Ecohydrological Controls on Temperate Wetland Shrub Dynamics
by
Hannah Elizabeth Ormshaw
A thesis submitted in conformity with the requirements for the degree of Master of Science Geography University of Toronto
© Copyright by Hannah Elizabeth Ormshaw 2014 Ecohydrological Controls on Temperate Wetland Shrub Growth and Stomatal Conductance
Hannah Elizabeth Ormshaw
Master of Science
Geography University of Toronto
2014 Abstract
In ecohydrology, soil moisture is the fundamental component to describe the underlying hydrologic regime at a site. Soil moisture, along with air temperature, vapour pressure deficit, and solar irradiance are significant controlling factors in plant growth and the physiological function of stomatal conductance (gs). To observe the ecohydrological controls on wetland shrub growth and physiology, an experimental plot within a post- agricultural field was established to manipulate soil moisture availability. Growth was measured as growing season increases in biomass under different levels of wetness, while gs was measured in-situ and correlated to soil moisture and site weather measurements using a logistic upper-quantile, non-linear regression approach. Two modelling techniques – a multiple linear regression model, and an adapted Jarvis-type phenomenological model – were used to illustrate growing season trends in gs. The results of this study describe plant tolerance to moisture as well as seasonal water demands, which is currently unknown for wetland shrub species.
ii Acknowledgments
I would like to express my deep gratitude to Dr. Tim P. Duval, my research supervisor, for his invaluable guidance, encouragement, and feedback throughout my research and analysis. His patience, superb teaching ability, and his attitude toward science was essential to my learning, and made this thesis possible. I am also particularly grateful for the assistance and advice provided by Dr. D. Scott Munro, in setting up the meteorological station at my study site, and in many of my climate-related calculations. I would also like to offer my special thanks to Dr. Yuhong He and Dr. Laura Brown for sitting on my thesis defense committee. I also would like to acknowledge the laboratory technicians, Ken Turner and Phil Rudz, for the lab assistance and resources that supported my work. The assistance provided by my many field assistants, Danielle Radu,
Bryn Fraser, Matthew Malone, Ahren Alfonso, Courtney Soden, Tammy Duong, Axel
Thomas, Michael Harris, Teresa Didiano, Christine Valancius, Alyssa Campitelli, Je-
Hyoeng Hung, Florence Thevabalan, and Adrian Lue, was invaluable throughout experiment set-up and data collection. I would like to thank Mattamy Homes Inc, Credit
Valley Conservation Authority, the Ministry of Natural Resources, the City of Brampton, and Urbantech Consulting for site access and project support. Finally, a huge thanks to my family, my boyfriend, and my friends for being a source of distraction from time to time, for the free therapy, and for their endless encouragement during completion of my degree.
iii Table of Contents
Acknowledgments ...... iii Table of Contents ...... iv List of Tables ...... vi List of Figures...... vii List of Appendices...... viii Chapter 1 ...... 1 Research Background and Rationale...... 1 1.1 Wetland Restoration...... 1 1.2 Ecohydrology...... 2 1.3 Study Rationale...... 8 1.4 References ...... 13 Chapter 2 ...... 18 Growth Responses of Ontario Native Wetland Shrubs to Increasing Soil Moisture 18 2.1 Introduction ...... 18 2.2 Methods ...... 24 2.2.1 Study Site ...... 24 2.2.2 Experimental Design...... 26 2.2.3 Data Analysis ...... 29 2.3 Results...... 31 2.3.1 Volumetric Moisture Content of the Soil...... 31 2.3.2 Stem Height ...... 35 2.3.3 Total Plant Biomass ...... 37 2.3.4 Partitioning of Aboveground and Belowground Biomass ...... 43 2.4 Discussion...... 45 2.4.1 Role of moisture in controlling growth and supporting literature evidence ...... 45 2.4.2 Applications ...... 53 2.5 Conclusion...... 57 2.6 References ...... 58 Chapter 3 ...... 63 Controlling Effects of Environmental Variables on Wetland Shrub Stomatal Conductance ...... 63 3.1 Introduction ...... 63 3.2 Methods ...... 70 3.2.1 Study Site ...... 70 3.2.2 Field data collection...... 72 3.2.3 Modelling...... 77 3.3 Results & Discussion ...... 79 3.3.1 Environmental Variables ...... 79 3.3.2 Observed Stomatal Conductance ...... 83 3.3.3 Controls on Stomatal Conductance...... 87
iv 3.3.4 Modelling Stomatal Conductance...... 98 3.3.5 Model performance...... 104 3.3.6 Applications ...... 107 3.4 Conclusion...... 108 3.5 References ...... 112 Chapter 4 ...... 122 Conclusion ...... 122 References ...... 127
v List of Tables
Chapter 1 ...... 1 1.1 2013 Site Weather Conditions Compared to Historic Climate Norms...... 11 Chapter 2 ...... 18 2.1 Native Wetland Species Characteristics...... 28 2.2 Volumetric Moisture Content for each Species and Treatment and Average Difference in Moisture between each Treatment through June, July, and August ...... 34 2.3 Average Percent Increase in Stem Height from Planting to Peak Height, and Highest Average Increase in Height between Measurement Dates ...... 36 2.4 Average Total Biomass and Percent Increase over 16 Weeks of Growth...... 38 2.5 Results of ANOVA and Tukey’s Post-Hoc for between-Treatment Total Biomass.....39 2.6 Average Ratio of Aboveground to Belowground Biomass ...... 43 2.7 Results of ANOVA and Tukey’s Post-Hoc for Aboveground to Belowground Ratios ...... 45 2.8 Coefficients of Wetness for each of the Wetland Species...... 55 Chapter 3 ...... 63 3.1 Native Wetland Species Characteristics...... 72 3.2 Volumetric Moisture Content for each Species and Treatment ...... 82 3.3 Observed Stomatal Conductance...... 84 3.4 Literature Values for Stomatal Conductance of Various Species ...... 86 3.5 Results of Multiple Linear Regression Model for Stomatal Conductance...... 99 3.6 Results of Jarvis-type Model for Stomatal Conductance ...... 103 Chapter 4 ...... 122 3.1 Moisture Tolerance of Wetland Species...... 123
vi List of Figures
Chapter 1 ...... 1 1.1 Range in Tolerance to Soil Moisture for Two Hypothetical Species ...... 7 1.2 Aerial Photograph of Study Site ...... 11 Chapter 2 ...... 18 2.1 Diagram of Treatment Design within the Experimental Plot ...... 27 2.2 Daily Precipitation at the Study Site from the Time of Planting to the End of the Measurement Period ...... 31 2.3 Average Volumetric Moisture Content of each Treatment and Species for each of the Maesurement Dates...... 33 2.4 Percentage Increase in Total Biomass from Initial Measurements...... 37 2.5 Average Total Plant Biomass per Treatment and Species ...... 40 2.6 Ratios of Aboveground to Belowground Biomass for each Species and Treatment....44 Chapter 3 ...... 63 3.1 Diagram of Treatment Design within the Experimental Plot ...... 73 3.2 Growing Season Trends in Measured Environmental Variables ...... 81 3.3 Variation in Measured Soil Moisture in Each Replicate for Each Species...... 83 3.4 Box Plots for Observed Stomatal Conductance for Each Species ...... 84 3.5 Logistic Upper-Quantile Response Curve for Observed Stomatal Conductance to Vapour Pressure Deficit...... 89 3.6 Logistic Upper-Quantile Response Curve for Observed Stomatal Conductance to Air Temperature ...... 91 3.7 Logistic Upper-Quantile Response Curve for Observed Stomatal Conductance to Incoming Shortwave Radiation...... 93 3.8 Logistic Upper-Quantile Response Curve for Observed Stomatal Conductance to Soil Moisture ...... 95 3.9 Growing Season Trends in Modelled Conductance for Each Species from Multiple Linear Regression Approach...... 100 3.10 Growing Season Trends in Modelled Conductance for Each Species from Jarvis-type Approach...... 104
vii List of Appendices
Chapter 2 ...... 18 2A Results of Repeat-Measures ANOVA for Measured Volumetric Moisture Content between Treatments and Dates ...... 62 Chapter 3 ...... 63 3A Comparison of Net Radiation between the Study Site Weather Station and the University of Toronto Mississauga Weather Station ...... 118 3B Logistic Growth Coefficients for each Species and Environmental Variable used to Generate Response Curves...... 119 3C Regression Coefficients for Multiple Linear Regression Model ...... 120 3D Results of ANOVA and Statistical Significance in Observed Stomatal Conductance of Each Species...... 121
viii Chapter 1 Research Background and Rationale
1.1 Wetland Restoration
Wetlands are among one of the most productive ecosystem types in the world, as well as one of the most economically valuable (Mitch & Gosselink, 2007; Zedler, 2000). Since settlement in
North America, however, it has been determined that over half of historic wetland coverage has been lost through conversion to other land-uses. The loss continues at a rate of between 28300-
36400 hectares a year (Moreno-Mateos, 2012). Wetland loss in Southern Ontario particularly has been a major concern, as it is estimated that 72% of pre-settlement wetland coverage has been lost (Ducks Unlimited Canada, 2010). Wetland restoration efforts are carried out in an attempt to recover some of this ecosystem loss, and to return some of the valuable ecosystem services that wetlands provide. These services include improving water quality, providing flood mitigation and storm water management, increasing habitat and biodiversity, acting as carbon sinks, and providing recreational and aesthetic value (Kardol & Wardle, 2010; Palmer & Filoso,
2009; Zedler, 2000).
Setting goals or targets for ecosystem self-regulation, plant community composition, and hydrologic characteristics helps determine restoration success. The common practice in restoring wetlands is manipulating physical hydrological features to allow for increased availability of water at the site, and introducing new species to the site (Suding et al., 2004;
Zedler, 2000). However, the current issue in ecological restoration is that project design is usually not process-based; rather it is based on structural features of wetland ecosystems
(Palmer & Filoso, 2009). Since the 1980s, when ecological restoration projects were emerging,
1 there has been significant work to include ecological concepts into the project design, such as succession theory, niche differentiation, threshold dynamics and state transitions, among others
(Kardol & Wardle, 2010; Zedler, 2000). There are very few projects, however, that incorporate knowledge on the interplay between ecology and hydrology into restoration plans (Kardol &
Wardle, 2010). Restoration projects often set specific targets for plant community assemblages, which requires a strong understanding of hydrological and substrate conditions suitable for these plants (Kardol & Wardle, 2010; Stroh et al., 2013; Zedler, 2000). Without proper incorporation of this knowledge, restored wetlands frequently fail to reach reference ecosystem levels, or fail to establish the expected plant community (Moreno-Mateos et al., 2012; Suding et al., 2004;
Zedler, 2000). Once species establish that respond differently from the historical or reference levels, a site shift to an alternative stable state can occur. Along with this shift comes a new set of ecosystem processes and a positive feedback begins which makes a site resilient to further restoration or alteration (Suding et al, 2004). Therefore, proper planning for species introduction is important to ensure that the proposed plant community will establish successfully, and persist for a long period of time. Since wetland ecohydrology is an area that has not yet been fully explored, increased knowledge of wetland ecohydrological relationships and plant responses to changing environmental conditions can provide valuable information to restoration practitioners on water demands, growth success, and plant physiology of wetland species, and help increase restoration success rates.
1.2 Ecohydrology
The study of ecohydrology focuses on the interacting relationships throughout the soil-plant- water-atmosphere continuum (D’Odorico et al., 2010; Rodriguez-Iturbe, 2000). It is understood
2 that the processes occurring amongst these spheres are very intimately linked and cannot operate independently of one another. The most influential component of this continuum is the hydrologic processes that relate to climatic conditions, soil characteristics, and species composition at a site (D’Odorico et al., 2010; Rodriguez-Iturbe, 2000). Hydrologic inputs of precipitation, surface water, and groundwater flows define the supply of water to a site. This water acts as the medium through which plants access nutrients and is critical to perform photosynthetic processes. Photosynthesis is the process of plant conversion of carbon dioxide and water into chemical energy, while the process of transpiration, which is driven by physiological and climatic variables, returns moisture to the atmosphere that can cycle and return as precipitation (D’Odorico et al., 2010; Rodriguez-Iturbe, 2000). One of the factors that determines the rate of photosynthesis, transpiration, and latent heat flux is the biological process of stomatal conductance (Collatz et al., 1991). Stomata on a leaf control the rate of gas exchange between a plant and the surrounding atmosphere. This gas exchange includes water loss from within the plant in the form of water vapour, and intake of CO2 from the atmosphere for carbon assimilation and photosynthesis. The rate of conductance can vary greatly depending on a variety of environmental conditions and physiological processes (Baird & Wilby, 1999; Buckley and Mott, 2013; Collatz et al., 1991; Lafleur, 1988). Stomatal aperture, or pore size, is directly related to turgor pressure of guard cells and leaf epidermal cells (Buckley, 2005). Guard cells are components of the leaf that surround the stomata, and control the pore opening, while epidermal cells sit adjacent to the guard cells. If water potential, or turgor pressure, within the guard cells is high, the stomatal aperture will increase, while high turgor pressure in the epidermal cells cause stomata to close (Buckley, 2005; Heatherington & Woodward, 2003).
3 The environmental variables that are considered to have the strongest control on stomatal conductance are sensible heat of the air mass measured as air temperature; vapour pressure deficit, which describes the drying power of the air; light and available energy through incoming shortwave radiation; and the leaf water potential, influenced by the volumetric soil moisture content (Collatz et al., 1991; Dolman et al., 2014; Stewart, 1988). CO2 partial pressure of the air and physiological processes within the plant itself are also important factors in determining the rate of conductance (Collatz et al., 1991). These environmental variables can influence guard cell closure and result in decreased, or limited, stomatal conductance. Limitation to stomatal conductance can occur across a wide spectrum, depending on which variable is being considered.
The response of stomata to light is related to photosynthetic processes. As light is a critical component of photosynthetic conversion of energy to carbohydrate molecules in a plant, stomata tend to remain open under high levels of solar irradiance to promote photosynthesis, assuming no other forces acting on a plant have a stronger control on conductance at that time
(Collatz et al., 1991; Kappen et al., 1987; Biscoe et al., 1977; Rejskova et al, 2012; Wilmer &
Mansfield, 1970). Leaves exposed to lower light levels through shading are found to have much lower rates of conductance compared to leaves in full light (Buckley, 2005; Buckley and Mott,
2000). While not all aspects of thermal relationships to stomata are well understood, stomatal response to air temperature most commonly shows that high air temperatures do not cause a decrease in conductance, but guard cell closure does occur when temperatures drop (Buckley &
Mott, 2013; Mott & Peak, 2010; Ikkonen et al., 2011; Wilmer & Mansfield, 1970). Forcings such as vapour pressure deficit and soil moisture, however, are limiting to both photosynthesis and stomatal conductance. It is understood that in atmospheric conditions where vapour pressure
4 deficit is high, the drying power of the air mass promotes increased evapotranspiration rates.
Since this can lead to significant water loss, stomata often respond to high vapour pressure deficit by decreasing their aperture, thus limiting water loss and maintaining leaf water potential. When humidity around a leaf is low, stomatal conductance initially will increase for a period of time, before ultimately declining and stopping entirely (Buckley, 2005). This response acts to maintain hydrologic equilibrium between the plant and the atmosphere. Without this response, transpiration would not be limited and soil moisture supply to the rooting zone of these plants could be depleted (Buckley, 2005; Kappen et al., 1987; Grantz, 1990; Monteith,
1995; Oren et al., 1999).
In ecohydrology, the soil moisture balance is considered to be the fundamental component to describe the underlying hydrologic mechanisms of ecological patterns and processes
(Rodriguez-Iturbe, 2000). Soil moisture is the greatest limiting factor in plant productivity, and is linked to leaf water supply, plant architecture (to allow for minimum possible energy expenditure in water transport), and physiological functioning of leaf stomata (D’Odorico et al.,
2010; Jones & Tardieu, 1998; Sperry et al., 2002). Because wetlands are water-controlled ecosystems, and because soil moisture levels will change drastically at a site throughout wetland restoration, this can be a significant determinant on plant growth success and seasonal water demands through transpiration within a restored system. Plant tolerance to moisture stress depends on plant-specific physiological adaptations, and the capacity of a system to supply resources to plant cells (Sperry et al., 2002). Moisture stress causing limitations to plant growth or stomatal conductance can occur at either end of the moisture spectrum – when soil moisture levels deplete and approach permanent wilting point, and when soils are consistently saturated creating anoxic conditions around plant rooting zones (Lovell & Menges, 2013).
5 Because guard cell function and plant water potential is a function of water supply to a plant, measurements of soil moisture are used as a proxy for plant water potential. Regulation of stomatal conductance in response to stressors such as water supply is necessary for plants to maintain hydrologic equilibrium in the soil-plant-atmosphere continuum (Sperry et al., 2002).
Stomata function as a pressure-regulator for plants by controlling flow rate of water through a plant. When moisture levels deplete, stomatal closure will maintain current water potential and hydraulic connectivity between roots and soil water (Sperry et al., 2002). Since soil moisture often changes over longer time periods than other factors, soil drying might be occurring without any significant change in leaf water potential. Plants are known to have drought sensing mechanisms in their roots that send signals through the plant. This signal triggers leaf guard cell turgor to drop despite plant water potential, and decrease stomatal aperture to preserve water
(Blatt, 2000; Buckley, 2005, Jones & Tardieu, 1998).
The supply of water to a plant throughout a growing season is also an important determinant for biomass accumulation (Kim & Wang, 2012). The energy expenditure that plants put towards growth is directly proportional to resource availability in a system (Sperry et al., 2002). Water is the main vector through which nutrients and compounds critical for growth are transported to the plant cells (Jones and Tardieu, 1998;). Not only will nutrient transport through a plant be limited by moisture supply, but also the overall nutrient enrichment and nutrient supply in the soil can be altered through site flooding (Bollens, Gusewell & Kltzli 2001; Gusewell et al.,
2003). Growth can also be limited by anoxic conditions around rooting zones which can occur with persistent periods of soil saturation, limiting oxygen supply to plants (Crawford &
Braendle, 1996). Water availability to a plant can also determine the rate of photosynthesis
(Booth & Loheide, 2012; D’Odorico, 2010). The rate of atmospheric CO2 uptake declines, and
6 thus the amount of carbon assimilation and photosynthesis may decline when guard cells on the leaf close in response to drying soil moisture conditions (Collatz et al., 1991; Others).
Figure 1.1: Range in tolerance to soil moisture and indication of the moisture stress response for two hypothetical species – one with a higher tolerance to moisture than the other.
Wetland plants possess characteristics that enable them to grow successfully in saturated soil conditions and low root oxygen levels, and can adapt to changes in soil chemistry that result from these anoxic conditions (Pezeshki, 2001). The tolerance that wetland species have to soil moisture levels before exhibiting a stress response, however, varies from species to species.
Figure 1.1 illustrates two hypothetical plant-moisture relationships. The ranges in tolerance seen in Figure 1.1 indicate that species want to exist in conditions where they are not limited by soil moisture availability, and are capable of their maximum potential stomatal conductance, and thus maximum ET and growth success. When moisture levels increase or decrease outside of this range, wilting would occur (Rodriguez-Iturbe, 2000). Wetland shrub species can exhibit these differences in tolerance to moisture, as species types can vary from being wetland obligate
7 to wetland facultative. These terms refer to the likelihood of a particular species being found within a wetland environment (Mills et al., 2012). Shrubs are also perennial species, which can have slower overall growth on a single growing season, as their reproductive success is not dependant on a single season of growth (Ehrlen & Lehtila, 2002).
1.3 Study Rationale
There is a lack of understanding on how site hydrologic conditions and stomatal responses are coordinated. Whether the plant-water feedbacks are controlled passively or actively (Buckley,
2005, Dolman et al., 2014; Jones & Tardieu, 1998; Nardini & Salleo 2000), and which environmental conditions are most limiting at any given time are questions that remain. The wide range of morphological and physiological responses possible under varying environmental conditions demands the investigation of species-specific relationships to the surrounding environment (Boyce et al., 2012; Ingram, 1983; Lafleur, 1990; Linacre, 1976; Rejskova, 2012).
It should not be assumed that a wetland species, for instance, with their own specific structural and environmental adaptations, would function identically to an upland or drought-tolerant species in terms of growth or stomatal conductance (Rodriguez-Iturbe, 2000).
There is currently a lack of existing research specific to wetland shrub responses to soil moisture. Shrubs are woody plants often with several main stems originating near the ground, and are smaller in size than trees (Gartner, 1995). Despite the lack of knowledge on these types of plants, however, Ontario native shrub species are commonly used in shrub thicket swamp restoration. The presence of swamps in Southern Ontario accounts for 17% of all identified land resource types based on land resource inventory data for this part of the province (Ontario
8 Ministry of Natural Resources, 2000). By placing a focus on wetland shrub species for this research, a gap in knowledge for these species types in temperate wetlands will be filled.
Currently, existing research that does focus on wetland plant responses to soil moisture look predominantly at hydrophytic reed, sedge, tree, and herbaceous species, as well as sphagnum moss (Biscoe et al., 1977; Bonilla-Warford and Zedler, 2002; Busch, 2000; Busch and Losch,
1998; Clarke and Baldwin, 2002; Ewers et al., 2009; Ewing, 1996; Hunter et al., 2000; Kellner,
2001; Kercher and Zedler, 2004; Lafluer, 1988; Lentz and Dunson, 1998; Lieffers and Shay,
1981; Rubio et al., 1995; Sala and Tenhunen, 1996). Any shrub-specific research looking at growth responses of wetland plants focuses on the competitive advantages of invasive shrubs in wetland ecosystems (Boyce et al., 2012; Mills et al., 2012; Zedler and Kercher 2004). In regards to the controlling effect of soil moisture on stomatal conductance, most studies look at drought effects on species rather than soil saturation. One study was found that did investigate shrub stomatal conductance, though the focus was on semi-arid and arid grassland species (Gao et al.,
2013).
To test the effects of soil moisture variation on a variety of native wetland shrub species, an experimental plot was established at the study site in which three treatments were defined to control water supply and limit water infiltration into the surrounding soil column. The treatments were effective in creating a wide range in moisture conditions within the plot. The wetland shrub species were planted within these treatments and measurements for stomatal conductance, growth, biomass, and soil moisture were gathered, as well as measurements for site weather conditions. Growth was determined as measurement of final biomass at the end of the growing season, compared against treatment moisture content and initial biomass of each species at the time of planting. Weather readings, along with treatment soil moisture
9 measurements can be correlated to specific stomatal conductance readings to determine the controlling relationships of each environmental variable, and modelling techniques can be implemented to depict growing season trends in stomatal conductance for each of the different species, based on the variation in moisture and atmospheric controls at the site (Buckley &
Mott, 2013). It is important that modelling techniques that correlate stomatal conductance to environmental and physiological variables are adjusted to site-specific vegetation and climatic characteristics to improve effectiveness (Collatz et al., 1991). The growing-season trends can be used to determine seasonal water demands for individual species by incorporating the values into estimates for evapotranspiration (Buckley & Mott, 2013). This will help provide accurate site-specific ecohydrological knowledge, which is valuable for determining which species will be most successful under expected hydrologic regimes and moisture availability (Busch, 2000).
This study took place over the growing period between May and September of 2013. The observed environmental conditions spanning this growing season provided insight into plant- environment interactions for a single growing season. In the context of historic climate norms for the region, the weather conditions over the growing season, as seen in Table 1.1, represent conditions that were warmer through spring and early summer and slightly cooler leading up to the end of September, compared to historic weather data. Humidity at the site was lower overall compared to climate norms. Rainfall was more frequent through June, July and August, though only June and July had higher rainfall amounts (with July being more than double the climatic norm). Despite the high rainfall in July, the lower precipitation amounts in May, August, and
September would indicate that the 2013 growing season was moderately wet, with lower humidity and air temperatures near normal (+/- 1.2 °C, on average).
10 2013 Monthly Observed Site Climate Data VS. Historic Climate Data for Brampton, ON (1981-2010) May June July August September Monthly Mean Observed 14.5 19.9 20.9 19.3 14.7 Temperature (°C) Historic 13.1 18.6 21.5 20.6 16.2 Monthly Mean Observed 65.2 65.3 75.7 73.7 77.7 Humidity (%) Historic 77.2 79.8 81.9 85.7 87.4 Monthly Total Observed 24.1 90 154.1 59.8 4.8 Precipitation(mm) Historic 74.3 71.5 75.7 78.1 74.5 Monthly Total Observed 9 12 19 11 1 Days With Rain Historic 12.5 10.8 10.4 10.2 10.5 Table 1.1: Site recorded monthly weather data compared against historic climate data from Lester B. Pearson International Airport Weather Station (Environment Canada, 2014).
The study site for this research is at a post-agricultural field in Brampton, ON that is slated to
undergo wetland restoration. Figure 1.2 shows an aerial photograph of the study site and its
surrounds. The site is located in an area undergoing residential development within the Credit
River watershed.
Figure 1.2: Aerial photo of study site, located in Brampton, ON (Lat: 43.68 ºN, Long: -79.83 ºW). The site is large brown-coloured field off-centre in the photo, bordered by forest on the SW, NW, and NE edges, and houses on the SE edge (City of Brampton, 2014).
