A Social-Ecological Analysis of Urban Tree Vulnerability for Publicly Owned Trees in a Residential Neighborhood

Abstract

The urban forest is a valuable ecosystem service provider, yet cities are frequently-degraded environments with a myriad of stressors and disturbances affecting trees. Vulnerability science is increasingly used to explore issues of sustainability in complex social-ecological systems, and can be a useful approach for assessing urban forests. The purpose of this study is to identify and explore drivers of urban forest vulnerability in a residential neighbourhood. Based on a recently-published framework of urban forest vulnerability, a series of indicators of exposure, sensitivity, and adaptive capacity that describe the built environment, urban forest structure, and human population, respectively, were assessed for 806 trees in Toronto. Tree mortality, condition, and diameter growth rates were then assessed using an existing 2007/2008 inventory. A bivariate analysis was first conducted to test for significant relationships of vulnerability indicators with mortality, condition, and growth. A multivariate analysis was then conducted using multiple linear regression for the continuous condition and growth variables and a multilayer perceptron neural network for the binary mortality variable. Commercial land uses and commercial buildings adjacent to trees consistently explained higher mortality rates and poor tree conditions. However, at finer spatial scales it is important to differentiate between different causes and correlates of urban forest decline within commercial land uses. Tree species, size, and condition were also important indicators of vulnerability. Understanding the causes of urban forest change and decline are essential for developing planning strategies to reduce long-term system vulnerability.

Full text

©2019 International Society of Arboriculture
10 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
mortality are avoided and the benets that urban
inhabitants receive from trees are maximized.
The built environment is a source of stress for urban
trees, especially in higher-density neighborhoods. Build-
ing density, height, and type affect irradiation (i.e.,
sunlight available for photosynthesis and plant
growth), the physical growing space for trees, and the
microclimate of urban areas (Jutras et al. 2010).
Moreover, construction activities and conicts with
above- and belowground utilities and other gray
infrastructure are common sources of urban tree
decline and mortality (Randrup et al. 2001; Koeser et
al. 2013; Steenberg et al. 2018). Land use is highly
inuential on urban forest ecosystems (Nitoslawski et
al. 2017), and is indeed indicative of the presence of
many of these stressors. Land uses with higher human
populations and building densities, as well as abundant
The urban forest is a valuable ecosystem service pro-
vider and represents essential green infrastructure for
many cities. However, cities are highly altered,
densely settled, and frequently degraded environ-
ments with a myriad of stressors and disturbances that
create difcult conditions for tree establishment and
growth (Nowak et al. 2004; Trowbridge and Bassuk
2004; Steenberg et al. 2017a). Consequently, urban
trees are often in poor condition and frequently have
reduced longevity (Roman and Scatena 2011; Koeser
et al. 2013), both of which translate to a reduction in
ecosystem services (Nowak and Dwyer 2007). Cases
and causes of decline in urban forest structure and
function need to be identied, assessed, and modeled.
Such research can inform the processes of urban
design and policy development, as well as urban for-
est management, so that unnecessary tree decline and
Arboriculture & Urban Forestry 2019. 45(1):10–25
A Social-Ecological Analysis of Urban Tree
Vulnerability for Publicly Owned Trees in a
Residential Neighborhood
James W. N. Steenberg, Andrew A. Millward, David J. Nowak,
Pamela J. Robinson, and Sandy M. Smith
Abstract. The urban forest is a valuable ecosystem service provider, yet cities are frequently degraded environments with a myriad of stressors and
disturbances affecting trees. Vulnerability science is increasingly used to explore issues of sustainability in complex social-ecological systems,
and can be a useful approach for assessing urban forests. The purpose of this study was to identify and explore drivers of urban forest vulnera-
bility in a residential neighborhood. Based on a recently published framework of urban forest vulnerability, a series of indicators of exposure,
sensitivity, and adaptive capacity that describe the built environment, urban forest structure, and human population, respectively, were assessed
for 806 trees in Toronto, Ontario, Canada. Tree mortality, condition, and diameter growth rates were then assessed using an existing 2007/2008
inventory. A bivariate analysis was rst conducted to test for signicant relationships of vulnerability indicators with mortality, condition, and
growth. A multivariate analysis was then conducted using multiple linear regression for the continuous condition and growth variables and a
multilayer perceptron neural network for the binary mortality variable. Commercial land uses and commercial buildings adjacent to trees con-
sistently explained higher mortality rates and poor tree conditions. However, at ner spatial scales it is important to differentiate between dif-
ferent causes and correlates of urban forest decline within commercial land uses. Tree species, size, and condition were also important indicators
of vulnerability. Understanding the causes of urban forest change and decline are essential for developing planning strategies to reduce long-
term system vulnerability.
Key Words. Condition; Growth; Mortality; Neighbourwoods; Urban Forest; Vulnerability Assessment.

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 11
Table 1. Description of exposure, sensitivity, and adaptive capacity indicators used to assess urban forest vulnerability and the
direction of their assumed relationship with vulnerability, where a positive assumption means an increase in indicator value
translates to an increase in vulnerability and a negative assumption means the opposite. Descriptive statistics are given for the
2014 data only, and where denoted by an asterisk (*), data represents the count of occurrences and percent of total measure-
ments for the binary (0/1) indicators.
Indicator description Vulnerability Mean/Count*(standard
assumption deviation/percent*)
Exposure
Built environment
Population density (persons/km2) Positive 14,834 (±8,146)
Built area intensity (%) Positive 50.2 (±21.2)
Land usez (categorical)
Site type (categorical)
Site size (m2 of growing environment) Negative 136.7 (±383.4)
Type of nearest building (categorical)
Height of nearest building (storeys) Negative 4.1 (±4.5)
Distance to nearest building (m) Negative 6.7 (±14.2)
Distance to street (m) Negative 4.1 (±3.0)
Width of sidewalk (m) Positive 2.7 (±1.9)
Width of street (m) Positive 11.2 (±6.7)
Impervious cover (%) Positive 47.3 (±32.1)
Light availabilityy (ordinal rank; 0-5) Negative 2.7 (±1.1)
Conicts
Conict of overhead utilities (0/1)x Positive 416 (51.6)*
Conict with sidewalk (0/1) Positive 76 (9.4)*
Conict with buildings (0/1) Positive 259 (32.1)*
Conict with building foundation (0/1) Positive 47 (5.8)*
Conict with other infrastructure (0/1) Positive 294 (36.5)*
Social stressors
Poor management (0/1) Positive 172 (21.3)*
Vandalism (0/1) Positive 92 (11.4)*
Sensitivity
Species (categorical)
DBH class (categorical)
Tree condition index (Neighbourwoods)w Positive 0.30 (±0.17)
In-grown tree (0/1)x Positive 41 (5.1%)*
Adaptive capacity
Social adaptive capacity
Median family income ($) Negative 54,194 (±11,676)
Average dwelling value ($) Negative 734,451 (±152,682)
Homeownership (%) Negative 44.0 (±14.8)
Population with a university degree Negative 4,313 (±1,130)
(individuals/10,000 people)
Signs of stewardship (0/1)v Negative 162 (20.1)*
Environmental adaptive capacity
Open green space (%) Negative 16.7 (±13.4)
Existing canopy cover (%) Negative 18.0 (±20.3)
z Land-use designation is based on categories described in the i-Tree Eco v. 5.0 manual. Land uses present in Harbord Village include commercial/industrial,
institutional, multi-unit residential, park, residential, and vacant.
y Light availability was measured using crown light exposure, which is a component of the i-Tree Eco measurement protocol.
x 0/1 measurement denotes a binary indicator, where 0 represents absence and 1 represents presence.
w An aggregate index that has a maximum value of 1.0 indicating extremely poor tree condition, which is based on the Neighbourwoods assessment protocol
(Kenney and Puric-Mladenovic 2001).
v Signs of stewardship include direct and obvious actions taken to protect trees or enhance growth (e.g., mulch, bicycle guards, pest protection; Lu et al. 2010).
0/1 measurement denotes a binary indicator, where 0 represents absence and 1 represents presence.

