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Iheringia. Série Zoologia

Print version ISSN 0073-4721On-line version ISSN 1678-4766

Iheringia, Sér. Zool. vol.108  Porto Alegre  2018  Epub June 11, 2018

http://dx.doi.org/10.1590/1678-4766e2018017 

Articles

Bird diversity in an urban ecosystem: the role of local habitats in understanding the effects of urbanization

Diversidade de aves em um ecossistema urbano: o papel dos habitat locais na compreensão dos efeitos da urbanização

Aline Goulart Rodrigues1 
http://orcid.org/0000-0003-0759-0464

Márcio Borges-Martins2 
http://orcid.org/0000-0001-9328-5794

Felipe Zilio3  * 
http://orcid.org/0000-0003-2207-9330

1Curso de Ciências Biológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil. (alinegrbio@gmail.com)

2Laboratório de Herpetologia, Departamento de Zoologia, Programa de Pós-graduação em Biologia Animal, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil. (borges.martins@ufrgs.br)

3Setor de Ornitologia. Seção de Zoologia de Vertebrados, Museu de Ciências Naturais, Fundação Zoobotânica do Rio Grande do Sul, Porto Alegre, RS, Brasil. (fzilio@msn.com)

ABSTRACT:

Urbanization causes environment changes that directly affect biotic diversity, and understanding the relationship between fauna and urban features is a key aspect of urban planning. Birds are particularly affected by urbanization. Noise levels, for instance, negatively affect birds’ behavior and social communication, while the presence of green areas promotes bird diversity. The effects of urbanization could differ according with the level of urbanization, and our goal was to understand how bird species assemblages are related to urban features in an intermediate stage of urbanization (a city in Brazil with 2,470 inhabitants/km²). We used canonical correspondence analysis (CCA) and generalized linear models (GLM) analyses to assess how bird species assemblages are affected by urban features (e.g., noise level, abundance of buildings) as well as habitat features (e.g., vegetation cover). Despite we did not find a clear pattern of urbanization both the urban and habitat features had, even if weak, an effect on bird species distribution. Bird species distribution was spatially correlated, and we identified three groups: 1) grassland and wetland species; 2) forest species; 3) species tolerant to habitat degradation. Species richness was positively related to the proportion of trees, abundance of people and presence of buildings, and negatively affected by higher levels of noise. The abundance of species decreased as noise levels increased, but the proportion of green areas (open or forest vegetation) had a positive effect. Agreeing with previous research, our study shows that noise levels and vegetation cover seem to be the best predictors of diversity in urban areas. Nevertheless, the presence of particular habitats (wetlands, grasslands, woodlots), patchily distributed in the urban matrix, could buffer the effects of urbanization on birds. These habitats should thus be taken into account in urban planning.

KEYWORDS Neotropic; urban noise; green spaces; species richness; bird assemblage

RESUMO:

A urbanização resulta em alterações no ambiente que afetam diretamente a diversidade biótica, sendo fundamental a compreensão das relações entre a fauna e as características do ambiente urbano para o planejamento de uma cidade. O ruído, por exemplo, é uma característica do ambiente urbano que afeta negativamente o comportamento e comunicação social das aves, enquanto a presença de áreas verdes promove a diversidade. Os efeitos da urbanização sobre a fauna podem variar conforme o estágio de desenvolvimento urbano, assim, nosso objetivo foi analisar a distribuição da avifauna em uma área com estágio intermediário de urbanização (uma cidade brasileira com 2.470 habitantes/km²) e sua relação com a paisagem urbana. Nós realizamos uma análise de Correspondência Canônica (CCA) e Modelos Lineares Generalizados (GLM) para avaliar como a avifauna é afetada pelos componentes da paisagem urbana (e.g., nível de ruído, número de construções, cobertura vegetal). Apesar de não termos encontrado um padrão claro de urbanização, tanto as características urbanas quanto as de habitat tiveram, mesmo que de forma branda, um efeito sobre a distribuição de espécies de aves. A distribuição das espécies foi espacialmente correlacionada, formando três grandes grupos: 1) espécies associadas aos ambientes campestres e úmidos; 2) espécies florestais; 3) espécies tolerantes aos ambientes degradados. A riqueza de espécies foi positivamente relacionada à proporção de árvores, à abundância de pessoas e à presença de prédios, porém teve efeito negativo com o aumento do nível de ruído. Áreas com maior nível de ruído apresentaram menor abundância de aves, enquanto as maiores abundâncias estiveram positivamente associadas à proporção de áreas verdes (vegetação campestre ou florestal). Nossos resultados concordam com estudos prévios que sugerem que o nível de ruído e a cobertura vegetal são as variáveis mais relevantes relacionadas à diversidade de aves em áreas urbanas. Contudo, a presença de habitat específicos (banhados, campos, matas), imersos na matriz urbana, poderiam amortizar os efeitos da urbanização sobre as aves, e estes deveriam ser considerados quando avaliado o planejamento urbano das cidades.

