CAPSTONE PROJECT:

THE EFFECTS OF TREE SPECIES DIVERSITY ON TREE MORTALITY

RATES IN DISTRICT SCHOOL BOARD (TDSB)

PROPERTIES

Rashid Azimov Master of Forest Conservation (MFC) Program

Faculty of Forestry University of Toronto - 2018

Abstract

Trees in schools of Toronto are important for providing shade to playgrounds, protecting children from the UV radiation and for cooling effects during hot days of the year. However, the current mortality rate of trees at school territories is relatively high. Probably, one of the main causes of the high tree mortality is the Emerald Ash Borer (EAB) infestation in the city. In urban forestry literature, researchers and forestry experts usually recommend increasing species diversity to avoid large-scale tree loss in the case of pest/disease outbreak. In this paper, we study how tree species diversity is changing and analyze potential effects of tree species diversity on reducing tree mortality rates in TDSB school properties. The analysis addressed the question if there is a significant statistical relationship between species diversity and tree mortality rates in the territory of 107 schools of TDSB. The potential relationship has been tested through using Simpson’s Diversity Index and estimated annual tree mortality rates. The regression analysis results showed a positive relationship between tree mortality rates and species diversity instead of the expected negative one. The existing literature in urban forestry explains this unexpected relationship with misuse of species in urban tree diversification process.

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ACKNOWLEDGEMENTS

I would like to thank to Professor Sean Thomas, my academic supervisor from Faculty of Forestry of the University of Toronto for his valuable advices and guidance during the preparation of this paper. As well, I would like to express my gratitude to Mr. Justin Nadeau, Ms. Gail Bornstein, and Ms. Karen Dobrucki from Sustainability office of Toronto District School Board for provided information about tree management practices in TDSB schools. I also thank to Mr. Dean Klomp, my class fellow, for his kind support during collection and processing of the tree inventory data used in this project.

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Table of Contents

Abstract ...... ii Acknowledgements ...... iii Table of contents ...... iv Introduction...... 1 Goal, hypothesis and objectives ...... 2 Methodology ...... 2 Data used for analysis...... 2 Analyzing current tree diversity trends ...... 3 Calculating annual tree mortality rates ...... 4 Measuring species diversity ...... 5 Regression analysis between the mortality rates and tree species diversity (Simpson’s diversity index) ...... 6 Results ...... 6 Current tree species diversity trends ...... 6 Estimation of the annual tree mortality rates ...... 11 Tree species diversity calculation ...... 13 Regression analysis ...... 13 Discussion ...... 16 Conclusion and recommendations ...... 18 References ...... 20 Appendix 1 Share of tree species in total urban forest population of the studied TDSB schools in 2018 ...... 22 Appendix 2 Estimated tree mortality rates using average and actual interval periods ... 24 Appendix 3 Tree mortality rates in different tree size classes ...... 27 Appendix 4 Tree species diversity estimation using Simpson’s Diversity Index ...... 28

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Introduction

Trees in Toronto District School Board (TDSB) schools are valued for providing shade on playgrounds, protecting children from potential negative effects ultraviolet radiation and for cooling effects to all school territory during hot days (TDSB 2013). Recently researches discovered that trees also contribute to improvement of student academic performance at primary schools (Sivarajah 2018).

However, currently trees in Toronto are experiencing stresses resulting from outbreaks of different pests, such as Emerald Ash Borer, Asian Long Horned Beetle, Gypsy Moth, etc. These pests are demonstrating considerable negative impact on overall status urban forest in the city (Cecco 2017, OMNRF 2018).

In order to increase the resilience to such situations, researchers and urban forestry practitioners usually recommend increasing tree species diversity in city tree population (CAP 2007, Ordòñez et al. 2013). These recommendations are mainly based on generally recognized assumption about high possibility of severe tree loss mortality from outbreaks of pests, specific to popular tree species in single - species - dominant urban tree populations (Sanders 1978, Raupp et all. 2006, Lacan & McBride 2008). As example of such situations, researchers usually refer to Dutch Elm Disease and Emerald Ash Borer infestations in the cities of North America. Urban forestry specialists have developed several targets for increasing urban tree species diversity. The most popular one can be considered the “10-20-30” approach published by Santamour (1990). This formula tells that in order to minimize tree loss due to pest infestations, any urban forest should contain no more than 10 percent of any single tree species, no more than 20 percent trees from the same genus and no more than 30 percent trees from the same tree family (Lacan & McBride 2008).

At the same time, other authors consider that counting only on species diversity may result in even higher tree mortality through planting less-adapted, untested trees in urban environments. According to them, tree adaptability is more important in decreasing mortality rates in urban tree populations than tree diversity (Richards 1983).

Considering that currently the urban trees in Toronto have been impacted greatly by EAB, now is a good time to test these two contradicting opinions about the potential relationship of tree species diversity and urban tree mortality. In this capstone project, I analyze possible effects of tree species diversity on tree mortality rates at TDSB school

1 properties and make relevant recommendations for the urban forestry managers if species diversity can be used as an effective tool in reducing tree mortality at school courtyards in Toronto.

Study goal, hypothesis and objectives

The primary objective of this study is to analyze the relationship between tree species diversity and tree mortality rates at TDSB school properties. I hypothesize a negative relationship between tree mortality and tree diversity. That schools with more species diversity (richness and evenness) have lower the tree mortality rates; and schools with less species diversity tree mortality rates are higher, due to mitigation effects of diversity.

In order to test this hypothesis, I conducted a four-step analysis: a) Analysis of current tree diversity trends; b) Estimation of annual tree mortality rates for the periods: 2004-2018, 2005-2018, 2006-2018 and 2007-2018; c) Measurement of tree species diversity at schools; d) Tests of the relationship between tree mortality rates and species diversity through linear regression analysis.

Methodology

Data used for analysis Currently TDSB possesses tree inventory database with information about 34,159 trees, which are located in the grounds of 547 schools. This database contains different tree measurement information, including tree trunk diameter, hard root surface, crown diameter measurements and general tree health assessment results.

For my analysis, I used the tree inventory data of 107 schools, first collected in 2004- 2007 and updated in 2018. These updated inventory records allow us to assess changes for periods of 14, 13, 12 and 11 years. The data analyzed in this study covers tree measurements information in 107 out of 547 TDSB schools (19.5%). The schools

2 are located in Downtown, , , and West York areas of Toronto (see Figure 1).

