some samples are run to determine
total DOC, assurning that the non-absorbing fraction remains constant over time.
Advantages are the relative ease with which sarnples are prepared and analyzed (only
filtration is necessary) and the cost of the method, being much more economical than
combustion.
Appendix B Techniques for Determining Aquatic Bacterial Biomass
Bacterial biomass and production are terms used throughout the literature with
little background given as to their meanings, methods to determine hem, and limitations.
The next two appendixes approach these topics so that the reader has a greater familiarity
with why these procedures are performed.
Bacterial biomass is a measurement of the living proportion of the bacterial
cornrnunity. The living proportion ranges from those bacteria which are taking up
enough resources to maintain themselves, but are not reproducing, to those bacteria which
are actively growing and undergoing ce11 division (Fry 1988). To observe this
population, which is typically under 1pm in size, a method is needed which will allow us
to visualize bactena and distinguish living cells from non-living cells and detritus. For
this purpose, fluorochrome stains are used, the two most cornrnon being acridine orange
(AO) and 4'6-diamidino-Zphenylindole(DAPI).
Acridine orange is the older of the twvo stains, with use on soi1 bacteria dating
back to the late 1940's. Standard protocols for use of the acridine orange technique for
aquatic bacteria was made by Hobbie et. al. (1 977) making it one of the most popular
techniques at the time. When stained, bacterial DNA complexes with acridine orange and
fluoresces a weak green at 436 or 4 9 0 m while the RNA-A0 complex fluoresces red
(Hobbie et. al. 1977). Rapidly growing bacteria have primarily RNA while inactive
bacteria contain mostly DNA (Hobbie et. al.1977, Fry and Zia 1982). Several
disadvantages exist when using this methodology as outlined by Porter and Feig (1 980)
and Robarts and Sephton (1 98 1), including:
1. Sediment staining and autofluorescence. The sediment-acridine orange
complex fluoresces a red to orange colour making it dificult to distinguish fiom living
bacteria, especially in sediment rich systems such as the Mackenzie Delta.
2. Short storage time of prepared slides. Acndine orange stained filters remain
stable for only two weeks before the acridine orange complex begins to autodejpde.
3. Filters must remain moist for counting. This may prove to be problematic if
prolonged counting is necessary due to bacterial densities.
M i l e these drawbacks make the acridine orange techniques less than ideal for
this experiment, it is still a commonly used method in low sediment system where slides
can be prepared and examined immediately. The more recent, and robust, method which
has gained wïdespreadacceptance is the use of the DAPI stain method.
DAPI is highly specific for DNA and at 365nm,the DAPI-DNA complex
fluoresces a bright blue. Unbound DAPI or DAPI bound to non-living detritus fluoresces
a weak yellow (Porter and Feig 1980, Fry 1988). Slides may be prepared and stored for
up to 24 weeks at 4°C (Porter and Feig 1980, Robarts and Sephton 1981, Fry 1988). As
well, filters do not have to remain moist while perfonning counts (Porter and Feig 1980).
This makes the DAPI method ideal for this expenment and for many others.
Selection and preparation of filters for bactenal biomass must also be considered.
Filter pore size commonly used is O.22pm. This retains up to 99% of the bacteria, as
opposed to a 0.4pm filter which retains only 56% (Hobbie et. al. 1977, Jones 1979). The
filters are usually composed of polycarbonate. The main advantage is the thimess
(approximately 10pm) as opposed to other filters such as cellulose and fibre filters which
174
may be 1OOpm or greater (Jones 1979). By virtue of this thin filter, fewer bacteria can
become trapped in the filter matrix with the majority being spread evenly over one plane
of depth ( f i y 1988). The main disadvantage is the slow flow rate when filtering samples,
which may be partially overcome by pre-filtering sarnples through a 3pm pore size filter
to remove larger detritus particles.
Filters are usually dyed black for ease of viewing the fluorescence produced by
light excitation. The most cornmonly used stain is Irgalan black, although other stains
such as No. 8. Ebony Black have been used successfûlly (Jones 1979).
