Water Usage vs. Tree Growth: The Ecological Trade-Off of Urban Trees in Southern California

By Peter Ibsen, Ph.D. Candidate; Department of Botany and Plant Science, University of California Riverside

For the past several years, I have taught ecology classes to hundreds of undergraduate students at University of California Riverside and have found that the urban tree is an ideal common ground to discuss the ecological concept of “trade-offs.” You see, every single student has experiences with urban trees, from backyard lemons, to tire swings, to taking a nap in the shade of a sycamore on a hot day. Urban trees provide all these services and so many more, yet, the future of urban trees in dry regions is in doubt, and we, as urban residents, have to make serious considerations about ecological trade-offs between water usage and tree growth.

With the assistance of hundreds of citizen scientists, I am conducting a study of urban tree function across the climate gradient of Southern California. My early results find that the urban forest comprises a broad spectrum of ecological strategies regarding tree growth and water usage. It is possible to find species exhibiting all combinations of “fast/slow” growth and “liberal/conservative” water usage.

Figure 1. Ten species of urban trees oriented in an ecological trait space. Each quadrant represents a growth-to-water use strategy. Trees are represented using the US Forest Service IDs by species.

However, my research has discovered two very important trade-offs of water use and tree growth. When separating out the urban trees found in coastal southern California from those found in desert regions, what appears is an interesting difference between the two communities. The desert urban forest exhibits functions of a faster growth and liberal water usage. This goes against some conventional thinking that people plant more water-conserving trees in the desert. They key finding here is that trees and parks are heavily irrigated, and as long as a species can withstand the heat, they can take advantage of both the water and abundant sunlight for growth.

Figure 2: The effects of irrigation in Palm Springs. The difference between the irrigated Desert Highland Park and the natural Palm Springs environment on the right highlights this effect.

I also studied how individual species may change functional strategies when planted from the coast to the desert. By measuring the difference of plant water pressure before dawn (when plants have low water pressure) and in the middle of the day (when plant have higher water pressure), I am able to calculate the water status of a species at a certain location. A higher water status implies that the tree is losing more water to the environment. I discovered that all species studied, save one, increase their water usage when moving from a coastal environment to a desert one.

Figure 3: The differences in water status of California urban tree species. Most species exhibit an increase in water status when they are planted in the urban desert.

When taking all these results together, there is a clear management trade-off. In hotter and more arid environments, urban trees have the potential to experience faster growth at the expense of increased water usage. For urban stakeholders, this is serious consideration. Faster growth means quicker establishment of shade and greater cooling of air temperature. However, increased water usage has serious consequences for an area prone to extended droughts. As the future Southern California climate is predicted to become hotter and drier, our results highlight the uncertainty of our urban forest. My research will continue to add more species to the study, with a goal of both higher resolution of results, and ultimately an idea of which species might be “the right trees, for the right place, in the right time.”

And, thank you to all the community scientists who helped find the trees included in my study, including partners across the Greater Los Angeles region! Please let me know if you have any questions.

Teasing the Signal from the Noise!

By Mark Chandler

Through the help of nearly 1,000 community member participants in Operation Healthy Air, we have collected hundreds of thousands of data points in various formats. After picking up the last field sensors in early October, we are now making sense of all this information. We are trying to discern the patterns and test ideas about trends in air quality and temperature over the region. We—as a project team—owe this next step of analysis to our funders, NASA, but also our institutions, partners, and community scientists.

While we are anxious to produce the definitive figures and results that increase our knowledge about air quality that can lead to action, there remain a few steps required to get there, and this is what we have been doing. In technical terms, we often call this data cleaning and curation; this often includes rolling up our sleeves and scrubbing. Using the Wikipedia definitions, data cleansing, data cleaning, or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Like museum curation, we need to “curate” data to ensure its value and use for analysis both today and to ensure it remains available for reuse—shared and into the future.

All data go through cleaning and curation—not just those collected by community scientist. Even data collected by expensive and highly accurate instruments need to go through this process. During the three campaigns of our Operation Healthy Air program, we have collected data from:

  • 17 ozone sensors
  • 237 iButton temperature sensors placed at community sites (e.g., homes and schools)
  • 96 iButton temperature sensors placed on street trees
  • 64 home indoor and outdoor temperature sensor data sheets
  • More than 150 habitat maps created with the help of 100+ community members

All of this data is the result of the help and participation of more than 20 local partner organizations, 150 community members, and ten schools—including 700 of their students! To view a map of where of this data was collected, visit the Operation Healthy Air Participants webpage.

One of the questions that the data will start to help us understand better is the extent to which local trees can help cool and/or alter the air quality around homes and schoolyards. To gather local-level information, many participants helped us map using the online tool, Habitat Network, which maps the kinds of habitats around the sensors. Many participants also collected information (using a tape measure) about the local tree under which the sensor was placed. While we have not completed pulling this information from all sensors together, here is an example from one home of what bringing this information together in one location looks like. A “Habitat Map,” created for a home in Riverside, is available below. The various shapes represent different kinds of habitats mapped and identified: darker grey is asphalt or concrete pavement, lighter grey is buildings, brown is soil, green is grass, etc. The round “bull’s-eye” objects are mapped trees.

Three iButton temperature sensors were placed around this home (three different colored arrows) and thanks to the help of the local community scientist, we have species identification and size of each tree where the sensors were placed. For example, sensor number CS-032 was placed in a crepe myrtle that was 15 feet tall and had an average canopy width of 13 feet (see the yellow lines).

We also plotted the average daily temperature (24 hours—zero is midnight) for each of these sensors. The black line along the bottom is the predicted temperature using a weather forecasting program, and the red line represents a separate sensor not mapped.

From this plot, we can see how different the temperature profiles are from one tree to the next. The green line for the Wilson peach is for the smallest tree heats up earlier and much hotter than the other two trees, and the elderberry (blue line) heats up earlier than the crepe myrtle.

We are planning to use the Habitat Maps to compare different homes and whether the amount of pavement predicts local temperature differences as well. While there is much more to do, we wanted to share these early results to illustrate how we are beginning to use the data so many local participants helped us collect. Thank you!

As always, please send any suggestions or ideas to me at mchandler@earthwatch.org.