Improving Extensive Green Roof Functioning Using Invasion Theory As a Management Tool

To Sedum or Not to Sedum: Improving Extensive Green Roof Functioning Using Invasion Theory as a Management Tool

Chih Julie Wang

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Ecology and Evolution University of Toronto

To Sedum or Not to Sedum: Improving Extensive Green Roof

Functioning Using Invasion Theory as a Management Tool

Chih Julie Wang

Master of Science

Department of Ecology and Evolution

University of Toronto

2020 Abstract

Green roofs have become increasingly popular to mitigate the negative impacts of urbanization and to restore key ecosystem functions in cities. In this thesis, I evaluate green roof functioning in two ways, using a meta-analysis of existing literature and a field-based manipulation experiment. The meta-analysis showed that Sedum survived better than non-Sedum, but there was no pattern of roof cooling and stormwater management. In the field, I conducted a factorial experiment using invasion theory as a management tool to increase native species diversity on extensive green roofs and evaluate green roof functioning. I hypothesized that resource addition via irrigation and disturbance treatments will enhance native species success and increase roof cooling and stormwater management. Results show plants aid in roof cooling and increase moisture retention, however evidence supporting diversity enhancing green roof functioning is lacking. Diversity effects on function theoretically require co-existence mechanisms, thus requiring observation over longer temporal scale.

Acknowledgments

I would like to express my sincere gratitude to everyone who has helped me throughout this project; this would not have been possible without them. First and foremost, I would like to thank my supervisors Dr. Marc Cadotte and Dr. Scott MacIvor for their insight, support, and guidance throughout this project. Second, I would like to extend my thanks to my supervisory committee members Dr. Eliana Gonzales-Vigil and Dr. Roberta Fulthorpe, for their invaluable comments, time, and encouragement. I would also like to thank my examination committee members, Dr. Peter Kotanen and Dr. Kenneth Welch for agreeing to supervise my examination despite having busy schedules. Third, I would like to thank the Cadotte lab members, Dr. Marta

Carboni, Dr. Carlos Alberto Arnillas, Dr. Deyi Yin, Adriano Roberto, Antonio Lorenzo, Chung

Chui, Menilek Beyene, Rachel Rigden, and Weihan (Bill) Liu, the MacIvor lab members,

Nicholas Sookhan, Garland Xie, Miranda Klymiuk, Praveen Jayarajan, and Stephen Grabinsky, work study students, Kelly Ray, Natasha Klasios, Nigarsan Kokilathasan, Raisa Chowdhury,

Rebecca Morris, Shannon Underwood, Tony Li, and Waqqas Khalid, and the numerous dedicated and hardworking volunteers who have all together made this project possible. Fourth, I would like to thank my family and friends for being supportive and encouraging throughout my project. Finally, I would like to thank the Department of Ecology and Evolutionary Biology at the University of Toronto for its resources and research opportunities that allow this project to be successful.

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Table of Contents

Acknowledgments ...... iii Table of Contents ...... iv List of Tables ...... vi List of Figures ...... vii List of Appendices ...... viii Chapter 1 Introduction ...... 1 Introduction ...... 1 1.1 Consequences of urbanization ...... 1 1.2 Green roofs can mitigate the negative impacts of urbanization ...... 2 1.3 Thesis overview ...... 3 1.3.1 Chapter 2: To Sedum or not to Sedum: Evaluating Sedum relative to Non-Sedum performance on extensive green roofs ...... 3 1.3.2 Chapter 3: Improving extensive green roof functioning using invasion theory as a management tool ...... 4 References ...... 5 Chapter 2 To Sedum or not to Sedum: Evaluating Sedum relative to Non-Sedum performance on extensive green roofs ...... 8 To Sedum or not to Sedum: Evaluating Sedum relative to Non-Sedum performance on extensive green roofs ...... 8 2.1 Introduction ...... 8 2.2 Materials and Methods ...... 10 2.2.1 Literature search ...... 10 2.2.2 Meta-analysis ...... 11 2.3 Results ...... 12 2.3.1 Green roof research trends ...... 12 2.3.2 Sedum verses non-Sedum survival ...... 12 2.3.3 Sedum verses non-Sedum roof cooling performance ...... 12 2.3.4 Sedum verses non-Sedum stormwater management ...... 13 2.4 Discussion ...... 13 2.4.1 Opportunities for future research ...... 16 References ...... 17 Figures...... 21 Appendix ...... 25

Chapter 3 Improving extensive green roof functioning using invasion theory as a management tool ...... 40 Improving extensive green roof functioning using invasion theory as a management tool . 40 3.1 Introduction ...... 40 3.2 Materials and methods ...... 42 3.2.1 Experimental set up ...... 42 3.2.2 Data collection ...... 44 3.2.3 Statistical analysis ...... 45 3.3 Results ...... 46 3.3.1 Native invader success ...... 46 3.3.2 Green roof functioning ...... 46 3.4 Discussion ...... 47 3.4.1 Native invader success ...... 47 3.4.2 Green roof functioning ...... 48 3.4.3 Conclusion ...... 50 3.4.4 Future directions ...... 51 References ...... 52 Tables ...... 57 Figures...... 59 Appendix ...... 62 Chapter 4 Thesis Summary ...... 67 Thesis summary ...... 67 References ...... 69

List of Tables

Table 3.1 Summary information of green roof sites used in this study……….…………………57

Table 3.2 List of extensive green roof plant species and diversity combinations used in field experiment………………………………………………………………………………………..58

List of Figures

Figure 2.1. Map of green roof studies evaluating plant survival, roof cooling functioning, and stormwater management performances………………………………………………………….21

Figure 2.2. Forest plot for Sedum relative to non-Sedum survival…………………………….....22

Figure 2.3 Forest plot for Sedum relative to non-Sedum cooling performance………………….23

Figure 2.4. Forest plot for Sedum relative to non-Sedum stormwater management functioning...24

Figure 3.1. Boxplots showing 2018 and 2019 plant cover………………………………………59

Figure 3.2 Boxplots showing July 2019 temperature………………………………………..…..60

Figure 3.3 Boxplots showing 2019 moisture retention………………………………………..…61

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List of Appendices

Appendix 2.1 Reference list of studies used in meta-analysis…………………………………...25

Figure 3.A1. Map of the University of Toronto Scarborough Campus………………………….62

Figure 3.A2. Photo of the experimental set up on top of MW roof at the University of Toronto Scarborough Campus…………………………………………………………………………….63

Figure 3.A3. Boxplots showing disturbance treatment effects on plant cover…………………..64

Figure 3.A4. Boxplots showing native invader plant cover……………………………………...65

Figure 3.A5. Rudbeckia hirta flowers on extensive green roof at University of Toronto Scarborough Campus…………………………………………………………………………….66

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Chapter 1 Introduction Introduction 1.1 Consequences of urbanization Urbanization is one of the major drivers of global environmental change, and currently more than 55% of people live in cities (United Nations, 2018). Humanity continues to experience a dramatic shift to urban living, as many ecosystems are rapidly being transformed into new, non- historical configurations differing in composition and/or function from past systems (Hobbs et al., 2009). These novel ecosystems are a new system driven by two major external forces: changes in species distribution and environmental alterations through land use change (Hobbs et al., 2009). Increasing incidences of anthropogenic modifications leading to novel ecosystems, requires serious considerations of how key ecosystem services can be restored and conserved in urban ecosystems.

Cities result in profound changes to the local environment, ecology, and biodiversity relative to its surrounding rural and natural areas. In urban environments, vegetation has been replaced by impervious surfaces, contributing to elevated air and surface temperatures in cities compared to their rural surroundings (i.e. urban heat island effect) (Grimm et al., 2008). In addition, impervious coverage increases the velocity and volume of runoff and the diversion of water causes flooding and pollution of streams (Arnold Jr. and Gibbons, 1996; Grimm et al., 2008).

