Abstract

Large, vegetatively diverse, and connected green space is often considered essential to conservation. Yet, these criteria can be impractical when applied to densely populated or under-resourced urban areas and evoke mixed sentiments from people. Conventional urban green space is often smaller, minimally vegetated, and contains open mowed areas that are inadequate for many wildlife species and fail to provide opportunities for nature connection in biodiverse environments. As songbirds are well liked and globally distributed, they present an opportunity to rethink urban green spaces, particularly small urban green spaces, that contain landscape qualities where the needs of people and songbirds converge. We conducted a comprehensive search of peer-reviewed publications to examine the natural, structural, and anthropogenic factors associated with songbird richness in small (<2 ha) urban green spaces. Overall, small urban green spaces that incorporated a variety of habitats and native plant species, particularly trees, supported songbird richness. In some cases, songbird richness in small green spaces was equivalent to richness reported in larger green spaces. Interestingly, connectivity between green spaces was not significant in the majority of articles that examined the factor. This outcome removes a possible obstruction to green space allocation and has the potential to jumpstart the placement of green space into historically and perpetually under-resourced communities. Finally, associations with anthropogenic factors had few positive associations with songbird richness, but this category of factors was also reported the least often. Collectively, these results provide feasible options to improve human health, nature connections, and songbird conservation.

Introduction

Urban green space is an area that contains varying degrees of vegetation that is associated with human-dominated landscapes (Taylor and Hochuli 2017). This highly inclusive definition results in the placement of urban green space along a broad and complex spectrum that includes vacant lots and conservation areas (Taylor and Hochuli 2017). Historically, the timing, placement, and criteria for incorporating public urban green space have been driven by social mediation, cultural objectives, and land values (Rosenzweig 1992; Loughran 2020). Green space allocation was often planned to simultaneously compensate for a lack of nature, reduce residential overcrowding, promote racial separation, and bolster current and future land values (Rosenzweig 1992; Loughran 2020; Schell et al. 2020). More recently, these former objectives have been revised to prioritize the value of urban green space in supporting a wide range of human welfare and conservation goals (Connop et al. 2016).

Urban green space can be private or public and vary in size, function, connectivity, biodiversity, accessibility, and history (McIntyre et al. 2008; Taylor and Hochuli 2017). As a result, each green space presents different types and degrees of ecological and ecosystem value to humans and wildlife, and the benefits accrued through one green space may not be equivalent to those of another (Gómez-Baggethun et al. 2013; Haase et al. 2014; Wood et al. 2018; Filazzola, Shrestha, and MacIvor 2019; Felappi et al. 2020). A neighborhood park, for example, may offer recreation opportunities and mitigate seasonal precipitation events, yet it may not provide habitat for a wide range of taxa due to fragmentation or lack of suitable vegetation for shelter or nesting (Beninde et al. 2015; Apfelbeck et al. 2020). Alternatively, a conservation area may support a resilient and biodiverse ecosystem, yet only appeal to a small subset of people due to perceptions of safety, recreation interests, or lack of accessibility (Porcherie et al. 2019). Both scenarios highlight the importance of educational interventions to improve human understanding of the multitude of benefits provided by ecologically valuable spaces and better unite the goals of residents, ecologists, and urban practitioners (Shanahan et al. 2015, Connop et al. 2016).

Although often multifaceted in the benefits they provide, green space installations designed to be biodiverse and resilient ecosystems provide a broader and more effective scope of solutions (Beninde et al. 2015; Connop et al. 2016; Ives et al. 2017; Lepczyk et al. 2017). Specifically, nature-based solutions can simultaneously offset the costs associated with maintaining air and water quality; contribute habitat for a variety of species; and offer mental restoration, physical recreation, and enhanced nature connection for people (Nardo, Saulle, and La Torre 2010; Kardan et al. 2015; Connop et al. 2016; Ives et al. 2017; Weisser and Hauck 2017; Felappi et al. 2020). Collectively, these benefits are thought to be key to establishing a strong pathway towards environmental sustainability (Beatley and Newman 2013; Rosa and Collado 2019; Barragan-Jason et al. 2022). Additionally, synergies have been found between supporting wildlife and human health with multiple factors, including increased tree cover, reduced noise, presence of water, and higher diversity of habitats (Felappi et al. 2020). However, human aesthetic preferences for smaller, open, and mowed greenscapes are in sharp contrast to the needs of many wildlife species (Beninde et al. 2015; Ives et al. 2017; Felappi et al. 2020; Whitburn, Linklater, and Abrahamse 2020). By balancing the importance of size and landscape composition that are key to wildlife conservation and are also associated with human preferences, urban green space may simultaneously meet the needs of wildlife and humans and strengthen nature connections (Beninde et al. 2015; Ives et al. 2017; Whitburn, Linklater, and Abrahamse 2020).

Urban green spaces are commonly planned and developed to offer a variety of amenities to people, the focal audience for urban planning. However, this type of green space is often unsuitable for a broad range of wildlife taxa and can instead result in smaller assemblages of urban utilizing (e.g. northern cardinal (Cardinalis cardinalis) and American robin (Turdus migratorius)) and urban dwelling (e.g. rock pigeon (Columba livia) and house sparrow (Passer domesticus)) species (Ikin et al. 2013; Aronson et al. 2014; Beninde et al. 2015; Lepczyk et al. 2017; Fidino et al. 2021). This outcome neither provides conservation from more ‘biodiversity-led’ or ‘wildlife inclusive’ designs nor enhances nature connection by allowing people to interact with more nature-based ecosystems (Dunn et al. 2006; Connop et al. 2016; Klein and Thurstan 2016; Weisser and Hauck 2017). Demonstrating to practitioners, policy makers, and residents the value and feasibility of designing green space to be ecologically and socially functional is a first step to incorporate these standards into comprehensive planning (Gobster et al. 2007; Connop et al. 2016; Kay et al. 2022). This step will likely necessitate a change in the narrative of conventional thinking and practices between city collaborators, requiring ecologists, landscape architects, and urban planners to collectively demonstrate the possibilities of blending aesthetics and ecology into multifunctional spaces (Gobster et al. 2007; Connop et al. 2016; Kay et al. 2022). The solutions will not be formulaic. In fact, they must remain adaptable to address multiple competing interests and/or challenges that change over time, including local and regional ecologies, policies, funding, and land availability (Rega-Brodsky, Nilon, and Warren 2018; Garcia-Garcia et al. 2020; Kay et al. 2022).

One of the priorities in reimagining urban green space is to address the expectations of urban residents (Dunn et al. 2006; Papworth et al. 2009; Klein and Thurstan 2016; Soga and Gaston 2018). That is, increasingly non-native or manicured nature containing low species richness becomes the benchmark of a natural state, and progressive generations have increasingly less endemic natural conditions as their reference of nature (Papworth et al. 2009; Klein and Thurstan 2016; Soga and Gaston 2018). One means of addressing this shifting baseline is recognizing that access to more natural and biodiverse urban green space is integral to our relationship with nature (Turner et al. 2004; Papworth et al. 2009; Nardo, Saulle, and La Torre 2010; Karacor and Parlar 2017; Lumber, Richardson, and Sheffield 2017; Klein et al. 2021). This relationship, hereafter referred to as human-nature connection (HNC), is often described as an emotional bond that develops from the physical and contextual interactions that we experience throughout our lives (Otto et al. 2016; Giusti 2019). Increasing nature-based interactions in urban settings has the potential to shape and strengthen this bond and may be key to advance pro-environmental engagement and securing global environmental sustainability (Mackay and Schmitt 2019; Whitburn, Linklater, and Abrahamse 2020; Wilkie and Trotter 2022).

An emerging area of investigation seeking to balance urban growth and wildlife conservation objectives involves exploring the relationship between HNC and songbirds (order Passeriformes) (Hedblom et al. 2014; Cox and Gaston 2016; Collins, Paton, and Gagné 2021). Much of this research has been conducted with humans in laboratories, classroom settings, and/or via surveys investigating bird species likeability, biodiversity perceptions, or restorative self-assessments associated with viewing virtual landscapes, bird species, and/or experiencing auditory playbacks of songbirds (Ratcliffe et al. 2013; Cameron et al. 2020; Deng et al. 2020; Liordos, Foutsa, and Kontsiotis 2020; Fisher et al. 2021). Collectively, this research demonstrates greater enjoyment and restorative value associated with vegetatively diverse landscapes containing higher songbird diversity (Hedblom et al. 2014; Cameron et al. 2020; Deng et al. 2020). Integrating evidence from nature-related experimentation and nature-based experiences may provide a guide to mitigating the effects of urbanization on avian conservation and increase HNC, particularly in urban areas where residents may have relatively few opportunities to engage with nature (Cox et al. 2017; Schell et al. 2020).

One of the most consistent outcomes from nature-based experiences is the relationship between HNC and time spent in a structurally complex and biodiverse green space (Beery and Wolf-Watz 2014; Scopelliti et al. 2016; Coldwell and Evans 2017; Colléony, White and Shwartz 2019). Individuals who spend time in these types of spaces report positive psychological, cognitive, physiological, and social effects (Fuller et al. 2007; Keniger et al. 2013; Wyles et al. 2019). Additionally, individuals who experience nature through recreation or in appreciative (e.g. bird watching) or consumptive (e.g. hunting) ways demonstrate increased engagement in a broad range of conservation behaviors and have greater positive sentiment and higher tolerance towards wildlife (Cooper et al. 2015; Cleary et al. 2020; Liordos, Foutsa, and Kontsiotis 2020; Hayes Hursh, Perry, and Drake 2024). Watching, feeding, and listening to bird taxa, specifically songbirds, whether in private gardens or public green spaces, has shown positive effects on people’s appreciation of nature and multiple measures of well-being (Cox and Gaston 2015; Cox and Gaston 2016; Zhu et al. 2020).

As songbirds are predominantly well liked, small-bodied, highly diverse, and globally ranging, with many demonstrating behavioral and/or phenotypic qualities that allow them to utilize urban landscapes, they are seemingly ideal candidates to navigate the possibilities of blending HNC and avian conservation into public urban green space (Chamberlain et al. 2009; Sol et al. 2014; Cox and Gaston 2015; Liordos et al. 2021; Collins, Paton, and Gagné 2021). Studies have shown that large urban areas with native vegetative heterogeneity, greater shrub and tree canopy cover, and perennial water are associated with greater avian richness (Clergeau, Jokimäki, and Savard 2001; de Toledo et al. 2012; Aronson et al. 2014; Ferenc, Sedláček, and Fuchs 2014; La Sorte et al. 2020). Yet, these components are not always applied in cities experiencing high demands for densification from population growth, land values, and limited available vacant areas (Clergeau, Jokimäki, and Savard 2001; de Toledo et al. 2012; Aronson et al. 2014; Ferenc, Sedláček, and Fuchs 2014; La Sorte et al. 2020).

Abundant research exists on urban avian populations, including investigations of avian species richness, composition, and abundance associated with varying degrees of urbanization and the potential role of urban green space in avian conservation (Marzluff 2017). Far fewer studies have examined the duality of small (< 2 ha) urban green space to enhance HNC and/or support resident songbird populations, although notable examples exist (Carbó-Ramírez and Zuria 2011; Stagoll et al. 2012; Jasmani et al. 2017; Amaya-Espinel et al. 2019; da Ferreira et al. 2021). With growing competition for available space within densely built and expanding cities, understanding the attributes of public urban green space, specifically small urban green space, that support avian populations may simultaneously support human well-being and HNC and provide a useful reference to urban practitioners interested in expanding beyond the conventional role of human-centered urban green space (Cox et al. 2017; Jasmani et al. 2017; da Ferreira et al. 2021; Miller and Hobbs 2022). Therefore, the objective of our study was to conduct a systematic review of the literature to extract site level (e.g. vegetation structure within a green space) and landscape level (e.g. connectivity across green spaces) land cover and use factors in urban green space associated with resident bird diversity, particularly songbird populations in small urban green space. The primary outcome of our study is to provide urban practitioners with a reference when planning, designing, and constructing urban green spaces to maximize songbird diversity and HNC.

Methodology

Search terms and filters

We conducted a comprehensive search of peer-reviewed publications in April 2023 to determine the factors associated with increased songbird diversity in urban green space, particularly small urban green space. All articles were searched within four databases: Web of Science and EBSCOhost Information Services, specifically GreenFILE, Urban Studies Abstracts, and Wildlife and Ecological Studies. The articles were searched using the term sequence (passeri* or songbird or (avian diversity) or (avian richness) or (bird diversity) or (bird richness)) AND (urban or city or cities or metropolitan or (urban area) or (urban landscape) or (urban landscape matri*) or (urban landscape attribute) or (urban landscape factor)) AND (greenspace or (green space) or (outdoor space) or (natural area) or parks). The literature filters applied to the search included English, full text, and available online access.

Eligibility criteria

These search terms and filters resulted in 678 articles: 315 from EBSCOhost and 363 from Web of Science. We further evaluated each relevant article using the following criteria: (1) did the article examine the richness of urban avian resident populations, including resident songbird populations, and (2) did the article examine associations between avian richness and landscape characteristics in specified urban green space locations. These two inclusion criteria resulted in 114 articles: 45 from EBSCOhost and 69 from Web of Science. These articles were selected for full text evaluation to determine suitability with our research objectives. We further excluded articles that examined urban avian populations, but (1) did not include site-specific landscape characteristics (quantitative or qualitative); (2) did not report the number of avian species associated with individual study sites or site types; (3) did not include songbird species numbers; (4) did not report at least one significant relationship (P < 0.05) between green space factors and outcomes of interest; (5) were purely qualitative; and/or (6) were duplicate articles. This resulted in a final dataset of 45 articles considered in our work. Results are reported with terminology and/or phrasing of the original article and the number of times that a factor was reported is indicated in parentheses following the factor.

Community structure

Predictors of avian abundance, composition, evenness, and species diversity can differ from those associated with richness (Hayes et al. 2020; de Groot et al. 2021; Wong et al. 2023). As we were primarily interested in identifying the attributes of the landscape that support the highest number of bird species, the studies were restricted to those reporting species richness (number of species in an area). Furthermore, included articles examined richness within the boundaries of urban and peri-urban zones (on the edge of urban development), including natural and semi-natural green spaces designated as parks, allotments, cemeteries, or woodlands, but excluding green roofs, green walls, and locations identified as rural (Sahana et al. 2023).

