Land use, scale, and bird distributions in the Phoenix metropolitan area

Landscape and Urban Planning 62 (2003) 55–68

Land use, scale, and bird distributions in the Phoenix metropolitan area Mark Hostetler a,∗ , Kim Knowles-Yanez b a

b

Department of Wildlife Ecology and Conservation, University of Florida, 215 Newins-Ziegler Hall, P.O. Box 110430, Gainesville, FL 32611-0430, USA Department of Liberal Studies, California State University San Marcos, San Marcos, CA 92096-0001, USA Received 23 October 2001; received in revised form 11 February 2002; accepted 29 May 2002

Abstract Most urban areas have land use maps, but these maps have not been used to explore whether land use categories affect bird distributions. We explored how land use, at 10 different scales, affected the distribution of bird species surveyed in the Phoenix metropolitan area during the breeding season. Based on vegetation cover and built structure, we randomly established 30, 1 km transects located in older residential neighborhoods, younger residential neighborhoods, remnant desert areas, and golf courses. Each transect was divided into five 200 m segments, and we surveyed transects between 1 May and 31 July 1998. From Maricopa Associations of Governments (MAG) land use data, we measured the amount of different land uses surrounding each segment from a small circular buffer, 100 m radius, to a large circular buffer, 2500 m radius. For each buffer area and species, we conducted multiple regressions between average bird counts and percent area represented by each land use category. Across all scales, results demonstrated that only 4 of 26 species had a significant coefficient of multiple determination >0.5 between average bird counts and land use. For most species, these results indicate that land use, as defined by MAG, has limited predictability on the number of birds found in an area. We hypothesize that the structural design of given area (e.g. quantity and types of trees planted) probably plays a primary role in affecting the distribution of most bird species in urban environments. Thus, regardless of land use designation, landscape design and management of an urban area may strongly influence whether an area is attractive to a given bird species. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Scale; Urban birds; Land use; Habitat selection

1. Introduction The design of urban landscapes is a result of complex social, cultural, economic, and political interactions. North American urban designs, in many instances, are controlled by peoples’ desires to have a neat, orderly landscape which indicates that a piece ∗ Corresponding author. Tel.: +1-352-846-0568; fax: +1-352-392-6984. E-mail address: [email protected] (M. Hostetler).

0169-2046/02/$20.00 © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 9 - 2 0 4 6 ( 0 2 ) 0 0 0 9 6 - 8

of land is being cared for (Nassauer, 1995a,b). Decisions are made by a variety of players that impact the landscape from limited scales (e.g. homeowners) to broad scales (e.g. city planners). Each of these decisions has the potential to affect different species of animals in urban environments, depending on the scale at which a species responds to landscape structure (Kotliar and Wiens, 1990; Holling, 1992; Hostetler, 1999; Hostetler and Holling, 2000). Overall, the end result of human decisions creates a heterogeneous urban landscape, where certain

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areas may or may not be attractive to wildlife species. Urban environments are becoming a significant landscape type in North America. The expansion of urban areas is quite rapid; the rate that lands are converted to urban use has been estimated to exceed population growth by a factor of 6–10 (Richmond, 1996). With population growth disproportionately increasing in certain regions of North America, an urban landscape matrix dominates many regions. For example, metropolitan Phoenix, AZ, annexed 82.6 km2 between 1990 and 1997 (Gober, 1998). The end result of urban sprawl is the potential loss of wildlife habitat and the fragmentation of large regions of North American landscapes. In particular, metropolitan areas have a documented impact on avian communities. Researchers have reported higher bird densities of only a few species in urban areas when compared to natural areas, and species composition and diversity changes as the degree of urbanization increases (e.g. Woolfenden and Rohwer, 1969; Emlen, 1974; Walcott, 1974; Degraaf and Wentworth, 1981; Blair, 1996). Of the studies that have looked at birds in urban areas, many have concentrated on how habitat structure affects the distribution of birds (e.g., Emlen, 1974; Penland, 1984; Horak, 1986; Sexton, 1987; Mills et al., 1989; Blair, 1996; Germaine et al., 1998). Researchers usually measure vegetation or the density of built structure to predict the distribution of a particular species. Although these studies have found that certain structures in a landscape can be a good predictor of bird distributions, these studies usually measured habitat structure at one scale. Different species probably respond to landscape structure at different scales, and single-scale studies may not adequately reflect what many species are responding to in a landscape (Holling, 1992; Hostetler and Holling, 2000). We define response as the ability of a bird to utilize structural objects in a landscape (e.g. tree patches). The range of scales relevant to animals is an important determinant in the spatial distribution and population dynamics of animals (Wiens, 1989; Levin, 1992; Wiens et al., 1993). Thus, in habitat-selection studies, it is important to measure structure across several scales. In addition to the lack of multi-scale studies, the potential effect of land use on urban avian distributions has not been addressed (see Blair, 1996). In

