Performing Department
Entomology/Nematology
Non Technical Summary
Over eighty percent of the US population now lives in cities, and by 2050 that number is expected to grow to almost 90%. Thus, ecosystem service provisioning within urban areas is increasingly important for the health and wellbeing of US citizens. Urban development has outpaced knowledge of how to optimize urban ecosystem function for communities, but, even in the face of this knowledge gap, scientists and urbanpractitioners agree that urban trees provide irreplicable services to residents; they promote human happiness, mitigate the impacts of heatwaves, clean the air, and reduce the incidence of and mortality from human cardiovascular and lung disease. The continuation of these services into the next century relies on the establishment, growth, and functioning of mature and newly-planted trees. Unfortunately, global climate warming may induce heat and water stress that threatens tree health and performance. However, specific effects of climate change on ecosystem services provided by trees are poorly measured and understood, in large part because we lack experimental techniques for manipulating climatic conditions experienced by trees at the city scale. In this project, we address this knowledge gap by measuring tree rings across urban heat gradients (hotter and cooler microregions) in three cities where trees receive different amounts of precipitation: Sacramento, Raleigh, and New Orleans. This study will directly measure tree responses to urban heat, climate change, and precipitation to improve tree species selection. To determine how to best integrate our results into tree species selection at the city level, we will apply social science approaches to determine how species selection decisions are currently made.
Animal Health Component
0%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
0%
Goals / Objectives
Objective 1. Measure detailed metrics of cooling provided by a diverse set of urban tree species with different traits in Sacramento across an urban heat gradient.Objective 2. Determine to what extent the effects of urban heat islands on cooling services depend on water availability by assessing long-term growth and associated services provided by commonly planted trees across gradients of urban heat in three cities with variable precipitation.Objective 3. Clarify how urban stakeholders--including nursery people, landscapers, arborists, green space managers--select urban tree species based on perceived ecosystem service provision and/or future climate suitability vs. other considerations.
Project Methods
Objective 1. Measure detailed metrics of cooling provided by a diverse set of urban tree species with different traits in Sacramento across an urban heat gradient.Field methods: In Year 1, investigations will focus on the city of Sacramento, where we will sample replicated tree stems, i.e., individual trees as defined by individual trunks, (n=10) for each target species (n=10, Table 1) within cool (trees located in coolest tenth percentile of the surface temperature distribution), medium-temperature (within the 40th to 60th percentile of the surface temperature distribution) and hot locations (trees located in hottest tenth percentile of the surface temperature distribution) within Sacramento, for a total of n= 300 tree stems sampled. Trees of each species will be randomly selected in each temperature category using ArcGIS Pro software licensed to UC Davis. In Years 2-3, we will replicate these methods in Raleigh and New Orleans.Measurements of each tree stem will include:Response variables: Wood cores to measure tree age and growth rates: Complete wood cores, after drying, mounting and scanning using a Velmex UniSlide system and a SM-2T-LED-HCM210 stereomicroscope, will provide dendrochronological series to be related to macroclimate and urban heat (surface temperature) data for each city (24), along with the accurate age of each tree stem. Where possible, two cores per stem will be collected (25). Shade quantity and quality: Single-tree crown projected areas, i.e., tree footprint, will be delineated from available remotely-sensed canopy cover GIS models to allow very precise estimates of current crown sizes; these data will allow calculation of species-specific crown expansion rates to model the creation of tree shade benefits over time (Section A3.3; Fig. 4). To validate these remote measurements and to validate and contribute to municipal tree inventories, crown spread measurements will be tape-measured for each stem and used as indicators of shade quantity in relation to DBH class. Leaf Area Index (LAI) and Sky View Factor (SVF) measurements, taken with hemispherical cameras, will be used as indicators of tree shade continuity and density (i.e., quality).Covariates accounting for variation in tree growth and cooling within models: Leaf traits: Leaves from upper, mid and lower portions of each tree crown and cardinal directions will be collected to calculate standard leaf traits related to water use and drought tolerance (e.g., stomatal density, leaf mass per area, average leaf area (44, 45)).Wood traits: We will