©2019 International Society of Arboriculture
12 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
Moreover, there are comparatively few empirical
eld studies investigating the effects of socioeco-
nomic variability on urban forest ecosystem decline.
Vulnerability science can offer a useful theoretical
framework for addressing these gaps and for bridging
the potential contributions of different disciplines that
investigate urban forests and their benets (Steenberg
et al. 2017a). Vulnerability science in social-ecological
systems is a useful approach for exploring issues of
sustainability and environmental change in both the-
oretical and applied research (Turner et al. 2003; Füssel
2010). Examples of applied vulnerability research
have ranged from agricultural systems and regional
land-use change to arctic systems and climate change
(Turner et al. 2003; Adger 2006). It was used in the
recent development of an urban forest vulnerability
framework (Steenberg et al. 2017a), where vulnera-
bility is dened as the likelihood of decline in urban
forest ecosystem service supply in response to stress,
and is comprised of exposure, sensitivity, and adaptive
capacity.
Exposure refers to the magnitude, frequency, dura-
tion, and spatial extent of stressors and disturbances
that affect a system (Burton et al. 1993; Adger 2006).
These are the external causes of tree decline and mor-
tality associated with the urban environment. Sensi-
tivity is the relative level of response by a system to
stressors or disturbances, and is determined by intrin-
sic characteristics of the system itself (Turner et al.
2003). Urban forest sensitivity is the internal struc-
ture of urban tree species assemblages, such as spe-
cies, size/age, condition, and diversity. Adaptive
capacity is the capacity for a system to shift or alter its
state to reduce its vulnerability or accommodate a
greater range in its ability to function while stressed
(Adger 2006; Füssel 2010). For urban forests, this
refers to associated human populations and their
behaviors regarding urban forest stewardship, as well
as the environmental capacity for increasing and
maintaining tree cover. By shifting research focus
away from external agents of stress and disturbance
only (i.e., impacts-only research), vulnerability analysis
may allow for a more comprehensive and integrative
mechanism for assessing urban forest structure, func-
tion, and change.
The purpose of this study is to explore the pro-
cesses of urban forest vulnerability for trees in the
public right of way in a residential neighborhood.
Specically, a conceptual framework of urban forest
impervious surfaces (e.g., commercial land uses),
have higher rates of tree mortality and urban forest
decline (Nowak et al. 2004; Lu et al. 2010). Cities are
also characterized by high rates of commercial trade
and shipping that can expose urban trees and forests
to invasive insects and pathogens (Laćan and
McBride 2008; Vander Vecht and Conway 2015),
such as the emerald ash borer (Agrilus planipennis;
EAB), Asian longhorned beetle (Anoplophora gla-
bripennis; ALB), and butternut canker (Sirococcus
clavigignenti-juglandacearum). These stressors and
disturbances can be interactive and cumulative, and
their ultimate effect on individual trees and urban for-
est ecosystems is dependent on tree condition, spe-
cies, age, and overall species and structural diversity.
The inuences of the human population and socio-
economic variability on urban forest structure and
function are complex, dynamic, and uncertain. There
are a number of social stressors, ranging from vandal-
ism and poor management practices, affecting indi-
vidual trees (Lu et al. 2010; Jack-Scott et al. 2013;
Koeser et al. 2013), to citywide issues of urban forest
policy and governance affecting the maintenance of
the entire urban forest resource (Conway and Urbani
2007). Furthermore, there is a growing body of
research that has investigated the inuence of the
socioeconomic characteristics of residents and their
association with urban forest condition as well as the
spatial distribution of city trees and their provision of
benets (Grove et al. 2006; Jack-Scott et al. 2013;
Shakeel and Conway 2014; Moskell et al. 2016). This
research points to strong positive relationships
between resident afuence and urban tree cover,
where higher levels of resident income, education,
and homeownership are spatially associated with
urban tree cover. Moreover, several studies highlight
direct relationships of these resident socioeconomic
attributes with participation in urban forest steward-
ship activities (Conway et al. 2011; Greene et al.
2011).
Research investigating the rates and causes of tree
mortality and declines in urban forest structure and
function is an important resource for urban forest
practitioners. The disciplines of ecology, urban plan-
ning, and geography continue to explore the dynam-
ics of urban forests and their relationship with human
populations. However, there is a considerable knowl-
edge gap on the combined effects of these stressors
and their interaction with urban forest structure.

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 13
alba), tree-of-heaven (Ailanthus altissima), and Man-
itoba maple (Acer negundo). Toronto has a continental
climate with hot, humid summers and cold winters,
with a mean annual precipitation is 834 mm and a
mean annual temperature of 9.2°C (Environment Can-
ada 2015). The city is within the Deciduous Forest
Region and Mixedwood Plains Ecozone (Ontario Min-
istry of Natural Resources 2012).
Data Collection and Processing
Data collection took place during the growing season
of 2014. A total of 806 publicly-owned trees (i.e.,
street trees and trees in front-yard rights-of-way,
parks, and schoolyards) were re-inventoried and
matched with data from the existing 2007/2008 tree
inventory. Of the 806 trees inventoried in 2007/2008,
672 were still living in 2014 during eld data collec-
tion. Residential backyard trees were omitted from
the study due to access constraints. The 806 trees rep-
resent a full survey of 24 city blocks covered in the
original inventory. In addition to the standard tree
inventory metrics of species, diameter at breast height
(DBH), and location, a series of indicators of urban
forest vulnerability were assessed for each tree (Table
1). Newly planted trees were also measured for
descriptive purposes but were not used in subsequent
statistical analysis.
The design of the urban forest vulnerability assess-
ment framework and selection of indicators are
described in Steenberg et al. (2017a). Specic indica-
tor selection and design were further rened accord-
ing to the study’s scale of assessment (i.e., individual
trees), data availability, and feasibility. Indicators in
the framework are assigned to the vulnerability sub-
categories of exposure, sensitivity, or adaptive capac-
ity. Exposure indicators (Table 1) represent external
stressors and disturbances that cause tree decline and
mortality, and subsequently a decline in ecosystem
service supply. While some of the exposure indica-
tors represent direct stressors (e.g., vandalism), most
characterize indirect relationships between stress and
the surrounding environment, all of which have been
previously identied as important causes and cor-
relates of tree decline and/or mortality (Randrup et al.
2001; Nowak et al. 2004; Trowbridge and Bassuk
2004; Jutras et al. 2010; Lu et al. 2010; Lawrence et
al. 2012; Koeser et al. 2013; Steenberg et al. 2018).
The main data source for exposure indicators was
eld data collected during this study. Additionally,
2011 census data were used to measure population
vulnerability (Steenberg et al. 2017a) was used to
assess 2014 data describing 806 public trees in a res-
idential neighborhood in Toronto, Ontario, Canada.
The framework consists of a series of quantitative
indicators of exposure, sensitivity, and adaptive
capacity that describe the built environment and asso-
ciated stressors, urban forest structure, and the neigh-
borhood’s human population, respectively. Tree
mortality, condition, and diameter growth rates were
then assessed using an existing tree inventory from
2007/2008. A bivariate analysis was rst conducted
to test for signicant relationships of vulnerability
indicators with mortality, condition, and growth. A
multivariate analysis was then conducted using mul-
tiple linear regression for the continuous condition
and growth variables and a multilayer perceptron
neural network for the binary mortality variable. With
much of the global population increasingly living in
cities and urbanization rates on the rise, ongoing
research and science-based tools for understanding
the causes of urban forest change and decline are
essential for developing planning strategies to reduce
long-term system vulnerability.
METHODS
Study Area
The study was conducted in a centrally located, down-
town residential neighborhood, Harbord Village, in
Toronto, Ontario, Canada. The neighborhood was
selected because of its existing, spatially-referenced
tree inventory. As of 2011, Harbord Village had 8,583
residents, a population density of 13,484 persons/
km2, and total area of 0.6 km2, and was predominately
comprised of semi-detached residential dwellings,
with approximately 1,600 households (Keller 2007;
Statistics Canada 2012). There are commercial land
uses along main street sections, with several larger
multi-unit and institutional parcels, and three small
public parks. Urban forest researchers and Harbord
Village residents conducted a tree inventory in 2007
and 2008 to inform their strategic urban forest man-
agement plan (Keller 2007). Dominant tree species in
the neighborhood include Norway maple (Acer plat-
anoides), green ash (Fraxinus pennsylvanica), hon-
eylocust (Gleditsia triacanthos), white cedar (Thuja
occidentalis), silver maple (Acer saccharinum), and
horsechestnut (Aesculus hippocastanum). Natural-
ized species that have grown from seed (in-grown)
that are common include white mulberry (Morus