particular, most metropolitan areas have land use maps, and these maps have not been used to explore whether land use categories affect bird distributions. Land use categories are related to landscape structure (i.e. land cover). For example, in terms of vegetative and built structure, a golf course is quite different from residential development. However, land use may or may not be a good predictor of underlying land cover that is important to birds (Hostetler, 1999). Is one golf course similar to another? Land cover may vary widely across similar land use categories, in part because land use designations rarely specify exacting landscape design guidelines. There are many elements that account for differences in land cover across neighborhoods, communities, cities, and metropolitan areas, even those given similar land use designations. These elements include differing tastes and preferences among landscape architects or developers, the landscape regulations in place at a municipality, and selective enforcement. Developers, including realtors, architects, and construction firms, exercise individual or collective preferences by making choices that shape the built environment. Cultural perception also plays a large role in influencing accepted landscape designs and the way people perceive or respond to landscape designs has a large influence on which designs are implemented (Nassauer, 1995a). Additionally, there is usually no overarching regulations that direct how the multitudes of individual landowners landscape and manage their private property. While a municipality may have general or subdivision site plan regulations, these have much room for creative and practical differences in outcome as seen on the landscape. For example, many cities have subdivision regulations, which may include site plan guidelines, and individual subdivisions may have covenants that describe the kind of landscape design that should occur, but these guidelines and covenants are unevenly interpreted, intentioned, and enforced. Often, subdivision covenants, which may address landscape design issues within a particular subdivision, are not enforced simply because neighbors have a difficult time filing suit against each other (Steiner, 1991). In the long run, the individual home or business owner brings even more to bear over the landscape structure as uneven attempts at caring for a landscape are enacted. In lieu of consistent approaches to landscape structure, a patchwork of different styles is evident.

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However, each land use category does have coarse landscape features that are common from one site to the next, e.g., all golf courses have large amounts of turf grass. From a policy perspective on how to manage urban areas for birds, it would be useful to know whether land use can predict whether a given area is attractive to a particular species. Such information will give pertinent information to homeowners, developers, landscape architects, and city planners to evaluate whether a piece of property (under a specific land use designation) could be designed for a given bird species. The objective of the study was to determine whether land use, across a range of scales, influences the abundance of bird species in the Phoenix metropolitan area. 2. Methods 2.1. Study design We conducted this study in the Phoenix metropolitan area in Arizona. The study area was bounded to the

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north by McDowell Rd., south by Pecos Rd., west by 40th street and portions of South Mountain Park, and to the east by Mesa/McQueen Rd. (Fig. 1). Phoenix is situated in the Sonoran desert and historically was a combination of Lower and Upland Sonoran vegetation with a mixture of vegetation associated with riparian corridors (Brown, 1994). We used a stratified random sampling design to select 30, 1 km transects within four major land cover types based on vegetative volume and built structure. The land cover types were older residential neighborhoods (eight transects), younger residential neighborhoods (eight transects), remnant desert areas (six transects), and golf courses (eight transects). Because land cover is somewhat correlated with land use, we used land cover to situate the transects within different locations within the study area. This was done to obtain a variety of land use categories across different scales. Remnant desert transects were limited to six due to limited availability of desert areas that could contain a non-overlapping, 1 km transect. Eight golf course transects were situated in eight separate golf

Fig. 1. Outline of the study area, cities, and major transportation routes.