11 The Credit Valley Conservation Authority, along with the Ontario Ministry of Natural
Resources and UrbanTech Consulting are developing restoration plans for this site as part of an agreement with Mattamy Homes Inc. to compensate for the loss of a wetland as part of their development plans. Selection of this site for restoration was based on its historical status as a wetland prior to agricultural conversion, and due to the existing patches of forested swamp in adjacent forest stands. The goal of the restoration project is to establish a shrub thicket swamp community at the site. This wetland type is well suited to the region, as the soils have high mineral and clay content, and the water supply will be predominantly through precipitation and groundwater (Mitch & Gosselink, 2007). Common native Ontario wetland shrubs will be introduced to the site. Plans to create wetland conditions involve blocking weeping tiles and in- filling drainage ditches to prevent water loss out of this site, and diversion of water from the rooftops of surrounding houses as additional water supply. Despite this, however, there is not much consideration for the amount of water that will be supplied to the site, or whether the supply will fit the demand for the species that will be introduced, which could limit the success of this project.
12 1.4 References
Baird, A. J., and R. L. Wilby. Eco-hydrology: Plants and Water in Terrestrial and Aquatic Environments. London: New York, 1999.
Biscoe, P. V., Incoll, L. D., Littleton, E. J., and Ollerenshaw, J. H. (1977). Barley and its environment. VII. Relationships between irradiance, leaf photosynthetic rate and stomatal conductance. Journal of Applied Ecology, 14(1): 293-302
Blatt, M. R. (2000). Cellular signaling and volume control in stomatal movements in plants. Annual Review of Cell and Developmental Biology, 16, 221-241
Bollens, U., Güsewell, S. and Klötzli, F. (2001). Vegetation changes in two Swiss fens affected by eutrophication and desiccation. Botanica Helvetica, 111, 139–155.
Bonilla-Warford, C., and Zedler, J.B., (2002). Potential for using native plant species in stormwater wetlands. Journal of Environmental Management, 29, 385–393.
Booth, E.G., and Loheide, S.P. (2012). Hydroecological model predictions indicate wetter and more diverse soil water regimes and vegetation types following floodplain restoration. Journal of Geophysical Research, 117
Boyce, R.L., Durtsche R.D., and Fugal, S.L. (2012). Impact of the invasive shrub Lonicera maackii on stand transpiration and ecosystem hydrology in a wetland forest. Biological Invasions, 14, 671–680.
Buckley, T.N. (2005). The control of stomata by water balance. The New Phytologist, 168, 275– 292
Buckley, T.N., Mott, K.A. (2013). Modelling stomatal conductance in response to environmental factors. Plant, Cell and Environment, 36: 1691–1699
Busch, J. (2000). Canopy transpiration rates in eutrophic wetlands dominated by sedges (Carex spp.) differ in a species specific way. Physics and Chemistry of the Earth, 25(7-8): 605- 610.
Busch, J., and Losch, R. (1998). Stomatal behaviour and gas exchange of sedges (Carex spp.) under different soil moisture regimes. Physics and Chemistry of the Earth, 23(4): 443- 448
City of Brampton. Brampton Maps-Air Photo Archive. Accessed at http://maps.brampton.ca/acris/default.aspx, September 3rd, 2014
Clarke, E., and Baldwin, A.H. (2002). Responses of wetland plants to ammonia and water level. Ecological Engineering, 18, 257–264.
13 Crawford, R.M.M, and Braendle, R. (1996). Oxygen deprivation stress in a changing environment. Journal of Experimental Botany, 47(295), 145-159
Collatz, G.J., Ball, J.T., Grivet, C., and Berry, J.A. (1991). Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agriculture and Forest Meteorology, 54, 107-136
Dolman, J.A., Miralles, D.G., and De Jeu, R.A.M. (2014). Ecohydrology bearings - invited commentary fifty years since monteith’s 1965 seminal paper: the emergence of global ecohydrology. Ecohydrology, 7, 897–902
D’Odorico, P., Laio, F., Porporato, A., Ridolfi, L., Rinaldo, A., and Rodriguez-Iturbe, I. (2010). Ecohydrology of terrestrial ecosystems. BioScience, 60(11) 898-907
Ducks Unlimited Canada (2010). Southern Ontario Wetland Conversion Analysis. Ducks Unlimited Canada.
Ehren, J., and Lehtila, K. (2002). How perennial are perennial plants? Oikos, 98, 308-322
Ewers, B.E., Mackay D.S., and Samanta S. (2007). Interannual consistency in canopy stomatal conductance control of leaf water potential across seven tree species. Tree Physiology, 27, 11 – 24
Ewing, K. (1996). Tolerance of four wetland plant species to flooding and sediment deposition. Environmental and Experimental Botany, 36, 131–146.
Gartner, B.L. (1995). Plant stems: physiology and functional morphology. Academic Press, Inc. ISBN 0-12-276460-9
Gao, Q., Yu M., Zhou C. (2013) Detecting the Differences in Responses of Stomatal Conductance to Moisture Stresses between Deciduous Shrubs and Artemisia Subshrubs. PLOS One, 8(12)
Grantz, D.A. (1990). Plant response to atmospheric humidity. Plant, Cell and Environment 13, 667-679
Güsewell, S., Bollens, U., Ryser, P. and Klötzli, F. (2003). Contrasting effects of nitrogen, phosphorus and water regime on first- and second-year growth of 16 wetland plant species. Functional Ecology, 17, 754–765
Hetherington, A.M., and Woodward, F.I. (2003) The role of stomata in sensing and driving environmental change. Nature, 424(6951), 901
Hunter, R.G., Combs, D.L., and George, D.B. (2000). Growth of softstem bulrush (Scirpus validus) in microcosms with different hydrologic regimes and media depths. Wetlands, 20, 15–22.
14 Ikkonen, E.N., Shibaeva, T.G., Sysoeva, M.I., and Sherudilo E.G. (2012). Stomatal Conductance in Cucumis sativus upon ShortTerm and LongTerm Exposures to Low Temperatures. Russian Journal of Plant Physiology, 59(5) 696–699
Ingram, H.A.P. (1983). Hydrology. Ecosystems of the World 4A–Mires: Swamp, Bog, Fen and Moor, Gore AJP (ed.). Elsevier: Amsterdam; 67 – 159.
Jones, H.G., Tardieu, F. (1998) Modelling water relations of horticultural crops: a review. Scientia Horticulturae, 74, 21–46
Kappen, L., Andresen, G., Losch, R. (1987). In situ observations of stomatal movements. Journal of Experimental Botany, 38(186), 126-141
Kardol, K., and Wardle, D.A. (2010). How understanding aboveground–belowground linkages can assist restoration ecology. Trends in Ecology and Evolution, 25(11), 670-679
Kercher, S.M., and Zedler, J.B. (2004). Flood tolerance in wetland angiosperms: a comparison of invasive and noninvasive species. Aquatic Botany, 80, 89–102
Kellner, E. (2001) Surface energy fluxes and control of evapotranspiration from a swedish sphagnum mire. Agricultural and Forest Meteorology, 110(2), 101-23
Kim, Y. and Wang G. (2012). Soil moisture-vegetation-precipitation feedback over North America: Its sensitivity to soil moisture climatology. Journal of Geophysical Research, 117.
Lafleur, P. (1988). Leaf conductance of four species growing in a subarctic marsh. Canadian Journal of Botany, 66(7): 1367-375.
Lentz, K.A., and Dunson, W.A. (1998). Water level affects growth of endangered northeastern bulrush, Scirpus ancistrochaetus Schuyler. Aquatic Botany, 60, 213–219.
Lieffers, V.J., and Shay, J.M. (1981). The effects of water level on the growth and reproduction of Scirpus maritimus var. paludosus. Canadian Journal of Botany, 59, 118–121
Linacre, E.T. (1976). Swamps. Vegetation and the atmosphere, 2, 329-347. Academic Press,New York,NY.
Lovell, J.T., and Menges, E. (2013). Dominant species responses to drought in seasonal wetlands: evidence from reciprocal transplants across a moisture gradient. The Journal of the Torrey Botanical Society, 140(2), 157-169
Mills, G., Hayes, F., Wilkinson, S., and Davies W.J. (2009). Chronic exposure to increasing background ozone impairs stomatal functioning in grassland species. Global Change Biology 15, 1522–1533
15 Mills, J.E., Meyer, G.A., Reinhartz, J.A., and Young, E.B (2012). An exotic invasive shrub has greater recruitment than native shrub species within a large undisturbed wetland. Plant Ecology, 213, 1425-1436
Mitch, W.J., and Gosselink, J.G. (2007). Wetlands. John Wiley & Sons Inc., 4th Edition
Monteith JL. (1995). A reinterpretation of stomatal responses to humidity. Plant, Cell and Environment, 18, 357-364
Mott, K.A., and Peak, D. (2010). Stomatal responses to humidity and temperature in darkness. Plant, Cell, and Environment, 33: 1084-1090.
Moreno-Mateos, D., Power, M.E., Comin, F.A., and Yockteng, R. (2012). Structural and functional loss in restored wetland ecosystems. PLoS Biology, 10(1)
Nardini, A., and Salleo, S. (2000) Limitation of stomatal conductance by hydraulic traits: sensing or preventing xylem cavitation? Trees, 15, 14–24
Ontario Ministry of Natural Resources (2000). Southern Ontario Land Resource Information System. Geospatial data, accessed Sept. 4 2014.
Oren, R., Sperry, J.S., Katul, G.G., Pataki, D.E., Ewers, B.E., Phillips, N., and Schafer K.V.R. (1999). Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell and Environment, 22(12), 1515-1526
Palmer, M.A., and Filoso S. (2009). Restoration of ecosystem services for environmental markets. Science 325, 575
Rejskova, A., Cizkova H., Brom J., and Pokorny J. (2012). Transpiration, evapotranspiration and energy fluxes in a temperate wetland dominated by Phalaris arundinacea under hot summer conditions. Ecohydrology 5: 19-27.
Rodriguez-Iturbe, I. (2000). Ecohydrology: A Hydrologic Perspective of Climate-soil- vegetation Dynamics. Water Resources Research, 36(1), 3-9.
Rubio, G., Casasola, G., and Lavado, R.S. (1995). Adaptations and biomass production of two grasses in response to waterlogging and soil nutrient enrichment. Oecologia, 102, 102– 105.
Sala, A., and Tenhunen, J.D (1996). Simulations of canopy net photosynthesis and transpiration in Quercus ilex L. under the influence of seasonal drought. Agriculture and Forest Meteorology, 76: 203-222
Sperry, J.S., Hacke, U.G., Oren, R. & Comstock, J.P. (2002). Water deficits and hydraulic limits to leaf water supply. Plant, Cell and Environment, 25, 251–263
16 Stewart, J.B. (1988). Modelling surface conductance of pine forest. Agriculture and forest meteorology, 43: 19-35.
Suding, K.N., Gross, K.L., and Houseman, G.R. (2004) Alternative states and positive feedbacks in restoration ecology. Trends in Ecology and Evolution, 19(1), 46-53
Willmer, C.M., and Mansfield, T.A (1970). Effects of some metabolic inhibitors and temperature on ion-stimulated stomatal opening in detached epidermis. New Phytologist, 69(4): 983-992
Zedler, J.B. (2000). Progress in wetland restoration ecology. Trends in Ecology and Evolution, 15(10), 402-407
Zedler, J.B., and Kercher, S. (2004). Causes and consequences of invasive plants in wetlands: Opportunities, opportunists, and outcomes. Critical Reviews in Plant Sciences, 23(5) 431- 452.
17 Chapter 2 Growth Responses of Ontario Native Wetland Shrubs to Increasing Soil Moisture
Abstract
To test the controlling effects of soil moisture on the growth of wetland shrub species, which is currently unknown for these plant types, an experimental plot within a post-agricultural field was established to manipulate soil moisture around individual plants and biomass accumulation over the growing season was measured by harvesting all plants. Variance and overall growth trends within each experimental treatment were determined to assess moisture tolerance and growth success of each species under a range of moisture availability. The results show that there are considerable differences in growth of the different wetland species, indicating either a positive, negative, or null response to increasing moisture levels. This information can be used to improve planting plans for restoration projects to ensure species are receiving appropriate moisture for optimal growth according to the wetland hydrologic regime.
2.1 Introduction
Ecohydrology places a substantial focus on increasing the understanding of the role of plant water use in the soil-water-plant-atmosphere continuum (Rodriguez-Iturbe, 2000). Water is the vector through which plants absorb nutrients, and is critical for the physiological processes of photosynthesis and transpiration (Rodriguez-Iturbe, 2000; D’Odorico, 2010; Tamea et al.,
2009). Because wetlands are water-dependant ecosystems, hydrologic characteristics have a critical control over wetland plant distribution and growth success across the ecosystem. The regional climate and landscape factors, as well as the source, supply, and residence time of water within an area will determine if a wetland will be established, and what the wetland type may be (Bedford, 1999). Soil moisture availability is an important factor to consider in wetland community structure, as it affects vegetation directly by controlling the availability of nutrients
18 and other resources, but also indirectly, by controlling abiotic factors such as infiltration rate, soil erosion that influence ecosystem dynamics (D’Odorico et al., 2010). Water table level is often used as a proxy for hydrological conditions such as soil moisture (Hajek et al., 2013); however, direct measurements of root zone soil moisture content is more physiologically important for vegetation interactions, as it describes the amount of liquid water or water vapour available in the soil pores that a plant can access (Booth & Loheide, 2012; Rodriguez-Iturbe et al., 2000). Because of the soil properties and duration of time between rainfall events, vastly different root zone soil moisture can exist for similar water table depths, both between and within individual sites (Tromp-van Meerveld & McDonnell, 2006). This variation in root zone moisture can translate directly into differences in plant growth rates.
Studies have indicated that the range in tolerance in terms of optimal growth conditions for individual species can vary greatly, so along a gradient of available soil moisture, plant communities can exhibit niche preferences for soil wetness and hydrology (Booth & Loheide,
2012). Even slight changes in topography across a site can result in significant variation in soil moisture, creating heterogeneity in the preferred moisture conditions for various species
(Moeslund et al., 2013). With this heterogeneity, plants can be exposed to varying degrees of water stress that can control community development and species composition. Water stress can limit a plant’s ability for transpiration and the ability for photosynthesis to occur (Booth &
Loheide, 2012; D’Odorico, 2010). In a continuously flooded wetland, growth would be limited by decreased oxygen supply available to the plants (Crawford and Braendle, 1996). When anoxic conditions exist in a wetland, the carrying capacity, or the maximum population of a species that this system can support, is greatly decreased. Conversely, if periods of water table drawdown are extended, a permanent wilting point is reached and plants exhibit die-off or loss
19 of biomass. This also decreases ecosystem carrying capacity by reducing transpiration capacity and decreasing diversity of the developed plant community (Palanisamy & Chui, 2013;
Rodriguez-Iturbe, 2000). Physiological mechanisms that plants exhibit in response to extreme fluctuations in water level include die-back and partitioning a greater amount of energy to the roots in attempt to access more water (Lovell & Menges, 2013). Plant responses to water levels may also influence nutrient uptake rates and/or their ability to use nutrients for growth. For instance, nutrient enrichment or nutrient supply may be altered under drought or flooding conditions, which can affect the success of fast growing plants, or reduce shifts in species composition (Bollens, Gusewell & Kltzli 2001; Gusewell et al., 2003).
In temperate wetlands in North America, the current knowledge of plant water interactions and growth success in the context of species-specific tolerance to moisture regimes looks at the responses of herbaceous hydrophytic species under different flood regimes or increasing soil moisture levels (Lieffers and Shay, 1981; Rubio et al., 1995; Ewing, 1996; Lentz and Dunson,
1998; Hunter et al., 2000; Clarke and Baldwin, 2002; Bonilla-Warford and Zedler, 2002;
Kercher and Zedler, 2004), but the focus is predominantly on grass, reed, or sedge species. For instance, soil saturation was found to be a significant factor in determining aboveground biomass and root adaptations in grassland species (Rubio et al., 1995). For wetland sedges, grasses, and other aquatic emergent species such as Scirpus maritimus, and Scolochloa festucacea, wetland flooding above the soil surface resulted in decreased vegetative growth and lower shoot survivorship, while the opposite was found for Myriophyllum verticillatum and
Utricularia spp. (Lieffers and Shay, 1981). Meanwhile, Carex species (C. rostrata and C. stipala) were found to be tolerant of seasonal fluctuation in soil moisture, as the photosynthetic recovery rate of these species was fast following experimental flooding and drying cycles
20 (Ewing, 1996). In a study of 16 wetland angiosperms (a mixture of various reed, sedge, and aster species) exposed to a range of flooding treatments, results indicated that Phalaris arundinacea and Typha latifolia were highly flood tolerant and produced the highest biomass of all species across all treatments, which can be indicative to their success as invasive species
(Kercher and Zedler, 2004).
To the best of our knowledge, there is no published research that looks specifically at the growth responses of temperate wetland shrub species, and how these types of plants interact with the surrounding environment, particularly in regard to soil moisture availability. The majority of existing studies investigating wetland shrub species in temperate environments focus on the competitive advantages of invasive shrubs in wetland ecosystems (Boyce et al., 2012; Mills et al., 2012; Zedler and Kercher 2004). For instance, the invasive shrub Frangula alnus was found to be more successful in establishing itself in a wider range of conditions within a large undisturbed wetland compared to four most common native species in the same system (Mills et al., 2012). A wetland forest stand invaded by Lonicera maackii was found to have basal area 5 times greater than a nearby native-dominant wetland forest, indicating higher species density in the invasive stand. Zedler and Kercher (2004) summarize the advantageous characteristics of wetland shrubs, including fast sprouting after burns, large seed dispersal distances, and adventitious and clonal roots, among others. A study that does provide some information on dryland shrub biomass accumulation in relation to soil moisture looked at four different shrub species planted in treatments that created a gradient of increasing moisture availability within the semi-arid Great Basin in the United States. The results of the study showed a positive relationship to plant growth and increased soil moisture across the treatments (Evans et al.,
2013). Whether this relationship can be applied to wetland shrubs remains to be tested.
21 An important application of this information on wetland plant species is in the context of wetland restoration. Wetland restoration projects are carried out in an attempt to increase wetland ecosystem coverage in areas where these systems have been lost or degraded (Bedford,
1999). This is particularly important in urbanized areas of Southern Ontario, Canada, where it is estimated that over 72% of this region’s original wetlands have been lost, and up to 98% in areas immediately adjacent or within urban centers (Bedford, 1999; Ducks Unlimited Canada,
2010; Moreno-Mateos, 2012; Rubec, 1994). Even globally, available information indicates that wetlands found in temperate regions show patterns of enormous losses in surface area and biodiversity (Brinson & Malvarez, 2002). The main reason behind the loss of these wetlands is due to anthropogenic landscape changes and increasing demand for land and resources over the past few centuries, and the conflicting land use demands where wetlands are found continues to be the largest threat to these ecosystems (Brinson & Malvarez, 2002). However, wetlands as an ecosystem provide a plethora of valuable services within the landscape, including but not limited to: improving water quality, carbon sequestration, aquifer recharge, flood mitigation and storm water management, providing habitat, increasing biodiversity, as well as being recreationally and aesthetically valuable (Ballantine et al., 2012). Due to the extensive loss of wetland systems in areas where these services are of considerable value, wetland restoration projects are undertaken in an attempt to return some of these ecosystem services back to these regions.
In wetland restoration planning, land uses such as agriculture and pasture are considered to be more easily reversed back to wetland status, compared to urban or industrial areas (Brinson &
Malvarez, 2002, Bedford, 1999). Low-lying agricultural areas with mineral soils that lack significant organic matter accumulation are still able to support hydrophytic vegetation, as long
22 as water residence time is long enough (Brinson & Malvarez, 2002). Due to the soil conditions and adjacent landscape features, end-goal communities for these restored systems are often that of a shrub thicket mineral swamp ecosystem. Shrubs are used in swamp restoration projects in order to facilitate succession to a forested swamp community, provided that site conditions are conducive to tree establishment (Cohen & Kost, 2007).
There are, however, limitations to the success of restoration efforts, some of which can stem from the improper, or lack of, effective integration of wetland hydrologic knowledge into the project plans (Fuselier et al., 2012). All too often, restoration practitioners do not have a thorough understanding of complex site hydrological functioning within a site prior to restoration planning (Large et al., 2007). The majority of wetland restoration efforts involve flooding or inundation of a site by blocking water drainage routes in order to increase water storage and residence time in a site, as well as introduction of wetland species (Fuselier et al.,
2012). To ensure that the species introduced through restoration are appropriate to the known or expected hydrologic regime, understanding the individual moisture tolerance ranges of each species can greatly increase their successful establishment. Ecological niche knowledge indicates that environmental stress resulting from increased soil moisture during restoration efforts can strongly control community composition and structure (Austin, 2002; Booth &
Loheide, 2012).
Throughout the process of wetland restoration, soil moisture levels at a site are expected to increase considerably. When wetland shrub species are introduced to a site as part of the restoration efforts, their growth success may be closely linked to the moisture availability at the site. Knowledge of these plant growth responses to soil moisture under the expected hydrologic
23 regime can increase the successful establishment and niche formation of wetland species introduced to the site. The objectives of this paper are to 1) directly observe the growth responses of native Ontario wetland shrub species under a gradient of increasing soil moisture availability, 2) to determine tolerance ranges and thresholds for growth in varying levels of wetness, and thus the preferred niche characteristics for individual species within a wetland environment, and 3) consider the applications of this information for wetland restoration projects. This will be done through an experimental approach where the species are planted in treatments designed to control moisture availability in the rooting zones. Through this, the direct relationships between biomass accumulation and soil moisture availability are observed, and species-specific plant-soil-water interactions can be identified.
2.2 Methods
2.2.1 Study Site
This research was conducted over the 2013 growing season, in a 2.5 hectare post-agricultural fallow field in Brampton, Ontario, Canada, (Lat: 43.68 ºN, Long: -79.83 ºW) that is part of the
Peel Plains physiographic region. This region is characterized by soils with high clay content, formed from iron-rich Queenston shale. The soil profile at the site showed topsoil to a depth of
30-40 cm, after which grey and blueish grey clays would dominate, with iron mottling throughout. After depths between 80 and 200 cm below the surface, layers of fine sand were present throughout the clay. Soil porosity in the upper topsoil layer was approximately 68%.
The water table at the site sits approximately 50 cm below the surface on average, though this fluctuates between 0 – 150 cm in response to rainfall amount and frequency. The growing season potential evapotranspiration (PET) rate at the site is 430 mm, with daily average PET of
24 3.2 mm (Ormshaw, unpublished data). This site is located within an area that is currently undergoing extensive residential development, and the site is slated to undergo restoration to a shrub thicket mineral swamp community. This site was selected for restoration in order to compensate for the loss of a nearby wetland as part of the region’s development plans, due to the fact that this site was historically a wetland prior to agricultural use, and is situated adjacent to established maple-dominant mineral swamp communities.
The vegetation of this fallow field is dominated by various obligate and facultative upland herbaceous species, predominantly Solidago altissima (Late Goldenrod, SOLALT), Solidago canadensis (Canada Goldenrod), Aster vimineus (Small White Aster), Euthamia graminifolia
(Grass-leaved Goldenrod), Sonchus arvensis (Field Sow Thistle), and Geum aleppicum (Yellow
Avens).
The restoration plans for this site involve in-filling drainage ditches that border the field, blocking of weeping tiles installed during initial conversion to agriculture, construction of berms to prevent overflow into nearby recreational spaces, as well as diversion of eaves-trough water from approximately 30 surrounding homes into the site as additional water supply after rainfall events. Plants to be introduced to the site include various native wetland shrub species to establish a mineral thicket swamp community. In fall 2012, 15 Acer rubrum (Red Maple) saplings were planted on an ad hoc basis. This site is to become a part of a natural heritage corridor along the East Huttonville Creek, defined by the Credit Valley Conservation Authority of Mississauga, Ontario. Apart from the sapling plantings, at the time of this study restoration efforts had not yet begun, which allowed for experimental manipulation of soil moisture conditions within the site for a variety of species that will be introduced when restoration efforts
25 begin. Conducting this experiment in a field setting allowed for observation of the natural controlling effects of rainfall, climate, and soil characteristics, rather than the artificial controls in a greenhouse.
2.2.2 Experimental Design
To conduct this shrub growth experiment, a 16x16 metre treatment plot was established within the site boundaries. Within this plot, three treatments were defined in order to test the effects of increasing soil moisture, which would be expected post-restoration, in which various shrub species were planted. Figure 2.1 illustrates the three treatments. The first treatment (TN) was a control treatment, which replicated the natural soil conditions. The second (TW) and third (TW2) treatments both used a barrier to prevent infiltration of rainwater into the surrounding soil column. The barrier was added by lining a 60 cm deep, 30 cm diameter hole with reinforced 6 mil plastic sheeting. These barriers retained water round the plant rooting zones creating small- scale wetland conditions around each plant. All treatments were rainwater fed; however, the third treatment (TW2) received twice the amount of water, by collecting precipitation in containers at the site and applying it to these replicates after each rainfall event. Over the growing season, precipitation totaled 270 mm across the site, which equates to 540 mm of water received by the TW2 treatments. The treatment design was able to establish a gradient of increasing soil moisture across the treatments.
26
Figure 2.1: Diagrammatic representation of the three treatments within the experimental plot – Natural (TN), Wet (TW), and Wettest (TW2)
Within each treatment, nine juvenile wetland species were planted in a random distribution across the plot. Of these species, seven were Ontario native wetland shrubs – Sambucus nigra L.