©2019 International Society of Arboriculture
14 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
Adaptive capacity indicators (Table 1) represent
components of the urban forest that enable it to reduce
its own vulnerability or increase its capacity to tolerate
greater change without adverse effects (Adger 2006).
In the context of this study, indicators of adaptive
capacity measure socioeconomic variables that are
likely to increase or be positively associated with eco-
system service supply, or environmental ones that are
likely to increase supply. All social adaptive capacity
indicators were measured using 2011 National House-
hold Survey data at the dissemination-area level,
excluding presence/absence indicators that were
assessed in the eld. Dissemination areas are the
smallest geographic unit for which census and
National Household Survey data are available, and
are delineated to contain between 400 and 700 peo-
ple. The environmental adaptive capacity indicators
were measured using 2007 land-cover data derived
from QuickBird satellite imagery with 0.6-m resolu-
tion, quantied at the parcel scale (City of Toronto
2010). Additional satellite-derived land cover data for
2014 would have been desirable but were not
available.
Analysis
Three metrics of ecological change were assessed by
comparing eld data collected for this study in 2014
with the existing 2007/2008 tree inventory. Tree mor-
tality was measured as presence/absence using
matched tree comparisons. Tree mortality was
recorded for both tree removals and for dead trees
still located on site. Annual mortality rates (Equation
1) were measured for the ten most abundant tree spe-
cies with the equation used by Nowak et al. (2004)
and adapted by Lawrence et al. (2012):
[1] m = 1 – (N1/N0)1/t
where m is the annual mortality rate (%), N0 is the
number of living trees at the time of the rst inven-
tory, N1 is the number of living trees at the time of the
second inventory, and t is the number of years
between inventories. Diameter growth rates (cm/yr)
were measured by dividing the difference in DBH
between matched trees by the time interval between
inventories. The third ecological change variable was
the Neighbourwoods-derived 2007/2008 and 2014
tree condition indices. However, change in tree con-
dition between inventories was not analyzed due to
the ordinal ranking method of Neighbourwoods and
density, and a combination of 2013 orthorectied
aerial photography and 2013 City of Toronto property
map data were used to measure built area intensity
(assessed as building site coverage; the ratio of building
footprint to parcel area), distances to nearest build-
ings, and widths of streets. The binary exposure indi-
cators resulting from the presence/absence of conicts
with infrastructure (Kenny and Puric-Mladenovic
2001), vandalism, and poor management were mea-
sured in the eld.
Sensitivity indicators (Table 1) represent the inter-
nal structure of the system, in this case the tree spe-
cies measured in the study, and its relative response to
exposures. In other words, they are elements of urban
forest structure that increase or decrease the likeli-
hood of tree decline and mortality in response to
stress. Species and DBH class were included to
account for potential variation in the vulnerability of
tree species and sizes (i.e., ages). A number of studies
have found that mortality rates uctuate by species
and are elevated in younger and newly planted urban
trees (e.g., Nowak et al. 2004; Roman and Scatena
2011; Koeser et al. 2013). Tree condition is another
predictor of urban tree mortality (Koeser et al. 2013)
and is itself an indicator of sensitivity to stress (Trow-
bridge and Bassuk 2004).
In this study, researchers derived tree condition
using an aggregated index calculated from data col-
lected as part of the Neighbourwoods assessment
protocol (Kenney and Puric-Mladenovic 2001). This
aggregate index has a maximum value of 1.0, indicat-
ing extremely poor tree condition. Neighbourwoods
is a tool for community-based urban forest steward-
ship, which was developed by Kenney and
Puric-Mladenovic (2001). It describes a standardized
procedure for community members to inventory and
monitor the location, composition, and condition of
their urban trees. The protocol describes 15 ordinal
metrics of tree condition (e.g., scars and cavities) and
structure (e.g., included bark), ranging from 0 (best
condition) to 3 (worst condition), giving a total possi-
ble score of 45, which researchers then standardized
to produce the aggregate condition index. A Neigh-
bourwoods assessment was conducted during the
2007/2008 Harbord Village tree inventory and was
again conducted for all trees measured in 2014. The
tree condition index was calculated for both
2007/2008 and 2014 data. All sensitivity indicators
were measured using eld data.

