Short-term Effects of a Lowered Water Table on
Carbon Cycling and Plant Community Structure
in a Temperate Bog Margin
Elissa M. Goud
Department of Geography
McGill University
Montreal, Quebec, Canada
August 2014
A thesis submitted to McGill University in partial fulfillment of the requirements
of the degree of Masters of Science
 Elissa M. Goud, 2014 All rights reserved
Abstract
Peatlands are key features of the northern landscape and play a critical role in the global carbon
cycle, storing approximately one-third of the global carbon pool. Although peatlands are
generally a net carbon sink, they are sources of atmospheric methane (CH4) and changes in
environmental conditions can amplify carbon release at both fine and large spatial scales.
Hydrological disturbances, either natural or anthropogenic, can alter the structure and function of
peatlands by changing their vegetation composition.
To better understand the relationship between water table position, vegetation and carbon
dynamics, I tested the short-term effects of a lowered water table on plant community structure,
carbon dioxide (CO2) and CH4 flux at a temperate bog margin flooded by a beaver pond. I
measured species abundance in 2012 and 2013 along three transects encompassing a hydrologic
gradient from ombrotrophic bog to beaver pond and compared field measurements of CO2
exchange and CH4 flux from static chambers across 27 sites and 9 vegetation groups. I identified
six distinct plant communities that contained characteristic species corresponding to changes in
water table depth. The primary drivers of plant community structure were variables related to
water table depth and water chemistry. I found unimodal or nearly unimodal responses of species
abundance to mean water table position; the narrowest tolerances were found in the wettest
vascular and bryophyte species and the widest tolerances were found in vascular and bryophyte
species that occupied both hummocks and hollows. Lowering the water table did not alter bog
species composition but minerotrophic species, particularly graminoids and aquatic Sphagnum
mosses, increased onto previously inundated areas. Changes in abundance could be explained by
differences in species optima and tolerances relative to water table depth. Vegetation
composition was an important control on both CO2 exchange and CH4 flux rates. The lowered
water table differentially affected the individual components of CO2 exchange due to differences
between plant species and assemblages. There was an overall reduction in ecosystem respiration
(ER), gross ecosystem photosynthesis (GEP) and CH4 flux after the drainage. I found unimodal
relationships between weekly (GEP) and water table position, with hummock, hollow, margin,
and moss vegetation groups clearly differentiating along the moisture gradient. My findings
emphasize the importance of vegetation dynamics in this system, and the need for a better
mechanistic understanding of the relationship between vegetation and carbon dynamics under
field conditions.
ii
Résumé
Les tourbières sont des éléments clés du paysage nordique et jouent un rôle essentiel dans le
cycle global du carbone, séquestrant le tiers du carbone contenu dans les sols. Bien que les
tourbières sont généralement un puit net pour le carbone, celles-ci représentent aussi des sources
de méthane (CH4) vers l'atmosphère et les changements dans les conditions environnementales
peuvent amplifier les émissions de carbone à différentes échelles spatiales. Les perturbations
hydrologiques naturelles ou anthropiques, peuvent modifier la structure et la fonction des
tourbières en changeant la composition de la végétation.
Pour mieux comprendre la relation entre la position de la nappe phréatique, la végétation et la
dynamique du carbone, j'ai testé les effets à court terme d'une baisse de la nappe phréatique sur
la structure des communautés végétales, les échanges de dioxyde de carbone (CO2) et les flux de
CH4 à la surface d’une tourbière tempérée, partiellement inondée par un étang de castor. J'ai
mesuré l'abondance des espèces végétales en 2012 et 2013 le long de trois transects couvrant un
gradient hydrologique allant de la partie ombrotrophe de la tourbière jusqu’à l'étang de castor, en
plus d’effectuer des mesures d'échanges gazeux (CO2 et CH4) à l’aide de chambres statiques sur
27 sites et 9 groupes de végétation. J'ai identifié six communautés végétales distinctes qui
contenaient des espèces caractéristiques correspondant au changement de profondeur de la nappe
phréatique. Les principaux facteurs de la structure des communautés végétales étaient des
variables liées à la profondeur de la nappe phréatique et à la de la chimie de l'eau. J'ai observé
des relations unimodales ou quasi unimodales entre l'abondance des espèces et la position de la
nappe phréatique; les niveaux de tolérances étaient plus étroits pour les espèces vasculaires et les
bryophytes des secteurs plus humides alors qu’ils étaient plus grands pour les espèces vasculaires
et bryophytes qui occupaient les buttes et les dépressions. L'abaissement du niveau de la nappe
n'a pas modifié la composition des espèces de la tourbière, mais l’abondance des espèces
minérotrophes, en particulier les graminées et les sphaignes aquatiques, a augmentée sur les
zones déjà inondées. Les changements dans l'abondance peuvent être expliquées par des
différences dans les niveaux optimaux et de tolérances relatifs à la position de la nappe
phréatique.
iii
Mes résultats démontrent que la composition de la végétation représentait un contrôle important
sur les échanges de CO2 et CH4. Les impacts de l’abaissement de la nappe phréatique sur les
échanges de CO2 étaient variables en raison des différences entre les espèces et les assemblages
végétaux. Dans l’ensemble, le drainage a engendré une réduction de la respiration de
l'écosystème, de la photosynthèse brute de l'écosystème (GEP) et des flux de CH4 . J'ai observé
des relations unimodales entre la GEP hebdomadaire et la position de la nappe phréatique, pour
les buttes, les dépressions, la marge, et les assemblages de végétation associés aux mousses le
long du gradient d'humidité. Mes résultats soulignent l'importance de la dynamique de la
végétation dans ce système, ainsi que la nécessité d'une meilleure compréhension mécanique de
la relation entre la végétation et la dynamique du carbone.
iv
Table of Contents
Abstract ........................................................................................................................................... ii
Résumé............................................................................................................................................ iii
Table of Contents ............................................................................................................................ v
List of Figures ............................................................................................................................... vii
List of Tables ................................................................................................................................ viii
Acknowledgments ........................................................................................................................... 9
Chapter 1: Introduction ............................................................................................................... 10
1.1 Research context .................................................................................................................. 10
1.2 Research objectives.............................................................................................................. 10
Chapter 2: Literature Review ...................................................................................................... 12
2.1 Carbon cycling in terrestrial ecosystems ............................................................................. 12
2.2 Peatlands and their role in the global carbon cycle .............................................................. 12
2.3 Vegetation dynamics in peatlands ....................................................................................... 12
2.3.1 Distribution along environmental gradients ................................................................. 13
2.3.2 Dynamics of the bog margin ........................................................................................ 14
2.4 Factors affecting carbon dioxide exchange in peatlands ...................................................... 15
2.5 Factors affecting methane emissions in peatlands ............................................................... 16
Chapter 3: Methodology .............................................................................................................. 18
3.1 Site description .................................................................................................................... 18
3.2 Experimental setup .............................................................................................................. 18
3.2.1 Vegetation sampling sites ............................................................................................. 18
3.2.2 Gas flux sampling sites ................................................................................................ 19
3.3 Vegetation sampling ............................................................................................................ 19
3.3.1 Species abundance........................................................................................................ 19
3.3.2 Green area .................................................................................................................... 20
3.4 Carbon dioxide exchange measurements ............................................................................. 20
3.5 Methane flux measurements ................................................................................................ 21
3.6 Ancillary measurements ...................................................................................................... 21
3.7 Data processing and analysis ............................................................................................... 22
Chapter 4: Results ........................................................................................................................ 24
4.1 Interannual variability in environmental conditions ............................................................ 24
4.2 Vegetation ............................................................................................................................ 25
4.2.1 Plant community structure along the bog-margin gradient .......................................... 25
4.2.2 Indirect and direct gradient analysis ............................................................................. 29
4.2.3 Plant community responses to a lowered water table ................................................... 32
4.2.4 Plant species optima and tolerances relative to mean water table position .................. 32
4.2.5 Green area .................................................................................................................... 34
4.3 Carbon dioxide..................................................................................................................... 37
4.3.1 Changes in CO2 exchange after draining the beaver pond ........................................... 37
4.3.1.1 NEE, GEP, and ER ............................................................................................... 37
4.3.1.2 Carbon use efficiency ........................................................................................... 38
4.3.2 Factors contributing to the spatial variation in CO2 exchange ..................................... 39
4.3.2.1 Vegetation ............................................................................................................ 39
4.3.2.2 Water table ........................................................................................................... 39
4.3.3 Factors contributing to the temporal variation in CO 2 exchange.................................. 40
4.3.3.1 Light ..................................................................................................................... 42
4.3.3.2 Water table and peat temperature ......................................................................... 44
4.4 Methane ............................................................................................................................... 49
4.4.1 Variations in CH4 flux .................................................................................................. 49
4.4.2 Biotic and abiotic correlates of methane flux at different spatial scales....................... 52
4.4.2.1 Landscape scale .................................................................................................... 52
4.4.2.2 Plant functional types ........................................................................................... 54
4.4.2.3 Individual vegetation groups ................................................................................ 54
4.4.2.4 Multiple regressions ............................................................................................. 56
v
Chapter 5: Discussion ................................................................................................................... 59
5.1 Vegetation ............................................................................................................................ 59
5.1.1 Plant community structure along the bog – margin gradient ........................................ 59
5.1.2 Plant community responses to a lowered water table ................................................... 59
5.2 Carbon dioxide..................................................................................................................... 62
5.2.1 Vegetation composition and spatial variation in CO2 exchange................................... 62
5.2.2 Changes in CO2 exchange after draining the beaver pond ........................................... 63
5.2.2.1 ER responses to lowered water levels .................................................................. 64
5.2.2.2 GEP responses to lowered water levels ................................................................ 66
5.3 Methane ............................................................................................................................... 70
5.3.1 Changes in CH4 flux after draining the beaver pond .................................................... 70
5.3.2 Abiotic correlates of methane flux ............................................................................... 71
5.3.3 Biotic correlates of methane flux ................................................................................. 73
Chapter 6: Summary & Conclusions .......................................................................................... 76
References ..................................................................................................................................... 80
Appendix ....................................................................................................................................... 87
vi
List of Figures
Figure 1: Aerial photo of the Mer Bleue beaver pond margin in August 2012 showing the location of the mire
expanse, forest edge, beaver dam and transects T1, T2 and T3. Source: Google Earth. ............................................. 19
Figure 2: Ordination plot of the non-metric multidimensional scaling analysis (NMdS) examining the strength of
associations between 33 plant species. Stress = 0.097. Five species groups are identified, corresponding to a water
table depth gradient: bog, bog margin, pond margin, mudflat and open water............................................................ 30
Figure 3: Biplot of the canonical correspondence analysis (CCA) examining the strength of associations between
environmental variables (water table (WT) mean, min and range, elevation (z), electrical conductivity (EC) and pH)
and 33 plant species. 2 primary axes accounted for 53.5% and 21.6% (75.1% combined). Four species groups are
identified, corresponding to a water table depth gradient: bog, bog margin, pond margin, and mudflat/open water. . 31
Figure 4: Relationship between species abundance and water table depth for eleven major species along the Mer
Bleue bog –margin gradient. Data is from both seasons combined, curve fit is a loess smooth function with Gaussian
weight. Species are listed in order of optima (dry to wet) and water table is in cm below the peat surface. ............... 33
Figure 5: Vascular and non-vascular green area of vegetation groups along the Mer Bleue bog – margin gradient for
2012 and 2013. ............................................................................................................................................................ 36
Figure 6: Results of one-way analysis of variance analyzing weekly CO2 flux measurements of NEE max, GEPmax,
and ER across vegetation groups and years. NEE max and GEPmax are calculated for PAR > 1000 μmol m-2 s-1. Data
are means with standard error. Asterisks denote significant differences (p < 0.05) between years from post hoc
comparison of means using Tukey’s honest significance test. .................................................................................... 38
Figure 7: Relationships between NEEmax (μmol m-2 s-1) and GEPmax (μmol m-2 s-1) with total green area (GA). Data
are seasonal averages (June – August) for 2012 and 2013. NEEmax and GEPmax are calculated for PAR > 1000 μmol
m-2 s-1. .......................................................................................................................................................................... 40
Figure 8: Seasonal variation in NEE (μmol m-2 s-1) for all vegetation groups from May – September 2012 (filled
circles) and 2013 (open circles). Variation in daily fluxes is mainly due to variation between vegetation groups and
differences in light levels between measurements. ...................................................................................................... 41
Figure 9: Relationship between NEE (μmol m-2 s-1) and PAR (μmol m-2 s-1) for eight* vegetation groups along the
Mer Bleue bog- margin gradient in 2012 and 2013, fitted with a rectangular hyperbola equation. ............................ 43
Figure 10: Relationships between ER (μmol m-2 s-1), water table position below the peat surface (WT, cm) and peat
temperature at 10 cm depth (oC) for graminoid, moss and shrub-dominated sites. Data are weekly averages from
May – September for 2012 (filled circles) and 2013 (open circles). Negative ER indicates the release of CO 2 to the
atmosphere. .................................................................................................................................................................. 46
Figure 11: Relationship between GEPmax (μmol m-2 s-1) and WT (cm below the peat surface) for hummock, hollow,
margin, bog and pond moss vegetation groups along the Mer Bleue bog- margin gradient in 2012 and 2013. Data are
weekly averages from June – August for 2012 (filled symbols) and 2013 (open symbols). Polynomial regression
coefficients (R2) for hummock, hollow, margin, bog and pond moss are 0.632, 0.588, 0.342, 0.321 and 0.512
respectively. Linear regression coefficients (R2) for hummock, hollow, and margin are 0.336, 0.725 and 0.474
respectively. All polynomial and linear regressions are significant at p < 0.01. ......................................................... 47
Figure 12: Seasonal average CH4 flux (mg m-2 d-1) for vegetation groups from May to August 2012 and 2013 and
the percentage change between years. Vertical lines are standard error. ..................................................................... 50
Figure 13: Seasonal patterns of growing season CH4 flux in 2012 and 2013 for bog, margin and pond vegetation
groups. Vertical lines are standard error. ..................................................................................................................... 51
Figure 14: Relationship between CH4 and water table depth (WT) for all collars in 2012 and 2013. Data are
instantaneous fluxes, CH4 is log-transformed, and WT is measured in cm below the peat surface. ........................... 53
Figure 15: Relationship between CH4 flux (mg m-2 d-1) and graminoid green area(m2 m-2) for all vegetation groups
in 2012, 2013 and both years combined. Data are seasonal averages, CH 4 is log-transformed, vertical lines are
standard errors. ............................................................................................................................................................ 53
Figure 16: Relationship between seasonal CH4 flux (mg m-2 d-1) and water table depth for shrub, moss and
graminoid plant functional types in 2012 and 2013. Linear regressions are for both years combined. Data are
seasonal averages, CH4 is log-transformed, and WT is measured in cm below the peat surface. ............................... 54
Figure 17: Relationships between CH4 flux (mg m-2 d-1) and GEPmax (μmol m-2 s-1) for nine vegetation groups from
2012 and 2013 combined. Data are monthly averages and CH 4 is log-transformed. Linear regression: R2 = 0.393, p
< 0.0001; Michaelis-Menten equation: R2 = 0.201, p < 0.0001. .................................................................................. 55
vii
List of Tables
Table 1: Climate data for the Mer Bleue Bog in 2012 and 2013 compared to climate normals (1970 – 2010) from
the Ottawa International Airport meteorological station, Ottawa, Ontario. .......................................................... 24
Table 2: Mean monthly and seasonal water table depth and peat temperature in 2012 and 2013 for bog, margin and
pond sites. Water table is in cm below the peat surface and temperature is measured at 10 cm depth in degrees
Celsius. Standard error is in parentheses. ......................................................................................................... 25
Table 3: Percent cover of 47 plant species along transects (T1, T2, T3) between 2012 and 2013. Asterisks (*)
indicate significant differences between years. Nomenclature follows the USDA online plants database
(http://plants.usda.gov) ................................................................................................................................... 27
Table 4: Six significant species assemblages identified by cluster analysis of the combined 2012 and 2013 datasets.
.................................................................................................................................................................... 29
Table 5: Optima and tolerances relative to water table depth for eleven major plant species along the bog – margin
gradient. Optima and tolerance values were calculated from the loess regression coefficients. Optima is calculated as
the water table depth corresponding to the maximum percent cover of a species and tolerance is calculated as the
total range of water table depths in which a species was present. Species are listed in order of optima (dry to wet)
and water table is in cm below the peat surface. ................................................................................................ 34
Table 6: Dominant plant species of the nine vegetation groups and average seasonal water table depth for 2012 and
2013. Water table is in cm below the peat surface and standard error are in parentheses. ...................................... 35
Table 7: Carbon use efficiency (CUE) values calculated as the ratio of NEE/GEP for the Mer Bleue Bog margin and
compared with reported CUE values for other ecosystems. ................................................................................ 39
Table 8: Rectangular hyperbola curve fit parameters for all sites and nine vegetation groups along the Mer Bleue
Bog margin from May – September, 2012* and 2013........................................................................................ 44
Table 9: Tolerance ranges and optima relative to water table position (WT) determined from unimodal relationships
between weekly GEPmax and WT and compared to tolerance ranges and optima determined from unimodal
relationships between species abundance and seasonal average WT. Optima is calculated as the water table depth
corresponding to the maximum GEPmax or percent cover of a species assemblage. Tolerance is calculated as the total
range of water table depths in which species photosynthesized or were present. Species assemblages are listed in
order of optima (dry to wet) and water table is in cm below the peat surface. ...................................................... 48
Table 10: Equations, R2, p-values and root mean square deviations (RMSE) for stepwise multiple regressions at
different timescales and spatial scales. logCH4 was the dependent variable with independent variables of water table
(WT), CO2 exchange (GEP and NEE), air and peat temperature (T_air, T_10), green area (GA), and graminoid area
(GrA). Variables not shown were not significant in the regression (α = 0.05). ..................................................... 57
viii
Acknowledgments
I would like to thank my co-supervisors Professor Tim Moore and Professor Nigel Roulet for
being tremendous mentors, encouraging me to grow as a research scientist, and for giving me the
opportunity to fall in love with peatlands. Tim, for your patience, humour and warm nature and
for consistently supporting me through both the excitements and challenges of the past two
years. Thank you for always keeping your door open and having faith in me. Nigel, for pushing
me to think critically and approach my problems, both scientific and otherwise, from a systems
point of view. Thank you for your generosity in supporting my graduate research at McGill, my
conference attendance opportunities and the pride you take in each of your students.
