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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 – 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 – 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 1950’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 1950’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

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

Me t h o d s

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 (R² = 0.82, n=200). 3

4

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

Tree height (y) estimated from diameter (x): y = 9.4079x0.5078 (R² = 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-PLANSŠ) 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-PLANSŠ) 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 “tipping pointâ€? (Cannon and Worley 1976, 1980). Prior to this 12 year tipping point, a logistics function was used to model the increasing mortality rate: (đ?‘… − đ?‘… )

1 − [1+ đ?‘’đ?‘›(6− đ?‘Œđ?‘’đ?‘›) ] + đ?‘ đ?‘Ž 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 Milwaukee’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 Milwaukee’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.) – 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

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

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

70.0

Condition

Percent

70.0

70.0 (1979)

Location

Percent

70.0

70.0

Interest Rate + 1

Percent

1.03

1.03

Replant Lost Trees?

Yes=1, No=0

1

1

Include Natural Survival

Yes=1, No=0

1

1

Variable Name Starting Diameter Starting Population

Unit

1

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 1950’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

2.00

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

x

City of Milwaukee Forestry cost records from 2014.

y

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?‘›

đ?‘‰đ?‘…đ?‘– = ⌊∑ [ đ?‘Ą=1

đ??śđ?‘? đ?‘‰đ?‘? đ??śđ?‘‘ đ??śđ?‘š đ??śđ?‘&#x; − − − − ]âŒŞ (1 + đ?‘‘)đ?‘Ą (1 + đ?‘‘)đ?‘Ą (1 + đ?‘‘)đ?‘Ą (1 + đ?‘‘)đ?‘Ą (1 + đ?‘‘)đ?‘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-PLANSŠ 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?‘ƒđ?‘‰đ?‘›đ?‘? − đ?‘ƒđ?‘‰đ?‘?đ?‘Ž ) [ ] đ?‘ƒđ?‘‰đ?‘šđ?‘?đ?‘Ž where: đ?‘ƒđ?‘‰đ?‘›đ?‘? = present value of lost trees for No Control alternative đ?‘ƒđ?‘‰đ?‘?đ?‘Ž = present value of lost trees for a control alternative (e.g., best, Good, or fair) đ?‘ƒđ?‘‰đ?‘šđ?‘?đ?‘Ž = 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-PLANSŠ 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, Milwaukee’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 – 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, Milwaukee’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 city’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 1960’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

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

Avg. Elm Elm Stormwater Stormwater DBH Population (ga) Value 11.78 14.16 16.2 19.6 21.98 25.38

106,738 102,355 50,634 9,181 4,994 3,318

2.77 M 3.58 M 2.27 M .5 M .31 M --

$.18 M $.24 M $.15 M $.03 M $.02 M $.02 M

Avg. Elm Elm Stormwater Stormwater DBH Population (ga) Value 11.78 14.16 16.2 19.6 21.98 25.38

106,738 83,623 26,311 3,240 748 92

2.77 M 2.92 M 1.18 M .18 M .05 M --

$.18 M $.19 M $.08 M $.01 M $3,059 $499

Avg. Elm Elm Stormwater Stormwater DBH Population (ga) Value 11.78 14.16 16.2 19.6 21.98 25.38

106,738 95,400 67,875 36,952 24,142 13,142

2.77 M 3.33 M 3.04 M 2.02 M 1.48 M --

$.18 M $.22 M $.2 M $.13 M $.1 M $.07 M

Avg. Elm Elm Stormwater Stormwater DBH Population (ga) Value 11.78 14.16 16.2 19.6 21.98 25.38

106,738 96,822 75,172 47,935 34,983 22,307

2.77 M 3.38 M 3.37 M 2.62 M 2.15 M --

$.18 M $.23 M $.22 M $.17 M $.14 M $.12 M

Avg. Elm Elm Stormwater Stormwater DBH Population (ga) Value 11.78 14.16 16.2 19.6 21.98 25.38

106,738 99,222 88,852 73,344 64,128 52,934

2.77 M 3.47 M 3.98 M 4.01 M 3.94 M --

$.18 M $.23 M $.27 M $.27 M $.26 M $.29 M

Structural Value $117.63 M $160.8 M $102.69 M $27.03 M $18.45 M $16.45 M Structural Value $117.63 M $131.37 M $53.36 M $9.54 M $2.76 M $.46 M Structural Value $117.63 M $149.87 M $137.65 M $108.79 M $89.18 M $65.15 M Structural Value $117.63 M $152.11 M $152.45 M $141.12 M $129.23 M $110.58 M Structural Value $117.63 M $155.88 M $180.19 M $215.92 M $236.89 M $262.4 M

Air Pollution Carbon Removal Storage $ $1.27 M $1.64 M $1.05 M $.23 M $.13 M $.12 M

$2.3 M $3.44 M $2.34 M $.67 M $.48 M $.47 M

Air Pollution Carbon Removal Storage $ $1.27 M $1.34 M $.55 M $.08 M $.02 M $3,277

$2.3 M $2.81 M $1.21 M $.24 M $.07 M $.01 M

Air Pollution Carbon Removal Storage $ $1.27 M $1.53 M $1.41 M $.91 M $.64 M $.47 M

$2.3 M $3.21 M $3.13 M $2.7 M $2.32 M $1.85 M

Air Pollution Carbon Removal Storage $ $1.27 M $1.55 M $1.56 M $1.18 M $.93 M $.79 M

$2.3 M $3.26 M $3.47 M $3.5 M $3.36 M $3.14 M

Air Pollution Carbon Removal Storage $ $1.27 M $1.59 M $1.85 M $1.81 M $1.71 M $1.89 M

$2.3 M $3.34 M $4.1 M $5.36 M $6.16 M $7.45 M

Carbon Storage (metri c tons )

36.43 M 54.47 M 36.96 M 10.62 M 7.6 M 7.39 M Carbon Storage (metri c tons )

36.43 M 44.5 M 19.21 M 3.75 M 1.14 M .2 M Carbon Storage (metri c tons )

36.43 M 50.77 M 49.55 M 42.74 M 36.72 M 29.27 M Carbon Storage (metri c tons )

36.43 M 51.53 M 54.88 M 55.44 M 53.21 M 49.68 M Carbon Storage (metri c tons )

36.43 M 52.81 M 64.86 M 84.82 M 97.54 M 117.88 M

Carbon Seques.

Energy Savings

Total Eco Value

.16 M .18 M .11 M .02 M .02 M .01 M

$.16 M $1.05 M $.49 M $.08 M $.04 M $.05 M

$1.61 M $2.93 M $1.69 M $.34 M $.19 M $.18 M

Carbon Seques.

Energy Savings

Total Eco Value

.16 M .14 M .05 M 8,761 2,373 372

$.16 M $.86 M $.25 M $.03 M $5,851 $1,268

$1.61 M $2.39 M $.88 M $.12 M $.03 M $5,044

Carbon Seques.

Energy Savings

Total Eco Value

.16 M .16 M .14 M .1 M .08 M .05 M

$.16 M $.98 M $.65 M $.32 M $.19 M $.18 M

$1.61 M $2.73 M $2.27 M $1.36 M $.93 M $.72 M

Carbon Seques.

Energy Savings

Total Eco Value

.16 M .17 M .16 M .13 M .11 M .09 M

$.16 M $.99 M $.72 M $.41 M $.27 M $.31 M

$1.61 M $2.77 M $2.51 M $1.77 M $1.35 M $1.22 M

Carbon Seques.

Energy Savings

Total Eco Value

.16 M .17 M .18 M .2 M .2 M .21 M

$.16 M $1.02 M $.85 M $.63 M $.5 M $.73 M

$1.61 M $2.84 M $2.97 M $2.71 M $2.47 M $2.9 M

Figure 18: Ecosystem benefits for cohort elm populations and sanitation levels.

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Annual Ecosystem Benefits for Actual vs. Best Sanitation Levels The annual loss of ecosystem benefits from DED elm removals is presented by comparing the extrapolated estimates of the elm populations in 2013 under the Actual vs. Best sanitation levels, or 960 elms vs. 35,505 elms, respectively. The figures in the following sections compare and present resulting benefit values under these two sanitation scenarios for each ecosystem service (stormwater runoff, air pollution mitigation, energy savings, and carbon storage).

Figure 19: Historical photo of a Milwaukee street in the 1960’s post-DED that was previously lined with elm canopy.

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Stormwater runoff benefits from elms under the Best sanitation (management) scenario would have averaged $266,000 per year over the study period vs. just $74,000 per year under the Actual sanitation scenario. Annual stormwater benefits would have increased from less than $200,000/year in 1956 to nearly $300,000/year in 2013 under Best sanitation. See Figure 20 below.

Figure 20: Stormwater runoff benefits for Actual vs. Best sanitation levels.

Air pollution mitigation benefits from elms under the Best sanitation (management) scenario would have averaged $1.8M per year over the study period vs. just $505,000 per year under the Actual sanitation scenario. See Figure 21 below.

Figure 21: Air quality and public health benefits for Actual vs. best sanitation levels.

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Energy savings from the shade of elm canopy along streets would have ranged from $.5M to $900,000 per year under the Best sanitation scenario, compared to virtually no energy benefits per year by the late 1970’s under Actual conditions. See Figure 22 below.

Figure 22: Energy conservation benefits for Actual vs. Best sanitation levels.

Carbon storage from the annual carbon sequestration of elms would have averaged roughly $200,000 per year under the Best sanitation scenario, compared to virtually no carbon sequestration benefit under Action conditions. See Figure 23 below.

Figure 23: Carbon storage benefits for Actual vs. Best sanitation levels.

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Structural values are not annual benefits. They represent the value of the tree population as an asset up to each year, meaning they are cumulative up to that point in time. Therefore, the delta in structural value between elm populations for Actual and Best sanitation is seen in Figure 24, or $249.7M.

Figure 24: Delta in 2013 structural value of elm ROW trees between Actual and Best sanitation levels.

By totaling the values of four ecosystem services per year for each sanitation scenario, it is clear how choices in management approach play out. By 2013, the remaining elm population for Best sanitation provides more than twice the monetary benefit annually than that of the Good sanitation scenario.