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 15
Lastly, to analyze the possible effects of the vul-
nerability indicators on the binary variable describing
tree mortality, researchers used a multilayer percep-
tron neural network using IBM® SPSS® Statistics
24 (Hastie et al. 2009; Jutras et al. 2009). Multilayer
perceptron neural networks are articial neural net-
works comprised of a collection of data structures
and algorithms in a network meant to loosely mimic
a biological brain. They fall within the discipline of
machine learning that has been growing in impor-
tance with the rise of computational power and large
data sets (Hastie et al. 2009). The mixed structure,
noisy, and highly variable nature of the vulnerability
data—and of urban social-ecological systems in gen-
eral—negate the use of many traditional inferential
statistics. While logistic regression has been used to
predict tree mortality in urban forests (e.g., Koeser et
al. 2013), researchers opted not to use this approach
because of the many categorical variables used in the
analysis and comparatively small sample size (Hair et
al. 2010). Neural networks have their origin in com-
puter science and articial intelligence, but have been
applied successfully in tree mortality research in both
rural (Guan and Gertner 1991; Hasenauer et al. 2011)
and urban settings (Jutras et al. 2009). For example,
Jutras et al. (2009) used them to investigate morpho-
logical parameters of street trees in Montreal, Qué-
bec, Canada. Multilayer perceptron neural networks
use a number of neurons (i.e., units) in one or more
layers, which communicate with each other via
weighted connections, or links (Hastie et al. 2009).
The independent variables (i.e., inputs) in the input
layer communicate to neurons in one or more hidden
the corresponding likelihood of assessment subjectiv-
ity among different researchers collecting data at the
two time instances.
Researchers rst conducted a bivariate analysis to
get an understanding of the inuence of individual
vulnerability indicators (i.e., exposure, sensitivity, and
adaptive capacity indicators in Table 1) on the three
ecological change variables, and insight into their
utility for vulnerability framework renement. The
analysis included simple signicance testing on rela-
tionships between vulnerability indicators and mor-
tality, condition, and growth, using the appropriate
nonparametric statistical test based on data type. Spear-
man’s Rho was used for tests between continuous
variables and Pearson’s chi-squared (χ2) test was used
for tests between categorical/binary variables. The
Mann-Whitney U test was used for tests between con-
tinuous and binary variables, while the Kruskal-Wallis
rank test was used for tests between continuous and
categorical variables with more than two groups.
A subsequent multivariate analysis was conducted
to evaluate the predictive capacity and explanatory
power of the vulnerability indicators on urban forest
ecological change in Harbord Village. Only those
vulnerability indicators that were found to have sta-
tistical signicance at the α = 0.05 level in the bivari-
ate analysis were included. Multiple linear (i.e.,
ordinary least squares) regression was used to predict
the continuous tree condition and growth rate vari-
ables. Condition and growth were used as dependent
response variables in separate regression models
using the reduced selection of vulnerability indicators
as independent predictor variables. These two models
were run on the 672 living trees only, as condition
and growth cannot be measured on dead/removed
trees. The site size (i.e., m2 of growing environment),
height of nearest building, distance to nearest build-
ing, distance to street, width of street, and width of
sidewalk variables were log transformed to meet nor-
mality assumptions for regression analysis. Tolerance
values indicated no issues with multicollinearity (i.e.,
tolerance values above 0.1; Hair et al. 2010) for all
variables except for some of the groups (i.e., dummy
variables) of the land use and building type categori-
cal variables. While this multicollinearity was to be
expected to some degree, it does reduce the effective-
ness of the models and is a source of uncertainty.
Only the top ve most abundant species were included
in the analysis as dummy variables.
Figure 1. Change in size-class distribution of measured trees
between the 2007/2008 (N = 806) and 2014 (N = 672; N = 1,056 with
newly planted trees) inventories in the Harbord Village
neighborhood in Toronto, Ontario, Canada.

©2019 International Society of Arboriculture
16 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
neural network used the vulnerability indicators iden-
tied in the bivariate analysis as inputs to predict
mortality outcomes (0/1). A single hidden layer with
six neurons was used. Two-thirds of the data records
(N = 565) were used as the training sample, and the
remaining one-third (N = 241) as the testing sample.
Training samples are important to use to avoid over-
tting of the model, which can lead to incorrect gen-
eralizations of the results.
RESULTS
The change in size-class distribution (Figure 1) and
species composition (Figure 2) between the 2007/2008
and 2014 inventories illustrates the demographic
change of public trees in the study area. The most
abundantly planted trees were white cedar, Japanese
maple (Acer palmatum), serviceberry (Amelanchier
spp.), Freeman maple (Acer × freemanii), dogwood
(Cornus spp.), eastern red cedar (Juniperus virgini-
ana), and mugho pine (Pinus mugo), which are con-
siderably different from the current dominant species
and are nearly all smaller-sized trees at maturity. The
total tree planting rate in the study area was 1.42
layers (i.e., one or more neurons in between the input
and output layers), which ultimately communicate to
the output layer. Supervised learning is used to train
and adapt the network using error values to identify
nal weight values and ultimately optimize its predic-
tive capacity (Hastie et al. 2009). In this study, the
Figure 2. Change in tree species distribution of the 10 most abundant
species measured between the 2007/2008 and 2014 inventories in the
Harbord Village neighborhood in Toronto, Ontario, Canada. ACPL:
Norway maple; FRPE: green ash; GLTR: honeylocust; THOC: white
cedar; ASCA1: silver maple; AEHI: horsechestnut; ACFR: Freeman
maple; TICO: littleleaf linden; AIAL: tree-of-heaven; MOAL: white
mulberry.
Table 2. Annual mortality rate (%), mean diameter growth rate (cm/yr), and mean condition index value of measured trees,
stratied by diameter class and 10 most abundant species.
Category N Annual Mean growth Mean condition
mortality rate rate (standard index value
(%) deviation) (standard deviation)
All trees 806 2.40 0.59 (±0.57) 0.30 (±0.17)
Size class
>0.1-10.0 cm DBH 168 6.56 0.27 (±0.25) 0.23 (±0.17)
10.1-20.0 cm DBH 174 2.67 0.64 (±0.42) 0.28 (±0.17)
20.1-30.0 cm DBH 133 1.36 0.72 (±0.47) 0.29 (±0.13)
30.1-50.0 cm DBH 200 0.75 0.69 (±0.49) 0.29 (±0.15)
50.1-75.0 cm DBH 89 1.09 0.56 (±1.08) 0.39 (±0.20)
>75.0 cm DBH 42 1.33 0.37 (±0.33) 0.43 (±0.15)
Species
Norway maple 163 2.10 0.46 (±0.23) 0.36 (±0.19)
Green ash 80 4.64 0.50 (±0.33) 0.40 (±0.16)
Honeylocust 80 0 0.59 (±0.44) 0.28 (±0.13)
White cedar 57 2.27 0.59 (±0.53) 0.17 (±0.14)
Silver maple 37 1.51 0.48 (±0.58) 0.39 (±0.13)
Horsechestnut 36 0.76 0.28 (±0.60) 0.37 (±0.16)
Freeman maple 27 1.56 1.10 (±0.70) 0.23 (±0.13)
Littleleaf linden 21 0 0.82 (±0.44) 0.29 (±0.12)
Tree-of-heaven 19 12.47 1.11 (±0.59) 0.18 (±0.10)
White mulberry 16 1.75 1.05 (±2.14) 0.33 (±0.12)
Other 270 2.93 0.65 (±0.56) 0.26 (±0.16)

