Issuu

RESILIENT SILICON VALLEY

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY CAROLINE GRUNEWALD | DINING LIU | WILL LOGSDON FINAL REPORT | ESPM 277 ADVANCED TOPICS IN CONSERVATION BIOLOGY | FALL 2017

ACKNOWLEDGMENTS This project would not have been possible without the guidance and support of the San Francisco Estuary Institute (SFEI), specifically Erica Spotswood and Steve Hagerty. Many thanks to Erica and Steve for all of their help throughout this project. We would also like to thank Professor Claire Kremen for her leadership and encouragement. COVER and FRONT MATTER CREDITS (front cover and citation page) Large California Oak Tree (Photo by Darvin Atkeson) (content page) A mix of California Native Plants and Garden, Garden library of Saxon Holt, PhotoBotanic

CONTENTS INTRODUCTION

METHODS

i-Tree Eco Software and Ecosystem Services

Data Preparation

Dummy Dataset

Santa Clara Street Tree Inventory

Spatial Analysis

RESULTS AND DISCUSSION

Dummy Dataset

Santa Clara Street Tree Inventory

Spatial Analysis

CONCLUSION

REFERENCES

EXECUTIVE SUMMARY

This report presents the results of a semester long grad-

uate-level research project for the course ESPM 277, Advanced Topics in Conservation Biology. This research was facilitated by the San Francisco Estuary Institute (SFEI) and contributes to ongoing research on urban ecology in Santa Clara County, California. As a team, we explored the variation in ecosystem services provided by native and non-native street trees, and measured trade-offs between the services and disserves provided by different species. Our analysis included three components: A Dummy Dataset, a street tree inventory of Santa Clara County, and a spatial analysis. The â€œDummy Datasetâ€? included 50 individuals of each species of street tree found in Santa Clara County, with each tree assigned a hypothetical diameter at breast height (DBH) of 1 to 50 inches. The second analysis used an inventory of street trees in Santa Clara County with the measured DBH of each tree. We ran both datasets through the US Forest Service i-Tree software, which calculates ecosystem services provided by individual trees. The results of the i-Tree calculations were then assigned to the location of each tree and analyzed spatially.

The results from the Dummy Dataset yielded the following insights: • Several ecosystem services are highly correlated, and the list of top ten performing species or genera is similar across some of these services. • Native oak trees tend to perform well from an ecosystem services perspective, but release more VOCs than non-native trees. From the Santa Clara County inventory, we determined that: • Across all native and non-native street trees included in the inventory, native species’ ecosystem services increase more rapidly with DBH than do ecosystem services provided by non-native species. • Among the 15 native and 14 most common non-native trees, natives have a higher proportion of low water-use level species and individual trees. The spatial analysis of the Santa Clara County street tree inventory showed that: • Native trees in general, and native oak trees specifically, are underrepresented among Santa Clara County’s street trees. • Native oak trees provide a disproportionately large amount of ecosystem services, and the ecosystem services provided per oak tree is especially high in areas with statistically significant clusters of native oaks. Native species provide not only ecological benefits, but also ecosystem services that benefit people. Our findings support the case for planting more native species, particularly native oak trees in Santa Clara County.

INTRODUCTION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

INTRODUCTION

Urban forests provide a range of benefits to communities of both the people and wildlife that call them home. From a conservation perspective, street trees can provide important wildlife habitat, helping to soften the urban matrix outside of open spaces and protected areas. The urban forest also provides benefits to people through ecosystem services, such as shade, stormwater capture, carbon sequestration, and pollution removal. This study explores the services and disservices provided by street trees in Santa Clara County, California to better understand the trade-offs between ecosystem services and the role of native species in urban forestry. We used the US Forest Service i-Tree software to quantify ecosystem services and disservices provided by street trees in Santa Clara County. Analyzing the results from i-Tree allowed us to answer the following research questions:

• How do we measure trade-offs between different ecosystem services? How can

we make decisions about which trees to plant in a way that integrates both ecosystem services and disservices?

• How do ecosystem service delivery and water-use levels differ between native

and non-native trees? Do native trees perform better from an ecosystem services perspective?

• What is the spatial distribution of native oak trees? How can this help inform future street tree planning efforts?

This study contributes to ongoing research on ecosystem health and resiliency in Silicon Valley by the San Francisco Estuary Institute (SFEI). A key component of SFEI’s work includes a “re-oaking” strategy for Silicon Valley, which aims to restore the region’s historic oak woodland landscape. Re-oaking has the potential to enhance the native biodiversity of urban ecosystems, provide important ecosystem services, and help cities adapt to climate change (SFEI 2017). This study supports SFEI’s case for re-integrating native oak woodlands by detailing the ecosystem services provided by native oak trees and providing recommendations for future street tree planning efforts. California’s native oak woodlands represent a significant priority for biological conservation. Oak woodlands support a variety of ecological functions through nutrient and hydrological cycling and support of food webs and wildlife (SFEI 2017). Historically, oak woodlands and savannas dominated much of Silicon