Figure 25: Total annual ecosystem benefits by sanitation alternative

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Cumulative Loss in Ecosystem Services By summarizing the benefits of remaining elms under the Actual sanitation scenario (n = 960 in 2013) and elms under the Best sanitation scenario (n = 35,505) from 1956 to 2013, the net difference in lost ecosystem benefits was quantified. Results for four ecosystem services are as follows: $11.2M in lost stormwater management benefits (* see Figure 26 below) $74.0M in lost air pollution removal benefits $27.3M in lost energy savings benefits $8.3M in lost carbon sequestration/storage benefits For a total of $120.8M in lost ecosystem benefits The cumulative foregone benefits over the study period are graphed below by ecosystem service type for the Best sanitation scenario (Figure 26).

Figure 26: Comparing the cumulative loss of ecosystem service benefits in dollars.

The above valuations are conservative in that they do not include many other direct or indirect ecosystem or societal benefits such as loss in property values, habitat, and structural value. Figure 27 compares the cumulative, lost stormwater management benefits for each source of valuation. Finally, Milwaukee’s forestry staff focused largely on elm removals for a decade, foregoing needed maintenance to existing trees such as cycle pruning, structural training of newly planted trees, and reforestation and afforestation of post WWII neighborhoods, the effects of which have not been accounted for in the benefit loss analysis. Figure 27: Comparing the cumulative loss of stormwater management benefits from i-Tree Eco to MMSD.

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Economic Analysis The economic analysis of DED included all common costs for managing the municipal tree population in Milwaukee. The value of the urban tree resource used two separate approaches to account for the functional value (i-Tree Eco) and compensatory value (worth of a tree based on size, condition, location, and species). Both costs and values are reported and provide important values to make management decisions. For example, the costs are used to manage the asset value of the tree population and to develop a budget. A net present value (NPV) is an important method to account for the total tree value – all costs. A negative NPV means more is being spent than the asset is worth. A positive NPV reports the level of value in excess of that being spent to maintain the asset. A benefit/cost analysis is a metric based on the sum of benefits of the tree population / costs to maintain the tree population. A B/C value reported as 0.5 means for every dollar spent on managing the tree population, 50 cents is returned. A B/C of 3.0 says 3 dollars is returned for every dollar invested. Dutch elm disease reduced the NPV and B/C of the Milwaukee elm street tree population. This finding occurred with both DED-PLANS and i-Tree Eco analyses. The actual outcome in Milwaukee was slightly better than No Control. If Milwaukee was able to integrate effective DED control approaches, both the DED-PLANS and i-Tree Eco NPV and B/C findings suggest control is better than No Control. Specific findings for the DED-PLANS (compensatory value) and i-Tree Eco (structural and functional value) follow. DED-PLANS The Milwaukee elm population was an asset that could potentially provide a NPV of $202 million over the 40 year time period (19561996) in the absence of DED (Table 7 below). Annually, the elm tree population had the potential to provide a mean $5 million NPV and a 1.93 B/C to Milwaukee citizens (Figures 30 and 31). With DED, the actual outcome realized was a $80 million NPV. The net result was a $122 million NPV reduction compared to a no DED situation. A lower 0.79 B/C also resulted as a consequence from DED. The actual outcome was somewhat better than a No Control situation and a potential $57 million NPV and 0.73 B/C.

Figure 28: Retained Net Present Value of public elms by DED sanitation option from DED-PLANS.

The ability to address DED through a control program was better than No Control or the actual outcome. Best Control gave the highest $175 million NPV which was $27 million less than a no DED scenario. Best Control also had favorable B/C’s with a 1.22 B/C. Fair ($112 million NPV) and Good ($130 million NPV) were also better than No Control, however both were lower than the Best Control alternative. The B/C was near 1.0 for both Fair Control (1.01) and Good Control (1.06). An alternative approach to calculate the B/C of DED management uses a comparison of a management outcome to No Control. The best management approach gave a 2.53 B/C compared to No Control. A Good Control program (1.36 B/C) would be better than No Control. A Fair Control (0.96 B/C) was slightly less than No Control.

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i-Tree Eco Similar to the DED-PLANS results, i-Tree Eco analysis provides a way to assess how DED reduced the value of the elm tree population. Without DED, a $363 million i-Tree Eco value from functional ($123 million) and structural ($239 million) benefits was possible during the 40 year simulated time period. Accounting for the $106 million management cost for the population gave a net $257 million benefit and a 3.42 B/C. A stark contrast occurred from DED with Figure 29: Retained Net Present Value of public elms the actual net benefit ($7 million) and B/C (1.07) by DED sanitation option from i-Tree Eco. compared to a no DED event. The actual outcome was still better than a No Control net benefit (-$7 million) and B/C (0.91) over a 40 year time period. Consistent with the DED-PLANS findings, a control program was better than No Control and the actual results. Best Control had a total $307 million value over 40 years, a $160 million NPV, and 2.10 B/C. 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. The take home message from both methods (DED-PLANS and i-Tree Eco) is that preventing a pathogen such as DED from causing complete removal & replacement is worth hundreds of millions of dollars over the lifespan of a tree population the size of public elms in Milwaukee in the 1950’s and 1960’s. If a pest arrives, active management is better than no management. With DED, even though it costs money to manage, the cost of inaction is greater by over a $100 million reduction in NPV. This finding is also true today in that active management of EAB is better than no management. Table 7: Comparing value in millions of dollars (2014 value) of Milwaukee elm street tree population by management alternative.

Analysis Area

Best Actual Control Outcome (1.0%)x

Good Control (3.5%)x

Fair Control (5.0%)x

No Control (18%)x

No DED

Total i-Tree Eco Functional Value

$42.7

$105.8

$75.3

$63.4

$28.4

$123.5

Mean i-Tree Eco Structural Value

$69.5

$201.6

$137.4

$112.8

$44.9

$239.5

Total i-Tree Eco Value

$112.2

$307.4

$212.8

$176.3

$73.3

$362.9

Total Management Cost

$104.7

$146.5

$126.1

$114.0

$80.7

$106.1

Net Benefit i-Tree Eco

$7.5

$160.9

$86.7

$62.2

-$7.4

$256.9

Net Benefit DED-PLANS

$80.2

$175.1

$130.2

$112.3

$56.6

$201.9

Benefit/Cost i-Tree Eco

1.07

2.10

1.69

1.55

0.91

3.42

Benefit/Cost DED-Plans

0.79

1.22

1.06

1.01

0.73

1.93

x

Percentage annual loss of elms trees under a control program alternative.

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$250

2.5 Net Present Value 1.93

$150

2.0

1.5 1.22 1.01

$100

0.73

1.06 1.0

0.79

$50

Benefit/Cost

Net Present Value (Millions of Dollars)

Benefit/Cost $200

0.5

$0

0.0 No Control

Actual Outcome

Fair Control

Good Control

Best Control

No DED

Figure 30: Net present value and benefit to cost ratio from DED-PLANS over 40 years for elm street trees.

Management Costs The cost of managing elm tree populations varied among the various DED management alternatives (Figure 31: Overall Costs). Total costs include tree planting, tree pruning, tree removal, and DED control costs. The least expensive alternative was No Control costing $80.7 million total over a 40 year simulation. Even though this was the least costly, it also provided the least net urban forest value since few elms (92) remained after 40 years. In addition, elm trees were smaller diameter (mean 13.9 inch DBH) since they were removed sooner than the control options (best = 17.7 mean dbh and no DED = mean 18.2 dbh). No Control had the highest tree removal ($25 million) and tree planting ($19 million) costs. This alternative also resulted in rapidly increasing management costs (Figure 33: Total Management Costs). As an example, the tree removal cost at the peak became 6 times higher than Best Control and over 2 times greater than Good Control (Figure 32: Removal Costs). In contrast, the control alternatives all avoid the budget spikes associated with No Control and the actual outcome in Milwaukee. An active management program to control DED would help to budget a more consistent cost over time. Best Control was the most expensive alternative with a $146.5 million 40 year total cost. Nearly 65% of this additional cost was from DED management that cost $42.6 million. Nearly 60% of the DED management cost is covered by $25 million in reduced tree planting and tree removal costs compared to No Control. Tree pruning ($85 million) was the highest cost for the Best Control alternative. Only a no Findings

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DED scenario had a higher pruning cost ($95 million). The reason for higher pruning costs with Best Control and no DED was these scenarios having more and larger trees. Finally, even though Best Control was the most costly option, the retention of benefits (presented above) far outweighs the costs. One unaccounted for cost of removing and replanting many trees in a short time period is a potential reduction of tree maintenance. The economic analysis reports maintenance costs over various management scenarios. A very important point is reduced pruning impacts in the actual outcome compared to best management. A lack of attention to structural and cycle pruning (exiting and new trees) during the heavy elm removal years has created tree structural problems in many species that contribute to storm damage, reduced life span, and overall poorer condition that is a legacy that remains today. So while tree canopy may have recovered over a 40 year period, the structural defects will plague the replacement trees for their life span. The current response to EAB should consider this unaccounted for cost of mass tree loss that the City’s additive treatment approach to EAB is avoiding and to retain important core services of the tree population.

$160,000,000 DED Management Cost $140,000,000 $120,000,000

Removal Cost Pruning Cost

$42.6M

Management Cost

Planting Cost

$27.8M $18.6M

$100,000,000 $10.0M

$80,000,000

$0

$15.9M

$8.9M

$0 $5.4M

$14.1M

$18.1M

$19.3M

$60,000,000

$40,000,000

$37.0M

$48.9M

$61.0M

$68.1M

$18.3M

$16.1M

$85.2M

$94.6M

$10.0M

$6.1M

Best

No DED

$20,000,000 $24.5M

$21.5M

$0

No Control

Actual Outcome

Fair

Good

Figure 31: Comparing costs for management alternatives from DED-PLANS over 40 years for elm street trees.

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10,000,000

No Control Good

9,000,000

Actual Fair

Best No DED

8,000,000

Cost ($)

7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994

0 Year

Figure 33: Cost to remove elm trees under a variety of management alternatives.

3,500,000 No Control Good

3,000,000

Actual Fair

Best No DED

Cost ($)

2,500,000 2,000,000

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

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

1960

1958

1956

0

Year Figure 32: Total cost to manage elm trees under a variety of management alternatives.

