Long-term effects and development of a tree preservation program on tree condition, survival, and growth

Landscape and Urban Planning 193 (2020) 103670

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Research Paper

Long-term effects and development of a tree preservation program on tree condition, survival, and growth

T

Richard J. Hauera, , Andrew K. Koeserb, Stephani Parbsa, Jim Kringerc, Randy Krousec, Ken Ottmanc, Robert W. Millera, David Sivyerc, Nilesh Timilsinaa, Les P. Wernera ⁎

a

College of Natural Resources, University of Wisconsin-Stevens Point, 800 Reserve Street, Stevens Point, WI 54481, United States University of Florida, Gulf Coast Research and Education Center, 14625 CR 672, Wimauma, FL 33598, United States c City of Milwaukee, 841 N. Broadway, Milwaukee, WI 53202, United States b

ARTICLE INFO

ABSTRACT

Keywords: Long-term research Street trees & construction Tree condition rating Tree growth & longevity Tree preservation Urban forest

A long-term research study in Milwaukee, Wisconsin, USA investigated how construction (i.e., repairs to streets, curbs, and sidewalks) affected tree condition, survival, and growth compared to control trees outside construction zones and effects of a tree preservation program on reducing construction impacts. This study is divided into three periods: (1) a limited implementation period (1979–1989) prior to a formal tree preservation program, (2) an intermediate implementation period (1989–2005) during the development and refinement of a tree preservation program, and (3) a full implementation period (2005–2018) after comprehensive execution of the tree preservation program. During the initial limited preservation period, baseline measurements from 1989 showed trees in construction zones died at a higher annual rate (4.1% actual) and had a lower tree condition rating (5.7% actual) in the limited implementation period (1979–1989) as compared to the other two periods. During the intermediate implementation period (i.e. program under development), construction activities resulted in a reduced effect on tree condition (2.4% actual) as measured in 2005. Tree survival and condition in the full implementation period were similar between trees associated with construction zones and non-impacted control trees measured in 2018. Results demonstrate the effect of a tree preservation program on promoting healthy trees in construction zones. Significant findings also show the importance of growing space with trees farther away from curbs and sidewalks having a higher tree condition and survival rate in all three periods. Findings from this study can encourage city-wide-preservation programs elsewhere to protect public street trees from construction.

1. Introduction Street trees are one of several urban infrastructure elements such as buildings, roads, sidewalks, curbs, belowground utilities (e.g., electrical, gas, telecommunications, sewer and water), and aboveground utilities (Coder, 1998; Gibson, 2017; McPherson, Costello, & Burger, 2001; Sydnor et al., 2000). Trees grown near streets, curbs, and sidewalks are routinely proximal to construction activities during the installation and repair of these components (Day, Dickinson, Wiseman, & Harris, 2010; Jim, 2003; McPherson et al., 2001; Morell, 1992). The design lifespan of infrastructure varies broadly with paved roads lasting 10–20 years, sidewalks lasting 20–25 years, and water mains and sewer pipes lasting 50–100 years (Gibson, 2017). The service life of infrastructure may differ from the functional lifespans noted above. The repair frequency depends upon many factors

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(e.g., climate, material and installation quality, exposure to elements, soils, and tree roots) acting upon the infrastructure (Costello, McPherson, Burger, & Dodge, 2000). Miller and Hauer (1995) found 3% of street trees were annually associated with construction activities in Milwaukee, WI USA. Assuming repairs occur evenly throughout the city, one could expect that a street tree location will experience construction on average once every 33 years. To put this into context, the estimated median street-tree lifespan in Milwaukee, WI is 28.5 years (Miller, Hauer, & Werner, 2015) – an estimate that is in line with the 19 to 28-year range of mean lifespan from Roman and Scatena (2011) in their meta-analysis of 16 community studies throughout the United States. As such, a street tree located near these infrastructure elements is likely to be present for at least one construction event. Tree survival data provides a discrete metric for assessing a tree’s response to disturbance (Hilbert, Roman, Koeser, Vogt, & van Doorn, 2019; Roman,

Corresponding author. E-mail address: [email protected] (R.J. Hauer).

https://doi.org/10.1016/j.landurbplan.2019.103670 Received 15 June 2019; Received in revised form 19 September 2019; Accepted 22 September 2019 0169-2046/ © 2019 Elsevier B.V. All rights reserved.

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Battles, & McBride, 2016; Roman & Scatena, 2011). The annual mortality rate, mean and median tree-life span, and tree-age mortality statistics provide further insight for assessing tree longevity (Miller et al., 2015; Roman et al., 2016). Tree condition provides an estimate of tree health and insights into future mortality. The visual observation of the roots, trunk, scaffold branches, twigs, and foliage or buds is commonly used to evaluate tree condition, with a 0 (dead) to 100% (excellent) scale typically used (Bond, 2010; Council of Tree and Landscape Appraisers, 2000). Hauer and Peterson (2016) found nearly 90% of municipal tree inventories in the United States included tree condition as a parameter. Measurements of tree stem diameter, total height, crown spread, live crown ratio (i.e., the proportion of total height that has foliage), and basal area (i.e., the cross-sectional area occupied by tree stems) are also common metrics collected to rate a tree’s current biometric properties, to assess change over time, and estimate ecosystem services and economic value (Morgenroth & Östberg, 2017; Nowak et al., 2008). Beyond the tree itself, planting location is often characterized based on tree lawn width, planting pit dimensions, soils (e.g., pH, bulk density, texture), presence or absence of utilities, and climate (Miller et al., 2015). Trees are ideally selected for site conditions, properly installed, and maintained as needed to provide decades of service life (Coder, 1998; Hirons & Sjöman, 2018; Miller et al., 2015; Mullaney, Lucke, & Trueman, 2015; Sanders, Grabosky, & Cowie, 2013; Vogt, Hauer, & Fischer, 2015). Likewise, the design and repair of streets, curbs, and sidewalks ideally considers the biological structure and function of trees to minimize construction damage and reduce future conflicts as trees grow (Baines, 1994; Coder, 1998; Day et al., 2010; Randrup, McPherson, & Costello, 2003; Vogt et al., 2015). Large stature trees grown in proximity (e.g., 1.5–2 m) to a street and sidewalk may cause infrastructure damage resulting in repair costs (Hauer, Miller, & Ouimet, 1994; Kopinga, 1994; McPherson, 2000; McPherson & Peper, 1995, 1996; Östberg, Martinsson, Stål, & Fransson, 2012; Wagar & Barker, 1983). Similarly, tree damage from direct conflicts (e.g., excavator damaging tree roots, stems, and/or branches) with construction activities can result in reduced tree growth, survival, and condition, which translates into decreased urban forest value and increased removal and replanting costs (Hauer et al., 1994; Koeser, Hauer, Norris, & Krouse, 2013; North, D'Amato, Russell, & Johnson, 2017; Watson, 1998). Trees in construction zones may have increased mortality, reduced tree condition, and reduced growth (Hauer et al., 1994; Koeser et al., 2013, Miller, 1994; North et al., 2017; O’Herrin, Hauer, Vander Weit, & Miller, 2016). Arboricultural standards in Australia, Hong Kong, United Kingdom, United States, and other locations provide specifications to reduce the effects of construction on trees to promote tree survival and health (AISWCD, 2017; British Standards Institute, 2005; Development Bureau, 2015; Matheny & Clark, 1998; Standards Australia, 2009; Tree Care Industry Association, 2012). The American National Standards Institute (ANSI) A300 (Part 5) Management of Trees and Shrubs During Site Planning and the accompanying International Society of Arboriculture (ISA) Trees and Construction Best Management Practices (BMPs) are industry-accepted practices for maintaining existing trees in the presence of construction (Fite & Smiley, 2016; Smiley & Fite, 2016; Tree Care Industry Association, 2012). Reviewed every five years, A300 standards are used by 60% of reporting communities when contracting tree care in the United States (Hauer & Peterson, 2016). Tree preservation practices along streets have evolved over the past three to four decades (Hauer et al., 1994; Matheny & Clark, 1998; Miller & Hauer, 1995; Watson, 1998; Watson, Hewitt, Custic, & Lo, 2014a, 2014b). The street tree preservation program in Milwaukee, WI USA is an example of this ongoing evolution (City of Milwaukee, 1996; Esposito, 2005; Ottman, Genich, & Boeder, 1996; Urbain, 2004). The tree preservation efforts progressed from a limited implementation period in the mid-1980s to an intermediate implementation period in the mid-1990s to mid-2000s. By the mid-2000s the program evolved to

