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Biological Conservation 142 (2009) 2578–2585
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Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Relationships between avian diversity, neighborhood age, income,
and environmental characteristics of an urban landscape
Scott R. Loss a,*, Marilyn O. Ruiz b, Jeffrey D. Brawn a
a
b
University of Illinois, Department of Natural Resources and Environmental Sciences, Shelford Vivarium, 606 E. Healey St., Champaign, IL 61820, USA
University of Illinois, Department of Pathobiology, 2001 South Lincoln Avenue, Urbana, IL 61802, USA
a r t i c l e
i n f o
Article history:
Received 17 December 2008
Received in revised form 8 May 2009
Accepted 4 June 2009
Available online 5 July 2009
Keywords:
Birds
Biodiversity conservation
Chicago
Housing age
Landscape ecology
Urbanization
a b s t r a c t
Maintaining biodiversity in urbanizing landscapes has become a top conservation priority. We examined
variation in bird communities across a diverse array of urban and suburban neighborhoods in the Chicago, Illinois, metropolitan region. Rather than taking the usual approach of focusing solely on natural
features of the urban landscape, we investigated how urban bird communities were related to neighborhood age and income, as well as environmental characteristics. We found that median housing age was
strongly related to avian species richness, with newer neighborhoods supporting more species. Housing
age was an important correlate of abundance for several species as well as abundance of exotic, migratory, and non-migratory species groups. Per capita income was inversely related to richness of native bird
species and positively related to exotic richness. Total richness was higher in urban sites with undeveloped patches and heterogeneous land cover types; moreover, richness decreased with increasing distance
from natural areas greater than 1 km2. Our findings suggest that bird richness is enhanced both by small
patches of natural land within the urban matrix and by close proximity to large natural preserves. Furthermore, these results suggest that investigating a combination of abiotic and environmental features
of the built landscape, rather than focusing solely on environmental features, may provide a more complete understanding of the factors influencing avian diversity in human-dominated landscapes.
Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction
Conservation of biodiversity in urban landscapes has emerged
as a top priority (Miller and Hobbs, 2002). Seventy percent of the
world’s population is expected to live in urban areas by the year
2050 (United Nations, 2007), and 80% of the United States population now lives in urban and suburban areas (U.S. Census Bureau,
2000; Globalis, 2009). The rate of land conversion to urban uses
is occurring faster than human population growth. The population
of Chicago, Illinois, for example, increased by 38% from 1950 to
1990, while its area of land cover increased by 124% (O’Meara,
1999). These trends are contributing to biotic homogenization,
the increased dominance of a few human-adapted species at the
expense of locally unique biota (McKinney and Lockwood, 1999;
McKinney, 2006).
Compared to adjacent natural areas, bird communities in urban
settings are generally characterized by low species richness and
high total density or biomass (Emlen, 1974; Beissinger and
* Corresponding author. Present address: University of Minnesota, Conservation
Biology Graduate Program, 1980 Folwell Avenue, St. Paul, MN 55108, USA. Tel.: +1
011 612 624 4796; fax: +1 011 625 5299.
E-mail addresses: [email protected] (S.R. Loss), [email protected] (M.O. Ruiz),
[email protected] (J.D. Brawn).
0006-3207/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biocon.2009.06.004
Osborne, 1982; Crooks et al., 2004; Sandstrom et al., 2006;
Chapman and Reich, 2007; Caula et al., 2008). Intermediate levels
of suburban development are often associated with a peak in bird
richness, due to the presence of both urban-avoiding native species
and urban-adapted exotics (Blair, 1996; Blair and Johnson, 2008).
Species common to urban settings, such as exotics, are thought
to have plentiful food and experience low predation pressure, thus
leading to high reproductive success and higher density (Marzluff,
2001; Shochat et al., 2004). The habitat loss and increased human
density associated with urbanization leads to homogenization of
avifaunas among urban areas in regions that are otherwise ecologically distinct (Blair, 2001; Clergeau et al., 2006; Blair and Johnson,
2008; Sorace and Gustin, 2008).