Figure 1. Inventoried TDSB schools. Green and purple colored stars represent the schools updated in 2018 and used for this study. Yellow stars represent the schools, where the second census was conducted in 2017. Small green dots represent all remaining TDSB schools. For analysis of current species diversity trends, I used information about 7377 trees (21% of all TDSB trees). For estimation of tree mortality and calculation of diversity index, I used information about 5733 trees (16.78% of all TDSB trees). There is difference in tree numbers, because for current diversity trends analysis I used information about trees of the first census and the new trees planted after 1st inventory. For mortality calculation and diversity index measurement I used records of only 5733 trees because for these calculations I needed information from the first and the second census.

Analyzing current tree diversity trends

In this stage of my analysis, I identified how the number of species (richness) changed at each individual school between the first and the second censuses to see the current trends of species richness at each individual school. I expected to identify if in the majority of schools, the species richness are increasing or decreasing during the study period.

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I also analyzed how the species composition is changing in overall 107 schools of TDSB. This analysis incudes identification how many species exist in these schools, the share of different species in the total urban tree population, and how this distribution of species has changed over the study period between two inventories.

Next, I calculated how many schools are meeting “30-20-10 diversity formula” by Santamour (1990). For this purpose, I identified the most popular family, genus and species at each school and calculated their share in total tree population of that individual school.

The current diversity trends analysis also included identification of the species composition among the newly planted trees and the species composition among the dead trees.

Calculating annual tree mortality rates

In order to test the possible correlation between diversity and mortality rates, we need to estimate annual tree mortality rates for the whole study period. For my calculations I used two formulas (see below eqn. (1) and (2)) with two different assumptions: a) tree mortality rate is the highest for young aged trees and it constantly decreases with the increase of tree age; b) tree mortality is constant for all aged trees. From our general observations in urban forestry, the first assumption is close to reality. Regarding the second formula, its accurateness was analyzed with the examples of urban tree populations by Roman et al. (2016) and the results showed that the estimated tree mortality rates are very close to true tree mortality rates.

We used following two equations to calculate constant annual mortality for the study period:

푙푛푁 −푙푛푁 (1) λ = 표 푠 , 푇

(Sheil et al. 1995: eqn.4) where, λ- estimated constant annual tree mortality rate, No – the number of trees in the beginning of the study period and Ns – the number of trees at the end of the study period.

and

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퐾푥 1/푥 (2) 푞푎푛푛푢푎푙 = 1 − ( ) 퐾0

(Sheil et al. 1995: eqn.6) where, qannual-estimated constant annual tree mortality rate, x- the period between the two census; Kx – number of trees in a population at the end of the study period; and K0 – number of trees in a population in the beginning of the study period.

As mentioned above, we have tree inventory data of different periods as 14, 13, 12 and 11 years(2004-2018;2005-2018; 2006-2018 and 2007-2018). These different time periods can decrease accurateness of our analysis. To avoid this bias, we also calculated the weighted average period between the censuses, using following formula:

푛 ∑푖 푇푖∗푁푖 (3) 푇푎푣푒 = 푛 ∑푖 푁푖

th In this formula, Tave – the average census interval, Ti-duration (years) of i time th interval, Ni number of trees, studied in i time period interval. The calculated average study period is used with two mortality calculation equations. Thus, we estimated four different tree mortality rates for each school.

Measuring species diversity

As our primary goal is to study potential relationship between species diversity and tree mortality, we need to measure tree diversity rates at each schools in the beginning of the study period. The diversity measurement usually requires considering both the richness (number of different species) and evenness (distribution of diversity in the population).

In many studies, diversity is considered higher in one population than another one, when the total number of species is higher and they are evenly distributed. There are different diversity indexes as Shannon index, Jaccard’s index, Simpson’s index, etc. However, all of these indices have their advantages and weaknesses. The diversity indices are selected and used according to which purpose they are used. For comparative studies, often biodiversity researchers use Simpson’s diversity index, which considers both species richness and dominance or evenness (Magurran, 2004).

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Simpson’s diversity index measures diversity in a population taking into account the number of species present, as well as abundance of each species. For calculation, we use following formulas:

∑ 푛 (푛 −1) (4) Simpson Diversity Index: 퐷 = 푖 푖 푁(푁−1)

(5) Inverse Simpson Diversity Index: 1 – D

Here, ni – total number of trees of a particular species, N - total number of trees of all species. The Simpson’s diversity index value ranges between 0 and 1. When the index equals to 0, it means infinite diversity. When the index equals to 1, it means no diversity. To have more intuitive values, showing higher value related to higher diversity, we subtracted the obtained values from 1.

Regression analysis between the mortality rates and tree species diversity (Simpson’s diversity index)

For testing the potential relationship between tree species diversity and mortality rates in a urban tree population, we calculated the inverse Simpson’s diversity index and estimated annual tree mortality rates with four different methods (two formulas and two different periods: average and true periods). All these variables have continuous values. For this reason, we use simple linear regression analysis to test if there is significant relationship between these variables. The estimated trees mortality rates will be dependent variable (Y). As the independent variable (x), we used the values of the Simpson’s diversity index calculated for each school. For comparison we run the linear modelling test four times.

We also ran linear regressions between tree mortality rates and the diversity richness (number of different species in each schoolyard), too. All analysis conducted with the use of statistical software R (R project 2018).

Results

Current tree species diversity trends

The diversity trends analysis showed that between the first and the second census of inventory in overall 107 TDSB school properties, the total number of species has increased from 100 to 107. Trees of 27 families were recorded in 1st census and this

6 number did not change in the 2nd census. Number of trees of 49 genus were counted in both 1st and this number increased to 50 in the 2nd census (see Table 1).

Table 1. Overall changes of taxa richness during the study period. Species richness Genus Family 1st census 100 49 26 2nd census 107 50 26

At the individual school level, in majority of schools, tree species richness has increased during the study period. In only 17 school out of 107, did tree species richness decrease. In the remaining 90 schools, the number of species increased (see Figure 2).

Figure 2. Tree species richness changes between two censuses.