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | INTRODUCTION

Valley and Santa Clara Valley. The historic landscape likely included less than 20 tree species, with oaks comprising the vast majority of trees (SFEI 2017). The settlement of Santa Clara County, as in most urban forests, resulted in the introduction of a variety of ornamental, non-native street tree species and a dramatic shift in tree composition. Today, the vast majority of street trees in Santa Clara County are introduced non-native species. While non-native trees provide ecosystem services to people, they generally do not provide the same ecological functions for wildlife that native species do. In order to present the case for restoring the native tree composition in Santa Clara County, the relative benefits to both wildlife and people must be well understood. This study therefore aims to shed light on the performance of native trees from an ecosystem services perspective. Ecosystem services are the benefits that the planetâ€™s natural systems provide to society. Ecosystem services include provisioning services, such as food, water, and fuel; regulating services such as water filtration, flood regulation, and climate regulation; cultural services that provide aesthetic or recreation value; and supporting services such as photosynthesis and nutrient cycling (MEA 2005). These services can be quantified by their physical or monetary benefit. From an urban forestry perspective, ecosystem services provided by street trees are often described in terms of monetary value as a way of attracting funding. Quantifying ecosystem services by their monetary value also provides a common unit for comparing different services to each other. The US Forest Service i-Tree software suite was created for the purpose of demonstrating the value of urban forests by quantifying the monetary value of ecosystem services provided by street trees. While the economic benefit of street trees is undoubtedly

important, street trees also have the potential to provide significant ecological benefits. This study uses the i-Tree software as a tool to demonstrate the trade-offs between ecosystem services provided by street trees, and to highlight the differences in ecosystem services provided by native and non-native trees. For instance, a tree species may provide more total ecosystem services, while another species may perform particularly well in a specific category, such as carbon sequestration. We use the economic value of ecosystem services as a means for comparing different ecosystem services to one-another; however, we emphasize that the ecological benefits of native trees transcend their monetary value. In addition to ecosystem services, street trees also provide disservices, such as volatile organic compound (VOC) emissions and high water use. The emissions of VOCs by street trees can contribute to ground-level ozone, which can be an air quality concern for communities. Water use is another variable with particular relevance in Californiaâ€™s drought-prone climate. This study examines the trade-offs between ecosystem services and disservices, and explores the water efficiency of native species compared to non-native species. The results of this research will contribute to future tree planning efforts in Santa Clara County, as well as other regions of California. Quantifying the ecosystem services provided by street trees allows us to compare tree species and make decisions about which trees to select based on the various benefits they provide. This dataset will therefore serve as an important resource for urban planners, landscape architects, and local conservation organizations with an interest in the urban forest.

METHODS | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

METHODS

i-Tree Eco Software and Ecosystem Services The ecosystem services (ES) of street trees in Santa Clara County were quantified using the US Forest Service i-Tree Eco software (Table 1). i-Tree is often used by community forestry groups to estimate the ecosystem services provided by urban forests. The software uses the US Forest Service Urban Forest Effects (UFORE) model to calculate physical benefits and monetary benefits of street trees. The minimum data requirements for i-Tree are the tree species and DBH for individual trees. Additional information can be provided to improve the model, including tree height, canopy dimensions, land use, and distance to buildings. For this analysis, only the tree species and DBH information were entered into i-Tree, as these were the only variables available for the entire inventory. The i-Tree software was used to quantify ecosystem services of the Santa Clara County street tree inventory, as well as a user-generated “Dummy Dataset” of street trees.

Data Preparation Street tree inventory data was obtained from SFEI for the cities of San Jose, Mountain View, Palo Alto, Cupertino, and East Palo Alto. The inventory included locations of street trees, species names, and diameter at breast height (DBH) of individuals. The street tree inventory is specific to street trees and does not include trees in backyards, parks, or open spaces. While the majority of the dataset was complete, approximately 10% of all street trees did not have DBH information. These trees were removed from the database, as DBH is a requirement for data entry into the i-Tree software. In addition to analyzing the Santa Clara County Inventory, we created a “Dummy Dataset” for analysis in i-Tree. The Dummy Dataset included 651 species from the Santa Clara County Tree Inventory, each with DBH ranging from 1 to 50 inches. A 1 to 50 DBH was used to represent the range of sizes and growth rates for each species. This allowed for standardization of DBH and comparison across species. Similarly, the actual Santa Clara County Inventory dataset was generated using i-Tree. The Santa Clara County Inventory dataset used the true DBH of individual species rather than a generalized 1 to 50 inch DBH range. The results of the Santa Clara County Inventory provided ecosystem services and disservices for individual trees associated with their spatial (latitude, longitude) information. This allowed for spatial

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | METHODS

Ecosystem Service

Definition

i-Tree Estimation

Carbon Storage (tons, $)

Total carbon stored as tree biomass

A species specific allometric equation is used to calculate biomass. If no allometric equation could be found for an individual species, the average of results from equations of the same genus was used. If no genus equations found, the average of results from all broadleaf or conifer equations used.

Gross Carbon Sequestration (tons/year, $/ year)

Net carbon sequestered annually

Average standardized growth rate is used to estimate DBH. The annual growth is not species specific. Interannual carbon storage difference uses species specific information when available.

Avoided Runoff (ft3/year, $/year)

Volume of water intercepted by tree canopy as leaf storage and evaporation

Interception is based on the leaf area index (LAI) of genus and species, which is calculated by species-specific shading coefficient (when available). Conifers do not appear to be species specific and use a general shading coefficient.

