ULTRA-Ex Project Summary - California State University, Fresno
Transcription
ULTRA-Ex Project Summary - California State University, Fresno
!"#$%$"&'"($&()&(*+,)&(#-'$-"'-%-.$')%(#/#0"12(3)0"+(1)&)."1"&0()#()(4+$5"+(-6(%)&4#')7"( )&4(,$-4$5"+#$0/($&(08"(9+"#&-:;%-5$#(<"0+-7-%$0)&(=+")>(;)%$6-+&$)? Project Summary Intellectual Merit: Urban land and water management decisions result from dynamic interactions between institutional and individual level factors. Landscaping and irrigation at any particular residence, for example, is a product of geography, hydrology, soil, and other local environmental conditions, the homeowners’ cultural preferences, socioeconomic status, neighborhood dynamics, as well as zoning laws, market conditions, city policies, and county/state/federal government regulations. Since land and water management are key determinants of habitat for other species, urban biodiversity is strongly driven by the outcome of interactions between these variables. This project will address the significance of water as a key variable in the Fresno Clovis Metropolitan Area (FCMA), shaping current patterns of landscape and biodiversity. This is additionally important because Fresno is making changes in water management, by installing water meters (like Clovis has done) in an effort to reduce overall water use. Over the next two years, we will analyze current patterns of water use in the FCMA, which can serve as a baseline before water-metering is fully implemented in Fresno. We will focus on: 1) institutional policy and decision making, 2) individual homeowner decision making, 3) landscape structure at multiple spatial scales, and, 4) patterns in the distribution of plant and bird diversity in the FCMA. Within a GIS framework, the project will analyze the above interactions using a variety of data obtained from: 1) aerial imagery; surveys of 2) institutions (city/county offices), 3) and individuals; 4) field surveys of habitat and plant diversity, and 5) bird census conducted by volunteer citizens for the Fresno Bird Count (http:// fresnobirds.org/). The hypothesis that metering will reduce water usage will be tested by analyzing the effect of Fresno’s introduction of water meters compared to Clovis, in a comparative Before-AfterControl-Impact experimental framework. Broader Impact: The research will be conducted by faculty and students from CSU-Fresno, UC-Davis, UC-Merced, and USDA-Forest Service working in close partnership with the planning, public utilities, and water departments of Fresno, Clovis, and Fresno County, California Dept. of Fish & Game, Audubon Society, and citizen participants. Student participation in the project through course-driven activities, independent studies, and graduate research, will enhance their critical thinking skills and equip them to apply academic knowledge to real world problems of natural resource governance. The project will also advance public engagement with science through citizen scientists helping measure biodiversity, survey respondents providing data on water use patterns and landscaping decisions. In addition to publication in scholarly journals, project results will be disseminated to all agencies/stakeholders involved, and the general public, via: an online data and map portal; local news media; presentations at local fora, e.g., the Central Valley Café Scientifique, Fresno Audubon Society, Archop; and events at local schools, public libraries, and urban parks. Insights generated into the dynamics of the FCMA as a coupled socioecological system (SES )should help guide future policy and planning for governing not only water, but other resources as well, towards greater sustainability even as the region prepares for greater urban growth. Results will also inform broader theory development to understand urban SES’s, with the FCMA serving as a model system, especially for cities in the arid regions of the US and elsewhere in the world. The theoretical framework developed to understand the governance dynamics of water should be applicable to other natural resource governance systems in other SES’s throughout the world. Future effort in this ULTRA will expand these analyses to other aspects of natural resource governance in the FCMA, and develop and test new fundamental theory about the dynamics of urban coupled human and natural systems, and related theory in various disciplines. Katti et al 2009! PROJECT DESCRIPTION Cities are complex, dynamic, coupled socioecological systems (SES, sensu Anderies et al 2004) that now constitute the dominant habitat for Homo sapiens. Understanding the resilience of cities to regional and global environmental changes amid rapid urban growth is a significant theoretical and practical challenge (Grimm et al 2008). Of particular significance is the governance of natural resources critical for humans as well as other species. A key natural resource is water, especially in the more rapidly urbanizing arid regions of the world (e.g., southwestern US; Alig et al 2004, Hope et al 2005; Grimm 2008). Understanding water management and use in cities is therefore critical to developing realistic theoretical models of urban SESs as well as effective urban policy and governance tools. Here we propose a 2-year research program to test the relationship among institutional, geographic, socioeconomic, and ecological variables as they relate to individual choices about household landscaping and water use, and resulting patterns in land cover and urban biodiversity. The Fresno Clovis Metropolitan Area (FCMA, Fig 2) is California’s fifth largest urban area and the major urban center of the San Joaquin Valley (SJV), a desert ecosystem that, through human adaptation, has been transformed into one of the largest agricultural areas in the world. Yet, in spite of vocal public discourse over water scarcity and heightened competition among various users, there has been no research to date on the relationship among variables underlying individual landscaping and water use choices in this rapidly growing metro. We bring to bear a multidisciplinary team with the resources and expertise to undertake a long-term exploration of the individual choices that drive water consumption in a major urban SES, thereby driving forward urban socioecological theory and resource governance. The American West faces a water crisis. Drought, urban growth, climate change and the continued demands of agriculture have combined to heighten the competition among water users. In California’s San Joaquin Valley, court-ordered water diversions under the Endangered Species Act have radically decreased water deliveries to many Valley farmers. A recent settlement providing for the restoration of the San Joaquin River and a third year of drought, in a region subject to repeated cycles of drought, have only exacerbated public debate about water and spurred the search for institutional arrangements to conserve it. Valley farmers have heightened experiments with dry land farming methods from the American Plains and Southwest. Valley cities are also seeking ways to reduce water use. In the FCMA, the City of Clovis already meters water use and the City of Fresno will transition to water meters by 2013. But the factors driving individual decisions about water use in the FCMA remain unknown, as do the consequences of those decisions for urban habitat and biodiversity. Therefore, key questions motivating this proposal are: How does resident water use affect urban landscape and habitat structure, and in turn, biodiversity? How are urban water users responding to water scarcity and to new institutional measures such as metering? Answers to these questions will both shed light on the nature of resilience in a major urban SES and inform public education and policy-making around the major resource issue facing the American West. The proposed project will address these questions through a combination of multidisciplinary observational studies and a “natural” experiment about to be conducted in the FCMA: the impending implementation of water-metering in Fresno by 2013. We will leverage existing projects, such as the Fresno Bird Count (FBC, http://fresnobirds.org/), and Fresno’s pilot water meter program, to obtain and disseminate data, and provide a sampling scheme for this project (details below in methods section). Our multidisciplinary team will work with city, county, and state agencies across multiple jurisdictions, and local nonprofit entities, to share data, provide inputs for future planning, and help disseminate information to the general public. The project will be carried out primarily through California State University, Fresno, a minority serving institution, and will directly involve undergraduate and graduate students. It will enhance their education, especially critical thinking skills, by engaging students in basic and applied research in classroom and thesis projects, and in outreach and service learning activities with local communities/organizations. The project will also establish a baseline for longer term modeling of potential impacts of projected regional and global climate change, related changes in regional agricultural practices and therefore the regional economy (agriculture being the main economic driver for the FCMA), as well as projected urban growth in the SJV as a transportation corridor (with new high-speed rail proposed) and due to relative affordability of real estate compared to coastal regions. Katti et al 2009 Page 1 Research questions for the ULTRA-Exploratory project: During the Exploratory phase of the ULTRA project proposed here (2010-11), we will focus on the following set of core questions: 1. How do interactions between institutions of governance and individual citizens influence water use and availability in an urban SES? 2. How resilient are patterns of water usage in the urban SES to drastic changes in water availability, such as water scarcity driven by drought cycles or climate change? i.e., 2a) how does imposing a direct economic cost on water through metering influence individual water usage decisions? and 2b) how well do institutions of governance prepare for long-term changes in water availability? 3. How do water usage decisions influence residential landscaping, e.g., mesic vs. xeric yards, and choice of plant species? 3a) What other factors influence this decision? 4. How do individual landscaping choices affect the land cover and habitat structure at broader scales? 4a) How do institutional landscaping decisions influence plant diversity? 4b) How does water usage differ between landscapes under individual vs. institutional control? 