its current state of a full implementation period as described in the methods section of this paper. In addition to guidelines related to the establishment of non-disturbed protection zones and the use of less invasive construction techniques, contractors were required to use tree protection BMPs. Enforcement of contractual standards and fines to deter improper practice is an important part of minimizing damage to trees in the Milwaukee program. This study aims to test the outcome of a tree preservation program on the condition, survival, and growth of street trees over a 39-year period. The effects of the City of Milwaukee’s tree preservation policy and management program were tested by comparing it to prior periods before implementation of the program. To achieve this, the study monitors a cohort of street tree sites first inventoried in 1979, with follow-up assessments in 1989, 2005, and 2018. The research questions central to this paper were: (1) do trees within a construction zone have similar growth, survival, and condition contrasted against comparable trees outside of these zones; (2) has a tree preservation program with three distinct implementation periods resulted in different outcomes in tree condition, survival, and growth; and (3) what other site and tree parameters beyond construction activity predict growth, condition, and longevity of street trees. We hypothesize no difference will occur between trees within construction zones and control areas in the full implementation period. In contrast, we expect to observe a difference in tree condition and survival during the periods prior to the current tree preservation program (Hauer et al., 1994; Koeser et al., 2013). We further hypothesize that tree lawn width, tree stem diameter, and past tree condition will significantly predict tree survival and tree condition. We also hypothesized that there will be species-specific survival and response to construction. 2. Methods The condition, survival, and growth of street trees within construction zones and nearby control trees not subjected to construction activities were evaluated. Trees were studied in three different periods corresponding to the implementation of a tree preservation program; 1) a limited implementation period from 1979 to 1989, 2) an intermediate implementation period from 1989 to 2005, and 3) a full implementation period from 2005 to 2018. 2.1. Trees & construction program The full implementation period involves a pre-construction tree assessment to evaluate tree stem diameter, condition, species, and requirements for tree protection as part of a developed construction plan (Table 1). The plan guides pre-construction meetings with forestry staff, city officials and construction contractors. Pre-construction tree pruning occurs to provide clearance for construction equipment operations. Additionally, changes in the full implementation period from past construction practices include: ▪ Removal of curbs occurs with the goal of minimizing root damage, especially structural roots growing against the curb (compared to the limited implementation period that involved soil removal and possible root damage at an approximate distance of 30 to 50 cm behind the curb). ▪ Construction equipment was modified as needed to reduce tree damage such as adding a 90-degree elbow on the exhaust pipe to direct exhaust heat away from rather than upwards and towards tree foliage. ▪ Slipform paver machines used to install curbs and gutters were customized to require less distance on the tree side from the standard 28.0 cm to 4.5 cm of clearance now needed for curb installation. Thus, nearly 24.0 cm of the tree root zone is no longer affected by construction. ▪ The width of streets was reduced by the distance needed for 2

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Table 1 Development of a tree preservation program for street trees in Milwaukee, WI and key program implementation periods. Implementation period No Implemented Program Before 1979 Limited Implemented Program (LIP) 1979–1989

Key program elements ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪

Intermediate Implemented Program (IIP) 1990–2004

▪ ▪ ▪

Fully Implemented Program (FIP) 2005–2018

▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪

No program in place 400 trees fall during a 1978 windstorm due to root severance from sidewalk damage prevention program Trees and construction program (aka tree preservation program) envisioned Pre-program tree inventory in 1979 (Miller & Sylvester, 1979) Initial development of program with hiring of forestry inspector in 1981 to develop program Program being conceptualized, developed, and applied on a limited basis by end of this period Concept of fines for non-compliance conceived as an approach to prevent damage to trees by contractors Initial study to quantify the baseline effects of construction on tree survival and condition developed Study implemented with trees subjected to construction from 1981 through 1985 compared to control trees outside of construction zones First assessment of the effects of construction on street trees conducted in 1989 and published (Hauer et al., 1994; Miller & Hauer, 1995) Program practices becoming formulated within street and sidewalk construction standards manual (City of Milwaukee, 1996) During this period trees and construction practices used in the 2005 to 2018 period become refined and commonly implemented (Ottman et al., 1996) By the end of this period, trees and construction practices are common and fully in place as a regular part of preventing damage to trees during construction (Urbain, 2004) Monitoring and fines for non-compliance continues with compliance becoming more regular Second assessment of the effects of construction on street trees published (Koeser et al., 2013) A pre-construction tree assessment occurs with tree stem diameter, condition, species, and requirements for tree protection developed by forestry inspector The developed tree preservation plan used to guide pre-construction meetings among forestry staff, city officials and construction contractors Removal of streets, curbs, and sidewalk occurs with the goal of no damage to structural roots Construction equipment such as concrete paver machines (slipform) modified to minimize tree damage by reducing the distance needed for equipment operation from 28.0 cm to 4.5 cm Street width narrowed to accommodate new reduced distance (e.g., 4.5 cm) of paver machine Sidewalk widths narrowed, summits over structural roots sidewalks used, and arcing around tree roots used as needed Monitoring and fines for non-compliance continues with compliance becoming common Follow-up arboricultural treatments used in cases of tree damage Third assessment of the effects of construction on street trees (This paper)

included street repair (surface replacement and utilities when needed), curb replacement, and/or sidewalk replacement, with one to all of these occurring during a construction event. All trees were maintained by municipal forestry staff and were located between a sidewalk and curb in tree lawns that varied between 58 and 724 cm in width. Trees were located primarily within residential neighborhoods of single and double story homes. The study site has an extended history of urban tree management beginning in 1918 (Miller et al., 2015) and the 989 tree planting locations (Fig. 1) included in our monitoring efforts were first inventoried in 1979 (Miller & Sylvester, 1979). Construction records were maintained by the municipality and were verified with construction dates stamped in the concrete. Final verification of the construction dataset was conducted by the Urban Forestry Specialist from the City of Milwaukee assigned to the construction location. A total of 989 planting sites that were locations with a tree or currently vacant (not planted) were randomly selected and measured across all three assessment periods (Fig. 1). A baseline inventory of these locations in 1979 (prior to any construction activities) found 845 of these locations were planted and 144 were vacant. Of the 845 treed sites inventoried in 1979, 432 had been subjected to construction activities between 1981 and 1985. (Hauer et al., 1994). The remaining 413 locations served as controls. All locations were reassessed in 1989 and 670 surviving trees from the original cohort were relocated. Given ongoing planting and tree replacement efforts, there were 942 planted locations and 47 vacant sites in 1989. Of the 942 trees present in 1989, 256 were subject to construction between 1990 and 2004, while the remaining 686 served as non-impacted controls. In 2005, 762 of the 942 trees from 1989 survived. Given replanting and replacement efforts there were 883 sites with trees (91 vacant) in 2005. Fifteen locations near Miller’s Plank Road that had previously been within the public right-of-way had ownership transferred to a private entity by 2018 and are no longer tapped for future assessments. When assessed for this latest study, 187 trees had been subjected to construction activities