Great appreciation is given to Mike Dalva for his immeasurable help in the field and lab, and in
the hours spent travelling between the two. Moreover, I am indebted to my field assistants and
lab mates for their help, valuable discussions and great friendship. Thanks to Professors Elyn
Humphreys and Jill Bubier who were always available for advice and perspective, and who
shared their experiences and equipment with me. Permission granted by the National Capital
Commission to use Mer Bleue as a research site is greatly acknowledged.
I would like to particularly thank my parents and step-parents for their tremendous support that
knew no bounds during this process. Countless thanks to my incredible friends and family for
their love and encouragement and to my son, Aryeh, who is my greatest joy and motivator.
Finally, I would like to express my special appreciation and thanks to Professor Marcia
Waterway for introducing me to the ever-exciting world of botany and scientific research. You
were, and remain, an inspirational teacher and I thank you for guiding me through my
undergraduate studies and trusting me to work in your laboratory, greenhouse and the McGill
Herbarium. You showed me how to teach and be taught, and the years I spent as a teaching
assistant to your courses had a tremendous impact on me. Thank you for giving me my first
opportunity for independent research as an undergraduate student and for continuing your
academic and personal support throughout my graduate studies. You continue to encourage me
to work harder and aim higher and I am forever indebted to you.
9
Chapter 1: Introduction
1.1 Research context
Peatlands are a globally important and persistent carbon sink which currently store
approximately one-third of the global soil carbon pool (Gorham 1991). The accumulation of
carbon in peatlands is primarily due to an imbalance between photosynthetic carbon fixation and
carbon loss through respiration. As net carbon sinks, peatlands play a mitigating role in climate
warming induced by increased greenhouse gases such as carbon dioxide (CO2) and methane
(CH4). However, peatlands are sources of atmospheric CH4 and changes in environmental
conditions and vegetation composition have been shown to amplify carbon release at both fine
and large spatial scales. The release of this large carbon stock could act as a positive feedback to
climatic warming (Roulet et al. 2007). Climate models predict warmer and potentially drier
conditions in northern latitudes due to positive feedbacks between temperature, precipitation and
evapotranspiration, which may persistently lower peatland water tables (Roulet et al. 1992).
Such feedbacks are already driving an increase in interannual hydroclimate variability, leading to
more frequent drought and flood events (Seager et al. 2012). Thus, it is imperative to further
understand the relationship between water table dynamics, vegetation composition and carbon
cycling in these globally important ecosystems.
1.2 Research objectives
To better understand the relationship between water table position, vegetation and carbon
dynamics, I tested the short-term effects of a lowered water table on plant community structure,
CO2 and CH4 flux at a temperate bog margin flooded by a beaver pond. Many studies have
looked at the effects of a slow and persistent water table drawdown over a number of years in an
attempt to understand how these systems may respond to future drier conditions (Roulet et al.
1993; Hughes et al. 1999; Strack et al. 2004; Strack & Waddington 2007; Murphy et al. 2009). I
was interested in more rapid and drastic changes in water table, akin to the increases in
interannual hydroclimate variability that are predicted to become more prevalent. I chose a
temperate bog in eastern Ontario located closer to the southern limit of peatland distribution
since these ecosystems are likely to experience the effects of climate change differently than
their boreal and arctic counterparts. Further, I conducted the study at a flooded bog margin
because disturbances at the margin (e.g., drainage ditches, flooding, drought) potentially affect
10
the bog as a whole through changes in hydrology and plant species composition (Howie & Van
Meerveld 2013). The dynamic nature of the physical environment at the margin characterized by
natural fluctuations in nutrient conditions and water levels, coupled with flooding from beaver
activity, promotes unique species assemblages that cover a range of bog, fen and marsh species
along a natural water table gradient (McMaster & McMaster 2001; Little et al. 2012).
Additionally, CO2 and CH4 flux from beaver-impacted bog margins are poorly quantified, and
less is known about the drivers of flux variability in these systems compared to bogs and fens
proper. By draining the pond, I was able to test the effects of a sudden shift in hydrology at fine
and broad spatial scales in a natural system, while retaining the natural fluctuations in water table
that occur throughout the season.
My objectives were to 1) characterize the distribution of plant species along the bog – margin
gradient and identify the environmental correlates of species distributions; 2) quantify CO2 and
CH4 flux rates from the range of vegetation along the bog – margin gradient, to identify the
biotic and abiotic correlates on flux rates and compare their strength and relative importance
across spatial and temporal scales; 3) assess the short-term effects of a lowered water table on
plant community structure, CO2 and CH4 flux.
11
Chapter 2: Literature Review
2.1 Carbon cycling in terrestrial ecosystems
The removal of atmospheric carbon dioxide (CO2) by terrestrial ecosystems has become an
increasingly important area of study since the onset of global climatic warming caused by
increased greenhouse gases in the atmosphere. The large uncertainties in predicting how the
carbon sink function of terrestrial ecosystems will respond to future climate conditions highlights
the necessity of understanding carbon cycling dynamics in terrestrial ecosystems (Bridgham et
al. 2006; Ahlström et al. 2012). An ecosystem’s ability to sequester or release carbon is
represented by the difference between gross ecosystem photosynthesis (GEP) and the sum of
autotrophic and heterotrophic ecosystem respiration (ER). This difference is the net ecosystem
carbon exchange of an ecosystem, or simply the net ecosystem production (NEP) (Chapin et al.
2006). Since the carbon sink function of an ecosystem depends on the balance between GEP and
ER it is important to understand the relative responses of photosynthesis and respiration to
changing environmental conditions.
2.2 Peatlands and their role in the global carbon cycle
Peatlands are key features of the northern landscape and are critical in the global carbon cycle,
storing approximately one-third of the global soil carbon pool. Saturated, cool and acidic
conditions allow net primary production to exceed decomposition and carbon is accumulated
over time (Gorham 1991). Although peatlands are generally a net carbon sink, they are sources
of atmospheric CH4. Vegetation dynamics and environmental variables of water table depth and
temperature are typically implicated as the primary drivers of carbon cycling patterns in
peatlands.
2.3 Vegetation dynamics in peatlands
Many studies in peatlands have shown that plant species composition directly affects carbon
cycling through physiological and ecological differences among plant species and different plant
functional types result in different rates of, for example, photosynthesis, respiration, methane
transport and litter decomposition (Moore et al. 2002; Bubier et al. 2003b; Laine et al. 2007;
Riutta et al. 2007a; Strack & Waddington 2007; Urbanová et al. 2012). Graminoid species have
12
higher carbon dioxide and methane flux rates than dwarf shrubs and bryophytes, which are
related to differences in physiology such as the presence of aerenchyma tissue in graminoids
(Bubier & Moore 1994).
2.3.1 Distribution along environmental gradients
While vegetation can directly affect carbon fluxes, plant community composition is the result of
biotic and abiotic interactions. Variations in community composition can arise through
differential habitat, or niche, partitioning resulting from differences in species’ abilities to
compete and acquire resources. Species are distributed according to their physiological responses
and tolerances along ecological gradients of resource availability such as light, water,
temperature and nutrients (Whittaker 1967).
Many studies in peatlands have found that vegetation differences among peatland types (e.g.,
fen, bog) and spatial heterogeneity within peatlands are primarily controlled by water table depth
and water chemistry gradients (e.g., Glaser et al. 1990, Okland et al. 2001) . In a comparison of a
Swedish and an Italian bog, Bragazza et al. (2005) identified water table depth and acidityalkalinity as the main environmental gradients driving plant and habitat distributions. Laine et al.
(2007) found that plant community structure in a lowland blanket bog in Ireland was strongly
controlled by water level, and that many species had distinct optima in relation to median water
table depth, with species diversity and plant biomass being highest in intermediate hydrological
conditions. Contrastingly, at Mer Bleue, Bubier et al. (2006) found that plant biomass was
highest at lower water table depths. They also found unimodal relationships between mean
water table depth and species abundance and that hydrological and water chemistry gradients
explained species distribution patterns.
Hydrological disturbances, either natural or anthropogenic, can alter the structure and function of
peatlands by changing their vegetation composition. Several studies have shown that a lowered
water table induced by either experimental or agricultural drainage, can result in community
shifts through increasing vascular plant cover over long (Strack et al. 2006a; Urbanová et al.
2012; Munir et al. 2014) and short (Strack & Waddington 2007) timescales. Raising water levels
has been shown to either promote (Tuittila et al. 2000; Asada et al. 2005) or cause declines in
bog vegetation (Mitchell & Niering 1993; Kelly et al. 1997). Peat core studies indicate that
sudden changes in water table can change carbon accumulation rates. Wetter conditions can
13
encourage rapid peat accumulation through increased plant productivity, whereas a sudden drop
in water table can encourage ombrotrophy by separating the peatland from the groundwater,
limiting minerotrophic species (sedges, minerotrophic Sphagnum species) and encouraging the
growth of bog species (dwarf shrubs, ombrotrophic Sphagnum species) (Hughes & Barber 2003).
2.3.2 Dynamics of the bog margin
Although the importance of water table position on vegetation dynamics has been emphasized in
numerous peatland studies, less attention has been given to the ombrotrophic bog margin. The
margin, or “lagg”, is important for the bog functioning as a whole, as it buffers the bog from
mineral-rich waters and maintains the water mound in the bog (Damman 1986). The margin is
subjected to a range of nutrient conditions, low summer flows and high winter runoff, which
limits the vegetation to species adapted to fluctuating environmental conditions. The plant
species composition of the margin is influenced by regional gradients of temperature and
precipitation and by local gradients of water level and chemistry both from the bog itself and
from the surrounding landscape (Gorham 1950; Bragazza et al. 2005; Howie & Van Meerveld
2013). The bog - margin gradient, also termed the “mire margin – mire expanse”, is similar to
other gradients in peatlands, i.e. depth to water table and “poor-rich” gradients, but the bogmargin gradient extends beyond the peatland therefore covering a larger range of plant species,
moisture and nutrient conditions. Differences in pH, Ca:Mg ratios and/or indicator plant species
along the bog – margin gradient can be used to identify the margin and the mineral soil water
limit (Gorham 1950; Sjörs 1950; Howie et al. 2009).
Natural or anthropogenic disturbances at the margin (e.g., drainage ditches, flooding, drought)
potentially affect the bog as a whole through changes in hydrology and plant species composition
over time. Beaver activity is known to disturb peatland margins by drastically changing their
hydrologic regime (Rosell et al. 2005). Beaver disturbance follows a 10-15 year cycle of dam
initiation, abandonment and recolonization. Dam initiation raises water levels, creating a pond
and flooding the adjacent peatland margin. Dam abandonment allows water levels to recede,
exposing substrate and allowing species to colonize previously submerged areas, or emerge from
the buried seed bank. Dam recolonization regresses plant succession to earlier submerged states
(Little et al. 2012). Plant colonization following dam abandonment may follow alternate
successional pathways resulting in a variety of wetland ecosystems, from sedge meadow to
14
upland forest. McMaster & McMaster (2001) found that successional direction was contingent
not only on water level but on pre-dam hydrology and the extent, frequency and duration of
reflooding events.
2.4 Factors affecting carbon dioxide exchange in peatlands
Previous studies have shown that water table depth as well as temperature and vegetation
dynamics all affect peatland NEP, but their relative effects on the individual components of NEP
(i.e., GEP and ER) are inconsistent across different spatial and temporal scales.
Interannual comparisons have demonstrated that lower water tables can cause peatlands to switch
from net sinks to sources of CO2 at the ecosystem (Shurpali et al. 1995; Griffis et al. 2000;
Bubier et al. 2003b) and species assemblage scale (Moore et al. 2002; Riutta et al. 2007b;
Pelletier et al. 2011) due to increased ER. Lowering the water table increases the depth of the
aerobic peat layer and respiration rates can be much greater under oxic conditions because of
increased oxygen availability which enhances microbial decomposition (Crill et al. 1988). ER is
also strongly correlated with peat temperature and can be more sensitive to changes in
temperature than water level because of the metabolic activity of microorganisms (Lafleur et al.
2005).
The response of GEP to lowered water tables are more variable, with studies reporting increases
(Bubier et al. 2003a; Strack & Waddington 2007), decreases (Moore et al. 2002; Riutta et al.
2007b) or no change in GEP rates (Bubier et al. 2003b; Muhr et al. 2011) in both natural and
manipulation studies. Physiological and ecological differences between plant species and
functional groups result in different rates of GEP; consequently, differential responses to water
table fluctuations are frequently reported for various plant functional types and species. For
example, Strack & Waddington (2007) observed an increase in vascular plant cover in poor fen
hollows in response to water table reductions, resulting in higher GEP rates whereas Bubier et al.
(2003a) and Riutta et al. (2007) found reductions in sedge and moss GEP, respectively, due to
physiological constraints. Additionally, the same functional types or plant species can react
differently to water table fluctuations depending on whether they are growing in a bog or a fen
(Bubier et al. 2003a; Bubier et al. 2003b), highlighting the importance of not only antecedent
moisture conditions but on plant tolerances and local adaptations. Pelletier et al. (2011)
hypothesized that a unimodal relationship exists between GEP as well as ER and water table
15
position at the species assemblage scale. Based on this theoretical relationship, each species
assemblage would possess an optimum water table position for GEP and ER and deviations from
the optimum would decrease carbon exchange rates (Pelletier et al. 2011). It is apparent that
species ecological tolerances to their environment play an important role in their functional
response to changes in the physical environment. Examining CO2 exchange from a more
ecological perspective may shed light on the apparent inconsistencies observed between
peatlands and plant species in response to lowered water tables.
2.5 Factors affecting methane emissions in peatlands
CH4 is a potent greenhouse gas having twenty-five times greater radiative forcing than CO2 and
its concentration in the atmosphere has significantly increased over the last century (IPCC,
2007). Studies across different peatland types have shown that water table position exerts a
strong control over CH4 flux, with larger fluxes being associated with higher water tables
(Freeman et al. 1992; Roulet et al. 1993; Bubier et al. 2005; Moore et al. 2011). This is due in
part by the depth of anaerobic versus aerobic peat that is available for methanogenesis and
oxidation, respectively. Lower peatland water tables increase the aerobic peat layer, and many
studies have observed that lowering the water table reduces CH4 emissions through increased
CH4 oxidation, reduced CH4 production or both (Freeman et al. 1992; Roulet et al. 1993; Bubier
et al. 2005; Turetsky et al. 2008; Urbanová et al. 2013). On the other hand, some studies have
observed no change in CH4 emissions or even an initial increase in emissions after a water table
drawdown due to factors that counteract the effects of the lowered water table, such as peat
subsidence (Strack & Waddington 2007), the release of stored CH4 (Hughes et al. 1999; Brown
et al. 2014), flux hysteresis between rising and falling limbs of water tables (Moore & Roulet
1993a; Moore & Dalva 1993; Brown et al. 2014), or increased plant productivity (Strack et al.
2006b).
Vegetation plays an important role in CH4 dynamics and can enhance both the transport and
production of CH4. Supply and quality of available substrate greatly influence methanogenesis
and can differ between plant species and environments (Öquist & Svensson 2002; Ström et al.
2005; Wilson et al. 2009; Lai et al. 2014). Since photosynthesis drives the supply of root
exudates used in methanogenesis, changes in photosynthetic activity are expected to affect CH4
production. CH4 can bypass the oxic zone and be directly transported from plant roots to the
16
atmosphere via aerenchyma, which is specialized plant tissue that allows for gas transport.
Aerenchymous plants are able to grow in saturated conditions and are common in minerotrophic
peatlands and wetter areas of ombrotrophic peatlands (Whalen 2005).
CH4 production is also strongly correlated with peat temperature, with seasonal patterns of CH4
flux often being attributed to the seasonal pattern of peat temperature, coincident with the
metabolic activity of microorganisms (Crill et al. 1988; Moore et al. 2011; Lai et al. 2014). It
can be difficult to disentangle the effects of temperature from other variables on CH4 fluxes
under field conditions, as temperature increases throughout the progression of summer coincide
with seasonal patterns of plant phenology and productivity, which together control substrate
quality and availability (Öquist & Svensson 2002).
Although water table, temperature and vegetation dynamics have repeatedly been shown to be
strong drivers of CH4 flux variability in a range of peatlands, their strength and relative
importance depend on the scale of measurement and data aggregation (Treat et al. 2007; Moore
et al. 2011), and the role of vegetation at finer spatial scales is still being determined. Moreover,
the temporal relationship between water table and CH4 emissions throughout the growing season
is inconsistent between peatlands and vegetation, yielding positive (Bubier et al. 1995b),
negative (Treat et al. 2007) or more typically, no statistical relationships (Yavitt et al. 1993;
Strack et al. 2004; Strack & Waddington 2007).
17
Chapter 3: Methodology
3.1 Site description
Mer Bleue is a large (25 km2) raised ombrotrophic bog 10 km east of Ottawa, Ontario. Three
distinct drainage fingers drain Mer Bleue into the Ottawa River Valley from east to west. The
climate of the area is cool-continental with a mean annual temperature of 5.8oC and mean
growing season temperature (May-October) of 15.5oC. Mean annual precipitation is 910 mm and
mean growing season precipitation is 491 mm (Lafleur et al. 2005). Peat depths reach 5-6 m at
the center and thin to 1-2 m towards the margins. Typical hummock-hollow microtopography is
found throughout the bog and vegetation is dominated by dwarf shrubs such as Chamaedaphne
calyculata, Rhododendron groenlandicum (previously Ledum groenlandicum) and Kalmia
angustifolia. The surface layer is primarily Sphagnum capillifolium, Sphagnum magellanicum,
and Polytrichum strictum (Bubier et al. 2007). A number of beaver ponds exist on the Mer Bleue
peatland margin. Multiple flooding, abandonment and re-colonisation events have created
transition zones characterized by freshwater emergents such as Typha latifolia, various sedges
and floating Sphagnum (Bubier et al. 2003b).
3.2 Experimental setup
Measurements were made along the Mer Bleue margin in an area impacted by beaver ponds. In
May 2012, I established three transects that covered the major variation in vegetation and water
table depths including typical bog hummock-hollow microtopography, the peatland margin and
beaver pond vegetation. Boardwalks were constructed parallel to the transects to avoid damaging
the vegetation and to allow easier access for data collection. The beaver pond was drained in
October 2012 by inserting pipes into the dam sediment to encourage and maintain water flow.
Figure 1 shows a map of the study site and location of transects T1, T2 and T3.
3.2.1 Vegetation sampling sites
Square vegetation plots (61 cm x 61 cm) were permanently installed every 2.5 m along the 3
transects. T1 was closest to the beaver dam and contained 18 plots, T2 contained 17 plots and T3
contained 12 for a total of 47 vegetation plots.