Air Pollution Removal (tons/year, $/ year)

Dry deposition of O3, SO2, NO2, CO, and PM10 to tree canopies

Environmental data including weather related information (e.g. wind speed, air temperature), solar radiation, hourly and average daily/ monthly pollution-concentrations are incorporated from local weather stations. Information about trees and shrubs, including total tree canopy coverage, LAI, percent leaf area that are evergreen, and local leaf-on and leaf-off dates are also used.

VOC Emissions (lbs/year)

Emissions of isoprene, monoterpenes, and other VOCs that contribute to O3 and CO

Given the environmental data including air temperature and solar radiation, i-Tree estimates VOC emissions based on species-specific emissions rates, species leaf biomass, and LAI.

Table 1. Ecosystem Services Definitions and i-Tree Calculations

METHODS | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

analysis on the location of ecosystem services in Santa Clara County. In addition to i-Tree data preparation, we added some important properties for the street tree including: scientific names summarized by genus, tree native/ non-native status to Santa Clara Valley, whether it is a top 15 common street tree according to counts of the Santa Clara County Inventory, and water use levels for selected species based on the WUCOLS IV Plant Search Database. The WUCOLS database allows users to select a region and a city within that region, and identifies a water-use level – very low, low, moderate, or high – for each species. We first selected “North Central Coastal” and “Santa Clara,” and collected water levels for as many inventory species as were available. We found additional target species by searching within “North Central Coastal” and “San Jose.” In our data, we indicated the water-use levels very low to high by ordered categorical variable 1,2,3,4.

Table 2. Focal Species List (left: native species; right: common species)

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | METHODS

Dummy Dataset i-Tree produced two types of reports for the Dummy Dataset: an individual tree summary and a genus summary of ecosystem services and disservices. In i-Tree Eco v6, carbon storage, gross carbon sequestration, avoided runoff, and pollution removal are quantified in both biophysical and monetary values. The ecosystem disservices summary provides annual emissions of total VOCs (lb/y), Monoterpene, and Isoprene. Using the Dummy Dataset results, we explored the relationships between ecosystem services and disservices. In particular, we wanted to answer three questions: (1) What individual factors affect single ecosystem services provided by street trees? (2) What is the relationship between different ecosystem services? (3) How do we measure trade-offs between different ecosystem services? Various factors, including DBH, native/non-native status, and whether a species is common or uncommon, can affect the ecosystem services it provides. To test whether and how these factors contribute to the provision of ecosystem services, we analyzed the Dummy Dataset results for 30 focal species or genera. The set of focal species consisted of the 15 species native to Santa Clara Valley and the 16 most common species or genera (Quercus agrifolia is both native and common). We visualized each ecosystem service by genus and species and highlighted native and common street trees in bar plots. We also used line plots to see how ecosystem service delivery by each focal species changed with DBH. Next, to explore the relationship among different ecosystem services, we examined ecosystem services in pairs, such as carbon storage and avoided runoff. We controlled for DBH by calculating average ecosystem services by DBH for each species, and then conducted regression analyses. We superimposed the linear regression lines, with their corresponding r and p-values, on the pairwise scatter plots. After measuring statistical distribution of ecosystem services across species, we ranked each species into

quartiles and assigned each species a score based on which quartile it fell into for each ecosystem service. We then generated radar plots to represent the tradeoffs between various ecosystem services, disservices, and water use level on one graph.

Santa Clara Street Tree Inventory After exporting the Dummy Dataset and analyzing the results, we imported the Santa Clara County street tree inventory to i-Tree. We calculated the frequencies of natives, non-natives, common species, common non-natives, and Quercus agrifolia (the only species that is both common and native). We divided these frequencies by the total number of tree observations to find the street tree composition breakdown. Then we separated all of the observations into two groups, native and non-native, and calculated the mean DBH and mean ecosystem service values for native and non-native species. The native and non-native lists were further divided into separate lists for each DBH. For example, all native trees with DBH measurements of five were grouped together. We then calculated the average total benefits in dollars provided by the trees in each DBH class. Using the same process, we calculated the average avoided runoff, pollution removal, and gross carbon sequestration provided according to DBH measurement and native or non-native status. We plotted each value with DBH on the x-axis and average benefit in dollars on the y-axis and ran regressions for each relationship. We then retrieved water-use level information from the WUCOLS IV Plant Search Database. Since WUCOLS did not include all of the species from the Santa Clara County inventory, we focused on comparing the water-use level information for all 15 native species and for the 14 most common non-natives. We counted the frequencies of each of these species and compared the distributions of native and non-native trees by water-use level. Then we created two histograms, one showing the number of species with each water-use level and another displaying the number of individual trees with each level. Finally, we conducted chi-squared tests of independence for both graphs to

METHODS | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

test whether native/non-native status has an effect on water-use levels.