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courses. All transects were along roads or trails and were 1 km long and divided into five 200 m segments. The four land cover types were identified from a 1993 Landsat image using a maximum likelihood supervised classification technique (see Ramsey et al., 1999). Each 30 m × 30 m pixel was classified based on the reflectance values of vegetative and built structures. An average reflectance value for a specific land cover category were obtained by visually estimating the percent cover of several land cover categories averaged over a four block training area (Ramsey et al., 1999). A pixel was given a specific land cover classification if it fell within a 2σ standard deviation of the training class mean. Essentially, older residential areas had larger vegetation volumes, with more lawn and canopy vegetation than younger residential neighborhoods. Desert remnants had a mixture of Lower and Upper Sonoran vegetation and golf courses were dominated by large expanses of mowed grass. 2.2. Bird survey We surveyed birds from 1 May to 31 July 1998 using a transect method (Emlen, 1971). The dates roughly correspond with breeding activity of most birds and are outside the fall and spring migration seasons. The 1 km transects were divided into five, 200 m segments. Observers walked transects at a slow pace (approximately 6 min/200 m segment) and counted all birds that were seen or heard within 40 m of either side of the transect. For the road transects, this meant 40 m from the curb on either side of the street. All surveys were conducted within 3 h after sunrise and each transect was surveyed three times per month. Three observers were used and to control for observer bias, each observer was randomly rotated so that each person surveyed each transect once per month. Each observer completed three transects each morning, and the start times for each transect were randomly rotated for any given month so that each transect had a start time in the early morning (0–45 min after sunrise), mid-morning (45 min to 1 h and 30 min after sunrise), and late morning (1 h and 30 min to 2 h and 30 min after sunrise). Desert and golf course transects were surveyed only in June and July due to permission problems. Surveys were not conducted during rainy or extremely windy conditions (greater than 20 mph).

2.3. Land use measurements and analyses To determine whether land use categories could predict the abundances of individual bird species, we measured the percent cover of recorded land use categories within 10 different buffer areas (i.e. scales). Buffer areas were circles with radii of 100, 200, 300, 400, 500, 750, 1000, 1500, 2000, and 2500 m. The center point of the buffer was the midpoint of a 200 m segment. Land use data were derived from the 1995 Maricopa Associations of Governments (MAG) data set (Maricopa Associations of Governments, 1995). From the MAG data set, the recreational open space land use category was subdivided into Desert Parks and Golf Courses. ESRI’s ArcView 3.1 and Spatial Analyst GIS software were used to calculate the percentages of cover represented by each land use category detected within a given buffer. We then determined the correlation between land use and species abundance using a multiple regression. The dependent variable was bird counts for each species (average number of individuals seen at each segment) and the independent variables were the percent cover of land use categories within a given circular buffer. Multiple regressions were done for each of the 10 buffer areas. Because each of the 30 transects had five segments, each regression had a sample size of 150. For a given circular buffer, we only conducted regressions on species that were present in at least 15 or more segments. Because we had a large sample size (n = 150), we only considered coefficient of multiple determination (r2 ) values >0.50 as significant. For a given circular buffer, we did not utilize a land use category in the multiple regression that represented a minute fraction of cover. Specifically, we disregarded land use categories that had fewer than 15 segments (of the 150 possible segments) with 10% of the buffer area or more occupied by the land use in question. This was to avoid spurious results that one can get from correlations with a land use that represented only a fraction of cover across all segments. For a significant correlation at a given scale, we explored which land use contributed the most to the regression. We conducted separate, simple linear regressions with all land use categories that were used in the multiple regression. The land use category with the highest r2 value (greater than 0.5) was reported and any other land use category that was within 0.1

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of this highest r2 value was also reported. If more than one land use category were reported, we explored collinearity between these categories and reported r2 values. In most cases, we expected collinearity among the percent cover of a land use across the 10 scales used in this study (see also Hostetler and Holling, 2000). In these situations, we expected that r2 values would be similar from one scale to the next. To ascertain which scale or range of scales may have played a significant role in affecting bird distributions, we employed the following procedure. First, for a given species across all scales, we looked for the one land use category with the largest r2 value that was greater than 0.50 (see above). Next, we considered all other scales that displayed r2 values within 0.09 of the highest r2 value. In these cases, we did not evaluate whether a species was responding to one scale more than another. Then, we looked for r2 values that were smaller than the largest r2 value by 0.10 or more. For those species that had at least one r2 value >0.50 and at least one r2 value that was <0.10 or more, we conducted a uniqueness index test (SAS Institute, 1994). This test takes in account the possible correlation in percent cover of a land use across scales. The purpose here was to determine whether a species seemed to primarily respond to land use at one particular scale or range of scales. The uniqueness index test simply compares the r2 value of the reduced regression (excluding the scale of interest) to the r2 value of a full, multiple regression. Only land use categories that displayed the highest r2 value (at each scale, from simple regressions) were entered into the multiple regression. For example, if one r2 value was 0.6 at the 100 m buffer and another r2 value was 0.45 at 200 m buffer (both from land use category A), then a multiple regression was conducted using the percent cover of land use category A at 100 and 200 m buffers (the two independent variables) against average bird counts. Here, we were looking for a significant drop in the r2 value (P < 0.05) when the scale with the highest r2 value was removed from the multiple regression. If the uniqueness test was significant, then this meant that the variation of bird counts was uniquely explained by the percent cover of land use at this excluded scale beyond the variance accounted for by the land use at the other scale.