18
3.2.2 Gas flux sampling sites
I selected nine vegetation groups to represent the range of plant species, microtopography and
water table depths along the three transects. Gas flux sampling sites were randomly distributed
along the transects, with 3 replicates of each vegetation group for a total of 27 sampling sites.
Collars (diameter 26 cm) were permanently installed to allow for repeated gas measurements in
the same location without disturbing the peat. I installed nine water level recorders with data
loggers to continuously measure water table position every hour from June – October 2012 and
from May – October, 2013. 3 recorders were installed per transect, located at the bog, margin
and pond.
mire expanse
beaver dam
forest edge
Figure 1: Aerial photo of the Mer Bleue beaver pond margin in August 2012 showing the location of the mire
expanse, forest edge, beaver dam and transects T1, T2 and T3. Source: Google Earth.
3.3 Vegetation sampling
3.3.1 Species abundance
I measured species abundance in late-July through mid-August in 2012 and 2013 for all
vegetation plots. I used the point-intercept method to estimate species percent cover for all plant
species in the 47 plots. Point intercept is an objective sampling method that is ideal for ground
cover and species that are less than 1 m in height (Bonham, 1989). For the gas flux sampling
sites (collars), the total percent cover for each plant species was determined visually in July 2012
and 2013. All species that did not represent 1% or more of the vegetation cover were recorded as
0.01%. Herbarium vouchers were collected for each species and placed in the McGill University
19
N
Herbarium. Nomenclature follows the USDA online plants database (http://plants.usda.gov).
Species names and their abbreviations are found in Appendix Table 1.
3.3.2 Green area
Total photosynthetic green area (GA, m2 m-2) was determined in each collar according to the
equations described in (Wilson et al. 2007) during the 2012 and 2013 peak growing seasons
(July-August). I counted the number of leaves of each vascular plant species present in each
collar and then multiplied this by the average leaf size in the collar. Non vascular green area was
determined by dividing total percent cover of each moss species by the surface area of the collar.
3.4 Carbon dioxide exchange measurements
Net ecosystem carbon dioxide exchange (NEE) is the balance between GEP (or GPP) and ER at
half-hour to decadal time scales (Wofsy et al. 1993) while NEP reflects the net carbon
accumulation by ecosystems over longer time scales (Randerson et al. 2002). I measured NEE
using the dynamic closed system chamber method every second week from June to September in
2012 and from May to September in 2013. The dynamic closed system chamber method involves
circulating air between a closed chamber and a gas analyzer. The linear rate of increase in CO2
concentration is used to calculate the flux (Rochette et al. 1992). CO2 concentrations were
determined in situ using a LI-COR 820 CO2 Gas Analyzer (LI-COR, Lincoln, Nebraska). The
LI-COR was zeroed and spanned with Ultra High Purity nitrogen gas (N2) and 450 ppm CO2,
respectively. Gas was sampled in the head space of a circular plexiglas chamber equipped with
water-circulating coils for cooling and a fan for air circulation. Cooling coils and fan controlled
chamber temperature and humidity within 1oC and 10% of ambient conditions, respectively. The
chambers were equipped with a removable top to allow the air in the chamber to equilibrate with
the ambient conditions before and after sampling. The chamber was placed upon a permanently
installed collar for 3 minutes, with CO2 concentration inside the chamber being measured every
10 seconds for the first minute and every 30 seconds for the remaining two minutes. Two
different chamber sizes were used to accommodate sites containing tall plants (e.g., Typha); 50.8
cm and 100.3 cm tall, both with a diameter of 26.7 cm.
Measurements were made under different light conditions: full light, half light and dark. Half
light was achieved by covering the chamber with a mesh cloth that effectively blocked 50% of
20
the incoming photosynthetically active radiation (PAR). PAR in µmol m−2 s−1 was measured
with an Apogee QMSW-SS Quantum Meter (Apogee Instruments, Logan, Utah). NEE was
measured under full and half light conditions and ecosystem respiration (ER) was measured
under dark conditions by covering the chamber with an opaque cloth. Gross ecosystem
photosynthesis (GEP) was calculated as the difference between NEE and ER. I adopt the
ecological convention that positive NEE values indicate uptake of CO2 by the ecosystem while
negative values indicate a release of CO2 to the atmosphere. Flux was calculated by linear
regression of the concentration change over the 3 minute sampling period and was converted
from mg m-2 s-1 to µmol m-2 sec-1. Fluxes without a significant regression correlation coefficient
(R2 > 0.80 at p < 0.05) were rejected. The rejection rate was less than 1%.
3.5 Methane flux measurements
CH4 flux measurements were made every second week from May to September in 2012 and
2013 using the static closed chamber technique (Bubier et al. 1995b). Opaque chambers of
various sizes depending on vegetation height (25.0 cm, 40.0 cm and 100.3 cm tall, all with a
diameter of 26.7 cm) were placed on collars for 30 minutes. Four air samples were taken every
10 minutes into a 10 mL syringe to measure the change in concentration of CH4 inside the
chamber head space. Samples were brought back to the laboratory and analysed within 48 hours
of collection. CH4 concentrations were determined using a MINI 2 gas chromatograph (flame
ionization detector model) (Shimadzu Scientific Instruments 1982). CH4 samples were calibrated
using 5.00 ppm as the standard with an N2 carrier gas at 40 ml/min. The detector is a flame
ionization detector (FID) at 100o C, while the column (Porapak Q) is at 50o C. The column is
stainless steel and 1/8 inch in diameter with a 80/100 mesh. Fluxes were calculated by linear
regression of the concentration change between the 4 air samples and fluxes without a significant
regression correlation coefficient (R2 > 0.80 at p < 0.05). There were, however, cases of zero flux
where concentrations did not change over time significantly enough to yield a correlation. The
rejection rate was less than 1% and zero fluxes were retained.
3.6 Ancillary measurements
Water table depth of the 47 vegetation plots was measured manually in 2 cm diameter PVC wells
every second week from June – October 2012 and from May – October 2013. Porewater pH was
measured in August 2012 and 2013 using a Hanna HI98103 portable pH meter. Porewater
21
electrical conductivity was measured in August 2013 using a Hanna Primo EC-4 portable EC
meter (Hanna Instruments, Woonsocket, Rhode Island). Elevation was measured by a
differential global positioning system (DGPS) (5800, Trimble Navigation Ltd, Dayton, Ohio) in
September, 2013. During each flux measurement, water table was measured manually adjacent
to each of the collars in 2 cm diameter PVC wells. Air temperature and peat temperature at 5 cm
and 10 cm below the peat surface were measured manually with a thermocouple.
3.7 Data processing and analysis
I examined the relationships among species’ abundances and environmental variables using
multivariate techniques. I classified a matrix of 33 species into different vegetation groups using
Ward’s agglomerative clustering based on plot similarity using Hellinger’s distance in species
abundance. I examined the groupings among vascular and non-vascular (bryophyte) species
using non-metric multidimensional scaling (NMdS) on the Hellinger-transformed abundance
matrix. I related species associations with environmental variables using canonical
correspondence analysis (CCA) and forward selection using the Aikaike criterion was used to
identify significant environmental variables to use in the CCA model (Legendre & Legendre
2012). All calculations were performed on 2012 and 2013 data both separately and combined in
R 3.0.1 (R Development Core Team 2013).
I estimated individual vascular and bryophyte plant species optima and tolerances relative to
mean seasonal water table position using a loess smooth function with Gaussian weight
(Cleveland 1979; Cleveland & Loader 1996). This technique assumes a Gaussian (unimodal)
response of species to environmental variables. Optima and tolerance values were calculated
from the loess regression coefficients; optima was calculated as the water table depth
corresponding to the maximum percent cover of a species and tolerance was calculated as the
total range of water table depths in which a species was present. Analyses were run using species
percent cover for 2012 and 2013 data combined in JMP v10 (SAS Institute Inc., Cary, North
Carolina).
I examined the relationships between CO2 exchange (NEE, GEP, ER), CH4 flux and independent
variables using linear and non-linear regressions and pairwise Pearson correlation analysis.
Statistical differences between years were determined using one-way analysis of variance
22
(ANOVA). I logarithmically transformed CH4 fluxes (log10 mg m-2 d-1 + 20) before statistical
analysis to reduce non-normality and to enable all data to be used.
The relationship between NEE and PAR was examined using a rectangular hyperbola (equation
1) using a curve-fitting technique computed in SigmaPlot 12.0, from Systat Software, Inc., San
Jose California USA. α is the initial slope of the curve and is a measure of photosynthetic
efficiency, PAR is the incoming photosynthetically active radiation in µmol m−2 s−1, GEPmax
(µmol m−2 s−1) is the asymptotic approach to the maximum rate of gross ecosystem
photosynthesis and ER (µmol m−2 s−1) is the y axis intercept at PAR = 0 and a measure of
ecosystem respiration (Bubier et al. 2003b).
Equation 1: NEE = [(α * PAR * GEPmax) / ((α * PAR)+ GEPmax)] + ER
23
Chapter 4: Results
4.1 Interannual variability in environmental conditions
Seasonal dynamics in the average daily air temperature and precipitation were significantly
different (p < 0.05) between years during the growing season (May – September). 2012 was
warmer and received less precipitation in June and July compared to 2013 and 2013 had less
precipitation in May than both 2012 and the climate normal (1970 – 2010) (Table 1). Draining
the beaver pond resulted in a decrease in surface water, peat water table depth and pond
dimensions. Monthly average water levels were lower (p < 0.05) in 2013 particularly in May and
August in the bog, margin and pond (Table 2). 2012 water levels were drastically reduced in
mid-July during a hot period with low precipitation, followed by a rise in water levels in August.
In 2013, water levels were less variable throughout the season; water levels were low in May,
rose in June during higher than average precipitation, then dropped in early July and continued to
recede into August. Monthly peat temperature at 10 cm depth was also significantly lower (p <
0.001) in 2013 for all months in all sites (Table 2).
Table 1: Climate data for the Mer Bleue Bog in 2012 and 2013 compared to climate normals (1970 – 2010) from
the Ottawa International Airport meteorological station, Ottawa, Ontario.
Air Temperature (oC)
Precipitation (mm)
Period
Normal
2012
2013
Normal
2012
2013
May
86.6
82.7
77.7
13.5
15.5
15.1
June
92.7
68.9
158.5
18.7
19.3
17.3
July
84.4
16.6
57.6
21.2
22.6
21.5
August
83.8
95.0
74.9
19.9
20.9
19.5
September
92.7
143.3
78.3
15.3
14.4
13.8
Mean
88.0
81.3
89.4
17.7
18.6
17.5
24
Table 2: Mean monthly and seasonal water table depth and peat temperature in 2012 and 2013 for bog, margin and
pond sites. Water table is in cm below the peat surface and temperature is measured at 10 cm depth in degrees
Celsius. Standard error is in parentheses.
Water Table Depth (cm)
Period
BOG
MARGIN
POND
Peat Temperature (oC)
2012
2013
2012
2013
May
18.2 (4.0)
28.3 (1.7)
21.2 (0.4)
9.7 (0.7)
June
21.5 (1.6)
23.7 (1.4)
20.3 (0.6)
13.5 (0.5)
July
29.2 (1.6)
32.0 (1.2)
20.2 (0.4)
17.2 (0.4)
August
20.1 (3.2)
36.7 (2.0)
19.2 (0.3)
16.7 (0.5)
Mean
22.3 (1.1)
30.2 (0.9)
20.2 (0.4)
14.3 (0.5)
May
1.7 (0.7)
11.3 (0.7)
21.3 (0.4)
8.5 (0.4)
June
3.4 (0.6)
9.4 (0.7)
20.4 (0.5)
11.4 (0.3)
July
12.6 (1.4)
17.4 (0.7)
20.3 (0.3)
16.5 (0.3)
August
5.3 (1.0)
19.2 (1.1)
19.4 (0.2)
15.8 (0.3)
Mean
5.6 (0.7)
14.3 (0.6)
20.4 (0.3)
13.0 (0.3)
May
0 (0)
6.5 (0.8)
21.4 (0.3)
10.4 (0.4)
June
0.2 (0.2)
4.5 (0.9)
22.2 (0.6)
13.7 (0.4)
July
4.6 (0.8)
13.3 (1.0)
20.7 (0.3)
18.2 (0.3)
August
2.1 (1.4)
15.7 (1.6)
19.1 (0.4)
16.4 (0.3)
Mean
1.7 (0.5)
10.0 (0.8)
20.8 (0.4)
14.7 (0.4)
4.2 Vegetation
4.2.1 Plant community structure along the bog-margin gradient
The three transects differed slightly from each other in species composition and distribution in
2012 and 2013 (Table 3). Cluster analysis segregated 33 species into six distinct groups
corresponding to topographic features: hummock (HM, n = 8), hollow (HL, n = 6), bog margin
(BM, n = 5), pond margin (PM, n = 6), mud flat (MF, n = 5), and open water (OW, n = 3), each
with characteristic species (Table 4).
HM and HL are typical ombrotrophic bog associations located at the northern portion of each
transect characterized by dwarf shrubs and a continuous Sphagnum moss ground layer.
Hummocks are perched higher above the water table than hollows, and are characterized by
vascular and bryophyte species adapted to drier conditions (e.g., Gaylussacia baccata,
25
Sphagnum capillifolium) than hollows (e.g. Rhododendron groenlandicum, Sphagnum
angustifolium).
BM is characterized by a continuous layer of Sphagnum fallax and a co-dominant canopy of
Chamaedaphne calyculata and Carex oligosperma. BM is where ombrotrophic and
minerotrophic zones mix and the water table is at or near the peat surface. BM is absent along
T3, where the bog instead transitions sharply into the pond margin.
PM is characterized by minerotrophic graminoid species (e.g., Typha angustifolia, Juncus
effusus, Calamagrostis canadensis) and a floating mat of Sphagnum cuspidatum. Typha
angustifolia is dominant and Juncus effusus occurs in high abundance along T1 and T2 in patchy,
dense stands but is absent along T3.
MF is only present along T2 and is characterized by minerotrophic graminoid and herbaceous
species (e.g., Bidens frondosa, Dulichium arundinaceum) that occupy exposed pond sediments
and mudflats created by decomposing Typha rhizomes and debris. OW species are floating
aquatics Brasenia schreberi, Hydrocharis morsus-ranae, and Utricularia macrorhiza (Tables 3
& 4).
26
Table 3: Percent cover of 47 plant species along transects (T1, T2, T3) between 2012 and 2013. Asterisks (*)
indicate significant differences between years. Nomenclature follows the USDA online plants database
(http://plants.usda.gov)
T1 2012
T1 2013
T2 2012
T2 2013
T3 2012
T3 2013
Acer rubrum
0
0.08
0
0
0
0.04
Betula populifolia
0
0.14
0
0
0.16
0.17
1.57
2.60
0.71
0.80
0.43
1.31
0
0
0
0
1.01
0.75
Chamaedaphne calyculata
24.56
21.68
21.60
24.40
27.65
32.09
Kalmia angustifolia
0.65
0.90
0.91
0.92
0.96
1.11
Kalmia polifolia
0.08
0.07
0.13
0.15
0
0
Vaccinium oxycoccus
0.34
0.28
0.10
0.15
0
0
0.82
1.50
0.85
0.82
0
0
0
0
0.81
0.92
0
0
Photinia melanocarpa
0.72
0.66
0.14
0.39
0.28
0.26
Rhododendron groenlandicum
2.85
2.57
1.69
1.44
6.16
5.87
Spiraea tomentosa
0.07
0.16*
0
0.11*
0
0
Vaccinium angustifolium
2.00
2.39
1.86
1.65
1.67
1.31
Vaccinium corymbosum
4.70
5.52
4.67
6.31
3.34
3.62
Vaccinium myrtilloides
1.10
1.65
0.32
0.36
0.84
0.74
0
4.86*
0.05
1.88*
4.53
8.01*
Carex oligosperma
7.27
7.29
13.69
14.39
4.19
5.80
Carex trisperma
2.17
3.68
0
0
0
0
Dulichium arundinaceum
0
0
0.40
0.19
0.19
0.56
Eleocharis spp.
0
0
2.24
2.31
0
0
Eriophorum virginicum
0
0.65*
0.19
0.95*
0
0
2.13
3.89
4.62
3.48
0
0
0
2.43*
3.13
5.94*
0
0
Sparganium americanum
1.23
3.87*
0.25
1.88*
0
0
Typha angustifolia
10.24
9.40
6.62
7.35
18.96
14.73*
0
0
0.37
0.75
0
0.14
8.65
1.83*
2.49
0.83*
1.20
0*
Trees
Larix laricina
Evergreen shrubs
Andromeda polifolia
Deciduous shrubs
Gaylussacia baccata
Nemopanthus mucronatus
Graminoids
Calamagrostis canadensis
Juncus effusus
Scirpus cyperinus
Herbs
Bidens frondosa
Brasenia schreberi
27
Calla palustris
0
0.19
0
0
1.01
3.49
Cypripideum acaule
0
0
0.09
0.13
0
0
0.11
0.01*
0.11
0*
0
0
Polygonum sagittatum
0
0.27*
0
0.22*
0
0.13*
Triadenum fraseri
0
0.15*
0.27
0.93*
0
0
1.22
0*
2.29
0*
0
0
0
0.37*
0
0.25*
0
0
Polytrichum strictum
0.12
0.18
0.23
0.21
0.36
0.30
Sphagnum angustifolium
0.71
0.67
0.69
0.77
3.54
3.33
Sphagnum capillifolium
2.45
2.12
1.99
1.67
1.56
1.45
Sphagnum cuspidatum
3.72
5.91*
6.67
9.35*
10.15
13.87*
Sphagnum fallax
3.96
1.12*
3.96
0.82*
4.28
1.88*
Sphagnum magellanicum
1.91
1.20
1.85
2.43
2.47
2.11
Sphagnum papillosum
0.09
0.02
0.76
0.14*
0
0
Hydrocharis morsus-ranae
Utricularia macrorhiza
Moss
Drepanocladus fluitans
28
Table 4: Six significant species assemblages identified by cluster analysis of the combined 2012 and 2013 datasets.
Hummock
Pond margin
Gaylussacia baccata
Calamagrostis canadensis
Kalmia angustifolia
Calla palustris
Photinia melanocarpa
Juncus effusus
Polytrichum strictum
Sparganium americanum
Sphagnum capillifolium
Sphagnum cuspidatum
Vaccinium angustifolium
Typha angustifolia
Vaccinium corymbosum
Vaccinium oxycoccus
Hollow
Mudflat
Larix laricina
Bidens frondosa
Nemopanthus mucronatus
Dulichium arundinaceum
Rhododendron groenlandicum
Eleocharis spp.