equipment clearance (e.g., 4.5 cm for the modified slipform paver machine) to avoid damage to roots. ▪ Sidewalk widths were decreased, arcing a new sidewalk around structural roots, and a raising (i.e., summit) the sidewalk over structural roots were implemented to avoid root damage. ▪ Monitoring and enforcement actions through fines for non-compliance provides a contractor an incentive to not damage tree roots, stems, and canopies as a proactive part of the construction process. For damaged trees, post-construction tree repair (e.g., pruning a damaged branch) is part of the plan. During the limited implementation period, the practices presented above were being conceptualized, developed, and applied on a limited basis by the end of the time period in 1989 (Hauer et al., 1994). Within the intermediate implementation period, the practices listed above were becoming formulated within street and sidewalk construction standards (City of Milwaukee, 1996), were becoming more commonly implemented over time (Ottman et al., 1996), and by the end of this period in 2005 were commonplace as a regular part of preventing damage to trees during a construction project (Urbain, 2004; Koeser et al., 2013). Further, contractor compliance became a regular occurrence by the late 1990/early 2000s due to fines for non-compliance (Urbain, 2004). Thus, the three study periods reflect the evolution of the program from little done (limited), becoming more common (intermediate), and most recently commonplace (fully) with implementation. 2.2. Study site Street trees occurring within construction zones (construction) or outside of construction zones (control) were studied to test the effects of a tree preservation program in Milwaukee, WI USA (43.0389° N, 87.9065° W). The city adjoins Lake Michigan and experiences a humid continental climate (Chen & Chen, 2013). Construction activities 3

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Fig. 1. Tree population flowchart during three periods: 1979 to 1989 a limited implementation period (LIP), 1989 to 2005 an intermediate implementation period (IIP), and 2005 to 2018 a full implementation period (FIP) for tree preservation. Each planting site is a street tree planting location with the possibility to be vacant (no tree), lost (no longer assessible), or planted (tree present).

between 2005 and 2017. In 2018, 701 of the 883 trees assessed in 2005 survived (Fig. 1, Table 2). Only locations with trees at the start of one of the three study periods (e.g., 1979, 1989, 2005) were part of the analysis during that study period. Trees planted during a study period were not part of the analysis during that period but did become part of the subsequent study period.

tree compared against the maximum of 20 possible points to generate the 0 to 100 percent scale outlined by the Council of Tree and Landscape Appraisers (CTLA) (Neely, 1988). This system was used in the previous study periods, and in the current study (Hauer et al., 1994; Koeser et al., 2013). One evaluator conducted all tree condition ratings during the current study. This was consistent with past inventories in 1979, 1989, and 2005. Although a different evaluator was used during each measurement year, the evaluator was trained by the authors of the paper. Each evaluator was an undergraduate-trained forestry student. The mean annual tree diameter growth increment (cm) was calculated as the change in diameter during a study period for each tree divided by the number of years between measurement periods and an overall mean calculated from all trees. Likewise, the mean annual basal area increment was the change in cross-sectional stem area (cm2) at DBH for each tree divided by the number of years between measurement periods and a mean basal area calculated from all individual trees.

2.3. Field measurements and data development Tree stem diameter (1.37 m above the ground surface, DBH), percent tree condition, tree lawn width (distance between curb and sidewalk), tree species, home address, and applicable field comments were recorded in May and June 2018, consistent with measurements in 1989 and 2005. Tree condition was visually rated using a five-point scale (1 = poor and 5 = excellent) with equal weight given each to roots, trunk, scaffold branches, and foliage. The total summed points for each 4

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Table 2 Variables tested for their effect on tree survival and tree condition used in initial and final regression models.1 Variable

Definition for Tree Measurement

Sample (n)

Mean (SE)

Construction89 Construction05 Construction18 LawnWidth89 LawnWidth05 LawnWidth18 TreeCond79 TreeCond89 TreeCond05 TreeCond18 TreeDiam79 TreeDiam89 TreeDiam05 TreeDiam18

1 if in construction zone 1981–1985, else 0 1 if in construction zone 1990–2004, else 0 1 if in construction zone 2005–2017, else 0 Lawn width as measured in 1989 (cm) Lawn width as measured in 2005 (cm) Lawn width as measured in 2018 (cm) Tree condition (%) in 1979 Tree condition (%) in 1989 Tree condition (%) in 2005 Tree condition (%) in 2018 Tree diameter (cm @1.37 m) in 1979 Tree diameter (cm @1.37 m) in 1989 Tree diameter (cm @1.37 m) in 2005 Tree diameter (cm @1.37 m) in 2018

334 191 162 670 762 701 845 670 762 701 845 670 762 701

N/A N/A N/A 234.95 (4.79) 226.21 (4.36) 228.78 (4.50) 75.05, (0.49) 73.88 (0.46) 74.72 (0.51) 69.42 (0.46) 18.95 (0.59) 30.05 (0.54) 41.95 (0.49) 48.58 (0.60)

Variable

Definition of Measured Tree Species

Sample (n)

2018 Survival (%)

Basswood05 GreenAsh05 Honeylocust05 LLeafLinden05 NorwayMaple05 SilverMaple05 SugarMaple05 WhiteAsh05

1 1 1 1 1 1 1 1

23 228 113 57 347 15 7 30

82.6 91.2 91.2 63.2 73.5 53.3 28.6 80.0

if if if if if if if if

present present present present present present present present

in in in in in in in in

2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005,

else else else else else else else else

0 0 0 0 0 0 0 0

1 Data was collected from Milwaukee, WI, USA inventory and construction data sets that spanned from 1979 to 2018 – a time frame that occurred before, during, and after the enactment of a comprehensive city-wide tree preservation program. Total planting sites (n = 989) could be planted, vacant, or lost during any measurement period. See Hauer et al. (1994) and Koeser et al. (2013) for species-specific statistics for the 1989 and 2005 measurement periods.

2.4. Statistical analysis

decisions for statistical significance were made at the p ≤ 0.05 level. The same approach was used for the limited and intermediate implementation study periods. Multiple linear regression models were used to detect attributes of the study sites associated with present (2018) tree condition (dependent variable) of surviving trees and test the a priori assumption of no effect of construction on tree condition. Attributes (independent variables) included 2005 tree diameter (cm), 2005 tree condition (%), 2018 tree lawn width (cm), construction (binary 1 = yes, 0 = no), and the eight tree species. This same model approach was used for the dependent tree growth variables (e.g., DBH and basal area). A one-way analysis of variance (ANOVA) was used to test for differences in tree condition between trees later allocated to control and construction groups. In model building, a p ≤ 0.25 significance level was adopted for initial screening of variables and a p ≤ 0.05 significance level was used when deciding what variables to retain for the final multiple regression model in SPSS Version 25 (IBM Corporation, 2017). Assumptions of normality, linearity, and homoscedasticity of model residuals were inspected using bivariate plots between independent and dependent variables, as well as plots of the standardized residuals and standardized predicted values from the final multiple regression model. Multicollinearity in models was tested using variance inflation factor (VIF) statistics with a lack of multicollinearity interpreted as the VIF < 4 (Mertler & Vannatta, 2005; Neter, Wasserman, & Kutner, 1990).