5. In turn, how do the above interactions influence patterns of bird diversity in cities? Residential land-use decisions as drivers of urban biodiversity: a conceptual model Urbanization is the dominant land use trend worldwide, and is growing fastest in arid regions (Collins et al. 2000, Grimm et al. 2008; Hope et al. 2005; Luck et al. 2001; McKinney 2002; Rees 2001). A city is a unique type of ecosystem where human social, economic, and cultural factors play a prominent role in structuring the landscape within any particular regional environmental context (Grimm et al 2000; 2008; McDonnell and Pickett 1990), in turn influencing the distribution and abundance of other species (Chace and Walsh 2006; McKinney 2002; Sandstrom 2006; Shochat et al 2006), and ultimately, regional patterns of biodiversity (Hope et al 2005). Urban ecosystems are therefore best studied as dynamic coupled socioecological systems, where strong feedbacks between the social and ecological components determine the overall structure and function of the entire system (Anderies et al 2004, Folke et al 1997, 2005). The long-term sustainability of cities is of increasing concern as they continue to grow, straining the infrastructure and pushing against environmental constraints on available natural resources (Ash et al 2008, Grimm et al 2008). Further, the sustainability of cities depends upon how resilient the SES is to perturbations from environmental as well as social and economic changes (Folke et al 2005). To improve the resilience of cities, one must develop a strong understanding of the dynamics of the SES, identify key variables and decision making nodes relevant to natural resource use, and use this knowledge to inform urban planning and policy (Ash et al 2008; Grimm et al 2008). In this project, we will explore the relationships among institutional and individual decision making, water use and availability, and biodiversity in the urban SES. Figure 1 below schematizes the conceptual framework of these relationships (adapted from Anderies et al 2004). Figure 1. A conceptual model of the core components of an urban SES; this proposal focuses on individual water usage decisions (highlighted in the oval) as drivers of ecological patterns. The sociopolitical part of the model includes institutional and individual decision making on water use and availability. The ecological part of the model addresses the impact of the sociopolitical elements on plant and animal diversity. Much of the literature on urban biodiversity (e.g., Marzluff et al 2000, Shochat Katti et al 2009 Page 2 et al 2006) has focused on a range of ecological variables while treating the sociopolitical variables as a “black box” often regarded simply as a source of “disturbance” to “natural” population dynamics and community patterns. Indeed, we now have a fairly good mechanistic understanding (Shochat et al 2006), supported by empirically tested (Shochat et al 2004) theoretical models (Anderies et al 2007) of the interactions in the ecological box. More recent treatments that look into the sociopolitical black box remain theoretical (Grimm et al 2008), with relatively little empirical testing of relationships (Hope et al 2005, Kinzig et al 2005, Syme et al 2004). Several recent studies have found that socioeconomic variables (e.g., income) have surprising explanatory power in multivariate analyses of urban plant (Hope et al 2003, 2006) and bird (Katti et al, 2002, manuscript in preparation) diversity patterns, but the exact pathways of influence remain unknown because the focus of these studies remains on ecological variables with social variables treated as covariates. In contrast, the relationships affecting individual decisions about water use (circled in Fig 1) and their resulting impacts on biodiversity form the core of our proposal. The interactions shown in Fig 1 are embedded, in the urban SES, within a broader environmental context defined by geography, geology, hydrology, soil biogeochemistry, air quality, and climate (Alberti 2005, Clergeau 2006, Grimm et al 2008). While these environmental variables set the context for the interactions in Fig 1, they are also affected by the outcomes of those interactions; the environment serves as both a source of resources (e.g., water) for the social and ecological components, and a sink for their outputs (e.g., pollutants from human activities). While recognizing this broader context, we focus on the more limited set of interactions in Fig 1 for the purposes of this Exploratory proposal, because they are at the core of the urban SES. Understanding these interactions will provide a foundation for developing a more comprehensive theoretical model of the urban SES in the next phase of the ULTRA. At the landscape level, cities are highly heterogeneous, consisting of a variety of land uses by humans (Alberti 2005, Clergeau 2006). Land use is determined by human individual and institutional actors, creating a mosaic of private and public land use types, with a variable mix of “natural” and “artificial” elements. As we describe in some detail below, this land use mosaic often masks even greater structural heterogeneity in urban land cover and habitat for non-human species (Cadenasso et al 2007). The distribution and diversity of these species (native and non-native) in cities then map on to this heterogeneous landscape mosaic in two ways: species are either deliberately introduced/placed by humans (most plants; some animals) or arrive and inhabit urban habitat patches on their own (most animals, some plants). Land use decisions at the parcel / home scale and their effects on land cover heterogeneity (aggregated at landscape scale) are thus key drivers of urban biodiversity (Hope et al 2005, Kinzig et al 2005, Martin et al 2004, Warren et al 2006). Understanding urban land cover heterogeneity Urban landscapes are physically heterogeneous at very fine scales. This heterogeneity results from a complex combination of built and non-built components such as vegetation, buildings and pervious and impervious surfaces. Existing urban classifications, however, inadequately capture this important heterogeneity. Ecology now recognizes that urban systems are ecosystems (Grimm et al 2000, 2008, Pickett et al 2000). As such, they maintain natural processes, such as nutrient cycles and wildlife population dynamics, and can provide numerous ecosystem services, such as water supply and other habitat provisions for non-human species. It has been hypothesized, but rarely tested, that the function of urban ecosystems is linked to the spatial heterogeneity of those systems. Not only is spatial heterogeneity core to landscape ecology, it is a key characteristic of urban systems (Pickett et al. 2001, Band et al. 2005, Shane 2005, Cadenasso et al. 2007). Urban expansion has led to a loss of forests, agricultural, and open lands (Alig et al. 2004, Brown et al. 2005, Foley et al. 2005). But ecologists have moved beyond simply documenting urban expansion and are increasingly working on understanding the ecological function of urban areas (Pickett et al. 1997, Grimm et al. 2000, 2008, Alberti 2005). To accomplish this finer scale understanding, the “closed box” of urban land use must be opened and the heterogeneity within metropolitan areas quantified (Effland & Pouyat 1997, Bauer and Steinnocher 2001, Pickett et al. 2001, Alberti et al. 2003, Cadenasso et al. 2006b, 2007). Traditional approaches to characterizing urban heterogeneity focus on land use and are pixel-based. The difference between land use and land cover (Kressler et al. 2001) is often ignored in ecology, but it is Katti et al 2009 Page 3 important for testing the link between heterogeneity and ecological function. Land cover is a physical pattern and is focused on structural heterogeneity. In contrast, land use defines the land in terms of social and economic function, or how people use the land. Land use is not an ecological variable and knowing the land use of an area does not necessarily lend insight into its ecological functioning (Cadenasso et al. 2007). As a physical descriptor of spatial heterogeneity, land cover may be more relevant to ecological processes than land use and would allow for testing links between structure and function. Urban heterogeneity is typically depicted by land use/land cover (LULC) classifications. Changes in LULC may alter ecosystem function (Vitousek 1994). Land use classifications have been developed to standardize approaches and terminology. Anderson et al. (1976) introduced a unified classification to: 1) standardize the delineation of land use, 2) apply at the continental scale, and 3) address the concerns of natural resource management. Anderson et al. (1976) specified tiers I and II of a hierarchical classification, leaving lower tiers to be defined based on characteristics of the specific location of interest. The Anderson et al. (1976, hereafter referred to as Anderson) scheme has become an “industry standard.” We hypothesize that the interacting environmental sociopolitical factors driving water use, and resultant patterns in the distribution of biodiversity, are better quantified using a new classification of urban heterogeneity that focuses on land cover rather than land use. This patch-based classification is built on a land cover logic which is distinct from readily available land use/land cover classifications that are pixelbased and built on a logic of mutually exclusive land uses. We expect that the amount and position of paved, permeable, and vegetated surfaces, buildings, and tree canopy drive the capacity of the urban system to support bird diversity, and may indicate patterns of water use. Standard LULC models have constraints that limit their application to urban systems (e.g. de Kok 2002): They 1) were developed for the continental scale, 2) separate built from non-built components, and 3) confound structure and function. For example, “residential” is a functional land use class in these schemes. But all residential land is not structurally the same due to the fine scale variation in building density, vegetation, and the amount of impervious surfaces. This unaccounted for heterogeneity may influence ecological functions such as the amount of cover for nesting birds within urban landscapes. In addition, these classification systems were designed to be used with remotely sensed images and were based on pixels. Though pixels can be large or small depending on data resolution, a pixel boundary is not inherently ecologically meaningful. Finer resolution data yields greater detail. HERCULES As a response to the constraints imposed by the application of pixel-based land use models for