EFFECTS OF DISSOLVED ORGANIC CARBON AS A BACTERIAL GROWTH

SUBSTRATE AND AS AN 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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or other means, without the permission of the author.

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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 rng -~ - l humic DOC), sufficient DOC to reduce UVB to 1% and substantially

increase bacterial food (*DOC, 12.5 rng - ~ - l humic DOC), Mylar-D screening to reduce

UVB to 1% without altering ambient DOC (-WB, 3.6 rng-~- l hurnic DOC), or were left

unaltered where W B penetration at 10 cm depth was 64% of surface values (Control, 3.6

r n g - ~ - l humic 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 ing 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 ing 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 OF CONTENTS . . APPROVAL PAGE ......................................................................................................... II

... ABSTRACT ................................................................................................................... 111

DEDICATION ................................................................................................................. v

............................................................................................ ACKNOWLEDGEMENTS vi ... ............................................................................................. TABLE OF CONTENTS viii

LIST OF TABLES ................................ .. ..................................................................... xi

LIST OF FIGURES ................... .. .............................................................................. xiv

................................................................................... CHAPTER 1 : INTRODUCTION 1

1 . 1 The Mackenzie Delta ................................................................................................ 1 .......................................................................................... 1 -2 Dissolved organic carbon 5

1.3 The microbial food-web .......................................................................................... I I .......................................................................................... 1 .3.1 Phytoplankton 13

1.3.2 Grazers ..................................................................................................... 17 ................................................................................................... 1.3.3 Viruses 19

................................................................................ 1.3.4 Higher trophic levels 20 1.4 Interactions within multiple trophic levels ....................................................... 22

...................................... ......... CHAPTER 2: MATERIALS AND METHODS ..... 30

2.1 Studyarea ................................................................................................................ 30 .................................................................................................................. 2.2 Lake site 31

2.3 Experimental design ............................................................................................... 34 2.4 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

...................................................... 2.6.3 Heterotrophic nano flagellate biomass 49 ........................................................................................... 2.6.4 Viral biomass 50

............................................................................... 2.6.5 Zooplankton biomass 51 ............................................................................ 2.6.6 Pkjoplankion biomass 51

viii

APPENDIX E: Detemination of Bacterial Production Through 3 ~ - T ~ R

........................................................................................ Incorporation 1 91

APPENDIX F: Averages, Standard Errors, and Number of Samples Collected for

....................................... Expenmental Microbial Biotic Components 1 94

APPENDIX G: Averages, Standard Errors, and Number o f Samples Collected for

...................................................... Expenmental Abiotic Components 1 95

LIST OF 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 1 7). 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 p-

value from the repeated measures ANOVA for the between subjects effect are

also listed. .......................................................................................................... 63

3 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 p-

value from the repeated measures ANOVA for the between subjects effect are

.......................................................................................................... also listed. 65

4 Planned cornparisons for virus 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 0.0 17). 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. 1 O @onferroni

adjustment to 0.033). A double asterisk indicated significance at an a level 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 fieedom, and p-value fiom the repeated measures ANOVA for the

between subjects effect are also listed. ........................... .. .....................-.. 74

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 p-

value fiom the repeated measures ANOVA for the between subjects effect are

also listed. .......................................................................................................... 82

xii

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 p-

value fiom the repeated measures ANOVA for the between subjects effect are

also listed. . ................ .......... ..... ........ ................ . . . . . . . . ..-.. . . . . .. 87

8 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 2 cornponents (perfect relationship, r =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 1 5

Bathymetric map of South Lake where DOC enrichment experiments were 3 3 conducted. ...................... ... ........................................................................... 23

General design of experimental enclosures. ...................................................... 38

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

South Lake over the course of experiment 2. ............................... .. .................. 84

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

for the Inuvik 40 lake survey. ................... ... ....... ....... .... ......... 120

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 ...................................................................................... Inuvik 40 lake survey. 1 3 5

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 survey. .................................... .... ...................................................................... 144

Relationship between bacterial biomass and heterotrophic nanoflagellate biomass for the Inuvik 40 lake survey. .......................................................................... 147

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 1 98 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. 199 1, 1 994). 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 198 1, 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 rng -~ - l reducing 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. 1 993, 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

1 995). 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

1 994).

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.

199 1 , 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 1 982).

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

1 982, 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 199 1, 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 recentl y (Ducklow 1 994). 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. 1 989, 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 fd 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 non-

selective 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).

I f 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 199 1). 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- (1 993, 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. ïhey 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~ch rnen t experiment 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.

