Assessing Forest Tree Communities and Assisted Migration Potential in Toronto’S Urban Forest As a Result of Climate Change

Assessing Forest Tree Communities and Assisted Migration Potential in Toronto’s Urban Forest as a Result of Climate Change

Peter Quincy Ng

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Physical and Environmental Sciences University of Toronto

Assessing Forest Tree Communities and Assisted Migration Potential in Toronto’s Urban Forest under Climate Change

Peter Quincy Ng

Doctor of Philosophy

Department of Physical and Environmental Sciences University of Toronto Scarborough

2018 Abstract

Projected climate change in the Toronto, Ontario, Canada area could greatly alter tree composition within the urban forest. Investigating biological change as a result of climate change is complicated due to the variability among climate model outputs and non-climatic influences on local vegetation. This study approaches the issues of composition change in tree species in Toronto through three interdependent studies. The first study is a comparison of

Global Climate Models (GCMs) available through the Intergovernmental Panel on Climate

Change's Fourth and Fifth Assessment Reports, AR4 and AR5 respectively. Using a performance metric based on how well the GCMs simulate climate relative to observation, GCMs GFDL-

CM3, IPSL-CM5A-LR and MPI-ESM-LR were determined to provide the best Canada-wide coverage for annual average temperature and precipitation changes. The second study correlates a series of biologically-relevant climatic variables to tree distributions of 134 North American trees east of the 100th meridian. The geographical absence or presence of a species was correlated to concurrent climate data, creating a species' climate envelope. Using climate projections from an ensemble of the GCMs GFDL-CM3, IPSL-CM5A-LR and MPI-ESM-LR for the year 2050, a suite of statistical regression and machine-learning techniques were used to predict a likelihood of suitability of trees in the Toronto area based on the derived climate

ii envelopes. While the dominant beech-maple communities are largely projected to stay intact, future climate suitability is likely to shift away from conifers and northern hardwoods into more southerly deciduous trees. The third study evaluates using projected tree suitability whether an assisted migration through the translocation of tree species would be effective in the Toronto area. Of particular interest is the increasing climate suitability that likely will be beneficial to

Ontario’s Species at Risk trees. However, the conservation of Ontario’s Species at Risk trees cannot be based solely upon projections of climate, a vulnerability assessment conducted in this study indicate that current pest and diseases threats coupled with and the loss of genetic diversity are also cause for concern.

iii

Acknowledgments

I would firstly and foremost I would like to thank my thesis supervisor, Dr. William A. Gough whom with utmost care and guidance directed me towards the completion of this doctoral degree. Additionally, I would like to thank to Dr. Jay Malcolm, who has introduced me to the concept of species modelling and to Dr. Adam Fenech who first motivated me to pursue doctoral studies and inspired many of the topics covered in this study. Furthermore, I would like to extend my thanks to Dr. Marney Isaac for her support and inspiration and Dr. Ken Butler and Dr. Mike Doughty, both who have offered me assistance in the implementation of statistical and GIS software. Finally, I would like to give special thanks to Elaine Pick and Shelley Eisner for their exceptional work in helping me complete all the required administrative duties required in this Ph.D.

This work would not be possible without my family and friends, who with their undying patience been with me along this long and arduous journey. To them, thank you for understanding and your support; it is all I have ever asked for. Thank you.

I would like to dedicate this work to my friends and colleagues in Sault Ste. Marie, located in the traditional territory of Baawaating. It is through your generosity that I have been able to apply what I have learned through my studies in order to fulfill my duties as a global citizen working towards the betterment of our collective lands and natural areas.

Table of Contents

Acknowledgments iv

Table of Contents v

List of Tables viii

List of Figures ix

List of Appendices xi

Chapter 1 Introduction 1

1 Introduction 1

1.1 Defining the Problem – Climate Change and its Effects on Tree Species Biodiversity in Toronto 1

1.2 Defining the Study Area – Toronto’s Vegetative Communities 3

1.3 Urban Forests 4

1.4 Climate’s Role in Plant Geography 5

1.5 Climatic Controls on Vegetation 8

1.6 Climate Change and the Global Climate Models 9

1.7 Research Objectives 12

1.7.1 Research Objectives – Study 1 12

1.7.2 Research Objectives – Study 2 13

1.7.3 Research Objectives – Study 3 13

1.8 References 13

Chapter 2 A Comparison between Canadian Observational Data and the AR4 and AR5 Climate Models 19

2.1 Abstract 19

2.2 Introduction 19

2.3 Methods 23

2.3.1 Comparing the GCM to Observational Data 23

2.3.2 Obtaining an Observational Dataset 23

2.3.3 Creating an Ensemble 25

2.4 Results 27

2.5 Discussion 41

2.6 Conclusion 43

2.7 References 43

Chapter 3 Future Distributions of 134 Eastern North American Tree Species in Toronto resulting from Climate Change Scenarios RCP 2.6 and RCP 8.5 for the Year 2050 47

3.1 Abstract 47

3.2 Introduction 47

3.3 Methods 50

3.3.1 Tree Species Distribution Data 50

3.3.2 Study Area 52

3.3.3 Theoretical Assumptions 53

3.3.4 Statistical Assumptions 55

3.3.5 Statistical Models Implemented from BIOMOD2 55

3.3.5 Model Selection for Predicting a Species Likelihood Suitability 58

3.4 Results 60

3.5 Discussion 68

3.5.1 Comparing Statistical Techniques for Species Distribution Modelling 68

3.5.2 Modelling a Projected Reality 69

3.5.3 Realizing a Climate Equilibrium 71

3.6 Conclusions 72

3.7 References 73

Chapter 4 Street Tree Suitability in Toronto’s Urban Forest for Future Climate Change Scenarios and an Assessment of an Assisted Migration of Ontario’s Species at Risk Trees 79

4.1 Abstract 79 vi

4.2 Introduction 79

4.3 Methods 84

4.3.1 Predicting Tree Species Suitability as a result of Projected Climate Change 84

4.3.2 Generating Maps to Visualize Toronto’s Tree Distributions 85

4.4 Results 86

4.5 Discussion 97

4.5.1 Other Non-Climate Considerations 98

4.5.2 Implementing an Assisted Migration 99

4.6 Conclusion 101

4.7 References 102

5.0 Conclusions 106

5.1 Summary of Main Findings 106

5.2 Recommendations for Further Research 112

6.0 References 114

7.0 Appendices 126

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List of Tables

Table 2.1 – Relative Rank of AR4 GCMs for Average Annual Temperature and Precipitation based on an aggregate score…………………………………………………..………………….36

Table 2.2 – Relative Rank of AR5 GCMs for Average Annual Temperature and Precipitation based on an aggregate score…………………………………………………..………………….37

Table 3.1 – Percentage suitability for 134 Eastern North American tree species under climate change outcomes RCP 2.6 and RCP 8.5……………...………………………………………….60

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List of Figures

Figure 1.1 – Location of Toronto Ecodistrict 7E-4………………………………………………3

Figure 1.2 – Chart Visualization of Special Report on Emissions Scenarios (SRES) climate change scenario…………………………………………………………………………...... 11

Figure 2.1 – Map of Canada showing the spatial resolution and NCEP observation points used for comparison of GCMs model output……………………………………………………….....25

Figure 2.2 – Map illustrating the differences between the AR4 Ensemble and the observational dataset (NCEP) for average annual temperature.………………………………………………...27

Figure 2.3 – Map illustrating the differences between the AR5 Ensemble and the observational dataset (NCEP) for average annual temperature …….…………….…...... …..28

Figure 2.4 – AR4 and AR5 GCM Averages for Annual Average Temperature per geographic region………………………………………………….…………….…...... …..29

Figure 2.5 – AR4 and AR5 GCM Averages for Annual Average Temperature per geographic region with outlier models removed…….… ……….…………….…...... …..31

Figure 2.6 – Map illustrating the differences between the AR4 Ensemble and the observational dataset (NCEP) for annual precipitation change………….…………….…...... 32

Figure 2.7 – Map illustrating the differences between the AR6 Ensemble and the observational dataset (NCEP) for annual precipitation change………….…………….…...... 33

Figure 2.8 – AR4 and AR5 GCM Averages for Annual Precipitaive Change per geographic region…….…………………………………...……….…………….…...... …..34

Figure 2.9 – AR4 and AR5 GCM Averages for Annual Precipitaive Change per geographic region with outlier models removed…………………...……...…….…...... …..35

Figure 3.1 – Location of areas selected to represent the Toronto area for species forecasts under climate change……………………………………………………………………………….…..52

Figure 3.2 – Ordination biplot showing the Eigenvalues of First (PC1) and Second (PC2) Principal Components based on tree species 2050 likelihoods for 134 Eastern North American Tree species under RCP 2.6 and RCP 8.5…………………………..……………….……….…..58

Figure 3.3 – Distribution of Likelihood Suitability in Eastern North American Tree Species in Toronto for Current Conditions and Future Climate Change ………………………….….…..66

Figure 4.1 – Toronto’s Most Populous Native Trees on Street Tree Allowances….…….….…..86

Figure 4.2 – Suitability Likelihood of Toronto’s Most Populous Native Trees on Street Tree Allowances under Current Projections, RCP 2.6 and RCP 8.5………………………………… 87 ix

Figure 4.3– The current distribution of Manitoba maple (Acer negundo) on street tree allowances..…………………………………………………………….…………………….…..88

Figure 4.4 – The current distribution of paper birch (Betula papyrifera) on street tree allowances………………………….…………………………………………………………….89

Figure 4.5 – The current istribution of common hackberry (Celtis occidentalis) on street tree allowances…………………………………………………………………………….89

Figure 4.6 – The current distribution of Kentucky coffee tree (Gymnocladus dioicus) on street tree allowances………………………...……………………………………………….90

Figure 4.7 – The current distribution of tulip tree (Liriodendron tulipfera) on street tree allowances………………………………………………………………………………..….90

Figure 4.8 – The current distribution of northern white cedar (Thuja occidentalis) on street tree allowances…………………………………………………………………………….91

Figure 4.9 – Tree suitability likelihood in Toronto under a current climate projection using an average of all tree likelihoods within a 500 m grid cell………………………………………….92

Figure 4.10 – Change in tree suitability likelihood in Toronto from current climate projections under RCP 2.6 using an average of all tree likelihoods within a 500m cell……………….…….93

Figure 4.11 – Tree occurrence likelihood in Toronto under RCP 8.5 using an average of all tree likelihoods within a 500 m grid cell………………………………………………….………….94

Figure 4.12: Future suitability of Ontario’s Species at Risk under current projections, RCP 2.6 and RCP 8.5 l………………………………………...……………………………….………….95

Figure 4.13: Future suitability of non-native candidate species for assisted migration under current projections, RCP 2.6 and RCP 8.5………………………………………………………96

List of Appendices

Appendix 7.1 – BIOMOD2 True Statistic Scores (TSS) for all 134 trees native to Eastern to North America……...…………………………………………………………………………..125

Appendix 7.2 – Percentage suitability likelihood for extant trees in Toronto’s Street Tree Inventory using current projections and concentration pathways RCP 2.6 and RCP 8.5 for the year 2050………………………………………………………………………………………..163

Chapter 1 Introduction 1 Introduction 1.1 Defining the Problem – Climate Change and its Effects on Tree Species Biodiversity in Toronto

Biodiversity is intrinsically linked to climatic conditions (Rapoport, 1982; Parmesan and Yohe, 2003) and thus susceptible to changing climate conditions. This poses important consequences for biodiversity in general, and for Toronto, Ontario, Canada, the focus of this work, in particular. With Canada’s climate projected to be warmer and wetter over the next hundred years, how may the biodiversity of Toronto’s tree species evolve as a result?

Due to Toronto’s location, both as a metropolitan area and a transitional zone between the more northerly conifer forests and southerly deciduous forests, there is potential for climate change to greatly alter Toronto’s urban forest. Warmer temperatures and greater amounts of precipitation are likely to facilitate the northward migration of temperate species from eastern North America while the more northerly tree species are likely to retreat. To assess this, my work analyzes the current tree biodiversity of Toronto’s urban forest and, based on climate projections, how these forested areas may transition in a changing climate. However, there are several important considerations that this work will examine in order to achieve this goal.

An important first consideration is the assessment of how well the global climate models (GCMs), mathematical representations of the climate, perform. How reliable will these models be in projecting a future climate? What methods are available to determine model reliability and in assessing which models work best and where? Will the GCMs stemming from the most recent assessment by the Intergovernmental Panel on Climate Change, the Fifth Assessment Report (AR5), produce more realistic climate projections than its predecessor, the Fourth Assessment Report (AR4)? Which Global Climate Model will provide a reliable assessment of a Canadian climate on a continental scale?

Second, is to address any changes in Toronto’s tree communities as a result of climate change. This work assesses the climate factors that influence changes in vegetative distribution, providing a critical biota-climate linkage. Furthermore, what is the role of climatic variables as a

1 2 driving mechanism for forest tree distribution? It is possible to create climate envelopes for Toronto’s current and potential future tree species to infer climate thresholds based on a tree species’ current distribution? Building on the correlation between current climate and the distributional presence of Eastern North American tree species, one might further ask how will tree distribution change under climate change in the Toronto area?

Finally, once the distributions of Toronto’s current and potential tree distribution are modelled, what are the consequences of climate change on the urban forest? What does this mean for Toronto, the region of study, which hosts tree species from both the Eastern deciduous region of North America and its more boreal neighbors? Will climate change threaten Toronto’s urban forest or will it provide a more hospitable environment for more southerly species to migrate to and inhabit the city?

To summarize, the aim of this research is, through the analysis of Toronto’s current and potential future climate, its current and potential tree species and the climate-vegetation link established through the construction of climate envelopes, to assess how the biodiversity of Toronto’s tree species may evolve as a result of climate change.

1.2 Defining the Study Area – Toronto’s Vegetative Communities

Figure 1.1: Toronto’s forest communities are located in Toronto Ecodistrict 7E-4 of the Lake Erie-Ontario Ecoregion of the Mixedwood Plains adapted from Crins et al. (2009).

At the northern limit of the Carolinian forest region, Toronto is located in Land Information Ontario’s Lake Erie-Lake Ontario Ecoregion classification and is bound by the Lake Simcoe- Rideau Ecoregion in the north (Crins et al., 2009). Situated in a transitional zone between the two ecoregions, Toronto shares a number of tree species with both the Lake Erie-Lake Ontario and Lake Simcoe-Rideau Ecoregion. Both ecoregions are noted as being dominated by American beech and sugar maple with eastern hemlock, black, green and white ash, red and silver maple, yellow birch and balsam fir commonly found in tree communities. However some of the southernmost Canadian distributions of tree species found in the Lake Erie-Lake Ontario ecoregion include butternut, bitternut hickory, rock elm, silver maple and blue beech that are interspersed among tree stands. Toronto’s urban forest is also influenced by human factors and the planting of ornamental trees is noted throughout the city. Of Toronto’s 132 different tree

4 species found on street allowances, only 64 are native to Eastern North America (east of the 100th meridian) (City of Toronto, 2016).

1.3 Urban Forests

Throughout this work, an urban forest is defined as all the trees in a city (Konijnendijk et al., 2006). Nuancing this definition, urban forests can be interpreted both ecologically and socially (Ordóñez and Duinker, 2014). Urban trees exist either as single, isolated trees or connected to natural forests, where interactions with other vegetation and wildlife are possible. Single urban trees however, are typically constrained by man-made influences, interacting with built infrastructure and human populations. Some of the obstacles to tree growth in urban environments include the heterogeneity of its soil surface, where paved surfaces often restrict aeration, nutrient cycling and soil organism activity (Watson et al., 2014). Additionally, the infrastructure of cities is known to cause urban heat island effects, where elevated temperatures can potentially lead to heat stress. The buildup of ground-level ozone which can lead to reduced photosynthetic ability (Jacob and Winner, 2009). However, the urban forest can also prove beneficial to trees with the removal of competitors and the suppression of wildfire through human intervention. Other human interactions that include watering during drought conditions (Foran et al., 2015), shielding from winter cold pruning and pest removal also improve tree survival.

Climate change poses an interesting question for urban forests especially given the various socioeconomic benefits provided by trees through ecosystem services. Urban trees benefit from the mitigation of minimum temperature extremes and reduced salt loadings during winter however trees that cannot adapt to climate change quickly enough will ultimately result in a reduction of tree numbers. Translocation of trees to match a suitable climate through an assisted migration plan becomes an increasingly powerful tool as a climate change adaptation response. If urban forests do not adapt to climate change, greater financial investments may be required to maintain urban forests. These investments are essential so that the ecosystem services such as cleaner air, reduced summer warming and flood and stormwater management provided by trees can be maintained (Brandt et al., 2017). However, a potential expansion of tree species range as a result of climate change may also coincide with the expansion potential of tree pests, diseases and their vectors (Yang, 2009).

1.4 Climate’s Role in Plant Geography

The climatological study of plant geography is not a new topic for biogeographers and stems from the ancient Greeks. The first recorded instance appears to be Menestor (5th Century, BCE) who first noted that climate influenced whether vegetation would have a deciduous or evergreen form. Menestor’s assertions were later more fully elaborated by philosopher Theophrastus (370- 285 BCE) who further stated that “each tree seeks an appropriate position and climate” and extended this understanding to the study of individual properties of plants such as survival and growth (Woodward, 1987). In modern times, the dependence of plant geography on climate, started with the work of Humboldt and Bonpland (1805) who proposed that climate exerts control over vegetation distribution as part of a larger, burgeoning philosophy of environmental determinism. Expanding on this work were the theories of Clements (1916) and Gleason (1926), who would later debate whether vegetation was strictly linked to climate or rather was a function of plant physiology. While Clements argued that vegetation was in equilibrium with its climate, Gleason argued for a physiological basis in which no two plant species share the same needs (Cox and Moore, 2016). The current understanding is that vegetative distribution is a combination of the two. Although individuals in a vegetative stand may be physiologically different from one another, vegetative communities still exhibit a sense of homogeneity in plant communities under similar climatological conditions. Today, vegetative geography is largely based on correlation to bioclimatic variables which affect vegetative growth and success. Vegetative geography is reflected as a multidirectional, probabilistic process that can have more than one outcome (Bazazz, 1996). Though vegetation can be seen as an expression of its climate, plant communities are also capable of influencing its climate. Albedo and controls on the water balance such as transpiration are also factors which can greatly influence climate on the local scale.

However, for the purposes of this work, climatic variables for individual species are obtained at the regional scale and thus local processes such as vegetative influences over the local climate are not captured. As this research will include the modelling of the climatic boundaries of individual species, modelling local influences on climate would be the subject of work outside the scope of this thesis. Typically, analyses of the vegetative interactions with the atmosphere occur within a rather generalized landscape scale, for example whether an area is forest or crop land (McPherson, 2007) rather than individual tree species.

One of the earliest attempts to categorize vegetation according to climate was the work of German climatologist Wladimir Köppen (1936). Recognizing that particular types of climate are associated with certain forms of vegetation, a map of vegetation could be drawn from a map of climate and vice-versa (Adams, 2007). By using rainfall and monthly temperature measures such as the maximum and minimum, Köppen demarcated areas accordingly. For example, the use of climatic terms such as humid, dry, polar, tropical, subtropical and desert are frequently used among Köppen’s classifications. Purely empirical in nature, Köppen matched his visual observations of vegetation to climate. However, this view of vegetation that assumes equilibrium with the current vegetation to climate, although reasonably accurate in a broad sense, failed to reproduce vegetation patterns in South America (Akin, 1991; Adams, 2007). This has been attributed to a lack of a quantitative measure for an evapotranspiration budget, which later plant geography approaches have implemented (Leemans et al., 1996).

Furthering Köppen’s efforts is the development of Leslie Holdridge’s Life Zone Classification (1959; 1967). Notably Holdridge used a “biotemperature” which he defined as a combination of three factors: a mean annual temperature, total annual precipitation, and unlike Köppen, a potential evapotranspiration ratio (Holdridge, 1959). However, considerable limitations occur due to the fact it is solely calibrated from two annual indices of temperature and precipitation (Leemans et al., 1996). Holdridge’s approach is however limited to a rigid scale of mean annual temperature that ranges from 0°C and 30°C and does not include considerations of fluctuating climatic extremes nor seasonality. In addition, the moisture balance is inadequately described and seasonal aspects are unaccounted for (Leemans et al., 1996; Adams, 2007).

More recent research has explored physiological aspects to plant distribution. Box (1981) proposed a model based on 77 different plant functional types. These plant functional types moved away from the general formation types such as biomes and described plant physiognomy according to seasonal variations in climate. Using physiognomic expression, (i.e. whether a plant is broad-leafed or an evergreen, for example), an eco-physiological basis was established based on eight different variables quantifying temperature and precipitation extrema in nuanced ways as well as an index for water loss evapotranspiration.

For the purposes of this study, I follow a hybridized, correlative approach using a climate envelope that combines the underlying mechanisms behind seasonally-based, climatologically-

7 induced thresholds on plant physiology known to limit plant distribution. Many contemporary studies implement more mechanistic models which implement biological interactions and species-specific data (Peterson et al., 1999; Svenning and Skov, 2005; Kerney and Porter, 2009), however the large number of individual considerations confounded with difficulty in obtaining species specific data often limits its usefulness to only a few select tree species (Peterson et al., 2011). Climatic controls that are utilized for this study include the seasonally-based maximum and minimum temperatures, along with a precipitation balance based on plant-water relationships. These climatic controls will be explained further in the following section (Section 1.5).

Prasad and Iverson (2007) investigated the potential changes of 134 Eastern North American tree species as a result of projected climate change. Their results using AR4 GCM output indicate that a potential increase in suitable habitat will occur for most tree species for projected climate change scenarios. However, conifers and northern hardwoods such as balsam fir (Abies balsamea), paper birch (Betula papyrifera), red spruce (Picea rubens), bigtooth and quaking aspen (Populus grandidentata and Populus tremuloides) showed notable declines (Prasad and Iverson, 2007). Similar studies by Pedlar et al. (2011), have used these findings to evaluate methods for implementing assisted migration and assess the capacity for tree species to move northwards as a result of projected climate change, while McKenney et al. (2001) have created a set of maps for Canadian Plant Hardiness zones based on Little’s Distribution of 134 Eastern North American (Prasad and Iverson, 2003).

The correlation between tree species distribution and climate is key to assisted migration. This is a process of translocating species to ecologically-appropriate areas by human intervention. Although the practice has been proposed as an adaptation measure to maintain biodiversity as a result of climate change (Minteer and Collins, 2010), assisted migration is not without its own issues. As addressed in Aubin et al. (2011), the translocation of tree species can introduce a host of different issues. These include the loss of genetic diversity through the spread of more widespread genomes when introducing transplanted stock to new areas and the introduction of diseases and pests (Aubin et al., 2011).

1.5 Climatic Controls on Vegetation

The most prominent factor in limiting trees species range is its minimum temperature tolerance (Woodward, 1987). Woodward (1987) concludes that minimum temperature is the key factor in the seasonal shedding of leaves in temperate deciduous tree species, as well as frost damage to plant structures. Trees that shed leaves during the winter season limit growth and photosynthetic gain and thus are excluded from warmer climates. However, trees of a deciduous nature that do not shed their leaves during winter are more likely to succumb to low temperature mortality. Thus, this physiognomic expression of vegetation, which shed their leaves during winter, can therefore establish a precise geographic boundary of where vegetation can grow.