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Emerald Ash Borer Analysis The Milwaukee Ash Tree Resource and Emerald Ash Borer The City of Milwaukee is confronted with the prospect of losing 16.2% of their street tree population to emerald ash borer. A 2014 inventory includes 194,544 total street trees with 6,408 potential planting locations and 31,567 ash trees (Table 8). Most are green ash (Fraxinus pennsylvanica) comprising 27,369 (14.1% of total trees), with 3,990 (2.1%) white ash (Fraxinus americana), and 208 (1.1%) other ash species. A 2008 urban forest ecosystem analysis in Milwaukee found ash as a common species across all lands (i-tree 2008). This ash tree population has an estimated $221 million structural value (Krouse, 2010; Sivyer 2010). A total 573,000 ash trees comprising 17.4% of all public and private trees and 13.7% of all leaf area was reported. To further put this in perspective, this is 11% of the entire estimated 5.2 million urban ash tree population occurring in Wisconsin (WiDNR undated).

Table 8: The number of ash street trees in Milwaukee, WI from a 2014 street tree inventory.

Green Ash

27,369

% of Total Population 14.1

White Ash

3,990

2.1

European Ash

62

0.0

Ash Spp.

146

0.1

Total Ash

31,567

16.2

Species

# of Trees

Other Tree Species 162,977 83.8 Emerald ash borer (EAB) was initially discovered in Total Street Trees 194,544 100.0 Wisconsin in 2008 near Saukville and Newburg which are approximately 30 miles north of Milwaukee (DATCAP 2015). In Milwaukee County EAB was found in 2009, approximately 10 to 15 miles south of Milwaukee in Oak Creek and Franklin (2009). The first confirmed EAB case in Milwaukee occurred in July 2012. By 2015 the mortality of ash trees by EAB is noticeable near locations of initial infestations and near La Crosse, Racine and Kenosha. Management Approaches Several integrated approaches have been developed over the past decade to manage ash tree populations (Herms and McCullough 2014, McCullough et al. 2015). These involve No Control, Preemptive Removal, and Treatment (VanNatta et al. 2012, VanNatta and Hauer 2012). A No Control approach, also referred to in the literature as a do nothing approach, takes a wait and see approach. Ash trees are left to die and dead trees are removed to prevent human injury or property damage. Preemptive Removal assumes ash trees will die anyway in the near future, so a planned removal of a proportion of trees annually is taken prior to a mass mortality caused by EAB. Treatment is an active approach with insecticides lethal to EAB used to prevent tree death. The protocols and effectiveness of treatments are well established (Herms et al. 2014). The first important point is that regardless of which EAB management strategy is taken, they will all cost money. The second important point is if the urban forest is being managed for benefits (social, environmental, and economic), then this important aspect needs to be considered in an analysis. No Control will result in most ash trees dying within 10 to 12 years of EAB entering a location (Knight et al 2013). This approach results in peak years of removal and replanting costs. A more rapid Preemptive Removal approach also costs money for removal and replanting. With this approach a community spends money sooner which has an additional cost when money is discounted for inflation. An active management approach to treat trees costs money to maintain the ash tree resource into a time in the future that tree removal will occur due to natural attrition or another reason warranting tree removal in

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an urban environment. This approach stabilizes the cost to manage EAB over the longest time period Table 9: Values used to evaluate the economic outcomes of four emerald ash borer management options.

Unit

Initial Value

Source

Starting Diameter

Mean Size (Inch)

17.90

1

Starting Population

Number of Trees

31,421

1

Preemptive Removal

Number of Years

5

User

Tree Growth Rate

Inches/Year

0.50

1

Maintenance Cost

$/Diameter Inch

2.03

2

Removal Cost

$/Diameter Inch

25.68

2

Treatment Cost

$/Diameter Inch

3.75

2

Treatment (Tx) Interval

Years Between Tx

2

Expected Tx Success

Percent

99.0%

2

Planting Survival

Percent

90.0%

2

Natural Survival

Percent

99.2%

2

No Control Survival (EAB)

Percent

80.0%

Replacement Size

Inches

2.00

User

Replacement Cost

Dollars

145

3

Installation Cost

Dollars

200

2

Unit Tree Cost

$/sq. in.

46.15

CTLA

Species

Percent

70.0%

CTLA

Condition

Percent

69.5%

CTLA

Location

Percent

70.0%

CTLA

Interest Rate + 1

Percent

1.03

User

Yes=1, No=0

1

User

Variable Name

Replant Lost Trees?

2

User

1

Milwaukee street tree inventory adjusted to 2014.

2

City of Milwaukee Forestry cost records from 2014.

3

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

CTLA = Council of Landscape Tree Appraisers User = Selected by user to meet management goals.

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City of Milwaukee Approach Prior to EAB being confirmed in the City of Milwaukee, the street tree population was put on a two year cycle of treat ash with Emamectin Benzoate (Tree-채ge). The initial plan implemented in 2009 was to treat each tree once every two years which is a recommended management approach (Smitley et al. 2010, Herms et al. 2014). This approach is currently being used. The Treatment approach was deemed the most economical based on an internal assessment of various management approaches (e.g., Preemptive Removal, No Control, or Treatment). Currently the cost for labor and all treatment related costs is $3.75 per diameter inch (Table 9). Thus, an average sized 18 inch diameter ash tree has an annual $33.75 cost for treatment. Mathematically, this cost is $3.75 (cost per diameter inch) * 18 (mean diameter of an ash tree) / 2 (treatment is once every two years) = $33.75 (annual cost). To verify the assessment of EAB management options, a follow-up economic analysis (total costs, net benefits, and a benefit/cost analysis) of these management options was conducted as part of this project.

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Economic Analysis Findings and Discussion Four EAB management scenarios were evaluated for the benefit/cost of implementation over a 20-year time period using the methods of VanNatta et al. (2012). The EAB-PlansŠ program was used to conduct the economic analysis (Table 9). The management costs include treatment cost, planting cost, and removal cost. Cost data from City of Milwaukee Forestry Services and tree purchase costs from the Milwaukee area wholesale nursery tree market were used in developing total cost estimates. Management options were financially discounted for inflation, tree growth was modeled, and tree mortality was included in the analysis. Tree mortality was used to account for natural ash tree mortality, planting mortality, and EAB specific mortality. Preemptive Removal and Planting was the most costly option at $26.9 million (Figure 34). This approach forgoes any current and future benefits that the large ash tree population provides. The Treatment option was the second most costly at $24.5 million. This approach retains tree benefits now and into the future allowing trees to naturally die or be removed as conflicts arise. If Milwaukee stopped treating trees today, the peak would likely occur in 4 to 6 years (Knight et al. 2013). The No Control

Figure 34: Management costs by approach in response to emerald ash borer for ash street trees.

option costs $22.5 million. This approach allows ash to die from EAB, retains current benefits for a few years, but ultimately will lead to a crisis event with mass ash mortality that could reach upwards of 5,000 to 10,000 dead public trees that will occur at the peak. Preemptive Removal without planting was Findings

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the least costly at $14.3 million. Although this approach is least costly, no future benefits are obtained for the over 31,000 locations that trees are not planted. While management costs are important in selecting the most appropriate scenario, selection of the appropriate option also requires consideration of tree benefits. Benefits were calculated as the compensatory tree value using the Council of Landscape Tree Appraisers (CTLA 2000) method. A compensatory value is a price one would expect to receive to recompense for a loss. This CTLA system calculates the value of a tree based on the size, location, condition, and species of a tree and using the market-based value of landscape trees. For example, larger trees are worth more than smaller trees and a tree in excellent health has greater value than a dead or dying tree. Therefore, a benefit cost (B/C) analysis and net benefits analysis were conducted using costs and benefits specific to the Milwaukee street tree population. A B/C is a measure of the value of a benefit relative to the cost. A B/C of 1 means the benefit is equal to the cost. A B/C of 3 means three dollars are returned for every dollar invested. A net benefit analysis is the sum of benefits minus all management costs. These are annually calculated, discounted for the value of money for inflation annually, and summed to a common time period. Both of these approaches provide a rigorous way to evaluate management alternatives. Not surprising, not having EAB provided the highest net benefit of $137.7 million and a 5.67 B/C. Since EAB is present in Milwaukee, unfortunately the ash tree population will no longer provide this value. The Treatment option provided the greatest net benefit with a $130.7 million value and a 2.96 B/C. Thus, the net effect of EAB in Milwaukee is $7 million over the 20 year time period as a management

Figure 35: Net present value and benefit to cost ratio by approach in response to emerald ash borer for ash street trees.

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cost to maintain the structural value and environmental services from ash street trees. A No Control approach resulted in a $58.1 million net benefit and 1.78 B/C. Thus, as an EAB infestation builds, the tree population still provides some value prior to the loss of trees and from the future benefits as replanted trees grow. This option however provided $72.6 million less in net benefits over the 20 year simulation period than Treatment. The worst options were Preemptive Removal with or without replanting. Both Preemptive B/C ratios were below 1 and also reduced the net benefits of the Milwaukee ash tree population by over $107 to $113 million compared to Treatment. Thus, the Treatment cost of $18.9 million over 20 years is estimated to provide $58 to $113 million of net benefit that would be lost by using the other options (No Control, Preemptive Removal and Replanting, or Preemptive Removal). An additional benefit is there is evidence that treatment of the public street trees will provide a residual effect on private property ash. By killing EAB adults, treatment slows the growth of an EAB population and increases the longevity of private ash trees (estimated at over 500,000 trees) and the benefits they provide. (McCullough and Mercader 2012). The findings from the economic analysis are consistent with the Treatment approach currently being used in Milwaukee. These results are also consistent with several separate studies that have concluded treatment is the most favored option (VanNatta et al. 2012, McCullough and Mercader 2012, Kovacs et al. 2013, Sargent et al. 2013). No studies were found that suggest No Control or Preemptive Removal were the best option. Treatment to conserve and maintain ash not only provides the highest net benefit and B/C, it also retains tree canopy over time. This has important ecological and social benefits. Ecologically, the tree canopy holds rain water and intercepts particulate matter, among several other benefits (e.g., energy conservation, wildlife habitat, property value). A finding in the Ohio region found that areas that lost ash to EAB also had significantly higher human mortality from respiratory ailments and cardiovascular disease (Donovan and Butry 2011). Although that study could not say a causal relationship exists, the correlation findings are responsible given less air pollution uptake and particulate matter interception occurred from less tree leaf area and this may result in the greater human health impacts. The No Control option would ultimately in the future lead to tree population with similar sized trees as exists today. However, using the growth rate of ash trees (0.50 inches stem diameter per year), it will take approximately 40 years to recover to the current 18 inch average ash tree diameter. The preemptive approaches likewise will set back the tree canopy by 30 years, are costly, and provide a fraction of the economic benefit of Treatment. Results from the EAB analysis are consistent with findings from the DED part of this project that showed active management with Best Control is better than all other options. Finally, an active Treatment program to retain ash is also consistent with the City of Milwaukee Forestry Services primary goal:

City of Milwaukee Forestry Services The primary goal of Forestry Services is to efficiently manage the urban landscape to provide a better quality of life for our citizens and visitors. This effort seeks to maximize the environmental and psychological benefits of the urban forest, while enhancing both landscape and property values.