Spatial Analysis After exporting the street tree ecosystem service data from i-Tree, the individual tree data was joined to the spatial point dataset using ArcGIS. Compiling the street tree ecosystem service data into a spatial dataset allowed for spatial patterns to be evaluated, including spatial clustering of high ecosystem services, clustering of native tree species, and distribution of ecosystem services across municipal jurisdictions. Given the lack of a contiguous dataset for the entire county, we limited our spatial analysis to the city of San Jose, which comprises the majority of street trees in this dataset. Street t ree point data was first aggregated into a 20-acre hexagonal grid that covered the San Jose municipal jurisdiction. Spatial aggregation allowed for the use of specific Spatial Analysis tools in ArcGIS and provided a consistent spatial resolution across the study area. An Optimized Hot Spot Analysis was performed to evaluate the spatial distribution of ecosystem services provided by street trees. The Optimized Hot Spot Analysis tool identifies statistically significant spatial clusters of high values (hotspots) and low values (cold spots) (ESRI 2017). The Hot Spot tool was used to identify hot spots of total ecosystem services provided by street trees in San Jose. The hot spot analysis was also performed to identify hot spots of native Quercus species in San Jose. Street tree data was also summarized by municipal jurisdiction by joining street tree ecosystem services data to the municipal boundaries of the cities of San Jose, Palo Alto, East Palo Alto, Mountain View, and Cupertino. Aggregating the street tree day by municipality allowed for comparison of total ecosystem services, street tree density, and street tree composition by municipality.

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | METHODS

Figure 0. Santa Clara County Map (grey: urban area; red: road system; yellow: wetland/water)

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

RESULTS AND DISCUS

Dummy Dataset Among the 615 species in Santa Clara County, different species perform well in different ecosystem services. We created bar graphs for each ecosystem service, as well as for VOC emissions, a disservice, with each graph presenting the top ten species for that service or disservice. In each graph we highlighted the native species and the most common species, or our set of focal species. For carbon storage and gross carbon sequestration, the histograms indicate that the overall distributions are skewed, with over 50 percent of species providing a high amount of both carbon storage and sequestration. Quercus agrifolia, native to Santa Clara Valley, provides relatively high quantities of carbon storage and gross carbon sequestration. Other oaks, such as Quercus chrysolepis, Quercus wislizeni, Quercus tomentella, and Quercus engelmannii, which are not native to Santa Clara Valley but are native and even endemic to California, also perform very well in terms of carbon storage and carbon sequestration (Figure 1 and Figure 2). The distribution of the amount of avoided runoff provided by different species is also skewed, with most species preventing between zero and 15 cubic feet of runoff, but with a few outlier species preventing over 25 cubic feet (Figure 3). The list of top ten species for pollution removal and VOC emissions did not include any native or common species. Juglan regia is the only species native to California that is in the top ten of pollution removal (Figure 4 and Figure 5). Although Eucalyptus species remove a large quantity of air pollutions, they also emit VOCs at a much higher rate than other species (see the distribution of VOCs in Figure 5).

Figure 1-5. (from up to down) Figure 1. Carbon Storage (t) across Species; Figure 2. Gross Carbon Sequestration (t/y) across Species; Figure 3. Avoided Runoff (ft3/y) across Species; Figure 4. Pollution Removal (t/y) across Species; Figure 5. Total VOCs (lb/y) across Species.

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

SSION

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

We also identified the top ten genera for each ecosystem service and highlighted the genera with native species in red and those with common street trees in blue (Figure 6). The rankings of genera are fairly consistent with the rankings of species. For example, Quercus and Prunus stand out in both carbon storage and gross carbon sequestration, Platanus does well in avoided runoff, and Eucalyptus provides the most avoided runoff and pollution removal while also emitting the most VOCs. Eucalyptus performs well in avoided runoff, mainly due to its high leaf area index (LAI), which is one of the main factors in i-Treeâ€™s avoided run-off calculation. This is surprising given that studies have shown that runoff is lower beneath oak trees in comparison to Eucalyptus trees (Thompson et al 2016). Eucalyptus also have oblong, hanging leaves, while oaks have wide, flat leaves. It is likely that i-Tree is not able to incorporate leaf geometries in the

avoided runoff calculation, which could help explain these results. Overall, at least one genus with native species shows up on the top ten lists for four ecosystem services. Quercus and Populus are both in the list of top ten producers of VOCs list. Interestingly, the compositions of the top ten genera of carbon storage and of gross carbon sequestration are similar, with only three genera varying between lists. The top ten genera of avoided runoff and pollution removal are quite similar as well, with only one inconsistency. Metrosideros is a top provider of avoided runoff but not of pollution removal, while Corymbia is a top provider of pollution removal but not avoided runoff. This finding addresses the second question â€“ the relationships between different ecosystem services.