Sphagnum angustifolium
Scirpus cyperinus
Sphagnum magellanicum
Triadenum fraseri
Vaccinium myrtilloides
Bog margin
Open water
Carex oligosperma
Brasenia schreberi
Chamaedaphne calyculata
Hydrocharis morsus-ranae
Eriophorum virginicum
Utricularia macrorhiza
Sphagnum fallax
4.2.2 Indirect and direct gradient analysis
Indirect gradient analysis using NMdS ordination showed that species were primarily distributed
along a water table depth gradient (Figure 2). Four groups identified by cluster analysis were
clearly represented in the NMdS ordination; BM, PM, MF and OW. Bog species were grouped
together but HM and HL were only slightly differentiated. Chamaedaphne calyculata and
Sphagnum fallax were in both bog and BM groups (Figure 3). Forward selection identified six
environmental variables as significant: water table mean, minimum and range, elevation (z),
electrical conductivity (EC) and pH. Direct gradient analysis with environmental variables using
CCA showed that variables related to water table and water chemistry explained 53.5% and
29
21.6% respectively of the variance in observed species distributions (75.1% cumulatively). The
CCA species groups were identical to the NMdS groupings except Chamaedaphne calyculata
and Sphagnum fallax were distinctly in the BM group and OW was not distinct from MF (Figure
3). For both NMdS and CCA analyses, the groupings and proportion of variation explained did
not differ between years or when the data was combined for both years. Results shown are for
both years combined.
Figure 2: Ordination plot of the non-metric multidimensional scaling analysis (NMdS) examining the strength of
associations between 33 plant species. Stress = 0.097. Five species groups are identified, corresponding to a water
table depth gradient: bog, bog margin, pond margin, mudflat and open water.
30
Figure 3: Biplot of the canonical correspondence analysis (CCA) examining the strength of associations between
environmental variables (water table (WT) mean, min and range, elevation (z), electrical conductivity (EC) and pH)
and 33 plant species. 2 primary axes accounted for 53.5% and 21.6% (75.1% combined). Four species groups are
identified, corresponding to a water table depth gradient: bog, bog margin, pond margin, and mudflat/open water.
31
4.2.3 Plant community responses to a lowered water table
Bog vegetation (HM and HL) along all three transects did not differ significantly between years
(Table 3). BM species Sphagnum fallax and Sphagnum papillosum significantly declined in 2013
while PM species Sphagnum cuspidatum significantly increased in 2013. PM and MF
minerotrophic graminoids (e.g., Calamagrostis canadensis, Scirpus cyperinus and Sparganium
americanum) and herbaceous species (e.g., Bidens frondosa, Polygonum sagittatum, Triadenum
fraseri) significantly increased in 2013. OW floating aquatic species Brasenia schreberi and
Hydrocharis morsus-ranae significantly declined and Utricularia macrorhiza was absent in
2013. The majority of OW plots were replaced by MF or PM vegetation in 2013.
4.2.4 Plant species optima and tolerances relative to mean water table position
Eleven major plant species were selected to represent the variation in vegetation and water table
depths along the bog – margin gradient. The relationship between species percent cover and
mean water table position differed among species (Figure 4). Bog, margin and pond species
occupied the drier, mesic and wet ends of the gradient, respectively. Sphagnum capillifolium had
the driest optimum (-31 cm) while T. angustifolia had the wettest optimum (0 cm).
Chamaedaphne calyculata had the widest tolerance (52 cm) while S. cuspidatum, J. effusus and
T. angustifolia had the narrowest tolerances (16 - 25 cm). Species with similar tolerances (e.g.,
R. groenlandicum, V. corymbosum, S. magellanicum) had different optima. Similarly, species
with similar optima (e.g., V. corymbosum, K. angustifolia, C. calyculata) had different tolerances
(Table 5).
32
Figure 4: Relationship between species abundance and water table depth for eleven major species along the Mer
Bleue bog –margin gradient. Data is from both seasons combined, curve fit is a loess smooth function with Gaussian
weight. Species are listed in order of optima (dry to wet) and water table is in cm below the peat surface.
33
Table 5: Optima and tolerances relative to water table depth for eleven major plant species along the bog – margin
gradient. Optima and tolerance values were calculated from the loess regression coefficients. Optima is calculated as
the water table depth corresponding to the maximum percent cover of a species and tolerance is calculated as the
total range of water table depths in which a species was present. Species are listed in order of optima (dry to wet)
and water table is in cm below the peat surface.
Species
Max* Optimum (cm)
Sphagnum capillifolium
16
-31
Rhododendron groenlandicum
29
-29
Chamaedaphne calyculata
83
-28
Kalmia angustifolia
13
-26
Vaccinium corymbosum
65
-24
Sphagnum fallax
19
-19
Sphagnum magellanicum
16
-18
Carex oligosperma
68
-8
Juncus effusus
37
-4
Sphagnum cuspidatum
71
-3
Typha angustifolia
70
0
*Maximum abundance in percent cover
Tolerance (cm)
50
44
52
46
46
40
48
38
25
16
20
R2
0.48
0.26
0.23
0.24
0.23
0.19
0.25
0.35
0.52
0.12
0.28
4.2.5 Green area
Dominant plant species and seasonal average water table depth for the nine vegetation groups are
described in Table 6. Seasonal summaries of green area (GA) and graminoid area (GrA) for each
collar are presented in Appendix A, Table 2. Total GA ranged from 0.8 to 14.9 m2 m-2 in 2012
and from 0.9 to 9.0 m2 m-2 in 2013 with larger values found in sites containing minerotrophic
graminoids (Carex, Rush and Typha) and sites with a high cover of ericaceous shrubs. Total GA
was significantly smaller (p < 0.05) in 2013 for hummock, mesic and wet margin, rush and typha
sites. Total GA was larger (p <0.05) in 2013 for shrub margin sites and was not significantly
different between years for hollows and moss (bog and pond). GrA (Carex, Juncus and Typha
species) ranged from 0 to 14.6 m2 m-2 in 2012 and from 0 to 7.3 m2 m-2 in 2013, with the largest
area in margin and pond sites. GrA declined in 2013 in mesic and wet margin sites and in rush
and typha sites but increased in shrub margin sites (Figure 5).
34
Table 6: Dominant plant species of the nine vegetation groups and average seasonal water table depth for 2012 and
2013. Water table is in cm below the peat surface and standard error are in parentheses.
Dominant Plant Species
Chamaedaphne calyculata, Kalmia
Hummock
Water Table (cm)
2012
35.8 (1.6)
2013
38.8 (1.5)
2012
22.8 (1.6)
2013
28.4 (1.1)
2012
16.5 (0.9)
2013
22.2 (1.0)
Carex oligosperma, C. calyculata,
2012
12.5 (1.3)
S. fallax
2013
17.9 (0.9)
Carex oligosperma, C. calyculata,
2012
6.8 (1.0)
2013
17.4 (0.8)
2012
1.8 (0.6)
2013
9.9 (0.8)
2012
2.6 (0.7)
2013
10.9 (0.9)
2012
0.4 (0.2)
2013
9.4 (1.0)
2012
6.0 (1.3)
2013
12.9 (2.0)
angustifolia, Vaccinium corymbosum,
Sphagnum capillifolium,
Polytrichum strictum
Hollow
C. calyculata, Rhododendron
groenlandicum, Sphagnum angustifolium,
S. magellanicum
Bog moss
Mesic margin
Wet margin
Sphagnum fallax, S. magellanicum
S. fallax,
S. cuspidatum
Shrub margin
Pond moss
Rush
Typha
C. calyculata, S. cuspidatum
S. cuspidatum
Juncus effusus
Typha angustifolia
35
Figure 5: Vascular and non-vascular green area of vegetation groups along the Mer Bleue bog – margin gradient for
2012 and 2013.
36
4.3 Carbon dioxide
4.3.1 Changes in CO2 exchange after draining the beaver pond
4.3.1.1 NEE, GEP, and ER
Seasonal averages of net ecosystem production under light saturating conditions where PAR >
1000 μmol m-2 s-1 (NEEmax), gross ecosystem photosynthesis under light saturating conditions
(GEPmax) and ecosystem respiration (ER) differed between sites and between years, with a larger
variance in fluxes observed in the pre-drained 2012 season. Average seasonal NEEmax from May
to August for each collar ranged from -1.3 to 20.7 μmol m-2 sec-1 in 2012 and from -0.8 to 9.0
μmol m-2 sec-1 in 2013. Average seasonal GEPmax ranged from 0.5 to 29.5 μmol m-2 sec-1 and
from 0.6 to 13.8 μmol m-2 sec-1 in 2012 and 2013, respectively. Average seasonal ER ranged
from -2.2 to -10.8 μmol m-2 sec-1 and from -1.0 to -5.1 μmol m-2 sec-1 in 2012 and 2013,
respectively. 2012 and 2013 seasonal summaries of CO2 exchange for each collar are presented
in Appendix A, Table 3.
When the collars were grouped according to their plant functional type (shrub, graminoid or
moss-dominated), NEEmax was significantly smaller (p < 0.05) in the drained 2013 season for
graminoids, significantly larger for moss and not significantly different for shrubs. GEPmax and
ER were significantly smaller (p < 0.05) in the drained 2013 season for both shrubs and
graminoids, but not for mosses (Figure 6). For individual vegetation groups, NEEmax was
significantly smaller (p < 0.05) in the drained 2013 season for margin (mesic, wet, shrub), rush
and typha collars, was not significantly different for hummocks and hollows and was larger in
both bog and pond mosses (Figure 6). GEPmax was smaller in the drained 2013 season for all
groups except bog moss (no difference) and pond moss (slightly larger). 2013 ER rates were
significantly smaller for all vegetation groups in the drained season (Figure 6). Despite
differences in CO2 flux magnitude and variance, the relationships among sites was consistent
between years, with flux relationships following patterns in green area more closely than water
table depth.
37
Figure 6: Results of one-way analysis of variance analyzing weekly CO2 flux measurements of NEEmax, GEPmax,
and ER across vegetation groups and years. NEEmax and GEPmax are calculated for PAR > 1000 μmol m-2 s-1. Data
are means with standard error. Asterisks denote significant differences (p < 0.05) between years from post hoc
comparison of means using Tukey’s honest significance test.
4.3.1.2 Carbon use efficiency
We calculated the carbon use efficiency (CUE) as the ratio of seasonal average NEEmax to
GEPmax (i.e., NEEmax / (NEEmax – ER)) for the nine vegetation groups in both seasons. CUE
values ranged from 0 to 0.7 in 2012 and from 0.2 to 0.6 in 2013. CUE values were larger in 2013
for all sites except shrub margin and typha. Our CUE values for shrub-dominated sites along the
bog – margin moisture gradient fall in between the reported range for tropical and boreal forests
and our minerotrophic graminoid sites fall within the reported range for cattail marshes (Table
7).
38
Table 7: Carbon use efficiency (CUE) values calculated as the ratio of NEE/GEP for the Mer Bleue Bog margin and
compared with reported CUE values for other ecosystems.
Site
Ratio
2012
2013
Mean
Study
Bog Moss
NEE/GEP
0.04
0.24
0.14
this study
Pond Moss
NEE/GEP
0.00
0.32
0.16
this study
Bog Hummock
NEE/GEP
0.14
0.44
0.29
this study
Bog Hollow
NEE/GEP
0.28
0.43
0.35
this study
(Americas & Asia)
NPP/GPP
-
-
0.35
Chambers et al. (2004), Malhi (2012)
Boreal Forests
NPP/GPP
-
-
0.39
Ryan et al. (1997)
Bog Margin
NEE/GEP
0.39
0.54
0.47
this study
Temperate Forests
NPP/GPP
-
-
0.54
Curtis et al. 2005, DeLucia et al. (2005)
Cattail Marshes
NPP/GPP
-
-
0.60
Bonneville et al. (2008), Rocha et al. (2009)
Rush
NEE/GEP
0.58
0.64
0.61
this study
Typha
NEE/GEP
0.58
0.72
0.65
this study
Crops
NPP/GPP
0.65
Van Iersel (2003)
Tropical Forests
4.3.2 Factors contributing to the spatial variation in CO2 exchange
Pairwise Pearson correlations were calculated between NEEmax, GEPmax, ER and independent
variables of GA, WT, T_10 and T_air at fine and broad spatial and temporal scales. Spatial
scales included averages of all sites combined, three plant functional types and nine vegetation
groups. Temporal scales included weekly, monthly and seasonal (June – August) averages. A
summary of Pearson correlations is presented in Appendix A, Table 4.
4.3.2.1 Vegetation
Vegetation composition was the strongest controller of spatial variation in CO2 exchange along
the bog-margin moisture gradient. The total green area of each collar was linearly correlated with
seasonal mean NEEmax, GEPmax and ER and was related to the species composition. GA was the
strongest correlate of seasonal average NEEmax and GEPmax in 2012 and 2013, although the R2
was smaller in 2013 (Figure 7).
4.3.2.2 Water table
Due to high productivity and plant biomass in both dry and wet sites owing to the presence of
shrubs and graminoids, respectively, water table depth was not a significant factor controlling
39
NEEmax or GEPmax spatial variation. However, WT was an important factor determining spatial
variation in ER rates, with drier sites closer to the bog respiring more than wet sites in the margin
and beaver pond.
Figure 7: Relationships between NEEmax (μmol m-2 s-1) and GEPmax (μmol m-2 s-1) with total green area (GA). Data
are seasonal averages (June – August) for 2012 and 2013. NEEmax and GEPmax are calculated for PAR > 1000 μmol
m-2 s-1.
4.3.3 Factors contributing to the temporal variation in CO2 exchange
Seasonal patterns of CO2 fluxes were observed in 2012 and 2013 with increasing CO2 rates
throughout the progression of summer followed by a reduction in rate through August into
September (Figure 8). The variance in NEE was considerably larger for all sites in the predrained 2012 season, which was warmer and subjected to a drought period in July.
40
Figure 8: Seasonal variation in NEE (μmol m-2 s-1) for all vegetation groups from May – September 2012 (filled
circles) and 2013 (open circles). Variation in daily fluxes is mainly due to variation between vegetation groups and
differences in light levels between measurements.
41
4.3.3.1 Light
The response of NEE to PAR was determined by curve-fitting a rectangular hyperbola to the
individual flux data. Parameter estimates of gross photosynthesis at maximum PAR (GEPmax),
respiration (ER) and the initial slope of the curve (α) differed spatially and interannually. GEPmax
ranged from 5.2 to 32.8 μmol m-2 sec-1 in 2012 and from 2.5 to 12.6 μmol m-2 sec-1 in 2013, ER
ranged from –3.0 to -8.8 μmol m-2 sec-1 in 2012 and from -1.2 to -3.8 μmol m-2 sec-1 in 2013 and
α ranged from 0.006 to 0.117 in 2012 and from 0.005 to 0.052 in 2013. In both years, the largest
values of GEPmax and α were found in minerotrophic graminoids (rush and typha) and the largest
values of ER were found in hummocks and minerotrophic graminoids. The smallest values of
GEPmax, α and ER were found in moss (bog and pond) sites (Figure 9). All sites had lower
parameter estimates of GEPmax and ER in the drained 2013 season with the exception of pond
moss (no relationship in 2012). α values were not significantly different between years for
hummock, hollow and moss sites but were lower in 2013 for all other sites (Table 8).
42
Figure 9: Relationship between NEE (μmol m-2 s-1) and PAR (μmol m-2 s-1) for eight* vegetation groups along the
Mer Bleue bog- margin gradient in 2012 and 2013, fitted with a rectangular hyperbola equation.
* note that collars from the three vegetation groups at the margin described in Table 3 have here been aggregated
according to their species composition i.e., graminoid or shrub-dominated, for stronger curve fit resolution.
43
Table 8: Rectangular hyperbola curve fit parameters for all sites and nine vegetation groups along the Mer Bleue
Bog margin from May – September, 2012* and 2013.
Site
Year
GEPmax
std error
All Sites
2012
9.8
0.94
All Sites
2013
5.1
Bog - Hummock
2012
Bog - Hummock
α
R2
std error
ER
std error
p
0.07
0.04
-5.1
0.40
0.42
< 0.0001
0.47
0.03
0.01
-2.1
0.15
0.44
< 0.0001
10.9
2.58
0.07
0.09
-8.8
1.02
0.58
< 0.0001
2013
5.1
0.86
0.07
0.13
-2.7
0.34
0.67
< 0.0001
Bog - Hollow
2012
11.3
2.15
0.02
0.01
-6.1
0.47
0.78
< 0.0001
Bog - Hollow
2013
4.6
0.57
0.02
0.01
-2.2
0.19
0.79
< 0.0001
Bog - Moss
2012
5.2
1.88
0.01
0.00
-3.0
0.27
0.58
< 0.0001
Bog - Moss
2013
2.5
0.77
0.01
0.01
-1.4
0.18
0.47
< 0.0001
Margin - Graminoid
2012
8.1
0.78
0.11
0.09
-4.3
0.40
0.70
< 0.0001
Margin - Graminoid
2013
4.0
0.47
0.02
0.01
-1.8
0.14
0.66
< 0.0001
Margin - Shrub
2012
11.4
1.59
0.04
0.02
-4.1
0.46
0.82
< 0.0001
Margin - Shrub
2013
4.7
0.81
0.02
0.01
-2.1
0.23
0.73
< 0.0001
Pond - Moss
2012
-
-
-
-
-
-
-
< 0.0001
Pond - Moss
2013
3.6
1.09
0.01
0.00
-1.2
0.14
0.64
< 0.0001
Pond - Rush
2012
23.8
3.12
0.12
0.57
-8.5
1.10
0.84
< 0.0001
Pond - Rush
2013
12.6
1.83
0.07
0.05
-3.8
0.62
0.73
< 0.0001
Pond - Typha
2012
32.8
5.68
0.10
0.05
-7.5
4.57
0.86
< 0.0001
Pond - Typha
2013
8.0
2.33
0.07
0.13
-2.9
0.86
0.51
< 0.0001
* note that the rectangular hyperbola could not be fitted to the pond moss groups in 2012. GEP max and ER units are
μmol m-2 s-1 and GEPmax is calculated for PAR > 1000 μmol m-2 s-1.
4.3.3.2 Water table and peat temperature
I found significant linear correlations between weekly average NEEmax, GEPmax, ER and
independent variables of WT and peat temperature at 10 cm (T_10) throughout the growing
season but their strength and relative importance differed between vegetation groups. T_10 was
the strongest predictor of weekly NEEmax for the graminoid-dominated sites while WT was the
strongest predictor of NEEmax for shrub-dominated sites. No significant linear correlations were
found for weekly moss NEEmax in 2012 or 2013. ER was strongly correlated with GEPmax and
T_10 at all spatial and temporal scales. ER was strongly correlated with WT for moss and shrub
sites but weakly so for graminoids (Figure 10). GEPmax was correlated with weekly T_10 in
44
graminoid and shrub sites in 2012 and 2013 growing seasons but was only significant for moss in
2013.
