A Cost Benefit Analysis of DED, EAB, and Historical Canopy in Milwaukee, WI by Plan-It Geo

The True Cost of Urban Forest Pathogens: A Cost/Benefit Analysis of Dutch Elm Disease, Emerald Ash Borer And Historical Tree Canopy in Milwaukee, Wisconsin Completed November 2015

Prepared by Plan-It Geo for the City of Milwaukee Department of Public Works

 “I walked New Year's Day beside the trees my father now gone planted” Lorine Niedecker, Wisconsin-born poet, 1903-1970

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North 16th Street between West Keefe Avenue & West Concordia Avenue

The True Cost of Urban Forest Pathogens: A Cost/Benefit Analysis of Dutch Elm Disease, Emerald Ash Borer, and Historical Tree Canopy in Milwaukee, Wisconsin

This project relied on a blend of professional, academic and local municipal expertise. The City of Milwaukee originated the project. Contract administration data research and review, and project oversight was provided by David Sivyer, Forestry Services Manager, and Ian Brown, Technical Services Manager. Plan-It Geo, LLC was the prime consultant and led canopy cover estimates, ecosystem services analysis, data analysis and interpretation, and reporting, specifically Ian S. Hanou, Rich Thurau Ph.D., Chris Peiffer, and TJ Wood. From the University of Wisconsin â&#x20AC;&#x201C; Stevens Point, Richard J. Hauer, Ph.D., led modeling of elm tree populations and economic cost/benefit analysis. He also supported the data analysis and interpretation. This document was funded in part by an urban forestry grant from the State of Wisconsin Department of Natural Resources Forestry Program as authorized under s.23.097, Wis. Stat.

Contents Executive Summary ................................................................................................................1 Background .............................................................................................................................4 Methods ...................................................................................................................................6 Study Area ............................................................................................................................................... 6 Estimating Historical Canopy Cover ....................................................................................................... 8 Estimating Historical Elm Populations .................................................................................................. 10 Ecosystem Services and Benefits ........................................................................................................ 13 Economic Analysis ................................................................................................................................ 17

Findings .................................................................................................................................23 Historical Trends in Canopy Cover ....................................................................................................... 23 Historical Elm Populations .................................................................................................................... 28 Ecosystem Services and Benefits ........................................................................................................ 31 Economic Analysis ................................................................................................................................ 39 Emerald Ash Borer Analysis ................................................................................................................. 44

Discussion .............................................................................................................................50 Comparables ......................................................................................................................................... 50 Recommendations and Future Applications ......................................................................................... 60

Appendix................................................................................................................................62 Key Acronyms and Terms ..................................................................................................................... 62 Sample Points used in Historical Canopy Estimation .......................................................................... 64 Historical Aerial Imagery ....................................................................................................................... 65 Historical Records of DED Removals and Tree Planting ..................................................................... 67 Economic Analysis Methods ................................................................................................................. 68 Historical Photos ................................................................................................................................... 70 References ............................................................................................................................................ 74

Tables & Figures Tables Table 1: Year and Specifications of Historical Imagery Used .................................................................... 9 Table 2: Examples of estimated mean tree stem diameter, crown width, tree height, and height to base of crown for American elm trees in Milwaukee by base year................................................................... 12 Table 3: Values for 1956, initial values used, and source to develop of an economic analysis to model Dutch elm disease management starting in 1956 in the City of Milwaukee using the Dutch Elm Disease PLANning Simulator. ................................................................................................................................ 19 Table 4: Cost data used in 1956 and comparison of adjusted 2014 to actual data. ............................... 20 Table 5: Summary of historical urban tree canopy results citywide from 1956 â&#x20AC;&#x201C; 2013 by time period. .. 24 Table 6: Summary of urban tree canopy assessment results within the street ROW by time period. .... 26 Table 7: Comparing value in millions of dollars (2014 value) of Milwaukee elm street tree population by management alternative. .......................................................................................................................... 40 Table 8: The number of ash street trees in Milwaukee, WI from a 2014 street tree inventory. .............. 44 Table 9: Values used to evaluate the economic outcomes of four emerald ash borer management options. ...................................................................................................................................................... 45 Table 10 (Appendix Table1): Values used in 1956, initial values used, and source to develop of an economic analysis to model Dutch elm disease management starting in 1956 in the City of Milwaukee using the Dutch Elm Disease PLANning Simulator ................................................................................. 68

Figures Figure 1: Historical aerial view of continuous street tree canopy cover in Milwaukee in 1956 ................. 3 Figure 2: Original vs. Revised 2014 ROW GIS Boundary for this Study ................................................... 7 Figure 3: The relationship between stem diameter and tree height for American elm in Milwaukee, WI. ................................................................................................................................................................... 11 Figure 4: The relationship between stem diameter and crown width for American elm in Milwaukee, WI. ................................................................................................................................................................... 11 Figure 5: Projected elm tree losses from Dutch elm disease under varying levels of control (Cannon and Worley 1976)...................................................................................................................................... 13 Figure 6: Ecosystem services provided by urban trees. .......................................................................... 13 Figure 7: Percent tree canopy citywide by time period. ........................................................................... 23 Figure 8: Historical and current aerial imagery example of citywide tree canopy increase. ................... 24 Figure 9: Percent tree canopy in the Rights-of-Way (ROW) by time period............................................ 25 Figure 10: Proportional percent of tree canopy in the ROW to all canopy citywide by time period. ....... 26 Figure 11: Aerial photos showing canopy gain in the ROW, near North Sherman Blvd. and West Florist Avenue, northwestern Milwaukee............................................................................................................. 27 Figure 12: Aerial photos showing typical elm canopy loss in the ROW. ................................................. 27 Figure 13: Loss of elms in Milwaukee WI and right of way canopy cover. .............................................. 28 Figure 14: Elm loss comparison under various Dutch elm disease management scenarios. ................. 29 Figure 15: Comparison between Actual loss in canopy and the potential effects of Active DED management on UTC. .............................................................................................................................. 30 Figure 16: Per tree ecosystem benefits based on average elm DBH for each time period. ................... 31

Figure 17: Historical photo of a typical Milwaukee street lined with elm canopy prior to DED loss. ....... 32 Figure 18: Ecosystem benefits for cohort elm populations and sanitation levels. ................................... 33 Figure 19: Historical photo of a Milwaukee street in the 1960’s post-DED ............................................. 34 Figure 20: Stormwater runoff benefits for Actual vs. Best sanitation levels. ........................................... 35 Figure 21: Air quality and public health benefits for Actual vs. best sanitation levels. ............................ 35 Figure 22: Energy conservation benefits for Actual vs. Best sanitation levels. ....................................... 36 Figure 23: Carbon storage benefits for Actual vs. Best sanitation levels. ............................................... 36 Figure 24: Delta in 2013 structural value of elm ROW trees between Actual and Best sanitation levels. ................................................................................................................................................................... 37 Figure 25: Total Annual Ecosystem Benefits by Sanitation Alternative (Management Scenario) .......... 37 Figure 26: Comparing the cumulative loss of ecosystem service benefits in dollars. ............................. 38 Figure 27: Comparing the cumulative loss of stormwater management benefits from i-Tree Eco to MMSD. ...................................................................................................................................................... 38 Figure 28: Retained Net Present Value of public elms by DED sanitation option from DED-PLANS. ... 39 Figure 29: Retained Net Present Value of public elms by DED sanitation option from i-Tree Eco. ....... 40 Figure 30: Net present value and benefit to cost ratio from DED-PLANS over 40 years for elm street trees. ......................................................................................................................................................... 41 Figure 31: Comparing costs for management alternatives from DED-PLANS over 40 years for elm street trees. ............................................................................................................................................... 42 Figure 32: Total cost to manage elm trees under a variety of management alternatives. ...................... 43 Figure 33: Cost to remove elm trees under a variety of management alternatives. ............................... 43 Figure 34: Management costs by approach in response to emerald ash borer for ash street trees. ..... 47 Figure 35: Net present value and benefit to cost ratio by approach in response to emerald ash borer for ash street trees. ........................................................................................................................................ 48 Figure 36: Aerials of North 24th Street between West Melvina Street and West Vienna Avenue in Milwaukee for 1956 and 2013 showing where canopy has not recovered from DED elm losses. ......... 51 Figure 37: Distribution of Dutch elm disease in North America showing the year of introduction within a state. (Adapted from Sinclair and Campana 1978). ................................................................................. 52 Figure 38: Loss of elms in Milwaukee and Minneapolis over 40 years and comparison to best sanitation practices. ................................................................................................................................................... 52 Figure 39: Victory Memorial Parkway in Minneapolis, MN lined with American elm circa early 1970’s. (photo by Mark Stennes) .......................................................................................................................... 53 Figure 40: The effects of Dutch elm disease management in Syracuse, NY with maximum sanitation compared to minimum and no sanitation. ................................................................................................ 53 Figure 42: Actual number of elm trees remaining compared to active management and minimal Dutch elm disease management in Minneapolis, MN. ....................................................................................... 54 Figure 41: Cost of Dutch elm disease management in Minneapolis, MN compared to predicted results of Baughman (1985) under two sanitation levels. .................................................................................... 54 Figure 43: Benefit to Cost Ratios of Urban Trees from Various Sources and Methods.......................... 56 Figure 44: Quantifying the annual reduction in stormwater runoff benefit of elms by DED management scenario. .................................................................................................................................................... 59 Figure 45 (Appendix Figure 1): (left) 500 points were assessed for each time period within the Revised ROW Layer; (right) 1,500 points were assessed for 1956, 1963, and 2013. 1,000 points were assessed for 1969, 1979, and 1986 within Milwaukee’s city limits. ......................................................................... 64 Figure 46 (Appendix Figure 2): Mosaic of tiles from the 1956 scanned, historical imagery over Milwaukee used in this study. ................................................................................................................... 65