Figure 6. Top 10 Genera for Each Ecosystem Service and Disservice

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

Figure 7. Carbon Storage by DBH (left: native oaks; right: 5 common street trees)

Figure 8. Gross Carbon Sequestration by DBH (left: native oaks; right: 5 common street trees)

In i-Tree, DBH is required to calculate a treeâ€™s value in terms of ecosystem services. Usually the bigger the DBH, the more ecosystem services the tree provides. Since each species in the dummy dataset has 50 trees with hypothetical DBHs of 1 to 50, we can use the results to analyze how rapidly ecosystem service quantities change with DBH increases. We can compare these rates across our set of focal species. Here, we specifically compared the relationships between DBH and ecosystem services across native oak trees and a few common non-native trees for ecosystem services. The line charts depicting carbon storage and gross carbon sequestration by DBH (Figure 7 and Figure 8) show that the increasing rate k of native oaks is much higher than that of common non-native species. All curves appear concave, as k becomes higher as DBH increases. However, there is some limit in i-Tree that when the DBH is over 40, some species lack data so that ES plummets at some thresholds, resulting in strange trends

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

When comparing line charts about avoided runoff and pollution removal by DBH (Figure 9 and Figure 10), it is hard to tell the difference between trends of avoided runoff by DBH and trends of pollution removal by DBH. All four native oak trees perform the same in both aspects based on i-Tree estimation. This is probably because of the indirect use of species in i-Tree generating LAI, which may not be species specific, to estimate both avoided runoff and pollution removal. This reason can also partly explain the similar compositions of top ten genera for these two ES and their relationship mentioned in the next section. VOC emissions increase at a constant rate with DBH, but rates vary significantly by species. Native oaks emit more VOCs than common non-native street trees (Figure 11).

Figure 9. Avoided Runoff by DBH (left: native oaks; right: 5 common street trees)

Figure 10. Pollution Removal by DBH (left: native oaks; right: 5 common street trees)

Figure 11. VOCs by DBH (left: native oaks; right: 5 common street trees)

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

Carbon Storage (t) Gross Carbon Sequestration (t/y) Avoided Runoff (ft³/y) Pollution Removal (t/y) Total VOCs (lb/y)

Carbon Storage (t)

Gross Carbon Sequestration (t/y)

Avoided Runoff (ft³/y)

Pollution Removal (t/y)

Total VOCs (lb/y)

0.990308749

0.100469264

0.097831785

0.124355321

0.990308749

0.083628745

0.078335158

0.085586799

0.100469264

0.083628745

0.989104132

0.600783254

0.097831785

0.078335158

0.989104132

0.619778359

0.124355321

0.085586799

0.600783254

0.619778359

Table 3. Correlation Coefficients between Different Ecosystem Services

Figure 12. Simple linear regression between different ES (left: carbon storage and gross carbon sequestration; right: avoided runoff and pollution removal) We explored the relationships between different ecosystem services by calculating pairwise correlation coefficients between different ecosystem services in a 5 x 5 grid (Table 3). The diagonal line filled with 1 can be considered as a hypothetical symmetry axis. Focusing on either half of the table, the correlation coefficients between carbon storage and gross carbon sequestration (0.990), avoided runoff and pollution removal (0.989), avoided runoff and total VOCs (0.601), as well as pollution removal and total VOCs (0.620) were relatively high. However, other relationships, especially between carbon storage and gross carbon sequestration, and total VOCs displayed on the bottom row, seemed to be nonlinear and less clear. For example, annual gross carbon sequestration

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

provided by the species with low annual VOCs emission ranges considerably from 0 to 2 dollars per year. The results of the linear regressions demonstrate that gross carbon sequestration and carbon storage have a linear relationship (y = 0.0172 x + 0.0562) with a p-value less than 0.005 and Pearson r = 0.990 (Figure 12, left). Pollution removal and avoided runoff also have a linear relationship (y = 0.0025 x) with a p-value less than 0.005 and Pearson’s r = 0.99 (Figure 12, right). This result also explains why the compositions of top ten genera for these two group of ES were quite similar (Figure 6), and why the trends of avoided runoff and pollution removal by DBH of some species appear similar (Figure 9 and Figure 10). This is likely because i-Tree uses the leaf area index to calculate both pollution removal and avoided runoff, which indicates trees with a higher leaf area index provide greater pollution removal and avoided runoff. It is necessary to measure trade-offs between different ecosystem services, especially between ecosystem services and disservices, so that we can compare different species comprehensively for better street tree planning. There are two main ways to do that. Firstly, using monetary value provides a common unit for comparison (Figure 13). In terms of total annual benefits, including annual benefits of gross carbon sequestration, avoided runoff, and pollution removal, there are 8 native species in the top ten focal species list, demonstrating that native species tend to provide more ecosystem services than common non-native species. We explored the tradeoff between ecosystem services and disservices among the top ten species for total benefits by juxtaposing total benefits (segmented into sections for carbon sequestration, avoided runoff, and pollution removal) with VOC emissions (lb/year) in a bar graph. Among these species, it is clear that native oaks and Populus fremontii produce a lot of VOCs but also have high total annual benefits, while Aesculus californica and Acer macrophyllum provide high total annual benefits and produce low total annual VOCs. However, total monetary value, in addition to total annual benefits, takes structural value and carbon storage monetary value into consideration. Structural value, also known as compensatory value, is species-specific and based on the replacement cost of a similar tree. Trunk area, species, condition, and location are used to determine value. Structural value accounts for a large proportion of a tree’s total monetary value, and, when we rank species by total monetary value, four native oaks occupy the list of top five species (Figure 13 right). Finally, we compared different species based on the biophysical value of different ecosystem services, disservices, and water use level, which were ranked into quartiles. We created two radar charts displaying the performance of native oak trees and 5 common non-native street trees, respectively (Figure 14). Each polar axis has four ticks 1, 2, 3, 4, with the higher value indicating better performance. For example, a species with VOC emissions score of “4” and water use score of “4” emits less VOCs and uses less water than another one with VOC emissions score of “1” and water use score of “1”. Above all, native oaks appear to perform well in all categories, with the exception of VOC emissions.