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3. Results 3.1. Land use From the Maricopa Associations of Governments existing land use map data set, 24 separate land use categories were found near the bird transects as delineated by the 100–2500 m buffers (Table 1). For complete description of the land use designations, see Maricopa Associations of Governments (1995). Most of the land use categories (e.g. office and educational) represented only a small fraction of the cover within each 100–2500 m buffer. Overall, across all scales, very few land use categories occupied greater than 10% of a buffer surrounding a particular segment (Table 1). For most scales, only Large Lot Residential, High Density Residential, Medium Density Residential, Small Lot Residential, Vacant, Golf Course, Desert Park had greater than 15 segments with 10% or more of the buffer occupied by these land use categories (Table 1). At broad scales (750 m radius and above) Agriculture, Warehouse/Distribution Centers, and Industrial categories were the only other land uses that had greater than 15 segments that covered more than 10% of a circular buffer. Across all of the scales, Small Lot Residential had the most segments where 10% or more of the buffer was covered by this land use (Table 1). 3.2. Bird surveys and regression results The golf course and desert remnant transects were surveyed six times and the older and younger residential transects were surveyed nine times. Across all transects, 65 species were seen but only 26 species (plus the unidentified Hummingbird category) were found in 15 different segments or more (Table 2). Only the Mourning Dove was found in all 150 segments and the House Sparrow, in terms of average number of birds per transect, was the most abundant (Table 2). Of the 26 species that were found in 15 or more segments (Table 2), from multiple regression results, only four species (Gambel’s Quail, House Sparrow, Killdeer, and Mourning Dove) had r2 values greater than 0.50 (Table 3). Gambel’s Quail had the largest r2 value at the 750 and 1000 m buffer (r 2 = 0.57), but it also had similar r2 values (i.e. within 0.09) from 200 to 2000 m buffer. From simple linear regressions, Desert

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Table 2 Number of transect segments in which an individual species was sighteda Common name

Scientific name

Number of segments

Average

Mourning Dove Verdin House Finch White-winged Dove House Sparrow Northern Mockingbird Great-tailed Grackle European Starling Gila Woodpecker Anna’s Hummingbird Inca Dove Abert’s Towhee Curved-billed Thrasher Brown-headed Cowbird Unidentified Hummingbird Cactus Wren Rock Dove Black-chinned Hummingbird Killdeer Bronzed Cowbird Cliff Swallow Northern Flicker Gambel’s Quail N.-roughed Winged Swallow Mallard Western Kingbird Loggerhead Shrike Black-tailed Gnatcatcher Bendire’s Thrasher Costa’s Hummingbird Northern Cardinal Ash-throated Flycatcher Black-crowned Night Heron Black-throated Sparrow Lesser Nighthawk Great Blue Heron Green Heron Rufous Hummingbird Say’s Phoebe Northern Oriole Black-headed Grosbeak Domesticated Duck Greater Roadrunner American Coot Lesser Goldfinch American Kestrel Brown-crested Flycatcher American Wigeon Brewer’s Sparrow Domesticated Goose Orange-crowned Warbler Warbling Vireo Ladder-backed Woodpecker

Zenaida macroura Auriparus flaviceps Carpodacus mexicanus Zenaida asiatica Passer domesticus Mimus polyglottos Quiscalus mexicanus Sturnus vulgaris Melanerpes uropygialis Calypte anna Columbina inca Pipilo aberti Toxostoma curvirostre Molothrus ater