Secondly, frost drought is another example of the importance of the minimum temperature threshold. Frost drought occurs when intracellular ice forms inside plant cells removing water. As water leaves the leaf through frost drought, this in turn leads to extracellular ice formation further restricting water. This loss of water through frost drought causes cell contraction which will eventually desiccate and kill the plant. Additionally, the removal of water through frost drought can notably change plant cell solute concentrations. These concentration changes alter the pH and ionic balance in plant structures and eventually lead to the denaturing of proteins and other molecular structures (Woodward, 1987). However, some species avoid frost drought through a variety of adaptations. Sakai and Weiser (1973) noted super-cooling of water beyond its freezing point as a demonstration of extreme cold survival with species such as the white pine weathering laboratory induced temperatures as low as -80°C. Furthermore, Larcher (1982) viewed minimum temperature as a potential agent of natural selection, proposing limiting temperatures for physiognomic types. Temperatures above 15°C were seen as non-limiting to plant growth, whereas chilling would begin below 15°C to freezing and super-cooling climates would eventually limit growth to conifer species.

Maximum temperature also plays a role in determining vegetation type. Maximum temperature can raise respiration beyond the point of a positive carbon balance and inherently hinder necessary plant functions including that reproduction and allocation of photosynthate resources (Box, 1996). However, unlike minimum temperature, plants are better able to cope with the extrema of maximum temperature. Adaptations such as heat-shock proteins prevent cell damage and plants and transpiration actively cools plant tissues.

Maximum temperature is inherently linked to the water balance. Low moisture levels in soil, which can be exacerbated under high temperatures and low precipitation, will inherently lead their plants reaching their permanent wilting point. When plants can no longer extract water from the soil as a result of decreased soil moisture, they will shed their leaves and ultimately wilt. Therefore, to control the amount of water lost through transpiration, or the evaporation from leaves, the availability of water has been shown to have a strong influence on leaf area. The expansion of the leaf is known to be sensitive to changes in water potential (Hsiao, 1973). Therefore, the availability of water has a direct influence on the leaf area and subsequently the expression of vegetative diversity. Grier and Running (1977) found positive correlations between rainfall and leaf area in both the conifers of in Oregon, USA. Waring et al. (1978) reached similar conclusions studying leaf-area index and its transpiration in Douglas fir. For example, lower amounts of precipitation favour conifers that are able to use their trunks as reservoirs and reduce the impact of drought. These conifers can then expand leaf area to accommodate for increases in water availability allowing for additional storage of water in its trunk. This phenomenon is contrasted with deciduous, hardwood species which shed their leaves during drought, a response absent in conifer species. Since plant growth is seen as a process of cell wall expansion, the evaporative budget is a determinant of plant survival, from which plant temperature and water balance play important roles. Although woody plants do not wilt, the decrease in water potential causes cavitation in the xylem and leaf abscission may take place.

1.6 Climate Change and the Global Climate Models

Used by decision makers to quantitatively assist in assessing the potential impacts of climate change, global climate models (GCMs) are mathematical models based on the basic physics and chemistry of the atmosphere, oceans, and land surface. GCMs describe climatic conditions evolving from the interaction of atmospheric, oceanic, cryospheric, and land processes. However each GCM simulates physical processes and feedbacks of the climate system differently. Differing research objectives will require different models in terms of resolution, spatial domain and sub-grid processes which vary among GCMs (Knutti et al., 2010). Scale is typically a cause for concern as climate simulations are limited to coarse resolutions on a global scale. For example, the United States’ National Centers for Environmental Prediction’s (NCEP) operate a resolution of approximately 2.5o x 2.5o latitude/longitude and is too coarse to simulate local climate conditions (Kalnay et al., 1996; World Climate Research Programme, 2013).

Over the last few decades, improvements in the understanding of the Earth’s atmospheric system have greatly increased. This increased knowledge of climate processes is complemented by improvements in observational systems as well as in advances in computational power (Cubasch et al., 2013). The observational record through the integration of new satellite systems, have increased the number of observations by several degrees of magnitude. Since the Earth’s climatic interactions exist over a variety of spatial and temporal time scales, these observations inherently reduce uncertainties resulting from a coarse spatial resolution and observational periods restricted to a narrow time frame. Increases in computational power have also allowed for the inclusion of “short” and “long-term” simulations and improved resolution. The CMIP5, the Coupled Model Intercomparison Project Phase 5, is run by the World Meteorological Organization’s (WMO) World Climate Research Programme (WCRP) and provides a framework for comparing models under identical climate forcing (Taylor et al., 2012). The CMIP5 has provided a framework for coordinated climate change experiments that have been reported in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (AR5).

To facilitate the assessment of the impacts of climate change, the Intergovernmental Panel on Climate Change (IPCC) created its first set of greenhouse gas scenarios in 1990 which was used in the IPCC’s First Assessment Report (FAR). These initial assessments were updated two years later in what have been commonly referred to as the IS92 scenarios. The IS92, comprised of six different greenhouse scenarios (IS92a – IS92f), were employed to portray a variety of economic, social, technological and environmental assumptions in order to project future greenhouse gas emissions. These scenarios were presented as climate storylines based on socioeconomic narratives and the physical aspects of climate.

In the Special Report on Emissions Scenarios (SRES) (Nakićenović et al., 1998) a new set of reference scenarios were developed. These scenarios were used in the IPCC’s Third (AR3) and Fourth Assessment Report (AR4) providing emission scenarios for GCMs. The scenarios were based on economic activity, technological change, population growth, and energy and fossil-fuel use intensities that directly determined greenhouse gas concentrations (Nakićenović et al., 1998). These emission profiles were split into four different storylines. Titled A1, A2, B1 and B2, these storylines depended on levels of economic integration and implementation of environmentally- friendly technologies. For example, the A2 model represented a fossil-fuel intensive emission scenario, focusing on a regional economy, while on the other spectrum a B1 scenario represented

11 an environmentally-friendly scenario in a deeply-integrated world economy (see Figure 1) below:

Figure 1.2: Greenhouse gas emission storylines adapted from the SRES scenarios developed by Nakićenović et al. (1998).

Probably the most substantial change in the Fifth Assessment Report (AR5) is the transition from “fixed” socio-economic storylines to that of representative concentration pathways (RCPs). These RCPs are based on the balance between incoming and outgoing radiation caused by fluxes in the atmosphere. Until the AR5, scenarios followed a restrictive storyline that reflected socioeconomic factors that would affect greenhouse gas emissions. The RCPs do not have a storyline associated with them but are more simply reflective of radiative forcing. With the RCPs the storylines are implied based on an end result rather than rigidly defined as in the SRES. Factors that affect radiative forcing can result from a variety of sources whether they represent economic, technological, demographic, policy factors as part of a strategy of mitigation. No longer having “definitive” storylines, the RCPs leave as an open question how socio-economic factors may achieve this level of radiative forcing.

Based on their specified increase of radiative forcing in W/m², the RCPs now incorporate a broader range due to the implementation of a low-emission scenario that implies mitigation (Knutti and Sedláček, 2013). This scenario, the RCP 2.6, implies a mitigation strategy as global greenhouse gas emissions are reduced by about 90% by 2100. Deriving their name from their levels of radiation forcing, four RCP scenarios exist: the RCP 2.6 (van Vuuren et al., 2011); RCP 4.5 (Thomson et al., 2011); RCP 6.0 (Masui et al., 2011) and RCP 8.5 (Riahi et al., 2011). The lower and upper bounds of the RCPs are represented by the RCP 2.6 as a lower bound,

12 presenting a mitigation-intensive scenario, and the RCP 8.5 as an upper bound, a business-as- usual scenario. RCP 4.5 and RCP 6.0, on the other hand, are intermediate scenarios with RCP 4.5 representing a moderate amount of radiative forcing and the RCP 6.0, an outcome that is approaching a business-as-usual response to climate change mitigation.

1.7 Research Objectives

The goal of the study is to assess the impacts of climate change on the distribution of 134 tree species from Eastern North America (US, Canada) and whether these species will find future climate habitat in Toronto and which of the current species in Toronto will no longer have viable climate conditions. This study will require a quantitative analysis of global climate models (GCMs) to assist in the projection of a future climate of Toronto. Following the selection of an appropriate climate model or models, projected tree habitat and distribution will be determined through a stochastic process built on climate-vegetation relationships. Finally, an assessment of Toronto’s urban forest evaluates the potential benefits and consequences to an assisted migration in the projected climate.

1.7.1 Research Objectives – Study 1

First, to assess the impact of projected climate change in Toronto requires the use of global climate models (GCMs). However, there exist a number of intermodel differences among the GCMs in projecting climate change. The goal of this first study is to choose an appropriate GCM or GCMs to model future climate change in Canada. The study will additionally compare GCM performance from both the AR5 and its predecessor the AR4 and choose an appropriate GCM (or GCMs) using a performance metric based on the ensemble (average of all models) to help select the GCMs most likely to project accurately a future climate in Canada. Since projected change cannot be validated directly, the first task is to compare the relative accuracy of these two sets of models to the climate record. This is accomplished by hindcasting the models to a known observational period, from which it is possible to determine a model's fidelity in reproducing observational statistics. Following these procedures, the next goal is to develop a performance metric from which it is possible to best select an appropriate GCM to simulate Canada's future climate.

1.7.2 Research Objectives – Study 2

The second objective is to determine which of the 134 Eastern North American tree species will be climate-suitable for Toronto based on the projected climate. This second study will infer a suitable climatic niche from spatial absences and presences in the species’ distribution by building what is known as a climatic envelope. Once these climatic envelopes are determined, a predicted outcome based on two climate change scenarios, RCP 2.6 and RCP 8.5, and on a number of regression and machine learning techniques through R package BIOMOD2 will be used to determine a projected likelihood of the species being climatically suitable for Toronto’s future urban forest.

1.7.3 Research Objectives – Study 3

The goal of this last section is to assess the species biodiversity of Toronto’s inventoried trees and assess any subsequent changes experienced in a changing climate. This builds on the results of the second objective, where the projected suitability of tree occurrences is assessed in an urban forestry context. An important step is to compare projections with Toronto’s current tree inventories. Using street tree data combined from previous surveys of Toronto’s urban forest, the goal is to create an inventoried assessment using two climate change outcomes RCP 2.6 and RCP 8.5. By mapping Toronto’s street tree data, it is possible to identify the distribution and possible climate-based vulnerabilities. To foster Toronto’s goals of increasing biodiversity under the assumption that there may be changes in the tree composition in Toronto’s urban forest, this study assesses the climatic feasibility of reintroducting trees from Ontario’s Species at Risk Tree Species through an assisted migration. A literature search examines suitable tree habitat, as well as current conservation threats and whether or not an assisted migration will be beneficial to Ontario’s Species at Risk trees, all of which are species from more southerly climes.

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Chapter 2 A Comparison between Canadian Observational Data and the AR4 and AR5 Climate Models 2.1 Abstract

With an increase in the number of total available global climate models (GCMs) to over forty models in the latest Intergovernmental Panel on Climate Change’s Fifth Assessment Report (IPCC AR5), choosing an appropriate GCM to assess the impacts of climate change for Canadian applications becomes increasingly challenging. Using observational data from across Canada obtained from the National Center for Environmental Prediction (NCEP) climate reanalysis, models were ranked based on the average absolute difference from this dataset. This work compares the performance of the IPCC AR5 GCMs to the models used in the previous assessment report, the Intergovernmental Panel on Climate Change’s Fourth Assessment Report (IPCC AR4). Using an ensemble of all models, the GCMs of the AR5 were noted as more consistently closer to observations than the models of the AR4 for temperature, while for precipitation the ensemble averages of the AR4 and AR5 were comparable. A second aspect of this study is the introduction of a “selected” ensemble of the most accurate models.

2.2 Introduction

With the introduction of forty-one models for Intergovernmental Panel on Climate Change’s Fifth Assessment Report (IPCC AR5), choosing an appropriate global climate model (GCM) becomes increasingly difficult for a particular location.

Used by decision makers to quantitatively assess the impacts of climate change, global climate models (GCMs) are mathematical models based on the basic physics and chemistry of the climate system (Taylor et al., 2012). GCMs describe climatic conditions evolving from atmospheric, oceanic, cryospheric and land processes. To simulate climate change, these physical models also provide regional representations of the response of the climate to changes in aerosol and greenhouse gas concentrations using scenarios of future emissions (Nakićenović et al., 1998; Matsui et al., 2011; van Vuuren et al., 2011; Thomson et al., 2011; Riahi et al., 2011). However, uncertainties in projections for a given emission scenario arise from a variety of sources. Each GCM simulates distinctively physical processes and feedbacks of the climate

19 20 system. These resulting uncertainties are caused by limitations in the theoretical understanding of the atmosphere as well as structural issues such as a model’s inability to describe certain high resolution climatic processes (Knutti et al., 2010) and can lead to equifinality issues (e.g. Gough, 2001). Arising from parameterization uncertainty, equifinality occurs when different sets of parameters for a single modelling system result in same or similar reproduction of conditions, given that the model, forcing data and observations used in calibration are not perfect (Beven and Freer, 2001). Equifinality is a well-known challenge in complex or finer-scale processes, such as the modelling of the water balance (Wilby, 2005).

Due to the variations in the modelled climate among the available GCMs, largely arising from the parametrization of physical processes within the GCMs, the Intergovernmental Panel on Climate Change (IPCC), the United Nations’ scientific review body on climate change, relies on the results of the Coupled Model Intercomparison Project (CMIP). The fifth and latest assessment report from the IPCC (AR5) uses input from the fifth phase of the CMIP (CMIP5), which provides a comparison framework for coordinated climate change experiments under the World Climate Research Programme (WCRP). With increases in computational power and the development of more sophisticated models that simulate the physical, chemical, and biological processes with greater precision and accuracy, the CMIP5 now assesses a total of over forty different GCMs. The latest update to the CMIP has a marked increase in the participating models compared to the fourth IPCC Assessment report (AR4) which had twenty-four. Other differences include the introduction of a new set of projection scenarios known as the representative concentration pathways (RCPs). These RCPs now incorporate mitigation strategies in their scenarios based on their specified increase of radiative forcing in W/m² relative to pre-industrial levels (Matsui et al., 2011; Van Vuuren et al., 2011; Thomson et al., 2011; Riahi et al., 2011) as opposed to the AR4’s SRES which assumes no mitigation and employed rigid climate storylines (Nakićenović et al., 1998). Instead, the RCP implies certain levels of mitigation through an expected climate change outcome through radiative forcing, while SRES assumes an emissions scenario based on pre-determined levels of economic integration, carbon economy, world population and integration of carbon-friendly technologies. In addition, the CMIP5 puts emphasis on near-term and long-term simulations, which can span anywhere as short as 10-30 years into the future or extend past 2100 for its long-term simulations (Taylor et al., 2012).

Assessing the skill of a climate model is more difficult than short-term weather prediction. Whereas, numerical weather projection can be verified against continuously-varying observed weather conditions, climate models are tested against observed climate patterns whose patterns evolve slowly and are less understood (Glecker et al., 2008). Given these circumstances, climate model forecasts cannot be compared with observation from a daily or even monthly basis, as the GCM is primarily used to make climate projections over much longer time scales, from decades to centuries, and their projections cannot be assessed until that time has passed (Pincus et al., 2008). As opposed to an initial value problem posed in weather forecasting, climate modelling becomes one of solving a boundary value problem. The direct approach to assessing model skill is to compare its model output to its differences from a long term observational record (Flato et al., 2013; Glecker et al., 2008; Pincus et al., 2008). Thus a feasible approach is to compare observation to earlier portions of the simulation, for example from years 1900 to the present.

Increasingly since the development of the AR4, is the increased use of quantitative statistical measures, often referred to as performance metrics (Flato et al., 2013). For the purpose of this study, the definition of a performance metric in Glecker et al. (2008) is used, as a scalar measure, limited only to metrics that compare model simulations with observationally-based reference data. These scalar metrics often refer to popular statistic measures and include but are not limited to mean error, standard deviation, root mean square and variance among others. Scalar metrics are distinguished from diagnostic measures which include but are not limited to maps, time series analysis and distributions. While scalar metrics help identify the scale of model differences, they do not diagnose their causes. A wide selection of climatic variables of interest exist, such as the present-day simulation of clouds, precipitation, and radiation in climate models (Pincus et al., 2008) yet their observational uncertainties are substantial but are poorly measured (Glecker et al., 2008). Previous examples of performance metrics using scalar measures include the Gough-Fenech Confidence Index (Fenech, 2009), which divides the model difference from observation to the standard deviation of observed baseline conditions. The Gough-Fenech Confidence Index has been used as a performance metric in the “selective ensemble” of Hewer and Gough (2016) to choose the GCMs most representative of observed temperature and precipitation conditions for a multi-model mean.

Using the GCMs from the AR5 and its previous assessment the AR4 (Fourth Assessment Report), a performance metric is developed for the two sets of climate models in a Canadian

22 context. These models were evaluated for their ability to recreate observations in average temperature and precipitation and were ranked relative to their difference from by a statistical reanalysis dataset. A multi-stage performance metric which combines the use of an ensemble and a rank histogram used to further eliminate models from a multi-model ensemble (Annan and Hargreaves, 2011) was used to assess model skill to simulate observational values. Not only is a multi-model ensemble or mean better than most models, it has been observed to be better than any individually selected GCM when describing the climate system as a whole (Annan and Hargreaves, 2011; Pincus et al., 2008). Pincus et al. (2008) provide two reasons for this unlikely coincidence. Firstly, an ensemble of models may better simulate large-scale, slowly varying features than the slowly varying features nearer the grid scale at monthly resolutions found in individual models. An ensemble mean averages large-scale agreement yet removes finer-scale error in model simulation. Secondly, the ensemble mean is justified as a random draw on a distribution centred on truth, therefore averaging across an entire suite of models reduces those systematic errors. Use of the ensemble is justified as being a random draw from a distribution centred on an observation value with each GCM that is perfectly independent of one another (Tebaldi and Knutti, 2007; Smith et al., 2009).

However, the calculation of this ensemble also leads to the question whether or not the ensemble itself can be improved upon. Although a selected ensemble of only the most accurate models is expected to improve upon an ensemble of all models, to what magnitude are these improvements? In addition, using the ensembles of measures of performance, is it possible to select the “best” climate model for a given location? For the remainder of this work, we refer to an ensemble that includes all climate models as the “full ensemble” and those that include a subset of these models as a “selected ensemble” (Hewer and Gough, 2016).

Despite generating a greater selection of GCMs to choose from in AR5, it is increasingly challenging to determine which models perform best, particularly for a given location of interest. Given the diverse climatic regions in Canada, choosing a specific model to represent its vast geography may not be straightforward. From this stems a number of questions that this study aims to answer. Firstly, are there any notable differences between the accuracy of the models of the AR4 and AR5 compared to observations? Of these which models best simulate Canada’s climate for each assessment? The next question pertains to the ensemble and whether its use can be refined using performance metrics. Developing criteria for a rank histogram can notably

23 eliminate poorly-performing models from analysis and the hope would be to limit GCM choices to a few models to most accurately portray a projection of Canada’s future temperature and precipitation.

2.3 Methods

2.3.1 Comparing the GCM to Observational Data

In order to assess how well output from the IPCC AR4 and AR5 models compare with observations, GCM output from the AR4 and AR5 models’ hindcasts were used to compare to an observational baseline. Although different projection scenarios (SRES and RCPs) are used for the AR4 and AR5, the time period of the hindcast (1900-2000) have identical radiative forcing, based on historical greenhouse gas and aerosol concentrations.

A ranking was developed based on how well the model simulates a region’s climate in Canada, with the lowest absolute mean difference, averaged over a region, between the model and observational dataset signifying the best model. Conversely having the highest absolute mean difference would signify the worst model.

2.3.2 Obtaining an Observational Dataset

While an ideal observational dataset would consist of a well sampled, homogenous network of in-situ observational centers across Canada, this is not the reality. As most in-situ observational networks are located near population centres, this inherently leads to a lack of coverage in more remote and sparsely populated areas. Although the introduction of satellite-based observations has improved coverage, there still exist issues of temporal and spatial homogeneity (Mason et al., 2003; Bengtsson et al., 2007). A lack of homogeneity has been noted across Canada by Vincent and Guillet (1998), in particular, large temporal and spatial disparities occur in observational centres north of 60°N latitude. North of 60°N latitude, the average station density is one observation point per 100 000 km2 and the longest span with a sufficient number of stations for climate analysis spans from 1945 to present day. South of 60°N latitude, there is a much denser network and longer observation period of one station per 30 000 km2 and climate records that span from 1895 to present day (Vincent and Guillet, 1998). To compensate for the vast spatial variability in climate observation, Bengtsson and Shukla (1988) and Trenberth and Olson (1988) proposed that atmospheric observations can be statistically reanalyzed by correlating climate

24 surfaces using a fixed dynamical system to assimilate observations (Mason et al., 2003). Correlated climate surfaces such as the reanalysis dataset from US National Center for Environmental Prediction (NCEP) for the thirty-year period between the years 1961-1990 was used as an observational record. The NCEP re-analysis dataset which has a 2.5° x 2.5° latitude/longitude resolution allows for a generalized picture of the regional climate (Kalnay et al., 1996). Values from this observational dataset are used to compare annual average temperature in degrees Celsius and precipitation in mm/day to corresponding values obtained from the climate models. The resolution of the NCEP re-analysis data is visualized in Figure 1 where each point represents the lower left corner of a 2.5° x 2.5° latitude/longitude resolution grid cell. It is worth noting a bias may exist in the re-analysis surface as typical of ensemble output with a large geographical coverage. Ensemble maxima may be mitigated due to averaging.

To quantify how well the climatic elements, precipitation and temperature, are represented by a model, a measurement of performance is required. Defined by the IPCC as a consistent measurement that gauges how well specific processes are represented by a model (Knutti et al., 2010), these performance metrics or measures can be used to describe which models work best and where. In its simplest form a performance metric can be summarized by their absolute difference between the model output and observational data and we adopt this measure.

Since Canada is a vast nation with a diverse geography, it is not particularly useful to establish a Canadian average in terms of the “best” GCM. Rather for this study the country was divided into six regions. Due to the coarseness and resulting overlap of the 2.5° x 2.5° NCEP latitude/longitude grid, Canada was divided into six provincial and territorial regions: (1) Atlantic (New Brunswick, Newfoundland, Nova Scotia, Prince Edward Island and Quebec); (2) British Columbia, (3) Northwest Territories and the Yukon; (4) Nunavut; (5) Ontario and (6) the Prairies (Alberta, Manitoba, Saskatchewan). While water locations were excluded from the analysis, due to the coarseness of the grid cells, some overlap occurs in land pixels or cells which overlying coastal areas. This notably affects some areas more than others, particularly around the Great Lakes Regions in Ontario; Atlantic Canada as well as British Columbia and the Nunavut Territory. The regional divisions are presented in Figure 2:

Figure 2.1: Map of Canada showing the spatial resolution of NCEP observation points used for comparison versus GCMs stemming from the AR4 and AR5 assessment reports.