capturing ecological heterogeneity in urban landscapes, a new classification has been developed (Cadenasso et al. 2007). This classification, called HERCULES (High Ecological Resolution Classification for Urban Landscapes and Environmental Systems) involves more than greater spatial resolution afforded by access to better imagery. It uses a different logic structure, employing land cover, not land use, and resolving the landscape heterogeneity into patches hypothesized to have ecological meaning (Cadenasso et al. 2007). HERCULES focuses on the biophysical structure of urban environments and uses the three recognized elements of urban heterogeneity – buildings, surface materials, and vegetation (Ridd 1995). These three elements are divided into six features in HERCULES: 1) coarse textured vegetation (shrubs and trees), 2) fine textured vegetation (grass and herbs), 3) bare soil, 4) pavement, 5) buildings, and 6) building typology. The type of vegetation, surface material, and buildings are hypothesized to influence ecosystem function because of their differential influence on the amount and distribution of organisms, material, and energy. Patches in the landscape are classified by the proportional cover of each of the first five features plus change in the building type. A change in either proportional cover or type of element leads to a change in patch type. Because the six elements are allowed to vary independently of each other, the resulting data set is a flexible descriptor of system heterogeneity that can be queried using any combination of the six features depending on the research question being addressed (Cadenasso et al. 2007). This approach overcomes the limitations of the available pixel-based classifications for urban systems because it 1) integrates human and natural components of the landscape, 2) recognizes that Katti et al 2009 Page 4 features can vary independently of each other, 3) accounts for all combinations of elements in the landscape, 4) has greater categorical resolution, and 5) does not confound structure and function. HERCULES has been applied to the Gwynns Falls, a 17,150 ha watershed in metropolitan Baltimore, which includes agricultural, suburban and urban lands. This watershed is a focal research watershed of the Baltimore Ecosystem Study. HERCULES is currently being applied to the Sacramento, CA metropolitan region. The FCMA provides another venue to test this model. Further, since the current project focuses on individual homeowner decision making processes which produce the fine scale structural heterogeneity, we may be able to develop a more comprehensive framework for reconciling land use (an administrative variable) and land cover (an ecological variable) in urban systems. Such a model framework will then allow future comparative studies between FCMA and other metropolitan regions. For example, Sacramento, also in California’s Central Valley, is going to implement water metering in the coming years as well, but it likely differs significantly in social, economic, and cultural, as well as some ecological variables. Another useful comparison is with Tucson, AZ, which is about the same size in population and spatial extent, receives somewhat greater precipitation, yet uses a third as much water per capita, and exhibits much greater xeriscaping, than the FCMA. Further, HERCULES can go beyond describing and quantifying landscape heterogeneity to serve as a sample stratifying tool for future projects in the FCMA. Individual and institutional decision making about land and water use Urban land use decisions result from dynamic interactions between institutional and individual level factors mediated by the local environment. Decisions about yard irrigation and landscaping at a parcel or single home, for example, is a product of local environmental conditions, the homeowners’ cultural preferences, socioeconomic status, neighborhood dynamics, and zoning regulations (Syme et al. 2004). This relationship is therefore at the heart of our conceptual model: to understand what drives urban biodiversity patterns, one must understand the dynamics of how landscaping decisions are made by individual actors. We recognize that institutions (government, non-government, and private) also directly influence urban land cover and habitat structure through their decisions on landscaping and water use in public, commercial, and similar tracts of land larger than individual homes. The Exploratory project will collect preliminary data on these decisions, allowing a more comprehensive analysis of these interactions during the next phase of the project. Institutional decisions determine the amount of water available for domestic use, influencing individuals’ decisions on how that water is used, particularly in the landscape. Institutions directly affect water usage by establishing rules for water pricing, metering, and enforcement of those decisions. Individual water use decisions are also indirectly affected by land use decisions by local governments such as zoning and subdivision regulations. The management agencies of multiple-family dwellings such as apartment complexes, gated communities, homeowners’ associations (HOAs) and market forces (e.g. plant availability in nurseries, cost of irrigation technologies, gardening expertise available through landscaping companies) add further layers of influence on individual landscaping and water use decisions, which can be addressed more comprehensively in future studies. Institutional and individual actors have the ability to influence each other’s decisions when a common understanding of the reasons behind their decisions is illuminated. It is expected from individuals that they learn, understand, and obey the water use and land use decisions imposed by the institutions. Likewise, the institutional decision makers are expected to be aware of the effectiveness and public support of the proposed and current decisions. The ecological side of the model is directly shaped by decisions made on the sociopolitical side of the model. Human decisions on water use influence the choice of landscape design and plants in the area, which determines habitat structure at different spatial scales, in turn affecting bird and other animal diversity in the urban core. Landscaping decisions which are formed by the institutions and individuals, will thus drive patterns in land cover, and plant and animal diversity. The Fresno-Clovis Metropolitan Area, an urban SES constrained by water availability: The FCMA is spread over approximately 600 Km2 within Fresno County, and is home to over 922,000 residents (US Census 2000), distributed over multiple jurisdictions: City of Fresno, City of Clovis, and Katti et al 2009 Page 5 Fresno County islands (Fig 2). The region is subject to a Mediterranean climate with hot, dry conditions in the summer and cool temperatures with a majority of precipitation in the winter. Fresno’s average annual high temperature is 24.7° C, average low is 10.2° C, and average rainfall is 27.9 cm (Western Regional Climate Center; www.wrcc.dri.edu). Figure 2: A map of the proposed ULTRA site, the Fresno-Clovis Metropolitan Area. The smaller bounded areas within the city limits are county islands—pockets of Fresno County that have remained unincorporated even as the cities grew around them. The points are randomly sampled point count locations from the Fresno Bird Count. In the FCMA, now the fifth largest urban center in California, a key environmental constraint on land use is expected to be water availability because it is embedded in an arid region. Surprisingly, however, water is currently used without constraint by most households, with mesic landscaping (lawn+ornamental shrubs+trees) being the norm throughout the area (Harmsen 2008). Fresno residents use an average of approximately 294 gallons per person per day (Fresno City Department of Water, cited in Harmsen et al 2008). This amount is substantially higher than other southwestern cities such as Las Vegas (230 gal.), Phoenix (165 gal.), Albuquerque (138 gal.), and Tucson (120 gal.) (Harmsen et al, 2008). Water meters, combined with a usage-based price structure, are important regulatory / microeconomic tools widely employed by governments to modulate water consumption, and encourage conservation during periods of water scarcity. The city of Fresno is currently installing water-meters at homes, and starting in 2013, residents will have to pay for water based on usage. While this is causing some concern about a new economic burden for residents, Fresno’s Department of Water Conservation has recently started a pilot Katti et al 2009 Page 6 program where 78 volunteer households, sampled from across the city’s socioeconomic gradient, have had meters installed to measure water consumption. Data from this pilot program will be used to set water rates once metering is turned on city-wide. We have initiated a collaboration with the Fresno government to study this issue, and city officials are enthusiastic about sharing data from the pilot program with our proposed ULTRA project (see attached Letter of Commitment). The city of Clovis has installed water meters on homes since 1913 (Dept. of Public Utilities, personal communication), although water rates have been low until recently. Clovis officials are also concerned about potential water scarcity, and are eager to collaborate with our project to help inform their future policies (see attached Letter of Commitment). We thus have a unique opportunity in the FCMA to study the impact of a regulatory tool, routinely used by institutional actors worldwide, on the water usage decisions and behaviors of individual actors, in a Before-After-Control-Impact (BACI; Krebs 1999) experimental framework. Specifically, we propose to use this experimental approach to test the hypothesis that metering will reduce water consumption due to the more explicit cost of water, in turn changing landscaping decisions, mediated by the socioeconomic status of individuals. We will also measure the impact of these landscaping decisions on the distribution and abundance of biodiversity. Figure 3: Map created by Planning Dept., City of Fresno, to illustrate the distribution of poverty as a percentage of the population in each census tract. The human population of the FCMA is culturally diverse and exhibits strong socioeconomic gradients: median income is $34,960 (US Census 2000) with a poverty rate of 22.7%, 9th highest among U.S. metropolitan areas. Lower income areas are concentrated near Fresno’s downtown and in the southeast Katti et al 2009 Page 7 and southwest portions of the city (Fig 3) while areas to the