Filter size is generally 2Smm which allows small volumes to be filtered evenly
over the surface. For an even distribution, a recommendation of 3ml-cm-* of membrane
area is usually followed (Jones and Simon 1985, Jones 1979, Fry 1988). This prevents
the problem of uneven distribution of bacteria at the filter edges. The prepared filter is
the placed on a coverslip with immersion oil and the bacteria are counted using
epifluorescence microscopy.
Once bacteria have been counted using graticules, and ce11 size measured using an
optical micrometer, the biovolume is obtained. This value is usually converted over to
dry weight biomass or ce11 organic carbon using a conversion factor. These conversion
factors have proven to be the largest source of errors when calculating carbon balance of
lakes. This is due to confiision over how much carbon exists in bactenal cells, ce11 coats,
and other structures which may contribute to the size, but not the carbon content per ce11
(van Veen and Paul 1979, Bratbak and Dundas 1984, Bratbak 1985, Nagata 1986). It
seems that his will have to be worked upon, although general concurrence of a suitable
conversion factor is unlikely due to the varying nature of bacteria in each individual lake.
Overall, enors in calculation of bacterial biomass may arise fiom four sources as
outlined by Jones (1979). These are:
1. The estimated number of organisms and percent viability. The percent
viability can be improved using DAPI stain which reduces the possibility of identifjhg
detritus and other non-living particles as living bacterial cells. The number of organisms
can be more closely estimated by counting a large number of bactena per filter and by
counting more than one filter per samples, so that confidence levels may be estimated.
Kirchman et. al. (1982) estimated the ideal nurnber of filters per sarnple to be two,
reducing error enough without costing the researcher excess time. Generally, about 10
random fields (including edges) and 400 bacteria per slide are counted (Jones 1979,
Kirchman er. al. 1982, Fry 1988).
Non-aquatic bacteria may also contaminate the sampfe if it is not properly
prepared. Proper preparation includes 0.22pm filtenng and autoclaving any solutions or
equipment used in the preparation of slides. Even afier this is done, control slides of
"pure" filtered water should be counted to estirnate the degree to which contamination
kvas present (Fry 1988).
2. Estimated size of organisms. Due to ce11 coats or other structures, ce11 size
may be overestimated. The size of organisms is ofien estimated by measuring a small
sample and classifjhg other cells into five or six size and shape categories. This may not
completely cover al1 the various size classes, but is done for the sake of time. Another
method which proved more accurate is by photography and digital analysis, allowing
computers to do the biovolume conversions (Sieracki et. al. 1985, Fry 1988).
3. Formula used to calculate size of organisms. When viewing fluorescing
bacteria, we view hem in a two-dimensional plane. Estimation of biovolume involves a
certain amount of guesswork, assurning bacterial cells are as deep as they are wide. This
may not prove to be tme, and although unpreventable, should be noted in studies. The
only thing which may help is measuring the size of more cells.
4. Conversion factor for converting biovolume to dry weight or ce11 carbon
content. As discussed already, this is the major source of error when estimating
heterotrophic bacterial biornass (van Veen and Paul 1979, Bratbak and Dundas 1984,
Bratbak 1985, Nagata 1986). To my knowledge, in the Mackenzie Delta, no previous
work has been conducted on bacterial biomass. Combined with the fact that this
experiment is not attempting to establish a carbon balance, just comparing results within
themselves, an average estimate of conversion factors used in the literature rnay be used.
Both heterotrophic nanoflagellates and viruses can be presenred, prepared, and
enumerated in a similar manner to bacteria. ï h e main difference being the pore filter size
used. With HNAN, a 8pm pore size polycarbonate filter is used to help reduce
contamination from bacterial cells (Sherr and Shen 1983, 1994, Pace and Funke 1991).
For viruses, samples are pre-filtered through a 0.22pm pore size filter to filter out bacteria
(Suale 1995). Slides are then prepared on 0.02pm pore size Al203 Anodisc membrane.