Although these regions vary greatly in size, the goal of dividing the nation into sub-national divisions is to more accurately represent the climate in their respective geographic contexts. To rank the “best” models in terms of spatial coverage, the average values of all cells within Canada per GCM will be used to represent the ability to simulate climate. The absolute delta differences were calculated to determine which models varied the least from the reference dataset (NCEP reanalysis). These models are then deemed as the “best” performing models for the given region.

2.3.3 Creating an Ensemble

With the variety of models available in the AR4 and AR5, a direct comparison between the assessment reports is not straightforward. It is possible however to compare the GCMs of both assessment reports using an average value. This can be accomplished by taking the ensemble of both the AR4 and AR5 GCMs as two separate values. This assumes by taking an ensemble that the result would represent a random draw from a distribution centred on an observation value with each GCM that is perfectly independent of one another (Knutti et al., 2010; Smith et al., 2009). For this work, the “full ensemble” weighs all models equally in an average. Weighting has not been recommended assuming that the models are independent and may not share the characteristics or parameterizations that all underlying models may contain (Knutti et al., 2010). Further guidelines on using an ensemble average is detailed in the Intergovernmental Panel on

Climate Change’s Task Group on Data and Scenario Support for Impact and Climate Assessment’s “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment” (IPCC-TGICA, 2007). While the IPCC-TGICA (2007), suggests that all ensemble members be used to maintain internal consistency, an ensemble based on selected models is explained later in this selection.

To tabulate model performance in a Canadian context, an aggregate score was developed by ranking the models in terms of model performance relative to observations for each of the regions. For example, the best model with the lowest absolute mean difference from observations in a subnational region has a score of 1, while the model with the largest departure from observations has a score of 25 for the AR4 for the 24 available climate models plus the full ensemble. Likewise, for the AR5 the model with the lowest absolute mean difference from observations has a score of one, but the highest absolute mean difference from observation has a score of 42 for the 41 different AR5 GCMs plus the full ensemble. A model’s aggregate score is a summation of the scores in all six regions in Canada and the lowest scores determines which models are best performing for all of Canada.

By developing an aggregate score based on the absolute average difference from observations, it is possible to evaluate a model’s performance relative to the full ensemble. Potentially improving on the full ensemble, a selected ensemble was created. Similar to the rank histogram proposed by Annan and Hargreaves (2011), this selected ensemble discards the models that fall below the performance for a full ensemble with regards to the absolute mean difference from observation. The intent of this procedure is to create a selected ensemble that only uses models that consistently perform better than the full ensemble. This standardized selected ensemble is applied to both temperature and precipitation for both the AR4 and AR5 simulations. As above, an aggregate score was created, however this time the selected ensemble is included with the full ensemble and GCMs, bringing the total number of compared models to 26 for the AR4 and 43 for the AR5.

2.4 Results

Figure 2.2: Annual average temperature (1961-1990) (oC) map depicting the differences between the AR4 ensemble and the observational dataset (NCEP).

The map visualization (Figure 2.2) created for the AR4 annual average temperature ensemble shows that the full ensemble generally represents Canada’s temperature to within ±1.5°C of the observational dataset for much of the country. With the exception of the high Arctic and the coastal Atlantic, this temperature difference is consistent throughout Canada. Temperatures that are higher (warmer) than observations are shown in red, while values lower (cooler) than observations are shown in blue. The greatest difference from observations are seen in areas of the high Arctic particularly the areas northeast of Nunavut with a temperature anomaly showing temperatures as much as 4°C cooler than observations occurring at 77.5°W longitude. This strip of cooler temperatures is the direct result of a poorly performing GCM, the Flexible Global Ocean-Atmosphere-Land System (FGOALS-g1.0) model. Values for the FGOALS-g1.0 can be as low as 12.85°C below observed values at 77.5°W, 85°N causing this distinctive anomaly in the ensemble. Excluding the FGOALS-g1.0 model, the maximum positive difference value from observation was recorded as 2.5°C at 62.5°W, 67.5°N, while maximum negative difference from the observation was recorded as -5.4°C at 50°W, 70°N.

Figure 2.3: The differences between the AR5 ensemble and the observational dataset (NCEP) for average annual temperature (1961-1990) (oC).

The annual average temperature visualization for the AR5’s ensemble shows a different result (Figure 2.3) from that of the AR4 analysis (Figure 2.2). It is uniformly warmer than its predecessor when inspected visually. Pockets of Alberta, Southern Ontario and Northwest Territories are simulated very well, compared to observations, with the AR5 ensembles ranging around ±1°C compared to ±1.5°C for AR4. The AR4 anomalous strip of lower temperatures is no longer present in the AR5 at 77.5°W. The maximum positive difference value from observation for the AR5 was recorded as 2.5°C at 62.5°W, 67.5°N, while maximum negative difference from the observation was recorded as -5.4°C at 50°W, 70°N.

GCM Ensemble Difference from Observation per Canadian Region for Average Annual

Temperature (°C) Prairies 1.51 1.73 1.88

2.21 Ontario 1.49 1.89 1.8

2.39 Nunavut 2.5 2.69 2.44

3.33 NWT and NWT Yukon 2.01 2.15 3.08

2.73 Columbia British 1.47 1.74 1.85

2.17 Atlantic 1.55 2.17 1.87

2.65 Canada 1.96 2.35 2.51 3.06

0 0.5 1 1.5 2 2.5 3 3.5 Difference from Observation (°C)

Average of Absolute Values (AR5) Average of Absolute Values (AR4) Standard Deviation AR5 Standard Deviation AR4

Figure 2.4: Difference between GCM averages and observations for annual average temperature per region (oC).

The AR4 and AR5 appear relatively similar in their difference from observation (see Figure 2.2 and 2.3). However, comparing to an observational baseline, the average temperature difference is measured as closer to observation in the AR5 compared to the AR4 when using a Canada-wide average (see Figure 2.4). When using the average of difference from observation in absolute values, the AR5 result was closer to observation in terms of absolute difference where it had a difference of 1.96°C, whereas the AR4 had a difference of 2.36°C.

Throughout Canada, there are regional variations between the AR4 and AR5. This can be seen through the intermodel variation shown through the standard deviations of annual average temperature and precipitation change. For Atlantic Canada, Ontario and the Prairies, the standard deviation measured approximately 0.3°C higher than in the AR5. This difference is more substantial when comparing that of the Nunavut region with a difference of 0.89°C. For almost all regions of the standard deviation was lower in the AR5 than in the AR4, where in the Northwest Territories and Yukon it ranked 0.35°C higher than its predecessor. The more homogenous distribution in the AR5 can be advantageous in that it better lends itself to model tuning by simply lowering the model output by 1-2°C through bias correction to match observed values.

In addition, it is noted that the temperature anomaly at 77.5°W no longer exists in the AR5, and is measured as significantly warmer in the AR5 due to the low temperature outliers in the AR4 caused by the FGOALS-g1.0 model1. To quantify this effect more fully, we show in Figure 2.5, the result of the AR4 ensemble with the outlier model FGOALS-g.1.0 removed.

1 Developed by the Chinese Academy of Sciences Institute of Atmospheric Physics, the Flexible Global Ocean-Atmosphere-Land System Model, gridpoint version 1.0 is an Earth System Model integrated to a complete carbon cycle. The carbon cycle of the FGOALS-g.10 model integrates an in-ocean component for biogeochemical markers to a dynamic global vegetation model (Institute of Atmospheric Physics, Chinese Academy of Sciences, 2005)

AR4 GCM Ensemble Difference from Observation per Canadian Region for Annual Average Temperature (°C)

** (with Outliers Removed ) Prairies 1.73 1.56 2.21

2.06 Ontario 1.89 1.74 2.39

2.26 Nunavut 2.69 2.53 3.33

3.14 NWT and NWT Yukon 2.15 2.01 2.73

2.57 Columbia British 1.74 1.65 2.17

2.02 Atlantic 2.17 2.05 2.65

2.47 Canada 2.35 2.19 3.06 2.8

0 0.5 1 1.5 2 2.5 3 3.5 Difference from Observation (°C)

Average of Absolute Values (AR4) Average of Absolute Values (AR4) ** Standard Deviation (AR4) Standard Deviation (AR4) **

Figure 2.5: GCM average difference from observation for annual average temperature per region (oC). **Denotes the removal of outlier model FGOALS-g.1.0.

The removal of the FGOALS-g.1.0 model (Figure 2.5) is noted by a decrease in intermodel variability. This exclusion of the FGOALS-g 1.0 from the ensemble is noted by a Canada-wide decrease in standard deviation by approximately 0.15oC. Nevertheless, despite the decrease in intermodel variability, the regions of Atlantic Canada, Northwest Territories and Nunavut still maintain a comparatively large standard deviation. Thus, as expected, removing outlier models such as the FGOALS-g.1.0 can improve the overall performance of an ensemble value.

Figure 2.6: The difference in precipitation (1961-1990) (mm/day) between the AR4 Ensemble and the observational dataset (NCEP) .

A visualization showing the precipitation difference of modelling results (mm/day) from observations was created for ensemble of the AR4 (Figure 2.6). Values that are higher (wetter) than observational values are noted in blue, while values lower (drier) than observations are denoted in yellow. There is an overestimate of precipitation of around 0.5-1.5 mm/day for many of the coastal areas of Canada. More inland areas such as the Prairies were simulated quite well, within a 0.5 mm/day error. Similar to the AR4 temperature ensemble there is a strip of outlier values at 77.5°W longitude. Inconsistent with trends seen throughout the ensemble, the Goddard Institute for Space Studies’ models GISS-EH and GISS-ER were at times much drier than observations, for example at 77.5°W, 57.5°N values were drier than observations by up to 1.67 mm/day. In addition, ranges between the highest and lowest recorded GCM values for 77.5°W longitude ranged anywhere from 0.57 to 1.69 mm/day showing a high degree of inconsistency. Maximum positive and negative differences from observations for the AR4 were recorded at 2.16 mm/day at 67.5°W, 40°N and -2.43 mm/day at 52.5°W, 62.5°N respectively.

Figure 2.7: The differences in precipitation (mm/day) between the AR5 Ensemble and the observational dataset (NCEP).

The visualization mapped for the AR5’s precipitation ensemble (Figure 2.7) shows a similar pattern from that of the AR4 analysis (Figure 2.6). The same overestimates of precipitation exist on the eastern coast of Canada at around 0.5 to 1.5 mm/day, although the west coast is slightly wetter at around 0 to 0.5 mm/day and wetter than the AR4. Again more inland areas such as the Prairies were simulated quite well, within 0.5 mm/day. However, the anomalous strip of lower precipitation is not present in the AR5. Maximum positive and negative differences from observations for the AR4 were recorded at 4.86 mm/day at 130°W, 52.5°N and -3.12 mm/day at 52.5°W, 62.5°N respectively.

GCM Ensemble Difference from Observation per Canadian Region for Annual Precipitative Change

(mm/day) Prairies 0.35 0.37 0.44

0.42 Ontario 0.42 0.38 0.39

0.43 Nunavut 0.12 0.4 0.48

0.52 NWT and NWT Yukon 0.39 0.4 0.5

0.54 Columbia British 0.72 0.74 0.89

0.92 Atlantic 0.56 0.62 0.69 0.76 Canada 0.46 0.52 0.7 0.72

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Difference from Observation (°C)

Average of Absolute Values (AR5) Average of Absolute Values (AR4) Standard Deviation (AR5) Standard Deviation (AR4)

Figure 2.8: GCM average difference from observation for precipitation for each region.

The difference between the AR4 and AR5 precipitation ensembles and observation in mm/day is recorded in Figure 2.8. As noted above, there are few notable differences between the AR4 ensemble and the AR5 ensemble (Figures 2.6 and 2.7) as both sets of GCMs yield results with an error of 0 to ±0.5mm/day from observations. There is a difference in the standard deviations between the two assessment models, with the AR5 having a lower standard deviation within a generally a hundredths of a millimeter. However, the coastal areas of Canada still yield the largest differences. Though relatively smaller this difference is most pronounced in Atlantic

Canada with a difference of about 0.07 mm/day, these differences are clearly visible from the maps shown in Figures 2.6 and 2.7 where British Columbia and the Atlantic provinces show substantial departures from observations. Similar to the comparison of AR4 and AR5 ensembles for temperature, for precipitation there is also an anomaly caused not by the FGOALS- g 1.0b but by AR4 models GISS-EH and GISS-ER at 77.5°W longitude. The exclusion of these models in the AR4 removes this anomaly from the ensemble as shown in Figure 2.9.

GCM Ensemble Difference from Observation per Canadian Region for Annual Precipitative Change (mm/day)

** (with Outliers Removed ) Prairies 0.37 0.36 0.42

0.42 Ontario 0.38 0.36 0.43

0.41 Nunavut 0.4 0.39 0.52

0.52 NWT and NWT Yukon 0.4 0.39 0.54

0.52 Columbia British 0.74 0.73 0.92

0.9 Atlantic 0.62 0.61 0.76

0.74 Canada 0.52 0.51 0.72 0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Difference from Observation (°C)

Average of Absolute Values (AR4) Average of Absolute Values (AR4) ** Standard Deviation (AR4) Standard Deviation (AR4) **

Figure 2.9: The difference between GCM averages and observations for precipitation per region (mm/day). **Denotes the exclusion of outlier models GISS-EH and GISS-ER

There were not substantial changes in the average and standard deviation after the removal of outlier models GISS-EH and GISS-ER from the original, full ensemble. Often these differences would be in the hundredths, and removal of the two outlier GCMs improved overall performance by approximately two hundredths of a millimeter per day.

A full ensemble was used as a performance metric to rate a GCM’s performance relative to the average value provided by the ensemble. This works analogously to a rank histogram, by removing all those models performing below an ensemble value. In this section we create a selected ensemble to further improve upon the full ensemble by only selecting models that rank above the full ensemble. The relative rankings of all the models to the full ensemble and selected ensemble for average annual temperature and precipitation are listed below in Tables 2.1 and 2.2 respectively.

Rank

AR4 GCM Temperature Precipitation AR4 GCM Temperature Precipitation

BCM2.0 21 22 GISS-EH 7 25

CGCM3T47 12 13 GISS-ER 9 26

CGCM3T63 22 11 HADCM3 5 3

CNRMCM3 2 15 HADGEM1 20 6

CSIROMk3.0 4 10 INGV-SXG 19 8

CSIROMk3.5 25 19 INMCM3.0 23 14

ECHAM5OM 3 18 IPSLCM4 13 20

ECHO-G 10 7 MIROC3.2 HIRES 24 21

MIROC3.2 ENSEMBLE 18 16 MEDRES 14 12

FGOALS-G1.0 26 23 MRI CGCM2.3.2A 8 5

GFDLCM2.0 16 1 NCARCCSM3 1 17

GFDLCM2.1 6 2 NCARPCM 17 4

GISS-AOM 15 24 SELECTED 11 9

ENSEMBLE

Table 2.1: Relative rank of AR4 GCMs for average annual temperature and precipitation based on an aggregate score. Values that are in light blue perform above ensemble values, while values in deep blue perform above selected ensemble values.

Rank

AR5 GCM Temperature Precipitation Temperature Precipitation

ACCESS1-0 23 5 HADGEM2-CC 39 6

ACCESS1-3 29 21 HADGEM2-ES 30 10

BCC-CSM1-1 7 15 INMCM4 22 30

BCC-CSM1-1-M 1 24 IPSL-CM5A-LR 3 9

BNU-ESM 12 43 IPSL-CM5A-MR 33 31

CANESM2 34 34 IPSL-CM5B-LR 16 42

CCSM4 15 41 MIROC4H 40 26

CESM1-BGC 14 39 MIROC5 31 19

CESM1-CAM5 13 40 MIROC-ESM 43 35

CMCC-CESM 37 11 MIROC-ESM-CHEM 42 36

CMCC-CM 26 29 MPI-ESM-LR 4 12

CMCC-CMS 19 13 MPI-ESM-MR 8 16

CNRM-CM5 9 37 MRI-CGCM3 5 27

CSIRO-MK3-6-0 20 8 NORESM1-M 11 17

FGOALS-G2 36 20 NORESM1-ME 25 18

FGOALS-S2 41 33

FIO-ESM 38 38 ENSEMBLE 32 25

SELECTED GFDL-CM3 2 17 3 ENSEMBLE 14

GFDL-ESM2G 18 1

GFDL-ESM2M 27 2

GISS-E2-H 28 32

GISS-E2-H-CC 21 22

GISS-E2-R 10 28

GISS-E2-R-CC 6 23

HADCM3 35 4

HADGEM2-AO 24 7

Table 2.2: Relative rank of AR5 GCMs for average annual temperature and precipitation based on an aggregate score. Values that are in light blue perform above ensemble values, while values in deep blue perform above selected ensemble values.

Using the aggregate score to rank which model appears closest to observations based on absolute mean difference, the NCARCCSM3 model from the National Center for Atmospheric Research was ranked as the best scoring for annual average temperature with an aggregate score of 18. This model can be contrasted to the worst performing model, the FGOALS-g1.0 model which scored 147. The full ensemble scored 97 falling below the median value 80. The full ensemble, ranking as the 17th best model out of 25, is not particularly useful in selecting an appropriate climate model as it ranks on the lower end of models. Subsequently the models that ranked below the full ensemble (INGV-SXG, HadGEM1, BCM2.0, CGCM3T63, INMCM3.0, MIROC3.2hires, CSIROMk3.5 and FGOALS-g1.0) are thus not recommended for use in climate change impact assessments that require annual temperature and precipitation in Canada.

The use of a selected ensemble (Table 2.1), proved to do substantially better than the full ensemble. The selected ensemble for AR4 temperatures ranked 11th out of all models, and scored an aggregate score of 67 compared to a median value of 81. Differences between full ensembles and selected ensembles are quite notable as the full ensemble scored 103 and was below the median value.

An aggregate score using the same technique to rank the AR4 models was repeated for the AR5 models. The first and most obvious difference is the introduction of several new modelling centres as well as improvements to pre-existing models. The best performing model for average annual temperature was a new model from the BCC-CSM1-1 with a score of 40, while the worst performing models belonged to the MIROC-ESM-CHEM and the MIROC-ESM models which scored 221 and 234 respectively. Similarly the ensemble scored in the lower middle range of the

39 models. The median models were the INMCM4.0 model scoring 136 and the ACCESS1-0 model scoring 139. It is worth noting that the full ensemble represents a lower performance than the median models at a value of 153, as was the case for the AR4. The models which rank below the full ensemble are MIROC5, HadGEM2-ES, IPSL-CM5A-MR, CANESM2, HadCM3, FGOALS- g2, FIO-ESM, CMCC-CESM, MIROC4h, HadGEM2-CC, FGOALS-s2, MIROC-ESM-CHEM, and MIROC-ESM. Out of a potential 42 models, the full ensemble placed as the 29th best model. Subsequently models that fell below the ensemble were removed to calculate the selected ensemble.

The effectiveness of a selected ensemble becomes far more evident when working with the larger number of GCMs available in the AR5 than the AR4. Out of a possible 43 models including the full ensemble and the selected ensemble, the selected ensemble was the 17th best model with an aggregate score of 110. The performance of the selected ensemble can be compared to median value of 140 and the much lower ranked full ensemble placed 32nd with a value of 159. From Tables 2.1 and 2.2, it can be seen that a selected ensemble removes many of the outlier values averaged in the ensemble. From Tables 2.1 and 2.2, it can be seen only a select number of models rank above the selected ensemble. This is particularly useful in selecting a GCM, especially in the AR5 where there is a substantial increase in the number of available GCMs.

Likewise with average annual temperature an aggregate score was used to rank the accuracy of global climate models to reproduce precipitation by ranking their difference from observation based on absolute mean difference. Ranked as the best model, the Geophysical Fluid Dynamics Laboratory’s models performed best with the models GFDLCM2.0 and GFDLCM2.1 scoring the two lowest for precipitation at 23 and 35 respectively. These values are contrasted to the Goddard Institute for Space Studies whose three models GISS-AOM, GISS-EH and GISS-ER scored the highest and took the bottom three places scoring 123, 126 and 134 respectively for their models. The median value was from the model of the Institute for Numerical Mathematics INMCM3.0 model with a score of 75, while the ensemble was below the median values at 87 points. Out of 25 available models, the ensemble ranked 15th. Models which scored below the ensemble were the NCARCCSM3, ECHAM5OM, CSIROMk3.5, IPSLCM4, MIROC3.2 hires, BCM2.0, FGOALS-g1.0, GISS-AOM, GISS-EH and GISS-ER models.

A selected ensemble for the models of the AR4 showed similar results in both temperature and precipitation, as the selected ensemble ranked substantially higher in its aggregate score than that of the full ensemble. The AR4 selected ensemble for precipitation placing 9th overall with a score of 64 tying with the CSIROMk3.0. The selected ensemble ranked higher, 9th out of 26, whereas the full ensemble ranked 16th with a score of 93. Full ensemble values for the AR4 were lower than the median score of 77.5. This shows that the careful selection of GCMs through a selected ensemble is much more representative of a climate reality. The selected ensemble performed higher than the median values for both temperature and precipitation. Selective ensembles for precipitation ranked 9th out of 26, whereas for temperature this was slightly lower, 11th out of 26.

Similar to the results from the AR4, the AR5 models from the Geophysical Fluid Dynamics Laboratory [GFDL-ESM2G (score = 41), GFDL-ESM2M (score = 51) and GFDL-CM3 (score = 58)] scored as the best in terms of having the lowest absolute mean difference from the observation for precipitation. These models were followed by the models from the Hadley Centre which had the next three lowest scores. These models, the HadCM3, HadGEM2-AO and HadGEM2-CC scored 73, 91 and 91. The full ensemble is more or less at the median value at 144, just falling below the Goddard Institute for Space Studies’ GISS-E2-R-CC and GISS-E2-H- CC which scored 139 and 140 respectively. Using a full ensemble appears to be representative of the average value ranking 23rd out of 42 different models and that about half of the GCMs fell below its value (BCC-CSM1-1M, MIROC4H, GISS-E2-R, MRI-CGCM3, CMCC-CM, INMCM4, IPSL-CM5A-MR, GISS-E2-H, FGOALS-S2, CANESM2, MIROC-ESM-CHEM, MIROC-ESM, CNRM-CM5, FIO-ESM, CESM1-BGC, CESM1-CAM5, CCSM4, IPSL-CM5B- LR, BNU-ESM). Unlike the aggregate scores for temperature in the AR4, AR5 and the aggregate score for AR4 precipitation, the full ensemble was comparable to the median value.

Further improving on the full ensemble shown in Table 2.2, a selected ensemble was ranked among all the models and the full ensemble of the AR5 models for precipitation. The selected ensemble in this case placed 14th out of 43 different models and ensembles with a value of 119, which is a marked improved over that of the full ensemble which placed 23rd out of 43 with a score of 149. Although the full ensemble was still relatively close to the median represented by the ACCESS1-3 GCM at 141, the selected ensemble not only improves on the ensemble but represents a value well above the median value. Again, this demonstrates the selected ensemble’s ability to rank among the best performing models, by exceeding both the ensemble and median.