(Common Elderberry, SAMNIG), Viburnum lentago L. (Nanny Berry, VIBLEN), Salix discolor
Muhl. (Pussy Willow, SALDIS), Cornus sericea L. (Red Osier Dogwood, CORSER), Spiraea alba Du Roi(Meadowsweet, SPIALB), Rosa palustris Marsh. (Swamp Rose, ROSPAL), and
Salix exigua Nutt. (Sandbar Willow, SALEXI) – and two were annual native goldenrod species
– Solidago uliginosa Nutt. (Bog Goldenrod, SOLULG), common to mineral thicket swamps in the region, and Solidago altissima L. (Late Goldenrod, SOLALT), which is the current dominant species at the site. Each species was replicated 9 times pre treatment (except for S. nigra, where only 8 healthy individuals could be obtained), giving a total of 240 replicates across all treatments and species. The rooting depth of the planted and existing shrubs were never
27 observed to exceed 30 cm, therefore the 60 cm depth of the treatments vastly exceeded the requirements for one year of root growth. Of the 240 plants, five (2.1%) died during the experiment. Of these plants, four were S. exigua and one was S. nigra. Information on species scientific classification and growth characteristics can be found in Table 2.1.
Native Wetland Species Characteristics Species Common Name Order Family Habitat Cornus sericea Red Osier Dogwood Cornales Cornaceae Wetland, riparian zone Rosa palustris Swamp Rose Rosales Rosaceae Marsh, swamp Spiraea alba Meadowsweet Rosales Rosaceae Wet grassland, swamp Solidago altissima Late Goldenrod Asterales Asteraceae Meadow Salix discolor Pussy Willow Malpighiales Salicaceae Forest, wetland Salix exigua Sandbar Willow Malpighiales Salicaceae Riparian, shore, wetland Sambucus nigra Common Elderberry Dipsacales Adoxaceae Lowland, wetland Solidago uliginosa Bog Goldenrod Asterales Asteraceae Bog, marsh, swamp Viburnum lentago Nanny Berry Dipsacales Caprifoliaceae Lowland forest, swamp Table 2.1: Species name, family, maximum height, and habitat of the nine species planted in the treatment plot
For each replicate, stem height measurements were carried out every third week throughout the growing season, for a total of five measurement dates. Volumetric moisture content of the soil for each replicate was measured weekly, over 10 weeks total from June 12 to August 23, using a
HydroSense® CS620/CD620 Soil Water Measurement System. Moisture content was measured up to a 20 cm depth, to account for the main rooting zone for these plants.
A full harvest of each replicate across the three treatments was carried out after a 16-week growth period, in order to determine overall dry biomass for each replicate. Each specimen was divided into aboveground and belowground biomass. The belowground biomass was carefully washed to remove all soil and debris from the root mass. The plant matter was dried for 72
28 hours at 40 °C and weighed. Three additional individuals of each species were selected at the time of planting in order determine average initial biomass. The same separation and drying technique was used.
2.2.3 Data Analysis
For each of the nine species planted in the experimental plot, statistical analysis was carried out on the stem height and plant biomass data. For the stem heights, the key value of interest was the proportional increase in growth from the time of planting to the peak height realized by each individual plant. This accounted for variation between individual plants, rather than just looking at the final height of each species. In terms of plant biomass, between-treatment comparisons of each species were made using average total plant biomass for each replicate after 16 weeks of growth, and comparing against the average initial total plant biomass for each species. The biomass was also separated out into aboveground and belowground biomass, in order to compare the partitioning of energy towards growth that is occurring within species under different moisture levels. Statistical analysis was done using ANOVA to determine significant differences among the treatments, and Tukey’s post-hoc test compared treatments against each other to find the relationships between these subgroups. The statistical tests were done using a confidence level of 0.1, as the variation among individual plants and the nature of the experiment (presence of environmental effects outside of control of the experiment), allowed for higher potential error. As well, the sample size for each species within each treatment was relatively small, supporting the use of a lower confidence level. Statistical analysis to compare the biomass measured between species within treatments was not carried out, as the variation in
29 initial biomass across all species was too great (anywhere from ~ 5 to 368 grams depending on
the species).
Effect size, which conveys the magnitude of the difference between two groups, or the distance
between means, is a valuable comparison tool to compare the strength of relationships on the
same scale. In this application, the effect size will measure the magnitude of the difference in
biomass between treatments. The Cohen’s d method of determining effect size was used, as it
uses a pooled standard deviation from both means in the comparison, as the standard deviation
values across the treatments vary. Equations 2.1 and 2.2 are used to determine Cohen’s d effect
size (Cohen, 1969).
M − M d = 1 2 (2.1) SDpooled
(SD12 + SD22 ) SDpooled = (2.2) € 2
Where M is the mean of each group and SD is the standard deviation. Effect size was
€ determined for the variation in biomass between treatments for each species. The values determined for the effect size between treatments will show if the differences in biomass
measured across these treatments are greater than or less than 1 standard deviation from the
mean. Any values of 1 or higher mean that the strength of the relationship between those
treatments is greater than 1 or more standard deviations, an indication that the majority of the
data points in one treatment fall outside of the range of values observed in the second treatment
in the comparison. The similarities in observed values between the two treatments being
compared would be low, and the strength of the treatment effect in these instances would be
considered very strong (Rosenthal, 1996).
30 2.3 Results
2.3.1 Volumetric Moisture Content of the Soil
The treatments in the experimental plot were rainwater fed, and the precipitation pattern throughout the measurement period can be seen in Figure 2.2. The volumetric moisture content of the soil as a result of precipitation across the growing season for the three treatments for each of the species is represented in Figure 2.3. The number of rainfall events for each month totaled
8, 12, 19, and 8 for May (after planting on May 17th), June, July and August, respectively. The number of rainfall events greater than 10mm in each month, however, was 1, 3, 4, and 2 from the time of planting in May, through June, July and August, respectively. The higher frequency of both total and large rainfall events (>10mm) through June and July resulted in larger differences in soil moisture between treatments, seen in Figure 2.3. Rainfall totaled 270 mm over the entire measurement period.
Figure 2.2: Daily total precipitation at the study site from time of planting (May 17th) to the end of the measurement period (August 23rd), of the 2013 growing season.
31 Within each treatment, each of the replicates received an equivalent water supply across the experimental plot, and differences in soil moisture between species and treatments were observed. On average, for each of the sampling weeks when soil moisture content was measured, the two barrier treatments (TW and TW2) had consistently wetter soil conditions than
TN. As seen in Table 2.2, the growing season average for TN replicates ranged from 28% to
39%, while TW and TW2 ranged from 39% - 56% and 38% - 55%, respectively. Of the nine species, V. lentago showed the lowest average moisture content across all treatments, while S. uliginosa was the wettest, on average, for all treatments. In terms of between-treatment differences within species, on the upper end of the scale S. uliginosa, S. discolor, and C. sericea showed the greatest seasonal range, with average differences across treatments of 17.1%, 16.1% and 16%, respectively. S. altissima had the most subtle difference in moisture across treatments for the whole growing season (3.6%), though at times during the summer the difference was as high as 7.5%. These treatment differences are illustrated in Figure 2.3.
When comparing the difference between treatments, in can be seen that on a month-to-month basis the difference in soil moisture between TN and both barrier treatments (TW and TW2) is much greater than the average difference in soil moisture readings between TW and TW2. Over the month of June, it was seen that the difference in soil moisture between TN and TW was 5 –
19%and 0.2 – 27% between TW2. In July, soil moisture in TN differed by 4 – 15% and 5 – 15% between TW and TW2, respectively, while August showed differences of 0.4 – 20% against TW and 0.1 – 20% to TW2. The average difference in values between TW and TW2 were often smaller, indicating that the water supply to the TW2 treatments was not always enough to increase soil moisture readings greatly. This is seen by differences of 0.8 – 12%, 0.1 – 7%, and 0.5 – 7% between TW and TW2 for June, July, and August, respectively.
32
Figure 2.3: Average volumetric moisture content of each replicate for the three treatments for each of the sampling dates across the 2013 growing season
In regards to growing season trends in soil moisture, on a per-species basis it can be seen that the difference in soil moisture between treatments is frequently greatest in June, and decreases through July and August. This trend can be exemplified by V. lentago, S. discolor, C. sericea, R. palustris, and S. exigua, where the moisture content in the barrier treatments are declining in
July and August to levels closer to what is measured in TN. Figure 2.3 illustrates this, by showing that the difference in moisture between treatments is often greatest in June to mid-July,
33 with a tapering-off occurring into August. This could be linked the amount of rainfall received
at the site during the months of June and July.
Volumetric moisture content for each wetland species in three treatments across the 2013 growing season
Species CORSER ROSPAL SALDIS SALEXI SAMNIG SPIALB VIBLEN SOLALT SOLULG TN Average 35 38 32.3 36.1 38.4 37.4 28.1 37.9 38.7 Range 21.1-55.1 23.8-57.7 22.4-43.1 22.9-49.1 23-56.8 20.4-66.3 15.4-44.8 24.6-52.4 24.5-51.7 TW Average 45.9 43.3 39.7 45.7 52.2 47.5 39.1 41.5 55.8 Range 21.8-59.3 21.7-54.7 19.5-55.1 25.4-55.5 34.5-68.9 29.2-55.4 23.1-60.3 22.5-56 40.7-64 TW2 Average 51 49.1 48.4 50.5 53.8 49.1 38.3 40.3 54.5 Range 27.6-61.8 27.7-74.1 20.9-68.7 24.1-63.4 34.7-62.8 27.2-58.7 18.7-57.7 22.3-50.1 40.1-63.2 Average difference in soil moisture between each treatment during June, July, and August 2013 June 16.7 8.9 14.6 12.7 14.9 12.8 12.7 5 18.8 TN – TW 19.9 15.6 26.6 21 16.8 16.8 15.3 0.2 17.9 TN – TW2 3.2 6.7 12 8.3 1.9 4 2.6 4.8 0.8 TW – TW2 July 9.3 4.4 5.1 10.4 13.2 7.8 10 4.6 14.5 TN – TW 14.7 10.8 12 13.4 13.9 9.5 7.2 5.3 14.4 TN – TW2 5.4 6.5 7 3 0.7 1.7 2.8 0.6 0.1 TW – TW2 August 7.7 3.3 1.6 5.2 13.6 11.2 7.3 0.4 19.6 TN – TW 14.4 7 7.1 9.9 16.6 10.3 6.8 0.1 15.8 TN – TW2 6.7 3.7 5.5 4.6 3 0.9 0.5 0.6 3.8 TW – TW2 Table 2.2: Growing season average and range in soil moisture in each treatment for the nine wetland species, and the average difference in soil moisture measurements between treatments for the months of June, July, and August, 2013.
Significant differences between treatments for each measurement date were found through
repeated measured ANOVA. The full results of this analysis can be seen in Appendix 2A. For
all species but S. altissima, significant differences in moisture content between treatments were
34 seen throughout the measurement period. For measurements leading up to July 15th, soil
th moisture in TN was significantly lower than both TW and TW2 for most species. For the July 15 measurement date, only one species, S. nigra, showed that TN and TW2 were significantly different. The lack of significant relationships at this point in time is due to the higher moisture readings in TN for all species, as this date closely followed the largest rainfall event in the summer. Most species showed that TN was significantly lower than TW and TW2 on the August
8th measurement date, while only S. nigra and S. uliginosa showed this relationship through to
rd August 23 . Significant differences in soil moisture between TW and TW2 were less frequent, though most often seen for treatments in which S. discolor was planted, and at one occasion for
C. sericea.
2.3.2 Stem Height
While the measurements of stem height provide information on the overall growth rate of the wetland species, it did not show much difference in height increases between treatments throughout the growing season. A summary of the average percent increase in stem height from the time of planting to the peak height can be seen in Table 2.3. As expected, both of the annual goldenrod species (S. altissima and S. uliginosa) exhibited the greatest overall increase in height from the time of planting to their peak growth. The shrub species that increased in height by the greatest amount was S. nigra, at least doubling in height in all treatments. Within a species, the results of one-way ANOVA and Tukey’s post-hoc analysis did not produce any significant relationships in height measurements between treatments. This information is valuable to see the rate of growth throughout the growing season, however. The stem height measurements show that the majority (78%) of plants reached their peak height by late July and mid-August (57% of
35 plants by August 16th, 21% by July 26th), and the greatest increase in height for each species across the replicates was experienced by early June to late July for the of majority of plants, as seen in Table 2.3. S. discolor, C. sericea, S. alba, R. palustris, and S. exigua all exhibited the greatest increases in growth by late-July. While increases in height were still observed at earlier measurement dates, the greatest most rapid increase was measured by this date. S. nigra grew fastest much earlier, adding the most height by early June for TN and TW, and late June for TW2.
The annual goldenrods, S. altissima and S. uliginosa, showed the most growth in late June as per the specification in Table 2.3. None of the species or treatments showed the greatest increases in height to be common leading up to the final measurement date, August 16th. This could be a result of the lower soil moisture readings at that stage of the growing season, as seen in Figure
2.3.
Stem Heights (cm) - Average percent increase in height from planting to peak height
Species
CORSER ROSPAL SALDIS SALEXI SAMNIG SPIALB VIBLEN SOLALT SOLULG
TN 22(16) 74(44) 23(19) 31(82) 114(83) 52(32) 3(3) 253(93) 200(85)
TW 41(48) 79(45) 26(11) 17(42) 191(95) 82(36) 4(5) 293(124) 303(211)
TW2 36(21) 83(42) 29(19) 25(25) 139(71) 104(59) 6(5) 268(250) 187(133) Highest average increase in height and corresponding dates TN Average 14% 50% 15% 21% 72% 28% 6% 76% 156% Date July 26 July 26 July 26 July 26 June 6 July 26 June 6 July 3 July 3 TW
Average 26% 53% 26% 62% 62% 43% 6% 101% 141% Date July 26 July 26 July 26 July 26 June 6 July 26 June 6 July 3 July 3 TW2
Average 78% 39% 53% 23% 102% 47% 4% 72% 94% Date July 26 July 26 July 26 July 26 July 3 July 26 June 6 July 3 July 3 Table 2.3: Summary of average and standard deviation of proportional increase in stem height (cm) data for the 9 measured wetland species. The date at which the largest increase in height was measured for each species in each treatment is also specified, along with the corresponding average percent increase in height.
36 2.3.3 Total Plant Biomass
There were clear differences observed for species in terms of their overall increase in biomass over the 16-week growth period, as well as the difference in biomass accumulation between treatments. The results of the biomass harvesting can be seen in Table 2.4. The measured biomass at the end for the growing season showed considerable increases in total biomass for most species, when compared against the initial biomass measurements for each species (see
Table 2.4). The relatively large standard deviations seen for the total biomass for the different species can be explained by the variation in individual plant size at the time of planting.
Figure 2.4: Percentage increase in overall total from initial measurements for each species and treatment, with standard error between replicates
For the various species, S. altissima, S. uliginosa, S. discolor, and C. sericea showed at least a doubling in biomass for all treatments, while V. lentago and R. palustris had the lowest overall
37 increases in biomass. R. palustris was the only species that experienced a decrease in biomass
by the end of the growing season in both the TN and TW treatments. The total increases in
biomass for each species in each treatment is illustrated in Figure 2.4. Overall, the test species
responded in one of three directions: a decrease in final biomass with increasing wetness, an
increase in biomass with increasing wetness, or no detectable difference across treatments. This
is illustrated in Figure 2.5, and summarized in Table 2.4. S. altissima showed decreases in
measured biomass with increasing moisture, while S. alba, R. palustris, and S. exigua had
increased biomass with increasing moisture. While the remaining five species did not indicate
significant differences in biomass between treatments, the results still provide insight into the
tolerance ranges each species has to moisture availability throughout the growing season. The
results of the statistical testing showed that four of the nine species had biomass measurements
that were significantly different between soil moisture treatments (P = <0.1) (Table 2.5).
Total Biomass - Average total plant biomass (g), and percent increase after 16 weeks of growth
Species
CORSER ROSPAL SALDIS SALEXI SAMNIG SPIALB VIBLEN SOLALT SOLULG
TN M (SD) 71(29) 37(17) 90(40) 51(14) 23(12) 46(19) 269(90) 229(119) 11(9) % ↑ 221 -33 186 88 22 44 18 470 257
TW M (SD) 75(19) 49(13) 130(63) 99(29) 33(27) 62(24) 255(83) 66(30) 16(11) % ↑ 239 -5 311 268 73 97 12 65 421
TW2 M (SD) 77(36) 61(26) 94(34) 86(35) 20(7) 79(24) 284(108) 92(36) 7(6) % ↑ 249 17 198 219 4 127 24 130 133 Table 2.4: Summary of average (M) and standard deviation (SD) of biomass data for the 9 measured wetland species, as well as the percentage increase in biomass in comparison to the initial measured biomass for each species.
38 Overall Biomass - Results of ANOVA and Tukey’s Post-Hoc
Species
CORSER ROSPAL SALDIS SALEXI SAMNIG SPIALB VIBLEN SOLALT SOLULG
F-Ratio 0.107 3.463 1.833 7.062 1.218 4.772 0.207 12.686 2.239 P-value 0.899 0.050 0.183 0.005 0.317 0.020 0.814 0.000 0.129 P-value 1-2 0.958 0.326 0.225 0.006 0.511 0.220 0.947 0.000 0.446 Effect size 1-2 0.160 0.961 0.737 2.272 0.469 0.770 0.162 1.881 0.524 P-value 1-3 0.891 0.040 0.985 0.038 0.914 0.016 0.942 0.002 0.642 Effect size 1-3 0.193 1.163 0.104 1.414 0.351 1.205 0.147 1.554 0.515 P-value 2-3 0.985 0.491 0.271 0.642 0.305 0.301 0.798 0.736 0.111 Effect Size 2-3 0.079 0.541 0.703 0.411 0.675 0.385 0.297 0.809 1.010 Table 2.5: Results of statistical analysis on plant biomass data, using ANOVA and Tukey’s post-hoc comparative analysis. Effect size between treatments using Cohen’s D is also indicated.
S. altissima species was planted in the treatment plot in order to represent the response of the
currently dominant species at the study site to increasing moisture availability after restoration
efforts proceed. This is the one species that responded to soil moisture with a significantly lower
final biomass in the wetter treatments, as seen in Figure 2.5. TN showed a 470% increase in
overall biomass from that measured initially, compared to TW and TW2, which only exhibited
increases in biomass of 65% and 130%, respectively. The growth of the S. altissima plants in TN
was the greatest overall increase in biomass over the growing season compared to all other
species planted.
The S. alba plants in the treatment plot showed a positive relationship between growth success
and soil moisture availability. The wettest treatments resulted in the highest measured biomass,
79 (+/-24) grams, while the natural soils were 72% lower on average, with a final biomass
measurement of 46 (+/-19) grams. The soil moisture levels across these treatments had a range
of 19% at the maximum difference, and on average TW2 was 12% wetter than TW for this
39 species. The magnitude of this relationship is expressed in an effect size of 1.2 between TN and
TW2.
Figure 2.5: Average total plant biomass per treatment for nine wetland species, with standard error bars, compared against initial biomass at time of planting for each species (indicated by black line across graphs). Letter notation indicates significantly different relationships, where ‘a’ is significantly different from ‘b’ (P = <0.1)
The trend with R. palustris is similar to that of S. alba, where the measured biomass is increasingly greater as moisture levels increase in the treatments. One key difference exhibited by these plants however is how the final measured biomass compared to the initial biomass. The most successful treatment, TW2, showed an increase of 17%, which is a relatively small increase
40 compared to other species in this treatment, and not significantly greater than the initial biomass.
However, the plants in the TN treatment showed that on average, they were declining in biomass
(33% decrease) as the growing season progressed, compared to those individuals measured at the time of planting. The TW treatment also showed that the biomass by the end of the growth period was relatively low compared to that of the initial measurements.
S. exigua showed that both the TW and TW2 treatments had significantly higher biomass than TN.
The biomass was highest in the TW treatment, with an average of 42 (+/-29) grams, which is
268% greater than the initial biomass. The TN treatment had an average biomass of 19 (+/-14) grams, only an 88% increase from initial. While the TW2 treatment was not significantly lower than that of TW, it did have a lower average biomass, which could indicate that a threshold was being reached in that when the soil moisture levels increase beyond a certain point, it may become detrimental to growth. Across the growing season, the soil moisture levels remained consistently higher in the barrier treatments, with the TW2 treatment being highest overall, at
51% (maximum of 63%), while TW was lower at 46% (maximum of 56%). This difference was greatest in June, when soil moisture in TW2 was 8% higher than soil moisture in TW. The effect size between TN and TW for S. exigua is the highest among all the species, being greater than two standard deviations from the mean (2.272).
S. discolor, S. uliginosa and S. nigra did not show any significant differences in total biomass between treatments, however, variation in average biomass between treatments was still measured. For all species, the TW treatment showed the highest average biomass, while the TN treatments were lowest overall. This could be indicative of thresholds for growth success, similar to that of the S. exigua plants, in which soil moisture levels that increase beyond a
41 certain threshold result in lower overall plant growth. For the TW treatment, S. discolor, S. uliginosa and S. nigra increased in biomass from the initial measurements by 311%, 420%, and
73%, respectively, while TW2 showed increases of only 198%, 133%, and 4%, respectively. For
S. uliginosa, this threshold-type response is supported by an effect size of 1.01 between TW and
TW2, showing that the magnitude of the difference between these treatments is over one standard deviation from the mean. For S. discolor plants, there was average difference in moisture of over
8% from TW to TW2. The higher moisture conditions were less optimal for overall growth of this species.
The final biomass measurements of C. sericea show that there was no significant effect of soil moisture availability on the growth of these plants. The average biomass measured across all three treatments was 71.0, 75.1, and 77.3 g for the TN, TW, and TW2 treatments, respectively. The small differences measured across treatments is seen despite an average difference in volumetric moisture content of the soil of 46% across the three treatments, and up to 66% different in late
June. This is supported by the small effect sizes between treatments (0.16, 0.193, 0.079). In terms of overall growth, it is apparent that the growth success of this species is high, regardless of moisture availability, as all treatments saw on average a 236% (+/-14) increase in biomass from the initial biomass measurement at the time of planting.
A similar trend is seen with the V. lentago species, in that there was not a significant difference in biomass between the three treatments. The range in soil moisture over the growing season was not as large, on average 12%, but the average biomass for each treatment was 269, 255, 284 g for TN, TW, and TW2, respectively. One difference to note with this species is that the increase in biomass from the time of planting was much lower – only ~18% (+/-6) increase.
42 2.3.4 Partitioning of Aboveground and Belowground Biomass
The ratio of aboveground to belowground biomass for each species and treatment is summarized in Table 2.6. By looking at these values in terms of a proportion of the overall biomass, comparisons can be made between treatments that are not affected by variation in size across replicates. In this comparison, a ratio value of 1 represents equivalent amounts of both stems and roots, while anything <1 indicates a greater proportion of belowground biomass, and >1 indicates a greater proportion of aboveground biomass. The results of the statistical tests are shown in Table 2.7.
Proportional Biomass – Average Ratio of Aboveground to Belowground Plant Biomass after 16 Weeks of Growth
Species
CORSER ROSPAL SALDIS SALEXI SAMNIG SPIALB VIBLEN SOLALT SOLULG
Treatment 1 1.08(0.3) 1.06(0.8) 1.89(0.9) 2.07(1.0) 0.38(0.1) 0.65(0.2) 0.98(0.3) 1.94(0.7) 0.73(0.4)
Treatment 2 1.33(0.4) 1.21(0.6) 1.52(0.9) 1.75(1.0) 0.64(0.3) 0.95(0.3) 0.83(0.4) 1.29(0.4) 2.52(2.4)
Treatment 3 1.34(0.5) 1.55(1.0) 1.6(0.7) 1.48(0.5) 0.44(0.2) 0.74(0.5) 0.82(0.2) 1.26(0.5) 1.32(0.5) Table 2.6: Summary of the average ratio and standard deviation of proportional aboveground biomass to belowground biomass for the 9 measured wetland species
There were differences between species in regard to the amount of energy that is partitioned to growing certain portions of the plant (Table 2.6). For instance, certain species, on average, have a consistently smaller proportion of aboveground biomass compared to their root mass (S. nigra and S. alba), since the ratios are all below 1, while others put a greater amount energy into developing their leaves and stems (S. altissima, S. discolor, C. sericea, R. palustris, and S. exigua), with ratios greater than 1 in all treatments.
43
Figure 2.6: Ratios of aboveground to belowground biomass for each species and treatment. The black line indicates a ratio of 1, where above and belowground biomass are equal.
Significant differences in biomass partitioning between treatments were seen for S. altissima, S. nigra, and S. uliginosa. The latter two showed that the TW treatments had significantly greater proportions of aboveground biomass compared to TN (ratios of 0.38 vs. 0.64, and 0.73 vs. 2.52, respectively), while S. altissima showed a significantly lower proportion of aboveground biomass in TW2 compared to TN (ratios of 1.94 in TN vs. 1.26 in TW2). The remaining relationships between treatments were non-significant, showing that moisture availability did not play a significant role in the development of aboveground vs. belowground biomass for the other species over one growing season.