Figure 47: Historical aerial imagery of the Milwaukee River in the vicinity of the UW-Milwaukee campus comparing 1956 (top) and 2010 (bottom). ................................................................................................ 66 Figure 48 (Appendix Figure 3): Historical photo from 1966 – Loss due to Dutch elm disease. .............. 70 Figure 49 (Appendix Figure 4): Historical photo from 1966- New plantings. ........................................... 70 Figure 50 (Appendix Figure 5): City of Milwaukee Forestry Staff, 1933. ................................................. 71 Figure 51 (Appendix Figure 6): Gordon Z. Rayner, Milwaukee City Forester from 1957 to 1972 during the peak of Dutch Elm Disease, outlining the schedule of Bidrin applications, the City’s only available DED control effort which targeted the vector (elm bark beetles) rather than the fungus because fungicidal treatments were not yet available. Note that with six, five-man crews, 30 of Milwaukee’s forestry staff were focused solely on DED treatments rather than typical work such as planting, pruning, and beautification. ..................................................................................................................................... 71 Figure 52 (Appendix Figure 7): Hydraulic spraying of an elm in Milwaukee in the 1930’s ..................... 72 Figure 53 (Appendix Figure 8): Bidrin DED treatment application where capsules were drilled into the tree truck and later removed..................................................................................................................... 72 Figure 54 (Appendix Figure 9): Push pins being used on a quarter section map, presumably indicating Bidrin treatment sites, and illustrating the widespread distribution of elm trees citywide........................ 73

Executive Summary The City of Milwaukee’s urban forest is comprised of public trees along streets and in parks as well as trees and forests on private property. This element of the City’s green infrastructure provides countless benefits to residents, the environment, and the economy. The City has a long-standing and nationally recognized street tree management program focusing on proactive management, maintenance, treatment, removals and tree planting. Non-native and often invasive forest pests threatened the stream of services that urban tree canopy provides. This study examined the functional, structural, and monetary impacts of Dutch elm disease (DED, Ophiostoma ulmi) in the City of Milwaukee by assessing historical tree canopy cover, foregone ecosystem services from elm removals, and the economics of management alternatives. In the 1950’s, Milwaukee’s street tree population was poorly diversified; the block and street monoculture planting of a single species devastated entire neighborhoods planted in elm. Given the parallels to emerald ash borer (EAB, Agrilus planipennis) that Milwaukee and other cities are grappling with, this study also discusses future economic scenarios that provide a strong case for improved forest pest and disease management.

Management Questions of this Study  When was the low point in Milwaukee’s canopy? Did this coincide with the stormwater Deep Tunnel?  What was the cumulative loss in ecosystem benefits from more than 100,000 DED elm removals?  What would it have cost to maintain elms to have sustained these benefits, and what level of DED management and tree maintenance would that have required?  How long did it take the City of Milwaukee to recover from DED in terms of canopy and benefits?  What lessons can be applied to EAB as urban forest management and policy move forward?  What are the structural changes that occurred; what biometric relationships exist between elm structural attributes (stem diameter and height, stem diameter and canopy spread); and what was the annual mortality (natural and DED), annual growth rate, and tree condition of the elm population in Milwaukee? Below are the key findings based on these project objectives: Historical Canopy  Average tree canopy citywide in Milwaukee increased from 9% in 1956 to 22.7% today, with the exception of a drop from 12.5% to 10% between 1963 and 1969 at the peak of DED.  Average canopy in the Street Rights-of-Way (ROW) decreased from 24% in 1963 to just 13% in 1979. Since then, it has steadily increased to around 23%, equal to the citywide average.  In 1956, half (51%) of all tree canopy in Milwaukee was found along the street ROW. In 1979 after the peak of DED, street trees made up just 20% of all canopy citywide.  The ROW canopy loss between 1963 and 1979 was nearly 50% (from 23.8% to 12.6% cover).  If available at the time, a “Best” DED management program (i.e. 1% annual elm loss) would have retained 23% average tree canopy in the ROW in 1979. The current approach to manage EAB will stabilize ash tree canopy in a similar way.  While tree canopy in Milwaukee is currently at its highest point in recent history based on these findings, cities such as Cincinnati (Hanou, 2011), Minneapolis (Bauer, 2011), and Washington D.C. (Hanou, 2013) average 39%, 32%, and 37% tree canopy, respectively.

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Elm Population  The elm population in the street ROW was approximately 107,000 in 1956 prior to DED. Currently less than 1% (approximately 1,000) of that cohort remains today.  DED was introduced to Milwaukee in 1956 prior to the development of effective approaches for DED management that became standard practice in the 1970’s and 1980’s.  Elm losses greatly increased to epidemic levels by the mid to late 1960’s prior to best DED management practices.  Milwaukee participated in developing effective DED management through the Federal DED Demonstration project (1978-1982) and once established annual elm loss greatly diminished.  If effective DED management knowledge was available in 1956 and implemented, under the “Best” management level scenario, approximately 1/3 (35,505) of these trees were modeled to remain nearly 60 years later as part of the 2013 ROW elm population.  Records document over 141,767 elm removals across all land, including 38,000 on private property which only represents a portion of overall private elm removals.  The peak number of elm removals on public street ROW occurred in 1968 at 16,580.  The average DBH of elms was modeled at 11.78” in 1956, 21.98” in 1986, and 31.16” in 2013. Ecosystem Benefits  The cumulative value of foregone ecosystem benefits due to DED elm removals in Milwaukee’s street ROW totals $120M, broken down by ecosystem service type as: o $11.1M in lost stormwater management services based on water treatment prices in iTree Eco, but approximately four times more ($44M) based on local pricing from MMSD. o $73.7M in lost air pollution mitigation services o $27.2M in lost energy savings services o $8.25M in lost carbon sequestration/storage benefits o Note: losses do not reflect benefits from new, replacement trees over the study period  Each mature American elm tree (31” DBH) provides $88 in annual ecosystem benefits with a structural value of $7,227. The stormwater benefit increases four-fold from $1.86/year vs. $7.96/year as DBH increases from 12” and 31” DBH, respectively.  Current documented green infrastructure sources (bioretention, cisterns, porous pavement, etc.) have an annual stormwater runoff reduction capacity of 14.0M gallons. Had the 1956 elm street tree population been actively managed under the Best Control scenario in this study, these elms would have provided 32.0M gallons of runoff control for the year 1996 (end of 40-year scenario). Economic Benefit/Cost Analysis  Using a compensatory analysis (DED-PLANS Program) over a 40 year time horizon, without DED the elm population would provide a $202 million Net Present Value (NPV, 2014 dollars). o A NPV analysis accounts for the benefits – costs and further uses an interest rate to account for the value of money over time. o Even with actual loss of trees from DED, the elm cohort provided an $80 million NPV over the 40 year time period, thus DED resulted in a $122 million dollar NPV reduction in urban forest value. o The “Best” management scenario gave an estimated $175 million NPV and an approximate benefit to cost ratio (B/C) of 2.0, meaning for every dollar invested the elm tree population returned $2 in benefit. Exe cu t i ve Su mma r y

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A “Best” management scenario would cost $42.6 million and spending this amount would return the $95 million greater NPV than the actual outcome in Milwaukee. o Even fair ($112 million NPV, 1.0 B/C) and Good ($130 million NPV, 1.1 B/C) management were better than the actual ($80 million NPV, 0.8 B/C) and No Control ($57 million NPV, 0.7 B/C).  The i-Tree Eco analysis conclusions were similar to the compensatory approach with a no DED scenario giving a $363 million value (ecosystem functional value = $123 million and standing tree structural value = $239 million) o Accounting for management costs ($106 million) gave a $257 million NPV (2014 dollars) and 3.42 B/C for a no DED scenario. o The actual outcome was a $7 million NPV (1.1 B/C), thus even with DED the elm population provided some positive value to Milwaukee. o A “Best” management gave a $307 million value from i-Tree Eco which after management costs ($147 million) resulted in a $160 million NPV and 2.1 B/C. o Comparatively, Good ($130 million NPV, 1.69 B/C) and fair ($112 million NPV, 1.55 B/C) control were better than No Control but not as favorable as a Best Control program. o

Discussion As Milwaukee and other cities manage emerald ash borer (EAB), a complete understanding of risk and value is essential for policy and decision makers. This economic analysis (cost/benefit, NPV) study takes the initial assumption that urban trees have value, and illustrates that if they do not, then removing trees is the most cost-effective alternative to managing pests. The results tell a compelling story that elevates the true cost of urban tree canopy loss into the decision matrix for invasive species policy and management. While traditional forest management “products” may be more obvious – such as maximizing timber volume, paper production, and wildlife habitat – there are outcomes and benefits from urban forest management in terms of public health, property values, and environmental services that are equally valued by the public. This study proves that active urban forest management is better than no management and quantifies this with multiple approaches, to make the case for proactive urban forest tree and pest management. Results from this study also support the current EAB risk management effort to actively manage the ash tree population in Milwaukee to retain healthy ash trees. Results from this study also show that the loss of the ash population would also have a significant effect on the Milwaukee tree canopy cover.