Tree attributes (past condition, DBH, species) and site attributes (tree lawn width, construction) were modeled as independent variables on the dependent variables of current tree condition, tree survival, and tree stem growth (Table 2, Appendix Table A-1). Each location in 2018 was recorded as either a surviving tree, replacement tree, or vacancy since the last 2005 monitoring. Tree survival was determined by comparing the current (2018) tree species and DBH to the previous record (2005). In the few cases that a determination of tree survival was not intuitive (e.g., small diameter tree, < 15 cm, of same species) planting records and/or consultation with Milwaukee forestry staff correctly determined tree survivability. The same process was previously used for the 2005 and 1989 measurement periods (Hauer et al., 1994; Koeser et al., 2013). A binary logistic regression model tested the effects of tree condition (%) in 2005, tree lawn width (cm) in 2018, DBH (cm) in 2005, construction binary variable, and tree species (only the eight most common species (n > 15) were included) on the dependent variable tree survival for the full implementation period timeframe. Seven of the tree species were compared against the eighth (reference) and each was coded as a series of species binary variables and added to the maximal (full) models (Table 2, Appendix Table A-1). Norway maple (Acer platanoides L.), silver maple (Acer saccharinum L.), white ash (Fraxinus americana L.), green ash (Fraxinus pennsylvanica Marsh), honeylocust (Gleditsia triacanthos var. inermis (L.) C. K Schneid), American basswood (Tilia americana L.), and little leaf linden, (Tilia cordata Mill) were tested against sugar maple (Acer saccharum Marshall) as the reference level species. A. saccharum was selected since it was the poorest performing species in past models (Koeser et al., 2013). Tree survival was analyzed through the logistic regression model using the glm() function in R (R Core Team, 2017). Initial model simplification was conducted using a backward and forward stepwise elimination function based on Akaike information criterion (AIC). Any remaining non-significant terms were removed one-at-a-time, with the initial and reduced models being compared using the anova() function in R (Crawley, 2013). All

3. Results 3.1. Tree condition Tree condition of construction and control trees varied by the three periods in the study area. Prior to any construction occurring in the study area, an initial 1979 inventory (baseline) demonstrated a comparable (F = 1.368, df = 1,843, p = 0.94) tree condition (mean = 75.0%, SE = 0.5%) (Fig. 2). Tree condition was significantly lower by 5.7% in construction zones during the limited implementation period prior to implementation of the tree preservation program 5

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100

Control

90

Condition Rating (Percent)

80

Construction p=0.04

p<0.0001

p=0.94

76.7

75.0 75.1

71.0

70

75.3

p=0.24

72.9

69.1 70.4

Fig. 2. Effects of construction on tree condition measured in 1979 (pre-construction), 1989 a limited implementation period (LIP), 2005 an intermediate implementation period (IIP), and 2018 a full implementation period (FIP) for tree preservation (bars are standard error; n = 845, 670, 762, 701 respectively for 1979, 1989, 2005, and 2018).

60 50 40 30 20 10 0

1979

1989

2005

(LIP)

Pre-construction

2018

(FIP)

(IIP)

(F = 36.424, df = 5,664, p < 0.0001, Adj R2 = 0.21) (Fig. 2, Table 3). No effect (p = 0.556) of construction on tree condition was found in the intermediate implementation period (F = 8.830, df = 5,756, p < 0.0001, Adj R2 = 0.06) through trees that survived construction (Table 4). The multiple regression model, which controlled for tree lawn width, past DBH, past tree condition, and species also showed no effect (p = 0.97) of construction on tree condition in the full implementation period (Table 5). The results suggest a tree preservation program effectively reduced the impact of street, curb, and/or sidewalk repair on tree condition. Over three measurement periods, four independent variables were found to be continuously significant (p < 0.001) – predicting between 5 and 20% (based on adjusted R2) of the variability of the dependent variable tree condition in the multiple regression models (Table 6). Tree

species were excluded from this modeling to allow for a more direct comparison of these four variables. As reported above, construction was significant for only the limited implementation period with a 5.7% greater tree condition for trees outside of construction zones (p < 0.001). As tree lawn width increased, tree condition increased and varied over the three measurement periods between 0.007 and 0.014 for each cm (p < 0.001). Thus, a 300 cm tree lawn width (~10 feet) would add ~2.1% to 4.2% to the overall tree condition when holding other factors constant. Past DBH had a negative effect on tree condition across all three periods, ranging between −0.075 and −0.104 per cm of DBH (p < 0.001). A tree with a 50 cm DBH (20 in), all other variables held constant, would reduce tree condition by ~3.6% to 5.0% compared to a small tree ~2 cm DBH (0.75 in). Past tree condition was also a consistent predictor across all three time-periods.

Table 3 Effect of construction, site attributes, and tree species on tree condition of surviving trees in 1989 in Milwaukee, WI, USA. Trees were subjected to construction activities or were trees outside of construction zones that grew between 1979 and 1989 (a limited implementation period for tree preservation). Unstandardized Coefficients B

Standardized Coefficients Beta

Sig.

Correlations Zero-order

Partial

0.233, std. error of est. = 10.521, F(13,656) = 669, p < 0.0001) 43.745 5.361 8.160 −6.502 0.874 −0.273 −7.439 0.013 0.004 0.130 3.589 0.436 0.045 0.335 9.673 −0.045 0.035 −0.057 −1.282 −6.413 4.639 −0.100 −1.382 0.249 4.548 0.004 0.055 −1.714 4.070 −0.063 −0.421 −0.101 4.158 −0.003 −0.024 0.752 4.311 0.015 0.175 −2.940 4.054 −0.120 −0.725 −6.750 4.971 −0.086 −1.358 −9.843 4.567 −0.150 −2.155 −7.466 4.585 −0.114 −1.628

0.000 0.000 0.000 0.000 0.200 0.167 0.956 0.674 0.981 0.862 0.469 0.175 0.032 0.104

−0.239 0.105 0.358 −0.106 −0.144 0.018 0.052 0.027 0.019 0.056 −0.067 −0.109 −0.054

−0.279 0.139 0.353 −0.050 −0.054 0.002 −0.016 −0.001 0.007 −0.028 −0.053 −0.084 −0.063

Final a priori Model (R2 = 0.215, R2adj = 0.209, std. error of est. = 10.613, F(5,664) = 669, p < 0.0001) (Intercept) 41.928 3.549 11.814 Construction89 −5.770 0.842 −0.242 −6.856 LawnWidth05 (cm) 0.012 0.003 0.120 3.380 TreeCond79 (%) 0.437 0.045 0.336 9.687 TreeDiam79 (cm) −0.084 0.028 −0.106 −3.028 SugarMaple79 −7.823 2.263 −0.119 −3.457

0.000 0.000 0.001 0.000 0.003 0.001

−0.239 0.105 0.358 −0.106 −0.109

−0.257 0.130 0.352 −0.117 −0.133

Model Variables 2

Initial full Model (R = 0.238 (Intercept) Construction89 LawnWidth05 (cm) TreeCond79 (%) TreeDiam79 (cm) American Elm79 Basswood79 GreenAsh79 Honeylocust79 LLeafLinden79 NorwayMaple79 SilverMaple79 SugarMaple79 WhiteAsh79

Std. Error

t-test Statistics t-value

R2adj =

6

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Table 4 Effect of construction, site attributes, and tree species on tree condition of surviving trees in 2005 in Milwaukee, WI, USA. Trees were subjected to construction activities or were trees outside of construction zones that grew between 1989 and 2005 (an intermediate implementation period for tree preservation). Unstandardized Coefficients B

Standardized Coefficients Beta

Sig.