The main disadvantages of the filter technique is that small organisrns may be missed, or
in the case of viruses, some bacteria smaller than 0.22pm diarneter will not be filtered
while large viruses (>0.22pmdiameter) will be (Sunle 1995).
Nanoflagellates are most often stained with Proflavin, fluorescein isothiocyanate
(FITC), or DAPI (Pace and Funke 1991, Sherr and Sherr 1992,1994). The main
disadvantage with these stains is that autotrophic and heterotrophic organisms are not
177
stained differentially. Staining does provide a rapid enmeration method as compared to
culture techniques or live ce11 counts which often underestimate total nmbers (Sherr and
Sherr 1994). Organic carbon content cari be estimated much the same way as bacteria, by
measuring a nurnber of cells to obtain ce11 volumes, then converting to wet and dry
weights and finally to organic carbon. Drawbacks to estimating organic C this way are
identical to those for bacteria,
Viruses can be enumerated by plaque assays, most probable numbers (MPN's),
transmission electron microscopy (TEM), and epifluorescence analysis. Plaque assays
and MPN's are used to estirnate lytic virus biomass. While this is a usefül measurement,
they ofien underestimate total viral biomass, plus are time consuming (Suttle 1995).
TEM which has been a favoured method in the past, has several drawbacks including
espense, time to prepare samples, and the fact that it appears to severely underestirnate
total viral biomass (Weinbauer and Suttle 1997). Fluorochrome staining cells and
subsequent epifluorescent analysis appears to be the preferred method. Stains include
DAPI and, more recently, Yo-Pro-1 (4-[3-methyl-2,3-dihydro-(benzo1,3-oxazo1e)-7methylmethyledeneJ-1-(3'-trimethylammoniumpropyl)-quinoliniudiiodide,a cyanine
based nucleic acid stain (Suttle 1993, Hemes and Suttle 1995). The disadvantage of
DAPI is that it is specific for double stranded DNA and thus misses RNA viruses
(Hemes and Suttle 1995). While Yo-Pro-1 does stain these RNA viruses, water samples
cannot be fixed with aldehydes, thus requiring immediate preparation of slides (Hennes
and Suttle 1995).
Appendix C Techniques for Determination of Aquatic Bacterial Production
Bacterial production c m Vary Hridely while biomass remains relatively stable. For
exarnple, an increase in DOC concentrations may increase bacterial productivity, but it
would be some time before biomass increases would be detected, or increased UVB
radiation resulting in ce11 death may keep biomass fiom increasing. Also, if bacterial
production increased at the same time grazing pressure increased, there would be little
change in biomass, even though production may be high. Production rates have
implications on the relative rate of nutrient and carbon uptake and cycling throughout the
foodweb.
A number of methods exist for measuring bacterial productivity, each with their
B HI TdR) is the most
own advantages and disadvantages. The [ 3 ~ - m ] t h ~ r n i d i n e
comrnonly used method and will be discussed in greatest detail. As OIDonovan (1978)
stated "a fundamental knowledge of thymidine metabolism (Section 2) is required of
anyone who routinely labels DNA for any purpose". This lack of knowledge has been
indicated as being one of the major problems associated with incorrect production
measurements.
The first three rnethods will be noted here, but not discussed in any great detail.
Phospholipid synthesis in bactena is closely coupled with bactenal growth rates in a
number of species (Robarts 1997). Samples are labeled with ~
~
3 incubated,
2 ~ 0
~
extracted and counted on a scintillation counter. This is converted to pmol P taken up per
rngC of bacterial biomass produced. The main disadvantage is the isotope ( 3 2 ~used
) for
this method, which poses a large risk to the experimenter if used improperly. Heavy
shielding must be wed, this making this technique less than ideal for field studies.
~
The second method is 3~-adenine.This measures bacteriai RNA synthesis,
although it can measure DNA synthesis as well (Kfissbacher et. ai. 1992, Robarts 1997).
However, besides bacteria, several microaigal species rnay also take up the adenine,
making the method less than specific for bacterial production.