Much like the AR4, the selected ensemble for precipitation was ranked 14th out of 43 and 17th out of 43 for temperature.

As seen in Tables 2.2 and 2.3, the use of a selected ensemble performed much better than the full ensemble as indicated by its aggregate score. Since the selected ensemble performed much better than most models of the AR4 and AR5 by limiting itself to models which performed better than the full ensemble, it is possible to choose only the best models for temperature and precipitation. In the AR4, there were a few models that were above both the full ensemble and selected ensemble and many which consistently fell below both ensembles. Using the AR4 models, the most consistent models which were above the full ensemble and selected ensemble for temperature and precipitation were ECHO-G, GFDLCM 2.1, HadCM3 and the MRI CGCM2.3.2A which were above ensemble values for both temperature and precipitation. On the other hand, models CSIROMK3.5, FGOALS-g1.0 and MIROC3.2HIRES were never above the full ensemble or selected ensemble for either temperature or precipitation.

Given the abundance of models available in the AR5, the process of being able to select an appropriate model was more challenging. In this case, the use of the different ensembles is particularly useful in screening what are the consistently stronger models. Since the selected ensemble ranked 17th for temperature and even higher in its aggregate score at 14th for precipitation, this inherently sets a high performance threshold for many models. Only the GFDL-CM3, IPSL-CM5A-LR and MPI-ESM-LR models exceeded the two different ensemble values for both temperature and precipitation while more models such as the CANESM2, FGOALS-S2, FIO-ESM, IPSL-CM5A-MR, MIROC4H, MIROC-ESM and MIROC-ESM- CHEM were consistently below both ensembles.

2.5 Discussion

Unlike in Annan and Hargreaves (2011) and Pincus et al. (2008), a multi-model ensemble of all models did not prove to be better than most models. In fact, when comparing its difference from observation for average annual temperature, the ensemble ranked below the AR4 and AR5 median. The ensemble ranked 32nd out of 42 model choices and 25th out of 42 model choices when assessing annual average temperature for the AR5. While for the AR4, the ensemble

42 ranked 18th out of 25 model choices and 16th out of 25 for average annual temperature and precipitation. Using an ensemble as a multi-model mean of all available GCMs has proven ineffective in the comparison of mean average temperature and annual precipitation to observed results. The use multi-model means that comprise of only the best GCMs to reproduce baseline conditions are emphasized in Annan and Hargreaves’ (2011) rank histogram, which removes all models which fall below a threshold and the selective ensemble approach used by Hewer and Gough (2016). The selective ensemble approach used by Hewer and Gough (2016) for maximum temperature and total precipitation uses a multi-model mean of the three best models ranked according to the Gough-Fenech Confidence Index (GFCI), a measure of model difference from observation divided by its standard deviation (Fenech, 2009). Based on previous research, the selected ensemble approach used in this study satisfies a need for a multi-model mean that moves beyond a simple average of all available GCMs.

Using a performance metric involving a rank histogram based on the concepts of Annan and Hargreaves (2011), all models which fell below an ensemble value were eliminated from the subsequent multi-model mean in what was deemed in this work as the selected ensemble. This selected ensemble greatly improved upon the results consistent with the findings of Annan and Hargreaves (2011) where a rank histogram was found to greatly improve an ensemble value deemed unreflective of observed results. However, it is important that this study only evaluated observation data to mean annual temperature and precipitation and did not holistically evaluate the climate system as a whole. An ideal for a given climate variable could fare poorly for another. Even when evaluating mean temperature and precipitation is it noted that the AR4 models GISS-EH and GISS-ER were poor performers for annual average precipitation, but were strong performing models for annual average temperature.

It is important to note that this study only considered the mean state of climate, since this is the most fundamental and best observed aspect of climate (Reichler and Kim, 2008). Understanding the cause of model error is difficult as this requires an understanding of a model and its processes as well as these processes in isolation. Processes such as ocean-atmosphere and cloud simulation remain problematic in AR5 GCM simulations (Flato et al., 2013) and validating more complex climate interactions becomes increasingly difficult (Reichler and Kim, 2008). More complex interactions such as ocean-atmosphere interactions are difficult to simulate and often require model tuning. Examples include Reichler and Kim (2008) which artificially adjusted for

43 imbalances in the mean temperature and salinity structure of the ocean and the study of Gough (2001) which used model tuning to alleviate the problem of equifinality caused by data deficiencies in parameterized processes. The use of these artificial corrections through model tuning underlines a need to address uncertainties in modelling especially with regards to the ocean and atmosphere, as well as any knowledge gaps in the understanding of the Earth’s climate. These issues are continually being improved upon with the development and introduction of new global climate models.

2.6 Conclusion

Consistent with the improvements noted in the latest IPCC Assessment Report (Flato et. al, 2013), the ensemble values of the AR5 proved more representative of observed conditions for annual temperature and precipitation than its predecessor the AR4 for Canada. When evaluating the climate system as a whole, the use of an ensemble value has been observed to be better than any singly selected GCM. However, when evaluating mean annual temperature and precipitation against observation, the study was able to improve on a multi-model ensemble by the use of a rank histogram. Through this study a repeatable and simplified method involving a performance metric based on a rank histogram and the ensemble was able to assess model skill and limit model selection using a highly discriminatory process bound by values obtained by observations. This limited model selection to only three models, the GFDL-CM3, IPSL-CM5A-LR and MPI- ESM-LR which were consistently above the criteria of the rank histogram method presented. It is important to note that this method is applicable only to the criteria of annual temperature and precipitation within a Canadian climate context, moving beyond mean climate conditions requires a more holistic outlook which is notably more difficult.

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Chapter 3 Future Distributions of 134 Eastern North American Tree Species in Toronto resulting from Climate Change Scenarios RCP 2.6 and RCP 8.5 for the Year 2050 3.1 Abstract

Situated between the Lake Erie-Lake Ontario Ecoregion and the Lake Simcoe-Rideau Ecoregion, tree species in the Toronto area include a combination of more northerly species as well as those found in the Carolinas of the United States. Projected climate change, therefore presents an interesting challenge for Toronto forests with the potential for changes in temperature and precipitation to alter range conditions. Using climate change projections RCP 2.6 and RCP 8.5, the potential future distributions of 134 tree species from eastern North America were projected based on statistical climate envelope techniques. By projecting the future climate of Toronto for the next 50 years, tree species suitability in the region was evaluated using a suite of models. Several species currently at or near the edge of their range limits were projected to be climatically unsuitable for the Toronto region. However, under an aggressive climate change scenario, a potential increase in biodiversity is shown by the incursion of trees from more southerly climes which will become climatically suitable for Toronto. With such a possibility, these results provide new opportunities for conservationists and urban planners looking to expand the habitat of Toronto’s Carolinian species in the Toronto area.

3.2 Introduction

Toronto is located in the Toronto Ecodistrict (7E-4), a transitional forest area in the Mixedwood Plains forest area lying on the northern limit of the Lake Erie-Lake Ontario Ecoregion (Ecoregion 7E) and the southern boundary of the Lake Simcoe-Rideau Ecoregion (Ecoregion 6E) (Crins et al., 2009; Figure 1.1). Toronto contains species typical of the Lake Simcoe-Rideau Ecoregion such as the dominant American beech and sugar maple common to both regions as well as eastern hemlock, black, green and white ash and red and silver maple which are represented in abundance. However more Carolinian species not found elsewhere in Canada and found in the Lake Erie-Lake Ontario Ecoregion include the black walnut (Juglans nigra), butternut (Juglans cinerea), bitternut hickory (Carya cordiformis), rock elm (Ulmus thomasii),

48 silver maple (Acer saccharinum) and occasional populations of black oak (Quercus velutina), red mulberry (Morus rubra) and sassafras (Sassafras albidum).

Climate change has the potential to affect the distribution of tree species substantively. Toronto is expected to see an increase in habitat for many of its Carolinian tree species while conversely seeing a decline in habitat for its more northern species. Similar results were demonstrated in previous research by Prasad et al. (2006) where a potential increase in suitable habitat was made available for many of the 134 Eastern North American (east of the 100th meridian) tree species (Prasad and Iverson, 2003) they sampled. Conifers and northern hardwoods, however, were subject to a decline in suitable habitat and these species included the balsam fir (Abies balsamea), paper birch (Betula papyrifera), red spruce (Picea rubens), bigtooth and quaking aspen (Populus grandidentata; Populus tremuloides). Using climate data obtained from Global Climate Models (GCMs) from the Intergovernmental Panel on Climate Change’s Fourth Assessment report (IPCC AR4), tree species absences and presences were correlated to 33 different variables of climate, as well as soil type and land use data for current tree distributions. These predictions were modeled using combination of classification tree analysis methods such as regression tree analysis, bagging trees and random forests.

Building on the research of Prasad et al. (2006), McKenney et al. (2011) created a set plant hardiness maps for 62 northerly tree species across Canada. Using the Prasad and Iverson (2003) dataset for eastern North American tree species, distribution was correlated to climatic variables to account for seasonal temperature and precipitation as well as wind speed. This correlation was modeled using ANUSPLIN, a form of Surface Range Envelope which uses spline-fitting techniques for interpolation and function smoothing. McKenney et al. (2001) found that climate change over the next 50 years resulted in an average northward shift of 57 kilometers, thus signaling an increase in the productivity and diversity of plants that can be grown in Canada.

This northward shift in tree species distribution is of particular interest to those wishing to implement an assisted migration strategy. Assisted migration as a conservation strategy would involve the translocation of species to track future climatic change. A climatic assessment of potential tree species under a climate expected to be warmer and wetter would be greatly beneficial to the understanding of assisted migration. As a novel conservation practice, assisted

49 migration is often constrained by the spatial uncertainty of land use and ecological interactions as well the possible introduction of unintended pests and diseases (Pedlar et al., 2012).

For this work, Toronto, Ontario, Canada was chosen as the study location for a number of reasons. Firstly, Toronto lies in a transitional area sitting between the Lake Erie-Lake Ontario ecoregion and the southern boundary of the Lake Simcoe-Rideau Ecoregion. Although future climate change is expected to experience a displacement of Toronto’s northerly hardwoods and conifer species in favour of more southerly species, it is unknown to what extent climate conditions will change and thus impact on species migration into the Toronto region. This research poses an interesting question for an urban forest setting, especially where tree growth is often limited by human influences. The research presented builds on a relatively new approach on assessing the vulnerability of urban trees such as in Ordóñez and Duinker (2014), Foran et al. (2015), Brandt et al. (2016) and Brandt et al. (2017) which analyze how temperature and precipitation extremes affect tree survival. However, there has been little research done to use this knowledge to implement an assisted migration of trees to a future climate in an urban forest context. Examining the implementation of an assisted migration under the context of an urban forest, Woodall et al. (2010) found that assisted migration served as a facilitator for native tree spread and a seed source or refuge for native trees along migration pathways. Furthermore, a potential change in the species composition of urban forest trees could greatly alter any ecosystem services provided by trees in an urban setting. This research could guide potential assisted migration practices under a Canadian urban forest context by maintaining or bolstering biodiversity to potentially improve ecosystem services through the socioeconomic benefits provided through the aesthetic, ecological and recreational values provided by trees.

This study builds upon the previous tree distribution maps of Little (1977) to provide a likelihood of suitability for 134 Eastern North American east of the 100th meridian in Toronto as a result of climate change. Previous climate change suitability studies have included the tree importance maps of Prasad et al. (2006) and the Canadian plant hardiness zones of McKenney et al. (2011). These aforementioned studies develop their indices of tree suitability as a result of climate change by relating species distribution to climate variables in a correlative process known as a climate envelope. Whereas Prasad et al. (2006) used solely used classification tree analysis and McKenney et al. (2011), a variant of the surface range envelope to model species distribution, this study aims to build on the work of its predecessors by using an ensemble of

50 machine-learning and regression methods made available through statistical package BIOMOD2. Furthermore, using BIOMOD2 predictions of species distribution will be produced using the most recent models (Chapter 2) from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report (IPPC AR5) and will implement these findings under a Canadian urban forest context for the City of Toronto.

3.3 Methods

3.3.1 Tree Species Distribution Data

Distributions for a total of 134 available tree species naturally occurring in North America east of the 100th meridian were considered for this work. Rasterized tree species distributions created by USGS based on the work of Elbert Little Jr. (Little, 1971) were available at 20 km2 resolution through the US Forest Service (see Prasad and Iverson, 2003). These distributions recorded the presence or absence of species over the study area defined as North America (excluding Mexico) east of the 100th meridian.

Climate data for the study region was obtained from the WorldClim database (http://www.worldclim.org) (Hijmans et al., 2005). This set of downscaled global climate grids was downloaded at a resolution of 5-arc-minutes or approximately 10 km2 at the equator. This downscaled climate data was paired to the tree distribution raster files (Prasad and Iverson, 2003) at a 5-arc-minute resolution by intersecting polygons of estimated distribution from the USGS dataset. To create models of the tree distributions, a number of climatic variables were chosen based on their ability to influence tree species distribution. This concept of choosing particular bioclimatic variables to model tree distributions was implemented in the work of E.O. Box (1981) and reiterated in Thuiller (2003), where temperature and precipitation extrema as well as an index for water loss and evapotranspiration were correlated with the appearance of certain vegetation types. For the purposes of the model, Annual Mean Temperature; Maximum Temperature of Warmest Month; Minimum Temperature of Coldest Month; Annual Precipitation; Precipitation of Driest Quarter and the Precipitation of Warmest Quarter were used. The justification for selecting these variables was based on annual temperature and precipitation patterns, as well variables such as Minimum Temperature of Coldest Month to measure low temperature mortality (Woodward, 1987) and variables such as Maximum

Temperature of Warmest Month, Precipitation of Driest Quarter, and the Precipitation of Warmest Quarter as a surrogate to measure evaporative water loss (Box, 1996).

In this work, a model based on the correlation between climatic variables and species range will be used. The model to be implemented, BIOMOD2, builds a species’ future distribution based on the correlation between a current species’ distribution and the climate variables within its range. BIOMOD2 then uses this correlation alongside those same climatic variables and climate models but with differing climate change scenarios to predict an outcome based on a variety of statistical techniques including Classification Tree Analysis, Flexible Discriminant Analysis, Multivariate Adaptive Regressive Splines, Random Forests and the Surface Range Envelope. These variables for BIOMOD were meant to replicate the variables chosen by Thuiller (2003) to model 61 native tree species across Europe. Precipitation of the Driest Quarter and Maximum Temperature of Warmest Month were used as surrogates for the growing degree days (>5°C) and relative humidity used by Thuiller (2003), which were not directly available through BIOCLIM.

As a baseline (current climate), climatic variables were similarly obtained from the WorldClim database for an average of the years 1950-2000. Climate change projections for the year 2050 (an average of the years 2041-2060) used two radiative forcing scenarios, known as the Representative Concentration Pathways (RCP) to project maximum and minimum levels of radiative forcing for future conditions. These maximum and minimum levels are achieved through the more aggressive climate change scenario RCP 8.5 with a radiative forcing of 8.5 W/m² and the more conservative RCP 2.6 with a radiative forcing of 2.6 W/m². To simulate future climate change, an ensemble of global climate models (GCMs) GFDL-CM3, IPSL- CM5A-LR and MPI-ESM-LR were used because of their accuracy in recreating Canadian climate observations (Chapter 2). It is worth noting that not all the bioclimatic variables chosen to project future species distribution as a result of climate change were available for all 41 GCMs of the AR5. Due to this limited availability, it was not possible to evaluate AR5 model accuracy across Canada for more specific bioclimatic variables other than average annual temperature and precipitation change. The bioclimatic variables used in this study were validated using a subset of available data using the method described in detail in Section 3.34. Using these GCMs, the future distributional range of trees were projected for climate scenarios RCP2.6 and RCP 8.5 to reflect climate change for the minimum and maximum radiative forcing levels of 2.6W/m2 and 8.5W/m2 respectively.

These climate change outcomes will represent a future climate that is warmer and wetter for the year 2050 (Environment and Climate Change Canada, 2016). RCP 2.6 scenario is used to represent a generally lower warming and carbon-friendly climate change outcome where fossil fuel reduction would be emphasized whereas RCP 8.5 represents a climate change scenario close to business-as-usual. For RCP 2.6, this represents a median 2.2ºC increase in winter temperature (December to February) and a median 1.4ºC increase in summer temperature (June to August) in the years 2046-2065 from a 1986-2005 baseline period for Ontario. While for precipitation, this represents a median increase in precipitation by 8.9% for the months of December to February and an increase in precipitation by 2.6% for the months of June to August ) in the years 2046- 2065 from a 1986-2005 baseline period for Ontario. For the RCP 8.5 climate scenario, this represents a median 4.6ºC increase in winter temperature (December to February) and a median 3.1ºC increase in summer temperature (June to August) in the years 2046-2065 from a 1986- 2005 baseline period for Ontario. While for precipitation, RCP 8.5 is represented by a median increase of 4.3% for the months of December to February and an increase in precipitation by 3.1% for the months of June to August) in the years 2046-2065 from a 1986-2005 baseline period for Ontario.

3.3.2 Study Area

Figure 3.1: Location of sampling areas within the Toronto area where climate data information was taken. The sample points have been shifted northwards as sample cells containing water surfaces led to null or skewed results.

For the 134 Eastern North American tree species, the BIOMOD2 software was used to forecast species distributions. Developed by the Centre d’Ecologie Fonctionnelle et Evolutive of Montpellier (Thuiller, 2003), the software, which is run in statistical software package R, provides forecasts of species distribution through the development of climate envelopes. A climate envelope is built based upon the statistical correlation between a current species distribution and the underlying (current-day) climate variables. This correlative method implies that particular combination of climatic conditions will similarly serve to define its future range.

For this work, a species’ likelihood of suitability in Toronto was defined an average of five 10- min resolution grid cells in an area representative of the city. These grid cells in a latitude/longitude projection were positioned just directly north of city limits, as any grid cell overlap with Lake Ontario resulted in flawed model predictions. Toronto was defined as an average of five different grid cells to decrease the potential sample bias of selecting a single cell. These cells (see Figure 3.1) are represented by a point at the bottom left corner are represented by the following latitude and longitudes: [(43.75°N, 79.58°W)]; [(-43.83°N, 79.50°W)]; [(43.83°N, 79.42°W)]; [(43.83°N, 79.33°W)] and [(43.83°N, 79.58°W)]

3.3.3 Theoretical Assumptions

The distributional limits of a species match particular combinations of climate variables, and that these limits shift accordingly to climatic changes (Araujo and Peterson, 2012). The use of a bioclimatic envelope model assumes that species distributions are in equilibrium with their current climate. This correlative approach stems from the long-established concept that climate in a broad sense controls vegetation patterns where the limits to distribution match particular combinations of climate variables and that these limits shift accordingly with changes in climate (Box, 1981; Rapoport, 1982; Woodward, 1987).

Of course, climate is not the sole determinant of distributions, and using a bioclimatic envelope ignores biotic interactions. An alternative to the bioclimatic envelope is the use of mechanistic models, which represent geographic, physiological and reproductive factors in conjunction with

54 climate variables. An example of a mechanistic model for tree distribution is the model proposed by Svenning and Skov (2005), which suggested that historical features such as the presence of glacial refugia limited the ranges of European tree species as well as its climate (Svenning and Skov 2005). Similarly Hijmans and Graham (2006) developed a mechanistic model on the physiology and growing period of 100-globally distributed plant species and predicted marked range reductions under both colder and warmer climates for past conditions (21 000 years before present).

Mechanistic models can also be used to more accurately determine tree species’ limits of tolerances. The mechanistic model of Chuine and Beaubien (2001) obtained detailed species data through controlled experiments by measuring the phenology or timing of biological events. Unfortunately, if one were to implement a mechanistic model such as that of Chuine and Beaubien (2001), it would be particularly data-demanding if a large number of species were to be studied. For many species, the required data are not known. While mechanistic models often aim to characterize a fundamental ecological niche, they are often limited in their representation of the full dimensions an ecosystem (Peterson et al., 2011). A mechanistic model is notably more difficult to develop as it requires a process-based modelling of a fundamental niche and all of the various factors that influence a species. Whereas the modelling of a realized niche concerns a realized outcome, it is important to note that all the factors which contribute to the fundamental niche may not be realized in their outcomes nor will they be realized in the future. Climate change and rapid evolutionary change along with genetic plasticity may alter environmental tolerances causing instability in the modelling of a fundamental niche (Pearson and Dawson, 2003).

Climate envelope approaches are criticized for excluding dispersal and biotic interactions. Although the climate envelope cannot account for dispersal as it assumes a species is in equilibrium with its climate, it does however model a realized niche by correlation. By correlating a geographic distribution to current climatic conditions implicitly considers such limitations. If it were not for these constraints, a species would achieve full equilibrium within its climate (Araujo and Peterson, 2012). Furthermore, since correlative approaches such as climate envelopes do not require detailed physiological data about individual species, this approach has the advantage of being able to be broadly applied to a large number of species. While mechanistic models may provide powerful tools as accurate simulations of individual species-

55 specific distributions, they do not estimate the magnitude of future changes for species, their assemblages, habitats and regions available to climate envelope models (Pearson and Dawson, 2003). As concluded by Hannah et al. (2002), climate envelope projections are the best available guide for policy-making and can be used as a consensus for possible conservation priorities, while being supplemented by local ecology and biogeography to compensate for model limitations (Hannah et al, 2002; Pearson and Dawson, 2003).

3.3.4 Statistical Assumptions

When modelling a species’ occurrences, an ideal model should be validated using an independent model dataset (Fielding and Bell, 1997). In Thuiller (2003) validation was achieved by using a subset of the data to calibrate the model and an independent subset to test it. However, the size of the subset used to calibrate the model is a contentious issue. Typically models use 70% of the data to calibrate (Thuiller, 2003); however, Araujo et al. (2005) suggest that 100% of the species occurrence data should be used to calibrate the model, as in many cases the validation affects performance results especially in the case of rare or data deficient species. As a compromise, for this work 80% of the data was used to calibrate the model and 20% to validate the model as done in Georges and Thuiller (2013).

All models were evaluated using the True Skill Statistic (TSS). The use of the TSS is to measure model agreement for absence-presence forecasts. Similar to Cohen’s Kappa, TSS incorporates the possibility of agreement by chance (Allouche et al., 2006). However, unlike Cohen’s Kappa, TSS is unaffected by the frequency and prevalence of its outcomes. Ranging from -1 to +1, a TSS score of +1 would indicate perfect agreement whereas a score of 0 would indicate an agreement no better than random. All models for all 134 trees for each modelling technique in this experiment passed the TSS threshold of 0.7, showing that there is reasonable agreement between the statistical models and their climatic variables (See Appendix 7.1).