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

Figure 13. Comparison of multiple ES provided by different species based on monetary value (left: total annual benefit and VOCs; right: total monetary value, including structural value, carbon storage and total annual benefit)

Figure 14. Comparison of multiple ES provided by different species based on biophysical value (left: native oaks, right: 5 common non-native street trees)

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

Santa Clara County Street Tree Inventory In the Dummy Dataset analysis, we examined the trade-offs between different ecosystem services. The Dummy Dataset included 50 trees from each species, with hypothetical diameter at breast height (DBH) measurements ranging from 1 to 50. The results provided insight into the relationship between services, and they also allowed us to compare different species’ performances, given standardized DBHs ranging from 1 to 50. However, the analysis evaluated only the 16 most common non-native species and the 15 native species. In total, 546 different species are recorded in the Santa Clara County Inventory. Also, since different species have different growth rates and varying DBH ranges, the Dummy Dataset analysis may not provide an accurate estimate of ecosystem services provided by individual species or by native and non-native species collectively.

One of the first and most obvious observations we made was that the 546 species in Santa Clara County are far from evenly represented. In total, the survey includes 310,892 trees. The 14 most common non-native species, which account for 2.6 percent of species, make up a disproportionate 51 percent of recorded trees (Figure 15). The 15 native species, in comparison, account for 6 percent of county street tree composition. Even within the native category, species are not evenly represented. The native Quercus agrifolia accounts for five percent of street trees, while the remaining 14 native species comprise only one percent of street trees.

Our county-wide inventory analysis provides a comprehensive summary of Santa Clara’s street trees and examines whether native and non-native consistently provide different levels of ecosystem services across the county. As with the Dummy Data set analysis, the summary of the complete inventory quantifies benefits in dollars.

Figure 16. Average total benefits segmented by ecosystem service for native and non-native trees

Figure 15. The proportions of total Santa Clara County street trees in five categories – common non-native, native, common native (Quercus agrifolia), and other.

The average native tree provides higher ecosystem services than the average non-native tree in Santa Clara County (Figure 16). This suggests that native species outperform non-native species in ecosystem service delivery. However, the average DBH is 14.3 inches for native species and 11.5 inches for non-native species. As the Dummy Dataset analysis demonstrated, ecosystem service levels are highly correlated with DBH. It is therefore possible that the differences in average ecosystem services is due simply to a difference in DBH. The DBH bar graphs (Figures 17.1 and 17.2) illustrate the distribution of native and non-native species by DBH. The first graph depicts the number of individual native and non-native trees by DBH measurement. The non-native tree distribution is right skewed, with the majority of trees having lower DBHs. In contrast,

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

the native species frequencies appear to decrease more consistently as DBH increases. Each bar in the second graph (Figure 17.2) measures the number of native or non-native trees with that DBH, divided by the total number of native or non-native trees with DBH values from one inch to 45 inches. In this relative frequency graph, the distributions of native and non-native species appear to be quite similar. However, compared with non-native trees, native trees with low DBH measurements account for a smaller proportion of all native trees. Native trees with high DBHs comprise a larger proportion of all native trees. This difference between native and non-native DBH relative frequency distributions explains the higher average DBH for native species.

# of trees

Frequency of native and non-native trees by DBH 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

DBH inches Native

Non-Native

Figure 17.1. Number of native and non-native trees in the inventory by DBHs 1 to 45 0.07

Relative frequency of native and non-native trees by DBH

Relative Frequency

0.06 0.05 0.04 0.03 0.02 0.01 0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

DBH (inches) Native Relative

Non-Native Relative

Figure 17.2. Relative frequencies of native and non-native trees in the inventory by DBHs 1 to 45

To account for differences in DBH across all 546 species, we calculated the average ecosystem services by DBH for native and non-native trees (Figure 18.1-4). We only plotted the averages corresponding to DBH levels 1 to 40, since i-Tree generated extremely irregular results for trees with DBHs larger than 40 inches. We surmise that i-Tree might not be capable of accurately calculating ecosystem services for very large trees. Figures 18.1-4 show that total benefits, carbon sequestration, avoided runoff, and pollution removal are all strongly correlated with DBH, with r-values greater than 0.82. The results resolve our concern that average benefits could be higher for native species simply because of differences in native and non-native DBH. For each ecosystem service, native species benefits increase more rapidly with DBH than do non-native species. These findings bolster the Dummy Dataset analysis, which showed the 15 native species outperforming the 16 non-native species. They suggest a consistent interaction between native or non-native status and ecosystem service levels.