Alterations in species distribution are leading to drastic changes in the biotic structure and composition of ecological communities, either from the loss of species or from the introduction of non-native species (Hooper et al., 2005). Humans introduce non-native species into cities to create, augment, or restore key ecosystem services. Some important ecosystem services include providing shade, improving aesthetics, fixing nitrogen, and controlling erosion (Potgieter et al., 2017). On the one hand, the introduced non-native species increase species diversity of cities and sustain ecosystem services on which humans depend (Elmqvist et al., 2008). On the other hand, non-native invasive species can form new communities in cities and homogenize biotic communities, which leads to unique native species being replaced by common non-native species (Trentanovi et al., 2013). Urban centres being key points of entry and foci for secondary

1 2 release of non-native species makes cities hotspots for biological invasions (Gaertner et al., 2017).

1.2 Green roofs can mitigate the negative impacts of urbanization Green infrastructure developments like green roofs are one of the ways to mitigate the negative impacts of urbanization and provide ecosystem services in cities. Green roofs reduce heat fluxes through the roofs by promoting evapotranspiration, physically shading the roof, and increasing insulation (Oberndofer et al., 2007). Green roofs also have a higher albedo (between 0.7 to 0.85) compared to conventional roofs (between 0.05 to 0.25) (Getter and Rowe, 2006). Albedo is a measure of how much light is reflected from a surface. High albedo surfaces have high reflectance of light and low albedo surfaces have low reflectance of light. In the summer, green roofs reduce the amount of heat transferred through the roof, thereby lowering the energy demands on the building’s cooling system. (Cook-Patton and Bauerle, 2012; Jim 2014). Temperatures under the substrate layers of the green roof were significantly lower and fluctuated less than on the surfaces of conventional roofs (Teemusk and Mander, 2010). Moreover, green roofs are ideal for urban stormwater management because they have an advantage of requiring no additional space and are able to retain rainwater and delay peak flow, thereby reducing the risk of flooding and preventing runoff (Villarreal and Bengtsson, 2004; Vijayaraghavan, 2016).

Green roofs are classified into two categories, intensive and extensive green roofs. Extensive green roofs are characterized with shallow substrate layer that is less than 15 cm and require minimal maintenance whereas intensive green roofs are characterized with thick substrate layer that is more than 20 cm (Oberndorfer et al., 2007; Vijayaraghavan, 2016). Intensive green roofs often require planning in design phase or structural improvements (Oberndorfer et al., 2007; Vijayaraghavan, 2016). Being light weight and less costly, extensive green roofs are often implemented on existing buildings, in new building projects, and research studies to quantify their benefits in cities.

Reducing the substrate depth decreases the type of plants that can successfully grow on the harsh extensive green roof environments, thus making Sedum spp. (hereafter referred to Sedum) the most common extensive green roof installations around the world and in many places where it is

3 not native. This has led to interest within the industry to broaden the type and number of plants suited for extensive green roofs. Sedum are extremely resilient, able to withstand drought, survive under low nutrient conditions, and provide good plant coverage (Snodgrass and Snodgrass, 2006). In addition, these low growing plants spread and cover the substrate in a short period of time, further reducing potential erosion and improving aesthetics (Getter and Rowe, 2008). Utilizing different life forms in mixed communities has been shown to improve the performance of extensive green roofs (Lundholm et al., 2010). Healthy and diverse green roof ecosystems can further enhance habitat provisioning (Williams et al., 2014), the aesthetic appeal of buildings (Jungels et al., 2013; Loder, 2014), noise reduction, and mitigate air pollution (Vijayaraghavan, 2016).

1.3 Thesis overview

Green roofs are constructed ecosystems engineered to produce valuable ecosystem services, featuring the interaction of living and non-living components (Lundholm, 2015). To some, green roofs are novel ecosystems composing new species combination providing important ecosystem services in cities. Novel ecosystems should not preclude management actions that can alter them to better support native species (Murica et al., 2014). The use of regionally native plants is often a goal in green roof research (Brenneisen, 2006; Schroll et al., 2011; Rowe et al., 2012). In this thesis, I assess the performance of Sedum relative to non-Sedum plants. Using extensive green roof as my study system, I also propose a novel management tool to increase native species diversity on extensive green roof ecosystems. I then evaluate whether increasing native species diversity improves green roof functioning.

1.3.1 Chapter 2: To Sedum or not to Sedum: Evaluating Sedum relative to Non-Sedum performance on extensive green roofs

In this chapter, I will use a meta-analysis to evaluate the performance of Sedum relative to non- Sedum green roof plants on extensive green roofs. I will assess whether Sedum outperforms non- Sedum green roof plant in terms of plant survival, roof cooling, and stormwater management.

1.3.2 Chapter 3: Improving extensive green roof functioning using invasion theory as a management tool

In this chapter, I will apply the current understanding of invasion biology to manage Sedum- based extensive green roofs. Native plant species will ‘invade’ Sedum communities on the green roof ecosystem. This field experiment will consist of two parts: (a) I will evaluate how disturbance and resource addition via irrigation affect native plant species success on green roofs. (b) I will assess whether increasing native species diversity improves green roof functions in terms of plant survival, cooling, and water capture.

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Chapter 2 To Sedum or not to Sedum: Evaluating Sedum relative to Non- Sedum performance on extensive green roofs To Sedum or not to Sedum: Evaluating Sedum relative to Non-Sedum performance on extensive green roofs 2.1 Introduction Sedum spp. (hereafter referred to Sedum) (Family: Crassulaceae) are the most widely used plant genus on green roofs around the world, including many places where they are not native. Their durability on green roofs has helped ‘grow’ an industry that contributes to urban greening initiatives in cities where impervious surfaces continually replace pre-existing green areas.

Green roofs consist of vegetation and substrate atop traditional roofing membranes. Many are built to be lightweight to reduce the load on existing buildings or the requirement of structural support on new ones. Reducing the weight of green roofs is mainly accomplished by reducing the volume of substrate, thereby reducing the types of plants that can be successfully grown. This also reduces the total volume of water that can be carried within that substrate, exacerbating drought conditions. Excess wind and exposure to the sun leads to very hot and dry growing conditions. The majority of green roofs that are constructed on new and retrofit buildings are extensive green roofs, where Sedum are most widely used. For instance, companies reporting to Green Roofs for Healthy Cities, a non-governmental organization representing green roof industries, covered 289,190 square meters of building roof tops across North America, the majority of which have been extensive green roofs (green roof industry survey, 2018). In most extensive green roof conditions, Sedum can achieve 100% cover (Rowe et al., 2006). This is important as plant cover is linked to a variety of green roof functions from cooling via evapotranspiration and shading (MacIvor et al., 2016) to aesthetics (Loder, 2014). High cover is typically a priority in cities that incentivize green roofs. For example, in Toronto, Canada, 80% plant cover must be achieved within the first three years of a green roof project (City of Toronto, 2017).

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Sedum consistently outperforms grasses and wildflowers (hereafter referred to non-Sedum) in survival and cover in shallow, extensive green roof conditions. Sedum evolved in environments that have similar characteristics as roof tops, thus they have time and again been shown to survive on green roofs where supplemental irrigation is limited or non-existent (Rowe et al., 2012). Sedum are succulents, also crassulacean acid metabolism (CAM) plants. Drought tolerance is a feature of CAM plants. CAM plants fix CO2 in the dark for later use in photosynthesis. They limit their water loss due to transpiration by opening their stomata to uptake CO2 at night. (Ting, 1985). Sedum spp. are also able to store extra water in their leaves and shoots and relocate water to vital plant tissues. After a period of drought, many Sedum spp. are able to recover (Bousselot et al., 2011).

Sedum is widely available in the horticulture industry and easily grown for green roofs. This is because Sedum can be spread via cuttings to grow dense ‘Sedum mats’ that can then be stacked and transported. Despite its global acceptance, Sedum is not native to most countries where it is widely deployed and might not be the most ideal green roof plant in certain regions. There is growing interest in trying to grow regional native plants in different green roof practices (Lundholm and Walker, 2018). Substrate depth is a limiting factor for plant survival and species selection for green roof projects (Dvorak and Volder, 2010). Greater substrate depth is known to allow additional plant diversity (Dunnett and Kingsbury, 2010), increase carrying capacity for water capture (VanWoert et al., 2005), and increase opportunities for wildlife (Williams et al., 2014).