3.3.5 Statistical Models Implemented from BIOMOD2

As an ensemble modelling tool for species distributions, BIOMOD2 possess a number of regression and machine-learning techniques which will be implemented into this study. These techniques each possess their individual advantages and disadvantages for species modelling. A basic description of the various statistical methods implemented in this study is explained below

56 and include the Surface Range Envelope (SRE), Classification Tree Analysis (CTA) and Random Forest (RF), Multivariate Adaptive Regression Splines (MARS) and Flexible Discriminant Analysis (FDA).

As a presence-only method to generate predictive values, the Surface Range Envelope calculates a fitted, species-specific, minimal rectilinear envelope for climatic variables (Heikkinen et al., 2006). In this study we use BIOMOD2. The model uses a rectilinear model known as SRE that is analogous to that of the ubiquitous BIOCLIM model (Busby, 1991). Defined by the extrema of each variable in the respective range space, this envelope model represents an ecological species niche bounded by the limits of the maximum and minimum values (Busby, 1991; Tsoar et al., 2007). Owing to its simplicity and use of presence-only data, models which employ both presence and absence data, such as the remainder below, are generally preferred as they are likely to give better predictions (Segurado and Araújo, 2004; Heikkinen et al., 2006; Pearson et al., 2006). Furthermore, it is worth noting that the TSS scores for the SRE were consistently the lowest, among all statistical models. The SRE consistently had a high cutoff rate with 495 data points removed for all tree species and had the lowest sensitivity (true positive rate) at roughly 80% as opposed to the >90% for all other models.

Classification tree analysis (CTA), also referred to as classification and regression trees (CART), involves rule-based methods that were used by Iverson and Prasad (1998) in a similar study involving tree species in the eastern United States. A recursive method built upon rules determined by individual explanatory variables, CTA splits input data into two exclusive, homogenous subsets (known ironically as “trees”) and continues until a termination point is reached. Classification trees can incorporate both numerical and categorical variables, however a drawback is a tendency to over-classify the data creating overly complex models that can limit its usefulness (Thuiller et al., 2003; Heikinnen et al., 2006). A popular form of CTA which is also used in this experiment is the Random Forest (Breiman, 2001). The Random Forest is a variation of the CTA by using a technique known as bootstrap aggregating or bagging each decision point. In bagging, training points are sampled with replacement from the full training set.

Multivariate Adaptive Regression Splines (MARS) represent a relatively new non-parametric regression technique that can be used for non-linear models (Friedman, 1991). By using spline

57 fitting, MARS uses polynomial functions in a piecewise manner and subsequent recursive partitioning. Through this recursive partitioning, MARS builds models in two phases in a forward pass and backwards pass. During the forward pass, MARS continually adds functions until it captures the maximum reduction in training error and any overfitting of models is then pruned in its backwards pass. However unlike other forms of recursive partitioning, the use of MARS is lauded for its ability to produce continuous models for high-dimensional data that are either additive or contain multiple predictor variable interactions (Lewis and Stevens, 1991).

The Flexible Discriminant Analysis (FDA) is a combination of an indicator response matrix to regression procedure. Whereas linear discriminant analysis (LDA) is restricted to linear boundaries, a flexible discriminant analysis uses multiple combinations of regression. Often the LDA has difficulty with larger datasets where having too many correlated predictors create model noise. The multiple regression techniques in a FDA, improve on the LDA by creating combinations of regression models so that its predictions from its sub-models are uncorrelated or weakly-correlated. Through this technique, it is possible to fit a large number of predictors into a reduced number of discriminant functions to optimally separate variables into groups (Hastie et al., 1994). These combinations of explanatory variables are selected to maximize the ratio of group mean discriminant scores to within-group variance (Manel et al., 1999).

3.3.5 Model Selection for Predicting a Species Likelihood Suitability

Figure 3.2: Ordination biplot showing the eigenvectors of first (PC1) and second (PC2) principal components based on tree species’ likelihoods in year 2050 for 134 Eastern North American tree species under RCP 2.6 and RCP 8.5. Eigenvectors for the various statistical models in BIOMOD2 are shown. PC1 explained 71.3% of total variation while PC2 explained 16.8%.

When the species distribution data of absences and presences are correlated to their current climate, BIOMOD2 uses statistical methods SRE, ANN, RF, CTA and FDA to provide suitability likelihood as a result of a future climate. However, deciding which of these models might best model a species distribution may be difficult. Alternatively, a principal component analysis (PCA) was used as a consensus (Kramm, 2008) in a method similar to that proposed by Araújo et al. (2005). A principal component analysis transforms a set of correlated model values into a set of linearly uncorrelated variables known as principal components. This transformation places the largest possible variance on the first principal component, such that models which contribute to the most to the first principal component can be determined to explain model variation.

To perform a Principal Component Analysis for the study, R package Vegan was used. Tree species and their respectively suitability for each model type (SRE, RF, CTA, MARS and FDA) were used. Each row (or "site") represented an individual tree species, while each column represented the percentage suitability given by one or another of the various statistical models ("species"). The first Principal Component (PC1) represents the axis that best explains the total variance in the data set. Therefore, a model projection which gives the highest loading on the PC1 can subsequently explain the greatest proportion of variance among all projections (Araújo et al, 2005). If several models all show high loadings (long eigenvectors) on this first axis, and low loadings on the second axis, then they show high consensus and jointly explain much of the variance in the data. By contrast, if models have loadings (eigenvectors) perpendicular to this group, then they show low consensus. So, the approach to consensus that I took here, following Kramm (2008), is to consider only those models with such high loadings.

Using the results from the RCP 2.6 climate change scenario, the first principal component (PC1) explained 71.3% of the variance; the second principal component (PC2) explained 16.8% (Figure 3.2). The MARS model had the highest loading on PC1, followed by the RF, CTA, FDA and the substantially weaker SRE model. SRE had the highest absolute loading on PC2, followed by the FDA, MARS, CTA and RF. Based on these loadings, to create a consensus estimate of a future

60 likelihood of suitability of a particular tree species, the suitability provided by FDA, MARS, CTA and RF were averaged to capture an ensemble of probable outcomes.

For the high carbon emission scenario, RCP 8.5, 76.3% was explained using PC1 and 13.3% by PC2. The model with the highest loading on the principal component again was MARS; however it was followed much more closely subsequently by the RF, FDA and CTA which only had marginally less loading on the PC1. Likewise, SRE had the lowest weight on PC1 and the substantially highest loadings on PC2. Based on loading weights from PC1, the models MARS, RF, FDA and CTA again provided consensus estimates. In both analyses, SRE had relatively low loadings on PC1; hence it was not used in the ensemble to predict a potential likelihood of suitability in Toronto.

3.4 Results

Available through BIOMOD2, an ensemble of statistical measures were used to project future tree distribution for two climate change scenarios, RCP 2.6 and RCP 8.5. This was accomplished through the creation of climate envelopes which correlated climatic variables to a species’ current distribution. The statistical models used to transform current distributions were a combination of regression and machine learning techniques available through BIOMOD2. Statistical models included the Surface Range Envelope (SRE), Classification Tree Analysis (CTA) and Random Forest (RF), Multivariate Adaptive Regressive Splines (MARS) and Flexible Discriminant Analysis (FDA). A Principal Component Analysis was used to determine which models could explain the variation between their predicted species suitability under future conditions. Since the SRE failed to explain the variation of its predicted suitability, this model was excluded from the projection of future species likelihood in Toronto. Below the average suitability likelihoods were derived from an ensemble of the statistical models CTA, RF, MARS and FDA was used as an estimate for potential likelihood of suitability and is listed in Table 3.1 below:

Species Current RCP RCP Species Current RCP RCP 2.6 8.5 2.6 8.5

Abies balsamea 58.5 3.6 2.0 Pinus clausa 8.3 11.0 8.3

Acer barbatum 0.0 2.0 1.6 Pinus echinata 3.9 8.7 61.7

Acer negundo 42.4 88.2 88.0 Pinus elliottii 3.1 4.1 3.1

Acer nigrum 86.0 98.6 77.5 Pinus glabra 1.7 2.3 1.7

Acer pensylvanicum 70.5 34.0 61.8 Pinus palustris 2.5 3.2 2.4

Acer rubrum 97.8 96.1 99.1 Pinus pungens 19.9 56.3 82.2

Acer saccharinum 83.8 98.7 98.5 Pinus resinosa 79.7 15.0 11.1

Acer saccharum 99.5 98.8 99.3 Pinus rigida 35.3 52.6 94.4

Acer spicatum 98.2 43.8 34.0 Pinus serotina 4.3 5.9 4.8

Aesculus glabra 2.6 36.1 57.7 Pinus strobus 99.3 94.3 92.3

Aesculus octandra 0.7 18.0 73.1 Pinus taeda 2.8 3.8 4.1

Alnus glutinosa 99.0 95.9 99.0 Pinus virginiana 2.5 37.3 68.4

Asimina triloba 12.1 94.6 81.8 Platanus occidentallis 66.7 99.3 99.4

Betula alleghaniensis 99.4 74.0 77.6 Populus balsamifera 61.9 23.9 9.4

Betula lenta 56.2 76.1 92.8 Populus deltoides 57.0 72.3 55.9

Betula nigra 5.6 9.4 20.0 Populus grandidentata 94.5 93.5 88.0

Betula papyrifera 68.8 8.1 6.1 Populus tremuloides 91.1 52.1 24.2

Betula populifolla 45.1 11.8 7.6 Prunus americana 31.9 93.4 94.7

Bumelia lanuginosa 4.0 5.1 4.0 Prunus pensylvanica 82.7 42.1 38.8

Carpinus caroliniana 92.7 99.8 99.4 Prunus serotina 94.2 98.0 99.4

Carya aquatica 3.7 4.8 3.6 Prunus virginiana 98.5 83.3 63.4

Carya cordiformis 53.1 94.4 98.9 Quercus alba 72.9 86.7 99.2

Carya glabra 28.9 67.3 89.5 Quercus bicolor 46.2 93.2 81.6

Carya illinoensis 3.6 7.1 7.9 Quercus coccinea 14.9 52.7 91.7

Carya laciniosa 23.1 71.7 61.3 Quercus durandii 3.7 4.5 3.2

Carya ovata 64.3 91.3 98.4 Quercus ellipsoidalis 9.1 9.3 5.7

Carya texana 0.9 2.2 2.4 Quercus falcata (2) 1.7 2.5 17.5

Carya tomentosa 6.8 53.3 59.2 Quercus falcata (3) 2.9 3.8 3.0

Castanea dentata 47.9 95.3 97.8 Quercus ilicifolia 12.9 45.9 80.8

Catalpa speciosa 1.1 0.0 0.6 Quercus imbricaria 8.6 63.9 50.2

Celtis laevigata 4.4 5.8 4.6 Quercus laevis 3.6 4.8 3.7

Celtis occidentalis 12.0 52.4 75.7 Quercus laurifolia 3.5 4.4 3.4

Cercis canadensis 6.2 50.4 97.1 Quercus lyrata 3.6 5.0 4.6

Chamaecyparis thyoides 8.7 11.7 3.3 Quercus macrocarpa 75.1 75.8 74.0

Cornus florida 38.3 80.6 98.7 Quercus marilandica 4.6 13.2 49.1

Diospyros virginiana 4.0 15.0 47.7 Quercus michauxii 2.7 3.8 3.5

Fagus grandifolia 96.7 96.4 99.1 Quercus 17.0 83.4 94.7 muehlenbergii

Fraxinus americana 98.4 97.6 99.0 Quercus nigra 2.4 3.1 2.4

Fraxinus nigra 98.0 62.4 30.1 Quercus nuttallii 1.6 2.2 1.7

Fraxinus pennsylvanica 82.8 86.1 88.3 Quercus palustris 12.1 83.0 70.8

Fraxinus quadrangulata 14.5 60.2 47.4 Quercus phellos 2.4 3.0 3.0

Gleditsia aquatica 2.5 2.8 1.9 Quercus prinus 33.5 87.7 97.8

Gleditsia triacanthos 4.6 21.7 34.2 Quercus rubra 99.4 91.3 99.2

Gordonia lasianthus 3.2 4.2 3.3 Quercus shumardii 4.9 7.9 32.6

Gymnocladus dioicus 11.1 49.7 51.2 Quercus stellata 4.0 14.1 77.9

Halesia spp. 3.0 3.8 2.9 Quercus stellata (4) 4.1 5.3 4.0

Ilex opaca 3.5 6.0 37.3 Quercus velutina 46.2 90.7 98.0

Juglans cinerea 90.9 98.8 99.0 Quercus virginiana 3.9 5.1 3.9

Juglans nigra 51.1 95.5 98.3 Robinia pseudoacacia 6.8 44.6 66.3

Juniperus virginiana 66.1 93.4 99.2 Salix amygdaloides 71.5 55.2 12.7

Larix laricina 70.4 5.1 10.1 Salix nigra 48.0 99.1 99.5

Liquidambar styraciflua 2.1 3.0 21.5 Sassafras albidum 61.2 88.4 99.5

Liriodendron tulipifera 29.0 95.0 98.3 Sorbus americana 32.0 2.4 12.9

Maclura pomifera 7.3 10.0 7.7 Taxodium distichum 3.9 5.1 3.9

Magnolia acuminata 45.8 78.0 96.5 Taxodium 4.1 5.4 4.1 distichum(5)

Magnolia grandiflora 1.4 1.9 1.4 Thuja occidentalis 92.2 27.6 27.4

Magnolia macrophylla 9.4 13.5 10.0 Tilia americana 98.8 92.6 98.1

Magnolia virginiana 4.1 5.3 4.5 Tilia heterophylla 5.8 28.0 56.4

Morus rubra 18.3 66.9 94.5 Tsuga canadensis 99.5 94.8 96.7

Nyssa aquatica 2.4 3.1 2.8 Ulmus alata 3.4 5.4 6.7

Nyssa ogechee 8.1 10.6 8.1 Ulmus americana 94.8 96.3 98.0

Nyssa sylvatica 36.1 79.3 97.0 Ulmus crassifolia 2.4 3.2 2.5

Nyssa sylvatica (1) 8.0 10.6 8.1 Ulmus rubra 92.9 100.0 99.4

Ostrya virginiana 91.9 91.2 85.8 Ulmus thomasii 95.7 94.8 80.7

Oxydendrum arboreum 2.1 4.0 10.9

Persea borbonia 4.8 6.4 4.8

Picea glauca 13.2 3.3 3.1

Picea mariana 47.5 4.7 3.6

Picea rubens 8.1 2.1 5.6

Pinus banksiana 16.2 8.1 6.5

(1) Nyssa sylvatica var. biflora, (2) Quercus falcata var. falcata, (3) Quercus falcata var. pagodilfolia, (4) Quercus stellata var. margaretta (5) Taxodium distichum var. nutans

Table 3.1: Percentage suitability likelihoods averaged across the four grid cells representing Toronto using an averaged likelihood from CTA, FDA, MARS and RF models for 134 Eastern North American tree species, using the current climate and representative concentration pathways RCP 2.6 and RCP 8.5 for the year 2050.

The likelihoods demonstrate a continued suitability for many of Toronto’s tree species. The two most dominant tree species, the American Beech (Fagus grandifolia) and Sugar Maple (Acer saccharum), were reported as well-established for current and future conditions with their suitability approaching near certainty with over a 95% likelihood. Other such important component species which were projected to have similar success and suitability of over 90% for current and future conditions include the American Elm (Ulmus americana), Basswood (Tilia americana), Blue beech (Carpinus caroliniana), Eastern Hemlock (Tsuga canadensis), Eastern Hemlock (Tsuga canadensis), Eastern White Pine (Pinus strobus), European Alder (Alnus glutinosa), Red Maple (Acer rubrum), Rock Elm (Ulmus thomasii), Slippery Elm (Ulmus rubra) and White Ash (Fraxinus americana). Although the results show the continued success of species such as the Eastern Hemlock (Tsuga canadensis) and Eastern White Pine (Pinus strobus), other conifers and northern hardwoods showed substantial declines in likelihoods. Examples include the Mountain Maple, Northern White Cedar (Thuja occidentalis) and Quaking Aspen, all which fall from a near-certainty (>90% likelihood) for current conditions to a decline in likelihood as high as 65% under the future conditions of RCP 2.6 and RCP 8.5. Similarly species which show only moderate success under current conditions (40-60%) such as the Paper Birch (Betula papyrifera), Tamarack (Larix laricina), Red Pine (Pinus resinosa) experience declines in likelihood declines of 40% or higher.

Not surprisingly, the values for RCP 2.6 are lower than that of the RCP 8.5. This is because under a more accelerated climate change scenario, southerly species are expected replace more rapidly communities of conifers and northern hardwoods lost under a more gradual climate change outcome such as RCP 2.6. Despite the decline of many conifer species and more northerly deciduous trees as a result of climate change, the change in suitability is positive for many species who find their northern limit in the Toronto region. These Carolinian species benefit from future climate change projected to bring warmer temperatures and an increase in precipitation. Some of these increases in likelihood were quite substantial; for example, Eastern Redbud (Cercis canadensis) increased to over 90% likelihood from current conditions under RCP 8.5. Chinkapin Oak (Quercus muehlenbergii), Scarlet Oak (Quercus coccinea), Red Mulberry (Morus rubra), Post Oak (Quercus stellata), Yellow Buckeye (Aesculus octandra) and Pawpaw (Asimina triloba) also increased in likelihood to at least 70%. Likewise, these changes were also noted in the less carbon intensive pathway RCP 2.6, where Pawpaw (Asimina triloba) had an increase of likelihood of over 82%, while Pin Oak (Quercus palustris), Chinkapin Oak (Quercus muehlenbergii), Tulip Tree (Liriodendron tulipifera) and Wild Plum (Prunus americana) all showed increases of likelihood of at least 60%. As many of these species are of Carolinian origin and already either currently exist in Toronto or Southern Ontario under a less optimal suitability, these trees could eventually provide suitable replacements for other tree species that lose suitability as a result of climate change. Although the climate suitability of these warmer weather tree species indicate that they will become increasingly compatible for future climates, a more detailed site-specific assessment will be needed to assess disease and pest risk, growth requirements, and which trees a city may want to plant as part of strategic management goals.

Distribution of Suitability Likelihoods for 134 Eastern North American Tree Species in Toronto for Various Climate Scenarios 60

30 Projected Current Values

20 RCP 2.6 NumberofSpecies 10 RCP 8.5

Percent Likelihood of Suitability

Figure 3.3: Distribution of likelihood suitability in Eastern North American tree species in Toronto for current conditions and projected climate change scenarios.

For current conditions from the BIOMOD2 model, 23 tree species from Little's distribution maps showed near-certain suitability (>90% likelihood) in Toronto. This number notably increases when comparing the results of the RCP 2.6 and RCP 8.5, where 31 and 37 tree species showed a near certainty of suitability in Toronto respectively. These results demonstrate that climate change is projected to increase species diversity positively. Conversely, species falling within the 1-10% range for likelihood of suitability decreased. The number of species in this likelihood range for current conditions was reported at 58, while for the RCP 2.6 and RCP 8.5 the numbers fell to 20 and 48.

Trees likelihoods between current conditions and future scenarios tend to be most affected on the most extreme likelihood ranges. Numbers of trees falling between a 20 to 80% likelihood stayed relatively constant for current and future conditions.

It is also interesting to note the varying likelihoods among conspecifics (or variations within the same species), as likelihood of suitability changed for variants of black tupelo (Nyssa sylvatica),

67 red oak (Quercus falcate), post oak (Quercus stellate) and bald cypress (Taxodium distichum). In the case of Quercus falcata, the southern red oak (Quercus falcata var. falcata) had a likelihood suitability of 17.5% while its conspecific cherrybark oak (Quercus falcata var. pagodifolia) had a likelihood of 3.0% suitability in Toronto for RCP 8.5. Such differences show variation in the climate envelopes that are reflective of intraspecific variation and genetic diversity.

Steady increases are seen for a number of Carolinian species such as in the shagbark hickory (Carya ovata) with an increase from 66.2% to a 98.4%, likelihood for RCP 2.6 and RCP 8.5 respectively. However, the most dramatic increases are seen in species such as the bear oak (Quercus ilicifolia), eastern redbud (Cercis canadensis), pitch pine (Pinus rigida), post oak (Quercus stellata), scarlet oak (Quercus coccinea) and shortleaf pine (Pinus echinata) which all show at least a 25% increase in likelihood when transitioning from the RCP 2.6 to RCP 8.5. Most notably, the yellow buckeye (Aesculus octandra) showed a 69.5% increase in likelihood transitioning from the RCP 2.6 to RCP 8.5. These substantive increases are the result of a warmer climate projected by RCP 8.5, and are seen as beneficial to the species, which occur naturally in the Appalachians.

For northern hardwoods common in Toronto’s forests, the largest decreases transitioning from RCP 2.6 to RCP 8.5 include that of black ash (Fraxinus nigra) at a 26.5% decrease and the quaking aspen (Populus tremuloides) decreasing 53.3%. The substantial change is a result of their climate envelopes which capture cooler, more northerly climates which may extend all the up to the northern limits of the Canadian tree line. However, the biggest loss occurred in the peachleaf willow (Salix amygdaloides) at a 42% decrease in likelihood. This low likelihood of suitability is possibly due to the low moisture regimes in which the peachleaf willow is naturally found in. Toronto’s climate which becomes increasingly warmer and wetter with the climate change scenarios RCP 2.6 and RCP 8.5, appear increasingly incompatible to the climate envelope of the peachleaf willow, native to the American Midwest and Canadian Prairies. The outcome of these results poses an interesting question, in which projections of future distribution are not simply a function of latitude that a species occupies. While latitudinal differences explain much of the variance for the overwhelming majority of species distributions projected here, it has proven to be inaccurate for species with a more longitudinal gradient such as the peachleaf willow.

3.5 Discussion

Future species suitability likelihood was determined based on climate envelopes using the statistical modelling package BIOMOD2. This section will provide a critical analysis of this model and will compare its results to previous species distribution studies of Eastern North American trees. Furthermore, this discussion will extend to ecological applications on whether such predicted suitability can be realized in terms of satisfying habitat requirements and whether species migration can track future changes in climate.

3.5.1 Comparing Statistical Techniques for Species Distribution Modelling

It is important to note that not all models used in BIOMOD2 performed equally when it came to predicting future species suitability using future climate scenarios RCP 2.6 and RCP 8.5. Using a Principal Component Analysis to determine which models could explain predicted species suitability, the SRE failed to explain its variation. The SRE is noted by its low weight on the first principal component and was excluded from analysis. For both the RCP 2.6 and RCP 8.5, MARS provided the highest loading on the first principal component estimate and SRE the lowest. SRE’s limited performance capabilities presumably are an indication of the limitation of presence-only methods in species and therefore use of presence-only methods should be viewed with a degree of caution. However, most concerning is that SRE views all climate variables equally and does not differentially weigh climate drivers instead creating a simple, bounded box.