44
Proportional Biomass - Results of ANOVA and Tukey’s Post-Hoc
Species
CORSER ROSPAL SALDIS SALEXI SAMNIG SPIALB VIBLEN SOLALT SOLULG
F-Ratio 1.233 0.093 0.627 0.303 2.889 0.984 0.986 3.373 3.230
P-value 0.301 0.912 0.543 0.742 0.079 0.390 0.388 0.051 0.058
P-value 1-2 0.408 0.997 0.512 0.927 0.073 0.442 0.397 0.118 0.055
Effect size 1-2 0.612 0.034 0.520 0.189 1.163 1.242 0.553 1.142 1.052
P-value 1-3 0.352 0.942 0.818 0.722 0.789 0.490 0.542 0.062 0.209
Effect size 1-3 0.675 0.144 0.341 0.383 0.369 0.453 0.609 1.083 1.107
P-value 2-3 0.998 0.915 0.858 0.935 0.228 1.00 0.965 0.943 0.801
Effect Size 2-3 0.026 0.276 0.235 0.231 0.811 0.001 0.117 0.141 0.291 Table 2.7: Results of statistical analysis on plant biomass data, using ANOVA and Tukey’s post-hoc comparative analysis. Effect size between treatments using Cohen’s D is also indicated.
2.4 Discussion
2.4.1 Role of moisture in controlling growth and supporting literature evidence
The results of this experiment show that wetland shrub species common to mineral thicket
swamp communities have a range of morphological responses to soil moisture availability
throughout a growing season. The measurements for stem height and biomass provided insight
into the growth patterns at moisture tolerance of these species.
The fluctuation in soil moisture in the treatments throughout the summer was directly related to
the seasonal precipitation patterns at the site, where more frequent precipitation events in June
and July created a larger difference in moisture between treatments, while the decrease in soil
45 moisture by the end of the growing season for all treatments was due to less frequent precipitation. This decrease, however, may not have impacted plant growth responses too greatly, as the greatest increases in stem height were seen leading up to the end of July, or earlier. This shows that in the earlier portion of the growing season, when rainfall was more frequent and the soil moisture availability in the treatments was higher, the growth rate for all species, on average, was more rapid. The rate of stem elongation in the vast majority of replicates during the month of August was slower overall.
Since there were no significant relationships between treatments explained through the analysis of the stem height measurements, there are likely more factors that can be considered that contribute to overall plant height measured than just the water availability. For instance, variability in heights at the time of planting within treatments, partitioning of energy to above or below ground biomass, as well as plant physiological characteristics of certain species – such as the time to reach peak height in a growing season (Kercher & Zedler, 2004). There are also characteristics that describe plant architecture aside from plant height, including canopy coverage and layering, which were not taken into account in this study, which could describe growth within taxon as well (Keer & Zedler, 2002). Accounting for horizontal growth, or shrub diameter, rather than relying strictly on measurements of vertical growth to indicate when peak growth is reached could improve measurements of plant architecture.
Biomass is often a better indicator of plant growth success, as it takes into consideration the extent of plant matter in the roots, leaves, and stems, rather than just relying on height, which can be highly variable between individual plants. By looking at biomass for each of the test
46 species across the three treatments, the role of soil moisture in the accumulation of biomass was observed.
As discussed, there were three different trends seen across the three treatments for total biomass
– an increase in biomass with increased soil moisture, a decrease in biomass with increased moisture, and no detectable difference in biomass between treatments.
The response of S. altissima, which had lower biomass in both barrier treatments, also showed the greatest increase in biomass from the initial measurement of all species in the TN treatment.
This is indicative of the success and tolerance of this species to more drought-like conditions, and that in a scenario where soil moisture levels at a site increases considerably, the change in conditions would be detrimental to the growth of these plants. The discrepancy in biomass production despite a relatively subtle difference in soil moisture availability between treatments can be explained by species-specific plant water use efficiency – the ratio of growth to water use over a certain period of time (Gulias et al., 2012; Van der Boogaard, 1997). The considerably lower growth in both the TW and TW2 treatments indicates poor water use efficiency for these species. The water stress was great enough to limit growth and photosynthetic functions for S. altissima, but poor stomatal control under increasing moisture levels allowed for continued water loss through transpiration (Gulias et al., 2012). This is supported by the observed stomatal conductance for S. altissima (Chapter 3.0; Table 3.3 and Figure 3.4), which was the highest of all species across the growing season despite variation in treatment soil moisture content.
Similar relationships were observed in other studies of plant water use efficiency, where total biomass production was greatest under soil-water deficit conditions for grass species F. arundinacea, D. glomerata, and T. aestivum (Gulias et al., 2012, Van der Boogaard, 1997). This shows that water use efficiency is not necessarily greatest when water availability increases, and
47 soil water depletion may continue as a result of poor stomatal control even in conditions where increasing soil water content is limiting to plant growth.
When increases in biomass were seen with increasing moisture, this is indicative that these species grow most successfully with higher measured soil volumetric moisture content. S. alba and R. palustris showed the greatest increases in biomass in the TW2 treatments, indicating a preference for the wettest possible conditions. Therefore, in a wetland environment these plants would be more sensitive to seasonal moisture availability. R. palustris is much more sensitive to moisture availability, and if the soils become too dry (when a decline in moisture across the treatments was seen throughout July and August) the plants actually begin to die off and lose matter.
S. exigua, S. uliginosa, S. discolor, and S. nigra all showed increases in biomass with greater moisture availability as well, though the greatest increases were seen in TW rather than TW2.
This relationship is indicative of a threshold for moisture tolerance, where growth of these species declines when soil moisture increases beyond a certain point. This sensitivity to moisture is apparent even with subtle differences in moisture over the growing season between
TW and TW2.
C. sericea and V. lentago, responded to soil moisture by showing no discernable variation in biomass across any of the treatments. The uniform growth of these plants regardless of moisture levels in the treatments characterizes a growth response that is highly tolerant to a wide range of available soil moisture.
48 These findings quantify expected habitat ranges for these types of species, as indicated in Table
2.1. Species such as R. palustris, S. alba, and S. uliginosa seem to be more restricted to very wet ecosystems, while S. exigua, S. dicolor, S. nigra, C. sericea and V. lentago can be found within wetlands, or in areas adjacent to wetlands or a water source. S. altissima, meanwhile, is a meadow or grassland species, not commonly found in wet environments. Table 2.8 also indicates the wetland obligate or facultative status of these species.
Since soil water is a vector through witch nutrients are absorbed by plants, and is critical for photosynthesis and plant cell functioning, it is understandable that water demands and appropriate moisture conditions for plants must be met for them to survive. When looking at water use of plants to biomass production, Evans et al (2013) found a clear trend that biomass production in herbaceous species was greater in treatments that received greater water supply.
The few shrub species that they looked at in the study (including S. exigua) showed a positive biomass production response only in the treatment receiving the most water. The treatment design for the study involved watering on a monthly basis with amounts equating to 1.3, 2.6, and 3.9 cm each month within the treatments, as well as a control treatment that was strictly rainwater fed. The moisture was not held within the treatments using any barrier, and was subject to infiltration through the soil column. Although direct measures of treatment soil moisture were not recorded, the treatment that received the highest irrigation throughout the growing season showed the highest biomass production for 8 of 10 species planted, especially in the second growing season when seasonal precipitation was also higher. Since many of the species planted are mostly found in wetland and riparian environments, it was determined that the low moisture availability in other treatments was too limiting to growth, therefore growth success was highest with the highest moisture availability (Evans et al., 2013). These findings
49 provide evidence that certain wetland species (such as S. alba and R. palustris in the present study) grow most successfully when they are not limited by low soil moisture availability.
In a study measuring the response of various meadow grasses in terms of biomass to modelled soil moisture (as a function of soil tension, precipitation inputs, and mean water table depth), it was found that when grown as a monoculture, all species showed a peak in biomass production when the water table depth for the model was between 20-35 cm below the surface (Silvertown et al., 1999). However, in a heterogeneous planting, niche overlap between species was uncommon, and there was clear segregation of species along a water table gradient. In these instances, the peak plant biomass production was seen across a water table depth range of 5-
110cm below the surface, depending on the species (Silvertown et al., 1999). This vast range in water table depth and corresponding peak growth of different species supports the argument that species responses can vary greatly depending on resource availability. The results of this study indicate that niches are not only defined by nutrient and light availability, but that soil moisture at a fine spatial scale can also have a strong control over plant community structures (Silvertown et al., 1999). This study, however, is using depth to water table (among other factors) to model soil moisture, rather than using direct measurements for volumetric moisture content. Water table fluctuation and soil aeration conditions can act as important environmental controls over the successful germination and establishment of plants either with the existing seed bank, or transported through natural dispersion processed (Stroh et al., 2013).
The competitive advantage of invasive species such as Typha latifolia and Phalaris arundinacea in response to moisture availability was tested in an experiment were these species were planted in a range of treatments replicating flooding regimes. It was found that under varying soil moisture treatments, there were clear observed differences in biomass accumulation across
50 species and treatments. Notably, the two invasive species, Typha and Phalaris, had the highest growth success overall compared to native sedge and reed species, which supports the known ecological capacity for these plants to out-compete other species in a system (Kercher & Zedler,
2004). While none of the species in the present study are considered invasive, competitive advantage of certain species may be realized by the more rapid increases in growth early in the growing season – as seen for S. nigra and V. lentago. Alternatively, the tolerance to high moisture levels seen by S. alba, R. palustris, and even C. sericea and V. lentago could result in competitive advantage of these species over others that do not exhibit a similar moisture tolerance. Other studies that have similarly looked at the growth success of invasive plant species have also found that flood tolerance was a significant factor in increasing plant biomass and growth rate (Newman et al., 1996; Miller & Zedler, 2003).
Existing studies looking at plant community interactions related to hydrologic conditions commonly rely on measures of species richness or distribution across a landscape. These studies take field measurements of species composition and/or coverage in a region, and relate that directly to the hydrologic gradients of the landscape. These metrics for plant diversity and dominance in specific hydrologic conditions does provide valuable information on niche formation in varying levels of wetness, but it is less accurate at portraying variations in growth responses within a species type, across different moisture conditions (Evans et al., 2013).
Studies agree that measurements of biomass are more sensitive to environmental variation than plant cover (Evans et al., 2013). By looking at canopy characteristics and species composition alone, questions still remain about plant physiological characteristics or environmental factors aside from moisture alone that can affect plant growth (Brandle et al., 1996; Crawford and
Braendle, 1996). For instance, in a developed plant community, certain species could have a
51 competitive advantage due to their capacity to increase their stem height or root extent during fluctuations in ground water. The annual goldenrod species in the present study, along with S. nigra plants, showed the most rapid increases in growth early in the growing season, taking full advantage of higher moisture availability at this point in time. In the realm of life cycle strategies of plants to respond to various environmental stressors, it is possible to see some variation in the partitioning of above and below ground biomass – the stems and leaves vs. the roots – and see if moisture availability affects the growth of these portions of a plant differently
(Mulamoottil et al., 1996). When looking at the ratios of aboveground to belowground biomass in the present study, differences are seen between the species in regard to how much biomass is produced for each portion. Morphologically speaking, S. nigra and S. alba had proportionately more expansive rooting zones compared to the other species planted. While there were few significant relationships between treatments when looking at the proportions of above to belowground biomass, there were instances where it was seen that moisture availability did play a role in plant partitioning of biomass. Since S. nigra and S. uliginosa showed significantly greater aboveground biomass in the TW treatments, this indicated that the species were not limited by moisture availability and were able to put more energy into developing leaves and stems, rather than expanding their root mass in search for more moisture. The lower proportion of belowground moisture in treatment TN for S. altissima indicates the opposite relationship, where the drier soil conditions were non-limiting to aboveground biomass growth. These findings also align with the overall measured biomass patterns for each of these species, as well as the increases in growth from the initial. While the rest of the results were not statistically significant, lower ratios of above to belowground biomass in TN for species such as R. palustris,
C. sericea, and S. alba could indicate similar growth responses to moisture as was seen for S. nigra and S. uliginosa.
52
Increased height and foliation allows for greater access to sunlight in a dense canopy, while greater rooting extent allows for greater access to moisture while soils are drier, and the ability to conserve pockets of oxygen during flooded periods (Blom et al., 1994). Additionally, the production of litter by species with high leaf area can increase nutrient cycling and limit germination of seed of competitor species (Grevilliot et al, 1998). The nature of the experiment in this study limited the competitive factors of surrounding plants by spacing individuals randomly in the plot and a minimum of 1 metre apart from each neighbour. The existing species at the site were clipped down regularly through the growing period to limit the effects of crowding and shading by other species. It also took into consideration direct measurements of plant biomass accumulation over a growing season for each individual plant measured, to observe the morphological and physiological characteristics of each species, rather than assessing growth based on randomly sampled surveys of the plant community across a site.
Overall, clear responses to moisture availability were seen for most of the test species, proven through statistical analysis of biomass within each of the three experimental treatments.
2.4.2 Applications
The results of this study can provide valuable information to restoration practitioners looking to introduce native wetland shrub species into a site through a wetland restoration project.
Knowledge of site hydrologic regime prior to, and throughout the restoration would be necessary to implement these findings effectively, as the hydrology and soil moisture availability will control the plant growth responses. By having a thorough understanding of the expected hydrologic regime, it may be possible to develop strategic planting plans that position
53 certain species in zone appropriate to their water demands, and increase the success of community establishment that might be missed if a random planting scheme is implemented.
Based on the results of this study, clear preferences or tolerance ranges were seen for different species. If these species are introduced to the site as part of the restoration plans, it might be expected that C. sericea or V. lentigo plants would grow quite successfully regardless of their positioning at the site, or should the soil moisture levels fluctuate significantly. These species were most tolerant to a wide range in moisture levels. Alternatively, many of the other species, particularly S. alba and R. palustris, would be much more closely tied to the soil moisture available to them. These particular plants grew most successfully in the wettest possible conditions, and would likely do best in an area of the site that won’t be limited by water supply or experience extreme fluctuations in moisture over a growing season. The wetland species that exhibited thresholds for moisture tolerance, such as S. exigua, S. nigra, S. uliginosa, and S. discolor, would thrive in conditions where soil moisture availability is neither too low nor too high. Meanwhile, the S. altissima species, which is the current dominant species at the study site, showed much lower growth as soil moisture levels increased. In a restoration project, it would be expected that this species would die off following site flooding.
A coefficient of wetness has been determined by Ontario conservation groups in order to place a value on to native plant species which describes the probability of them existing in a wetland versus an upland environment (Oldham et al., 1995). These values range from -5 to +5 and describe if that species is an obligate or facultative wetland or upland species, and the likelihood of them existing in either one of these environments. In the spectrum of wetland plants, they can almost always occur in wetlands (-5: >99% probability), or can be equivalently likely to exist in
54 wetlands or uplands (0: ~50% probability). Anywhere in between these values on the scale, the likelihood of a particular plant existing in a wetland is greater than an upland environment, to varying degrees. Table 2.8 lists the wetness coefficients of the 9 species in this experiment, and whether the findings of this study concur. Where a ‘yes’ is indicated for agreement to the coefficient of wetness, that shows that the results of the biomass measurements agree with the assumed moisture tolerance levels for the coefficient of wetness, based on the likelihood of the species to be found growing within a wetland.
Coefficient of Wetness for Wetland Species Coefficient Species Description Agree with findings? of Wetness Lower growth in wettest treatment Salix exigua -5 Obligate could indicate a facultative wetland status rather than obligate Rosa palustris -5 Obligate Yes Solidago uliginosa -5 Obligate Spiraea alba -4 Facultative Wetland Yes Salix discolor -3 Facultative Wetland More similar to V. lentigo in Cornus sericea -3 Facultative Wetland moisture tolerance (facultative) Sambucus nigra -2 Facultative Wetland Viburnum lentigo -1 Facultative Yes Highest growth in drier soils, yet Solidago altissima +3 Facultative Upland increases in biomass over 100% in wetter treatments Table 2.8: Ministry of Natural Resources defined coefficients of wetness for each of the native Ontario species in this study.
For S. exigua and R. palustris, the increases in biomass seen under increased soil moisture levels agree with the obligate wetland status, whereas S. alba showed increases in biomass in all treatments, though the highest increase was seen in the wettest treatment. Like S. alba, C. sericea is considered a facultative wetland species, which for this shrub is likely indicative of it’s high tolerance to a wide range in moisture levels. V. lentago is classified as a facultative species, and the agreement from the results is due to the tolerance to varying moisture, but also
55 the lower overall growth. Meanwhile, S. altissima is given a facultative upland classification, agreeing with the lower tolerance to increased soil moisture levels, and the highest growth success in the TN treatment. The findings for the remaining species do not disagree with these assigned classifications, but a lack of a significant relationship between treatments doesn’t provide solid conclusive evidence in these instances.
A study looking at the changes in biodiversity related to flooded meadows and fertilization regimes found that unless the hydrologic and trophic balances that were found in the original floodplain were maintained, a loss in biodiversity was experienced regardless of the fertilization and flooding regimes used in their treatments (Grevilliot et al., 1998). In the majority of their treatments it was found that species with rapid growth strategies meant to competitively exclude other species were most successful, and that once biodiversity was lost in the treatments it was much more difficult to restore it again (Grevilliot et al., 1998). This supports the understanding that site planting plans in a restoration project should incorporate ecological niche knowledge, and to the best of one’s ability plant species in areas where their access to resources will not be limiting, and the risk of being out-competed by other species is lower.
Fine scale hydrologic conditions are also key factors in determining plant community composition; however, this is dependant on various macro-scale hydrologic inputs (Magee &
Kentula, 2005). Because plant growth success is so closely linked to hydrology, understanding of site water balance, and thus the availability of water to various taxa is critical in wetland restoration planning. In order to incorporate hydrologic knowledge into restoration plans, particularly in regard to promoting niche development, pre-restoration monitoring of the site water balance is essential. Allowing for natural topography to assist in creating moisture
56 variability should be coupled with educated efforts to introduce appropriate wetland species to their expected niche (Large et al., 2007, Bedford 1999). Additionally, an understanding of how the new species that are introduced to the site will use the water that is available to them, in terms of their physiological characteristics, is valuable (Bedford, 1999).
2.5 Conclusion
The variation in volumetric moisture content across the three experimental treatments throughout the growing season resulted in clear differences in overall growth of the wetland species planted in the plot. The measurements varied from a positive, negative, or null response to increasing moisture within the treatments, and also showed that the greatest increases in growth were seen at times of the year when moisture content in the treatments was higher on average. This understanding of shrub growth responses in mineral soils, and tolerance to soil moisture variation, is valuable from an ecohydrological standpoint as it sheds more light on the plant-soil-water interactions in water-controlled environments. Applications of these findings to wetland restoration can improve the successful establishment of a shrub thicket swamp community, when used in conjunction with and understanding of site hydrological conditions.
57 2.6 References
Austin, M. P. (2002). Spatial prediction of species distribution: An interface between ecological theory and statistical modeling. Ecological Modelling, 157(23), 101-118
Ballantine, K., Schneider, R., Groffman, P., and Lehmann, J. (2012). Soil properties and vegetative development in four restored freshwater depressional wetlands. Soil Science Society of America Journal, 76(4), 1482-1495.
Bedford, B.L. (1999). Cumulative effect on wetland landscapes: Links to wetland restoration in the United States and southern Canada. Wetlands, 19(4) 775-788.
Blom, C. W.P.M., Voesenek, L. A.C.J., Banga, M., Engelaar, W. M.H.G., Rijnders, J. H.G.M., Van De Steeg, H. M., and Visser, E. J.W. (1994). Physiological ecology of riverside species: adaptive responses of plants to submergence. Annals of Botany, 74(3), 253-263.
Bollens, U., Güsewell, S. and Klötzli, F. (2001). Vegetation changes in two Swiss fens affected by eutrophication and desiccation. Botanica Helvetica, 111, 139–155.
Booth, E.G., and Loheide, S.P. (2012). Hydroecological model predictions indicate wetter and more diverse soil water regimes and vegetation types following floodplain restoration. Journal of Geophysical Research, 117
Bonilla-Warford, C., and Zedler, J.B., (2002). Potential for using native plant species in stormwater wetlands. Journal of Environmental Management, 29, 385–393.
Boyce, R.L., Durtsche R.D., and Fugal, S.L. (2012). Impact of the invasive shrub Lonicera maackii on stand transpiration and ecosystem hydrology in a wetland forest. Biological Invasions, 14, 671–680.
Brändle, R., Pokorny, J., Kvet, J., and Cizkova H. (1996). Wetland plants as a subject of interdisciplinary research. Folia Geobotanica et Phytotaxonimica. 31, 1-6.
Brinson, M.M., and Malvarez, A.I. (2002). Temperate freshwater wetlands: types, status, and threats. Environmental Conservation, 29(2) 115-133.
Clarke, E., and Baldwin, A.H. (2002). Responses of wetland plants to ammonia and water level. Ecological Engineering, 18, 257–264.
Cohen, J. (1969). Statistical power analysis for the behavioral sciences. New York: Academic Press
Cohen, J.G., and M.A. Kost. 2007. Natural community abstract for northern shrub thicket. Michigan Natural Features Inventory, Lansing, MI.
Crawford, R.M.M, and Braendle, R. (1996). Oxygen deprivation stress in a changing environment. Journal of Experimental Botany, 47(295), 145-159
58
D’Odorico, P., Laio, F., Porporato, A., Ridolfi, L., Rinaldo, A., and Rodriguez-Iturbe, I. (2010). Ecohydrology of terrestrial ecosystems. BioScience, 60(11) 898-907
Ducks Unlimited Canada (2010). Southern Ontario Wetland Conversion Analysis. Ducks Unlimited Canada.
Ewing, K. (1996). Tolerance of four wetland plant species to flooding and sediment deposition. Environmental and Experimental Botany, 36, 131–146.
Evans, T.L., Mata-González R., Martin, D. W., McLendon, T. and Noller, J. S. (2013). Growth, water productivity, and biomass allocation of Great Basin plants as affected by summer watering. Ecohydrology, 6, 713–721
Fuselier, L.C., Donarski, D., Novacek, J., Rastedt, D., and Peyton, C. (2012). Composition and biomass productivity of bryophyte assemblages in natural and restored marshes in the prairie pothole region of northern Minnesota. Wetlands, 32(6), 1067-1078.
Grevilliot, F., Krebs, L., and Muller, S. (1998). Comparative importance and interference of hydrological conditions and soil nutrient gradients in floristic biodiversity in flood meadows. Biodiversity and Conservation, 7, 1495-520.
Gulias, J., Seddaiu, G., Cifre, J., Salis, M., & Ledda, L. (2012). Leaf and plant water use efficiency in cocksfoot and tall fescue accessions under differing soil water availability. Crop Science, 52, 2321-2331.
Güsewell, S., Bollens, U., Ryser, P. and Klötzli, F. (2003). Contrasting effects of nitrogen, phosphorus and water regime on first- and second-year growth of 16 wetland plant species. Functional Ecology, 17, 754–765.
Hajek, M. Hajkova, P., Koci, M., Jirousek, M., Mikulakova, E., Kintrova K., (2013). Do we need soil moisture measurements in the vegetation–environment studies in wetlands? Journal of Vegetation Science, 24(1), 127-137
Hunter, R.G., Combs, D.L., and George, D.B. (2000). Growth of softstem bulrush (Scirpus validus) in microcosms with different hydrologic regimes and media depths. Wetlands, 20, 15–22.
Keer, G., and Zedler, J.B. (2002). Salt marsh canopy architecture differs with the number and composition of species. Journal of Applied Ecology, 12, 456–473.
Kercher, S.M., and Zedler, J.B. (2004). Flood tolerance in wetland angiosperms: a comparison of invasive and noninvasive species. Aquatic Botany, 80, 89–102.
Large, A.R.G., Mayes, W.M., Newson, M.D., and Parkin, G. (2007). Using long-term monitoring of fen hydrology and vegetation to underpin wetland restoration strategies. Applied Vegetation Science, 10, 417-428.
59
Lentz, K.A., and Dunson, W.A. (1998). Water level affects growth of endangered northeastern bulrush, Scirpus ancistrochaetus Schuyler. Aquatic Botany, 60, 213–219.
Lieffers, V.J., and Shay, J.M. (1981). The effects of water level on the growth and reproduction of Scirpus maritimus var. paludosus. Canadian Journal of Botany, 59, 118–121
Lovell, J.T., and Menges, E. (2013). Dominant species responses to drought in seasonal wetlands: evidence from reciprocal transplants across a moisture gradient. The Journal of the Torrey Botanical Society, 140(2), 157-169
Magee, T.K., and Kentula, M.E. (2005). Response of wetland plant species to hydrologic conditions. Wetlands Ecology and Management, 13, 163–181.
Miller, R.C., and Zedler, J.B. (2003). Responses of native and invasive wetland plants to hydroperiod and water depth. Plant Ecology, 167, 57–69.
Mills, J.E., Meyer, G.A., Reinhartz, J.A., and Young, E.B (2012). An exotic invasive shrub has greater recruitment than native shrub species within a large undisturbed wetland. Plant Ecology, 213, 1425-1436
Moeslund, J.E., Arge, L., Bocher, P.K., Dalgaard, T., Ejrnæs, R., Odgaard, M.V., and Svenning, J.C. (2013). Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodiversity and Conservation, 22, 2151–2166
Moreno-Mateos, D., Power, M.E., Comin, F.A., and Yockteng, R. (2012). Structural and functional loss in restored wetland ecosystems. PLoS Biology, 10(1)
Mulamoottil, G., Warner, B.G., and McBean, E.A. (1996). Wetlands: Environmental gradients, boundaries, and buffers. Lewis Publishers, Boca Rotan. ISBN: 1566701473
Newman, S., Grace, J.B., and Koebel, J.W. (1996). Effects of nutrients and hydroperiod on Typha, Cladium and Eleocharis: implications for Everglades restoration. Journal of Applied Ecology, 6, 774–783.