Small urban green space

To assess the extent that small urban green space supports songbird diversity, a subset of qualifying articles was extracted that (1) included quantitative landscape metrics for study sites < 2 ha and (2) listed the number of songbird species at each study site or type of site. The categorization of ‘small’ green space is not standardized in the literature, and studies include areas from < 1 ha to < 10 ha or simply refer to a study site as ‘small’ without an operational definition (Carbó-Ramírez and Zuria 2011; Zuñiga-Palacios et al. 2020; da Ferreira et al. 2021; Gavrilidis et al. 2022). As a definitive classification of small green space remains variable, we assigned ‘small’ to areas < 2 ha. This approach is cognizant of the demand for highly coveted urban vacant areas while retaining a variety of size alternatives for urban practitioners to consider.

Each of these study inclusion criteria was considered essential to determine the natural, structural, and anthropogenic factors that predict the richness of the avian community in urban green space and remain in accordance with our main objective of the study: to provide a condensed and user-friendly reference for urban practitioners interested in expanding the supportive role of urban green space, particularly small urban green space, to include opportunities to enhance HNC through songbird richness.

Results

Forty-five peer-reviewed qualifying articles conducted in 24 countries (21 temperate, 14 tropical, and 10 subtropical or semitropical) and published between 2000 and 2023 evaluated predictors of bird and/or songbird species richness (Table A.1). A total of 1665 study sites, ranging from < 0.1 ha to nearly 6000 ha, reported 179 associations with a wide range of predictive factors, classified in this study as natural (vegetation, habitat, and water), structural (area, shape, age, and connectivity), or anthropogenic (human activity (including noise level and number of pedestrians/minute), degree of urbanization, percentage of impervious surface (including structures and road cover), building factors (including building height and density), percentage asphalt, and number of vehicles/minute) (Fig. 1, Table A.2).

The number of natural, structural, and anthropogenic factors associated with avian richness from the full dataset of 45 peer-reviewed articles dated 2000 to 2023.
Figure 1:

The number (in parentheses) of natural (yellow), structural (blue), and anthropogenic (gray/black) factors associated with avian richness from the full dataset of 45 peer-reviewed articles dated 2000 to 2023.

Natural factors

Natural factors (72) accounted for 40.2% of the explanatory associations with bird and/or songbird richness: vegetation (54), water (9), and habitat (9) (Fig. 1, Table A.2). Vegetation factors (including the number of large trees, percentage tree cover, and vegetation diversity) had more significant positive effects on richness (57.4%) than negative (9.3%) or nonsignificant (33.3%) (Fig. 2, Table A.2). Habitat (number, type, composition, and diversity) and the presence of water, amount of water, or waterbody shape had a significant positive effect on richness excluding one nonsignificant occurrence for habitat type (Fig. 2, Table A.2). Distance to water outside of the green space had positive (33.3%) and nonsignificant (66.7%) effects (Fig. 2, Table A.2).

The natural, structural, and anthropogenic factors having a positive, negative, or nonsignificant effect on bird species richness from the full dataset of 45 peer-reviewed articles published between 2000 and 2023.
Figure 2:

Type and number of natural (vegetation, habitat, water); structural (size, connectivity, shape, and age); and anthropogenic (human factors (human), degree of urbanization (urban), percentage impervious surface (imperv.), building factors (buildings), percentage asphalt (asphalt), and number of vehicles/minute (vehicles) factors and the significance (positive, negative, or not significant) of each factor on bird species richness examined from the full dataset of 45 peer-reviewed articles published between 2000 and 2023.

Vegetation

There were 54 vegetation-specific occurrences (Fig. 3, Table A.2). Factors related to the number, diversity, or age of trees, canopy cover, or percentage or presence of forest were reported in 48.1% of vegetation-specific occurrences and had a significant positive effect on richness in 65.4% of occurrences, negative in 11.5% of occurrences, and nonsignificant in 23.1% of occurrences. Woody vegetation, shrubs, and shrubs/grass had significant positive (36.4%), negative (9.1%), and nonsignificant (54.5%) effects on richness. Grass and barren ground had 40.0% positive, 20.0% negative, and 40.0% nonsignificant effects on richness. Vegetation diversity, density, or height had mostly positive effects on richness compared to nonsignificant (57.1% and 42.9%, respectively) and no negative effects. There were five unique occurrences pertaining to tree cavities (1), surrounding green space (3), or native understory (1) (Garizábal-Carmona and Mancera-Rodríguez 2021; Strohbach, Lerman, and Warren 2013; Aida et al. 2016). The number of tree cavities and percentage of surrounding green space had a significant positive effect on richness, and native understory had a nonsignificant effect.

The type and number of vegetation factors having a positive, negative, or nonsignificant effect on bird species richness examined from the full dataset of 45 peer-reviewed articles.
Figure 3:

Type and number of vegetation (forest/canopy, trees, woody vegetation, shrubs/grass, grass/bare ground, vegetation diversity/vegetation height, and other) factors and the significance (positive, negative, or not significant) of each factor on bird species richness examined from the full dataset of 45 peer-reviewed articles.

Structural factors

Structural factors (66) accounted for 36.9% of the explanatory associations with bird and/or songbird richness: patch area (35), connectivity (18), patch shape (9), and patch age (4) (Fig. 1, Table A.2). The size of green space had a significant positive effect (74.3%) on richness more often than a nonsignificant effect (25.7%) (Fig. 2, Table A.2). Interestingly, connectivity was a significant positive factor in only 11.1% of occurrences compared to 88.9% of occurrences as a nonsignificant factor (Fig. 2, Table A.2). Patch shape had a significant positive effect on richness in 44.4% of occurrences and a nonsignificant effect in 55.6% of occurrences (Fig. 2, Table A.2). Patch age had a significant positive effect in 75.0% of occurrences and a nonsignificant effect in 25% of occurrences (Fig. 2, Table A.2). No negative effects were reported for structural factors.

Anthropogenic factors

Anthropogenic factors (41) accounted for 22.9% of the explanatory occurrences with bird and/or songbird richness: human factors (13), degree of urbanization (10), percentage impervious surface (6), buildings (6), percentage asphalt (3), and number of vehicles/minute (3) (Fig. 1, Table A.2). Only 7.4% of the associations with anthropogenic factors had a significant positive effect on richness, while 34.1% were negative and 58.5% were nonsignificant (Fig. 2, Table A.2). Human factors included results associated with noise level (5), pedestrians/minute (6), number of off-leash dogs (1), and number of cats (1). Overall, human factors had zero significant positive effects on richness and fewer negative (38.5%) than nonsignificant (61.5%) effects. The level of pedestrians had equal negative and nonsignificant effects on richness, noise level had fewer negative effects compared to nonsignificant effects, and neither the number of dogs off-leash nor the number of cats had a significant influence on richness (Fig. 2, Table A.2). One article reported a significant positive effect on richness with the degree of urbanization (Oliver et al. 2011) and one article reported a significant positive effect on richness with the percentage of impervious surface (Charre et al. 2013). All other occurrences for degree of urbanization, number of vehicles/minute, percentage impervious surface, and percentage asphalt were split between negative (31.8%) and nonsignificant (59.1%) effects (Fig. 2, Table A.2). Building-related factors had a predominately negative (33.3%) or nonsignificant (50.0%) effect on richness, with a single article reporting a significant positive effect on richness (number of buildings) (Kumdet, Ivande, and Dami 2021) (Fig. 2, Table A.2).

Small green spaces

Of the 45 articles reviewed, 12 provided landscape factors associated with songbird richness for small green spaces (< 2 ha) (Table A.3). Seven articles reported 55 sites that averaged 1.1 ha (< 1 ha to 1.98 ha) and contained an average of 15.0 bird species (range 5 to 53 species), including an average of 12.8 songbird species (range 3 to 35 species). Each of the seven articles reported results related to different measured factors within green spaces and surrounding matrices, often with minimal overlap. Eighteen sites averaged 21.7% canopy cover (range 0.2% to 70.5%). These same sites averaged 38.5% impervious surface (range 1.8% to 94.0%). An additional six sites averaged 37.5% tree cover (range 2.6% to 80%). Thirty sites averaged 5.1% shrub cover (range 0 to 45.2%) and 26 sites averaged 27.0% grass cover (range 0 to 74.4%). Overall, vegetation, habitat, and abundance of water had a significant positive effect on richness, except in one case of no significance (canopy diversity) (Schütz and Schulze 2015). The size of green space had a significant positive effect on richness in all articles that examined green space area (Oreja et al. 2012; Schütz and Schulze 2015; Matthies et al. 2017; Chaiyarat et al. 2019; González- de la Hera 2019; Melo and Piratelli 2023). Closer proximity to a green space or water source had a positive effect on richness in one article (Chaiyarat et al. 2019), was not significant in four articles (Imai and Nakashizuka 2010; González-Oreja et al. 2012; Matthies et al. 2017; Melo and Piratelli 2023), and was not examined in two articles (Schütz and Schulze 2015; de la Hera 2019). Anthropogenic factors had a negative effect on richness in articles that examined noise level, degree of urbanization, or percentage of impervious surface (Imai and Nakashizuka 2010; González-Oreja et al. 2012; Schütz and Schulze 2015). A negative effect on richness was found in one article that specifically examined noise associated with songbird richness and measured an average noise level of 61.4 dB at four sites < 2 ha (González-Oreja et al. 2012). The number of building and vehicle related factors and off-leash dogs and the number of cats had a nonsignificant effect on richness (Melo and Piratelli 2023).

Five of the 12 articles did not include site-specific landscape metrics but are worth noting since they provide information regarding natural, structural, and anthropogenic land cover predictors of songbird richness (Carbó-Ramírez and Zuria 2011; Stagoll et al. 2012; Amaya-Espinel et al. 2019; da Ferreira et al. 2021; Rico-Silva, Cruz-Trujillo, and Colorado 2021). One article reported avian and songbird richness specifically at small sites by site type, (7 gardens (x̄ = 0.3 ± 0.05 ha); 6 parks (x̄ = 1.2 ± 0.3 ha); and 6 road strip corridors (x̄ = 1.1 ± 0.26 ha)) (Carbó-Ramírez and Zuria 2011). The reported percentage of canopy cover and ground cover was 45.4 ± 3.64 | 53.0 ± 7.89 (gardens); 54.0 ± 4.67 | 55.3 ± 3.85 (parks); and 64.8 ± 7.23 | 69.0 ± 13.18 (road strip corridors). The percentage of asphalt and building cover in adjacent landscapes was 39.7 ± 1.85 | 48.1 ± 3.77 (gardens); 47.4 ± 4.49 | 40.1 ± 6.23 (parks); and 42.1 ± 2.66 | 29.5 ± 7.95 (road strip corridors). Bird species ranged from 26 to 32 species and songbird species ranged from 21 to 27 species. The second article with an average green space size of 0.4 ± 0.05 ha for 18 study sites reported ‘10–25’ bird species at all sites (the majority of species noted as songbird species) (Rico-Silva, Cruz-Trujillo, and Colorado 2021). The reported tree richness and tree abundance was 11.3 ± 6.0 | 28.9 ± 13.5, and the percentage of road cover and buildings in adjacent landscapes was 20.2 ± 3.8 | 63.2 ± 10.1. The third article evaluated 60 sites ranging from 0.5 to 2.0 ha that were ‘vegetated by ornamental trees and grass located around and between buildings, publicly accessible, and of relatively rounded form’ (Amaya-Espinel et al. 2019). Bird species were reported as 28 total species with 16 songbird species per site. The reported percentage of arboreal cover and ground cover was 51.17 ± 15.61 | 46.17 ± 17.62, and the percentage of road cover and building density in adjacent landscapes was 13.59 ± 4.24 | 41.71 ± 30.66. The fourth article with area of green space ranging from 0.25 to 2.0 ha, reported a mean number of bird species (including unspecified songbird species) as 7.8 ± 2.6 species/site (Stagoll et al. 2012). Study sites (109) were described as ‘located in residential areas, containing varying sizes of native trees of the genus Eucalyptus, and >500 m from other parks’. The fifth article evaluated 28 sites ranging from 0.1 to 0.8 ha that were described by tree richness (range 0 to 60 species) and noise level (range 59 to 75 dB) (da Ferreira et al. 2021). Bird species ranged from 9 to 36 per site and songbird species ranged from 7 to 23.

Collectively, the five articles found a significant positive effect on richness associated with height of herbaceous plants, percentage of woody vegetation, vegetation diversity, native tree species richness, or the number of large native trees, but tree density, tree abundance, and shrub abundance were nonsignificant factors. No negative effects were reported with vegetation factors. Patch area had a significant positive effect on richness in two occurrences and was nonsignificant in one occurrence. Connection to a land or water source was nonsignificant in the articles that examined the factor. Negative or nonsignificant effects on richness from anthropogenic factors were reported for percentage of asphalt or road cover, building density, building height, percentage of buildings, number of pedestrians or noise level.

Discussion

The purpose of our study was to conduct a comprehensive search of peer-reviewed publications to (1) determine the natural, structural, and anthropogenic factors (qualitative or quantitative) associated with resident songbird richness in urban green space and (2) determine site-specific quantitative landscape metrics and site-associated numbers of songbird species for small (< 2 ha) green spaces. Large patch area, vegetation (type, structure, and/or diversity), water (presence and/or abundance), and habitat (number, type, composition, and/or diversity) had predominantly positive effects on bird and/or songbird richness. Connectivity was nonsignificant in the majority of occurrences. Predictive anthropogenic factors were reported the least often, with the majority of effects being negative or nonsignificant. Below, we interpret the effects of individual natural, structural, and anthropogenic factors on bird richness and the conceptual advances these factors provide toward understanding the characteristics of urban green space, particularly small urban green space, that support songbird richness.