Studies of urban bird communities typically examine the influence of natural environmental factors, such as small-scale vegetation features (Sewell and Catterall, 1998; Daniels and Kirkpatrick,
2006a,b) and the size and spatial arrangement of natural habitat
patches (Donnelly and Marzluff, 1998; Mortberg, 2001; Melles
et al., 2003; Campbell, 2009; Evans et al., 2009). These types of
studies have contributed greatly to our understanding of avian
habitat relationships in urban areas. However, non-environmental
factors associated with urban areas, such as landscape planning,
structural and architectural features of neighborhoods, and socioeconomic status of urban residents, also have an important influ-
S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
ence on urban habitat. For example, both housing age and socioeconomic status are related to the amount and structure of native
and ornamental vegetation (Hope et al., 2003; Martin et al., 2004;
Smith et al., 2005). Despite these observations, studies of the relationship between bird communities and non-environmental factors are rare (but see Kinzig et al., 2005; Melles, 2005).
Furthermore, no such studies have been conducted in the Midwestern United States. Clarification of such relationships will be
useful to conservation planners, urban planners, and land managers, since databases of non-environmental and socio-economic
data are often accessible via the internet. In addition, these data
present an opportunity to extend understanding of the urban characteristics (both environmental and abiotic) that influence urban
bird diversity.
We investigated the possible relationships among environmental landscape characteristics (e.g., land cover heterogeneity), housing age and income, and variation in avian richness, by studying a
set of neighborhoods ranging from high density urban to outer
suburban in the Chicago, Illinois, metropolitan region. This region
includes more than 90,000 hectares of preserved natural land (Chicago Wilderness, 2009) and is ideal for an analysis of urban and
suburban bird communities in relation to a human-dominated
landscape that contains an extensive network of preserves. We
sought to: 1) determine whether urban bird diversity is related
to neighborhood age and income in addition to environmental
characteristics of the landscape, 2) assess whether neighborhood
age and income are useful predictors for other aspects of avian
community structure (i.e., relative density of individual species
and richness and density of species groups), and 3) investigate
whether the amount and proximity of surrounding natural habitat
influences the richness of bird communities within the urban/suburban matrix. The impetus for these objectives was to inform avian
conservation efforts in human-dominated landscapes while more
broadly contributing to an understanding of the relationship between human-built environments and biological systems.
2. Materials and methods
2.1. Study area
We selected 42 total study sites in the Chicago metropolitan
area to represent a diverse set of urban neighborhoods with varying proximity to undeveloped lands (Fig. 1). All sites were originally developed for a study of broad and fine-scale urban and
ecological factors influencing West Nile virus transmission (Loss
et al., 2009). The wide array of urban typologies sampled also lent
itself to an analysis of the abiotic and environmental landscape
determinants of avian community structure. Below we describe
how broad and fine-scale sites were selected. Sites were different
between broad and fine-scale categories with regard to size and
the process of site selection. However, all other methods, including
bird surveys and landscape analysis, were equivalent across all
sites.
Sixteen of the sites (hereafter, ‘‘metro” sites) represent the twocounty Chicago metropolitan area (Cook and DuPage Counties) and
were selected using an urban classification technique from a previous broad-scale study of West Nile virus (WNV; Ruiz et al., 2007).
Random selection of census tracts, stratified by general urban classifications (outer suburban, inner suburban, high density urban
upper income, high density urban lower income, and urban open
space) resulted in a diverse representation of urban neighborhoods
across the metropolitan area. This suite of sites was supplemented
by two more sites in the WNV high risk areas of 2002 (Ruiz et al.,
2004). These sixteen sites (mean area = 4406 ha) comprised from
one to three census tracts.
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We also selected 26 smaller sites (hereafter, ‘‘south-side” sites)
to represent Chicago’s south-side urban/suburban interface in an
intensive fine-scale study of WNV ecology (Loss et al., 2009).