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 17
Table 3. Bivariate analysis of the exposure, sensitivity, and adaptive capacity indicators with tree mortality, condition, and
growth rate, showing test statistic values and signicance levels.
Independent variable Mortality Condition Growth
Exposure
Built environment
Population density (persons/km2) 42,919z -0.15y,*** -0.01y
Built area intensity (%) 42,112z 0.02y -0.12y,**
Land use (categorical) 33.89x,*** 23.99w,*** 25.20w,***
Site type (categorical) 9.78x 17.69w, * 24.55w, **
Site size (m2) 40,486z 0.01y 0.14y,***
Type of nearest building (categorical) 16.487x,* 13.52w 20.71w, **
Height of nearest building (storeys) 43,545z 0.08y, * -0.04y
Distance to nearest building (m) 39,449z,* 0.14y,*** 0.06y
Distance to street (m) 44,949z -0.16y,*** 0.16y, ***
Width of sidewalk (m) 44,826z 0.11y,** -0.07y
Width of street (m) 43,203z 0.10y,** -0.04y
Impervious cover (%) 41,420z 0.22y, *** -0.06y
Light availability (ordinal rank; 0-5) 7.52w 0.03y 0.02y
Conicts
Conict of overhead utilities (0/1) 142.98x,*** 49,752z 46,913z
Conict with sidewalk (0/1) 6.11x,* 14,756z,* 18,497z
Conict with buildings (0/1) 59.45x,*** 44,628z,** 39,982z,***
Conict with building foundation (0/1) 6.24x,* 9,381z 8,456z
Conict with other infrastructure (0/1) 5.45x,* 47,964z 44,596z,*
Social stressors
Poor management (0/1) N/A 30,348z,*** 36,580z
Vandalism (0/1) N/A 18,189z,*** 21,593z
Sensitivity
Species (categorical) 67.69x,*** 95.77w,*** 164.32w,***
DBH (cm) 95.76x,*** 0.25y, *** 0.04y
Tree condition index (Neighbourwoods) 40,988z N/A -0.21y, ***
In-grown tree (0/1) 32.22x,*** 5,175z 5,670z
Adaptive capacity
Social adaptive capacity
Median family income ($) 44,798z -0.03y -0.03y
Average dwelling value ($) 43,134z -0.09y* 0.03y
Homeownership (%) 43,693z 0.05y -0.03y
Population with a university degree 43,985z 0.09y* -0.03y
(individuals/10,000 people)
Signs of stewardship (0/1) N/A 35,058z 32,406z,*
Environmental adaptive capacity
Open green space (%) 40,491z -0.08y,* 0.11y,**
Existing canopy cover (%) 40,833z -0.01y 0.01y
z Mann-Whitney U test.
y Spearman’s Rho.
x Pearson’s chi-squared (χ2) test.
w Kruskal-Wallis rank test.
Notes: single asterisk (*) indicates signicant at the α = 0.05 level; double asterisk (**) indicates signicant at the α = 0.01 level; and triple asterisk (***)
indicates signicant at the α = 0.001 level.
trees/ha/yr, and white cedar, which was frequently
planted along fence lines, represented 43% of all new
trees planted. Diameter growth rates slowed with
increases to tree size; the condition of measured trees
also consistently worsened with greater tree size
(Table 2). However, the lower diameter growth rate

©2019 International Society of Arboriculture
18 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
trees were far more likely to experience mortality than
planted trees. There were no signicant relationships
between mortality and adaptive capacity indicators.
Tree condition had the highest number of signi-
cant relationships with the vulnerability indicators,
many of which were associated with increasing inten-
sity of the built environment, like land use and site
type (Table 3). More impervious surface cover and
larger sidewalks and streets were all associated with
poor tree condition. Incidences of poor management
(e.g., improper pruning, unremoved tethers causing
damage), vandalism (e.g., torn branches), and con-
icts with sidewalks were also associated with poor
tree condition, while conicts with buildings were
associated with better condition. Tree condition
declined consistently with increasing DBH (Table 3).
Green ash, silver maple, and horsechestnut were in
worse condition, while white cedar and tree-of-heaven
were in better condition. There were signicant but
fairly weak correlations of dwelling value, education,
and open greenspace with tree condition (Table 3),
although the relationship between education and tree
condition was counter to vulnerability assumptions.
With tree diameter growth rates, land use, site
type, and building type were again found to have sig-
nicant relationships (Table 3), with slower growth
rates associated with higher-density commercial areas
(i.e., commercial land uses and buildings). Multi-family
residential land uses and apartment towers were asso-
ciated with faster growth rates. Built area intensity
was also associated with lower growth rates and
greater distances from streets with higher ones. Simi-
lar to the counterintuitive mortality results, trees in
conicts with buildings and other types of infrastruc-
ture were associated with faster growth rates. As
expected, trees in poor condition had slower growth
rates and growth rates declined with increasing DBH
class (Table 3). The exception to the latter were trees
in the smallest DBH class, which combined with the
high mortality rate of these trees, is likely explained
by transplant shock and establishment failure (Trow-
bridge and Bassuk 2004). Open greenspace was asso-
ciated with faster growth rates while the presence of
stewardship activities (e.g., watering bags) were asso-
ciated with lower growth rates (Table 3).
The regression models predicting tree condition
and growth rates in the multivariate analysis yielded
some additional insight (Table 4). The condition
model explained 32.1% of the variation in tree condi-
tion. Evidence of poor management and DBH were
of the >0.1-10.0 cm tree size class was anomalous. It
should be noted that multiple-year DBH measure-
ments and growth rates derived from there are likely
to have high measurement error, which is a potential
explanation for this anomaly.
Of the measured trees present in both the 2007/2008
and 2014 inventories, Norway maple was the most
abundant (Figure 2). White cedar exceeded Norway
maple in 2014 in abundance when trees planted
during the time between inventories were incorpo-
rated. Honeylocust, white cedar, Freeman maple, and
littleleaf linden (Tilia cordata) all increased in popu-
lation size when planted trees were incorporated,
while Norway maple, green ash, silver maple, hor-
sechestnut, tree-of-heaven, and white mulberry
decreased. No planted green ash, horsechestnut, tree-
of-heaven, or white mulberry were observed. Tree-
of-heaven had a substantially higher mortality rate
than other trees (Table 2), followed by green ash, both
of which were higher than the study area average
annual mortality rate of 2.4% (Table 2). Green ashes
were in the worst condition, which was likely attrib-
utable to the ongoing EAB infestation in the study
area, while white cedar were consistently in better con-
dition. Tree condition of other species was generally
reective of tree size, where consistently larger species
(e.g., silver maple and horsechestnut) were in worse
condition.
The bivariate analysis revealed a number of signif-
icant relationships between vulnerability indicators
and tree mortality. Land use is known to be an inu-
ential driver of urban forest structure and function,
which was corroborated by the ndings (Table 3).
The χ2 test revealed that commercial land uses had a
high occurrence of tree mortality (36 observed versus
22 expected), while institutional land uses had a
lower occurrence (15 observed versus 20 expected).
Distance to the nearest building and building type
were other signicant built environment indicators,
with shorter distances being associated with higher
mortality. The ve conict with infrastructure indica-
tors all had signicant relationships with mortality,
yet some were counter to a priori vulnerability
assumptions (i.e., increased mortality with conict).
In particular, the presence of conicts with overhead
utility wires had an observed 6 incidences of mortality
compared to the expected value of 69. Tree mortality
was much higher for trees in the smallest DBH class
(67 observed versus 28 expected) and for green ash com-
pared to other species (Table 3). Additionally, in-grown