Bactenal

biomass

DOC )tC

Bacterial

production

'P

UV-B 'P or 'l'

Both 0 't'

Phytoplankton

biomass

HNAN Virus

biomass biomass biomass

'T'

*

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

1 2,000 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 Sm 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 1 Sm 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 kd) , 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 d at 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 1 Ocm 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

Overhead view

Support fiame

I

UV variable cover \:l 3m

I

Si de view UV variable cover Lake

- - -, d a c e Support h n e

Floaîs

Enclosure bag

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 .Sm wooden 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

3 9

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 (1 98 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 0.1N NaOH. 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 1 Ocm 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 = irradiance at subsurface (1 00%)

IZ = irradimce at depth (50% or 1%)

z = depth (O. 10m)

k = attenuation coefficient K ~ I B

K ~ I B = 0.4 15 (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 1 Ocm 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 4 1

r n g - ~ - l ) needed 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.m to 365m (De Haan and De Boer 1987, DeHann

1993). I f 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-C model 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

spectrophotometrically according 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 ~ 3 - and 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 pre-

combusted g las 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 1 Onrn 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, 1 L 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,

1 L 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 - ~ - l DAPI 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 las filter holders. The backing filter ensured even distribution of bactena on

the 0.22pm filter. Preserved water sarnples were shaken vigorously and 2ml aliquots

placed on filters. DAPI stain was added to a final concentration of 0.0 l P g - ~ - l 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 (1 979) 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 - 3 (Fry 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 (1 983, 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 = 1 pg) 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 1 pg-~-l. Sarnples 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 0.75 to 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 ~vhich were - 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.):

C yanophytes C = B x 0.22

Dinoflagellates C=Bx0.13

Diatoms C=BxO. l l

C hIorophytes 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 (2OnM final concentration) added

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 (1 0

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.

Standards for each experiment were also prepared by adding 1 0 0 ~ 1 of 3 ~ - ~ d ~ to

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

(Fil ter-Count, Packard) was added.

Upon arriva1 at SFU, filters were dissolved with the addition of IOml of Filter-

Count (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 = CWF where 1 = g C produced per M 3 ~ - T ~ R uptake

C = cells produced per M 3 ~ - T ~ R uptake (2.0-1 018)

V = average ce11 volume (0.0914~m3 for this experiment)

F = carbon conversion factor (3.16- 10-1 3 g c-pn3 for this

experiment)

From the calculated uptake of 3 ~ - T ~ R per liter per day, and the amount of C

produced per mole of 3 ~ - T ~ R uptake, 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 0.037 using 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 rng 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 71 00 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 1 Smg-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 . 0 0 1) which 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 treatment

59

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 1 1 I I O Cmtrd 200 205 210 215 220

Umn ôay of year

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 0.01 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 from the repeated measures

ANOVA for the between subjects effect are also listed.

Bacterial Biomass

Experiment I Experiment 2

Control versus treatments p=0.01 O** p<O.OOl***

MS error

Error d.f. 8 8

p-value 0.004 0.00 1

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 Heterotroph ic 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-ml-

1 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.

Control versus treatments

+DOC versus HDOC

++DOC versus -UV-B

MS error

Error d.f.

p-value

Nanoflagellate Biomass

Expenment 1 Experiment 2

p=0.004* p=O.0OS4

p=0.004* * p=O.O33 *

p=O.OlO** p=0.024*

1-10-9 1.10-9

8 8

0.00 1 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~onding to 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 -UVB treatments (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-mlg1 to 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 ***

p=O.OOl***

4.18-10-6

++DOC versus -UV-B p=0.006*

MS error 3.73-10-6

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 -UVB treatment were equal,

virus biomass may possibly be greater in the -UVB because 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 -UVB enclosures. 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 1 7). 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.

Control versus treatments

+DOC versus *DOC

+DOC versus -UV-B

MS error

Error d.f.

p-value

Chlorophyll Phytoplankton

Experiment 1 Experirnent 2 Experiment 2

p<O.OO 1 * ** p<O.OO 1 * * p<O.OOf ***

p<O.OO 1 * * 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.

205 210 2 15

Julian day of year

Lake -WB ++DOC +DOC Con trol

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 -LM3 treatrnent 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

1 O).

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 relative1 y 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 - ~ J respectively (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 8 1

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.

Control versus treatments

+DOC versus *DOC

*DOC versus - W - B

MS error

Error d.f.

p-value

Zooplankton Biomass

Experiment 2

p<0.001***

p<O.OOl***

p<o.oo 1 *

10.045

8

<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.

205 210 215

Julian day of year

Lake -UV% ++DOC +DOC Con t rol

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 -cellol to 3 -0- 10-8 pg c.1-1 .day-l-

cell-l in experiment 1 and fiom 1 S.10-8 pg ~- l - l -da~- l - ce l l - l to 3.9.1 0-8 pg C-1-1 -day-1-

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 h only 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.