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Discussion This first-of-its-kind study retrospectively quantified the historical loss in trees and tree canopy from a major urban tree pest and how this resulted in reduced ecological services and economic costs. The project used DED as a model to determine the community costs and impacts and how the benefits and costs of various pest management alternatives compared to no active management and the actual outcome in Milwaukee. The methodology and outcomes for DED serve as a proxy for EAB and other future pest management decisions. The findings provide urban forest managers a lens to see how management decisions can be used to conserve tree canopy and meet social desires of a community. From a tactical and budgetary standpoint, this study demonstrates the forgone “core services and budget impacts� and long-term consequences accompanying inordinate municipal resource allocations to tree removal and reforestation strategies. In addition, this demonstrates the extraordinary length of time (e.g. a generation) needed to fully recover from devastating canopy loss. Results show that applying active management to slow the loss of trees from DED was economically favored compared to letting the disease kill the tree population with no control. The effect of the loss of elm trees on increased stormwater was a primary interest of this study. Results show that the value of stormwater interception alone would cover the management costs associated with reducing the loss of elms to 3 to 5% annually. Much of the cost of a best management approach that would lead to 1% of elms dying from DED could be covered through stormwater savings. Coupled with other ecological benefits, a best management approach would lead to the greatest number of trees retained over time and gave the highest net present value (NPV) and benefit / cost (B/C) ratio of all options. A best management approach is also the least impacting on other important core services such as cycle pruning and young tree structural pruning during the disease/pest management period. It is well established that urban trees have values that surpass the costs over the typical life span to plant, maintain, and remove trees when needed (Miller et al. 2015. It takes approximately 10 to 20 years for the net value of urban trees to surpass all costs to date with planting and establishing trees Urban Trees Appreciate in Value (Miller and Schuman 1981, McPherson et al 1997). It takes approximately 10 to 20 years for the Expending resources to protect a tree from insects net value of urban trees to surpass all costs and diseases that result in the premature loss of the to date with planting and establishing trees. tree is a rational decision (Hauer et al. 2014, Vogt et After that time period urban trees provide al. 2015). Findings from this study used these value that exceeds management costs. concepts and found that Best sanitation levels of DED management and EAB treatment in Milwaukee yield the greatest economic outcome. The benefits provided by mature trees are greater than the financial costs to extend the lifespan of an established and mature tree. How the study’s results compare to the body of existing literature is below, followed by recommendations and future applications for EAB and overall urban forest pest management.

Comparables Historical Trends in Canopy Cover Urban tree cover is commonly used as a metric to describe an urban forest goal expressed as a certain percentage of the ground will be covered by UTC at a point in time (Nowak et al. 1996, Nowak and Greenfield 2012, Miller et al. 2015). Several local units of government have set UTC goals within the Discussion

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United States (McPherson et al. 2011). Approaches to assess UTC are readily available and involve ground-based plots and remotely sensed imagery (Nowak and Greenfield 2012, Miller et al. 2015). Specific protocols for developing a statewide urban forest assessment (Brown 2007) and urban forest health (Cumming et al. 2002) monitoring also exist for Wisconsin. The loss of trees from DED or EAB ultimately affects UTC. The implemented management practice is set in place to maintain UTC. For example, a good sanitation program (3.5 % annual elm loss) would have led to a stable UTC. This means that the rate of elm canopy loss from removed trees was approximately similar to the level of growth in UTC of surviving elm trees. Best Control of DED would have resulted in an increased elm UTC, assuming no other events (i.e., storms, infrastructure changes, etc.) would lead to a loss of elm trees. Thus, a take home message is active management of DED and EAB can maintain UTC.

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.

With EAB found in Milwaukee, lessons learned from the loss of elms can be applied to the ash tree population. The Milwaukee urban forest has an estimated 16% ash (Fraxinus spp.) UTC (Souci et al. 2009). Likewise, a 2008 i-Tree urban forest assessment for Milwaukee found ash trees inhabit 17.4% of all urban trees (i-Tree Ecosystem Analysis 2008). The structural value alone exceeds $200 million. Thus, without active management of the ash UTC, it is expected that this canopy would be lost to EAB within one to two decades (Herms and McCullough 2014). Management of DED and EAB in Milwaukee Dutch elm disease arrived in Milwaukee in 1956. At that time effective approaches for DED management were in the testing and conceptual phase (Hafstad et al. 1965, Miller and Sylvester 1979, Cannon and Worley 1980, Tomlinson and Potter 2010, Harwood et al. 2011). The loss of American elm from DED in Milwaukee followed a pattern observed in surrounding states to the east and south (Sinclair 1978, Sinclair and Campana 1978). Indiana, Illinois, Iowa, and Michigan experienced the introduction of DED in 1934, 1950, 1957, and 1950 respectively in these states (Figure 37). In these locations, the science to effectively manage DED was not fully understood, transferred in applicable management recommendations, and mass elm tree loss occurred. Milwaukee experienced the catastrophic loss of elm trees and associated ecological services detailed in this study.

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Figure 37: Distribution of Dutch elm disease in North America showing the year of introduction within a state. (Adapted from Sinclair and Campana 1978).

By the late 1960’s and into the 1970’s the concepts of sanitation to remove elm wood breeding sites for elm bark beetles such as the native elm bark beetle (Hylurgopinus rufipes), European elm bark beetle (Scolytus multistriatus), and banded elm bark beetle (Scolytus schevyrewi) and best management practices were becoming well established (Cannon and Worley 1980, Miller and Schuman 1981). Technology transfer was further proceeding through state and federal programs and demonstration projects (WIDNR 1980, Hanisch et al. 1983).

Percent of Original Elms Remain

The loss of 16,580 elms during the recorded peak (1969) was a 32% loss rate of the remaining elms in Milwaukee. By comparison, Sinclair (1978) reported in Champaign IL 32% of elms died at the peak year (1959) and in Rockford, IL 51% of elms died at the peak year (1962). Minneapolis, MN also did have a peak year of 21% (1977) which was soon brought back to a low rate through DED best management practices refined in the 1970’s 100 Milwaukee Actual Outcome which were implemented Milwaukee Best Sanitation by Minneapolis City 80 Minneapolis Actual Outcome Forester David Devoto. If one could turn back the 60 clock and implement the effective DED 40 management in Milwaukee, findings from this study suggest tens of 20 thousands of elms would remain today. The $160 0 to $175 million in 1 4 7 10 13 16 19 22 25 28 31 34 37 40 ecological benefits and Years Since DED Discovered economic value would Figure 38: Loss of elms in Milwaukee and Minneapolis over 40 years and have likely occurred and comparison to best sanitation practices. stabilized the loss of elms trees (Figure 38). The methods detailed by Cannon and Worley (1976) were used to predict the rate of loss for Milwaukee. The City of Minneapolis which is approximately 300 miles to the northwest recorded Discussion

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the first DED case five years later in 1963. This brief time lag and the advancement of DED management science that was implemented in Minneapolis greatly reduced the loss of elms (Baughman 1985). The Minneapolis DED management program since 1978 has followed closely the predicted outcome of a good sanitation program (3% annual loss) except for a brief time period about 10 years ago (Figure 38). Current losses again follow the predicted losses following intensive sanitation. The City of Winnipeg in Manitoba, Canada was likewise able to effectively apply the biologic science of sanitation and chemical treatment against insect vectors. Winnipeg still has over 50% of the elm population after the first DED incidence in 1975 (Kerr 2010). Fredericton in New Brunswick, Canada had the first DED case in 1961 and retained 70% of the elm population after thirty years through active management (Magasi et al. 1993). By comparison, surrounding communities with limited DED management lost most of their elms trees. Thus, Minneapolis, Winnipeg, and Fredericton provide case examples of the potential elm canopy that Milwaukee could have prolonged if the science and management technology was available when DED was discovered in 1956. The experience in Syracuse, NY points out the value of sanitation (active management) and the result of later inaction (Miller et al. 1969). Elm losses were kept under control with sanitation and then returned to heavy elm losses when sanitation was stopped (Figure 40). Figure 39: Victory Memorial Parkway in Minneapolis, MN lined with American elm circa early 1970’s. (photo by Mark Stennes)

Economic Findings

Percentage Residual Elm Mortality

Dutch elm disease Effects of Sanitation on Elm Losses in Syracuse, NY management costs 18 No Sanitation money to inspect for 16 diseased elm trees and firewood, remove 14 diseased trees promptly 12 (e.g., within 20 days), replace lost trees, and 10 treat infected elm trees. 8 Even with these costs, Maximum 6 this study found the retention of elm trees 4 Minimum and ultimately elm UTC 2 was economically more favorable than No 0 19511952195319541955195619571958195919601961196219631964196519661967 control. This result was Year consistent with several Figure 40: The effects of Dutch elm disease management in Syracuse, NY others studies that found with maximum sanitation compared to minimum and no sanitation. control programs cost 37 to 76% less than No Control programs when the cost of tree planting and tree removal are considered (Cannon and Worley 1976, Miller and Schumann 1981, Sherwood and Betters 1981, Westwood 1991). Also, the environmental and aesthetic services of retained trees add significant additional benefit. The application of intensive sanitation in Minneapolis helped to budget for ongoing impacts from natural and

Discussion

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manmade causes (e.g., DED, EAB, Storms) and avoid peak budgets that require 2 to 3 times more money to remove dead trees and replant trees (Figure 41: Predicted). Active management also lead to a manageable annual loss of elm trees from DED (Figure 42). As an example case study, a DED control program between 1979 and 1982 in Little Falls, MN cost $183,835 (Hanisch et al. 1983). In contrast, it was estimated that without a control program the community would have spent $444,864, or 2.4 times more as a result of removing an additional 4,536 elm trees that would have died from DED. Communities with highly effective programs can be expected to potentially retain 75% of the elm population 20 to 25 years after DED is first introduced to the community (Cannon and Worley 1976, Magasi et al. 1993). Without a program it may take as few as 5 years to lose 25% of the elm tree population and within 9 more years only 25% remain. Hence, multiple tree inspections for diseased trees and prompt removal of infected trees not only reduce tree losses but can also cost less in the short and long term.