Twenty-one of these sites were classified as residential and were
made up of 20–30 contiguous and relatively homogeneous 150 m
hexagons (mean area of sites = 585 ha). The hexagons were classified as residential if >35% of land use was ‘‘low/medium density
urban” using a published digital land cover map of 30 m resolution
from the years 1999 to 2000 (Illinois Dept. of Agriculture, 2009). The
residential hexagons were then classified as low, medium or high
housing unit density (U.S. Census Bureau, 2000) and as <0.5 km or
>0.5 km from an undeveloped area (i.e., >40% in a non-urban land
use class). The final set of south-side sites represented combinations
of the three urban types (Ruiz et al., 2007) present in this area, varieties of housing unit density, and distances to undeveloped areas. In
addition, we selected five predominantly undeveloped sites (e.g.,
cemeteries) that were surrounded by an otherwise urban matrix.
The goal of this entire process was to select a broad range of sites
representing an array of human population densities, neighborhood
ages, income levels, and landscape contexts.
2.2. Estimation of bird richness and abundance
We conducted point surveys (Bibby et al., 2000) along routes
consisting of 5–10 survey points, depending on size of the site, following previously published survey methods (Loss et al., 2009). We
used street-level maps prior to site visits to evenly distribute survey points across sites. Survey points were established at least
0.5 km apart to prevent double counting of birds, in accordance
with Breeding Bird Survey protocols (U.S. Geological Survey,
1998). We then verified point locations and relocated points that
were inaccessible or noisy, owing to traffic.
We conducted 5-min unlimited radius point counts (Reynolds
et al., 1980) and estimated distance to each bird. Counts of longer
duration were not used in order to allow for sampling of all survey
points (185 total points in 2005; 145 points in 2006) twice during
each breeding season. All censuses were conducted between 0.5 h
before sunrise and 4.0 h after sunrise (05:30–10:00) on relatively
calm days with no precipitation. We surveyed each study site once
in June and once in July to coincide with the peak avian breeding
season in the Chicago area. South-side sites were surveyed in
2005 and 2006 while metro sites were surveyed only in 2005, owing to logistic constraints.
2.3. Measurement of variables for landscape analysis
We estimated median housing age, per capita income, and environmental variables using ArcMap 9.0 geographical information
system (Environmental Systems Research Institute, Redlands, California). Landscape measurements encompassed the study site
scale and did not include buffer analysis of surrounding land. Buffer measurements would not have been independent, due to the
close proximity of some sites to each other (Hostetler and Knowles-Yanez, 2003). We selected candidate landscape variables (Table
1) based on results of previous studies that reported important
associations with bird species richness, local densities, or both
(Mortberg, 2001; Melles et al., 2003; Martin et al., 2004; Palomino
and Carrascal, 2005).
Environmental variables measured for each site included proportion of undeveloped land within the site, land cover heterogeneity, and average distance of sites to natural habitat preserves
with area greater than 1 km2. We also measured percent imperviousness (i.e., amount of impermeable surface, such as pavement or
buildings), which is inversely proportional to vegetative cover (U.S.
Geological Survey, 2009). We used Illinois land cover data (Illinois
Dept. of Agriculture, 2009) to classify cover types within each site.
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S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
Fig. 1. Map of study area, including locations of ‘‘metro” (gray polygons) and ‘‘south-side” (black polygons) study sites, used for bird community landscape analysis, Chicago
metropolitan area, Cook and DuPage Counties, Illinois, 2005–2006.
Table 1
Mean, range, standard deviation, and source of independent landscape variables measured for bird community landscape analysis, Chicago, Illinois, 2005–2006.
Variable
Mean
Range
St. Dev.
Source
Land cover heterogeneity
Imperviousness (%)
Median housing age (years)
Per capita income ($)
Proportion undeveloped
Distance to 1 km2 natural preserve (m)
1.01
45.36
45.64
26912.99
0.20
2014.43
0.16–7.69
0.45–86.41
20.00–67.00
10081.00–85196.00
0.00–0.87
15.61–12150.20
1.20
17.24
13.33
12495.85
0.25
2589.53
Illinois Dept. of Agriculture
U.S. Geological Survey (EROS)
U.S. Census Bureau
U.S. Census Bureau
Illinois Dept. of Agriculture
Illinois Dept. of Agriculture
Classifications included agriculture, woodland, wetland, savanna,
and low, medium, and high density urban. Using these classifications, we defined undeveloped land within sites to include agriculture, woodland, wetland, and savanna cover types. We estimated
land cover heterogeneity within each site using all seven cover
types, and by calculating Shannon’s diversity index. This metric
simultaneously considers the number of land cover types and the
evenness of distribution of cover types within a study site (Magurran, 2004). Since large sites are likely to include more land cover
types, we standardized heterogeneity values by dividing by site
area. Finally, we calculated average distance of study sites to the
nearest natural area (forest, savanna, or grassland cover type) with
area greater than 1 km2 using ArcMap’s Spatial Analyst extension.