15.1% of all studied trees were represented by Acer platanoides (Norway maple). This proportion of Norway maple has not changed in the second census (see Fig.3 and Fig.4). In general, schools have good tree species diversity (103 species). However, this diversity is not equally distributed. Many species are represented by one or two trees. For example, only 13 out of 103 species are represented by more than 1% of all trees. The other 90 species make up less than 1% of the total tree population (Appendix 1).

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Figure 3. Share of tree species in overall 107 schools in the first inventory (2004-2007)

The most common 13 species represent more than 77 percent of all trees. And the remaining 90 species together represent only 22.93% of all trees. It worth to note that in 2018 Ash species (Fraxinus americana and Fraxinus pennysylvanica) still represent 10.86% of total tree population in TDSB properties (see Fig. 4).

Figure 4. Share of tree species in overall 107 schools in the second inventory (2018)

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During the study period the share of popular species Norway maple (Acer platanoides), honey locust (Gleditsia triacanthos) and Austrian pine (Pinus nigra) did not change considerably. However, share of relative less popular three species - blue spruce (Picea pungens), red oak (Quercus rubra) and American mountainash (Sorbus americana) decreased to less than 1% in total tree population (see Fig. 3 and Fig.4).

We also checked the degree to which tree diversity at schools meet the requirements of the “30-20-10 diversity management formula” (Santamour 1990) using tree data from the second census (2018). The most common tree family was Sapindaceae. This family represents 31.31% of all studied trees in the 107 school properties.

The most common genus is represented by Acer with 31.94%. This value is much higher than the required 20% share by the formula.

Similarly, we calculated the most common species and it is represented by 15.51% Norway maple (Acer platanoides) trees. This value is higher than the required 10%.

When we calculated at the individual school level, only 25 schools out of 107 meet the family diversity rule. Each of these 25 schools have trees with the most popular family, represented by trees lees than 30%.

Concerning diversity at the genus level in individual schools, only 2 schools (FH Miller Junior Public School and Williamson Road Junior Public School) have the most common genus representating less than 20%. So, only these two schools meet the diversity requirements at genus level. In other schools, the most common genus is more than 20 % of all trees.

Only two schools (Fairbank Memorial Community School and Rawlingson Community School) meet the species diversity requirements. The two schools have the most common species represented by less than 10% of all trees in its property.

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Next, we analyzed the species distribution among newly planted trees. 1643 new trees were planted between the first and the second census. These new trees represent 66 tree species. 20 species comprise more than 1% among newly planted trees. The most frequent newly planted species are silver maple (Acer saccharinum) – 19.72%, honey locust (Gleditsia triacanthos) – 11.93% and sugar maple (Acer saccharum) – 9.01%, etc. The detailed distribution of species among the new trees is given below in Figure 5.

Figure 5. Species distribution among new planted trees during the study period.

We also analyzed the species distribution of trees dead or cut between the first and second census. 1202 trees died or were cut during the studied period at 107 schools of TDSB.

These dead trees were representatives of 72 species. When we analyzed the share of these species among dead/cut trees, only 19 species comprised more than 1% of the tree loss (see Fig. 6).

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Figure 6. Share of species among the lost (dead or cut) trees

Estimation of annual tree mortality rates

Next step of our study required estimation of annual tree mortality rates at each school property. To reduce the bias due to difference in interval periods between the 1st and 2nd census (11,12,13 and 14 years), we calculated the weighted mean interval period, using the equation (3). Our calculated mean interval period is 12.09 years.

Overall calculated tree mortality rates of all schools are 1.98% and 1.96%, using two equations (eqn. 1) and (eqn. 2) and the average interval period (12.09 years).

Regarding the mortality rates at each school, we have calculated 4 different tree mortality rates using two different equations (eqn.1 and 2) and two interval periods (the observed interval between censuses of each school and average interval period). The estimated tree mortality rates are given in a table in Appendix 2. These data are used in subsequent steps (in the regression analysis) to identify if there is a significant statistical relationship between tree mortality and species diversity.

In order to have general idea about tree mortality at different tree size classes during the study period, we also estimated tree mortality for the trees with 7 different DBH size classes (see Fig.7). The calculations were done using two equations and with the estimated average period. Despite of difference in the assumptions, both formulas

11 estimated very similar mortality rate values. The calculated mortality data are given in Appendix 3.

Fig 7. Tree mortality estimation at different tree diameter size classes

On the graph, we can see that the lowest mortality rates are observed with the trees in 41- 50 cm DBH size class. The highest mortality rates are observed with the trees of less than 10cm DBH size.

However, this distribution of dead/cut trees may be impacted by massive loss of Ash trees. To check this, we calculated the same distribution without Ash trees (Fig.8). We discovered that general trend did not change: the mortality of trees with smallest diameter is the highest. But the lowest tree mortality changed from the 41-50cm range to 31-40cm.

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Fig 8. Tree mortality estimation at different tree diameter size classes excluding Ash species.

Tree species diversity

In this step of our study, we used Simpson’s Diversity Index, which considers both the tree species richness (number of species) and the evenness. For this purpose, we used equations (4) and (5). The calculated diversity data are provided in Appendix 4. We use these diversity data in the next step of our study in the regression analysis between diversity and tree mortality rates.

For overall 107 schools, we obtained a Simpson’s index of 0.93. As this value was obtained after subtracting from 1, we can consider that the diversity level at overall 107 schools is relatively high (close to 1).

Regression analysis

Simple linear regression analysis was used to test if tree species diversity significantly predicted annual tree mortality rates at TDSB schools. As we used 4 different methods for annual tree mortality estimation, we have four different scatter-plots (Fig.9).

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Figure 9. Scatter-plots of four combination of tree mortality and Simpson’s Diversity Index

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The linear regression analysis results are provided in Table 2 below.