Oldham, M.J., Bakowsky, W.D., and Sutherland D.A. (1995). Floristic quality assessment system for Southern Ontario. Natural Heritage Information Centre, Ontario Ministry of Natural Resources. Peterborough, Ontario. 23 pp.
Palanisamy, B., and Chui, T.F.M. (2013). Understanding wetland plant dynamics in response to water table changes through ecohydrological modelling. Ecohydrology, 6, 287-296
Rodriguez-Iturbe, I. (2000). Ecohydrology: A Hydrologic Perspective of Climate-soil- vegetation Dynamics. Water Resources Research, 36(1), 3-9.
Rosenthal, J.A. (1996). Qualitative descriptors of strength of association and effect size. Journal of Social Science Research, 21(4), 37-59
60
Rubec, C.D.A. (1994) Canada’s federal policy on wetland conservation: a global model. Global Wetlands: Old World and New, ed. W.J. Mitsch, 909–917.
Rubio, G., Casasola, G., and Lavado, R.S. (1995). Adaptations and biomass production of two grasses in response to waterlogging and soil nutrient enrichment. Oecologia, 102, 102– 105.
Silvertown, J., Dodd, M.E., Gowing, D.J.G., and Mountford J.O. (1999). Hydrologically defined niches reveal a basis for species richness in plant communities. Nature, 400, 61-63.
Stroh, P.A., Mountford, J.O., Araya, Y.N., and Hughes, M.R. (2013). Quantifying soil hydrology to explain the development of vegetation in and ex-arable wetland restoration site. Wetlands, 33, 311-320
Tamea, S., Liao, F., Ridolfi, L., D’Odorico, P., and Rodriguez-Iturbe, I. (2009). Ecohydrology of groundwater-dependant ecosystems: 2. Stochastic soil moisture dynamics. Water Resources Research, 45.
Tromp-van Meerveld, H.J. and McDonnell, J.J. (2006). On the interrelations between topography, soil depth, soil moisture, transpiration rates and species distribution at the hillslope scale. Advances in Water Resources, 29, 293–310
Van den Boogaard, R., Alewijnse, D., Veneklaas, J., & Lambers, H. (1997). Growth and water- use efficiency of 10 Triticum aestivum cultivars at different water availability in relation to allocation of biomass. Plant, Cell, and Environment, 20, 200-210.
61 Appendix 2A: Results of Repeat-Measures ANOVA for Measured Volumetric Moisture Content between Treatments and Dates
Results of Repeat-Measures ANOVA for Measured Volumetric Moisture Content between Treatments and Dates Date Species Jun 14 Jun 19 Jun 25 Jul 02 Jul 15 Jul 24 Jul 30 Aug 08 Aug 13 Aug 23 T1-T2 0.000 0.001 0.000 0.000 0.512 0.007 0.199 0.011 0.039 0.976 CORSER T1-T3 0.000 0.000 0.000 0.000 0.358 0.003 0.002 0.000 0.000 0.122 T2-T3 0.752 0.524 0.716 0.053 0.958 0.919 0.111 0.213 0.155 0.178
T1-T2 0.032 0.999 0.000 0.009 0.996 0.332 0.103 0.094 0.408 0.818 ROSPAL T1-T3 0.003 0.386 0.000 0.000 0.999 0.220 0.038 0.047 0.082 0.506 T2-T3 0.569 0.369 0.971 0.269 0.999 0.962 0.882 0.937 0.613 0.213
T1-T2 0.022 0.004 0.002 0.000 0.206 0.003 0.310 0.003 0.018 0.066 SPIALB T1-T3 0.001 0.000 0.001 0.000 0.111 0.001 0.061 0.001 0.059 0.177 T2-T3 0.362 0.358 0.870 0.634 0.936 0.840 0.641 0.895 0.855 0.863
T1-T2 0.498 0.181 0.455 0.503 0.780 0.557 0.945 0.988 0.826 0.610 SOLALT T1-T3 0.742 0.773 0.993 0.465 0.789 0.279 0.230 0.873 0.588 0.557 T2-T3 0.165 0.497 0.520 0.998 0.999 0.861 0.372 0.797 0.915 0.996
T1-T2 0.016 0.006 0.015 0.017 0.607 0.861 0.797 0.646 0.662 0.475 SALDIS T1-T3 0.000 0.000 0.000 0.000 0.114 0.200 0.057 0.008 0.172 0.826 T2-T3 0.006 0.103 0.065 0.015 0.506 0.436 0.194 0.062 0.593 0.826
T1-T2 0.018 0.052 0.024 0.001 0.459 0.039 0.238 0.241 0.454 0.702 SALEXI T1-T3 0.000 0.001 0.000 0.000 0.939 0.001 0.036 0.024 0.007 0.926 T2-T3 0.114 0.228 0.234 0.207 0.663 0.187 0.594 0.478 0.105 0.901
T1-T2 0.001 0.007 0.007 0.007 0.193 0.013 0.193 0.025 0.011 0.044 SAMNIG T1-T3 0.000 0.001 0.001 0.002 0.029 0.029 0.006 0.001 0.003 0.007 T2-T3 0.768 0.832 0.966 0.918 0.704 0.843 0.305 0.526 0.932 0.792
T1-T2 0.004 0.000 0.000 0.000 0.189 0.000 0.137 0.005 0.000 0.000 SOLULG T1-T3 0.005 0.000 0.000 0.001 0.814 0.000 0.003 0.008 0.000 0.003 T2-T3 0.991 0.938 0.946 0.438 0.469 0.302 0.193 0.981 0.637 0.116
T1-T2 0.059 0.026 0.541 0.208 0.092 0.171 0.496 0.000 0.044 X VIBLEN T1-T3 0.039 0.001 0.346 0.014 0.225 0.841 0.871 0.000 0.311 X T2-T3 0.977 0.364 0.934 0.390 0.876 0.409 0.246 0.735 0.545 X
62 Chapter 3 Controlling Effects of Environmental Variables on Wetland Shrub Stomatal Conductance
Abstract
To test the controlling effects of these environmental factors on gs of wetland shrub species, which is currently unknown for these plant types, an experimental plot within a post- agricultural field was established to manipulate soil moisture around individual plants and gs was measured throughout the growing season. A logistic upper-quantile, non-linear regression approach was used to reveal the species-specific controlling relationships of each environmental variable. Two modelling techniques were implemented to calculate growing season trends in gs at the study site – a multiple linear regression model, and an adapted Jarvis-type phenomenological model. The adapted Jarvis-type model incorporated the species response curves to the environmental controls generated through the logistic upper-quantile regression.
The results of the models show that gs can be up to 1.5 times greater between species over a growing season, which could result in significantly greater water loss through evapotranspiration comparatively.
3.1 Introduction
The study of ecohydrology focuses on the interacting relationships throughout the soil-plant- water-atmosphere continuum (D’Odorico et al., 2010). Plants play a crucial role in controlling terrestrial ecosystem water balances, as the evapotranspiration (ET) component of a water balance can account for up to 70% of all water loss at a site (Landmeyer, 2012). Stomatal conductance of a plant is an important metric for determining plant transpiration and photosynthetic rates, as it describes the rate of gas exchange through the leaf stomata (Lafleur,
1988). Stomata are the small pores on a leaf that change in aperture as a function of guard cell water content, to control water vapour loss and CO2 intake of a plant. There are various
63 environmental factors affect stomata aperture at any given time (Baird & Wilby, 1999; Buckley and Mott, 2013; Lafleur, 1988). When leaf water potential is high, the guard cells surrounding stomata are turgid, and stomatal aperture is large. When environmental conditions place stress on a plant, guard cells shrink and stomata close (Heatherington & Woodward, 2003). The environmental variables that are considered to have the strongest control on stomatal conductance are sensible heat of the air mass measured as air temperature; vapour pressure deficit, which describes the drying power of the air; light and available energy through incoming shortwave radiation; and the leaf water potential, influenced by the volumetric soil moisture content (Dolman et al., 2014; Stewart, 1988).
Guard cell closure for different species may occur under varying levels of stress, and the stress response may not be the same from one species to another, even for plants exposed to similar climatic and soil conditions (Rodriguez-Iturbe, 2000). The rate of conductance between species is related to leaf hydraulic characteristics, which can vary depending on leaf size, stomatal shape and density, as well as leaf petiole and vein orientation (Sack et al, 2003). Variation in these plant characteristics can be beneficial for differential water use for different species to limit competition for water resources, as well physiological and growth adaptations of differing species to preserve energy and promote growth and reproductive success (Chaves et al., 2002;
Niklas & Hammond, 2013; Rodriguez-Iturbe, 2000). Plants exposed to moisture stress can respond by exhibiting immediate stress avoidance and stomatal closure, however the trade-off is decreased photosynthesis and carbon assimilation. One particular stress adaptation that is commonly seen in herbaceous annual species is the tendency to avoid moisture-limiting conditions by growing rapidly and seeding before drying soil conditions occur (Chaves et al.,
2002).
64 Because wetlands are water-controlled ecosystems, understanding the role of water on plant physiological functioning in these systems is essential. In regard to plant responses to soil moisture, it is known that increasing soil moisture through persistent water supply will result in increased plant growth for moisture tolerant species. However, increased moisture can also mean greater capability for the plants to transpire and thus deplete the soil moisture available
(Kim & Wang, 2012). However, the way in which these plants ‘sense’ moisture stress, and how soil moisture interacts with key controls over conductance is largely unknown (Dolman et al.,
2014). Since physiological characteristics of plant species can vary greatly and species-specific responses to changing environmental conditions will differ, understating how each environmental control can elicit a stomatal response for individual species is necessary, and can contribute to greater understanding of plant-water interactions (Boyce et al., 2012; Ingram,
1983; Lafleur, 1990; Linacre, 1976; Rejskova et al., 2012).
The existing research that does investigate environmental controls on wetland plant stomatal conductance focuses predominantly on trees, reed and sedge species (Biscoe et al., 1977; Busch,
2000; Busch and Losch, 1998; Ewers et al., 2009; Sala and Tenhunen, 1995). There is currently a lack of information, however, on wetland shrub stomatal conductance. One study was found that did investigate shrub stomatal conductance focused on deciduous upland and grassland shrub species in semi-arid and arid environments. In this study, stomatal conductance was determined as a function of hydraulic conductivity, water potential, and osmotic potential, along with atmospheric vapour pressure deficit. Mechanistic controls such as volumetric moisture content of the soil, air temperature, and solar radiation were not considered (Gao et al., 2013).
To increase knowledge on wetland shrubs this study will focus on native Ontario wetland shrub species common to mineral thicket swamp communities in temperate regions. Swamps are one
65 of four main wetland types in Ontario (Ontario Ministry of Natural Resources, 2011), and mineral thicket swamps are a common variety.
When looking at the relationships between plant and the soil-water-atmosphere continuum, and the stomatal controls of these aforementioned environmental factors, typical methods of ordination are not always appropriate to represent the data. The nature of these relationships is inherently non-linear and non-symmetrical, as a result of numerous confounding factors acting on a plant at any given time (Austin, 1979; Cade, 1999; Duval et al., 2012). Yet ordination methods are used which make assumptions on ecological models that represent the form of a species response to an environmental control as a linear correlation (Austin, 1999). For instance, when attempting a correlation between factor X and Y, least-squares linear regression assumes that Y is a function of X, while there may be numerous limiting Z factors that are not taken into in the comparison (Cade, 1999). In the case of stomatal conductance, a linear regression analysis using vapour pressure deficit as the controlling factor may not show a strong linear correlation, because at any given measurement point a different environmental variable may have a more significant control. This issue limits the effectiveness of typical methods of correlating vegetation data to explanatory variables because only a small subset of all influencing factors can be reasonably measured and analyzed. Because of this, there may be unknown or unmeasured variables that are determining the results, which cannot be represented by the data available (Cade, 1999; Schroder et al., 2005). The use of non-linear ordination methods such as applying a unimodal, symmetrical Gaussian curve to species responses is not always effective either, as this method still only accounts for a single optimum environmental condition, and assumes symmetry in the relationship, which is not always the case (Austin, 1999). A method that has been proven successful in improving on these analysis and correlation issues is to use a
66 non-linear, logistic upper quantile regression approach (Cade, 1999; Duval et al., 2012; Scharf et al., 1998; Schroder et al, 2005). By using upper quantile regression, it is possible to elucidate the constraints of a particular limiting variable over another, as it properly accounts for the upper and lower bounds of the response variable (Cade, 1999; Cade & Guo, 2000; Duval et al.,
2012; Scharf et al., 1998; Schroder et al., 2005). For instance, the curve generated for the response of conductance due to a change in solar irradiance can bend to the shape of the data, and accounts for the full range of a dataset. Despite a number of studies that have proven that this method is valuable solving regression problems in ecological research (Cade & Guo, 2000;
Duval et al., 2012; Strachan and McCaughey, 2002), it has been relatively under-utilized
(Dolman et al., 2014; Schroder et al., 2005), and never before attempted in the context of stomatal conductivity. Studies that have looked at the upper bounds of controls on stomatal conductance have used “boundary-line” analysis rather than a logistic upper quantile regression approach. The “boundary-line” for the stomatal responses determine the shapes of these lines based on measurement points along the upper bounds for the Y variable on a point cloud in a scatter plot (Lafleur, 1998; Strachan and McCaughey 2002, Stewart, 1988). This is less analytical and more subjective than logistic upper-quantile regression, which takes into account the entire dataset to calculate the response curves. Additionally, the boundary-line analysis methods assume uniform responses to environmental conditions for a group of species, when in reality the responses may differ between species.
The knowledge of environmental controls on stomatal conductance is also valuable from a modelling standpoint. Stomatal conductance of plants can be directly measured in the field.
However, these measurement techniques are labour intensive, time consuming and not practical for large spatial or temporal scales. In order to extrapolate field measurements to a full-site or
67 full growing season scale, data modelling techniques are necessary (Dolman et al., 2014). To assess growing season variation in stomatal conductance based on the environmental limiting factors of interest in this study, there are two methods of statistical modelling that will be presented which rely on easily measureable values (Buckley & Mott, 2013). The first is a multiple linear regression model that assumes linear functions for each relationship, while the more complex “phenomenological” model incorporates a logistic upper-quantile non-linear regression equation (Jarvis, 1976; Stewart, 1988).
The phenomenological approach was first proposed by Jarvis (1976), and is based on the knowledge that an environmental variable can have a similar controlling effect over stomatal conductance of different species when other variables are made non-limiting (Strachan and
McCaughey, 2002). The model proposes that these relationships can be determined independently, and then incorporated and combined as various functions within the model
(Strachan and McCaughey, 2002). For instance, studies have shown that leaf stomata on many species begin to close when vapour pressure deficit is too high in order to limit water loss
(Buckely & Mott, 2013; Lafluer, 1988; Streck, 2013), stomata open in response to increasing light levels, and can exhibit a parabola shaped response to air temperature due to maximum and minimum thresholds (Adams, 1991; Munro, 1989; Strachan and McCaughey, 2002). The logistic upper quantile regression approach inherently generates functions that represent these limiting relationships, which fits ideally with the Jarvis-type phemenological model.
The resulting modelled conductance values can elucidate how wetland plants are affected by soil hydrologic characteristics. The clearly defined results of these models – stomatal aperture or conductance – can also be easily compared to physical measurements, and is valuable for many
68 plant science and ecosystem applications (Buckley & Mott, 2013). One such application would be to improving hydrological knowledge in wetland restoration projects.
Wetland restoration is an increasingly important activity, particularly in North America where it is determined that less than half of historical wetland coverage remains, and loss continues at a rate between 28300 to 36400 hectares a year (Moreno-Mateos, 2012). However, the success of restoration projects is limited by the knowledge practitioners have of the ecohydrological interactions. The common practice in restoring wetlands is manipulating physical hydrological features to allow for increased availability of water at the site. Successful plant community establishment occurs over a long time scale, and frequently does not approach the reference level expected for the restoration (Moreno-Mateos et al., 2012). Restoration projects often set specific vegetation assemblage targets, for which a strong understanding of hydrological and substrate requirements is necessary, but rarely determined. The need for highly prescriptive management practices often results in restoration projects that do not meet the original aims
(Stroh et al., 2013), and generalizations of wetland hydrologic functions are of little use due to variability in catchment topography, climatology, and in the wetland type and location (Staes et al., 2009). Therefore, increased knowledge of wetland ecohydrological relationships and plant responses to changing environmental conditions can provide valuable information to restoration practitioners on water demands and plant physiology of wetland species. The growing season stomatal conductance measurements determined through modelling is a metric that can be included in calculations for site ET. Proper determination of seasonal ET will help quantify a significant portion of the wetland water balance. This is needed to determine if there will be sufficient water availability throughout a growing season. Accurate knowledge of site hydrologic conditions can also help determine which species will be most successful under
69 expected hydrologic regimes and moisture availability, because of the variation in physiological responses from species to species (Busch, 2000).
Because we do not know much about wetland shrub species, despite their use in restoration projects, calculation of thicket swamp ET is difficult. There is also a lack of understanding of site water balances following restoration of these systems, as well as the possible responses these species have to change in climate (such as increasing temperatures, increasing soil moisture, and a change in frequency and intensity of rainfall events).
The objectives for this study, then, are to 1) Determine the limiting relationships of atmospheric and soil moisture controls on stomatal conductance for native Ontario wetland shrub species, 2) assess the ability of non-linear quantile regression to determine these relationships to stomatal conductance, and 3) compare the results of two modelling techniques for extrapolating growing- season stomatal conductance along natural fluctuations in site soil moisture and we will then discuss the relevance of these findings to wetland restoration efforts.
3.2 Methods
3.2.1 Study Site
This research was conducted in a 2.5 hectare field in Brampton, Ontario, Canada, (Lat: 43.68,
Long: -79.83), over the 2013 growing season. The site was formerly used for agriculture, and is located within an area that is currently undergoing extensive residential development. Current vegetation coverage at the site is predominantly goldenrod, aster, and thistle species. The physiographic region that this site fall within is known as the Peel Plains, which is characterized
70 by soils with high clay content, eroded from iron-rich Queenston shale. Soil porosity in the upper topsoil layer was approximately 68%. The soil profile at the site showed topsoil to a depth of 30-40 cm, with presence of some clay-ey sediment. Below this depth, grey and blueish grey clays dominate the profile, with iron mottling throughout. After depths between 80 and 200 cm below the surface, layers of fine sand were present throughout the clay. During this study period, water table at the site sat 50 cm below the surface on average, with fluctuation between
0 – 150 cm in response to rainfall amount and frequency. The growing season potential evapotranspiration (PET) rate at the site is 430 mm, with daily average PET of 3.2 mm
(Ormshaw, unpublished data).
As part of the development plans in this region, the site is slated to undergo restoration to a shrub thicket mineral swamp community, in order to compensate for the loss of a nearby wetland. Prior to agricultural use, this site was historically a wetland, and is situated adjacent to established maple-dominant mineral swamp communities, characteristics that should help aid in wetland restoration at the site. The restoration plans will include blocking the drainage of water out of the site by in-filling drainage ditches, removing weeping tiles, and constructing a berm in the northeastern corner. Approximately 30 houses adjacent to the site will be connected to a drainage ditch that will allow rainwater to be re-directed into the site to provide additional water supply. A number of Acer rubrum saplings were transplanted on to the site, and the shrub mineral thicket swamp community will be established by introducing native wetland shrub species. This fits with the community types of the wetland areas found in the surrounding maple-dominant forest stands, therefore increasing connectivity with these systems. This site will also become a part of a natural heritage corridor defined by the Credit Valley Conservation
Authority of Mississauga, Ontario, that borders the East Huttonville creek. Restoration efforts
71 had not yet begun at the time of this study, allowing for the experimental manipulation of soil
moisture conditions surrounding a variety of species that will be introduced when restoration
efforts begin.
3.2.2 Field data collection
To observe the responses and variation in stomatal conductance (gs) of wetland shrub species
under differences in soil moisture availability, an experimental 16x16 metre experimental plot
was established within the site boundaries. Within this plot, 27 replicates of 9 juvenile wetland
species were planted in a random distribution. Planting was done by May 17th, and each plant
grew over a total of 16 weeks from the time of planting.
Native Wetland Species Characteristics Species Common Name Family Wetland Height Habitat Occurrence Sambucus nigra Common Elderberry Adoxaceae Facultative 6 m Lowland, wetland Solidago altissima Late Goldenrod Asteraceae Upland 0.5 – 2 m Meadow Viburnum lentago Nanny Berry Caprifoliaceae Facultative 9 m Lowland forest, swamp
Solidago uliginosa Bog Goldenrod Asteraceae Obligate 0.3 – 1.2 m Bog, marsh, swamp Salix discolor Pussy Willow Salicaceae Facultative 7 m Forest, wetland Cornus sericea Red Osier Dogwood Cornaceae Facultative 1.4 – 6 m Wetland, riparian zone Spiraea alba Meadowsweet Rosaceae Facultative 1 – 1.5 m Wet grassland, swamp Rosa palustris Swamp Rose Rosaceae Obligate 0.3 – 2.5 m Marsh, swamp Salix exigua Sandbar Willow Salicaceae Obligate 1 – 7 m Riparian, shore, wetland Table 3.1: Species name, family, maximum height, and habitat of the nine species planted in the treatment plot
Of the species planted, 7 were Ontario native wetland shrubs – Cornus sericea (Red Osier
Dogwood), Rosa palustris (Swamp Rose), Salix discolor (Pussy Willow), Salix exigua (Sandbar
Willow), Sambucus nigra (Common Elderberry), Spiraea alba (Meadowsweet), and Viburnum
lentago (Nanny Berry), – and two were native goldenrod species – Solidago altissima (Late
72 Goldenrod) and Solidago ulginosa (Bog Goldenrod) – the former of which is typically an upland species, and is the current dominant species at the site. Table 3.1 describes some of the characteristics and of each species.
Figure 3.1: Diagrammatic representation of the three treatments within the experimental plot – Natural (TN), Wet (TW), and Wettest (TW2)
Of the 27 replicates for each species, 9 were planted in the natural soil conditions, where precipitation was able to drain through the soil column, lowering soil moisture levels in the plant root zone. The remaining replicates featured a barrier to prevent infiltration of rainwater into the surrounding soil column. Lining a 30 cm diameter by 60 cm deep hole with reinforced 6 mil plastic sheeting created the barrier. These barriers effectively retained water around the plant rooting zones, creating small-scale wetland conditions around each plant. All treatments were rainwater fed; however, 9 of these 18 replicates received a doubled supply of water, by
73 collecting precipitation in containers at the site and applying it to these replicates after each rainfall event. Thus, a gradient of increasing soil moisture across the treatments was established, which allowed for observations of plant physiology in response to a wide range of soil moisture.
Figure 3.1 illustrates the replicate design within the experimental plot.
-1 In order to establish the gs rates of the native wetland shrub species, the conductance in mm s was measured using a Delta-T AP4 leaf diffusion porometer. For each replicate, gs was measured on a weekly basis over a 16-week growing period, by randomly selecting a subset of 5 of 9 replicates for each species and treatment. For each replicate on any given sampling date, three leaves were measured representing the upper 25%, middle 50% and lower 25% of the plant, respectively. This equated to approximately 133 plants measured weekly, which is equivalent to approximately 400 individual readings of leaf level gs per week. A total of 3240 individual gs measurements spanning the growing season were recorded. Porometer use was restricted to days with no rainfall and when ambient relative humidity (RH) was below 80%.
Due to the sensitivity of stomata to changes in environmental conditions, the porometer would be calibrated each day to the specific temperature and RH at the site. For accurate readings, recalibration was necessary when temperature fluctuation was greater than +/- 2.5 °C, or RH changed by 5%. The range in accurate readings for stomatal conductance from the porometer is between 0.25 and 30 mm s-1.
A micro-meteorological station was erected at the study site, which continuously scanned weather variables at 5-second intervals and recorded as 30-minute averages to a CR-1000 data logger, save for the tipping bucket, which recorded total tips within a 30-minute time interval.
The station featured a Kipp & Zonen NR-Lite net radiometer mounted 3.5 metres above the
74 ground; a Vaisala HMP155 air temperature and RH probe within a Gill Screen and a R.M
Young cup anemometer mounted 2 metres above the ground; a CS616 soil moisture probe
averaging volumetric moisture content of the soil up to 20 cm below the surface; and a Texas
Electronics 25M tipping bucket rain gauge mounted 1 metre above the ground. The gs
measurements were directly correlated to meteorological controls of air temperature (T), solar
irradiance (K↓), and vapour pressure deficit of the air mass (VPD), as these variables are
particularly important to determine the controlling relationships over gs. For the environmental
controls that are of interest for this study, air temperature and volumetric moisture content are
direct measurements taken from instrumentation at the site. Vapour pressure deficit was
calculated based on the difference between actual vapour pressure of the air (ea) and vapour
pressure of the air at saturation (es), using equation (3.1).
