Ecological homogenization of urban America

a research project funded by the U.S. National Science Foundation program on “MacroSystems Biology: Research on Biological Systems at Regional to Continental Scales.”

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Background and Literature Review

The “urban homogenization” hypothesis

Within the United States, the conversion of agricultural and native ecosystems to residential urban and suburban use represented the greatest human-driven source of landscape change during the 20th century. There is great interest in the social and environmental consequences of this land change process. Of particular environmental concern is the proliferation of suburban turfgrass, which now covers 10-16 million ha, forming the nation’s largest irrigated crop (Robbins and Birkenholtz 2003, Milesi et al. 2005). While such land covers afford spaces of personal and social value (e.g., recreation and aesthetics), there are significant concerns about fundamental changes in ecological structure and function associated with lawns and other components of urban and suburban ecosystems (Robbins et al. 2001, McKinney 2008).

Residential land management is fundamentally a local process, an expression of the decisions of individual land managers and households. However, non-local actors, structures and processes may influence residents’ decisions as well as mediate their social and biophysical impacts. Thus, decisions on yardscaping and other management may be tied not only to variables at the local-scale, but also to household structure, socioeconomic status, and neighborhood-level norms (Grove et al. 2006, Robbins 2007). 

residential landscapes

Figure 1.  Hypothesized ecological structure in residential landscapes  across four US cities, showing that a) differences between residential and native ecosystems within each city will be greater than the differences between residential ecosystems in different cities and b) that differences in native ecosystems across the continent will be larger than differences in urban and suburban ecosystems across the continent.

We hypothesize that the multi-scalar drivers and dynamics of residential land management lead to two important continental-scale patterns in urban ecosystem structure and function. First, similarity in human decision making processes across broad areas leads to convergence and homogenization in urban ecosystem structure and function across biophysically dissimilar settings.  Thus, residential ecosystems in different places are more similar to each other than they are to the native ecosystems that they replaced, e.g., a Phoenix suburban lawn is more ecologically similar to a Baltimore yard than to its neighboring Sonoran Desert ecosystems (Figure 1). Second, because residential management is driven to a large extent by household structure and socioeconomic characteristics and neighborhood-level norms, we hypothesize that neighborhoods with similar demographic and lifestyle characteristics (e.g., age, socioeconomic status, life stage, ethnicity) and social preferences (e.g., values and interests) across different cities will have more similar landscaping preferences and practices than different neighborhoods within a city, e.g., similar neighborhoods in Phoenix and Baltimore will have more similar ecosystem structure and function than dissimilar neighborhoods within each site (Figure 2).

lifestyle

Figure 2.    Hypothesized relationship between lifestyle characteristics and landscaping, where neighborhoods with similar lifestyle characteristics across different cities will have more similar landscaping preferences and practices than nearby neighborhoods within the same city with differing lifestyle characteristics, e.g., yellow neighborhoods in different cities (P1, BO1, BA1, M1) are more similar than yellow and green neighborhoods within a city (e.g., P1 vs P2 and P3). 

The ecology of residential landscapes

Perhaps the most obvious aspect of urban/suburban land use change is the replacement of natural vegetation assemblages by turfgrass yards, popular plant species, and impervious surfaces (Turner et al. 1990, Byrne 2007, Cadenasso et al. 2007, Walker et al. 2009). Within suburban parcels, lawns (or xeriscaped yards in arid regions) are the dominant land cover (Blanco-Montero et al. 1995, Robbins and Birkenholtz 2003). While there is great concern about the environmental performance of lawns in relation to air and water quality and use (Robbins et al. 2001), there is considerable uncertainty about carbon and nitrogen dynamics in these ecosystems.  Lawns can have high nitrogen losses, especially if over- fertilized and -watered (Morton et al. 1988, Petrovic 1990, Qian et al. 2003, Townsend-Small and Czimczik 2010b).  But lawns have also been shown to have considerable potential for nitrogen retention (Gold et al. 1990, Raciti et al. 2008) and carbon sequestration (Kaye et al. 2005, Golubiewski 2006, Raciti et al. submitted). 