The third method, and second most cornmonly used method, is 3 ~ 4 e u c i n e .This
method is based upon the knowledge that protein constitutes up to half of dry bacterial
weight. By labeling an exogenous supply of a protein precursor, bactenal growth rnay
then be measured (Servais 1992). Disadvantages of this method include incorporation
into protein even if ce11 production is zero, the rates of protein synthesis rnay be high
relative to ce11 production when shifting from low to high growth rates, very high
concentrations rnay be necessary and then phytoplankton rnay use this source, and finally,
there may be a relationship between 3 ~ 4 e u c i n eand the supply of DOC (Robaris 1997).
Since this experiment involved a manipulation of DOC concentration, this Iast
disadvantage alone rnakes use of the 3~-leucinemethod a poor choice.
The final, and most commonly utilized method for measuring heterotrophic
J uptake. Thymidine is a precursor to DNA and since
bactenal production is [ ~ HTdR
DNA synthesis is closely coupled to ce11 division and production, this method rnay be
used to estimate ce11 growth (Robarts and Zohary 1993, Robarts 1997). To understand
the use of thymidine in estimating ce11 growth, and its drawbacks, its uptake and
conversion into DNA must be examined.
Figure 30 shows the stmcture of thymidine and the location of its label.
HI TdR
is supplied exogenously by the experimenter and thus must be taken up by the bactena
through a salvage pathway (Figure 3 1). Enough exogenous thymidine must be supplied
to satuate the bactenal de novo pathway. After a certain period, thymidine
180
Figure 30. Chernical structure of [3~--]
the label is indicated by an asterisk.
thymidine @HI TdR). The location of
Figure 31. Pathway by which DNA becornes labeled with 3~ via uptake of exogenously
supplied [ 3 ~ TdR.
]
The CH3 group containing the 3~ label (indicated by an asterisk)
can be lost from the thymine group and may be the major pathway of non-specific
labeling occurring in experiments. De novo synthesis of dTMP fiom UDP accounts for
20% of DNA synthesis, while the CDP accounts for the other 80% when the salvage
pathway is not in use. dTMP, dTDP, and dTTP are thymidine mono- di- and
triphosphates respectively. dUMP, dUDP, and dUTP are deoxyuridine mono-, di-, and
triphosphates respectively. dC is deoxycytidine. dCMP, dCDP, and dCTP are
deoxycytidine mono-, di-, and triphosphates respectively. (Modified from Robarts and
Zohaxy, 1993)
Cellular metabolism
Salvage
Paîhway
de novo
-Paîhway
UDP
phosphorylase is induced and non-specific labeling may occur (Figure 3 1). n i e extent to
which demethylation and non-specific labeling of RNA and protein is unknown,
however, if the experïment is short enough and labeled DNA isolated, this does not
present a large problem (Wicks and Robarts 1987, Robarts and Zohary 1993, Robarts
1997).
The basic assumption of the above is that most bacteria have the transport
enzymes and thymidine kinase allowing them to use exogenously supplied
[jwTdR
(Wicks and Robarts 1993). This is not always true of al1 bacteria and as will be seen, a
number of problerns do exist with the use of [ 3 ~ TdR.
]
These problems and solutions are
discussed in greater detail below.
1. Non-specific labeling. Labeling of DNA synthesized by the salvage pathway
occurs linearly for approximately 1 hour with approximately 82% of the label being
associated with DNA. However, after this period, thymidine phosphorylase is activated
and proteins and lipids may be labeled by
HI TdR using it for storage for later use in
cellular processes (Robarts and Zohary 1993). Also possible is labeling of RNA.
However, few organisms are able to degrade pyrimidines along the pathway involving the
reduction of wacil or thymine (Robarts 1997).