PALAVRAS-CHAVE Neotrópico; ruído urbano; áreas verdes; riqueza de espécies; assembleia de aves

Urban ecosystems are complex, heterogenic and dynamic, characterized mainly by dense agglomerations of people living in the same place. The urbanization process involves changes in the landscape, soil modifications, climate changes, and biodiversity loss, resulting in a new, distinct ecosystem (Pickett et al., 2011). City growth changes the landscape - destroying natural habitats and creating new ones - and native species are replaced by a pool of a few species adapted to the urban environment (urban exploiters), promoting biotic homogenization (Blair, 1996, 2004; Rolando et al., 1997; Clergeau et al., 1998; Tait et al., 2005; Chace & Walsh, 2006; McKinney, 2006; Evans et al., 2009; Pickett et al., 2011; Aronson et al., 2014; Puga-Caballero et al., 2014; Beninde et al., 2015; Dallimer et al., 2015). However, cities are not homogeneous environments, but rather have zoning according to the type of activity in or usage given to certain areas (parks, industrial zone, residential zone). Thus, in urban areas, bird species distribution is both related to the local habitat features (tree and shrub cover, density of houses and other buildings) and the degree of urbanization of the city (Rolando et al., 1997; Evans et al., 2009; Pickett et al., 2011; Fontana et al., 2011; Ortega-Álvarez & MacGregor-Fors, 2011; Aronson et al., 2014).

The availability of green areas and the level of noise are two of the most important features affecting urban avian species assemblages (Chace & Walsh, 2006; Evans et al., 2009; Fontana et al., 2011; Toledo et al., 2012; Njoroge et al., 2013, Beninde et al., 2015; Sacco et al., 2015). High bird diversity in urban landscapes has been associated with high densities of trees and the presence of large green spaces connected or near to each other (i.e., not fragmented, but connected by corridors or acting as stepping stones) (Evans et al., 2009; Aronson et al., 2014, Beninde et al., 2015). High densities of human dwellings - and people - and high levels of noise are associated with lower levels of bird diversity (Evans et al., 2009; Fontana et al., 2011), but higher bird abundances (Evans et al., 2009). This pattern of continuous decline of diversity and increase in abundance is exhibited along the rural-urban gradient (Blair, 1996; Chace & Walsh, 2006; McKinney, 2006; Puga-Caballero et al., 2014; Bino et al., 2008; Ortega-Álvarez & MacGregor-Fors, 2011), although diversity could peak at intermediate levels of disturbance, as in peri-urban areas (Blair, 1996, 2004; Tratalos et al., 2007).

Birds’ responses to living in urban centers and the effects of disturbance in these areas have been studied for decades in the northern hemisphere (e.g., Marzluff et al., 2001; Chace & Walsh, 2006; Evans et al., 2009; Pickett et al., 2011; Davis et al., 2012; Taylor et al., 2013; Aronson et al., 2014; Sol et al., 2014; Beninde et al., 2015), but are a relatively new research focus in South America (e.g., Fontana et al., 2011; Ortega-Álvarez & MacGregor-Fors, 2011; Toledo et al., 2012; Njoroge et al., 2013; Puga-Caballero et al., 2014; Leveau et al., 2015; Sacco et al., 2015). Although urban species assemblages in South and North America show similar patterns, still there are several gaps to be filled (e.g., demographic patterns, physiological responses, behavioral ecology, biotic homogenization; Ortega-Álvarez & MacGregor-Fors, 2011). Our goal was to evaluate how urbanization affects the bird species assemblage (species richness and abundance) of a medium-sized city in southern Brazil. We use features of the urban landscape to test the predictions that (1) the degree of urbanization affects bird species distribution, and intensely urbanized areas have lower species richness and higher species abundance than less urbanized areas; (2) both species richness and abundance diminished in proportion to noise level; (3) vegetation is an important component of the urban landscape for birds, and bird diversity will increase in proportion to the area of green space in the city (parks, gardens, orchards).

METHODS

Study area. Canoas (29°55’12”S, 51°10’48”W) is part of the metropolitan area of Porto Alegre (the capital and largest city of the state of Rio Grande do Sul, Brazil) known as Greater Porto Alegre. Built in the Depressão Central region (Central valley) on the Guaiba river basin, Canoas is bordered by the dos Sinos and Gravataí rivers, and is in the transition zone between Planície Costeira (Coastal Plain) and the Planalto Meridional (Meridional Plateau) (www.canoas.rs.gov.br). Climate is temperate (Cfa; Köppen, 1918), with a hot and humid summer. In the Bioma Pampa, which spreads over 63% of Rio Grande do Sul political territory, the municipality territory is classified as region of ecological tension, where grass, shrub and wetlands are predominant in surrounding areas of the urban zone. Currently Canoas has no rural areas and the population of 323,827 inhabitants is settled in an area of 131.1 km² (demography of 2,470.13 inhabitants/km²; IBGE, 2010). The city grew in a disordered way, scattered in patches of neighborhoods and villages that were settled in marshy and flooded areas. Industry had large impact on the city demography, as well as the local economy (Mayer, 2009). Despite the level of urbanization, Canoas has 16.2 m² of green areas per capita, making a total of 5.49 km² in the city (Estado da Cidade, 2014).

Sampling design. We randomly selected, 120 sites based on 60 maps of the municipality of Canoas (www.geo.canoas.rs.gov.br). Each map covers a 1.1 km² area, divided in 20 quadrants of 0.4 km². We randomly selected two quadrants on each map and established one sample unit in the center of each quadrant. Sample units were required to be a public area (i.e., street, sidewalk, square) and were moved to the public location nearest the chosen center if necessary. Sample units were at least 200 m apart to guarantee independence between sampling units (Ralph et al., 1993). We used this sampling design to facilitate a more homogeneous evaluation of the study area and to avoid a concentration of points in few regions of the municipality. We could not access two sites located on private properties, and so removed them because we did not find any accessible location nearby. Thus, we sampled a total of 118 sites (Fig. 1).