VPD = es − ea (3.1)
€ The values for es and ea are determined with measurements for relative humidity and air
temperature. The measurements for incoming shortwave radiation were collected from the
nearby University of Toronto Mississauga meteorological station. While the site meteorological
station featured a net radiometer for recording available solar energy, it is not possible to
separate out each component of the solar radiation from these measurements. Because the
incoming shortwave radiation is more important in controlling stomatal closure, these
measurements were preferable for analysis. It was determined that the close proximity of the
stations, and the averaging of readings over a half hour interval resulted in a minimal difference
in radiation readings between the sites. A comparison of net radiation between the two
meteorological stations can be seen in Appendix 3A. Over the study period, it can be seen that
75 net radiation follows a similar day-to-day trend, with an average standard deviation between stations of +/- 22.3 W m-2.
Volumetric moisture content (θ) of the soil for each replicate was measured weekly using a
HydroSense® CS620/CD620 Soil Water Measurement System. θ was measured up to a 20 cm depth, to account for the main rooting zone for these types of plants. Measurements were recorded in a percentage value, indicating the percentage of soil pores that are occupied or saturated with water molecules instead of air. The HydroSense® was calibrated to the soils specific to the study site, which have high clay content and are thus more conductive. Soil cores were taken to correlate direct measurements from the HydroSense® to actual soil moisture readings. A three-point calibration procedure was used, where a series of cores were brought to fully saturated, field capacity, and dry soil moisture levels in a controlled setting. At each stage,
θ in the cores was measured with the HydroSense® to gather the probe output period value (the speed of return of a measurement), and a set of cores was oven dried for 48 hours at 80 °C. The soil moisture content was determined as the loss of water mass from the soils and was correlated to the probe period output measured. This was repeated for each degree of saturation for the soil cores. The results were plotted and the slope equation for the linear relationship between soil core θ and period output at the three levels of saturation was applied to the raw data for θ from the experimental plot.
76 3.2.3 Modelling
The response curves for environmental controls on shrub conductance were modelled using
logistic growth functions as per equation (3.2) (Huisman et al., 1993; Oksanen and Minchin,
2002).
1 1 y = M (3.2) 1+ ea+bx 1+ ec+dx
-1 Where y is the species response (gs, mm s ), x is the environmental variable that is affecting this € response: T, VPD, K↓, or θ. In this equation, M represents the maximum possible value that y
-1 could reach, which in this case was set to 30 mm s (the theoretical maximum gs value for
plants, and the porometer’s upper limit of accuracy). a, b, c, and d are the coefficients to be
estimated. The non-linear regression was carried out by comparing the 95th quantile of each
species gs against each environmental variable of interest. In logistic upper-quantile regression,
the absolute upper envelope of the measured values would provide the best representation for
the limiting effects of an environmental variable, as these values will be least affected by other
controlling factors. However, to account for measurement error while maintaining a best
representation of the envelope of the relationship, a 95% regression quantile is considered
effective (Schroder et al. 2005). This regression analysis was performed in STATISTICA®
using equation 3.2 along with the user-defined asymmetric loss function provided in the
Appendix of Schröder et al (2005). After fitting the growth function, if b and d had the same
sign (which would indicate that they are both describing the same direction of change in species
response), d was then set to zero to eliminate this effect (Schröder et al, 2005). The logistic
growth coefficients generated for the 95th regression quantile can be found in Appendix 3B.
77 Multiple linear regression is a type of generalized linear model, and is an approach used in order
to model relationships between two or more explanatory variables to a response variable by
fitting a linear equation to the observed data. This method estimates unknown model parameters
by assuming value y is a linear function of the observed x variables, using determined regression
coefficients. The equation for modelling gs as a linear function of different environmental
variables is seen in equation (3.3) (Lafleur, 1988).
gs = a + bT + cVPD + dK ↓+eθ (3.3)
€ Where a is the constant regression coefficient, and b, c, d, e are regression coefficients based on
the linear relationships of the observed environmental variables for each measurement of gs.
These coefficients were determined in SYSTAT using the least squares multiple linear
regression analysis function. gs is modelled by incorporating known measurements for T (°C),
VPD (kPa), K↓ (W m-2) and θ (m3 m-3) across the growing season to the determined regression
coefficients. The coefficients for each species and treatment can be seen in Appendix 3C.
The phenomenological approach as proposed by Jarvis (1976), is used to model gs using the
same environmental variables that are incorporated in the multiple linear regression model.
However, instead of assuming a linear correlation between conductance and these
environmental variables, it includes the non-linear quantile regressions determined from the
analysis of the observed values. Equation (3.4) is used to model gs using this approach (Lafluer,
1988).
78 gs = gs max (aT)(bVPD)(cK ↓)(dθ) (3.4)
€ Where gs max is the maximum observed value for each species, and a, b, c, and d are functions to represent the response of conductance to each of the individual environmental variables (T,
VPD, K↓, and θ). The functions are expressed as a normalized value between 0-1, representing
the proportionate gs as a response to each individual variable, where 1 would be the maximum
possible gs (Lafluer, 1988; Jarvis, 1976). The approach used for this study is an adaptation to the
methodology initially presented in Jarvis (1976), as it statistically accounts for variability in
observed gs responses for different species, rather than using functions designed based on
assumed boundary line relationships for the different environmental variables (Buckley & Mott,
2013; Chambers et al., 1985). The upper-quantile regression curves for each environmental
variable were used to calculate the corresponding normalized function of gs under each value of
the variable. Equation (3.2) was used to calculate gs using the same logistic growth coefficients
for each response curve, and then normalized between 0-1 as a fraction of maximum observed gs
for that species. This allows for the controlling relationship of each environmental variable to be
used to calculate gs under known conditions. Equation (3.4) effectively combines the potential
controlling effect of all four variables of interest.
3.3 Results & Discussion
3.3.1 Environmental Variables
The growing season trends for 24-hour averages of the four controlling environmental variables
measured for the 2013 growing season are seen in Figure 3.2. K↓ does not indicate any seasonal
pattern, as there is a great degree of day-to-day fluctuation in these readings throughout the
79 entire growing season. The trend in VPD shows similarity in fluctuations with changing air temperature – low VPD values are frequently seen on days when T drops, and higher VPD with higher T. Over the growing season, daily average VPD exceeded 1.0 kPa 49% of the time, while
VPD of 1.5 kPa was exceeded for 11% of the measurement dates. On only one day of the growing season was a daily average exceeding 2.0 kPa seen. Generally speaking, plants begin to exhibit stress to VPD at levels of 1.5 kPa or greater, while 2.0 kPa is considered very stressful.
For air temperature, while peaks and dips where seen throughout the full growing season, much lower daily T was seen in May and September (when daily average temperatures reached between 5-10 ºC), while the period from early June to late August only showed 6 days where the average T was below 20 °C. Seasonal average daily T was 20.4 °C, while monthly averages for
May, June, July, August, and September are 17.3, 20.8, 23.4, 22.6, and 17.5 °C, respectively.
Throughout the growing season, fluctuation in measured θ was a function of rainfall frequency and intensity; however, θ through May was 38.2% on average, with a range of only 11.4%
(compared to a range of 40% in July), despite infrequent precipitation. The minimal fluctuation in θ for May was due to the remaining moisture left from spring thaw that was not yet depleted by vegetation and ET. For the remaining months, average θ was 40.7, 40.4, 30.8 and 20.9% for
June, July, August, and September, respectively, with ranges of 25.9, 40.4, 25.8, and 17.8 %.
Measured θ from the meteorological station reached a peak of 66% following the largest rainfall event of the summer on July 9th. At this time, soils were fully saturated, however this moisture content reading is still lower than some values observed for θ within the experimental plot (see
Table 3.2). This would be a result of heterogeneity in soil porosity at the points of θ measurement across the site; areas of higher soil porosity may be found adjacent to plants within
80 the experimental plot, compared to a lower porosity near the meteorological station. Following this large rainfall event on July 9th, T and VPD also saw peaks up to 30.4 °C and 1.9 kPa.
Figure 3.2: Growing season trend in 24-hour average air temperature (T), vapour pressure deficit (VPD), incoming shortwave radiation (K↓), and volumetric moisture content of the soil (θ) gathered from the meteorological station at the site for the 2013 growing season. Daily total precipitation is included on the graph with the average θ.
81
Beneath the experimental shrub replicates, measured θ fluctuated throughout the growing season, and Table 3.2 and Figure 3.3 summarize the range in soil moisture seen across the species replicates. It was observed that average soil moisture across all replicates ranged from
33.5 – 49.1 %. For most species, average θ was near 43%, however S. altissima and V. lentago were lower at 33.5% and 39.1%, while S. uliginosa was highest at 49.1%. For S. altissima and
V. lentago, the lower average moisture content is likely due to differences in soil bulk density in these replicates, which would result in lower maximum θ due to lower porosity. This is evident as well by lower maximum measured θ in the replicates even after large rainfall events.
Measured θ overall ranged from as low as 11.2%, to a maximum of 87% across all replicates throughout the growing season. Of the species measured, S. uliginosa saw the highest average soil moisture (49.1%), and V. lentago had the lowest (33.5%). A high range in moisture values from minimum to maximum measured θ was still observed, however. The overall range in moisture for all species was 57.2% (+/- 6.2%) from minimum to maximum observations. This variation in moisture between treatments throughout the growing season shows that the experimental design was effective in creating a wide range in θ for each species, allowing for an array of measurements for gs under different θ.
Volumetric moisture content for each wetland species in all replicates across the 2013 growing season (%)
Species C. sericea R. S. alba S. S. discolor S. exigua S. nigra S. V. lentago palustris altissima uliginosa Average 42.9 42.5 43.5 39.1 40.2 43.4 47.1 49.1 33.5 Range 15.5-71.5 11.2-74.1 18.0-72.3 14.6-64.6 12.9-77.5 13.7-75.8 15.5-74.9 19.7-87 13.7-64.6
Table 3.2: Experimental plot volumetric moisture content of the soil per treatment. Average seasonal θ and range in θ
82
Figure 3.3: Variation in measured θ in each replicate of the three treatments within the experimental plot. The boxes represent the upper and lower quartiles of each set of data points, with the solid and dashed lines indicating the median and mean values, respectively. The black dots represent 5th and 95th quantiles for outliers.
3.3.2 Observed Stomatal Conductance
The observed stomatal conductance from porometer measurements throughout the growing season for each test species is presented in Table 3.3, showing the mean, median and range in gs.
The species with the highest seasonal mean gs were S. altissima, S. discolor, S. uliginosa, and C.
-1 sericea, at 12.4, 11.3, 10.2 and 10.2 mm s , respectively. The lowest seasonal observed gs values were 6.5, 7.8 and 8.1 mm s-1 for V. lentago, S. exigua, and S. alba. Figure 3.2 illustrates the quartiles and variance in the data.
83
Observed Stomatal Conductance (mm s-1) Species C. R. S. S. S. V. sericea palustris S. alba altissima discolor S. exigua S. nigra ulginosa lentago Mean gs 10.2(4.3) 9.4(5.9) 9.1(4.2) 12.4(4.8) 11.3(5.9) 8.2(6.17) 9.8(6.2) 10.2(4.8) 7.1(4.4)
Median gs 9.9 8.6 8.1 11.8 11.1 7.8 8.9 9.6 6.5 Range 1.5-20.8 0.5-25.3 1.2-21.9 4.2-28.6 0.2-27.9 0.8-20.3 0.3-28.1 0.2-23.2 0.2-20.8 Table 3.3: Average (standard deviation), median, and range in observed gs for each species planted in the experimental plot.
Figure 3.4: Box plots for observed gs for each species throughout the sampling period. The boxes represent the upper and lower quartiles of each set of data points, with the solid and dashed lines indicating the median and mean values, respectively. The black dots represent 5th and 95th quantiles for outliers.
Statistical analysis on the gs for each species using ANOVA and Tukey’s post-hoc analysis
indicated that 18 of 36 relationships between species were significantly different. The full
results of the statistical analysis can be seen in Appendix 3D. Of the 18 significantly different
84 relationships between the species, S. altissima and V. lentago had greatest number of low p- values (less than 0.1) between species. S. altissima was found to have significantly higher observed gs from all other species in the study, while gs for V. lentago was significantly lower than all species but S. exigua. For willow species, S. discolor was conducting 31% higher than
S. exigua, while for the annual goldenrod species, observed gs for S. altissima was 19% greater than S. uliginosa. P-values between these sets of species are 0.000 and 0.023 for the Salix and
Solidago species, respectively, indicating that these are significant differences in conductance.
Table 3.4 provides a wide range of gs values for a variety of species collected from studies looking at plant stomatal conductance. The regions and biomes where these studies were conducted vary greatly, from areas in Northern China and Japan, to the Southern United States and Northern Canada. Globally, for all species included in Table 3.3, the average gs varies from
-1 -1 0.6 to 16.7 mm s , though maximum gs is seen up to 47.7 mm s . Looking just within temperate
-1 regions, the literature values for average gs varies from 3.75 to 16.5 mm s , with several values in the 7 to 11 mm s-1 range. This range fits closely with the observed values for the species in
-1 this study, which have average gs ranging from 7.1 to 12.4 mm s . For wetland species
-1 specifically, the literature shows that average gs can range from 0.6 to 16.5 mm s , though
-1 nearly half of these species had average gs greater than 7.1 mm s (the lowest value of the observed species). Of the values for various wetland species found within the literature, the species measured in the experimental plot conducting at a higher rate comparatively. Literature values found for willow species ranged from 0.6 to 6.4 mm s-1, while the S. discolor and S.
-1 exigua plants in this study had average gs of 11.3 and 8.2 mm s , respectively. Similarly, observed average gs for S. nigra and V. lentago in this study were 9.8 and 7.1 mm s-1, while literature values for a S. williamsii and a V. rhytidophyllum shrub were 6.2 and 5.1, respectively.
85 -1 Literature values for gs of various species (mm s ) Species Growth Species Mean gs gs Range form Habitat Source Chimonanthus praecox 7.4 -- Shrub Temperate forest Gao et al., 2013 Mussaenda esquirollo 6.3 -- Shrub Tropics and subtropics Gao et al., 2013 Lespedeza bicolor 4.1 -- Legume Semi-arid grassland Gao et al., 2013 Vitex negundo 6.0 -- Shrub Grassland, mixed forest Gao et al., 2013 Caragana pygmaea 2.5 -- Legume High altitude desert Gao et al., 2013 Artemisia frigida 5.8 -- Subshrub Dry grassland, shrubland Gao et al., 2013 Salix psammophila 0.6 -- Shrub Alpine, swamp, floodplain Gao et al., 2013 Viburnum rhytidophyllum 5.1 -- Shrub Mixed and deciduous forest Gao et al., 2013 Sambucus williamsii 6.2 -- Shrub Alpine slope and scrubland Gao et al., 2013 Liquidambar formosana 11.4 2.3-47.7 Tree Temperate forest Krober, 2014 Homalanthus populneus 12.1 4.7-17.7 Tree Tropical secondary forest Jurbrandt et al, 2004 Grewia cf. glabra 14.2 9.1-18.6 Tree Tropical secondary forest Jurbrandt et al, 2004 Trema orientalis 15.5 4.5-18.8 Tree Tropical secondary forest Jurbrandt et al, 2004 Acalypha cf. caturus 14.7 4.8-17.8 Tree Tropical secondary forest Jurbrandt et al, 2004 Pipturus argenteus 14.9 4.5-18.2 Tree Tropical secondary forest Jurbrandt et al, 2004 Mallotus barbatus 16.7 4.1-18.6 Tree Tropical secondary forest Jurbrandt et al, 2004 Macaranga hispida 10.4 4.5-13.6 Tree Tropical secondary forest Jurbrandt et al, 2004 Macaranga tanarius 8.9 4.3-13.6 Tree Tropical secondary forest Jurbrandt et al, 2004 Leontodon hispidus 6.8 1.1-12.5 Forb Grassland Mills et al., 2009 Dactylis glomerata 5.1 1.1-9.1 Gramminoid Grassland Mills et al., 2009 Acer saccharum 1.0 0.8-1.2 Tree Mixed and deciduous forest Ewers et al., 2007 Tillia americana 3.0 2.7-3.2 Tree Mixed and deciduous forest Ewers et al., 2007 Populus tremuloides 3.0 2.9-3.0 Tree Mixed and deciduous forest Ewers et al., 2007 Thuja plicata 3.2 2.7-3.8 Tree Forested wetland Ewers et al., 2007 Alnus rugosa 3.5 3.2-3.9 Shrub Forested wetland Ewers et al., 2007 Juncus effusus 6.7 3.6-9.8 Reed Freshwater Marsh Mann& Wetzel,1999 Sasa palmata 13.1 3.4-22.7 Gramminoid Temperate bog Takagi et al., 1998 Moliniopsis japonica 7.9 3.4-12.5 Forb Temperate bog Takagi et al., 1998 Myrica gale 16.5 4.5-28.4 Shrub Temperate bog Takagi et al., 1998 Ilex crenata 9.4 3.0-15.9 Tree Temperate bog Takagi et al., 1998 Cladium jamaicens 2.9 -- Reed Everglades Koch& Rawlik,1983 Typha domingen 10.5 -- Reed Everglades Koch& Rawlik,1983 Picea sitchensis 3.75 2-5.5 Tree Temperate rainforest Jarvis, 1979 Salix discolor 6.4 2.7-10.5 Shrub Sub-arctic wetland Lafleur, 1988 Carex palacea 7.6 4.2-11.1 Sedge Sub-arctic wetland Lafleur, 1988 Salix bebbianna 6.1 2.9-10.2 Shrub Sub-arctic wetland Lafleur, 1988 Alnus rugosa 6.1 2.6-9.2 Shrub Sub-arctic wetland Lafleur, 1988
Table 3.4: Literature values for gs of a range of species and ecosystems. Where necessary, values for gs were converted from mmol m-2 s-1 to mm s-1 using standard atmospheric conditions
Comparatively, to other wetland plant types, the observed shrub species were often conducting at a higher rate than many grass, reed, or sedge species from the literature (ranging from 2.9 to
86 10.5), and had higher seasonal gs that many tree species as well (ranging from 3.2 to 9.4). Of the wetland shrub species in the literature, Myrica gale was found within the most similar
-1 ecosystem type, and had a much higher average gs of 16.5 mm s . This indicates that in general, the shrub species, and particularly those observed in this study, have a higher seasonal water demand than many other wetland species. Wetland shrub species, then, would likely have ecosystem requirements in which water supply is sufficient to support this rate of water loss.
This can also have implications on ecosystem succession, as a site with higher water supply may only be suitable to shrub species that are adapted to use that water effectively. Secondary or tertiary succession to a closed-canopy forest swamp community can be limited by fluctuating groundwater tables and lack of disturbance, which allows shrub thicket swamp communities to persist (Cohen & Kost, 2007). The higher gs and thus higher water demands of wetland shrub species should be considered for restoration efforts, as higher seasonal ET rates from a shrub canopy can deplete soil water supply faster than a reed-dominant or forested wetland with lower plant gs. To ensure that restoration design provides sufficient water for the restored plant community, species-specific water loss through ET should be considered.
3.3.3 Controls on Stomatal Conductance
The results of the logistic non-linear upper quantile regression on each limiting environmental variable indicate a range in responses of different species to each of the prevailing factors. The curves in the plots in Figures 3.5-3.8 represent the upper bound of the control that each variable has on gs, or in other words the maximum conductance that is plausible for each individual species under observed environmental conditions. The points are separated out by treatment,
87 though one curve is generated for the full data set for each species, as it can be assumed that the forcing of any one variable will be relatively equal across the experimental plot.
The response of gs to vapour pressure deficit for each species is represented in Figure 3.5. The parabolic shaped curves for S. nigra and S. altissima indicate a relationship where the maximum potential gs was seen at moderate VPD, around 1.4-1.5 kPa. Vapour pressure at this point was not too high or low, and the drying power of the air mass had less of an effect on leaf water potential. When VPD was higher than this range, a decrease in maximum potential gs of up to
43% and 30% for S. nigra and S. altissima, respectively, was observed. A decrease in VPD also showed gs drop by up to 53% for S. nigra and 23% for S. altissima. This showed that these two species seem to be most sensitive to VPD.
Studies show that for many terrestrial plant species, the stomatal response to air vapour pressure is usually a mechanism of guard cell closure with lowered leaf water potential. When there is greater drying power of the air, there is higher potential for plant transpiration. However, consistent high rates of ET can cause a decline in leaf water potential, leading to guard cell shrinkage and stomatal closure to occur (Buckley & Mott, 2013; Busch & Losch, 1998; Mott &
Peak, 2010; Streck 2003). This relationship is apparent in a number of the shrub species planted, though the decrease is relatively subtle. S. discolor, S. alba, S. exigua, and V. lentago show this response, with a percent decrease in gs of 29, 37, 39, and 61%, respectively, with increasing
VPD. V. lentago has the most drastic decline in gs, indicating that this species is most sensitive to high VPD.
88
-1 Figure 3.5: Logistic Upper-Quantile response curves for observed gs (mm s ) to vapour pressure deficit (kPa)
For other species, such as R. palustris and S. uliginosa, the observed trend showed that as VPD increased, and the air becomes drier, gs also increased. This is indicative of a lack of guard cell control for these species. These two species are both wetland obligate species, indicating their adaptation to grow in conditions with consistently high θ. Because of this, there may be no evolutionary adaptation that requires stomata of these plants to close under high VPD, as θ is rarely limiting. This could cause potential problems should this species be planted in a system
89 with insufficient θ, as the lack of stomatal control to high VPD would cause θ to be depleted below a permanent wilting point for these species. For relationships where plants experience an increase in gs with increasing VPD, research has also indicated that this could be a result of increased leaf water potential with increasing leaf temperature, a function of water molecule vibration and expansion within leaf cells with increased heat (Mott & Peak, 2010; Wilmer &
Mansfield, 1970).
The limiting effect curves for VPD indicate that independent of the controlling influence of θ, the stomata on the plants begin to close and limit water loss as the air above became drier. This could indicate that the overall surface conductance is mainly independent of wetness, and that there is not a strong linkage between processes occurring below the surface and the ambient air mass (Kellner, 2001). Stomatal closure was experienced under high VPD regardless of the soil moisture content at the time of measurement, which would indicate a physiological benefit for these species to limit loss of water vapour as an early stress response (Guo & Sun, 2012), and may be linked the accessibility of the soil water in clay soils. For species showing a parabolic response, Guo & Sun (2012) found that the latent heat flux would only increase with a rising vapour pressure deficit up to 1.0 kPa, and that beyond that the relationship ceased, and stomatal closure began.
The species response to temperature in this environment showed that high temperatures allowed for near maximum potential gs for most species, illustrated in Figure 3.6. S. nigra, S. uliginosa,
S. alba, S. altissima, S. discolor, and C. sericea showed a plateau response, where gs peaked between 18 to 23 °C and did not show a decline as temperatures continued to increase. Of these
90 species, S. nigra, S. uliginosa, and S. altissima were very sensitive to temperatures below 20 °C, as these species show a rate of decline in gs between 64-183% down to temperatures of 16 °C.
By the time temperatures reached 18 °C, the maximum potential gs had declined to between 41-
56% of peak gs. V. lentago and S. exigua did not have much change in potential gs over the full range of temperature, indicating that they were insensitive to temperature, while R. palustris was very sensitive to temperature. Peak maximum potential gs for this species was seen at 23 °C, and at either side of this peak for gs, declines up to 29% and 42% of peak potential gs for was seen to temperatures of 16 °C and 30 °C, respectively.
-1 Figure 3.6: Logistic upper-quantile response curves for observed gs (mm s ) to air temperature (°C)
91 This is supported by research indicating that stomatal aperture is highly temperature dependent, as it causes the leaf vapour mole fraction to increase. Peak stomatal apertures for the species in these studies were seen up to 35 °C (Buckley & Mott, 2013; Mott & Peak, 2010; Wilmer &
Mansfield, 1970). As well, a study of Juncus effusus in temperate wetlands showed a strong correlation between daily gs and T in all seasons but autumn (Mann & Wetzel, 1999). There is evidence that T plays an important role in mediating leaf water potential as well, by controlling changes in water vapour density within individual leaf pores, which can trigger physiological mechanisms to maintain equilibrium between guard cell and pore-air water potential (Buckley and Mott, 2013).
The response of species to incoming solar radiation, as seen in Figure 3.7, showed that S. alba,
S. altissima, S. uliginosa, R. palustris, C. sericea, and S. exigua had increasing potential gs with increasing K↓. The most responsive species to increasing K↓ were S. uliginosa and R. palustris,
-2 where gs increased by 83% and 77%, respectively, from 200 up to 1000 W m . These two species are wetland obligate species, therefore when moisture is non-limiting to these plants they take can advantage of high light levels to increase photosynthetic processes. S. altissima and S. alba were moderately influenced by K↓ , having small increases of 36% and 22% respectively. C. sericea and S exigua showed increases of 50% and 53%. Alternatively, S. nigra,
S. discolor, and V. lentago all showed relationships to irradiance where the gs began to decrease
-2 at levels greater than 700 W m . This decrease in gs with high K↓ for these species may be due to the clustering of low gs observations at the high end of the x-axis, which pulls down the quantile curve. This clustering is also seen for C. sericea, R. palustris, and S.exigua, however, and the curves are not pulled down in the same way. The maximum gs for S.nigra, V. lentago,
92 and S. discolor is seen at a mid-range K↓, causing the peak in response to occur at this point.
Because these species are conducting the most at moderate K↓ levels, it is likely that the sensitivity of these species to other factors such as T and VPD is greater despite the light availability.