north and west contain some of the most expensive homes in the SJV. The smaller city of Clovis shows a similar north-south wealth gradient. Most of the recent urban growth has occurred along the northern fringe of the metro. The FCMA is also an ideal location for developing urban anthropological research because of its high ethnic/cultural heterogeneity, with a significant proportion of immigrant communities (settled over a range of time periods) from, e.g., Armenia, Mexico and other Latin American nations, South and Southeast Asia, as well as Native Americans, African Americans, and Europeans. Given these social and economic gradients, the cost of metering is likely to have heterogeneous effects on the land-use decisions of individual homeowners in different parts of the city, resulting in variable and complex outcomes in terms of habitat and species diversity. For example, if water becomes too scarce or expensive, outcomes could vary from lawns being abandoned and becoming weedy; a shift away from water-hungry ornamental plants to perhaps native plants or food plants; to, more comprehensive xeriscaping of yards (in richer neighborhoods). These various outcomes will in turn alter habitat for other species, potentially affecting species composition in terms of native vs. non-native taxa, and alter overall biodiversity. Such changes might also bring issues of environmental justice into sharper focus if poorer neighborhoods/communities are harder hit; on the other hand, lack of resources may encourage such communities to develop more sustainable water-efficient practices, potentially alleviating some environmental inequities. Water availability is already a hot political issue in the SJV, and water availability may decline even for urban areas given the continuing drought, and predictions from some regional climate change models (Harmsen et al 2008). Understanding these dynamics and being able to predict a range of potential outcomes should help urban planners, developers, and city/county/state/federal govt. agencies to modify their governance policies and practices towards greater resilience to future water scarcity. Further, a deeper understanding of relationships between social and ecological variables, and key nodes of decision-making, can also help us identify/ analyze and redress patterns of environmental inequity in many cities, whereby poorer neighborhoods and communities are also most often deprived of access to nature and related environmental goods. A well informed approach to policy making, built upon a deeper scientific understanding of the urban SES, can help create new models of natural resource governance that can accommodate population growth while improving the quality of the urban environment in ways that sustain healthy human communities and urban wildlife populations. METHODOLOGY Project organization and study design: To address the 5 main research questions (p.2) within the conceptual framework of an urban SES (p.2, Fig 1) we’ve organized this project’s activities around four core components, with a team of personnel responsible for each (as listed in Table 1, p. 14). These include three domains of variables to be measured: Social Science (all of the human social variables), Ecology (diversity and abundance of plants and birds), and Environment (climate, geology, geography, hydrology, soils, and build environment); and a unified GIS framework that encompasses all variables. The HERCULES land cover model will provide the main layer for the GIS, with other variables added in separate layers. The following sections describe, for each cluster, the key variables, sources of available data, methodology for collecting data, and team responsible for the same. Given the time and resource constraints of the Exploratory phase of this ULTRA, this proposal focuses field data gathering efforts on Social Science and Ecology variables, as indicated below. Sampling framework: The ongoing Fresno Bird Count (FBC) project has already established a field sampling framework for longer-term monitoring of bird diversity and habitat variables, modeled after similar programs in the Central Arizona-Phoenix (CAP) and Baltimore Ecosystem Study (BES) Long-Term Ecological Research (LTER) projects and other urban research sites. Two of the co-PIs were part of the research team in these urban LTER projects: MK (initials refer to names in Table 1, here and throughout the following sections) in CAP, and MLC in BES. For the FBC, we have deployed a 1km X 1km grid across the FCMA (Fig 2; see FBC website http://fresnobirds.org/ for an interactive map) extending from latitude 36.71°N to 36.89°N and longitude -119.67°W to -119.92°W, encompassing a total area of 460 Km2. This sample grid is similar to that developed for the Tucson Bird Count in Arizona (Turner 2003). Within each grid cell, a Katti et al 2009 Page 8 single point was chosen randomly as a bird census location. Points which landed on private property, were moved to the nearest public accessible location, typically the curb or street. A subsample of the bird census locations are being surveyed to measure habitat variables, i.e., tree/shrub/grass cover, tree diversity, tree density, canopy cover, impermeable surface cover. The grid provides a robust spatially explicit sampling framework useful to measure most of the component variables, and for GIS analysis and modeling. We will, as much as possible, use this grid to sample variables across all three domains, to maintain consistency in the spatial scale of sampling which will enable more efficient spatial analyses. Social Science domain: The sociopolitical component of the conceptual model will focus on Institutional and Individual actors, and data will be gathered primarily through surveys and interviews. Detailed surveys of institutions and individuals will be carried out during the Exploratory phase and are part of the core of this proposal. Institutional Review Board clearance at CSU-Fresno for these surveys is still pending, and we will submit the approval notice electronically to NSF once that process is complete. Institutional Actors: These include the public works and water departments of Fresno, Clovis, and Fresno County, who have agreed (see attached Letters of Commitment) to share documents and data on water use policies and regulations, water pricing, zoning regulations, and enforcement. Key personnel (department directors and enforcement officers) in the institutions will participate in a detailed survey designed (by ARJ, CDOC, and LMT) to assess: decision making (water policy, land use regulations), institutional behavior (enforcement), perceptions about individual actors, and perceptions of regional environment and long-term climate change trends. At least 2-3 key individuals will be surveyed in each of 3 jurisdictions, so the sample size will be relatively small. Surveys will be conducted using standard forms filled out during interviews with key personnel. CDOC and ARJ will be primarily responsible for conducting these interviews, with help from upper division undergraduate or graduate students from Geography and Sociology departments, enrolled in Independent Study projects (for 1-3 units of credit). In addition MS will conduct a policy analysis as a case-study in an upper division Political Science class (PL SI 157) graduate course in quantitative analysis (MPA 120G). Several team members (MS, SCB, CDOC, and MK) are currently engaged in several projects with city and county agencies to develop new urban green spaces, convert canals into green corridors, and restore riparian vegetation. We will leverage these connections to begin broader scale analyses of how institutions directly influence the urban land cover and habitat structure in public spaces. This broader scale institutional role in structuring urban land cover will be addressed more comprehensively in the next phase of the ULTRA. Individual (or household) Actors: Individuals will be surveyed primarily from among households selected from a random sample of FBC sites (stratified by income levels, Fig 3). A secondary nonrandom sample will come from participants in Fresno’s pilot project for water metering, which will allow us to correlate survey responses directly with actual measured water use. At least 2 individual homeowners will be surveyed at at least 60 FBC sites within the urban areas of Fresno and Clovis (excluding urban fringe sites) using standardized forms (developed by ARJ, LMT, HDD, and JCI) to document socioeconomic status, ethnicity, cultural background, education level, home price, size of house, size and type of yard/ garden, age of house, area housing density, landscape choice (incl. presence/absence of pool), water usage, irrigation technique and frequency, plant preferences, bird feeding practices, as well as perceptions about institutional decision making, enforcement of regulations, long term water policies, and perceptions of regional environment and long-term climate change trends. Surveys will be conducted by upper division students enrolled in several social science classes (Environment & Society; Intermediate Ethnographic Fieldwork; Environmental Planning) and Independent Study projects under the supervision of ARJ, CDOC, HDD, LMT, and MS. Survey forms will be coded to ensure anonymity of respondents, and data entered into a secure database for subsequent analyses. Survey data will be augmented with aggregated data on some of these variables from public databases such as the US Census, and local government planning, public works and water departments, and California Water Institute (DFZ). To measure the true impact of individual decision making on water use, we will analyze the correspondence between survey responses (i.e., self reporting by individuals) and actual water use in two ways: 1) using direct measures of water used in households participating in Fresno’s pilot metering Katti et al 2009 Page 9 program; and, 2) detailed interviews and direct observation of behavior using techniques of ethnographic and behavioral analyses, to be conducted by HDD and his anthropology students. Since the latter is more time intensive, we will choose a stratified random sub-sample (c. 10 households) of the survey respondents for such detailed ethnography, to allow for qualitative analyses during the Exploratory project. Our surveys will provide preliminary data to examine the influence of cultural background on urban water use, urban agriculture, and landscaping decisions. More comprehensive urban ethnographic/ anthropological research will be part of longer term work in the ULTRA. Ecology domain: Urban biodiversity is under strong influence from