Non-specific labeling may be relatively high and thus isolation of DNA is
necessary. A series of steps are used in the removal and isolation of labeled DNA fiom
bacterial cells. Trichloroacetic acid ( K A ) is used to lyse bacterial cells and precipitate
labeled DNA and other macrornolecules. 50% (w/v) phenol-chloroforrn removes labeled
proteins and 80% ethanol removed labeled lipids. Using this method, it is assumed that
RNA is not labeled, or is done so at very low levels (Wicks and Robarts 1987, Robarts
and Zohary 1993, Robarts 1997).
185
2. Isotope dilution. Dilution may occur by either other exogenous sources of
thymidine competing for uptake sites, or more commonly, by de novo synthesis of dTMP
(Figure 3 1). Isotope dilution may be prevented by storing
HI TdR in 3% ethanol at 4'C
to prevent autodegradation and dilution before use (Robarts and Zohary 1993, Robarts
1997). To prevent extemal non-labeled thymidine or de novo dilution of labeled
thymidine, concentrations of [ 3 ~ TdR
]
between 10 and 20nM should be used to saturate
bacterial ceIls (Wicks and Robarts 1987, Robarts and Zohary 1993, Robarts 1997).
3. Specificity of [ ~ H TJ I R for growing heterotrophic bacteria. Al1 growing
heterotrophic bacteria should take up [ 3 ~ TdR
]
while non-gron-ing bacteria or other
organisms should not. Of the aerobic heterotrophic bacteria, only Pseudomonas species
appear to lack thymidine kinase and thus can not use exogenous sources (Saito et. al.
1985). These bacteria generally do not comprise a large proportion of freshwater
bacterioplankton, and generally do not influence results significantly.
One problem associated wïth other bacterial production techniques is uptake by
phytopld-ton of the label. Afier twelve hours of incubation with [ ~ HTdR,
J
less than
1% of the label was associated with pemate diatoms and flagellates (Fuhrman and Azam
1982). Thus, it appears that [ 3 ~ TdR
]
is relatively specific for heterotrophic bactena.
4. Cellular DNA content. For conversion of the rate of DNA synthesis into the
number of bacteria produced, the DNA content must be known. This varies depending on
the status of bacteria, actively growing having more DNA content per cell. However, a
DNA content ranging fiom 2 to 5 fg-cell-1 appears typical (Robarts and Zohary 1993).
5. Conversion factors. The conversion factor may be used to convert the rate of
[ j ~TdR
]
incorporation to number of cells produced per unit volume and tirne. Both
theoretical and empirical values exist for conversion factors. Empirical values have the
advantage in that they are specific for the particular system the researcher is interested in,
but are disadvantageous in that they do require the development of a dilution curve
(Robarts and Zohary 1993).
Ducklow et. al. (1992) went through a number of methods for calculating
conversion factors. They concluded that the modified derivative method, where the
conversion factor \vas estimated as the y-intercept of regression equations of ce11 numbers
and [
j ~TdR
]
incorporation over tirne, was the most ideal since maximum weight is
given to ce11 numbers. However, Robarts and Zohary (1993) suggest that using data of
carbon per ce11 and DNA per ceIl, instead of empirical or theoretical conversion factors,
would be ideal as this eliminates the problems associated with calcdating conversion
factors.
From the above, it appears that despite limitations, the
DE HI TddR is the ideal
method for estimating heterotrophic bacterial production in lakes. This method is
specific for heterotrophic bacteria, and is applicable under a number of growth States. As
well,
HI TdR is of Iow danger to the experimenter and may be used with relative ease
in the field. Finally, this method is generally reliable, precise, and sensitive.
Standardization of the methodology used to extract and puri@ labeled DNA and
conversion factors still need to be agreed upon, but when limitations of al1 methods are
]
uptake is still the most reliable one curtently in use.
considered, [ 3 ~ TdR
Appendix D
Determination of Dissolved Organic Carbon Concentration
Using Gas Chromatography.
1. Combust empty lOml glass ampoules for 4-6 hours at 475-525OC to remove any
contaminating organics. Gold band ampoules are preferred as they break much cleaner
along the score lines.