Table 2. The results of regression analysis between tree mortality and Simpsons Diversity Index calculated for TDSB schools. Dependent Independent F statistic R2 p-value Estimated linear variable, (y) variable, (x) regression formula

Mortality estimated, using Eqn1 Simpson’s F(1,105)=3.068 0.02839 0.08276 Y(x)= 0.028214x -0.00197 9 and actual Diversity Index

interval period Mortality estimated, using Eqn1 Simpson’s F(1,105)=3.449 0.0318 0.0661 Y(x)= 0.029493x -0.00295 and mean Diversity Index 1 interval period Mortality estimated, using Eqn 2 Simpson’s F(1,105)=3.261 0.03012 0.0738 Y(x)= 0.027914x - and actual Diversity Index 0.002124 interval period Mortality estimated, using Eqn 2 Simpson’s F(1,105)=3.645 0.03355 0.05897 Y(x)= 0.029136x- and mean Diversity Index 0.003046 interval period

The test results show that the relationship between the estimated annual mortality rates and the calculated Simpson’s Diversity index is not strong enough to report a statistically significant relationship.

During the analysis we noted that residuals were not good (fan-shaped) and that the data should be re-analyzed to take this into account. As our both estimated values - mortality and the Simpson Diversity Index are between 0 and 1, we transformed our dataset to “the natural logarithm +1” of these values to accommodate zero values. The regression analysis conducted with the transformed dataset showed positive linear relationship between Simpson Diversity Index and tree morality rate (see Table 3).

Table 3. The results of regression analysis using transformed values1 of mortality and Simpson Diversity Index

Dependent Independent F statistic R2 p-value Equaion variable, (y) variable, (x)

1 Transformed with natural logarithm (ln) of “mortality or diversity index value +1” 15

ln (mortality values ln (Simpson y(x)= F (1,105) = of Eqn1 with actual divertsity index 0.07129 0.003148 2.2635x- 9.137 interval period +1) +1) 0.3542 ln (mortality values ln(Simpson y(x)= 2.3171x- F (1,105) = of Eqn1 with mean divertsity index 0.07559 0.002414 0.3822 9.668 interval period +1) +1) ln (mortality values ln(Simpson y(x)= of Eqn1 with actual divertsity index F (1,105)= 9.206 0.07258 0.002906 2.2479x - interval period +1) +1) 0.3524 ln (mortality values ln(Simpson y(x)= 2.2977x- of Eqn1 with mean divertsity index F (1,105)= 9.8 0.07665 0.002261 0.3785 interval period +1) +1)

Discussion

According to species diversity analysis, overall tree species diversity is high with 107 species in 2018. The Simpson Diversity Index value for total tree population in these schools is 0.93. As this value is relatively close to 1, which means infinite diversity, general diversity is estimated very high. However, these species are distributed unequally in TDSB urban forest population. Many species are represented by a small number of trees (often by one or two trees). This means that this diversity richness is in a vulnerable condition, because loss of any tree of these species may lead to decrease of diversity.

The analysis showed that tree species richness is increasing at individual school level. This trend is good for species diversification in each schoolyard, but may not be enough. TDSB should continue planting less represented tree species in school territories.

During our communication with the experts of Sustainability office of TDSB, we discovered that within Large Tree Program TDSB planting currently 13 deciduous and 7 coniferous native tree species. This list includes basswood, eastern redbud, hackberry, honey locust, black locust, Kentucky coffee tree, ironwood, red maple, silver maple, sugar maple, black maple and tulip tree. We believe that TDSB should review this list, as Acer is the most common genus and represents more than 31% of trees planted. In case of possible pest outbreak specific to Maple trees, 31% of TDSB trees can be under risk. So, it is more reasonable to avoid planting any maple species. Moreover, currently honey locust is also representing more than 10% of all trees. The invasive growing characteristics of black locust also can be a convincing reason to review abovementioned species list recommended for planting.

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In developing a new list of species, the following factors should be considered: species, survivorship, suitability of the growing environment, and representation these species in the total tree population. In this process, “10-20-30” approach can be the final objective. Of course, it is difficult to conform totally to the requirements of this formula. But, if species share in total population is closer to the requirements of this formula, the probability of catastrophic tree loss from pests will be lower. Ash tree replacement can be used as a good opportunity for species diversification.

Another interesting point of species composition trend is that tree mortality is the highest among small diameter size trees. This signifies that tree maintenance and stewardship activities are not enough. Thus, TDSB should improve tree stewardship activities at schools, through enhanced education, tree care and tree planting campaigns.

The regression analysis results are completely opposite of what we expected. We expected negative relationship between tree mortality and diversity, but we have found significant positive relationship. Therefore, our hypothesis that higher diversity results in lower tree mortality is not supported.

We also recognize some important statistical weaknesses of the used methodology. First, the mortality rates we use are all approximate, because we estimated annual mortality rates basing on tree census data in two intervals. Variable census interval at different schools also could affect negatively to accuracy of our estimations.

The observed strong positive relationship between tree diversity and mortality also can be explained with the fact that the efforts to increase tree diversity can be mis-guided. They can result in more trees being planted that are vulnerable to pests or “off-site”, and so increase tree mortality and reduce overall tree performance (Richards 1983; Berland and Elliot 2014; Berland and Hopton 2016). For example, Richards (1983) tested this relationship using tree inventory data of street trees of Syracuse, NY. This author concluded from his results that simply increasing tree species diversity may increase tree mortality rates due to planting less adapted to urban environment. He concluded that species adaptability is more critical than species diversity.

On the other hand, Raupp et al. (2008) emphasizes existence of generalist pests and diseases (ex. Asian Longhorned Beetle), which attacks several tree families (Sawyer 2005, Raupp et al. 2006). This argument also can potentially explain why we did not see negative relationship between diversity and tree mortality rates. Although we do 17 not have evidence about infestation of Asian Longhorned Beetle or any other generalist pests or diseases in Toronto during the study period, at a certain extent Asian Longhorned Beetle and European Gypsy Moth may have contributed to deteriorating tree conditions in schools.

Another group of scientists (Lacan & McBride 2008) consider that other factors such as susceptible tree age, environmental conditions and time, may have more significant impact in tree mortality rates than species diversity. During data collection this summer, I often observed detrimental effects of trees by people. For example, I often encountered situations, where trees were cut due to construction projects, due to physical damage by children, etc. I also observed that usually TDSB construction team plant young trees in school yards, and further maintenance of these young trees are trusted to school staff on voluntary basis. These staff members have their primary responsibilities at school, (teachers should teach, caretakers should ensure cleanness and security of facilities, etc.); taking care of trees is often as a secondary task for them.