While the ability of urban and suburban soils to accumulate carbon is well established (Pouyat et al. 2006), there is more uncertainty about aboveground carbon in residential areas.  On average, urban land in the northeast U.S. has 33.0% tree canopy cover (Nowak and Crane 2002), and more surprisingly, over the entire U.S., counties defined as “metropolitan” by the U.S. Census have an average of 33.4% canopy cover (Dwyer et al. 2000).  Older residential areas tend to have larger amounts of C stored in tree biomass as a result of increased tree cover (McPherson 1998).  Analysis with the Urban Forest Effects (UFORE) model (Nowak and Crane 1998) suggests that woody biomass in “urban” areas (as defined by the U.S. Census) sequesters 0.8 Mg C ha-1 yr-1 (Nowak and Crane 2002), or about 71% of the average amount stored annually per hectare in live trees on forestland (1.12 Mg C ha-1 yr-1) in the U.S. (Birdsey 1992). 

We suggest urban and suburban land use change increases carbon sequestration at the continental scale.  This increase occurs because in humid regions, carbon stocks in unpaved soils (the largest reservoir) are either increased, or not changed by urbanization, and in arid regions soil and vegetation carbon stocks are increased by urbanization.  We hypothesize that these soil effects are larger than any declines in vegetation carbon in humid regions, resulting in a net continental increase in ecosystem carbon stocks

Much of the ability of urban and suburban ecosystems to accumulate carbon is related to human modification and homogenization of microclimate in cities (Brazel et al. 2000).  Of particular importance is the role of watering in arid cities that should result in homogenization of soil moisture and relative humidity in residential ecosystems across the continent.

The isotopic composition of plants and soils has the potential to provide a unique means of tracing the effects of altered environmental factors and management practices on urban biogeochemical cycles. Plant nitrogen isotope ratios (δ15N) have been recognized as an integrative indicator of N sources with distinct δ15N signatures as well as soil N processes that have significant fractionation factors (Hogberg 1997, Evans 2001).  In addition, C isotope ratios (δ13C) are distinct for the two major plant photosynthetic pathways, C3 and C4, both of which are prevalent in turfgrass cultivars. 

We hypothesize that the N isotope composition of organic matter will be significantly enriched in sites with greater fertilizer application and that δ15N will be a strong indicator of fertilization intensity across sites. Pataki and Wang (2010b) showed that δ15N of unfertilized grasses in the Los Angeles basin was correlated with the spatial distribution of atmospheric NO2  and that quantifying spatial variability in both soil δ15N and atmospheric NO2 concentrations explained about 85% of the spatial variability in plant δ15N of unfertilized grasses (Wang and Pataki 2010a).  These results suggest that large changes in N cycling due to fertilization could be detectable at the scale of this multi-city comparison.

For carbon, we hypothesize that the C isotope composition of urban lawn grasses will be primarily related to water availability for C3, cool season grasses, and to temperature for mixtures of warm season and cool season grasses.  Plant δ13C has already been shown to be indicator of water stress in common lawn grasses such as fescue (Bijoor et al. 2008).  However, when analyzing data at the urban regional scale, the influence of δ13C of urban atmospheric CO2 must be taken into account, as it is commonly depleted due to local fossil fuel combustion (Pataki et al. 2003, Pataki et al. 2007, Wang and Pataki 2010a) .  We propose to use C4 plants as indicators of the isotopic composition of CO2, as has been shown previously (Marino and McElroy 1991, Pepin and Körner 2002).  Bijoor et al. (2008) used δ13C of grass clippings to calculate the proportion of C4 weeds in a fescue-dominated lawn, and found that weed cover increased with temperature, as would be expected from the quantum yield model of C3 vs C4 photosynthesis (Ehleringer and Bjorkman 1977).  We expect these patterns to be detectable at the spatial scale of this study, both within and across cities and to be a powerful indicator of the homogenization of controls or primary production (temperature, moisture, atmospheric CO2) in urban ecosystems across the continent. 