Figure 1: Historical aerial view of continuous street tree canopy cover in Milwaukee in 1956 along West Capitol Drive (Top), North 27th Street (West), North Teutonia Ave (East), West Vienna Ave (South)

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Background The City of Milwaukee experienced significant loss of urban tree canopy due to Dutch elm disease (DED, Ophiostoma ulmi), which based on historical records has resulted in the removal of over 140,000 elms from the late 1950â&#x20AC;&#x2122;s to present time. Today in the face of EAB, Milwaukee is fortunate that several years of research and lessons learned from communities in Michigan, Indiana, and Ohio provide best management approaches for this insect (Herms and McCullough, 2014). This was not the case with DED, when in the late 1950â&#x20AC;&#x2122;s Milwaukee had fewer management options, science, and resources, therefore tree removal and replacement was the outcome (Sinclair 1978). This study takes into account the long-term impacts of that consequence. Additionally, while the City replanted streets with Norway maples, ash and other urban tolerant species, many are smaller stature trees with less leaf area that provision less ecosystem benefits than American elm(Schuman 1984). This first-of-its-kind study evaluated historical tree canopy in the City and provides complete picture of the economic and ecosystem trade-offs of species selection, treatment/retention, and maintenance/growth through a cumulative, lifetime valuation. This study allows managers to understand the long-term impacts of widespread canopy loss from invasive pests and disease by examining the impact not just on structural value (removal and replacement costs) but also on stormwater, air quality, carbon, and energy benefits compared with actual costs. Impacts from Invasive Forest Pests Invasive species, pests and disease present one of the most serious threats to urban and community forests (Pimentel 2005, Pimentel et al. 2005, Ball et al. 2007). While diversification of urban tree community composition and structure is recognized today as the best defense against unforeseen threats, in Milwaukee and many other cities across the country, DED reduced urban tree canopy (UTC) cover and associated economic and environmental benefits significantly in the latter part of the 20th century (Campana and Stipes 1981, Stipes 2000). With the current invasion of emerald ash borer (EAB), originally found in Detroit in 2002 and now spreading quickly across the United States, it is timely to assess the historical change in UTC and benefits in Milwaukee as a way to increase the understanding of the connection of urban forest management with the societal benefits and services provided.

Major Devastating U.S. Forest Pests

Chestnut Blight: Large American chestnut was eliminated from eastern forests as a dominant species by chestnut blight (Cryphonectria parasitica). The tree produced valuable timber and was and important food source for wildlife and humans. This tree species is now an understory species of minor importance.

Dutch Elm Disease: In 1950, Syracuse, New York, had 53,000 elms along its streets. Today it has fewer than 300 as a result of the ravages of Dutch elm disease (Ophiostoma ulmi). By the 1970s, it was estimated that over 40 million of the northeast United States 77 million elm trees were dead (Sinclair, 1978).

Emerald Ash Borer: Emerald ash borer (Agrilus planipennis), a phloem-feeding beetle native to Asia, was discovered near Detroit, Michigan and Windsor, Ontario in 2002. A ten-year discounted cost of $10.7 billion was estimated to treat, remove, and replace 17 out of 38 million ash trees on developed land within communities in a 25-state study area (Kovacs et al., 2010).

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Stormwater and Water Quality Management Coincidentally, at the peak of UTC loss in Milwaukee due to DED (1979) was the decision by Milwaukee Metropolitan Sewer District (MMSD) to fund the stormwater “Deep Tunnel” construction project at a cost of over $1 billion for the initial phase (Behm 2013). This project was undertaken to mitigate flooding and the release of untreated stormwater and sewage into surface water bodies and was part of a $3 billion water pollution abatement program (MMSD 2009). One research question for this study asked whether the construction of Milwaukee’s stormwater “Deep Tunnel System” – a 28.5 mile long storage system completed 1993 with total storage volume of 521 million gallons – could have been avoided altogether or reduced in size and expense had DED not resulted in the loss of over 100,000 elm trees in the 1960’s and 1970’s (MMSD 2009, Sivyer 2014). A greater emphasis was placed on research, discussions, available data sources and models, and the process of quantifying the impact of DED on runoff volume (water quantity) and lost stormwater management benefits compared with other urban forest ecosystem services. The i-Tree Eco model was used to quantify the amount of water captured by elm trees and prevented from becoming an overland flow of water. Economic Approaches for Making for Management Decisions Management decisions are ideally developed from a comparison of several alternative approaches that could be used to achieve a desired end result. Economic criteria that are used to support decision making need to bring all alternatives into a common framework that allows for comparison. Discounting money for the value over time, selecting a base year for comparison, using management cost data that reflects actual expenditures, and accounting for benefits that accurately reflect an outcome are all important. Even with the best available information, using a variety of approaches provides a better understanding if one alternative is truly superior to another. Thus, this study used multiple approaches. Objectives of this Study This study aimed to connect historical changes in UTC with the loss of American elm and to understand the losses incurred resulting from DED and similar threats. Specific goals for the project include: 1. Establish and implement peer review supported scientific protocols and methodology for conducting a comprehensive historical urban tree canopy analysis in Milwaukee using georeferenced historical aerial photography. 2. Estimate the foregone monetary ecosystem benefits resulting from elm loss and DED. 3. Model elm population characteristics and analyze cost & benefit of DED sanitation scenarios. 4. Relate historical lessons of DED to current efforts to manage ash trees and EAB.

Timeline of Urban Forestry Milestones and Studies in Milwaukee, WI            

1911 … Milwaukee establishes a tree nursery at Evergreen Park, planting 32,000 trees 1918 … Milwaukee Forestry Program begins Otto W. Spidel hired as city forester with a $15,000 budget 1919 … 4,000 elm, Norway maple, ash and linden planted; forestry budget $17,419.61 1924 … Milwaukee establishes a 160-Ac tree nursery in neighboring Franklin, Wisconsin 1956 … DED discovered in Milwaukee and Beloit Wisconsin 1979 … A Partial Street Tree Inventory Study, Dr. Robert Miller 1996 … Urban Ecosystem Analysis, American Forests 2008 … i-Tree Eco Analysis, City of Milwaukee Forestry Services 2009 … EAB Hyperspectral Remote Sensing Analysis, NCDC Imaging 2012 … Emerald ash borer (EAB) discovered in Milwaukee 2013 … Milwaukee included among the 10 best U.S. cities for urban forests, by American Forests 2015 … An Economic Cost/Benefit Analysis of Dutch Elm Disease and Historical Tree Canopy

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Methods This project incorporated expertise in geospatial data, aerial imagery interpretation, urban forest management, economic cost/benefit modeling, environmental modeling/ecosystem services analysis, and local historical resources. The broad and integrated expertise was important to reach the following objectives and steps which are described in this section and in detail in the Appendix: 1. Estimate historical tree canopy percent across six time periods citywide and within Milwaukee’s street rights-of-way (ROW). 2. Estimate historical elm populations and structure (mean elm tree size, condition, number of trees) across six time periods. 3. Quantify the annual and cumulative UTC benefit loss and costs attributed to DED and the documented loss of 103,625 American elm street trees in Milwaukee using environmental and economic parameters with through i-Tree Eco and the CTLA compensatory tree valuation. 4. Use the results derived from Milwaukee’s DED economic analysis in conjunction with EAB cost calculators to evaluate and discuss various approaches to urban forest pest management from treatment (conservation) to actual or preemptive removal and replacement of Milwaukee elm trees from DED and ash street trees at risk to EAB.

Study Area Two areas of interest (AOI) were assessed for historical canopy cover – the official city limits of Milwaukee and a revised Street Rights-of-Way (ROW). Citywide Estimating citywide urban tree canopy (UTC) is an important metric on the extent of the entire urban forest on both public and private property and allows for comparisons to other cities. The citywide AOI for this study encompassed 96.7 square miles, or 61,881 acres. Street Rights-of-Way (ROW) Estimating historical UTC in the public street ROW is an important metric to benchmark the extent and maturity of trees managed and maintained by City Forestry staff. No ROW layer existed for the 1950’s. After receiving the original (2014) GIS ROW boundary from the City and comparing with the historical aerial imagery, it was decided to modify the current (2014) ROW extent to reflect conditions in 1956. The following modifications were performed to the street ROW AOI using Esri ArcGIS software:  Areas that were undeveloped in the 1950’s and 1960’s were excluded from the ROW analysis in three main areas to the north, west, and southeast given the focus of studying the impact of DED on elm street tree canopy and associated lost ecosystem benefits and cost scenarios.  The reduced AOI was then buffered by 15-feet (30-feet total) on each side of the ROW to ensure that randomly generated sample points would accurately depict a greater amount of the canopy from public street trees that extended into private property. See Figure 2 below.  Finally, large water bodies were removed from within the public ROW area such as the Milwaukee River downtown.  The original ROW covered 21.9 square miles (14,023 acres). The final revised ROW AOI after removing undeveloped areas, water areas, and buffering by 15-feet totaled 22.2 square miles (14,222 acres). See maps in Figure # below.

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Milwaukee City Limit

ROW Layer

Revised ROW Layer

Figure 2: Original vs. Revised 2014 ROW GIS Boundary for this Study and Inset of Revised ROW buffer. Me t h o d s

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Estimating Historical Canopy Cover To estimate canopy cover and change over time, a statistical point-based sampling approach was conducted (Nowak and Greenfield 2012) using aerial imagery interpretation for six (6) periods of time: 1956, 1963, 1969, 1979, 1986, and 2013. A canopy estimate based on 2008 remote sensing analysis (Souci et al. 2009) was also available and incorporated as a 7th time period for canopy trends and ecosystem benefit analysis. Aerial imagery interpretation was conducted using the i-Tree Canopy application from the USDA’s Forest Service i-Tree suite of tools for 2013 imagery and using Esri ArcGIS software for all historical imagery years (excluding 2008 which was based on high resolution hyperspectral imagery and LiDAR classification). i-Tree Canopy allows users to estimate tree and other cover classes (e.g., grass, building, roads, etc.) within a geographic boundary. The tool randomly generates points onto imagery from Google Maps where the user classifies (“tallies”) each point by the underlying land cover type based on the aerial photo. As random point locations are assessed, the program updates estimates for canopy cover percent and standard error (SE). For this study, the default land cover classes of “canopy” and “non-canopy” were used. Other land cover classes (e.g. grass, buildings, water, etc.) were not classified in order to expedite the process and focus on accurately estimating canopy cover and other tasks. While an infinite number of randomly generated points can be used, the following number of points was evaluated for a targeted SE %: 1,500 points citywide for 2013, 1956 and 1963 to achieve a SE of approximately 1% 1,000 points citywide for 1969, 1979, and 1986 to achieve a SE of 2% or better 500 points in the public street ROW for all time periods to achieve a SE of 2% or better The location of the points remained the same for each of the six time periods in which tree canopy was assessed (i.e. each point was assessed six times). One exception is within the ROW. To achieve the targeted SE, additional random points were selected in order to reach a 500 point sample size because only 350 of the original 1,500 sample points citywide fell within the Revised ROW AOI. The Results section details the number of points classified as “canopy” along with the estimated canopy cover for each of the six time periods. Related Historical Tree Canopy to Elm Populations Biometric relationships (detailed later in Methods) were used to estimate tree canopy area based on tree stem diameter. This was used to estimate the percentage of canopy comprised by elm trees over time and how this changed over time. An initial attempt was made to quantify the percentage of elm tree canopy using the aerial images, however, the image quality did not sufficiently allow for species identification. Thus the biometric approach was used as the better approach. Note that ultimately, the results of the statistical canopy sampling approach were not used to quantify the lost ecosystem benefits of elms from DED. Ecosystem benefits were based on the number of elm trees and their size in a time period, described further below. Historical Imagery Imagery was obtained from the USDA Farm Services Agency (FSA) Aerial Photography Field Office in Salt Lake City, Utah and required digitally scanning and orthorectifying dozens of tiles of imagery for each time period. Table 1 lists the year, color bands available, spatial (pixel) resolution, and example visual of each imagery data set. The most current imagery in i-Tree Canopy which uses Google Maps

