Abstract
Reliable models of urban tree growth over time are useful for selecting appropriate species for available planting sites, anticipating future tree maintenance and removal costs, and quantifying the benefits provided by trees. There is a need to develop growth models for multiple cities within the same climate region to understand the degree of variability for the same species in different cities. In this study, we developed tree growth models for 13 common street tree species in Cincinnati, Ohio, USA, based on field data and planting records. These models relate tree age to diameter at breast height. Then we compared the modeled tree growth curves for Cincinnati to analogous models from nearby Indianapolis, Indiana. To estimate how differences in modeled tree growth translate to differences in ecosystem services, we compared annual ecosystem service estimates from Cincinnati and Indianapolis using the i-Tree Eco model. The comparisons showed varying levels of difference between cities; for example, modeled growth curves for Acer platanoides were nearly identical, while models for Pyrus calleryana differed by > 47% over 35 years of growth. These results advance our understanding of urban tree growth rates by comparing models from two nearby cities, and by underscoring the inherent variability in urban tree growth that will drive attendant differences in the ecosystem services provided by trees.
Similar content being viewed by others
Data availability
Online Resource 3.
Code availability
Online Resource 2.
References
Bartoń K (2019) MuMIn: Multi-model inference. R package, version 1.43.6
Caris C, Rentschler G, Olson E (nd) Appendix 7: DBH measuring standards. In: USDA APHIS New pest response guidelines: Asian longhorned beetle (Anoplophora glabripennis). URL: https://www.aphis.usda.gov/plant_health/plant_pest_info/asian_lhb/downloads/alb_response_guidelines.pdf. Accessed 10 March 2020
Dobbs C, Kendal D, Nitschke CR (2014) Multiple ecosystem services and disservices of the urban forest establishing their connections with landscape structure and sociodemographics. Ecol Ind 43:44–55
Escobedo FJ, Kroeger T, Wagner JE (2011) Urban forests and pollution mitigation: Analyzing ecosystem services and disservices. Environ Pollut 159:2078–2087
Galenieks A (2017) Importance of urban street tree policies: A comparison of neighbouring Southern California cities. Urban Forestry Urban Greening 22:105–110
Hauer R, Peterson W (2016) Municipal tree care and management in the United States: A 2014 urban & community forestry census of tree activities. College of Natural Resources, University of Wisconsin-Stevens Point, Stevens Point
Hilbert DR, North EA, Hauer RJ, Koeser AK, McLean DC, Northrop RJ, Andreu M, Parbs S (2020) Predicting trunk flare diameter to prevent tree damage to infrastructure. Urban Forestry Urban Greening 49:126645
i-Tree (2019) Eco Guide to Data Limitations. URL: https://www.itreetools.org/resources/manuals/Ecov6_ManualsGuides/Ecov6Guide_DataLimitations.pdf. Accessed 1 March 2020
i-Tree (2020) i-Tree Tools. URL: https://www.itreetools.org/. Accessed 1 March 2020
Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends in Ecology Evolution 19:101–108
Larsen FK, Kristoffersen P (2002) Tilia’s physical dimensions over time. J Arboric 28:209–214
Lyytimäki J, Sipilä M (2009) Hopping on one leg – The challenge of disservices for urban green management. Urban Forestry Urban Greening 8:309–315
McPherson EG, Peper PJ (2012) Urban tree growth modeling. Arboriculture Urban Forestry 38:172–180
McPherson EG, van Doorn NS, Peper PJ (2016) Urban Tree Database and Allometric Equations. Gen Tech Rep PSW-GTR-253. USDA Forest Service, Pacific Southwest Research Station, Albany
McPherson G, Simpson JR, Peper PJ, Maco SE, Xiao Q (2005) Municipal forest benefits and costs in five US cities. J Forest 103:411–416
Monteiro MV, Doick KJ, Handley P (2016) Allometric relationships for urban trees in Great Britain. Urban Forestry Urban Greening 19:223–236
Mullaney J, Lucke T, Trueman SJ (2015) A review of benefits and challenges in growing street trees in paved urban environments. Landscape Urban Planning 134:157–166
NOAA NCEI (2020) 1981–2010 Climate Normals. URL: https://www.ncdc.noaa.gov/cdo-web/datatools/normals. Accessed 1 March 2020
Nowak DJ, Crane DE, Stevens JC, Hoehn RE, Walton JT, Bond J (2008) A ground-based method of assessing urban forest structure and ecosystem services. Arboriculture Urban Forestry 34:347–358
Peper PJ, McPherson EG, Mori SM (2001a) Equations for predicting diameter, height, crown width, and leaf area of San Joaquin Valley street trees. J Arboric 27:306–317
Peper PJ, McPherson EG, Mori SM (2001b) Predictive equations for dimensions and leaf area of coastal southern California street trees. J Arboric 27:169–180
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. URL: https://www.R-project.org/. Accessed 12 Dec 2019
Randrup TB, McPherson EG, Costello LR (2001) A review of tree root conflicts with sidewalks, curbs, and roads. Urban Ecosystems 5:209–225
Semenzato P, Cattaneo D, Dainese M (2011) Growth prediction for five tree species in an Italian urban forest. Urban Forestry Urban Greening 10:169–176
Signorell A, Aho K, Alfons A et al (2019) DescTools: Tools for descriptive statistics. R package, version 0.99.28
Stoffberg GH, van Rooyen MW, van der Linde MJ, Groeneveld HT (2009) Modelling dimensional growth of three street tree species in the urban forest of the City of Tshwane, South Africa. Southern Forests: A Journal of Forest Science 71:273–277
Stokes MA, Smiley TL (1996) An introduction to tree-ring dating. University of Arizona Press, Tucson
Troxel B, Piana M, Ashton MS, Murphy-Dunning C (2013) Relationships between bole and crown size for young urban trees in the northeastern USA. Urban Forestry Urban Greening 12:144–153
Van Domelen DR (2019) dvmisc: Convenience functions, moving window statistics, and graphics. R package, version 1.1.3
Yoon TK, Park C-W, Lee SJ, Ko S, Kim KN, Son Y, Lee KH, Oh S, Lee W-K, Son Y (2013) Allometric equations for estimating the aboveground volume of five common urban street tree species in Daegu, Korea. Urban Forestry Urban Greening 12:344–349
Acknowledgements
We are grateful to Cincinnati Parks Urban Forestry staff, and Robin Hunt in particular, for providing tree removal work orders and planting records that made data collection possible. Matt Hopton assisted with field work planning, and Nicholas Sylvest assisted with data collection.
Funding
Field data collection for this research was conducted while AB held a National Research Council Research Associateship Award at the US Environmental Protection Agency; any opinions expressed in this paper are those of the author and do not necessarily reflect the views of the Agency.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest/Competing interests
None.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Berland, A. Urban tree growth models for two nearby cities show notable differences. Urban Ecosyst 23, 1253–1261 (2020). https://doi.org/10.1007/s11252-020-01015-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11252-020-01015-0