In addition to turfgrass, lawns contain weeds, and residential landscapes additionally contain annual and perennial plants and a variety of trees and shrubs, all of which contribute to the overall diversity of urban landscapes and which reflect social and demographic drivers of landscaping decisions. We hypothesize that differences in plant community composition and aboveground biomass between biophysically dissimilar regions are reduced by urbanization because residential areas in different regions have more similar landscaping and therefore plant community composition relative to that of native ecosystems in these regions.  More specifically, across regions, the cultivated flora will have lower turnover in species and phylogenetic composition than the native flora. However, within a region, there will be higher species richness and phylogenetic diversity within yards in the cultivated flora relative to flora in natural areas (controlling for area). For the spontaneous (non-cultivated) flora, the proportion of exotic species will be higher in residential landscapes than in the surrounding natural landscape, and this exotic component of the spontaneous flora will be phylogenetically more homogeneous across regions than the native component.  We expect the proportion of exotic species to be higher with the presence of open land (Von Holle and Motzkin 2007) and previous agricultural land use (Neill et al. 2007).  Structural similarities between residential landscapes across regions will lead to more similar aboveground biomass across regions in residential than in natural areas.

Human modification of residential landscapes includes substantial modification of the structure, distribution, and character of surface water systems, often introducing novel aquatic ecosystems where they were absent and eliminating others where they are abundant.  For example, residential development in Phoenix includes construction of lakes and canals (Roach et al. 2008), while abundant small and ephemeral streams are lost in mesic temperate zones (Paul and Meyer 2001).  These changes can reflect aesthetic preferences and/or engineering necessity (Roach et al. 2008), with the potential for urban hydrographies to converge on a moderate to low density of a variety of surface water features, in contrast to the divergent distributions and connectivity of these features in non-urbanized reference landscapes.

In addition to these landscape-scale changes, urban water bodies also exhibit notable changes in physical and biological structure and ecosystem-scale processes.  In streams, where the effects of urbanization are best-studied, ‘urban stream syndrome’ describes a suite of changes including flashier hydrographs, reduced channel complexity, nutrient enrichment, and loss of species diversity (Meyer et al. 2005, Walsh et al. 2005).  Much less is known about the landscape- or system-scale effects of urbanization on lakes. 

We propose to evaluate the extent to which urbanization results in a convergence of hydrographic features (i.e., the distribution of surface waters) as well as structural and functional characteristics of individual water bodies.  This research element will include analysis of surface water distributions (type, size, density and connectivity/isolation) obtained from high-resolution mapping and sampling of aquatic sediments for structural characterization and N-cycling measurements.  Because of the relative imbalance of existing data on urban streams vs. urban lakes, we will focus our sampling efforts on lentic systems, but landscape analyses will include all surface waters.  We expect that urban areas will converge toward moderate density and connectivity of predominantly larger water bodies, along with the loss of small and ephemeral features.  At the ecosystem scale, we hypothesize that nutrient and organic matter enrichment result in an increase in potential denitrification rate and that the increased nitrogen entering waterbodies will cause a  convergence toward carbon as the limiting resource for denitrification in urban areas.

Land management and ecology at the household/parcel scale:  Implications for scaling

The fundamental actors in residential land management are individual residents and the household units to which they belong. Residential managers make decisions to maintain their yards in particular ways for a variety of reasons, affecting the structure and function of urbanized ecosystems in complex ways (Law et al. 2004, Osmond and Hardy 2004, Zhou et al. 2009b).  Understanding and mapping parcel-scale dynamics is thus critical to evaluating and understanding the impact of residential land management on ecosystem structure and function at large scales.

Methods for mapping ecological structure at the highly detailed parcel scale over large areas and for relating ecological structure to variation in residential social groups have developed only recently (Liverman et al. 1998, Grove et al. 2006, Troy et al. 2007). First, the empirical ability to simultaneously describe ecological structure at both a parcel level and across large regional extents is relatively new. The widespread adoption of Geographic Information Systems (GIS) by federal, state, and local governments and recent advances in aerial and satellite remote sensing have greatly increased the availability of high-resolution geospatial data.  As well, cadastral information maintained by local governments in hardcopy format is increasingly available digitally.  Cadastral maps include information such as the boundaries, ownership and often assessed value of land parcels, as well as infrastructure such as streets, storm drains, and retention ponds.  High resolution imagery can be used to derive vegetation cover and combined with cadastral data and digital surface water data to distinguish vegetation extent, structure, and productivity among different land uses, residential owners and neighborhoods (Zhou and Troy 2008, Zhou et al. 2008).