It is interesting to note that the results of this study run somewhat counter to the findings of Prasad et al. (2006) in examining which model best explained variation as a result of climate change. Prasad et al. (2006) note the instability of MARS in predicting species range for future climates in being too reliant on local data. It is likely that MARS in their experiment was subject to the instability of using an extensive multivariate approach using 33 different variables of climate, soil type, and land use data. The weighing of variable selection seems to be problematic in MARS especially for rarer species for future climate projections (Prasad et al., 2006), and is likely that the selection relevant predictor variables explaining absences or presences (whichever is larger) are likely influence predictors for a species with low prevalence (Leathwick et al., 2006). Leathwick et al. (2006) describe a risk of over-fitting with having rare species modelling under the same environmental conditions as either their absences or presences depending on which is more prevalent. It may be possible to alleviate these issues of prevalence by altering

69 variable weights or reducing variable space for rarer species so that results are not necessarily skewed by its absences or presences.

Our study uses a reduced variable space only selecting seven different climate variables expressing annual averages, maxima and minima of temperature and precipitation extrema. Conversely, MARS consistently showed highest scores along PC1 and hence was best able to explain variation within the BIOMOD2 simulations. Support for MARS has been shown by Hastie and Tibshirani (1996) and Leathwick et al. (2005) which describe the merits of MARS as successfully able to fit more complex and non-linear relationships and to describe relationships between multiple species, their predictors and environment.

3.5.2 Modelling a Projected Reality

The projected results of the simulation of 134 different Eastern Northern American tree species demonstrate that future climate change is expected to encourage the northern migration of tree species into Toronto’s future climate space. These results, which use the latest iteration of IPCC Global Climate Models, are consistent with the findings of Canada’s Plant Hardiness Zones published by McKenney et al. (2001) and the previous modelling efforts of Iverson and Prasad (1998) and Prasad et al. (2006).

Although describing vegetation at a much broader landscape approach rather than a species approach, McKenney et al. (2001, 2006) describe a shift in Toronto’s current Plant Hardiness Zone from 6A to 7A, an approximate shift of about 5°C in average annual minimum temperature. This would place Toronto’s current tree communities somewhere closer to a climate similar to that of Southern Ohio and the Virginias and this is consistent with the incursion of more southerly tree species as projected by BIOMOD2. However, unlike this work, McKenney et al. (2001) does not describe tree communities at the species level. Additionally, the prediction of plant hardiness zones using ANUSPLIN, a thin plate spline prediction method that uses presence-only data may not necessarily produce robust results as shown through the use of presence-only Surface Range Envelope method used in this paper. This is evidenced by SRE’s non-differential weighting of variables as in the other regression techniques used in the paper.

The results of this paper are consistent with the findings of Iverson and Prasad (1998); Prasad et al., (2006). Although their work examined only the eastern United States, they also projected

70 declines of spruces and firs in and an advancement of southerly oak species. Notably, Iverson and Prasad (1998) predict a northward shifts in excess of 250 km while retaining the southern boundary of the distributional limits for the eastern redbud (Cercis canadensis), black cherry (Prunus serotina), scarlet oak (Quercus coccinea), blackjack oak (Quercus marilandica), scarlet oak (Quercus stellata), eastern hemlock (Tsuga canadensis), and winged elm (Ulmus alata). While the distributional limits of the blackjack oak and winged elm are likely too southerly to reach Toronto even for climate change projections, the success of the remaining five species retain likelihoods of suitability as high as 90% for RCP 2.6 and RCP 8.5.

It is important to note that these suitability likelihoods are the result of the modelling of a realized niche. This assumes the likelihood captures the entire set of conditions from which a tree species can grow in, including most notably competition and any limiting factors present in its habitat which are implied through its climatic envelope. A notable example is the northern catalpa (Catalpa speciosa), commonly planted as an ornamental tree and is widely noted in Toronto’s Street Tree Inventory (City of Toronto, 2016). The likelihood of suitability was estimated to be very low under current conditions and even decreasing for projected climate change scenarios, RCP 2.6 and RCP 8.5 with likelihoods of 11.4%, 4.3% and 5.9% respectively. It is important to note that it is not a failing of the model, rather that the implied niche captured by its climatic envelope represents a more narrow (realized) range of environmental conditions. Therefore it is unlikely that the climatic envelope of the northern catalpa will provide any suitable habitat outside its small population centers near the Mississippi and Ohio Rivers. Woodall et al. (2010) also found the northern catalpa and various magnolia species to be much farther north than what their current forestland ranges should be. The northern catalpa is not a major constituent of forests throughout its range and is poorly represented within Toronto’s urban forest outside its Street Tree Inventory representing only 0.3% of all forest cover (Novak et al., 2013). The reason for this anomaly is that the derived climate envelope is a realized niche rather than a fundamental niche. They may still thrive under managed conditions, for example as street trees, however these species may be stressed and living in suboptimal conditions and which may also make them vulnerable to pests, diseases or competition.

3.5.3 Realizing a Climate Equilibrium

An important question is the extent to which the projected increases in tree species likelihood in Toronto will actually occur. If migration does not track climate change, what instead may occur is that the northerly trees expected to move out of their areas climatic are instead surviving under suboptimal conditions, and species projected to arrive from the south fail to migrate northward. In the fragmented landscapes of Eastern North American, historically observed rates of migration 10–50 km per century are unlikely to occur (Prasad and Iverson, 1998). While Kirilenko and Solomon (1998) have hypothesized migration at post-glacial rates or a migration rate even higher, even the mechanisms behind historical geographical migrations are poorly understood (Clark et al., 1998). Dispersal of seeds for one remains problematic, if migration is to track climate change. Low seed availability, as noted by Clark et al. (1998) was often encountered for even common tree species in closed canopies, while Dyer (1995) found that dispersal by birds or wind to fall short projected warming by at least an order of a magnitude (see also Malcolm et al., 2002). Further any large-scale change is considered to be unlikely, when considering that the populations of the two major component species from Ontario’s Mixedwood Plains, the American beech (Fagus americana) and sugar maple (Acer saccharum) see a future likelihood of suitability that approaches a near-certainty (>95%) for the RCP 2.6 and RCP 8.5 scenarios. As these trees occupy a substantial part of the ecological niche, with Sugar Maple covering over 10% of Toronto’s urban forest (Nowak et al., 2013), compositional change is only likely to occur if these dominant trees die off and are replaced.

What the projections provided by BIOMOD2 show are the modelling of a realized niche under the restrictions of climate and climate change. For a changing climate, assisted migration as proposed by Pedlar et al. (2011) may be of interest to conservationists and decision-makers to afforest Carolinian species that are rare in Ontario. This poses the issue of adapting to an assisted migration approach to mitigate the loss of biodiversity. By moving species intentionally to climatically suitable locations away from areas of natural occurrence, assisted migration is a novel conservation practice. Although its critics propose that resources are better used in preserving existing communities, where resources and knowledge have more appropriately been placed, Minteer and Collins (2010) propose a “move it, or lose it” approach. It is possible that for future climate change, that species may fail to catch up to climate change resulting in substantial losses of biodiversity. However, the concerns of translocating species include a potential

72 maladaptation and the introduction of pests and diseases from translocated stock (Pedlar et al, 2012). Further concerns include the loss of genetic diversity by selecting widely distributed genotypes if these trees are chosen from migration stock (Aubin et al., 2011). However, in human-altered environments such as an urban forest which already has been greatly influenced by land use change and the introduction of ornamental trees, assisted migration in limited trials may be of particular interest if properly managed (Minteer and Collins, 2010). Woodall et al. (2010), found that for climate change not only translocated trees spread rapidly, these targeted tree species can serve as a facilitator of native tree spread enhancing local biodiversity and ecological services. Furthermore, these trees can serve as seed sources or climate refugia for native trees along migration pathways.

3.6 Conclusions

The potential likelihood of 134 Eastern North American trees was simulated using software BIOMOD2 for future climate change under a high and low representative concentration pathway, RCP 2.6 and RCP 8.5. While many of the dominant species in Toronto’s urban forest such as the American Beech and sugar maple are expected to retain a near-certainty of likelihood (>95%), projected climate change will support an increase in biodiversity. Compared to current conditions, a higher carbon emission pathway, such as RCP 8.5 would suggest a gain in the number of species with high likelihood of suitability and decrease in the number of species with low suitability. This suggests a replacement of more northerly species with the more numerous temperate species from the south. These results are consistent with the results of McKenney (2001) which suggests a shift in Toronto’s ecoregion to a climate similar to the Appalachians or an approximate increase of roughly 5ºC. Similarly, Iverson and Prasad (1998) and Prasad et al. (2006) note the dramatic decline of aspen and fir communities. The models proposed in the paper, include a more robust ensemble of CTA, FDA MARS and RT, whereas McKenney (2001) uses a presence-only method of estimation similar to the SRE which provided erratic predictions in BIOMOD2 and Iverson and Prasad (1998, 2007) which used only variations of CTA methods. A concise selection of climate variables that only affect vegetation directly was chosen and apparently allowed for MARS to provide a reasonable estimation that best explained species distribution in the model. The erratic behavior seen in the experiments by Iverson and Prasad (1998) and Prasad et al. (2006) were likely due to the large selection of variables on vegetation such as land use which makes prediction very unstable. This leads variable selection for rarer

73 species to be guided by the prevalence of absences or presences, in whichever is more numerous of the two. As both global climate and statistical models continually evolve over time, predictions of future tree distribution will likely change as well. More recent iterations of GCMs show a reduction of uncertainty from observational outcomes as evidenced in Chapter 2 using annual average temperature and precipitation.

While projections of future climate change propose a replacement of more northerly hardwoods and pines with that of more temperate species, this is unlikely to become a reality in the near future. Estimations of tree migration suggest that distributions will lag behind climate (Malcolm et al., 2002) and even the mechanisms behind post-glacial tree migration are poorly understood. However, what this study does contribute to is a potential for future tree species diversity. Although an assisted migration is a contentious topic with the potential introduction of diseases and pests or even the maladaptation of species (Pedlar et al., 2012; Aubin et al., 2011), a potential inaction would result in a “move it, or lose it” outcome according to Minteer and Collins (2010). Conservationists and planners wishing to implement an assisted migration of more southerly species to rehabilitate of afforest areas may certainly find this research of importance. However, if implemented in experimental trials of limited scale, an assisted migration could be conducive to native tree spread and a seed source and refuge for native trees (Woodall et al., 2010).

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Pedlar, J.H., McKenney, D.W., Aubin, I., Beardmore, T., Beaulieu, J., Iverson, L., O'Neill, G.A., Winder, R.S. and Ste-Marie, C., (2012). Placing forestry in the assisted migration debate. BioScience, 62(9), pp.835-842.

Prasad, A. M. and L. R. Iverson.( 2003). Little's range and FIA importance value database for 135 eastern US tree species. http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html, Northeastern Research Station, USDA Forest Service, Delaware, Ohio.

Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2), 181-199.

Rapoport, E. H. (1982). Areography. Geographical Strategies of Species. Translated by B.

Drausal. Pergamon Press, Oxford, pp. 269.

Segurado, P., & Araujo, M. B. (2004). An evaluation of methods for modelling species distributions. Journal of Biogeography, 31(10), 1555-1568.

Svenning, J. C., & Skov, F. (2005). The relative roles of environment and history as controls of tree species composition and richness in Europe. Journal of Biogeography, 32(6), 1019-1033.

Thuiller, W., Araújo, M.B. and Lavorel, S. 2003. Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. Journal of Vegetation Science 14, 669–80.

Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). BIOMOD–a platform for ensemble forecasting of species distributions. Ecography, 32(3), 369-373.

Tsoar, A., Allouche, O., Steinitz, O., Rotem, D., & Kadmon, R. (2007). A comparative evaluation of presence‐only methods for modelling species distribution. Diversity and distributions, 13(4), 397-405.

Woodall, C. W., Nowak, D. J., Liknes, G. C., & Westfall, J. A. (2010). Assessing the potential for urban trees to facilitate forest tree migration in the eastern United States. Forest Ecology and Management, 259(8), 1447-1454.

Woodward, F.I. (1987). Climate and Plant Distribution. Cambridge University Press, Cambridge,pp. 174.

Chapter 4 Street Tree Suitability in Toronto’s Urban Forest for Future Climate Change Scenarios and an Assessment of an Assisted Migration of Ontario’s Species at Risk Trees

4.1 Abstract

Toronto is located at the boundary between Ontario’s Carolinian forest zone and the Great Lakes - St. Lawrence forest and projected climate change may pose important consequences for the heterogeneous mix of trees that currently inhabit its urban forest. To provide insight into the impact of climate change on Toronto’s urban forest, trees occurring on roadside allowances were mapped to show potential changes in tree suitability.

This study also explores the potential rehabilitation of Ontario’s Species at Risk in Toronto through assisted migration. Because all of Ontario’s tree Species at Risk are Carolinian species that thrive in the projected warmer and wetter climates of Southwestern Ontario, the rehabilitation of these Carolinian species may be increasingly possible as a result of Toronto’s evolving climate. An assessment of threats to conservation was used as criteria in addition to climate suitability to evaluate an assisted migration of translocated trees in the context of Toronto’s strategic goals of afforestation. In the case of Toronto's urban trees, it appears that projected climates will become increasingly accommodating to these Species at Risk. However, a closer analysis reveals disease, pest risk and loss of genetic diversity currently threaten Ontario’s Species at Risk and a potential introduction of translocated tree stock as part of an assisted migration may further exacerbate its threats to conservation.

4.2 Introduction

Urban forests are typically represented by a network of heterogeneous spaces whose complex interactions are determined by an intersection of biophysical and social factors (Sanders and Stevens, 1984; Conway and Urbani, 2007). Toronto, Ontario, Canada is defined by a humid, continental climate characterized by warm, humid summers and cold winters. However, this description of Toronto’s climate becomes more nuanced when considering its proximity to Lake

Ontario and the effect of lake breezes in addition to the effects of urbanization (Mohsin and Gough, 2012). Describing tree distributions in an urbanized environment consists of a complicating set of challenges, where land surface variations are a notable constraint. The often impervious surfaces of urbanized landscapes may blanket an area such as the downtown core, with many cities having as much as 40% of their total land area unavailable for trees and vegetation (Sanders and Stevens, 1984). Urban environments are highly heterogeneous landscapes with substantial spatial and vertical variation to their surfaces. Paved surfaces leave an impenetrable crust over the soil surface, restricting aeration, nutrient cycling, soil and soil organism activity (Watson et al., 2014). Such limitations caused by urban environments often impede tree growth and lead to stressed trees that are more vulnerable to pests and disease (Tubby and Webber, 2010).

Toronto lies in the Toronto Ecodistrict 7E-4 of Ontario’s Mixedwood Plains region (Figure 1.1). The Toronto Ecodistrict is a low-lying area with an elevation from 73-280 m above sea level bound by the communities of Burlington and Pickering at its southwestern and northeastern extents, respectively. Currently, Toronto’s current street tree assemblage in its urban forest differs from Toronto’s strategic management plans (City of Toronto, 2013a). Low diversity through the dominance of a few species, are potential threats to Toronto’s goals of achieving greater biodiversity and increased canopy cover through expansion and equitable distribution of tree assets (City of Toronto, 2013a). Overreliance on a relatively small number of species can be particularly harmful to the overall health of the urban forest. The current Toronto tree community represented by its street trees is dominated by only a few species, many of which are introduced exotics. Such species include the eastern white cedar (Thuja occidentalis), sugar maple (Acer saccharum), Norway maple (Acer platanoides), white ash (Fraxinus americana), Manitoba maple (Acer negundo), green ash (Acer pensylvanicum), white spruce (Picea glauca), eastern hophornbeam (Ostrya virginiana), Siberian elm (Ulmus pumila), and European crab apple (Malus sylvestris. Together these 10 most populous tree account for over 57.7% of Toronto’s tree assemblage (Novak et al., 2013). A low diversity makes the urban forest particularly vulnerable to the introduction of diseases or pests, which could greatly affect some tree populations. These stresses caused by suboptimal conditions link to increased susceptibility to insect pests (Tubby and Webber, 2010). Inevitably, this could lead to increased costs due to treatment, replanting and subsequent removal (McPherson et al., 1997). This low diversity stems

81 from the selection of younger, ornamental trees on city property. Only 14% of Toronto’s trees have a diameter greater than 30.6 cm (City of Toronto, 2013). However, where biophysical constraints are less evident within the city, there is a greater potential fostering biodiversity, especially in a human-influenced environment, which could decidedly reduce the impact of natural stressors.

Adding another level of complexity to Toronto’s urban forest is climate change. Although to what extent climate change affects biological interactions is an active research area, a projected warmer and wetter climate (Environment and Climate Change Canada, 2016) is expected to have important consequences. For the years 2046-2065, a conservative climate change scenario RCP 2.6 projects an 8.9% increase in precipitation and an increase of 2.2°C for the winter months of December to February. During the summer months of June to August, an increase in precipitation by 2.6% and an increase of temperature by 1.4°C is projected. For carbon-intensive climate change outcome such as RCP 8.5, there is an average projected increase in precipitation of 17.5% and an increase in temperature of 4.6°C for the winter months of December to February. However, for the summer months of June to August, there is an projected average of 3.1°C in temperature and an increase in precipitation of 1.3%.

Although Toronto’s Strategic Management Goals state that trees should be selected to not only include more species and increase robustness (City of Toronto, 2013a), the projected change in climate should be assessed for new opportunities to foster biodiversity, and whether the effects of an assisted migration would be positive or negative. Climate change may pose many risks and threats in the form of new diseases and pests as well as the unfavorable climatic conditions for its more northerly species, however on the other hand it may prove to produce beneficial conditions that foster new habitat for new and current tree species. Assisted migration for the purposes of the study refers to the human-assisted translocation of trees that are native to Eastern North America, that may not currently exist within Toronto. A list of tree species native to the Toronto is provided in Appendix 7.2.

Of particular interest is the assisted migration of Ontario’s Tree Species at Risk in Ontario, all of which are species of more southerly Carolina origin. An adaptation approach to mitigate the loss of biodiversity, assisted migration is an in-situ method of intentionally moving of species to climatically suitable locations. However, the practice of implementing an assisted migration is a

82 contentious topic. Numerous studies explore whether or not assisted migration should be implemented and whether those effects will be beneficial to biodiversity. It is a relatively new management technique, and managers often lack best practices (Minteer and Collins, 2010; Pedlar et al., 2012). An imperfect knowledge of climatic conditions, which determine species distribution and their competitive interactions with their environment, coupled with the unknown future migration outcomes as a result of climate change all make assisted migration extremely challenging.

Under assisted migration, tree species are selected based on their suitability to the future climatic conditions from a given location. As Aubin et al. (2011) states there may be unintended consequences of an assisted migration. Firstly, a potential for maladaption may occur from intraspecific variation. Potential tree stock may have adapted to its localized conditions and introducing a particular genetic stock throughout its distribution, may lead to an ill-fitted introduction. Conversely, if selected genetic stocks are from more widely-distributed genotypes, a potential loss in local genetic diversity may result from its chosen migration stock (Aubin et al., 2011).

Assisted migration becomes increasingly important when the evidence suggests that natural migration is too slow to replace the potential loss of trees through climate change. In the fragmented landscapes of Eastern North America, historically observed rates of migration at 10– 50 km per century are unlikely to occur (Iverson and Prasad, 1998). Even at these post-glacial rates, forests need to migrate at least one order of magnitude faster than historically observed migrations to adequately respond to current levels of warming (Malcolm et al., 2002; Woodall et al., 2010). Inaction on assisted migration could lead to a potential extinction or extirpation events as a result of climate change as proposed by the “move it or lose it” strategy proposed by Minteer and Collins (2010). Simply put, there is a potential loss for biodiversity if species cannot adapt to their new environments as a result of climate change. As a new and novel conservation approach, Minteer and Collins (2010) have proposed the implementation of assisted migration in environments that have substantial human influence, yet do not require constant management as an important starting point. These novel ecosystems can provide new habitat for species that may be introduced in anticipation of any harmful changes that may occur in native and unaltered ecosystems (Minteer and Collins, 2010). Toronto is an ideal study for the evaluation of a potential assisted migration strategy for a human-altered landscape. As a transitional area, the

Toronto area boasts a number of tree species from the both Ontario’s boreal regions and its more temperate Carolinian south. It is expected that many of these temperate trees could potentially expand their range into the Toronto area as a result of projected climate change (see Chapter 3). Assessing urban forest vulnerability as a result of climate change is a relatively new topic. The cumulative effects of climate change include insect and disease susceptibility, extreme weather events and sensitivity of young trees (Brandt et al., 2016; Brandt et al., 2017; Foran et al., 2015) and tree species with specific temperature and moisture requirements. Ordóñez and Duinker (2014) also suggest socioeconomic impacts of climate change. While some research assesses the future vulnerability of urban tree species as a result of climate change, few studies such as Woodall et al. (2010), focus on an assisted migration of native trees within an urban forestry context. Woodall et al. (2010) found assisted migration served as a facilitator for native tree spread and a seed source or refuge for native trees along migration pathways. Tree species found within a city were often reflective of their surrounding forestlands and if climate change provides competitive advantage to introduced trees, it is very likely they will spread rapidly. Furthermore, if urban trees are maintained through proper irrigation, fertilization and suppression of competitive effects, the assisted of migration of native trees could provide for potential seed sources for threatened trees. Although assisted migration superficially seems to be a simple solution to a relatively complicated problem, assisted migration will only seek to serve a small fraction of species whose climate envelopes overlap with Toronto’s future climate.

This study builds on the work of Woodall et al. (2010) by examining forest tree distributions as a result of climate change. Specifically, this study addresses an assisted tree migration within an urban forestry context. Detailed information regarding Toronto's tree composition at present is limited to street tree data. Therefore, analysis regarding Toronto's current tree inventories will be derived from street tree data. This chapter analyzes projected climate change and the likelihoods of Toronto’s tree species using two methods. The first objective is quantifying any changes in the suitability likelihood as a result of projected climate change for the most populous trees in Toronto’s urban forest composition. This will result in the creation of maps to visualize how the future suitability likelihoods of Toronto’s native tree species will change as a result of climate change for the low and high emission scenarios of RCP 2.6 and RCP 8.5. Secondly, using the likelihoods calculated from the varying climate change scenarios, an assessment will be done for tree species listed under Ontario's Species at Risk Act. Finally, the possible introduction or

84 rehabilitation of Ontario’s tree Species at Risk in Toronto’s changing climate will be explored in this chapter.

4.3 Methods

4.3.1 Predicting Tree Species Suitability as a Result of Projected Climate Change

Projecting tree species suitability for a future climate builds on the results of the previous chapter. In order to project tree species diversity as a result of projected climate change, 134 available tree species naturally occurring in North America east of the 100th meridian were considered for this experiment with trees native to the Toronto area noted in Appendix 7.2. Using the digitized maps based on the work of Elbert L. Little, Jr. between 1971 and 1977, the tree species distributions were transformed and made available as GIS vector data shapefiles through the US Forest Service with an approximate resolution of 20 km2 (Prasad and Iverson, 2003). By correlating current distribution to climate, it is possible to infer the climatic boundaries of the various tree species. A number of bioclimatic variables known to limit tree distribution were used for this work. Available through the WorldClim database (http://www.worldclim.org) (Hijmans et al., 2005), a number of bio-climatic variables were considered with species absences and presences: Annual Mean Temperature; Maximum Temperature of Warmest Month; Minimum Temperature of Coldest Month; Annual Precipitation; Precipitation of Driest Quarter and the Precipitation of Warmest Quarter. These variables were considered to account for tree stressors, which ultimately limit distribution (Thuiller et al., 2003). The justification for the selection of these variables is given in the Chapter 3.