Figure 1  Bird species richness and overall abundance recorded in point counts (surveyed on September 2013) in the municipality of Canoas, Rio Grande do Sul, Brazil. 

In each sample unit (50 m radius from a central point), we measured the following variables related to the degree of urbanization (descriptions of each variable are in Tab. I): 1) Noise; 2) density of ‘Trees’; 3) density of ‘People’; 4) density of ‘Pets’. We measured the percentage of ‘Vegetation cover’ and ‘Grass’ (open areas: grassland, gardens; Tab. I) - using satellite images provided by Google Pro (for Canoas, the images with the best available resolution were dated from January/2009 to December/2013). We also measured the abundance of buildings in each sample (number of ‘Houses’, ‘Buildings’, ‘Pavilions’, and ‘Other structures’; all but houses later transformed into categorical variables; Tab. I).

Table I  Explanatory variables (Ca, categorical; Co, continuous) measured on each sample unit in the urban area of Canoas, Rio Grande do Sul, Brazil. 

Variables Class Description
Houses Co Number of houses
Buildings Ca, binary Number of buildings with more than two floors
Pavilions Ca, binary Number of large horizontal buildings typically for industrial purposes
Other structures Ca, binary Number of buildings with low flow of people (supermarkets, sports facilities, parking lots)
People Co Number of persons passing by or standing at the point-count area
Pets Co Number of pets observed on the point-count area
Vegetation cover Co Percent of the sample unit covered by trees (aerial image)
Trees Co Number of trees higher than 3 m
Grass Co Percent of the sample unit covered by open areas (grassland, gardens)
Noise Co The mean of the three measures of sound frequency (measured at 0 min, 5 min and 10 min during bird counts)

We carried out bird surveys in September 2013, at the beginning of the breeding season. We conducted 10-min point-count surveys using a 50m fixed radius (Ralph et al., 1993), starting at dawn and lasting for 4 h (until 10:00 AM). We recorded all birds seen or heard, except birds flying above 20 m over the area, which we ignored in order to avoid double-counting during the census (annotated as an occasional record to compose the list of birds of Canoas).

Data analysis. We first constructed three matrices: 1) species abundance (number of individuals per point-count); 2) variables indicating the urban gradient (eight non-collinear variables); and 3) a spatial matrix with the geographical coordinates of each point-count (latitude and longitude). To avoid multi-collinearity, we selected only variables with Spearman correlation index below |0.6|: we excluded ‘Traffic’ and ‘Pets’ from the analyses and instead used the correlated variables ‘Houses’ (rho = 0.63) and ‘Noise’ (rho = 0.75). After the first investigation of data we eliminated two samples. These samples were outliers because they were located in an open, not urbanized area, where the major source of disturbance was traffic noise from the highway BR-386.

We tested the spatial correlation between species distribution and urban variables using a Mantel test (Legendre & Legendre, 1998), performed with R (R Development Core Team, 2015) using vegan package (Oksanen et al., 2015). The Spearman rho was used as the correlation coefficient. We used 9999 iterations with permutations of the matrix elements to calculate the P value for the test statistic, assuming no correlation between matrices as null hypothesis. The species distribution was spatially correlated (P = 0.01), so we performed a partial Mantel test to evaluate the correlation between species abundance and urban variables, weighting the spatial correlation.

To analyze the relationships between bird species assemblage and the urban variables, we performed a canonical correspondence analysis (CCA) (Legendre & Legendre, 1998) using on CANOCO v4.5 (Ter Braak & Šmilauer, 2002). Variables were centralized and standardized and the rare species were down-weighted to minimize their individual effects. To test the correlation between species abundance and urban variables, we used a Monte Carlo test with 9999 unrestricted permutations, assuming no correlation as the null hypothesis (Ter Braak & Šmilauer, 2002).

Finally, we used generalized linear models (GLM) to evaluate how urbanization affected bird species richness and abundance. To model species richness, we used the residuals of the linear regression of these variables as the response variable and also used Gaussian error and identity function. Because species richness is correlated with abundance (because more birds are detected as richness increased), we used the residuals as surrogate of species richness. To model the abundance (logarithmically transformed) we used the number of individuals in each point-count as the response variable, and used Gaussian error and identity function. We started by including eight variables as predictors in each model, and searched for the best subset using a backward stepwise procedure. We used second-order Akaike’s Information Criterion (AICc) to select competing models, assuming that models with ∆AICc ≤ 2 explain the data equally well. We performed the GLMs with R (R Development Core Team, 2015), using the MuMIn package (Barton, 2014) to build and select models.

RESULTS

We recorded 2,897 individuals from 100 bird species (13 only recorded occasionally, outside the point-count area) and 38 families (Appendix 1). Most of these species inhabit open habitats (grassland, shrublands and open areas; 46%), and are omnivorous (36%) (Appendix 1). Sites outside the urban core had more species richness and abundance (Fig. 1). These are dominated by grass and wetlands, where large flocks of Shiny cowbird [Molothrus bonariensis (Gmelin, 1789); 85 individuals] and Bare-faced ibis [Phimosus infuscatus (Lichtenstein, 1823); 90 individuals], for instance, were recorded.