Correlations Zero-order

Partial

Initial Full Model (R2 = 0.069 R2adj = 0.053, std. error of est. = 13.861, F(13,748) = 761, p < 0.0001) (Intercept) 70.995 5.147 13.792 Construction05 −0.672 1.142 −0.021 −0.589 LawnWidth05 (cm) 0.018 0.004 0.072 1.915 TreeCond89 (%) 0.172 0.043 0.146 4.005 TreeDiam89 (cm) −0.160 0.046 −0.152 −3.496 American Elm89 0.708 6.661 0.005 0.106 Basswood89 2.825 5.248 0.032 0.538 Green Ash89 −5.554 4.302 −0.175 −1.291 Honeylocust89 −4.770 4.404 −0.117 −1.083 LLLinden89 −10.911 4.615 −0.193 −2.364 Norway Maple89 −9.129 4.261 −0.314 −2.142 Silver Maple89 −6.524 5.819 −0.064 −1.121 Sugar Maple89 −5.943 6.720 −0.040 −0.884 White Ash89 −8.005 5.079 −0.098 −1.576

0.000 0.556 0.056 0.000 0.001 0.915 0.590 0.197 0.279 0.018 0.032 0.263 0.377 0.115

0.008 0.059 0.145 −0.073 0.016 0.093 0.072 0.057 −0.070 −0.091 −0.060 0.021 −0.036

−0.022 0.070 0.145 −0.127 0.004 0.020 −0.047 −0.040 −0.086 −0.078 −0.041 −0.032 −0.058

Final a priori Model (R2 = 0.055, R2adj = 0.049, std. error of est. = 13.889, F(5,756) = 761, p < 0.0001) (Intercept) 65.889 3.276 20.110 LawnWidth05 (cm) 0.009 0.004 0.074 2.014 TreeCond89 (%) 0.166 0.042 0.141 3.947 TreeDiam89 (cm) −0.143 0.039 −0.136 −3.639 LLeafLinden89 −5.927 2.064 −0.105 −2.871 NorwayMaple89 −4.169 1.084 −0.144 −3.847

0.000 0.044 0.000 0.000 0.004 0.000

0.059 0.145 −0.073 −0.070 −0.091

0.073 0.142 −0.131 −0.104 −0.139

Model Variables

Std. Error

t-test Statistics t-value

Table 5 Effect of construction, site attributes, and tree species on tree condition of surviving trees in 2018 in Milwaukee, WI, USA. Trees were subjected to construction activities or were trees outside of construction zones that grew between 2005 and 2018 (a full implementation period for tree preservation). Unstandardized Coefficients B

Standardized Coefficients Beta

t-test Statistics t-value

Sig.

Correlations Zero-order

Partial

11.016 −0.044 4.703 7.992 −7.066 1.934 7.796 2.425 2.737 −0.174 2.029 −1.446 0.889

0.000 0.965 0.000 0.000 0.000 0.053 0.000 0.016 0.006 0.862 0.043 0.149 0.374

0.045 0.129 0.282 −0.115 −0.003 0.411 −0.070 0.014 −0.312 −0.017 −0.041 −0.058

−0.002 0.176 0.291 −0.260 0.074 0.285 0.092 0.104 −0.007 0.077 −0.055 0.034

Final a priori Model (R2 = 0.323, R2adj = 0.315, std. error of est. = 10.213, F(8,692) = 700, p < 0.0001) (Intercept) 42.019 3.362 12.498 LawnWidth18 (cm) 0.016 0.003 0.153 4.621 TreeCond05 (%) 0.327 0.040 0.258 8.114 TreeDiam05 (cm) −0.129 0.016 −0.245 −7.194 Basswood05 5.707 2.453 0.075 2.327 GreenAsh05 13.880 0.934 0.514 14.865 Honeylocust05 4.711 1.182 0.135 3.984 LLeafLinden05 6.467 1.810 0.116 3.573 SilverMaple05 8.557 3.769 0.074 2.271

0.000 0.000 0.000 0.000 0.020 0.000 0.000 0.000 0.023

0.129 0.282 −0.115 −0.003 0.411 −0.070 0.014 −0.017

0.173 0.295 −0.264 0.088 0.492 0.150 0.135 0.086

Model Variables 2

Initial Full Model (R = 0.326 (Constant) Construction18 LawnWidth18 (cm) TreeCond05 (%) TreeDiam05 (cm) Basswood05 Green Ash05 Honeylocust05 LLeafLinden05 NorwayMaple05 SilverMaple05 SugarMaple05 WhiteAsh05

Std. Error

R2adj

= 0.314, std. error of est. = 10.217, F(12,688) = 700, p < 0.001) 41.909 3.805 −0.044 0.987 −0.001 0.017 0.004 0.161 0.328 0.041 0.259 −0.192 0.027 −0.247 5.596 2.893 0.074 13.770 1.766 0.510 4.632 1.910 0.133 6.389 2.335 0.114 −0.295 1.697 −0.012 8.399 4.139 0.072 −10.685 7.392 −0.046 2.338 2.628 0.034

Tree condition in the full implementation period would be a 3.3% higher for each additional 10% condition rating in 2005. The residual effects of construction from a previous program period were examined for surviving trees in a more recent program period with no significant effect found (data not shown) on trees during a current measurement period. For example, construction that occurred during the limited implementation period had no detected residual effect on tree condition in the intermediate implementation period. Likewise, in the full implementation period no residual effect of tree condition in the intermediate implementation period was found (data not shown).

3.2. Tree survival The effects of construction on tree survival varied over the three measurements periods. During the recent full implementation period, no effect (p = 0.81) of construction on tree mortality was found when controlling for tree species, lawn width, prior DBH, and past tree condition in the logistic model (Tables 7 and 8). Results from 2005 showed a negative effect (p = 0.01) of construction on tree survival in the intermediate implementation period (Koeser et al., 2013). Trees in construction zones had 4.1% greater mortality (p = 0.004) during the 7

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Table 6 Comparison of four independent variables and coefficients on tree condition from three multiple regression models over three-time periods that varied in implementation of a tree preservation program: 1979 to 1989 (limited implementation period), 1989 to 2005 (intermediate implementation period), 2005 to 2018 (full implementation period) in Milwaukee, WI, USA. Independent Variable (unit)

1979 to 1989

1989 to 2005

2005 to 2018

Lawn Width1 (cm) Construction2 (0 = no, 1 = yes) Past Tree Condition3 (%) Past Tree Diameter3 (cm) Model Summary Sample Size (n) Adjusted R2 Model Significance (p-value)

0.011 −5.723 0.44 −0.075

0.007 ns4 0.17 −0.104

0.014 ns4 0.33 −0.097

670 0.20 < 0.001

762 0.03 < 0.001

701 0.10 < 0.001

1 2 3 4

Table 8 Final modela and logistic regression results of street tree survival between 2005 and 2018 for trees subjected to construction activities before 2018 or trees outside of construction zones. Data was collected from a Milwaukee, WI, USA inventory and construction data sets spanning from 2005 to 2017 – a time frame that occurred during a highly developed city-wide tree preservation program. (n = 824).

Measurement taken at the start of a measurement period. Measurement taken during a measurement period. Measurement taken at the end of a measurement period. ns = Not significant.

Coefficient

Standard Error

P value

Odds Ratio

95% CI Lower

95% CI Lower

(Intercept) Construction18 LawnWidth18 TreeCond05 TreeDiam05 Basswood05 GreenAsh05 Honeylocust05 LLeafLinden05 NorwayMaple05 SilverMaple05 WhiteAsh05

−7.289 −0.066 0.008 0.066 −0.013 2.960 4.523 4.166 2.668 3.026 2.238 3.480

1.184 0.267 0.003 0.008 0.018 1.044 0.938 0.947 0.941 0.901 1.081 1.011

< 0.0001 0.8060 0.0018 < 0.0001 0.4726 0.0046 < 0.0001 < 0.0001 0.0046 0.0008 0.0383 0.0006

0.001 0.937 1.008 1.068 0.987 19.30 92.15 64.43 14.42 20.62 9.380 32.47

0.0001 0.560 1.003 1.053 0.953 2.720 15.68 10.78 2.433 3.765 1.195 4.825

0.0064 1.600 1.014 1.085 1.022 186.8 740.7 524.9 116.2 156.5 94.21 293.9

Coefficient

Standard Error

P value

Odds Ratio

95% CI Lower

95% CI Lower

(Intercept) LawnWidth18 TreeCond05 Basswood05 GreenAsh05 Honeylocust05 LLeafLinden05 NorwayMaple05 SilverMaple05 WhiteAsh05