human actors, and is therefore treated as a set of dependent variables in our conceptual model (Hope et al 2002, 2005; Kinzig et al 2005). We will measure plant diversity, habitat structure, and bird diversity in the FCMA using the FBC sampling framework. Bird diversity: The bird census follows a standard point count protocol as used in CAP-LTER, Tucson Bird Count, Ottawa Breeding Bird Count, and similar projects. The random sampling locations are grouped into routes, each consisting of 8 points, and FBC volunteers can choose which routes they want to census. Routes are censused once a year from April 15th to May 15th, with about 200 points having been sampled during the 2008 and 2009 census. Ongoing outreach efforts, through the local Audubon Society chapter (see attached Letter of Commitment), will increase the number of volunteers, and therefore the census sample size, over the next several years. Five minute counts, conducted within a 40 m fixed-radius circle around each sampling site, are used to measure species richness and abundance. The 2008-09 censuses recorded a total of c.80 species at 200 sites surveyed. Preliminary analysis indicates that diversity increases slightly as one moves north through Fresno, suggesting a potential correlation with the socioeconomic gradient, as has been reported elsewhere (Hope et al 2005; Kinzig et al 2005). Point counts with a fixed radius and short duration are reliable measures for both regional and local studies (Hutto et al 1986). This seasonal period coincides with the breeding season of most local bird species, and bird censuses in Tucson and Phoenix. Species detectability is greater during this period of time due to a higher frequency and intensity of vocalizations (Selmi and Boulinier 2003). The census is conducted within a time window between 30 minutes before sunrise and 4 hours after sunrise. The routes have been designed such that a single volunteer can survey one route on a morning. Birds are identified to species by both visual and auditory methods. Volunteers are experienced birders from the local communities who are trained by experienced faculty and graduate students to conduct a rigorous scientific survey. They are screened to ensure adequate bird identification skills (visual and auditory), and trained to improve these skills by experts and peers. Volunteers also collect data during the workshop to compare across observers, so that training leads to greater consistency and reduced bias among observers. There are several advantages of this type of volunteer model: it allows for higher resolution sampling than would be feasible with the few student or paid technicians in the project; a large number of volunteers enables us to conduct the census within a narrow seasonal window; and, it cultivates a strong sense of community ownership in the project, adding to the local community’s ecological knowledge base (Cooper et al 2007). This citizen science network will form a core of our community outreach efforts, and the participation of urban naturalitsts help us generate further insights into water use and bird diversity, and keep a closer eye on long-term temporal trends. The FBC was founded by MK and is currently coordinated by BS (whose MS thesis is based on FBC data), and they will consult with KLP and PRC during this project. Bird monitoring also provides opportunities for students in field ecology classes (e.g., MK’s Biology of Reptiles and Birds class, Biol 135) to learn how to conduct point counts and analyze census data. We will also develop a database of avian life-history information from the literature, as MK did in CAP LTER. This database will facilitate analyses of functional group or ecological diversity and community structure in the urban avifauna, and experimental studies of bird behavior and population dynamics in cities (Shochat et al 2004, 2006, Warren et al 2006, Anderies et al 2007) Habitat assessment: In conjunction with the FBC, MK and BS have already begun surveying habitat at a manageable subset of the FBC locations that have already been censused. Of the 200 sites censused by FBC volunteers, we are sampling 50 sites, stratified by the 3 income zones in Fig 3, to measure habitat variables. Variables currently being measured for BS’ thesis project include the density of trees and shrubs, canopy closure (using a spherical densiometer), ground cover, and tree height, all measured within Katti et al 2009 Page 10 a 20m radius circle centered at the bird point count location. Ground cover is measured using a systematic point intercept sampling method consisting of two 20 meter transects aligned in cardinal directions and centered at the bird census point (Krebs 1999). At each meter along these transects, ground cover is classified as grass, woody plant, bare ground, litter, water (pool), pavement, or other impermeable surface. Additional variables recorded include grass height, apparent irrigation level (on a 4-point scale), and land use type (residential, rural, commercial). If the sampling circle overlaps inaccessible private property, percent ground cover (in the same categories as above) is visually estimated. We also take a series of 7 overlapping panoramic digital photographs (>6 megapixel resolution DSLR camera shooting in RAW mode, 28mm lens) to document the habitat around each point. This photographic database can be used for long-term monitoring of habitat change, and may be useful for tree identification, although we are not doing this currently due to constraints of resource and taxonomic expertise. ULTRA funding will allow us to expand these habitat surveys to a larger sample of points, to be surveyed periodically for longterm habitat monitoring (as in CAP and BES LTERs). The larger sample size will increase the resolution of habitat measurement, which will be useful for ground truthing the HERCULES land cover model. Habitat surveys also provide excellent pedagogic opportunities to train students (e.g., in MK’s Field Methods in Ecology, and JTB’s Urban Forestry classes) in ecological methodology and data analyses. Plant diversity: Identifying plant species in cities can be challenging because urban floras typically contain a high proportion of introduced species and horticultural cultivars (Hope et al 2002). This is one reason MK and BS have not yet attempted to measure plant diversity, but this proposed project will fill this crucial gap. Measuring plant diversity will be the primary responsibility of JTB, in consultation with JVHC. All tree and woody plant species taller than 1m will be identified at least to Genus within the same 20m radius circles used for habitat surveys. We will develop a plant diversity database, where we will also begin compiling life-history and phenology data from the literature. As with the birds, this database will facilitate future analyses of the urban flora in terms of functional groups and life-forms, in addition to species diversity. The life history database will also allow a preliminary analysis (by JVHC and JTB) of the water requirements of species planted in the city, since this is likely to play an important role if planting decisions change due to changes in water availability. This will also lay the groundwork for future research on the impact of human activities and decisions on urban plant physiology and phenology. Environment domain: This proposal will initiate a preliminary analysis of the effects of environmental variables on water use decisions, and resultant biodiversity patterns, establishing a baseline for more comprehensive research on these relationships during the ULTRA. The limited resources available under the Exploratory phase do not allow detailed monitoring of hydrological and climatic variables which tend to be technology intensive. Instead, we will begin to characterize and quantify the abiotic environment by leveraging existing regional monitoring programs and datasets. We will collate data on environmental variables by mining the databases of city, county, and state agencies, including departments of water, public works, flood control and irrigation districts, air and water resource boards, California and from ongoing environmental research in the region. The primary sets of variables include: • Climate - temperature, humidity, precipitation, during the project period and long term records; we will begin analyzing these in the context of the urban heat island effect (Baker et al 2002, Hedquist and Brazel 2006). • Hydrology - the main source of water applied to the landscape (ground/surface/recycled gray water), amount of water available, amount used (at the residential scale), runoff; mostly secondary source data, augmented by data on usage from Fresno’s pilot water meter program (Anon. 2008). • Water infrastructure: Presence/absence of meters, metered water rate, irrigation infrastructure (Hillaire et al 2008), distribution mechanisms; Data will be obtained from public works and water departments, databases available through the California Water Institute at CSUF, and through institutional and individual surveys (described above). • Built environment - building density, landscape and building architecture, amount and distribution of impermeable substrates, distribution and maintenance of urban parks and other green spaces; we will begin collating data from City planning departments, and initiate preliminary analysis in collaboration Katti et al 2009 Page 11 with local architects from Archop (http://www.archop.org/; see attached Letter of Commitment) engaged in urban renewal through sustainable architecture and landscaping practices. The Exploratory project brings together scholars conducting research on these variables in the region, if not in the urban area, with practitioners engaged in urban governance. Specifically, we will draw upon ongoing empirical and theoretical work (e.g., Dettinger et al 2004, Domagalski at al 2008, Hillaire et al 2008, Meixner et al, 2004) on regional—i.e., SJV and nearby Sierra Nevada watersheds—hydrology (by ZW), climate (by LK), soils (by AAB), and water infrastructure (DFZ), to develop a preliminary conceptual model of these dynamics in the urban core of the FCMA. This will form the basis of more detailed model development during the long-term phase. MK will coordinate this effort in consultation with ZW, LK, AAB, DFZ, SCB, and MLC. Unifying GIS framework: The patch-based HERCULES layer, based on a land-cover logic, will be generated for the study region using high resolution air photos (obtained from aerial surveys conducted annually by the state of California, and available through the ISIS Center at CSUF). An object-oriented classification