Sedum is unable to perform ecosystem functions as well as some non-Sedum plants. For instance, Stachys spp. outperformed Sedum and other non-Sedum species in terms of roof cooling because of its leaf morphology (Blanusa et al., 2013). The hairs on Stachys’ leaves reduced the intensity of incoming irradiance and provided higher albedo, which significantly cooled the leaf surface and substrate temperature below the canopy (Blanusa et al., 2013). Small, narrow, succulent leaves with thick cuticle are unlikely to offer evapotranspirative cooling (VanWoert et al., 2005). Furthermore, Sedum produced significantly more runoff volumes than grass seeded green roof model trays (Mickovski et al., 2013). Surface runoff was lessened by grass that had more developed foliage and root systems that intercepted the rainfall better than Sedum with potential modification of the precipitation distribution (Mickovski et al., 2013). However, studies have

10 combined Sedum with other non-Sedum plantings and find that Sedum can act as nurse plants (Butler et al., 2011), facilitating the survival of non-Sedum. Thus, utilizing these different life forms in mixed communities can improve performance of extensive green roofs (Lundholm et al., 2010; Lundholm et al., 2015).

In this chapter, I aim to evaluate the performance of Sedum relative to non-Sedum green roof plants. I conducted a meta-analysis by first surveying the literature, then extracting the standard effect sizes of performance of Sedum compared to non-Sedum plants for three criteria for green roofs i) survival ii) roof cooling, and iii) stormwater management. It is appropriate to conduct this meta-analysis because there is a great growth in the peer-reviewed literature on green roofs in the last few decades and so sufficient studies should be available for comparison. Due to Sedum’s ability to tolerate drought, I hypothesize that Sedum survives better than non-Sedum on extensive green roofs (Rowe et al., 2006; Rowe et al., 2012). However, Sedum have low evapotranspiration rates especially on hot and dry days (Blanusa et al., 2013), and so I hypothesize non-Sedum outperforms Sedum in roof cooling and stormwater management on extensive green roofs (Mickovski et al., 2013; MacIvor et al., 2016).

2.2 Materials and Methods 2.2.1 Literature search The literature search was conducted in March 2018 using Web of Science for all peer-reviewed journal articles. This included all English language studies. I used the search terms “Sedum AND Green Roof” and “Sedum AND Vegetated Roof” to capture all studies that used Sedum plants on green roofs. The terms returned 186 results and they were screened for their relevance to this study. A total of 96 studies were excluded for reasons such as (a) examined other effects of ecosystem services that were not survival, cooling, or water capture, (b) review paper and no data collected, or (c) could not gain access to paper. The final selection had 90 studies that evaluated the performance of survival, roof cooling, and water capture, and 42 studies qualitatively described Sedum in relation to non-Sedum performances on green roofs.

The selected studies were then reviewed to extract the type of green roof functioning examined (e.g. survival, roof cooling, water capture), the response variable measured (e.g. survival, substrate temperature, retention), species planted on the green roof, and location of the study.

Comparisons of Sedum to non-Sedum performances were conducted within an individual study by calculating the Hedges’ g effect size estimate before they were compared among studies. Some studies had multiple data types that were used in the meta-analysis. For instance, if they measured different ecosystem services, had a different soil depth, or over multiple years.

2.2.2 Meta-analysis I conducted a meta-analysis to statistically compare Sedum to non-Sedum performance using data extracted from relevant studies. All analysis and data aggregation were conducted in R Version 3.6.1 (R Core Team, 2019). I followed an approach similar to Filazzola et al. (2019) that provides a clear workflow of data aggregation, calculating effect sizes, and conducting statistical models. Studies that were included in the meta-analysis had to include the following: (a) List of Sedum and non-Sedum species used, (b) green roof substrate depth, (c) a measure of ecosystem functions examined in this study (survival, roof cooling, and/or water capture), and (d) data reported as either means with standard deviations or data where means and standard deviation could be calculated. The number of replicates in each study was recorded to be used as the n value in the meta-analysis. In studies where means were recorded but not the standard deviation, I calculated the standard deviation among the means that were recorded. In these studies, the number of recoded means were used as the sample size (n).

To contrast Sedum vs. non-Sedum performance, the effect size was calculated using the mean, standard deviation, and n from each possible comparison (function escalc, package metafor; Viechtbauer, 2010). I computed the standardized mean difference (referred to as Hedges’ g): ̅ ̅ �� = , where �̅1 − �̅2 is the difference of means and s* is the pooled standard ∗ deviation. Hedges’ g was used to calculate the effect size as it corrects for positive bias when comparing the mean difference between two groups (Hedges, 1982). The meta-analysis was then conducted using a mixed-effects model (function rma, package metafor). A mixed-effects model was used because it assumes the selected comparisons are a random subset of a larger population of studies conducting similar comparisons and it accounts for different study methodologies.

2.3 Results 2.3.1 Green roof research trends A total of 90 studies out of the 186 studies that were reviewed examined survival, cooling, and/or stormwater management performances of green roof plants. From this, 50 studies evaluated green roof plant survival, 23 evaluated roof cooling functioning, and 34 evaluated stormwater management performances. A large proportion were conducted in North America (46%) and Europe (37%). Only 18% were conducted elsewhere (Fig. 2.1). These studies were conducted across 46 cities in 22 countries. The most common Sedum plants used on green roofs are S. spurium (41%), S. album (37%), S. acre (32%), S. kamtschaticum (27%), and S. reflexum (18%). The most common non-Sedum plants used on green roofs are Allium cernuum, Koeleria macrantha, and Coreopsis lanceolata. A. cernuum, K. macrantha, and C. lanceolata are native to North America. These species are the most common non-Sedum plants used since the largest proportion of green roof studies examined were done in North America (Fig. 2.1).

A total of 42 studies qualitatively described Sedum in relation to non-Sedum survival, roof cooling, and stormwater management performances on green roofs. From these studies, 25 studies evaluated green roof plant survival, 6 evaluated roof cooling functioning, and 12 evaluated stormwater management performances.

2.3.2 Sedum verses non-Sedum survival I found that Sedum survived better than non-Sedum on green roofs (mean effect ± SE = 0.952 ± 0.417; z = 2.282; p = 0.023) (Fig. 2.2). There was strong evidence of heterogeneity among studies (Q = 910.562; p < 0.0001). There was no significant publication bias (z = 0.649; p = 0.516) assessed by funnel plot.

2.3.3 Sedum verses non-Sedum roof cooling performance I found that there were no significant differences between Sedum and non-Sedum green roof cooling performance (mean effect ± SE = -2.025 ± 1.616; z = -1.253; p = 0.210), with some studies finding a substantial cooling benefit with non-Sedum plantings (Fig. 2.3). There was strong evidence of heterogeneity among studies (Q = 50.308; p < 0.0001) and significant publication bias (z = -3.715; p = 0.0002) assessed by funnel plot.

2.3.4 Sedum verses non-Sedum stormwater management From the studies analyzed, I found that there were no significant differences between Sedum and non-Sedum plants in stormwater management (mean effect ± SE = 0.3841 ± 0.3158; z = 1.2165; p = 0.2238). Studies found Sedum produced more runoff than non-Sedum plants and non-Sedum plants experienced more water loss than Sedum plants (Fig. 2.4). There were strong evidences of heterogeneity among studies (Q = 103.417; p < 0.0001), and significant publication bias (z = - 2.1006; p = 0.0357) assessed by funnel plot.

2.4 Discussion The use of regional native plants has become a popular goal in green roof research (Brenneisen, 2006; Schroll et al., 2011; Rowe et al., 2012), but their performance relative to Sedum plants has not been effectively quantified. This study is the first meta-analysis quantifying performances of Sedum relative to non-Sedum plants on extensive green roofs. Using a systematic review and meta-analysis, I found that 90 studies evaluate plant survival, roof cooling, and stormwater management of extensive green roofs, but only about half of the studies compare Sedum to non- Sedum performance. The majority of studies focuses on survival, only a small number of studies quantified roof cooling and stormwater management performance.