2. To each combusted ampuole, add approximately 50mg potassium persulfate
( K ~ S 2 0 8 ) .This is suficient for DOC concentrations up to 1 0 0 m ~ - ~Above
- ~ . this,
water samples should be diluted as additional K2S208 results in formation of large
amounts of free CO2 gas wvhich ofien ruptures the ampuole during autoclaving. Higher
levels of K2S208 should be used in cases where mercuric chloride is used to preserve
water samples for DOC analysis as the mercuric chloride may interfere with the oxidation
process.
3. Add 10ml of 0.45pm filtered lake water to ampuole.
3. Add 0.2ml of O.O5M H2SO4 to reduce the pH to below 4. In well buffered systems,
or systems with high inorganic carbon concentrations, the concentration of the acid used
may need to be increased.
5. Sparge sample for 15 minutes with He (technical grade or pre-purified) by bubbling
the liquid through a g l a s pipette connected to the He tank. The addition of acid and
sparging releaçes inorganic carbon as free CO2. Less than 12 minutes can result in
retention of some inorganic carbon, and sparging for greater than 20 minutes can result in
partial oxidation of the organic carbon. While He is the preferred sparging gas, virtually
any CO2 free gas may be used in sparging.
188
6. Remove sarnple from sparging gas and immediately seal ampuole with a propane
burner.
7. Autoclave samples for 1 hour at 121-1 30°C in a slow-exhaust release autoclave.
Rapid release of pressure may result in explosion of ampuole. For best results, ailow the
autoclave to cool ovemight.
8. Allow samples to cool to room temperature. Sarnples are now stable indefinitely,
provided the arnpuole was properly sealed, and can be stored at 4°C or room temperature.
Freezing will rupture the arnpuole.
9. For analysis, first warm-up and calibrate gas chromatography analyzer ~ 4 t glucose
h
standards. Prepare selected sample by first crushing tip.
10. Transfer 8ml of sarnple to 20 ml syringe using clean Nalgene tubing attached to the
syringe.
1 1. Remove tubing and replace with a 3-way stopcock. Add l2ml of CO2 free carrier
gas using 3-way stopcock.
12. Seal off syringe with stopcock and shake 50 times to release dissolved CO2 from
liquid.
13. Inject 5ml of sample gas into GC and analyze for CO2 concentration.
14. From linear regression of standard concentrations versus CO2 peak area, convert
unknown sample CO2 peak area to total DOC concentration.
For standards, glucose at known concentrations are used. Standards are prepared
using the identical protocol for samples. Loss of CO2 in the fiee headspace of ampoules
is generally less than 7% and is quite consistent across a11 samples. Resolution can be
down to O.lrng-~-lbut is more typically in the o
. s ~ ~ Lrange.
-*
Appendix E
Determination of Bacterial Production Through 3 ~ - T ~ R
Incorporation.
Whenever sterile water is referred to in this protocol, this is meant to imply
distilled deionized Mater 0.22pm filtered and stenlized at 130°C for 1 hour in a slowexhaust autoclave. Water should be prepared fresh for each experiment. Al1 equipment
should be acid-washed (10% HCI solution) and rinsed with stenle water.
1. Soak 0.22pm nitro-cellulose filters in sterile water at 4°C for two hours prior to start
of esperiment to reduce background interference.
2. Place lOml of sarnple into a sterilized autoclavable 20ml glass via1 with a screw-on
cap.
3. Add 10p1 of 3 ~ - T ~(Amersharn)
R
to each sample. Stock is preserved in 5% ethanol.
4. For controls, irnrnediately add 0.5ml of formaldehyde (37% v/v) and allow to sit for 5
minutes. For samples, allow to incubate at 30 minutes at room temperature and ambient
lighting before addition of formaldehyde. n i e addition of formaldehyde prevents the 3 ~ -
TdR from bonding with DOC which may possibly cause high false readings. It is
generally not necessary in low DOC systems (below 2 0 m g ~ - l ) .
5. Add 0.25ml 1ON NaOH and let sarnple stand at room temperature for 20 minutes or
place in refngerator for 1 to 20 hours before proceeding to step 6.