collect triplicate wood samples to calculate wood structural and anatomical parameters and wood density as metrics of drought tolerance (45, 46).Tree structural measurements: A complete urban forestry assessment will be performed to update information from the respective municipal tree inventory such as: tree height, DBH, observable recent management actions (e.g., pruning, topping), rooting space, and tree condition (47).Pest damage: Insect pest pressure can increase in response to urban heat (36), which can reduce tree growth (38). We will survey a predetermined number of leaves per tree for diverse types of insect herbivory using methods from Pearse et al. (48) and Meineke et al. (36).Soil coring and penetrometer analysis: Soil superficial bulk density and local soil compaction (0-30 cm) will be measured in triplicates to determine soil water hydraulic conductivity and the amount of tree-available precipitation and stormwater runoff. Soil samples will be also characterized in the lab in terms of soil organic matter content, texture, and water holding capacity.Environmental and planting conditions: We will measure key parameters in situ and with remote sensing (GIS) comprising: i) planting space, presence of utilities and infrastructures, including pavements and powerlines, ii) historical land use through imagery photo interpretation and analyses of cadastral and land records, and iii) socio-economic covariates for census block groups (e.g., median income, education) to account for socio-ecological effects (50).Microclimatic conditions: We are currently assessing correlations between air and surface temperatures in Sacramento. We expect that they will be highly correlated, and we will thus be able to use surface temperatures in models as predictors of historical tree growth and cooling.Macroclimatic conditions: We will extract macroclimate data from PRISM (52).Air pollution: We will use the DARTE vehicle emissions database (53) to extract spatially explicit values of vehicle pollution within various buffer distances of each tree. This value represents a general metric of local pollution, which can have strong effects on tree growth (54).Objective 2. Analyze whether urban stakeholders select urban tree species based on perceived ecosystem service provision and/or future climate suitability considerations.Methods - To fully understand the extent to which urban stakeholders are integrating perceived ecosystem services and climate change into their decision-making, we will implement a mixed methods social science approach using qualitative case studies along with quantitative surveys. A foundational component of the social science approach is identifying who are the key decision-makers, i.e., the population to be surveyed. Identifying the population in the case of urban trees is complex because diverse individuals influence urban tree management on private and public land. These actors include landscapers and arborists who help homeowners select and maintain trees, nurseries that sell trees, local government agencies that manage public lands, and local non-governmental organizations that advocate and implement tree-planting projects.We will study these types of decision-makers starting with qualitative methods in each focal city. We will begin by qualitatively interviewing local government parks officials and any non-governmental organizations working on urban tree issues. These stakeholders can usually be easily found via internet searches. From there, we will use a "snowball" approach to identify the most active arborists, nurseries, and landscapers working on urban trees. These more hands-on tree managers will usually be known by the local government and non-governmental organizations. To get a broader picture of tree management in each city, we will design an online survey to target all urban tree managers that we can find. The city-level survey will be complemented by a national survey based on lists. These same organizations will also encourage their members to respond to the survey.Drivers of decision making: The most important component of the quantitative survey will be attitudes and behaviors related to climate change in urban forestry. This portion of the survey will cover the following topics:1. To what extent does climate change and its effects on tree function/survival affect decision-making around tree species and/or cultivar selection?2. What tree species do they think will be more resilient to climate change?3. What management practices do they think should be used to adapt to climate change?4. How aware/concerned are they about the local impacts of climate change, such as increasing heat or changing water availability?5. What are their general climate change attitudes, in terms of accepting that climate change is happening and the role of human behavior? These questions will be drawn from general public opinion research projects such as the Yale Climate studies, which can provide a basis for comparison.These behavioral and attitude variables can variously be used as dependent or independent variables in different analyses, based on the target research question and hypotheses.