I found unimodal relationships between weekly GEPmax and WT for hummock, hollow, margin,
bog and pond moss sites. Hummock, hollow, margin, and moss vegetation groups were clearly
differentiated along the moisture gradient and I found distinct optima and tolerances relative to
water table position for GEPmax using weekly averages from June to August. Hummocks had the
driest optima along the gradient at 42 cm below the peat surface and displayed the broadest
tolerance range (31 cm), followed by hollows and bog moss both in terms of optima (-27 and -22
cm, respectively) and tolerance range (25 and 30 cm, respectively). Margin and pond moss sites
had wetter optima of -15 and -11 cm, respectively and also displayed narrower tolerances (22
and 23 cm, respectively). Only linear relationships were found in 2013 for hummock, hollow and
margin sites (Figure 11). I compared WT optima and tolerances using the values determined
from the GEP-WT response curves to the values obtained from the loess smooth fit functions for
species abundance; values from the individual species that make up the vegetation groups were
averaged. Optimum WT position for hollows, bog moss and margin sites were approximately the
same for both abundance and photosynthetic performance. The photosynthetic optimum that I
determined for hummocks and pond moss were considerably drier than the optimum found for
species abundance, but were within both tolerance ranges. Tolerance ranges determined from
species abundance were broader than those determined from GEPmax with the exception of pond
moss, whose photosynthetic tolerance range (23 cm) was broader than its abundance range (16
cm) (Table 9).
45
Figure 10: Relationships between ER (μmol m-2 s-1), water table position below the peat surface (WT, cm) and peat
temperature at 10 cm depth (oC) for graminoid, moss and shrub-dominated sites. Data are weekly averages from
May – September for 2012 (filled circles) and 2013 (open circles). Negative ER indicates the release of CO 2 to the
atmosphere.
46
Figure 11: Relationship between GEPmax (μmol m-2 s-1) and WT (cm below the peat surface) for hummock, hollow,
margin, bog and pond moss vegetation groups along the Mer Bleue bog- margin gradient in 2012 and 2013. Data are
weekly averages from June – August for 2012 (filled symbols) and 2013 (open symbols). Polynomial regression
coefficients (R2) for hummock, hollow, margin, bog and pond moss are 0.632, 0.588, 0.342, 0.321 and 0.512
respectively. Linear regression coefficients (R2) for hummock, hollow, and margin are 0.336, 0.725 and 0.474
respectively. All polynomial and linear regressions are significant at p < 0.01.
47
Table 5: Tolerance ranges and optima relative to water table position (WT) determined from unimodal relationships
between weekly GEPmax and WT and compared to tolerance ranges and optima determined from unimodal
relationships between species abundance and seasonal average WT. Optima is calculated as the water table depth
corresponding to the maximum GEPmax or percent cover of a species assemblage. Tolerance is calculated as the total
range of water table depths in which species photosynthesized or were present. Species assemblages are listed in
order of optima (dry to wet) and water table is in cm below the peat surface.
Vegetation Group
Hummock
Hollow
Species
Chamaedaphne calyculata,
Kalmia angustifolia,
Vaccinium corymbosum,
Sphagnum capillifolium
C. calyculata,
Rhododendron
groenlandicum, Sphagnum
magellanicum
Bog moss
Sphagnum fallax, S.
magellanicum
Margin
Carex oligosperma, C.
calyculata, S. fallax, S.
cuspidatum
Pond moss
S. cuspidatum
Tolerance (cm)
31
Optimum (cm)
-43
Data Source
GEPmax (Chapter 4.3.3.2)
49
-27
abundance (Chapter 4.2.4)
25
-27
GEPmax (Chapter 4.3.3.2)
48
-25
abundance (Chapter 4.2.4)
30
-22
GEPmax (Chapter 4.3.3.2)
44
-18
abundance (Chapter 4.2.4)
22
-15
GEPmax (Chapter 4.3.3.2)
37
-14
abundance (Chapter 4.2.4)
23
-11
GEPmax (Chapter 4.3.3.2)
16
-3
abundance (Chapter 4.2.4)
48
4.4 Methane
4.4.1 Variations in CH4 flux
CH4 flux varied between vegetation groups but differences between groups were not consistent
between years (Figure 3 & 4). In 2012, seasonal averages for hummock, hollow and moss collars
were significantly different from each other, but hollows were not significantly different from
pond moss collars. Margin (mesic, wet and shrub) and typha collars were not significantly
different from each other and rush collars were significantly different from all other collars.
Rush, typha and mesic margin collars remained distinct and had the largest fluxes in 2013, but no
statistical differences were found between bog and other margin collars (Figure 12).Seasonal
CH4 fluxes were significantly smaller (p < 0.001) in 2013 for all sites with reductions ranging
from 55 to 98% in 2013 compared to 2012. Average seasonal CH4 fluxes from May to August
for each collar ranged from 8.2 to 1458.0 mg CH4 m-2 d-1 in 2012 and from -0.3 to 786.9 mg CH4
m-2 d-1 in 2013, with the largest fluxes for both years occurring in sites dominated by graminoids.
The seasonal patterns of CH4 flux in 2012 and 2013 both showed increasing emissions
throughout the progression of summer with peaks in late June and July followed by a reduction
in emissions through August into September. Emissions were larger in the spring than late
summer in 2012 but the opposite was true in 2013, with larger emissions in August and
September than in May and early June (Figure 13). We observed large CH4 emissions in 2012,
particularly during a summer drought period and immediately after the pond was drained in
October (Figure 13), followed by a drastic overall reduction in emissions in the 2013 drained
season.
49
Figure 12: Seasonal average CH4 flux (mg m-2 d-1) for vegetation groups from May to August 2012 and 2013 and
the percentage change between years. Vertical lines are standard error.
50
Figure 13: Seasonal patterns of growing season CH4 flux in 2012 and 2013 for bog, margin and pond vegetation
groups. Vertical lines are standard error.
51
4.4.2 Biotic and abiotic correlates of methane flux at different spatial scales
I found significant correlations between CH4 flux (log-transformed) and independent variables of
GA, GrA, WT, T_10, T_air, NEE and GEP but the strength of the correlations varied at different
spatial and temporal scales (Appendix A, Table 4). In general, abiotic variables of WT and
temperature were stronger correlates with CH4 flux in 2012 and biotic variables related to CO2
exchange and vegetation were stronger correlates with CH4 flux in 2013. Further, the statistical
strength of the correlations increased over longer temporal scales, i.e. seasonal > monthly >
weekly.
4.4.2.1 Landscape scale
At the broadest spatial scale (all sites combined), weekly, monthly and seasonal average CH4
fluxes all showed the strongest correlations with WT, GrA, NEE and GEP. The slope of the
linear regression between logCH4 and WT (all data, unaveraged) was not significantly different
between years (0.03), and the intercept was larger in 2012 (Figure 14). Air and peat temperature
were significant at weekly timescales for both years but became insignificant in 2012 for
monthly and seasonal timescales. Seasonal averages showed the strongest correlations (seasonal
> monthly > weekly), particularly for WT and T_10. In 2012, seasonal average WT yielded the
strongest correlation with CH4 fluxes (R2 = 0.565) followed by GrA, NEE and GEP (R2 = 0.379,
0.338, 0.228 respectively). In 2013, seasonal average NEE yielded the strongest correlation with
CH4 fluxes (R2 = 0.803) followed by GEP, GrA and WT (R2 = 0.782, 0.461, 0.300 respectively).
For both years combined, T_10 and WT yielded the strongest correlations with CH4 fluxes (R2 =
0.463 and 0.414, respectively). GrA displayed a positive linear relationship with logCH4,
yielding similar slopes between years but a smaller intercept in the drained 2013 season (Figure
15).
52
Figure 14: Relationship between CH4 and water table depth (WT) for all collars in 2012 and 2013. Data are
instantaneous fluxes, CH4 is log-transformed, and WT is measured in cm below the peat surface.
Figure 15: Relationship between CH4 flux (mg m-2 d-1) and graminoid green area(m2 m-2) for all vegetation groups
in 2012, 2013 and both years combined. Data are seasonal averages, CH 4 is log-transformed, vertical lines are
standard errors.
53
4.4.2.2 Plant functional types
When collars were grouped according to their plant functional type (graminoid, shrub or moss
dominated), the relative strength of the independent variables with CH4 varied between
functional type, between years and at different temporal scales, with seasonal averages again
yielding the strongest correlations with CH4 fluxes. At this finer spatial scale, WT, GrA, NEE
and GEP were still important variables but the influence of peat temperature increased at both
monthly and seasonal timescales. Linear relationships were found between seasonal average
logCH4 and water table depth for shrub, moss and graminoids, with larger CH4 fluxes associated
with increasing WT (Figure 16).
Figure 16: Relationship between seasonal CH4 flux (mg m-2 d-1) and water table depth for shrub, moss and
graminoid plant functional types in 2012 and 2013. Linear regressions are for both years combined. Data are
seasonal averages, CH4 is log-transformed, and WT is measured in cm below the peat surface.
4.4.2.3 Individual vegetation groups
For individual vegetation groups, the relative strength of the independent variables with CH4
varied between years but did not vary as much between vegetation groups or temporal scales.
Weekly and monthly averages yielded similar relationships within each vegetation group;
seasonal averages were not appropriate for analysis at this scale for individual years (only 3 data
points per year), but relationships for both years combined revealed similar relationships as
54
weekly and monthly timescales, but with different relative importance between independent
variables. CH4 flux increased with higher WT in 2013 at both weekly and monthly timescales for
hummock, moss, mesic and wet margin and rush collars but this relationship was only significant
in 2012 for typha (weekly) and mesic margin (monthly) collars. For both years combined, the
positive relationships between WT and CH4 was significant for hollow, pond moss, shrub and
wet margin collars at both weekly and monthly timescales.
I found both linear and non-linear relationships between monthly logCH4 fluxes and GEPmax at
the vegetation group scale (Figure 17). Fitting a Michaelis-Menton equation to the values yielded
an equally significant relationship as a linear fit (p < 0.0001), but with a lower correlation
coefficient.
Figure 17: Relationships between CH4 flux (mg m-2 d-1) and GEPmax (μmol m-2 s-1) for nine vegetation groups from
2012 and 2013 combined. Data are monthly averages and CH 4 is log-transformed. Linear regression: R2 = 0.393, p
< 0.0001; Michaelis-Menten equation: R2 = 0.201, p < 0.0001.
55
4.4.2.4 Multiple regressions
To reveal which combination of independent variables could explain the most variation in CH4
flux, stepwise multiple regressions (mixed backwards and forwards) were performed at all
spatial and temporal scales. Equations, R2, p-values and root mean square deviations (RMSE) for
stepwise multiple regressions are presented in Table 10. For all sites combined, WT was an
important predictive variable for both combined and individual years and was the most important
variable for 2012 and for both years combined; however, GEP was the most important variable
for 2013. The strongest predictive model for 2012 revealed seasonal WT as the most important
variable in combination with GEP, explaining 70% of the variability in CH4 flux. For 2013, the
strongest predictive model included seasonal WT in combination with GEP, GA and GrA,
explaining 96% of the variability in CH4 flux and for both years combined, WT, T_10 and NEE
together explained 79% of the variability in CH4 flux.
For graminoid collars, peat temperature was the most important variable at both monthly and
seasonal timescales followed by WT and GEP at monthly and seasonal timescales (R2 = 0.681
and 0.870, respectively). Moss collars also had peat temperature as the most important variable,
followed in importance by WT at monthly and WT and GEP at seasonal timescales. For shrub
collars, water table depth was the most important variable, followed by T_10 at both monthly
and seasonal timescales. The addition of GEP significantly improved the model at the monthly
scale, but not at the seasonal scale (R2 = 0.765 and 0.842 for monthly and seasonal scales,
respectively).
For individual vegetation groups, monthly average GEP was the most important variable for
hummock, hollow and moss whereas monthly T_10 was the most important variable for all
margin and pond groups. Seasonal average GEP was the most important variable for all groups
except pond moss, rush and typha, which responded most positively to WT, T_10 and T_air.
56
Table 10: Equations, R2, p-values and root mean square deviations (RMSE) for stepwise multiple regressions at
different timescales and spatial scales. logCH4 was the dependent variable with independent variables of water table
(WT), CO2 exchange (GEP and NEE), air and peat temperature (T_air, T_10), green area (GA), and graminoid area
(GrA). Variables not shown were not significant in the regression (α = 0.05).
All data
Scale
Equation
R2
2012
Seasonal
2.44 + 0.025(0.005)*WT + 0.021(0.008)*GEP
2013
Seasonal
Both
p
RMSE
0.695
< 0.0001
0.25
0.955
< 0.0001
0.10
0.785
0.001
0.26
0.765
< 0.0001
0.21
0.762
< 0.0001
0.19
1.47 + 0.090(0.021)*GEP + 0.066(0.013)*GrA 0.063(0.022)*GA + 0.009(0.003)*WT
0.53 + 0.022(0.021)*WT + 0.096(0.023)*T_air +
Seasonal
0.029(0.048)*NEE
Functional
Type
1.22 + 0.012(0.175)*WT+ 0.038(0.089)*T_10 +
Shrub
Monthly
0.026(0.733)*GEP
1.31 + 0.032(0.637)*T_10 + 0.015(0.054)*WT +
Moss
Monthly
0.073(0.046)*GEP
0.86 + 0.087(0.713)*T_10 + 0.017(0.086)*WT +
Graminoid
Monthly
0.029(0.072)*GEP
0.681
< 0.0001
0.32
Shrub
Seasonal
0.35 + 0.023(0.815)*WT + 0.109(0.615)*T_10
0.842
< 0.0001
0.20
Moss
Seasonal
0.02 + 0.106(0.106)*T_10 + 0.01(0.035)*WT
0.917
< 0.0001
0.12
-0.81 + 0.20(0.005)*T_10 + 0.024(0.008)*GEP +
Graminoid
Seasonal
0.025(0.336)*WT
0.870
< 0.0001
0.20
Monthly
1.06 + 0.016(0.183)*GEP + 0.017(0.077)*T_air
0.898
0.001
0.06
Vegetation
Group
Hummock
1.46 + 0.021(0.088)*GEP + 0.016(0.246)*WT +
Hollow
Monthly
0.036(0.230)*T_10
0.740
0.005
0.21
Bog moss
Monthly
1.01 + 0.095(0.597)*GEP + 0.021(0.119)*T_air
0.796
0.001
0.14
Monthly
1.02 + 0.063(0.216)*T_10 + 0.047(0.629)*GEP
0.813
0.001
0.25
0.806
0.005
0.25
Mesic
margin
1.23 + 0.056(0.529)*T_10 + 0.036(0.172)*GEP +
Wet margin
Monthly
Shrub
0.020(0105)*WT
1.00 + 0.067(0.743)*T_10 + 0.011(0.022)*GEP +
margin
Monthly
0.037(0.0430)*WT
0.866
0.001
0.20
Pond moss
Monthly
1.22 + 0.038(0.510)*T_10 + 0.141(0.235)*GEP +
0.743
0.003
0.22
57
0.013(0.065)*WT
0.89 + 0.086(0.088)*T_10 - 0.022(0.629)*WT +
Rush
Monthly
0.003(0.001)*NEE
0.677
0.001
0.29
Typha
Monthly
0.56 + 0.111(0.933*T_10 + 0.018(0.396)*WT
0.664
0.002
0.33
Hummock
Seasonal
-0.87 + 0.010(0.020)*GEP + 0.096(0.09)*T_air
0.980
< 0.0001
0.04
0.997
< 0.0001
0.03
0.955
< 0.0001
0.10
0.961
< 0.0001
0.18
0.996
< 0.0001
0.05
1.14 + 0.007(0.001)*GEP + 0.032(0.040)*WT +
Hollow
Seasonal
0.099(0.011)*T_10
2.54 + 0.170(0.009)*GEP + 0.347(0.015)*T_10 +
Bog moss
Seasonal
Mesic
margin
0.140(0.008)*WT
-0.48 + 0.014(0.003)*GEP - 0.051(0.038)*WT +
Seasonal
0.108(0.036)*T_10
-0.95 + 0.010(0.001)*GEP + 0.191(0.081)*T_10 +
Wet margin
Seasonal
Shrub
0.013(0.002)*WT
0.43 + 0.019(0.003)*GEP + 0.081(0.285)*WT +
margin
Seasonal
0.125(0.031)*T_10
0.978
< 0.0001
0.14
Pond moss
Seasonal
0.81 + 0.034(0.027)*WT + 0.069(0.034)*T_10
0.902
< 0.0001
0.16
0.952
< 0.0001
0.11
0.999
< 0.0001
0.01
7.10 + 0.252(0.385)*T_10 - 0.071(0.178)*WT Rush
Seasonal
0.391(0.117)*T_air
1.64 + 0.037(0.256)*WT - 0.056(0.080)*T_10 -
Typha
Seasonal
0.009(0.001)*T_air
58
Chapter 5: Discussion
5.1 Vegetation
5.1.1 Plant community structure along the bog – margin gradient
We found six distinct plant communities that corresponded to changes in water table depth along
the bog – margin gradient. Hummock and hollow communities are similar to those reported for
the dome section of Mer Bleue (Bubier et al. 2006) with differences in the deciduous shrub
species, notably the dominance of deciduous shrub Vaccinium corymbosum over Vaccinium
myrtilloides owing to the higher water table bordering the margin, which promotes V.
corymbosum (Mitchell & Niering 1993).
The bog margin community is similar to poor fen and wet bog associations (Yavitt et al. 1993;
Bubier et al. 2006; Gąbka & Lamentowicz 2008) and is found at the Mer Bleue margin because
of the high water table and minerotrophic conditions maintained by the beaver pond. Pond
margin, mudflat and open water communities are comparable to other studies of peatland beaver pond margins (Mitchell & Niering 1993; McMaster & McMaster 2001; Little et al. 2012)
including those conducted at Mer Bleue (Bubier et al. 2003b).
The primary drivers of plant community structure identified through direct gradient analysis
were variables related to water table depth and water chemistry, which is in agreement with other
studies in peatlands (Glaser et al. 1990; Økland 1990; Bragazza et al. 2005; Bubier et al. 2006;
Laine et al. 2007; Hájková et al. 2008) and peatland margins (McMaster & McMaster 2001;
Bragazza et al. 2005; Howie & Van Meerveld 2013). In this study, water table variables were
found to be more important (53.5%) than pH (21.6%) in explaining species associations and
distribution likely due to the greater range of water table depths than pH values along the
transects.