6. Add 3ml of 100% K A (tnchloroacetic acid; stored at 4°C) and stand on ice for 15
minutes. As Iow as 50% TCA may be used as long as the pH is reduced to 2 or lower.
19 1
7. Filter through pre-soaked membrane.
8. Rinse with 3ml5% TCA (stored at 4°C) by adding to filter and filter apparatus. Filter
and repeat 3 times.
9. Filter 5ml of 50% phenol-chlorofonn (stored at room temperature)
10. Filter 5ml of ice-cold (stored at 40°C) 80% ethanol.
1 1. Rernove non-filtering margins plus 7% of filter and place in clean, unused
scintillation vials (plastic or glass). The removal of the non-filtering region overcomes
the problem of lateral creep of the isotope.
12. Add 10ml of Filter-Count or other suitable scintillation cocktail and allow it to
completely dissolve filter (5 to 15 minutes) before ruming on scintillation counter.
Sarnples should be checked for quench, however, this is usually not a problem if dried
filters are used.
13. After counting on scintillation counter, multiply values by 1.O7to account for
removal of 7% of filtering margin.
14. For standards, 100pl of the 3 ~ - T ~stock
R solution in (3) should be added to Sm1 of
sterile water. Two 1 0 0 ~aliquots
~1
should be removed, placed in separate scintillation
vials, and diluted with 9 0 0 ~ of
1 sterile water and 9ml of scintillation count.
1. NaOH = 1ON sodium hydroxide solution.
Purpose:
Increase pH of sample enough to shock or kill bacteria and prevent
R
M e r 3 ~ - T ~incorporation.
2. 100% TCA = 1OOg TCA (trichloroacetic acid) made up to 1OOml total volume with
stenle water.
Purpose:
Lyse bacterial cells and precipitate leveled DNA and other
macromolecules.
3. 5% TCA = 5g K A made up to 1OOml with sterile water
Purpose:
Rime and M e r precipitate out any lefiover DNA and
macromolecules.
4. 5056 phenoI-chloroform = 50g phenol made up to lOOml total volume with
chloroform.
Purpose:
Removes labeled proteins.
5. 80% ice-cold ethanol = 80ml HPLC grade ethanol plus 20ml sterile water.
Purpose:
Removes labeled lipids.
Appendix F
Averages, Standard Errors, and Number of Samples Collected
for Experimental Microbial Biotic Components.
The abbreviation SE in the first row is for standard error of the mean. Samples
are generally biomass in pg c d - 1 or g Cl-1 for phyto (phytoplankton biomass) and
zoop (zooplanklon biomass). Chlorophyll concentration (abbreviated Chl) is in pg-l-l.
Bacterial production (abbreviated bac prod) is in units of pg c.1-ldayml.
Appendix G
Averages, Standard Errors, and Number of Samples Collected
for Experimental Abiotic Components.
The abbreviation SE in the first row is for standard error of the mean. Units for
the abiotic parameters are as follows; conductivity (prnhos-crn-l)ytemperature (OC),
phosphorus and nitrogen (abbreviated P and N; PM), hurnic DOC (mgl-l), and total
suspended sediment concentration (abbreviated TSS; mgl-1).
000(s
O0020
Ow12
00021
Om l 0
O0010
O0017
om15
O0010
O0010
O W
OaM6
OWM
0 1z1
0 0219
0 1M7
OaJm
0 1251
0 1258
OOldd
OIWI
0 0764
OO764
Oo5n
0 0500
O 1251
OO764
O0017
00023
O W
O0050
OOObl
O0026
05nO
00092
O0052
00100
O0010
Oibml
00079
00030
Oan4
00046
00061
O W
O0012
OW5
00055
OWIO
00011
O0024
O0014
OW l 4
OW12
00021
0 Wl5
O0020
OWIS
OWl7
O0017
O0010
OWIO
OWIO
O00lO
000l5
O0012
OM l 0
O0010
OI W l
O 1151
00866
OOs00
02021
O lm
O I 155
OIU3
O0764
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