Whereas, study sites contained relatively small patches (typically
<2 ha) of undeveloped land, such as municipal parks or railroad
rights-of-way, the natural preserves were characterized by unbroken tracts of protected forest and parkland that provide significant
habitat for breeding birds.
S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
To understand how urban morphology, landscape planning, and
socio-economic status influence urban bird communities, we also
measured median housing age and per capita income for each site
(U.S. Census Bureau, 2000). Since sites comprised of greater than
one census tract, we averaged census data across tracts for each
site. Thus, the housing age and per capita income values used for
the remainder of the analysis represent the study site scale. These
measurements have been used in previous studies of urban vegetation and bird communities and have been suggested to vary predictably with avian diversity (Kinzig et al., 2005; Melles, 2005).
Such metrics are readily available via the internet (U.S. Census Bureau, 2000) and may be indirectly related to environmental landscape measures, such as amount of natural area.
2.4. Data analyses
We used ‘‘site” as the unit of replication rather than individual
survey points, and we restricted analyses to bird species that were
likely breeding in the region (i.e., migrants or rare species were not
considered). Nor did we consider waterfowl, gulls, herons, raptors,
and shorebirds, owing to their wide ranging habits and tendency
for extensive daily movements in the region (See Appendix A for
a full list of species included in the study). We derived density estimates based on detection functions using the program Distance 5.0
(Thomas et al., 2005). Estimated densities were natural log-transformed to meet distributional assumptions. Annual species richness was estimated by combining all bird species observed in the
June and July surveys. We standardized species richness by number of survey points, since larger study sites contained more survey
points and were thus likely to produce higher species richness estimates. Since there were no significant differences between 2005
and 2006 for any of the richness and density estimates of southside sites, we pooled data across years for these sites.
We used multiple regression models to analyze factors affecting
total species richness and total density. Multiple regression was
also used to identify factors affecting densities of six common species that were present at all sites. To determine responses of different species groups to urbanization, we analyzed richness and
density of native, exotic, migratory, and non-migratory species
groups. Chicago’s avifauna includes several non-native bird species. Those that met the above criteria for inclusion in the study included rock pigeons (Columba livia), Eurasian collared-doves
(Streptopelia decaocto), monk parakeets (Myiopsitta monachus),
European starlings (Sturnus vulgarus), house sparrows (Passer
domesticus), and house finches (Carpodacus mexicanus). We also
used logistic regression models to examine factors contributing
to the presence of 12 species that were frequently absent from
sites.
We used an information-theoretic approach to determine the
best model(s) for species richness, density, and presence. Akaike’s
Information Criteria (AIC) is based on the principle of parsimony,
whereby the best model or model set is selected from a set of candidate models based on how well it explains the relationship with
the observed data, while using relatively few parameters (Burnham and Anderson, 2002). To minimize multi-collinearity, we
identified correlated independent variables using Pearson’s correlations. Each candidate model was therefore comprised of variables
that were not strongly correlated (r < 0.5).
Nine candidate models were developed for each dependent variable (total richness, total density, species group density and richness, and density or presence of individual species) based on
previous analyses of urban bird density and richness (Mortberg,
2001; Melles et al., 2003; Kinzig et al., 2005; Palomino and Carrascal, 2005). We conducted preliminary data analysis to arrive at a
set of likely candidate models for each dependent variable. A global
model comprising all variables was also used for each analysis.
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We used mean square errors from multiple regression models
and log-likelihood from logistic regression models to calculate
Akaike’s Information Criterion, corrected for small sample sizes
(AICc). We report DAIC values (the difference in AIC value between
the best model and the model of interest) and AIC weights (the relative support for a model). If more than one model was strongly
supported (i.e., DAIC = 0–2), we considered variables from all
strong models, and averaged regression coefficients for independent variables that were represented in more than one strong
model (Burnham and Anderson, 2002). We also compared variables by calculating variable importance weights (sum of AIC
weights for all models containing the variable of interest; Burnham
and Anderson, 2002).