5.1.2 Plant community responses to a lowered water table
Short-term responses to the lowered table may be explained by differences in species water table
depth tolerances. I found unimodal or nearly unimodal responses of species abundance to mean
water table position. Optima and tolerance varied between species, suggesting niche
differentiation along the water table gradient from bog to pond. The narrowest tolerances were
59
found in the wettest vascular and bryophyte species (e.g. Juncus, Typha, S. cuspidatum) and the
widest tolerances were found in vascular and bryophyte species that occupied both hummocks
and hollows (C. calyculata, R. groenlandicum, V. corymbosum, S. magellanicum). Species with
wider tolerances are able to occupy a larger range of water table depths which may confer a
stronger competitive advantage over species with narrow tolerances (Whittaker 1967;
Vandervalk 1981).
Bubier et al. (2006) measured the optima and tolerances for dominant hummock, hollow and
poor fen species at Mer Bleue. My values are in agreement with Bubier et al. (2006) for C.
oliogosperma but are different for C. calyculata, K. angustifolia and R. groenlandicum. I found
much wider tolerances and wetter optima for these three bog species which is not surprising
since I sampled a considerably longer gradient than the previous study and because the bog
communities near the margin have a shallower water table depth than the bog center. The four
dominant Sphagnum species S. capillifolium, S. magellanicum, S. fallax and S. cuspidatum had
slightly drier optima and wider tolerances then those reported in Bubier et al. (1995) for
Sphagnum species in the Clay Belt (Ontario) and Labrador Trough (Quebec) peatlands (Bubier et
al. 1995b).
Lowering the water table did not alter bog species composition because the water table did not
drop below the tolerance range of bog hummock and hollow species. Mitchell & Niering (1993)
also found that a long-term beaver disturbance regime at a bog margin in Connecticut did not
negatively affect species richness of the adjacent bog communities. Peatlands have been
described as self-regulating with respect to water table, which can be maintained within narrow
ranges of depth (Ingram 1983; Belyea 2009). This is likely why, even though the range and
variance in water table depth differed between years, the mean water table depth in the bog was
relatively unaffected in the first season of the drainage. If the lower water depth is maintained
over the long term, changes in species composition may occur. However, internal feedbacks
among Sphagnum species may prevent severe reductions in water level, since Sphagnum mosses
transport and store water by capillary action, maintaining wet and anoxic conditions (Rydin et al.
2006; Laine et al. 2011b).
Contrary to my expectations, mesic Sphagnum species at the bog margin (S. fallax, S.
papillosum) significantly declined. This short-term response in the moss layer indicates that the
60
new water level is suboptimal for S. fallax and S. papillosum growth and expansion. Also
contrary to my expectations, ombrotrophic shrub and sedge cover at the bog margin was not
significantly different with the exception of Eriophorum virginicum, which increased along T1
and T2 but was absent along T1 and in very low abundance along T2 in 2012. Eriophorum
virginicum is an ombrotrophic sedge that is known to co-occur with Sphagnum fallax in mesic
peatlands (Wieder et al. 1990; Yavitt et al. 1993; Gąbka & Lamentowicz 2008). It is likely that
the new lower water level is preferable for E. virginicum growth. At the bog margin, the water
table shifted towards the optimum for C. calyculata and S. magellanicum but shifted away from
the optimum for C. oligosperma. I anticipated S. fallax cover to increase and the significant
decline in S. fallax was unexpected, as S. fallax is reported to have a wide tolerance for water
table depths and nutrient conditions (Gąbka & Lamentowicz 2008; Laine 2009) and I found a
broad tolerance range of 40 cm. However, Bubier et al. (1995) reported a tolerance of 22 cm for
S. fallax in Northern Ontario and Quebec peatlands. It is possible that the ecological tolerance of
S. fallax and other peatland species are locally adapted and vary with geography, but more
studies are needed to clarify this. Moreover, the observed decline in species abundance may be
attributed to competitive interactions between other bryophyte and/or vascular plant species that
limit growth.
Although C. calyculata and C. oligosperma percent cover did not differ between years, Moore et
al. (2002) showed that shrub and sedge foliar biomass (photosynthetically active tissues) differed
between a wet and a dry growing season with shrub biomass increasing and sedge biomass
decreasing in the dry year. Changes in foliar biomass should occur sooner than changes in
percent cover, since both woody and foliar biomass contribute to total percent cover. If water
levels are maintained at the lower level, this bog margin community may become replaced either
by an expanding bog (C. calyculata, S. magellanicum) or an encroaching meadow (pond
margin/mudflat). It is also possible that the community will shift further down the water table
gradient in time.
The amount of open water in the pond was reduced, causing the floating aquatic species to
decline or be completely absent (e.g. Utricularia macrorhiza) in 2013. Lowering the water table
resulted in a significant increase in minerotrophic species in pond areas, notably graminoids
Calamagrostis canadensis, Scirpus cyperinus, Sparganium americanum and herbs Polygonum
61
sagittatum and Triadenum fraseri. Exposing the pond sediments allowed seedbank species to
germinate and expand onto previously inundated areas (Vandervalk 1981). The increase in
Scirpus cyperinus is also likely attributed to the ability of many Scirpus species to survive as
tubers in water that is too deep for growth (Squires & Vandervalk 1992). There was a decline in
Typha angustifolia along T3, the transect furthest from the dam, which experienced the lowest
water table depths in 2013. The water table shifted away from the optimum for T. angustifolia
along all three transects and approached the tolerance limit along T3. Typha species are reported
to be restricted to areas of minimal water table fluctuations (Grace & Wetzel 1981; Grace 1989).
Open water communities are being replaced by pond margin and mudflat species and this shift is
expected to continue if the water levels remain low.
Surprisingly, aquatic (floating) Sphagnum species did not decline; rather, the Sphagnum
cuspidatum mat expanded onto previously inundated areas both towards the bog and further into
the pond. This indicates that the previously high water table caused by beaver impoundment was
suboptimal for S. cuspidatum expansion. Sphagnum cuspidatum is reported to occur as a floating
aquatic species preferring submerged and open water areas (Laine 2009; Laine et al. 2011a). My
results show that S. cuspidatum has a wider tolerance range (0 – 16 cm) and an optimum around
3 cm below the peat surface. Over time, S. cuspidatum may make conditions more favourable for
other peatland species through acidification of local conditions, encouraging paludification
(Rydin et al. 2006; Laine et al. 2011b).
5.2 Carbon dioxide
5.2.1 Vegetation composition and spatial variation in CO2 exchange
The spatial variation of CO2 exchange along the bog – margin moisture gradient was controlled
by the vegetation composition, with distinct species assemblages being associated with
characteristic water table positions and rates of NEE. Bog vegetation at the margin was
comparable to the Mer Bleue center (Bubier et al. 2006; Lai et al. 2014) but with some
differences in deciduous shrub and Sphagnum moss composition. Margin vegetation was
composed of evergreen shrub, sedge and Sphagnum moss species similar to poor fen and wet bog
associations (Yavitt et al. 1993; Bubier et al. 2006; Gąbka & Lamentowicz 2008) while the
62
beaver pond was dominated by tall minerotrophic graminoids J. effusus and T. angustifolia. The
relative CO2 exchange rates between vegetation groups was consistent between years and agreed
with previous studies (minerotrophic graminoids > fens (shrubs + sedges) > bogs (shrubs >
mosses)) (Knapp & Yavitt 1995; Moore et al. 2002; Bubier et al. 2003a; Bubier et al. 2003b;
Laine et al. 2007); however, the magnitude of CO2 fluxes along the bog - margin gradient were
higher in the pre-drained 2012 season than reported for similar vegetation groups in
ombrotrophic bogs, including Mer Bleue (Moore et al. 2002; Bubier et al. 2003b; Lai et al.
2014), and were also higher than reported for poor fens (Moore et al. 2002; Bubier et al. 2003a;
Bubier et al. 2003b). After reductions in the drained 2013 season, CO2 exchange rates for bog
and margin sites fell within the previously reported range while T. angustifolia and J. effusus
rates were within the reported range in both years (Knapp & Yavitt 1995; Mann & Wetzel 1999;
Bonneville et al. 2008). Peatland margins have naturally high levels of variance in both water
levels and nutrients (Howie & Van Meerveld 2013) which, coupled with flooding by beaver
activity, are likely promoting higher rates of CO2 exchange compared to the peatland proper.
5.2.2 Changes in CO2 exchange after draining the beaver pond
Draining the beaver pond lowered the average position of the water table across the bog – margin
gradient and I observed differential responses by vegetation groups to the lowered water levels.
Seasonal average NEEmax declined in graminoid-dominated sites but was not significantly
different for shrub-dominated sites and even increased in moss sites. On the other hand, both
GEPmax and ER seasonal averages were lower in the drained season for all vegetation groups
except moss. Bog moss had similar GEPmax but lower ER in 2013 and pond moss had higher
GEPmax and lower ER, which accounts for the increase in NEEmax between years. NEEmax was
not significantly different for bog shrubs because GEPmax and ER were proportionally reduced in
hummock and hollow sites whereas greater reductions in GEPmax relative to ER in graminoiddominated sites resulted in lower NEEmax in 2013. Although all sites were net carbon sinks in the
drained season, the sink strength of graminoids was weakened due to reduced GEP.
The carbon sink potential of ecosystems can be represented by their carbon use efficiency
(CUE), which is the ratio of GEP to NPP. CUE provides a measure of what fraction of total
carbon assimilation becomes incorporated into new tissues and is typically treated as a constant
(~ 0.5) in climate models, but CUE has been shown to be sensitive to changes in environmental
63
conditions and consequently environmental change (Chambers et al. 2004; Curtis et al. 2005;
DeLucia et al. 2005; DeLucia et al. 2007). I observed an increase in CUE for all sites in the
drained season, which suggests that the carbon assimilated through photosynthesis was more
efficiently converted into biomass and plant growth in the drained compared to the pre-drained
season. Lower ER rates certainly contributed to lower CUE values, as plants and ecosystems
with a lower respiratory requirement display higher CUE values (Van Iersel 2003). Due to
nutrient limitations and evergreen vegetation, I expected bog CUE values to be closer to boreal
and tropical forests than temperate forests and our range of values for hummocks and hollows
(0.14 to 0.44, average 0.35) agrees with average estimates of 0.32 to 0.40 and 0.35 to 0.46 for
boreal and tropical forests, respectively (Ryan et al. 1997; Chambers et al. 2004; Malhi 2012).
The average moss CUE of 0.15 is driven by the extremely low CUE range in 2012 (0 to 0.04)
caused by disproportionately low GEP rates during the summer drought, whereby the moss
surface dried out and sites became sources of CO2 to the atmosphere. The actual CUE of
Sphagnum mosses likely resembles the 2013 values which were 0.24 and 0.30 for bog and pond
moss, respectively. Freshwater temperate marshes have reported CUE values of around 0.61
(Bonneville et al. 2008; Rocha & Goulden 2009), and our rush and typha sites are in accordance
with this value, ranging from 0.58 to 0.72 with an average of 0.63. The bog margin, which
resembles fen vegetation and physical conditions, fell in between bog/boreal/tropical forests and
freshwater marsh values, with CUE ranging from 0.39 to 0.54 with an average of 0.47. To our
knowledge there are no formal estimations of CUE for peatlands or peatland plant species and I
report first estimations for ombrotrophic bog, margin and beaver pond vegetation, however
preliminary considering the limited number of seasons and the high variability between years.
Further research is needed to evaluate the effects of changing environmental conditions on CUE
in general, and for peatlands specifically.
5.2.2.1 ER responses to lowered water levels
Water table position was an important factor determining spatial variation in ER rates, with drier
sites closer to the bog respiring more than wet sites in the margin and beaver pond. I expected to
find higher ER rates in the drained season because of an increased oxic peat layer, which has
been shown to enhance respiration through increased oxygen availability for plant roots and
microbes (Scanlon & Moore 2000). Interannual comparisons have reported substantial increases
in ER during particularly dry summers, with peatlands even becoming sources of CO2 during
64
dry periods (Shurpali et al. 1995; Alm et al. 1999). During a substantial water table drop in late
summer, Moore et al. (2002) found that hummock, hollow and poor fen sites at Mer Bleue
switched from being net CO2 sinks to sources due to increased respiration rates and decreased
photosynthetic rates. Similarly, in a study by Bubier et al. (2003b) also at Mer Bleue, a dry
summer decreased NEE due to greater ER rates in hummock, hollow, poor fen and beaver pond
margin sites. GEP was reduced in the poor fen and margin sites but not in the bog. In a temperate
poor fen in New Hampshire, a dry season resulted in higher ER rates for shrub, sedge and moss
sites but NEE was not reduced in sedges and actually increased in shrub and moss sites (Bubier
et al. 2003a). I found that weekly ER increased with lower water levels within both growing
seasons, as seen by the positive relationship between weekly ER and water table depth for
individual plant groups (Figure 5). However, I report lower seasonal ER rates for all vegetation
groups in the drained season, reflecting the difference between short term and long term
responses of ER to changes in water level position. Lower ER in the drained season can be
attributed to lower peat temperatures and plant productivity, indirectly caused by the drainage.
The positive relationship between ER and peat temperature has been previously established in
peatlands (Crill et al. 1988; Chivers et al. 2009); key metabolic processes involved in respiration
are temperature-limited, such as the citric acid cycle which is responsible for aerobic respiration
in both plants and microbes (Wiskich & Dry 1985). Peat temperatures in the drained season were
colder in all sites, particularly in the spring. My results show that lower peat temperatures, both
between years and within the growing season, were strongly correlated with lower ER rates for
all sites and individual plant groups. It is likely that factors other than air temperature contributed
to the lower peat temperatures in the drained season, since air temperature was only slightly
lower in 2013, and was not lower than the long term average. Strack & Waddington (2007) also
found consistently colder peat temperatures in experimentally drained sites compared to control
plots in a poor fen in Quebec, which they attributed to soil ice being maintained later into the
season in drier sites than in adjacent wet sites. It is likely that this also occurred in this study,
although I did not measure the timing of ice melt. Moreover, the removal of standing water,
which I did observe, can also lower peat temperatures (Scott et al. 1999).
I also found strong positive relationships between ER and GEPmax at all spatial and temporal
scales and, although GEP is derived from the difference between NEE and ER, it is probable that
the observed declines in plant productivity also contributed to lower overall ER rates. Water
65
stress reduces respiration due to diminished substrate availability caused by stress-induced
reductions in photosynthetic rates (Atkin & Macherel 2009). My results support the idea that
substrate availability ultimately limits microbial activities even if oxygen availability is increased
by deepening of the oxic zone. My results are in agreement with other peatland studies that
observed a lack of response in ER to experimental water table drawdown with accompanying
low GEP rates (Strack & Waddington 2007; Chivers et al. 2009; Muhr et al. 2011). Thus, lower
peat temperatures and/or plant productivity can counteract the positive effects of a deeper oxic
layer on ER, resulting in decreases or no net change in ER rates.
5.2.2.2 GEP responses to lowered water levels
Spatial variation in GEPmax was largely controlled by photosynthetic green area, with the largest
rates found in sites dominated by tall minerotrophic graminoids, intermediate rates in shrubdominated sites and the smallest rates in mosses. GA declines in the drained season contributed
to declines in photosynthesis by reducing the amount of photosynthesizing tissue. Adjusting
biomass allocation between shoots and roots is a well documented response to water stress in a
variety of plant species including graminoids (Soukupová 1994; Gold 2000; Luo et al. 2008),
bog herbs, shrubs and trees (Murphy et al. 2009; Murphy & Moore 2010). Other studies in
peatlands have observed GA declines during seasons with lower water levels (Bubier et al.
2003b; Chivers et al. 2009). This response is not uniform across plant groups; for example, in a
poor fen in New Hampshire, deciduous shrubs produced more leaves while sedges reduced their
leaf area during a particularly dry growing season (Bubier et al. 2003a). I also observed a
differential response in plant groups; shrub green area decreased in hummocks but increased in
hollows and shrub margin sites. Graminoid green area decreased in wet margin, rush and typha
sites but increased in shrub margin sites. Moss green area was not significantly different between
years although slight declines were observed in mesic margin, rush and typha sites.
Reductions in photosynthetic performance are also shown by differences in light response curve
parameters. The response of the relationship between NEE and PAR differed between years,
with lower values of GEPmax and ER in the drained 2013 season for all groups, as well as lower
variance in these parameters. α was not significantly different between years for hummock,
hollow and bog moss although it was slightly lower in 2013. These results show that, even
though photosynthetic rates were reduced in the bog sites, their photosynthetic efficiency was
66
not. Conversely, α was greatly reduced in the margin, rush and typha sites demonstrating a
weaker photosynthetic efficiency in 2013 in addition to lower GEPmax rates for graminoid sites
along the bog margin and beaver pond.
Photosynthetic reductions concomitant with lower water levels were observed both between and
within seasons. In the pre-drained 2012 season, a summer drought caused reductions in GEP
across plant groups and mosses became a net source of CO2 during this period. However, GEP
recovered when water levels rose to pre-drought levels. Water table position in the drained
season was lower overall, but water levels were also more stable because there was not a
pronounced summer drought. Consequently, GEP rates were minimized and did not display
strong fluctuations throughout the growing season.
Species assemblages displayed distinct water table preferences for photosynthesis performance,
demonstrated by unimodal relationships between GEPmax and WT. Hummock, hollow, margin,
and moss vegetation groups were clearly differentiated along the moisture gradient and I found
distinct optima and tolerances relative to water table position for GEPmax using weekly averages
from June to August. Hummocks had the driest optima and displayed the broadest tolerance
range, followed by hollows and bog moss both in terms of optima and tolerances. Margin and
pond moss sites had wetter optima and narrower tolerances. My results agree with Pelletier et al.