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 19
17.5% of the variation in growth rates with several
counter intuitive relationships. Additionally, DBH
was measured manually using diameter tapes by dif-
ferent researchers and at different time periods, so
sampling error resulting from variability in measure-
ments was likely. Institutional land uses were strong
signicant predictors of faster tree growth rates,
strong predictors of poorer condition. Norway maple
and green ash were strongly associated with poor tree
condition, while white cedar was associated with bet-
ter condition. Both park land uses and exposure to
vandalism were associated with poor tree condition
as well. The regression model predicting diameter
growth rates did not perform as well, explaining only
Table 4. Beta coefcients (β) and P-values for the multiple linear regression analysis predicting individual 2014 tree condition
index values and diameter growth rates (cm/yr) of individual trees using the urban forest vulnerability indicators.
Independent variable Condition P-value Growth P-value
β β
Tree condition index (Neighbourwoods) -0.161 <0.0001
Population density (persons/km2) -0.073 0.160
Built area intensity (%)
Height of nearest building (storeys) 0.076 0.152
Distance to nearest building (m) -0.046 0.370
Distance to street (m) 0.035 0.540 0.101 0.096
Width of sidewalk (m) 0.079 0.176
Width of street (m) 0.037 0.587
Impervious cover (%) -0.040 0.418
Conict with sidewalk (0/1) 0.012 0.782
Conict with buildings (0/1) -0.033 0.405 0.206 <0.0001
Conict with other infrastructure (0/1) 0.099 0.012
Poor management (0/1) 0.308 <0.0001
Vandalism (0/1) 0.109 0.004
Land use – Commercial -0.153 0.254 -0.272 0.066
Land use – Institutional 0.063 0.494 0.366 <0.0001
Land use – Multi-family 0.031 0.767 -0.094 0.355
Land use – Park 0.118 0.011 0.026 0.670
Site type – Fence Line 0.027 0.517 -0.126 0.005
Site type – Bare 0.040 0.272 -0.012 0.753
Site type – Lawn/grass 0.031 0.480 0.074 0.116
Site type – Grass median 0.123 0.021 0.044 0.380
Site type – Raised planter 0.053 0.330 0.075 0.211
Site type – Tree pit/sidewalk 0.163 0.086 -0.015 0.867
Building type – Apartment -0.050 0.595 0.105 0.290
Building type – Commercial 0.208 0.099 0.212 0.138
Building type – Detached house -0.017 0.642 -0.019 0.622
Building type – Institutional <0.0001 0.996 -0.264 0.003
Building type – Row house -0.071 0.057 -0.064 0.103
DBH (cm) 0.335 <0.0001
Norway maple 0.127 0.002 -0.206 <0.0001
Green ash 0.115 0.020 0.054 0.288
Honeylocust -0.085 0.058 0.038 0.435
White cedar -0.107 0.006 -0.016 0.697
Silver maple 0.058 0.128 -0.146 <0.0001
Average dwelling value ($) -0.013 0.785
Population with a university degree -0.041 0.353
(individuals/10,000 people)
Signs of stewardship (0/1) -0.061 0.153 -0.002 0.962
Open greenspace (%) -0.076 0.112 0.052 0.318
R2 0.321 0.175

©2019 International Society of Arboriculture
20 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
differentiate between different causes and correlates
of urban forest decline for trees growing within com-
mercial land uses. For example, street width (i.e.,
wider streets) can be a positive correlate of tree stress
(Nagendra and Gopal 2010). Current ndings also
suggest that distance from streets and buildings are
important indicators of urban tree vulnerability.
While land use is a fairly established mechanism for
stratifying urban landscapes and conducting urban
forest research (Nowak et al. 1996; Steenberg et al.
2015), the results of this study suggest that at the
household scale, differentiated indicators (e.g., building
type, impervious cover, street geometry) are necessary
components of urban forest vulnerability assessment.
There are myriad physical, biological, and social
stressors and disturbances that afict urban trees and
forests (Trowbridge and Bassuk 2004; Steenberg et
al. 2017a). Consequently, there are many opportuni-
ties to improve upon frameworks of urban forest vul-
nerability assessment. In this study, exposure
indicators were mainly limited in scope to those
stressors associated with the built environment and
urban form. However, the intent was that the sensitivity
indicators would, in part, address these other dimen-
sions of exposure for which quantication and/or data
availability were limiting factors for measurement.
For example, vulnerability to biological threats
(e.g., EAB) or storm events can be captured in the
sensitivity metrics of species composition (e.g., ash
abundance and distribution; Laćan and McBride
2008; Vander Vecth and Conway 2015) and age struc-
ture (e.g., structural diversity and over-mature cano-
pies; Staudhammer and LeMay 2001; Lopes et al.
2009). Additionally, it is possible that the widespread
though adjacency to institutional buildings was also a
strong predictor and explained slower growth rates.
This unexpected nding can be explained, in part, by
the fact that land use was assessed in the eld at the
parcel level while building type was assessed for the
building with the shortest distance to a given tree.
Poor tree condition explained slower tree growth
rates, as did tree species (i.e., Norway maple and sil-
ver maple). Conicts with infrastructure again were
associated with faster growth rates. Lastly, the multi-
layer perceptron neural network used to analyze tree
mortality performed well using the selected vulnera-
bility indicators, which reinforces the utility of these
indicators in future vulnerability assessment. The net-
work had an accuracy of 89.2% with the training
sample and 86.7% with the testing sample, and was
more effective in predicting living trees than dead
trees (Table 5).
DISCUSSION AND CONCLUSIONS
The ndings of this study suggest that the highest
exposure and corresponding levels of urban tree
decline and mortality were most inuenced by the
intensity of land use and the conditions encountered
in the built environment. Trees growing in land clas-
sied as commercial land uses, and circumstances in
which commercial buildings were adjacent to trees,
consistently explained higher mortality rates and
poor tree conditions. While studies have found vary-
ing effects of commercial land uses on urban trees
(e.g., Lawrence et al. 2012), it is generally established
that these inuences are among the most detrimental
for tree health (Nowak et al. 2004; Jutras et al. 2010).
However, at ner spatial scales it is important to
Table 5. Classication accuracy of the multilayer perceptron neural network for predicting tree mortality (0/1) in the testing and
training samples using the vulnerability indicators.
Predicted Predicted Percent
no mortality (0) mortality (1) correct
Training sample
Observed no mortality (0) 454 20 95.8
Observed mortality (1) 41 50 54.9
Overall accuracy 89.2
Testing sample
Observed no mortality (0) 183 15 92.4
Observed mortality (1) 17 26 60.5
Overall accuracy 86.7