−7.423 0.008 0.066 2.879 4.480 4.131 2.631 2.996 2.033 3.425

1.153 0.003 0.008 1.037 0.934 0.943 0.937 0.895 1.041 1.005

< 0.0001 0.0021 < 0.0001 0.0055 < 0.0001 < 0.0001 0.0050 0.0008 0.0507 0.0007

0.001 1.008 1.068 17.81 88.26 62.24 13.89 20.00 7.640 30.72

0.00005 1.003 1.053 2.541 15.16 10.49 2.364 3.692 1.054 4.616

0.005 1.013 1.085 170.6 704.1 503.9 111.1 150.5 71.89 275.1

a Tree survival in the absence or presence of adjacent road construction activities was modeled using multiple logistic regression. Positive coefficients indicate an increased likelihood of survival (S). Negative coefficients indicate an increased likelihood of death (D). For example, a Fraxinus pennsylvanica (species green ash = 1) is 88 times more likely to have survived this period than an Acer saccharum (sugar maple base level). Additionally, for each percentage increase of tree condition at the start of this time period in 2005, a tree is 1.068 times more likely to survive. AIC: 674.02

Table 7 Full modela and logistic regression results of street tree survival between 2005 and 2018 for trees subjected to construction activities before 2018 or trees outside of construction zones. Data was collected from a Milwaukee, WI, USA inventory and construction data sets spanning from 2005 to 2017 – a time frame that occurred during a highly developed city-wide tree preservation program. (n = 824). Variable

Variable

was the greatest in the limited development program (1.24 cm) and decreased to intermediate implementation period (1.12 cm) and full implementation period (0.84 cm). No difference (data not shown) was found in annual basal area increment for all three periods when controlling for other factors (e.g., tree lawn width, past DBH, species, past tree condition, data not shown). Mean annual basal area increment was 4.36 cm2, 5.82 cm2, and 5.72 cm2 for the limited, intermediate, and full implementation periods respectively per tree. Even though annual stem diameter growth increment declined over time, the annual basal area increment was greatest and constant in the two most recent time periods. Finally, the mean tree diameters increased over time (Fig. 6).

a Tree survival in the absence or presence of adjacent road construction activities was modeled using multiple logistic regression. Positive coefficients indicate an increased likelihood of survival (S). Negative coefficients indicate an increased likelihood of death (D). For example, a Fraxinus pennsylvanica (Species green ash = 1) is 92 times more likely to have survived this period than an Acer saccharum (sugar maple base level). Additionally, for each percentage increase of tree condition at the start of this time period in 2005, a tree is 1.068 times more likely to survive. AIC: 677.45

4. Discussion The study of construction impacts on street trees in Milwaukee demonstrates how policy, management, and sustained monitoring fostered increased retention of trees and tree condition (City of Milwaukee, 1996; Hauer et al., 1994; Koeser et al., 2013; Ottman et al., 1996). The trees and construction program was initiated after hundreds of trees with damaged roots from construction toppled after a windstorm. Baseline tree data was collected before the program started and through three subsequent periods as the program evolved. The timeframe of this study also led to a more reasoned understanding of trees and a program designed to reduce the impacts of construction on street trees. Evaluation of a measurement period, without consideration of other measurement periods and management actions, will likely lead to incorrect or incomplete conclusions.

limited implementation period (Koeser et al., 2013). Odds ratios were calculated to show an effect of site and tree attributes (Tables 7 and 8; Figs. 3 and 4). Trees with a higher tree condition were more likely to survive construction. Odds of trees survival increases as tree lawn width increases. Green ash, honey locust, and white ash had respectively an 88, 62, and 31 times greater odds of survival than sugar maple in the full implementation period.

4.1. Species, condition, survival, and growth

3.3. Tree growth

In this study, we investigated if trees in construction zones have similar growth, survival, and condition to trees outside of these areas. We also asked if other site and tree parameters predicted the overall tree growth, survival, and condition of street trees. This question is important as approximately 6000 street trees (200,000 street tree population) in Milwaukee could annually be affected by construction and potentially damaged without a tree preservation program since construction on average occurs once every 33 years at a location (Hauer

No difference (p = 0.129) in annual growth rate in DBH was found, when controlling for other factors (e.g., tree lawn width, past DBH, species, past tree condition), between control and construction trees for the full implementation period (Fig. 5). No difference was also detected with the annual growth increment for surviving construction and control trees in the intermediate implementation period (p = 0.62) and limited implementation period (p = 0.55). Annual DBH growth rate 8

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Fig. 3. Effects of tree condition measured at the start of a study period on the odds of tree survival during three-time periods in Milwaukee, WI USA: 1979 to 1989 a limited implementation period, 1989 to 2005 an intermediate implementation period, and 2005 to 2018 a full implementation period for tree preservation.

Fig. 4. Effects of tree lawn width on the odds of tree survival during three-time periods in Milwaukee, WI USA: in 1979 to 1989 a limited implementation period, 1989 to 2005 an intermediate implementation period, and 2005 to 2018 a full implementation period for tree preservation.

et al., 1994). We found no difference in survival and growth between trees in construction zones or trees outside these areas during the full implementation period, suggesting the current trees and construction program in Milwaukee is effective at protecting trees. An initial 1989 study by Hauer et al. (1994) found that trees associated with construction had lower survivability and reduced tree condition. A subsequent follow-up study in 2005 found a tree preservation program in an intermediate stage of development resulted in a reduced effect of construction on tree survival and tree condition that was approximately half the impact as discovered in 1989 (Hauer, 2009; Koeser et al., 2013). Hauer et al. (1994) found that approximately 350 street trees died prematurely from construction and another 5650 trees experienced a 5.7% (0 to 100% scale) reduction in tree condition in Milwaukee. Decreased tree condition (e.g., tree health) makes trees more

susceptible to tree decline and will also reduce the financial value of the tree population value as prescribed in the CTLA methodology (Council of Tree and Landscape Appraisers, 2000; Miller et al., 2015). Thus, the current street trees and construction program has diminished potential effects with no difference between trees in construction zones and control trees not associated with construction. Protection of trees prevents mortality, maintains tree health, and should lead to greater ecosystem services and street tree value (Miller et al., 2015). The study also found the survival of commonly grown tree species varied. Koeser et al. (2013) found sugar maple trees had the highest mortality of the species studied and were six times less likely to survive than American elm (Ulmus americana L.). In this same Milwaukee study, silver maple and honeylocust performed best with 14-times greater odds of survival compared to American elm from 1989 to 2005. 9

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Fig. 5. Mean per tree growth (annual stem diameter increment and annual basal area increment at 1.37 m) during three-time periods in Milwaukee, WI USA: 1979 to 1989 a limited implementation period, 1989 to 2005 an intermediate implementation period, and 2005 to 2018 a full developed program period for tree preservation (bars are standard error; n = 670, 762, 701 respectively for 1979 to 1989, 1989 to 2005, and 2005 to 2018).