Vegetation and structural factors

Large green spaces had a significant positive effect on bird and/or songbird richness in the majority of articles that examined patch size, including studies that exclusively examined sites <4 ha (Carbó-Ramírez and Zuria 2011; Jasmani et al. 2017; Amaya-Espinel et al. 2019), with increments of 0.02 ha associated with an additional species being observed (Strohbach, Lerman, and Warren 2013). Yet patch size alone did not support bird richness, as several articles reported comparable or greater richness at green space locations <2 ha (Rico-Silva, Cruz-Trujillo, and Colorado 2021), <5 ha (Thompson, Tamayo, and Sigurðsson 2022), and <10 ha (Zuñiga-Palacios et al. 2020) when compared to larger green spaces examined. However, larger areas can be more effective in reducing the amount of patch area exposed to edges, providing larger core areas that, when comprised of greater vegetation and/or habitat richness, offer qualities important to many urban avian species, including urban avoiding species less tolerant of urban noise and/or human activity (Garizábal-Carmona and Mancera-Rodríguez 2021; González-Oreja et al. 2012; Matthies et al. 2017; Chaiyarat et al. 2019). In fact, vegetation factors had a significant positive effect on bird richness in the majority of articles, with one article reporting the addition of native mature trees (> 100 cm) increasing bird richness by more than 150% and woodland dependent bird richness by more than 300% (Stagoll et al. 2012).

Incorporating different tree species with varying growth rates will likely require planning ahead, in some cases for decadal landscape transformation, and encourages the design of green space that includes a variety of supportive vegetation cover in the interim (Stagoll et al. 2012). Furthermore, the presence of tree-related factors, such as tree richness, percentage tree cover, or percentage woody vegetation (trees and shrubs), may explain why area did not have a significant effect on bird species diversity in several articles that examined the factor (Morelli et al. 2017; Korányi et al. 2021; Kumdet, Ivande, and Dami 2021; Rico-Silva, Cruz-Trujillo, and Colorado 2021). Finally, patch shape had mixed predictive value in the nine studies that evaluated the factor. However, reducing edge effects (patch perimeter to area ratio) was often cited as an important factor to support richness as well as support a broader range of uncommon or specialist species (Garizábal-Carmona and Mancera-Rodríguez 2021; Peris and Montelongo 2014; Huang et al. 2022; Thompson, Tamayo, and Sigurðsson 2022). Together, these outcomes favor the concurrent incorporation of patch area and vegetation richness, and potentially shape, in designing green space that supports bird diversity.

For humans, there are conflicting results regarding the appeal of large patch size and vegetation characteristics, with preferences for more open and mowed green space with reduced tree canopy (Felappi et al. 2020). Small and vegetatively diverse green space that includes open area could help alleviate safety concerns associated with larger green space, increase frequency of contact with nature, and offer support to a variety of bird species (Felappi et al. 2020).

Connectivity

Connectivity is often cited as an important consideration in green space planning and wildlife conservation strategies but had a nonsignificant effect on avian richness in the majority of studies investigating the factor. This outcome occurred in studies evaluating richness and the distance of small areas to species rich large areas (González-Oreja et al. 2012; Charre et al. 2013; La Sorte et al. 2020) and in studies that exclusively examined the isolation of small green spaces and species richness (Amaya-Espinel et al. 2019; da Ferreira et al. 2021). This is important information, as emphasizing the establishment of a network of green spaces may place unnecessary restrictions on municipalities concerning the placement of green space and diminish the value of more isolated locations in avian conservation (Fahrig 2020; Riva and Fahrig 2022). To be clear, we are not discrediting the importance of connectivity and recognize that green space that is distributed in an equitable and accessible pattern is important for humans and terrestrial wildlife movement (Rigolon 2016; Schell et al. 2020; Larson et al. 2021). However, the results of this review indicate that when the focal animal is avian, connectivity may be a secondary concern. In fact, rethinking connectivity with avian communities in mind can jumpstart benefits to historically and perpetually under-resourced human communities that lack access to adequate green space and connections with existing green space can be retained through vegetated corridors, such as street trees (Fernández-Juricic 2000; da Ferreira et al. 2021).

Anthropogenic factors

Anthropogenic factors were associated with species richness less often than natural or structural factors and had a positive effect on richness in only three occurrences (number of buildings, degree of urbanization, and the percentage of impervious surface) (Charre et al. 2013; Kumdet, Ivande, and Dami 2021; Thompson, Tamayo, and Sigurðsson 2022). In each case, the presence of abundant surrounding woody vegetation was largely attributed to the positive effect of anthropogenic factors on overall richness rather than any direct benefit from anthropogenic factors, as an increase in more common species was also found (Charre et al. 2013; Kumdet, Ivande, and Dami 2021; Thompson, Tamayo, and Sigurðsson 2022). Noise level had mixed significance on richness in the articles that examined the factor, but higher intensity of sound (> 70 dB; gas-powered lawn mower ≈ 80 dB; car horn ≈ 100 dB; sirens ≈ 120 dB) for extended periods of time can damage human hearing and has been associated with reduced cognition, hypertension, stress, anxiety, and depression (van den Berg et al. 2010; Liu et al. 2019; Uebel et al. 2022; Müller, Forssén and Kropp 2023). In addition, there is evidence that higher noise levels interfere with avian communication, distribution, and reproduction (González-Oreja et al. 2012; Marzluff 2017; Perillo et al. 2017; da Ferreira et al. 2021). Green spaces with noise levels < 52 dB have been associated with higher resident avian species, whereas exotic avian species were more common in green spaces with higher noise levels (Arévalo et al. 2022). Avian species, such as the American Robin (Turdus migratorius), can make vocal adjustments in response to anthropogenic noise, but plasticity in vocal repertoire, particularly frequency characteristics, is not present in all avian species and can interfere with attracting mates or avoiding predation (Dowling, Luther, and Marra 2012; Slabbekoorn 2013). From this perspective, abating the effect of noise in urban green space could be advantageous for people and songbirds. How this is accomplished will be multifaceted, whether green space area and the diversity of vegetation, water features, zoning, and/or transportation related factors are implemented to interrupt or mask noise transmission (Counts and Newman 2020; Cicort-Lucaciu et al. 2022). For example, noise dampening measures might include berms and/or mature native trees acting as sound barriers, natural sounds dominating the landscape (e.g. audible water), innovative pavement types to dampen nearby vehicle noises, and designated lanes to support the use of active transportation (e.g. pedestrians and bicycles). On a positive note, a wide range of anthropogenic factors were measured in the majority of articles, but few factors analyzed were found to have an effect (positive, negative, or nonsignificant) on richness. To be clear, we are not downplaying the impact of anthropogenic factors on avian richness, but we interpret these results as a promising opportunity to incorporate vegetation and structural enhancements to urban green spaces that may offset the effects of urbanization, resulting in a positive outcome for people and songbirds.

Small green space and songbirds

Valuable information regarding site and landscape level factors supporting songbird diversity were identified across the dataset where studies focused on both small and larger green spaces, and particularly in the articles focusing on small green spaces. Large patch area had a significant positive effect on richness in the majority of articles containing small (< 2 ha) green spaces and in articles that exclusively examined small green spaces. Although size may be viewed as an essential and primary consideration to conserving songbird diversity, several articles reported comparable or greater richness in small green space locations and large green spaces may not be an achievable option in many urban areas. To that point, natural factors, particularly tree-related factors, had an overwhelmingly positive effect on songbird richness. When patch size is a restriction, green space with increased vegetation structure (herbaceous plant richness, woody vegetation, and/or mature trees) may be able to compensate for smaller patch sizes for many species. In addition, as results indicated that connectivity was nonsignificant in a majority of occurrences, this outcome removes another possible obstruction to green space allocation. Emphasizing vegetation structure within green spaces may be a suitable tradeoff for a lack of connectivity as well as patch size. As urban communities consider ‘pocket parks’ and other green and creative repurposing of smaller vacant spaces, this point may help drive decisions about the types of vegetation to include. For anthropogenic factors, the absence of positive effects on richness was a recurring outcome, but this category of factors was also reported the least often, indicating that natural and structural factors likely have a more significant role in supporting songbird richness. Finally, for articles that provided site-specific landscape metrics and songbird richness, there was a wide range of factors regarding the amount of impervious surface, canopy cover, woody and herbaceous vegetation, and multiple tree-related factors that supported songbirds. This outcome is encouraging, as it demonstrates that a range of effective factors can be combined to provide suitable urban habitat for songbirds.

Limitations and recommendations

This review can serve as a guide for urban practitioners interested in expanding the conventional role of urban green space to incorporate qualities to enhance HNC and songbird conservation. However, we do not claim it to be an exhaustive search of the literature (e.g. non-English, technical reports) nor a comprehensive list of evidence pertaining to songbird richness in urban green space. For example, our search criteria focused on resident songbird richness, as this criterion aligns with providing people with year-round bird diversity that has been associated with enhanced HNC. However, migratory birds are a dynamic part of many urban avian communities and a similar approach as the one used in this manuscript could be applied in future studies. Additionally, other metrics, such as abundance and/or composition, may not be supported by the same factors as richness and result in dominance by a few species or lower numbers of uncommon or guild-specific species.

Publishing site and landscape level data along with site-specific numbers of bird and/or songbird species, often collected regardless of study objectives, is key to improve and expand the accessibility of this type of research to a broader audience, particularly urban practitioners trying to manage multiple interests. In addition, our search resulted in only four articles that specifically evaluated small green space (<2 ha). Future research investigating the supportive conditions and compositions of small urban green spaces could result in a broad assortment of valuable applications, particularly as urban populations continue to expand and face growing challenges to human health, nature connection, and environmental sustainability.

Conclusions

The overwhelming appeal of songbirds, as well as their diversity and global distribution, presents a compelling opportunity to rethink urban green space design that supports people and songbirds. The conventional list of green space qualities that are often viewed as paramount to conservation must be reevaluated to develop a shortlist of factors that have known positive effects and are feasible for densely populated or under-resourced areas. To do otherwise disregards the conservation potential of small green spaces, as well as their possible role in broadening conservation awareness and support, namely, by keeping people connected to nature through the spaces they share with songbirds. Although green space designs may vary, based on our findings, we present several recommendations: (1) allocate green space of any feasible size; (2) incorporate a variety of native plant species, particularly tree species; (3) incorporate native habitat diversity; (4) integrate water; (5) place green spaces in under-resourced areas and connect spaces through green corridors; and (6) plan for the temporal transformation of green spaces. Collectively, these recommendations have the potential to empower urban practitioners with viable options to generate design ideas that have the capacity to meet the interests of urban residents, human and avian.

Author contributions

Sheryl Hayes Hursh (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Validation [equal], Visualization [lead], Writing—original draft [equal], Writing—review & editing [equal]), Elizabeth E. Perry (Validation [equal], Writing—original draft [equal], Writing—review & editing [equal]), and David Drake (Validation [equal], Writing—original draft [equal], Writing—review & editing [equal])

Conflict of interest statement

None declared.

Data availability

Data supporting the findings of this study are available in Dryad at https://datadryad.org/stash at DOI: 10.5061/dryad.sqv9s4n93.

APPENDIX 1

Table A.1:

Reference number, author, date of publication, and study location examining avian richness from the full dataset of 45 articles published between 2000 and 2023 in our review

Reference numberAuthorDate of publicationStudy location
1Aida et al. DOI2016Klang Valley, Malaysia
2Amaya-Espinel et al.2019Santiago, Chile
3Bonança et al. DOI2017São Paulo, Brazil
4Carbó-Ramírez and Zuria2011Pachuca, Mexico
5Chaiyarat et al. DOI2019Bangkok, Thailand
6Chang and Lee DOI2016Tainan, Taiwan
7Charre et al.2013Mexico City, Mexico
8Dale DOI2018Oslo, Norway
9Fernández-Juricic2000Madrid, Spain
10da Ferreira et al. DOI2021Rio Claro, Brazil
11Garizábal-Carmona and Mancera-Rodríguez DOI2021Medellin, Columbia
12González-Oreja et al. DOI2012Puebla, Mexico
13de Groot et al.2021Ljubljana, Slovenia
14Hayes et al. DOI2020Georgetown, Guyana
15de la Hera 2019Vitoria-Gasteiz, Spain
16Huang et al.2022Fuzhou, China
17Imai and Nakashizuka DOI2010Sendai, Japan
18James Barth et al. DOI2015Queensland, Australia
19Jasmani et al. DOI2017Petaling Jaya, Malaysia
20Kaushik et al. DOI2022Dehradun, India
21Khera et al. DOI2009Delhi, India
22Kontsiotis et al. DOI2019Dehradun, India
23Korányi et al. DOI2021Gottingen, Germany
24Kumdet et al.2021Plateau State, Nigeria
25La Sorte et al. DOI2020New York City, USA
26MacGregor-Fors et al.2018Veracruz, Mexico
27Machar et al. DOI2022Olomouc City, Czech Republic
28Matthies et al. DOI2017Hannover, Germany
29Melo and Piratelli DOI2022São Paulo, Brazil
30Morelli et al. DOI2017Beijing, China
31Mühlbauer et al. DOI2021Munich, Germany
32Oliver et al. DOI2011St Louis, USA
33Peris and Montelongo2014Salamanca, Spain
34Rico-Silva et al. DOI2021Florencia, Columbia
35Sandström et al.2006Orebro, Sweden
36Schütz and Schulze2015Vienna, Italy
37Shih DOI2018Taipei City, Taiwan
38Shwartz et al. DOI2008Tel Aviv, Israel
39Stagoll et al. DOI2012Canberra, Australia
40Strobach et al.2013Boston, USA
41Thompson et al.2022Reykjavik, Iceland
42Wong et al.2023Singapore
43Xie et al. DOI2016Beijing, China
44Zorzal et al.2021Vitoria, Brazil
45Zuñiga-Palacios et al. DOI2020Pachuca, Mexico
Reference numberAuthorDate of publicationStudy location
1Aida et al. DOI2016Klang Valley, Malaysia
2Amaya-Espinel et al.2019Santiago, Chile
3Bonança et al. DOI2017São Paulo, Brazil
4Carbó-Ramírez and Zuria2011Pachuca, Mexico
5Chaiyarat et al. DOI2019Bangkok, Thailand
6Chang and Lee DOI2016Tainan, Taiwan
7Charre et al.2013Mexico City, Mexico
8Dale DOI2018Oslo, Norway
9Fernández-Juricic2000Madrid, Spain
10da Ferreira et al. DOI2021Rio Claro, Brazil
11Garizábal-Carmona and Mancera-Rodríguez DOI2021Medellin, Columbia
12González-Oreja et al. DOI2012Puebla, Mexico
13de Groot et al.2021Ljubljana, Slovenia
14Hayes et al. DOI2020Georgetown, Guyana
15de la Hera 2019Vitoria-Gasteiz, Spain
16Huang et al.2022Fuzhou, China
17Imai and Nakashizuka DOI2010Sendai, Japan
18James Barth et al. DOI2015Queensland, Australia
19Jasmani et al. DOI2017Petaling Jaya, Malaysia
20Kaushik et al. DOI2022Dehradun, India
21Khera et al. DOI2009Delhi, India
22Kontsiotis et al. DOI2019Dehradun, India
23Korányi et al. DOI2021Gottingen, Germany
24Kumdet et al.2021Plateau State, Nigeria
25La Sorte et al. DOI2020New York City, USA
26MacGregor-Fors et al.2018Veracruz, Mexico
27Machar et al. DOI2022Olomouc City, Czech Republic
28Matthies et al. DOI2017Hannover, Germany
29Melo and Piratelli DOI2022São Paulo, Brazil
30Morelli et al. DOI2017Beijing, China
31Mühlbauer et al. DOI2021Munich, Germany
32Oliver et al. DOI2011St Louis, USA
33Peris and Montelongo2014Salamanca, Spain
34Rico-Silva et al. DOI2021Florencia, Columbia
35Sandström et al.2006Orebro, Sweden
36Schütz and Schulze2015Vienna, Italy
37Shih DOI2018Taipei City, Taiwan
38Shwartz et al. DOI2008Tel Aviv, Israel
39Stagoll et al. DOI2012Canberra, Australia
40Strobach et al.2013Boston, USA
41Thompson et al.2022Reykjavik, Iceland
42Wong et al.2023Singapore
43Xie et al. DOI2016Beijing, China
44Zorzal et al.2021Vitoria, Brazil
45Zuñiga-Palacios et al. DOI2020Pachuca, Mexico
Table A.1:

Reference number, author, date of publication, and study location examining avian richness from the full dataset of 45 articles published between 2000 and 2023 in our review

Reference numberAuthorDate of publicationStudy location
1Aida et al. DOI2016Klang Valley, Malaysia
2Amaya-Espinel et al.2019Santiago, Chile
3Bonança et al. DOI2017São Paulo, Brazil
4Carbó-Ramírez and Zuria2011Pachuca, Mexico
5Chaiyarat et al. DOI2019Bangkok, Thailand
6Chang and Lee DOI2016Tainan, Taiwan
7Charre et al.2013Mexico City, Mexico
8Dale DOI2018Oslo, Norway
9Fernández-Juricic2000Madrid, Spain
10da Ferreira et al. DOI2021Rio Claro, Brazil
11Garizábal-Carmona and Mancera-Rodríguez DOI2021Medellin, Columbia
12González-Oreja et al. DOI2012Puebla, Mexico
13de Groot et al.2021Ljubljana, Slovenia
14Hayes et al. DOI2020Georgetown, Guyana
15de la Hera 2019Vitoria-Gasteiz, Spain
16Huang et al.2022Fuzhou, China
17Imai and Nakashizuka DOI2010Sendai, Japan
18James Barth et al. DOI2015Queensland, Australia
19Jasmani et al. DOI2017Petaling Jaya, Malaysia
20Kaushik et al. DOI2022Dehradun, India
21Khera et al. DOI2009Delhi, India
22Kontsiotis et al. DOI2019Dehradun, India
23Korányi et al. DOI2021Gottingen, Germany
24Kumdet et al.2021Plateau State, Nigeria
25La Sorte et al. DOI2020New York City, USA
26MacGregor-Fors et al.2018Veracruz, Mexico
27Machar et al. DOI2022Olomouc City, Czech Republic
28Matthies et al. DOI2017Hannover, Germany
29Melo and Piratelli DOI2022São Paulo, Brazil
30Morelli et al. DOI2017Beijing, China
31Mühlbauer et al. DOI2021Munich, Germany
32Oliver et al. DOI2011St Louis, USA
33Peris and Montelongo2014Salamanca, Spain
34Rico-Silva et al. DOI2021Florencia, Columbia
35Sandström et al.2006Orebro, Sweden
36Schütz and Schulze2015Vienna, Italy
37Shih DOI2018Taipei City, Taiwan
38Shwartz et al. DOI2008Tel Aviv, Israel
39Stagoll et al. DOI2012Canberra, Australia
40Strobach et al.2013Boston, USA
41Thompson et al.2022Reykjavik, Iceland
42Wong et al.2023Singapore
43Xie et al. DOI2016Beijing, China
44Zorzal et al.2021Vitoria, Brazil
45Zuñiga-Palacios et al. DOI2020Pachuca, Mexico
Reference numberAuthorDate of publicationStudy location
1Aida et al. DOI2016Klang Valley, Malaysia
2Amaya-Espinel et al.2019Santiago, Chile
3Bonança et al. DOI2017São Paulo, Brazil
4Carbó-Ramírez and Zuria2011Pachuca, Mexico
5Chaiyarat et al. DOI2019Bangkok, Thailand
6Chang and Lee DOI2016Tainan, Taiwan
7Charre et al.2013Mexico City, Mexico
8Dale DOI2018Oslo, Norway
9Fernández-Juricic2000Madrid, Spain
10da Ferreira et al. DOI2021Rio Claro, Brazil
11Garizábal-Carmona and Mancera-Rodríguez DOI2021Medellin, Columbia
12González-Oreja et al. DOI2012Puebla, Mexico
13de Groot et al.2021Ljubljana, Slovenia
14Hayes et al. DOI2020Georgetown, Guyana
15de la Hera 2019Vitoria-Gasteiz, Spain
16Huang et al.2022Fuzhou, China
17Imai and Nakashizuka DOI2010Sendai, Japan
18James Barth et al. DOI2015Queensland, Australia
19Jasmani et al. DOI2017Petaling Jaya, Malaysia
20Kaushik et al. DOI2022Dehradun, India
21Khera et al. DOI2009Delhi, India
22Kontsiotis et al. DOI2019Dehradun, India
23Korányi et al. DOI2021Gottingen, Germany
24Kumdet et al.2021Plateau State, Nigeria
25La Sorte et al. DOI2020New York City, USA
26MacGregor-Fors et al.2018Veracruz, Mexico
27Machar et al. DOI2022Olomouc City, Czech Republic
28Matthies et al. DOI2017Hannover, Germany
29Melo and Piratelli DOI2022São Paulo, Brazil
30Morelli et al. DOI2017Beijing, China
31Mühlbauer et al. DOI2021Munich, Germany
32Oliver et al. DOI2011St Louis, USA
33Peris and Montelongo2014Salamanca, Spain
34Rico-Silva et al. DOI2021Florencia, Columbia
35Sandström et al.2006Orebro, Sweden
36Schütz and Schulze2015Vienna, Italy
37Shih DOI2018Taipei City, Taiwan
38Shwartz et al. DOI2008Tel Aviv, Israel
39Stagoll et al. DOI2012Canberra, Australia
40Strobach et al.2013Boston, USA
41Thompson et al.2022Reykjavik, Iceland
42Wong et al.2023Singapore
43Xie et al. DOI2016Beijing, China
44Zorzal et al.2021Vitoria, Brazil
45Zuñiga-Palacios et al. DOI2020Pachuca, Mexico
Table A.2:

Observed effects of structural, natural, and anthropogenic factors predicting overall avian richness examined from the full dataset of 45 peer-reviewed articles published between 2000 and 2023

Predictors of avian richness
Structural and natural factors
Anthropogenic factors
Reference numberStudy site size (ha)Green space areaConnectivity1 Shape2 Age3Vegetation, habitat, and water factorsBuilding, road, and vehicle factorsImpervious surface and degree of urbanizationHuman factors
Number of study sites
10.06–127 | 80(+)
  • (+)

  • % vegetation (surrounding green space)

  • (+)

  • number of woody trees

  • (NS)

  • degree/type of urbanization

  • (suburbs or business district)

20.5–2.0 | 60(+)(NS)(1)
  • (+)

  • vegetation diversity

  • (−)

  • % asphalt

  • (−)

  • % buildings

  • (NS)

  • building height

311.9–50 | 4(+)
  • (NS)(2)

  • (NS)(3)

  • (+)

  • presence of water/proximity of water bodies

40.1–2.0 | 19(+)
  • (+)

  • height of herbaceous plants

  • (−)

  • % buildings

  • (NS)

  • % asphalt

  • (NS) vehicles/minute

  • (NS)

  • pedestrian density

  • (NS)

  • noise level

51.4–400 | 10(+)(+)(1)
  • (+)

  • habitat composition

60.3–52.8 | 54(+)(NS)(1)
  • (−)

  • number of non-native tree species

  • (NS)

  • % canopy cover

  • (NS)

  • ground heterogeneity

  • (−)

  • pedestrian density

711.0–1100 | 12(+)(NS)(1)
  • (NS)

  • tree-related factors

  • (+)

  • % impervious surfaces

80.6–98.1 | 93(+)(NS)(1)
  • (+)

  • presence of native forests

  • (NS)

  • degree/type of urbanization

  • (inner/

  • residential or outer/

  • near-forest buffer zone)

91.0–118.2 | 25(+)
  • (NS)(1)

  • (+)(3)

100.1–0.77 | 28(NS)(1)
  • (+)

  • native tree species richness

  • (NS)

  • tree density

  • (NS)

  • % native tree species

  • (NS)

  • distance to water

  • (−)

  • noise level

110.1–103.7 | 44(+)(+)(2)
  • (−)

  • introduced tree dominance (lower crown coverage, basal area, and average height)

  • (+)

  • % area dominated by shrubs/grass

  • (NS)

  • native understory

  • (−)

  • % impervious surface

120.7–702 | 22(+)(NS)(1)
  • (−)

  • noise level

13<39.0–666 | 39(NS)
  • (NS)

  • habitat type

  • (urban forests or parks)

  • (NS)

  • degree/type of urbanization

  • (urban or peri-urban, i.e. inside or outside the motorway ring road)

14NP | 114
  • (+)

  • habitat type

  • -managed (e.g. parks and cemeteries)

150.5–17.01 | 31(+)
163.54–34.19 | 9(+)(+)(2)
  • (+)

  • % woodland

  • (+)

  • waterbody shape index

170–66.6 | 20(NS)(1)
  • (+)

  • % water

  • (prevalence of water)

  • (−)

  • degree of urbanization

  • (200m and 1000m buffer zone)

18<900 | 95
  • (+)

  • number of mature trees

190.7–3.5 | 9(+)(NS)(2)
  • (−)

  • % canopy cover

  • (+)

  • % open grass/ground

  • (+)

  • % native vegetation species

  • (+)

  • % exotic vegetation

  • species

  • (NS)

  • human presence

  • (NS)

  • noise level

200.3–224 | 18(+)
  • (+)

  • tree species richness

  • (+)

  • woody species richness

  • (NS)

  • % impervious surface

212.0–2135 | 19(NS)
  • (+)

  • woody species richness

  • (+)

  • density of medium-sized trees

  • (−)

  • exotic woody species density

  • (NS)

  • shrub diversity

  • (NS)

  • shrub density

22<3–1100 | 113
  • (+)

  • habitat type

  • (forest ecotone)

231.0–9.0 | 34(NS)
  • (+)

  • % tree cover

  • (NS)

  • % shrub cover

  • (NS)

  • % impervious surface

2416.78–116.54 | 15(NS)(+)(3)
  • (+)

  • number of shrubs

  • (+)

  • number of tree species

  • (NS)

  • grass height

  • (NS)

  • density of flowering/fruit plants

  • (NS)

  • % bare ground

  • (+)

  • number of buildings

  • (−)

  • number of vehicles

  • (−)

  • pedestrian density

250.10–1119.41 | 102(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • % tree canopy

26NP | 6
  • (+)

  • habitat

  • (well-preserved cloud forests)

272.9–4.2 | 6(+)(1)
  • (NS)

  • degree of urbanization

  • (heritage city or floodplain forest)

280.72–62.26 | 32(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • number of habitat types

  • (+)

  • diversity of habitat types

291.1–5300 | 25(+)
  • (+)

  • % shrub cover

  • (+)

  • distance to water

  • (NS)

  • number of glass panes

  • (NS)

  • number of vehicles

  • (NS)

  • number of off-leash dogs

  • (NS)

  • number of cats

3024.82–2050.93 | 10(NS)(NS)(1)
  • (+)

  • presence of large trees

  • (+)

  • % water coverage

310.09–6.71 | 103(+)(NS)(1)
  • (+)

  • % grass cover

  • (+)

  • density of trees

  • (+)

  • number of mature trees

  • (NS)

  • % shrub cover

  • (−)

  • mean number of people

3215.0–5923 | 20(+)
  • (+)

  • degree of urbanization

  • (1 km and 5 km buffer zones)

330.2–5.95 | 20(+)(NS)(1)
  • (+)

  • % tree cover

340.12–2.47 | 18(NS)
  • (+)

  • % woody vegetation cover (area surrounding green space)

  • (NS)

  • tree abundance

  • (NS)

  • shrub abundance

  • (NS)

  • % buildings

  • (NS)

  • % asphalt

3596.1–810.40 | 8
  • (−)

  • degree of urbanization (city center, residential, greenway and periphery)

360.7–34.5 | 36(+)
  • (NS)

  • canopy diversity

  • (−)

  • % impervious surface

374.0–547 | 30(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • habitat diversity

  • (+)

  • % water area

  • (NS)

  • degree of urbanization

  • (building index (NDBI) and building height)

38262 | 1
  • (+)

  • % woody plant species richness

  • (−)

  • % lawn cover

  • (NS)

  • distance to water

390.25–2.0 | 109
  • (+)

  • number of large native trees

400.01–210 | 30(+)(NS)(1)
  • (+)

  • number of tree cavities

410.5–40.93 | 15(NS)
  • (+)(2)

  • (+)(−)(3)

  • highest in intermediate and old-central higher than young-suburban

  • (NS)