In studies that measured survival, I found support for my hypothesis that Sedum survives better than non-Sedum plants on extensive green roofs. Harsh environmental conditions found on rooftops present challenges for plant survival. Extensive green roofs have shallow substrate, excess wind, sun exposure, extreme heat, cold, and drought which restrict plant selection to species capable of tolerating these extreme conditions (Durhman et al., 2006; Getter and Rowe, 2006). Water availability is a limiting factor on green roofs, thus drought-tolerant mechanisms are essential for survival on extensive green roofs. Sedum have physiological and morphological advantages over non-Sedum plants. Sedum are CAM plants and are drought tolerant. CAM plants fix CO2 in the dark for later use in photosynthesis. They limit their water loss due to transpiration by opening their stomata to uptake CO2 at night. (Ting, 1985). Sedum are also able to store extra water in their leaves and shoots and relocate water to vital plant tissues (VanWoert et al., 2005). They extract water from growing media even when there is no rainfall for long periods of time (Graceson et al., 2014). After more than 28 days of drought, many Sedum spp. are able to fully recover after watering (Durhman et al., 2006; Bousselot et al., 2011). Sedum are low growing

14 plants that exhibit fast establishment rate, thus providing high coverage on extensive green roofs (Rowe et al., 2012). These physiological and morphological characteristics allow Sedum survival without supplemental irrigation and provide high coverage on green roofs. Contrary to Sedum species, non-CAM (non-Sedum) plants die unless they receive frequent supplemental irrigation as frequent as every other day (Durhman et el., 2006). Under water stress conditions, Sedum accumulates greater biomass than other non-Sedum species (Durhman et al., 2006).

Some non-Sedum plants evaluated in this meta-analysis survived better than Sedum plants. For instance, at the end of the seven-year long study by Rowe and colleagues (2012), the non-Sedum plant, Phedimus spurius, was the dominant species in 7.5 cm soil depth. Phedimus spurius was formerly known as Sedum spurium. To account for taxonomic changes, all non-Sedum species should be individually examined and regrouped in future analysis. In the study by Bousselot and colleagues (2010), non-Sedum plants, Antennaria parvifolia, Bouteloua gracilis, Delosperma cooperi, Eriogonum umbellatum, and Opuntia fragilis had higher average plant cover compared to Sedum lanceolatum. B. gracillis and D. cooperi had the highest plant cover compared to the other plants. D. cooperi are commonly known as Hardy Iceplant which are CAM plants like Sedum. These observations of non-Sedum plants outperforming Sedum plants are driven by non- Sedum species functionally similar to Sedum. For future analysis, a comparison among different plant functional groups can be a more accurate measure of plant survivability on extensive green roofs.

Very few studies compare Sedum versus non-Sedum roof cooling abilities (n = 6) and these studies are heterogenous in what they measured (e.g. leaf area index, substrate temperature, heat flux, and energy consumption). Meta-analysis results do not support my hypothesis that non- Sedum plants provide better cooling than Sedum plants. Plants can cool roofs in two ways: plant canopy shading the roof surfaces and evapotranspiration. Additionally, plant physiology and morphology characteristics such as leaf area index (LAI), stomatal resistance, height, coverage, and albedo can attribute to roof cooling performance (Eksi, 2017). Plants with higher transpiration rates are expected to be more efficient at cooling. Plant coverage of the substrate surface may also be important (Yaghoobian and Srebric, 2015). Shade provided by higher LAI will influence the overall albedo of the roof and decrease solar radiation that reaches the substrate surface (Yaghoobian and Srebric, 2015). In this way, broadleaved plants with high

15 transpiration rates may provide better roof cooling services than CAM plants like Sedum (Blanusa et al., 2013). For example, Arachis pintoi are broadleaved groundcover herbs that grow quickly to form high cover on roof tops. These non-Sedum plants are often C3 plants and offer evapotranspirational cooling in the daytime, significantly cooling the A. pintoi surface compared to Sedum surface (Jim, 2014a). Other studies that measure energy consumption find a different trend where non-Sedum plants perform worse than Sedum plants. Non-Sedum plants are grown on larger volumes of substrate compared to Sedum substrate, this acts as a heat sink and continues to transfer heat into the building during cooler times of the day (Eksi, 2017; Jim, 2014b). Energy consumption is increased for non-Sedum roofs, but they are a result of different substrate depth instead of vegetation type. Given the heterogeneous methods quantifying roof cooling, the effects of Sedum versus non-Sedum roof cooling are ambiguous.

The main goals of stormwater management are to reduce stormwater runoff and maximize stormwater retention (VanWoert et al., 2005). According to studies that evaluate stormwater management on extensive green roofs, non-Sedum plants do not outperform Sedum plants. The amount of water lost could be a function of at least three separate properties of the system: uptake and transpiration by plants, shading by plants that might reduce the rate of evaporation from the soil surface, and greater holding capacity of substrates containing plant roots (Wolf and Lunholm, 2008). All three functions benefit from different physiological or morphological characteristics of plants. Studies that measure runoff show Sedum roofs produced more runoff than non-Sedum roofs (Monterusso et al., 2004; Nagase and Dunnet, 2012; Mickovski et al., 2013). In terms of stormwater capture, Sedum may not transpire water from green roofs fast enough between rainfalls for it to contribute substantially to stormwater retention (Starry et al., 2014). Sedum plants allow moisture to retain in substrate because they have low water requirements and they grow close to the substrate surface, thereby reducing evaporation form the soil surface (Durhman et al., 2006). Herbaceous non-Sedum plants transpire more water than Sedum, which will create more space for water capture in subsequent rain events (MacIvor and Lundholm, 2011). Due to the complexity of stormwater management, the effects of Sedum versus non-Sedum are unclear.

2.4.1 Opportunities for future research Research in green roofs is increasing, there is growing interest in trying to plant regionally native species in green roof practices (Lundholm and Walker, 2018). This meta-analysis shows that Sedum survives better than non-Sedum on extensive green roofs. The extreme environmental conditions are advantageous for drought-tolerant species like Sedum to thrive and provide high coverage. This is important as plant cover is linked to a variety of green roof functions such as roof cooling and stormwater management (MacIvor et al., 2016). Identifying plant species sharing one or more traits (i.e. trait-based research) is a good direction for future research. However, when evaluating green roof functioning, there is a lack of control for biomass and cover. Current green roof research also lacks consistent metrics and methodologies when measuring green roof functioning. Thus, roof cooling and stormwater management of Sedum relative to non-Sedum plants on extensive green roofs are unclear. Researchers and industries should work together to form consistent metrics when quantifying green roof functioning. For example, when measuring roof cooling, temperature should be measured in standardized soil depth or roof height. Furthermore, directly measuring stormwater runoff and evapotranspiration could be beneficial to untangle the complex relationship between plant species and stormwater management.

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Figures

Figure 2.1. Map of green roof studies evaluating plant survival, roof cooling functioning, and stormwater management performances. Green points represent the area of study, their size indicates number of studies.