Control versus treatments

+DOC versus ++DOC

UDOC versus -UV-B

MS error

Error d.f.

p-value

Total bacterial production Production rate per bacteria

Experiment I Experiment 2 Expenment 1 Experiment 2

p=0.007** p=O.O 12** p=0.002*** p=0.043

p=0.003*** p=0.002*** p=0.033* p=0.02 1

p=O.O11** p=0.006** p=0.023* p=0.03 1 *

8.21 5 8.782 O. 1384 0.1412

8 8 8 8

<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 HDOC 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 -UVB and 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

1 Ocm 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 non-

coloured, photobleached DOC which may offer linle or no nutrition for bacteria (Reitner

es. al. 1 997). It may have been that the bacteria benefited from the further decrease in

UVB in the -UVB treatment (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 1 997).

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 250m to that at 365m

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

HNAhT may 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 1 997, 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

9 8

the diet of macrozooplankton (Sanders et. al. 1989, Pace et. al. 1998). If

macrozooplankton increased in the -UVB but 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 1 l), 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, 1 O0

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 HDOC 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 -UVB treatment, 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 1 1% 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,

Mostaj ir 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

1 O3

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, ~vhere the 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 -UVB treatment 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~ving strong 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 1 os

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 -UVB treatments

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 non-

selective feeders and likely consumed bactena and predators as well as phytoplankton 1 O6

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

WB) , 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 1 O7

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). 1 O8

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 S components (perfect relationship, ?=l .O, no relationship, r =O).

1 Key: bactena = bacterial biomass (pg C d - ) 1 Total DOC = total dissolved organic carbon (mg-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 X Bacteria Total DOC Bacteria HNAN Bacteria Virus Bacteria Humic DOC Bacteria TSS Bacteria DOC size Bacteria Si11 elevôtion Bacteria Chlorophyll a HNAN Total DOC HNAN Humic DOC Virus Total DOC Virus Hurnic DOC Chlorophyll a Total DOC Chlorophyll a Hurnic DOC Chlorophyll a TSS Total DOC Humic DOC Total DOC Si11 elevation Humic DOC Si11 elevation HumicDOC TSS TSS Si11 elevation

3 Multiple r' 0.296 0.780 0.352 0.239 0.023 <o.oo 1 O. 199 €0 .O0 1 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

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 1 Ocm 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 4

Sill eleva tion (ml

Figure 16. Relationship between total dissolved organic carbon concentration and si11

elevation for the Inuvik 40 lake survey.

2 3 4

Sill elevation (m)

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 4

Si11 elevation (m)

Figure 19. ReIationship between bacteriai biomass and totai dissolved organic carbon

concentration for the Inuvik 40 Iake s w e y .

1 O 20 30 40 50 60 70

Total DOC concentration (mg Ï '1

Figure 20. Relationship between bacterial biomass and humic dissolved organic carbon

concentration for the Inuvik 40 lake survey.

4 6 8 10 12 14 - 1

Humic DOC concentration (mg 1

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 60

Total DOC concentration (mg 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 60 70

Total DOC concentration (mg 1- 1

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 14 - 1

Humic DOC concentration (mg I 1

Figure 25. Relationship between bacterial biomass and chlorophyll concentration for the

Inuvik 40 lake survey.

O 2 4 6 8 10 - 1

Chlorophyll concentration (pg I 1

Figure 26. Relationship between chlorophyll concentration and total suspended sediment

concentration for the Inuvik 40 lake survey.

O 2 4 6 8 10 - 1

Total suspended sediments (mg I

Figure 27. Relationship between chlorophyll concentration and humic dissolved organic

carbon concentration for the Inuvik 40 lake survey.

4 6 8 10 12 14

iiurnic DOC concentration (ma Ï1 )

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 0.02 - 1

Virus biomass (pg C ml 1

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 n between the t ~ \ ~ o , but 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 I 1 1

0.0000 0.000 1 0.0002 0.0003 0.0004

Nanoflagellate biomaçs (pg C mi-')

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 sunfey indicated 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 - ~ - l range (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 mer 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 non-

humic 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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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.

1 996). 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. al-

1996). 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 1 pm 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 490m 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 wïdespread acceptance 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 198 1, 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 1 OOpm 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 iy 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 199 1).

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 (>0.22pm diameter) will be (Sunle 1995).