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was from 2013. Figure 46 (Appendix Figure 2) in the Appendix provides mosaic of the 1956 scanned, historical imagery over Milwaukee used in this study. Table 1: Year and Specifications of Historical Imagery Used

Imagery Year

Spectral Bands

Known or Approximate Resolution

1956

Black & White

¼ meter

1963

Black & White

¼ meter

1969

Black & White

¼ meter

1979

Black & White

½ meter

1986

Color Infrared

1 meter

2013

True Color (NAIP)

1 meter

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Example

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Estimating Historical Elm Populations When quantifying the effects of a pest or disease problem, a tree inventory that describes a tree population is important. Ideally the inventory would be updated annually, in an ongoing manner through maintenance tracking, or as often as needed. Among other uses, this is done to develop conclusions on how various management alternatives compare to achieve a desired outcome. Modeling the Required Elm Characteristics No tree inventory was found that described all elm tree population attributes in 1956 that were needed for this project. Attributes necessary for each annual time period include: number of elms, mean stem diameter, canopy width, tree height, and height to tree canopy base. The only known value from 1956 was the initial number of 106,738 elms in the public-right-of-way just prior to the first confirmed DED case in Milwaukee (Sivyer 2014). In addition, an annual accounting of the removal of elms exists (Miller and Schuman 1981, Sivyer 2014). Other required attributes were estimated based on best available records and research as described next. The American elm street tree population was simulated over a 40 year time period (1956-1996). Accounting for Tree Growth to Backcast Elm Populations Annual tree growth rates are commonly used to forecast future tree diameters (Bragg 2003). The same approach can be used to estimate tree size in the past through backcasting. The mean stem diameter (dbh, 4.5 feet) in 1956 was estimated by backcasting the annual growth rate of elm street trees in Milwaukee. A cohort of 43 elm street trees tracked from 1979 to 2005 had a mean 0.34 inch annual growth rate (Hauer et al. 1994, Koeser et al. 2014). By comparison, a similar 0.37 inch annual growth rate for 105 elms was found in Minneapolis, MN (Hanson et al. 2008, Hanson 2009). A mean 19.6 inch stem diameter in 1979 (n= 5,998 elms) was reduced by 0.34 inches annually to derive an estimated 11.78 inch mean dbh in 1956 (Schuman 1984). The formula to backcast stem diameter was:

DBHtp = DBHt0 – MGR * TP where: DBHtp = stem diameter in past time period DBHt0 = starting stem diameter MGR = stem diameter mean annual growth rate TP = time period difference between starting year and backcast year The same formula above can be used to forecast growth by adding the MGR * TP product to the starting stem diameter. Regression Models to Backcast Elm Populations Biometric relationships are commonly used to predict tree parameters based on actual measurements (Pillsbury et al. 1998, Peper et al. 2001, McHale et al. 2009). Estimates of tree height, canopy width, and height to tree canopy base were based on regression models developed from American elms in a Milwaukee street inventory collected between 2008 and 2011 (Figure 3: Tree Height, Figure 4: Canopy Spread, Table 2: Year Biometric). A crown width (y) prediction model from tree diameter (x) is y = 4.8611 + 1.7001x (R² = 0.8198, n=200). Tree height (y) estimated from diameter (x) is y = 9.4079x 0.5078 (R² = 0.8706, n=200). The mean height to the tree canopy base is estimated as 35% of the tree height. A regression model from Milwaukee street trees poorly predicted the height to tree canopy base (y) by Me t h o d s

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diameter (x) as y = 3.5591 + 1.0717x (R² = 0.2741, n=75). A mean 65% live crown of total tree height was found for American elm street trees in Milwaukee and this value was used to estimate base of crown by subtracting 65% of tree height from tree height. Accounting for Tree Mortality Annually in Elm Populations Annual tree attrition was estimated based on natural tree survival (99.1%) for American elm and planting survival (90%) for all tree species as described by Miller and Schuman (1981). Additional tree mortality as a result of DED used methods of Cannon and Worley (1976, 1980) and Sinclair (1978). Elm Populations under Various Management Intensities (“Sanitation Levels”) Epidemiology models provide a basis of how elm populations change over time given various DED management intensities (Figure 5: Cannon and Worley). The economic efficiency of various sanitation levels was studied by Cannon and Worley (1976, 1980) for 39 Midwestern communities collecting information on elm tree populations, elms lost to DED, control measures used, and control costs. Control programs for DED were disaggregated into 4 program performance groups: Best (1% annual mortality), Good (3.5%), Fair (5%) and No Control (18%). The percentage additional mortality from Me t h o d s

Figure 3: The relationship between stem diameter and tree height for American elm in Milwaukee, WI.

Figure 4: The relationship between stem diameter and crown width for American elm in Milwaukee, WI. 1 1 | Pa g e

DED is a function of control program effectiveness. No control results in an average 18% annual mortality. Actual elm loss in Milwaukee used official records from Milwaukee. Table 2: Examples of estimated mean tree stem diameter, crown width, tree height, and height to base of crown for American elm trees in Milwaukee by base year.

Year

Mean Stem Diameter (Inches) 1

Crown Width (Feet)2

Tree Height (Feet) 3

Mean Height to Base of Crown 4

1956 1963 1969 1979

11.8 14.2 16.2 19.6

24.9 29.0 32.4 38.2

32.9 36.2 38.7 42.6

11.5 12.7 13.5 14.9

1986

22.0

42.3

45.2

15.8

2008

29.5

55.0

52.5

18.4

2013

31.2

57.9

54.0

18.9

1 2

Crown width (y) prediction model from tree diameter (x): y = 4.8611 + 1.7001x (RÂ˛ = 0.82, n=200). 3

Stem diameter growth rate = 0.34 inch per year (n=43).

Tree height (y) estimated from diameter (x): y = 9.4079x0.5078 (RÂ˛ = 0.87, n=200).

Derived from subtracting tree height by 65% of tree height as elms on average had 65% live crown.

Software Programs used to Backcast Elm Populations The effectiveness of control approaches occurs as a function of the intensity of diseased elm surveys and prompt removal of infected trees prior to adult beetle movement to non-diseased trees (Barger 1977). The Emerald Ash Borer PLANning Simulator (EAB-PLANSÂŠ) as described by VanNatta et al. (2012) was modified using mortality rates described by Cannon and Worley (1976, 1980) to create the Dutch Elm Disease PLANning Simulator (DED-PLANSÂŠ) Version MKE (Milwaukee) program. An assumption was made that DED was introduced to the tree population at the start of the model simulation. For each control alternative, annual mortality was set at the full rate after a 12-year DED population â&#x20AC;&#x153;tipping pointâ&#x20AC;? (Cannon and Worley 1976, 1980). Prior to this 12 year tipping point, a logistics function was used to model the increasing mortality rate: (đ?&#x2018;&#x2026; â&#x2C6;&#x2019; đ?&#x2018;&#x2026; )

1 â&#x2C6;&#x2019; [1+ đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;(6â&#x2C6;&#x2019; đ?&#x2018;&#x152;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;) ] + đ?&#x2018; đ?&#x2018;&#x17D; where: Rn = natural percent elm survival rate without DED present Re = percent elm survival rate after tipping point for a DED control program Yn = years from present Na = normal elm mortality rate without DED present e = natural log

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These methods produced the elm populations and attributes for each historical time period as shown in Table 2 and Figures 3 and 4 above.

Figure 5: Projected elm tree losses from Dutch elm disease under varying levels of control (Cannon and Worley 1976).