Figure 42: Cost of Dutch elm disease management in Minneapolis, MN compared to predicted results of Baughman (1985) under two sanitation levels.

Figure 41: Actual number of elm trees remaining compared to active management and minimal Dutch elm disease management in Minneapolis, MN.

This study used the work of Cannon and Worley (1976, 1980) as a basis of DED epidemiology and ultimate DED mortality of elm trees. They tracked 39 Midwestern communities and the incidence of elms lost to DED, control measures used, and control costs were quantified. Programs were easily disaggregated into 4 program performance groups: Best (1% annual mortality), Good (3.5%), Fair (5%) and No Control (18%). The most intensive level of sanitation (best performance) was the most costeffective. After a 15-year model scenario, doing nothing was the most costly option. All levels of Discussion

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program performance were less costly than No Control due to reduced tree removals. The results of this study using cost data specific to Milwaukee were similar to that of Cannon and Worley (1980) with best management giving the highest Net Present Value and Benefit/Cost. This study also used a 40year planning horizon and further found doing nothing was the least favorable economic decision and Best Control was most favored. A NPV analysis is an approach to account for the value after subtracting all management costs from the accumulated benefits and discounting for the value of money over time (Peterson and Straka 2011, VanNatta et al. 2012, Miller et al. 2015). Miller and Schuman (1981) investigated changes in net urban forest value for public trees in 3 communities in Wisconsin (Bloomer, Milwaukee, Wisconsin Rapids) over a 20-year period. Similar to this study, the Council of Tree and Landscape Appraisers (CTLA) evaluation method was used to develop individual tree values that were summed together to produce the composite urban forest value. The DED mortality followed that of Cannon and Worley (1976), Best (1%), Good (3.5%), Fair (5%), and No Control (18%). Net forest value (benefits minus costs) was greatest for Best Control and decreased as DED management was less effective throughout the 20year horizon. Net forest value was 50% to over 200% higher for Best Control compared to No Control after a 20-year model period. This finding was consistent regardless if elm trees lost to DED were replaced or not. The current Milwaukee study also found the same conclusion over the 40-year time frame to the 20-year period used by Miller and Schuman (1981). A benefit/cost (B/C) analysis is another approach to compare alternative management approaches. A benefit-cost analysis model was developed for evaluating DED management programs in Colorado (Sherwood and Betters 1981). The model uses average annual elm loss, elm losses under various management options, net number of prevented tree losses, and costs of various management options to generate B/C analysis. Tree value was derived using the CTLA method. The B/C is calculated from the avoided tree loss divided by the management cost to prevent tree loss. Intensive surveys along with insecticide application produced the highest B/C of 3.41 compared to inconsistent sanitation (B/C 1.24) and consistent 2-surveys/year sanitation (B/C 1.62) over the 10-year simulation period using a 7% discount rate per year. This recent Milwaukee study over 40 years found Best Control to have a 2.53 B/C and Good Control was 1.36 when compared to No Control. Fair (0.96 B/C) was comparable to No Control. Several urban forestry studies have used a B/C approach to evaluate various management practices (Miller et al. 2015). In this study, the Milwaukee elm population had B/C values ranging from 1.93 (CTLA method) and 3.42 (i-Tree Eco method) if DED did not occur. These values are consistent with several studies (1.4 to 3.8 B/C) throughout the United States (McPherson 1994, Maco and McPherson 2003, McPherson et al. 2005). Results from this study depict a wide variety of tree values over time. Ideally the value of any management cost or tree benefit would be known for each time period that information is calculated and summed. As with many studies, that was not the case with this study and those that use a discounted cash flow analysis to discount the value of money over time (Peterson and Straka 2011, Peterson and Straka 2012). A composite Consumer Price Index CPI and Primary Producer Index (PPI) adjustment for inflation was used in this analysis to adjust tree management and tree purchase costs to a common time frame. Current and historical market prices were compared to adjusted values and no issues were found with adjusted prices. For example, the unit tree cost was adjusted from the price of trees in 1979 ($50 per two inch caliper tree) to 1956 ($18.99 per two inch caliper tree). A market place unit tree price of $6.05 per square inch was derived from a tree replacement cost in 1956 and comparable to the fixed $5 value from that time period (Cullen, 2005, Watson 2001, Watson 2002). The Discussion

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current CTLA method to Benefit/Cost Public Tree Management appraise tree value is through a market approach National High Estimate Public Trees as used in this study (Cullen 2005). Adjusting the 1956 I-Tree Estimate Milwaukee Elm Trees and 1979 values to 2014 Best Sanitation Practice gives $145.34 which is CTLA Method Milwaukee Elm Trees consistent with the current Best Sanitation Practice wholesale market price ($130 to $146) for two inch National Low Estimate Public Trees caliper trees comparable to American elm (Wolter 0 1 2 3 4 2015). Further, maintenance Benefit/Cost costs ($0.88 per diameter inch) and tree removal costs Figure 43: Benefit to Cost Ratios of Urban Trees from Various Sources and ($5.66 per diameter inch) Methods. derived from 1980 data were comparable to 2014 values for maintenance ($2.07 to $2.54 per diameter inch) and removal ($15.30 per diameter inch) using cost accounting data from City of Milwaukee Forestry Operations. Two tree valuation systems were used to evaluate management options. Both the compensatory value (CTLA) and the environmental services (i-Tree Eco) approaches gave the conclusion that active management was better than no management and in order from Best > Good > Fair > Actual Outcome > No Control. VanNatta et al. 2012 used this same approach to evaluate management approaches for EAB management. They found that both the compensatory (CTLA) and environmental services (i-Tree Streets) were consistent and active management was better than No Control. McPherson (2007) also used a multiple approach using a cost approach (CTLA compensatory value) and benefit approach (environmental services) finding that after 40 years green ash (Fraxinus pennsylvanica) gave $5,807 per tree using the cost approach and between $3,102 and $5,022 from the benefit-based approach. This study found after 40 years the CTLA approach ranged from $2,125 (Fair control) to $2,254 (no DED scenario) on average for all trees that survived or died during the simulation time period included in the calculation. The benefit-based approach yielded a $6,297 total value from 40 years between the sum of $1,466 in ecological values and $4,831 in structural value at year 40 for the 11.78 inch dbh elm tree that survived and became larger. Tree Structure Modeling Knowing the structure (e.g., tree species, size, condition, canopy dimensions) of an urban forest is necessary to estimate environmental and aesthetic functions. No tree inventory existed for 1956 except the number of ROW elms trees. To solve this technical challenge, more recent tree inventory information was backcasted to reflect the 1956 population. Backcasting tree growth by subtracting a known tree growth rate has been used in rural forests modeling (Pommerening and Muszta, 2015). In this study, the use of this approach is the first time we know of that it was done with an urban tree population. To account for missing information on the size, height, and canopy dimensions of elms, models relating tree diameter to canopy spread and tree height used data for American elm street trees from Milwaukee. Estimating tree height and canopy spread has been commonly used with urban trees with

Discussion

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highly predictive models resulting (Pillsbury et al. 1998, Peper et al. 2001, McHale et a. 2009). This study was no exception with high R2 values for tree height (R2 = 0.82) and canopy spread (R2 = 0.87). The models to estimate the height to base of the canopy were poor. These had R2 values below 0.30 using Milwaukee elm tree data. A variety of reasons in addition to stem diameter explain the height to canopy base parameter. Regardless of tree diameter, branches of street trees that grow over streets and sidewalks get pruned for vehicle clearance to a height of at least 14 to 16 feet on the road side and 10 to 12 feet on the sidewalk side (Miller et al 2015). Also, branches get broken by storms, vandalism, getting hit by vehicles, and may naturally die. Thus, many events independent of tree diameter affect height to canopy and a simple model using the mean living tree canopy (2/3rds of total tree height) was used. An assumption was the living crown goes from the top of the tree to a distance that is 1/3 of tree that is crown free from the ground upward. Ecosystem Services and Benefits Elms were an important and dominant source of ecological value in the 1950’s. They provided approximately half the total ecological services the 1950’s with the elm cohort providing a $1.7 million annual ecological value in 2014 dollars. The elm population had a $118 million structural value. A 2008 i-Tree Eco analysis report for Milwaukee placed a $1.43 billion structural value for all 3.4 million trees across all land (i-Tree Ecosystem Analysis. 2008). Annually these trees provide $12.8 million dollars of ecosystem services. A discussion on the functional contribution of stormwater management services follows. Stormwater The loss of elm trees and the resulting increased stormwater runoff and treatment costs was a primary research question of this study. Construction of the deep tunnel due to stormwater related Combined Sewer Overflows (CSO) occurred at the same time when street tree canopy cover was at or near its lowest point due to DED. While attempts were made to obtain historical CSO and stormwater treatment data from MMSD, a lack of volumetric data for the defined area of interest didn’t allow for making direct correlations in this regard. Per storm event, the deep tunnel has a 521 million gallon capacity to hold water. Prior to the deep tunnel there were 40-60 CSO events annually (MMSD 2013). Currently there are 2-3 combined sewer overflow (CSO) events each year, though several variables can impact whether a rain event triggers a CSO such as the area of the rain event and level of ground saturation at the time of event. The tunnel is needed because of the nature of peak flows, or the timing of storm runoff and a system’s (gray or green) ability to store, absorb, or infiltrate large volumes in a short period of time. Trees on the other hand intercept approximately the initial 1/10th inch of rainfall during a storm event and continuously absorb stormwater infiltrating pervious surfaces long after the storm event, thus recharging the green infrastructure stormwater storage capacity. Therefore, MMSD looks at the capacity of green infrastructure best management practices (BMPs) per storm except with trees. While trees work continuously, particularly during warmer months, the tunnel is used episodically to store larger volumes of stormwater than can be managed with green infrastructure. While alternative best management practices have the potential to help reduce the amount of annual storm runoff, it does not remove entirely the need for additional stormwater mitigation. Storms typical of the region will always have the potential to exceed what current infrastructure/canopy best management practices are capable of mitigating in terms of stormwater. For these reasons, it is important not only to highlight the benefits of reducing annual runoff through canopy goals, but to consider an episodic or event-based approach through the construction of the deep tunnel. Because Discussion