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 21
Additionally, assessed trees were consistently in poor
condition with increasing diameter. Larger sites with
more open greenspace, and those that were farther
from adjacent buildings, were more likely to have
larger trees, and therefore trees in poor condition,
despite more suitable growing conditions than higher-
density commercial areas. Again this highlights the
inuence of specic conditions in Harbord Village,
and subsequently limits further generalizations. How-
ever, declining tree condition with age is an estab-
lished pattern (Nowak et al. 2004), which suggests
higher sensitivity and subsequent vulnerability of
mature urban forest ecosystems, often found in older,
established residential neighborhoods. Importantly, it
may also reveal that the processes driving decline in
tree condition may sometimes differ from those driv-
ing mortality.
Overall, adaptive capacity indicators were less
inuential on ecological change and vulnerability
than exposure and sensitivity indicators. For one,
they were limited by the scale of available socioeco-
nomic data (i.e., census dissemination areas as
opposed to households). However, this limitation
does not preclude them from being important in long-
term urban forest vulnerability. Many studies support
a strong positive relationship of both urban forest
structure and stewardship with the socio-demographic
characteristics of city residents at broader spatial
scales (e.g., Grove et al. 2006; Troy et al. 2007; Con-
way et al. 2011; Greene et al. 2011; Schwarz et al.
2015). Political processes are also important drivers
of urban forest distribution and stewardship. For
instance, Kendal et al. (2012) found that income
inequality in tree cover distribution was more pro-
nounced in public streetscapes than in residential
properties. Füssel (2010) emphasizes that while
observed empirical data are more objective and reli-
able, they cannot reveal all aspects of system vulner-
ability, especially long-term risks. It is likely that the
comparatively short time span (e.g., six to seven
years) between tree inventories in this study, as well
as the spatial scale of data used in the analysis, might
explain this lower inuence of adaptive capacity on
ecological change. Importantly, the quantitative
nature and specic indicators of the Steenberg et al.
(2017a) vulnerability framework restrict the concep-
tion of adaptive capacity considerably, especially given
its emphasis on census data, and might imply that
adaptive capacity is restricted to afuent communities.
decline of ash might also inate the inuence of some
other exposure indicators.
Nonetheless, this study’s ndings suggest that
quantifying known biological exposures would be
benecial in future vulnerability assessments, given
the high levels of decline and mortality of green ash
attributable to EAB. One nding that ran contrary to
the a priori vulnerability assumptions was the exceed-
ingly high survival rate of trees in conict with over-
head utility wires, compared to those that were not.
This may be due to the hardiness of the species
selected for street tree plantings. However, the study
authors offer the one theory requiring further investi-
gation: that trees most often in conict were the
municipally-owned, larger trees in the public right-
of-way. Despite the conict with utility wires, more
frequent maintenance of these trees by urban forest
practitioners could potentially explain this trend, but
this requires further research to be substantiated.
Urban forest structural elements that characterize
sensitivity were found to be valuable in examining
overall vulnerability. Specically, tree condition was
a highly inuential predictor of mortality and diame-
ter growth. This nding conrms existing research
supporting condition as an effective predictor of mor-
tality (Koeser et al. 2013). This nding also suggests
that more detailed frameworks for assessing tree con-
dition (i.e., not just percent crown dieback) are valu-
able. Conversely, the ndings also highlight important
drivers of condition decline, such as poor manage-
ment and vandalism, where poor management was
most often identied as improper pruning practices
and vandalism as torn branches on smaller trees (Lu
et al. 2010). Decline, mortality, and vulnerability of
the studied trees were likely a function of the compo-
sition and age distribution of the neighborhood and
tolerance of individual species to urban conditions
(e.g., high tolerance of honeylocust, and therefore
low sensitivity and minimal mortality; Burns and
Honkala 1990). One notable species-level effect was
the much higher likelihood of mortality for in-grown
species (e.g., tree-of-heaven), which emphasizes the
importance of differentiating between planted and
in-grown trees in urban forest vulnerability assessment.
Tree size was a highly inuential metric of urban
forest sensitivity, both in its interaction with expo-
sures and as a predictor of tree condition. Trees in the
smallest size class had by far the highest mortality
rates, as might be expected (Roman and Scatena 2011).

©2019 International Society of Arboriculture
22 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability
Vulnerability science offers an integrative lens
through which to explore risk and loss of function in
highly complex, social-ecological systems like the
urban forest (Turner et al. 2003; Adger 2006; Grove
2009; Steenberg et al. 2017a). Vulnerability also has
many synergies with the concept of resilience that is
of increasing importance in urban planning, though
Steenberg et al. (2017a) argue that a vulnerability lens
addresses drivers of change that are often external to
resilience frameworks. Much of the research investi-
gating mortality and decline in urban forests focuses
primarily on stressors and disturbances. Moreover,
vulnerability assessment might also be a useful sup-
plement to existing assessments of tree safety and
risk (Ellison 2005). This study afrms that there is a
need to investigate how these stressors interact with
urban forest structure and surrounding human popu-
lations to reduce or inate vulnerability in order to
reliably predict the likelihood of potential loss of eco-
system services. Moreover, many of the established
relationships between urban forests and socioeco-
nomic variability are based on two-dimensional tree
canopy cover data at broader spatial scales. There are
far fewer studies (e.g., Shakeel and Conway 2013)
investigating urban forest ecological processes at
ner scales using empirical eld data from multiple
time periods. However, further research is needed
that tests both the reliability and validity of indicator
design in different neighborhoods, cities, and scales.
With increasing attention paid to urban forests by
municipalities (Ordόñez and Duinker 2013) and com-
munity groups (Conway et al. 2011), the demand for
management information that goes beyond quantify-
ing ecosystem structure and function to assessing
urban forest vulnerability is of increasing interest.
Acknowledgements. We are indebted to Ryerson University
research assistants Amber Grant and Claire Stevenson-Blythe,
who assisted with eld data collection. Thank you to the residents
of the Harbord Village neighborhood and the Harbord Village
Residents Association for permitting eld data collection and
sharing existing data. Funding for this project was provided by
the Natural Sciences and Engineering Research Council of Can-
ada (NSERC) and Ryerson University. Some statistical consult-
ing support was provided by David Kremelberg. This research
was, in part, conducted and funded during the lead author’s Ful-
bright exchange at the USDA Forest Service’s Northern Research
Station in Syracuse, New York. Fulbright Canada is a joint,
bi -national, treaty-based organization created to encourage mutual
understanding between Canada and the United States of America
through academic and cultural exchange.
Research has shown that adaptive capacity is driven
by an array of social processes not necessarily afxed
to wealth, including place attachment, common concern
for neighborhood improvement (e.g., crime reduc-
tion), and the presence of community leaders (West-
phal 1993; Manzo and Perkins 2006; Tidball and
Krasny 2007). Household-scale, qualitative research
will provide valuable insight into these social pro-
cesses in future work.
Given their longevity and stationary nature, trees
and forests are generally vulnerable to environmental
change, where manifestations of change in urban for-
est structure and function may lag considerably in
their response to drivers of change (e.g., changes in
management practices). Current urban forest struc-
ture is often a function of decades-old management
decisions (Boone et al. 2010). The disparity between
commonly-planted tree species and overstory species
composition in the neighborhood, coupled with ongo-
ing decline of green ash and its removal from tree
planting schedules, points toward the likelihood of
considerable future change in ecosystem conditions.
Moreover, Norway maple, which was the dominant
overstory species, was an extremely popular urban
tree in previous decades but is now no longer favored
in Toronto’s urban forestry plan and planting sched-
ules because of its potential to become invasive (City
of Toronto 2013). In addition to these potential lag
effects in species composition, the observed species-
specic mortality and shifts towards smaller, orna-
mental species may also correspond to declines in
future ecosystem service supply irrespective of urban
stressors and disturbances. Many ecosystem services
are strongly associated with larger, longer-lived tree
species with large leaf areas (Nowak and Dwyer
2007), which may indicate future declines in ecosys-
tem service supply due to changing planting prefer-
ences in tree species. Moreover, populations of
mature urban trees, especially with low species and
age diversity, may provide high levels of ecosystem
services but also be highly vulnerable due to their
sensitivity to pests, storms, and age-related decline
(Steenberg et al. 2017a). These issues reinforce the
temporal nature of vulnerability and associated
impacts (Adger 2006; Steenberg et al. 2017a). Urban
forest vulnerability assessments require both hind-
sight in the form of monitoring (Roman et al. 2013),
but also foresight in the form of ecological modeling
to explore future scenarios of management and dis-
turbance (Steenberg et al. 2017b).