approach will be used to calculate the percent cover of the 5 HERCULES land cover elements for each patch. An object-oriented classification approach is best suited for this because it uses information such as texture, shape and context to discriminate among elements of similar reflectance and it defines objects that may be made up of heterogeneous pixels (Zhou & Troy 2008). Using eCognition software (Definiens 2007), this approach was tested in the Baltimore research referred to previously and an overall classification accuracy of 92% was obtained. This full development of the land cover model with extensive ground truthing will be a core activity for the ULTRA; during this Exploratory phase, a preliminary model will be developed and tested. MLC, who developed HERCULES, will be primarily responsible for land cover modeling, and will be assisted by a graduate student (SW), the GIS and Data Manager (XY), in consultation with MK, CDOC, JTB, and ZW. All other variables will also be measured in as spatially explicit a manner as possible, to allow combining them in a comprehensive GIS model. Plant and bird distributions will be mapped using techniques such as indicator kriging which has been tested in the Phoenix area to analyze bird distributions in heterogeneous urban landscapes (Walker et al 2008). Data analyses and hypothesis testing: The conceptual model described on p.3 will be first analyzed in separate smaller models at different scales. Different techniques of analyses will be initially applied to the sociopolitical and ecological parts of the study to understand patterns within those components, before combining them in multivariate, spatially explicit models. We recognize that analyzing interactions between a complex set of variables measured at different spatial and temporal scales, using different techniques from diverse disciplines, can be a statistical challenge. We therefore plan to spend a significant amount of effort to explore patterns in the data before testing specific hypotheses about interactions between complex sets of variables. The Exploratory project will allow our multidisciplinary team to work together in learning and developing effective strategies to analyze such multivariate, multi-scale, long-term datasets. The 5 main research questions (p.2) provide a focus for analyzing the relationships between social, ecological, and physical environmental variables in a GIS framework. Here we outline our basic approach to those questions: Questions 1 and 2 belong primarily to the sociopolitical component of our conceptual model. To answer these, a sociopolitical model of analysis will be applied (led by JCI, CDOC, ARJ) to the survey data on two scales: aggregate and individual. We will employ linear regression analysis of the variables that express the relations between individuals and water use and institutional actors’ impacts on them. In this model, it is assumed that the water use and availability is a function of the institutional decision making and behavior, individual decision making and behavior, and environmental factors. Relevant variables will be measured, as described earlier, through surveys of institutions, and a random sample of individuals near FBC sites, and theses data will be used for quantitative analyses. The secondary sample of individuals surveyed, from Fresno’s pilot metering program, will be analyzed to “ground truth” the perceptions of survey respondents in terms of landscape outcomes. We will also apply more qualitative analysis to data from the smaller sample of more detailed ethnographic studies (led by HDD), to develop Katti et al 2009 Page 12 a deeper understanding of water use and landscaping behaviors. This urban ethnographic analysis will be broadened during the next phase of the ULTRA project, if funded. We can address question 2a (effect of metering on water usage) in a Before-After-Control-Impact (BACI) experimental design framework (Krebs 1999) because Fresno does not currently meter water, but will start metering in 2013, while Clovis already meters water. The BACI design allows us to directly test the hypothesis that the regulatory/microeconomic tool of metering water use and imposing a price on water will reduce water consumption, and change landscaping decisions. We can conduct this experiment (led by MK, in consultation with SCB) in two parts, by: 1) comparing current water use by residents in Fresno vs. Clovis, at sample sites matched by statistically controlling for variation in socioeconomic, cultural, and governance differences among them; and, 2) comparing water use by Fresno residents before and after metering is implemented with matched sites in Clovis serving as contemporary controls. The timeframe of the Exploratory project allows us to carry out part 1 of this experiment, establishing a baseline comparison for part 2 during the next phase of the ULTRA. Given the relative rarity of such “natural” experimental opportunities in urban environments (Shochat et al 2006), and the difficulty of designing proper BACI studies in cities, we are motivated to pursue this experiment even if we don’t get funded by the ULTRA program. Indeed several graduate student research projects at CSUF (BS, SW) are beginning to address some variables in this experiment at least partially; ULTRA funding will enable us to conduct a more comprehensive analysis. To address questions 3-5, we will combine independent variables from the social, ecological, and environmental components in a GIS to examine their effects upon the dependent variables of landscape choice, land cover, plant diversity, and bird diversity. We will analyze the biodiversity variables in multiple ways drawing upon species’ life-histories, to measure diversity in terms of species richness as well as functional groups (e.g. life-forms for plants; avian foraging guilds). Results from these analyses will also provide a basis for future studies testing theoretical predictions about urban animal behavior and population dynamics (Shochat et al 2006, Anderies et al 2007). We anticipate most of our statistical analyses to be built around linear models. As a number of variables may be inter-correlated, we expect to use techniques such as principle components analysis (PCA) to reduce the dimensionality of datasets. Further, since there isn’t yet, as far as we are aware, a standard statistical modeling framework established to analyze such complex systems, we anticipate using a variety of approaches to hypothesis testing, such as, multiple regression, model-based inference, regression-tree analyses, structural models (path analysis), and spatial econometrics models. Finally, for the larger scale GIS analyses, data on most variables will be aggregated to a common spatial scale, most likely at the census tract or census block group levels. Expected Outcomes and Broader Impacts: The project we are proposing represents a true partnership between academic researchers from multiple disciplines and campuses, and institutions involved in governing the urban SES, working together to address fundamental theoretical problems in urban ecology, as well as practical challenges in sustainable governance of urban environments. We have already initiated collaborative relationships with key institutions in the FCMA, and expect these to strengthen through the course of this project. Regional and state agencies such as the Central Valley offices of California Departments of Fish and Game (see attached letter), and Transportation (Caltrans) have expressed a strong interest in this project because they need deeper insights into land use decision making by individual and institutional stakeholders in the region, and are sensitive about urban growth in the valley. We therefore expect the FCMA ULTRA team to become an exemplar of the kind of participatory multidisciplinary collaboration necessary to understand the dynamics of coupled human and natural systems, and to apply this understanding to improve governance. Understanding the relationships among the variables in the urban SES model will help us push urban planning towards building more sustainable, resilient, and environmentally healthy cities. Further, a deeper understanding of these mechanisms and key variables can also help us identify, analyze and redress patterns of environmental inequity in many cities, whereby poorer neighborhoods and communities are also most often deprived of access to nature and related environmental goods. More Katti et al 2009 Page 13 specifically, this project will produce 1) several academic papers developing and empirically testing theory, to be submitted to the peer-reviewed literature; 2) reports to city, county, and state govt. offices which will inform future policy making; 3) a comprehensive multidisciplinary database (see attached Data Management Plan) as a baseline for the next phase of the ULTRA, and subsequent long-term analyses; and, 4) web portals to disseminate results and some aggregated datasets in the public domain. We have identified a number of other broader impacts throughout the proposal above. These include impacts on 1) pedagogy: opportunities to engage students enrolled in a variety of classes and independent study projects at a minority-serving institution in field research and data analyses, thereby improving their critical thinking skills; 2) community engagement: service learning opportunities for students where they can learn to transfer academic knowledge to practical applications; 3) close collaboration between academic researchers and practitioners in government and non-government organizations; 4) citizen science: advancing public understanding of science through citizen participation in primary research (e.g. FBC); 5) K-12 partner programs at CSU-Fresno; and 6) public dissemination of research outputs through local news media, reports to governance agencies, and presentations to the community through venues like Central Valley Café Scientifique (http://www.valleycafesci.org/). Timeline: The project will be carried out during the calendar years of 2010-2011. To facilitate good communication among participants from different disciplines, which is a key to the success of this project, we plan to organize at least three 1-2 day workshops bringing together the entire team: 1. January 2010 - to establish a common framework and starting point for the different clusters at the outset of their work; we will also invite at least one scholar with longer term experience working in similar collaborative teams studying coupled human-natural systems (e.g., from one of the urban LTER sites) to share their perspective with our nascent collaborative team. 2. Summer / early Fall 2010 - to allow sharing of preliminary data, identify any issues or problems in data collection, and iron out any issues in database management and data entry; and 3. Summer 2011 - to begin wrapping up analyses, outline manuscripts, and work on the proposal for the ULTRA (subject to program dates when announced). The research activities described above will be carried out as follows: 1. Social Science: Institutional and individual surveys will be carried out primarily during 2010, with class participation where appropriate. Survey data will be entered into a secure database as soon as possible to allow maximum time for data analyses and interpretation. More detailed ethnographic work will begin thereafter, in Fall 2010 / Spring 2011. 