Study, First author, Soil depth, Measure Mean [95% CI]

118,Graceson,15,Biomass 1.08 [ 0.36, 1.80] 171,Krawczyk,10,Biomass 0.88 [−0.26, 2.02] 144,Eksi,10,Biomass accumulation 2.62 [ 0.44, 4.81] 14,Durhman,7.5,Biomass accumulation 0.43 [−0.38, 1.24] 30,Getter,8,Cover 0.28 [−0.03, 0.59] 30,Getter,12,Cover 0.28 [−0.03, 0.60] 52,Rowe,2.5,Cover 0.29 [−0.09, 0.68] 52,Rowe,5,Cover −0.91 [−1.30, −0.52] 52,Rowe,7.5,Cover −5.45 [−6.12, −4.77] 147,MacIvor,10,Cover 1.99 [ 1.07, 2.91] 76,MacIvor,10,Cover 2.05 [ 1.45, 2.66] 77,Mickovski,7.5,Cover 4.32 [ 2.06, 6.58] 84,Vinson,5.08,Cover 4.11 [ 2.28, 5.94] 84,Vinson,5.08,Cover 2.51 [ 0.96, 4.06] 124,Bevilacqua,NA,Cover −0.19 [−1.79, 1.42] 40,Bousselot,10,Cover −6.31 [−9.34, −3.28] 88,Dvorak,8.9,Cover 3.33 [ 1.26, 5.40] 149,Ferrante,10,Cover −1.82 [−3.37, −0.27] 147,MacIvor,10,Vegetative structure −1.01 [−1.81, −0.21] 7,Monterusso,10,Survival 7.47 [ 6.42, 8.52] 13,Rowe,10,Survival 8.48 [ 6.91, 10.06] 13,Rowe,10,Survival 5.39 [ 3.96, 6.81] 18,Durhman,2.5,Survival 0.05 [−0.48, 0.59] 18,Durhman,5,Survival 0.12 [−0.42, 0.65] 18,Durhman,7.5,Survival 0.24 [−0.29, 0.78] 33,Nagase,5.7,Survival 1.19 [ 0.67, 1.72] 37,Thuring,3,Survival 0.91 [ 0.14, 1.68] 37,Thuring,6,Survival 0.74 [−0.01, 1.50] 37,Thuring,12,Survival 1.10 [ 0.31, 1.88] 49,Price,10.2,Survival 1.27 [ 0.75, 1.79] 50,Schroll,12.7,Survival 0.54 [−0.07, 1.15] 51,Whittinghill,8.9,Survival −1.20 [−1.64, −0.75] 56,Farrell,16,Survival 1.58 [ 1.21, 1.95] 80,Dvorak,11.4,Survival −0.30 [−1.95, 1.34] 80,Dvorak,11.4,Survival −1.13 [−2.49, 0.23] 1,Boivin,15,Winter survival damage 1.21 [−1.04, 3.47] 1,Boivin,5,Winter survival damage −0.21 [−2.36, 1.94] 1,Boivin,10,Winter survival damage 0.64 [−1.54, 2.81] 1,Boivin,15,Winter survival damage 0.48 [−1.69, 2.64]

RE Model 0.95 [ 0.13, 1.77]

Non-Sedum Sedum

−10 −5 0 5 10 15 Standardized Mean Difference

Figure 2.2. Mean effect sizes (Hedges’ g) of Sedum relative to non-Sedum survival. The study number represents a unique identifier from the list of studies that were systematically reviewed. The first author’s last name and soil depth (cm) used in the study was listed. The measure is the estimate of survival used in that study. Error bars represent 95% confidence intervals and bars not overlapping zero (dashed line) are considered significant.

22 23

Study, First author, Soil depth, Measure Mean [95% CI]

93,Jim,5,Energy consumption (khW) −0.37 [ −1.51, 0.77]

99,Jim,5,Substrate temperature 2.16 [ 0.28, 4.04]

120,Jim,5,Daily electricity energy consumption (kW) −0.74 [ −2.40, 0.91]

147,MacIvor,10,Average147,MacIvor,10,Avergage TemperatureTemperature −0.43 [ −1.20, 0.33]

166,Monteiroa,10,LAI (m^2/m^2) −10.55 [−15.03, −6.06]

166,Monteiroa,10,LAI (m^2/m^2) −7.39 [−10.51, −4.27]

177,Eksi,5, 20,Heat flux (Wm^−2) 0.81 [ −0.36, 1.99]

RE Model −2.03 [ −5.19, 1.14]

Non-Sedum Sedum −20 −15 −10 −5 0 5 Standardized Mean Difference

Figure 2.3. Mean effect sizes (Hedges’ g) of Sedum relative to non-Sedum cooling performance. The study number represents a unique identifier from the list of studies that were systematically reviewed. The first author’s last name and soil depth (cm) used in the study was listed. The measure is the estimate of survival used in that study. Error bars represent 95% confidence intervals and bars not overlapping zero (dashed line) are considered significant.

Study, First author, Soil depth, Measure Mean [95% CI]

164,Soulis,8,Runoff (%) −0.11 [−0.94, 0.73] 164,Soulis,16,Runoff (%) 0.32 [−0.52, 1.16] 5,Monterusso,10,Runoff (L) 1.23 [ 0.72, 1.73] 54,Nagase,5,Runoff (mL) 2.01 [ 1.13, 2.88] 72,Graceson,10,Runoff depth (mm) 0.11 [−0.37, 0.59] 77,Mickovski,7.5,Surface runoff (mL) 3.15 [ 1.99, 4.31] 176,Hill,10,Volumetric runoff coefficient (Cvol) 0.53 [−0.28, 1.35] 48,Aitkenhead−Peterson,2,Moisture loss (%) −0.17 [−1.56, 1.22] 48,Aitkenhead−Peterson,4,Moisture loss (%) −0.13 [−1.52, 1.26] 47,Buccola,5,Water loss (%) −0.24 [−1.63, 1.15] 47,Buccola,14,Water loss (%) −0.53 [−2.15, 1.09] 105,Volder,8.9,Water loss (%) 1.80 [ 1.17, 2.43] 130,Whittinghill,10.5,Water loss (%) −4.90 [−7.56, −2.25] 22,Wolf,7,Water loss (g) −0.63 [−0.87, −0.39] 77,Mickovski,7.5,Basal drainage (mL) 0.52 [−0.29, 1.33] 118,Graceson,15,Water holding capacity (%v/v) 0.20 [−0.50, 0.89]

RE Model 0.38 [−0.23, 1.00]

Non-Sedum Sedum −10 −5 0 5 Standardized Mean Difference

Figure 2.4. Mean effect sizes (Hedges’ g) of Sedum relative to non-Sedum stormwater management functioning. The study number represents a unique identifier from the list of studies that were systematically reviewed. The first author’s last name and soil depth (cm) used in the study was listed. The measure is the estimate of survival used in that study. Error bars represent 95% confidence intervals and bars not overlapping zero (dashed line) are considered significant.

Appendix

Appendix 2.1. List of papers used in the meta-analysis.

Ahmed, S., S. Buckley, A. E. Stratton, F. Asefaha, C. Butler, M. Reynolds, and C. Orians. 2017.

Sedum groundcover variably enhances performance and phenolic concentrations of

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matter content on the development of green roof vegetation: A six-year experiment.

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Bengtsson, L., L. Grahn, and J. Olsson. 2005. Hydrological function of a thin extensive green

roof in southern Sweden. Hydrology Research 36:259–268.

Berndtsson, J., T. Emilsson, and L. Bengtsson. 2006. The influence of extensive vegetated roofs

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Brunetti, G., J. Šimůnek, and P. Piro. 2016. A Comprehensive Analysis of the Variably Saturated

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Butler, C., and C. M. Orians. 2011. Sedum cools soil and can improve neighboring plant

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Durhman, A. K., D. B. Rowe, and C. L. Rugh. 2006. Effect of watering regimen on chlorophyll

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Durhman, A. K., D. B. Rowe, and C. L. Rugh. 2007. Effect of substrate depth on initial growth,

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foamed glass on plant growth and water retention. Urban Forestry & Urban Greening

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33:265–277.

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conventional and controlled release fertiliser on nutrient runoff from various vegetated roof

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hot and dry climates: Interacting effects of plant water use, succulence and substrate.

Ecological Engineering 49:270–276.

Farrell, C., C. Szota, N. S. G. Williams, and S. K. Arndt. 2013. High water users can be drought

tolerant: using physiological traits for green roof plant selection. Plant and Soil 372:177–

193.

Feng, C., Q. Meng, and Y. Zhang. 2010. Theoretical and experimental analysis of the energy

balance of extensive green roofs. Energy and Buildings 42:959–965.

Fernandez-Cañero, R., T. Emilsson, C. Fernandez-Barba, and M. Á. Herrera Machuca. 2013.

Green roof systems: A study of public attitudes and preferences in southern Spain. Journal

of Environmental Management 128:106–115.