Nanoflagellates are most often stained with Proflavin, fluorescein isothiocyanate

(FITC), or DAPI (Pace and Funke 199 1, 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-(benzo- 1 ,3-oxazo1e)-7-

methylmethyledeneJ- 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

own advantages and disadvantages. The [ 3 ~ - m ] t h ~ r n i d i n e B HI TdR) is the most

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 2 ~ 0 ~ ~ incubated,

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 3~4eucine . 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 e and the supply of DOC (Robaris 1997).

Since this experiment involved a manipulation of DOC concentration, this Iast

disadvantage alone rnakes use of the 3~-leucine method a poor choice.

The final, and most commonly utilized method for measuring heterotrophic

bactenal production is [ ~ H J TdR uptake. Thymidine is a precursor to DNA and since

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~--] thymidine @HI TdR). The location of

the label is indicated by an asterisk.

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

UDP

Salvage Paîhway

de novo -- Paîhway

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 [jw TdR

(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 J T 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 [ ~ H J TdR, 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

considered, [ 3 ~ ] TdR uptake is still the most reliable one curtently in use.

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~S208) . 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 12 1-1 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 h glucose

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-~-l but is more typically in the o . s ~ ~ L - * range.

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 slow-

exhaust 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 ~ R (Amersharn) 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 1 ON 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. Fil ter 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.

1 3. After counting on scintillation counter, multiply values by 1 .O7 to account for

removal of 7% of filtering margin.

14. For standards, 100pl of the 3 ~ - T ~ R stock solution in (3) should be added to Sm1 of

sterile water. Two 1 0 0 ~ ~ 1 aliquots should be removed, placed in separate scintillation

vials, and diluted with 9 0 0 ~ 1 of sterile water and 9ml of scintillation count.

1. NaOH = 1 ON sodium hydroxide solution.

Purpose: Increase pH of sample enough to shock or kill bacteria and prevent

M e r 3 ~ - T ~ R incorporation.

2. 1 00% TCA = 1 OOg TCA (trichloroacetic acid) made up to 1 OOml 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 1 OOml 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-l dayml.

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)y temperature (OC),

phosphorus and nitrogen (abbreviated P and N; PM), hurnic DOC (mgl-l), and total

suspended sediment concentration (abbreviated TSS; mgl-1).

000(s O 0020 O w12 00021 O m l 0 O 0010 O 0017 om15 O 0010 O 0010 O W OaM6 O W M 0 1 z 1 0 0219 0 1M7 O a J m 0 1251 0 1258 OOldd O I W I 0 0764 O O764 O o 5 n 0 0500 O 1251 O O764 O 0017 00023 O W O 0050 OOObl O 0026 05nO 00092 O 0052 00100 O 0010 Oibml 00079 00030 Oan4 00046 00061 O W O 0012 O W 5 00055 OWIO 00011 O 0024 O 0014 O Wl4 OW12 00021

0 Wl5 O 0020 O WIS O Wl7 O0017 O 0010 O WIO O WIO O 00lO 000l5 O 0012 O M l 0 O 0010 O I W l O 1151 00866 O Os00 02021 O lm O I 155 O IU3 O 0764 O 1607 O 0577 o m O II55 O 076J 0001o o m 2 O -7 O 0078 O W16 OOIW O O30 00112 O M l 2 O W31 0- OOlH O w71 O M69 O 0031 O -0 O al24 O w2o O M21 O 0013 OOWt OWIS O Wl6 O 0013 O w32 00034 0 0036 O 0019

OdOlO O ml5 O 0012 O W3l O W20 O 0010 0 0010 O 0010 O WlO O wro 00015 OWIO 00015 O WI5 0 2021 O 0 7 a O orn O 0219 O 0764 O 1756 0 1443 0 1251 O lanl O Io00 O 1193 O 0219 0 IW1 0 1x2 00010 0 0055 O W2i 0 0031 O 0143 O 0067 00069 00069 0 0057 0 0103 0 m o 0 m 7 00023 0 ml O 0112 1 O m72 0003.4 OW36 O 0031 0 0035 0.0021 O WlO 00011 O m 5 OOObl O m n O m n 0-

OQJl2 00015 0002l O m l 0 O m l 0 00006 O 0010 00006 0 0019 00006 O O010 00006 00015 O ZV30 O osa6 0 0764 O ID00 O 133 O Io00 O 1607 0 1012 0 0219 O 02S9 O o s n O o s n 0 0764 O Il55 00021 O 0032 O 0036 O Olrn 00064 OOlOl O 0075 OQYo O 0154 00120 ow66 OOlW OOtl2 0 W35 O 0021 00022 O 0021 O 0053 00022 O 0 0 s 00065 O 0032 0 0034 0- 0 0038 O m l 0 0 W16 O 0030