By establishing the climatic baseline for current conditions, future climates can then be used to project potential tree distribution changes. Two radiative forcing scenarios, known as the Representative Concentration Pathways (RCP), roughly representing the maximum and minimum levels of radiative forcing for future conditions were used for the year 2050 (an average of the years 2041-2060). These RCPs, RCP 2.6 and RCP 8.5, were used representing a full range of possibilities as a result of climate change, and an optimized global climate model (GCM) ensemble using the models GFDL-CM3, IPSL-CM5A-LR and MPI-ESM-LR (Chapter 2).

Using the results reported in the Chapter 3, the projected likelihoods of species resulting from climate change will be used to facilitate two different analyses. Firstly, the future likelihood of tree species distribution will be used in conjunction with Toronto’s Street Tree Data. By using these tree species likelihoods together with Toronto’s Street Tree Data, it is possible to create maps to visualize Toronto’s tree community as a result of climate change. A map would not only visualize which trees exist and where, but also identify climatic suitability for its projected species likelihoods using climate change scenarios RCP 2.6 and RCP 8.5.

Secondly, the projected likelihoods of tree species as a result of climate change are used to assess the viability of introducing or rehabilitating Ontario’s Species at Risk as a result of projected climate change. Viability of Ontario’s Species at Risk will use the given likelihoods of suitability under the conservative climate change scenario RCP 2.6 and an aggressive climate change scenario, RCP 8.5, to assess climatic suitability.

4.3.2 Generating Maps to Visualize Toronto’s Tree Distributions

Approximately 10.2 million trees currently provide Toronto with 17,000 to 18,000 hectares of urban forest canopy cover (Novak et al., 2013). Toronto has a tree canopy cover of approximately 28%, and of these 10.2 million trees roughly 6% (600,000 trees) are city-owned street trees, 34% (3.5 million trees) are in city parks and natural areas, and 60% (6.1 million trees) exist on private lands (City of Toronto, 2013a). To assess the current status of Toronto’s tree species, street tree data was available through Toronto’s Open Data Network. This continually-updated dataset pertains to city-owned trees located on road allowances throughout Toronto and is intended to provide inventories for inspection and maintenance. While Toronto’s street tree data only accounts for 530 136 trees as of April 2016, it provides insight and analysis of trends regarding Toronto’s public planting strategies. This is the case for many urban forest policies where financial allocations and planning initiatives are restricted to publicly owned trees (Conway and Urbani, 2007).

This study will seek to quantify changes in Toronto’s urban forest by projecting the change in likelihood for its 15 most populous native trees. Of the 530 136 trees in Toronto's Street Tree data, 115 156 or roughly 22% are considered native to Eastern North America. Although 63 different native tree species exist in Toronto, the top 15 most numerous species represent species represent approximately 85% the urban forest’s native trees. Among these 15, those that show

86 substantial changes in likelihood will be mapped to show how their potential outcomes may shape Toronto’s urban forest. Any losses to these few component species could have an important impact on tree species biodiversity and could dramatically reduce canopy cover in the city. However, these losses may prove beneficial for other tree species looking to occupy an unfilled niche. A changing climate could either prove to be beneficial to Ontario’s trees that are identified as Species at Risk or further exacerbate the problems of invasive species within the city. Climate change poses unique challenges and potential opportunities for some Species at Risk, many of which reside in isolated populations in Ontario’s Carolinian forests.

4.4 Results

Toronto's Most Populous Street Tree Natives 25000

20000

15000

10000

NumberofIndividuals 5000

Picea glauca

Acer rubrum

Acer negundo

Quercusrubra

Acer saccharum

Catalpa speciosa

Betulapapyrifera

Acer saccharinum

Celtisoccidentalis

Thujaoccidentalis

Fraxinusamericana

Gleditsiatriacanthos

Gymnocladusdioicus Liriodendrontulipifera Fraxinuspennsylvanica

Figure 4.1: Toronto’s Fifteen Most Populous Native Trees on Street Tree Allowances

Toronto’s Street Tree Data shows skewed distributions its native tree species, even among its most populous species. As seen in Figure 4.1, the city is dominated by a combination of silver maplee (Acer saccharinum), honey locust (Gleditsia triacanthos), sugar maple (Acer saccharum), green ash (Fraxinus pennsylvanica), paper birch (Betula Papyrifera) and white spruce (Picea glauca) each representing over 10 000 individuals. As seen below in Figure 4.2

87 their future likelihoods as a result of climate change scenarios, RCP 2.6 and RCP 8.5, are largely positive with the exception of the paper birch (Betula Papyrifera), northern white cedar (Thuja occidentalis) and white spruce (Picea glauca). However, although largely positive, projected climate change does not affect each species evenly, and the differences are seen in Figure 4.2 where the Manitoba maple (Acer negundo), common hackberry (Celtis occidentalis), Kentucky coffee tree (Gymnocladus dioicus) and tulip tree (Liriodendron tulipfera) will experience increases greater than 35%, whereas northern white cedar (Thuja occidentalis), paper birch (Betula Papyrifera) will experience declines of over 50% from current suitability. These declines in suitability are noted by the areas captured by their climate envelopes under their current distribution. For example, the paper birch’s climate envelope captures a suitability that reflects a more northerly environment, with species suitability captured in areas as far north as Alaska. Similarly for the northern white cedar, its climate envelope captures conditions compatible with more boreal environments. As a result of projected climate change, the paper birch and the northern white cedar will experience dramatic declines in its climatic suitability.

Future Likelihood of Suitability for Toronto's Most Populous Street Tree Natives Under Climate Change 100 90 80

70 60 50 40

Likelihood(%) 30 Current 20 RCP 2.6 10 RCP 8.5

Picea glauca

Acer rubrum

Acer negundo

Quercusrubra

Acer saccharum

Catalpa speciosa

Betulapapyrifera

Acer saccharinum

Celtisoccidentalis

Thujaoccidentalis

Fraxinusamericana

Gleditsiatriacanthos

Gymnocladusdioicus Liriodendrontulipifera Fraxinus pennsylvanicaFraxinus

Figure 4.2: The Suitability Likelihood of Toronto’s Fifteen Most Populous Native Trees on Street Tree Allowances under Current Projections, RCP 2.6, and RCP 8.5

Using the data from 15 of the most numerous native trees in Toronto’s Street Tree Data (see Figure 4.1), a set of maps were created showing the location of current street trees native to Eastern North America. To illustrate this change, representative climate change pathways, RCP 2.6 and RCP 8.5 were used to simulate future conditions in Toronto. These maps show changes in likelihood of suitability for native tree species from Toronto’s Street Tree Inventory from current conditions.

Figure 4.3: The current distribution of Manitoba maple (Acer negundo) on street tree allowances

Figure 4.4: As in Figure 4.3 but for the current distribution of paper birch (Betula papyrifera)

Figure 4.5: As in Figure 4.3 but for the current distribution of common hackberry (Celtis occidentalis)

Figure 4.6: As in Figure 4.3 but for the current distribution of Kentucky coffee tree (Gymnocladus dioicus)

Figure 4.7: As in Figure 4.3 but for the current distribution of tulip tree (Liriodendron tulipfera)

Figure 4.8: As in Figure 4.3 but for the current distribution of northern white cedar (Thuja occidentalis)

A comparison of the honey locust (Gleditsia triacanthos), paper birch (Betula papyrifera), Kentucky coffee tree (Gymnocladus dioicus), northern white cedar (Thuja occidentalis), common hackberry (Celtis occidentalis) and tulip tree (Liriodendron tulipifera) for the climate change scenarios are shown individually as trees which influence the overall character of Toronto’s trees (see Figures 4.3 through 4.8). The overall character of Toronto’s urban forest is shown in a composite showing tree suitability likelihood of all extant native trees averaged within a 500 m2 radius. From these maps, which range from current reproduction to projected climate change using RCP 2.6 and RCP 8.5 scenarios, it is possible to see the effects of Toronto’s most populous trees on overall tree suitability throughout the city.

Figure 4.9: Tree suitability likelihood in Toronto under a current climate reproduction using an average of all tree likelihoods within a 500 m grid cell

Figure 4.10: Change in tree suitability likelihood in Toronto from current climate projections under RCP 2.6 using an average of all tree likelihoods within a 500 m grid cell

Figure 4.11: As in Figure 4.10 but under future climate outcome RCP 8.5

Here shown in Figures 4.9 through 4.11, the effects of its most populous trees and dominants such as silver maple, sugar maple, red maple, green and white ash and red oak are shown through current and future distributions as areas with high likelihoods of suitability. Most poignantly, the changes observed in the paper birch and northern white cedar have notably created areas of low suitability that are scattered throughout the city in areas formerly populated by areas of moderate-to-high suitability likelihood (see Figures 4.4 and 4.8). Slight gains in suitability are made in the northeastern portion of the city where natural tree communities exist. This is held in contrast to more urbanized portions of the city where singly-planted trees are planted for ornamental purposes. Though these trees may be native to the Toronto area, they are not necessarily selected for ecological fitness in Toronto’s current or future climates.

While there may be individual differences when comparing the outcomes of RCP 2.6 and RCP 8.5, when using an average of all tree likelihoods within a 500 m radius, the results are nearly indistinguishable other than a few scattered pockets of model output where there are slight differences (compare Figures 4.10 and 4.11). This potentially could lead to trees living in suboptimal conditions, which may not necessarily result in extirpation but a perhaps a gradual decline, which would make the trees more vulnerable to pests, diseases and competition without management or human intervention. Areas of high suitability are the result of the maintained dominance of species such as silver maple (Acer saccharinum), sugar maple (Acer saccharum), green ash (Fraxinus pennsylvanica), red oak (Acer rubrum), and white ash (Fraxinus americana).

Future Likelihood of Suitability for Ontario's Species at Risk in Toronto Under Climate Change 100 90 80

60 50

40 Likelihood(%) 30 Current 20 RCP 2.6 10 RCP 8.5

Betulalenta

Morusrubra

Cornusflorida

Juglanscinerea

Castanea dentata

Quercusshumardii

Magnolia acuminata Gymnocladusdioicus Fraxinusquadrangulata

Figure 4.12: Projected suitability of Ontario’s Species at Risk with current climate conditions, RCP 2.6 and RCP 8.5

However, from a strictly climatological aspect, several other tree species will benefit as a result of projected climate change for climate scenarios RCP 2.6 and RCP 8.5. Of particular, interest is the potential to expand habitat for Ontario’s Species at Risk. As seen in the chart below, all of Ontario’s Species at Risk which include the American chestnut (Castanea dentata), blue ash (Fraxinus quadrangulata), butternut (Juglans cinerea), cherry birch (Betula lenta), cucumber tree (Magnolia acuminata), eastern flowering dogwood (Cornus florida), Kentucky coffee tree (Gymnocladus dioicus), red mulberry (Morus rubra) and humard Oak (Quercus shumardii) have an increase in suitability for Toronto’s projected climate (see Figure 4.12).

Future Likelihood of Suitability for Non-Native Canidate Species in Toronto For Climate Change Scenarios 100 90 80

70 60 50 40

Likelihood(%) Current 30 20 RCP 2.6 10 RCP 8.5

Pinusrigida

Pinus pungens Pinus

Asimina triloba

Quercusstellata

Quercusilicifolia Quercuscoccinea

Platanusoccidentallis

Figure 4.13: Future suitability of non-native candidate species for assisted migration under current projections, RCP 2.6 and RCP 8.5

It is also of interest besides Ontario’s Species at Risk, to foster future habitat for trees which will fit Toronto’s future climate. A number of species which do not currently exist in Toronto’s Street Tree Inventory may potentially benefit from its projected climate (see Figure 4.13). These trees

97 include the bear oak (Quercus ilicifolia), American sycamore (Platanus occidentalis), pawpaw (Asimina triloba), pitch pine (Pinus rigida), post oak (Quercus stellata), scarlet oak (Quercus coccinea) and table mountain pine (Pinus pungens). The aforementioned trees continue to see their suitability increase moving from current conditions to projected RCP 8.5 conditions, although the Pawpaw takes a slight decrease from projected RCP 2.6 values. Several of these trees currently already exist in Ontario, such as the American sycamore, cucumber tree and pitch pine, while trees such as the bear and scarlet oaks occur just south of Ontario’s borders. Although oaks such as the Bear Oak and Post Oak have their populations far south from Toronto, these species under RCP 8.5 make substantial gains to reach high suitability in the city’s projected climate.

4.5 Discussion

Through Toronto’s Street Tree Inventory, it was possible to assess the geographical distribution of Toronto’s street trees as well as its species richness. It is important to note that this study pertains to only Toronto’s street trees as a representation of its urban forest. These inventories reflect trees on street allowances and do not pertain to trees in natural forested areas or on private property. Although more detailed records exist for urban forests such as through the i-Tree tool database, these records are spatially heterogeneous and often incomplete (Lerman et al., 2014). With its city-wide coverage, these street trees represent an assemblage of trees previously chosen by foresters to best adapt to conditions of Toronto’s urban forest. Since these trees are publicly- owned, street trees remain flexible to any adaptation measures needed to better cope with climate change.

To illustrate changes of tree suitability under climate change, an overall average of all tree species likelihoods within a 500 meter radius was visualized in a series of composite maps for current climate conditions and climate change scenarios RCP 2.6 and RCP 8.5. The results of these visualizations show a large-scale decline of species suitability under the RCP 2.6 and RCP 8.5 scenarios from current values. This decline is largely in part to declines in the Northern White Pine, Paper Birch and White Spruce. Although Toronto’s Street Tree Data boasts 63 different native tree species, the data shows Toronto’s native trees are largely dominated by only a small number of tree species. Given that Toronto’s urban forest is disproportionally represented by only a small number of tree species, any changes in Toronto’s most representative tree species

98 will notably affect overall tree suitability within the city. However, despite this decline in suitability resulting from the suitability likelihoods of the northern white pine, paper birch and white spruce as a result of climate change, future conditions may also prove beneficial to other tree species in Toronto’s urban forest.

4.5.1 Other Non-Climate Considerations

With the expected increase in temperature and precipitation in Toronto under climate change, an assisted migration may become increasingly feasible. This may prove beneficial to more southerly species, notably, Ontario’s Species at Risk. For Ontario Species at Risk many of which are of southern, Carolinian origins, conservation records and historical distributional data were used to better assess feasibility for introduction and reforestation under an assisted migration. To compliment findings of projected suitability using climate change scenarios RCP 2.6 and RCP 8.5 will be an assessment of tree species habitat and their associated risks to tree species survival including vulnerability to disease and pests. A particular focus will be given to Ontario’s Species at Risk in this assessment of tree species suitability.

From a purely climatic perspective, the results of climate change are largely positive to Ontario’s Species at Risk (Figure 4.12). While the projected increases of precipitation and temperature as a result of climate change notably increase our measured likelihood of suitability in Toronto for Ontario's Species at Risk, the reality may be far different. An assessment of Ontario's Species at Risk shows that among these species are concerns of diseases, pests and the loss of genetic diversity. Substantial gains occur in the likelihood of all species including the American chestnut (Castanea dentata), blue ash (Fraxinus quadrangulata), cherry birch (Betula lenta), cucumber tree (Magnolia acuminata), eastern flowering dogwood (Cornus florida), Kentucky coffee tree (Gymnocladus dioicus), red mulberry (Morus rubra) and shumard Oak (Quercus shumardii) though the likelihood of the butternut (Juglans cinerea) increased only slightly retaining its near certainty of suitability in Toronto's projected climate. These numbers are particularly encouraging for an assisted migration where a potential expansion of range could be potentially implemented. Possible tree stock for translocation can be taken from common species or wide-ranging distributions south of the Canadian border such as with the blue ash, cherry birch and eastern flowering dogwood This would be advantageous for Ontario's Species at Risk

99 which exist as either scattered or sporadic trees with low dispersal and limited population centers within the province.

4.5.2 Implementing an Assisted Migration

However, no matter how tempting an assisted migration would seem for Ontario's Species at Risk trees, implementation might be a precarious endeavour. A possible assisted migration may potentially undermine conservation attempts if translocated specimens reduce genetic diversity or further exacerbate the effects of disease and other harmful effects. Rising temperatures have been attributed to the geographic expansion of pests, pathogens and their vectors, and it has been expected that increases in temperature may speed up their growth and development and increase winter survival (Yang, 2009). Pests and diseases are documented and well known, such as with the chestnut blight that has historically decimated the Amercian chestnut. Virtually all mature American chestnuts have succumbed to the disease, causing the tree to rarely if ever grow to maturity leaving the trees as only a number of sprouts on old stumps and root systems (Anagnostakis, 1995). Similarly, the butternut has been threatened with extirpation in Ontario, whose typically short-life spans are compounded by the introduction of butternut canker (Sirococcus clavigigenti-juglandacearum). The threat has been so serious that in 1995, the US Forest Service indicated that up to 77% of all trees have been infected with butternut canker. Causing branch and stem cankers, the butternut canker has been largely limited to riparian areas where heavily infected trees are no longer reproducing (Schlarbaum et al., 1997). Another Species at Risk tree under disease threat is the eastern flowering dogwood, and although not currently in Toronto, its projected climatic suitability for the two climate change scenarios could potentially see this tree translocated using an assisted migration strategy. The eastern flowering dogwood was once listed as population secure yet now experiences annual declines of 7-8% due to dogwood anthracnose funguses (Discula sp.) (Bickerton and Thompson-Black, 2010). However, the tree is grown as an ornamental and resistant cultivars have been known to exist in Maryland (Windham and Witte, 1998), but translocating such trees could potentially act as vectors for other pests and diseases while reducing the local genetic diversity of the eastern flowering dogwood. Pests such as the emerald ash borer also pose threats to local ash trees, and although Ontario's Species at Risk tree, the blue ash is a typically less-preferred species, the insect pest is still a cause for concern. Since the tree typically occurs in soils too dry or unsuitable for other trees (Farrar, 1995), its lower population densities as a scattered tree species

100 could lead it to be vulnerable if emerald ash borer densities become too high or if all other ash communities have succumbed to infestation. Furthermore, although a future climatic suitability implies success, this does not necessarily translate to a perfect, ecological fit. Although some Species at Risk trees are generalists, for example, the American chestnut which can grow under a variety light requirements and soil conditions and the cherry birch which grows over a long- ranging distribution from Southern Ontario to Georgia, many species end up growing in specialized environments due to their non-dominant nature in forest stands. Species such as the cucumber tree require deep, loose, moist and fertile soils, while the Kentucky coffee tree only exists on floodplain woodlands and the edge of marshes, these specialized requirements would be particularly demanding for an assisted migration. Furthermore, the isolated nature of some species would result in a loss of genetic diversity if translocated trees are taken from stocks derived from widely-ranging genomes. Species such as the red mulberry are particularly vulnerable to the hybrizidation with the white mulberry, an exotic from Northern China. The tree is not particularly pest nor disease resistant and its leaves are vulnerable to predation and diseases, which include twig blight and dieback, cankers and root rot (Parks Canada, 2011). In particular, species that are poorly documented in Southern Ontario which include the cherry birch and Shumard Oak, which is often misidentified as the red oak which it hybridizes with (Donley et al., 2013), face losing their unique genetic diversity if replaced by translocated trees.

The aforementioned concerns of disease and pest risk leave the cucumber tree (Magnolia acuminata) and Kentucky coffee tree (Gymnocladus dioicus) and cherry birch (Betula lenta) being potentially the only suitable Species at Risk candidates for an assisted migration. However, the future climate of Toronto can prove increasingly suitable a select number of other tree species which reside outside its street tree inventories (see Figure 4.13 and Appendix 7.2 for a full list of species suitability likelihoods). Species such as the American sycamore, pawpaw and pitch pine are already native to Ontario, while oaks such as the bear and scarlet Oaks occur just south of Ontario’s borders. Other trees such as the post oak and table mountain pine whose population centres are much further south of Ontario also see their suitability coincide with Toronto’s future climate. Certain trees such as the pawpaw would be welcome additions to an urban forest bringing edible fruit, while southern pines such as the pitch and table mountain pine could replace more northerly pines lost through warming temperatures. However, the translocation of trees to Toronto’s urban forest could bring similarly bring disease and pest issues

101 as with Ontario’s Species at Risk. As an example, the American sycamore may find suitability through Toronto’s projected climate especially given its ability to survive on a variety of soil types. However this ornamental tree also experiences many disease and pest issues with lace bugs and anthracnose known to be major defoliating agents for the tree (Filer, 1977).

Although translocating trees through an assisted migration may introduce unintended outcomes such as the loss of genetic diversity and disease and pest risks, an assisted migration could provide climate refugia and a potential native tree source for rarer species of trees (Woodall et al., 2010). However, as Minteer and Collins (2010) suggest, that if assisted migrations are to occur it may beneficial to start in small-scale, human-controlled environments first to anticipate any harmful changes. An assisted migration could only work in isolated patches as street trees far away from naturally occurring forest areas where risk of infestation from pests and diseases could be either monitored or controlled. Similarly, trees can be grown ex-situ in greenhouses or nurseries from local stock where trees from an assisted migration could be quarantined before serving as potential seed stock.

4.6 Conclusion

By investigating the future likelihood as a result of projected climate change for trees in Toronto’s Street Tree Inventory, it was possible to visualize how projected change could shape the city’s urban forest. Visualizing the likelihood of suitability for current conditions and climate change scenarios, RCP 2.6 and RCP 8.5, showed that while the climate suitability likelihood for Toronto’s trees are largely positive on the city’s eastern end. However, for the central and western portion of the city, the results are more negative with the decline of the Paper Birch,

Northern White Cedar and White Spruce greatly reducing the overall likelihood of suitability for Toronto’s urban forest. For species that decline in climate suitability, assisted migration to replace any trees lost due to potential climate change is an option. Building on the work of Woodall et al. (2010), this work uses future species suitability due to climate change to assess Ontario’s Species at Risk in an urban forest and assisted migration context. Comparing the individual tree needs and their prospective risks to introduction, the study balanced between the “move it or lose it” outcome proposed by Minteer and Collins (2010) against its detractors Aubin et al. (2011) and Pedlar et al. (2012). The study’s findings were consistent with that of Aubin et al. (2011) and Pedlar et al. (2012) where a potential assisted migration would be detrimental to

102 biodiversity due to the risks concerning the loss of genetic diversity and the introduction of pests and diseases. Assessing the current conservation threats to Ontario’s Species at Risk, many of which currently suffer from pest and risk disease as well as loss of genetic diversity through hybridization, an assisted migration should be limited in scale and proceed with caution. It may be preferable to focus on single urban trees and choose from established local or nearby stock that will compliment Toronto’s future climate.

4.7 References

Anagnostakis, S. L. (1995). The pathogens and pests of chestnuts. Advances in botanical research, 21, 125-145.

Aubin, I., Garbe, C. M., Colombo, S., Drever, C. R., McKenney, D. W., Messier, C., & Winder, R. (2011). Why we disagree about assisted migration 1: Ethical implications of a key debate regarding the future of Canada's forests. The Forestry Chronicle, 87(6), 755-765.