Conclusion and recommendations

We conclude that the hypothesis about negative relationship between tree species diversity and mortality rates is not proved. Our analysis results showed completely opposite trend: a positive relationship between diversity and mortality. Basing on these results, I recommend TDSB to consider seriously the adaptability to urban stresses in selecting new species for planting. I recognize that tree species diversity provides more ecosystem services than single species dominant population, but diversification should not increase tree mortality.

From my results, I have developed several recommendations for Sustainability office of TDSB, which is responsible for urban forestry management:

• TDSB needs further studies on adaptability of existing species in its properties, using tree assessment information from the inventory database. Basing on this information, I advise to develop of a new list of tree species, which is more adapted to environmental conditions of TDSB schools and less represented in current tree population.

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• All potential consequences should be considered before planting some invasive native tree species (ex., black locust) on TDSB properties. • TDSB should implement activities related to strengthening stewardship for young planted trees at schools, because tree mortality rate is the highest at young ages. • In diversification of species composition, urban forestry specialists should understand that just increasing tree species diversity does not reduce tree mortality rates. Because, trees can also die from the factors other than pests and diseases.

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List of references:

Berland, A., & Elliott, G. P. (2014). Unexpected connections between residential urban forest diversity and vulnerability to two invasive beetles. Landscape ecology, 29(1), 141-152.

Berland, A., & Hopton, M. E. (2016). Asian longhorned beetle complicates the relationship between taxonomic diversity and pest vulnerability in street tree assemblages. Arboricultural Journal, 38(1), 28-40.

Cecco Leyland (2017) An urban forest in crisis: Why tree selection is important for Toronto’s canopy. The globe and mail. Issue of November 12, 2017.

CAP (Clean Air Partnership) (2007). Climate Change Adaptation Options for Toronto’s Urban Forest. Available at: https://www.glslcities.org/wp- content/uploads/2015/09/Climate_Change_Adaptation_Options_for_Torontos_Urban_Forest_2 007.pdf Laćan, Igor & Mcbride, Joe. (2008). Pest Vulnerability Matrix (PVM): A graphic model for assessing the interaction between tree species diversity and urban forest susceptibility to insects and diseases. Urban Forestry & Urban Greening. 7. 291-300. 10.1016/j.ufug.2008.06.002. Magurran, A.E. 2004. Measuring Biological Diversity. Blackwell Publishing, Oxford, UK. OMNRF ( Ministry of Natural Resources and Forestry) (2018). Asian long-horned beetle. Available at: https://www.ontario.ca/page/asian-long-horned-beetle Ordòñez, C., & Duinker, P. N. (2013). An analysis of urban forest management plans in – Implications for urban forest management. Landscape and Urban Planning, 116, 36–47

Raupp, M.J., Cumming, A.B. Raupp, E.C. (2006). Street tree diversity in eastern North America and its potential for tree loss to exotic borers. Arboriculture and Urban Forestry 32(6) 297-304.

Richards, N. A. (1983). Diversity and stability in a street tree population. Urban Ecology, 7(2), 159-171.

Roman, L. A., Battles, J. J., & McBride, J. R. (2016). Urban tree mortality: a primer on demographic approaches.Gen. Tech. Rep. NRS-158. Newtown Square, PA: US Department of Agriculture, Forest Service, Northern Research Station. 24 p., 158, 1-24 Sanders, R.A. (1978) Managing for species diversity in city – owned trees. In: Little, S. (Ed.), Urban Forester’s Notebook. General Technical Report NE 49 of the USDA Forest Service. Northeastern Forest Experiment Station, Broomall, PA. Santamour Jr., F.S., 1990. Trees for urban planting: diversity, uniformity, and common sense. In: Proceedings of the Seventh Conference of the Metropolitan Tree Improvement Alliance (METRIA) 7, pp. 57-65. Sawyer, A. (2005). Annotated Categorization of ALB Hosts, Updated by Baode Wang in January 2015, USDA-APHIS-PPQ, Otis Plant Protection Laboratory. Retrieved November 25, 2018 from: < http://www.aphis.usda.gov/plant_health/plant_pest_info/asian_lhb/downloads/hostlist.pdf>

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Sivarajah, S., Smith, S. M., & Thomas, S. C. (2018). Tree cover and species composition effects on academic performance of primary school students. PloS one, 13(2), e0193254. Sheil, D.F.; Burslem, R.P.; Alder, D. 1995. The interpretation and misinterpretation of mortality rate measures. Journal of Ecology. 83: 331-333. TDSB (2013). Urban Forest Management Plan. Internal document of Toronto District School Boars. Provided by Sustainability office of TDSB, September 2018.

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Appendix 1

Share of tree species in total urban forest population of the studied TDSB schools in 2018

Species Share in total Species Share in total Species Share in total Species Share in total tree tree population, tree tree population, % population, % population, % % Abies balsamea 0.02 Crataegus laevigata 0.09 Picea abies 0.41 Salix × sepulcralis 0.09 'Chrysocoma'

Abies spp. 0.07 Cupressus cashmeriana 0.02 Picea glauca 1.31 Salix alba 0.04

Acer campestre 0.09 Elaeagnus angustifolia 0.21 Picea omorika 0.04 Salix discolor 0.04

Acer ginnala 0.25 Euonymus europaeus 0.02 Picea pungens 3.50 Sorbus americana 1.06

Acer negundo 0.46 Fagus grandifolia 0.23 Pinus nigra 13.56 Sorbus spp. 0.09

Acer nigrum 0.05 Fagus sylvatica 0.07 Pinus resinosa 0.11 Syringa reticulata 0.55

Acer palmatum 0.07 Fraxinus americana 3.48 Pinus strobus 0.69 Syringa spp. 0.00

Acer platanoides 15.50 Fraxinus excelsor 0.26 Pinus sylvestris 0.79 Syringa vulgaris 0.05

Acer rubrum 1.96 Fraxinus pennsylvanica 7.38 Platanus orientalis 0.14 Taxus baccata 0.53

Acer saccharinum 5.93 Fraxinus quadrangulata 0.04 Platanus x 0.28 Thuja occidentalis 3.65 acerifolia

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Species Share in total Species Share in total Species Share in total Species Share in total tree tree population, tree tree population, % population, % population, % % Acer saccharum 2.74 Ginkgo biloba 0.49 Populus alba 0.19 Thuja plicata 0.21