Similarly, green ash and white ash tree were 6-times more likely to survive than American elm during this same time period (Koeser et al., 2013). Given these findings, we used sugar maple as a baseline when comparing species in this study. Results from species tolerance to street tree settings are important for future planting and retention decisions. Sugar maple performs poorly, or at least the genetic source historically planted in Milwaukee performs poorly as a street tree. Despite confirmation of Agrilus planipennis Fairmaire (emerald ash borer; EAB) in Milwaukee in 2012, ash survived at higher rates than the baseline species from their site tolerance. The observed survival and growth rates support the current plan to protect existing ash species and tree canopy through chemical control of EAB (Krouse, 2010; Sivyer, 2010; VanNatta, Hauer, & Schuettpelz, 2012). The current condition of trees is an important predictor for future survival and tree health. This finding is consistent with Manion (1991) and Clark and Matheny (1991) who presented models that explain factors associated with tree declines and death. Abiotic factors such as moisture stress, compacted soils, construction, and biotic organisms may predispose a tree to reduced health and premature mortality. The age or size of a tree is also a factor with young or smaller trees less prone for predisposing factors than older or larger trees (Francis, Parresol, & de Patino, 1996; Koeser et al., 2013). We found DBH had a negative relationship with present tree condition on average. This effect became more pronounced in future measurement periods, presumably a function of larger trees more recently (2018) than in the past (1989). This effect was also found in 2005 with tree mortality being nearly nine times greater for a 100 cm (~40 in) DBH tree than a comparison 25 cm (~10 in) DBH tree (Koeser et al., 2013). Past tree condition was also an important predictor of future tree condition and was consistent through all time periods. For trees in fair or poor condition, the importance of avoiding disturbances is more important to promote tree survival (Clark & Matheny, 1991). An odds ratios analysis for tree condition provides a useful way to make management recommendations when tree health (tree condition) is considered for tree retention in construction zones. We used a 50% condition rating (fair) tree as a comparison for the odds ratios of tree survival since a tree of that condition would likely be one an urban forest manager would consider for removal and replacement (Bond, 2010; Miller et al., 2015). A tree with a 70% (good) condition would be nearly three times more likely to survive. A 90% (excellent) rated tree would be over eight times more likely to survive compared to the 50% rated tree (Fig. 3). Thus, condition rating can be used as a predictor of

tolerance to construction and with decision making for trees to retain in a construction zone. This study also documents the growth of trees over an extended time. Our study found the annual stem diameter increment declined over time from 1.24 to 0.84 cm per year and the mean DBH increasing over the study time period (Koeser et al., 2013). The current growth rate is like the 0.83 cm annual DBH growth rate in Baltimore, MD USA (Nowak, Kuroda, & Crane, 2004). Rather than a linear increase in tree size, basal area or basal area increment provides another way to monitor tree growth. North et al. (2017) used basal area increment to assess the effects of site conditions and construction on trees and found trees subjected to construction had a reduced basal area increment. We found construction had no effect on basal area increment rather it has remained constant over the past three decades. These periods also correspond to the intermediate and full implementation periods and an important historical foundation for the current findings. However, only trees that survived between measurement periods were included in the analysis and construction did result in increased tree mortality in the limited and intermediate program periods. Thus, biomass would be lower for trees in construction zones in the initial two study periods, but likely not today since no difference was found in tree survival. 4.2. Historical foundation The history of site and tree management efforts of the city were important factors that contributed to the success of this long-term study. In response to Dutch elm disease (Ophiostoma ulmi (Buisman) Melin & Nannf.), the United States Department of Agriculture Forest Service sponsored numerous urban forestry demonstration projects in the late 1970s and early 1980s including a pilot tree inventory in Milwaukee in 1979 (Hauer, Casey-Widstrand, & Miller, 2008; Hauer & Johnson, 2008; Miller & Schuman, 1981; Miller & Sylvester, 1979). The initial baseline inventory from 1979 laid the foundation to test the effects of construction. Municipal records on construction projects by the City of Milwaukee were critical to assign trees to control groups or a tree cohort associated with construction (Hauer et al., 1994; Miller & Sylvester, 1979). Prior to the implementation of its preservation program, the City of Milwaukee was losing urban forest value and incurring greater management costs associated with construction-related tree removals. An estimated reduction of $792,000 (1990 real USD) in urban forest value from construction-induced- mortality (4.1%) and lower tree condition (5.7%) was a significant outcome of the limited implementation period 10

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Fig. 6. Change in tree diameter distribution in Milwaukee, WI USA, 1979 to 2018. Data from 1979, 1989, and 2005 adapted from Koeser et al. (2013).

(Hauer et al., 1994; Miller & Hauer, 1995). Adjusted for inflation (Consumer Price Index), the 1990 impact would be $1.5 million in 2018. Results from that study supported the implementation of a tree preservation program (City of Milwaukee, 1996; Ottman et al., 1996). The tree preservation program costs approximately $235,000 annually in 2018, thus the program produces an estimated 6.4 benefit to cost ratio (B/C) for the associated costs for pre-construction tree pruning (equipment clearance) and two full-time staff foresters (salary, fringe benefits, and overhead costs) to implement the program. The benefit from the estimated retained urban forest value ($1.5 million) would be lost if construction practices in the early 1980s continued today. For decision-makers, the program produces at least six dollars for every dollar invested. This nearly 40-year story demonstrates how science and policy, when implemented through urban forestry management activities, can result in a healthier and longer-lived tree population with an economically favorable outcome. The City of Milwaukee implemented a program to prevent construction damage to street trees several decades ago (City of Milwaukee,

1996). Through a combination of methods to minimize tree damage through alternative construction practices, modification of construction equipment, fines, preconstruction meetings, and monitoring during construction, the program has reduced above and belowground damage to street trees and improved their long-term health and survival (Esposito, 2005; Ottman et al., 1996; Urbain, 2004). No one solution fits all situations and all street and sidewalk construction plans must comply with applicable local, state, and national standards for accessibility (Costello et al., 2000; Dodge & Geiger, 2003; Fite & Smiley, 2016; Seattle Department of Transportation, 2015). Understanding the tolerance of tree species to a given construction situation is also important when making retention or removal decisions (Koeser et al., 2013). 4.3. Growing space and tree size In this study, tree lawn width consistently and significantly predicted tree survival over three different measurement periods. The 11

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width of a tree lawn was positively related to tree condition. The result is consistent with several studies that also found a positive relationship with the distance between the curb and sidewalk (tree lawn width) and tree growth, condition, and survival (Hauer et al., 1994; Koeser et al., 2013; North et al., 2017; Scholz, Uzomah, & Al-Faraj, 2016). The probability of tree survival through an odds ratios analysis provides a way to comparatively assess management situations. Odds ratios from the logistic regression analysis found a tree grown in a 3 m (10 foot) wide tree lawn was approximately 2.5 times more likely to survive than a tree grown in a smaller 0.6 m (2 foot) wide tree lawn (Fig. 4). The findings from our study were not surprising and consistent with Berrang, Karnosky, and Stanton (1985) who found trees growing in larger tree lawns had a greater tree condition in New York City, USA. Tree growth was greater in larger tree lawns in Minneapolis/Saint Paul, MN USA (North et al., 2017). The larger the tree lawn or distance away from infrastructure the lower the risk of damaging tree stems, roots, and scaffold branches during construction and likewise the infrastructure itself (Hauer et al., 1994; Scholz et al., 2016). Maintaining a minimum distance of 1.5–2 m from a tree trunk at maturity to the edge of a sidewalk or curbs/street is one solution as found in a temperate climate to decrease damage to the hardscape (Hauer et al., 1994; Johnson & North, 2016; Miller, 1994; North et al., 2017; Scholz et al., 2016). This distance corresponds to the zone of rapid taper for several temperate North American tree species (Perry, 1992). Halwatura, Jayawardena, and Somarathna (2013) found in some tropical species that 4–11 m from the tree trunk might be needed to avoid damage to buildings. Francis et al. (1996) also suggested spacing large tropical trees at least 5 m or more away from structures to avoid infrastructure damage. Buttress roots of tropical trees were cited as a reason for these greater minimum spacing dimensions. Thus, designing tree planting locations to avoid damage to infrastructure varies by species, soils, and climate. This is more easily done when tree lawns are first created. Retrofitting existing planting locations by arcing sidewalks around planting trees is an additional option (Ottman et al., 1996; Seattle Department of Transportation, 2015; Urban, 2008). Findings from this study can also be used to prevent damage to trees in non-street tree development areas. As remediation of construction damage can be challenging and costly, BMPs for trees and construction typically give guidelines for restricting the damage to tree stems and branches, twigs and foliage, and root damage within a defined Tree Protection Zone (TPZ). The TPZ varies given the standard used but ultimately considers tree size and root area needed to sustain long-term growth. While protecting as large an area as possible is always ideal from a tree health perspective, development constraints necessitate the definition of a critical root zone (CRZ) – the minimal root area that must be protected to avoid damage to the tree (Smiley & Fite, 2016). The CRZ minimum distance of 6 to 18x DBH to restrict construction activities depends on a tree species susceptibility to construction (Smiley & Fite, 2016). Tree preservation BMP’s, standards, plans, and policies provide a means to avoid tree damage and negate the potential effects of construction (Miller et al., 2015).