3. Results
We observed 76 bird species during the 2 years of the study.
Fifty-four of these were probable breeders in the region. On average, 17 species (Range = 8–32, SE = 1.01) and 67 individuals per
hectare (Range = 29.80–152.24, SE = 4.51) were observed at each
site. The most common and widespread species were the house
sparrow, American robin (Turdus migratorius), European starling,
mourning dove (Zenaida macroura), and common grackle (Quiscalus
quiscula).
3.1. Associations between species richness and housing age, income,
and environmental measurements
Spatial variation in total species richness was related to both
housing age and to environmental measurements of the landscape, but not to per capita income (Table 2; also see Appendix
A for full model selection results). Notably, richness was more
strongly related to median housing age than to any of the environmental variables (Table 2). Newer neighborhoods were found
to support greater richness than old developments (Fig. 2). The
AIC importance weight for housing age (0.89; maximum possible weight = 1) further identified an important relationship between housing age and richness. Species richness was also
greater in sites containing more undeveloped land and greater
land cover heterogeneity, and richness decreased with increasing distance from the natural preserves with area greater than
1 km2 (Table 2, Fig. 2). AIC importance weights for land cover
heterogeneity (0.34), undeveloped land (0.13), and distance to
natural preserves (0.12), suggest that these variables were less
important than housing age for explaining variation in total
richness.
Correlation results indicate that median housing age summarized a combination of environmental features of the urban landscape. This result may provide an explanation for the
relationship between avian richness and housing age. For example,
older neighborhoods had more impervious surface (r = 0.61), less
undeveloped area (r = 0.43) and were further from the large natural preserves (r = 0.39). Housing age is thus positively related to
intensity of urban development and isolation from natural habitats. Per capita income was not strongly related to any of the environmental variables we investigated, but declined slightly with
increasing imperviousness (r = 0.22).
3.2. Associations between species groups and individual species and
housing age, income, and environmental measures
Richness and density of avian species groups (i.e., exotic vs. native and migratory vs. non-migratory) were related to housing age
and income as well as to environmental measurements (Table 2).
Densities of exotic and non-migratory groups were higher in areas
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S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
Table 2
Estimated regression coefficients for landscape variables and model R2 values for best multiple regression models selected using AICc. Coefficients and R2 values for variables that
appeared in more than one strongly supported model (DAIC 6 2) for a single dependent variable were averaged across models. Full AIC results are presented in Appendix A.
Regression coefficients
Variable
Intercept
Richness
Total
Native
Exotic
Migratory
Nonmigratory
2.25
1.71
0.30
1.21
0.70
Density
Total
Native
Exotic
Migratory
Nonmigratory
3.31
2.41
1.99
2.05
3.05
Land cover
heterogeneity
0.10
Imperviousness
–
–
2.16
0.02
0.01
0.06
0.02
–
0.08
0.06
–
–
0.12
–
–
–
–
Median housing
age
Per capita
income
–
–
–
0.01
1.03
0.30
–
–
–
0.04
2.12
–
0.89
Proportion
undeveloped
Distance to
natural
0.29
0.50
0.50
1.40
–
0.02
–
–
0.16
0.43
0.17
–
–
–
–
–
0.45
0.84
2.56
0.92
1.46
0.01
–
–
–
–
–
0.12
–
Model
R2
0.36
0.32
0.18
0.35
0.06
0.07
0.39
0.44
0.46
0.50
Fig. 2. Relationship between avian species richness and land cover heterogeneity, median housing age, proportion of undeveloped land, and distance to natural preserves
>1 km2, across 42 sites in the Chicago metropolitan area, 2005–2006. Richness indicates average number of species recorded per survey point.
with older housing. Richness of migratory species was lower in
older developments. Neighborhoods with high per capita income
had more exotic species (imp. wt. = 0.19) while lower income sites
were characterized by more native (imp. wt. = 0.27) and migratory
species (imp. wt. = 0.35; Table 2). Variable importance weights
from best models for species groups were lower than for the total
richness model, suggesting less support for these relationships. The
best models for richness of non-migratory species were uninformative (R2 = 0.06).