2. Ecology: The Fresno Bird Count will be conducted next in April-May 2010, adding a third year to the existing database. Habitat assessment for some of the sites has already begun, and more detailed surveys of plants and habitats will be carried out during Spring-Fall 2010, when deciduous plants are in leaf and/or most plant species are in bloom facilitating taxonomic identification. We (MK, BS) are already developing statistical models to analyze these ecological data, which should make it easier to analyze data collected during the ULTRA project. 3. Environment: We will begin collating data from public databases, and ongoing related research projects, during Spring 2010. Given the limited resources in this project, most of the research in this cluster will be relatively low intensity, and focus on applying ongoing regional work by participants to the FCMA. We will begin developing models to address changes in hydrology and water availability under likely changes in regional climate, and continued urban growth. We will also initiate collaboration with local architects working on sustainable urban design through Archop to start addressing the built environment and landscape architecture. These analyses will be developed more comprehensively during the next phase of the ULTRA. 4. GIS: Work in this cluster consists of two main components - compiling aerial imagery, and the different datasets on all the variables into spatially explicit relational databases to facilitate multivariate analyses; and, developing and testing the land cover model using HERCULES. Work on Katti et al 2009 Page 14 both of these components will begin in Spring 2010, building upon currently ongoing work in the FBC. Given the limited resources in the Exploratory phase, and the level of expertise needed to conduct the remote sensing and GIS analyses, we anticipate developing a preliminary land cover model by the second year of the project, with full development and testing to occur when additional funding is available in the ULTRA proper or from other sources. Table 1. Project organization: The research team is organized into 4 teams addressing different components of the project: Environment (climate, hydrology, soils, built environment), GIS (land cover modeling, remote sensing, spatial analyses), Social Science (anthropology, sociology, urban planning and policy, political science), and Ecology (biodiversity, distributions, population & community ecology). The following table describes the role (PI/Co-PI, Senior Personnel, or Project Participant) and cluster affiliation for all team members. Initials Name Dept. & Institution Team PI and Co-PI MK Madhusudan Katti Biology, CSU-Fresno CDOC C. Derya Özgöç-Ça!lar Geography, CSU-Fresno MLC Mary L. Cadenasso Plant Sciences, UC-Davis JTB John T. Bushoven Plant Sciences, CSU-Fresno ARJ Andrew R. Jones Sociology, CSU-Fresno Ecology (and a bit of all) Social Science, GIS Ecology, Environment, GIS Ecology, GIS Social Science Senior Personnel HDD Henry D. Delcore Anthropology, CSU-Fresno Social Science JCI LMT Jason C. Immekus Lara M. Triona Education, CSU-Fresno Psychology, CSU-Fresno Social Science Social Science XY Xiaoming Yang Interdisciplinary Spatial Info Systems Center, CSU-Fresno (Data Manager) AAB JVHC Project Participants Asmeret Asfaw Berhe School of Natural Sciences, UC-Merced John V. H. Constable Biology, CSU-Fresno SCB Steven C. Blumenshine Biology, CSU-Fresno PRC Paul R. Crosbie Biology, CSU-Fresno LMK KLP Lara M. Kueppers Kathryn L. Purcell School of Natural Sciences, UC-Merced USDA-Forest Service, Pacific SW Station MS Mark Somma Political Science, CSU-Fresno Social Science ZW Zhi (Luke) Wang Earth & Env. Sci, CSU-Fresno Environment, GIS DFZ David F. Zoldoske CA Water Inst., CSU-Fresno BS Bradley Schleder Biology, CSU-Fresno (grad student) SW Sarah Wallace Plant Sci., CSU-Fresno (grad student) Katti et al 2009 GIS, and overall Data Management Environment Ecology Ecology, Environment Ecology Environment Ecology Environment Ecology GIS Page 15 Resilience in an urban socioecological system: water management as a driver of landscape and biodiversity in the Fresno-Clovis Metropolitan Area, California. References Cited Anon. 2008. City of Fresno Urban Water Management Plan. Dept. of Public Utilities, Fresno. Alberti, M., J. M. Marzluff, E. Shulenberger, G. Bradley, C. Ryan, and C. Zumbrunnen. 2003. Integrating humans into ecology: opportunities and challenges for studying urban ecosystems. BioScience 53:1169-1179. Alberti, M. 2005. The effects of urban patterns on ecosystem function. International Regional Science Review 28:168-192. Alldredge Mathew W., P. K. H., Simons Theodore R. 2006. Estimating Detection Probabilities from Multiple-Observe Point Counts. The Auk 123:1172-1182. Alig, R. J., J. D. Kline, and M. Lichtenstein. 2004. Urbanization on the US landscape: looking ahead in the 21st century. Landscape and Urban Planning 69:219-234. Anderies, J.M., Janssen, M., and Ostrom, E. 2004. A framework to analyze the robustness of socialecological systems from an institutional perspective. Ecology and Society, 9(1): 18. [online] URL: http://www.ecologyandsociety.org/vol9/iss1/art18 Anderies, J. M., Katti, M., and E. Shochat. 2007. Living in the city: Resource availability, predation, and bird population dynamics in urban areas. Journal of Theoretical Biology 247: 36-49 Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. Land use and land cover classification systems for use with remote sensor data. USGS Professional Paper 964, US Geological Survey. Ash, C., Jasny, B.R., Roberts, L., Stone, R., and Sugden, A.M. 2008. Reimagining cities. Science, 319: 739. Baron, J. S., Poff, N. LeRoy, Angermeier, Paul L., Dahm, Clifford N., Gleick, Peter H., Hairston, Jr., Nelson G., Jackson, Robert B., Johnston, Carol A., Richter, Brian D., Steinman, Alan D. 2002. Meeting Ecological and Societal Needs for Freshwater. Ecological Applications 12:1247-1260. Band, L. E., M. L. Cadenasso, C. S. B. Grimmond, J. M. Grove, and S. T. A. Pickett. 2005. Heterogeneity in urban ecosystems: patterns and process. Pages 257-278 in G. Lovett, C. G. Jones, M. G. Turner, and K. C. Weathers, editors. Ecosystem function in heterogeneous landscapes. SpringerVerlag, New York. Baker, L. A. Brazel, A. J. Selover, N. Martin, C. McIntyre, N. Steiner, F. R. Nelson, A. Musacchio, L. 2002. Urbanization and warming of Phoenix (Arizona, USA) Impacts, feedbacks and mitigation. Urban Ecosystems 6:183-203 Bauer, T. B., and K. T. Steinnocher. 2001. Per-parcel land use classification in urban areas applying a rule-based technique. GIS 6:24-27. Bibby, C.J., Burgess, N.D. and Hill, D.A.1992. Bird census techniques. London: Academic Press. Bino, G., Levin, N., Darawshi, S., Van der Hal, N., Reich-Solomon, A., Kark, S. 2008. Accurate Prediction of Bird Species Richness Patterns in an Urban Environment Using Landsat-Derived NDVI and Spectral Unmixing. International Journal of Remote Sensing:3675-3700. Brewer, C. 2001. Cultivating conservation literacy: "trickle-down" education is not enough. Conservation Biology 15:1203–1205. Brewer, C. 2002. Conservation education partnerships in schoolyard laboratories: a call back to action. Conservation Biology 16:577–579. Broton, L., Manosa, Santi, Estrada, Joan. 2004. Modelling the effects of irrigation schemes on the distribution of steppe birds in Mediterranean farmland. Biodiversity and Conservation 13:1039-1058. Brown, D. G., K. M. Johnson, T. R. Loveland, and D. M. Theobald. 2005. Rural land-use trends in the conterminous United States, 1950-2000. Ecological Applications 15:1851-1863. Cadenasso, M.L. S.T.A. Pickett, and J.M. Grove. 2006b. Dimensions of Ecosystem Complexity: Heterogeneity, Connectivity, and History. Ecological Complexity 3:1-12. Katti et al 2009 Page 1 Cadenasso, M. L., S. T. A. Pickett, and K. Schwarz. 2007. Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Frontiers in Ecology and Environment 5:80-88. Chace, J. F., and J. J. Walsh. 2006. Urban effects on native avifauna: a review. Landscape and Urban Planing 74:46-69. Clergeau, P., J. Jokimaki, and R. Snep. 2006. Using hierarchical levels for urban ecology. Trends in Ecology & Evolution 21:660-661. Collins J.P., Kinzig A.P., Grimm N.B., Fagan W.F., Hope D., Wu J. & Borer E.T. (2000) A new urban ecology. American Scientist 88, 416-25 Cook, W. H., Faeth, Stanley H. 2006. Irrigation and Land Use Drive Ground Arthropod Community Patterns in an Urban Desert. Environmental Entomology 35:1532-1540. Cooper, C. B., Dickinson, Janis, Phillips, Tina, Bonney, Rick. 2007. Citizen Science as a Tool for Conservation in Residential Ecosystems. Ecology and Society 12. Daniels, G. D., Kirkpatrick, J.B. 2006. Does Variation in Garden Characteristics Influence the Conservation of Birds in Suburbia. Biological Conservation 133:326-335. Definiens, 2007. Definiens Professional. Software: http://www.definiens.com/. de Kok, R. 2000. Comparison of object oriented classification techniques and standard image analysis or the use of change detection between SPOT multispectral satellite images and aerial photos. International Archives of Photogrammetry and Remote Sensing 33:214-221. Dettinger, M.D., Cayan, D.R., Meyer, M.K. and Jeton, A.E. 2004. Simulated Hydrologic Responses to Climate Variations and Change in the Merced, Carson, and American River Basins, Sierra Nevada, California, 1900–2099. Climatic Change 62 (1-3):283-317 Effland, W. R., and R. V. Pouyat. 1997. The genesis, classification, and mapping of soils in urban areas. Urban Ecosystems 1:217-228. Domagalski, J.L., Phillips, S.P., Bayless, E.R., Zamora, C., Kendall, C., Wildman, R.A. Jr and Hering, J.G.. 2008 Influences of the unsaturated, saturated, and riparian zones on the transport of nitrate near the Merced River, California, USA. Hydrogeology Journal 16(4):1431-2174 Effland, W. R., and R. V. Pouyat. 