systems, the involvement of a qualified arborist was the least likely activity to occur during construction practices in wooded lots. The perception, knowledge, and importance of tree preservation approaches by construction practitioners in street tree settings is not known, or at least not found by the authors of this paper. Working with contractors to develop modifications to construction practices was an important part of the tree preservation program now in place and led to reducing damage to tree root systems (Esposito, 2005; Ottman et al., 1996; Urbain, 2004). Further, inspection and enforcement of tree preservation requirements and fining those responsible for tree damage at a rate of $40 per cm of DBH ($100 per inch) is an effective part of the program to reduce contractor damage to tree stems, canopies, and root systems. The current study also shows the importance of long-term urban forestry research. The development and continuation of long-term research studies in urban forests provide a mechanism to monitor change or lack thereof and the potential to develop policy and test management actions for an effect on trees (Driscoll et al., 2012). Long-term studies in urban forestry are uncommon but important to depict longitudinal change and contributions of trees and associated vegetation through air pollution abatement, water quality improvement, energy conservation, public health contributions, and other societal services (Bodnaruk et al., 2017; Ko, Jun-Hak, McPherson, & Roman, 2015; Nowak et al., 2008; Pataki et al., 2011; Roman, McPherson, Scharenbroch, & Bartens, 2013). 4.5. Study limitations Tree condition is commonly evaluated visually, and a limitation is internal wood conditions and below-ground root systems that were not evaluated in this study may affect tree health (Bond, 2010). This visual assessment is adequate for most situations and routinely used in urban tree assessments (Klein, Koeser, Hauer, Hansen, & Escobedo, 2019). Visual observations or the scope of work may necessitate a more advanced assessment of internal wood condition and below ground observations. In this study, the time-consuming nature of more advanced assessment was deemed not important and any root system or internal conditions not observed were likely a source of error associated with both control and construction trees. Tree condition was evaluated by a different person during each measurement period and inter-rater reliability is not known. Thus, potential differences in evaluator bias are possible and any potential source is not known. This study also used the CTLA tree condition evaluation system which uses both tree health and tree structure to develop a tree condition rating and the terms used in this system may vary from other tree rating systems (e.g., ISA Tree Risk Assessment Qualification rating system). The effects of construction on tree health were one of several factors that affect tree survival, condition, and growth in this study. The avoidance of past construction practices has decreased the impact on annual tree mortality and tree condition which explained about 10% of the variability when comparing the model R2 statistic from 1989 and in 2018. Other factors not studied such as soil moisture content, soil bulk density, soil texture, soil volume, tree genotypes, insects, pathogens, and cultural practices would also likely increase model explanation of tree survival, condition, and growth. The study sites were randomly selected within the City of Milwaukee and believed to be reflective of the street tree population, nonetheless, we cannot say with 100% certainty this sample represents all street trees.

4.4. Policy and ordinance The development of regulatory and governance mechanisms through law, policies, ordinances, standards, and fines for non-compliance to follow required specifications should be part of construction projects (Fite & Smiley, 2016; Matheny & Clark, 1998; Miller et al., 2015). Along with specifying what needs to be done through written specifications, understanding contractor knowledge, and perceptions of construction near trees is vital to tree preservation programs (Despot & Gerhold, 2003; O’Herrin et al., 2016). Contractor knowledge of correct practices of construction near trees has increased over the past several decades in a regional study of builders in nearby Central Wisconsin, USA (O’Herrin et al., 2016). However, even though contractors generally understand the importance of avoiding damage to tree root

5. Conclusions This study suggests you can have construction and protect trees too. Given the nearly 40-year period and implementation of management actions to reduce the effects of construction on trees, we conclude that a tree preservation program can successfully be implemented to promote survival and maintain the condition of street trees in a construction zone. A street tree preservation program can be economically 12

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advantageous compared to no program with a B/C > 6 in the Milwaukee program. A construction damage prevention program or an often similarly labeled tree preservation program requires a wellthought-out process that involves all parties from the design and engineering stages that include trees, monitoring and enforcement from initial through the final stages of construction, and potentially followup arboricultural treatments. This study demonstrates how the observation of a problem (e.g., tree construction and declines in tree condition and survival) and implementation of management actions through a tree preservation program led to a healthier and longer-lived tree population associated with the repair of streets, sidewalks, curbs, and other infrastructure. This would likely not occur without actions taken to reduce the effects of construction on trees. Communities can use findings from this study and program approaches to foster healthier and more sustained street tree populations.

Acknowledgements The authors thank anonymous reviewers for their suggestions with the improvement of this paper. We thank the assistance of Jeff Laufenberg (Urban Forestry Manager) and Kurt Klemstein (Urban Forestry Crew Leader) for their logistical assistance with locating records and data collection. We are also grateful for the financial support from the Undergraduate Research and Creative Activity Grant from the University of Wisconsin-Stevens Point (UWSP) Office of Research and Sponsored Projects. We also thank the UWSP College of Natural Resources for Financial Support and sponsorship with the publication of this paper. Finally, we sincerely thank the TREE Fund, Wisconsin Arborist Association, USDA – Forest Service, and the USDA McIntireStennis Program for funding that was crucial for prior research associated with this project and was vital to this current effort.

Appendix

Table A-1 Variables used in this study, the definition of the variable, and the measurement unit. Variable

Definition for Tree Measurement

Unit

Construction89 Construction05 Construction18 LawnWidth89 LawnWidth05 LawnWidth18 TreeCond79 TreeCond89 TreeCond05 TreeCond18 TreeDiam79 TreeDiam89 TreeDiam05 TreeDiam18

Indicator that records if construction occurred between 1981 and 1985 Indicator that records if construction occurred between 1990 and 2004 Indicator that records if construction occurred between 2005 and 2017 Distance between curb and sidewalk measured in 1989 Distance between curb and sidewalk measured in 2005 Distance between curb and sidewalk measured in 2018 Overall condition of tree measured in 1979 Overall condition of tree measured in 1989 Overall condition of tree measured in 2005 Overall condition of tree measured in 2018 Stem diameter measured at @ 1.37 m above the ground in 1979 Stem diameter measured at @ 1.37 m above the ground in 1989 Stem diameter measured at @ 1.37 m above the ground in 2005 Stem diameter measured at @ 1.37 m above the ground in 2018

Binary Binary Binary cm cm cm % % % % cm cm cm cm

Tree Species1

Definition of Measured Tree Species

Unit

AmericanElmYY BasswoodYY GreenAshYY HoneylocustYY LLeafLindenYY NorwayMapleYY SilverMapleYY SugarMapleYY WhiteAshYY

Indicator Indicator Indicator Indicator Indicator Indicator Indicator Indicator Indicator

Binary Binary Binary Binary Binary Binary Binary Binary Binary

that that that that that that that that that

American elm was present in 1979 or 1989 American basswood was present in 1979, 1989, or 2005 green ash was present in 1979, 1989, or 2005 honeylocust was present in 1979, 1989, or 2005 little leaf linden was present in 1979, 1989, or 2005 Norway maple was present in 1979, 1989, or 2005 silver maple was present in 1979, 1989, or 2005 sugar maple was present in 1979, 1989, or 2005 white ash was present in 1979, 1989, or 2005

1

The two digit code (YY) following the tree species in this study coincides with the measurement period: 79 = 1979, 89 = 1989, 05 = 2005, and 18 = 2018 (e.g., AmericanElm79 means this species was present in 1979 at the measured tree location).

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