  • degree of urbanization

  • (suburban or central)

42NP | 64(NS)(+)(2)
  • (+)

  • presence of freshwater bodies

  • (NS)

  • tree cover

432.27–22.04 | 29(+)
  • (+)

  • largest patch index of woodland (surrounding green space)

  • (NS)

  • foliage height diversity

  • (−)

  • degree urbanization

  • (NS)

  • human visitations

443.12–227.2 | 7(+)(NS)(1)
  • (+)

  • habitat heterogeneity

  • (NS)

  • noise level

450.2–4.11 | 17(NS)
  • (NS)

  • any vegetation structure variables

  • (NS)

  • %impervious surface

Predictors of avian richness
Structural and natural factors
Anthropogenic factors
Reference numberStudy site size (ha)Green space areaConnectivity1 Shape2 Age3Vegetation, habitat, and water factorsBuilding, road, and vehicle factorsImpervious surface and degree of urbanizationHuman factors
Number of study sites
10.06–127 | 80(+)
  • (+)

  • % vegetation (surrounding green space)

  • (+)

  • number of woody trees

  • (NS)

  • degree/type of urbanization

  • (suburbs or business district)

20.5–2.0 | 60(+)(NS)(1)
  • (+)

  • vegetation diversity

  • (−)

  • % asphalt

  • (−)

  • % buildings

  • (NS)

  • building height

311.9–50 | 4(+)
  • (NS)(2)

  • (NS)(3)

  • (+)

  • presence of water/proximity of water bodies

40.1–2.0 | 19(+)
  • (+)

  • height of herbaceous plants

  • (−)

  • % buildings

  • (NS)

  • % asphalt

  • (NS) vehicles/minute

  • (NS)

  • pedestrian density

  • (NS)

  • noise level

51.4–400 | 10(+)(+)(1)
  • (+)

  • habitat composition

60.3–52.8 | 54(+)(NS)(1)
  • (−)

  • number of non-native tree species

  • (NS)

  • % canopy cover

  • (NS)

  • ground heterogeneity

  • (−)

  • pedestrian density

711.0–1100 | 12(+)(NS)(1)
  • (NS)

  • tree-related factors

  • (+)

  • % impervious surfaces

80.6–98.1 | 93(+)(NS)(1)
  • (+)

  • presence of native forests

  • (NS)

  • degree/type of urbanization

  • (inner/

  • residential or outer/

  • near-forest buffer zone)

91.0–118.2 | 25(+)
  • (NS)(1)

  • (+)(3)

100.1–0.77 | 28(NS)(1)
  • (+)

  • native tree species richness

  • (NS)

  • tree density

  • (NS)

  • % native tree species

  • (NS)

  • distance to water

  • (−)

  • noise level

110.1–103.7 | 44(+)(+)(2)
  • (−)

  • introduced tree dominance (lower crown coverage, basal area, and average height)

  • (+)

  • % area dominated by shrubs/grass

  • (NS)

  • native understory

  • (−)

  • % impervious surface

120.7–702 | 22(+)(NS)(1)
  • (−)

  • noise level

13<39.0–666 | 39(NS)
  • (NS)

  • habitat type

  • (urban forests or parks)

  • (NS)

  • degree/type of urbanization

  • (urban or peri-urban, i.e. inside or outside the motorway ring road)

14NP | 114
  • (+)

  • habitat type

  • -managed (e.g. parks and cemeteries)

150.5–17.01 | 31(+)
163.54–34.19 | 9(+)(+)(2)
  • (+)

  • % woodland

  • (+)

  • waterbody shape index

170–66.6 | 20(NS)(1)
  • (+)

  • % water

  • (prevalence of water)

  • (−)

  • degree of urbanization

  • (200m and 1000m buffer zone)

18<900 | 95
  • (+)

  • number of mature trees

190.7–3.5 | 9(+)(NS)(2)
  • (−)

  • % canopy cover

  • (+)

  • % open grass/ground

  • (+)

  • % native vegetation species

  • (+)

  • % exotic vegetation

  • species

  • (NS)

  • human presence

  • (NS)

  • noise level

200.3–224 | 18(+)
  • (+)

  • tree species richness

  • (+)

  • woody species richness

  • (NS)

  • % impervious surface

212.0–2135 | 19(NS)
  • (+)

  • woody species richness

  • (+)

  • density of medium-sized trees

  • (−)

  • exotic woody species density

  • (NS)

  • shrub diversity

  • (NS)

  • shrub density

22<3–1100 | 113
  • (+)

  • habitat type

  • (forest ecotone)

231.0–9.0 | 34(NS)
  • (+)

  • % tree cover

  • (NS)

  • % shrub cover

  • (NS)

  • % impervious surface

2416.78–116.54 | 15(NS)(+)(3)
  • (+)

  • number of shrubs

  • (+)

  • number of tree species

  • (NS)

  • grass height

  • (NS)

  • density of flowering/fruit plants

  • (NS)

  • % bare ground

  • (+)

  • number of buildings

  • (−)

  • number of vehicles

  • (−)

  • pedestrian density

250.10–1119.41 | 102(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • % tree canopy

26NP | 6
  • (+)

  • habitat

  • (well-preserved cloud forests)

272.9–4.2 | 6(+)(1)
  • (NS)

  • degree of urbanization

  • (heritage city or floodplain forest)

280.72–62.26 | 32(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • number of habitat types

  • (+)

  • diversity of habitat types

291.1–5300 | 25(+)
  • (+)

  • % shrub cover

  • (+)

  • distance to water

  • (NS)

  • number of glass panes

  • (NS)

  • number of vehicles

  • (NS)

  • number of off-leash dogs

  • (NS)

  • number of cats

3024.82–2050.93 | 10(NS)(NS)(1)
  • (+)

  • presence of large trees

  • (+)

  • % water coverage

310.09–6.71 | 103(+)(NS)(1)
  • (+)

  • % grass cover

  • (+)

  • density of trees

  • (+)

  • number of mature trees

  • (NS)

  • % shrub cover

  • (−)

  • mean number of people

3215.0–5923 | 20(+)
  • (+)

  • degree of urbanization

  • (1 km and 5 km buffer zones)

330.2–5.95 | 20(+)(NS)(1)
  • (+)

  • % tree cover

340.12–2.47 | 18(NS)
  • (+)

  • % woody vegetation cover (area surrounding green space)

  • (NS)

  • tree abundance

  • (NS)

  • shrub abundance

  • (NS)

  • % buildings

  • (NS)

  • % asphalt

3596.1–810.40 | 8
  • (−)

  • degree of urbanization (city center, residential, greenway and periphery)

360.7–34.5 | 36(+)
  • (NS)

  • canopy diversity

  • (−)

  • % impervious surface

374.0–547 | 30(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • habitat diversity

  • (+)

  • % water area

  • (NS)

  • degree of urbanization

  • (building index (NDBI) and building height)

38262 | 1
  • (+)

  • % woody plant species richness

  • (−)

  • % lawn cover

  • (NS)

  • distance to water

390.25–2.0 | 109
  • (+)

  • number of large native trees

400.01–210 | 30(+)(NS)(1)
  • (+)

  • number of tree cavities

410.5–40.93 | 15(NS)
  • (+)(2)

  • (+)(−)(3)

  • highest in intermediate and old-central higher than young-suburban

  • (NS)

  • degree of urbanization

  • (suburban or central)

42NP | 64(NS)(+)(2)
  • (+)

  • presence of freshwater bodies

  • (NS)

  • tree cover

432.27–22.04 | 29(+)
  • (+)

  • largest patch index of woodland (surrounding green space)

  • (NS)

  • foliage height diversity

  • (−)

  • degree urbanization

  • (NS)

  • human visitations

443.12–227.2 | 7(+)(NS)(1)
  • (+)

  • habitat heterogeneity

  • (NS)

  • noise level

450.2–4.11 | 17(NS)
  • (NS)

  • any vegetation structure variables

  • (NS)

  • %impervious surface

Explanatory factors had a positive (+), negative (−), nonsignificant effect (NS), or conditional significance (+) (−) on richness. Shaded areas are factors that were not examined, not in the final analysis, or not referenced in the significant results presented in the articles. Additional factors may have been examined but are not listed below unless referenced in the significant results presented in the articles. Site information not provided in the articles is indicated by NP. Reference numbers correspond to reference numbers used in Table A.1.

Table A.2:

Observed effects of structural, natural, and anthropogenic factors predicting overall avian richness examined from the full dataset of 45 peer-reviewed articles published between 2000 and 2023

Predictors of avian richness
Structural and natural factors
Anthropogenic factors
Reference numberStudy site size (ha)Green space areaConnectivity1 Shape2 Age3Vegetation, habitat, and water factorsBuilding, road, and vehicle factorsImpervious surface and degree of urbanizationHuman factors
Number of study sites
10.06–127 | 80(+)
  • (+)

  • % vegetation (surrounding green space)

  • (+)

  • number of woody trees

  • (NS)

  • degree/type of urbanization

  • (suburbs or business district)

20.5–2.0 | 60(+)(NS)(1)
  • (+)

  • vegetation diversity

  • (−)

  • % asphalt

  • (−)

  • % buildings

  • (NS)

  • building height

311.9–50 | 4(+)
  • (NS)(2)

  • (NS)(3)

  • (+)

  • presence of water/proximity of water bodies

40.1–2.0 | 19(+)
  • (+)

  • height of herbaceous plants

  • (−)

  • % buildings

  • (NS)

  • % asphalt

  • (NS) vehicles/minute

  • (NS)

  • pedestrian density

  • (NS)

  • noise level

51.4–400 | 10(+)(+)(1)
  • (+)

  • habitat composition

60.3–52.8 | 54(+)(NS)(1)
  • (−)

  • number of non-native tree species

  • (NS)

  • % canopy cover

  • (NS)

  • ground heterogeneity

  • (−)

  • pedestrian density

711.0–1100 | 12(+)(NS)(1)
  • (NS)

  • tree-related factors

  • (+)

  • % impervious surfaces

80.6–98.1 | 93(+)(NS)(1)
  • (+)

  • presence of native forests

  • (NS)

  • degree/type of urbanization

  • (inner/

  • residential or outer/

  • near-forest buffer zone)

91.0–118.2 | 25(+)
  • (NS)(1)

  • (+)(3)

100.1–0.77 | 28(NS)(1)
  • (+)

  • native tree species richness

  • (NS)

  • tree density

  • (NS)

  • % native tree species

  • (NS)

  • distance to water

  • (−)

  • noise level

110.1–103.7 | 44(+)(+)(2)
  • (−)

  • introduced tree dominance (lower crown coverage, basal area, and average height)

  • (+)

  • % area dominated by shrubs/grass

  • (NS)

  • native understory

  • (−)

  • % impervious surface

120.7–702 | 22(+)(NS)(1)
  • (−)

  • noise level

13<39.0–666 | 39(NS)
  • (NS)

  • habitat type

  • (urban forests or parks)

  • (NS)

  • degree/type of urbanization

  • (urban or peri-urban, i.e. inside or outside the motorway ring road)

14NP | 114
  • (+)

  • habitat type

  • -managed (e.g. parks and cemeteries)

150.5–17.01 | 31(+)
163.54–34.19 | 9(+)(+)(2)
  • (+)

  • % woodland

  • (+)

  • waterbody shape index

170–66.6 | 20(NS)(1)
  • (+)

  • % water

  • (prevalence of water)

  • (−)

  • degree of urbanization

  • (200m and 1000m buffer zone)

18<900 | 95
  • (+)

  • number of mature trees

190.7–3.5 | 9(+)(NS)(2)
  • (−)

  • % canopy cover

  • (+)

  • % open grass/ground

  • (+)

  • % native vegetation species

  • (+)

  • % exotic vegetation

  • species

  • (NS)

  • human presence

  • (NS)

  • noise level

200.3–224 | 18(+)
  • (+)

  • tree species richness

  • (+)

  • woody species richness

  • (NS)

  • % impervious surface

212.0–2135 | 19(NS)
  • (+)

  • woody species richness

  • (+)

  • density of medium-sized trees

  • (−)

  • exotic woody species density

  • (NS)

  • shrub diversity

  • (NS)

  • shrub density

22<3–1100 | 113
  • (+)

  • habitat type

  • (forest ecotone)

231.0–9.0 | 34(NS)
  • (+)

  • % tree cover

  • (NS)

  • % shrub cover

  • (NS)

  • % impervious surface

2416.78–116.54 | 15(NS)(+)(3)
  • (+)

  • number of shrubs

  • (+)

  • number of tree species

  • (NS)

  • grass height

  • (NS)

  • density of flowering/fruit plants

  • (NS)

  • % bare ground

  • (+)

  • number of buildings

  • (−)

  • number of vehicles

  • (−)

  • pedestrian density

250.10–1119.41 | 102(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • % tree canopy

26NP | 6
  • (+)

  • habitat

  • (well-preserved cloud forests)

272.9–4.2 | 6(+)(1)
  • (NS)

  • degree of urbanization

  • (heritage city or floodplain forest)

280.72–62.26 | 32(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • number of habitat types

  • (+)

  • diversity of habitat types

291.1–5300 | 25(+)
  • (+)

  • % shrub cover

  • (+)

  • distance to water

  • (NS)

  • number of glass panes

  • (NS)

  • number of vehicles

  • (NS)

  • number of off-leash dogs

  • (NS)

  • number of cats

3024.82–2050.93 | 10(NS)(NS)(1)
  • (+)

  • presence of large trees

  • (+)

  • % water coverage

310.09–6.71 | 103(+)(NS)(1)
  • (+)

  • % grass cover

  • (+)

  • density of trees

  • (+)

  • number of mature trees

  • (NS)

  • % shrub cover

  • (−)

  • mean number of people

3215.0–5923 | 20(+)
  • (+)

  • degree of urbanization

  • (1 km and 5 km buffer zones)

330.2–5.95 | 20(+)(NS)(1)
  • (+)

  • % tree cover

340.12–2.47 | 18(NS)
  • (+)

  • % woody vegetation cover (area surrounding green space)

  • (NS)

  • tree abundance

  • (NS)

  • shrub abundance

  • (NS)

  • % buildings

  • (NS)