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tree impacts on runoff as reported from the i-Tree Eco model are annual reductions while deep tunnel storage and other green infrastructure BMPs are measured per storm event, making an apples-toapples comparison presents a challenge. To address this, annual values from i-Eco were divided by an average of 50 storm events per year and compared further below to avoided runoff from other BMPs that MMSD is implementing and tracking. With regards to avoided runoff from the street tree population and DED, elms in 1956 had a 21 million gallon annual capacity at 194 gallons per 12” DBH elm tree. The capacity increased to 26.9 million gallons in 1963 as average DBH increased to 14”. As elm trees died and were removed, the capacity decreased to 13.6 million gallons in 1969, 3.4 million gallons in 1979, 2.2 million gallons in 1986, 1.6 million gallons in 2008, and 0.9 million gallons in 2013. At 31” average DBH in 2013, each elm resulted in 894 gallons of avoided runoff per year. The 40-year simulation of elm tree survival through DED management showed a potential to retain this capacity. The Best Control scenario found the avoided runoff capacity to increase to approximately a 32 million gallon capacity at the end of the 40 years in 1996. All control scenarios would be further enhanced by the replanting of trees that would provide additional capacity for runoff reduction over this time period. The mean tree diameter today (~12”) is similar to what it was in 1956 (Miller et al. 2015). Given elms represented roughly half of all street trees then, the findings from this study suggest a stormwater value for all street trees is two times that amount, or close to 40 million gallon capacity compared to a “No Street Tree” scenario. Assuming 50 storm events per year, this street tree population of over 190,000 trees provides 800,000 gallons of avoided runoff per storm. This equates to roughly 4 gallons per 12” tree per event and is likely underestimated. An inventory of current and known green infrastructure in the City of Milwaukee was recently completed (Green Infrastructure Baseline Inventory 2015). A total of 14.0 million gallons of stormwater capture per event was quantified from documented green infrastructure sources. The top three were bioretention (7.3 million gallons, rainwater catchment/cisterns (2.7 million gallons), and porous pavement (2.6 million gallons) accounting for 90% of the documented storage sources. To date, only 395 “stormwater” trees were documented with a total of 9,875 gallons of avoided runoff. This is based on a modeled estimate of 25 gallons per tree per storm event. A goal of 172,692 stormwater trees by 2035 is proposed by the Green Infrastructure Baseline Inventory (2015) report. At 25 gallons/tree/event, this would result in 4.3M gallons of avoided runoff per storm event. Given MMSD already has accounting measures in place recognizing 25 gal/tree/event, the City of Milwaukee may consider applying this multiplier to existing street trees for the inventory of known green infrastructure. Accounting for the current Milwaukee street tree population of over 190,000 trees would surpass the GIBI goal and immediately account for 4.75M gallons of avoided runoff per storm. Furthermore, the 3.1 million trees that exist on other lands further provide runoff capacity, however, likely at a lower rate as many of the trees are situated on more pervious land and smaller average DBH. As another model, future studies could look at comparing street tree stormwater values from the Minimal Impact Design Standards (MIDS) BMP Calculator from the Minnesota Pollution Control Agency (http://stormwater.pca.state.mn.us/index.php/MIDS_calculator). The City of Milwaukee is also closely watching green stormwater management efforts in cities like Philadelphia, PA which aim to avoid expensive, single-benefit gray infrastructure construction projects over green infrastructure initiatives providing triple bottom line benefits.

Discussion

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Figure 44: Quantifying the annual reduction in stormwater runoff benefit of elms by DED management scenario.

By comparison to Milwaukee, the City of Minneapolis has a similar street tree population with 198,633 trees (McPherson et al. 2005). The annual interception of rainfall was estimated at 334.7 million gallons (Figure 44). Per tree an estimated 1,685 gallons of stormwater valued at $45.67 is annually intercepted. It is likely that using the same model developed by Xiao et al. (1998) and used in the McPherson et al. study (2005) would produce a similar magnitude of water interception in Milwaukee. Results of this study are lower and more conservative than avoided runoff estimates found by McPherson et al. (2005) in Minneapolis. The elm tree population in Minneapolis existing in 2005 accounted for 10% of trees and 28% of all benefits (McPherson et al. 2005). The elm trees account for 43.1% of rainfall interception (144 million gallons). Throughout much of the DED management time period in Minneapolis a Good Control program occurred. Thus, the potential impact of elm trees in Milwaukee today with a similar 3.5% annual loss through a Good Control program is substantial. These elm street trees could have contributed 40% or more of the water storage capacity today. Finally, the default value for water treatment in the i-Tree model ($0.0089 /gallon) is 4.06 times lower than current costs ($.036092/gallon) at the MMSD plant. Thus, the estimated i-Tree annual value of $7.96 per a mean sized elm today is $32.28 per tree with MMSD rates. At the elm population level, the Best Control scenario had a $265,249 mean annual value with i-Tree default values. Adjusting this to the MMSD rate gave a $1,075,659 mean annual value for Best Control. As a take-home message from this study, the mean stormwater value alone would have covered the estimated mean cost of $1.03M

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for the Best Control program. All other ecosystem benefit values provided by the elm population further enhance a decision to actively manage the street tree resource to reduce canopy losses from DED. The Milwaukee urban forestry operation currently receives funding from stormwater fees. Findings from this study suggest this funding is supported by the stormwater interception, storage, and reduced stormwater runoff. Further, the current public tree resource is known and supports green infrastructure goals of MMSD (Green Infrastructure Baseline Inventory 2015). While covering the region with trees would not reduce the episodic need for the deep tunnel, increasing public and private urban tree canopy in Milwaukee can help MMSD meet its goals of reducing CSO events from 2-3 per year to zero and capturing the first .5” of runoff from storm events through various types of green infrastructure. Trees are one part of green infrastructure and this study shows that proactive management of tree pests is an important aspect of maintaining robust UTC for stormwater management goals.

Recommendations and Future Applications What a difference-maker it is for Milwaukee to be 10 years ahead of EAB instead of 10 years behind as they were with DED. Cities now have more science, tools, research, and staff and can take proactive risk management. This study not only validates the City of Milwaukee’s current approach to EAB management but the importance of understanding the total costs to the community and long-term operational impacts associated with forest pest management strategies. This study models a very comprehensive approach that justifies the benefit costs of aggressive forest pest management and maximizes the environmental service benefits afforded by urban tree canopy. The alternative is a shortterm approach that cities take all too often during the mode of crisis management but this study suggests there may be a better benefit and outcome.

EAB Management Validation As cities grappled with EAB and other future pests, a management and budget question then is “should we do Thus, the Milwaukee forestry approach is something or do something differently?” In traditional currently a model for cost efficiency. forest management economics, resource managers Without treatment, you can expect up to evaluate scenarios from do-nothing to intensive 10,000 ash to be lost at the peak followed management in order to maximize the growth and profit by the replanting of a like amount. of a timber yield or other management objectives such as recreation, wildlife habitat, or fire risk. Managing urban forests and street/park trees is no different if you assume urban trees have a different value and you manage the resource to maximize those outcomes and benefits, including human health and wellbeing, stormwater regulation, carbon storage/sequestration, energy savings, property values, and others. The story of the loss of ecosystem benefits and the 40 year time period to recover for the loss of elms should not be forgotten. This is the same scenario that Milwaukee currently must respond to with EAB. One artifact of the catastrophic loss of elms was the limited choices with planting material to reforest. Green ash, Norway maple, honeylocust, and linden were selected as they were available. The availability of nursery stock has become more limited in the recent time due to nurseries closing as an economic result from the great recession and other nurseries scaling back on lining out nursery plantings. Thus, consideration must be taken that a do nothing and remove ash trees after they die approach may result in delayed tree planting if nursery stock is not available. An important concept is if trees have no value then getting rid of them is your lowest cost alternative. That is not the case as verified through this study of ecosystem benefits and the structural value of a Discussion

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tree. Financially, the retention of ash trees through the current approach Milwaukee is using is the best option. It retains trees and retains the highest return on the urban forest investment. Even though EAB will cost money, the cost of inaction is greater through the loss of ecosystem services which citizens easily notice as a reduction in shade cast by trees. The treatment approach used by Milwaukee is also at the low end for treatment costs in the nation. The $3.75 per diameter inch mentioned by forestry staff was verified independently through this study. By comparison, the lowest cost for EAB treatment on a commercial level for multi thousand tree contracts is approximately $5 to $6 per diameter inch. Thus, the Milwaukee forestry approach is currently a model for cost efficiency. By comparison, initial EAB treatment modeling conducted at the University of Wisconsin – Stevens Point found that $33 per diameter was the tipping point for treatment being too expensive. The current Milwaukee EAB treatment program is a factor of 10 lower than that threshold. The treatment option also helps stabilize neighborhoods that would otherwise be greatly impacted by EAB. It provides a science based solution that is not only budget-friendly (flat line budgeting over time), it is politically and publically advantageous, and reinforces the importance of a professionally managed urban forest. The ash treatment program also helps retain a tree maintenance cycle designed to prolong tree longevity and tree health. Without treatment, you can expect up to 10,000 ash to be lost at the peak followed by the replanting of a like amount. That was a similar story from the late 1960’s into the 1970’s as a result of DED. Future applications from this work can be used in the decision matrix as cities prepare for new threats with a long-term view to formulate their strategy so they don't repeat the cycle and feel the devastating impacts of DED as Milwaukee did or with EAB as in Michigan. Similar studies could go a step further and estimate the population of trees planted and grown after pest removals and subtract the cumulative benefits of this canopy from the loss of elm benefits that were never realized. This would be provide a more complete picture of the dynamics of canopy benefits over time and is an item for future research. New tools such as i-Tree Landscape and i-Tree Forecast may make it easier to analyze tradeoffs in benefit cost scenarios and management alternatives for individual trees or entire populations. Additionally, ecosystem benefit valuations can change over time as new research is conducted and models are improved.

Discussion

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Appendix The following sections are included in the appendix:       

Key Acronyms and Terms Sample Points used in Historical Canopy Estimation Historical Aerial Imagery Historical Records of DED Removals and Tree Planting Economic Analysis Methods Historical Photos References

Key Acronyms and Terms The following key terms are provided as a supporting glossary for this study.         