Ferrante, P., M. La Gennusa, G. Peri, G. Rizzo, and G. Scaccianoce. 2016. Vegetation growth

parameters and leaf temperature: Experimental results from a six plots green roofs’ system.

Energy 115:1723–1732.

Franzaring, J., L. Steffan, W. Ansel, R. Walker, and A. Fangmeier. 2016. Water retention, wash-

out, substrate and surface temperatures of extensive green roof mesocosms—Results from

a two-year study in SW-Germany. Ecological Engineering 94:503–515.

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Chapter 3 Improving extensive green roof functioning using invasion theory as a management tool Improving extensive green roof functioning using invasion theory as a management tool 3.1 Introduction Green roofs are constructed ecosystems engineered to produce valuable ecosystem services, featuring the interaction of living and non-living components (Lundholm, 2015). The majority of green roofs consist of Sedum-based communities which are non-native to where they are widely deployed, making green roofs novel ecosystems. Green roofs represent a new state of species combination providing important ecosystem services in cities. These ecosystem services include thermoregulation (Sookhan et al., 2018), stormwater management (Voyde et al., 2010), enhance habitat provisioning (Williams et al., 2014), improve aesthetic appeal of buildings (Jungels et al., 2013; Loder, 2014), noise reduction, and mitigate air pollution (Vijayaraghavan, 2016). Novel ecosystems are often viewed as irreversible where they are so profoundly transformed that no effort will be able to return them to their pre-disturbed, historical state. While this might be true in many cases (Yates et al., 2000a; Yates et al., 2000b), the existence of novel ecosystems should not prevent management actions that can alter them to better support native species and ecosystem services (Murcia et al., 2014). Here, I propose a novel ecosystem management method to increase native biodiversity and species coexistence on novel ecosystems.

A positive link between plant biodiversity and ecosystem functioning is well established in ecological literature (Hooper et al., 2005). Although empirical research linking plant biodiversity with green roof performance is limited (Cook-Patton and Bauerle, 2012), utilizing different life forms in mixed communities has been shown to improve the performance of extensive green roofs (Lundholm et al., 2010; Lundholm et al., 2015). Two mechanisms explain this positive biodiversity-ecosystem functioning relationship, the sampling effect and complementarity effect. Sampling effect is where mixtures perform better compared to monocultures because they probably contain a highly productive species (Huston, 1997; Loreau and Hector, 2001). Complementarity is where species coexist through resource partitioning or positive interactions, leading to increased total resource use and thus perform better (Tilman et al., 1997; Loreau and

40 41

Hector, 2001; Petchey, 2003). Increasing plant diversity on green roofs should enhance green roof functioning.

The study of invasion biology has been a quest to understand the causal mechanisms that explain the invasiveness of non-native species as well as the invasibility of ecosystems (Drake et al., 1989; Elton, 1958; Inderjit et al., 2005). Many theories and hypotheses have been proposed to explain mechanisms driving invasions. According to the classic invasion framework by Blackburn and colleagues (2011), the non-native species must overcome a series of biotic and abiotic barriers to complete the pathway from introduced species to problematic invasive species. Among the popular theoretical descriptions explaining the mechanisms overcoming these hurdles, I will highlight two commonly referenced hypotheses which will be applied to manage extensive green roof ecosystems.

First is the “Resource Fluctuation Hypothesis”, which states that availability of resources in an ecosystem fluctuates and plant communities become more susceptible to invasion whenever there is an increase of unused resources (Davis et al., 2000). Resources in a community can increase when resource uptake decreases or when resource supply increases. Either disturbance can open space in the community and release resources, or resource supply can arise (e.g. irrigation increases water supply), resulting in more resources for invaders. The second commonly referenced hypothesis is the “Propagule Pressure Hypothesis”, which predicts that species introduced in greater numbers will be more likely to establish and spread (Lockwood et al., 2005; Colautti et al., 2006). These two mechanisms of invasion, amongst others, supply the framework of managing novel ecosystems like green roofs to increase native biodiversity.

Here, native species that managers might wish to invade a novel ecosystem are coined ‘native invaders’. For native species to become native invaders, they must be able to establish, spread, proliferate, and persist in the novel ecosystem. Native invaders, just like non-native invaders, must overcome a series of abiotic and biotic barriers that prevent successful establishment and spread (Blackburn et al., 2011). Unlike non-native invaders, geography is not a barrier for native invaders since they are native to this region, however, there could be other barriers like unfavorable environmental conditions or local dispersal barriers (like building height for green roof communities) associated with novel ecosystems that exclude them. Using this invasion

42 theory management framework, I will help native species overcome hurdles to successfully ‘invade’ extensive green roof ecosystems.

Sedum spread and cover the substrate in a short period of time, resulting in resilient communities able to inhibit weeds (Getter and Rowe, 2008). While that improves aesthetics and reduce potential erosion, it is difficult for native species to establish within the Sedum community. Using principles of invasion biology, I will increase the invasibility of the Sedum community, thereby increasing establishment success of native invaders on green roof ecosystems. Invasibility is defined as the susceptibility of a community to the colonization and establishment of individuals not currently part of the community (Davis et al., 2005). According to the “Resource Fluctuation Hypothesis”, a plant community’s susceptibility to invasion increases as the amount of unused resources increases (Davis et al., 2000). By disturbance and irrigation, the availability of resources increases in the ecosystem and so should native invader success. If the native invader is able to establish, meaning that they will form self-sustaining populations and become incorporated within the resident flora (Richardson et al., 2000), then native species diversity is increased on green roof ecosystems. Further, according to “Propagule Pressure Hypothesis”, greater introduction efforts of native species should translate into increased establishment success.

Coexistence of native invaders and Sedum species will increase overall plant diversity on green roof ecosystems. Combination of Sedum with non-Sedum plantings had been successful, where Sedum may act as nurse plants, facilitating the survival of non-Sedum plants (Butler et al., 2011). I hypothesize that (a) disturbance and resource addition via irrigation will increase native invader success, and (b) green roof modules with higher native invader diversity will perform better, in terms of green roof cooling and stormwater management, than green roof modules with low native invader diversity.

3.2 Materials and methods 3.2.1 Experimental set up 3.2.1.1 Site set up Eight sites were set up in mid-May 2018 across the University of Toronto Scarborough campus (Fig 3.A1). There were six roof sites and two ground-level control sites (Table 3.1). In a fully

43 factorial experiment, each site consisted of 44 modules in a randomized pattern. The experimental modules were surrounded by non-experimental modules to mitigate edge effect. Modules are the Hydropack model from Vegetal i.D. Each module comes in a black tray 100% HDPE from bottle caps, the dimensions are 60 cm by 40 cm, soil depth is 10 cm, and the site is approximately 4.4 m by 3.8 m (Fig 3.A2). Each green roof module (experimental unit) consisted of different percentage of Sedum acre, Sedum album, and Sedum spurium cover. Each green roof module was assigned a different treatment: (a) ‘Disturbance’ (two levels: disturbed and not disturbed), (b) ‘irrigation’ (two levels: watered and not watered), and (c) ‘Diversity’ (11 levels: one Sedum only control, one soil only control, five native invader monocultures grown with Sedum, three low native invader diversity mixtures grown with Sedum, and one high native invader diversity mixture grown with Sedum; refer to Table 3.2 for details on different diversity levels).

3.2.1.2 Disturbance treatment Disturbance treatment involved clearing Sedum vegetation. Green roof modules assigned the disturbance treatment had vegetation removed at each corner and the centre of the module. A cylinder of 9.5 cm in diameter was used to standardize the amount of vegetation removed. Vegetation within the five areas where the cylinder covers were removed from the module. Approximately 15% of vegetation was removed per module in this treatment.

3.2.1.3 Irrigation treatment Irrigation treatments were applied weekly where treated green roof modules were watered 4 L each. All modules received natural precipitation. In the 2018 field season, all eight sites were watered on one day each week. In the 2019 field season, irrigation treatments were on two consecutive days each week. Irrigation order of the sites was randomized each week.