  • % asphalt

3596.1–810.40 | 8
  • (−)

  • degree of urbanization (city center, residential, greenway and periphery)

360.7–34.5 | 36(+)
  • (NS)

  • canopy diversity

  • (−)

  • % impervious surface

374.0–547 | 30(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • habitat diversity

  • (+)

  • % water area

  • (NS)

  • degree of urbanization

  • (building index (NDBI) and building height)

38262 | 1
  • (+)

  • % woody plant species richness

  • (−)

  • % lawn cover

  • (NS)

  • distance to water

390.25–2.0 | 109
  • (+)

  • number of large native trees

400.01–210 | 30(+)(NS)(1)
  • (+)

  • number of tree cavities

410.5–40.93 | 15(NS)
  • (+)(2)

  • (+)(−)(3)

  • highest in intermediate and old-central higher than young-suburban

  • (NS)

  • degree of urbanization

  • (suburban or central)

42NP | 64(NS)(+)(2)
  • (+)

  • presence of freshwater bodies

  • (NS)

  • tree cover

432.27–22.04 | 29(+)
  • (+)

  • largest patch index of woodland (surrounding green space)

  • (NS)

  • foliage height diversity

  • (−)

  • degree urbanization

  • (NS)

  • human visitations

443.12–227.2 | 7(+)(NS)(1)
  • (+)

  • habitat heterogeneity

  • (NS)

  • noise level

450.2–4.11 | 17(NS)
  • (NS)

  • any vegetation structure variables

  • (NS)

  • %impervious surface

Predictors of avian richness
Structural and natural factors
Anthropogenic factors
Reference numberStudy site size (ha)Green space areaConnectivity1 Shape2 Age3Vegetation, habitat, and water factorsBuilding, road, and vehicle factorsImpervious surface and degree of urbanizationHuman factors
Number of study sites
10.06–127 | 80(+)
  • (+)

  • % vegetation (surrounding green space)

  • (+)

  • number of woody trees

  • (NS)

  • degree/type of urbanization

  • (suburbs or business district)

20.5–2.0 | 60(+)(NS)(1)
  • (+)

  • vegetation diversity

  • (−)

  • % asphalt

  • (−)

  • % buildings

  • (NS)

  • building height

311.9–50 | 4(+)
  • (NS)(2)

  • (NS)(3)

  • (+)

  • presence of water/proximity of water bodies

40.1–2.0 | 19(+)
  • (+)

  • height of herbaceous plants

  • (−)

  • % buildings

  • (NS)

  • % asphalt

  • (NS) vehicles/minute

  • (NS)

  • pedestrian density

  • (NS)

  • noise level

51.4–400 | 10(+)(+)(1)
  • (+)

  • habitat composition

60.3–52.8 | 54(+)(NS)(1)
  • (−)

  • number of non-native tree species

  • (NS)

  • % canopy cover

  • (NS)

  • ground heterogeneity

  • (−)

  • pedestrian density

711.0–1100 | 12(+)(NS)(1)
  • (NS)

  • tree-related factors

  • (+)

  • % impervious surfaces

80.6–98.1 | 93(+)(NS)(1)
  • (+)

  • presence of native forests

  • (NS)

  • degree/type of urbanization

  • (inner/

  • residential or outer/

  • near-forest buffer zone)

91.0–118.2 | 25(+)
  • (NS)(1)

  • (+)(3)

100.1–0.77 | 28(NS)(1)
  • (+)

  • native tree species richness

  • (NS)

  • tree density

  • (NS)

  • % native tree species

  • (NS)

  • distance to water

  • (−)

  • noise level

110.1–103.7 | 44(+)(+)(2)
  • (−)

  • introduced tree dominance (lower crown coverage, basal area, and average height)

  • (+)

  • % area dominated by shrubs/grass

  • (NS)

  • native understory

  • (−)

  • % impervious surface

120.7–702 | 22(+)(NS)(1)
  • (−)

  • noise level

13<39.0–666 | 39(NS)
  • (NS)

  • habitat type

  • (urban forests or parks)

  • (NS)

  • degree/type of urbanization

  • (urban or peri-urban, i.e. inside or outside the motorway ring road)

14NP | 114
  • (+)

  • habitat type

  • -managed (e.g. parks and cemeteries)

150.5–17.01 | 31(+)
163.54–34.19 | 9(+)(+)(2)
  • (+)

  • % woodland

  • (+)

  • waterbody shape index

170–66.6 | 20(NS)(1)
  • (+)

  • % water

  • (prevalence of water)

  • (−)

  • degree of urbanization

  • (200m and 1000m buffer zone)

18<900 | 95
  • (+)

  • number of mature trees

190.7–3.5 | 9(+)(NS)(2)
  • (−)

  • % canopy cover

  • (+)

  • % open grass/ground

  • (+)

  • % native vegetation species

  • (+)

  • % exotic vegetation

  • species

  • (NS)

  • human presence

  • (NS)

  • noise level

200.3–224 | 18(+)
  • (+)

  • tree species richness

  • (+)

  • woody species richness

  • (NS)

  • % impervious surface

212.0–2135 | 19(NS)
  • (+)

  • woody species richness

  • (+)

  • density of medium-sized trees

  • (−)

  • exotic woody species density

  • (NS)

  • shrub diversity

  • (NS)

  • shrub density

22<3–1100 | 113
  • (+)

  • habitat type

  • (forest ecotone)

231.0–9.0 | 34(NS)
  • (+)

  • % tree cover

  • (NS)

  • % shrub cover

  • (NS)

  • % impervious surface

2416.78–116.54 | 15(NS)(+)(3)
  • (+)

  • number of shrubs

  • (+)

  • number of tree species

  • (NS)

  • grass height

  • (NS)

  • density of flowering/fruit plants

  • (NS)

  • % bare ground

  • (+)

  • number of buildings

  • (−)

  • number of vehicles

  • (−)

  • pedestrian density

250.10–1119.41 | 102(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • % tree canopy

26NP | 6
  • (+)

  • habitat

  • (well-preserved cloud forests)

272.9–4.2 | 6(+)(1)
  • (NS)

  • degree of urbanization

  • (heritage city or floodplain forest)

280.72–62.26 | 32(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • number of habitat types

  • (+)

  • diversity of habitat types

291.1–5300 | 25(+)
  • (+)

  • % shrub cover

  • (+)

  • distance to water

  • (NS)

  • number of glass panes

  • (NS)

  • number of vehicles

  • (NS)

  • number of off-leash dogs

  • (NS)

  • number of cats

3024.82–2050.93 | 10(NS)(NS)(1)
  • (+)

  • presence of large trees

  • (+)

  • % water coverage

310.09–6.71 | 103(+)(NS)(1)
  • (+)

  • % grass cover

  • (+)

  • density of trees

  • (+)

  • number of mature trees

  • (NS)

  • % shrub cover

  • (−)

  • mean number of people

3215.0–5923 | 20(+)
  • (+)

  • degree of urbanization

  • (1 km and 5 km buffer zones)

330.2–5.95 | 20(+)(NS)(1)
  • (+)

  • % tree cover

340.12–2.47 | 18(NS)
  • (+)

  • % woody vegetation cover (area surrounding green space)

  • (NS)

  • tree abundance

  • (NS)

  • shrub abundance

  • (NS)

  • % buildings

  • (NS)

  • % asphalt

3596.1–810.40 | 8
  • (−)

  • degree of urbanization (city center, residential, greenway and periphery)

360.7–34.5 | 36(+)
  • (NS)

  • canopy diversity

  • (−)

  • % impervious surface

374.0–547 | 30(+)
  • (NS)(1)

  • (NS)(2)

  • (+)

  • habitat diversity

  • (+)

  • % water area

  • (NS)

  • degree of urbanization

  • (building index (NDBI) and building height)

38262 | 1
  • (+)

  • % woody plant species richness

  • (−)

  • % lawn cover

  • (NS)

  • distance to water

390.25–2.0 | 109
  • (+)

  • number of large native trees

400.01–210 | 30(+)(NS)(1)
  • (+)

  • number of tree cavities

410.5–40.93 | 15(NS)
  • (+)(2)

  • (+)(−)(3)

  • highest in intermediate and old-central higher than young-suburban

  • (NS)

  • degree of urbanization

  • (suburban or central)

42NP | 64(NS)(+)(2)
  • (+)

  • presence of freshwater bodies

  • (NS)

  • tree cover

432.27–22.04 | 29(+)
  • (+)

  • largest patch index of woodland (surrounding green space)

  • (NS)

  • foliage height diversity

  • (−)

  • degree urbanization

  • (NS)

  • human visitations

443.12–227.2 | 7(+)(NS)(1)
  • (+)

  • habitat heterogeneity

  • (NS)

  • noise level

450.2–4.11 | 17(NS)
  • (NS)

  • any vegetation structure variables

  • (NS)

  • %impervious surface

Explanatory factors had a positive (+), negative (−), nonsignificant effect (NS), or conditional significance (+) (−) on richness. Shaded areas are factors that were not examined, not in the final analysis, or not referenced in the significant results presented in the articles. Additional factors may have been examined but are not listed below unless referenced in the significant results presented in the articles. Site information not provided in the articles is indicated by NP. Reference numbers correspond to reference numbers used in Table A.1.

Table A.3:

The number of sites, number of avian and songbird species, study objectives, factors measured, observed effects, and author comments and recommendations for twelve articles that provided landscape factors for small green spaces (<2 ha) from the full dataset of 45 peer-reviewed articles published between 2000 and 2023.

Reference numberNumber of sites < 2 ha | total sitesStudy objectivesFactors measuredObserved effectsAuthor comments and recommendations
Number of species avian | songbird
260 | 60explore whether several distinct site and matrix-level variables influence the diversity and composition of bird communities occupying small urban green spacebuilding density (%); average building height (m); road coverage (%); area (ha); distance to nearest neighboring green space (m); Shannon native vegetation diversity index; vegetation basal area (cm); bush coverage (%); and tree coverage (%)As the matrix around a green space had increased building densities, bird richness and abundance of native and insectivorous species decreased and invasive, urban-dweller, and omnivorous species increased. As road coverage increased, both the richness and abundance tended to decrease across all bird categories. Distance to the nearest green space, and native vegetation diversity had a significant effect on several bird categories.(1) city planners may need to take into account both the surrounding urban matrix and site-level characteristics in order to improve bird diversity within small urban green spaces
28 | 16
419 | 19analyze how green space characteristics, those of the adjacent landscape, and human disturbance variables affect bird species richness, abundance, and community composition (during summer and winter)area (ha); perimeter (m); tree and ground cover (%); tree and shrub species richness; tree, shrub, and herbaceous plant height (m); buildings (%); asphalt (%); area covered by green space (%); distance to the closest area covered by native vegetation; distance to the closest green space; distance to the closest main road; number of pedestrians and vehicles (min); and noise level (dB)Green space area was the most important variable that positively influenced bird species richness, for both the summer and the winter communities. Summer bird species richness was lower in places that had a greater percentage of area covered by buildings in the adjacent landscape. Generalist and opportunistic species were favored by urbanization. We did not obtain any significant models with the human disturbance variables.
  • (1) green spaces should have the largest possible area and contain a complex vegetative cover to support resident and migratory birds

  • (2) the landscape matrix around green spaces should maximize the amount of vegetative cover and connectivity between sites, taking advantage of well-designed road strip corridors

  • (3) small green spaces could function as steppingstones which could be temporarily used by different species while moving through urban landscapes

26-32 | 21-27
51 | 10evaluate the relationships between bird diversity, park size, distance to the nearest main park, and habitat compositionsarea (ha); human population density (people/km2); average building density (%); average building height (m); distance to the nearest mainland urban park (km2); and trees, wetland, and grassland (%)Large areas had the highest overall species richness (migratory and resident). Parks closer to mainland parks had more overall species richness than isolated parks. More resident species in small parks. More migratory species in large parks. Species richness was positively correlated with grasslands and wetlands and negatively correlated with increasing buildings.
  • (1) maintain and expand large parks in the city to increase biodiversity and complexity of the urban ecosystem by increasing grassland and reduce buildings in the park areas

  • (2) plant more trees and increase wetland in surrounding park areas, along with controlling human population, building density and height

16 | 14
1028 | 28assess how noise, vegetation aspects, distance from a major habitat patch and from water are related to species composition, species richness, total abundance and feeding guildsarea (ha); distance from the border of large green space and from water bodies; tree species richness, proportion of native tree species, tree density; and noise level (dB)Noise level was negatively related to bird species richness, composition, total abundance, and abundance of granivorous species. Tree species richness presented positive relationships with bird species richness, composition, and total abundance.
  • (1) negative effects of noise can be offset by increasing tree species' richness

  • (2) small public urban green spaces are important for human wellbeing and quality of life and we demonstrate that their vegetation characteristics can also positively influence urban bird communities.