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

Figure 18.1, 18.2, 18.3, 18.4. Average total benefits, gross carbon sequestration, avoided runoff, and pollution removal by DBH for all native and non-native trees. Figure 19 shows the 15 native species and the 14 common non-native species grouped into water-use level bins. If there were no relationship between native status and water efficiency, we would expect the native and non-native species to be evenly distributed across the four levels. Instead, more than half of the native species have very low or low water use levels, while 11 of the 14 non-native species have moderate or high water-use levels. Therefore, at least among the 15 native and 14 most common non-native species, natives have a higher proportion of water efficient species. However, when we conducted a chi-squared test of independence, we received a p-vale of 0.14 and so were unable to confidently conclude that, among all native and non-native species, natives have different proportions of very low, low, moderate and high water-use level species than non-natives. This may well be because our test was under-powered, since many of our expected values were below 5. Figure 20 shows the counts of individual trees belonging to the 15 native species and 14 most common non-native species by water-use level. Seventy-seven percent of native trees recorded in the county inventory have very low water-use levels, and nearly 94 percent have very low or low water-use levels. By contrast, of the 159,622 trees belonging to the 14 common non-native species, none have very low and 36 percent have a low water-use level (Figure 20). Therefore, at least among the street trees belonging to these 29 species, natives have a higher proportion of water efficient individual trees. When we conducted a chi-squared test, we received a chi-squared statistic of 134,636.6, which yielded a p-value of nearly zero. Based on these results, we can confidently conclude that the distribution of individual street trees across water-use levels is dependent on native or non-native status.

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

Number of native and non-native trees by water-use level 95434

57529

14285 0 Very Low

3084

951

Low

Moderate Native

Figure 19. Distribution of the 15 native species and 14 most common non-native species across water-use levels (WUCOLS IV Plant Type Database)

185

6659

High

Non-Native

Figure 20. Frequencies of very low, low, moderate, and high water-use native and non-native trees

Further research is necessary to understand what is driving the relationship between native species and lower water-use levels. It would be helpful to obtain water-use data with more specific categories or with continuous values. A deeper exploration of how different traits contribute to water-use levels would also be valuable. Our results have only begun to examine water efficiency trends among native and non-native trees, but the appearance of a relationship between native species status and higher levels of water efficiency is encouraging and suggests that further research would be worthwhile.

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

Spatial Data Analysis

City

Total Street Trees

Area (mi2)

Street Tree Density (indiv/mi2)

Total Annual Ecosystem Services

Total Annual Ecosystem Services per mi2

San Jose

213,738

180.5

1,184

$828,757

$4,591

Mountain View

27,265

12.3

2,222

$102,968

$8,391

Palo Alto1

8,808

25.8

342

$72,035

$2,795

Cupertino

5,860

11.3

518

$17,128

$1,514

East Palo Alto

2,371

2.6

904

$6,963

$2,656

Total2

260,826

$1,038,161

1A large number of trees in the city of Palo Alto street tree inventory did not include DBH data and theref ore are

not included here. 2

The total number of street trees/ecosystem services shown here is greater than the sum of the cities because a small fraction of trees in the inventory were located just outside of city jurisdictional boundaries and were included in the final calculation, but were not attributed to individual cities.

Table 4. Street Tree Ecosystem Services in Select Cities of Santa Clara County

Figure 21. Street Tree Composition and Ecosystem Services Provided by Native Quercus

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

In total, street trees provide over $1,000,000 of annual benefits in the cities of San Jose, Mountain View, Palo Alto, Cupertino, and East Palo Alto (Table 4). Native Quercus species comprise only 5.2% of the total number of street trees in the study area (Figure 21). However, these trees provide almost 10% of all annual ecosystem services in the study area. In East Palo Alto, native Quercus species account for only 5% of street trees, but provide over 14% of all annual ecosystem services in the city. Therefore, it appears that native Quercus species provide a disproportionately large amount of ecosystem services in relation to their abundance. This supports the case for re-oaking the valley by prioritizing native Quercus species in future street tree planning. The results of the spatial data analysis provide an overview of the distribution of ecosystem services provided by street trees in the city of San Jose. The street tree data are displayed as spatially-aggregated 20-acre polygons. Street trees are most dense in the central portions of San Jose to the north and east of the downtown city center (Figure 22). The commercial and industrial areas in northern San Jose generally have fewer street trees than the residential areas in the southern portion of the city. Annual benefits from ecosystem services provided by street trees in San Jose include gross carbon sequestration, avoided stormwater runoff, and annual pollution removal. The distribution of street tree ecosystem services in San Jose is displayed in Figure 23. In general, the distribution of ecosystem services is consistent across services provided, meaning that areas with high values for one service will also have high values for other services. This is not surprising given the results of the Dummy Dataset Analysis, which indicate that many ecosystem services are positively correlated with one another.

Figure 22. Street Tree Density in San Jose

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

Figure 23. Street Tree Ecosystem Services in San Jose

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

Figure 24. Hot-Spot Analysis of Annual Street Tree Ecosystem Services in San Jose

RESULTS & DISCUSSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

Figure 25. Native Street Tree Density in San Jose

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | RESULTS & DISCUSSION

The hot-spot analysis of annual ecosystem services provided by street trees shows several large areas of statistically significant clustering of high ecosystem services. Hot-spots of high annual ecosystem services are general located in residential areas of San Jose (Figure 24). The cold-spots for ecosystem services provided by street trees generally correspond with undeveloped hillsides, tidal marsh, and areas near the San Jose airport. It should be noted that undeveloped hillsides and tidal marshes do contribute ecosystem services, although in this case they are not provided by street trees. Only 5% of street trees in the Santa Clara County inventory are native to the Santa Clara Valley. The distribution of native street trees in San Jose is shown in Figure 25. Native street trees are sparse throughout San Jose and many parts of the city have no native street trees. The spatial distribution of both ecosystem services and native street trees can be used to identify areas where ecosystem services and native trees are lacking. These areas are opportunities for re-oaking, as oaks have been shown to provide significant ecosystem services and ecological benefits. The hot-spot analysis of native Quercus species is shown in Figure 26. There are statistically significant clusters of native Quercus species in several locations that are spread throughout San Jose. The individual clusters of native Quercus do not appear to follow a