Ecosystem Services and Benefits Years of research and development by the U.S. Forest Service and academia now provide data and software to quantify many of the ecosystem services and benefits provided by Milwaukeeâ&#x20AC;&#x2122;s urban forest in greater detail than ever before. This section outlines the overall process used to quantify the loss of ecosystem benefits due to American elm removals in Milwaukeeâ&#x20AC;&#x2122;s public street ROW, including the models that were considered and used, the parameters applied, dollar values used to estimate each ecosystem service, and other considerations taken into account. Four ecosystem services were analyzed for benefit values in dollars and resources units (e.g. gallons, lbs., etc.) â&#x20AC;&#x201C; stormwater management, air pollution removal, energy savings, and carbon storage / sequestration. This was a technically challenging aspect of the project requiring collaborative discussions with the City and project team to use the best modeling assumptions and data available, within budget and reason. Me t h o d s

Figure 6: Ecosystem services provided by urban trees. 1 3 | Pa g e

Available Models, Software, and Data Sources for Ecosystem Benefits The following models, software programs, and data sources were evaluated for this objective of the study: CITYgreen software (American Forests) i-Tree suite of tools from the U.S. Forest Service, specifically: o i-Tree Streets o i-Tree Eco (previously the Urban Forest Effects model, or UFORE) o i-Tree Hydro Milwaukee Metropolitan Sewer District (MMSD) o Stormwater impacts of trees o Data on combined sewer overflow (CSO) events o Costs for the stormwater deep tunnel Minnesota Pollution Control Agency’s “Minimal Impact Design Standards” (MIDS) Calculator The decision was made to use the i-Tree Eco model for several reasons. First, the i-Tree Streets program was not yet integrated with the UFORE model in i-Tree Eco. The i-Tree Streets model does not include many of the latest ecosystem valuations such as PM 2.5 air pollution mitigation and it uses total interception of stormwater for canopy benefit analysis rather than the avoided net runoff which iTree Eco uses. Second, with scripting using the statistical package R and MS Access, i-Tree Eco could mimic various populations and characteristics of elms over time, a critical requirement for this analysis. Ultimately, results showed that individual trees run through i-Tree Eco provided the same ecosystem benefits on a per tree basis and that multiple trees with the same characteristics were not needed. Third, while the i-Hydro model provides the latest science within the i-Tree suite of tools, it is not suited for individual tree characteristics, which was a requirement of this study. In addition many components of the i-Hydro model are used in i-Tree Eco for stormwater runoff benefit calculations. Efforts to obtain usable data from MMSD such as combined sewer overflow (CSO) events and water pollutant loading data was unsuccessful. While historical reports were provided and some data records existed, records would have required searching and scanning to electronic format and it was still unclear whether the effort would provide data inputs that could be used to associate or correlate CSO events with DED elm removals and loss of canopy. Finally, the MIDS Calculator was evaluated but determined to be beyond the scope of this study. Therefore utilizing i-Tree Eco application provided the best fit for analyzing the four targeted ecosystem services. Additionally, the i-Tree Landscape and i-Tree Forecast models were not yet available. Overview of Elm Benefits Analysis using i-Tree Eco Using the i-Tree Eco model, a series of elm cohort populations and an associated average diameter at breast height (DBH) were developed for the years 1956, 1963, 1969, 1979, 1986, 2008, and 2013, corresponding to the years of available historical imagery. Based on these inputs, per tree values were then calculated for each ecosystem service. These were used to interpolate i-Tree Eco values between time periods, e.g. the value of each ecosystem service based on the elm population and size in 2013 minus the value in 2008, divided by the 6 years between time periods. Aside from population and DBH, Leaf Area Index (LAI) was the primary driver for quantifying ecosystem benefits based on results of tests and a basic sensitivity analysis, specifically live crown measurements and the percent crown missing. Other noteworthy parameters/assumptions applied in the i-Tree Eco model included:

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“Good” average rating for elm tree condition 3% for level of tree crown dieback live crown measurements Urban Trees and Stormwater Management crown height crown width An i-Tree Streets report from Minneapolis, MN showed that % crown missing entries 10% of their street tree population is elm but that provides

Stormwater Management

22.5% of their heating/cooling benefit and 41.2% of their total street tree stormwater benefit due to their high leaf area index (LAI) and size in the total street tree population, or what's known as their Importance Value (IV).

Urban trees and forests intercept stormwater, reducing runoff and filtering out pollutants that would otherwise enter rivers and lakes. Trees and other vegetation help mitigate stormwater runoff by intercepting precipitation, naturally aerating soil and thus increasing stormwater absorption, and through evapotranspiration from respiration processes.

i-Tree Eco uses the i-Tree Hydro model which incorporates climatic information such as rain event length and intensity as well as soil saturation to estimate the volume and cost of additional runoff that would occur in the absence of trees. While strategic placement of trees near impervious surfaces and waterways can maximize stormwater quality and quantity management, these are not parameters that can be adjusted in the i-Tree Eco model. Specifically, the following methods and steps were used: The City’s i-Tree Eco plot data was originally processed using version 4 -UFORE (September 2008) which did not include stormwater benefits. As an initial step, the 2008 field plot data was ran through the latest version of i-Tree Eco (v5.1.7 as of November 2014) to quantify citywide ecosystem service values for the entire urban forest. The price of avoided stormwater runoff in i-Tree v.5.1.7 ($0.0089/gallon) was compared with MMSD’s cost of $.036092/gallon for water treatment at the plant. The value from i-Tree was used for consistency with other ecosystem services. Therefore, estimates based on the iTree stormwater runoff benefit valuations are conservative by approximately ¼ the value MMSD uses. The impact of trees on avoided stormwater runoff in gallons was also compared between i-Tree Eco and MMSD. A simulation in i-Tree Eco estimated 194 gallons (25.94 ft3 * 7.4805 gal/ft3) per 12” elm tree (1956 average) while MMSD has an estimated 169 to 449 gallons/year/tree (MMSD 2009), therefore i-Tree Eco results are on the low side of MMSD’s range. Preliminary investigation occurred to compare the number and intensity of CSO events with canopy cover changes. A lack of data records existed in print and electronic from or could readily be provided. Additionally, several variables can impact whether a rain event triggers a Me t h o d s

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CSO such as the area and level of ground saturation at the time of event, and this study focused largely on elms in the street ROW citywide whereas CSO events only pertain to a portion of the overall city’s footprint and beyond the ROW.

Air Quality Trees naturally remove pollutants and lower air temperature. The three main effects of urban trees that lead to improved air quality are: 1) Lower air temperatures resulting from shade and latent heat absorption, which reduces ozone formation and smog. 2) Air is “cleansed” through the direct removal of a variety of pollutants. 3) Indirectly, shade from trees reduces the amount of energy used for cooling, therefore limiting pollutants emitted from power plants. Some tree species emit biogenic or naturally occurring volatile organic compounds (VOCs) that can contribute to ozone formation. However, in most cases the positive effects of these trees result in an overall reduction in ozone. Trees can substantially lower O3 production by blocking sunlight and lowering temperatures on surfaces that emit NOx and VOCs (asphalt, fuel tanks, buildings, etc.) which contribute to the formation of ground-level ozone. i-Tree Eco models apply a value to trees for their air pollution removal capacity based on several variables including vegetation composition such as Leaf Area Index (LAI), climatic conditions, pollutant concentration, human population estimates, and avoided human health issues based on the Environmental Protection Agency’s (EPA) BenMAP (Benefits Mapping) Program Pollution removal value is calculated based on the following prices: $1,253 per metric ton for Carbon Monoxide (CO) $9,923 per metric ton for Ozone (O3) $1,356 per metric ton for Nitrogen Dioxide (NO2) $481 per metric ton for Sulfur Dioxide (SO2) $49,558 per metric ton for Particulate Matter (PM 10) less than 10 microns and greater than 2.5 microns $428,689 per metric ton for Particulate Matter (PM 2.5) less than 2.5 micron

Carbon Storage and Sequestration Atmospheric carbon (CO2) is sequestered through plant processes and stored in trees as biomass over time. The cumulative or lifetime value was measured using i-Tree Eco as total carbon storage and was calculated for historical elm populations in the public street ROW along with annual carbon sequestration. Default dollars values in i-Tree Eco can be edited as new research, social cost of carbon, or market prices becomes available. The monetary benefits of carbon used in this study are based on the following:

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i-Tree originally used values by Faunkauser (1994) and updated the values based on the 2010 U.S. Interagency Working Group. At the time of this study, carbon was valued in i-Tree Eco at $78 per metric ton. The 2013 Interagency Working Group recently on Social Cost of Carbon released new carbon values and the default value in i-Tree Eco will be changing soon after this report is finalized, therefore the new value of $139.33 per metric ton of carbon was used for this study (United States Government 2013). It is important to note that the price of elemental carbon was used for this study, not CO2.

Energy Savings Through shade and summer cooling as well as winter wind block, tree canopy reduces energy use which can be measured in kWh, BTUs, and dollar savings. Indirectly, trees reduce radiant heating from impervious surfaces such as asphalt, and provide evaporative cooling through their respiration processes. In addition, less energy use at the home means fewer carbon emissions produced at the power plant. Energy impacts reported by i-Tree Eco reflect these building-tree interactions based on work by McPherson and Simpson (2001). Energy use impacts are dependent on the relative proximity and direction of trees from buildings as well as their species and height. The “right tree in the right place” is particularly important for increasing energy benefits. In i-Tree Eco, elm trees in each mock cohort population were equally spaced in each of the four cardinal directions (north, west, east, and south) at an average distance from buildings of 30 feet. The following default prices in i-Tree Eco where used to calculate energy savings: Price of electricity = $0.1331/kWh ($133.1 per MWH) Price of heating = $1.391/Therm ($13.91 per MBTU) Given there was less air conditioning in use in homes in the 1960’s and 1970’s, the cooling values modeled in i-Tree Eco prior to 1980 were reduced by 50% to conservatively estimate lost benefit.

What is a Structural Value? Other Ecosystem Services A Structural Value is the worth based on the physical resource itself (e.g., the cost of having to replace a tree with a similar tree)

Urban trees provide many other ecosystem services that are often more difficult to quantify which were not included in this study. Examples include reduced crime rates, fewer sick days and decreased recovery from illnesses, increased property values, enhanced wildlife habitat, pavement longevity from shade, erosion control along streams, and increased retail spending (Miller et al. 2015). These values were not directly accounted for in i-Tree and are indirectly part of the structural value of trees.