Aesculus glabra 0.04 Gleditsia triacanthos 10.73 Populus 0.02 Tilia americana 0.28 balsamifera Aesculus 0.32 Gymnocladus dioicus 0.09 Populus deltoides 0.12 Tilia cordata 5.97 hippocastanum Ailanthus altissima 0.12 Juglans cinerea 0.05 Populus 0.09 Tilia spp. 0.04 tremuloides Amelanchier laevis 0.37 Juglans nigra 0.12 Prunus americana 0.09 Tsuga canadensis 0.18

Amelanchier spp. 0.00 Juniperus chinensis 0.19 Prunus spp. 0.56 Ulmus americana 0.49

Betula papyrifera 0.21 Juniperus spp. 0.02 Pseudotsuga 0.60 Ulmus glabra 0.07 menziesii Betula pendula 0.19 Larix laricina 0.09 Pyrus spp. 0.49 Ulmus minor 0.02

Betula spp. 0.04 Magnolia acuminata (L.) 0.04 Quercus alba 0.48 Ulmus procera 0.07 L. Catalpa speciosa 0.11 Magnolia soulangeana 0.18 Quercus 0.04 Ulmus pumila 1.75 macrocarpa Celtis occidentalis 0.88 Malus spp. 0.04 Quercus palustris 0.14 Ulmus rubra 0.11

Cercis canadensis 0.02 Malus sylvestris 3.11 Quercus robur 0.65 Ulmus spp. 0.02

Cladrastis kentukea 0.09 Morus rubra 0.35 Quercus rubra 2.26 Ulmus x hollandica 0.04

Corylus colurna 0.23 Ostrya virginiana 0.23 Rhus coriaria 0.39 unknown species 0.11

Cotinus coggygria 0.02 Phellodendron 0.02 Robinia 0.28 Viburnum lentago 0.04 and Cotinus amurense pseudoacacia obovatus

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Appendix 2

Estimated tree mortality rates using average and actual interval periods

# School name Equation (1) Equation (2) Actual Mean Mean Actual interva interval interval interval l 1 Adam Beck Junior Public School 0.29% 0.29% 0.28% 0.29% 2 Anson Park Public School 2.60% 2.85% 2.56% 2.81% 3 Balmy Beach Community School 1.28% 1.28% 1.27% 1.28% 4 Bennington Heights Elementary School 1.78% 1.66% 1.77% 1.65% 5 Bessborough Drive Public School 3.66% 3.40% 3.59% 3.34% 6 Heights Public School 1.28% 1.40% 1.27% 1.39% 7 Birch Cliff Public School 1.36% 1.50% 1.35% 1.49% 8 Birchmount Park Collegiate Institute 0.50% 0.55% 0.50% 0.55% 9 Blake Street Public School 2.08% 2.09% 2.06% 2.07% 10 Blantyre Public School 0.51% 0.56% 0.51% 0.56% 11 Bliss Carman Senior Public School 2.50% 2.75% 2.47% 2.71% 12 Bowmore Road Junior and Senior Public School 1.30% 1.31% 1.29% 1.30% 13 Bruce Junior Public School 2.57% 2.58% 2.53% 2.55% 14 Carleton Village Junior & Senior Public School 0.00% 0.00% 0.00% 0.00% 15 Chester Elementary School 5.24% 5.28% 5.11% 5.14% 16 Chine Drive Public School 4.14% 4.55% 4.06% 4.45% 17 Public School 2.08% 2.28% 2.06% 2.26% 18 Cliffside Junior Public School 2.38% 2.62% 2.35% 2.58% 19 Cordella Junior Public School 3.83% 3.56% 3.76% 3.50% 20 Corvette Junior Public School 1.64% 1.80% 1.62% 1.78% 21 Cosburn Middle School 2.78% 2.80% 2.75% 2.76% 22 Courcelette Public School 1.18% 1.30% 1.18% 1.29% 23 ES 8.74% 8.80% 8.37% 8.42% 24 DA Morrison Middle School 1.57% 1.58% 1.56% 1.57% 25 Danforth Collegiate and Technical Institute 1.07% 1.08% 1.06% 1.07% 26 Danforth Gardens PS 0.63% 0.69% 0.63% 0.69% 27 Davisville Junior Public School 2.88% 2.68% 2.84% 2.64% 28 Deer Park Junior and Senior Public School 0.96% 0.89% 0.95% 0.88% 29 Dennis Avenue Community School 1.51% 1.52% 1.50% 1.51% Duke of Connaught Junior and Senior Public 30 School 1.87% 1.88% 1.85% 1.86% Dundas Jr PS / First Nations School of Toronto Jr 31 & Sr 0.40% 0.37% 0.40% 0.37% 32 Earl Beatty Junior and Senior Public School 0.87% 0.88% 0.87% 0.87% 33 Earl Grey Senior Public School 1.10% 1.11% 1.10% 1.11% 34 Earl Haig Junior Public School 2.08% 2.09% 2.06% 2.07% 35 East York Collegiate Institute 0.75% 0.75% 0.75% 0.75% 36 Eastdale Collegiate Institute 7.58% 7.64% 7.30% 7.35% 37 Eglinton Junior Public School 11.17% 10.38% 10.57% 9.86% 38 Fairbank Memorial Community School 7.16% 7.21% 6.91% 6.95% 39 Fairmount Junior Public School 3.60% 3.96% 3.54% 3.88% 40 FH Miller Junior Public School 1.96% 1.82% 1.94% 1.80% 41 Forest Hill Junior and Senior Public School 1.64% 1.53% 1.63% 1.51% 42 Frank Oke Secondary School 0.44% 0.41% 0.44% 0.41% 43 Frankland Community School Junior 1.46% 1.47% 1.44% 1.46% 44 Gateway Public School 1.44% 1.45% 1.43% 1.44% 24