-1 Figure 3.7: Logistic upper-quantile response curves for observed gs (mm s ) to incoming solar radiation (W m-2)
Research indicates that gs and irradiance are linearly correlated; so that gs is highest when incoming solar radiation hitting the leaf surface is high (Biscoe et al., 1977; Rejskova et al,
2012; Wilmer & Mansfield, 1970), particularly with well watered plants (Guyot et al., 2011).
93 Somewhat contrary to this however, a low correlation between irradiance and gs was found for
Juncus effusus in temperate wetlands, as water losses through transpiration for this species were measured at all times throughout the day, and declines were not seen during low light levels
(Mann & Wetzel, 1999). A similar low responsiveness to light was seen for four of the nine observed species, where increasing light levels did not elicit a large increase in maximum potential gs. This could be related to the productivity of these species over a growing season, and the access to other resources affecting photosynthesis, particularly θ, which may not require high responsiveness to light energy to promote photosynthesis. The wetland shrubs showing this type of response are perennial, having lower increases in growth over one growing season than annual herbaceous species (Figure 2.4 in Chapter 2), and are adapted to grow in environments where θ is more available.
Similar to air temperature, many of the wetland species, save for S. exigua and V. lentago, produced a plateau response for maximum potential gs to increasing soil moisture, as seen in
Figure 3.8. The rate of increase in maximum potential gs slows considerably for R. palustris,
S.alba, S. nigra, and S. altissima once θ reaches 22-26 %, while the rate of increase in gs slows for C. sericea, S. uliginosa, and S. discolor at θ between 30-34 %. As θ increases beyond this point, peak potential gs is realized and no declines in gs are seen as θ continues to increase to the point of field capacity. It is important to note, that on the opposite end of the scale when θ drops below these ranges, these species exhibit a drastic decline in maximum potential conductance.
For S. nigra and C. sericea, a steep drop in gs is seen at 28% θ, S. altissima drops at 25%, and maximum gs for S. alba and R. palustris drops in θ less than 22%. The declines for S. discolor and S. uliginosa are more gradual, but begins at wetter soil conditions when θ is 36%. This
94 indicates that the guard cells of these species are very responsive to θ levels below these levels, and are able to close rapidly as soil moisture levels decrease. Soil saturation in the plot is experienced when θ is at 68%, and field capacity is at 63%. The θ where gs begins to decline for these species is between one half to one third of field capacity. S. exigua and V. lentago are unique in their response to θ, as they did not exhibit a rapid decline in maximum potential gs in drier soil conditions. S. exigua shows a very gradual decline in gs to decreasing θ, while V. lentago is non-responsive to lowered θ.
-1 Figure 3.8: Logistic upper-quantile response curves for observed gs (mm s ) to volumetric moisture content of the soil (m3 m-3)
95 The plateau response seen could be due to the fact that physiologically, wetland plants are generally well adapted to function under high soil moisture levels, so it is not unexpected that the shrub species measured were not experiencing a negative response to saturated soils
(Pezehski, 2001). In well-watered environment such as wetlands, daily evapotranspiration rates have been recorded that range from 1.2 to 6.9 mm, compared to grassland systems that only showed daily ET ranging from 0.8 to 3.6 mm (Burba, 1999; Frank, 2003; Jaksic et al., 2006;
Kellner, 2001; Kim & Verma, 1995; Lafleur, 1988; Rejskova, 2012). The higher evapotranspiration rates seen in wetlands indicate that plants are capable of high transpiration when their rooting zones are not moisture-limited (Kellner, 2001; Rejskova, 2012). Extended periods of saturation have been linked to decreasing stomatal conductance compared to that in aerated soils due to lowered root permeability and slower water uptake rates, though this was tested in crop-species predominantly (Hiron & Wright, 1973; Kramer, 1940; Pezehski, 2001).
The decreased root permeability was linked to decreased root metabolic activity and changes in root cellular membranes as a result of persistent soil saturation and lack of oxygen, which created resistance to water movement (Kramer, 1940). Because the water supply to the observed species was rainfall dependant, the intervals between rainfall events could have resulted in soil saturation levels that were not consistently high enough in the treatments to cause much of a stress response (Pezehski, 2001). Declines in treatment θ between rainfall events may allow for root aeration to promote water uptake. Based on the literature values for gs from Table 3.4, upland grassland or forest species were seen to have lower overall average gs readings compared to species found in wetland environments (1.0 – 11.4 mm s-1 for upland species, 2.9 – 16.5 mm
-1 s for wetland species). Within these ranges, 85% of upland species had gs values lower than
-1 7.1 mm s (the lowest average observed gs of the test species), while 63% of wetland species
96 -1 had average gs readings greater than 7.1 mm s , showing that wetland species frequently have higher water usage rates compared to other plant types.
The response of declining gs with decreased soil moisture is common in many terrestrial plant species, as the ability for stomata to close when soil moisture levels are low prevents potentially harmful water loss. Similar to the mechanism that occurs with high vapour pressure deficit, stomata close to preserve water (Franks, 2004; Guyot et al., 2011), and this protects plants from damaging effects of drought conditions by maintaining leaf water potential (Guyot et al., 2011;
Medlyn et al., 2011; Sala & Tenhunen, 1996). It has also been shown that for plants in low moisture conditions, stomata close regardless of the light energy availability (Guyot et al.,
2011). The two species, S. exigua and V. lentago, that did not show drastic declines in gs with lower soil moisture, have less guard cell control and higher potential gs with fluctuations in moisture throughout a growing season, resulting in high potential transpiration rates in low θ.
This could create conditions where moisture demand of these species is greater than the θ supply, further depleting θ. The lack of guard cell control in dry soils could mean that this species is better suited to consistently wetter environments, in order to not reach their permanent wilting point.
Most studies looking at plant physiology and morphology in regard to water stress approach the topic in terms of drought impacts on vegetation rather than in the context of water saturated soils
(Saha et al., 2008; Xu et al., 2007). A study by Asensio et al (2007) found that when soil moisture levels declined by 30%, the gs of Quercus ilex declined by 53% on average. For
Leymus chinensis exposed to soil moisture levels below 35% of field capacity, significant
97 declines in stomatal conductance were also observed (Saha et al., 2008; Xu et al., 2007). This is a similar level of θ at which the test species experienced significant declines in gs as well.
Comparatively, when observing the differences in conductance between dry season and wet season, it was found that despite variation between species, gs was consistently lower during the dry season for a number of native Floridian shrub species (Saha et al., 2008). Overall, however, these responses vary on a species to species basis, and depend greatly on the length of the drought period, the soil type, and the depth to the water table (Saha et al., 2008). Seven out of nine species measured in this study showed a significant decline in maximum potential gs to low soil moisture availabilities, with species-specific thresholds for these soils.
3.3.4 Modelling Stomatal Conductance
The results of the multiple linear regression model, which relates the four environmental variables to observed gs for each species, are summarized in Table 3.5, indicating the seasonal
2 average and range in gs, along with the R and p-value resulting from a comparison of modelled values to field observations for gs for each species. Applying the coefficients of the model to the growing season environmental data allowed for comparison of gs throughout the growing season between species and varying θ. As seen in Figure 3.9, the gs that the model estimates show clear differences between species, as a function of variation in each environmental control across the growing season. The results of this model show that despite the environmental controls over any given plant being equal any point in time, there was considerable variation in species conductance across the growing season. This shows that each species responds differently to all environmental variables, which are incorporated into the model as coefficients specific to each controlling factor, producing seasonal trends that vary from one species to the next. The largest
98 peaks in site θ correspond to peaks in modelled gs for all species, particularly the late May and
early July precipitation events. The lowest dips in modelled gs for all species are seen in May
and September, which correspond to dates where air temperatures were low (daily averages
between 5-10 ºC), seen in Figure 3.2. The R2 values for the model results compared against
observed gs range from 0.004 to 0.374, with the highest correlation seen for S. exigua plants.
As seen in Table 3.5, and illustrated in Figure 3.9, R. palustris, S. nigra, V. lentago, and S.
-1 exigua had the lowest seasonal modelled gs, at 6.7, 7.7, 7.9 and 8.0 mm s , respectively. The
highest season modelled gs values were seen by S. altissima, S. discolor, and C. sericea, with gs
of 12.2, 11.3, and 9.4 mm s-1. S. uliginosa and S. alba sat at a mid-range in the modelled results,
-1 with seasonal gs of 9.1 and 8.4 mm s . There is a total variation in conductance between species
of 57%. The fact that S. altissima was the highest conducting species under existing site θ
corresponds with the findings related to plant growth that showed that this species was most
successful and had the greatest increases in biomass in drier soil conditions (Chapter 2, Figure
-1 2.5). Peaks in gs of up to 24.4 mm s at any given time point were modelled, though some
-1 species never reached modelled gs higher than 15.9 mm s at any point in the growing season.
Results of Multiple Linear Regression Model for Stomatal Conductance (mm s-1) Species C. R. S. S. S. V. sericea palustris S. alba altissima discolor S. exigua S. nigra ulginosa lentago Mean gs 9.4(4.2) 6.7(3.2) 8.4(3.1) 12.2(2.9) 11.3(4.5) 8.0(3.6) 7.5(3.3) 9.1(3.3) 7.9(2.9) Range 0-20.3 0-17.0 0-15.8 5.3-21.4 0.7-24.4 0-16.3 0-16.9 0.1-15.9 0-15.9 R2 0.142 0.199 0.071 0.275 0.006 0.374 0.004 0.072 0.264 RMSE 3.121 1.989 1.723 2.090 3.812 1.675 1.716 1.491 1.829 p-Value 0.006 0.004 0.100 0.000 0.623 0.000 0.735 0.091 0.000
Table 3.5: Results of multiple linear regression approach for modelling stomatal conductance. Average 2 seasonal gs and range in gs modelled from site θ, and resultant R and P-value from linear regression analysis of variance to observed gs measurements.
99
Because the multiple linear regression model does not factor in the shape of these response curves, to determine the effects of any of the four environmental controls, gs is estimated by assuming a least squares linear relationship between the observed gs and the measured environmental conditions. The variation seen between species at any given time point may be a result of one factor being a more significant control at that time than any of the others. Due to the nature of this model, it is difficult to discern which control is most limiting at any given time.
Figure 3.9: Growing season trends in modelled stomatal conductance using the multiple linear regression approach
100 When modelling gs throughout the growing season for each treatment using the adjusted Jarvis- type model, the resultant values were considerably different from those determined with the multiple linear regression approach. The modelled daily gs values fluctuate rapidly on a day-to- day basis, showing large peaks and dips in gs as a function of the strongest controlling factor
2 over gs for those conditions, which can be seen in Figure 3.10. The R values for the model results compared against observed gs range from 0.000 to 0.189, with the highest correlation seen for C. sericea plants. The strength of the controlling influences of different environmental variables can be explained by the response curves of gs as seen in Figures 3.5 to 3.8. In the equation used to estimate gs with this model, the functions represent the weight that each controlling environmental variable has in determining what the maximum potential gs could be in those conditions. Because most species showed that θ was non-limiting after θ levels reached above 35 – 57% of field capacity, any time θ was above that surrounding an individual plant, the model estimated that gs would be near it’s maximum potential conductance for those conditions.
Table 3.6 summarizes these findings, and the average function values for each species and treatment indicate that in most instances the weight that θ carried in the equation was non- limiting to gs. For all months the ƒθ values ranged from 0.83 – 1 for all species, meaning that θ would allow for modelled gs to be up to 83 – 100% of maximum potential gs. The lower the value of the function for each environment variable, the more limiting that variable is to gs. For the species that did indicate that θ played a role in limiting gs at certain stages in the growing season (S. uliginosa, S. discolor and S. exigua), this is directly linked to the fact that the response curves for these species varied from the norm. Additionally, θ was found to be more limiting for these species in August and September, when site θ was lower overall. In these months, ƒθ was between 0.83 – 0.84 on average for these species, compared to 0.94 – 1 earlier
101 in the growing season. This did not appear to significantly affect daily average modelled gs, however, since species that were not limited by θ in August and September still showed low dips in gs at the same points in time – particularly S. altissima, C. sericea, R. palustris, and S. nigra. This indicates that a different environmental variable is a more important control over gs than θ. As seen in Table 3.6, T was the most limiting factor to gs for most species (save for S. exigua) in May, June, and September (ƒT ranging from 0.45 – 0.78), followed by K↓ in July and
August (ƒK↓ ranging from 0.69 – 0.86). This is likely due to the colder overall temperatures in
May, June, and September, which ceased to be limiting in the warm months of July and August.
VPD was not found to be a particularly strong control over the full growing season, as VPD is below 1.5 kPa 89% of the time, as seen in Figure 3.2. This shows that most measurements days across the growing season were sufficiently humid to prevent a stress response, and stomatal closure would not occur as a result of VPD. Looking at the modelled gs between species, compared to the results of the multiple linear regression model, there is often a greater span in day to day gs estimates for the Jarvis model results, particularly in the early an late portion of the
-1 - growing season when gs may be estimated between 0-5 mm s and then increase up to 20 mm s
1 or greater the following day. S. exigua, S. alba, V.lentago, and S. altissima had the highest
-1 seasonal average modelled gs, at 14.9, 14.4, 13.7, and 13.5 mm s , respectively. The lowest seasonal modelled gs values were seen for S. uliginosa, S. nigra, and R.palustris, at 10.1, 10.9,
-1 and 10.9 mm s respectively. S. exigua had the least fluctuation in gs over the growing season, primarily as a result of this species’ low responsiveness to both air temperature and θ. Based on the Jarvis-type model results, there is a 38% difference in seasonal average modelled conductance between S. exigua and S. ulignosa, the highest and lowest conducting species on average, respectively. Between the two models, the Jarvis-type model resulted seasonal average
102 gs that was between 10 – 60% greater than average modelled conductance from the multiple
linear regression model for each species.
Results of Jarvis-type Model for Stomatal Conductance (mm s-1) Species C. R. S. S. S. V. sericea palustris S. alba altissima discolor S. exigua S. nigra uliginosa lentago
Mean gs 11.9(6.3) 10.9(6.8) 14.4(4.6) 13.5(8.5) 12.9(7.3) 14.9(2.4) 10.9(7.7) 10.1(7.2) 13.7(5.6) Range 0.1-24.4 0-25.1 0.7-22.1 0 - 25.3 0 - 25.0 6.7-22.7 0-25.4 0-24.8 0.5-24.9 R2 0.189 0.032 0.143 0.006 0.113 0.005 0.000 0.047 0.148 RMSE 2.971 3.173 2.080 2.861 4.138 2.757 5.057 3.807 2.387 p-Value 0.006 0.301 0.027 0.670 0.060 0.685 0.981 0.240 0.023 Functions May ƒT 0.48 0.52 0.60 0.50 0.51 0.98 0.47 0.52 0.76 ƒVPD 0.98 0.80 0.92 0.87 0.95 0.91 0.76 0.71 0.90 ƒK↓ 0.79 0.74 0.86 0.84 0.84 0.78 0.76 0.78 0.70 ƒθ 0.99 1.00 1.00 1.00 0.97 0.89 1.00 0.94 0.99 June ƒT 0.67 0.75 0.78 0.78 0.77 0.97 0.69 0.73 0.95 ƒVPD 0.99 0.82 0.92 0.93 0.95 0.91 0.85 0.79 0.90 ƒK↓ 0.83 0.78 0.88 0.87 0.87 0.82 0.78 0.84 0.74 ƒθ 0.99 1.00 1.00 1.00 0.97 0.90 1.00 0.95 0.99 July ƒT 0.79 0.86 0.85 0.89 0.87 0.94 0.82 0.83 0.95 ƒVPD 0.98 0.81 0.92 0.90 0.95 0.91 0.80 0.74 0.90 ƒK↓ 0.77 0.71 0.85 0.82 0.83 0.76 0.76 0.75 0.69 ƒθ 0.97 0.99 1.00 0.99 0.94 0.89 0.99 0.93 0.98 August ƒT 0.78 0.86 0.84 0.88 0.87 0.96 0.81 0.82 0.96 ƒVPD 0.99 0.82 0.92 0.92 0.95 0.90 0.84 0.78 0.90 ƒK↓ 0.78 0.73 0.85 0.83 0.86 0.77 0.80 0.79 0.74 ƒθ 0.89 0.96 0.94 0.92 0.84 0.84 0.94 0.84 0.96 September ƒT 0.45 0.49 0.61 0.45 0.48 0.98 0.42 0.50 0.81 ƒVPD 0.99 0.75 0.96 0.85 0.97 0.95 0.71 0.63 0.95 ƒK↓ 0.74 0.67 0.83 0.80 0.80 0.73 0.74 0.69 0.66 ƒθ 0.90 0.97 0.97 0.94 0.84 0.83 0.96 0.83 0.95
Table 3.6: Results of Jarvis-type approach for modelling stomatal conductance. Average seasonal gs and 2 range in gs modelled using site θ, and resultant R and P-value from linear regression analysis of variance to observed gs measurements. Average values of the normalized functions for each environmental control used in the model are shown, separated by month
103
Figure 3.10: Growing season trends in modelled stomatal conductance using the Jarvis-type model
3.3.5 Model performance
When comparing the observed gs readings for each species against modelled gs for the same time points, the variance in the modelled values was not highly correlated to the observed readings, based on low coefficients of variation ranging from 0.004 to 0.374 for the multiple linear regression model, and from 0.000 to 0.189 for the Jarvis-type model. The average seasonal gs for each species from the results of the multiple linear regression model was within 0
– 34% of observed average gs for each species, while the average gs from the Jarvis-type model
2 results were between 1 to 64% greater than observed gs. While the R values are lower than
104 typically accepted to indicate strong correlation, they still provide proof of correlation. The results for gs determined by the multiple linear regression model were more highly correlated to observed gs for S nigra, S. altissima, R. palustris, S. exigua, S. uliginosa, and V. lentago, while the Jarvis-type model showed higher correlations for C. sericea, S. alba, and S. discolor. For context, the study by Lafluer (1988) modelled stomatal conductance of different wetland species using a multiple linear regression and boundary-line Jarvis-type approach, and when comparing
2 modelled gs to observed gs, found R values from 0.35 to 0.42 through multiple linear regression modelling, and between 0.18 and 0.32 using the Jarvis-type model. Though these are higher than some of the R2 values determined in this study, it shows that high R2 in this type of comparison are not expected. When low R2 values result from a linear correlation, looking at the variation in residuals is of value. The plotting of residuals suggests homoscedasticity in the variance of the dependant variable to the independent, and no correlation in the residuals from one observation to the next for both models. This means that the variance in the datasets for modelled gs values were similar to the variance in observed gs from the porometer. The models were effective in accounting for time-point specific controls on gs, shown by the wide range of modelled gs values. The low R2 values of the multiple linear regression and Jarvis-type models, in this study and in Lafleur’s study could be due to the fact that an important controlling factor is not being considered, or it could be due to the manner in which the weather and soil moisture values are being accounted for. Weather variables were averaged over a 30-minute interval, and soil moisture was measured weekly and corresponded gs by selecting the value measured closest in time to each gs observation, which may result in discrepancies in time-point specific conditions for each measurement of gs. Additionally, microsite variation in T and VPD at the leaf level are not accounted for, and the effects of leaf shading throughout the plant are not considered in this experimental approach. Leaf temperature and humidity can vary throughout a canopy, as cooler,
105 more humid conditions exist where sunlight does not reach. Also, the controlling effect of K↓ in this study assumes that each leaf measured is in direct sunlight, though in reality partial shading of measured leaves would occur. Model results could be improved if instantaneous microsite or leaf-level and soil moisture conditions can be recorded and used for comparison to measurements of gs. Additionally, environmental variables that do not have a particularly strong controlling influence over gs for certain species can be removed from the model.
Other applications of the Jarvis model have been effective when used in other studies related to plant science (Lafluer 1988, Medlyn et al., 2011; Wang et al., 2013). In these studies, the functions in the equation used based on the assumption that the boundary line response to an environmental control for all species of interest are the identical, with only occasional adjustments or improvements to the model by accounting for additional variables (Wang et al.,
2013), The adjustment to the model made in this study is unique in that it generates the functions based on individual species response curves to each environmental control. Buckley and Mott (2013) state that the obvious direction of future stomatal modelling is to make models more mechanistic, and increase the complexity and detail of the factors included in the models.
The amount of detail being accounted for can be increased by incorporation of species-specific responses to environmental controls. The model effectiveness may be further improved by including microsite measurements of leaf level T and VPD to improve correlation to observed plant conductance.
In the manner that the Jarvis model was developed, the main disadvantage is that in order to estimate gs, it relies on environmental data which spans large spatial and temporal scales and changes in plant growth characteristics over time can have an effect on how gs of different
106 plants respond to these factors over time (Buckley & Mott, 2013, Whitehead et al., 2011). It has been found that a lowered rate of photosynthesis in aging leaves is highly attributable to stomatal limitation (Farquhar & Sharkey 2012; Whitehead et al., 2011). Though the Jarvis-type model used in this study cannot effectively account for this type of temporal change in gs responses, the improvements made by accounting for species-specific responses to each controlling variable may increase the applicability of this model.
3.3.6 Applications
The ability to predict or model species gs as a response to the surrounding environment can be valuable for wetland restoration planning. Both of the models presented in this study are valuable for quantifying the variation in seasonal gs for different species, as well as for predicting such trends based on the measurable environmental conditions at a site. The capacity of these models for prediction of gs is valuable to estimates of ET, as ET is responsible for a large portion (up to 70%) of water loss for any ecosystem (Landmeyer, 2012). Because gs is an important factor to consider in estimates for ET using the Penman-Monteith equation, the rate at which certain species are conducting water vapour will have direct effects on the estimates for
ET at a site. Consistent high stomatal conductance of wetland species can result in a greater net loss of soil moisture over a growing season when compared to the water demands of other species, which can greatly alter site hydrologic conditions over time (Boyce et al., 2012). For example, a study found that invasive Lonicera maackii trees were conducting at rates that equated to seasonal transpiration losses 5 times greater than a nearby native forest stand, which resulted in only 10% of water leaving the invasive-dominant site as stream flow (Boyce et al.,
2012). This information is valuable for estimates for ET, and understanding the water demands
107 of different wetland species in relation to the water supply. In a wetland restoration project, it may then be expected that seasonal ET rates will increase along with the increasing soil moisture availability depending on which species are introduced to a site, and the ability of those species to conduct water at high rates in saturated soils. As restoration efforts at this study site proceed, and these species are introduced to the system, based on the results of these models it would be anticipated that certain species would have higher seasonal water demands than others. Some species were conducting up of 1.5 times greater than others on average based on the multiple linear regression model results, and up to 38% higher from the Jarvis-type model.
For example, S. exigua shrubs could transpire up to 18 to 31% (depending on the model) more water on average than R. palustris plants. This type of species-to-species difference in water usage should be accounted for when anticipating how much water will be supplied to the site through restoration, in order to ensure that the shrubs are not limited by θ that is too low. As seen in Figure 3.8, soil moisture levels below 35% of field capacity resulted in stomatal closure for many species, which would result in decreased plant photosynthetic and conductance rates.
3.4 Conclusion
Stomatal control of plant gas exchange, and thus the ecosystem carbon and water balances, creates a close linkage between plant physiology to site-specific climate and hydrology. With knowledge of how environmental variables control the leaf level gas exchange, models for species-specific carbon and water fluxes can be used to properly estimate wetland water and carbon budgets (Busch and Losch, 1998). Modelling stomatal conductance using these methods which call for easily measureable climatic variables gives a better insight into the movement of water throughout the growing season in the plant-soil-atmosphere continuum (Strachan and
108 McCaughey, 2002). Dynamic vegetation models are critical to improve our understanding of the climatic connections to biosphere carbon & nutrient cycles, as well as hydrological cycles
(Medlyn et al., 2011)
Limiting environmental variables, as investigated in this study, have direct effects on the ability of a plant to conduct gas or water vapour at their maximum potential (Busch and Losch, 1998).
If limiting conditions persist for a long enough period of time, or at a great enough spatial scale where these species are found, then there is the possibility for this to have a significant impact on wetland water budget and the climatic conditions of the site and immediate surroundings
(Pezehski, 2001).
Of the environmental controls investigated in this study, air temperature and irradiance were found to be the most limiting to gs over the growing season. The controlling effects of temperature were more prominent in May, June, and September, when average temperatures were lower, and solar irradiance was the main limiting factor during July and August. The shrub responses to moisture availability showed high responsiveness to soil moisture levels below
35% of field capacity, at which point the drop in gs was rapid for most species. Prolonged periods of soil saturation were not found to have a limiting effect on gs for the majority of species. For the controlling effect of K↓, S. nigra, S. discolor, and V. lentago showed a parabolic shaped response curve, which is somewhat unexpected compared to the more typical linear increase with increasing irradiance with increasing light. Similarly, S. nigra and S. altissima showed this type of atypical parabolic response to VPD, as well as R. palustris to T.
These unique responses support the idea that boundary-line analysis that assumes uniform
109 responses of all species to an environmental control may not fully capture the range of possible responses experienced by different species.
The average seasonal gs for each species from the results of the multiple linear regression model was within 0 – 34% of observed average gs for each species, while the average gs from the
Jarvis-type model results were between 1 to 64% greater than observed gs. The model results show that there is considerable between-species variation in gs across the growing season. It was seen that some species were conducting upwards of 1.5 times higher than others on average from the multiple linear regression model, and up to 38% higher from the Jarvis-type model.
This has the potential to translate into significant variation in water demands of these species over a growing season.