Economic Analysis An economic analysis was used to identify the costs, benefits, net present value (NPV), and benefitcost ratio (B/C) associated with DED management intensity alternatives, the actual outcome with the Me t h o d s

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loss of elm trees, and a scenario in which DED did not occur in Milwaukee. These parameters were used to gauge which management alternatives were most to least favorable in a given context (Hauer et al. 2014). For example, the costs associated with an alternative are important for budget planning and if costs are the only decision factor, then the lowest cost alternative would be most favored. Likewise, if benefits (value) are the sole factor of interest, then the alternative providing the greatest value would be most favored. A NPV and B/C integrate both benefits and costs so management alternatives can be decided based on both of these economic parameters combined (Miller et al. 2015). A variety of approaches exist to examine the economics and financial effectiveness and efficiency used to evaluate management alternatives (Miller et al. 2015). Cost information was used that best represented Milwaukee Forestry Operations. A sensitivity analysis was used to identify how a change in one variable would change the decision making. Tree valuation used two approaches, a compensatory method and a functional and structural approach (Cullen 2005, Maco and McPherson 2003, Nowak 1993, McPherson et al. 1997, Nowak et al. 2002, Nowak 2008, Nowak et al. 2008). Using a variety of approaches provides a greater confidence with decision making, especially if the same conclusion occurs under different approaches. Methods used to identify costs and benefits are covered below. Costs Associated with Dutch Elm Disease Management Sinclair (1978) lists five cost categories associated with DED: (1) intrinsic value of the trees, (2) diminished real estate value associated with loss of trees, (3) costs of premature tree removal and replacement, (4) costs of control efforts, and (5) cost of research necessary to understand the disease. Forgone ecosystem services and benefits (e.g., stormwater, air quality, carbon uptake, and energy use) and social goods (e.g., aesthetics, commerce, human health, etc.) are additional costs resulting from the loss of elms from DED or a value resulting from retained elms through management (Miller et al. 2015). Understanding the total costs of DED management is important to determine how the economic benefits and costs compare and to ultimately evaluate the effectiveness and efficiency of various management options that range from No Control to Best Control (Cannon and Worley 1976, Cannon and Worley 1980, Baughman 1985, Hauer 2012, VanNatta and Hauer 2012). Cost data was adjusted for inflation to a common 1956 starting point. Results were also adjusted for inflation and reported in 2014 dollars unless otherwise noted. Adjustment for inflation used a mean composite of both the Consumer Price Index (CPI) and Primary Producer Index (PPI) using data from the Bureau of Labor Statistics (2014, 2015). A mean PPI value by year was calculated using monthly data for non-seasonally adjusted all commodities (Bureau of Labor Statistics 2015). CPI data were also tabulated as a mean value by year using monthly index values using data current through November 2014 (Bureau of Labor Statistics. 2014). Adjusted data was compared to available cost data within a time period to provide a relative comparison for validation of adjusted data (Table 4, 2014 Values). No concerns were found with values adjusted and compared to 2014 values. Costs associated with premature tree removal, DED control, and urban forest management (i.e., tree planting, tree pruning, and tree removal) used data from the City of Milwaukee when possible and regional data from the Midwest as needed. Table 3 (Elm Economic Variables and Assumptions) provides the starting value, year it was collected, and the data source. A $2.12 per diameter inch cost for tree removal in 1956 was based on a $5.66 value from 1980 (Kostichka and Cannon 1984, Schuman 1984). Tree maintenance costs were $0.33 per diameter inch adjusted from $0.88 in 1980 (Kostichka et al.1984, Schuman 1984). Tree maintenance was from pruning trees on a five year cycle. Dutch elm disease management on a per tree basis included survey ($0.14), sanitation ($0.01), and

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Table 3: Values for 1956, initial values used, and source to develop of an economic analysis to model Dutch elm disease management starting in 1956 in the City of Milwaukee using the Dutch Elm Disease PLANning Simulator.

1956 Value Used1

Initial Value (year)

Mean Size (Inches)

11.78

19.6 (1979)

Number of Trees

106,738

106,738 (1956)

Tree Growth Rate

Inches/Year

0.34

0.34 (1979 to 2005)

Maintenance Cost

$/Diameter Inch

0.33

0.88 (1980)

Removal Cost

$/Diameter Inch

2.12

5.66 (1980)

Survey Costs

$/Elm

0.14

0.43 (1980)

Sanitation Costs

$/Elm

0.01

0.04 (1980)

Combined Costs

$/Elm

2.14

6.00 (1980)

Planting Survival

Percent

90.0

90.0 (1950s to 1980s)

Natural Survival

Percent

99.1

No Control Survival

Percent

82.0

82.0 (1970’s)

Best Control Survival

Percent

99.0

99.0 (1970’s)

Good Control Survival

Percent

96.5

96.5 (1970’s)

Fair Control Survival

Percent

95.0

95.0 (1970’s)

Replacement Size

Inches

2.00

Replacement Cost

Dollars

18.99

50.00 (1979)

Installation Cost

Dollars

18.23

48.00 (1979)

Unit Tree Cost

$/sq. in.

6.05

Derived

Species

Percent

70.0

Condition

Percent

70.0

70.0 (1979)

Location

Percent

70.0

Interest Rate + 1

Percent

1.03

Replant Lost Trees?

Yes=1, No=0

Include Natural Survival

Yes=1, No=0

Variable Name Starting Diameter Starting Population

Unit

adjusted to 1956 from 1979 and 1980 data using change from a composite Consumer Price Index (CPI) and Primary Producer Index (PPI) for all Commodities (mean change both indexes). 2

The source of data and description of the data is contained in the Appendix (Table 10).

other combined ($2.14) costs (Kostichka et al. 1984). Survey intensity to identified diseased elm tree varies by control approach with Best = 4, Good = 3, Fair = 2, Actual = 1, and No Control = 0. Sanitation,

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which is a ground-based survey for elm wood piles, assumes that all control options do this once annually, except the No Control option. Additional costs to conduct all other management activities such as root graft-disruption, chemical treatment, etc. assumes that Best control is 100% of this value, Good control is 80%, Fair is 60%, Actual is 40%, and No Control is 0%. Note in the Kostichka et al. (1984) study they defined sanitation as a wood pile and abatement survey. Sanitation in this report is taken as the removal of all elm wood (standing dying elms and downed elm wood) before adult elm bark beetles emerge and this definition is consistent with the general use of the term sanitation.

Compensatory Value A compensatory tree value approach using the Council of Tree and Landscape Appraisers (CTLA) system was used to estimate the value of elm trees (CTLA 2000). A compensatory value reflects the overall contribution of the tree to the landscape (e.g., property values, environmental services, aesthetics, etc.) and is the value to compensate (replace) that associated with a tree. The CTLA system bases value on the size, species, condition, and location of a tree. A 1956 unit tree cost of $6.05 per in2 of stem area was derived from an $18.99 cost for a two inch caliper replacement tree following current CTLA guidelines (CTLA 2000). The unit tree cost is comparable to a fixed $5 value used during the 1950â&#x20AC;&#x2122;s time period (Cullen, 2005, Watson 2001, Watson 2002). A 70% species and 70% location were used and follow percentages consistent for the study area (Hasselkus undated, Simons et al. 2009, Hauer 2014). The 70% condition percentage determined by Schuman (1984) was used and assumes the elm population in 1956 was similar to the percentage derived from a 1979 tree inventory. A tree installation cost was set at $18.23 per tree (Miller and Schuman 1981). Table 4: Cost data used in 1956 and comparison of adjusted 2014 to actual data.

Variable Name

Unit

1956 Value ($)

2014 Value Inflation Adjusted ($)

2014 Comparison Municipal and Private Costs ($)

Maintenance Cost

$/Diameter Inch

0.33

2.49

2.07 to 2.54x

Removal Cost

$/Diameter Inch

2.12

16.22

15.30x

Survey Costs

$/Elm

0.14

1.10

Sanitation Costs

$/Elm

0.01

0.10

Combined Costs

$/Elm

2.14

16.35

Replacement Size

Inches

2.00

Replacement Cost

Dollars

18.99

145.34

130 to 146y

Installation Cost

Dollars

18.23

139.52

Unit Tree Cost

$/sq. in.

6.05

46.26

41.38 to 46.47

City of Milwaukee Forestry cost records from 2014.

Wholesale cost of trees similar to American elm from a commercial Milwaukee area nursery.

Tree maintenance and management activities were included in all cost analysis scenarios. These include tree planting, tree pruning, tree removal, DED management costs, and EAB treatment costs Me t h o d s

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(Table 4: 2014 Values). A validation of these 1956 cost data were done by adjusting for inflation to 2014 and compared to current market values. For example, maintenance costs per stem diameter inch from 1956 ($0.33) adjusted to 2014 ($2.49) were consistent with current City of Milwaukee maintenance costs ($2.07 to $2.54 per diameter inch). Also, the 2014 inflation adjusted value of $145.34 per two inch caliper tree is consistent with current market rates of $130 to $146 for a similar size and cost to produce replacement trees (hackberry, linden, and Freeman maple) comparable to American elm (Wolter 2015).

Net Present Value and Benefit Cost The NPV followed an approach from VanNatta et al. (2012). The cost of treatment for emerald ash borer used by VanNatta et al. (2012) was modified to account for management costs associated with a DED control program. Net present value for the remaining elm trees was calculated by subtracting all tree costs from the value of retained trees in each management alternative (Miller and Schuman 1981, Sherwood and Betters 1981, CTLA 2000, Miller et al. 2015). The following equation was used to calculate the net value of remaining and lost elm trees: đ?&#x2018;&#x203A;

đ?&#x2018;&#x2030;đ?&#x2018;&#x2026;đ?&#x2018;&#x2013; = â&#x152;Šâ&#x2C6;&#x2018; [ đ?&#x2018;Ą=1

đ??śđ?&#x2018;? đ?&#x2018;&#x2030;đ?&#x2018;? đ??śđ?&#x2018;&#x2018; đ??śđ?&#x2018;&#x161; đ??śđ?&#x2018;&#x; â&#x2C6;&#x2019; â&#x2C6;&#x2019; â&#x2C6;&#x2019; â&#x2C6;&#x2019; ]â&#x152;Ş (1 + đ?&#x2018;&#x2018;)đ?&#x2018;Ą (1 + đ?&#x2018;&#x2018;)đ?&#x2018;Ą (1 + đ?&#x2018;&#x2018;)đ?&#x2018;Ą (1 + đ?&#x2018;&#x2018;)đ?&#x2018;Ą (1 + đ?&#x2018;&#x2018;)đ?&#x2018;Ą