      

Area of Interest (AOI) – the areas where canopy cover was estimated for this study, specifically modified citywide and street rights-of-way areas. Backcasting – modeling predictions for past conditions rather than future ones Benefit / Cost (B/C) Ratio – the sum of all benefits divided by the sum of all costs Biometrics – the relationship of a tree attribute (e.g., tree height) based on another tree attribute (e.g., stem diameter) Cohort – a group of trees in a population that are followed over time Confidence interval (C.I.) – a statistical bounds around a mean value, often expressed as a 95% confidence interval that means 95% of the values will be within the reported range Compensatory Value – the compensation a property owner could expect for the loss of a tree Deep Tunnel – the underground storm and sanitary storage sewer constructed by MMSD, initial first phase completed in 1993 Diameter at Breast Height (DBH) – the diameter in inches of a tree, a cohort population, or an average population at breast height, or approximately 4.5 feet high and expressed as stem diameter in this report. Dutch elm disease (DED) – a tree disease caused by the fungal causal agent Ophiostoma ulmi, Ophiostoma novo-ulmi Ecosystem Services – the functional values provided by trees and analyzed in this study Emerald Ash Borer (EAB) – an exotic insect (Agrilus planipennis) from Asia that attacks trees in the ash (Fraxinus spp.) genera and first discovered in North America in 2002 Importance Value (IV) – the relative value of a tree that takes into account both the relative abundance and canopy cover of the tree species Leaf Area Index (LAI) – the collective surface area of leaves in tree canopy, the largest driver of urban forest ecosystem benefit values, and particularly high for American elms Live Crown Ratio (LCR) – the percentage of total tree height that is comprised by a living crown National Agriculture Imagery Program (NAIP) – USDA Farm Services Agency multispectral aerial imagery flown statewide on a two or three-year rotating basis, provided at the county level.

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     

 

Net Present Value (NPV) – the sum of discounted benefits minus the discounted costs accrued in managing the tree population. Net Value – the accounting of total benefits minus the total costs used to manage a tree population Particulate Matter (PM) – Material suspended in the air in the form of minute solid particles or liquid droplets, especially when considered as an atmospheric pollutant. Population – for this project, population refers to the actual or modeled American elm tree population at any given year Rights-of-Way (ROW) – for this study, the public street corridor where the City manages street trees. Sanitation – An active management approach used to identify and dispose of wood of standing elm trees and elm wood piles that may harbor elm bark beetles before the beetles leave. The effectiveness of sanitation is a key part of management scenarios listed below. o Actual – the outcome that Milwaukee experienced with elms mortality from DED o No Control – a projected 18% annual loss of elms under no DED management o Fair – a projected 5% annual loss of elms under a fair management scenario o Good – a projected 3.5% annual loss of elms under a good management scenario o Best – a projected 1% annual loss of elms under a best management scenario Standard Error (S.E.) – the amount of error that can be explained by the standard deviation of a sampling distribution, most commonly of the mean Urban Forest – defined as the sum of all woody and associated vegetation in and around dense human settlements, ranging from small communities in rural settings to metropolitan regions Urban Forest Management – the art, science and technology of managing trees and forest resources in and around urban community ecosystems for the physiological, sociological, economic, and aesthetic benefits trees provide society.

Appendix

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Sample Points used in Historical Canopy Estimation 2013Canopy Canopy Loss From 2013 to 1956 Canopy Points All Other Points

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.

Appendix

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Historical Aerial Imagery

Figure 46 (Appendix Figure 2): Mosaic of tiles from the 1956 scanned, historical imagery over Milwaukee used in this study.

Appendix

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Figure 47: Historical aerial imagery of the Milwaukee River in the vicinity of the UWMilwaukee campus comparing 1956 (top) and 2010 (bottom).

Appendix

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Historical Records of DED Removals and Tree Planting

Year

# Street Tree Elms Removed

Cumulative # Street Tree Elms Removed

# Private Elms Trees Removed

# Total Elms Trees Removed

# Total Street Elms Left

# Trees Planted

% Street Tree Elm Loss From Original #

% Street Tree Elm Loss From Remaining # Trees

% Street Tree Elms Remaining

1956

6

6

5

11

106732

4,513

0

0

100

1957

36

42

22

58

106,696

7,129

0

0

100

1958

203

245

139

342

106,493

7,206

0

0

100

1959

228

473

187

415

106,265

6,962

0

0

100

1960

423

896

266

689

105,842

9,190

0

0

99

1961

869

1,765

463

1,332

104,973

8,593

1

1

98

1962

1,688

3,453

591

2,279

103,285

9,705

2

2

97

1963

2,365

5,818

760

3,125

100,920

8,829

2

2

95

1964

1,910

7,728

415

2,325

99,010

11,329

2

2

93

1965

5,517

13,245

1,272

6,789

93,493

11,309

5

6

88

1966

11,421

24,666

1,788

13,209

82,072

10,626

11

14

77

1967

14,398

39,064

1,797

16,195

67,674

10,724

13

21

63

1968

16,580

55,644

3,038

19,618

51,094

15,443

16

32

48

1969

10,509

66,153

1,681

12,190

40,585

14,655

10

26

38

1970

6,944

73,097

983

7,927

33,641

18,966

7

21

32

1971

6,250

79,347

967

7,217

27,391

16,266

6

23

26

1972

3,070

82,417

854

3,924

24,321

12,326

3

13

23

1973

1,825

84,242

650

2,475

22,496

15,283

2

8

21

1974

3,498

87,740

760

4,258

18,998

16,169

3

18

18

1975

2,956

90,696

552

3,508

16,042

13,297

3

18

15

1976

2,631

93,327

1,076

3,707

13,411

8,005

2

20

13

1977

2,862

96,189

458

3,320

10,549

11,676

3

27

10

1978

1,285

97,474

746

2,031

9,264

12,687

1

14

9

1979

888

98,362

691

1,579

8,376

8,703

1

11

8

1980

732

99,094

769

1,501

7,644

8,841

1

10

7

1981

957

100,051

650

1,607

6,687

10,241

1

14

6

1982

717

100,768

2,134

2,851

5,970

10,044

1

12

6

1983

306

101,074

900

1,206

5,664

8,485

0

5

5

1984

385

101,459

905

1,290

5,279

8,468

0

7

5

1985

240

101,699

1,166

1,406

5,039

7,692

0

5

5

1986

237

101,936

1,156

1,393

4,802

7,476

0

5

5

1987

157

102,093

1,112

1,269

4,645

7,066

0

3

4

1988

190

102,283

1,192

1,382

4,455

5,882

0

4

4

1989

178

102,461

1,432

1,610

4,277

7,033

0

4

4

1990

240

102,701

1,396

1,636

4,037

7,390

0

6

4

1991

103

102,804

1,201

1,304

3,934

5,334

0

3

4

1992

150

102,954

2,059

2,209

3,784

3,621

0

4

4

1993

176

103,130

233

409

3,608

4,037

0

5

3

1994

161

103,291

720

881

3,447

3,545

0

5

3

1995

99

103,390

956

1,055

3,348

4,040

0

3

3

1996

235

103,625

No data

235

3,113

No data

0

8

3

Appendix

6 7 | Pa g e


Economic Analysis Methods 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

Variable Name

Unit

1956 Value Used1

Initial Value (year) 19.6 (1979)

Source

Variable Description

Starting Diameter

Mean Size (Inches)

11.78

Starting Population

Number of Trees

106,738

Inches/Year

0.34

Maintenanc $/Diameter e Cost Inch

0.33

0.88 (1980)

Kostichka et al. 1984; Schuman 1984

Total annual cost per tree diameter inch (DBH, 4.5 feet) to maintain trees in the management area, $6.40 per tree / 7.24 mean DBH = $0.88/inch and CPI/PPI adjusted1

Removal Cost

$/Diameter Inch

2.12

5.66 (1980)

WIDNR 1980; Schuman 1984

Cost to remove a tree per diameter inch (DBH, 4.5 feet), mean $111 value (range: 44 to 210) from 1980 / 19.6 mean elm DBH = $5.66/inch and CPI/PPI adjusted1

Survey Costs

$/Elm

0.14

0.43 (1980)

Kostichka et al. 1984

Cost for each survey to identify diseased elms for removal, Mean value ($0.43) from 15 WI communities (range: 0.10 to 1.59) and CPI/PPI 1 adjusted

Sanitation Costs

$/Elm

0.01

0.04 (1980)

Kostichka et al. 1984

Cost for a lot by lot examination of potential elm wood brood wood, Mean value ($0.04) from 15 WI communities (range: $0.01 to $0.25) and CPI/PPI 1 adjusted

Combined Costs

$/Elm

2.14

6.00 (1980)

Kostichka et al. 1984

Cost to conduct all other management activities such as root graft-disruption, chemical treatment, insecticide sprays, etc., in addition to survey and sanitation (range: 2.82 to 8.88) and CPI/PPI adjusted1

Planting Survival

Percent

90.0

Natural Survival

Percent

99.1

No Control Survival

Percent

82.0

Tree Growth Rate

Appendix

Schuman 1984; Mean stem diameter (DBH, 4.5 feet) and 1956 value Hauer et al. 1994; projected from 19.6 mean DBH in 1979 (n=5998 Koeser et al. elms) by back casting an annual growth of 0.34 to 2014 1956.

106,738 Sivyer 2014; (1956) Miller and Schuman 1981

Number of elm street trees in management area at the start of the simulation. Official records from City of Milwaukee Forestry Services.

0.34 Hauer et al. 1994 The average (mean) annual increase in tree (1979 to diameter (DBH, 4.5 feet), sample cohort of initial 43 2005) elms trees measured in 1979, 1989, and 2005.

90.0 Miller and (1950s to Schuman 1981 1980s) 99.1

Percent survival of trees following planting until established, survival typical for 2” DBH trees planted in Milwaukee (80% survival 1” trees)

Miller and Schuman 1981

Percent annual survival normally expected for elm trees without regard to DED, 0.9% annual mortality for established trees

82.0 Cannon and (1970’s) Worley 1976, 1980; VanNatta

Projected annual survival of elms assuming a 12 year tipping point under no control; based on municipal approaches to DED management, tipping 6 8 | Pa g e


et al. 2012

point assumes inflection point at year 6

Best Control Survival

Percent

99.0

99.0 Cannon and (1970’s) Worley 1976, 1980; VanNatta et al. 2012

Projected annual survival of elms assuming a 12 year tipping point under Best Control; based on municipal approaches to DED management, tipping point assumes inflection point at year 6

Good Control Survival

Percent

96.5

96.5 Cannon and (1970’s) Worley 1976, 1980; VanNatta et al. 2012

Projected annual survival of elms assuming a 12 year tipping point under Good Control; based on municipal approaches to DED management, tipping point assumes inflection point at year 6

Fair Control Survival

Percent

95.0

95.0 Cannon and (1970’s) Worley 1976, 1980; VanNatta et al. 2012

Projected annual survival of elms assuming a 12 year tipping point under Fair Control; based on municipal approaches to DED management, tipping point assumes inflection point at year 6

Replaceme nt Size

Inches

2.00

2.00

Miller and Schuman 1981

Average (mean) diameter (DBH, 4.5 feet) of a replacement tree; typical historic replanting of 2” caliper material in late 1970’s

Replaceme nt Cost

Dollars

18.99

50.00 (1979)

Miller and Schuman 1981

Cost to purchase a replacement tree; cost reflective of time period

Installation Cost

Dollars

18.23

48.00 (1979)

Miller and Schuman 1981

Cost to install a replacement tree; cost reflective of time period

Unit Tree Cost

$/sq. in.