Housing age and income also explained variation in density and
presence of individual bird species. Model R2 values were greater
than 0.30 for most species (Table 3). Per capita income was represented in the best models for 10 species and median housing age
was represented in the best models for 9 species, when considering
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S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
Table 3
Estimated regression coefficients for landscape variables and model R2 values for best individual species models selected using AICc. Multiple regression was used for density
models of common species and logistic regression was used for presence/absence models of uncommon species. Coefficients and R2 values for variables that appeared in more
than one strongly supported model (DAIC 6 2) for a single dependent variable were averaged across models.
Regression coefficients
Variable
Intercept
Density
American robin
House sparrow
Northern cardinal
European starling
Common grackle
Mourning dove
1.72
2.86
0.91
1.16
0.09
0.30
Presence/absence
Blue jay
Black-capped chickadee
Gray catbird
Baltimore oriole
Red-eyed vireo
Indigo bunting
Red-winged blackbird
Rock pigeon
Chipping sparrow
Downy woodpecker
House wren
Brown-headed cowbird
Song sparrow
0.46
1.83
4.16
0.41
6.61
7.62
3.38
7.43
7.99
4.97
1.89
1.42
7.16
Land cover
heterogeneity
0.04
–
–
0.76
0.01
–
Imperviousness
Median housing
age
Per capita
income
–
–
–
–
–
–
–
–
–
–
0.01
0.01
0.30
0.28
–
–
1.44
3.37
–
–
–
0.15
0.77
0.07
–
0.09
0.12
0.05
–
0.79
–
–
1.13
0.28
0.10
–
–
–
species models with R2 P 0.20. Densities of native species, including black-capped chickadees (Poecile atricapillus), Baltimore orioles
(Icterus galbula), red-winged blackbirds (Agelaius phoeneceus), song
sparrows (Melospiza melodia) and chipping sparrows (Spizella
passerina) tended to be greater in new developments. Density of
the exotic house sparrow, the most abundant species recorded
(comprising 49% of total bird density), was highest in older
neighborhoods.
4. Discussion
We found that total species richness and the abundances of several common species were strongly related to median housing age.
Moreover, housing age was related to other aspects of avian community structure, including richness of the migratory species group
and density of exotic and non-migratory groups. In general, as
neighborhoods aged, species richness eroded and bird communities
shifted from being primarily composed of native and migratory species to increased dominance by exotic and non-migratory species.
We also observed that proximity to large natural preserves enhances avian richness. For example, distributions of some common
species, including mourning doves and common grackles were independent of distance, while most neotropical migrants, including
scarlet tanagers (Piranga olivacea), eastern wood-pewees (Contupus
virens), and great crested flycatchers (Myiarchus crinitus) occurred
exclusively at sites within 0.5 km of large natural preserves.
4.1. Value of housing age and income metrics for urban bird studies
Our results suggest the potential utility of housing age for
describing and predicting avian diversity in urban areas. In contrast to our findings, previous studies have reported that older
neighborhoods contain more bird species, owing to more diverse
and complex vegetation cover compared to new developments
(Vale and Vale, 1976; Munyenyembe et al., 1989; Palomino and
Carrascal, 2005; but see Hope et al., 2003; Martin et al., 2004).