# School name Equation (1) Equation (2) Actual Mean Mean Actual interva interval interval interval l 45 General Brock Public School 1.14% 1.26% 1.14% 1.25% 46 General Mercer Junior Public School 1.47% 1.37% 1.46% 1.36% 47 George Harvey Collegiate Institute 0.66% 0.67% 0.66% 0.66% 48 George Webster Elementary School 0.67% 0.68% 0.67% 0.67% 49 Gledhill Junior Public School 2.31% 2.32% 2.28% 2.30% 50 Gordon A Brown Middle School 4.36% 4.39% 4.27% 4.30% 51 Grenoble Public School 2.32% 2.34% 2.30% 2.31% 52 HA Halbert Junior Public School 3.04% 3.34% 3.00% 3.29% 53 Harwood Public School 0.81% 0.82% 0.81% 0.82% 54 Hodgson Senior Public School 1.28% 1.19% 1.27% 1.18% 55 Humewood Community School 0.00% 0.00% 0.00% 0.00% 56 J G Workman Public School 0.49% 0.53% 0.49% 0.53% 57 J R Wilcox Community School 2.57% 2.39% 2.53% 2.36% 58 Jackman Avenue Junior Public School 4.10% 4.13% 4.02% 4.04% 59 John A Leslie Public School 2.60% 2.85% 2.56% 2.81% 60 Keelesdale Junior Public School 1.92% 1.93% 1.90% 1.91% 61 Kew Beach Junior Public School 1.16% 1.17% 1.16% 1.16% 62 Kimberley Junior Public School 1.75% 1.76% 1.73% 1.75% 63 High School 2.50% 2.33% 2.47% 2.30% 64 Junior Public School 0.64% 0.64% 0.63% 0.64% 65 Malvern Collegiate Institute 1.10% 1.11% 1.10% 1.11% 66 Marc Garneau Collegiate Institute 1.51% 1.52% 1.50% 1.51% 67 Mason Road Junior Public School 1.51% 1.66% 1.50% 1.64% 68 Maurice Cody Junior Public School 4.16% 3.87% 4.08% 3.80% 69 Monarch Park Collegiate Institute 0.87% 0.88% 0.87% 0.87% 70 Morse Street Junior Public School 1.70% 1.72% 1.69% 1.70% 71 Norman Cook Junior Public School 5.74% 6.30% 5.57% 6.11% 72 Norway Junior Public School 0.97% 0.97% 0.96% 0.97% 73 Oakridge Junior Public School 3.10% 3.41% 3.05% 3.35% 74 Oakwood Collegiate Institute 0.57% 0.53% 0.57% 0.53% 75 O'Connor Public School 2.04% 2.06% 2.02% 2.04% 76 Ogden Junior Public School 0.84% 0.72% 0.83% 0.72% 77 Oriole Park Junior Public School 2.26% 2.10% 2.24% 2.08% 78 Pape Avenue Junior Public School 1.77% 1.78% 1.75% 1.76% 79 Parkside Public School 1.66% 1.67% 1.65% 1.66% 80 Presteign Heights Elementary School 1.02% 1.03% 1.01% 1.02% 81 Rawlinson Community School 2.46% 2.29% 2.43% 2.26% 82 Regent Heights Junior Public School 0.70% 0.77% 0.70% 0.77% 83 R H King Academy 2.18% 2.40% 2.16% 2.37% 84 RH McGregor Elementary School 0.24% 0.25% 0.24% 0.24% 85 Riverdale Collegiate Institute 3.63% 3.65% 3.56% 3.59% 86 Robert Service Senior Public School 0.00% 0.00% 0.00% 0.00% 87 Roden Junior Public School 0.54% 0.54% 0.54% 0.54% 88 Rolph Road Elementary School 1.73% 1.61% 1.72% 1.60% 89 Rose Avenue Junior Public School 2.92% 2.71% 2.88% 2.68% 90 Runnymede Collegiate Institute 1.10% 1.03% 1.10% 1.02% 91 Samuel Hearne Senior Public School 0.00% 0.00% 0.00% 0.00% 92 SATEC @ WA Porter Collegiate Institute 0.88% 0.97% 0.88% 0.96% 93 Secord Public School 1.68% 1.70% 1.67% 1.68% 25

# School name Equation (1) Equation (2) Actual Mean Mean Actual interva interval interval interval l 94 Selwyn Elementary School 4.46% 4.49% 4.36% 4.39% 95 Swansea Junior and Senior Public School 2.83% 2.63% 2.79% 2.60% 96 School 6.95% 7.00% 6.72% 6.76% 97 4.42% 3.82% 4.33% 3.75% 98 Valley Park Middle School 2.42% 2.43% 2.39% 2.40% 99 0.00% 0.00% 0.00% 0.00% 100 Victoria Park Elementary School 0.53% 0.54% 0.53% 0.54% 101 Warden Avenue Junior Public School 1.25% 1.37% 1.24% 1.36% 102 Westwood Middle School 0.69% 0.69% 0.69% 0.69% 103 Whitney Junior Public School 4.59% 4.27% 4.49% 4.18% 104 Wilkinson Junior Public School 0.00% 0.00% 0.00% 0.00% 105 William J McCordic School 2.38% 2.62% 2.35% 2.58% 106 Williamson Road Junior Public School 2.51% 2.52% 2.48% 2.49% 107 Withrow Avenue Junior Public School 1.96% 1.97% 1.94% 1.95%

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Appendix 3

Tree mortality rates in different tree size classes DBH size Mean # trees in # of trees in Mortality Mortality classes interval 1st census 2nd census estimation using estimation using Eqn.1 Eqn.2 [0-10] 12.08564451 1200 797 3.39% 3.33% [11-20] 12.08564451 1395 1063 2.25% 2.22% [21-30] 12.08564451 1310 1109 1.38% 1.37% [31-40] 12.08564451 829 709 1.29% 1.29% [41-50] 12.08564451 420 363 1.21% 1.20% [51-60] 12.08564451 200 166 1.54% 1.53% [61<] 12.08564451 313 252 1.79% 1.78% Total number of trees 5667 4459