©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 23
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James W. N. Steenberg (corresponding author)
Environmental Applied Science and Management
Ryerson University
and
School for Resource and Environmental Studies
Dalhousie University
6100 University Avenue
Halifax, Nova Scotia, B3H 4R2, Canada
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Andrew A. Millward
Urban Forest Research & Ecological Disturbance (UFRED)
Group
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350 Victoria Street
Toronto, Ontario, M5B 2K3, Canada
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©2019 International Society of Arboriculture
Arboriculture & Urban Forestry 45(1): January 2019 25
wurden anhand eines existierenden Katasters von 2007/2008 un-
tersucht. Eine zweidimensionale Analyse wurde zuerst ausgeführt,
um die signikanten Beziehungen der Anfälligkeitsfaktoren mit
Sterberate, Zustand und Wachstum zu testen. Eine multidimen-
sionale Analyse wurde anschließend unter der Verwendung von
einer multiplern linearer Regression für die fortgesetzten Kondi-
tionen und Wachstumsvariablen und eines mehrlagigen Perzep-
trons (vereinfachtes künstliches Netzwerk) für die binäre
Sterberate -Variable durchgeführt. Gewerbliche Landnutzung und
Gewerbebauten in der Nachbarschaft von Bäumen hatten konsis-
tent höhere Sterberaten und armselige Baumkonditionen. Bei
feineren räumlichen Skalen ist es wichtig, zwischen verschie-
denen Ursachen und Korrelaten beim Rückgang urbaner Wälder
bei kommerzieller Landnutzung zu differenzieren. Baumart,
-größe und Kondition sind ebenso wichtige Indikatoren von An-
fälligkeiten. Das Verständnis der Ursachen des Wandels und
Rückgang von urbanen Wäldern ist essentiell für die Entwick-
lung von Planungsstrategien, um die Verletzbarkeit auf lange
Sicht zu reduzieren.
Resumen. El bosque urbano es un proveedor valioso de servicios
ecosistémicos, pero las ciudades son entornos frecuentemente de-
gradados con innumerables factores estresantes y disturbios que
afectan a los árboles. La ciencia de la vulnerabilidad se usa cada
vez más para explorar temas de sostenibilidad en sistemas so-
cio-ecológicos complejos y puede ser un enfoque útil para evalu-
ar los bosques urbanos. El propósito de este estudio fue identicar
y explorar los impulsores de la vulnerabilidad de los bosques ur-
banos en un vecindario residencial. Sobre la base de un marco de
vulnerabilidad de bosques urbanos recientemente publicado, se
evaluaron 806 árboles en Toronto, Ontario, Canadá, una serie de
indicadores de exposición, sensibilidad y capacidad de adapta-
ción que describen el entorno construido, la estructura de los
bosques urbanos y la población humana, respectivamente. . Las
tasas de crecimiento, la condición y el diámetro del árbol se eval-
uaron utilizando un inventario existente de 2007/2008. Primero
se realizó un análisis bivariado para probar las relaciones signi-
cativas de los indicadores de vulnerabilidad con la mortalidad, la
condición y el crecimiento. Luego se realizó un análisis multi-
variado utilizando regresión lineal múltiple para las variables de
condición y crecimiento continuas y una red neuronal de percep-
tora multicapa para la variable de mortalidad binaria. Los usos
comerciales de la tierra y los edicios comerciales adyacentes a
los árboles explicaron de manera consistente las mayores tasas de
mortalidad y las malas condiciones de los árboles. Sin embargo,
a escalas espaciales más nas es importante diferenciar entre las
diferentes causas y los correlatos de la disminución del bosque
urbano dentro de los usos comerciales de la tierra. Las especies de
árboles, el tamaño y la condición también fueron indicadores im-
portantes de vulnerabilidad. Comprender las causas del cambio y
la disminución de los bosques urbanos es esencial para desarrol-
lar estrategias de planicación para reducir la vulnerabilidad del
sistema a largo plazo.
Résumé. La forêt urbaine est une précieuse pourvoyeuse de
services écosystémiques malgré que les villes soient fréquem-
ment dégradées sur le plan environnemental avec une myriade de
facteurs de stress et de perturbations affectant les arbres. Une ap-
proche scientique de vulnérabilité est de plus en plus utilisée
an d’explorer les questions de durabilité dans les systèmes du
complexe socio-écologique et peut s’avérer utile pour apprécier
les forêts urbaines. L’objet de cette étude était d’identier et d’ex-
plorer les éléments conducteurs de la vulnérabilité de la forêt
urbaine dans un quartier résidentiel. Sur la base d’un cadre réce-
mment publié sur la vulnérabilité des forêts urbaines, une série
d’indicateurs d’exposition, de sensibilité et de capacité adaptative
décrivant respectivement le milieu bâti, la structure de la forêt
urbaine et la population humaine fut utilisée pour évaluer 806
arbres à Toronto, Ontario, Canada. La mortalité des arbres, leur
condition et le taux de croissance en diamètre furent évalués en
référant à un inventaire datant de 2007/2008. Une analyse à deux
variables fut d’abord effectuée an de vérier les possibles rela-
tions signicatives des indicateurs de vulnérabilité avec la mortalité,
la condition et la croissance. Par la suite, une analyse multidimen-
sionnelle fut réalisée en recourant à une régression linéaire multi-
ple pour les données continues de condition et de croissance alors
qu’un réseau neuronal de perceptron multicouche était utilisé pour
les variables binaires de mortalité. L’usage commercial des sites et
la présence d’édices commerciaux adjacents aux arbres expli-
quaient de manière consistante, le taux plus élevé de mortalité et
les pauvres conditions de croissance des arbres. Toutefois, à une
échelle spatiale plus ne, il est important de distinguer entre les
diverses causes et leurs corrélations en lien avec le déclin de la
forêt urbaine dans les zones commerciales. L’espèce des arbres,
leur dimension et leur condition sont également d’importants in-
dicateurs de vulnérabilité. La compréhension des causes affectant
le changement et le déclin des forêts urbaines est essentielle au
développement d’une planication stratégique an de réduire
leur vulnérabilité à long terme.
Zusammenfassung. Der Urbane Forst ist ein wertvoller Lief-
erant von Ökosystemen, dennoch haben Städte gelegentlich
heruntergekommene Ökosysteme mit unendlich viele Stressfak-
toren und Störungen, die Bäume beeinträchtigen. Die Wissenschaft
zur Erforschung der Verletzbarkeit wird zunehmend eingesetzt, um
die Themen wie Nachhaltigkeit in komplexen sozio-ökologischen
Systemen zu erforschen und es kann auch ein nützlicher Ansatz
für die Untersuchung urbaner Wälder sein. Die Absicht hinter
dieser Studie bestand darin, die treibenden Kräfte in der Verlet-
zlichkeit von urbanen Wäldern in einem besiedelten Umfeld zu
identizieren und erforschen. Basierend auf einem kürzlich
veröffentlichen Rahmenwerk zur Anfälligkeit von urbanen Wäl-
dern wurden in Toronto, Kanada, an 806 Bäumen eine Serie von
Indikatoren der Exposition, Sensitivität und adaptiver Fähigkeit,
welche das bebaute Umfeld beschreiben, die urbane Forststruktur
und die menschliche Population in Bezug dazu untersucht.
Baumsterblichkeit, Zustand und durchschnittliche Wachstumsraten

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