While the models showed low correlation to observed valued for gs for the same measurement time-points, analysis of the variation in residuals showed that both models were effective in capturing a high degree variation in gs under a range of environmental conditions, which also was observed in the measured gs values.
The results of these two modelling techniques can provide valuable information for restoration planning, as gs is an important metric for estimating ET using the Penman-Monteith equation.
By incorporating plant physiological characteristics into estimates for ET, a more accurate depiction of seasonal water demands of certain species is possible. In order to ensure that the water demands of the plants introduced to the site during restoration do not outweigh the water supply needed to establish a wetland environment, these types of considerations are very important. Currently ET in wetlands being restored or created is insufficiently characterized
110 despite being the most prominent hydrologic component, because of the diversity of wetlands interactions and the complexity of surface characteristics (Staes et al., 2009). Increased research into wetland ecohydrological processes can work to fill some of these gaps in knowledge.
111 3.5 References
Adams, R.S., Black, T.A., and Fleming R.L. (1991). Evapotranspiration and surface conductance in a high elevation, grass‐covered forest clear‐ cut. Agriculture Forest Meteorology, 56, 173–193.
Asensio, D., Penuelas, J., Ogaya, R., and Llusia, J., (2007) Seasonal soil and leaf CO2 exchange rates in a Mediterranean Holm oak forest and their responses to drought conditions. Atmospheric Environment, 41: 2447–2455
Austin, M., (1976). On non-linear species response models in ordination. Vegetatio, 33: 33–41.
Austin, M. (1999). A silent clash of paradigms: some inconsistencies in community ecology. Oikos, 86: 170–178.
Baird, A. J., and R. L. Wilby. Eco-hydrology: Plants and Water in Terrestrial and Aquatic Environments. London: New York, 1999.
Biscoe, P. V., Incoll, L. D., Littleton, E. J., and Ollerenshaw, J. H. (1977). Barley and its environment. VII. Relationships between irradiance, leaf photosynthetic rate and stomatal conductance. Journal of Applied Ecology, 14(1): 293-302
Boyce, R.L., Durtsche R.D., and Fugal, S.L. (2012). Impact of the invasive shrub Lonicera maackii on stand transpiration and ecosystem hydrology in a wetland forest. Biological Invasions, 14, 671–680.
Breshears, D. D., Cobb, N.S., Rich, P.M., Price, K.P., Allen, C.D., and Balice, R.G. (2005). Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences, 102(42): 15144-5148.
Buckley, T.N., Mott, K.A. (2013). Modelling stomatal conductance in response to environmental factors. Plant, Cell and Environment, 36: 1691–1699
Burba, G.G., Verma, S.B., and Kim J. (1999). A comparative study of surface energy fluxes of three communities (phragmites australis, scirpus acutus, and open water) in a prairie wetland ecosystem. Wetlands, 19(2) 451-57.
Busch, J. (2000). Canopy transpiration rates in eutrophic wetlands dominated by sedges (Carex spp.) differ in a species specific way. Physics and Chemistry of the Earth, 25(7-8): 605- 610.
Busch, J., and Losch, R. (1998). Stomatal behaviour and gas exchange of sedges (Carex spp.) under different soil moisture regimes. Physics and Chemistry of the Earth, 23(4): 443- 448
Cade, B.S., Terrell, J.W., and Schroeder, R.L. (1999). Estimating effects of limiting factors with
112 regression quantiles. Ecology 80: 311–323.
Cade, B.S., and Guo Q. (2000). Estimating effects of constraints on plant performance with regression quantiles. Oikos 91: 245–254.
Chambers J.L., Hinckley T.M., Cox G.S., Metcalf C.L. and Aslin R.G. (1985) Boundary-line analysis and models of leaf conductance for four oak-hickoryforest species. Forest Science 31, 437–450.
Chaves, M.M., Pereira, J.S., Maroco, J., Rodrigues, M.L., Ricardo, C.P.P., Osorio, M.L., Carvalho, I., Faria, T., and Pinheiro, C. (2002). How plants cope with water stress in the field; photosynthesis and growth. Annals of Botany, 89, 907-916
D’Odorico, P., Laio, F., Porporato, A., Ridolfi, L., Rinaldo, A., and Rodriguez-Iturbe, I. (2010). Ecohydrology of terrestrial ecosystems. BioScience, 60(11) 898-907
Dolman, A.J., Miralles, D.G., and de Jeu R.A.M. (2014) Ecohydrology bearings – invited commentary fifty years since Monteith’s 1965 seminal paper: the emergence of global ecohydrology. Ecohydrology, 7, 897–902
Duval, T.P., Waddington, M.J., and Branfireun, B.A (2012). Hydrological and biogeochemical controls on plant species distribution within calcareous fens. Ecohydrology, 5: 73-89.
Ewers, B.E., Mackay D.S., and Samanta S. (2007). Interannual consistency in canopy stomatal conductance control of leaf water potential across seven tree species. Tree Physiology, 27, 11 - 24
Farquhar, G.D. and Sharkey, T.D. (1982). Stomatal conductance and photosynthesis. Annual Review of Plant Physiology. 33: 317–345.
Fensham, R.J., Fairfax, R.J. (2007) Drought related tree death of savanna eucalypts: species susceptibility, soil conditions and root architecture. Journal of Vegetation Science, 18:71–80
Frank, A. B. (2003). Evapotranspiration from northern semiarid grasslands. Agronomy Journal ,95(6), 1504.
Franks P.J. (2004). Stomatal control and hydraulic conductance, with special reference to tall trees. Tree Physiology, 24: 865–878.
Gao Q., Yu M., Zhou C. (2013) Detecting the Differences in Responses of Stomatal Conductance to Moisture Stresses between Deciduous Shrubs and Artemisia Subshrubs. PLOS One, 8(12)
113 Guo, Y., and Sun, L. (2012) Surface energy fluxes and control of evapotranspiration from a Carex lasiocarpa mire in the Sanjiang Plain, Northeast China. International Journal of Biometeorology, 56, 221–232
Guyot, G., Scoffini, S., and Sack, L., (2012). Combined impacts of irradiance and dehydration on leaf hydraulic conductance: insights into vulnerability and stomatal control. Plant, Cell and Environment, 35: 857–871.
Heatherington, A.M., and Woodward, F.I. (2003) The role of stomata in sensing and driving environmental change. Nature, 424(6951), 901
Hiron, R.W.P., and Wright, S.T.C., (1973). The role of endogenous abscisic acid in the response of plants to stress. Journal of Experimental Botany 24, 769–781.
Huisman J., Olff H., Fresco L.F.M. (1993). A hierarchical set of models for species response analysis. Journal of Vegetation Science, 4: 37–46.
Ingram, H.A.P. (1983). Hydrology. Ecosystems of the World 4A–Mires: Swamp, Bog, Fen and Moor, Gore AJP (ed.). Elsevier: Amsterdam; 67 – 159.
Jaksic, V., G. Kiely, J. Albertson, R. Oren, G. Katul, P. Leahy, and Byrne K. A. (2006). Net ecosystem exchange of grassland in contrasting wet and dry years." Agricultural and Forest Meteorology, 139(3-4) 323-34.
Jarvis, P.G (1976). The interpretation in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society Land, 273: 593- 610
Juhrbandt, J., Leuschner, C., and Holscher, D. (2004). The relationship between maximal stomatal conductance and leaf traits in eight Southeast Asian early successional tree species. Forest Ecology and Management, 202, 245-256
Kellner, E. (2001) Surface energy fluxes and control of evapotranspiration from a swedish sphagnum mire. Agricultural and Forest Meteorology, 110(2), 101-23.
Kim, J., and Verma S.B. (1996). Surface exchange of water vapour between an open sphagnum fen and the atmosphere. Boundary-Layer Meteorology, 79(3) 243-64.
Kim, Y. and Wang G. (2012). Soil moisture-vegetation-precipitation feedback over North America: Its sensitivity to soil moisture climatology. Journal of Geophysical Research, 117.
Koch, M.S., Rawlik, P.S (1993). Transpiration and stomatal conductance of two wetland macrophytes (Cladium jamaicense and Typha domingensis) in the subtropical everglades. American Journal of Botany, 80(10), 1146-1154
114 Kramer, P.J., (1940). Causes of decreased absorption of water by plants in poorly aerated media. American Journal of Botany 27, 216– 220.
Krober, W., and Bruelheide, H. (2014).Transpiration and stomatal control: a cross-species study of leaf traits in 39 evergreen and deciduous broadleaved subtropical tree species. Trees, 28, 901-914
Lafleur, P. (1988). Leaf conductance of four species growing in a subarctic marsh. Canadian Journal of Botany, 66(7): 1367-375.
Lafleur, P. (1990). Evapotranspiration from sedge-dominated wetland surfaces. Aquatic Botany, 37(4) 341-53.
Landmeyer, J. E. (2012). Plant and groundwater interactions under pristine conditions. Introduction to Phytoremediation of Contaminated Groundwater, 115-27.
Linacre, E.T. (1976). Swamps. Vegetation and the atmosphere, 2, 329-347. Academic Press,New York,NY.
Mann, C.J., and Wetzel, R.G. (1999). Photosynthesis and stomatal conductance of Juncus effusus in a temperate wetland ecosystem. Aquatic Botany, 63, 127-144.
Medlyn, B.E., Duursma, R.A., Eamus, D., Ellsworth, D.S., Prentice, C.I., Barton, C.V.M., Crous, K.Y., De Angelis, P., Freeman, M., and Wingate L., (2011). Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology, (2011). 17: 2134–2144.
Mills, G., Hayes, F., Wilkinson, S., and Davies W.J. (2009). Chronic exposure to increasing background ozone impairs stomatal functioning in grassland species. Global Change Biology 15, 1522–1533
Mott, K.A., and Peak, D. (2010). Stomatal responses to humidity and temperature in darkness. Plant, Cell, and Environment, 33: 1084-1090.
Moreno-Mateos, D., Power, M.E., Comin, F.A., and Yockteng, R. (2012). Structural and functional loss in restored wetland ecosystems. PLoS Biology, 10(1)
Munro, D. S. (1979). Daytime energy exchange and evaporation from a wooded swamp. Water Resources Research 15(5): 1259.
Niklas, K.J.,and Hammond, S.T. (2013). Biophysical effects on plant competition and coexistence. Functional Ecology, 27, 854–864
Oksanen J, Minchin P.R. (2002). Continuum theory revisited: what shape are species responses along ecological gradients? Ecological Modelling, 157: 119–129.
115 Ontario Ministry of Natural Resources (2011). Wetlands. Ministry of Natural Resources and Forestry.
Pezehski, S.R. (2001) Wetland plant responses to soil flooding. Environmental and Experimental Biology, 46: 299-312
Rejskova, A., Cizkova H., Brom J., and Pokorny J. (2012). Transpiration, evapotranspiration and energy fluxes in a temperate wetland dominated by Phalaris arundinacea under hot summer conditions. Ecohydrology 5: 19-27.
Sack, L., Cowan, P.D., Jaikumar, N., and Holbrook, N.M. (2003). The ‘hydrology’ of leaves: co-ordination of structure and function in temperate woody species. Plant, Cell and Environment, 26, 1343–1356
Saha, S., Strazisar, T.M., Menges, E.S., Ellsworth, P., and Sternberg, L. (2008). Linking the patterns in soil moisture to leaf water potential, stomatal conductance, growth, and mortality of dominant shrubs in the Florida scrub ecosystem. Plant Soil, 313: 113–127.
Sala, A., and Tenhunen, J.D (1996). Simulations of canopy net photosynthesis and transpiration in Quercus ilex L. under the influence of seasonal drought. Agriculture and Forest Meteorology, 76: 203-222
Scharf, F.S., Juanes, F., and Sutherland, M. (1998). Inferring ecological relationships from the edges of scatter diagrams: comparison of regression techniques. Ecology 79: 448–460.
Schröder H.K., Andersen H.E., and Kiehl K. (2005). Rejecting the mean: estimating the response of fen plant species to environmental factors by non-linear quantile regression. Journal of Vegetation Science, 16: 373 – 382.
Staes, J., Rubarenzya, M.H., Miere, P., and Willems, P. (2009) Modelling hydrological effects of wetland restoration: a differentiated view. Water Science & Technology, 59(3).
Stewart, J.B. (1988). Modelling surface conductance of pine forest. Agriculture and forest meteorology, 43: 19-35.
Strachan, I.B., and McCaughey, J.H. (2002). Stomatal conductance of Populus trichocarpa in southern Iceland in relation to environmental variables. Scandinavian Journal of Forest Research. 17: 7-14
Streck, N.A. (2003) Stomatal response to vapour pressure deficit: an unsolved issue. Revista Brasileira de Agrociencia, 9(4): 317-322
Stroh, P.A., Mountford, J.O, Araya Y.N., and Hughes, F.M.R (2013). Quantifying soil hydrology to explain the development of vegetation at an ex-arable wetland restoration site. Wetlands, 33, 311-320
116 Takagi, K., Tsuboya, T., and Takahashi, H. (1998). Diurnal hysteresis of stomatal and bulk surface conductance in relation to vapour pressure deficit in a cool-temperate wetland. Agriculture and Forest Meteorology, 91, 177-191
Wang, C., Yang, P., Li, Y., and Ren, S. (2013). Characteristics of E. japonicus stomatal conductance under water-deficit stress using a nonlinear Jarvis modified model. Mathematical and Computer Modelling, 58: 799-806
Whitehead, D., Barbour, M.M., Griffin, K.L., Turnbull, M.H., and Tissue, D.T. (2011). Effects of leaf age and tree size on stomatal and mesophyll limitations to photosynthesis in mountain beech (Nothofagus solandrii var. cliffortiodes). Tree Physiology, 31: 985-996.
Willmer, C.M., and Mansfield, T.A (1970). Effects of some metabolic inhibitors and temperature on ion-stimulated stomatal opening in detached epidermis. New Phytologist, 69(4): 983-992
117 Appendix 3A: Comparison of net radiation between the study site weather station and the University of Toronto Mississauga weather station
118 Appendix 3B: Logistic growth coefficients for each species and environmental variable, used to generate upper-quantile response curves
Appendix A Table 1: Logistic Growth Coefficients for Species Response to Air Temperature Species Coefficients a b c d C.sericea 6.19617 -0.32208 -1.8898 0.034891 R.palustris 7.21806 -0.37458 -8.943 0.284591 V.lentago -0.99511 0.040058 4.06117 -0.31347 S.alba 3.84328 -0.2527 -0.51751 S.altissima 26.8041 -1.4811 -0.82714 S.discolor 11.6946 -0.66152 -1.2656 S.exigua -6.6827 0.16906 -0.3587 S.nigra 10.8753 -0.58489 -1.1185 S.ulginosa 6.54846 -0.37748 -0.78115
Appendix A Table 2: Logistic Growth Coefficients for Species Response to Vapour Pressure Deficit Species Coefficients a b c d C.sericea -0.46196 -0.31887 -2.6482 0.799246 R.palustris -0.20343 -1.016200 -1.386200 V. lentago -3.2003 1.28105 -0.52535 S.alba -2.4173 0.777812 -0.75462 S.altissima 0.200816 -1.3393 -3.3283 1.27984 S.discolor -2.8078 0.730904 -1.8128 S.exigua -1.4911 0.550988 -1.3014 S.nigra -4.958 1.97232 0.946081 -1.9182 S.ulginosa 1.11392 -2.5748 -0.81381
Appendix A Table 3: Logistic Growth Coefficients for Species Response to Incoming Solar Radiation Species Coefficients a b c d C.sericea 0.492282 -0.11354 -7.0319 R.palustris 0.59891 -0.188380 -1.539100 V.lentago 1.66773 -0.442390 -6.042100 0.66701 S.alba 0.224266 -0.0699 -3.9945 S.altissima -0.01581 -0.11593 -4.3308 S.discolor 0.634432 -0.43988 -4.1155 0.362854 S.exigua 0.647286 -0.10604 -3.2938 S.nigra 0.706752 -0.39443 -11.163 1.15913 S.ulginosa 1.3162 -0.50591 -1.7682 0.112587
119
Appendix A Table 4: Logistic Growth Coefficients for Species Response to Volumetric Moisture Content of the Soil Species Coefficients a b c d C.sericea 4.063310 -0.233670 -0.565040 R.palustris 5.513810 -0.347930 -1.063700 V.lentago -2.149800 0.024882 0.313349 -0.022810 S.alba 10.653300 -0.557570 -0.277500 S.altissima 5.841880 -0.325130 -1.143000 S.discolor 3.052880 -0.169380 -1.553400 S.exigua 0.232086 -0.033440 -1.076500 0.007157 S.nigra 6.594520 -0.372060 -1.003800 S.ulginosa 1.665870 -0.117280 -0.650670
Appendix 3C: Regression coefficients for multiple linear regression model for stomatal conductance
Appendix B: Coefficients for Environmental Controls over Stomatal Conductance using Multiple Linear Regression Modelling Species Coefficients a b c d e C.sericea -9.672 1.041 -7.021 0.001 0.107 R.palustris -11.000 0.481 -0.168 0.005 0.152 V.lentago 1.099 0.544 -6.453 0.001 0.054 S.alba -3.915 0.785 -6.372 0.004 0.013 S.altissima -0.540 0.590 -4.709 0.001 0.145 S.discolor -3.980 0.927 -8.344 -0.001 0.137 S.exigua -5.649 0.889 -6.699 0.001 0.048 S.nigra -7.628 0.804 -6.466 0.004 0.084 S.ulginosa -5.267 0.813 -3.212 0.001 0.004
120 Appendix 3D: Results of statistical analysis of variance in observed gs for each test species (ANOVA and Tukey’s Post-Hoc test)
Appendix B Table 1: p-Values for variance between species observed gs
Species R. S. S. C. sericea S. alba S. discolor S. exigua S. nigra V. lentago palustris altissima uliginosa
C. sericea X 0.955 0.772 0.014 0.689 0.091 1.000 1.000 0.000
R. X X 1.000 0.000 0.072 0.723 0.999 0.930 0.009 palustris
S. alba X X X 0.000 0.019 0.935 0.979 0.713 0.000
S. X X X X 0.751 0.000 0.003 0.023 0.000 altissima
S. discolor X X X X X 0.000 0.350 0.771 0.000
S. exigua X X X X X X 0.346 0.074 0.725
S. nigra X X X X X X X 0.999 0.001
S. X X X X X X X X 0.000 uliginosa
V. lentago X X X X X X X X X
121 Chapter 4 Conclusion
The findings of this study provides information on Ontario native wetland shrub species that, up to this point, has been largely unknown, and untested in a scientific capacity. The morphological and physiological dynamics of these plants in mineral soils throughout one growing season were seen to vary considerably between species. Ecohydrological plant interactions with soil moisture and various atmospheric forces were observed, and provide information on plant growth, gas exchange, and seasonal water demands.
When planted in the experimental plot within the study site, the treatment design was effective in exposing the different species to a wide range of moisture conditions. The variation in volumetric moisture content within the treatments throughout the growing season resulted in clear differences in growth responses of the nine wetland species.
Growth was expressed a measured plant biomass, and the trends in biomass accumulation varied from a positive, negative, or null response to increasing moisture availability. The ranges in growth success and moisture tolerance observed for each species within the three treatments is summarized in Table 4.1. The results indicate that certain species, namely C. sericea and V. lentago, were most tolerant to a wide range in soil moisture availability, as growth was near equivalent in all treatments. Species such as S. alba and
R. palustris, however, showed a clear preference to the highest moisture levels, where the highest growth was observed. The idea of a moisture tolerance threshold, as introduced in chapter 1, where plants experience detrimental growth responses outside of the range in moisture they are best adapted to, can be observed for the remaining species. S. exigua, S.
122 nigra. S. discolor, and S. uliginosa, while showing increases in biomass for all treatments, had the highest growth success in the TW treatment, which predominantly had average soil moisture ranges lower than that seen in TW2. The S. altissima plants, which are the annual goldenrod species currently dominant at the site, had vastly greater growth in the TN treatment, showing an adaptation of this species to have lower tolerance to high soil moisture than most wetland plants in the study. These observed responses relate back to the understanding that optimal ranges for moisture tolerance exist, and that plants are adapted to exist in conditions where they are not limited by moisture availability, and are capable of their maximum potential for photosynthesis and transpiration (Rodriguez-
Iturbe, 2000).
Moisture Tolerance of Wetland Species
Species TN TW TW2 Cornus sericea ✔ ✔ ✔ Viburnum lentago ✔ ✔ ✔ Solidago altissima ✔ ✗ ✗ Sambucus nigra ✗ ✔ ✗ Salix exigua ✗ ✔ ✗ Solidago uliginosa ✗ ✔ ✗ Salix discolor ✗ ✔ ✔ Spiraea alba ✗ ✔ ✔ Rosa palustris ✗ ✗ ✔ Table 4.1: Results of biomass harvest expressed as observed tolerance to treatment soil moisture. A check mark indicates higher growth success in a treatment, while an X is lower growth success and thus a lower tolerance to moisture content in a treatment.
123 In the event that these species are introduced during wetland restoration at a site with mineral soils, it would be expected that any existing S. altissima plants would begin to die off as moisture levels at the site increase, due to the much lower growth seen in the wetter treatments. Additionally, C. sericea and V. lentago plants would likely be most successful in establishing at the site, as moisture variation was not particularly limiting to growth. The remaining species, as seen in Table 4.1, would be more sensitive to soil moisture availability, and placement within the site should be considered to ensure optimal moisture supply is met. This knowledge on wetland shrub growth can improve the successful establishment of a shrub thicket swamp community, when used in conjunction with and understanding of site hydrological conditions
The wide range in moisture conditions established in the experimental plot also allowed for observation of the controlling effects of moisture on wetland shrub stomatal conductance, alongside the controlling effects of atmospheric vapour pressure deficit, air temperature, and incoming shortwave radiation. The logistic upper-quantile non-linear regression approach used to assess these controlling relationships showed that the factors eliciting the most rapid guard cell closure response for most species were air temperatures below 20 °C, and drying soil moisture conditions below 35% of field capacity. Many species also indicated decreasing conductance with increasing vapour pressure deficit, and increasing conductance with higher irradiance. Over the full growing season, air temperature and irradiance were found to be the most limiting to stomatal conductance. The controlling effects of temperature were more prominent in May, June,
124 and September, when average temperatures were lower, and solar irradiance was the main limiting factor during July and August.
Because stomatal conductance is the rate of gas exchange of a plant, including CO2 uptake and release of water vapour, measurements of conductance can provide valuable insight into the water use and transpiration of individual species. Stomatal conductance can be incorporated into estimates for the evapotranspiration rate of a canopy using the
Penman-Monteith equation; therefore, accurate values for growing season stomatal conductance for various species can increase the precision of evapotranspiration estimates using this method. The evapotranspiration component of the water balance can account for a significant portion of water loss at a site (up to 70%), so accurate measurement of plant water use and water demands can improve the characterization and quantification of site hydrologic processes (Landmeyer, 2012, Medlyn et al., 2011).
Methods that were implemented for modelling stomatal conductance over the growing season fill the measurement gaps while still capturing variation caused by any environmental limiting factors. The model results show that there is considerable difference in stomatal conductance across the growing season between species. It was seen that some species were conducting upwards of 57% higher than others on average from the multiple linear regression model, and up to 38% higher from the Jarvis-type model. This has the potential to translate into significant variation in water demands of these species over a growing season.
125 In a wetland restoration context, in order to ensure that the water demands of the plants introduced to the site do not outweigh the water supply needed to establish a wetland environment, these types of considerations are very important. Currently ET in wetlands is insufficiently characterized despite being the most prominent hydrologic component, because of the diversity of wetlands interactions and the complexity of surface characteristics (Staes et al., 2009). Increased research into wetland ecohydrological processes can work to fill some of these gaps in knowledge.
Recommendations to restoration practitioners based on the findings of this research would be to implement pre-restoration site monitoring. This monitoring would involve measures to characterize the current site hydrologic regime, including estimates for seasonal evapotranspiration. Knowledge of stomatal conductance of shrub species that are to be introduced to the site can be used in estimates for evapotranspiration for a theoretical “post-restoration” canopy. This could bring to light any disparity in pre- and post-restoration water loss through evapotranspiration due to changes in species composition. Restoration efforts can then work to ensure that water supply to the restored wetland site will account for any differences in water loss. Understanding site topography as well as groundwater depth and flow paths would also help in determining water residence time, and useful for determining heterogeneity in soil moisture availability within a site. Knowledge of plant growth success under varying moisture conditions can inform practitioners of which species are most appropriate for the expected hydrologic regime, as well as the locations within the site that moisture availability will be ideal for the introduced species.
126 References
Landmeyer, J. E. (2012). Plant and groundwater interactions under pristine conditions. Introduction to Phytoremediation of Contaminated Groundwater, 115-27.
Medlyn, B.E., Duursma, R.A., Eamus, D., Ellsworth, D.S., Prentice, C.I., Barton, C.V.M., Crous, K.Y., De Angelis, P., Freeman, M., and Wingate L., (2011). Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology, (2011). 17: 2134–2144.
Rodriguez-Iturbe, I. (2000). Ecohydrology: A Hydrologic Perspective of Climate-soil- vegetation Dynamics. Water Resources Research, 36(1), 3-9.
Staes, J., Rubarenzya, M.H., Miere, P., and Willems, P. (2009) Modelling hydrological effects of wetland restoration: a differentiated view. Water Science & Technology, 59(3).
127