9-36 | 7-23
124 | 22study the nested subset pattern of songbird assemblagesarea (ha); distance from large green space (km); tree nestedness; and background noise level (dB)Size of green space was the most explanatory factor with noise the second most explanatory. The effect of noise was more noticeable in open, sub-open, and non-forest habitats. Rare species could be found only in the most species-rich sites, whereas widely distributed species could be found in most sites.
  • (1) maintain a minimum area of suitable patch

  • (2) control noise pollution

9-14 | 9-14
1515 | 31evaluate environmental seasonality and park features on species composition, diversity and nestedness of the breeding and wintering avian communitiesarea (ha); shape (ha/km); grass cover (%); shrub cover (%); tree density (no/ha); mean tree height (m); mean tree trunk diameter (cm); tree diversity; and mean noise level (dB)Avian diversity was significantly greater during breeding than during the winter period, although the most diverse parks during breeding were also the most diverse during winter. Most of the among–park variation in diversity was explained by park size, while tree density had a marginal contribution that was only significant during winter. Seasonality affected distribution but not diversity or nestedness.(1) favor the existence of a few relatively large parks (over 10 ha) instead of many small ones to maintain a diverse urban avifauna all year round
16 | 15
1711-20evaluate ecological traits associated with avian communities and the environmental factors important in changing the structure of avian communitiesarea (ha); tree canopy (%); shrubs (%); grass (%); ground surface paved with asphalt (%), open area (%); artificial structures (%); degree of isolation; urbanization; visibility (%); and water (%)The avian community tended to be dominated by a few species and lower numbers of uncommon species. The overall species richness decreased in areas categorized as urban vegetation, those surrounded by urban areas, and at the sites with many artificial structures. Species richness was positively influenced by the prevalence of water. All study parks and green spaces exhibited similar levels of the isolation index.
  • (1) increase shrubs

  • (2) urban avian community dominated by a few species or lower numbers of uncommon species

5-12 | 3-10
2812 | 32examine determinants of species-area effects, distance effects, and the effects of habitat structure on total, native, and endangered species richness for vascular plants, birds, and mammalsarea (ha); shape (perimeter/area ratio); distance to urban edge and nearest green space (m); number of habitat types; green space (%); and diversity of habitat typesPatch area in combination with habitat heterogeneity was most important for bird richness (total, native, and endangered).(1) conserve large green spaces that include a high diversity of habitats
14-23 | 13-20
295 | 25test which biotic (i.e. vegetation characteristics and human and pet disturbances) and abiotic variables (i.e. area size, number of vehicles, and glass panes) influence functional diversity indices of dietary guilds, migrants, residents, and total bird communityarea (ha); herbaceous cover (%); herbaceous height (cm); shrub height (cm), cover (%), and morpho-richness; tree height (m), cover (%), abundance; distance to water (m); number of glass panes; number of pedestrians; number of vehicles; and number of homeless and/ or off-leash dogs and catsLarge-sized areas of urban green spaces and shrub cover are the main characteristics that drive bird richness and functional richness of all bird guilds (frugivore-nectarivore, insectivore, resident, and migrant) and the total avian community. 90% of recorded avian species were resident birds.
  • (1) prioritize large areas with high shrub cover

  • (2) mitigate the negative impact caused by glass panes, traffic of vehicles, and domestic animals

22-53 | 13-35
3417 | 18assess the effect of local and landscape level vegetation, building cover, and urbanization on native and exotic bird speciesarea (ha); tree and shrub richness and abundance; plant height (cm); paved surface (%); building cover (%); grassland cover (%); woody vegetation cover (%); and road cover (%)On the local scale, environmental factors did not have a significant effect on bird richness. On the landscape scale, native bird richness and abundance were positively related to woody vegetation cover. Exotic birds were positively influenced by variables associated with urbanization.
  • (1) increase the taxonomic and structural complexity of native vegetation within green spaces to improve habitat quality

  • (2) prevent the proliferation of exotic bird populations

10-25 | 10-25
368 | 36effect of park size, canopy heterogeneity within the park, and the proportion of sealed area surrounding each parkarea (ha); natural green space (%); manmade green space (%); sealed areas (%); and forest/tree-covered areas (%)Species richness increased with increasing park size and decreased with increased percentage of sealed areas.(1) bird assemblages of parks embedded in an urban landscape matrix with a high permeability for forest birds most likely provide an increased ecosystem function and promote and maintain high diversity and ecosystem function
7-12 | 6-10
39109 | 109role of large native treesarea (ha); number and diameter of trees (cm)Large trees had a consistent, strong, and positive relationship with bird diversity and as trees became larger in size, their positive effect on bird diversity increased.
  • (1) proactively plan for large trees and implement tree preservation policies that recognize biodiversity values

  • (2) the addition of five trees >100 cm increased species richness by 157%, average abundance by 91%, probability of breeding by 158%, and woodland species richness by 301%

7.8±2.6 | 7.8±2.6
Reference numberNumber of sites < 2 ha | total sitesStudy objectivesFactors measuredObserved effectsAuthor comments and recommendations
Number of species avian | songbird
260 | 60explore whether several distinct site and matrix-level variables influence the diversity and composition of bird communities occupying small urban green spacebuilding density (%); average building height (m); road coverage (%); area (ha); distance to nearest neighboring green space (m); Shannon native vegetation diversity index; vegetation basal area (cm); bush coverage (%); and tree coverage (%)As the matrix around a green space had increased building densities, bird richness and abundance of native and insectivorous species decreased and invasive, urban-dweller, and omnivorous species increased. As road coverage increased, both the richness and abundance tended to decrease across all bird categories. Distance to the nearest green space, and native vegetation diversity had a significant effect on several bird categories.(1) city planners may need to take into account both the surrounding urban matrix and site-level characteristics in order to improve bird diversity within small urban green spaces
28 | 16
419 | 19analyze how green space characteristics, those of the adjacent landscape, and human disturbance variables affect bird species richness, abundance, and community composition (during summer and winter)area (ha); perimeter (m); tree and ground cover (%); tree and shrub species richness; tree, shrub, and herbaceous plant height (m); buildings (%); asphalt (%); area covered by green space (%); distance to the closest area covered by native vegetation; distance to the closest green space; distance to the closest main road; number of pedestrians and vehicles (min); and noise level (dB)Green space area was the most important variable that positively influenced bird species richness, for both the summer and the winter communities. Summer bird species richness was lower in places that had a greater percentage of area covered by buildings in the adjacent landscape. Generalist and opportunistic species were favored by urbanization. We did not obtain any significant models with the human disturbance variables.
  • (1) green spaces should have the largest possible area and contain a complex vegetative cover to support resident and migratory birds

  • (2) the landscape matrix around green spaces should maximize the amount of vegetative cover and connectivity between sites, taking advantage of well-designed road strip corridors

  • (3) small green spaces could function as steppingstones which could be temporarily used by different species while moving through urban landscapes

26-32 | 21-27
51 | 10evaluate the relationships between bird diversity, park size, distance to the nearest main park, and habitat compositionsarea (ha); human population density (people/km2); average building density (%); average building height (m); distance to the nearest mainland urban park (km2); and trees, wetland, and grassland (%)Large areas had the highest overall species richness (migratory and resident). Parks closer to mainland parks had more overall species richness than isolated parks. More resident species in small parks. More migratory species in large parks. Species richness was positively correlated with grasslands and wetlands and negatively correlated with increasing buildings.
  • (1) maintain and expand large parks in the city to increase biodiversity and complexity of the urban ecosystem by increasing grassland and reduce buildings in the park areas

  • (2) plant more trees and increase wetland in surrounding park areas, along with controlling human population, building density and height

16 | 14
1028 | 28assess how noise, vegetation aspects, distance from a major habitat patch and from water are related to species composition, species richness, total abundance and feeding guildsarea (ha); distance from the border of large green space and from water bodies; tree species richness, proportion of native tree species, tree density; and noise level (dB)Noise level was negatively related to bird species richness, composition, total abundance, and abundance of granivorous species. Tree species richness presented positive relationships with bird species richness, composition, and total abundance.
  • (1) negative effects of noise can be offset by increasing tree species' richness

  • (2) small public urban green spaces are important for human wellbeing and quality of life and we demonstrate that their vegetation characteristics can also positively influence urban bird communities.

9-36 | 7-23
124 | 22study the nested subset pattern of songbird assemblagesarea (ha); distance from large green space (km); tree nestedness; and background noise level (dB)Size of green space was the most explanatory factor with noise the second most explanatory. The effect of noise was more noticeable in open, sub-open, and non-forest habitats. Rare species could be found only in the most species-rich sites, whereas widely distributed species could be found in most sites.
  • (1) maintain a minimum area of suitable patch

  • (2) control noise pollution

9-14 | 9-14
1515 | 31evaluate environmental seasonality and park features on species composition, diversity and nestedness of the breeding and wintering avian communitiesarea (ha); shape (ha/km); grass cover (%); shrub cover (%); tree density (no/ha); mean tree height (m); mean tree trunk diameter (cm); tree diversity; and mean noise level (dB)Avian diversity was significantly greater during breeding than during the winter period, although the most diverse parks during breeding were also the most diverse during winter. Most of the among–park variation in diversity was explained by park size, while tree density had a marginal contribution that was only significant during winter. Seasonality affected distribution but not diversity or nestedness.(1) favor the existence of a few relatively large parks (over 10 ha) instead of many small ones to maintain a diverse urban avifauna all year round
16 | 15
1711-20evaluate ecological traits associated with avian communities and the environmental factors important in changing the structure of avian communitiesarea (ha); tree canopy (%); shrubs (%); grass (%); ground surface paved with asphalt (%), open area (%); artificial structures (%); degree of isolation; urbanization; visibility (%); and water (%)The avian community tended to be dominated by a few species and lower numbers of uncommon species. The overall species richness decreased in areas categorized as urban vegetation, those surrounded by urban areas, and at the sites with many artificial structures. Species richness was positively influenced by the prevalence of water. All study parks and green spaces exhibited similar levels of the isolation index.
  • (1) increase shrubs

  • (2) urban avian community dominated by a few species or lower numbers of uncommon species

5-12 | 3-10
2812 | 32examine determinants of species-area effects, distance effects, and the effects of habitat structure on total, native, and endangered species richness for vascular plants, birds, and mammalsarea (ha); shape (perimeter/area ratio); distance to urban edge and nearest green space (m); number of habitat types; green space (%); and diversity of habitat typesPatch area in combination with habitat heterogeneity was most important for bird richness (total, native, and endangered).(1) conserve large green spaces that include a high diversity of habitats
14-23 | 13-20
295 | 25test which biotic (i.e. vegetation characteristics and human and pet disturbances) and abiotic variables (i.e. area size, number of vehicles, and glass panes) influence functional diversity indices of dietary guilds, migrants, residents, and total bird communityarea (ha); herbaceous cover (%); herbaceous height (cm); shrub height (cm), cover (%), and morpho-richness; tree height (m), cover (%), abundance; distance to water (m); number of glass panes; number of pedestrians; number of vehicles; and number of homeless and/ or off-leash dogs and catsLarge-sized areas of urban green spaces and shrub cover are the main characteristics that drive bird richness and functional richness of all bird guilds (frugivore-nectarivore, insectivore, resident, and migrant) and the total avian community. 90% of recorded avian species were resident birds.
  • (1) prioritize large areas with high shrub cover

  • (2) mitigate the negative impact caused by glass panes, traffic of vehicles, and domestic animals

22-53 | 13-35
3417 | 18assess the effect of local and landscape level vegetation, building cover, and urbanization on native and exotic bird speciesarea (ha); tree and shrub richness and abundance; plant height (cm); paved surface (%); building cover (%); grassland cover (%); woody vegetation cover (%); and road cover (%)On the local scale, environmental factors did not have a significant effect on bird richness. On the landscape scale, native bird richness and abundance were positively related to woody vegetation cover. Exotic birds were positively influenced by variables associated with urbanization.
  • (1) increase the taxonomic and structural complexity of native vegetation within green spaces to improve habitat quality

  • (2) prevent the proliferation of exotic bird populations

10-25 | 10-25
368 | 36effect of park size, canopy heterogeneity within the park, and the proportion of sealed area surrounding each parkarea (ha); natural green space (%); manmade green space (%); sealed areas (%); and forest/tree-covered areas (%)Species richness increased with increasing park size and decreased with increased percentage of sealed areas.(1) bird assemblages of parks embedded in an urban landscape matrix with a high permeability for forest birds most likely provide an increased ecosystem function and promote and maintain high diversity and ecosystem function
7-12 | 6-10
39109 | 109role of large native treesarea (ha); number and diameter of trees (cm)Large trees had a consistent, strong, and positive relationship with bird diversity and as trees became larger in size, their positive effect on bird diversity increased.
  • (1) proactively plan for large trees and implement tree preservation policies that recognize biodiversity values

  • (2) the addition of five trees >100 cm increased species richness by 157%, average abundance by 91%, probability of breeding by 158%, and woodland species richness by 301%

7.8±2.6 | 7.8±2.6

Reference numbers correspond to reference numbers used in Table A.1.

Table A.3:

The number of sites, number of avian and songbird species, study objectives, factors measured, observed effects, and author comments and recommendations for twelve articles that provided landscape factors for small green spaces (<2 ha) from the full dataset of 45 peer-reviewed articles published between 2000 and 2023.

Reference numberNumber of sites < 2 ha | total sitesStudy objectivesFactors measuredObserved effectsAuthor comments and recommendations
Number of species avian | songbird
260 | 60explore whether several distinct site and matrix-level variables influence the diversity and composition of bird communities occupying small urban green spacebuilding density (%); average building height (m); road coverage (%); area (ha); distance to nearest neighboring green space (m); Shannon native vegetation diversity index; vegetation basal area (cm); bush coverage (%); and tree coverage (%)As the matrix around a green space had increased building densities, bird richness and abundance of native and insectivorous species decreased and invasive, urban-dweller, and omnivorous species increased. As road coverage increased, both the richness and abundance tended to decrease across all bird categories. Distance to the nearest green space, and native vegetation diversity had a significant effect on several bird categories.(1) city planners may need to take into account both the surrounding urban matrix and site-level characteristics in order to improve bird diversity within small urban green spaces
28 | 16
419 | 19analyze how green space characteristics, those of the adjacent landscape, and human disturbance variables affect bird species richness, abundance, and community composition (during summer and winter)area (ha); perimeter (m); tree and ground cover (%); tree and shrub species richness; tree, shrub, and herbaceous plant height (m); buildings (%); asphalt (%); area covered by green space (%); distance to the closest area covered by native vegetation; distance to the closest green space; distance to the closest main road; number of pedestrians and vehicles (min); and noise level (dB)Green space area was the most important variable that positively influenced bird species richness, for both the summer and the winter communities. Summer bird species richness was lower in places that had a greater percentage of area covered by buildings in the adjacent landscape. Generalist and opportunistic species were favored by urbanization. We did not obtain any significant models with the human disturbance variables.
  • (1) green spaces should have the largest possible area and contain a complex vegetative cover to support resident and migratory birds

  • (2) the landscape matrix around green spaces should maximize the amount of vegetative cover and connectivity between sites, taking advantage of well-designed road strip corridors

  • (3) small green spaces could function as steppingstones which could be temporarily used by different species while moving through urban landscapes

26-32 | 21-27
51 | 10evaluate the relationships between bird diversity, park size, distance to the nearest main park, and habitat compositionsarea (ha); human population density (people/km2); average building density (%); average building height (m); distance to the nearest mainland urban park (km2); and trees, wetland, and grassland (%)Large areas had the highest overall species richness (migratory and resident). Parks closer to mainland parks had more overall species richness than isolated parks. More resident species in small parks. More migratory species in large parks. Species richness was positively correlated with grasslands and wetlands and negatively correlated with increasing buildings.
  • (1) maintain and expand large parks in the city to increase biodiversity and complexity of the urban ecosystem by increasing grassland and reduce buildings in the park areas

  • (2) plant more trees and increase wetland in surrounding park areas, along with controlling human population, building density and height

16 | 14
1028 | 28assess how noise, vegetation aspects, distance from a major habitat patch and from water are related to species composition, species richness, total abundance and feeding guildsarea (ha); distance from the border of large green space and from water bodies; tree species richness, proportion of native tree species, tree density; and noise level (dB)Noise level was negatively related to bird species richness, composition, total abundance, and abundance of granivorous species. Tree species richness presented positive relationships with bird species richness, composition, and total abundance.
  • (1) negative effects of noise can be offset by increasing tree species' richness

  • (2) small public urban green spaces are important for human wellbeing and quality of life and we demonstrate that their vegetation characteristics can also positively influence urban bird communities.