The nature of the association between avian richness and housing
age may depend on landscape and ecoregional contexts and the ex-
–
–
–
0.11
Distance to
natural
0.27
2.92
2.00
1.08
–
0.07
0.22
0.24
–
0.14
0.12
0.05
–
–
–
–
0.51
0.41
0.09
–
–
1.13
Proportion
undeveloped
0.31
4.21
3.61
–
–
–
4.14
8.63
0.34
0.34
0.41
0.15
0.18
0.41
0.09
0.28
–
0.14
0.11
0.11
0.18
0.30
0.66
0.09
0.26
0.01
0.06
0.01
0.25
0.29
0.23
0.42
0.44
0.39
0.30
0.24
0.54
0.27
0.31
0.30
0.45
–
–
–
–
–
–
–
6.05
0.06
–
5.41
–
0.01
–
2.21
3.35
2.33
Model R2
0.20
0.11
0.21
tent to which development alters natural elements of avian habitats. In the Chicago area, newer developments (20–30 years old)
were established in previously forested or mixed woodland-agricultural settings. Newer suburban developments that retained
components of this natural habitat appear to support relatively
great avian diversity. Subsequent urbanization, however, leads to
increased habitat loss and erosion of habitat diversity (structurally
and floristically, see also Walcott, 1974). We observed this phenomenon in Chicago, as the oldest neighborhoods are generally
typified by high human density and few remaining patches of
undeveloped land. In contrast, developments in formerly forested
landscapes that do not retain natural habitat are characterized by
low initial structural complexity and gradual maturation of forest
structure over time. This pattern was noted in Baltimore, Maryland
(Grove et al., 2006) and in isolated Chicago neighborhoods greater
than 50 years old that now contain rich vegetative communities
with complex structure. The implication of ours and previous findings is that planners and habitat managers that seek to retain significant natural components of vegetative habitat during all stages
and ages of urban development may be able to minimize the decay
of species richness and diversity that may otherwise occur.
As the Chicago metropolitan area continues to expand into surrounding agricultural land, the newest housing developments
(<20 years old) are established in landscapes dominated by rowcrop agriculture. Since our study did not include these exurban
areas, the relationship between housing age, vegetation, and avian
richness remains unclear for them. However, we would expect to
observe increased avian richness over time, as the initially sparsely
vegetated neighborhoods would likely develop greater vegetation
cover and structure due to planting of ornamental shrubs and trees
(Hope et al., 2003). Further research is needed to determine how
the relationship between housing age and avian richness varies
with regional and landscape context.
Though per capita income was unrelated to total avian richness,
the result was likely due to offsetting relationships of native and
exotic species groups. We found that high-income areas contain
more exotic bird species, while low-income neighborhoods support more native and migratory species. Income is likely related
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S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
to environmental characteristics that were not measured in our
study. For example, fine-scale vegetation characteristics, such as
proportion of native and exotic plant species, density and structure
of the understory, and plant diversity, have been linked to neighborhood socio-economic status (Hope et al., 2003; Martin et al.,
2004). Importantly, our results differ from previous studies that
detected a positive relationship between socio-economic status
and avian richness (Kinzig et al., 2005; Melles, 2005). Mechanisms
responsible for our conflicting findings are unclear. Future research
should continue to identify and measure intermediate variables to
clarify the mechanistic link between income and avian richness.
4.2. Importance of small undeveloped patches and large habitat
preserves in an urban landscape
Even though most study sites were distant from large nature
preserves (greater than 2 km, on average) and contained only small
undeveloped patches (less than 20% of total land cover, on average), such as municipal parks or railroad rights-of-way, it was clear
that these patches contributed to enriching bird richness. In addition to a positive relationship with total richness, and relationships
with several species groups, the proportion of undeveloped land
was positively related to density of twelve individual species
(when considering species models with R2 P 0.20), more than
any other variable. Species group responses to this measure varied,
with density of native and migratory species increasing and density of exotic and non-migratory species decreasing with increased
undeveloped area. The inverse relationship between undeveloped
area and density of house sparrows, the most abundant species,
was especially strong (Table 3). Undeveloped patches often contributed to increased avian richness, supporting species not typically associated with predominantly urban settings, such as
indigo buntings (Passerina cyanea) and Baltimore orioles. Higher
proportions of undeveloped land (40% or greater) tended to occur
in newer neighborhoods.
Notably, urban sites with the highest richness values were comparable to nature reserves in rural parts of north-central and northwestern Illinois (Robinson et al., 1997), when analyzing the same
orders of birds considered here (i.e., Columbiformes, Coraciformes,
Piciformes, Passeriformes). Though some species differed between
these two areas, with more exotic species occurring in urban sites,
native species richness of urban sites still approached that of natural preserves. Preservation efforts are usually focused on relatively pristine natural areas; however, these results suggest the
potential value of small patches of appropriate habitat in urbanized landscapes for avian conservation. Though small habitat
patches can function as ecological sinks, resulting in negative local
scale population growth rates within a patch (Pulliam, 1988), some
species associated with highly disturbed habitats have been found
to breed successfully in small fragments of natural habitat (Brawn
et al., 2002; Leston and Rodewald, 2006). Thus, further research
should address nest success and productivity in urban habitat
patches to determine the importance of urban sites for conservation of viable bird populations.