Second, there is a growing body of research focused on the social factors affecting variations in residential land management in urban areas.  A number of studies have used measures of income and education to examine the relationship between socio-economic status and vegetation cover (Whitney and Adams 1980, Palmer 1984, Grove 1996, Grove and Burch 1997, Dow 2000, Vogt et al. 2002, Martin et al. 2004). Even more recent is the growing area of social-ecological research addressing the relationships between households, their lifestyle behaviors, and their ecologies (Grove et al. 2006, Troy et al. 2007, Boone et al. 2009a, Zhou et al. 2009b).  A critical finding to this body of research is that lifestyle factors such as family size, life stage, and ethnicity may be weakly correlated with socio-economic status, but these lifestyle factors also play a critical role in determining how households manage their properties.

There is a growing use of marketing techniques based upon social science theory to examine similarities and differences among different lifestyle groups and their consumption behaviors at local, regional, and national levels (Weiss 1988, 1990, Holbrook 2001, Troy 2008).  Geo-demographic segmentation refers to a range of methods used for classifying and characterizing neighborhoods or localities based on the principle that residents living near each other are likely to have similar demographic, socio-economic and lifestyle characteristics. Geo-demographic segmentation can be used to measure and generalize (scale up) different lifestyle groups’ preferences and motivations for various environmental behaviors in a spatially explicit context (Troy 2008). We propose to investigate whether similar relationships between lifestyle groups and their residential land management are observed, regardless of differences in regional climate (Holbrook 2001).  We also seek to determine whether there is “lifestyle homogenization” such that the same lifestyle groups in different parts of the nation have more similar land management practices than different lifestyle groups in the same city (Weiss 1988, Grove et al. 2006).

The empirical basis for characterizing the relationships between ecological structure and social groups at the parcel level and regionalizing to the national level is based upon Claritas, Inc.’s PRIZM™ (Potential Rating Index for Zipcode Markets; hereafter referred to as “PRIZM”) categorization system which has been developed by demographers and sociologists for market research (Weiss 1988, 1990, Grove et al. 2006).  There are two primary objectives of the PRIZM classification system.  The first is to segment the 290 million people of the American population and their urban, suburban, and rural neighborhoods into clusters using Census data related to household education, income, occupation, race/ancestry, family composition, and housing.  Claritas uses factor analysis and U.S. Census data at the block group level to categorize neighborhoods. The variables used by Claritas to classify neighborhoods include social rank (e.g. income, education), household (e.g. life stage, size), mobility (e.g. length of residence), ethnicity (e.g. race, foreign versus U.S. born), urbanization (e.g. population, housing density), and housing (e.g. owner and renter status, home values) (Lang et al. 1997, Claritas 1999). The second objective of the PRIZM classification system is to associate these clusters with consumer spending patterns and household tastes and attitudes using additional data such as market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM categorization system is useful for several reasons.  First, because PRIZM is designed to predict variations in expenditures on different types of consumer goods and services, of which yard care products and services are an example, it is well-suited for understanding variations in household land-management preferences and behavior.  Second, since every U.S. Census Block Group (approximating neighborhoods) is assigned a specific PRIZM category, these data provide the basis for scaling from neighborhood to continental levels.

By combining remote sensing and parcel data with plot level sampling, we will create an extensive-intensive data strategy (Zimmerman et al. 2009, Pickett et al. 2010) that we hypothesize will facilitate scaling the effects of urban/suburban land use change from local to regional and continental scales. We will use extensive sampling (e.g., the development/use of spatially contiguous/exhaustive social or ecological datasets) to determine patterns over large scales. We will use intensive sampling (e.g., the derivation of in-depth, rich and more time/labor resource intensive data that are derived from selected locations) to quantify process and mechanisms and  the motivations of social actors (Boone et al. 2009b, Buckley and Boone 2010). The intensive sampling will be used to validate extensive data.  For instance, we will produce intensive, fine-scale remote-sensing derived land cover-class maps that include lawns, coniferous and deciduous vegetation, impervious surfaces and lakes for small areas in which we conduct field sampling to infer vegetation composition, carbon and nitrogen stocks and cycling rates across socioeconomic strata.  We will then apply the findings from the intensive scale over larger areas using  data layers that capture land cover and socioeconomic characteristics of residents (e.g., Census, PRIZM).