Arboriculture & Urban Forestry 45(5): September 2019 areas). Evaluating what rates of mortality are fairly typical for urban trees, and what rates are catastrophic, can help managers interpret program performance and researchers design realistic projection models (Roman 2014; Roman et al. 2016). Tree mortality is also a fundamental component of managing urban forest population cycles: planting, growth, pruning, removal, and replacement. In heavily managed portions of the urban forest, such as street- scapes, yards, and landscaped parks, human interven- tions drive tree population cycles (Roman et al. 2016; Roman et al. 2018). Several models have incorpo- rated mortality rates into projections to assist urban forest managers with decision-making about planting and removal actions. For instance, Miller and Marano (1984) and Bartsch et al. (1985) used tree inventory data combined with user-defined planting, growth, mortality, and removal rates to present street tree pop- ulation simulations. These models were designed to help meet management objectives related to costs and desired benefits, but they are part of software pro- grams no longer available to managers. More recently, researchers have proposed several projection models to assist with planning for tree removals, replacements, and pesticide treatment regimens due to Agrilus pla- nipennis (emerald ash borer, EAB), which threatens widely planted Fraxinus spp. (Hauer 2012; VanNatta et al. 2012; Sadof et al. 2017). Another projection example is the i-Tree Forecast model (currently part of i-Tree Eco), which uses urban forest inventory data, default or user adjusted mortality rates, and species/ location specific growth models to estimate forest structure and ecosystem services produced under alternative planting scenarios (Nowak et al. 2013). Similarly, projected ecosystem services for million tree planting campaigns in New York City, NY and Los Angeles, CA have assumed mortality scenarios (Morani et al. 2011; McPherson et al. 2008). Each of these projections is essentially a demographic population model: a simulation of population size and structure over time due to adding and subtracting individuals (Roman et al. 2016). Yet as Morani et al. (2011) pointed out in their projection model for tree planting in New York City, “the main limit for the population projector” was the lack of empirical mortality rate information. Furthermore, past research has shown that assumed survival rates in ecosystem services models can be higher than actual rates (Roman et al. 2014b; McPherson 2014; Ko et al. 2015a; Ko et al. 169 2015b). The potential value of urban forest popula- tion projection models is their capacity to reasonably predict urban forest changes (and associated benefits) under varying scenarios. Urban forest population models can enable managers to weigh the trade-offs regarding when, where, and how much to plant, and illustrate how maintenance and removal decisions relate to decadal-scale population cycles. Urban foresters, ecologists, and arborists need accurate mortality information from empirical field data to understand the process of urban tree death, improve best management practices, and enhance projection models. Particularly in the context of max- imizing return-on-investments of public dollars, survival rates are an important yet missing piece of cost-benefit considerations for municipalities (McPherson and Simpson 2002; McPherson and Kendall 2014; Ko et al. 2015b; Widney et al. 2016). Knowing the survival rates for public and private trees planted by munici- palities, nonprofits, homeowners, and other parties is crucial to not only justifying expenditures on tree planting, but also to estimating the benefits these trees will provide to city residents into the future (Widney et al. 2016). In this review, we gathered existing liter- ature on urban tree mortality to: (1) summarize reported mortality and survival rates to determine what levels of mortality could be considered typical in urban for- ests; and (2) identify and categorize biophysical and human factors associated with urban tree mortality. METHODS Literature Search We conducted a literature search to find studies reporting urban tree mortality field data. We carried out systematic keyword and article title searches of urban forestry, urban ecology, and arboriculture jour- nals using Web of Science, ScienceDirect, JSTOR, Google Scholar, the US Forest Service’s TreeSearch, and the Urban Forestry database at the University of Minnesota library. We searched for prospective arti- cles in non-English languages by searching in Google Scholar, where non-English publications are better represented (Jascó 2005), as well as using the “all languages” options in the search engines listed above. In addition to the keyword searches, we conducted an exhaustive search (i.e., we scanned the titles and abstracts of all publications) of all volumes of Journal of Arboriculture/Arboriculture & Urban Forestry ©2019 International Society of Arboriculture
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