(2011), who proposed theoretical unimodal relationships between photosynthesis and water table
position at the plant community scale; however, the magnitude of our GEP rates and WT depths
were considerably lower than the values proposed by Pelletier et al. (2011), likely attributed to
climatic and vegetation differences between different peatlands. The classification of
assemblages in peatlands (e.g., hummock, hollow, lawn) are more related to the topographical
position, and the vegetation composition will vary considerably between peatlands in different
climates and nutrient status (i.e., ombrotrophic, minerotrophic). Results from my margin sites are
comparable to those found in a lowland blanket bog by Laine et al. (2007), who also found a
unimodal relationship between GEP and WT and observed a photosynthetic optima of 10 cm
below the peat surface and a tolerance range of approximately 20 cm for the bog as a whole,
without making a distinction between different vegetation groups. Chivers et al. (2009) found
unimodal relationships in a rich fen dominated by sedges and moss with a photosynthetic optima
ranging from 15 to 19 cm below the peat surface. Unimodal relationships between net
67
photosynthesis and tissue water content revealed key differences between Sphagnum fallax,
which is abundant in wet environments and the hummock-dominating Sphagnum capillifolium
(previously S. nemoreum). Sphagnum capillifolium had a broader tolerance range than S. fallax
but S. fallax was able to fix carbon more effectively at lower tissue water contents than S.
capillifolium (Titus et al. 1983; Wagner & Titus 1984).
Being able to maximize carbon assimilation at different water table depths allows for species to
coexist along the bog – margin moisture gradient. The mechanisms underlying water table
partitioning are most likely related to differences in plant morphology and tolerance strategies.
Being able to tolerate a broad range of environmental conditions confers an advantage over
species with narrower tolerances and stress-tolerant species may displace superior competitors
simply because they are better equipped to survive under high stress (Laine et al. 2011b). My
bog sites displayed broader tolerances compared to the margin, reflecting differences in
morphology and life strategies. The dwarf shrubs that dominate hummocks and hollows are
known to be more drought-resistant than sedges due to sclerophyllous leaves and extensive root
systems. Many shrub species have evergreen leaves and bog Sphagnum have greater waterretaining capacity than the aquatic Sphagnum located at the beaver pond margin. However, these
species are not able to thrive under the inundated conditions of the margin, as evidenced by the
decline in photosynthetic performance at higher water table positions. The sedges and Sphagnum
species growing at the margin are better adapted to inundated conditions yet their success was
limited to a narrower range of water table positions and they were more negatively affected by
the drainage.
It is logical that species have preferred environmental conditions for the performance of essential
functions such as carbon assimilation, but the quantification of such preferences is difficult under
field conditions. I expected that the GEPmax values from the 2013 drained season would fall
along the tolerance curves determined from the pre-drained 2012 data and the moss sites did
meet this expectation; lower water levels corresponded with lower GEPmax rates, filling in the dry
ends of the response curves for both bog and pond moss. GEPmax rates for hummock, hollow and
margin sites in the drained 2013 season did not fall along the response curves; rather, they
displayed a linear relationship. The lack of a unimodal response to water table in the drained
season for vascular species assemblages suggests that differences between vascular and
68
bryophyte plants may be influencing the GEP-WT relationship, such as the lack of roots in
bryophytes and their ability to retain water through capillary action (Laine et al. 2011b). My
results also suggest that GEPmax is influenced not only by the position of the water table, but by
seasonal influences. It has been suggested that the spring conditions influence carbon exchange
patterns for the rest of the growing season (Griffis et al. 2000). Spring water levels in the drained
season were considerably lower and were augmented by lower than average precipitation in
May, which may have limited productivity in this crucial period of plant development and
dampened production for the rest of the season, even after water levels rose to the predetermined optimal positions in late June.
My results using weekly GEPmax reflect the photosynthetic responses to water level fluctuations
throughout the growing season but the long term water level position may reflect a more accurate
photosynthetic tolerance range. The distribution of plant species in peatlands is strongly
controlled by water level and chemistry gradients (Glaser et al. 1990; Bragazza et al. 2005) and
unimodal relationships have been observed between species abundance and the seasonal average
water table position for a variety of peatland species (Bubier et al. 1995b; Bubier et al. 2006)
including those along the Mer Bleue margin from this study. Comparing optima for species that
compromise the vegetation groups with those determined from our GEP-WT response curves
revealed that the optimum WT position for hollows, margin and bog moss were approximately
the same for both abundance and photosynthetic performance (Table 6). The photosynthetic
optimum that I determined for hummocks and pond moss were considerably drier than the
optimum reported for species abundance, but were within both tolerance ranges. Fine scale
hydrologically defined niches have been shown to structure a variety of plant communities
(Silvertown et al. 1999; Araya et al. 2011), but physiological tradeoff s and interspecific
competition may cause discrepancies between a species optimum for some essential function, in
this case photosynthesis, and where a species is most abundant. This could reasonably explain
the differences I observed between the functional (GEP) and ecological (abundance) optima that
I observed. The overlap in short term (weekly) functional and long term (seasonal) ecological
optima indicates that my results are not transient and further studies are needed to determine the
causal mechanisms behind these differences, how these mechanisms respond to changing
environmental conditions and the implications for ecosystem structure and function.
69
5.3 Methane
CH4 flux varied greatly among bog, margin and pond vegetation groups, with seasonal averages
ranging from -0.28 to 1458 mg CH4 m-2 d-1 across both years. Before the drainage, the hierarchy
and distinctness of CH4 flux among vegetation groups generally agreed with previous studies
(minerotrophic graminoids > fens (shrubs + sedges) > hollows > hummocks) (Dise 1993; Bubier
et al. 2005; Ström et al. 2005) but were larger than reported from other wetlands with
comparable vegetation. The pond had a floating mat of S. cuspidatum and was dominated by
minerotrophic graminoids J. effusus and T. angustifolia. Floating peat has been shown to emit
more methane than submerged or intact peat because the peat is warmer and oxygen diffusion
from the atmosphere into the peat is restricted (Scott et al. 1999). Juncus effusus and T.
angustifolia are large and productive graminoids that are known to emit large quantities of
methane by stimulating methanogenesis through labile root exudates and transporting methane
via aerenchyma (Sebacher et al. 1985; Strom et al. 2005; Kao-Kniffin et al. 2010).
5.3.1 Changes in CH4 flux after draining the beaver pond
The hierarchy and distinctness of CH4 flux among vegetation groups observed in 2012 did not
hold true after the drainage. Rush, typha and mesic margin sites remained distinct and had the
largest fluxes in 2013, but no statistical differences were found between bog and other margin
sites. On the other hand, the hierarchy and distinctness of gross photosynthetic rates among
vegetation groups was consistent between years, despite considerable declines in 2013 GEP
rates. These results indicate that factors other than species composition were affecting CH4 flux
dynamics in the drained season and that CH4 emissions may be more sensitive to changes in
water table position than CO2 exchange (Lai et al. 2014).
CH4 emissions were substantially smaller at all sites in the season after the drainage with
reductions from 55- 98% and even CH4 consumption from bog hummock, hollow and moss sites.
Lowered water levels can inhibit methanogenesis and strengthen CH4 oxidation by increasing the
depth of the aerobic peat layer. Draining the beaver pond effectively lowered the seasonal
average water table at all sites from 7 – 30 cm, increasing the aerobic zone in the peat and
reducing or eliminating standing water. After the drainage, emissions fell within the low end of
the reported range for Juncus effusus and Typha angustifolia (Sebacher et al. 1985; Strom et al.
70
2005; Kao-Kniffin et al. 2010), for poor fens (Dise 1993; Strack et al. 2006b; Turetsky et al.
2008) and bog sites (Yavitt et al. 1993; Bubier et al. 2005; Moore et al. 2011).
Reduced CH4 flux after lowering the water table has been demonstrated in numerous studies
using peat mesocosms from bogs (Moore & Dalva 1993; Updegraff et al. 2001; Blodau et al.
2004), fens (Moore & Dalva 1993; Updegraff et al. 2001) and sites dominated by Juncus
(Freeman et al. 1992). Field observations confirm this response as well, in both manipulated
studies and natural observations. For example, in a temperate poor fen fragment near Quebec,
Strack & Waddington (2007) did not find reduced CH4 flux in the first season of a water table
drawdown, but emissions were reduced by 60 to 100% after 3 years of drainage for vegetation
groups comparable to our margin sites. At a different location in this same fen, Strack et al.
(2004) found CH4 reductions of 71 to 97% for similar vegetation groups after 8 years of
drainage. Bubier et al. (2005) observed increases in CH4 flux by 35 - 90% in response to a wetter
and warmer season in a variety of fens near Thompson, Manitoba.
5.3.2 Abiotic correlates of methane flux
I found a strong positive relationship between average seasonal CH4 flux and WT at the broadest
spatial and temporal scale, which agrees with numerous other studies that have found that
seasonal average water table position to be the dominant variable explaining the spatial
distribution of methane fluxes across a range of peatlands (Bubier et al. 1995a; Treat et al. 2007;
Couwenberg et al. 2011; Moore et al. 2011; Urbanová et al. 2012). The slope of the regression
was not significantly different between years and fell within the middle range of slopes
determined for peatlands across Canada (Moore & Roulet 1993a; Moore et al. 2011), thus
strengthening the suggestion that there is a similar functional relationship between CH4 flux and
WT across geographic regions and environmental conditions (Bubier et al. 1995a).
Although WT was an important variable at all spatial and temporal scales, the strength and
relative importance of WT weakened at finer scales of observation. For shrub, moss and
graminoid functional types, seasonal average WT strongly correlated with CH4 flux in both the
flooded and drained seasons, in agreement with the patterns observed at the broader spatial scale.
At the monthly timescale, this relationship was significant only when both years were combined;
in 2012, water table declines throughout the growing season correlated with higher CH4 fluxes
from moss and shrub sites and WT was not a significant driver of seasonal CH4 variability in
71
2013. For individual vegetation groups, WT did not explain seasonal CH4 flux patterns in 2012
but strong positive correlations were found in 2013. Intuitively, one would expect to observe
smaller CH4 flux in response to WT declines throughout the growing season, yet this relationship
has been difficult to quantify under field conditions. This is likely due to a host of collinear
variables that confound results in addition to the possibility that the microorganisms responsible
for CH4 oxidation and production may not immediately respond to water table fluctuations.
Despite these challenges, I observed water table declines throughout the 2013 growing season
correlating with smaller CH4 fluxes for hummock, moss, rush and all 3 margin groups (mesic,
wet, shrub).
The importance of peat temperature became more pronounced at finer scales and contributed
more to the seasonal patterns (weekly and monthly variations) for plant functional types and all
nine vegetation groups. The positive relationship between CH4 flux and peat temperature has
been previously determined in peatlands and is explained by the metabolic activity of
microorganisms, which is enhanced by rising temperatures (Crill et al. 1988). The colder peat
temperatures in the drained season certainly contributed to the overall smaller CH4 emissions.
Large CH4 emissions in 2012 were associated with falling water levels and rising temperatures
during a summer drought period and immediately after the pond was drained in October. Peat
temperatures in 2013 were consistently cooler, natural summer drawdown was less pronounced,
and increases in CH4 flux throughout the growing season did not correlate with falling water
levels. Other studies have observed similar increases in CH4 flux following a drop in water table,
either experimentally induced or by natural drawdown (Moore & Dalva 1993; Moore & Roulet
1993b), followed by smaller overall emissions in the next growing season (Hughes et al. 1999).
The release of stored CH4 has been linked to changes in pressure gradients associated with a
falling water table and/or declines in atmospheric pressure (Brown et al. 2014). The large
emissions observed in 2012 are likely due in part to episodic CH4 fluxes triggered by falling
summer water tables. A falling summer water table is typical in wetlands, with the magnitude
and duration of the drawdown largely influenced by precipitation and evapotranspiration rates,
resulting in episodic CH4 fluxes independent of vegetation dynamics that otherwise influence
methanogenesis, such as substrate supply and species composition. Treat et al. (2007) also found
the largest CH4 fluxes in a temperate fen during a season with an exceptionally pronounced
72
summer drawdown, which they attributed to changes in pressure gradients and increased CH4
production coincident with warming temperatures. Similar to this study, they also found the
lowest fluxes in a season that was cooler and with relatively stable water levels. It appears that
episodic fluxes are triggered by physical cues and significantly contribute to seasonal CH4
emissions.
5.3.3 Biotic correlates of methane flux
Episodic CH4 fluxes were not observed in 2013 because the peat was drier, and standing water
was absent and biotic variables became more important. To illustrate this, my results showed that
biotic variables related to CO2 exchange and green area were stronger correlates on CH4
emissions in 2013 than 2012 and explained as much as or more of the seasonal variations CH4
flux than temperature and WT at finer scales. Supply and quality of available substrate greatly
influences methanogenesis and can differ between plant species and environments (Öquist &
Svensson 2002; Ström et al. 2005; Lai et al. 2014), with graminoids generally providing higher
quality substrate in the form of acetate than shrubs and Sphagnum mosses (Bellisario et al.
1999). In addition to substrate quality, the quantity of photosynthate that is emitted as CH4 can
vary between species even of the same functional type. For example, more photosynthate from
Typha and Phalaris species is emitted as CH4 than from Carex and Eriophorum species (Wilson
et al. 2009). GEP positively correlated with CH4 fluxes and increased in importance at finer
spatial and temporal scales. GEP was included in all multiple regression models at monthly
scales for the three plant functional types and all vegetation groups with the exception of rush
and typha. GEP was the most important predictive variable for hummock, hollow and moss at
the monthly scale and for all vegetation groups at the seasonal time scale, with the exception of
pond moss and minerotrophic graminoids rush and typha. Oquist & Svensson (2002) observed
that minerotrophic and ombrotrophic graminoid species influenced CH4 flux in different ways
and proposed that CH4 transport via aerenchyma was the prominent influence on CH4 flux in
minerotrophic sites whereas substrate-based interactions regulated by photosynthesis dominated
at ombrotrophic sites. This is due, in part, to differences in the available nutrient pool between
ombrotrophic and minerotrophic sites, the former relying more heavily on plant-mediated carbon
input (Öquist & Svensson 2002). This may explain why rush and typha sites were less dependent
on GEP than margin and bog sites and why peat temperature was stronger than GEP in
explaining CH4 flux for margin and pond sites at the monthly scale.
73
Interestingly, fitting a Michaelis-Menten equation to the monthly data for vegetation groups
yielded a significant positive relationship explaining 20% of the variance in monthly average
methane fluxes (Figure 17). Michaelis-Menten equations have been used to describe the positive
relationship between gross CH4 production and substrate concentration (e.g., acetate), hence
GEP may be useful as a proxy for substrate concentration in the field. My results demonstrate
that GEP is a strong control over the seasonal patterns in CH4 flux by providing substrate for
methanogenesis throughout the growing season. Additionally, smaller GEP after the drainage
contributed to the smaller overall CH4 emissions.
In addition to substrate dynamics mediated by photosynthesis, the presence and productivity of
graminoids were of almost equal importance in explaining spatial patterns of CH4 flux, with
larger seasonal fluxes for both years associated with larger graminoid green area (Figure 15).
Many graminoids, including the species in our study, possess aerenchyma, which allow for the
movement of CH4 from the anaerobic zone directly to the atmosphere, bypassing aerobic
oxidation (Whalen 2005). A 5-year study of CH4 fluxes at Mer Bleue revealed that the
combination of aerenchymous vegetation (Eriophorum vaginatum and Maianthemum trifolium),
peat temperature at 40 cm and water table depth explained significantly more of the variation
than water table alone (Moore et al. 2011) and my results are in agreement with this study.
Strack et al. (2006) predicted that sedge biomass and production are likely to be enhanced under
drier conditions, and suggested that ecological succession of sedges onto previously inundated
areas is an important consideration for peatland carbon models (Strack et al. 2006b). In my study
site, the peatland sedge Carex oligosperma dominates the margin while the pond is dominated by
freshwater graminoids Juncus effusus and Typha angustifolia. I observed considerably lower
graminoid productivity (green area, NEE and GEP) after the drainage, but an increase in pond
moss (S. cuspidatum) productivity and abundance. Increased sedge abundance and productivity
in response to lower peatland water tables does not capture the behavior of all sedge or
graminoid species, as there are physiological differences between species even of the same
genus. For example, differences in aerenchyma formation and structure can confer differential
flooding tolerance in Carex, Juncus and Typha species (Smirnoff & Crawford 1983; Visser et al.
2000). Tradeoffs between drought tolerance and flooding tolerance have been observed for
freshwater graminoids and species with a high tolerance to flooding often have a low tolerance to
drought (Luo et al. 2008). Further, species may display short- and long-term adaptations in
74
response to hydrological disturbances, either flooding or drought. For example, adjusting
biomass allocation between shoots and roots is a well documented response to water stress in a
variety of graminoid species (Soukupová 1994; Gold 2000; Luo et al. 2008) as well as bog
herbs, shrubs and trees (Murphy et al. 2009; Murphy & Moore 2010). It is likely that reduction
in green area and photosynthesis not only in graminoids but in also shrubs and bog mosses was
induced by water stress, and species may have allocated shoot biomass belowground as a shortterm strategy. If the new lower water table depth is maintained, plant species will need to shift
their distribution to follow their preferred water levels or be replaced by more competitive or
adapted species.
75
Chapter 6: Summary & Conclusions
This study has demonstrated that distinct plant groups found along a flooded bog margin respond
differentially to a lowered water table. Moreover, feedback mechanisms between vegetation,
CO2 and CH4 were identified and their implications are discussed in closing.
My first objective was to characterize the distribution of plant species along the bog – margin
gradient and identify the environmental correlates of species distributions. I identified six distinct
plant communities that contained characteristic species corresponding to changes in water table
depth; hummock, hollow, bog margin, pond margin, mud flat and open water. The primary
drivers of plant community structure were variables related to water table depth and water
chemistry, which is in agreement with other peatland studies. I found unimodal or nearly
unimodal responses of species abundance to mean water table position. Optima and tolerance
varied between species, suggesting niche differentiation along the water table gradient from bog
to pond. The narrowest tolerances were found in the wettest vascular and bryophyte species (e.g.
Juncus, Typha, S. cuspidatum) and the widest tolerances were found in vascular and bryophyte
species that occupied both hummocks and hollows (C. calyculata, R. groenlandicum, V.
corymbosum, S. magellanicum).
My second objective was to quantify CO2 and CH4 flux rates from the range of vegetation along
the bog – margin gradient, to identify the biotic and abiotic correlates on flux rates and compare
their strength and relative importance across spatial and temporal scales. Rates of NEE, ER, GEP
and CH4 flux differed between sites and between years, with a larger variance in fluxes observed
in the pre-drained 2012 season. The spatial variation of CO2 exchange along the bog – margin
moisture gradient was controlled by the vegetation composition, with distinct species
assemblages being associated with characteristic water table positions and rates of NEE. CH4
flux rates were greatly influenced by the presence of graminoids, but distinct rates of CH4 flux
for other species assemblages were inconsistent between years. Flux relationships followed
patterns in green area more closely than water table depth (minerotrophic graminoids > fens
(shrubs + sedges) > bogs (shrubs > mosses)).