Where: VRi = net annual value remaining for management alternative i Vc = CTLA value Cd = DED management costs Cm = maintenance costs Cr = removal costs Cp = planting costs d = discount interest rate t = number of years in the future Annual values were discounted by a 3% interest rate in all cases. The discount rate was selected to approximate the long-term mean CPI and PPI composite during the project time period. The net present value is analogous to profit in a business in which the gross revenue - expenses = profit. Thus, a higher NPV of the urban forest is more desirable than a lower NPV. The DED-PLANSÂŠ simulator estimates elm populations that might exist in Milwaukee given management intensity (e.g., best, Good, fair, No Control). This planning tool also develops costs and benefits associated with potential and actual results in Milwaukee over a 40 year planning scenario. Two B/C approaches were used to evaluate management alternatives in DED-PLANS. One approach compares the benefits to costs within the alternative. For example, the mean present value (PV) of retained trees within an alternative (e.g., Best Control) was divided by the PV of all costs that occurred over the simulation time period within that same alternative. A second approach used that of Sherwood and Betters (1981) which contrasts the PV of an avoided loss divided by the PV of a control program (e.g., best, Good, or fair) used to reduce the loss of elms (CNC). The formula used follows:

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(đ?&#x2018;&#x192;đ?&#x2018;&#x2030;đ?&#x2018;&#x203A;đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;&#x192;đ?&#x2018;&#x2030;đ?&#x2018;?đ?&#x2018;&#x17D; ) [ ] đ?&#x2018;&#x192;đ?&#x2018;&#x2030;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x17D; where: đ?&#x2018;&#x192;đ?&#x2018;&#x2030;đ?&#x2018;&#x203A;đ?&#x2018;? = present value of lost trees for No Control alternative đ?&#x2018;&#x192;đ?&#x2018;&#x2030;đ?&#x2018;?đ?&#x2018;&#x17D; = present value of lost trees for a control alternative (e.g., best, Good, or fair) đ?&#x2018;&#x192;đ?&#x2018;&#x2030;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x17D; = present value of all DED management costs for a control alternative A B/C greater than one indicates for every dollar spent, the value retained was greater. A B/C and NPV was also conducted to assess composite functional and structure value of all management alternatives and the actual outcome using i-Tree Eco version 5.1.7. The B/C was derived by dividing the total management costs from DED-PLANSÂŠ by the i-Tree Eco composite value using 2014 values to generate the i-Tree Eco B/C (ECO). The NPV in 2014 using the i-Tree approach was derived by subtracting total management costs (e.g., DED control, tree planting, tree pruning, tree removal) from the i-Tree Eco composite value.

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Findings The sections below present key findings from this study, including historical canopy cover, elm populations over time, modeling of lost ecosystem benefits by year, per tree, and cumulatively, and a comprehensive economic benefit/cost analysis.

Historical Trends in Canopy Cover Results are presented below for historical canopy cover estimates in Milwaukee. These were derived from aerial imagery interpretation for six (6) time periods beginning with citywide results and then within the public street rights-of-way (ROW). Canopy estimates based on a remote sensing and GIS analysis of tree canopy from 2008 are also included. Citywide Canopy Results Based on the point sampling approach, Milwaukeeâ&#x20AC;&#x2122;s tree canopy has increased every year since 1956 with the exception of 1969 at the peak of DED and elm removals. Results yielded average citywide canopy of 8.7% (5,365 acres) in 1956 and 22.7% (14,066 acres) in 2013, a difference of 8,701 acres, or a 162% relative increase. Standard Error (SE) for the estimates was +/- 0.7 and 1.1%, respectively, well within the targeted sampling design. Note that for comparison, the 2008 i-Tree Eco study yielded 21.6% canopy vs. 20.5% from remote sensing.

Citywide Tree Canopy by Time Period 25.0%

20.5%

22.7%

2008

2013

20.0% 15.0% 10.0%

14.3%

14.6%

1979

1986

12.5% 10.0%

8.7%

5.0%

0.0% 1956

1963

1969

Figure 7: Percent tree canopy citywide by time period.

In the time period between 1956 and 1963, citywide tree canopy increased 8.7% to 12.5%, or a 44.5% relative gain. During the peak of DED elm removals, tree canopy declined dramatically between 1963 and 1969 by 1,566 acres, or -20.2%. In a ten year span from 1969 â&#x20AC;&#x201C; 1979, there was another increase in citywide canopy from 10% to 14.3% (2,661 acres), or a 43% gain. The estimated change from 19791986 was nominal however it is important to remember that the canopy estimate for each image period has +/- 1% SE. Between 2008 and 2013, Milwaukeeâ&#x20AC;&#x2122;s tree canopy has increased by 2.2%, or a 10.7% relative increase. Given ~1% standard errors in the random point sampling technique used for 2013 and the GIS-based canopy analysis from 2008, the percent canopy cover in the two time periods illustrates Findings

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signs of an expanding citywide tree canopy. Results also show that even in the face of EAB, Milwaukee’s canopy has not declined in recent years. See Table 5 for a complete summary of canopy cover estimates from 1956 to 2013. Figure 8 below illustrates historical gain in tree canopy as an example outside the public street ROW. Table 5: Summary of historical urban tree canopy results citywide from 1956 – 2013 by time period.

Year

Canopy Points (of 500)

Canopy Cover

S.E. (±)

1956 1963 1969 1979 1986 2008 2013

1,500 1,500 1,000 1,000 1,000 0 1,500

8.7% 12.5% 10.0% 14.3% 14.6% 20.5% 22.7%

0.7 0.9 0.9 1.1 1.1 N/A 1.1

C.I. (±)

Canopy Area (Acres)

Canopy Change by time period (Acres)

% Change from prior assessment year

1.4 1.8 1.8 2.2 2.2 N/A 2.2

5,365 7,754 6,188 8,849 9,035 12,663 14,066

N/A 2,389 -1,566 2,661 186 3,628 1,403

N/A 44.5% -20.2% 43.0% 2.1% 40.2% 11.1%

Figure 8: Historical and current aerial imagery example of citywide tree canopy increase.

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Rights-of-Way Canopy Results Aerial image interpretation was used to estimate tree cover within the cityâ&#x20AC;&#x2122;s rights-of-way (ROW) for the six time periods. The ROW is a distinct management area for City Forestry staff. Including a 30-foot buffer (15-feet on each side) to capture the full extent of canopy from street trees maintained by the City, the average canopy in the ROW has increased and decreased over the years from 19.2% in 1956 to 23.2% in 2013. This represents a difference of 568 acres or a 21% relative increase.

ROW Tree Canopy by Time Period 30.0% 25.0% 20.0%

25.7%

23.8%

23.2%

19.2% 16.0%

15.0%

14.2% 12.6%

10.0% 5.0% 0.0%

1956

1963

1969

1979

1986

2008

2013

Figure 9: Percent tree canopy in the Rights-of-Way (ROW) by time period.

Elm removals from DED were greatest from 1963 to 1979 with 87% of public elms removed in that time period. This corresponded to a relative reduction of nearly half of the ROW canopy cover. Adjacent residents to the ROW experienced a significant loss of canopy cover and benefits. From 1963 to 1969, the canopy loss was most striking in the ROW and totaled 1,109 acres (-32.8% loss), resulting in a canopy cover of 16%. By 1979, an additional 484 acres of ROW canopy was lost, resulting in just 12.6% average ROW canopy. The reduction from 23.8% in 1963 to 12.6% in 1979 equals a loss of 11.2% cover, however in relative terms this equals a staggering loss of 47% of all ROW canopy in just 16 years (1,792 acres in 1979 divided by 3,385 acres in 1963). The 2013 canopy cover recovered to the level that existed prior to peak DED losses in 1963. Thus, it took 50 years for the ROW canopy cover to recover from DED. As the City recovered from focusing on DED removals and took initiatives to replant, canopy in the ROW increased 1,636 acres (an 81% increase) in a 22-year span (1986 to 2008). This increase was likely initiated from tree planting that exceeded removals by more than 2 trees planted for every tree removed during the peak DED years. From 1963 to 1979 a total of 216,293 trees were planted for both elm replacement and new developing areas of the City outside of the AOI for this study. Within the ROW, the time period where the most tree cover existed was in 2008 (based on LiDAR remote sensing analysis) at 25.7% (3,655 acres). The most recent canopy cover estimate in 2013 was 23.2% (3,299 acres). The higher value in 2008 falls within the 3.7% confidence interval bounds from the 2013 canopy cover estimate.

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Table 6: Summary of urban tree canopy assessment results within the street ROW by time period.

Year

Canopy Points (of 500)

Canopy Cover

S.E. (±)

C.I. (±)

Canopy Area (Acres)

Proportion of All Canopy Citywide

Canopy Change (Acres)

% Change

1956 1963 1969 1979 1986 2008 2013

96 119 80 63 71 0 116

19.2% 23.8% 16.0% 12.6% 14.2% 25.7% 23.2%

1.8 1.9 1.6 1.5 1.6 N/A 1.9

3.5 3.7 3.1 2.9 3.1 N/A 3.7

2,731 3,385 2,275 1,792 2,019 3,655 3,299

51% 44% 37% 20% 22% 29% 23%

N/A 654 -1,109 -484 228 1,636 -356

N/A 24.0% -32.8% -21.3% 12.7% 81.0% -9.7%

Within the ROW, it is possible to see how the distribution of canopy has changed for each time period. Figure 10 illustrates that in 1956, tree canopy in the ROW had a significant influence on the overall citywide tree canopy cover at 51% whereas by 1979 after DED elm removals the proportion of ROW canopy to citywide canopy dropped to only 20%. This was calculated as 2,731 acres of ROW canopy / 5,365 of citywide canopy in 1956 (51%) vs. 1,792 acres of ROW canopy / 8,849 acres of citywide canopy in 1979 (20%). Since then, the proportion of tree canopy in the ROW to the rest of the City has increased but slowly. Approximately ¼ of the all canopy cover in the City today exists in the public ROW. By comparison, this is a 50% reduction from the study starting period in 1956. One likely reason is a function of increased canopy in non-ROW areas (i.e. private property), even though the absolute ROW canopy cover is greater today than in 1956. Additionally, the reduction in ROW canopy cover between 2008 and 2013 is influenced in part by an increasing number of aging DED replacement trees removed annually during this period.