9-36 | 7-23
124 | 22study the nested subset pattern of songbird assemblagesarea (ha); distance from large green space (km); tree nestedness; and background noise level (dB)Size of green space was the most explanatory factor with noise the second most explanatory. The effect of noise was more noticeable in open, sub-open, and non-forest habitats. Rare species could be found only in the most species-rich sites, whereas widely distributed species could be found in most sites.
  • (1) maintain a minimum area of suitable patch

  • (2) control noise pollution

9-14 | 9-14
1515 | 31evaluate environmental seasonality and park features on species composition, diversity and nestedness of the breeding and wintering avian communitiesarea (ha); shape (ha/km); grass cover (%); shrub cover (%); tree density (no/ha); mean tree height (m); mean tree trunk diameter (cm); tree diversity; and mean noise level (dB)Avian diversity was significantly greater during breeding than during the winter period, although the most diverse parks during breeding were also the most diverse during winter. Most of the among–park variation in diversity was explained by park size, while tree density had a marginal contribution that was only significant during winter. Seasonality affected distribution but not diversity or nestedness.(1) favor the existence of a few relatively large parks (over 10 ha) instead of many small ones to maintain a diverse urban avifauna all year round
16 | 15
1711-20evaluate ecological traits associated with avian communities and the environmental factors important in changing the structure of avian communitiesarea (ha); tree canopy (%); shrubs (%); grass (%); ground surface paved with asphalt (%), open area (%); artificial structures (%); degree of isolation; urbanization; visibility (%); and water (%)The avian community tended to be dominated by a few species and lower numbers of uncommon species. The overall species richness decreased in areas categorized as urban vegetation, those surrounded by urban areas, and at the sites with many artificial structures. Species richness was positively influenced by the prevalence of water. All study parks and green spaces exhibited similar levels of the isolation index.
  • (1) increase shrubs

  • (2) urban avian community dominated by a few species or lower numbers of uncommon species

5-12 | 3-10
2812 | 32examine determinants of species-area effects, distance effects, and the effects of habitat structure on total, native, and endangered species richness for vascular plants, birds, and mammalsarea (ha); shape (perimeter/area ratio); distance to urban edge and nearest green space (m); number of habitat types; green space (%); and diversity of habitat typesPatch area in combination with habitat heterogeneity was most important for bird richness (total, native, and endangered).(1) conserve large green spaces that include a high diversity of habitats
14-23 | 13-20
295 | 25test which biotic (i.e. vegetation characteristics and human and pet disturbances) and abiotic variables (i.e. area size, number of vehicles, and glass panes) influence functional diversity indices of dietary guilds, migrants, residents, and total bird communityarea (ha); herbaceous cover (%); herbaceous height (cm); shrub height (cm), cover (%), and morpho-richness; tree height (m), cover (%), abundance; distance to water (m); number of glass panes; number of pedestrians; number of vehicles; and number of homeless and/ or off-leash dogs and catsLarge-sized areas of urban green spaces and shrub cover are the main characteristics that drive bird richness and functional richness of all bird guilds (frugivore-nectarivore, insectivore, resident, and migrant) and the total avian community. 90% of recorded avian species were resident birds.
  • (1) prioritize large areas with high shrub cover

  • (2) mitigate the negative impact caused by glass panes, traffic of vehicles, and domestic animals

22-53 | 13-35
3417 | 18assess the effect of local and landscape level vegetation, building cover, and urbanization on native and exotic bird speciesarea (ha); tree and shrub richness and abundance; plant height (cm); paved surface (%); building cover (%); grassland cover (%); woody vegetation cover (%); and road cover (%)On the local scale, environmental factors did not have a significant effect on bird richness. On the landscape scale, native bird richness and abundance were positively related to woody vegetation cover. Exotic birds were positively influenced by variables associated with urbanization.
  • (1) increase the taxonomic and structural complexity of native vegetation within green spaces to improve habitat quality

  • (2) prevent the proliferation of exotic bird populations

10-25 | 10-25
368 | 36effect of park size, canopy heterogeneity within the park, and the proportion of sealed area surrounding each parkarea (ha); natural green space (%); manmade green space (%); sealed areas (%); and forest/tree-covered areas (%)Species richness increased with increasing park size and decreased with increased percentage of sealed areas.(1) bird assemblages of parks embedded in an urban landscape matrix with a high permeability for forest birds most likely provide an increased ecosystem function and promote and maintain high diversity and ecosystem function
7-12 | 6-10
39109 | 109role of large native treesarea (ha); number and diameter of trees (cm)Large trees had a consistent, strong, and positive relationship with bird diversity and as trees became larger in size, their positive effect on bird diversity increased.
  • (1) proactively plan for large trees and implement tree preservation policies that recognize biodiversity values

  • (2) the addition of five trees >100 cm increased species richness by 157%, average abundance by 91%, probability of breeding by 158%, and woodland species richness by 301%

7.8±2.6 | 7.8±2.6
Reference numberNumber of sites < 2 ha | total sitesStudy objectivesFactors measuredObserved effectsAuthor comments and recommendations
Number of species avian | songbird
260 | 60explore whether several distinct site and matrix-level variables influence the diversity and composition of bird communities occupying small urban green spacebuilding density (%); average building height (m); road coverage (%); area (ha); distance to nearest neighboring green space (m); Shannon native vegetation diversity index; vegetation basal area (cm); bush coverage (%); and tree coverage (%)As the matrix around a green space had increased building densities, bird richness and abundance of native and insectivorous species decreased and invasive, urban-dweller, and omnivorous species increased. As road coverage increased, both the richness and abundance tended to decrease across all bird categories. Distance to the nearest green space, and native vegetation diversity had a significant effect on several bird categories.(1) city planners may need to take into account both the surrounding urban matrix and site-level characteristics in order to improve bird diversity within small urban green spaces
28 | 16
419 | 19analyze how green space characteristics, those of the adjacent landscape, and human disturbance variables affect bird species richness, abundance, and community composition (during summer and winter)area (ha); perimeter (m); tree and ground cover (%); tree and shrub species richness; tree, shrub, and herbaceous plant height (m); buildings (%); asphalt (%); area covered by green space (%); distance to the closest area covered by native vegetation; distance to the closest green space; distance to the closest main road; number of pedestrians and vehicles (min); and noise level (dB)Green space area was the most important variable that positively influenced bird species richness, for both the summer and the winter communities. Summer bird species richness was lower in places that had a greater percentage of area covered by buildings in the adjacent landscape. Generalist and opportunistic species were favored by urbanization. We did not obtain any significant models with the human disturbance variables.
  • (1) green spaces should have the largest possible area and contain a complex vegetative cover to support resident and migratory birds

  • (2) the landscape matrix around green spaces should maximize the amount of vegetative cover and connectivity between sites, taking advantage of well-designed road strip corridors

  • (3) small green spaces could function as steppingstones which could be temporarily used by different species while moving through urban landscapes

26-32 | 21-27
51 | 10evaluate the relationships between bird diversity, park size, distance to the nearest main park, and habitat compositionsarea (ha); human population density (people/km2); average building density (%); average building height (m); distance to the nearest mainland urban park (km2); and trees, wetland, and grassland (%)Large areas had the highest overall species richness (migratory and resident). Parks closer to mainland parks had more overall species richness than isolated parks. More resident species in small parks. More migratory species in large parks. Species richness was positively correlated with grasslands and wetlands and negatively correlated with increasing buildings.
  • (1) maintain and expand large parks in the city to increase biodiversity and complexity of the urban ecosystem by increasing grassland and reduce buildings in the park areas

  • (2) plant more trees and increase wetland in surrounding park areas, along with controlling human population, building density and height

16 | 14
1028 | 28assess how noise, vegetation aspects, distance from a major habitat patch and from water are related to species composition, species richness, total abundance and feeding guildsarea (ha); distance from the border of large green space and from water bodies; tree species richness, proportion of native tree species, tree density; and noise level (dB)Noise level was negatively related to bird species richness, composition, total abundance, and abundance of granivorous species. Tree species richness presented positive relationships with bird species richness, composition, and total abundance.
  • (1) negative effects of noise can be offset by increasing tree species' richness

  • (2) small public urban green spaces are important for human wellbeing and quality of life and we demonstrate that their vegetation characteristics can also positively influence urban bird communities.

9-36 | 7-23
124 | 22study the nested subset pattern of songbird assemblagesarea (ha); distance from large green space (km); tree nestedness; and background noise level (dB)Size of green space was the most explanatory factor with noise the second most explanatory. The effect of noise was more noticeable in open, sub-open, and non-forest habitats. Rare species could be found only in the most species-rich sites, whereas widely distributed species could be found in most sites.
  • (1) maintain a minimum area of suitable patch

  • (2) control noise pollution

9-14 | 9-14
1515 | 31evaluate environmental seasonality and park features on species composition, diversity and nestedness of the breeding and wintering avian communitiesarea (ha); shape (ha/km); grass cover (%); shrub cover (%); tree density (no/ha); mean tree height (m); mean tree trunk diameter (cm); tree diversity; and mean noise level (dB)Avian diversity was significantly greater during breeding than during the winter period, although the most diverse parks during breeding were also the most diverse during winter. Most of the among–park variation in diversity was explained by park size, while tree density had a marginal contribution that was only significant during winter. Seasonality affected distribution but not diversity or nestedness.(1) favor the existence of a few relatively large parks (over 10 ha) instead of many small ones to maintain a diverse urban avifauna all year round
16 | 15
1711-20evaluate ecological traits associated with avian communities and the environmental factors important in changing the structure of avian communitiesarea (ha); tree canopy (%); shrubs (%); grass (%); ground surface paved with asphalt (%), open area (%); artificial structures (%); degree of isolation; urbanization; visibility (%); and water (%)The avian community tended to be dominated by a few species and lower numbers of uncommon species. The overall species richness decreased in areas categorized as urban vegetation, those surrounded by urban areas, and at the sites with many artificial structures. Species richness was positively influenced by the prevalence of water. All study parks and green spaces exhibited similar levels of the isolation index.
  • (1) increase shrubs

  • (2) urban avian community dominated by a few species or lower numbers of uncommon species

5-12 | 3-10
2812 | 32examine determinants of species-area effects, distance effects, and the effects of habitat structure on total, native, and endangered species richness for vascular plants, birds, and mammalsarea (ha); shape (perimeter/area ratio); distance to urban edge and nearest green space (m); number of habitat types; green space (%); and diversity of habitat typesPatch area in combination with habitat heterogeneity was most important for bird richness (total, native, and endangered).(1) conserve large green spaces that include a high diversity of habitats
14-23 | 13-20
295 | 25test which biotic (i.e. vegetation characteristics and human and pet disturbances) and abiotic variables (i.e. area size, number of vehicles, and glass panes) influence functional diversity indices of dietary guilds, migrants, residents, and total bird communityarea (ha); herbaceous cover (%); herbaceous height (cm); shrub height (cm), cover (%), and morpho-richness; tree height (m), cover (%), abundance; distance to water (m); number of glass panes; number of pedestrians; number of vehicles; and number of homeless and/ or off-leash dogs and catsLarge-sized areas of urban green spaces and shrub cover are the main characteristics that drive bird richness and functional richness of all bird guilds (frugivore-nectarivore, insectivore, resident, and migrant) and the total avian community. 90% of recorded avian species were resident birds.
  • (1) prioritize large areas with high shrub cover

  • (2) mitigate the negative impact caused by glass panes, traffic of vehicles, and domestic animals

22-53 | 13-35
3417 | 18assess the effect of local and landscape level vegetation, building cover, and urbanization on native and exotic bird speciesarea (ha); tree and shrub richness and abundance; plant height (cm); paved surface (%); building cover (%); grassland cover (%); woody vegetation cover (%); and road cover (%)On the local scale, environmental factors did not have a significant effect on bird richness. On the landscape scale, native bird richness and abundance were positively related to woody vegetation cover. Exotic birds were positively influenced by variables associated with urbanization.
  • (1) increase the taxonomic and structural complexity of native vegetation within green spaces to improve habitat quality

  • (2) prevent the proliferation of exotic bird populations

10-25 | 10-25
368 | 36effect of park size, canopy heterogeneity within the park, and the proportion of sealed area surrounding each parkarea (ha); natural green space (%); manmade green space (%); sealed areas (%); and forest/tree-covered areas (%)Species richness increased with increasing park size and decreased with increased percentage of sealed areas.(1) bird assemblages of parks embedded in an urban landscape matrix with a high permeability for forest birds most likely provide an increased ecosystem function and promote and maintain high diversity and ecosystem function
7-12 | 6-10
39109 | 109role of large native treesarea (ha); number and diameter of trees (cm)Large trees had a consistent, strong, and positive relationship with bird diversity and as trees became larger in size, their positive effect on bird diversity increased.
  • (1) proactively plan for large trees and implement tree preservation policies that recognize biodiversity values

  • (2) the addition of five trees >100 cm increased species richness by 157%, average abundance by 91%, probability of breeding by 158%, and woodland species richness by 301%

7.8±2.6 | 7.8±2.6

Reference numbers correspond to reference numbers used in Table A.1.

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