150 139 135 130 128 125 123 121 118 110 102 89 88 83 80 79 71 70 42 42 33 32 26 26 18 18 17 13 11 9 9 8 8 7 7 6 6 6 6 5 5 4 4 3 3 3 3 2 2 2 2 2 1

97.4 35.8 94.2 77.3 345.0 46.3 111.9 70.4 21.6 11.9 103.1 13.8 7.2 6.8 4.4 11.2 50.9 4.7 4.1 2.5 3.9 0.9 4.1 1.4 8.8 1.5 0.8 0.9 0.5 0.5 0.3 0.4 0.8 0.4 0.5 0.3 0.3 0.2 0.2 0.2 0.2 1.5 0.2 0.1 0.2 0.1 0.1 0.1 0.1 0.3 0.1 0.2 0.0

Campylorhynchus brunneicapillus Columba livia Archilochus alexandri Charadrius vociferus Molothrus aeneus Hirundo pyrrhonota Colaptes auratus Callipepla gambelii Stelgidopteryx serripennis Anas platyrhynchos Tyrannus verticalis Lanius ludovicianus Polioptila melanura Toxostoma bendirei Calypte costae Cardinalis cardinalis Myiarchus cinerascens Nycticorax nycticorax Amphispiza bilineata Chordeiles acutipennis Ardea herodias Butorides striatus Selasphorus rufus Sayornis saya Icterus galbula Pheucticus melanocephalus Geococcyx californianus Fulica americana Carduelis psaltria Falco sparverius Myiarchus tyrannulus Anas americana Spizella breweri Vermivora celata Vireo gilvus Picoides scalaris

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Table 2 (Continued ) Common name

Scientific name

LeConte’s Thrasher Olive-sided Flycatcher Red-winged Blackbird Rock Wren Song Sparrow Broad-billed Hummingbird Bullock’s Oriole Common Ground Dove MacGillivray’s Warbler Phainopepla Red-Tailed Hawk Snowy Egret Spotted Sandpiper Wilson’s Warbler Yellow Warbler

Toxostoma lecontei Contopus borealis Agelaius phoeniceus Salpinctes obsoletus Melospiza melodia Cynanthus latirostris Icterus galbula bullockii Columbina passerina Oporornis tolmiei Phainopepla nitens Buteo jamaicensis Egretta thula Actitis macularia Wilsonia pusilla Dendroica petechia

a

Number of segments 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Average 0.1 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0

The average number of birds seen per 1 km transect is also listed.

Park land use had the largest r2 value across all of these scales with Golf Course land use contributing to the r2 value at 2000 m (Table 3). At 2000 m buffer, correlation between the percent cover of Golf Course and Desert Park land use was quite low (r 2 = 0.05, P < 0.05). The uniqueness value index test was significant (P < 0.05) for all tests between the 750 and 1000 m buffer and the 100 and 2500 m buffers that had smaller r2 values by at least 0.10. The results of these tests indicate that Gambel’s Quail abundance patterns correlate to the amount of Desert Park land use at a range of scales from 200 to 1500 m. At 2000 m buffer, the combination of Desert Park and Golf Course land use contributed to the significant multiple regression result. House Sparrow had the largest r2 value at the 100 m and 200 m buffer (r 2 = 0.74), but it also had similar r2 values (i.e. within 0.09) from the 300 m to the 500 m buffer (Table 3). From simple linear regressions, Small Lot Residential land use had the largest r2 value across all of these scales (Table 3). The uniqueness value index test was significant (P < 0.05) for all tests between the 100 m buffer and the 750, 1000, 1500, 2000, and 2500 m buffers that had smaller r2 values by at least 0.10. Thus, House Sparrow abundance patterns correlate to the amount of Small Lot Residential land use at a range of scales from 100 to 500 m. Killdeer had the largest r2 value at the 100 m buffer 2 (r = 0.51), but it also had similar r2 values (i.e. within 0.09) from 200 to 400 m buffer (Table 3). From simple linear regressions, Golf Course land use had