Bird species distribution were spatially correlated (Mantel test, p < 0.01), while urban variables were not (Mantel test, p = 0.98). Species assemblages, in turn, were not correlated with urban variables (partial Mantel test, p = 0.25). The canonical axes of CCA, despite significance (p < 0.01) and the average correlation between species and environmental variables (Pearson correlation, axis I = 0.75, axis II = 0.66), explained only 13% of variability in the data (axis I = 7.1%, axis II= 1.9%). Notwithstanding, CCA separated more-urbanized areas from those that were less urbanized, the latter having a larger proportion of open vegetation cover (grass and wetlands) (left to right on axis I) and wooded areas, with greater vegetation cover (distinguished from other variables on axis II) (Fig. 2). Synanthropic and/or exotic species [e.g. House sparrow Passer domesticus (Linnaeus, 1758), Rock dove (Columba livia Gmelin, 1789), Blue-and-white swallow Pygochelidon cyanoleuca (Vieillot, 1817)] were recorded more frequently in more urbanized sites, characterized by higher densities of people and houses; the presence of buildings, pavilions and other structures; and higher levels of noise. Sites with greater proportions of open vegetation, lower levels of noise, and an absence or lower frequency of urban structures (house, buildings) were dominated by wetland species [e.g., White-browed meadowlark Sturnella superciliaris (Bonaparte, 1850), White-faced ibis Plegadis chihi (Vieillot, 1817) and Chestnut-capped blackbird Chrysomus ruficapillus (Vieillot, 1819)], and grass/shrubland species [e.g., White monjita Xolmis irupero (Vieillot, 1823), Shiny cowbird]. Forested species, such as Variable antshrike (Thamnophilus caerulescens Vieillot, 1816) and Golden-crowned warble [Basileuterus culicivorus (Deppe, 1830)] were present and more abundant in sites with a greater abundance of vegetation cover (percentage cover and number of trees).

Figure 2  Ordination diagram presenting the first two axes of the Canonical Correspondence Analysis (CCA) (percent of explained variability: axis I = 7.1%, axis II = 1.9%) based on the distribution of species abundance in 118 sample units (dots) in the urban area of Canoas, Rio Grande do Sul, Brazil, and its correlation with seven explanatory variables (arrows). The first axis shows the urbanization gradient (negatives values on left = more urbanized regions; positive values on right = less urbanized regions). All axes were significant (Monte Carlo test with 9,999 permutations: P < 0.001). Species names are given in full in Appendix 1. Variables are described in Tab. I

We selected four competing models for analysis of species richness (Tab. II). The most important variables, retained in all models, were abundance of houses, noise and percentage of vegetation cover. Abundance of people, presence of buildings and other structures were the other variables selected in our GLMs (each only in one model). Species richness was positively related to the proportion of arboreal vegetation (‘Trees’), abundance of people and presence of buildings (Tab. II), while areas with higher noise levels, abundance of houses and presence of other structures have low species richness. We selected six models for analysis of species abundance (Tab. III). Species abundance decreased as noise levels increased (noise level was selected in all models), and also decreased with the abundance of houses and presence of buildings. The proportion of vegetation cover (both open and arboreal) in a site had a positive effect on bird abundance (as we can see in Fig. 1), as did the presence of pavilions.

Table II Competing models (∆AICc < 2) for the influence of environmental variables in bird species richness (considering the residual of the linear regression of bird species richness and abundance). Variables with negative coefficients are indicated by a minus sign within the brackets. Variables retained in the best candidate model are in bold (df, degrees of freedom; AICc, corrected Akaike’s Information Criterion; ∆AICc, difference in AICc between the current model and the best model; w, Akaike weights). 

Modelo df AICc ∆AICc Weight
(-Houses)+(-Noise)+Vegetation 5 531.21 0.00 0.46
(-Houses)+(-Noise)+Vegetation+People 6 532.84 1.63 0.20
(-Houses)+(-Noise)+Vegetation+(-Other) 6 533.17 1.96 0.17
(-Houses)+(-Noise)+Vegetation+Buildings 6 533.19 1.97 0.17

Table III  Competing models (∆AICc < 2) for the influence of environmental variables in bird species abundances. Variables with negative coefficients are indicated by a minus sign within the brackets. Variables retained in the best candidate model are in bold (df, degrees of freedon; AICc, corrected Akaike’s Information Criterion; ∆AICc, difference on AICc between the current model and the best model; w, Akaike weights).  

Modelo df AICc ∆AICc Weight
Grass+(-Noise) 4 155.83 0.00 0.29
Grass 3 156.95 1.12 0.17
Grass+(-Noise)+(-Houses) 5 157.22 1.39 0.15
Grass+(-Noise)+Pavilions 5 157.35 1.52 0.14
Grass+(-Noise)+(-Buildings) 5 157.43 1.60 0.13
Grass+(-Noise)+Vegetation 5 157.62 1.79 0.12

DISCUSSION

Our results suggest that the city of Canoas has a rural-urban gradient similar to those of other urban centers (Blair, 1996; Chace & Walsh, 2006; McKinney, 2006; Bino et al., 2008; Ortega-Álvarez & MacGregor-Fors, 2011; Puga-Caballero et al., 2014). The urbanized areas are in the core of the city, surrounded by areas of grass, shrub and wetlands (Fig. 1). Hence, the bird species assemblage varies along the gradient from grass and wetland species (e.g., Shiny cowbird, Chestnut-capped blackbird, White-faced ibis) to urban-adapted species, such the Gilded hummingbird [Hylocharis chrysura (Shaw, 1812)], Bananaquit [Coereba flaveola (Linnaeus, 1758)] and Sayaca tanager [Tangara sayaca (Linnaeus, 1766)], which are common in parks and gardens (Sick, 1997; Fontana et al., 2011). Introduced species like the House sparrow, Rock dove and Common waxbill [Estrilda astrild (Linnaeus, 1758)], also dominate the urban landscapes (Blair, 2004; Bino et al., 2008; Fontana et al., 2011; Ortega-Álvarez & MacGregor-Fors, 2011; Aronson et al., 2014).