3.2.1.4 Diversity treatment Five native invaders previously shown to survive on green roofs were selected for this experiment (Monterusso et al., 2005; Rowe et al., 2006). Allium cernuum, Festuca rubra, Achillea millefolium, Rudbeckia hirta, and Symphyotrichum novae-angliae were combined in varying diversity levels and seeded into the modules with Sedum already established (Table 3.2).

The green roof modules were seeded on June 22, 2018. The modules were watered with 2 L of water before seeding. The same modules were seeded again on May 7, 2019.

3.2.2 Data collection 3.2.2.1 Plant cover Plant cover was measure by using a grid to reduce observer bias. The grid was made to cover one green roof module and each square inside the grid was 10 cm by 10 cm. The plant cover for each species of Sedum and native invader was estimated by counting the number of squares each species covered in the grid. The number of squares was then converted to percent plant cover. There were two observers that recorded plant cover six times in the 2018 field season. In a trial where the two observers recorded plant cover of the same plot on the same day, there was a statistically significant difference between the plant cover data recorded (t = 3.788, df = 39, p < 0.001). A correction factor was applied. Cover was measured four times in the 2019 field season by a single observer.

3.2.2.2 Temperature Thermochron temperature logging ibuttons were set to record temperature of modules every four hours, started on July 3, 2018 at 12:00 pm. They were taken out to reset to log winter data on October 16 and set up to log on October 19 at 12:00 pm. HOBO temperature loggers were set to record temperature at the site every 4 hours starting on August 2, 2018. Both temperature loggers recorded temperature data until the end of the 2019 field season (October 2019). Air temperature and precipitation data was also obtained from the UTSC weather station.

Due to a limited number of ibutton temperature loggers available, on July 3, 2018, 13 ibuttons were randomly placed per site. Although this captures all treatment combinations, each combination does not have enough replicate for statistical power. On October 19, 2018, the ibuttons were placed in disturbance and watering treatment combinations of all soil control, high diversity, and Sedum only modules.

3.2.2.3 Soil moisture In the 2018 field season, soil moisture was measured with a moisture meter type HH2 by Delta-T Devices Ltd. Soil moisture was measured the day before and after the irrigation event. Additional

45 days of soil moisture was measured (up to four times a week including before and after the irrigation event) to better understand the fluctuation of soil moisture levels. In 2019 field season, soil moisture was measured the day before, immediately after watering, one hour after watering, the day after watering, and two days after watering.

3.2.3 Statistical analysis All statistical analyses were conducted in R version 3.6.1 (R Core Team, 2019).

3.2.3.1 Native invader success Plant cover data was analyzed using linear mixed-effect models to see how disturbance and irrigation treatment affected plant cover and native invader cover. ‘Disturbance’ (with two levels: disturbed and not disturbed) and ‘Irrigation’ (with two levels: watered and not watered) were fixed effects, ‘site’ was the random effect, and native invader plant cover was the dependent variable (Native invader plant cover ~ disturbance * water + (1|site)). Soil only control modules were removed from the analysis. Statistical significance was determined by Type III Analysis of Variance with Satterthwaite’s method.

3.2.3.2 Green roof functioning 3.2.3.2.1 Green roof cooling Temperature data were analyzed using linear mixed-effect models to see how diversity, disturbance, and irrigation treatment affected temperatures from summer 2019. ‘Species’ (with 3 levels: Sedum only, High diversity, Soil only control), ‘Disturbance’ (with two levels: disturbed and not disturbed) and ‘Irrigation’ (with two levels: watered and not watered) were fixed effects, ‘site’ was the random effect, and temperature was the dependent variable (Temperature ~ disturbance * water * species + (1|site)). Statistical significance was determined by Type III Analysis of Variance with Satterthwaite’s method.

3.2.3.2.2 Stormwater management Soil moisture data from the 2019 field season were analyzed using linear mixed-effect model to see how diversity, disturbance, and irrigation treatment affected moisture retention. Species richness was calculated for each module and categorized into soil only control (richness = 0), low diversity (richness = 3), medium diversity (richness = 4, 5), and high diversity (richness = 6,

7). Moisture retention was defined as soil moisture one hour after watering subtracted by soil moisture before watering. ‘Species’ (with 4 levels: Soil only control, Low diversity, Medium diversity, and High diversity), ‘Disturbance’ (with two levels: disturbed and not disturbed) and ‘Irrigation’ (with two levels: watered and not watered) were fixed effects, ‘site’ was the random effect, and native invader plant cover was the dependent variable (Moisture retention ~ disturbance * water * species + (1|site)). Statistical significance was determined by Type III Analysis of Variance with Satterthwaite’s method.

3.3 Results 3.3.1 Native invader success Disturbance treatments significantly altered plant cover of green roof modules (F= 66.35; df = 2; p < 0.001) (Fig 3.A3). In 2018, disturbance (t = 3.543; df = 308; p < 0.001) and irrigation (t = 2.198; df = 308; p = 0.029) significantly affected plant cover (Fig. 3.1a). There is no significant interaction between disturbance and irrigation. Irrigation significantly affected native invader plant cover (t = 3.326; df = 308; p < 0.001). Disturbance had no significant effect on native invader plant cover. There was no significant interaction between disturbance and irrigation (Fig. 3.1b). In 2019, disturbance (t = 2.184; df = 308; p = 0.030) and irrigation (t = 3.795; df = 308; p < 0.001) significantly affected plant cover (Fig. 3.1c). There was no significant interaction between disturbance and irrigation. Irrigation significantly affected native invader plant cover (t = 5.862; df = 308, p < 0.001). Disturbance had no significant effect on native invader plant cover. There was no significant interaction between disturbance and irrigation (Fig. 3.1d). Rudbeckia hirta flowered at all eight sites and had the highest percentage plant cover compared to other native invaders (Fig. 3.A4).

3.3.2 Green roof functioning 3.3.2.1 Green roof cooling Irrigation significantly affected mean summer temperature, maximum summer temperature, and diurnal temperature range (Fig. 3.2). Irrigated modules had significantly lower mean temperature (t = -3.890; df = 151; p < 0.001) and soil only control modules had higher mean temperatures (t = 2.466; df = 152; p = 0.015) than vegetated modules (Sedum only and high diversity modules). Irrigated modules had significantly lower maximum temperature (t = -2.158; df = 151; p = 0.033)

47 and soil only control modules had higher temperatures (t = 2.876; df = 152; p = 0.004) than vegetated modules. Irrigated modules had significantly less diurnal temperature range (t = - 2.090; df = 150; p = 0.038) and soil only control modules had larger diurnal temperature range (t = 2.794; df = 152; p = 0.006) than vegetated modules. There was no significant effect of species, disturbance, and irrigation on minimum summer temperature.

Average air temperature was calculated using the UTSC weather station data. The average temperature of summer 2018 (June to August) was about 0.74 degrees Celsius warmer than the average temperature of summer 2019.

3.3.2.2 Stormwater management Irrigation significantly affected moisture retention (t = 3.193; df = 329; p = 0.002). Diversity and disturbance showed no significant effect on moisture retention (Fig. 3.3).

3.4 Discussion 3.4.1 Native invader success Extensive green roofs are often planted with Sedum plants because they are extremely drought tolerant and able to spread and cover the substrate in a short period of time, resulting in resilient communities (Snodgrass and Snodgrass, 2006; Getter and Rowe, 2008). This makes it difficult for non-Sedum species to establish within the Sedum community and increase native diversity on extensive green roof ecosystems. According to current understandings of invasion biology, native invaders must overcome a series of biotic and abiotic hurdles to establish, proliferate, spread, and persist in Sedum-based green roof ecosystems (Blackburn et al., 2011). Increasing invasibility of Sedum communities can promote native invader success on green roof ecosystems.