the largest r2 value across all of these scales (Table 3). The uniqueness value index test was significant (P < 0.05) for all tests between the 100 m buffer and the 500, 750, 1000, and 1500 m buffers that had smaller r2 values by at least 0.10. Thus, Killdeer abundance patterns correlate to the amount of Golf Course land use at a range of scales from 100 to 400 m. Mourning Dove had the largest r2 value at the 2000 m buffer (r 2 = 0.60, Medium Density Residential land use), and only the 2500 m buffer had an r2 value within 0.09 (Table 3). From simple linear regressions, Medium Density Residential land use had the largest r2 value at both scales (Table 3). The uniqueness value index test was significant (P < 0.05) between the 2000 and 100–1500 m buffers that had smaller r2 values by at least 0.10. Thus, Mourning Dove abundance patterns correlate to the amount of Medium Density Residential land use at a range of scales from 2000 to 2500 m.

4. Discussion 4.1. Bird species abundance and land use Although 65 species were recorded in this study, only 26 species were abundant and found in greater than 15 segments across the study. Several urban studies have found that many recorded bird species have low densities and limited distribution in urban

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areas (Mills et al., 1989; Blair, 1996; Germaine et al., 1998). Having such low, urban bird densities for many species is a common problem when attempting to ascertain which habitat variables influence the distribution of birds in urban environments. In Tucson, AZ, Germaine et al. (1998) found that many native species occurred in a fraction of surveyed plots, including the Lesser Goldfinch, Greater Roadrunner, American Kestrel, Loggerhead Shrike, Say’s Phoebe, and Bullock’s Oriole. The relative infrequent occurrence of 38 species from our study could be a result of the fact that these species are normally not abundant in the wild (e.g. Lesser Nighthawk). However, many of these species may not be able to exploit urban environments and are relatively absent from urban areas because necessary habitat variables are absent. Urbanization typically alters plant composition with an introduction of ornamentals and lawn, and changes in vegetation structure (Emlen, 1974; Beissinger and Osborne, 1982; Rudnicky and Mcdonnell, 1989). Studies have found that native bird species are dependent on the amount of native vegetation present in an urban area (e.g. Mills et al., 1989). In this study, transects located in the residential areas and golf courses typically were planted with turf grass and ornamentals and consisted of limited native vegetation (personal observation), probably limiting the number of species occurring on these transects. Also, when viewed at broad scales, transects in our study are in a “sea” of residential development and probably influenced both the abundance and presence of a species within a given transect. Birds do respond to landscape features at broad scales. For example, Hostetler and Holling (2000) found that the abundance of many species were correlated with the amount of tree canopy cover from such broad scales as 0.2–85 km2 . Many of the infrequently seen bird species in our study may respond negatively to transects situated within residential development. We found quite a mixture of different land uses, but at all scales, Small Lot Residential was the most prevalent land use, indicating that most transects were situated in a residential matrix. 4.2. Regression and land use We found significant multiple regression correlations for only 4 of 26 species. This indicates that for

most species, land use was not a good predictor of bird abundance. The purpose behind regression analyses is to describe the dependence of a variable Y on an independent variable X with the idea of supporting hypotheses regarding the possible causation in changes of Y by X (Sokal and Rohlf, 1995). Thus, for most species in this study, the land use categories probably plays a minor role in predicting the increase or decrease in the amount of birds. We hypothesize that land use was not a good predictor of bird densities because for a given land use category, land cover, such as the amount of trees, is probably quite variable from one site to the next. For example, Single Lot Residential can look quite different from one subdivision to the next. The structural characteristics of a residential area are influenced by the combination of decisions made by homeowners, developers, and city planners (Hostetler, 1999; Hostetler and Holling, 2000). Walking through a neighborhood, one yard can be quite different from another, based on individual decisions made by homeowners. Some homeowners may prefer to have a lawn with ornamental vegetation and others may prefer to have xeriscaped yard with native vegetation. Likewise, a developer determines the overall layout of one neighborhood, such as the types of homes constructed, the types of vegetation left, and the amount of open space. Even golf courses and desert parks can look structurally different from one site to the next, depending on who designed and managed these. An increase or decrease in bird counts was probably more associated with the amount of land cover variables such as the types and the amount of vegetation. This follows several studies that demonstrated that bird abundance and distribution was most often affected by habitat structure (Emlen, 1974; Penland, 1984; Horak, 1986; Sexton, 1987; Mills et al., 1989; Blair, 1996; Germaine et al., 1998). Abundance patterns, for a few species, correlated with the amount of a land use category surrounding a segment. Over 50% of the variation in the counts for Gambel’s Quail, Killdeer, House Sparrow, and Mourning Dove could be explained by the amount of land use surrounding a segment. Variation in Gambel’s Quail counts were correlated to variation in the amount of Desert Park, Killdeer with Golf Course, House Sparrow with Single Lot Residential, and Mourning Dove with Medium Density Residential. This indicates that