Although some effects of urbanization on the bird species assemblage were not so clear, the regression models nonetheless showed well known effects of urbanization. The proportion of arboreal vegetation cover was the most important variable predicting an increase in species richness in Canoas, corroborating the known role of green areas as biodiversity enhancers in urban centers (Chace & Walsh, 2006; Evans et al., 2009; Fontana et al., 2011; Ortega-Álvarez & MacGregor-Fors, 2011; Toledo et al., 2012; Njoroge et al., 2013; Aronson et al., 2014; Beninde et al., 2015; Sacco et al., 2015). Open landscapes prevail in the Canoas region, and the arboreal component is characterized by gardens, squares and parks in the urban area; this contrasts with cities with large forested areas on the borders, for instance around Porto Alegre (Fontana et al., 2011). As observed in other studies, plots of open vegetation (squares, gardens, golf courses) usually have higher biodiversity (Evans et al., 2009; Aronson et al., 2014; Beninde et al., 2015). Therefore, these areas are home to particular bird species assemblages that are distinct from rural and peri-urban avifauna.

On the other hand, the negative effects of urbanization upon bird diversity are clearly showed in the relationships between species richness and the level of noise and the density of houses. Noise is a striking feature of urban centers, usually related to low species richness and abundance (Fontana et al., 2011; Pickett et al., 2011; Sacco et al., 2015). Together with other human activities, noise may constrain species’ ability settle in urban centers, which leads to loss of diversity and differentiation in bird species assemblages (Francis et al., 2009; Fontana et al., 2011; Rolando et al., 1997; Slabbekoorn & Peet, 2003; Bisson et al., 2011; Pickett et al., 2011; Chávez-Zichinelli et al., 2013).

In addition to noise, the density of houses and presence of buildings affected both species richness and abundance in our study. Although both variables are related to urbanization, they are not good indicators of urbanization, as their effects are dependent on the scale analyzed and the particular process of urbanization for each city (Evans et al., 2009). In Canoas, the density of houses appears to indicate densely urbanized areas, and adds to other urban features (noise, buildings) in negatively affecting the avifauna. Although negatively related to bird abundance, presence of buildings was positively related to species richness, probably due to the occurrence of aerial foragers (e.g., Hirundinidae) and species that nest or live in rocky habitats (e.g., Apodidade) (Blair, 1996, 2004; Aronson et al., 2014).

Bird abundance tends to increase with urbanization, which is an artifact of the higher density of a few urban exploiters, often exotic species (Croci et al., 2008; Chace & Walsh, 2006; Bino et al., 2008; Ortega-Álvarez & MacGregor-Fors, 2011; Aronson et al., 2014; Francis, 2015). Omnivores and synanthropic species [e.g., Rock dove, Rufous-bellied thrush (Turdus rufiventris Vieillot, 1818), Great kiskadee Pitangus sulphuratus (Linnaeus, 1766)] were very abundant in Canoas. These species are generalists in diet and habitat use, and have great ecological plasticity: they are more efficient at exploiting resources in urban areas that are new to native species (Blair, 1996).

We found a high abundance of urban exploiters, like House sparrows, Rock doves and Eared doves [Zenaida auriculata (Des Murs, 1847)], in more urbanized areas, in agreement with the pattern found in other studies (Blair, 2004; Chace &Walsh, 2006; Bino et al., 2008; Fontana et al., 2011; Ortega-Álvarez & MacGregor-Fors, 2011; Aronson et al., 2014; Puga-Caballero et al., 2014). However, his pattern of abundant urban exploiters does not reflect a positive overall effect of urbanization on bird abundance. In fact, pavilions were the only urban feature positively related to abundance, probably because they offer local resources for a few common species. On the other hand, peri-urban areas of the city, which were used for agriculture in past decades, showed high abundance of a few species (e.g., Bare- and White-faced ibises, Shiny cowbird). This concentration of birds in peri-urban areas (Fig. 1) masks the expected pattern seen along a rural-urban gradient: increase in abundance from rural areas to urban centers (Blair, 1996; Chace & Walsh, 2006; McKinney, 2006; Bino et al., 2008; Ortega-Álvarez & MacGregor-Fors, 2011; Puga-Caballero et al., 2014).

The city of Canoas does not have a clear pattern of urbanization, reflecting the disorganized growth of the city (Mayer, 2009). Despite the existence of a core area, with buildings and commercial areas, there are still plots of habitats patchily distributed inside the city, buffering bird species distribution from urban effects. The presence of wetlands, grasslands (vacant lots, lawns and squares) and woodlots (urban parks, gardens) offers habitats to species less adapted to the urban environment (urban avoiders), reducing the biotic homogenization and maintaining part of the pre-urbanization pool of species. We agree with Fontana et al. (2011), that the level of noise seems to be the best variable indicating the degree of urbanization. Along with vegetation cover or related parameters (density of trees, presence of green areas) (Chace & Walsh, 2006; Evans et al., 2009; Aronson et al., 2014; Beninde et al., 2015), the level of noise would be the best variable to use in order to recognize levels of urbanization.

