The services that urban trees provide to human society and the natural environment are widely recognized, but urban trees are in jeopardy due to climate change and urban stressors. With drought as a major threat in many areas, it is important for the future of urban forestry to select species composition based upon performance under water stress. Certain leaf functional traits can help horticulturalists more accurately predict water usage of urban trees. Comprehension through rigorous experimentation is lacking, partly due to the thousands of mostly exotic species. Previous studies suggest that species whose leaves have a denser arrangement of smaller stomata and a higher leaf mass per area (LMA) are better adapted to low water availability. We sampled 70 urban tree species California and analyzed their stomatal length, stomatal density, and LMA. We compared the traits with water use data from the Water Use Classification of Landscape Species to assess possible correlations. All pairwise trait comparisons show significant correlation (P < 0.05), and LMA is significantly higher in low water use species compared to medium water use species (P= 0.0045). After using independent contrasts to incorporate phylogenetic relationships, significance was lost, implying that basal divergences are responsible for observed trends. Other potential explanations for differences in species water usage are foliar longevity (deciduous vs. evergreen) and stomatal distribution (amphistomatous vs. hypostomatous). Low water use species are more likely to be evergreen and amphistomatous compared to medium water use species. Consideration of all these traits in combination with good management practices can help ensure future success of urban forests.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3494 |
| Date | 01 June 2019 |
| Creators | Huang, Sophia |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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