Seasonal patterns in CO2 exchange were related to peat temperature and changes in WT, with
temperature being the strongest predictor of weekly NEE for graminoid-dominated sites while
76
WT was the strongest predictor of NEE for shrub-dominated sites. ER was controlled by peat
temperature at all scales of observation, but WT and GEP became stronger predictors at finer
spatial and temporal scales, indicating feedbacks between plant productivity and both autotrophic
and heterotrophic respiration. I found unimodal relationships between weekly GEPmax and WT,
with hummock, hollow, margin, and moss vegetation groups clearly differentiating along the
moisture gradient. Hummocks had the driest optima along the gradient and displayed the
broadest tolerance range, followed by hollows and bog moss both in terms of optima and
tolerance. Margin and pond moss sites had wetter optima and narrower tolerances. When
compared to the optima calculated for species abundance, the optimum WT positions for
hummocks and pond moss were considerably drier than the optimum found for species
abundance, but were within both tolerance ranges. Tolerance ranges determined from species
abundance were broader than those determined from GEPmax with the exception of pond moss,
whose photosynthetic tolerance range was broader than its abundance range.
The seasonal variability in CH4 flux was related to peat temperature and GEP, whereas the long
term position of the water table explained spatial patterns by controlling the depth of the aerobic
layer. The presence and productivity of graminoids were of almost equal importance in
explaining spatial patterns of CH4 flux, with larger fluxes for both years associated with larger
graminoid green area. Although both WT and GEP were important for CH4 at all scales of
observation, finer spatial and temporal scales captured more of the biological dynamics related to
plant productivity and microbial activity. In the absence of strong fluctuations in water levels
during the growing season, plant productivity had a stronger influence than water table.
My third objective was to assess the short-term effects of a lowered water table on plant
community structure, CO2 and CH4 flux rates. Lowering the water table induced differential
changes in species composition that were seen even in the first season of the drainage. Lowering
the water table did not alter bog species composition because the water table did not drop below
the tolerance range of bog hummock and hollow species. There was, however, a significant
increase in minerotrophic species in pond areas, particularly tall graminoids (e.g., Calamagrostis
canadensis, Scirpus cyperinus, Sparganium americanum). Exposing the pond sediments allowed
seedbank species to germinate and expand onto previously inundated areas. Additionally, the
amount of open water in the pond was reduced, causing the floating aquatic species to decline or
77
become completely absent in 2013. Although S. fallax cover significantly declined at the
margin, the S. cuspidatum mat expanded onto previously inundated areas both towards the bog
and further into the pond. Changes in abundance could be explained by differences in species
optima and tolerances relative to water table depth.
The lowered water table differentially affected the individual components of CO2 exchange. In
graminoid-dominated sites, I observed greater reductions in GEPmax relative to ER resulting in
smaller NEEmax. Larger NEEmax in moss sites was caused by decreased ER relative to GEPmax
and there were no significant changes in NEEmax for shrub-dominated sites because GEPmax and
ER were proportionally reduced. I report first estimations of carbon use efficiency (CUE) values
for ombrotrophic bog, margin and beaver pond vegetation and CUE for all plant groups
increased in the drained season due to lower seasonal ER accompanied by less pronounced
reductions in GEPmax. Overall lower ER rates were attributed to colder peat temperatures in the
drained season and reduced plant productivity which counteracted the positive effects of a deeper
oxic layer on ER. Photosynthetic responses to the lowered water table were, again, different
between plant groups. Photosynthetic rates (GEPmax) and efficiency (α) were minimally reduced
in bog vegetation whereas margin and pond photosynthetic rates and efficiency significantly
declined. Decreases in photosynthetic performance were attributed to declines in green area and
species-specific tolerances to water level position.
CH4 flux was substantially smaller at all sites after the pond was drained, with reductions from
55- 98%. Reductions were primarily attributed to the lower water levels, but cooler peat
temperatures and reduced plant productivity also contributed. I demonstrated that graminoid
cover greatly enhances CH4 emissions and that tall graminoid species increased in abundance at
the pond margin in the first season of the drainage. Conversely, other species such as S. fallax
and C. oligosperma declined in abundance and/or green area. Thus, if the new lower water table
depth is maintained, plant species will need to shift their distribution to follow their preferred
water levels or be replaced by more competitive or adapted species. Continued species shifts
may alter important processes related to greenhouse gas fluxes through changes in productivity,
substrate quality or plant mediated transport, with significant implications for feedback
mechanisms in a changing climate.
78
My findings emphasize the importance of vegetation dynamics in this system, and the need for a
better mechanistic understanding of the relationship between vegetation and carbon dynamics
under field conditions. I highlight the importance of considering functionally distinct plant
species and assemblages separately in both plant ecology and carbon cycling studies. Taking into
account the peatland margin is equally important since it is in a state of constant flux between
changing environmental conditions and species interactions, the direction of which has
consequences for peatland structure and function.
The importance of photosynthesis is also emphasized; my results suggest that GEP is influenced
not only by the position of the water table, but by seasonal influences. Suboptimal spring
conditions may have limited productivity in this crucial period of plant development and
dampened production for the rest of the season, even after water levels rose to optimal positions
later in the season. This necessarily affected both ER and CH4 dynamics, which greatly
contribute to an ecosystem’s carbon balance. The majority of carbon exchange studies in
peatlands focus on the peak growing season period from June to August, but dynamics in the
spring may be more important in driving seasonal and interannual variation. Future research that
focuses on interannual spring conditions at fine spatial scales have the potential to improve our
understanding of which environmental conditions are optimal for the performance of essential
functions such as carbon assimilation for different species assemblages. Determining the optimal
environmental range for essential plant functions is important not only for environmental
management (e.g., habitat restoration) but for improving climate models and reducing the
uncertainty in forecasting responses to future climate scenarios.
79
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Appendix
Table 1: Species names and abbreviations used in data analyses. Herbarium vouchers were collected for each
species and placed in the McGill University Herbarium. Nomenclature follows the USDA online plants database
(http://plants.usda.gov).
Abbr.
Genus
Species
Species authority
Family
Veg Group
Androm
Andromeda
polifolia
L. var. glaucophylla (Link)
DC.
Ericaceae
Bog Hollow
Aronia
Photinia
melanocarpa
(Michx.) K.R. Robertson &
Phipps
Rosaceae
Bog Hummock
Calamag
Calamagrostis
canadensis
(Michx.) P. Beauv.
Poaceae
Pond Margin
Calla
Calla
palustris
L.
Araceae
Pond Margin
Caroligo
Carex
oligosperma
Michx.
Cyperaceae
Bog Margin
Cham
Chamaedaphne
calyculata
(L.) Moench
Ericaceae
Bog Margin
Dulich
Dulichium
arundinaceum
(L.) Britton
Cyperaceae
Pond Margin
Eleocharis
Eleocharis
sp.
Cyperaceae
Pond
Eriophvirg
Eriophorum
virginicum
L.
Cyperaceae
Bog Margin
Spargan
Sparganium
americanum
Nutt.
Sparganiaceae
Pond Margin
Bidens
Bidens
frondosa
L.
Asteraceae
Pond
Juncus
Juncus
effusus
L.
Juncaceae
Pond Margin
Kalmiang
Kalmia
angustifolia
L.
Ericaceae
Bog Hummock
Larix
Larix
laricina
(Du Roi) K. Koch
Pinaceae
Bog Hollow
Ledum
Ledum
groenlandicum
Oeder
Ericaceae
Bog Hollow
Scirpus
Scirpus
cyperinus
(L.) Kunth
Cyperaceae
Pond
Triaden
Triadenum
fraseri
(Spach) Gleason
Clusiaceae
Pond
Typha
Typha
angustifolia
L.
Typhaceae
Pond Margin
Vaccang
Vaccinium
angustifolium
Aiton
Ericaceae
Bog Hummock
Vaccorym
Vaccinium
corymbosum
L.
Ericaceae
Bog Hummock
Vaccmyrt
Vaccinium
myrtilloides
Michx.
Ericaceae
Bog Hollow
Brasenia
Brasenia
schreberi
J.F. Gmel.
Cabombaceae
Pond
Polytrich
Polytrichum
strictum
Brid.
Polytrichaceae
Bog Hummock
Spang
Sphagnum
angustifolium
(C.E.O. Jensen ex Russow)
C.E.O. Jensen
Sphagnaceae
Bog Hollow
Spcapil
Sphagnum
capillifolium
(Ehrh.) Hedw.
Sphagnaceae
Bog Hummock
Spcusp
Sphagnum
cuspidatum
Ehrh. ex Hoffm.
Sphagnaceae
Pond Margin
Spfallax
Sphagnum
fallax
(Klinggr.) Klinggr.
Sphagnaceae
Bog Margin
Spmag
Sphagnum
magellanicum
Brid.
Sphagnaceae
Bog Hollow
Sppapill
Sphagnum
papillosum
Lindb.
Sphagnaceae
Bog Margin
Utric
Utricularia
macrorhiza
Leconte
Lentibulariaceae
Pond
87
Table 2: Summary of seasonal green area and graminoid area for individual collars in 2012 and 2013. Units are in
area (m2 m-2).
Collar
BW1_C1
BW1_C2
BW3_C2
BW1_C4
BW3_C1
BW3_C4
BW1_C3
BW2_C1
BW3_C3
BW1_C5
BW2_C2
BW2_C3
BW1_C6
BW1_C7
BW2_C4
BW1_C8
BW2_C6
BW3_C6
BW2_C5
BW3_C5
BW3_C7
BW1_C10
BW2_C7
BW2_C8
BW1_C9
T_1
T_2
Vegetation Group
1 - Hummock
1 - Hummock
1 - Hummock
2 - Hollow
2 - Hollow
2 - Hollow
3 - Bog moss
3 - Bog moss
3 - Bog moss
4 - Mesic margin
4 - Mesic margin
4 - Mesic margin
5 - Wet margin
5 - Wet margin
5 - Wet margin
6 - Shrub margin
6 - Shrub margin
6 - Shrub margin
7 - Pond moss
7 - Pond moss
7 - Pond moss
8 - Rush
8 - Rush
8 - Rush
9 - Typha
9 - Typha
9 - Typha
Green Area
2012
2013
8.3
5.1
4.4
3.2
3.5
4.1
4.8
4.4
1.7
1.7
3.2
4.9
1.3
1.4
2.0
1.9
1.1
0.5
6.9
4.1
6.7
3.6
2.6
6.1
6.0
4.4
3.3
2.7
3.3
2.9
6.9
8.4
2.9
4.5
3.8
5.8
1.1
1.1
0.7
0.5
0.5
1.1
10.3
6.4
5.7
7.7
10.0
6.6
5.0
7.8
0.7
22.0
21.4
88
Graminoid Area
2012
2013
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
5.3
3.4
5.5
2.5
1.5
5.5
2.3
1.7
1.5
1.1
2.1
0.7
2.6
5.8
0.2
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
9.9
6.4
5.7
7.1
8.9
6.4
5.0
7.1
0.5
22.0
21.2
Table 3: Summary of seasonal NEEmax, ER, GEPmax (μmol m-2 s-1) and CH4 (mg m-2 d-1) for individual
collars in 2012 and 2013.
Collar
BW1_C1
BW1_C2
BW3_C2
BW1_C4
BW3_C1
BW3_C4
BW1_C3
BW2_C1
BW3_C3
BW1_C5
BW2_C2
BW2_C3
BW1_C6
BW1_C7
BW2_C4
BW1_C8
BW2_C6
BW3_C6
BW2_C5
BW3_C5
BW3_C7
BW1_C10
BW2_C7
BW2_C8
BW1_C9
T_1
T_2
Vegetation Group
1 - Hummock
1 - Hummock
1 - Hummock
2 - Hollow
2 - Hollow
2 - Hollow
3 - Bog moss
3 - Bog moss
3 - Bog moss
4 - Mesic margin
4 - Mesic margin
4 - Mesic margin
5 - Wet margin
5 - Wet margin
5 - Wet margin
6 - Shrub margin
6 - Shrub margin
6 - Shrub margin
7 - Pond moss
7 - Pond moss
7 - Pond moss
8 - Rush
8 - Rush
8 - Rush
9 - Typha
9 - Typha
9 - Typha
NEEmax
2012
2013
4.0
3.7
-0.1
1.8
0.7
2.0
2.4
1.4
1.5
1.8
3.2
2.3
0.1
0.2
-0.3
1.4
-0.2
-0.2
6.8
2.3
4.0
2.1
1.0
0.9
4.9
3.0
2.2
0.6
2.6
1.5
5.9
2.6
4.2
2.0
6.5
2.0
-0.2
1.2
-0.9
-0.8
-1.3
1.2
12.6
9.0
7.9
7.9
15.7
6.3
8.7
18.3
-0.2
20.7
5.1
ER
2012
-9.2
-10.8
-7.5
-6.1
-6.0
-7.4
-2.7
-3.9
-2.5
-4.4
-4.3
-4.5
-6.5
-4.2
-4.5
-4.3
-3.4
-6.0
-0.9
-2.4
-2.2
-10.1
-7.1
-8.8
-6.8
-8.8
89
2013
-2.9
-4.1
-2.6
-2.4
-2.4
-2.3
-1.5
-1.8
-1.3
-2.4
-2.4
-1.5
-4.0
-1.7
-1.2
-2.8
-2.2
-2.1
-1.0
-1.3
-1.3
-4.8
-5.1
-3.3
-2.8
-3.0
-4.0
GEPmax
2012
2013
10.8
6.6
12.6
5.8
9.6
4.6
8.1
3.8
7.7
4.2
10.0
4.5
3.2
1.7
3.6
3.2
2.1
1.2
11.2
4.6
8.3
4.5
5.1
2.4
11.4
7.0
6.5
2.3
7.0
2.7
9.9
5.4
7.9
4.2
12.7
4.1
0.8
2.2
1.2
0.5
0.5
2.6
22.6
13.8
15.3
12.9
24.7
9.6
11.5
24.5
2.8
29.5
9.1
CH4
2012
2013
8.2
3.3
37.2
3.2
65.9
1.3
179.6
11.8
111.7
3.5
184.2
4.1
90.2
3.1
47.0
1.3
104.3
-0.3
599.8
49.8
645.8
38.8
397.6
18.5
539.9
32.5
307.9
19.6
391.1
11.9
499.2
41.4
469.5
4.8
227.9
6.8
130.6
5.4
114.1
2.8
241.4
32.8
817.6
786.9
1458.0 359.6
912.6
216.1
443.9
316.6
23.7
916.7
269.5
Table 4: Pearson correlation coefficients between independent variables and NEE, ER, GEP and logCH4
at seasonal and monthly scales for all sites and plant functional types. NS: not significant, ** significant
at 0.01 level, * significant at 0.05 level.
Seasonal
NEE x GA
NEE x T_10
NEE x T_air
NEE x WT
ER x GA
ER x T_10
ER x T_air
ER x WT
GEP x GA
GEP x T_10
GEP x T_air
GEP x WT
logCH4 x GA
logCH4 x GEP
logCH4 x GrA
logCH4 x NEE
logCH4 x
T_10
logCH4 x
T_air
logCH4 x WT
Monthly
NEE x GA
NEE x T_10
NEE x T_air
NEE x WT
ER x GA
ER x T_10
ER x T_air
ER x WT
GEP x GA
GEP x T_10
GEP x T_air
GEP x WT
logCH4 x GA
logCH4 x GEP
logCH4 x GrA
logCH4 x NEE
logCH4 x
T_10
logCH4 x
T_air
logCH4 x WT
All sites
2012
2013
0.86**
0.52**
0.58**
0.56**
-0.48**
0.45*
NS
NS
-0.61**
-0.54**
-0.56**
-0.52**
0.44*
-0.49**
NS
NS
0.87**
0.56**
0.62**
0.59**
-0.49*
0.49**
NS
NS
0.47*
0.52**
0.48*
0.88**
0.62**
0.68**
0.58**
0.90**
Graminoid
2012
2013
0.83**
NS
0.71*
0.81**
NS
NS
NS
0.82**
-0.69*
NS
-0.74**
-0.74**
NS
NS
-0.82**
NS
0.83**
NS
0.75**
0.84**
NS
NS
0.61*
0.72**
NS
NS
NS
0.97**
NS
NS
NS
0.97**
Moss
2012
2013
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.87*
NS
NS
NS
NS
NS
0.86*
0.85*
NS
NS
NS
NS
-0.96**
NS
-0.88*
NS
-0.89*
NS
NS
NS
NS
NS
Shrub
2012
2013
NS
NS
NS
NS
NS
NS
0.66*
NS
NS
NS
NS
NS
NS
NS
0.86**
NS
NS
NS
0.73*
NS
NS
NS
NS
NS
NS
0.79*
NS
NS
NS
0.95**
NS
NS
NS
0.37*
NS
0.87**
NS
NS
NS
NS
NS
0.75**
2012
0.80**
0.40**
NS
0.27*
-0.38**
-0.53**
-0.37**
0.36**
0.80**
0.49**
NS
NS
0.36**
0.40**
0.58**
0.48**
0.56**
0.55**
2013
0.41**
0.41**
0.34**
NS
-0.38**
-0.36**
-0.61**
-0.48**
0.44**
0.52**
0.42**
NS
0.45**
0.79**
0.60**
0.79**
NS
NS
2012
0.76**
0.52**
NS
0.39*
-0.46**
-0.64**
-0.51**
NS
0.76*
0.59**
NS
0.41**
0.42**
NS
0.43**
NS
NS
0.72**
2013
0.29**
0.53**
0.35**
NS
-0.31*
-0.32*
-0.64**
-0.43**
0.32*
0.60**
0.41**
NS
0.33**
0.87**
0.38**
0.85**
NS
0.86*
2012
NS
-0.66**
0.45**
NS
-0.49*
0.49*
NS
0.56*
NS
0.47*
NS
NS
NS
NS
NS
NS
NS
0.82*
2013
0.58**
NS
NS
NS
NS
NS
-0.64**
-0.57**
0.53**
NS
NS
NS
NS
NS
NS
NS
NS
0.98**
2012
NS
NS
NS
0.42*
NS
-0.57**
-0.40*
0.54**
NS
0.58**
0.44*
NS
NS
NS
0.49**
0.48*
NS
0.65*
2013
NS
0.53**
0.61**
NS
NS
NS
-0.71**
-0.70**
NS
0.75**
0.80**
-0.46*
0.46**
NS
0.57**
NS
NS
0.38**
0.33*
0.64**
NS
0.38*
0.31*
NS
NS
0.56**
0.30**
0.25**
0.32*
NS
0.40**
NS
NS
-0.62**
NS
NS
0.39**
0.67**
NS
NS
90