Finally, given the increasing concern with sustainable urban development, seeking environment-friendly urban growth that preserves cities’ biodiversity (Pickett et al., 2011; Aronson et al., 2014; Beninde et al., 2015), urban planners need to take into account how the city works ecologically (Pickett et al., 2011), for example, how the biota responds to an urban environment. This is essential for conservation and management purposes. The effects of the level of noise and presence of green areas on urban-dwelling birds, for instance, are well known (e.g., Chace & Walsh, 2006; Evans et al., 2009; Aronson et al., 2014; Beninde et al., 2015), and should be considered in urban planning policies. Supporting citizens in maintaining residential vegetation (e.g., private yards), and, hence, keeping areas of native vegetation inside the urban area, is a simple example of how to increase a city’s green areas and promote biological conservation (Smith et al., 2014).

Acknowledgements

We thank Sandra M. Hartz and Jan K. F. Mähler Jr for valuable contributions to first draft of the manuscript. We appreciate the improvements in English usage made by Jessica Barker through the Association of Field Ornithologists’ program of editorial assistance.

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Appendix 1

Species abundance (SA, number of individuals) and frequency of occurrence (Fr, number of points with where target species was recorded divided by the total of point-counts) recorded in 118 point-counts in the urban area of Canoas, Rio Grande do Sul, Brazil. Taxonomy and common names are listed according to the Brazilian Ornithological Records Committee (Piacentini et al., 2015). Diets and foraging habitats are given according to Sick (1997). Species marked with a ‘X’ in SA column were those recoded only occasionally (i.e., outside the point-count limits). Diet: Omn, omnivore; Car, carnivore; Pis, piscivore; Ins, insectivore; Sca, scavenger; Gra, granivore; Fru, frugivore; Nec, nectarivore; Snail, snail. Habitat: Wet, wetland; Grass, grassland; For, forest; Wood, woodlots; Palm, palm forest; Shrub, shrublands; Marsh, marshes; Mang, mangroves; Water, river, lakes and/or coastal areas; Rice, rice fields; Urb, urban; Gen, generalists; Open, open areas. 