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these species may be responding to a landscape variable(s) that is consistent in these land use categories. Considering the natural history characteristics of these species (Ehrlich et al., 1988), Gambel’s Quail was most likely responding to the presence of large areas of desert vegetation, Killdeer to the presence of large open spaces consisting of turf grass, and House Sparrow and Mourning Dove to the presence of built structure (e.g. homes). However, at 2000 m buffer for Gambel’s Quail, Golf Course and Desert Park categories contributed to a significant regression result. Golf Course and Desert Park land use categories were not correlated with each other at this scale, indicating that surveyed segments near golf courses also had more Gambel’s Quails. Several of the golf courses had desert vegetation mixed in with the turf and this may have attracted more birds in the vicinity. An inspection of segments that had Gambel’s Quail revealed several segments with these golf courses nearby, but these golf courses were not recorded until a broader area was analyzed. In addition, why Mourning Doves and House Sparrows responded to one particular residential land use category versus another, at this point, needs more investigation. It may be that density of artificial bird amenities (e.g. roosting and nesting sites on buildings, birdbaths, bird feeders, etc.) may be different between the various residential land uses and more attractive for one species versus another. The multi-scale analyses were necessary to explore whether birds were responding to a particular land use category. One may erroneously interpret the result of a habitat-selection study if analyses were constricted to data gathered at one arbitrary scale (Hostetler, 1999; Hostetler and Holling, 2000). For example, in our study, had we restricted our measurements of land use at 100 m radius buffers, we would not have found that the Mourning Dove and Gambel’s Quail may be responding to Medium Density Residential and Desert Park land use categories at broader scales. Interestingly, these two species are larger than the House Sparrow and Killdeer, which responded to land use categories at more limited scales. The scales that recorded the highest r2 value for the Mourning Dove, Gambel’s Quail, House Sparrow, and Killdeer were 2000 m, 750 and 1000, 100 and 200, and 100 m, respectively (Table 3). Hostetler and Holling (2000) found that body sizes of birds

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were an approximate predictor of the scales at which birds responded to urban tree patches. Many ecological, physiological, and behavioral traits are correlated to body size (Schoener, 1968; Peters, 1983; Calder, 1984), and avian home range area is somewhat correlated to body size (Schoener, 1968). It may be that Mourning Doves and Gambel’s Quails are primarily responding to a land use category at broader scales because they have fairly large home ranges. We have explained the avian distribution patterns in terms of urban land use, however urban development can impact birds indirectly through human activities. Traffic, noise pollution, and the presence of humans can cause birds to avoid urban developments. Birds take flight, or “flush” when humans are nearby, which may result in nest abandonment (e.g. Hockin et al., 1992) or an energetic cost through increased metabolic rate (Gabrielson and Smith, 1995). In addition, the presence of cats and dogs has a substantial impact on bird species found in urban environments (Churcher and Lawton, 1987; Coleman and Temple, 1993). In our study, the unexplained variation in avian abundance and distribution could be a result of some combination between the amount of certain landscape variables (e.g. trees and cacti) and the degree of human disturbance. Also, we conducted the surveys during the breeding season, and we most likely counted a mixture of immature/newly-fledged birds along with mature adults. Animals may respond to different features in the landscape during different life history stages (Levin, 1992). If mature and immature birds (of the same species) respond to different land use categories, this could cause non-significant regression results, and we could not distinguish between breeding and non-breeding birds. 4.3. Urban design and management implications We found only four bird species where abundance patterns were correlated to the amount of land use in our Phoenix study area. Thus, land use, as defined by the Maricopa Associations of Governments, was not a good predictor of bird distributions. These results have several implications for how city planners, landscape architects, developers, and homeowners can design and manage urban areas for birds.