6.05

Derived Neely 1979; CTLA 2000

Species

Percent

70.0

70.0

Condition

Percent

70.0

70.0 (1979)

Location

Percent

70.0

70.0

Interest Rate + 1

Percent

1.03

1.03

Replant Yes=1, No=0 Lost Trees?

1

1

Replant trees killed by DED in all the management options; user selected to include (1) or not include (0) tree planting in management costs

Include Natural Survival

1

1

DED management scenarios also include natural survival in the analysis

Yes=1, No=0

CTLA calculated unit tree cost of replacement tree (user does not modify, based on replacement size); derived from replacement cost / square area of replacement sized tree

Hasselkus CTLA species percentage of the elm tree population; undated; Simons reflects value of elm based on longevity that can be 2009; Hauer retained under DED management programs. 2014 Schuman 1984

CTLA average (mean) condition of the elm tree population; measured mean value from 5998 elms trees

Neely 1979; CTLA 2000

CTLA average (mean) percent for elm trees in the management area; reflect of contribution of city street trees to properties in Milwaukee, 70% due importance to site (ranges 50 to 80% for street trees in residential settings) Discount interest rate (Enter value as 1 + the interest rate, i.e. 6% interest rate enter as 1.06); assumes historic inflation rate of 3.21% rounded to 3 %

1

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).

Appendix

6 9 | Pa g e


Historical Photos The following photos came from Milwaukee Forestry records. Credit: City of Milwaukee – Department of Public Works - Forestry Section.

Figure 48 (Appendix Figure 3): Historical photo from 1966 – Loss due to Dutch elm disease.

Figure 49 (Appendix Figure 4): Historical photo from 1966New plantings.

Appendix

7 0 | Pa g e


Figure 50 (Appendix Figure 5): City of Milwaukee Forestry Staff, 1933.

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.

Appendix

7 1 | Pa g e


Figure 52 (Appendix Figure 7): Hydraulic spraying of an elm in Milwaukee in the 1930’s

Figure 53 (Appendix Figure 8): Bidrin DED treatment application where capsules were drilled into the tree truck and later removed.

Appendix

7 2 | Pa g e


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.

Appendix

7 3 | Pa g e


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Donovan, G.H. and D. Butry. 2011. The Effect of Urban Trees on the Rental Price of Single-Family Homes in Portland, Oregon. Urban Forestry and Urban Greening. 10(3):163-168. Fankhauser, S. 1994. The Social Costs of Greenhouse Gas Emissions: an Expected Value Approach. The Energy Journal 15:157-184. French, D.W. 1993. History of Dutch Elm Disease in Minnesota. University of Minnesota Agricultural Experiment Station Report 229-1993. 49 p. Groth L., J. Krawczyk, C.J. Kostichka. 1982. Wisconsin Dutch Elm Disease Control Demonstration Program Accomplishment Report, 1981. Wisconsin Department of Natural Resources, University of Wisconsin-Extension, Wisconsin Department of Agriculture, Trade and Consumer Protection, Cooperating Municipalities. 119 pp. Hafstad, G.E., J. Libby, and G.L. Worf. 1965. Dutch Elm Disease Manual for Wisconsin. University of Wisconsin Extension and Wisconsin Department of Agriculture Madison. 92 p. Hanisch, M.A., H.D. Brown, and E.A. Brown. 1983. Dutch Elm Disease Management Guide. USDA FS Bulletin One. 23 pp. Hanou, I. 2011. Urban Tree Canopy Forum, Cincinnati, Ohio. Accessed September 15th, 2015 <https://www.planning.org/cm/search/event/?EventID=16948> Hanson, D. L. 2009. Stratified Random Sample Analysis of Minneapolis, Minnesota’s Urban Forest: Status of the Urban Forest and Applying Lessons Learned from Ulmus americana. M.S. Thesis. University of Minnesota. 119 pp. Hanson, D.L., R.J. Hauer and G.R. Johnson. 2008. Ulmus americana in Minneapolis, MN: Stratified Sample Analysis and Age to Diameter at Breast Height (dbh) Biometrics. International Society of Arboriculture 84th Annual Conference. Poster Presentation. Saint Louis, MO July 26 – 30, 2008 Harwood T.D., I. Tomlinson, C.A. Potterb and J.D. Knight. 2011. Dutch Elm Disease Revisited: Past, Present and Future Management in Great Britain. Plant Pathology. 60(3):545–555. Hasselkus, E. undated. Species Factors for Establishing Values of Trees in Wisconsin. Hauer, R.J. 2012. Emerald Ash Borer Economics, Management Approaches, and Decision Making. Tree Care Industry. August 2012. 23(8):14-17. Hauer, R.J. 2014. FOR 444 Urban Forestry Class Notes: Tree Appraisal. Hauer, R.J., R.W. Miller, and D.M. Ouimet. 1994. Street Tree Decline and Construction Damage. Journal of Arboriculture. 20:94–97. Hauer R.J., J.M. Vogt. and B.C. Fischer. 2014. What is the Cost of Not Maintaining the Urban Forest. Arborist News 24(1):12–17. Herms D.A. and D.G. McCullough. 2014. Emerald Ash Borer Invasion of North America: History, Biology, Ecology, Impacts, and Management. Annual Review of Entomology. 59:13–30. Herms D.A., D.G. McCullough, D.R. Smitley, C.S. Sadoff, W. Cranshaw. 2014. Insecticide Options for Protecting Ash Trees from Emerald Ash Borer. North Central IPM Center Bulletin. 2nd Edition. 16 pp. i-Tree. undated. Manuals & Wrokbooks. Assessed September 15th, 2015 <https://www.itreetools.org/resources/manuals.php >

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Peterson, K.S. and T.J. Straka. 2012. Urban Forest and Tree Valuation Using Discounted Cash Flow Analysis: Impact of Economic Components. Open Journal of Forestry. 2(3):174–181. Pillsbury, N.H., J.L. Reimer, and R.P. Thompson. 1998. Tree Volume Equations for Fifteen Urban Species in California (Technical Report Number 7). San Luis Obispo: California Polytechnic State University. Pimentel, D. 2005. Environmental Consequences and Economic Costs of Alien Species. pp. 269–276. In. S. Inderjit (editor). Invasive Plants: Ecological and Agricultural Aspects. Birkhäuser Basel. Switzerland. Pimentel D., R. Zuniga and D. Morrison. 2005. Update on the Environmental and Economic Costs Associated with Alien-Invasive Species in the United States. Ecological Economics. 52: 273–288. Pommerening A. A. Muszta. 2015. Methods of Modelling Relative Growth Rate. Forest Ecosystems. 2:5 doi:10.1186/s40663-015-0029-4. Sargent, C., H.M. Martinson, R.A. Bean, S. Grimard, B. Raupp, S.C. Bass, E.J. Bergmann, D.J. Nowak, and M.J. Raupp. 2013. Approaches for Predicting the Movement and Potential Economic and Ecological Impacts of the Emerald Ash Borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), in Maryland Municipalities and a Discussion of Possible Management Options. The Maryland Entomologist. 6(1):14–29. Schuman, S.P. 1984. Analysis of Street Tree Species Adaptability to Urban Conditions. M.S. Thesis. University of Wisconsin – Stevens Point. 93 pp. Sherwood, S.C. and D.R. Betters. 1981. Benefit-Cost Analysis of Municipal Dutch Elm Disease Control Programs in Colorado. Journal of Arboriculture. 7:291-298. Simons, K. 2009. Minnesota Supplement To the Guide For Plant Appraisal with Regional Tree Appraisal Factors. Minnesota Society of Arboriculture. http://msa-live.org/wpcontent/uploads/2013/08/mn_plant_appraisal_supplement-1.pdf. 24 pp. Sinclair, W.A. 1978. Epidemiology. in. Dutch elm disease perspectives after 60 years W.A. Sinclair and R.J. Campana (eds.). NE. Reg. Res. Pub. Search Agri. Cornell Univ. 8(5):27-30 Sinclair, W.A. and R.J. Campana. 1978. Dutch elm disease perspectives after 60 years. in. Dutch elm disease perspectives after 60 years W.A. Sinclair and R.J. Campana (eds.). NE. Reg. Res. Pub. Search Agri. Cornell Univ. 8(5):5-6 Sivyer, D. 2010. Mapping the Future for EAB Readiness and Response Planning in Milwaukee: An Update. 46(1):16 –18, 35 Sivyer, D. 2014. Elm Loss Records 1956 to 1996. City of Milwaukee Forestry Operations. Smitley D.R., J.J Doccola, and D.L. Cox. 2010. Multiple-Year Protection of Ash Trees from Emerald Ash Borer with a Single Trunk Injection of Emamectin Benzoate, and Single-Year Protection with an Imidacloprid Basal Drench. Arboriculture & Urban Forestry. 36:206–11. Souci, J. S., I. Hanou, and D. Puchalski. 2009. High-Resolution Remote Sensing Image Analysis for Early Detection and Response Planning for Emerald Ash Borer. Photogrammetric Engineering and Remote Sensing 75(8):905–909. Stipes, R.J. 2000. The Management of Dutch Elm Disease. in. The Elms: Breeding, Conservation, and Disease Management. C.P. Dunn (ed.) Kluwer Acad. Pub. Boston, MA. pp. 157-172

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A Cost Benefit Analysis of DED, EAB, and Historical Canopy in Milwaukee, WI  

Cost benefit analysis, urban forest pest management, Dutch elm disease (DED), Emerald Ash Borer (EAB), urban tree canopy, i-Tree Eco, i-Tree...

A Cost Benefit Analysis of DED, EAB, and Historical Canopy in Milwaukee, WI  

Cost benefit analysis, urban forest pest management, Dutch elm disease (DED), Emerald Ash Borer (EAB), urban tree canopy, i-Tree Eco, i-Tree...

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