Bird species have been suggested to ‘‘spillover” from small
patches of natural habitat (15–136 ha) into directly adjacent urban
neighborhoods in Chicago (Lussenhop, 1977). Our result that richness increased with increasing proximity to large patches of natural
habitat suggests the possibility of a larger scale spillover effect from
natural preserves into surrounding urban sites greater than one-half
kilometer away. For example, densities of blue jays (Cyanocitta cristata), red-winged blackbirds, chipping sparrows, house wrens (Troglodytes aedon), and song sparrows, were highest within 0.5 km of
large natural areas. Densities of these species decreased with
increasing isolation from the reserves. Thus, large blocks of natural
area may influence bird communities of surrounding urban areas.
These findings agree with previous work suggesting that birds in urban settings respond to both local and landscape resource cues
(Hostetler, 1999; Bakermans and Rodewald, 2006).
5. Conservation implications
Vast databases of social and economic metrics are easily accessible online through the U.S. Census Bureau (http://www.census.gov), and they can be merged with spatial data using
geographic information systems software. Used in combination
with vegetation and habitat data, as well as existing avian point
count data, these variables may facilitate identification of urban
biodiversity hotspots and inform management that seeks to enhance or eradicate certain species groups (e.g., to increase diversity
of migratory species or reduce density of exotics). Though our findings are likely applicable to cities in the Midwestern U.S., our results contrast previous similar work in other regions
(Munyenyembe et al., 1989; Kinzig et al., 2005; Melles, 2005).
Thus, further investigation is warranted to clarify the relationship
between housing age, income, and avian community structure in
other biomes and regions in the U.S. and globally.
While large nature preserves may support large numbers of
species, budgetary and spatial constraints often prevent conservation at such large scales. Small preserves, however, can also be useful for conserving naturally patchy habitats and species with
isolated distributions (Schwartz, 1999). Moreover, a series of small
preserves that captures a variety of land cover types can be especially favorable for conservation of many species (Schwartz and
Van Mantgem, 1997). Our results further suggest that bird richness
in urban areas is enriched by small habitat patches as small as
1–10 hectares. In addition to providing habitat for breeding birds,
urban patches of natural and undeveloped habitat are important as
stop-over sites for long-distance migrant birds (Brawn and Stotz,
2001; Pennington et al., 2008).
This study suggests that assessment of a combination of nonenvironmental (e.g., housing age and income) and environmental
measurements, rather than focusing solely on environmental factors, may provide a more complete understanding of the factors
influencing avian diversity in an urban setting. Investigating relationships between these types of variables and biological patterns,
including community structure of birds, as well as plants and other
animal taxa, will determine whether such non-environmental variables are generally useful for understanding and predicting patterns of biological diversity in human-dominated landscapes.
Acknowledgements
We thank the Village of Oak Lawn for generously providing field
lab space during the study, as well as the municipalities who cooperated with us during field research (Alsip, Blue Island, Burbank,
City of Chicago, Dolton, Evanston, Evergreen Park, Harvey, Indian
Head Park, Orland Park, Palos Hills, Western Springs). We also
thank Judy Pollock of Audubon Chicago Region, who helped coordinate citywide counts, and Eric Secker, who assisted with avian surveys. We thank Robert Blair, Tony Goldberg, and Uriel Kitron, who
provided valuable comments on earlier versions of the manuscript.
This work was supported by the NSF/NIH program in the Ecology of
Infectious Diseases (Grant 04-29124) and by the Jonathan Baldwin
Turner Graduate Fellowship from the University of Illinois – College of Agriculture, Consumer, and Environmental Sciences.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.biocon.2009.06.004.
S.R. Loss et al. / Biological Conservation 142 (2009) 2578–2585
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