Brandt, L., Lewis, A. D., Fahey, R., Scott, L., Darling, L., & Swanston, C. (2016). A framework for adapting urban forests to climate change. Environmental Science & Policy, 66, 393-402.

Bickerton, H. and M. Thompson-Black. 2010. Recovery Strategy for the Eastern Flowering Dogwood (Cornus florida) in Ontario. Ontario Recovery Strategy Series. Prepared for the Ontario Ministry of Natural Resources, Peterborough, Ontario. vi+ 21 pp.

City of Toronto (2013). Every Tree Counts: A Portrait of Toronto’s Urban Forest. City of Toronto, Parks, Forestry and Recreation, Urban Forestry.Toronto, Ontario. Available at: http://www1.toronto.ca/City%20Of%20Toronto/Parks%20Forestry%20&%20Recreation/Urb an%20Forestry/Files/pdf/E/every_tree_counts.pdf

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City of Toronto (2013a). Sustaining & Expanding the Urban Forest: Toronto’s Strategic Forest Management Plan. City of Toronto, Parks, Forestry and Recreation, Urban Forestry. Available at: http://www1.toronto.ca/City%20Of%20Toronto/Parks%20Forestry%20&%20Recreation/Urb an%20Forestry/Files/pdf/R/Reports/effects-and-values.pdf

Conway, T. M., & Urbani, L. (2007). Variations in municipal urban forestry policies: a case study of Toronto, Canada. Urban Forestry & Urban Greening,6(3), 181-192.

Donley, R.N., J. Jalava, and J. van Overbeeke. (2013). Management Plan for the Shumard Oak (Quercus shumardii) in Ontario. Ontario Management Plan Series. Prepared for the Ontario Ministry of Natural Resources, Peterborough, Ontario. v + 59 pp.aa

Environment and Climate Change Canada. (2016). Climate data and scenarios for Canada: synthesis of recent observation and modelling results. Environment and Climate Change Canada, Ottawa. 29pp.

Farrar, J. L. (1995). Trees in Canada. Fitzhenry & Whiteside Ltd., Markham, Ont., and Canadian Forest Service. Natural Resources Canada, Ottawa, Ont, 106-107.

Iverson, L. R., & Prasad, A. M. (1998). Predicting abundance of 80 tree species following climate change in the eastern United States. Ecological Monographs, 68(4), 465-485.

Lerman, S. B., Nislow, K. H., Nowak, D. J., DeStefano, S., King, D. I., & Jones-Farrand, D. T. (2014). Using urban forest assessment tools to model bird habitat potential. Landscape and urban planning, 122, 29-40.

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Malcolm, J. R., Markham, A., Neilson, R. P., & Garaci, M. (2002). Estimated migration rates under scenarios of global climate change. Journal of Biogeography, 29(7), 835-849.

McPherson, E. G., Nowak, D., Heisler, G., Grimmond, S., Souch, C., Grant, R., & Rowntree, R. (1997). Quantifying urban forest structure, function, and value: the Chicago Urban Forest Climate Project. Urban ecosystems, 1(1), 49-61.

Minteer, B. A., & Collins, J. P. (2010). Move it or lose it? The ecological ethics of relocating species under climate change. Ecological Applications, 20(7), 1801-1804.

Mohsin, T., & Gough, W. A. (2012). Characterization and estimation of urban heat island at Toronto: impact of the choice of rural sites. Theoretical and Applied Climatology, 108(1-2), 105- 117.

National Centers for Environmental Prediction (2013). Subjective List of Model Performance Characteristics. Available at: http://www.wpc.ncep.noaa.gov/mdlbias/biastext.shtml

Ordóñez, C., & Duinker, P. N. (2015). Climate change vulnerability assessment of the urban forest in three Canadian cities. Climatic change, 131(4), 531-543. Chicago

Parks Canada. 2011. Recovery Strategy for the Red Mulberry (Morus rubra) in Canada. Species at Risk Act Recovery Strategy Series. Parks Canada Agency. Ottawa, Ontario. vi + 47 pp.

Pedlar, J. H., McKenney, D. W., Aubin, I., Beardmore, T., Beaulieu, J., Iverson, L., ... & Ste- Marie, C. (2012). Placing forestry in the assisted migration debate. BioScience, 62(9), 835-842.

Sanders, R. A., & Stevens, J. C. (1984). Urban forest of Dayton, Ohio: A preliminary assessment. Urban Ecology, 8(1-2), 91-98.

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Schlarbaum, S. E., Hebard, F., Spaine, P. C., & Kamalay, J. C. (1997). Three American tragedies: chestnut blight, butternut canker, and Dutch elm disease. Exotic pests of eastern forests. USDA Forest Service, Southeastern Research Station, Asheville, NC. Tennessee Exotic Pest Council, 8-10.

Tubby, K. V., & Webber, J. F. (2010). Pests and diseases threatening urban trees under a changing climate. Forestry, 83(4), 451-459.

Windham, M. T., & Witte, W. T. (1998). Naturally occurring resistance to powdery mildew in seedlings of Cornus florida. Journal of Environmental Horticulture, 16(3), 173-177.

Yang, J. (2009). Assessing the impact of climate change on urban tree species selection: a case study in Philadelphia. Journal of Forestry, 107(7), 364-372.

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5.0 Conclusions 5.1 Summary of Main Findings

The aim of this thesis was to assess the future tree species diversity as a result of changing climate conditions in Toronto’s urban forest. Addressed individually in Chapters 2, 3, and 4, the main research questions of this thesis were addressed as standalone chapters. The aim of Chapter 2 was to determine which GCMs could best model average temperature and precipitation under a Canadian climate context. This was accomplished through the comparison of GCMs through the Intergovernmental Panel on Climate Change’s Fourth and Fifth Assessment Report to NCEP reanalysis data. A performance metric based on a rank histogram was used to refine an ensemble value, creating a selected ensemble of the most accurate climate models to help guide model selection. The most accurate models for average temperature and precipitation were then used to create projections of tree species distribution as a result of climate change in Chapter 3. To model species distribution as a result of climate change, a climate envelope was created by correlating current species range to current climatic conditions in Chapter 3. Modelling package BIOMOD2 then used an ensemble of statistical modelling techniques to transform these species distributions using climate change projections from the most accurate GCMs that were determined in Chapter 2. An ensemble of statistical models using Classification Tree Analysis, Random Forest, Multivariate Adaptive Splines and Flexible Discriminant Analysis was used to create a predicted likelihood of suitability under future climate change. Future likelihood of suitability as predicted through species modelling showed that climate change appears to encourage the incursion of more southerly trees while showing a decline in suitability for northern hardwoods and conifers. Chapter 4 culminates in using the work of the two previous chapters to assess street tree suitability for Toronto’s urban forest under climate change. Additionally, this chapter assesses the feasibility of an assisted migration of Ontario’s Species at Risk Trees. To visualize changes in Toronto’s urban forest under climate change, a composite map was created showing the suitability likelihoods of all Toronto street trees within a 500m radius. This map showed large areas of decline under climate change outcomes RCP 2.6 and RCP 8.5, which were attributed to the decreasing suitability from the Northern White Cedar, Paper Birch and White Spruce. A decline in the aforementioned tree species however appears to benefit Ontario’s Species at Risk as many of them come from more southerly, Carolinian

107 climates. While a warmer climate should suggest conditions beneficial to Ontario’s Species at Risk, assessment of disease and pest risk as well as a potential loss of genetic diversity shows that an assisted migration of translocated trees should be avoided.

Chapter 2 of this thesis evaluates the accuracy of these Global Climate Models to model temperature and precipitation by validating climate data by hindcasting climate model output to a known concurrent climatological baseline. The chapter assesses the ability of the GCMs developed by the CMIP used in the Fourth (AR4) and Fifth (AR5) Intergovernmental Panel on Climate Change Assessment Reports to reproduce historically observed climate conditions for annual average of temperature and precipitation. The goal of the research were to determine what the differences were between the two sets of GCMs. Using the NCEP reanalysis dataset to provide a climatic baseline, the AR4 and AR5 ensembles were compared using absolute difference from observation. For a climate change impact assessment, an ensemble is typically used to project climate change. The AR5 GCMs provided more accurate results both for annual average temperature and precipitation. This difference between AR4 and AR5 is largely due to a number of anomalies modelled in the high Arctic in AR4 models FGOALS- g 1.0b, GISS-EH and GISS-ER. Contrary to the findings of Annan and Hargreaves (2011) and Pincus et al (2008), a multi-model mean of all models fared relatively poorly in this analysis. This shows a high degree of intermodel variability in both the AR4 and AR5, therefore a performance metric based on the rank histogram of Annan and Hargreaves (2011) was used to remove poorly performing GCMs. To devise this performance metric, an aggregate score was created to determine on how well each GCM simulated Canada’s regional climates. These GCMs were then ranked relative to an ensemble value for the two different sets of GCMs provided by AR4 or AR5. The models which fell below an ensemble value were then discarded and a new “selected ensemble” of all GCMs ranking above the original ensemble value was taken. Those models which ranked above this new selected ensemble for both temperature and precipitation were determined to be the most accurate models and were determined to be the GFDL-CM3, IPSL-CM5A- LR and MPI- ESM- LR when using AR5 GCMs and if the AR4 is to be selected, ECHO-G, GFDLCM2.1, HADCM3 and MRI CGCM2.3.2A would be the most accurate GCMs. Chapter 2’s contributions to research include developing a computationally straightforward method of determining which GCM provides the best coverage for annual average temperature and precipitation within Canada

108 by created an improved ensemble based on a regional validation using historically-observed climate data.

It is important to note that this study only concerns a mean state of climate, as this would result in the most fundamental and best observed aspect of climate (Reichler and Kim, 2008). However, to understand the cause of model error is difficult and will require an approach that both isolates individual processes while also holistically evaluating the climate model itself. As noted in AR5 GCM simulation, progress is still being made on ocean-atmosphere and cloud and more complex studies will often involve model tuning. Model tuning is often used to artificially adjust for gaps in understanding and has been demonstrated in previous studies like the sea-ice study of Gough (2001) to alleviate the problem of equifinality from data deficiency.

Chapter 3 uses the GCMs chosen by the selected ensemble in Chapter 2 to be the most accurate in simulating past climate in Canada to determine future tree species range in Toronto. Using tree species occurrence data for Eastern North American trees east of the 100th meridian, a climate envelope or a suite of bioclimatic variables associated with tree occurrence was inferred by a species’ geographic absence or presence. Current and projected climate data was obtained through WorldClim for bioclimatic variables Annual Mean Temperature; Maximum Temperature of Warmest Month; Minimum Temperature of Coldest Month; Annual Precipitation; Precipitation of Driest Quarter and the Precipitation of Warmest Quarter which were determined to limit distribution. Using these bioclimatic variables which have been correlated to current tree distribution, BIOMOD2 implements a number of statistical techniques such as Classification Tree Analysis (CTA), Flexible Discriminant Analysis (FDA), Multi Adaptive Regressive Splines (MARS), Random Forests (RF) and the Surface Range Envelope (SRE) to provide estimates of a future likelihood of suitability under two different climate change prediction pathways, RCP 2.6 and RCP 8.5 for the year 2050.

The addition of these models build upon the tree importance maps of Iverson and Prasad (1998; 2007) and the tree hardiness maps created by McKenney et al. (2001) as a result of climate change. Additional models provided by BIOMOD2 offer a more nuanced approach than Iverson and Prasad's (1998) classification tree analysis approach and the SRE methods used by McKenney et al. (2001). BIOMOD2 model variation was then tested using a principal

109 component analysis. A principal component analysis was used to determine which statistical models explain variation between tree species and their bioclimatic variables. Through this procedure, it was determined that with the exception of the Surface Range Envelope, there was a strong agreement between the other statistical measures. This is particularly interesting, as these results may greatly affect the tree hardiness maps created by McKenney et al. (2001). For this experiment, it is interesting to note that MARS best explained variation regarding future climate change and tree species distribution. These results run contrary to the results of Prasad and Iverson (2007), which state the highly-inconsistent values given by MARS. These inconsistencies are likely due to the large number of bioclimatic variables used in Prasad and Iverson (2007) which seemingly appear to have affected their future predictions as MARS is heavily-reliant on neighborhoods of localized data. This chapter improved on these outcomes by using only a handful of bioclimatic variables known to influence vegetative change.

To capture an entire range of potential future tree species distributions an ensemble of likelihoods was produced in BIOMOD2 from the CTA, FDA, MARS and RF with SRE excluded to do its poor predictive ability. These likelihoods were predicted over an area representing the city of Toronto. These results showed that future climate change outcomes RCP 2.6 and RCP 8.5 showing an increase in the number of tree species from current values. The number of tree species with a 90-100% likelihood of suitability increased as a result of climate using RCP 2.6 and RCP 8.5 projections, while conversely, the number of tree species between 0-10% likelihood of suitability decreased. Between the RCP 2.6 and RCP 8.5, the more carbon-intensive climate change outcome RCP 8.5 experienced greater gains and fewer losses in species likelihood than the less carbon-intensive RCP 2.6. This is explained by the northward movement of northern hardwoods and conifers from Toronto and the incursion of more temperate species into Toronto. These effects on tree species distribution in eastern North American as a result of climate change are consistent with earlier findings with studies by McKenney et al. (2001) and Prasad and Iverson (2007, 2011).

The findings in Chapter 3 help provide an assessment of future tree biodiversity in Toronto as a result of climate change. Although this study was specific to the Toronto area, the methods used to construct a correlative climate envelope are transferrable to any Canadian city. Given the findings of Chapter 2 to compliment the availability of high-resolution climate data, determining

110 future tree distributions should be only a matter of obtaining the necessary tree species inventories. Furthermore, this chapter improves on the work of its predecessors by providing predicting species outcomes using an ensemble of statistical modelling techniques. Previous studies relied on the singular use of surface range envelopes McKenney et al. (2011) and classification and regression tree analysis Iverson and Prasad (1998) and Prasad et al., (2013), whereas this study provides an averaged selection of statistical regression and machine learning techniques.

Chapter 4 examines the future tree diversity in Toronto’s urban forest while assessing the feasibility of implementing an assisted migration for Ontario’s rare tree species as a result of climate change. An assisted migration is a climate change adaption approach used to mitigate the loss of biodiversity, by moving species to climatically suitable locations away from their natural range. Although an assisted migration exploits projected climates to bolster biodiversity, it is a contentious topic due to the possibility of unintended consequences such as maladaptation or the introduction of pests, diseases and invasive species. Although the contentious nature of assisted migration has been discussed by both its proponents (Minteer and Collins, 2011) and critics (Aubin et al., 2011; Pedlar et al., 2012), the study is still a relatively new. While a number of climate change vulnerability studies have been implemented in an urban forest context (Ordóñez and Duinker (2014), Foran et al. (2015), Brandt et al. (2016) and Brandt et al. (2017), a few studies such as Woodall et al. (2011) examine assisted migration under an urban forestry context.

To assess the potential for an assisted migration, the current likelihood of suitability was plotted against the future likelihoods predicted under climate change outcomes RCP 2.6 and RCP 8.5. Assessing Toronto’s urban forest relied on city-wide estimates (Nowak et al., 2013) and a detailed inventory of trees on public allowances provided by Toronto’s Street Tree Data (City of Toronto, 2016). Toronto’s Street Trees were mapped showing their respective likelihoods for current conditions and projected climate change. It is important to note that the Toronto’s Street Tree Data is not to be conflated with the urban forest in its entirety, but rather is the most representative dataset that exists for the city. Often financial restraints and planning initiatives are limited to publicly owned trees with regards to urban forests (Conway and Urbani, 2007).

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For the most part, Toronto’s most populous native trees such as the silver maple (Acer saccharinum, sugar maple (Acer saccharum), green ash (Fraxinus pennsylvanica), red oak (Acer rubrum) and white ash (Fraxinus americana) remain unaffected under both current conditions and projected climate change. However, there were some notable gains with the species the honey locust (Gleditsia triacanthos), Kentucky coffee tree (Gymnocladus dioicus), common hackberry (Celtis occidentalis) and tulip tree (Liriodendron tulipifera) and drastic decreases in paper Birch (Betula papyrifera) and northern white cedar (Thuja occidentalis). The coverage of these species with large changes in likelihood were illustrated in maps in Chapter 3 to show both changes for a projected climate and their difference from the rest of Toronto’s native tree assemblage.

Trees from Ontario’s Species at Risk were assessed based on current distribution and habitat requirements, conservation status and threats; contributions to biodiversity and ecosystem services and feasibility of introduction. All trees pertaining to Ontario’s Species at Risk listing have increasing in likelihood from current conditions under climate change. However, it is of note that the blue ash had a noted decrease in likelihood transitioning from the RCP 2.6 to RCP 8.5, due to a potentially wetter climate which would be less representative of its climate envelope. Although these Species at Risk trees may experience improved climate suitability from the projections of potential likelihood, there are other external threats such as insect pests, disease and the risk of hybridization. Of these species, diseases include the American chestnut with the chestnut blight; the butternut with the butternut canker and eastern flowering dogwood with various forms of anthracnose. Other threats include the emerald ash borer potentially affecting the blue ash and the current hybridization of the red mulberry with the white mulberry. Therefore although an assisted migration could potentially undermine contributions to biodiversity through the unintended consequences listed above. Nevertheless, the merits of assisted migration in providing seed sources or climate refugia for rare species (Woodall et al, 2011) should not be discounted. A number of trees nearby and local would make good candidates than the more pest and disease vulnerable Species at Risk trees. These include oaks and southern pines not currently in Toronto’s Street Tree Inventories and could include bear oak, pawpaw, pitch pine, post oak, scarlet oak and table mountain pine. If assisted migration is to occur it may be perhaps worthwhile to approach assisted migration in controlled experiments in

112 human-altered environments as single, isolated street trees opposed to unaltered forestlands (Minteer and Collins, 2011).

The results of Chapter 4 provide a novel approach to assessing street tree suitability by incorporating future species distribution under climate change to its current street tree inventories. By overlaying predicted climate suitability over the city’s tree inventories, this allows decision makers to visually identify both opportunities and potential vulnerabilities within the urban forest. A special focus was given to Ontario’s Species at Risk, which may be able to exploit changes in climate and the decline in suitability from conifers and northern hardwoods. Further analysis however proved that there are other non-climate issues which may affect suitability of Ontario’s Species at Risk in Toronto’s urban forest. These threats include but are not limited to disease and pest risk, hybridization and genetic loss. Likewise to Chapter 3, although Toronto was chosen as the study area, the methods produced in this chapter are not exclusive to Toronto. Given the availability of high-resolution bioclimatic data, it is possible to reproduce this study in other locales as long as spatial data is available for its tree inventories.

5.2 Recommendations for Further Research

Chapter 2 was able to identify which GCMs were best able to reproduce observations in annual average temperature and precipitation change, using performance metric based on the model to observational difference. Despite the GCMs GFDL-CM3, IPSL-CM5A-LR and MPI-ESM-LR being noted as the most accurate models, it is important to note that this study only considered annual average temperature and precipitation change. Although annual average temperature and precipitation change are among the most fundamental and best-observed aspects of climate, to understand modelling error requires a more holistic approach that examines a model and its processes in isolation (Reichler and Kim, 2008). Processes as such as ocean-atmosphere and cloud simulation remain are known issues AR5 simulations (Flato et al., 2013) and model tuning (Reichler and Kim, 2008; Gough, 2001) has often been proposed to accommodate for the difficulty in simulation. However, as understanding in such processes improve, coupled with improvements in observational and computational ability (Cubasch et al., 2013), predictive

113 accuracy in the GCMs will likely increase as well. Therefore, the comparative study of GCMs is an iterative process, and will likely be a topic of continual research.

While Chapter 3 identified a suite of climatic variables that were selected to determine the future distribution of 134 eastern North American trees in Toronto’s urban forest, there remain are many factors which influence vegetative geography beyond climate itself. It is important to note that this study uses an ensemble of statistical techniques under BIOMOD2 to model the realized niche under the restrictions of both current and future climates. A number of other limiting factors include, but are not limited to the effects of insect pests and disease, land use change, inter and intraspecies competition, species dispersion and genetic diversity among others. A subsequent study could potentially aim to incorporate some or all of those previous listed mechanisms, however should proceed with caution such that an inclusion of the selected variable has a direct relation to vegetative distributive change.

Chapter 4, aims to answer some of the uncertainties behind a modelling a realized niche in an urban forest context. Additionally, this chapter explores the climatic potential for an assisted migration with an emphasis on rehabilitating habitat for Ontario’s Species at Risk. While this chapter combines future projections of tree suitability to Toronto’s urban forest, this study was limited to the analysis of trees on street allowances. A more detailed inventory of Toronto’s tree estimates would be beneficial to the study of tree species. Due to either accessibility or the lack of resources, studies of urban forests are typically limited to street tree data. However, trees in urban forests also comprise of trees on private property, parks and natural woodlots, which will have very different levels of biological and human interaction as well as variations in the soil surface. While the chapter has given a preliminary assessment of the feasibility in an assisted migration of Ontario’s Species at Risk trees, it may be of interest to test the actual suitability of suitable trees in in small-scale, human-controlled or in-situ environments to quantify possible benefits or harmful changes (Minteer and Collins, 2010). Such research could provide as climate refugia for potential seed sources along migration pathways (Woodall et al., 2011), and assist and guide adaptation to disease, pest risk and the loss of genetic diversity.

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6.0 References

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Anagnostakis, S. L. (1995). The pathogens and pests of chestnuts. Advances in botanical research, 21, 125-145.

Akin, W.E. (1991). Global Patterns: Climate, Vegetation and Soils. Univ. Oklahoma Press.

Norman, OK, 370 pp.

Annan, J. D., & Hargreaves, J. C. (2011). Understanding the CMIP3 multimodel ensemble. Journal of Climate, 24(16), 4529-4538.

Araújo, M. B., Whittaker, R. J., Ladle, R. J., & Erhard, M. (2005). Reducing uncertainty in projections of extinction risk from climate change. Global ecology and Biogeography, 14(6), 529-538.

Araújo, M. B., and A. T. Peterson. 2012. Uses and misuses of bioclimatic envelope modeling.Ecology 93: 1527–1539.

Bazzaz, F.A. (1996). Plants in Changing Environments: Linking Physiological, Population and Community Ecology, Cambridge University Press, Cambridge, 320 pp.

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Bengtsson, L., and Shukla, J. (1988). Integration of space and in situ observations to study global climate change. Bulletin of the American Meteorological Society, 69(10), 1130-1143. Chicago

Box E.O. (1981). Macroclimate and Plant Forms. Dr W. Junk, The Hague, 258 pp

Box, E. O. (1996). Plant functional types and climate at the global scale. Journal of Vegetation Science, 7(3), 309-320.

Brandt, L., Lewis, A. D., Fahey, R., Scott, L., Darling, L., & Swanston, C. (2016). A framework for adapting urban forests to climate change. Environmental Science & Policy, 66, 393-402.

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Busby, J. (1991). BIOCLIM- A bioclimate analysis and prediction system.Plant Protection Quarterly (Australia).

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