Taxon Common name SA Fr Diet Habitat Species code
Anseriformes
Anatidae
Callonetta leucophrys (Vieillot, 1816) Ringed Teal X Omn Wet Cal_leu
Amazonetta brasiliensis (Gmelin, 1789) Brazilian Teal 5 0.025 Omn Wet Ama_bra
Ciconiiformes
Ciconiidae
Ciconia maguari (Gmelin, 1789) Maguari Stork X Car Wet Cic_mag
Suliformes
Phalacrocoracidae
Nannopterum brasilianus (Gmelin, 1789) Neotropic Cormorant X Pis Water Nan_bra
Pelecaniformes
Ardeidae
Nycticorax nycticorax (Linnaeus, 1758) Black-crowned Night-Heron X X Car Water Nyc_nyc
Bubulcus ibis (Linnaeus, 1758) Cattle Egret 25 0.008 Ins Grass Bub_ibi
Ardea cocoi Linnaeus, 1766 Cocoi Heron 2 0.008 Car Water Ard_coc
Ardea alba Linnaeus, 1758 Great Egret 3 0.017 Car Water; Marsh Ard_alb
Syrigma sibilatrix (Temminck, 1824) Whistling Heron X X Car Grass; Rice Syr_sib
Threskiornithidae
Plegadis chihi (Vieillot, 1817) White-faced Ibis 98 0.051 Ins Wet; Rice Ple_chi
Phimosus infuscatus (Lichtenstein, 1823) Bare-faced Ibis 120 0.025 Omn Wet Phi_inf
Platalea ajaja Linnaeus, 1758 Roseate Spoonbill X Ins Wet; Mang Pla_aja
Cathartiformes
Cathartidae
Coragyps atratus (Bechstein, 1793) Black Vulture 3 0.008 Sca Gen; Urb Cor_atr
Accipitriformes
Accipitridae
Circus buffoni (Gmelin, 1788) Long-winged Harrier X Car Wet; Grass Cir_buf
Rostrhamus sociabilis (Vieillot, 1817) Snail Kite 4 0.025 snail Wet Ros_soc
Heterospizias meridionalis (Latham, 1790) Savanna Hawk 2 0.008 Car Grass Het_mer
Rupornis magnirostris (Gmelin, 1788) Roadside Hawk 1 0.008 Car Grass; For Rup_mag
Gruiformes
Aramidae
Aramus guarauna (Linnaeus, 1766) Limpkin 1 0.008 Snail Wet Ara_gua
Rallidae
Aramides saracura (Spix, 1825) Slaty-breasted Wood-Rail 1 0.008 Omn For; Wet Ara_sar
Gallinula galeata (Lichtenstein, 1818) Common Gallinule 2 0.008 Omn Water Gal_gal
Porphyriops melanops (Vieillot, 1819) Spot-flanked Gallinule 1 0.008 Omn Water Por_mel
Charadriiformes
Charadriidae
Vanellus chilensis (Molina, 1782) Southern Lapwing 83 0.263 Ins Wet, Grass Van_chi
Recurvirostridae
Himantopus melanurus Vieillot, 1817 White-backed Stilt 6 0.017 Ins Wet, Water Him_mel
Scolopacidae
Gallinago paraguaiae (Vieillot, 1816) South American Snipe X Omn Wet Gal_par
Jacanidae
Jacana jacana (Linnaeus, 1766) Wattled Jacana 17 0.051 Omn Wet Jac_jac
Columbiformes
Columbidae
Columbina talpacoti (Temminck, 1810) Ruddy Ground-Dove 76 0.347 Gra Open; Wet Col_tap
Columbina picui (Temminck, 1813) Picui Ground-Dove 65 0.314 Gra Grass Col_pic
Columba livia Gmelin, 1789 Rock Pigeon 118 0.280 Gra Urb Col_liv
Patagioenas picazuro (Temminck, 1813) Picazuro Pigeon 4 0.025 Gra, Fru For; Wood Pat_pic
Zenaida auriculata (Des Murs, 1847) Eared Dove 118 0.390 Gra Grass Zen_aur
Leptotila verreauxi Bonaparte, 1855 White-tipped Dove 1 0.008 Fru, Gra Grass; Shrub; For Lep_ver
Cuculiformes
Cuculidae
Piaya cayana (Linnaeus, 1766) Squirrel Cuckoo 1 0.008 Car For Pia_cay
Crotophaga ani Linnaeus, 1758 Smooth-billed Ani X Car Open Cro_ani
Guira guira (Gmelin, 1788) Guira Cuckoo 8 0.025 Car Grass Gui_gui
Strigiformes
Strigidae
Athene cunicularia (Molina, 1782) Burrowing Owl 1 0.008 Car Grass Ath_cun
Apodiformes
Apodidae
Chaetura meridionalis Hellmayr, 1907 Sick’s Swift 4 0.025 Ins Grass; Urb Cha_mer
Trochilidae
Chlorostilbon lucidus (Shaw, 1812) Glittering-bellied Emerald 2 0.017 Nec Shrub; Urb Chl_luc
Hylocharis chrysura (Shaw, 1812) Gilded Hummingbird 17 0.127 Nec For; Shrub Hyl_chr
Piciformes
Picidae
Melanerpes candidus (Otto, 1796) White Woodpecker X Omn Grass Mel_can
Veniliornis spilogaster (Wagler, 1827) White-spotted Woodpecker 1 0.008 Ins For Vem_spi
Colaptes melanochloros (Gmelin, 1788) Green-barred Woodpecker 3 0.025 Ins For; Shrub; Open Col_mel
Colaptes campestris (Vieillot, 1818) Campo Flicker 22 0.110 Ins Grass Col_cam
Falconiformes
Falconidae
Caracara plancus (Miller, 1777) Southern Caracara 1 0.008 Omn Grass; Open Car_pla
Milvago chimachima (Vieillot, 1816) Yellow-headed Caracara 1 0.008 Omn Open Mil_chm
Milvago chimango (Vieillot, 1816) Chimango Caracara 2 0.017 Car Grass; Open Mil_chg
Falco sparverius Linnaeus, 1758 American Kestrel X Car Grass Fal_spa
Psittaciformes
Psittacidae
Myiopsitta monachus (Boddaert, 1783) Monk Parakeet 33 0.136 Fru Grass, Wood Myi_mon
Amazona aestiva (Linnaeus, 1758) Turquoise-fronted Parrot 2 0.008 Fru For, Palm Ama_aes
Passeriformes
Thamnophilidae
Thamnophilus ruficapillus Vieillot, 1816 Rufous-capped Antshrike 2 0.008 Ins Grass; For Tha_ruf
Thamnophilus caerulescens Vieillot, 1816 Variable Antshrike 2 0.008 Ins For; Shrub Tha_cae
Furnariidae
Furnarius rufus (Gmelin, 1788) Rufous Hornero 154 0.712 Ins Open Fur_ruf
Schoeniophylax phryganophilus (Vieillot, 1817) Chotoy Spinetail 2 0.008 Ins Grass; Shrub Sch_phr