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Appendix 4

Tree species diversity estimation using Simpson’s Diversity Index Number of Simpson's trees at 1st Simpson's Index Diversity # School name census (D) Index (1-D) 1 Adam Beck Jr PS 59 0.55 0.45 2 Anson Park PS 26 0.15 0.85 3 Balmy Beach Jr PS 28 0.19 0.81 4 Bennington Heights ES 67 0.17 0.83 5 Bessborough Drive ES 14 0.11 0.89 6 Birch Cliff Heights PS 35 0.10 0.90 7 Birch Cliff PS 79 0.33 0.67 8 Birchmount Park CI 102 0.15 0.85 Blake Street Jr PS / East Alternative School 63 9 of Toronto 0.23 0.77 10 Blantyre PS 50 0.06 0.94 11 Bliss Carman Sr PS 46 0.13 0.87 12 Bowmore Road Jr & Sr PS 62 0.24 0.76 13 Bruce Jr PS 15 0.18 0.82 14 Carleton Village Jr & Sr PS 5 0.60 0.40 15 Chester ES 49 0.32 0.68 16 Chine Drive PS 33 0.05 0.95 17 Clairlea PS 17 0.05 0.95 18 Cliffside JPS 4 0.33 0.67 19 Cordella Jr PS 54 0.16 0.84 20 Corvette Jr PS 39 0.14 0.86 21 Cosburn Middle School 70 0.15 0.85 22 Courcelette PS 30 0.10 0.90 23 Crescent Town ES 92 0.10 0.90 24 D A Morrison JHS 50 0.13 0.87 25 Danforth Collegiate & Technical Institute 33 0.11 0.89 26 Danforth Gardens PS 41 0.38 0.62 27 Davisville Jr PS & Metro School for the Deaf 51 0.15 0.85 28 Deer Park Jr & Sr PS 55 0.19 0.81 29 Dennis Avenue Community School 12 0.36 0.64 30 Duke of Connaught Jr & Sr PS 94 0.11 0.89 Dundas Jr PS / First Nations School of 128 31 Toronto Jr & Sr 0.20 0.80 Earl Beatty Jr & Sr PS and Community 40 32 Centre 0.11 0.89 33 Earl Grey Senior Public School 8 0.18 0.82 34 Earl Haig Jr PS 27 0.28 0.72 35 East York CI 104 0.13 0.87 36 Eastdale CI 30 0.35 0.65 Eglinton Jr PS / Spectrum Alternative School 27 37 Sr 0.25 0.75 38 F H Miller Jr PS 38 0.04 0.96 39 Fairbank Memorial Community School 57 0.11 0.89 40 Fairmount Jr PS 17 0.07 0.93 41 Forest Hill Jr & Sr PS 239 0.48 0.52 42 Frank Oke SS 58 0.46 0.54 Frankland Community JS & Community 93 43 Centre 0.12 0.88 28

Number of Simpson's trees at 1st Simpson's Index Diversity # School name census (D) Index (1-D) 44 Gateway PS 75 0.08 0.92 45 General Brock PS 62 0.13 0.87 46 General Mercer Jr PS 43 0.13 0.87 47 George Harvey CI 13 0.26 0.74 48 George Webster ES 77 0.14 0.86 49 Gledhill Jr PS 111 0.20 0.80 50 Gordon A Brown MS 61 0.17 0.83 51 Grenoble PS 49 0.08 0.92 52 H A Halbert Jr PS 13 0.04 0.96 53 Harwood PS 32 0.19 0.81 54 Hodgson Sr PS 35 0.09 0.91 55 Humewood Community School 16 0.11 0.89 56 J G Workman PS 35 0.10 0.90 57 J R Wilcox Community School 30 0.16 0.84 58 Jackman Avenue Jr PS 64 0.08 0.92 59 John A Leslie PS 26 0.10 0.90 60 Keelesdale Jr PS 29 0.07 0.93 61 Kew Beach Jr PS 61 0.09 0.91 Kimberley Jr PS / Beaches Alternative 42 62 School Jr 0.14 0.86 63 Leaside HS 69 0.17 0.83 64 Leslieville Jr PS 27 0.24 0.76 65 Malvern CI 8 0.32 0.68 66 Marc Garneau CI 72 0.20 0.80 67 Mason Road Jr PS 30 0.25 0.75 68 Maurice Cody Jr PS 43 0.07 0.93 69 Monarch Park Collegiate 40 0.21 0.79 70 Morse Street Jr PS 43 0.25 0.75 71 Norman Cook Jr PS 20 0.21 0.79 72 Norway Jr PS 136 0.33 0.67 73 Oakridge Jr PS 32 0.05 0.95 74 Oakwood CI 30 0.08 0.92 75 O'Connor PS 32 0.19 0.81 76 Ogden Jr PS 52 0.15 0.85 77 Oriole Park Jr PS 46 0.13 0.87 78 Pape Avenue Jr PS 26 0.29 0.71 79 Parkside ES 66 0.15 0.85 80 Presteign Heights ES 69 0.21 0.79 81 R H King Academy 82 0.08 0.92 82 R H McGregor ES 69 0.08 0.92 83 Rawlinson Community School 35 0.10 0.90 84 Regent Heights Jr PS 37 0.10 0.90 85 Riverdale CI 62 0.14 0.86 86 Robert Service Sr PS 28 0.24 0.76 87 Roden Jr PS 79 0.33 0.67 88 Rolph Road ES 53 0.10 0.90 89 Rose Avenue Jr PS 74 0.14 0.86 90 Runnymede CI 48 0.44 0.56 91 Samuel Hearne Sr PS 30 0.22 0.78 92 SATEC @ W A Porter CI 99 0.32 0.68 29

Number of Simpson's trees at 1st Simpson's Index Diversity # School name census (D) Index (1-D) 93 Secord ES 76 0.23 0.77 94 Selwyn ES 24 0.17 0.83 95 Swansea Jr & Sr PS 69 0.27 0.73 96 Thorncliffe Park ES 197 0.17 0.83 Ursula Franklin/Western Tech- 99 97 CommSchool/ 0.09 0.91 98 Valley Park MS 79 0.10 0.90 99 Vaughan Road Academy 8 0.46 0.54 100 Victoria Park ES 32 0.14 0.86 101 Warden Avenue Jr PS 93 0.11 0.89 102 Westwood MS 25 0.19 0.81 103 Whitney Jr PS 54 0.15 0.85 104 Wilkinson Jr PS 98 0.22 0.78 105 William J McCordic School, Jr & Sr 32 0.06 0.94 106 Williamson Rd PS & Glen Ames PS 88 0.08 0.92 Withrow Avenue Jr PS / Quest Alternative 38 107 School Sr 0.19 0.81 0.07 0.93 All schools

30