According to the “Resource Fluctuation Hypothesis”, I hypothesized that disturbance and irrigation will increase native invader success (Davis et al., 2000). I found that irrigation significantly increased native invader cover (Fig. 3.1). This result is consistent with the finding of Rowe and colleagues (2006) that survival of natives was dependent on water availability. However, disturbance did not significantly increase native invader success. Disturbance treatment significantly decreased overall plant cover in 2018 as expected (Fig. 3.1). In both

48 years, disturbed and undisturbed modules had similar native invader cover, suggesting that there is no effect of disturbance on native invader success. Disturbance opens up space in the community and releases resources which may not be important to native invader success if native invaders and Sedum plants are competing for different above and below ground resources. In other words, native invaders and Sedum are able to coexist due to niche partitioning (MacArthur, 1958), regardless of disturbance. Sedum are ground covering plants with shallow and fibrous root systems that do not make use of the resources in deeper substrate profiles (Nektarios et al., 2015). Whereas the native invaders are tall herbaceous plants with deeper roots than Sedum that acquire a different niche than Sedum. Therefore, niche partitioning facilitates the coexistence of Sedum and native invaders regardless of disturbance.

Overall, native invaders had low establishment success. This is expected as extensive green roofs are extremely harsh environments for plants. Nevertheless, the herbaceous native invaders chosen in this study have been shown to survive on green roofs (Monterusso et al., 2005; Rowe et al., 2006). Previous research has also shown Sedum may act as nurse plants for non-Sedum plants during summer water deficits by reducing peak soil temperature (Butler et al., 2011). The difference between 2018 and 2019 native invader cover may be attributed to year-to-year climate variation and seeding time. The higher average temperature in summer 2018 than summer 2019 combined with the later seeding time in 2018 could explain why the 2018 native invader cover was significantly lower than the 2019 native invader cover (Fig 3.1). I seeded in late spring 2018, when it was already too hot and dry for seedlings to establish. Young plants can wilt and become stressed especially in a water-limited environment (Snodgrass and Snodgrass, 2006). Hence, the low germination and establishment success in 2018, shallow substrate, and harsh winter requires reseeding the modules. By reseeding in early spring 2019, there was higher native invader establishment success in 2019 than 2018.

3.4.2 Green roof functioning A positive link between plant biodiversity and ecosystem functions is most often reported in ecological literature (Hooper et al., 2005). Utilizing different life forms in mixed communities has also been shown to improve the performance of extensive green roofs (Lundholm et al., 2010; Lundholm et al., 2015). I hypothesized that green roof modules with higher native invader diversity will perform better, in terms of green roof cooling and stormwater management, than

49 green roof modules with low native invader diversity. In contrast, my results show that plant diversity did not affect roof cooling and stormwater management (Fig 3.2; Fig 3.3). Sedum has an advantage over native invaders as it had already established in the modules prior to the experiment, the germination and immature stage of native invaders is a susceptible life stage on harsh green roof environments. High diversity modules perform slightly worse than Sedum only modules in terms of roof cooling. Native invaders are potentially drawing resources from Sedum, causing Sedum to perform worse. Sedum are unlikely to offer evapotranspirative cooling under stressful conditions (VanWoert et al., 2005). In addition, native invaders competing for resources may be exerting stress on the Sedum community, causing Sedum to go dormant earlier. Dormant Sedum will absorb heat as opposed to keeping the roof cool. Native invaders may have a negative impact on Sedum, where competition decrease evapotranspiration rates and cause colouration of Sedum, thereby reduced overall roof cooling performance. This is consistent with literature where competitive relationships among species can result in negative diversity- ecosystem function relationships (Maynard et al., 2017).

Plant diversity did not affect stormwater management. Diversity effects require co-existence mechanisms because the magnitude of complementarity increases as experiments run longer (Cardinale et al., 2007). For instance, Lundholm and colleagues (2015) found that the positive relationship between planted species richness and ecosystem services grew stronger over the four-year experiment. Ecosystem service is better explained by the dominant plant species’ performance than diversity (Veen et al., 2018). Soil only controls have higher evaporation from the surface compared to vegetated modules (Wolf and Lundholm, 2008; MacIvor and Lundholm, 2011). Sedum may not transpire water from green roofs fast enough between rainfalls for it to contribute substantially to stormwater retention (Starry et al., 2014). Native invaders are species that can evapotranspirate more water than Sedum in drought conditions, which will create more space for water capture in subsequent rain events (MacIvor and Lundholm, 2011). However, the trade-off is that they could exacerbate impending drought conditions on the roof, making plant survival difficult. My results show that the addition of native invaders to Sedum communities do lose moisture at a slightly faster rate than Sedum only communities, meaning diversity could potentially contribute to stormwater retention as the experiment runs longer. It is worth noting; however, moisture retention is the convoluted result of stormwater runoff and

50 evapotranspiration. A direct measure of evapotranspiration or water runoff may be a better method to evaluate stormwater retention.

There were no strong evidence of native invaders improving roof cooling and stormwater management in this experiment. Nevertheless, there may be other ecosystem services that native invaders can provide that were not quantified in this study. The native invaders, Festuca ruba and Rudbeckia hirta, flowered in the 2019 field season, which improved aesthetics (Fig 3.A5) and potentially habitat provisioning for native arthropod species. Further research may be done to quantify other ecosystem services native invaders may offer.

3.4.3 Conclusion This is a two-part study of the cause and consequence of diversity on extensive green roof ecosystems. I use invasion theory as a management tool to increase native species diversity on extensive green roofs and then evaluate diversity effects on green roof functioning.

The results show low native invader success, indicating that invasion theory may not be the best management approach for harsh environments such as extensive green roofs. Irrigation did facilitate native invader plant growth, whereas disturbance had no effect on native invader success. Sedum and native invaders are able to coexist (Butler et al., 2011) due to niche partitioning regardless of disturbance.

Native invader diversity did not enhance green roof functioning in terms of roof cooling and stormwater management. The magnitude of complementarity increases as experiments run longer, thus diversity effects are not apparent in this two-year field experiment (Cardinale et al., 2007). Nevertheless, vegetation does provide better roof cooling and reduce stormwater runoff compared to non-vegetated green roof modules. Soil only controls had the highest temperatures and least moisture retention compared to vegetated modules. There are no clear effects of native invaders in this experiment. Sedum community is established and mature, whereas native invader seedlings have low abundance. Functional traits of dominant species can be the major driver for green roof functions as opposed to diversity effects (Xie et al., 2018). Sedum being the dominant plant species may explain why there is no effect of native invader diversity on moisture retention. Furthermore, native invaders may be negatively impacting Sedum, resulting in

51 negative diversity-ecosystem function relationships (Maynard et al., 2017). Native invaders can potentially draw resources from Sedum, causing Sedum to perform worse in roof cooling. In studies that show non-Sedum outperforming Sedum plants, physiological stress of Sedum is not measured (Wolf and Lundholm, 2008). Quantifying physiological stress of Sedum in a competitive relationship with other green roof plants is an area of research that need to be further explored.

This experiment demonstrates a low-cost method to increase native species diversity on extensive green roofs. Although my results do not show strong diversity effects of roof cooling and stormwater management, there may be other ecosystem services, such as improved aesthetics (Fig 3.A5) and habitat provisioning, a diverse green roof ecosystem can provide.

3.4.4 Future directions Using invasion theory as a management tool is a cost-effective way to improve native species diversity on extensive green roofs. The commercially available high cover Sedum communities do not require disturbance to increase native invader success. However, practitioners should consider long-term supplemental irrigation on extensive green roofs to ensure native species survival. This novel approach is also a high-risk method resulting in low establishment success of native invaders. The harsh extensive green roof ecosystem may require alternative methods to ensure native invader establishment success. Studies have shown increasing substrate depth and/or having organic substrate type (MacIvor et al., 2013; Graceson et al., 2014) can increase survival of green roof plants. Future experiments can manipulate the age of the planted native invaders (seeds versus plugs), substrate depth, and substrate type to increase native invader success. With higher native invader cover, there may be stronger diversity effects on green roof functioning than found in this experiment. The effectiveness of this novel management tool can be evaluated in other novel ecosystems such as wetland detention basins, vegetated roadsides, or substrate-rich intensive green roofs.

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