Figure 26. Hot-Spot Analysis of Native Quercus Species in San Jose

clear pattern, however several potential relationships were observed. For example, at least two clusters of native Quercus are found in neighborhoods with names that reference oak trees (Oak Canyon, Oak Grove). At least two other clusters are abutted by open space or hillsides. The remaining clusters are generally residential areas of San Jose. The proportion of ecosystem services provided by native Quercus species was evaluated by subtracting the percentage of native Quercus trees from the percentage of ecosystem services provided by native Quercus in each 20-acre polygon (Figures 27). Large negative values indicate that Quercus species provide a disproportionately small amount of ecosystem services, while large positive values indicate that Quercus provide a disproportionately large amount of ecosystem services. Locations with a disproportionately high amount of ecosystem services provided by Quercus generally correspond with the locations of Quercus clusters in Figure 9. This likely indicates that large native oaks provide significant ecosystem services that benefit both people and wildlife in these areas. These locations should be prioritized for conservation and management strategies that preserve large, native oak trees. Future tree planting strategies could also consider establishing additional oak nodes in areas of San Jose, and planting additional oaks in close proximity to existing nodes to promote ecological processes, as suggested in SFEIâ€™s Re-Oaking report (SFEI 2017).

Figure 27. Proportion of Ecosystem Services Provided by Native Quercus

CONCLUSION | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

CONCLUSION

This analysis underscores the value native species provide for both people and wildlife. From an anthropogenic perspective, planting more native species will increase services that benefit local communities, such as carbon sequestration and air pollution removal. From a biological conservation perspective, native oaks will enhance ecological services, including wildlife habitat, food webs, and hydrological and nutrient cycling. Native street trees, particularly native oak species, perform well from an ecosystem services perspective. The Santa Clara County Inventory indicates that native tree species perform better than non-native species in all ecosystem service categories. Native oak trees also provide a disproportionately large amount of ecosystem services in every city in this inventory. Since native species are under-represented in Santa Clara County, there is opportunity to increase ecosystem services by selecting native species in future tree planting. Overall, native oaks appear to perform better than the five most common non-native trees, using less water, providing more carbon storage, gross carbon sequestration, avoided runoff and pollution removal, though producing more VOCs. The full suite of ecosystem services and disservices should be considered at both the individual tree scale and ecosystem scale when comparing different species. While individual oaks do emit more VOCs, the cumulative effects of high VOC emissions should be evaluated in street tree plan-

QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY | CONCLUSION

ning. For example, the spatial distribution of VOCs in neighborhoods can be evaluated to identify opportunity areas with lower annual VOC emission rates where oaks would have less cumulative impacts on air quality. Â Â The results of this analysis can contribute to future street planning efforts by providing a tool for quantifying tradeoffs in the ecosystem services provided by different species. For example, this dataset can be used in decision-making by urban planners to compare the ecosystem services and disservices of individual species. We provided two methods for evaluating trade-offs between species: a biophysical value ranking system, and monetary value. Both methods allow for comparison between services and disserves with various units. Planners can use this system to target specific species based on the services they provide. For example, trees that perform well for pollution removal could be targeted for highway buffer zones, and trees that provide high annual avoided runoff

could be planted in areas with localized flooding. The spatial distribution of ecosystem services and existing oaks can also be used in decision-making to identify areas to focus tree planting resources that benefit both people and wildlife. Our spatial analysis identified several key nodes of large native oak trees that could serve as a baseline for re-oaking in San Jose. The spatial distribution of remaining oaks could contribute to network and corridor planning of native trees in San Jose. This analysis provides a novel method for quantifying and comparing the ecosystem services provided by street trees. Future research could continue to expand the use of i-Tree and similar tools for understanding the relationship between ecosystem services, native trees, and biodiversity in urban forests.

REFERENCES | QUANTIFYING STREET TREE ECOSYSTEM SERVICES IN SANTA CLARA COUNTY

APPENDIX: REFERENCES ESRI. 2017. ArcMap Tool Reference. Redlands, CA: Environmental Systems Research Institute. Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well-being: Synthesis. Island Press, Washington, DC. San Francisco Estuary Institute-Aquatic Science Center. 2017. Re-Oaking Silicon Valley: Building Vibrant Cities with Nature. Publication # 825, San Francisco Estuary Institute, Richmond, CA. Thompson, Arnold, Jerry D. Davis, and Andrew J. Oliphant. “Surface Runoff and Soil Erosion under Eucalyptus and Oak Canopy.” Earth Surface Processes and Landforms 41, no. 8 (June 30, 2016): 1018–26. https://doi.org/10.1002/esp.3881.