Distribution of ROW Canopy to All Canopy 60% 51% 50%

44%

37%

40%

29%

30% 20%

23%

22%

20% 10% 0% 1956

1963

1969

1979

1986

2008

2013

Figure 10: Proportional percent of tree canopy in the ROW to all canopy citywide by time period.

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The images below illustrate historical gains (Figure 11) and losses (Figure 12) in tree canopy as examples inside the public street ROW.

Figure 11: Aerial photos showing canopy gain in the ROW, near North Sherman Blvd. and West Florist Avenue, northwestern Milwaukee.

Figure 12: Aerial photos showing typical elm canopy loss in the ROW.

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Historical Elm Populations A simulation of the loss of elms under a variety of DED management scenarios was compared to the actual outcome. These scenarios were also a foundation for the modeling of the economic costs and benefits. In addition, they were used to estimate potential ecological services associated with the Milwaukee street tree elm population. Simulation of Elm Populations in Milwaukee Starting in 1956, the street tree elm population was recorded to have 106,738 trees (City of Milwaukee removal data). An actual loss of elms from DED was low during the initial years with six trees dying in 1956 from the disease. In 1957 a total of 36 trees died from DED followed by 203 in 1958 and 228 in 1959. By 1962 to 1964, elm losses were near 2,000 trees annually. This rate of death increased to over 10,000 trees annually dying between 1966 and 1969, peaking at 16,580 elms removed in 1968 (Figure 13). By 1969 the rate of annual loss started declining as over 60% of the elm population was removed by then. The annual loss of elm trees relative to the remaining population, however, was consistently at rates of approximately 10 to 20 percent between 1966 and 1982 (Figure 13). Interestingly, after the DED Federal Demonstration Project occurred between 1979 and 1982, the annual loss averaged 4.5% (WI DNR 1980, Groth et al. 1982, French 1993). Prior to that, between 1966 and 1982 the annual elm loss averaged 14.8%.

Figure 13: Loss of elms in Milwaukee WI and right of way canopy cover. Note that actual ROW canopy cover percent by time period is shown in red and is not associated with either y-axis.

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The loss of elms trees varied between four simulated sanitation practices and the actual recorded public elm population (Figure 14: Elm Loss Comparison). Without DED an estimated 74,347 elms would remain in 1996 assuming a 0.9% annual mortality (Miller and Schuman 1981). Over the 40 year simulation period, best sanitation had 52,934 remaining elms compared to 92 trees under No Control and 3,348 elms recorded as actually left at the start of the 1996 growing season. Thus, Best Control results in approximately 16 times more elm trees remaining than the actual population. More elms would also exist under fair (13,142 elms) and Good (22,307 elms) control with 3 to 6 times more elms than the actual population. Today less than 1,000 elms from the population in 1956 remain. It is likely that early records (e.g., initial 5 to 7 years following recorded DED) had only elms dying from DED tallied as lost trees. The loss tallies did not include naturally expected mortality from a variety of causes (e.g., construction, insects, disease, lighting strikes, and moisture stress). It would be expected that approximately 900 to 1,000 elms would die annually in the 1956 population based on work by Miller and Schuman (1981) that estimate a 0.9% annual natural mortality. Thus, the actual loss of elms is likely underestimated in the initial years and DED cases only recorded in the actual loss records.

Figure 14: Elm loss comparison under various Dutch elm disease management scenarios. (Elm population in Milwaukee over a 40 year period comparing the actual outcome and four management approaches and anticipated percentage annual loss. Simulated losses adapted from Cannon and Worley (1976) with a starting population 106,738 elm trees).

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Effect of DED Management on Elm Tree Canopy in the Street Rights-of-Way in Milwaukee A reduction in elm street tree canopy cover occurred initially in the 1960â&#x20AC;&#x2122;s from a peak of 23.8% in 1963 to a low of 12.6% in 1979 (Figure 15). In 1956 elm trees in the ROW comprised 44% of the total 19.2% ROW UTC by all tree species. In 1963 elms were contributing a relatively similar 45% of the total 23.8% ROW UTC. The relative elm UTC started to decline and was 34% in 1969, 12% in 1979, and 7% by 1986. Active DED management would have slowed the loss of elm canopy under Fair Control, conserved canopy cover levels under Good Control, or even resulted in a gain in elm canopy under Best Control (Figure 14). Thus, if the knowledge to apply active DED management was available and started in 1956, elm canopy loss could have been stabilized or even grown and bought time for other trees to be planted and become the future replacement UTC. Rather, the outcome was most of the elm UTC was lost within two decades and it took another three decades to recover from the UTC loss.

Figure 15: Comparison between Actual loss in canopy and the potential effects of Active DED management on UTC.

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Ecosystem Services and Benefits Results are presented below based on the simulated elm populations in Milwaukee’s street ROW applied through the i-Tree Eco model for each historical imagery time period and interpolated between time periods. All “foregone” (lost) monetary benefits are in present (2014) dollar values on an annual basis unless otherwise stated. Results are presented in four sections: Annual benefit values by ecosystem service for individual elms as DBH increases over time Annual benefit values summarized for each cohort elm population by ecosystem service and by sanitation levels Annual benefit values for Actual vs. Best management (sanitation) levels by ecosystem service Cumulative lost benefits due to DED elm removals for the Best management (sanitation) level for each ecosystem service. Annual Ecosystem Benefits for Individual Elm Trees Annual ecosystem benefits from i-Tree Eco were first analyzed on a per tree basis for the following ecosystem services: air pollution mitigation, carbon sequestration/storage, stormwater runoff, and energy savings. Monetary per tree benefit increased from $15.80 per year based on 11.78” average DBH in 1956 to $87.94 per year in 2013 at 31.16” average DBH. Figure 16 presents benefits for each ecosystem service, then summarized per tree (Total Eco), followed by the structural (compensatory) value. Per Tree Values (Elms) Year 1956 1963 1969 1979 1986 1996 2008 2013

Avg. Elm DBH 11.78 14.16 16.20 19.60 21.98 25.38 29.46 31.16

CO $0.01 $0.01 $0.01 $0.01 $0.02 $0.02 $0.03 $0.03

PM10 $4.99 $6.73 $8.70 $10.23 $10.98 $14.70 $19.54 $21.73

NO2 $0.06 $0.08 $0.10 $0.12 $0.14 $0.19 $0.25 $0.27

O3 $1.86 $2.51 $3.27 $4.12 $4.81 $6.32 $8.29 $9.22

SO2 $0.01 $0.01 $0.02 $0.02 $0.02 $0.03 $0.04 $0.05

PM2.5 $4.97 $6.69 $8.71 $10.12 $10.67 $14.35 $19.13 $21.27

Energy CO2 Seq. Runoff $0.66 $1.52 $1.73 $8.97 $1.73 $2.33 $8.15 $2.08 $2.98 $6.92 $2.70 $3.64 $7.83 $3.17 $4.09 $13.79 $4.01 $5.43 $21.53 $5.09 $7.16 $21.22 $6.20 $7.96

Total Eco $15.80 $29.05 $34.01 $37.90 $41.74 $58.83 $81.06 $87.94

Structural Value $1,102.00 $1,571.00 $2,028.00 $2,944.00 $3,694.00 $4,957.04 $6,599.00 $7,227.00

Figure 16: Per tree ecosystem benefits based on average elm DBH for each time period.

Stormwater benefit results increased over time as the average DBH grew. For example, elms with 11.78” average DBH in 1956 provided $1.73/tree/year (194 gal.), while this value increases to $7.96/tree/year at 31.16” average DBH (894 gal.). The DBH increase during this time period is 2.6 times while the stormwater dollar value benefit is 4.6 times, where with other ecosystem services the dollar benefit increases approximately 4 times. This is largely a function of increased Leaf Area Index (LAI) as well as stem flow and infiltration. Overall, however, stormwater results from i-Tree Eco on a per tree basis represent approximately 11% of total ecosystem value in 1956 and 9% in 2013. Stormwater dollar values should be considered conservative based on valuations from i-Tree Eco using $.0089/gallon whereas MMSD uses $.036092/gallon treatment cost, or approximately four times the value per unit of runoff benefit.

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Benefits for air pollution removal, carbon sequestration, and structural value resulted in linear increases over time as DBH increases. Air pollution removal represents 75% of total benefit per elm tree in 1956 and 60% in 2013 due to greater increases in the annual benefits coming from energy savings. The resulting energy values were more inconsistent than expected, even while keeping the parameters of distance and direction from buildings equal across time periods in the i-Tree Eco model. The overall trend was as expected, with energy benefits increasing from $0.66/year to $21.22/year, however a large increase between 1956 and 1963 could not be explained or the slight reduction in benefits between 1969 and 1986. By 2013, energy benefits represent 24% of total ecosystem benefit per elm tree, compared to just 4% in 1956. Note that the cooling component of energy benefits prior to the 1980 was reduced by 50% to account for fewer homes with air conditioning.

Figure 17: Historical photo of a typical Milwaukee street lined with elm canopy prior to DED loss.

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Annual Ecosystem Benefits for Cohort Elm Populations by Sanitation Level Figure 18 below presents annual ecosystem benefit results for each cohort elm population (i.e. each imagery assessment year) based on sanitation levels. In all cases, the values for 1956 are the same because they are prior to DED elm removals, and Structural Value is not included in Total Eco Value. As one example, the difference in 1986 between Actual and Best sanitation for stormwater runoff is 3.6M gallons ($240k) that year. Note that the 1956 stormwater savings of $184k/year is 25% of the $747k stormwater value modeled using MMSD's treatment cost of $.036092/gallon, therefore this estimate in particular is conservative.

No Control

Actual

Year 1956 1963 1969 1979 1986 1996

Fair

Year 1956 1963 1969 1979 1986 1996

Good

Year 1956 1963 1969 1979 1986 1996

Best

Year 1956 1963 1969 1979 1986 1996