1997. The genesis, classification, and mapping of soils in urban areas. Urban Ecosystems 1:217-228. Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. S. Chapin, M. T. Coe, G. C. Daily, H. K. Gibbs, J. H. Helkowski, T. Holloway, E. A. Howard, C. J. Kucharik, C. Monfreda, J. A. Patz, I. C. Prentice, N. Ramankutty, and P. K. Snyder. 2005. Global consequences of land use. Science 309:570-574. Folke, C., Jansson, A., Larsson, J. and Costanza, R. 1997. “Ecosystem appropriation by cities.” Ambio 26: 167-172. Folke, C., Colding, J., Olsson, P. and Hahn, T. 2005. “Interdependent Social-Ecological Systems and Adaptive Governance for Ecosystem Services.” In J. Pretty, A. Ball, T. Benton, J. Guivant, D. Lee, D. Orr, M. Pfeffer and H. Ward (eds) The SAGE Handbook of Environment and Society. Los Angeles: Sage Publications. pp. 537-552. Grimm, N. B., J. M. Grove, S. T. A. Pickett, and C. L. Redman. 2000. Integrated approaches to long-term studies of urban ecological systems. BioScience 50:571-584. Grimm, N.B., Faeth, S., Golubiewski, N., Redman, C., Wu, J., Bai, X., and, Briggs J. 2008. Global Change and the Ecology of Cities. Science vol. 319 (5864) pp. 756-760 Harmsen, F., Hunsaker, Donald, Van de Water, Peter, Luo, Yupeng Vivian. 2008. Mitigation and Adaptation Strategies for Climate Change in Fresno California. Institute of Climate Change, Oceans and Atmosphere, Fresno. Hedquist, B. C., and A. J. Brazel. 2006. Urban, residential, and rural climate comparisons from mobile transects and fixed stations: Phoenix, Arizona. Journal of the Arizona-Nevada Academy of Science 38:77-87. Hillaire, R. S., Arnold, M.A., Wilkerson, D.C., Devitt, D.A., Hurd, B.H., Lesikar, B.J., Lohr, V.I., Martin, C.A., McDonald, G.V., Morris, R.L., Pittenger, D.R., Shaw, D.A., Zoldoske, D.F. 2008. Efficient Water Use in Residential Urban Landscapes. Hortscience 43:2081-2092. Katti et al 2009 Page 2 Holling, C.S. 1994. “An ecologists view of the Malthusian conflict.” In K. Lindahl-Kiessling and H. Landberg (eds) Population, Economic Development and the Environment. Oxford University Press: Oxford, UK. pp. 79-103. Hope, D., Gries, Corinna, Zhu, Weixing, Fagan, William F., Redman, Charles L., Grimm, Nancy B., Nelson, Amy L., Martin, Chris, Kinzig, Ann. 2003. Socioeconomics Drive Plant Diversity. Proceedings of the National Academy of Sciences of the United States of America 100:8788-8792. Hope, D., Gries, D., Warren, P., Katti, M., Stuart, G., Oleson, J., Kaye, J. 2005. How do humans restructure the biodiversity of the Sonoran desert? In: Connecting Mountain Islands and Desert Seas: Biodiversity and Management of the Madrean Archipelago II, 2004 May 11-15, Tucson, AZ. USDA Forest Service Proceedings RMRS-P-26: 189-194 (Fort Collins, CO). Hope, D., C. Gries, D. Casagrande, C. L. Redman, N. B. Grimm, and C. Martin. 2006. Drivers of spatial variation in plant diversity across the central Arizona - Phoenix ecosystem. Society & Natural Resources 19:101-116. Hutto, R. L., Pletschet, Sandra M., Hendricks, Paul. 1986. A Fixed-Radius Point Count Method for Nonbreeding and Breeding Season Use. The Auk 103:593-602. Katti M., E. Shochat, D, Stuart, J, Lemmer, and B. Rambo, 2002. Environmental and socioeconomic influences on the Phoenix avifauna. 4th Annual Poster Symposium, CAP LTER. http:// caplter.asu.edu/docs/symposia/symp2002/Katti.pdf. Katti, M., Warren, P.S., Shochat, E., and Hope, D. Manuscript in preparation. Cities as oases of stability for birds of variable habitats: socioeconomic and ecological determinants of bird diversity in a desert metropolis. Kinzig, A. P., Warren, P. S., Martin, C., Hope, D., and M. Katti. 2005. The Effects of Human Socioeconomic Status and Cultural Characteristics on Urban Patterns of Biodiversity Ecology & Society 10 (1): 23. [online] URL: http://www.ecologyandsociety.org/vol10/iss1/art23/. Krebs, C. J. 1999. Ecological Methodology Seconnd Edition. Second edition. Addison-Welsey Educational Publishers, Menlo Park. Kressler, F. P., T. B. Bauer, and K. T. Steinnocher. 2001. Object-oriented per-parcel land use classification of very high resolution images. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas:164-167. Lepczyk, C. A. 2005. Integrating published data and citizen science to describe bird diversity across a landscape. Journal of Applied Ecology 42:4 672 Lepczyk, C. A., Mertig, Angela G., Liu, Jianguo. 2004. Assessing Landowner Activities Related to Birds Across Rural-to-Urban Landscapes. Environmental Management 33:110-125. Luck, M. A., Jenerette, G. D., Wu, J., Grimm, N. B. 2001. The urban funnel model and the spatially heterogeneous ecological footprint. Ecosystems, 4:782-796. MacGregor-Fors, I. 2008. Relation Between Habitat Attributes and Bird Richness in a Western Mexico Suburb. Landscape and Urban Planning 84:92-98. Martin, C. A., P. S. Warren, and A. P. Kinzig. 2004. Neighborhood socioeconomic status is a useful predictor of perennial landscape vegetation in residential neighborhoods and embedded small parks of Phoenix, AZ. Landscape and Urban Planning 69:355-368. Marzluff, J. M., Bowman, Reed, Donnelly, Roarke. 2001. Avian Ecology and Conservation in an Urbanizing World. Kluwer, Norwell. McCaffrey, R. E. 2005. Using citizen science in urban bird studies. Urban Habitats 2:70-86. McDonnell, M. J., and S. T. A. Pickett. 1990. Ecosystem structure and function along urban rural gradients - an unexploited opportunity for ecology. Ecology 71:1232-1237. McKinney, M. L. 2002. Urbanizaiton, Biodiversity, and Conservation. Bioscience 52:884-890. Meixner, T., C. Gutmann, R. Bales, A. Leydecker, J. Sickman, J. Melack and J. McConnell, 2004. Multidecadal Hydrochemical Response of a Sierra Nevada Watershed: Sensitivity to Weathering Rate and Changes in Deposition,Journal of Hydrology,272-285, Melles, S., J. 2005. Urban Diversity as an Indicator of Human Social Diversity and Economic Inequality in Vancouver, British Columbia. Urban Habitats 3:25-40. Moore, J W. 2000. “Environmental Crises and the Metabolic Rift in World-Historical Perspective.” Organization & Environment, Vol. 13, No. 2, 123-157 Katti et al 2009 Page 3 Pickett, S. T. A., W. R. Burch, Jr., S. D. Dalton, and T. W. Foresman. 1997. Integrated urban ecosystem research. Urban Ecosystems 1:183-184. Pickett, S. T. A., M. L. Cadenasso, J. M. Grove, C. H. Nilon, R. V. Pouyat, W. C. Zipperer, and R. Costanza. 2001. Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annual Review of Ecology and Systematics 32:127-157. Redman, C.L. 1999. Human Impact on Ancient Environments. The University of Arizona Press: Tucson, AZ. Rees W.E. 2001. Global change, ecological footprints, and urban sustainability. In: How green is the city? Sustainability assessment and the management of urban environments. (eds. Devuyst D., Hens L., & De Lannoy W.) pp. 340-363. Columbia University Press, New York. Ridd, M. K. 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int.J.Remote Sensing 16:2165-2185. Rosenberg, K. V., Terrill, S B., Rosenberg, G H. 1987. Value of Suburban Habitats to Desert Riparian Birds. The Wilson Bulletin 99:642-654. Sandstrom, U. G., P. Angelstam, and G. Mikusinski. 2006. Ecological diversity of birds in relation to the structure of urban green space. Landscape and Urban Planning 77:39-53. Scheiner, S. M., Gurevich, J, editor. 2001. Design and Analysis of Ecological Experiments. Second edition. Oxford University Press, New York. Schlesinger, M. D., Manley, Patricia N., Holyoak, Marcel. 2008. Distinguishing Stressors Acting on Land Bird Communities in an Urbanizing Environment. Ecology 89:2302-2314. Schwartz, M. W. 2006. How Conservation Scientists Can Help Develop Social Capital for Biodiversity. Conservation Biology. DOI:10.1111/j.1523-1739.2006.00421.x. Selmi Slaheddine, B. T. 2003. Does time of season influence bird species number determined from pointcount data? A capture-recapture approach. Journal of Field Ornithology 74:349-356. Shane, D. G. 2005. Recombinant urbanism: conceptual modeling in architecture, urban design, and city theory. John Wiley & Sons, Hoboken. Shochat, E., S. Lerman, M. Katti, and D. Lewis. 2004. Linking optimal foraging behavior to bird community structure in an urban-desert landscape: field experiments with artificial food patches. American Naturalist 164:232-243. Shochat, E., Warren, P.S., Faeth, S.H., McIntyre, N.E. and Hope, D. 2006. From patterns to emerging processes in mechanistic urban ecology. Trends in Ecology & Evolution 21:186-191. Stabler, B. L. 2008. Management regimes affect woody plant productivity and water use efficiency in an urban ecosystem. Urban Ecosystems 11:197-211. Steffen, W., Sanderson, A., Jager, J., Tyson, P.D., Moore III, B., Matson, P.A. et al. 2004. Global Change and the Earth Systgem: A Planet under Pressure. Springer Verlag: Heidelberg, Germany. Stow, D., Coulter, L., Kaiser, J., Hope, A., Service, D., Schutte, K., Walters, A. 2003. Irrigated Vegetation Assessment for Urban Environments. Photogrammetric Engineering and Remote Sensing 69:381-390. Syme, G., Shao, Q., Po, M. and Campbell, E. 2004. Predicting and understanding home garden water use. Landscape and Urban Planning 68:121-128 Turner II, B. L., Clark, W.C., Kates, R.W., Richards, J.F., Mathews, J.T., Meyer, W.B. (eds). 1990. The Earth as Transformed by Human Action: Global and Regional Changes in the Biosphere over the Past 300 Years. Cambridge University Press: Cambridge, UK. Vitousek, P. M. 1994. Beyond global warming: ecology and global change. Ecology 75:1861-1876. Turner, W.R., Nakamura, T, and Dinetti, M. 2004. Global Urbanization and the Separation of Humans from Nature. Bioscience 54:585-590. Turner, W. R. 2003. Citywide Biological Monitoring as a Tool for Ecology and Conservation in Urban Landscapes: The Case of the Tucson Bird Count. 65. Vitousek, P. M. 1994. Beyond global warming: ecology and global change. Ecology 75:1861-1876. Walker, J. S., Balling, R. C. Jr., Briggs, J. M., Katti, M., Warren, P. S., Wentz, E. A. 2008. Birds of a feather: interpolating distribution patterns of urban birds. Computers, Environment, and Urban Systems. 32: 19-28. Katti et al 2009 Page 4 Warren, P. S., M. Katti, M. Ermann, and A. Brazel. 2006 Urban Bioacoustics—It’s not just noise. Animal Behaviour 71:491-502. Warren, P. S., Kinzig, A. P., Cox, M., Grove, J. M., Martin, C. and Nilon, C. H. 2003. Human socioeconomic factors predict avian diversity in two cities. 5th Annual Poster Symposium, Central Arizona–Phoenix, Long-Term Ecological Research (CAP-LTER), Tempe. Arizona State University. http://caplter.asu.edu/docs/symposia/symp2003/warren_et_al.pdf Zhou, W. and A. Troy. 2008. An object-oriented approach for analyzing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing 29:3119-3135. Katti et al 2009 Page 5