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4.3.1. Homeowners For a given subdivision, the land use category in which properties are contained may not be a good indicator of where birds will occur within the subdivision. For example, if you own a house that is situated in a Small Lot Residential area, the way you design and manage your landscape could have a measurable affect on the quantity and types of birds that visit your yard. Certain species reside in this type of land use (e.g. Anna’s Hummingbird) but where they appear may be dependent on how one manages a property. In addition, some species may not be that abundant in residential areas (e.g. Gambel’s Quail and Killdeer), but this does not mean a homeowner could not attract these species to their yard. For example, if the property backed-up to a desert park or a golf course, where the Gambel’s Quail and Killdeer are present, one may be able to attract these species to a yard. Homeowners should look beyond the boundaries of their yard and be aware of the surrounding landscape matrix and the types of birds that could in occur in these landscapes. Further, this study indicated that a Gambel’s Quail might need a large area of desert vegetation (i.e. an area representing a circle of 100–2000 m). If a group of adjoining property owners were to eliminate lawns and plant natural desert vegetation, the cumulative effect may be significant enough to attract a Gambel’s Quail. In general, to attract larger bird species, homeowners need to think at much broader scale than their own lot. 4.3.2. Developers, city planners, and landscape architects How one designs and manages a golf course, a subdivision, or a desert remnant probably plays a large role in attracting or excluding a bird species. Each golf course, subdivision, and desert remnant can look quite different depending on the historical development and current management of the property. For example, property designated as a golf course does not automatically preclude which species will be found on this piece of property. Although particular golf course features are common across all golf courses (e.g. turf grass) and may be attractive to certain species (e.g. a Killdeer), the design and management of a golf course could have a dramatic effect on which bird species occur on which golf courses. Several studies in Tucson, AZ found that the amount of native vegetation was instrumental in maintaining sensitive

native bird species (Mills et al., 1989; Germaine et al., 1998). Thus, golf courses planted with native vegetation would probably attract a greater diversity of species. The important message for developers, planners, and architects is that a land use category can have a variety of vegetative patterns that may or may not be attractive to bird species. Our study demonstrated that land use was not a good predictor of avian abundance patterns, and this suggests that specific design and management strategies will have an appreciable impact on birds, regardless of land use designation. If a city wants to minimize the impact of urban areas on bird communities, we recommend that various interpretive, outreach, and educational programs be established that inform all citizens about designing and managing landscapes for birds. Urban landscapes mature over time, and the realm of different decisions made by a wide variety of people can change the original design and appearance of an area. For example, a residential area could have been designed with primarily native landscaping, however, as homeowners come and go, each makes an individual decision about how to maintain their individual property. Over time, if each homeowner decides to plant exotic vegetation, the cumulative impact could drastically affect the landscape. A potential exists to create urban landscape structure in which birds could thrive. A key ingredient appears to be educating individual homeowners and developers about the relationships between particular bird species, land use, and vegetation. As homeowners and developers become more aware of these relationships and begin to implement appropriate management strategies, they may be rewarded by a wider variety of avian visitors.

Acknowledgements We thank our colleagues at the Center for Environmental Studies, Arizona State University, for their support and input in developing this project. We also thank the Department of Wildlife Ecology and Conservation, University of Florida, for an atmosphere in which to analyze data and write this paper. This research was in part funded by the National Science Foundation Grant DEB-9714833. This research is contribution number R-08915 of the Florida Agricultural Experiment Station.

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currently working with developers, planners, architects, and homeowners to design and manage sustainable, residential communities. Long-term goals include developing training courses for the private and public sector on sustainable developments, creating certification criteria for eco-intelligent communities, and constructing on site, education programs for homeowners to promote functionality of an environmental community. Prior to his graduate studies, he was a US peace corps volunteer in Senegal. More information is available at . Kimberley [Kim] Knowles-Yánez holds a PhD in urban and regional planning from the University of Illinois at Urbana-

Champaign and a Master’s in regional planning from Washington State University. She is currently an assistant professor of urban and regional planning in the Liberal Studies Department at California State University San Marcos. Previously, she was a research associate at the Center for Environmental Studies and adjunct professor in the School of Planning & Landscape Architecture at Arizona State University, where she conducted land use planning research for the NSF-funded Central Arizona Phoenix Long-Term Ecological Research Project. Her areas of research interest are land use planning, GIS, and children’s issues. Prior to her graduate studies, she was a US peace corps volunteer in Ecuador.