Certhiaxis cinnamomeus (Gmelin, 1788) Yellow-chinned Spinetail 9 0.051 Ins Open Cer_cin
Synallaxis cinerascens Temminck, 1823 Gray-bellied Spinetail 1 0.008 Ins For Syn_cin
Synallaxis spixi Sclater, 1856 Spix's Spinetail 4 0.034 Ins For Syn_spi
Rhynchocyclidae
Phylloscartes ventralis (Temminck, 1824) Mottle-cheeked Tyrannulet 4 0.034 Ins For Phy_ven
Tyrannidae
Camptostoma obsoletum (Temminck, 1824) Southern Beardless-Tyrannulet 10 0.059 Omn For; Shrub Cam_obs
Elaenia flavogaster (Thunberg, 1822) Yellow-bellied Elaenia 27 0.152 Omn Grass; Shrub Ela_fla
Serpophaga subcristata (Vieillot, 1817) White-crested Tyrannulet 5 0.034 Ins Grass; Shrub Ser_sub
Pitangus sulphuratus (Linnaeus, 1766) Great Kiskadee 165 0.771 Omn Gen; Urb Pit_sul
Machetornis rixosa (Vieillot, 1819) Cattle Tyrant 5 0.025 Ins Grass Mac_rix
Satrapa icterophrys (Vieillot, 1818) Yellow-browed Tyrant 5 0.042 Ins For; Shrub Sat_ict
Xolmis irupero (Vieillot, 1823) White Monjita 5 0.042 Ins Grass; Shrubs Xol_iru
Vireonidae
Cyclarhis gujanensis (Gmelin, 1789) Rufous-browed Peppershrike X Omn For; Shrub Cyc_guj
Hirundinidae
Pygochelidon cyanoleuca (Vieillot, 1817) Blue-and-white Swallow 78 0.339 Ins Gen; Urb Pyg_cya
Progne chalybea (Gmelin, 1789) Gray-breasted Martin 38 0.161 Ins Gen; Urb Pro_cha
Tachycineta leucorrhoa (Vieillot, 1817) White-rumped Swallow 8 0.034 Ins Gen; Urb Tac_leu
Troglodytidae
Troglodytes musculus Naumann, 1823 Southern House Wren 82 0.534 Omn Gen; Urb Tro_mus
Turdidae
Turdus leucomelas Vieillot, 1818 Pale-breasted Thrush 4 0.034 Omn Gen; Urb Tur_leu
Turdus rufiventris Vieillot, 1818 Rufous-bellied Thrush 119 0.636 Omn Gen; Urb Tur_ruf
Turdus amaurochalinus Cabanis, 1850 Creamy-bellied Thrush 5 0.042 Omn Gen; Urb Tur_ama
Mimidae
Mimus saturninus (Lichtenstein, 1823) Chalk-browed Mockingbird 16 0.076 Omn Open; Shrub Mim_sat
Motacillidae
Anthus lutescens Pucheran, 1855 Yellowish Pipit 4 0.017 Omn Grass; Wet Ant_lut
Passerelidae
Zonotrichia capensis (Statius Muller, 1776) Rufous-collared Sparrow 13 0.093 Omn Grass; Open Zon_cap
Ammodramus humeralis (Bosc, 1792) Grassland Sparrow 2 0.017 Gra Grass Amm_hum
Parulidae
Setophaga pitiayumi (Vieillot, 1817) Tropical Parula 1 0.008 Ins For Set_pit
Geothlypis aequinoctialis (Gmelin, 1789) Masked Yellowthroat 8 0.059 Ins Wet; Shrub Geo_aeq
Basileuterus culicivorus (Deppe, 1830) Golden-crowned Warbler 7 0.042 Ins For Bas_cul
Myiothlypis leucoblephara (Vieillot, 1817) White-browed Warbler 2 0.017 Ins For Bas_leu
Icteridae
Amblyramphus holosericeus (Scopoli, 1786) Scarlet-headed Blackbird X Omn Wet Amb_hol
Chrysomus ruficapillus (Vieillot, 1819) Chestnut-capped Blackbird 19 0.034 Omn Wet Chr_ruf
Pseudoleistes guirahuro (Vieillot, 1819) Yellow-rumped Marshbird 2 0.008 Omn Wet Pse_gui
Agelaioides badius (Vieillot, 1819) Grayish Baywing 17 0.042 Omn Open Age_bad
Molothrus bonariensis (Gmelin, 1789) Shiny Cowbird 315 0.254 Omn Grass; Open Mol_bon
Sturnella superciliaris (Bonaparte, 1850) White-browed Meadowlark 2 0.008 Omn Grass Stu_sup
Thraupidae
Pipraeidea bonariensis (Gmelin, 1789) Blue-and-yellow Tanager 6 0.042 Fru For Pip_bon
Paroaria coronata (Miller, 1776) Red-crested Cardinal 3 0.008 Omn Grass; Shrub Par_cor
Tangara sayaca (Linnaeus, 1766) Sayaca Tanager 112 0.492 Fru Gen; Urb Tan_sy
Tangara palmarum (Wied, 1821) Palm Tanager 13 0.059 Omn For; Palm Tan_pal
Sicalis flaveola (Linnaeus, 1766) Saffron Finch 61 0.161 Gra Grass Sic_fla
Coryphospingus cucullatus (Statius Muller, 1776) Red-crested Finch 4 0.025 Omn For Lan_cuc
Coereba flaveola (Linnaeus, 1758) Bananaquit 99 0.627 Nec For Coe_fla
Sporophila caerulescens (Vieillot, 1823) Double-collared Seedeater 3 0.025 Gra Grass Spo_car
Embernagra platensis (Gmelin, 1789) Great Pampa-Finch 2 0.008 Omn Grass; Wet Bem_pla
Saltator similis d’Orbigny & Lafresnaye, 1837 Green-winged Saltator 1 0.008 Omn For Sat_sim
Poospiza nigrorufa (d’Orbigny & Lafresnaye, 1837) Black-and-rufous Warbling-Finch 2 0.017 Omn For Poo_nig
Fringillidae
Euphonia chlorotica (Linnaeus, 1766) Purple-throated Euphonia 7 0.059 Fru For Eup_chl
Euphonia cyanocephala (Vieillot, 1818) Golden-rumped Euphonia 2 0.008 Fru For Eup_cya
Estrildidae
Estrilda astrild (Linnaeus, 1758) Common Waxbill 18 0.025 Gra Gen; Urb Est_est
Passeridae
Passer domesticus (Linnaeus, 1758) House Sparrow 568 0.881 Omn Gen; Urb Pas_dom

Received: November 11, 2016; Accepted: April 08, 2018

*Corresponding author: Universidade Federal de Viçosa (UFV) - Campus Rio Paranaíba, Rodovia MG-230, km 8, s/n, Caixa Postal 22 , 38810-000, Rio Paranaíba, MG, Brasil. E-mail: fzilio@msn.com

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